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Ferrara, Carla, Francesca Martella, and Maurizio Vichi. "Probabilistic Disjoint Principal Component Analysis." Multivariate Behavioral Research 54, no. 1 (November 7, 2018): 47–61. http://dx.doi.org/10.1080/00273171.2018.1485006.
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Vichi, Maurizio, and Gilbert Saporta. "Clustering and disjoint principal component analysis." Computational Statistics & Data Analysis 53, no. 8 (June 2009): 3194–208. http://dx.doi.org/10.1016/j.csda.2008.05.028.
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Lamboy, Warren F. "Disjoint Principal Component Analysis: A Statistical Method of Botanical Identification." Systematic Botany 15, no. 1 (January 1990): 3. http://dx.doi.org/10.2307/2419010.
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Hu, Qiguo, and Jinyin He. "Path Sets Combination Method for Reliability Analysis of Phased-Mission Systems Based on Cumulative Exposure Model." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 5 (October 2018): 995–1003. http://dx.doi.org/10.1051/jnwpu/20183650995.
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The modeling of phased-mission systems is difficult and the solving process is complex because of the relevance of the phase tasks and the sharing of components existing in different phases or between phases. To solve the problem, based on the cumulative exposure model, the path sets combination method of phased-mission systems is proposed. Aiming at the problem of the cross-stage correlation of components and its different failure rate in each phase, the cumulative exposure model considering the historical damage of components is used to solve by obtaining the cumulative damage distribution of each component in each phase. Firstly, a phased-mission systems reliability model is build by mapping phased-mission system fault trees into a Bayesian network. By traversing the Bayesian network, the minimal path sets of each phase are obtained. Secondly, the disjoint formulas introduced by variable elimination method are used to do the disjoint operation of the minimal path sets of each phase and the conditional probability relations of the common components are used to reduce the minimal path sets scale. Finally, the minimum disjoint path sets of each phase are combined and summed according to the component conditional probability relation. The path sets combination method of phased-mission systems avoids the large conditional probability table, large storage and large computation problems caused by the excessive discrete states in the traditional Bayesian method and the problem that the PMS-BDD method has strict requirements for variable ordering and is difficult to solve the system reliability with multiple failure distribution types of components. In the end, a phased-mission systems reliability modeling and solving is carried out for a geosynchronous orbit satellite, and compared with the PMS-BDD method, which verifies the correctness of the method.
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Martin-Barreiro, Carlos, John A. Ramirez-Figueroa, Xavier Cabezas, Víctor Leiva, and M. Purificación Galindo-Villardón. "Disjoint and Functional Principal Component Analysis for Infected Cases and Deaths Due to COVID-19 in South American Countries with Sensor-Related Data." Sensors 21, no. 12 (June 14, 2021): 4094. http://dx.doi.org/10.3390/s21124094.
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In this paper, we group South American countries based on the number of infected cases and deaths due to COVID-19. The countries considered are: Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Peru, Paraguay, Uruguay, and Venezuela. The data used are collected from a database of Johns Hopkins University, an institution that is dedicated to sensing and monitoring the evolution of the COVID-19 pandemic. A statistical analysis, based on principal components with modern and recent techniques, is conducted. Initially, utilizing the correlation matrix, standard components and varimax rotations are calculated. Then, by using disjoint components and functional components, the countries are grouped. An algorithm that allows us to keep the principal component analysis updated with a sensor in the data warehouse is designed. As reported in the conclusions, this grouping changes depending on the number of components considered, the type of principal component (standard, disjoint or functional) and the variable to be considered (infected cases or deaths). The results obtained are compared to the k-means technique. The COVID-19 cases and their deaths vary in the different countries due to diverse reasons, as reported in the conclusions.
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Meghanathan, Natarajan. "Quantifying the Theory Vs. Programming Disparity using Spectral Bipartivity Analysis and Principal Component Analysis." International Journal of Computer Science and Information Technology 14, no. 5 (October 31, 2022): 1–15. http://dx.doi.org/10.5121/ijcsit.2022.14501.
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Some students in the Computer Science and related majors excel very well in programming-related assignments, but not equally well in the theoretical assignments (that are not programming-based) and vice-versa. We refer to this as the "Theory vs. Programming Disparity (TPD)". In this paper, we propose a spectral bipartivity analysis-based approach to quantify the TPD metric for any student in a course based on the percentage scores (considered as decimal values in the range of 0 to 1) of the student in the course assignments (that involves both theoretical and programming-based assignments). We also propose a principal component analysis (PCA)-based approach to quantify the TPD metric for the entire class based on the percentage scores (in a scale of 0 to 100) of the students in the theoretical and programming assignments. The spectral analysis approach partitions the set of theoretical and programming assignments to two disjoint sets whose constituents are closer to each other within each set and relatively more different from each across the two sets. The TPD metric for a student is computed on the basis of the Euclidean distance between the tuples representing the actual numbers of theoretical and programming assignments vis-a-vis the number of theoretical and programming assignments in each of the two disjoint sets. The PCA-based analysis identifies the dominating principal components within the sets of theoretical and programming assignments and computes the TPD metric for the entire class as a weighted average of the correlation coefficients between the dominating principal components representing these two sets.
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Nose-Filho, Kenji, and Joao Marcos Travassos Romano. "Low-Rank Decomposition Based on Disjoint Component Analysis With Applications in Seismic Imaging." IEEE Transactions on Computational Imaging 3, no. 2 (June 2017): 275–81. http://dx.doi.org/10.1109/tci.2017.2691548.
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LONG, HAO, and XIAO-WEI LIU. "A UNIFIED COMMUNITY DETECTION ALGORITHM IN LARGE-SCALE COMPLEX NETWORKS." Advances in Complex Systems 22, no. 03 (May 2019): 1950004. http://dx.doi.org/10.1142/s0219525919500048.
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A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.
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Yu, Kong Shuai, and Dong Hu. "Appearance Model Based Moving Object Matching across Disjoint Camera Views." Advanced Materials Research 760-762 (September 2013): 1322–26. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1322.
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A new object tracking scheme for multi-camera surveillance with non-overlapping views is proposed in this paper. Brightness transfer function (BTF) is used to establish relative appearance correspondence between different views. Mixtures of probabilistic principal component analysis (MPPCA) is incooperated to learn the subspace of brightness transfer function with the concern to deal with multiple different brightness areas in a scene. The incremental major color spectrum histogram (IMCSH) is used as similarity measure for reliable matching. Experimental results with real world videos show the effectiveness of the proposed algorithm.
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Vesper, Stephen, Jennie Wakefield, Peter Ashley, David Cox, Gary Dewalt, and Warren Friedman. "Geographic Distribution of Environmental Relative Moldiness Index Molds in USA Homes." Journal of Environmental and Public Health 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/242457.
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Objective. The objective of this study was to quantify and describe the distribution of the 36 molds that make up the Environmental Relative Moldiness Index (ERMI).Materials and Methods. As part of the 2006 American Healthy Homes Survey, settled dust samples were analyzed by mold-specific quantitative PCR (MSQPCR) for the 36 ERMI molds. Each species' geographical distribution pattern was examined individually, followed by partitioning analysis in order to identify spatially meaningful patterns. For mapping, the 36 mold populations were divided into disjoint clusters on the basis of their standardized concentrations, and First Principal Component (FPC) scores were computed.Results and Conclusions. The partitioning analyses failed to uncover a valid partitioning that yielded compact, well-separated partitions with systematic spatial distributions, either on global or local criteria. Disjoint variable clustering resulted in seven mold clusters. The 36 molds and ERMI values themselves were found to be heterogeneously distributed across the United States of America (USA).
