Journal articles on the topic 'Data-dependent complexity'
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Synnevåg, J.-F., A. Austeng, and S. Holm. "A low-complexity data-dependent beamformer." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 58, no. 2 (February 2011): 281–89. http://dx.doi.org/10.1109/tuffc.2011.1805.
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Fritzsche, Paula, Dolores Rexachs, and Emilio Luque. "Defining Asymptotic Parallel Time Complexity of Data-dependent Algorithms." New Generation Computing 32, no. 2 (April 2014): 123–44. http://dx.doi.org/10.1007/s00354-014-0202-2.
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Di, J., and E. Kolaczyk. "Complexity-penalized estimation of minimum volume sets for dependent data." Journal of Multivariate Analysis 101, no. 9 (October 2010): 1910–26. http://dx.doi.org/10.1016/j.jmva.2010.04.009.
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Bazin, Jérémie, Pilar Bustos-Sanmamed, Caroline Hartmann, Christine Lelandais-Brière, and Martin Crespi. "Complexity of miRNA-dependent regulation in root symbiosis." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1595 (June 5, 2012): 1570–79. http://dx.doi.org/10.1098/rstb.2011.0228.
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The development of root systems may be strongly affected by the symbiotic interactions that plants establish with soil organisms. Legumes are able to develop symbiotic relationships with both rhizobial bacteria and arbuscular mycorrhizal fungi leading to the formation of nitrogen-fixing nodules and mycorrhizal arbuscules, respectively. Both of these symbiotic interactions involve complex cellular reprogramming and profound morphological and physiological changes in specific root cells. In addition, the repression of pathogenic defence responses seems to be required for successful symbiotic interactions. Apart from typical regulatory genes, such as transcription factors, microRNAs (miRNAs) are emerging as riboregulators that control gene networks in eukaryotic cells through interactions with specific target mRNAs. In recent years, the availability of deep-sequencing technologies and the development of in silico approaches have allowed for the identification of large sets of miRNAs and their targets in legumes . A number of conserved and legume-specific miRNAs were found to be associated with symbiotic interactions as shown by their expression patterns or actions on symbiosis-related targets. In this review, we combine data from recent literature and genomic and deep-sequencing data on miRNAs controlling nodule development or restricting defence reactions to address the diversity and specificity of miRNA-dependent regulation in legume root symbiosis. Phylogenetic analysis of miRNA isoforms and their potential targets suggests a role for miRNAs in the repression of plant defence during symbiosis and revealed the evolution of miRNA-dependent regulation in legumes to allow for the modification of root cell specification, such as the formation of mycorrhized roots and nitrogen-fixing nodules.
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Yuen, Hok Pan, and Andrew Mackinnon. "Performance of joint modelling of time-to-event data with time-dependent predictors: an assessment based on transition to psychosis data." PeerJ 4 (October 19, 2016): e2582. http://dx.doi.org/10.7717/peerj.2582.
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Joint modelling has emerged to be a potential tool to analyse data with a time-to-event outcome and longitudinal measurements collected over a series of time points. Joint modelling involves the simultaneous modelling of the two components, namely the time-to-event component and the longitudinal component. The main challenges of joint modelling are the mathematical and computational complexity. Recent advances in joint modelling have seen the emergence of several software packages which have implemented some of the computational requirements to run joint models. These packages have opened the door for more routine use of joint modelling. Through simulations and real data based on transition to psychosis research, we compared joint model analysis of time-to-event outcome with the conventional Cox regression analysis. We also compared a number of packages for fitting joint models. Our results suggest that joint modelling do have advantages over conventional analysis despite its potential complexity. Our results also suggest that the results of analyses may depend on how the methodology is implemented.
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El-Yaniv, R., and D. Pechyony. "Transductive Rademacher Complexity and its Applications." Journal of Artificial Intelligence Research 35 (June 22, 2009): 193–234. http://dx.doi.org/10.1613/jair.2587.
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We develop a technique for deriving data-dependent error bounds for transductive learning algorithms based on transductive Rademacher complexity. Our technique is based on a novel general error bound for transduction in terms of transductive Rademacher complexity, together with a novel bounding technique for Rademacher averages for particular algorithms, in terms of their "unlabeled-labeled" representation. This technique is relevant to many advanced graph-based transductive algorithms and we demonstrate its effectiveness by deriving error bounds to three well known algorithms. Finally, we present a new PAC-Bayesian bound for mixtures of transductive algorithms based on our Rademacher bounds.
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Chen, Bo, Yuanyuan Guo, Xiue Gao, and Yunming Wang. "A Novel Multi-Attribute Decision Making Approach: Addressing the Complexity of Time Dependent and Interdependent Data." IEEE Access 6 (2018): 55838–49. http://dx.doi.org/10.1109/access.2018.2872636.
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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.
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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.
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Romero, E., and R. Sullivan. "Complexity of the outward K+ current of the rat megakaryocyte." American Journal of Physiology-Cell Physiology 272, no. 5 (May 1, 1997): C1525—C1531. http://dx.doi.org/10.1152/ajpcell.1997.272.5.c1525.
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Megakaryocytes isolated from rat bone marrow express a voltage-dependent, outward K+ current with complex kinetics of activation and inactivation. We found that this current could be separated into at least two components based on differential responses to K+ channel blockers. One component, which exhibited features of the "transient" or "A-type" K+ current of excitable cells, was more strongly blocked by 4-aminopyridine (4-AP) than by tetrabutylammonium (TBA). This current, which we designated as "4-AP-sensitive" current, activated rapidly at potentials more positive than -40 mV and subsequently underwent rapid voltage-dependent inactivation. A separate current that activated slowly was blocked much more effectively by TBA than by 4-AP. This "TBA-sensitive" component, which resembled a typical delayed rectifier current, was much more resistant to voltage-dependent inactivation. The relative contribution of each of these components varied from cell to cell. The effect of charybdotoxin was similar to that of 4-AP. Our data indicate that the voltage-dependent K+ current of resting megakaryocytes is more complex than heretofore believed and support the emerging concept that megakaryocytes possess intricate electrophysiological properties.
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Tompkins, Catherine J. "SURVIVING THE COMPLEXITY: USING GROUNDED THEORY TO UNDERSTAND KINSHIP CAREGIVING." Innovation in Aging 3, Supplement_1 (November 2019): S803—S804. http://dx.doi.org/10.1093/geroni/igz038.2956.
