Pairwise Matrix Research Articles

Selection of Additive Manufacturing Machine Using Analytical Hierarchy Process

3D printing or additive manufacturing (AM) is considered to be the most important technology among the emerging technologies. 3D printing technology is considered as an alternative to the conventional manufacturer machine traditionally used in the manufacturing sector. 3D printing technology is generally classified into seven types. Each type of 3D printing technology has its separate own uniqueness (i.e., operation, material usage, and no wastage). The price of a manufactured item includes all its costs. The most important of these is to take into account the price of the machine being manufactured and the features of the machine. Moreover, the price of the product produced in AM will depend on all the costs required to produce it. Then, it is possible to reduce the cost of the product by choosing the AMM that has significant features and the right price. Therefore, this paper aims to solve a decision-making problem from the AMM selection by using one of the multicriteria decision-making (MCDM) tools, i.e., analytical hierarchy process (AHP). This paper outcome is meant to meet the expectation of end-users. As an initial step, the Micro, Small, and Medium Enterprise (MSME) company gets quotations from some AM companies to choose a type of AM machine known FDM for its structure product and doll product. The first step is to select the most appropriate machines based on cost, size/volume, extruder type, and weight of the machine. Criteria for AHP are derived from decision-makers. Also, in AHP, the pair-wise matrix is obtained from the decision-makers by answering the standard Saaty’s scale criteria questions. In this paper, such a selection method is explored. The outcome of this paper may vary depending on the expectations of the decision-makers. The end of this paper helps to choose the AMM with the right price and features to suit the decision-makers.

Groundwater potential zoning by integrating multi-criteria decision and bivariate analysis methods – a case study on Cheyyar River Basin, Tamil Nadu, India

The study aims to improve the pairwise decision matrix of the widely used analytical hierarchy process (AHP) to generate groundwater potential zones for Cheyyar River Basin through multi-influencing factor (MIF) and bivariate analysis. MIF prioritizes 11 time-independent input thematic layers, and Cramer’s V dependency value from bivariate analysis is used to assign the decision values of the pairwise matrix. The resulted potential zones are classified into four categories: Poor, Moderate, Good and Very-Good, with 15.33, 70.78, 13.88 and 0.002% of the total area. Well yield data from fourteen wells are used for validation, and the accuracy value is 85.67%. HIGHLIGHTS Utilizing the Cramer’s V value for pairwise matrix enhanced the reliability of decision. The spatial variation of potential is influenced majorly by the Lithology. Annual average rainfall and groundwater fluctuation indicating the groundwater availability matches with the zones of groundwater potential.

Integrating multiple sequence features for identifying anticancer peptides

As one of the most terrible diseases, cancer causes millions of deaths worldwide every year. The popular treatment approaches, such as radiotherapy and chemotherapy, have been used in against cancer cells. However, those traditional therapies have side effects on normal cells, time-consuming and expensive. Recent studies showed that anticancer peptides (ACP) may be a potential choice instead of traditional approaches for treating cancer. Therefore, it is desired to develop a computational method to identify anticancer peptides. In this study, a support vector machine (SVM) based computational model was proposed to discriminate anticancer peptides from non-anticancer peptides. In the model, peptide sequences were firstly encoded by amino acids physicochemical (PC) properties and residue pairwise energy content matrix (RECM). Then, Pearson’s correlation coefficient, high-order correlation information, and discrete wavelet transform were employed to extract useful information from PC and RECM matrix. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select discriminative features. Finally, these selected features were fed into SVM for distinguishing ACP from non-ACP. Experimental results demonstrated that the proposed method is powerful, it indicates that our proposed method may be a hopeful tool in discriminating anticancer peptides from non-anticancer peptides. The codes and datasets used in current work are available at https://figshare.com/articles/online_resource/iACP/16866232.

