Journal articles on the topic 'Data / knowledge partitioning and distribution'
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1
Rota, Jadranka, Tobias Malm, Nicolas Chazot, Carlos Peña, and Niklas Wahlberg. "A simple method for data partitioning based on relative evolutionary rates." PeerJ 6 (August 28, 2018): e5498. http://dx.doi.org/10.7717/peerj.5498.
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Background Multiple studies have demonstrated that partitioning of molecular datasets is important in model-based phylogenetic analyses. Commonly, partitioning is done a priori based on some known properties of sequence evolution, e.g. differences in rate of evolution among codon positions of a protein-coding gene. Here we propose a new method for data partitioning based on relative evolutionary rates of the sites in the alignment of the dataset being analysed. The rates are inferred using the previously published Tree Independent Generation of Evolutionary Rates (TIGER), and the partitioning is conducted using our novel python script RatePartitions. We conducted simulations to assess the performance of our new method, and we applied it to eight published multi-locus phylogenetic datasets, representing different taxonomic ranks within the insect order Lepidoptera (butterflies and moths) and one phylogenomic dataset, which included ultra-conserved elements as well as introns. Methods We used TIGER-rates to generate relative evolutionary rates for all sites in the alignments. Then, using RatePartitions, we partitioned the data into partitions based on their relative evolutionary rate. RatePartitions applies a simple formula that ensures a distribution of sites into partitions following the distribution of rates of the characters from the full dataset. This ensures that the invariable sites are placed in a partition with slowly evolving sites, avoiding the pitfalls of previously used methods, such as k-means. Different partitioning strategies were evaluated using BIC scores as calculated by PartitionFinder. Results Simulations did not highlight any misbehaviour of our partitioning approach, even under difficult parameter conditions or missing data. In all eight phylogenetic datasets, partitioning using TIGER-rates and RatePartitions was significantly better as measured by the BIC scores than other partitioning strategies, such as the commonly used partitioning by gene and codon position. We compared the resulting topologies and node support for these eight datasets as well as for the phylogenomic dataset. Discussion We developed a new method of partitioning phylogenetic datasets without using any prior knowledge (e.g. DNA sequence evolution). This method is entirely based on the properties of the data being analysed and can be applied to DNA sequences (protein-coding, introns, ultra-conserved elements), protein sequences, as well as morphological characters. A likely explanation for why our method performs better than other tested partitioning strategies is that it accounts for the heterogeneity in the data to a much greater extent than when data are simply subdivided based on prior knowledge.
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Shaikh, M. Bilal, M. Abdul Rehman, and Attaullah Sahito. "Optimizing Distributed Machine Learning for Large Scale EEG Data Set." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (June 30, 2017): 114. http://dx.doi.org/10.30537/sjcms.v1i1.14.
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Distributed Machine Learning (DML) has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential) which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.
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Liu, Richen, Liming Shen, Xueyi Chen, Genlin Ji, Bin Zhao, Chao Tan, and Mingjun Su. "Sketch-Based Slice Interpretative Visualization for Stratigraphic Data." Journal of Imaging Science and Technology 63, no. 6 (November 1, 2019): 60505–1. http://dx.doi.org/10.2352/j.imagingsci.technol.2019.63.6.060505.
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Abstract In this article, the authors propose a stratigraphic slice interpretative visualization system, namely slice analyzer. It enables the domain experts, i.e., geologists and oil/gas exploration experts, to interactively interpret the slices with domain knowledge, which helps them get a better understanding of stratigraphic structures and the distribution of the geological materials, e.g., underground flow path (UFP), river delta, floodplain, slump fan, etc. In addition to some domain-specific slice edit manipulations, a sketch-based sub-region partitioning approach is further presented to help users divide the slice into individual sub-regions with homologous characteristics according to their domain knowledge. Consequently, the geological materials they are interested in can be extracted automatically and visualized by the proposed geological symbol definition algorithm. Feedback from domain experts suggests that the proposed system is capable of interpreting the stratigraphic slice, compared with their currently used tools.
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Zhu, Zichen, Xiao Hu, and Manos Athanassoulis. "NOCAP: Near-Optimal Correlation-Aware Partitioning Joins." Proceedings of the ACM on Management of Data 1, no. 4 (December 8, 2023): 1–27. http://dx.doi.org/10.1145/3626739.
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Storage-based joins are still commonly used today because the memory budget does not always scale with the data size. One of the many join algorithms developed that has been widely deployed and proven to be efficient is the Hybrid Hash Join (HHJ), which is designed to exploit any available memory to maximize the data that is joined directly in memory. However, HHJ cannot fully exploit detailed knowledge of the join attribute correlation distribution. In this paper, we show that given a correlation skew in the join attributes, HHJ partitions data in a suboptimal way. To do that, we derive the optimal partitioning using a new cost-based analysis of partitioning-based joins that is tailored for primary key - foreign key (PK-FK) joins, one of the most common join types. This optimal partitioning strategy has a high memory cost, thus, we further derive an approximate algorithm that has tunable memory cost and leads to near-optimal results. Our algorithm, termed NOCAP (Near-Optimal Correlation-Aware Partitioning) join, outperforms the state of the art for skewed correlations by up to 30%, and the textbook Grace Hash Join by up to 4×. Further, for a limited memory budget, NOCAP outperforms HHJ by up to 10%, even for uniform correlation. Overall, NOCAP dominates state-of-the-art algorithms and mimics the best algorithm for a memory budget varying from below √||relation|| to more than ||relation||.
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Sineglazov, Victor, Olena Chumachenko, and Eduard Heilyk. "Semi-controlled Learning in Information Processing Problems." Electronics and Control Systems 4, no. 70 (January 4, 2022): 37–43. http://dx.doi.org/10.18372/1990-5548.70.16754.
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The article substantiates the need for further research of known methods and the development of new methods of machine learning – semi-supervized learning. It is shown that knowledge of the probability distribution density of the initial data obtained using unlabeled data should carry information useful for deriving the conditional probability distribution density of labels and input data. If this is not the case, semi-supervised learning will not provide any improvement over supervised learning. It may even happen that the use of unlabeled data reduces the accuracy of the prediction. For semi-supervised learning to work, certain assumptions must hold, namely: the semi-supervised smoothness assumption, the clustering assumption (low-density partitioning), and the manifold assumption. A new hybrid semi-supervised learning algorithm using the label propagation method has been developed. An example of using the proposed algorithm is given.
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Sirbiladze, Gia, Bidzina Matsaberidze, Bezhan Ghvaberidze, Bidzina Midodashvili, and David Mikadze. "Fuzzy TOPSIS based selection index in the planning of emergency service facilities locations and goods transportation." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1949–62. http://dx.doi.org/10.3233/jifs-210636.
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The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example.
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Smith, Bruce R., Christophe M. Herbinger, and Heather R. Merry. "Accurate Partition of Individuals Into Full-Sib Families From Genetic Data Without Parental Information." Genetics 158, no. 3 (July 1, 2001): 1329–38. http://dx.doi.org/10.1093/genetics/158.3.1329.
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Abstract Two Markov chain Monte Carlo algorithms are proposed that allow the partitioning of individuals into full-sib groups using single-locus genetic marker data when no parental information is available. These algorithms present a method of moving through the sibship configuration space and locating the configuration that maximizes an overall score on the basis of pairwise likelihood ratios of being full-sib or unrelated or maximizes the full joint likelihood of the proposed family structure. Using these methods, up to 757 out of 759 Atlantic salmon were correctly classified into 12 full-sib families of unequal size using four microsatellite markers. Large-scale simulations were performed to assess the sensitivity of the procedures to the number of loci and number of alleles per locus, the allelic distribution type, the distribution of families, and the independent knowledge of population allelic frequencies. The number of loci and the number of alleles per locus had the most impact on accuracy. Very good accuracy can be obtained with as few as four loci when they have at least eight alleles. Accuracy decreases when using allelic frequencies estimated in small target samples with skewed family distributions with the pairwise likelihood approach. We present an iterative approach that partly corrects that problem. The full likelihood approach is less sensitive to the precision of allelic frequencies estimates but did not perform as well with the large data set or when little information was available (e.g., four loci with four alleles).
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Grard, Aline, and Jean-François Deliège. "Characterizing Trace Metal Contamination and Partitioning in the Rivers and Sediments of Western Europe Watersheds." Hydrology 10, no. 2 (February 16, 2023): 51. http://dx.doi.org/10.3390/hydrology10020051.