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Abstract The role of a caregiver often goes beyond the task of caring for someone who is dependent in managing activities of daily living. Children are dependent on others to care for them due solely to their age and maturity; others are dependent due to chronic ailments or short-term disabilities. Regardless of why someone is dependent, the caregiving relationship is complex. This paper focuses on a grounded theory, developed and applied to understand the complexities of kinship caregiving. The literature continues to support the identified needs of kinship caregivers (Tompkins, 2015; Lee, Clarkson-Hendrix, & Lee, 2016). To understand the unique needs of kinship families, the following grand tour question was asked: What is it like for you to live within a kinship caregiving household? The theory was developed over several years based on observational data and 15 interviews with grandparent caregivers and at least one of the grandchildren they were raising. The theory, Surviving the Complexity, is a survival process of taking on the caregiving role and doing one’s best in spite of multiple obstacles. Surviving the complexity consists of three stages: rescuing, taking-on and role reversal. The theory identifies and explains emotional, relationship and situational complexity within kinship families. Hope and denial are factors of emotional complexity: “It’s not that she (my daughter) does not love him (the child), she is just unable to right now. She will get better.” Theory development and further application of the theory will be discussed.
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Compton, Ryan. "A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311881872. http://dx.doi.org/10.1177/2378023118818720.
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Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and predictive model optimization to address a sociological data challenge. The author’s group develops improved generalizable models to identify at-risk families. Principal-components analysis and decision tree modeling are used to predict six main dependent variables in the Fragile Families Challenge, successfully modeling one binary variable but no continuous dependent variables in the diagnostic data set. This indicates that some binary dependent variables are more predictable using a reduced set of uncorrelated independent variables, and continuous dependent variables demand more complexity.
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Kabán, Ata. "Dimension-Free Error Bounds from Random Projections." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4049–56. http://dx.doi.org/10.1609/aaai.v33i01.33014049.
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Learning from high dimensional data is challenging in general – however, often the data is not truly high dimensional in the sense that it may have some hidden low complexity geometry. We give new, user-friendly PAC-bounds that are able to take advantage of such benign geometry to reduce dimensional-dependence of error-guarantees in settings where such dependence is known to be essential in general. This is achieved by employing random projection as an analytic tool, and exploiting its structure-preserving compression ability. We introduce an auxiliary function class that operates on reduced dimensional inputs, and a new complexity term, as the distortion of the loss under random projections. The latter is a hypothesis-dependent data-complexity, whose analytic estimates turn out to recover various regularisation schemes in parametric models, and a notion of intrinsic dimension, as quantified by the Gaussian width of the input support in the case of the nearest neighbour rule. If there is benign geometry present, then the bounds become tighter, otherwise they recover the original dimension-dependent bounds.
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Zhang, Ronggen. "Corpus-based study on syntactic complexity of texts by L1 and L2 learners." ITM Web of Conferences 45 (2022): 01081. http://dx.doi.org/10.1051/itmconf/20224501081.
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Based on 90 argumentative texts from the corpora of LOCNESS and WECCL, the paper attempts to make a study of the syntactic complexity of those texts. All the data are processed online by the Web-based L2 Syntactic Complexity Analyser, and then processed with the corpus tool AntConc and IBM SPSS Statistics 20. The findings include: for one thing, L1 learners use more complex sentences with more verb phrases, and more embedded clauses, in their writings, compared with L2 learners. For another, in writing L2 learners may use dependent clauses with longer size than those by L1 learners despite the L1 learners tend to use sentences embedded with dependent clauses more often. Finally, some pedagogical suggestions are made on how to improve L2 writing course teaching.
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Li, Yu Mei, Bin Zhao, and Xin Ping Guan. "Unbiased H∞ Infinite Filtering for Stochastic Systems with Data Packet Losses." Advanced Materials Research 204-210 (February 2011): 400–405. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.400.
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This paper presents the unbiased H∞ filter design for stochastic systems with data packet losses. By constructing unbiased filter, the complexity and computational burden of the real-time filtering process are reduced greatly. Delay-dependent sufficient conditions for stochastic system with data packet losses are proposed in terms of linear matrix inequalities (LMIs). Numerical example demonstrates the proposed approaches are effective.
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Diehl, J. Alan, Wensheng Yang, Ronald A. Rimerman, Hua Xiao, and Andrew Emili. "Hsc70 Regulates Accumulation of Cyclin D1 and Cyclin D1-Dependent Protein Kinase." Molecular and Cellular Biology 23, no. 5 (March 1, 2003): 1764–74. http://dx.doi.org/10.1128/mcb.23.5.1764-1774.2003.
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ABSTRACT The cyclin D-dependent kinase is a critical mediator of mitogen-dependent G1 phase progression in mammalian cells. Given the high incidence of cyclin D1 overexpression in human neoplasias, the nature and complexity of cyclin D complexes in vivo have been subjects of intense interest. Besides its catalytic partner, the nature and complexity of cyclin D complexes in vivo remain ambiguous. To address this issue, we purified native cyclin D1 complexes from proliferating mouse fibroblasts by affinity chromatography and began to identify and functionally characterize the associated proteins. In this report, we describe the identification of Hsc70 and its functional importance for cyclin D1 and cyclin D1-dependent kinase maturation. We demonstrate that Hsc70 associates with newly synthesized cyclin D1 and is a component of a mature, catalytically active cyclin D1/CDK4 holoenzyme complex. Our data suggest that Hsc70 promotes stabilization of newly synthesized cyclin D1, thereby increasing its availability for assembly with CDK4. In addition, our data demonstrate that Hsc70 remains bound to cyclin D1 following its assembly with CDK4 and Cip/Kip proteins, where it ensures the formation of a catalytically active complex.
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Maximov, Yury, Massih-Reza Amini, and Zaid Harchaoui. "Rademacher Complexity Bounds for a Penalized Multi-class Semi-supervised Algorithm." Journal of Artificial Intelligence Research 61 (April 11, 2018): 761–86. http://dx.doi.org/10.1613/jair.5638.
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We propose Rademacher complexity bounds for multi-class classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing k predominant classes using the labeled training examples such that the proportion of their non-predominant classes is below a fixed threshold stands for clustering consistency. In the second step, a classifier is trained by minimizing a margin empirical loss over the labeled training set and a penalization term measuring the disability of the learner to predict the k predominant classes of the identified clusters. The resulting data-dependent generalization error bound involves the margin distribution of the classifier, the stability of the clustering technique used in the first step and Rademacher complexity terms corresponding to partially labeled training data. Our theoretical result exhibit convergence rates extending those proposed in the literature for the binary case, and experimental results on different multi-class classification problems show empirical evidence that supports the theory.
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Yakiwchuk, Erin M., Michelle M. Foisy, and Christine A. Hughes. "Complexity of Interactions Between Voriconazole and Antiretroviral Agents." Annals of Pharmacotherapy 42, no. 5 (April 15, 2008): 698–703. http://dx.doi.org/10.1345/aph.1k530.