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Akhonda, M. A. B. S., Ben Gabrielson, Suchita Bhinge, Vince D. Calhoun, and Tülay Adali. "Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data." Journal of Neuroscience Methods 358 (July 2021): 109214. http://dx.doi.org/10.1016/j.jneumeth.2021.109214.
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Butyaev, R., D. Chernyshev, E. Mikhailets, L. Plotnikova, A. Garifullin, A. Kuvshinov, S. Voloshin, and A. Polyanichko. "APPLICATION OF THE PRINCIPAL COMPONENT ANALYSIS TO IR SPECTRA OF BLOOD SERUM OF PATIENTS WITH ONCOHEMATOLOGICAL DISEASES." Russian Journal of Biological Physics and Chemisrty 7, no. 3 (September 28, 2022): 462–66. http://dx.doi.org/10.29039/rusjbpc.2022.0545.
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Multiple myeloma and chronic lymphocytic leukemia are oncological diseases of the blood, which remain incurable today. The paper proposes a method for classifying blood serum samples from patients with multiple myeloma, chronic lymphocytic leukemia and healthy donors based on the analysis of their spectra in the mid-infrared (IR) range. IR spectra of blood serum were recorded using a Tensor 27 IR Fourier spectrometer in D2O solution. To analyze the obtained spectra in this work, a machine learning algorithm was implemented – the principal component analysis. The use of the principal component analysis made it possible to significantly simplify the representation of the array of spectral data. 45 samples of blood serum were analyzed in the work. As a result of applying this approach, the studied set of samples is divided into three disjoint sets corresponding to blood serum samples of patients with multiple myeloma, chronic lymphocytic leukemia and healthy donors. Thus, the principal component method can be successfully applied to classify blood serum samples of patients with diagnoses of multiple myeloma and chronic lymphocytic leukemia. The universality of the proposed algorithm allows us to expect that in the future it is possible to apply a similar approach for other oncohematological diseases.
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Hamada, Nozomu. "Separation of Multiple Speech Signals by usingTriangular Microphone Array." ECTI Transactions on Electrical Engineering, Electronics, and Communications 6, no. 1 (January 30, 2008): 15–21. http://dx.doi.org/10.37936/ecti-eec.200861.171743.
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Speech source separation has been an important topic to realize speech-based human-machine interfaces or high quality hand-free communication with machines. For source separation, Independent Component Analysis (ICA) and time-frequency masking are powerful methods as a tool of Blind Source Separation (BSS) of speech mixtures. The latter method is based on the assumption called \W-Disjoint Orthogonality" which implies the cell component sparsity of speech in the time-frequency domain. One of the topics treated in this article is to introduce the time-frequency masking scheme is applied to the equilateral triangular array where the three delay estimates from each microphone pairs are obtained. In addition, it is used to improve histogram-mapping algorithm by integrate and coordinate transformation of three delay estimates. Some experiments in real environment for separating multiple sources are performed to verify the effectiveness.
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WU, Xian, Boustany ROGER, Simon BOULEY, Olivier MINCK, Hervé DENAYER, Jérôme ANTONI, and Konstantinos GRYLLIAS. "Deep learning-enhanced blind separation of incoherent and spatially disjoint sound sources." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 4 (October 4, 2024): 7596–607. http://dx.doi.org/10.3397/in_2024_3981.
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Separating incoherent sound sources within complex acoustical fields presents a significant challenge in acoustic imaging. Existing methods, such as Principal Component Analysis (PCA) applied to the Cross-Spectral Matrix (CSM), yield 'virtual' sources based on statistical orthogonality. However, this approach often fails to accurately identify distinct physical sources, primarily due to its reliance on statistical orthogonality solely. A state-of-the-art method involves computing a rotation matrix to enforce criteria such as least spatial entropy, or spatial orthogonality among sources, a process that, while effective, significantly increases computational complexity and time. This work introduces a hybrid approach combining PCA and deep learning for predicting spatially disjoint source maps from virtual sources. By simulating sound sources in random quantities and locations, we train a neural network tailored to this task. We address the order mismatch between PCA-derived virtual sources and pre-simulated labels by framing source separation as a set prediction problem, utilizing the Hungarian loss for efficient mismatch resolution. This method simplifies the separation process, offering faster post-training computations and eliminating the need for complex optimizations. Validation in simulated environments and real-world datasets has shown the model's effectiveness in source separation for acoustic imaging, indicating the potential of integrating deep learning with existing methods.
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Galan-Vasquez, Edgardo, and Ernesto Perez-Rueda. "A landscape for drug-target interactions based on network analysis." PLOS ONE 16, no. 3 (March 17, 2021): e0247018. http://dx.doi.org/10.1371/journal.pone.0247018.
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In this work, we performed an analysis of the networks of interactions between drugs and their targets to assess how connected the compounds are. For our purpose, the interactions were downloaded from the DrugBank database, and we considered all drugs approved by the FDA. Based on topological analysis of this interaction network, we obtained information on degree, clustering coefficient, connected components, and centrality of these interactions. We identified that this drug-target interaction network cannot be divided into two disjoint and independent sets, i.e., it is not bipartite. In addition, the connectivity or associations between every pair of nodes identified that the drug-target network is constituted of 165 connected components, where one giant component contains 4376 interactions that represent 89.99% of all the elements. In this regard, the histamine H1 receptor, which belongs to the family of rhodopsin-like G-protein-coupled receptors and is activated by the biogenic amine histamine, was found to be the most important node in the centrality of input-degrees. In the case of centrality of output-degrees, fostamatinib was found to be the most important node, as this drug interacts with 300 different targets, including arachidonate 5-lipoxygenase or ALOX5, expressed on cells primarily involved in regulation of immune responses. The top 10 hubs interacted with 33% of the target genes. Fostamatinib stands out because it is used for the treatment of chronic immune thrombocytopenia in adults. Finally, 187 highly connected sets of nodes, structured in communities, were also identified. Indeed, the largest communities have more than 400 elements and are related to metabolic diseases, psychiatric disorders and cancer. Our results demonstrate the possibilities to explore these compounds and their targets to improve drug repositioning and contend against emergent diseases.
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Миргород, Володимир Федорович, and Ірина Маратівна Гвоздева. "ОЦІНКА ПОТУЖНОСТІ КРИТЕРІЇВ ТРЕНДУ." Aerospace technic and technology, no. 7 (August 31, 2020): 129–36. http://dx.doi.org/10.32620/aktt.2020.7.18.
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An approach to the selection and comparison of the criteria that are used in the analysis of time series of registration parameters of the technical camp of power and power plants based on gas turbine engines is proposed. The approach is based on established important characteristics of trending criteria, namely the power of such criteria, which are considered as criteria for distinguishing complex hypotheses. For analysis, we propose a statistical model for generating data in the form of a combination of deterministic trends and random components. A deterministic component is considered as a linear approximation of its expansion in a Taylor series. This assumption is justified by the need to show a trend in the shortest period of time at which the trend component allows a linear approximation. A random compound is taken as a sample of a general population of independent random variables that have a normal distribution. For analysis, the most common trend criteria were selected: Student's criterion for equality of means; Fisher dispersion ratio criterion; correlation criterion and its varieties. The supporting hypothesis has the form of belonging of a time series to a sample from the general set of independent random variables, and an alternative is belonging to a sample with a linear trend. Trend statistics of the relevant criteria generated on a moving or sectional disjoint analysis window of a given dimension. The trend development parameter was selected as the ratio of the trend growth during the analysis to the standard deviation of the random component. For the considered trend criteria, the obtained dependences of their power on the trend development parameter and the probability of an error of the first kind (erroneous alarm), as well as the operational characteristics of the criteria. The analysis was performed by the methods of analytical estimates and statistical modeling. It has been established that in the case of an alternative, the statistics of the correlation criterion and the Fisher criterion are quickly normalized, and student statistics do not change their type. A comparison of trending power criteria with equal values of the probability of an error of the first kind allows us to establish the advantage of the Student criterion, and the correlation criterion has the worst performance. Obtaining indicators of the power of trend criteria are important for applied applications since it allows you to establish the probability of the second kind of error (skipping a trend).