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Adsorption and desorption processes occurring on suspended and bed sediments were studied in two datasets from western Europe watersheds (Meuse and Mosel). Copper and zinc dissolved and total concentrations, total suspended sediment concentrations, mass concentrations, and grain sizes were analyzed. Four classes of mineral particle size were determined. Grain size distribution had to be considered in order to assess the trace metal particulate phase in the water column. The partitioning coefficients of trace metals between the dissolved and particulate phases were calculated. The objective of this study was to improve the description of the processes involved in the transportation and fate of trace metals in river aquatic ecosystems. Useful data for future modelling, management and contamination assessment of river sediments were provided. As it is confirmed by a literature review, the copper and zinc partitioning coefficients calculated in this study are reliable. The knowledge related to copper and zinc (e.g., partitioning coefficients) will allow us to begin investigations into environmental modelling. This modelling will allow us to consider new sorption processes and better describe trace metal and sediment fates as well as pressure–impact relationships.
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McDonald, H. Gregory. "Yukon to the Yucatan: Habitat partitioning in North American Late Pleistocene ground sloths (Xenarthra, Pilosa)." Journal of Palaeosciences 70, no. (1-2) (September 10, 2021): 237–52. http://dx.doi.org/10.54991/jop.2021.17.
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The late Pleistocene mammalian fauna of North America included seven genera of ground sloth, representing four families. This cohort of megaherbivores had an extensive geographic range in North America from the Yukon in Canada to the Yucatan Peninsula in Mexico and inhabited a variety of biomes. Within this latitudinal range there are taxa with a distribution limited to temperate latitudes while others have a distribution restricted to tropical latitudes. Some taxa are better documented than others and more is known about their palaeoecology and habitat preferences, while our knowledge of the palaeoecology of taxa more recently discovered remains limited. In order to better understand what aspects of their palaeoecology allowed their dispersal from South America, long–term success in North America and ultimately the underlying causes for their extinction at the end of the Pleistocene more information is needed. A summary overview of the differences in the palaeoecology of the late Pleistocene sloths in North America and their preferred habitats is presented based on different data sources.
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Dalton, Lori A., and Mohammadmahdi R. Yousefi. "Data Requirements for Model-Based Cancer Prognosis Prediction." Cancer Informatics 14s5 (January 2015): CIN.S30801. http://dx.doi.org/10.4137/cin.s30801.
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Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated using optimal Bayesian classification, given patient data. It was assumed that (1) disease is the result of several mutations of a known healthy network and that these mutations and their probability distribution in the population are known and (2) only a single snapshot of the patient's gene activity profile is observed. It was shown that, even in ideal settings where cancer in the population and the effect of a drug are fully modeled, a single static measurement is typically not sufficient. Here, we study what measurements are sufficient to predict prognosis. In particular, we relax assumption (1) by addressing how population data may be used to estimate network probabilities, and extend assumption (2) to include static and time-series measurements of both population and patient data. Furthermore, we extend the prediction of prognosis classes to optimal Bayesian regression of prognosis metrics. Even when time-series data is preferable to infer a stochastic dynamical network, we show that static data can be superior for prognosis prediction when constrained to small samples. Furthermore, although population data is helpful, performance is not sensitive to inaccuracies in the estimated network probabilities.
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Kerkweg, A., P. Jöckel, A. Pozzer, H. Tost, R. Sander, M. Schulz, P. Stier, E. Vignati, J. Wilson, and J. Lelieveld. "Consistent simulation of bromine chemistry from the marine boundary layer to the stratosphere, Part I: model description, sea salt aerosols and pH." Atmospheric Chemistry and Physics Discussions 8, no. 2 (April 14, 2008): 7217–62. http://dx.doi.org/10.5194/acpd-8-7217-2008.
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Abstract. This is the first article of a series presenting a detailed analysis of bromine chemistry simulated with the atmospheric chemistry general circulation model ECHAM5/MESSy. Release from sea salt is an important bromine source, hence the model explicitly calculates aerosol chemistry and phase partitioning for coarse mode aerosol particles. Many processes including chemical reaction rates are influenced by the particle size distribution, and aerosol associated water strongly affects the aerosol pH. Knowledge of the aerosol pH is important as it determines the aerosol chemistry, e.g., the efficiency of sulphur oxidation and bromine release. Here, we focus on the simulated sea salt aerosol size distribution and the coarse mode aerosol pH. A comparison with available field data shows that the simulated aerosol distributions agree reasonably well within the range of measurements. In spite of the small number of aerosol pH measurements and the uncertainty in its experimental determination, the simulated aerosol pH compares well with the observations. The aerosol pH ranges from alkaline aerosol in areas of strong production down to pH values of 1 over regions of medium sea salt production and high levels of gas phase acids, mostly polluted regions over the oceans in the northern hemisphere.
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Kerkweg, A., P. Jöckel, A. Pozzer, H. Tost, R. Sander, M. Schulz, P. Stier, E. Vignati, J. Wilson, and J. Lelieveld. "Consistent simulation of bromine chemistry from the marine boundary layer to the stratosphere – Part 1: Model description, sea salt aerosols and pH." Atmospheric Chemistry and Physics 8, no. 19 (October 15, 2008): 5899–917. http://dx.doi.org/10.5194/acp-8-5899-2008.
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Abstract. This is the first article of a series presenting a detailed analysis of bromine chemistry simulated with the atmospheric chemistry general circulation model ECHAM5/MESSy. Release from sea salt is an important bromine source, hence the model explicitly calculates aerosol chemistry and phase partitioning for coarse mode aerosol particles. Many processes including chemical reaction rates are influenced by the particle size distribution, and aerosol associated water strongly affects the aerosol pH. Knowledge of the aerosol pH is important as it determines the aerosol chemistry, e.g., the efficiency of sulphur oxidation and bromine release. Here, we focus on the simulated sea salt aerosol size distribution and the coarse mode aerosol pH. A comparison with available field data shows that the simulated aerosol distributions agree reasonably well within the range of measurements. In spite of the small number of aerosol pH measurements and the uncertainty in its experimental determination, the simulated aerosol pH compares well with the observations. The aerosol pH ranges from alkaline aerosol in areas of strong production down to pH-values of 1 over regions of medium sea salt production and high levels of gas phase acids, mostly polluted regions over the oceans in the Northern Hemisphere.
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Molinié, Dylan, Kurosh Madani, Véronique Amarger, and Abdennasser Chebira. "Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA)." Machine Learning and Knowledge Extraction 5, no. 3 (August 4, 2023): 979–1009. http://dx.doi.org/10.3390/make5030051.
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This paper introduces a non-parametric methodology based on classical unsupervised clustering techniques to automatically identify the main regions of a space, without requiring the objective number of clusters, so as to identify the major regular states of unknown industrial systems. Indeed, useful knowledge on real industrial processes entails the identification of their regular states, and their historically encountered anomalies. Since both should form compact and salient groups of data, unsupervised clustering generally performs this task fairly accurately; however, this often requires the number of clusters upstream, knowledge which is rarely available. As such, the proposed algorithm operates a first partitioning of the space, then it estimates the integrity of the clusters, and splits them again and again until every cluster obtains an acceptable integrity; finally, a step of merging based on the clusters’ empirical distributions is performed to refine the partitioning. Applied to real industrial data obtained in the scope of a European project, this methodology proved able to automatically identify the main regular states of the system. Results show the robustness of the proposed approach in the fully-automatic and non-parametric identification of the main regions of a space, knowledge which is useful to industrial anomaly detection and behavioral modeling.
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Benmouiza, Khalil. "NONLINEAR CLUSTERED ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODEL FOR HOURLY SOLAR IRRADIATION ESTIMATION." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 68, no. 1 (April 1, 2023): 7–11. http://dx.doi.org/10.59277/rrst-ee.2023.68.1.1.
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Solar energy occupies an important place among the various sources of renewable energy. A precise knowledge of the distribution of solar irradiation in a specified location is needed before any solar irradiation system installation. This paper introduces a nonlinear clustering, adaptive-network-based fuzzy inference system (ANFIS) model to estimate the hourly solar irradiation data using meteorological inputs and clustering algorithms: grid partitioning, subtractive clustering, and fuzzy c-means. Comparing these clustering algorithms is investigated to classify the inputs into clusters, which helps the solar irradiation estimation model build better. This method's advantage is understanding and simplifying the nonlinearity presented in the input’s datasets. Moreover, the FCM algorithm gives the best results from comparing the testing data; the RMSE is 43.2274 W/m2, and MSE equals 2001.34 W/m2 with an R2 equal to 0.9893.
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Moura, Aloysio Souza de, Felipe Santana Machado, Ravi Fernandes Mariano, Cléber Rodrigo de Souza, Urica Carolina Lemos Mengez, and Marco Aurélio Leite Fontes. "Mesoscale bird distribution pattern in montane phytophysiognomies along an ecotone between two hotspots." Acta Scientiarum. Biological Sciences 43 (December 8, 2021): e56931. http://dx.doi.org/10.4025/actascibiolsci.v43i1.56931.