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Objective: To evaluate and summarize pertinent aspects of the literature on interactions between voriconazole and antiretroviral agents. Data Sources: Primary literature was identified through MEDLINE (1950-February 2008), EMBASE (1988-February 2008), and International Pharmaceutical Abstracts (1970-February 2008) using the search terms voriconazole, ritonavir, protease inhibitors, nonnucleoside reverse transcriptase inhibitors, raltegravir, maraviroc, and drug interactions. Additionally, relevant abstracts from infectious diseases and HIV conferences (2004-February 2008), reference citations from relevant publications, and product information monographs were reviewed. Study Selection And Data Abstraction: All articles identified from the data sources and published in English were reviewed. Of these, studies and reports addressing voriconazole pharmacokinetics or interactions with antiretroviral agents were evaluated. Data Synthesis: The interactions between voriconazole and antiretroviral drugs are complex. Voriconazole and ritonavir exhibit a time- and dose-dependent interaction. Ritonavir initially inhibits voriconazole metabolism, but, with chronic administration, subsequently induces voriconazole metabolism. This interaction is more pronounced with high doses of ritonavir. Coadministration of voriconazole and efavirenz at usual doses is contraindicated because of a 2-way interaction resulting in efavirenz toxicity and decreased therapeutic effect of voriconazole. Dosage adjustments of both drugs are required. Based on pharmacokinetic characteristics, interactions between voriconazole and other protease inhibitors, nonnucleoside reverse transcriptase inhibitors (including etravirine), and maraviroc are likely but have not been well characterized in the literature. Interactions between voriconazole and nucleoside reverse transcriptase inhibitors or raltegravir are not anticipated. Conclusions: Interactions between voriconazole and antiretrovirals have the potential for serious consequences. However, because there is limited information available, further studies are warranted to establish these interactions and clarify their appropriate management. Until then, clinicians should be aware of the potential for interactions between voriconazole and antiretroviral agents and how to monitor for these interactions in clinical practice.
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Ranjitkar, S., A. Turan, C. Mann, G. A. Gully, M. Marsman, S. Edwards, J. A. Kaidonis, et al. "Surface-Sensitive Microwear Texture Analysis of Attrition and Erosion." Journal of Dental Research 96, no. 3 (December 7, 2016): 300–307. http://dx.doi.org/10.1177/0022034516680585.
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Scale-sensitive fractal analysis of high-resolution 3-dimensional surface reconstructions of wear patterns has advanced our knowledge in evolutionary biology, and has opened up opportunities for translatory applications in clinical practice. To elucidate the microwear characteristics of attrition and erosion in worn natural teeth, we scanned 50 extracted human teeth using a confocal profiler at a high optical resolution (X–Y, 0.17 µm; Z < 3 nm). Our hypothesis was that microwear complexity would be greater in erosion and that anisotropy would be greater in attrition. The teeth were divided into 4 groups, including 2 wear types (attrition and erosion) and 2 locations (anterior and posterior teeth; n = 12 for each anterior group, n = 13 for each posterior group) for 2 tissue types (enamel and dentine). The raw 3-dimensional data cloud was subjected to a newly developed rigorous standardization technique to reduce interscanner variability as well as to filter anomalous scanning data. Linear mixed effects (regression) analyses conducted separately for the dependent variables, complexity and anisotropy, showed the following effects of the independent variables: significant interactions between wear type and tissue type ( P = 0.0157 and P = 0.0003, respectively) and significant effects of location ( P < 0.0001 and P = 0.0035, respectively). There were significant associations between complexity and anisotropy when the dependent variable was either complexity ( P = 0.0003) or anisotropy ( P = 0.0014). Our findings of greater complexity in erosion and greater anisotropy in attrition confirm our hypothesis. The greatest geometric means were noted in dentine erosion for complexity and dentine attrition for anisotropy. Dentine also exhibited microwear characteristics that were more consistent with wear types than enamel. Overall, our findings could complement macrowear assessment in dental clinical practice and research and could assist in the early detection and management of pathologic tooth wear.
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Deleersnyder, Wouter, Benjamin Maveau, Thomas Hermans, and David Dudal. "Inversion of electromagnetic induction data using a novel wavelet-based and scale-dependent regularization term." Geophysical Journal International 226, no. 3 (May 6, 2021): 1715–29. http://dx.doi.org/10.1093/gji/ggab182.
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SUMMARY The inversion of electromagnetic induction data to a conductivity profile is an ill-posed problem. Regularization improves the stability of the inversion and a smoothing constraint is typically used. However, the conductivity profiles are not always expected to be smooth. Here, we develop a new inversion scheme in which we transform the model to the wavelet space and impose a sparsity constraint. This sparsity constrained inversion scheme will minimize an objective function with a least-squares data misfit and a sparsity measure of the model in the wavelet domain. A model transform to the wavelet domain allows to investigate the temporal resolution (periodicities at different frequencies) and spatial resolution (location of the peaks) characteristics of the model, and penalizing small-scale coefficients effectively reduces the complexity of the model. The novel scale-dependent regularization term can be used to favour either blocky or smooth structures, as well as high-amplitude models in globally smooth structures in the inversion. Depending on the expected conductivity profile, a suitable wavelet basis function can be chosen. The scheme supports multiple types of regularization with the same algorithm and is thus flexible. Finally, we apply this new scheme on a frequency domain electromagnetic sounding data set, but the scheme could equally apply to any other 1-D geophysical method.
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Wang, Yingxu, and Vincent Chiew. "Empirical Studies on the Functional Complexity of Software in Large-Scale Software Systems." International Journal of Software Science and Computational Intelligence 3, no. 3 (July 2011): 23–42. http://dx.doi.org/10.4018/ijssci.2011070103.
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Functional complexity is one of the most fundamental properties of software because almost all other software attributes and properties such as functional size, development effort, costs, quality, and project duration are highly dependent on it. The functional complexity of software is a macro-scope problem concerning the semantic properties of software and human cognitive complexity towards a given software system; while the computational complexity is a micro-scope problem concerning algorithmic analyses towards machine throughput and time/space efficiency. This paper presents an empirical study on the functional complexity of software known as cognitive complexity based on large-scale samples using a Software Cognitive Complexity Analysis Tool (SCCAT). Empirical data are obtained with SCCAT on 7,531 programs and five formally specified software systems. The theoretical foundation of software functional complexity is introduced and the metric of software cognitive complexity is formally modeled. The functional complexities of a large-scale software system and the air traffic control systems (ATCS) are rigorously analyzed. A novel approach to represent software functional complexities and their distributions in software systems is developed. The nature of functional complexity of software in software engineering is rigorously explained. The relationship between the symbolic and functional complexities of software is quantitatively analyzed.