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Jahan, F., and M. Awrangjeb. "PIXEL-BASED LAND COVER CLASSIFICATION BY FUSING HYPERSPECTRAL AND LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 711–18. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-711-2017.
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Land cover classification has many applications like forest management, urban planning, land use change identification and environment change analysis. The passive sensing of hyperspectral systems can be effective in describing the phenomenology of the observed area over hundreds of (narrow) spectral bands. On the other hand, the active sensing of LiDAR (Light Detection and Ranging) systems can be exploited for characterising topographical information of the area. As a result, the joint use of hyperspectral and LiDAR data provides a source of complementary information, which can greatly assist in the classification of complex classes. In this study, we fuse hyperspectral and LiDAR data for land cover classification. We do a pixel-wise classification on a disjoint set of training and testing samples for five different classes. We propose a new feature combination by fusing features from both hyperspectral and LiDAR, which achieves competent classification accuracy with low feature dimension, while the existing method requires high dimensional feature vector to achieve similar classification result. Also, for the reduction of the dimension of the feature vector, Principal Component Analysis (PCA) is used as it captures the variance of the samples with a limited number of Principal Components (PCs). We tested our classification method using PCA applied on hyperspectral bands only and combined hyperspectral and LiDAR features. Classification with support vector machine (SVM) and decision tree shows that our feature combination achieves better classification accuracy compared to the existing feature combination, while keeping the similar number of PCs. The experimental results also show that decision tree performs better than SVM and requires less execution time.
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Malapert, Arnaud, Jean-Charles Régin, and Mohamed Rezgui. "Embarrassingly Parallel Search in Constraint Programming." Journal of Artificial Intelligence Research 57 (November 20, 2016): 421–64. http://dx.doi.org/10.1613/jair.5247.
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We introduce an Embarrassingly Parallel Search (EPS) method for solving constraint problems in parallel, and we show that this method matches or even outperforms state-of-the-art algorithms on a number of problems using various computing infrastructures. EPS is a simple method in which a master decomposes the problem into many disjoint subproblems which are then solved independently by workers. Our approach has three advantages: it is an efficient method; it involves almost no communication or synchronization between workers; and its implementation is made easy because the master and the workers rely on an underlying constraint solver, but does not require to modify it. This paper describes the method, and its applications to various constraint problems (satisfaction, enumeration, optimization). We show that our method can be adapted to different underlying solvers (Gecode, Choco2, OR-tools) on different computing infrastructures (multi-core, data centers, cloud computing). The experiments cover unsatisfiable, enumeration and optimization problems, but do not cover first solution search because it makes the results hard to analyze. The same variability can be observed for optimization problems, but at a lesser extent because the optimality proof is required. EPS offers good average performance, and matches or outperforms other available parallel implementations of Gecode as well as some solvers portfolios. Moreover, we perform an in-depth analysis of the various factors that make this approach efficient as well as the anomalies that can occur. Last, we show that the decomposition is a key component for efficiency and load balancing.
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Van Der Wurff, Wim. "The syntax of participial adjuncts in Eastern Bengali." Journal of Linguistics 25, no. 2 (September 1989): 373–416. http://dx.doi.org/10.1017/s0022226700014158.
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In this article I will discuss the syntactic properties of participial adjuncts in Eastern Bengali. From a GB-point of view, these constructions are quite interesting, because they can contain a nominative which is apparently not assigned by AGR, and because they show a quite intricate pattern of possibilities for coreference and disjoint reference, with some seemingly arbitrary differences among the three types of adjuncts. In Section 2, I shall present the empirical data for these constructions. In Section 3 I will discuss the relevant general syntactic principles of Eastern Bengali, specifically those responsible for Case-assignment, word-order, pro-drop (including ergative verbs) and binding properties. In Section 4, I will show that the characteristics of the participial constructions, including the apparently haphazard binding properties, follow from the general syntactic principles of Eastern Bengali, if we assume one simple statement for each type of adjunct. No further construction-specific stipulations need be made. To the extent that the analysis I propose can be upheld, it will constitute indirect support for the GB-mechanisms that are crucially involved in it. Apart from various principles of configurationality and binding, I will make use of the idea that there is no abstract AGR, in these cases at least, and also of the analysis of pro-drop as a silent clitic phenomenon, proposed in Safir (1985). It is of course a fact that the principles of grammar I appeal to still need to be investigated more carefully, and may have to be modified on the basis of data not yet taken into account or accurately analyzed. However, as they stand, the relevant principles of GB-theory appear to be able to account for the Eastern Bengali facts I discuss here. Apart from these more theoretical concerns, this paper naturally has an important descriptive component too, which I hope will make it also interesting to linguists working in a different theoretical framework and may stimulate linguists specifically concerned with Bengali to explore further the intricascies of this area of Bengali grammer.
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Zellers, Edward T., Tin-Su Pan, Samuel J. Patrash, Mingwei Han, and Stuart A. Batterman. "Extended disjoint principal-components regression analysis of SAW vapor sensor-array responses." Sensors and Actuators B: Chemical 12, no. 2 (April 1993): 123–33. http://dx.doi.org/10.1016/0925-4005(93)80008-y.
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Martin-Barreiro, Carlos, John A. Ramirez-Figueroa, Xavier Cabezas, Victor Leiva, Ana Martin-Casado, and M. Purificación Galindo-Villardón. "A New Algorithm for Computing Disjoint Orthogonal Components in the Parallel Factor Analysis Model with Simulations and Applications to Real-World Data." Mathematics 9, no. 17 (August 26, 2021): 2058. http://dx.doi.org/10.3390/math9172058.
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In this paper, we extend the use of disjoint orthogonal components to three-way table analysis with the parallel factor analysis model. Traditional methods, such as scaling, orthogonality constraints, non-negativity constraints, and sparse techniques, do not guarantee that interpretable loading matrices are obtained in this model. We propose a novel heuristic algorithm that allows simple structure loading matrices to be obtained by calculating disjoint orthogonal components. This algorithm is also an alternative approach for solving the well-known degeneracy problem. We carry out computational experiments by utilizing simulated and real-world data to illustrate the benefits of the proposed algorithm.
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Martin-Barreiro, Carlos, John A. Ramirez-Figueroa, Ana B. Nieto-Librero, Víctor Leiva, Ana Martin-Casado, and M. Purificación Galindo-Villardón. "A New Algorithm for Computing Disjoint Orthogonal Components in the Three-Way Tucker Model." Mathematics 9, no. 3 (January 20, 2021): 203. http://dx.doi.org/10.3390/math9030203.
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One of the main drawbacks of the traditional methods for computing components in the three-way Tucker model is the complex structure of the final loading matrices preventing an easy interpretation of the obtained results. In this paper, we propose a heuristic algorithm for computing disjoint orthogonal components facilitating the analysis of three-way data and the interpretation of results. We observe in the computational experiments carried out that our novel algorithm ameliorates this drawback, generating final loading matrices with a simple structure and then easier to interpret. Illustrations with real data are provided to show potential applications of the algorithm.
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Clapp, Mónica, Juan Carlos Fernández, and Alberto Saldaña. "Critical polyharmonic systems and optimal partitions." Communications on Pure & Applied Analysis 20, no. 11 (2021): 3991. http://dx.doi.org/10.3934/cpaa.2021141.