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Brazil has a high diversity of birds and presents the largest number of threatened bird species in the neotropical region. Even so, there are gaps in the bird knowledge, especially in ecotonal montane regions. Given this panorama, this study aimed to analyse the bird community distribution (richness, composition, and beta diversity between phytophysiognomies) of an ecotonal montane landscape of southeastern Brazil, with the purpose of detecting substitution patterns of bird species on a meso-scale. Using bird data performed during the years 1998 to 2015 in 46 sampling points, we found high bird richness in montane phytophysiognomies along an ecotone between Cerrado and Atlantic Forest hotspots. The composition present species of both domains, with high turnover component. We highlight the field environments and candeais are considered homogeneous and threathened, which would directly affect birds. The present study contributes to future conservation strategies, as it demonstrates ecotonal regions as transition zones and reinforces the need to consider as particular ecological units. These ecotonal regions are key locations for understanding ecological patterns in response to environmental changes or phytophysiognomies. Knowing how partitioning of the composition occurs within an environmental mosaic is essential to understand the limits and distributions of the species and conserve them.
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Robinson, Kevin P., Duncan A. I. MacDougall, Connor C. G. Bamford, William J. Brown, Ciaran J. Dolan, Rebecca Hall, Gary N. Haskins, et al. "Ecological habitat partitioning and feeding specialisations of coastal minke whales (Balaenoptera acutorostrata) using a recently designated MPA in northeast Scotland." PLOS ONE 18, no. 7 (July 19, 2023): e0246617. http://dx.doi.org/10.1371/journal.pone.0246617.
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In the design of protected areas for cetaceans, spatial maps rarely take account of the life-history and behaviour of protected species relevant to their spatial ambit, which may be important for their management. In this study, we examined the distribution and feeding behaviours of adult versus juvenile minke whales (Balaenoptera acutorostrata) from long-term studies in the Moray Firth in northeast Scotland, where a Marine Protected Area (MPA) has recently been designated. Data were collected during dedicated boat surveys between 2001 and 2022 inclusive, from which 784 encounters with 964 whales of confirmed age-class (471 juveniles and 493 adults) were recorded from 56,263 km of survey effort, resulting in 238 focal follows. Adults and juveniles were occasionally seen together, but their distributions were not statistically correlated, and GIS revealed spatial separation / habitat partitioning by age-class―with juveniles preferring shallower, inshore waters with sandy-gravel sediments, and adults preferring deeper, offshore waters with greater bathymetric slope. GAMs suggested that the partitioning between age-classes was predominantly based on the differing proximity of animals to the shore, with juveniles showing a preference for the gentlest seabed slopes, and both adults and juveniles showing a similar preference for sandy gravel sediment types. However, the GAMs only used sightings data with available survey effort (2008 to 2022) and excluded depth due to collinearity issues. Whilst adult minkes employed a range of “active” prey-entrapment specialisations, showing inter-individual variation and seasonal plasticity in their targeted prey, juveniles almost exclusively used “passive” (low energy) feeding methods targeting low-density patches of inshore prey. These findings corroborate the need to incorporate demographic and behavioural data into spatial models when identifying priority areas for protected cetacean species. Not all areas within an MPA have equal value for a population and a better knowledge of the spatial preferences of these whales within the designated Scottish MPAs, appointed for their protection, is considered vital for their conservation.
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Wang, Ziyu, Kai Liu, Jingjing Cao, Liheng Peng, and Xin Wen. "Annual Change Analysis of Mangrove Forests in China during 1986–2021 Based on Google Earth Engine." Forests 13, no. 9 (September 14, 2022): 1489. http://dx.doi.org/10.3390/f13091489.
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Mangroves are a key type of protected coastal wetland, with a range of benefits such as protection from wave damage, sand fixation, water purification and ecological tourism. As the academic knowledge of mangroves has gradually increased, the use of remote sensing to monitor their dynamic changes in China has become a hot topic of discussion and has received attention in academic circles. Remote sensing has also provided necessary auxiliary decision-making suggestions and data support for the scientific and rational conservation, restoration and management of mangrove resources. In this paper, we used Landsat satellite series data combined with the normalized difference vegetation index (NDVI) and adaptive threshold partitioning (OTSU method) to monitor mangrove dynamics in coastal China from 1986 to 2021 based on Google Earth Engine (GEE), with three main results. (1) Based on the massive data and efficient computational capability of the GEE platform, we achieved large-scale interannual mangrove distribution extraction. The overall classification accuracy for 2019 exceeded 0.93, and the mangrove distribution extraction effect was good. (2) The total mangrove area and the mean patch size in China showed overall increasing trends, and Guangdong and Guangxi were the top two provinces in China in terms of the largest mangrove area. (3) Except for Dongzhaigang National Nature Reserve, the mangrove areas in other national mangrove reserves mainly showed increasing trends, confirming the effectiveness of the reserves. Data on the spatial structure and area trends of mangroves in China can provide scientific references for mangrove conservation and development, and serve in the further restoration of mangrove ecosystems.
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Butler, Rebecca A., Mona Papeş, James T. Vogt, Dave J. Paulsen, Christopher Crowe, and Rebecca T. Trout Fryxell. "Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling." PLOS Neglected Tropical Diseases 18, no. 2 (February 14, 2024): e0011919. http://dx.doi.org/10.1371/journal.pntd.0011919.
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Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014–2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.
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Sánchez-Cabanes, Alicia, Maja Nimak-Wood, Nicola Harris, and Renaud De Stephanis. "Habitat preferences among three top predators inhabiting a degraded ecosystem, the Black Sea." Scientia Marina 81, no. 2 (June 14, 2017): 217. http://dx.doi.org/10.3989/scimar.04493.07a.
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This study investigated whether there is evidence of widespread niche partitioning based on environmental factors in the Black Sea and tested the hypothesis that physiographic factors may be employed as predictors. It addresses poorly researched areas with good habitat potential for the only three cetacean subspecies living in this area: the Black Sea short-beaked common dolphin (Delphinus delphis spp. ponticus), the Black Sea bottlenose dolphin (Tursiops truncatus spp. ponticus) and the Black Sea harbour porpoise (Phocoena phocoena spp. relicta). Generalized additive models (GAMs) were used to analyse data collected from multiple sources. In total, 745 sightings of the three species between 1998 and 2010 throughout the Black Sea were included. The analysis found depth and sea surface temperature to be the most important variables for separating the occurrence of the three species. Common dolphins occurred mainly in deep waters and in areas where the sea surface temperature was low, bottlenose dolphins were distributed primarily in shallower and warmer waters than common dolphins, and harbour porpoises were distributed in shallower waters with lower sea surface temperature than bottlenose dolphins. This study suggests strong niche segregation among the three cetacean species. The study is also the first contribution to the basic information of cetacean species distribution and habitat preferences in the Black Sea as a whole. Knowledge of the distribution of the three dolphin species in the study area is essential to establish conservation measures for these populations.
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Reyes-Palomeque, Gabriela, Juan Manuel Dupuy, Kristofer D. Johnson, Miguel Angel Castillo-Santiago, and J. Luis Hernández-Stefanoni. "Combining LiDAR data and airborne imagery of very high resolution to improve aboveground biomass estimates in tropical dry forests." Forestry: An International Journal of Forest Research 92, no. 5 (June 14, 2019): 599–615. http://dx.doi.org/10.1093/forestry/cpz037.
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Abstract Knowledge of the spatial distribution of aboveground biomass (AGB) is crucial to guide forest conservation and management to maintain carbon stocks. LiDAR has been highly successful for this purpose, but has limited availability. Very-high resolution (<1 m) orthophotos can also be used to estimate AGB because they allow a fine distinction of forest canopy grain. We evaluated the separate and joint performance of orthophotos and LiDAR data to estimate AGB in two types of tropical dry forests in the Yucatan Peninsula. Woody plants were surveyed in twenty 0.1 ha plots in a semideciduous forest at Kaxil Kiuic Biocultural Reserve (RBKK) and 28 plots in a semievergreen forest at Felipe Carrillo Puerto (FCP). We fitted three regression models: one based on LiDAR data, another based on orthophoto variables calculated for forest canopy and canopy opening fractions, and a third model that combined both sets of variables. Variation in AGB was decomposed into LiDAR, orthophotos and joint components using variation-partitioning analyses. In FCP, regression models using LiDAR data only showed higher fit (R2 = 0.82) than orthophoto variables only (R2 = 0.70). In contrast, orthophotos had a slightly higher fit (R2 = 0.91) than LiDAR (R2 = 0.88) in RBKK, because orthophoto variables characterize very well the horizontal structure of canopies on this site. The model that combined both data sets showed a better fit (R2 = 0.85) only in FCP, which has a more complex forest structure. The largest percentage of AGB variation (88 per cent in RBKK and 67 per cent in FCP) was explained by the joint contribution of LiDAR and orthophotos. We conclude that both LiDAR and orthophotos provide accurate estimation of AGB, but their relative performance varies with forest type and structural complexity. Combining the two sets of variables can further improve the accuracy of AGB estimation, particularly in forests with complex vegetation structure.