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Vasudevan, Ayyem Pillai, and R. Sudhakar. "A Low Complexity Near-Optimal MIMO Antenna Subset Selection Algorithm for Capacity Maximisation." International Journal of Antennas and Propagation 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/956756.
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Multiple input multiple output (MIMO) wireless systems employ a scheme called antenna subset selection for maximising the data rate or reliability for the prevailing channel conditions with the available or affordable number of radio frequency (RF) chains. In this paper, a low-complexity, and near-optimal performance fast algorithm is formulated and the detailed algorithm statements are stated with the exact complexity involved for capacity-maximising receive-only selection. The complexities of other receive-only selection comparable algorithms are calculated. Complexities have been stated in terms of both complex-complex flops and real-real flops. Significantly, all the algorithms are seen in the perspective of linear increase of capacity with the number of selected antennas up to one less than the total number of receive antennas. It is shown that our algorithm will be a good choice in terms of both performance and complexity for systems, which look for linear increase in capacity with the number of selected antennas up to one less than the total receive antennas. Our algorithm complexity is much less dependent on the number of transmit antennas and is not dependent on the number of selected antennas and it strikes a good tradeoff between performance and speed, which is very important for practical implementations.
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Wang, Xiaoyin, Xiaoyan Wei, and Zhibin Pan. "On the Convergence Rate of Kernel-Based Sequential Greedy Regression." Abstract and Applied Analysis 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/619138.
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A kernel-based greedy algorithm is presented to realize efficient sparse learning with data-dependent basis functions. Upper bound of generalization error is obtained based on complexity measure of hypothesis space with covering numbers. A careful analysis shows the error has a satisfactory decay rate under mild conditions.
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Chen, Luo, Zebang Liu, and Mengyu Ma. "Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model." Applied Sciences 12, no. 15 (July 31, 2022): 7710. http://dx.doi.org/10.3390/app12157710.
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The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods.
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Sharif Moghadasi, Leila. "The study of Economic Complexity and GDP Effect on Inflation Rate and Income Inequality in Persian Gulf States 2002 -2015." Mapta Journal of Mechanical and Industrial Engineering (MJMIE) 2, no. 3 (December 20, 2018): 31–39. http://dx.doi.org/10.33544/mjmie.v2i3.83.
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The purpose of this study was to examine the effect of economic complexity and gross domestic product (GDP) on inflation rate and income inequality between 2002 and 2015. The statistical population of this research is Persian Gulf states, and independent variables are economic complexity and GDP and dependent variables are inflation rate and income inequality. The present research is an applied research and is essentially a descriptive research, and also in terms of methodology, it is considered as a correlational research. The theoretical literature and subjective history and research data collection had been done using library method and document mining method, respectively. Descriptive and inferential statistics have been used to describe and summarize the collected data. Firstly, variance heterogeneity pre-tests, F lemmer test, Hausman test and Jarque-Bera test were used to for analyzing data and then multivariate regression test and Eviews software were used to confirm and reject the research hypotheses. The results of the research show that economic complexity and GDP have a positive and significant effect on inflation rate, while economic complexity and GDP have a negative effect on income inequality.
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Abada, Sharon, Leslie E. Clark, Arup K. Sinha, Rui Xia, Kathleen Pace-Murphy, Renee J. Flores, and Jason Burnett. "Medication Regimen Complexity and Low Adherence in Older Community-Dwelling Adults With Substantiated Self-Neglect." Journal of Applied Gerontology 38, no. 6 (June 23, 2017): 866–83. http://dx.doi.org/10.1177/0733464817714565.
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Objective: Determine whether medication regimen complexity predicts medication adherence levels in a sample of community-dwelling adults 65 years and older with Adult Protective Services–substantiated self-neglect. Methods: A cross-sectional analysis of baseline data ( N = 31 participants) from a pilot intervention to increase medication adherence among the target group was performed. The Medication Regimen Complexity Index (MRCI) and the 8-item Morisky Medication Adherence Scale (MMAS-8)™ were the primary independent and dependent measures, respectively. A multivariable linear regression analysis, adjusting for potential confounders, was conducted to estimate the association between complexity and adherence. Results: Regimen complexity was high (mean MRCI = 19.6) and adherence was low (mean MMAS = 5.1). Even after controlling for confounders, increased complexity was significantly associated with lower adherence. Discussion: Older community-dwelling adults who self-neglect have complex medication regimens that contribute to low medication adherence. Medication regimen complexity may be a modifiable contributor to low adherence that can be targeted by future interventions to reduce self-neglect and its consequences.
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Zhe, Cao, Sun Xiangwen, and Niu Xinwen. "Harmonic Frequency Estimation Algorithm Based on MSWF in Power System." Open Electrical & Electronic Engineering Journal 9, no. 1 (October 28, 2015): 524–29. http://dx.doi.org/10.2174/1874129001509010524.
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This paper pointed out the disadvantage of harmonic frequency estimation algorithm in current power system, presented the MUSIC (multiple signal classification)-based harmonic frequency estimation algorithm in power system, and analyzed the computational complexity of the MUSIC algorithm. In order to reduce the computational complexity of conventional MUSIC algorithm and to increase the real-time characteristic of harmonic frequency estimation algorithm, we combined the multi-stage wiener filter (MSWF) recursive algorithm and MUSIC algorithm so as to avoid the subspace decomposition process of the conventional MUSIC algorithm, thus realizing the purpose of significantly reducing the computational complexity of the MUSIC algorithm. Through theoretical analysis and simulation experiments, we find that the algorithm proposed in this paper is of excellent resolution characteristic, and less dependent on data volume.
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Hidayatullah, Rahmat, Didik Santoso, and Abdul Syukur. "The Value of Habitat Complexity and Diversity of Reef Fish Species in Sangiang Island, Bima Regency, West Nusa Tenggra." Jurnal Biologi Tropis 20, no. 3 (December 30, 2020): 514. http://dx.doi.org/10.29303/jbt.v20i3.2261.
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Coral reef ecosystems in the marine environment have a vital role, especially in providing habitat for the diversity of commercial and non-commercial fish species. This study aims to assess the species richness of coral reef fish based on the complexity of the habitat of Sangiang Island, Bima Regency. The data collection method used Underwater Visual Census on the belt transect, and the habitat complexity value is determined by the chain intercept transect method. Data analysis used Shannon-Wiener Index Diversity (H '), Abundance (Ki), and Habitat complexity (C). Next is a simple regression analysis between habitat complexity as the independent variable and fish abundance as the dependent variable. In this study, 106 species were found and covering 24 families. The three fish families, Pomacentridae, Labridae, and Lutjanidae, are the most dominant of the other families. Meanwhile, the diversity index ranged from 1.874-2.753 with an average value of ± SD 2.35 ± 0.317, and the abundance value ranged from 0.774-2.528 individuals/m2 with an average value of ± SD 1.505 individuals / m2 ± 0.745. Furthermore, the analysis showed that hábitate complexity had a significant effect on fish abundance. The coefficient of determination (R2) of 80% can explain the relationship of habitat complexity to fish abundance in the study location. This study concludes that coral reef habitat complexity is a determining factor for the level of fish diversity and abundance in the study area. Therefore, the protection of coral habitat is a strategy for the protection of coral reef fish resources.