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<p style='text-indent:20px;'>We establish the existence of solutions to a weakly-coupled competitive system of polyharmonic equations in <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{R}^N $\end{document}</tex-math></inline-formula> which are invariant under a group of conformal diffeomorphisms, and study the behavior of least energy solutions as the coupling parameters tend to <inline-formula><tex-math id="M2">\begin{document}$ -\infty $\end{document}</tex-math></inline-formula>. We show that the supports of the limiting profiles of their components are pairwise disjoint smooth domains and solve a nonlinear optimal partition problem of <inline-formula><tex-math id="M3">\begin{document}$ \mathbb R^N $\end{document}</tex-math></inline-formula>. We give a detailed description of the shape of these domains.</p>
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Birrell, Paul J., Richard G. Pebody, André Charlett, Xu-Sheng Zhang, and Daniela De Angelis. "Real-time modelling of a pandemic influenza outbreak." Health Technology Assessment 21, no. 58 (October 2017): 1–118. http://dx.doi.org/10.3310/hta21580.
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BackgroundReal-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible.ObjectivesTo advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software.MethodsMarkov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with ‘gold-standard’ MCMC-derived inferences in terms of estimation quality and computational burden.ResultsThe PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g.R0is consistently estimated to be 1.76–1.80, dropping by 43–50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden ‘shock’ in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use.LimitationsThe PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance.ConclusionsFollowing the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation.Future work recommendationsModelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling.Trial registrationCurrent Controlled Trials ISRCTN40334843.FundingThis project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full inHealth Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.
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Milleana Shaharudin, Shazlyn, Shuhaida Ismail, Siti Mariana Che Mat Nor, and Norhaiza Ahmad. "An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (December 1, 2019): 237. http://dx.doi.org/10.11591/ijai.v8.i3.pp237-243.
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<p>In this study, hybrid RPCA-spectral biclustering model is proposed in identifying the Peninsular Malaysia rainfall pattern. This model is a combination between Robust Principal Component Analysis (RPCA) and bi-clustering in order to overcome the skewness problem that existed in the Peninsular Malaysia rainfall data. The ability of Robust PCA is more resilient to outlier given that it assesses every observation and downweights the ones which deviate from the data center compared to classical PCA. Meanwhile, two way-clustering able to simultaneously cluster along two variables and exhibit a high correlation compared to one-way cluster analysis. The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA. Meanwhile, the number of clusters has improved from six disjointed cluster in Robust PCA-kMeans to eight disjointed cluster for the proposed model. Further analysis shows that the proposed model has smaller variation with the values of 0.0034 compared to 0.030 in Robust PCA-kMeans model. Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation of the clustering result.</p>
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FUCHS, PAUL. "THE DEVELOPMENT OF AN OBJECT-ORIENTED SYSTEM WHICH INTEGRATES SIMULATION AND RECONSTRUCTION WITHIN A COMMON FRAMEWORK." International Journal of Modern Physics C 06, no. 04 (August 1995): 481–88. http://dx.doi.org/10.1142/s0129183195000320.
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A common problem for small experiments is that the design of the data acquisition, the simulation of the experiment and the reconstruction and analysis of real data is broken up into several disjoint development phases. Software development is necessary in all phases, yet continuity and an overall design strategy is lacking. This leads to duplication of software components and interface problems from one phase to the next and prohibits the generation of code which is reusable over the lifetime of many experiments. We address this problem within an object-oriented paradigm.
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Bagchi, Susmit. "The Sequential and Contractible Topological Embeddings of Functional Groups." Symmetry 12, no. 5 (May 8, 2020): 789. http://dx.doi.org/10.3390/sym12050789.
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The continuous and injective embeddings of closed curves in Hausdorff topological spaces maintain isometry in subspaces generating components. An embedding of a circle group within a topological space creates isometric subspace with rotational symmetry. This paper introduces the generalized algebraic construction of functional groups and its topological embeddings into normal spaces maintaining homeomorphism of functional groups. The proposed algebraic construction of functional groups maintains homeomorphism to rotationally symmetric circle groups. The embeddings of functional groups are constructed in a sequence in the normal topological spaces. First, the topological decomposition and associated embeddings of a generalized group algebraic structure in the lower dimensional space is presented. It is shown that the one-point compactification property of topological space containing the decomposed group embeddings can be identified. Second, the sequential topological embeddings of functional groups are formulated. The proposed sequential embeddings follow Schoenflies property within the normal topological space. The preservation of homeomorphism between disjoint functional group embeddings under Banach-type contraction is analyzed taking into consideration that the underlying topological space is Hausdorff and the embeddings are in a monotone class. It is shown that components in a monotone class of isometry are not separable, whereas the multiple disjoint monotone class of embeddings are separable. A comparative analysis of the proposed concepts and formulations with respect to the existing structures is included in the paper.
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Andrienko, Natalia, Gennady Andrienko, Siming Chen, Dirk Burghardt, Alexander Dunkel, and Ross Purves. "Geovisual analysis of VGI for understanding people's behaviour in relation to multifaceted context." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-10-2019.
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<p><strong>Abstract.</strong> Volunteered Geographic Information (VGI) in the form of actively and passively generated spatial content offers extensive potential for a wide range of applications. Realising this potential however requires methods which take account of the specific properties of such data, for example its heterogeneity, quality, subjectivity, spatial resolution and temporal relevance. The creation and production of such content through social media platforms is an expressive aspect of human behaviour, and as such influenced strongly by the co-occurrence of events and context external to the social media. In this project we are developing geovisual analysis methods which show how actors interact in location based social media (LBSM), and how their interactions influence, and are influenced by, their physical and social environment and relations.</p><p>In the first phase of the project, we developed and demonstrated a conceptual model enabling the extraction, analysis and visualisation of events and reactions to events in LBSM. A central element of this model and its implementation is the integration of spatial, temporal, thematic and social dimensions, or <i>facets</i>, combined with an explicit link between events and reactions. We have developed a conceptual model of collective reactions in LBSM [1] which includes a task matrix underpinning our methodological efforts. A key output of this conceptual model, and the resulting task matrix was the acknowledgement of the importance of exploring multiple dimensions in LBSM reactions to events, namely the spatial, temporal, thematic and social which relate to where, when, what and who questions which can be posed of such data.</p><p>The conceptual model formed a basis for our research on bridging the gap between visually-driven analysis and visual communication, or story telling [2]. Findings and results of the analysis often need to be communicated to an audience that lacks expertise in visualization and analysis methods. This requires analysis outcomes to be presented in simpler ways than that are typically used in analysis supporting systems. Not only analytical visualizations may be too complex for target audiences but also the information that needs to be presented. Analysis results may consist of multiple components, which may involve multiple heterogeneous facets. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, within which two main challenges lie: information complexity and display complexity. We address this problem by proposing a general framework for story synthesis, in which the analyst creates and organises story contents from analysis results. Story synthesis includes selecting and assembling findings and arranging them in meaningful layouts that take into account the structure of information and inherent properties of its components (facets). Paper [2] proposes a facet-based generic framework for story synthesis which can be applied to different kinds of VGI and LBSM data.