Omicron variant receptor-binding domain phylogenetics and molecular dynamics

BackgroundWe investigated the evolutionary relationships, mutations, antigenic epitopes, and structural dynamics of the receptor-binding domain (RBD) of SARS-CoV-2, Omicron and other recently evolved variants. MethodsThe RBD of SARS-CoV-2 and its Omicron, Alpha, Beta, Gamma, Delta, and Mu variants were subjected to pairwise sequence matrix evaluation, antigenic epitope prediction, and phylogenetic relationship and structural dynamics analyses. ResultsThe Omicron RBD contained 13–15 amino acid mutations, of which 12 were new and three conserved with other variants. In addition, two mutations found in the Alpha, Beta, Gamma, and Mu variants were not found in the Omicron RBD. The ultrametric clustering unweighted pair group method with arithmetic mean identified Omicron as a novel monophyletic class, but the neighbor-joining method clustered Omicron with Alpha and Delta variants. In the SARS-CoV-2 RBD, five main antigenic epitopes were predicted, and these epitopes were conserved across all SARS-CoV-2 variants tested. Surprisingly, the additional mutations in the Omicron variant increased the size of the expected antigenic sites in two of these antigenic epitopes. Molecular dynamics (MD) simulations revealed higher root-mean-square deviation in the Omicron RBD, greater residue fluctuation at residues 32–42 and 140–160, and increased solvent-accessible surface area. ConclusionsThe Omicron RBD mutations indicate the variant is within a new phylogenetic class of SARS-CoV-2 and significantly impact RBD structure, conformation, and molecular dynamics. However, conserved anticipated antigenic sites may imply partial changes in receptor affinity and response to immune reactions. Omicron RBD binding with the angiotensin-converting enzyme 2 receptor was suggested to be weaker than the original SARS-CoV-2 binding in MD simulations.

A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations.

Nutrient regulation in aquaponic environments has been a topic of research for many years. Most studies have focused on appropriate control of nutrients in an aquaponic set-up, but very little research has been conducted on commercial-scale applications. In our model, the input data were sourced on a weekly basis from three commercial aquaponic farms in Southeast Texas over the course of a year. Due to the limited number of data points, dimensionality reduction techniques such as pairwise correlation matrix were used to remove the highly correlated predictors. Feature selection techniques such as the XGBoost classifier and Recursive Feature Elimination with ExtraTreesClassifier were used to rank the features in order of their relative importance. Ammonium and calcium were found to be the top two nutrient predictors, and based on the months in which lettuce was cultivated, the median of these nutrient values from the historical dataset served as the optimal concentration to be maintained in the aquaponic solution to sustain healthy growth of tilapia fish and lettuce plants in a coupled set-up. To accomplish this, Vernier sensors were used to measure the nutrient values and actuator systems were built to dispense the appropriate nutrient into the ecosystem via a closed loop.

Causality-aware Enhanced Model for Multi-hop Question Answering over Knowledge Graphs

To improve the performance of knowledge graph-based question answering system (KGQA), several approaches have been developed to construct a semantic parser based on entity linking, relation identification and logical/numerical structure identification. However, existing methods arrive at answers only by maximizing the data likelihood only on the sparse or imbalanced explicit relations, ignoring the potentially large number of latent relations. It makes KGQA suffer from a high level of spurious entity relations and missing link challenge. In this paper, we propose a causal filter (CF) model for KGQA (CF-KGQA), which performs causal interference on the relation representation space to reduce the spurious relation representation in a data-driven manner, i.e., the goal of this work is to comprehensively discover disentangled latent factors to alleviate the spurious correlation problem in KGQA. The model comprises a causal pairwise aggregator (AP) and a disentangled latent factor aggregator (AC). The former filters out most spurious entity relations inconsistent to their dense groups’ neighborhood, and generates a causal pairwise matrix among all the candidate relations. The latter learns the latent relation representation via an encoder–decoder on the causal pairwise matrix. It disconnects the latent factor and the causal confounder beneath the knowledge embedding space by causal intervention. To prove the effectiveness and efficiency of the proposed approach, we test CF-KGQA and other state-of-the-art methods on four public real-world datasets. The experiments indicate that our approach outperforms the recent methods and is also less sensitive to the spurious correlation problem, thus demonstrating the robustness of CF-KGQA.

Complete Genome Characterization of Reticuloendotheliosis Virus Detected in Chickens with Multiple Viral Coinfections.