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Johnson, James D., and John P. Chang. "Function- and agonist-specific Ca2+signalling: The requirement for and mechanism of spatial and temporal complexity in Ca2+signals." Biochemistry and Cell Biology 78, no. 3 (April 2, 2000): 217–40. http://dx.doi.org/10.1139/o00-012.
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Calcium signals have been implicated in the regulation of many diverse cellular processes. The problem of how information from extracellular signals is delivered with specificity and fidelity using fluctuations in cytosolic Ca2+concentration remains unresolved. The capacity of cells to generate Ca2+signals of sufficient spatial and temporal complexity is the primary constraint on their ability to effectively encode information through Ca2+. Over the past decade, a large body of literature has dealt with some basic features of Ca2+-handling in cells, as well as the multiplicity and functional diversity of intracellular Ca2+stores and extracellular Ca2+influx pathways. In principle, physiologists now have the necessary information to attack the problem of function- and agonist-specificity in Ca2+signal transduction. This review explores the data indicating that Ca2+release from diverse sources, including many types of intracellular stores, generates Ca2+signals with sufficient complexity to regulate the vast number of cellular functions that have been reported as Ca2+-dependent. Some examples where such complexity may relate to neuroendocrine regulation of hormone secretion/synthesis are discussed. We show that the functional and spatial heterogeneity of Ca2+stores generates Ca2+signals with sufficient spatiotemporal complexity to simultaneously control multiple Ca2+-dependent cellular functions in neuroendocrine systems.Key words: signal coding, IP3receptor, ryanodine receptor, endoplasmic reticulum, Golgi, secretory granules, mitochondria, exocytosis.
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Visser, Thomas R., and Remy Spliet. "Efficient Move Evaluations for Time-Dependent Vehicle Routing Problems." Transportation Science 54, no. 4 (July 2020): 1091–112. http://dx.doi.org/10.1287/trsc.2019.0938.
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In this paper, we introduce several new methods for efficiently evaluating moves in neighborhood search heuristics for routing problems with time-dependent travel times. We consider both the case that route duration is constrained and the case that route duration appears in the objective. We observe that the composition of piecewise linear functions can be evaluated in various orders when computing the route duration. We use this to develop a new tree-based data structure to improve the complexity of computations and memory usage. This approach also allows us to present methods that have the best known computational complexity but that do not require a lexicographic order of search. Our numerical experiments illustrate the trade-off between computation time and memory usage among the various methods. For 1,000 customer instances, our methods are able to speed up a construction heuristic by up to 8.89 times and an exchange neighborhood improvement heuristic by up to 3.94 times, without requiring excessive amounts of memory.
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Rheem, Abdul, and Musheer Ahmad. "APPLICATION OF MATLAB IN REAL DECISION MAKING PROBLEM." Jnanabha 52, no. 01 (2022): 08–21. http://dx.doi.org/10.58250/jnanabha.2022.52102.
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The aim of this paper is to analyze the maximum age group of women affected by the divorce problem using fuzzy matrix method. Study of this real world problem is based on four types of different matrices, known as initial raw data matrix (IRDM), average time dependent data matrix (ATDM), refined time dependent data matrix ( RTDM), and combined effect time dependent data matrix ( CETDM). For this study the data has been obtained from 110 divorced women in Delhi and NCR, India. In order to estimate maximum age group of women influenced by divorce problem, some graphical representations are shown for different values of ?, 0 ≤ α ≤ 1 using algebraic applications of fuzzy matrices. Abdul et al. [13] faced different type of problem like if we add one or more attributes row wise or column wise then matrix will become bigger and complexity will increase during calculation. Due to it we use matlab code to solve each part of this problem in this paper and got best results.
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Xiao, Zhenyu, Jie Wang, Teng Yuan Cheng, and Kuiran Shi. "A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets." Discrete Dynamics in Nature and Society 2020 (October 22, 2020): 1–23. http://dx.doi.org/10.1155/2020/9673623.
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Financial data usually have the features of complexity and interdependence structure, such as asymmetric, tail, and time-varying dependence. This study constructs a new multivariate skewed fat-tailed copula, namely, noncentral contaminated normal (NCCN) copula, to analyze the dependent structure of financial market data. The dynamic conditional correlation (DCC) model is also incorporated into constructing the time-varying NCCN copula model. This study comprehensively examines the effects of the DCC-NCCN copula and related models on fitting dependence structures of Hong Kong stock markets. The results show that the DCC-NCCN copula model can better depict the dependence structures of returns. Considering the flexibility and complexity, the DCC-NCCN copula model is a relatively ideal, time-varying, multivariate skewed fat-tailed copula model.
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Stemmer, Ralf, Hai-Dang Vu, Sébastien Le Nours, Kim Grüttner, Sébastien Pillement, and Wolfgang Nebel. "A Measurement-Based Message-Level Timing Prediction Approach for Data-Dependent SDFGs on Tile-Based Heterogeneous MPSoCs." Applied Sciences 11, no. 14 (July 20, 2021): 6649. http://dx.doi.org/10.3390/app11146649.
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Fast yet accurate performance and timing prediction of complex parallel data flow applications on multi-processor systems remains a very difficult discipline. The reason for it comes from the complexity of the data flow applications w.r.t. data dependent execution paths and the hardware platform with shared resources, like buses and memories. This combination may lead to complex timing interferences that are difficult to express in pure analytical or classical simulation-based approaches. In this work, we propose the combination of timing measurement and statistical simulation models for probabilistic timing and performance prediction of Synchronous Data Flow (SDF) applications on MPSoCs with shared memories. We exploit the separation of computation and communication in our SDF model of computation to set-up simulation-based performance prediction models following different abstraction approaches. We especially propose a message-level communication model driven by a data-dependent probabilistic execution phase timing model. We compare our work against measurement on two case-studies from the computer vision domain: a Sobel filter and a JPEG decoder. We show that the accuracy and execution time of our modeling and evaluation framework outperforms existing approaches and is suitable for a fast yet accurate design space exploration.
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Neagu, Olimpia. "Economic Complexity and Ecological Footprint: Evidence from the Most Complex Economies in the World." Sustainability 12, no. 21 (October 30, 2020): 9031. http://dx.doi.org/10.3390/su12219031.