</p><p>To introduce our concepts, we use an example based on the IEEE VAST Challenge 2011 [3], requiring analysis of the circumstances of an epidemic outbreak in a fictive city Vastopolis. The data are geographically referenced microblog messages, some of which include keywords indicating disease symptoms, such as fever, chills, sweats, aches and pains, coughing, etc. The time span of the data is 3 weeks. An analyst needs to find out when and where the outbreak started and how it developed. The analyst uses a visual analytics system providing multiple types of interactive visual displays and supporting database queries and data transformations. Fig.1 shows how analysis artefacts are managed. In the course of the analysis, the analyst has obtained a set of findings (labelled F1-F5), which include the outbreak start time, the spatial clusters and the times of their existence, the differing sets of frequent keywords associated with the clusters, the location and time of the truck crash, and the ways of spreading and temporal development of two diseases. As the next natural step, these findings need to be communicated to any interested audience, but not as disjoint information pieces but as an integrated story. The pieces need to be arranged in appropriate ways revealing the relationships between the information pieces, such as temporal and spatial relationships. Figures 1C and 1D show examples of arrangements that might be created by the VAST Challenge analyst for conveying temporal and spatial relationships between the findings. Another kind of relationship the analyst may wish to reflect is the differences between the symptoms of two diseases that were discovered in the course of the analysis. For this purpose, the analyst may juxtapose the lists of the keywords corresponding to the central-eastern and south-western clusters. Analysts should be able to create and edit such arrangements in order to construct understandable and interesting stories.</p><p>A process of story synthesis includes the following activities: aggregate and summarize (as a means of simplification and achieving a desired level of detail), embed details (enable drilling down into aggregates), arrange (put information pieces in a meaningful layout), show facets (exhibit information structure), and annotate (include explanations and comments). Information facets play an important role in story synthesis. They need to be presented to story recipients to enable proper understanding of information. However, heterogeneous facets, such as space, time, population, semantics of message texts (represented by keywords or topics), etc. may be hard to present simultaneously while keeping the display simple and easy to understand and avoiding information overload. Such facets may be represented in complementary views providing different perspectives on the information. The task matrix introduced in paper [1] suggests taking into account only two facets at once. Inherent properties of information facets can be used for meaningful arrangement of story slices and for aggregation. Thus, temporal and spatial arrangements, as in Fig.1 (C, D), exploit the inherent properties of time (temporal ordering and distances) and space (spatial distances, neighbourhood, and relative directions). Paper [2] describes an example of analysing expressions of people’s reactions to political events and processes, such as the Brexit, in LBSM and organizing analysis findings in stories with the use of various facet-based layouts.</p><p>In the further work, we extend the research scope to studying reactions as a component of behaviour (along with human activities and emotions), incorporating external social and physical context to better allow events to be related and compared. This will not only include development of new analysis methods and workflows but also definition of new analysis tasks and, respectively, new types of analytical results. These extensions will require further work on finding, on the one hand, effective representations for analytical visualizations, on the other hand, expressive and easily understandable representations for communication of analysis findings. Besides, a general problem to be tackled is how to incorporate analyst’s input, such as background knowledge and context information that is not reflected in available data, in both analytical visualizations and stories presenting analysis results. It would be interesting to go beyond mere use of textual annotations towards representing such inputs in a visual form, which needs to be distinguishable from the representation of the data.</p>
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Lu, Chi-Jie, and Chi-Chang Chang. "A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/624017.
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Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.
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Weiß, Marcel, and Sebastian E. Ahnert. "Neutral components show a hierarchical community structure in the genotype–phenotype map of RNA secondary structure." Journal of The Royal Society Interface 17, no. 171 (October 2020): 20200608. http://dx.doi.org/10.1098/rsif.2020.0608.
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Genotype–phenotype (GP) maps describe the relationship between biological sequences and structural or functional outcomes. They can be represented as networks in which genotypes are the nodes, and one-point mutations between them are the edges. The genotypes that map to the same phenotype form subnetworks consisting of one or multiple disjoint connected components–so-called neutral components (NCs). For the GP map of RNA secondary structure, the NCs have been found to exhibit distinctive network features that can affect the dynamical processes taking place on them. Here, we focus on the community structure of RNA secondary structure NCs. Building on previous findings, we introduce a method to reveal the hierarchical community structure solely from the sequence constraints and composition of the genotypes that form a given NC. Thereby, we obtain modularity values similar to common community detection algorithms, which are much more complex. From this knowledge, we endorse a sampling method that allows a fast exploration of the different communities of a given NC. Furthermore, we introduce a way to estimate the community structure from genotype samples, which is useful when an exhaustive analysis of the NC is not feasible, as is the case for longer sequence lengths.
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Chartrand, Gary, Ritabrato Chatterjee, and Ping Zhang. "Ramsey Chains in Linear Forests." Axioms 12, no. 11 (October 29, 2023): 1019. http://dx.doi.org/10.3390/axioms12111019.
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Every red–blue coloring of the edges of a graph G results in a sequence G1, G2, …, Gℓ of pairwise edge-disjoint monochromatic subgraphs Gi (1≤i≤ℓ) of size i, such that Gi is isomorphic to a subgraph of Gi+1 for 1≤i≤ℓ−1. Such a sequence is called a Ramsey chain in G, and ARc(G) is the maximum length of a Ramsey chain in G, with respect to a red–blue coloring c. The Ramsey index AR(G) of G is the minimum value of ARc(G) among all the red–blue colorings c of G. If G has size m, then k+12≤m<k+22 for some positive integer k. It has been shown that there are infinite classes S of graphs, such that for every graph G of size m in S, AR(G)=k if and only if k+12≤m<k+22. Two of these classes are the matchings mK2 and paths Pm+1 of size m. These are both subclasses of linear forests (a forest of which each of the components is a path). It is shown that if F is any linear forest of size m with k+12<m<k+22, then AR(F)=k. Furthermore, if F is a linear forest of size k+12, where k≥4, that has at most k−12 components, then AR(F)=k, while for each integer t with k−12<t<k+12 there is a linear forest F of size k+12 with t components, such that AR(F)=k−1.
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PAPADIMITRIOU, STERGIOS, and SPIRIDON D. LIKOTHANASSIS. "KERNEL-BASED SELF-ORGANIZED MAPS TRAINED WITH SUPERVISED BIAS FOR GENE EXPRESSION DATA ANALYSIS." Journal of Bioinformatics and Computational Biology 01, no. 04 (January 2004): 647–80. http://dx.doi.org/10.1142/s021972000400034x.
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Self-Organized Maps (SOMs) are a popular approach for analyzing genome-wide expression data. However, most SOM based approaches ignore prior knowledge about functional gene categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known functional characteristics of genes. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.
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Cecchini, Simone. "A long neck principle for Riemannian spin manifolds with positive scalar curvature." Geometric and Functional Analysis 30, no. 5 (September 22, 2020): 1183–223. http://dx.doi.org/10.1007/s00039-020-00545-1.
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AbstractWe develop index theory on compact Riemannian spin manifolds with boundary in the case when the topological information is encoded by bundles which are supported away from the boundary. As a first application, we establish a “long neck principle” for a compact Riemannian spin n-manifold with boundary X, stating that if $${{\,\mathrm{scal}\,}}(X)\ge n(n-1)$$ scal ( X ) ≥ n ( n - 1 ) and there is a nonzero degree map into the sphere $$f:X\rightarrow S^n$$ f : X → S n which is strictly area decreasing, then the distance between the support of $$\text {d}f$$ d f and the boundary of X is at most $$\pi /n$$ π / n . This answers, in the spin setting and for strictly area decreasing maps, a question recently asked by Gromov. As a second application, we consider a Riemannian manifold X obtained by removing k pairwise disjoint embedded n-balls from a closed spin n-manifold Y. We show that if $${{\,\mathrm{scal}\,}}(X)>\sigma >0$$ scal ( X ) > σ > 0 and Y satisfies a certain condition expressed in terms of higher index theory, then the radius of a geodesic collar neighborhood of $$\partial X$$ ∂ X is at most $$\pi \sqrt{(n-1)/(n\sigma )}$$ π ( n - 1 ) / ( n σ ) . Finally, we consider the case of a Riemannian n-manifold V diffeomorphic to $$N\times [-1,1]$$ N × [ - 1 , 1 ] , with N a closed spin manifold with nonvanishing Rosenebrg index. In this case, we show that if $${{\,\mathrm{scal}\,}}(V)\ge \sigma >0$$ scal ( V ) ≥ σ > 0 , then the distance between the boundary components of V is at most $$2\pi \sqrt{(n-1)/(n\sigma )}$$ 2 π ( n - 1 ) / ( n σ ) . This last constant is sharp by an argument due to Gromov.