Reticuloendotheliosis virus (REV) is a retroviral pathogen capable of infecting several avian hosts and is associated with immunosuppression, anemia, proventriculitis, neoplasia, and runting–stunting syndrome. Its genome contains the three major genes, gag, pol, and env, and two flanking long terminal repeat (LTR) regions. Complete genome sequences of REV are limited in terms of geographical origin. The aim of this study was to characterize the complete genome of REV detected in Brazilian chickens with multiple viral coinfections and analyze the polymorphisms in the deduced amino acids sequences corresponding to its encoded proteins. We tested the presence and completeness of REV as well as other viral pathogens in samples from Brazilian poultry farms by qPCR. The complete genomes of two REV strains were sequenced by overlapping fragments through the dideoxy method. Phylogenetic analysis, pairwise identity matrix, polymorphism identification and protein modeling were performed along the entire genome. We detected REV in 65% (26/40) of the tested samples. Concomitant viral infections were detected in 82.5% (33/40) of the samples and in 90% (9/10) of the farms. Multiple infections included up to seven viruses. Phylogenetic analysis classified both Brazilian strains into REV subtype 3, and the pairwise comparison indicated that strains from the USA and fowlpox virus (FWPV)-related strains were the most identical. The subdomain p18 in gag, the reverse transcriptase/ribonuclease H in pol, and the surface (SU) in the env protein were the most polymorphic in genomic comparisons. The relevant motifs for each protein were highly conserved, with fewer polymorphisms in the fusion peptide, immunosuppression domain, and disulfide bonds on the surface (SU) and transmembrane (TM) of env. This is the first study to include complete genomes of REV in Brazil and South America detected in farms with multiple viral coinfections. Our findings suggest an involvement of REV as an immunosuppressor and active agent in the emergence and progression of multiple infectious diseases. We also found a possible etiological relationship between Brazilian strains and the USA and FWPV recombinant strains. This information highlights the need for epidemiological vigilance regarding REV in association with another pathogens.

Spatial Modeling with Repopulation Potential for Three Flora Species of Huaytapallana Regional Conservation Area, Peru

In the high mountain ecosystems of the Huaytapallana Regional Conservation Area (ACR-H) there are three species of flora (Krapfia macropetala, Gentianella scarlatinostriata and Senecio canescens) of social, economic, cultural and medicinal importance, however their population status and undefined local distribution make these species area more vulnerable to extinction. Therefore, the objective of this work is to determine the population distribution for repopulation purpose in the ACR-H from the potential distribution in Peru using Maxent algorithm and a local model developed with the Saaty pairwise hierarchy matrix, adding a soil sample for a better application of the final model. The results show that the Species Distribution Models (SDMs) have a high confidence because the Area Under the Curve (AUC) surpass 0.90. Otherwise, the local model is consistent by showing a Consistency Ratio (CR) of less than 0.10. As a final result, all species obtained optimal spaces for repopulation near the Huaytapallana Cordillera, where Krapfia macropetala obtained the largest extension (715.334 ha) and Gentianella scarlatinostriata is the smallest (650.096 ha). Further there were no differences in the parameters evaluated in the three soil samples, which facilitates the application of the models for the repopulation of these three species.

Stability and feedback stabilizability of delay periodic differential equations with pairwise permutable matrix functions

Abstract Representation of solutions of delayed differential equations with multiple delays and periodic coefficients is established. Consequently, results on stabilizability of weakly delayed closed-loop systems and stability of non-weakly delayed periodic systems are proved. The stabilizability result is an extension of the classical Brunovský theorem for linear periodic systems of ordinary differential equations to a class of delay differential equations with pairwise permutable matrix functions.

ITTCA-MFF: identifying tumor T cell antigens based on multiple feature fusion.

Cancer isa terrible disease, recent studies reported that tumor T cell antigens (TTCAs) may play a promising role in cancer treatment. Since experimental methods are still expensive and time-consuming, it is highly desirable to develop automatic computational methods to identify tumor T cell antigens from the huge amount of natural and synthetic peptides. Hence, in this study, a novel computational model called iTTCA-MFF was proposed to identify TTCAs. In order to describe the sequence effectively, the physicochemical (PC) properties of amino acid and residue pairwise energy content matrix (RECM) were firstly employed to encode peptide sequences. Then, two different approaches including covariance and Pearson's correlation coefficient (PCC) were used to collect discriminative information from PC and RECM matrixes. Next, an effective feature selection approach called the least absolute shrinkage and selection operator (LAASO) was adopted to select the optimal features. These selected optimal features were fed into support vector machine (SVM) for identifying TTCAs. We performed experiments on two different datasets, experimental results indicated that the proposed method is promising and may play a complementary role to the existing methods for identifying TTCAs. The datasets and codes can be available at https://figshare.com/articles/online_resource/iTTCA-MFF/17636120 .