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The paper introduces economic complexity as an explanatory variable of ecological footprint change, along with income per capita and fossil fuel energy consumption. The link between the ecological footprint and economic complexity is explored within a panel of 48 complex economies over the period 1995–2014. The panel analysis is based on the annual data series of the economic complexity index (ECI), fossil fuel energy consumption, income per capita, and the ecological footprint of production. The econometrical analysis, based on second-generation unit root tests, cointegration testing, and estimation of fully modified ordinary square (FMOLS) and dynamic ordinary least square (DOLS) models in a heterogeneous panel of countries, revealed a validated positive long-run association between the ecological footprint of production as dependent variable and the economic complexity index, gross domestic product per capita, and fossil fuel energy consumption. The paper sheds light on the critical situation of environmental sustainability, taking into consideration that 75% of countries under examination are in ecological deficit.
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Pinkas, Daniel M., Caroline E. Sanvitale, Joshua C. Bufton, Fiona J. Sorrell, Nicolae Solcan, Rod Chalk, James Doutch, and Alex N. Bullock. "Structural complexity in the KCTD family of Cullin3-dependent E3 ubiquitin ligases." Biochemical Journal 474, no. 22 (November 1, 2017): 3747–61. http://dx.doi.org/10.1042/bcj20170527.
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Members of the potassium channel tetramerization domain (KCTD) family are soluble non-channel proteins that commonly function as Cullin3 (Cul3)-dependent E3 ligases. Solution studies of the N-terminal BTB domain have suggested that some KCTD family members may tetramerize similarly to the homologous tetramerization domain (T1) of the voltage-gated potassium (Kv) channels. However, available structures of KCTD1, KCTD5 and KCTD9 have demonstrated instead pentameric assemblies. To explore other phylogenetic clades within the KCTD family, we determined the crystal structures of the BTB domains of a further five human KCTD proteins revealing a rich variety of oligomerization architectures, including monomer (SHKBP1), a novel two-fold symmetric tetramer (KCTD10 and KCTD13), open pentamer (KCTD16) and closed pentamer (KCTD17). While these diverse geometries were confirmed by small-angle X-ray scattering (SAXS), only the pentameric forms were stable upon size-exclusion chromatography. With the exception of KCTD16, all proteins bound to Cul3 and were observed to reassemble in solution as 5 : 5 heterodecamers. SAXS data and structural modelling indicate that Cul3 may stabilize closed BTB pentamers by binding across their BTB–BTB interfaces. These extra interactions likely also allow KCTD proteins to bind Cul3 without the expected 3-box motif. Overall, these studies reveal the KCTD family BTB domain to be a highly versatile scaffold compatible with a range of oligomeric assemblies and geometries. This observed interface plasticity may support functional changes in regulation of this unusual E3 ligase family.
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Parashchuk, Valentyna, Laryssa Yarova, and Stepan Parashchuk. "Automated Complexity Assessment of English Informational Texts for EFL Pre-service Teachers and Translators." Arab World English Journal 7, no. 1 (July 15, 2021): 155–64. http://dx.doi.org/10.24093/awej/call7.11.
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Automated text complexity assessment tools are of enormous practical value in solving the time-consuming task of analyzing English informational texts for their complexity at the pre-reading stage. The present study depicts the application of the automated text analysis system the TextEvaluator as an effective tool that helps analyze texts on eight dimensions of text complexity as follows: syntactic complexity; academic vocabulary; word unfamiliarity; word concreteness; lexical cohesion; interactive style; level of argumentation; degree of narrativity, with further summarizing them with an overall genre-dependent complexity score. This research examines the complexity dimensions of English informational texts of four genres – legal, linguistic, news, and medical – that are used for teaching reading comprehension to EFL (English as a foreign language) pre-service teachers and translators at universities in Ukraine. The data obtained with the help of the TextEvaluator has shown that English legal texts are the most difficult for reading comprehension in comparison to linguistic, news, and medical texts. In contrast, medical texts are the least challenging out of the four genres compared. The TextEvaluator has provided insight into the complexity of English informational texts across their different genres that would be useful for assembling the corpora of reading passages scaled on specific dimensions of text complexity that predict text difficulty to EFL pre-service teachers and translators.
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Hussein, Saad. "The association between cost-standard setting and work performance: The role of information asymmetry and goal complexity." Accounting 7, no. 7 (2021): 1757–68. http://dx.doi.org/10.5267/j.ac.2021.4.021.
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This paper is an empirical testing of the association between Cost Standard-Setting (PSS) on Work Performance (WP), mediated With Information Asymmetry (IA), and Goal Complexity (GC). It is a rule of thumb that PSS setting can lower the level of IA and GC between the managers and the employees and leads to better WP. The present work uses a path model to measure the direct, indirect, and spurious effect between the dependent and independent variables of this study. Data were collected from ten corporate firms in Iraq via a pre-designed questionnaire survey, the questionnaire forms were distributed randomly to the firm’s top management personnel, departmental managers, engineers, accountants, and administrators who are involved in PSS. Around 350 forms were distributed for data collection, however, only 198 forms were considered for this analysis, the rest of the forms were discarded due to incompleteness or missing values. The findings of the study showed a significant direct effect of PSS on WP. Likewise, there was clear evidence of an indirect effect via the mediating variables (IA and GC). The influence of IA and GC confirms the strong association between the independent variable (PSS) on the dependent variable (WP).
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Bompard, Guillaume, and Emmanuelle Caron. "Regulation of WASP/WAVE proteins." Journal of Cell Biology 166, no. 7 (September 27, 2004): 957–62. http://dx.doi.org/10.1083/jcb.200403127.
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Despite their homology, the regulation of WASP and WAVE, activators of Arp2/3-dependent actin polymerization, has always been thought to be different. Several recent studies have revealed new aspects of their regulation, highlighting its complexity and the crucial role of post-translational modifications. New data also suggest additional functions for WASP family proteins, pushing us to reconsider existing models.
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Gunadi, Willy, and Diana Melliza Stephanie. "Dampak Visual Complexity Terhadap Minat Beli Konsumen dalam Bisnis Ritel." Jurnal Manajemen Teknologi 20, no. 1 (2021): 1–20. http://dx.doi.org/10.12695/jmt.2021.20.1.1.