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Xing, Xu-Feng, Mir Abolfazl Mostafavia, and Chen Wang. "EXTENSION OF RCC TOPOLOGICAL RELATIONS FOR 3D COMPLEX OBJECTS COMPONENTS EXTRACTED FROM 3D LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 425–32. http://dx.doi.org/10.5194/isprs-archives-xli-b3-425-2016.
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Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in <i>R</i><sup>3</sup>. Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in <i>R</i><sup>3</sup>. We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.
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Xing, Xu-Feng, Mir Abolfazl Mostafavia, and Chen Wang. "EXTENSION OF RCC TOPOLOGICAL RELATIONS FOR 3D COMPLEX OBJECTS COMPONENTS EXTRACTED FROM 3D LIDAR POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 425–32. http://dx.doi.org/10.5194/isprsarchives-xli-b3-425-2016.
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Topological relations are fundamental for qualitative description, querying and analysis of a 3D scene. Although topological relations for 2D objects have been extensively studied and implemented in GIS applications, their direct extension to 3D is very challenging and they cannot be directly applied to represent relations between components of complex 3D objects represented by 3D B-Rep models in <i>R</i><sup>3</sup>. Herein we present an extended Region Connection Calculus (RCC) model to express and formalize topological relations between planar regions for creating 3D model represented by Boundary Representation model in <i>R</i><sup>3</sup>. We proposed a new dimension extended 9-Intersection model to represent the basic relations among components of a complex object, including disjoint, meet and intersect. The last element in 3*3 matrix records the details of connection through the common parts of two regions and the intersecting line of two planes. Additionally, this model can deal with the case of planar regions with holes. Finally, the geometric information is transformed into a list of strings consisting of topological relations between two planar regions and detailed connection information. The experiments show that the proposed approach helps to identify topological relations of planar segments of point cloud automatically.
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Kim, Jong-Suk, Jie Chen, Seo-Yeon Park, Dian Wu, Hyeon-Cheol Yoon, and Joo-Heon Lee. "OneMap Drought Monitoring Analysis Based on Statistical Models." Applied Sciences 12, no. 19 (September 26, 2022): 9669. http://dx.doi.org/10.3390/app12199669.
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As the effects of droughts on agriculture and industrial water availability intensify with climate change, developing suitable drought prevention and mitigation measures has become increasingly important. However, measuring drought conditions using different indices leads to disjointed drought management responses by ministries and agencies. Additionally, indices based on only one variable are insufficient to accurately assess drought conditions. Therefore, creating and adopting a OneMap drought index would be beneficial in the assessment of drought conditions and the implementation of appropriate measures. In this study, we used multivariate statistical modeling using Bayesian principal component analysis to develop a OneMap drought index that unifies existing measures of drought conditions, including meteorological, agricultural, and hydrological drought indices. After evaluating the accuracy of the corrected OneMap drought index based on the self-organizing migrating algorithm optimization technique, it was found that the applicability of the OneMap drought index and its ability to regenerate drought were excellent for ground and satellite data. Therefore, the authors recommend implementing step-by-step drought management action plans using the integrated index to generate drought forecasts and warnings, thus promoting concerted and effective responses of local governments and authorities.
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Gojkovic, Vesna, Jelena Dostanic, and Veljko Djuric. "Тhe Dark Triad, Amorality, and Impulsivity." Psihologija 52, no. 1 (2019): 53–73. http://dx.doi.org/10.2298/psi170803028g.
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The ongoing research on socially toxic behaviors has been dominated by the Dark Triad approach. However, there are other theoretical approaches on antagonistic personalities that are not incepted by the Dark Triad approach. The goal of the present study was to investigate empirical overlap between the Dark Triad and Amorality constructs. Our data indicated that there is a substantial overlap between the two constructs, save for the Narcissism component of the Dark Triad which is somewhat distinct from the common Dark Triad/Amorality space. When Impulsivity was included into the analyses it disjointed the relatively monolithic Dark Triad/Amorality structure that was observed by the earlier analysis; indicating that the Dark Triad and Amorality do not unreservedly belong to the same measurement space. Thus, theoretically and empirically separable traits (amalgamated in the Drak Triad, Amorality, and Impulsivity constructs) combine in an intricate fashion to form distinctive patterns of socially malignant behaviors.
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Băban, Călin Florin, and Marius Băban. "An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance." Mathematics 10, no. 15 (July 29, 2022): 2672. http://dx.doi.org/10.3390/math10152672.
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Since open innovation between industry–university is a highly complex phenomenon, its orchestration may be of great support for better collaboration between these organizations. However, there is a lack of evidence on how an orchestration framework impacts the collaboration performance between these organizations in such a setting. Based on a research model that investigates the influence of the main orchestration dimensions on the performance of collaboration, this study offers one of the first perspectives of an orchestration process between the industry and university actors in open innovation. The developed research model was assessed using a deep learning dual-stage PLS-SEM and artificial neural network (ANN) analysis. In the first stage, the hypotheses of the research model were tested based on a disjoint two-stage approach of PLS-SEM, and the results reveal the orchestration dimensions that have a significant impact on collaboration performance. In the second stage, a deep learning network approach was successfully employed to capture the complex relationships among the significant orchestration dimensions identified through the PLS-SEM analysis. An importance–performance map analysis provided useful insights into the relative importance of the components of each orchestration dimension based on their effects on the collaboration performance.
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Tuztaşı, Uğur, and Pınar Koç. "A spatial analysis of the physical properties of Sivas Cumhuriyet University Campus." Journal of Human Sciences 18, no. 4 (November 7, 2021): 564–77. http://dx.doi.org/10.14687/jhs.v18i4.6123.
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Research problem/aim: This study aimed to reveal the physical characteristics of the Sivas Cumhuriyet University Campus. University campuses are equipped with highly diversified physical qualities in terms of the design and planning stages. The quality and property of the physical space are the values that enrich and develop the livable, accessible, and perceptible features of the campus space. In this study, the basic physical components that make up a university campus were identified in order to analyze their qualities. Method: The method was based on fieldwork documentation tools such as observation, on-site examination, photography, and drawing. Accordingly, the Sivas Cumhuriyet University campus was evaluated in terms of its basic physical characteristics such as borders, building usage patterns, morphology, occupancy-vacancy, common areas, and green areas. Findings: It was determined that the campus space has a scattered and disjointed group of relationships, and individual and independent spatial arrangements present a fragmented appearance throughout the built environment. Accordingly, it was determined that the common and green areas and the occupancy-vacancy relations were disturbed, and a morphologically unstable and scattered physical space presentation emerged. Conclusions: Ultimately, this study, which analyzed the current physical environment of the campus, proposed the preparation of an urgent design guide for the campus in order to transform the built environmental components that have been constructed in a disconnected, dispersed, fragmented and unrelated manner into holistic, coherent and continuous spatial organizations.