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Wen, Hang, Pamela L. Sullivan, Gwendolyn L. Macpherson, Sharon A. Billings, and Li Li. "Deepening roots can enhance carbonate weathering by amplifying CO<sub>2</sub>-rich recharge." Biogeosciences 18, no. 1 (January 5, 2021): 55–75. http://dx.doi.org/10.5194/bg-18-55-2021.
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Abstract. Carbonate weathering is essential in regulating atmospheric CO2 and carbon cycle at the century timescale. Plant roots accelerate weathering by elevating soil CO2 via respiration. It however remains poorly understood how and how much rooting characteristics (e.g., depth and density distribution) modify flow paths and weathering. We address this knowledge gap using field data from and reactive transport numerical experiments at the Konza Prairie Biological Station (Konza), Kansas (USA), a site where woody encroachment into grasslands is surmised to deepen roots. Results indicate that deepening roots can enhance weathering in two ways. First, deepening roots can control thermodynamic limits of carbonate dissolution by regulating how much CO2 transports vertical downward to the deeper carbonate-rich zone. The base-case data and model from Konza reveal that concentrations of Ca and dissolved inorganic carbon (DIC) are regulated by soil pCO2 driven by the seasonal soil respiration. This relationship can be encapsulated in equations derived in this work describing the dependence of Ca and DIC on temperature and soil CO2. The relationship can explain spring water Ca and DIC concentrations from multiple carbonate-dominated catchments. Second, numerical experiments show that roots control weathering rates by regulating recharge (or vertical water fluxes) into the deeper carbonate zone and export reaction products at dissolution equilibrium. The numerical experiments explored the potential effects of partitioning 40 % of infiltrated water to depth in woodlands compared to 5 % in grasslands. Soil CO2 data suggest relatively similar soil CO2 distribution over depth, which in woodlands and grasslands leads only to 1 % to ∼ 12 % difference in weathering rates if flow partitioning was kept the same between the two land covers. In contrast, deepening roots can enhance weathering by ∼ 17 % to 200 % as infiltration rates increased from 3.7 × 10−2 to 3.7 m/a. Weathering rates in these cases however are more than an order of magnitude higher than a case without roots at all, underscoring the essential role of roots in general. Numerical experiments also indicate that weathering fronts in woodlands propagated > 2 times deeper compared to grasslands after 300 years at an infiltration rate of 0.37 m/a. These differences in weathering fronts are ultimately caused by the differences in the contact times of CO2-charged water with carbonate in the deep subsurface. Within the limitation of modeling exercises, these data and numerical experiments prompt the hypothesis that (1) deepening roots in woodlands can enhance carbonate weathering by promoting recharge and CO2–carbonate contact in the deep subsurface and (2) the hydrological impacts of rooting characteristics can be more influential than those of soil CO2 distribution in modulating weathering rates. We call for colocated characterizations of roots, subsurface structure, and soil CO2 levels, as well as their linkage to water and water chemistry. These measurements will be essential to illuminate feedback mechanisms of land cover changes, chemical weathering, global carbon cycle, and climate.
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GUILLER, ANNIE, ALAIN BELLIDO, ALAIN COUTELLE, and LUC MADEC. "Spatial genetic pattern in the land mollusc Helix aspersa inferred from a ‘centre-based clustering’ procedure." Genetical Research 88, no. 1 (August 2006): 27–44. http://dx.doi.org/10.1017/s0016672306008305.
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The present work provides the first broad-scale screening of allozymes in the land snail Helix aspersa. By using overall information available on the distribution of genetic variation between 102 populations previously investigated, we expect to strengthen our knowledge on the spread of the invasive aspersa subspecies in the Western Mediterranean. We propose a new approach based on a centre-based clustering procedure to cluster populations into groups following rules of geographical proximity and genetic similarity. Assuming a stepping-stone model of diffusion, we apply a partitioning algorithm which clusters only populations that are geographically contiguous. The algorithm used, which is actually part of leading methods developed for analysing large microarray datasets, is that of the k-means. Its goal is to minimize the within-group variance. The spatial constraint is provided by a list of connections between localities deduced from a Delaunay network. After testing each optimal group for the presence of spatial arrangement in the genetic data, the inferred genetic structure was compared with partitions obtained from other methods published for defining homogeneous groups (i.e. the Monmonier and SAMOVA algorithms). Competing biogeographical scenarios inferred from the k-means procedure were then compared and discussed to shed more light on colonization routes taken by the species.
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Islam, M. J., A. W. Reza, A. S. M. Z. Kausar, and H. Ramiah. "New Ray Tracing Method to Investigate the Various Effects on Wave Propagation in Medical Scenario: An Application of Wireless Body Area Network." Scientific World Journal 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/306270.
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The advent of technology with the increasing use of wireless network has led to the development of Wireless Body Area Network (WBAN) to continuously monitor the change of physiological data in a cost efficient manner. As numerous researches on wave propagation characterization have been done in intrabody communication, this study has given emphasis on the wave propagation characterization between the control units (CUs) and wireless access point (AP) in a hospital scenario. Ray tracing is a tool to predict the rays to characterize the wave propagation. It takes huge simulation time, especially when multiple transmitters are involved to transmit physiological data in a realistic hospital environment. Therefore, this study has developed an accelerated ray tracing method based on the nearest neighbor cell and prior knowledge of intersection techniques. Beside this, Red-Black tree is used to store and provide a faster retrieval mechanism of objects in the hospital environment. To prove the superiority, detailed complexity analysis and calculations of reflection and transmission coefficients are also presented in this paper. The results show that the proposed method is about 1.51, 2.1, and 2.9 times faster than the Object Distribution Technique (ODT), Space Volumetric Partitioning (SVP), and Angular Z-Buffer (AZB) methods, respectively. To show the various effects on received power in 60 GHz frequency, few comparisons are made and it is found that on average −9.44 dBm, −8.23 dBm, and −9.27 dBm received power attenuations should be considered when human, AP, and CU move in a given hospital scenario.
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Velluet, C., J. Demarty, B. Cappelaere, I. Braud, H. B. A. Issoufou, N. Boulain, D. Ramier, et al. "Building a field- and model-based climatology of local water and energy cycles in the cultivated Sahel – annual budgets and seasonality." Hydrology and Earth System Sciences Discussions 11, no. 5 (May 13, 2014): 4753–808. http://dx.doi.org/10.5194/hessd-11-4753-2014.
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Abstract. In the African Sahel, energy and water cycling at the land surface is pivotal for regional climate, water resources and land productivity, yet it is still extremely poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt, rainfed millet crop and fallow bush. These estimates build on the combination of a 7 year field dataset from two typical plots in southwestern Niger with detailed physically-based soil-plant-atmosphere modelling, yielding a continuous, comprehensive set of water and energy flux and storage variables over the 7 year period. In this study case in particular, blending field data with mechanistic modelling is considered as making best use of available data and knowledge for such purpose. It extends observations by reconstructing missing data and extrapolating to unobserved variables or periods. Furthermore, model constraining with observations compromises between extraction of observational information content and integration of process understanding, hence accounting for data imprecision and departure from physical laws. Climatological averages of all water and energy variables, with associated sampling uncertainty, are derived at annual to subseasonal scales from the 7 year series produced. Similarities and differences in the two ecosystems behaviors are highlighted. Mean annual evapotranspiration is found to represent ~82–85% of rainfall for both systems, but with different soil evaporation/plant transpiration partitioning and different seasonal distribution. The remainder consists entirely of runoff for the fallow, whereas drainage and runoff stand in a 40–60% proportion for the millet field. These results should provide a robust reference for the surface energy- and water-related studies needed in this region. The model developed in this context has the potential for reliable simulations outside the reported conditions, including changing climate and land cover.
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Asyaev, Grigorii, Alexander Sokolov, and Alexey Ruchay. "Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks." Mathematics 11, no. 18 (September 16, 2023): 3939. http://dx.doi.org/10.3390/math11183939.