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Abstract. Along with the rapid economic growth in Indonesia, high buying power led to significant emergence of new-comers brand in retail market. Bottled tea business that used to dominated by two brands, now become highly competitive with more than 20 brands in the market, also an increase in chocolate consumption by more than 25 percent per year making chocolate display in supermarkets become remarkably widespread. This is challenge for manufacturers and retailers to develop strategy to gain maximum profit. This research aims to analyze the effect of display's visual complexity on consumers evaluation process and purchase intention. This research also analyzes the effect of consumer field dependence and shopping goal on perceived target attractiveness. This research consists of 3 experimental studies, each in ANOVA 2x2. Study 1 analyze abstract stimuli, study 2 analyze real product and study 3 complements with eye tracking data. The results prove that the more complex is the product's display, the more product target attractiveness will be rated lower, especially for utilitarian customer with field dependent character. Keywords: Package, processing fluency, purchase intention, retail, visual complexity
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Aruan, Deasy Arisandy, Hasrat Naeni Gulo, Ana Karelina Lumban Nahor, Natasya Br Ginting, and Esti Trinita Wahyuni. "Pengaruh Kompetensi, Kompleksitas Tugas dan Skeptisme Profesional Terhadap Kualitas Audit pada Kantor Badan Pengawasan Keuangan dan pembangunan Provinsi Sumatera Utara." Owner 3, no. 2 (July 27, 2019): 215. http://dx.doi.org/10.33395/owner.v3i2.141.
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Audit quality is not easy to define because there are many factors that affect quality. This study aims to determine the effect of competence, task complexity, and Auditor Professional Skepticism, on Audit Quality. The data used in this study is primary data by distributing questionnaires to all auditors at the BPKP Representative Office in North Sumatra Province. The population in this study were all auditors at the BPKP Representative Office in North Sumatra Province. The collection of data and information needed in this study was carried out with field research. The data used are primary data collected directly from the research subjects with a questionnaire form. Testing the effect of independent variables on the dependent variable is done by using multiple regression models. The results of this study indicate that both partially and simultaneously, competence, task complexity, and Auditor Professional Skepticism, have an effect on Audit Quality in the BPKP Representative office in North Sumatra Province
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Jaeger, Herbert. "Observable Operator Models for Discrete Stochastic Time Series." Neural Computation 12, no. 6 (June 1, 2000): 1371–98. http://dx.doi.org/10.1162/089976600300015411.
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A widely used class of models for stochastic systems is hidden Markov models. Systems that can be modeled by hidden Markov models are a proper subclass of linearly dependent processes, a class of stochastic systems known from mathematical investigations carried out over the past four decades. This article provides a novel, simple characterization of linearly dependent processes, called observable operator models. The mathematical properties of observable operator models lead to a constructive learning algorithm for the identification of linearly dependent processes. The core of the algorithm has a time complexity of O (N + nm3), where N is the size of training data, n is the number of distinguishable outcomes of observations, and m is model state-space dimension.
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Hoehn, Richard S., Peter L. Jernigan, Alex L. Chang, Michael J. Edwards, Charles C. Caldwell, Erich Gulbins, and Timothy A. Pritts. "Acid Sphingomyelinase Inhibition Prevents Hemolysis During Erythrocyte Storage." Cellular Physiology and Biochemistry 39, no. 1 (2016): 331–40. http://dx.doi.org/10.1159/000445627.
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Background/Aims: During storage, units of human red blood cells (pRBCs) experience membrane destabilization and hemolysis which may cause harm to transfusion recipients. This study investigates whether inhibition of acid sphingomyelinase could stabilize erythrocyte membranes and prevent hemolysis during storage. Methods: Human and murine pRBCs were stored under standard blood banking conditions with and without the addition of amitriptyline, a known acid sphingomyelinase inhibitor. Hemoglobin was measured with an electronic hematology analyzer and flow cytometry was used to measure erythrocyte size, complexity, phosphatidylserine externalization, and band 3 protein expression. Results: Cell-free hemoglobin, a marker of hemolysis, increased during pRBC storage. Amitriptyline treatment decreased hemolysis in a dose-dependent manner. Standard pRBC storage led to loss of erythrocyte size and membrane complexity, increased phosphatidylserine externalization, and decreased band 3 protein integrity as determined by flow cytometry. Each of these changes was reduced by treatment with amitriptyline. Transfusion of amitriptyline-treated pRBCs resulted in decreased circulating free hemoglobin. Conclusion: Erythrocyte storage is associated with changes in cell size, complexity, membrane molecular composition, and increased hemolysis. Acid sphingomyelinase inhibition reduced these changes in a dose-dependent manner. Our data suggest a novel mechanism to attenuate the harmful effects after transfusion of aged blood products.
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Albreem, Mahmoud A., Mohammed H. Alsharif, and Sunghwan Kim. "A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems." Symmetry 12, no. 2 (February 21, 2020): 306. http://dx.doi.org/10.3390/sym12020306.
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Fifth-generation (5G) communications system is commercially introduced by several mobile operators where sub-6 GHz bands are the backbone of the 5G networks. A large-scale multiple-input multiple-output (MIMO), or massive MIMO (mMIMO), technology has a major impact to secure high data rate, high spectral efficiency, and quality of service (QoS). It could also have a major role in the beyond-5G systems. A massive number of antennas seek advanced signal processing to detect and equalize the signal. However, optimal detectors, such as the maximum likelihood (ML) and maximum posterior (MAP), are not desirable in implementation due to extremely high complexity. Therefore, sub-optimum solutions have been introduced to obtain and guarantee enough balance between the performance and the computational complexity. In this paper, a robust and joint low complexity detection algorithm is proposed based on the Jacobi (JA) and Gauss–Seidel (GS) methods. In such iterative methods, the performance, complexity, and convergence rate are highly dependent on the initial vector. In this paper, initial solution is proposed by exploiting the benefits of a stair matrix to obtain a fast convergence rate, high performance, and low complexity. Numerical results show that proposed algorithm achieves high accuracy and relieve the computational complexity even when the BS-to-user-antenna ratio (BUAR) is small.
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Bimo, Arif Arya, and Intan Rahma Sari. "THE EFFECT OF AUDIT COMPLEXITY, FINANCIAL DISTRESS AND INSTITUTIONAL OWNERSHIP ON AUDIT REPORT LAG." CASHFLOW : CURRENT ADVANCED RESEARCH ON SHARIA FINANCE AND ECONOMIC WORLDWIDE 2, no. 1 (October 22, 2022): 75–89. http://dx.doi.org/10.55047/cashflow.v2i1.421.