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Casarrubea, Maurizio, Giuseppe Di Giovanni, and Giuseppe Crescimanno. "Effects of Different Anxiety Levels on the Behavioral Patternings Investigated through T-pattern Analysis in Wistar Rats Tested in the Hole-Board Apparatus." Brain Sciences 11, no. 6 (May 27, 2021): 714. http://dx.doi.org/10.3390/brainsci11060714.
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The Hole-Board is an ethologically based tool for investigating the anxiety-related behavior of rats following manipulation of the central anxiety level. The present paper aims to assess behavioral patterning following pharmacological manipulation of emotional assets in Wistar rats tested in this experimental apparatus. For this purpose, the behavior of three groups of rats injected with saline, diazepam or FG7142 was evaluated using conventional quantitative and multivariate T-pattern analyses. The results demonstrate that quantitative analyses of individual components of the behavior, disjointed from the comprehensive behavioral structure, are of narrow utility in the understanding of the subject’s emotional condition. Among the components of the behavioral repertoire in rodents tested in the Hole-Board, Edge-Sniff and Head-Dip represent the most significant ones to rate anxiety level. They are characterized by a strong bivariate relationship and are also firmly part of the behavioral architecture, as revealed by the T-pattern analysis (TPA), a multivariate technique able to detect significant relationships among behavioral events over time. Edge-Sniff → Head-Dip sequences, in particular, are greatly influenced by the level of anxiety: barely detectable in control animals, they completely disappear in subjects with a reduced level of anxiety and are present in almost 25% of the total of T-patterns detected in subjects whose anxiety level increased.
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Dakić, Vedran, Zlatan Morić, Ana Kapulica, and Damir Regvart. "Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations." Journal of Cybersecurity and Privacy 5, no. 1 (December 30, 2024): 2. https://doi.org/10.3390/jcp5010002.
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The Zero Trust Architecture (ZTA) security system follows the “never trust, always verify” principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation takes time and effort. Azure’s extensive services and customizations require careful design and implementation. Azure administrators need help navigating and changing configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTAs comprehensive policy definitions, multi-factor and passwordless authentication, and other advanced features are tested in a mid-size business scenario. The document delineates several principal findings concerning the execution of Azure’s ZTA within mid-sized enterprises. Azure ZTA significantly improves security by reducing attack surfaces via ongoing identity verification, stringent access controls, and micro-segmentation. Nonetheless, its execution is resource-demanding and intricate, necessitating considerable expertise and meticulous planning. A notable disparity exists between theoretical ZTA frameworks and their practical implementation, characterized by disjointed management interfaces and user fatigue resulting from incessant authentication requests. The case studies indicate that although Zero Trust Architecture enhances organizational security and mitigates risks, it may disrupt operations and adversely affect user experience, particularly in hybrid and fully cloud-based settings. The study underscores the necessity for customized configurations and the equilibrium between security and usability to ensure effective ZTA implementation.
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Bowden, A. Brooks. "Designing Field Experiments to Integrate Research on Costs." AERA Open 9 (January 2023): 233285842311715. http://dx.doi.org/10.1177/23328584231171536.
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Although experimental evaluations have been labeled the “gold standard” of evidence for policy (U.S. Department of Education, 2003), evaluations without an analysis of costs are not sufficient for policymaking (Monk, 1995; Ross et al., 2007). Funding organizations now require cost-effectiveness data in most evaluations of effects. Yet, there is little guidance on how to integrate research on costs into efficacy or effectiveness evaluations. As a result, research proposals and papers are disjointed in the treatment of costs, implementation, and effects, and studies often miss opportunities to integrate what is learned from the cost component into what is learned about effectiveness. To address this issue, this paper uses common evaluation frameworks to provide guidance for integrating research on costs into the design of field experiments building on the ingredients method (Levin et al., 2018). The goal is to improve study design, resulting in more cohesive, efficient, and higher-quality evaluations.
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Carpenter, Charlie M., Weiming Zhang, Lucas Gillenwater, Cameron Severn, Tusharkanti Ghosh, Russell Bowler, Katerina Kechris, and Debashis Ghosh. "PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes." PLOS Computational Biology 17, no. 10 (October 22, 2021): e1008986. http://dx.doi.org/10.1371/journal.pcbi.1008986.
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High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the “-omics” family. For this work, we focus on subsets that interact with one another and represent these “pathways” as graphs. Observed pathways often have disjoint components, i.e., nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway, which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This work extends previous kernel machine approaches. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or “smoothed” graph into a score test, PaIRKAT can provide more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.
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Becker, Douglas A. "Environmental Quality, Extreme Heat, and Healthcare Expenditures." International Journal of Environmental Research and Public Health 21, no. 10 (October 5, 2024): 1322. http://dx.doi.org/10.3390/ijerph21101322.
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Although the effects of the environment on human health are well-established, the literature on the relationship between the quality of the environment and expenditures on healthcare is relatively sparse and disjointed. In this study, the Environmental Quality Index developed by the Environmental Protection Agency and heatwave days were compared against per capita Medicare spending at the county level. A general additive model with a Markov Random Field smoothing term was used for the analysis to ensure that spatial dependence did not undermine model results. The Environmental Quality Index was found to hold a statistically significant (p < 0.05), multifaceted nonlinear association with spending, as was the average seasonal maximum heat index. The same was not true of heatwave days, however. In a secondary analysis on the individual domains of the index, the social and built environment components were significantly related to spending, but the air, water, and land domains were not. These results provide initial support for the simultaneous benefits of healthcare financing systems to mitigate some dimensions of poor environmental quality and consistently high air temperatures.
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Vilutiene, Tatjana, Diana Kalibatiene, M. Reza Hosseini, Eugenio Pellicer, and Edmundas Kazimieras Zavadskas. "Building Information Modeling (BIM) for Structural Engineering: A Bibliometric Analysis of the Literature." Advances in Civil Engineering 2019 (August 25, 2019): 1–19. http://dx.doi.org/10.1155/2019/5290690.
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Building information modeling (BIM) is transforming the way of work across the architecture, engineering, and construction (AEC) industry, where BIM offers vast opportunities for improving performance. BIM is therefore an area of great interest across the AEC industry in general and for the structural engineering field in particular. This paper is aimed at providing a broad picture of published papers that relate BIM with structural engineering. This overview will enhance understanding of the state of the research work on this subject, drawing upon bibliometric analysis of 369 papers. Findings provide an updated picture of how now-available studies that link BIM developments and applications in structural engineering are distributed chronologically, across journals, authors, countries, and institutions. Detailed analyses of citation networks present the cooccurrence map of keywords, citation patterns of journals and articles, the most cited journals, and the top 15 most cited articles on BIM in the area of structural engineering. Discussions demonstrate that research on BIM applications for structural engineering has been constantly growing with a sudden increase after 2014. This study reveals that research attempts on this area have been dominated by exploring generic issues of BIM like information management; however, technical issues of structural engineering, to be resolved through BIM capabilities, have remained overlooked. Moreover, the research work in this area is found to be conducted largely in isolation, comprising disjointed and fragmented research studies. Gaps and important areas for future research include modeling of structural components, automation of the assembly sequence, planning and optimization of off-site construction, and dynamic structural health monitoring.
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Sleeman, Katherine E., Javiera Leniz, Irene J. Higginson, and Katherine Bristowe. "Is end-of-life care a priority for policymakers? Qualitative documentary analysis of health care strategies." Palliative Medicine 32, no. 9 (July 17, 2018): 1474–86. http://dx.doi.org/10.1177/0269216318786333.