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This paper considers the main approaches to building algorithms for the decision support systems of information protection strategies against cyberattacks in the networks of automated process control systems (the so-called recommender systems). The advantages and disadvantages of each of the considered algorithms are revealed, and their applicability to the processing of the information security events of the UNSW-NB 15 dataset is analyzed. The dataset used contains raw network packets collected using the IXIA PerfectStorm software in the CyberRange laboratory of the Australian Cyber Security Centre (Canberra) in order to create a hybrid of the simulation of the real actions and the synthetic behavior of the network traffic generated during attacks. The possibility of applying four semantic proximity algorithms to partition process the data into clusters based on attack type in a distribution control system (DCS) is analyzed. The percentage of homogeneous records belonging to a particular type of attack is used as the metric that determines the optimal method of cluster partitioning. This metric was chosen under the assumption that cyberattacks located “closer” to each other in the multidimensional space have similar defense strategies. A hypothesis is formulated about the possibility of transferring knowledge about attacks from the vector feature space into a semantic form using semantic proximity methods. The percentage of homogeneous entries was maximal when the cosine proximity measure was used, which confirmed the hypothesis about the possibility of applying the corresponding algorithm in the recommender system.
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STRATHE, A. B., H. JØRGENSEN, E. KEBREAB, and A. DANFÆR. "Bayesian simultaneous equation models for the analysis of energy intake and partitioning in growing pigs." Journal of Agricultural Science 150, no. 6 (April 4, 2012): 764–74. http://dx.doi.org/10.1017/s0021859612000275.
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SUMMARYThe objective of the current study was to develop Bayesian simultaneous equation models for modelling energy intake and partitioning in growing pigs. A key feature of the Bayesian approach is that parameters are assigned prior distributions, which may reflect the current state of nature. In the models, rates of metabolizable energy (ME) intake, protein deposition (PD) and lipid deposition (LD) were treated as dependent variables accounting for residuals being correlated. Two complementary equation systems were used to model ME intake (MEI), PD and LD. Informative priors were developed, reflecting current knowledge about metabolic scaling and partial efficiencies of PD and LD rates, whereas flat non-informative priors were used for the reminder of the parameters. The experimental data analysed originate from a balance and respiration trial with 17 cross-bred pigs of three genders (barrows, boars and gilts) selected on the basis of similar birth weight. The pigs were fed four diets based on barley, wheat and soybean meal supplemented with crystalline amino acids to meet or exceed Danish nutrient requirement standards. Nutrient balances and gas exchanges were measured at c. 25, 75, 120 and 150 kg body weight (BW) using metabolic cages and open circuit respiration chambers. A total of 56 measurements were performed. The sensitivity analysis showed that only the maintenance component was sensitive to the prior specification, and hence the maintenance estimate of 0·91 MJ ME/kg0·60 per day (0·95 credible interval (CrI): 0·78–1·09) should be interpreted with caution. It was shown that boars’ ability to deposit protein was superior to that of barrows and gilts, as these had an estimated maximum PD (PDmax) of 250 g/day (0·95 CrI: 237–263), whereas the barrows and gilts had a PDmax of 210 g/day (0·95 CrI: 198–220). Furthermore, boars reached PDmax at 109 kg BW (0·95 CrI: 93·6–130), whereas barrows and gilts maximized PD at 81·7 kg BW (0·95 CrI: 75·6–89·5). At 25 kg BW, the boars partitioned on average 5–6% more of the ME above maintenance into PD than barrows and gilts, and this was progressively increased to 10–11% more than barrows and gilts at 150 kg BW. The Bayesian modelling framework can be used to further refine the analysis of data from metabolic studies in growing pigs.
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Velluet, C., J. Demarty, B. Cappelaere, I. Braud, H. B. A. Issoufou, N. Boulain, D. Ramier, et al. "Building a field- and model-based climatology of local water and energy cycles in the cultivated Sahel – annual budgets and seasonality." Hydrology and Earth System Sciences 18, no. 12 (December 10, 2014): 5001–24. http://dx.doi.org/10.5194/hess-18-5001-2014.
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Abstract. In the sub-Saharan Sahel, energy and water cycling at the land surface is pivotal for the regional climate, water resources and land productivity, yet it is still very poorly documented. As a step towards a comprehensive climatological description of surface fluxes in this area, this study provides estimates of long-term average annual budgets and seasonal cycles for two main land use types of the cultivated Sahelian belt: rainfed millet crop and fallow bush. These estimates build on the combination of a 7-year field data set from two typical plots in southwestern Niger with detailed physically based soil–plant–atmosphere modeling, yielding a continuous, comprehensive set of water and energy flux and storage variables over this multiyear period. In the present case in particular, blending field data with mechanistic modeling makes the best use of available data and knowledge for the construction of the multivariate time series. Rather than using the model only to gap-fill observations into a composite series, model–data integration is generalized homogeneously over time by generating the whole series with the entire data-constrained model simulation. Climatological averages of all water and energy variables, with associated sampling uncertainty, are derived at annual to sub-seasonal scales from the time series produced. Similarities and differences in the two ecosystem behaviors are highlighted. Mean annual evapotranspiration is found to represent ~82–85% of rainfall for both systems, but with different soil evaporation/plant transpiration partitioning and different seasonal distribution. The remainder consists entirely of runoff for the fallow, whereas drainage and runoff stand in a 40–60% proportion for the millet field. These results should provide a robust reference for the surface energy- and water-related studies needed in this region. Their significance and the benefits they gain from the innovative data–model integration approach are thoroughly discussed. The model developed in this context has the potential for reliable simulations outside the reported conditions, including changing climate and land cover.
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Wu, Tianjun, Jiancheng Luo, Lijing Gao, Yingwei Sun, Wen Dong, Ya’nan Zhou, Wei Liu, et al. "Geo-Object-Based Vegetation Mapping via Machine Learning Methods with an Intelligent Sample Collection Scheme: A Case Study of Taibai Mountain, China." Remote Sensing 13, no. 2 (January 13, 2021): 249. http://dx.doi.org/10.3390/rs13020249.
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Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.
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Ervens, B., B. J. Turpin, and R. J. Weber. "Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies." Atmospheric Chemistry and Physics Discussions 11, no. 8 (August 8, 2011): 22301–83. http://dx.doi.org/10.5194/acpd-11-22301-2011.
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Abstract. Progress has been made over the past decade in predicting secondary organic aerosol (SOA) mass in the atmosphere using vapor pressure-driven partitioning, which implies that SOA compounds are formed in the gas phase and then partition to an organic phase (gasSOA). However, discrepancies in predicting organic aerosol oxidation state, size and product (molecular mass) distribution, relative humidity (RH) dependence, color, and vertical profile suggest that additional SOA sources and aging processes may be important. The formation of SOA in cloud and aerosol water (aqSOA) is not considered in these models even though water is an abundant medium for atmospheric chemistry and such chemistry can form dicarboxylic acids and "humic-like substances" (oligomers, high-molecular-weight compounds), i.e., compounds that do not have any gas phase sources but comprise a significant fraction of the total SOA mass. There is direct evidence from field observations and laboratory studies that organic aerosol is formed in cloud and aerosol water, contributing substantial mass to the droplet mode. This review summarizes the current knowledge on aqueous phase organic reactions and combines evidence that points to a significant role of aqSOA formation in the atmosphere. Model studies are discussed that explore the importance of aqSOA formation and suggestions for model improvements are made based on the comprehensive set of laboratory data presented here. A first comparison is made between aqSOA and gasSOA yields and mass predictions for selected conditions. These simulations suggest that aqSOA might contribute almost as much mass as gasSOA to the SOA budget, with highest contributions from biogenic VOC emissions in the presence of anthropogenic pollutants (i.e., NOx) at high relative humidity and cloudiness. Gaps in the current understanding of aqSOA processes are discussed and further studies (laboratory, field, model) are outlined to complement current data sets.
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Ervens, B., B. J. Turpin, and R. J. Weber. "Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies." Atmospheric Chemistry and Physics 11, no. 21 (November 9, 2011): 11069–102. http://dx.doi.org/10.5194/acp-11-11069-2011.
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Abstract. Progress has been made over the past decade in predicting secondary organic aerosol (SOA) mass in the atmosphere using vapor pressure-driven partitioning, which implies that SOA compounds are formed in the gas phase and then partition to an organic phase (gasSOA). However, discrepancies in predicting organic aerosol oxidation state, size and product (molecular mass) distribution, relative humidity (RH) dependence, color, and vertical profile suggest that additional SOA sources and aging processes may be important. The formation of SOA in cloud and aerosol water (aqSOA) is not considered in these models even though water is an abundant medium for atmospheric chemistry and such chemistry can form dicarboxylic acids and "humic-like substances" (oligomers, high-molecular-weight compounds), i.e. compounds that do not have any gas phase sources but comprise a significant fraction of the total SOA mass. There is direct evidence from field observations and laboratory studies that organic aerosol is formed in cloud and aerosol water, contributing substantial mass to the droplet mode. This review summarizes the current knowledge on aqueous phase organic reactions and combines evidence that points to a significant role of aqSOA formation in the atmosphere. Model studies are discussed that explore the importance of aqSOA formation and suggestions for model improvements are made based on the comprehensive set of laboratory data presented here. A first comparison is made between aqSOA and gasSOA yields and mass predictions for selected conditions. These simulations suggest that aqSOA might contribute almost as much mass as gasSOA to the SOA budget, with highest contributions from biogenic emissions of volatile organic compounds (VOC) in the presence of anthropogenic pollutants (i.e. NOx) at high relative humidity and cloudiness. Gaps in the current understanding of aqSOA processes are discussed and further studies (laboratory, field, model) are outlined to complement current data sets.