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This research was carried out on property and real estate sector companies during the time period 2017-2021, with the goal of determining and proving that Audit Complexity, Financial Distress, and Institutional Ownership will affect the Audit Report Lag. The sample companies for this study were chosen at random. In addition to dependent variables, this investigation also takes into account independent factors including Audit Complexity, Financial Distress, and Institutional Ownership. The method of research that was utilized in this investigation was descriptive research that took a quantitative approach. Purposive sampling was utilized as the method of sampling throughout this investigation. Secondary data and other data collecting methods, notably documentation, were used for the purpose of gathering information for this study. The research was carried out on 42 firms from the property and real estate sector that were listed on the Indonesia Stock Exchange between 2017 and 2021. Statistical analysis carried out with Eviews 9 The findings of the experiment demonstrate that Audit Complexity influences Audit Repot Lag, whereas Financial Distress policies and institutional ownership do not have any bearing on Audit Repot Lag. In addition, the results of this study demonstrate that Audit Complexity, Financial Distress, and Institutional Ownership all have an effect on Audit Report Lag simultaneously.
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Rijali, Ahmad. "ANALISIS DATA KUALITATIF." Alhadharah: Jurnal Ilmu Dakwah 17, no. 33 (January 2, 2019): 81. http://dx.doi.org/10.18592/alhadharah.v17i33.2374.
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Data collection is interactive with data analysis, data collection is an integral part of data analysis activities. Data reduction is an attempt to deduce data, then sorting through data in certain conceptual units, certain categories, and certain themes. Data reduction results are processed in such a way as to look more fully in their figure. It may take the form of sketches, synopsis, matrices, and other forms; it is very necessary to facilitate the explanation and affirmation of conclusions. The process, not once, but interacts back and forth. The process of occurrence in qualitative research is very dependent on the complexity of the problems to be answered and the sharpness of the researchers' traceability in making comparisons during the data collection process. How can the data analysis process be operated? This paper will attempt to answer and describe the parts (1) understanding the meaning of data analysis, (2) analysis when collecting data; (3) data reduction; (4) data presentation; (5) drawing conclusions and verification.
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Wu, Yirui, Yukai Ding, Yuelong Zhu, Jun Feng, and Sifeng Wang. "Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution." Complexity 2020 (March 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/7670382.
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With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors. Essentially, growing data category and size greatly contribute to solve problems happened in physical space. In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood. To reduce impacts induced by floods, hydrological factors acquired from physical space and data-driven models in cyber space have been adopted to accurately forecast floods. Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space. Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers. The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors. Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results. Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.
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Wanke, Peter Fernandes, and Henrique Luiz Corrêa. "The relationship between the logistics complexity of manufacturing companies and their supply chain management." Production 24, no. 2 (August 23, 2013): 233–54. http://dx.doi.org/10.1590/s0103-65132013005000052.
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This study aims to investigate whether, and the means by which, supply chain managers of large manufacturing companies adopt a context-dependent approach (also called contingency approach) in their supply chain decisions; it empirically explores the correlation between logistics complexity-related contextual conditions and supply chain management (SCM) objectives and decision areas. The study involves a comprehensive literature review, followed by an analysis of survey data (based on a sample of 108 large manufacturing companies in Brazil), using cluster analysis, factor analysis and binary logistic regression. In this study, we not only investigate the major effects of supply chain objectives and decision areas as predictors of the logistics complexity of manufacturing but also investigate their second order interactions. Statistically significant relationships were found between logistics complexity-related contextual conditions and objectives and decision areas involving the supply chain. The managers of large companies who were surveyed considered different objectives and decision areas to be critical to the achievement of supply chain excellence when their companies had different levels of logistics complexity.
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Ulacha, Grzegorz, and Mirosław Łazoryszczak. "Lossless Image Coding Using Non-MMSE Algorithms to Calculate Linear Prediction Coefficients." Entropy 25, no. 1 (January 12, 2023): 156. http://dx.doi.org/10.3390/e25010156.
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This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec).
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AMARTAVIA, RENSIA, and MUHAMMAD ARIEF EFFENDI. "PENGARUH RASIO KEUANGAN, TATA KELOLA PERUSAHAAN, KARAKTERISTIK AUDITOR TERHADAP AUDIT REPORT LAG." E-Jurnal Akuntansi TSM 2, no. 3 (September 30, 2022): 393–406. http://dx.doi.org/10.34208/ejatsm.v2i3.1716.
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The purpose of this research is to obtain empirical evidence about the effect of firm size, profitability, leverage, auditor reputation, audit tenure, board size, board independence, audit committee size and complexity of corporate as independent variables to audit report lag as dependent variable. The population in this research is manufacturing companies that listed in Indonesia Stock Exchange (BEI) from 2018 to 2020. The sample used for this research consist of 78 listed manufacturing companies. The selection of these sample used the purposive sampling method with total 234 research data and the data analysis method in this research is used multiple regression analysis. The result in this research indicated that audit tenure and board independence had effect to audit report lag. While firm size, profitability, leverage, auditor reputation, board size, audit committee size, dan complexity of corporate had no effect to audit report lag.
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Zamani, Abbas-Ali, Saeed Tavakoli, Sadegh Etedali, and Jafar Sadeghi. "Modeling of a magneto-rheological damper: An improved multi-state-dependent parameter estimation approach." Journal of Intelligent Material Systems and Structures 30, no. 8 (March 19, 2019): 1178–88. http://dx.doi.org/10.1177/1045389x19835938.
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A magneto-rheological damper, which is controlled by a magnetic field, is an effective smart damping device in structural control. Because of the complexity of its dynamics, however, the modeling and control of magneto-rheological dampers is still a challenging problem. In this article, an improved multi-state-dependent parameter estimation method is introduced and employed for magneto-rheological damper modeling. To provide a comprehensive training data, a modified Bouc–Wen model is used as the reference model. According to various tests, the proposed multi-state-dependent parameter model can predict the magneto-rheological damper response over a wide range of operating conditions fairly accurately.
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Almilia, Luciana S., Nurul H. U. Dewi, and Putri Wulanditya. "The effect of visualization and complexity tasks in investment decision making." HOLISTICA – Journal of Business and Public Administration 10, no. 1 (April 1, 2019): 68–77. http://dx.doi.org/10.2478/hjbpa-2019-0006.
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Abstract This study examines the effect of visualization level and level of assignment complexity on investment decision making as measured by level of accuracy, level of confidence and calibration level. This research uses experimental method. The experimental method in this research is 2x2 (mixed design) include: (1) Level of visualization (high and low) and (2) Complexity of task (high and low). The dependent variable in this research is the level of accuracy, level of confidence and calibration level. Data analysis technique used in this research is independent sample t-test. Participants in this study are students of Accounting Undergraduate Program in one Private University in East Java. The total participants in this study were 103 people, but only 77 participants who passed the check manipulation and can be analyzed further. The results of this study indicate that the visualization effect in decision-making has an effect only when decision makers receive assignment with low complexity of assignment. The results of this study indicate that the effect of the complexity of the assignment influences both decision-making as measured by the level of accuracy, level of confidence and calibration level.