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Background: Prioritisation of end-of-life care by policymakers has been the subject of extensive rhetoric, but little scrutiny. In England, responsibility for improving health and care lies with 152 regional Health and Wellbeing Boards. Aim: To understand the extent to which Health and Wellbeing Boards have identified and prioritised end-of-life care needs and their plans for improvement. Design: Qualitative documentary analysis of Health and Wellbeing Strategies. Summative content analysis to quantify key concepts and identify themes. Data sources: Strategies were identified from Local Authority web pages and systematically searched to identify relevant content. Results: In total, 150 strategies were identified. End-of-life care was mentioned in 78 (52.0%) and prioritised in 6 (4.0%). Four themes emerged: (1) clinical context – in 43/78 strategies end-of-life care was mentioned within a specific clinical context, most often ageing and dementia; (2) aims and aspirations – 31 strategies identified local needs and/or quantifiable aims, most related to the place of death; (3) narrative thread – the connection between need, aim and planned intervention was disjointed, just six strategies included all three components; and (4) focus of evidence – where cited, evidence related to evidence of need, not evidence for effective interventions. Conclusion: Half of Health and Wellbeing Strategies mention end-of-life care, few prioritise it and none cite evidence for effective interventions. The absence of connection between need, aim and intervention is concerning. Future research should explore whether and how strategies have impacted on local populations.
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Mascia, Leno, Yannis Kouparitsas, Davide Nocita, and Xujin Bao. "Antiplasticization of Polymer Materials: Structural Aspects and Effects on Mechanical and Diffusion-Controlled Properties." Polymers 12, no. 4 (April 1, 2020): 769. http://dx.doi.org/10.3390/polym12040769.
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Antiplasticization of glassy polymers, arising from the addition of small amounts of plasticizer, was examined to highlight the developments that have taken place over the last few decades, aiming to fill gaps of knowledge in the large number of disjointed publications. The analysis includes the role of polymer/plasticizer molecular interactions and the conditions leading to the cross-over from antiplasticization to plasticization. This was based on molecular dynamics considerations of thermal transitions and related relaxation spectra, alongside the deviation of free volumes from the additivity rule. Useful insights were gained from an analysis of data on molecular glasses, including the implications of the glass fragility concept. The effects of molecular packing resulting from antiplasticization are also discussed in the context of physical ageing. These include considerations on the effects on mechanical properties and diffusion-controlled behaviour. Some peculiar features of antiplasticization regarding changes in Tg were probed and the effects of water were examined, both as a single component and in combination with other plasticizers to illustrate the role of intermolecular forces. The analysis has also brought to light the shortcomings of existing theories for disregarding the dual cross-over from antiplasticization to plasticization with respect to modulus variation with temperature and for not addressing failure related properties, such as yielding, crazing and fracture toughness.
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Couto, Dayvid Rodrigues, André Paviotti Fontana, Ludovic Jean Charles Kollmann, Vitor Da Cunha Manhães, Talitha Mayumi Francisco, and Gláucio De Mello Cunha. "Vascular epiphytes in seasonal semideciduous forest in the Espírito Santo State and similarity with other seasonal forests in Eastern Brazil." Acta Scientiarum. Biological Sciences 38, no. 2 (October 24, 2016): 169. http://dx.doi.org/10.4025/actascibiolsci.v38i2.31320.
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In this study, we evaluated the floristic composition of vascular epiphytes in seasonal semideciduous forest fragments of the Itapemirim River basin, Southern State of Espírito Santo, in order to verify its similarity to other semideciduous forests studied in Brazil. Excursions were conducted every fortnight between June 2008 and May 2009, and epiphytes were collected and recorded (55 species, 34 genera and six families). Orchidaceae (21 species) was the richest family, whereas the genera presenting the greatest richness were Tillandsia (seven), Rhipsalis (four), Aechmea, Epidendrum and Peperomia (three species each). The category the most representative was characteristics holoepiphytes (84%). The riparian forests were the most important environments for the epiphytic flora. Similarity analysis and PCA supported four groups, where the study area appears disjointed from the others, supporting the hypothesis that geographical proximity, elevation and climate have a strong effect on the floristic composition, conditioning the formation of distinct floras. Detailed surveys on the floristic composition and structure of this community are important for the elaboration of studies on coherent environmental impacts, since epiphytes are typical in tropical rainforests, and they are an important floristic, structural and functional component of these ecosystems.
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Fisher, Rebecca J., Lal Russell, Frances Riley-Bennett, Trudi Cameron, Marion Walker, and Cath Sackley. "Barriers and facilitators in providing home-based rehabilitation for stroke survivors with severe disability in the UK: an online focus group study with multidisciplinary rehabilitation teams." BMJ Open 13, no. 8 (August 2023): e071217. http://dx.doi.org/10.1136/bmjopen-2022-071217.
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ObjectivesIn the UK, over 20% of stroke survivors leave hospital with severe disability. Limited evidence-based clinical guidance is available to support the rehabilitation of these individuals. Our previous research has focused on establishing consensus regarding the core components of home-based rehabilitation for this under investigated group. This study explores the barriers of providing rehabilitation and identifies strategies to overcome them.DesignThree focus group interviews were conducted with n=20. The context coding framework was employed to organise the transcribed data and to facilitate inductive and deductive analysis and synthesis.SettingOnline, MSTeams, UK.ParticipantsA purposive sample of 20 National Health Service clinical staff participants, from 3 multidisciplinary teams providing home-based stroke rehabilitation for this population (n=7, 6 and 7).ResultsHigh levels of need were reported across multiple domains for survivors including continence, communication and physical function. Interventions often required multiagency collaboration in order to optimise the available resources and specialist skills. There was lack of clarity regarding who was ultimately responsible for providing components of rehabilitation for stroke survivors with severe disability. Teams provide rehabilitation for this population but are insufficiently commissioned or resourced to fully meet their needs. In-complete and disjointed pathways with resultant healthcare inequalities were commonly reported. Teams used a variety of strategies to overcome these barriers and optimise rehabilitation opportunities. These included upskilling a diverse range of partners to capitalise on the skills and resources across health, social care and voluntary sector boundaries employing multiagency collaboration. Teams established and engaged networks of stakeholders in order to advocate on behalf of stroke survivors.ConclusionsCollaboration and partnership working is important in the delivery of rehabilitation for stroke survivors with severe disability. Commissioners need to be aware that cross-agency multidisciplinary expertise is required, if rehabilitation opportunities are to be realised and existing health inequalities addressed.
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Mousazadeh, Hossein. "Environmental social science and urban environment management: A review of pathways link- ing policies and planning to citizens living by the river." Quaestiones Geographicae 41, no. 4 (December 30, 2022): 127–39. http://dx.doi.org/10.14746/quageo-2022-0043.
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Urban environment management (UEM) and environmental social science (ESS) are two interconnected approaches that show significant potential for creating more sustainable and liveable cities. Their research has, however, been mainly disjointed, which may be because planning and policy have placed more emphasis on UEM than ESS at the same time. This research examined ESS in terms of the quality of life (QOL) criteria and sense of place (SOP) of citizens living by the river with the UEM intervention in the form of policies and programmes. The effects of fieldwork in the form of specialised and spatial interviews are presented in this research, which is usually ignored in the literature review of this type of analysis. The study aims to fill the current gap in this field in three European capitals by providing a reflective methodological account of fieldwork and the ‘elite interviewing’ approach. This paper produces a distinct form of the localisation of the research and sampling techniques and is as new, interactive and developing as possible. The results of the present study are underpinned by the conceptual model of research, which supports the main aim of this analysis and tries to examine the components of research in a new perspective. The findings high- lighted that maintaining and strengthening ESS for citizens living next to rivers require innovative UEM, which uses urban creativity in the field of goals, urban settlements, strategies and management solutions to provide the base for increasing the liveability of the city and improving the quality of urban life.