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Butenko, Olga, and Anna Topchiy. "Modeling fires based on the results of correlation analysis." Ukrainian journal of remote sensing 10, no. 3 (September 29, 2023): 28–33. http://dx.doi.org/10.36023/ujrs.2023.10.3.245.
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In order to monitor and study in more detail the causes and probability of the occurrence and spread of fires in the east of Ukraine in the combat zone, mathematical modeling of the factors influencing the occurrence of fires based on linear regression was performed in this study. The initial assessment of a priori information presented in a discrete form is a time—consuming process. A large dataset with a time interval requires application of ready—made methods and solutions. By applying statistical analysis techniques and historical analogies, it becomes possible to visually and graphically evaluate the initial data. This evaluation serves as the foundation for classifying factors, which enables their division into samples for subsequent analysis and modeling.The expediency of application of correlation analysis is demonstrated by its ability to establish and illustrate the connections between fires and hostilities across different time intervals. To examine the connection between fires and the factors contributing to their occurrence, the widely used method of linear regression was applied, which is common in solving problems of ecological monitoring of the Earth.Consequently, a program code was developed to provide the implementation of the linear regression algorithm. Since a large data set requires ready—made mathematical tools with a visualization function, therefore, the Python programming language was chosen as a tool for mathematical modeling of fires in eastern Ukraine caused by ongoing active hostilities. To facilitate simulation, random variables are partitioned with a distribution ratio of 40% for testing models and 60% for training models. The visual materials in this study encompass the initial data for subsequent analysis, the outcomes of data set partitioning, and their corresponding models. The tabular data comprises quantitative assessments of test and training models, serving as a basis for decision—making regarding the degree to which prediction results align with the study's objectives. These quantitative evaluations of prediction outcomes highlight the necessity of a comprehensive initial set of factors influencing fire initiation, along with their qualitative and quantitative classification. The implementation of the mathematical algorithm confirms the ease of application of regression methods.However, when employing regression analysis to model fires without prior knowledge, it highlights the importance of conducting supplementary analysis through other established methods and synthesizing additional data. This can be achieved by utilizing interval estimates with the aid of fuzzy logic.
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Crespo, Jorge, Martin Reich, Fernando Barra, Juan José Verdugo, Claudio Martínez, Mathieu Leisen, Rurik Romero, Diego Morata, and Carlos Marquardt. "Occurrence and Distribution of Silver in the World-Class Río Blanco Porphyry Cu-Mo Deposit, Central Chile." Economic Geology 115, no. 8 (October 26, 2020): 1619–44. http://dx.doi.org/10.5382/econgeo.4778.
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Abstract Porphyry Cu-Mo deposits (PCDs) are the world’s major source of Cu, Mo, and Re and are also a significant source of Au and Ag. Here we focus on the world-class Río Blanco PCD in the Andes of central Chile, where Ag is a by-product of Cu mining. Statistical examination of an extensive multielemental inductively coupled plasma-mass spectrometry data set indicates compositional trends at the deposit scale, including Ag-Cu (r = 0.71) and Ag-In (r = 0.53) positive correlations, which relate to Cu-Fe sulfides and Cu sulfosalts in the deposit. Silver is primarily concentrated in Cu ores in the central core of the deposit, and significant variations in the Ag concentration are related to the different hydrothermal alteration types. The concentration of Ag is highest in the potassic core (avg 2.01 ppm) and decreases slightly in the gray-green sericite (phyllic) zone (avg 1.72 ppm); Ag is lowest in the outer propylitic alteration zone (avg 0.59 ppm). Drill core samples from major hydrothermal alteration zones were selected for in situ analysis of Ag and associated elements in sulfide and sulfosalt minerals. To ensure representativeness, sample selection considered the spatial distribution of the alteration types and ore paragenesis. Chalcopyrite is the most abundant Cu sulfide in Río Blanco, with Ag concentration that ranges from sub-parts per million levels to hundreds of parts per million. The highest concentration of Ag in chalcopyrite is associated with the high-temperature potassic alteration stage. Bornite is less abundant than chalcopyrite but has the highest Ag concentration of all studied sulfides, ranging from hundreds of parts per million up to ~1,000 ppm. The Ag concentration in bornite is higher in lower-temperature alteration assemblages (moderate gray-green sericite), opposite to the behavior of Ag in chalcopyrite. Pyrite has the lowest Ag content, although concentrations of other critical elements such as Co, Ni, and Au may be significant. The highest Ag concentrations, i.e., thousands of parts per million up to weight percent levels, were detected in late-stage Cu sulfosalts (enargite, tennantite, and tetrahedrite). The Ag content in these sulfosalts increases with increasing Sb concentrations, from the Sb-poor enargite to the Sb-rich tetrahedrite. The earliest Ag mineralization event is related to the potassic alteration stage represented by early biotite and transitional early biotite-type veinlets and where the predominant sulfides are chalcopyrite and bornite. Silver mineralization during this stage was predominantly controlled by crystallization of Cu-Fe sulfides. The second Ag mineralization event at Río Blanco is associated with the transitional Cu mineralization stage, which is represented by the gray-green sericite alteration (C-type veinlets). In this alteration type, Ag was partitioned preferentially into chalcopyrite, bornite, and to a lesser extent pyrite. The last Ag mineralization event is related to the late quartz-sericite alteration stage, characterized by D- and E-type veinlets with pyrite-chalcopyrite and enargite-tennantite-tetrahedrite. Our data indicate that Ag was associated with several Cu mineralization episodes at Río Blanco, with Ag concentration apparently controlled by cooling, changes in pH, fO2 and fS2 of the hydrothermal fluids, and the intensity of alteration. Overall, our results provide information on critical metal partitioning between sulfides, plus the distribution of critical element resources at the deposit scale. Knowledge of the mineralogical occurrence of critical metals in PCDs is necessary to better assess their resources and evaluate the potential for their recovery.
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Ganjdanesh, Alireza, Shangqian Gao, Hirad Alipanah, and Heng Huang. "Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (March 24, 2024): 12118–26. http://dx.doi.org/10.1609/aaai.v38i11.29100.
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Generative Adversarial Networks (GANs) have shown remarkable success in modeling complex data distributions for image-to-image translation. Still, their high computational demands prohibit their deployment in practical scenarios like edge devices. Existing GAN compression methods mainly rely on knowledge distillation or convolutional classifiers' pruning techniques. Thus, they neglect the critical characteristic of GANs: their local density structure over their learned manifold. Accordingly, we approach GAN compression from a new perspective by explicitly encouraging the pruned model to preserve the density structure of the original parameter-heavy model on its learned manifold. We facilitate this objective for the pruned model by partitioning the learned manifold of the original generator into local neighborhoods around its generated samples. Then, we propose a novel pruning objective to regularize the pruned model to preserve the local density structure over each neighborhood, resembling the kernel density estimation method. Also, we develop a collaborative pruning scheme in which the discriminator and generator are pruned by two pruning agents. We design the agents to capture interactions between the generator and discriminator by exchanging their peer's feedback when determining corresponding models' architectures. Thanks to such a design, our pruning method can efficiently find performant sub-networks and can maintain the balance between the generator and discriminator more effectively compared to baselines during pruning, thereby showing more stable pruning dynamics. Our experiments on image translation GAN models, Pix2Pix and CycleGAN, with various benchmark datasets and architectures demonstrate our method's effectiveness.
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Siqueira, Ricardo Almeida de, Daniel Alejandro Vila, and João Maria de Sousa Afonso. "The Performance of the Diurnal Cycle of Precipitation from Blended Satellite Techniques over Brazil." Remote Sensing 13, no. 4 (February 17, 2021): 734. http://dx.doi.org/10.3390/rs13040734.
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The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels.
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Lopez, Gerardo, Romeo R. Favreau, Colin Smith, and Theodore M. DeJong. "L-PEACH: A Computer-based Model to Understand How Peach Trees Grow." HortTechnology 20, no. 6 (December 2010): 983–90. http://dx.doi.org/10.21273/hortsci.20.6.983.
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L-PEACH is a computer-based model that simulates the growth of peach [Prunus persica (L.) Batsch] trees. The model integrates important concepts related to carbon assimilation, distribution, and use in peach trees. It also includes modeling of the responses to horticultural practices such as tree pruning and fruit thinning. While running L-PEACH, three-dimensional (3D) depictions of simulated growing trees can be displayed on the computer screen and the user can easily interact with the model. Quantitative data generated during a simulation can be saved to a file or printed for visualization and analysis. L-PEACH is a powerful tool for understanding how peach trees function in the field environment, and it can be used as an innovative method for dissemination of knowledge related with carbohydrate assimilation and partitioning. In this study, we describe the version of L-PEACH that runs on a daily time-step (L-PEACH-d) and how users can run the model and interact with it. To demonstrate how L-PEACH-d works, different pruning and fruit thinning strategies were analyzed. Regarding pruning, model outputs showed 3D depictions of unpruned trees and pruned trees trained to a perpendicular V system. For the fruit thinning studies, we simulated different intensities and dates of fruit thinning in mature peach trees. Total simulated yield increased with crop load but the opposite was observed for average fruit weight. An optimal balance between simulated total yield and average fruit weight was obtained by leaving 150 fruit per tree. Simulating different dates of fruit thinning indicated that fruit weight at harvest was higher on earlier compared with later-thinned trees. The model indicates that fruit thinning should be therefore carried out early in the season to maximize fruit size. The simulation results demonstrate that L-PEACH-d can be used as an educational tool and facilitate the adoption of suitable cultural practices for efficient production.
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Lopez, Gerardo, Romeo R. Favreau, Colin Smith, and Theodore M. DeJong. "L-PEACH: A Computer-based Model to Understand How Peach Trees Grow." HortTechnology 20, no. 6 (December 2010): 983–90. http://dx.doi.org/10.21273/horttech.20.6.983.
Full textAbstract:
L-PEACH is a computer-based model that simulates the growth of peach [Prunus persica (L.) Batsch] trees. The model integrates important concepts related to carbon assimilation, distribution, and use in peach trees. It also includes modeling of the responses to horticultural practices such as tree pruning and fruit thinning. While running L-PEACH, three-dimensional (3D) depictions of simulated growing trees can be displayed on the computer screen and the user can easily interact with the model. Quantitative data generated during a simulation can be saved to a file or printed for visualization and analysis. L-PEACH is a powerful tool for understanding how peach trees function in the field environment, and it can be used as an innovative method for dissemination of knowledge related with carbohydrate assimilation and partitioning. In this study, we describe the version of L-PEACH that runs on a daily time-step (L-PEACH-d) and how users can run the model and interact with it. To demonstrate how L-PEACH-d works, different pruning and fruit thinning strategies were analyzed. Regarding pruning, model outputs showed 3D depictions of unpruned trees and pruned trees trained to a perpendicular V system. For the fruit thinning studies, we simulated different intensities and dates of fruit thinning in mature peach trees. Total simulated yield increased with crop load but the opposite was observed for average fruit weight. An optimal balance between simulated total yield and average fruit weight was obtained by leaving 150 fruit per tree. Simulating different dates of fruit thinning indicated that fruit weight at harvest was higher on earlier compared with later-thinned trees. The model indicates that fruit thinning should be therefore carried out early in the season to maximize fruit size. The simulation results demonstrate that L-PEACH-d can be used as an educational tool and facilitate the adoption of suitable cultural practices for efficient production.
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Grose, Christopher J., and Juan C. Afonso. "Chemical Disequilibria, Lithospheric Thickness, and the Source of Ocean Island Basalts." Journal of Petrology 60, no. 4 (March 2, 2019): 755–90. http://dx.doi.org/10.1093/petrology/egz012.
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Abstract We examine REE (Rare-Earth Element) and isotopic (Sr–Hf–Nd–Pb) signatures in OIB (Ocean Island Basalts) as a function of lithospheric thickness and show that the data can be divided into thin- (<12 Ma) and thick-plate (>12 Ma) sub-sets. Comparison to geophysically constrained thermal plate models indicates that the demarcation age (∼12 Ma) corresponds to a lithospheric thickness of about 50 km. Thick-plate OIB show incompatible element and isotopic enrichments, whereas thin-plate lavas show MORB-like or slightly enriched values. We argue that enriched signatures in thick-plate OIB originate from low-degree melting at depths below the dry solidus, while depleted signatures in MORB and thin-plate OIB are indicative of higher-degree melting. We tested quantitative explanations of REE systematics using melting models for homogeneous fertile peridotite. Using experimental partition coefficients for major upper mantle minerals, our equilibrium melting models are not able to explain the data. However, using a new grain-scale disequilibrium melting model for the same homogeneous lithology the data can be explained. Disequilibrium models are able to explain the data by reducing the amount of incompatible element partitioning into low degree melts. To explore new levels of detail in disequilibrium phenomena, we employ the Monte-Carlo Potts model to characterize the textural evolution of a microstructure undergoing coarsening and phase transformation processes simultaneous with the diffusive partitioning of trace elements among solid phases and melt in decompressing mantle. We further employ inverse methods to study the thermochemical properties required for models to explain the OIB data. Both data and theory show that OIB erupted on spreading ridges contain signatures close to MORB values, although E-MORB provides the best fit. This indicates that MORB and OIB are produced by compositionally indistinguishable sources, although the isotopic data indicate that the source is heterogeneous. Also, a posteriori distributions are found for the temperature of the thermomechanical lithosphere-asthenosphere boundary (TLAB), the temperature in the source of OIB (Tp, oib) and the extent of equilibrium during melting (i.e. grain size). TLAB has been constrained to 1200–1300°C and Tp, oib is constrained to be <1400°C. However, we consider the constraints on Tp, oib as a description of all OIB to be provisional, because it is a statistical inference from the global dataset. Exceptional islands or island groups may exist, such as the classical ‘hotspots’ (Hawaii, Reunion, etc) and these islands may originate from hot sources. On the other hand, by the same statistical arguments their origins may be anomalously hydrated or enriched instead. Mean grain size in the source of OIB is about 1–5 mm, although this is also provisional due to a strong dependence on knowledge of partition coefficients, ascent rate and the melting function. We also perform an inversion in which partition coefficients were allowed to vary from their experimental values. In these inversions TLAB and Tp, oib are unchanged, but realizations close to equilibrium can be found when partition coefficients differ substantially from their experimental values. We also investigated bulk compositions in the source of OIB constrained by our inverse models. Corrections for crystallization effects provided ambiguous confirmations of previously proposed mantle compositions, with depleted mantle providing the poorest fits. We did not include isotopes in our models, but we briefly evaluate the lithospheric thickness effect on isotopes. Although REE data do not require a lithologically heterogeneous source, isotopes indicate that a minor enriched component disproportionately contributes to thick-plate OIB, but is diluted by high-degree melting in the generation of thin-plate OIB and MORB.
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Crooks, P., and R. H. Perrott. "Language Constructs for Data Partitioning and Distribution." Scientific Programming 4, no. 2 (1995): 59–85. http://dx.doi.org/10.1155/1995/656010.
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This article presents a survey of language features for distributed memory multiprocessor systems (DMMs), in particular, systems that provide features for data partitioning and distribution. In these systems the programmer is freed from consideration of the low-level details of the target architecture in that there is no need to program explicit processes or specify interprocess communication. Programs are written according to the shared memory programming paradigm but the programmer is required to specify, by means of directives, additional syntax or interactive methods, how the data of the program are decomposed and distributed.
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Bo, Shukui, and Yongju Jing. "Data Distribution Partitioning for One-Class Extraction from Remote Sensing Imagery." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 09 (February 16, 2017): 1754018. http://dx.doi.org/10.1142/s0218001417540180.
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One-class extraction from remotely sensed imagery is researched with multi-class classifiers in this paper. With two supervised multi-class classifiers, Bayesian classifier and nearest neighbor classifier, we firstly analyzed the effect of the data distribution partitioning on one-class extraction from the remote sensing images. The data distribution partitioning refers to the way that the data set is partitioned before classification. As a parametric method, the Bayesian classifier achieved good classification performance when the data distribution was partitioned appropriately. While as a nonparametric method, the NN classifier did not require a detailed partitioning of the data distribution. For simplicity, the data set can be partitioned into two classes, the class of interest and the remainder, to extract the specific class. With appropriate partitioning of the data set, the specific class of interest was well extracted from remotely sensed imagery in the experiments. This study will be helpful for one-class extraction from remote sensing imagery with multi-class classifiers. It provides a way to improve the one-class classification from the aspect of data distribution partitioning.
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Kurrant, Douglas, Muhammad Omer, Nasim Abdollahi, Pedram Mojabi, Elise Fear, and Joe LoVetri. "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning." Journal of Imaging 7, no. 1 (January 7, 2021): 5. http://dx.doi.org/10.3390/jimaging7010005.
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Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.
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Kurrant, Douglas, Muhammad Omer, Nasim Abdollahi, Pedram Mojabi, Elise Fear, and Joe LoVetri. "Evaluating Performance of Microwave Image Reconstruction Algorithms: Extracting Tissue Types with Segmentation Using Machine Learning." Journal of Imaging 7, no. 1 (January 7, 2021): 5. http://dx.doi.org/10.3390/jimaging7010005.
Full textAbstract:
Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.
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AGRAWAL, GAGAN. "DATA DISTRIBUTION ANALYSIS FOR IRREGULAR AND ADAPTIVE CODES." Parallel Processing Letters 09, no. 01 (March 1999): 135–46. http://dx.doi.org/10.1142/s0129626499000153.
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An important component in compiling for distributed memory machines is data partitioning. While a number of automatic analysis techniques have been proposed for this phase, none of them is applicable for irregular problems. In this paper, we present compile-time analysis for determining data partitioning for such applications. We have developed a set of cost functions for determining communication and redistribution costs in irregular codes. We first determine the appropriate distributions for a single data parallel statement, and then use the cost functions with a greedy algorithm for computing distributions for the full program. Initial performance results on a 16 processor IBM SP-2 are also presented.
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Marmor, Yariv N., and Emil Bashkansky. "Reliability of Partitioning Metric Space Data." Mathematics 12, no. 4 (February 18, 2024): 603. http://dx.doi.org/10.3390/math12040603.
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The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtained by a simulation study for a given partition under the homogeneity null hypothesis to draw a conclusion using standard statistical procedures. The proposed concept is illustrated using three examples representing different types of objects under study. Some general considerations are given regarding the nature of the SP distribution under the null hypothesis and its dependence on the number of divisions and the amount of data within them. A detailed modus operandi (working method) for analyzing a metric data partition is also offered.
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Kholiev, Vladyslav, and Olesia Barkovska. "Analysis of the of training and test data distribution for audio series classification." Інформаційно-керуючі системи на залізничному транспорті 28, no. 1 (March 27, 2023): 38–43. http://dx.doi.org/10.18664/ikszt.v28i1.276343.
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The effectiveness of machine learning algorithms for any given task largely depends on the training and test datasets. This manifests itself not only in the amount of data, but also in its content (that is, its relevance for the task at hand), as well as in its organization. Generally, the common approach is to split the dataset into training and testing sets to avoid model overfitting. In addition, to achieve better metrics for the selected criteria (accuracy, learning rate, etc.) of model performance, different ratios of training and test sets are used in the partitioning. The goal of this paper is to analyze methods of data set partitioning for use in training neural networks and statistical models. One of the reviewed methods, specifically the cross-validation method, was applied to a dataset developed from the LibriSpeach corpus, an open English speech corpus based on the LirbiVox project of voluntarily contributed audio books. The result of applying the selected data partitioning method on the selected data set is demonstrated.
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COENEN, FRANS, and PAUL LENG. "Partitioning strategies for distributed association rule mining." Knowledge Engineering Review 21, no. 1 (March 2006): 25–47. http://dx.doi.org/10.1017/s0269888906000786.
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In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.
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Nguyen Thai, B., and A. Olasz. "RASTER DATA PARTITIONING FOR SUPPORTING DISTRIBUTED GIS PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 543–51. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-543-2015.
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In the geospatial sector big data concept also has already impact. Several studies facing originally computer science techniques applied in GIS processing of huge amount of geospatial data. In other research studies geospatial data is considered as it were always been big data (Lee and Kang, 2015). Nevertheless, we can prove data acquisition methods have been improved substantially not only the amount, but the resolution of raw data in spectral, spatial and temporal aspects as well. A significant portion of big data is geospatial data, and the size of such data is growing rapidly at least by 20% every year (Dasgupta, 2013). The produced increasing volume of raw data, in different format, representation and purpose the wealth of information derived from this data sets represents only valuable results. However, the computing capability and processing speed rather tackle with limitations, even if semi-automatic or automatic procedures are aimed on complex geospatial data (Krist´of et al., 2014). In late times, distributed computing has reached many interdisciplinary areas of computer science inclusive of remote sensing and geographic information processing approaches. Cloud computing even more requires appropriate processing algorithms to be distributed and handle geospatial big data. Map-Reduce programming model and distributed file systems have proven their capabilities to process non GIS big data. But sometimes it’s inconvenient or inefficient to rewrite existing algorithms to Map-Reduce programming model, also GIS data can not be partitioned as text-based data by line or by bytes. Hence, we would like to find an alternative solution for data partitioning, data distribution and execution of existing algorithms without rewriting or with only minor modifications. This paper focuses on technical overview of currently available distributed computing environments, as well as GIS data (raster data) partitioning, distribution and distributed processing of GIS algorithms. A proof of concept implementation have been made for raster data partitioning, distribution and processing. The first results on performance have been compared against commercial software ERDAS IMAGINE 2011 and 2014. Partitioning methods heavily depend on application areas, therefore we may consider data partitioning as a preprocessing step before applying processing services on data. As a proof of concept we have implemented a simple tile-based partitioning method splitting an image into smaller grids (NxM tiles) and comparing the processing time to existing methods by NDVI calculation. The concept is demonstrated using own development open source processing framework.
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Erdeljan, A., D. Capko, S. Vukmirovic, D. Bojanic, and V. Congradac. "Distributed PSO Algorithm for Data Model Partitioning in Power Distribution Systems." Journal of Applied Research and Technology 12, no. 5 (October 2014): 947–57. http://dx.doi.org/10.1016/s1665-6423(14)70601-7.
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Yaşar, Abdurrahman, Muhammed Fati̇h Balin, Xiaojing An, Kaan Sancak, and Ümit V. Çatalyürek. "On Symmetric Rectilinear Partitioning." ACM Journal of Experimental Algorithmics 27 (December 31, 2022): 1–26. http://dx.doi.org/10.1145/3523750.
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Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block distribution). More specifically, we address the problem of symmetric rectilinear partitioning of two dimensional domains, and utilize sparse matrices to model them. By symmetric, we mean both dimensions (i.e., the rows and columns of the matrix) are identically partitioned yielding a tiling where the diagonal tiles (blocks) will be squares. We first show that the optimal solution to this problem is NP-hard, and we propose four heuristics to solve two different variants of this problem. To make the proposed techniques more applicable in real life application scenarios, we further reduce their computational complexities by utilizing effective sparsification strategies together with an efficient sparse prefix-sum data structure. We experimentally show the proposed algorithms are efficient and effective on more than six hundred test matrices/graphs.
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Adli, AWS, FI Prastyasari, DW Handani, and KB Artana. "LNG Distribution Optimization using Set Partitioning Problem Method." IOP Conference Series: Earth and Environmental Science 972, no. 1 (January 1, 2022): 012082. http://dx.doi.org/10.1088/1755-1315/972/1/012082.
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Abstract The Indonesian government’s commitment to increase the use of gas for domestic demand, by issuing the Decree of the Minister of Energy and Mineral Resources Number 13K/13/MEM/2020 concerning the Assignment of the Implementation of the Supply and Development of LNG Infrastructure and the Gasification 52 power plants in Indonesia. Therefore, a study on the supply chain design that can support the gasification process of the 52 power plants is crucial. Power plant data is imperative to identify receiving terminals which are then grouped into 8 clusters using the K-Means method. The design will use operating LNG refineries, which will then go to the hub for each cluster. Considering the feasibility factor of the receiving terminal and using the center of gravity method carries the determination of the hub. Considering the investment and operational costs form the feasible route as the most optimal by using the Set Partitioning Problem (SPP) method. The optimization considers several types of ships with six different sizes. The total investment cost required was $107,815,749.11, and the operational cost was $68,993,709.11. The results of the economic analysis indicate the distribution will reach a payback period within 10 years if gas sales use a margin of 1,75 USD.
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Jen, Tien-Chien, and Aloysius U. Anagonye. "An Improved Transient Model of Tool Temperatures in Metal Cutting." Journal of Manufacturing Science and Engineering 123, no. 1 (April 1, 2000): 30–37. http://dx.doi.org/10.1115/1.1334865.
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A model for predicting cutting tool temperatures under transient conditions is presented. The model of Stephenson et al. [10] is extended to include the initial transient response to the tool temperature and nonuniform heat flux distributions. The main goal in this paper is to be able to accurately predict the initial transient tool temperature response, or temperatures in interrupted cutting for cases where the cutting time is short. A method to predict the true transient energy partitioning instead of quasi-steady energy partitioning (Stephenson et al., [10]), without seeking the full numerical analysis, has been developed. In this paper, the transient energy partitioning is obtained through a fixed-point iteration process by modifying the quasi-steady energy partitioning method presented by Loewen and Shaw [11]. The predicted transient tool temperatures are compared quantitatively to the experimental data. Utilizing a semi-empirical correlation for heat flux distribution along the tool-chip interface, the temperature distribution is calculated and compared qualitatively to existing experimental data.