Identification of tumor homing peptides by utilizing hybrid feature representation

Cancer is one of the serious diseases, recent studies reported that tumor homing peptides (THPs) play a key role in treatment of cancer. Due to the experimental methods are time-consuming and expensive, it is urgent to develop automatic computational approaches to identify THPs. Hence, in this study, we proposed a novel machine learning methods to distinguish THPs from non-THPs, in which the peptide sequences firstly encoded by pseudo residue pairwise energy content matrix (PseRECM) and pseudo physicochemical property (PsePC). Moreover, the least absolute shrinkage and selection operator (LAASO) was employed to select optimal features from the extracted features. All of these selected features were fed into support vector machine (SVM) for identifying THPs. We achieved 89.02%, 88.49%, and 94.58% classification accuracy on the Main, Small, and Main90 dataset, respectively. Experimental results showed that our proposed method outperforms the existing predictors on the same benchmark datasets. It indicates that the proposed method may be a useful tool in identifying THPs. The datasets and codes used in current study are available at https://figshare.com/articles/online_resource/iTHPs/16778770. Communicated by Ramaswamy H. Sarma

Enhancing urban resilience evaluation systems through automated rational and consistent decision-making simulations

• Urban resilience evaluation system (URES) is 16 indicators and 75 parameters. • URES has five interrelated dimensions that used as scenarios in ARCDM. • ARCDM is to pre-conceptualize and simulates scenarios for decision-makers. • Virtual PoE (ARCDM) reduce time and resources on urban resilience decision-making. Urban resilience is an increasingly important topic because of the need to protect and optimize the value derived from the urban built environment with its constructed assets. But there is still no harmonized definition or a consensual set of descriptors for this multi-dimensional concept. This paper aims to address this knowledge gap by outlining an Urban Resilience Evaluation System (URES) with a breakdown structure of 16 indicators and 75 parameters grouped into five interrelated dimensions: environmental, economic, organizational, social, and technical. The resilience scoring of the constructed assets relies on a novel Multi-Criteria Decision Analysis (MCDA) approach adapted from the Analytic Hierarchy Process (AHP) method. This novel approach involves Automated Rational and Consistent Decision Making (ARCDM) to pre-conceptualize and simulate behavior scenarios of a virtual panel of experts with different perspectives. These behavior scenarios are organized into a pairwise multilayer decision-making matrix that overrides the need for surveys. The authors use a portfolio of buildings with seven different use types (residential, research facilities, schools, hospitals, industrial facilities, shopping centers, and hotels) to test the applicability of the proposed URES breakdown structure for buildings with different levels of importance. This allows the comparison and validation of various ranges of results expressing different perspectives. The proposed methodology can be readily used by various stakeholders of the Architecture, Engineering, Construction, and Operation (AECO) sectors involved in the lifecycle management decisions and activities of constructed assets. It impacts feasibility studies, design, construction, operation and maintenance, rehabilitation, and disposal of constructed assets that comprise the built environment for cities and societies.

Climate change and livelihood vulnerability of the rice farmers in the North Central Region of Vietnam: A case study in Nghe An province, Vietnam

This study aimed at assessing the livelihoods of rice households in Nghe An province of Vietnam. The study was undertaken through household surveys of 396 households in three districts of this province. In this study, the livelihood vulnerability index (LVI) was used for investigating the level of vulnerability to climate change of rice households in Nghe An province, pairwise correlation matrix and beta regression were used for examining the factors affecting the level of vulnerability climate stresses. Overall, the computed LVI showed that respondents were slightly vulnerable to climate change, indicating that households in the study area still had capacities to cope with the change. The beta regression analysis showed that floods, droughts, cold spells, irrigation, institutional factors, and socio-demographic factors were the major factors significantly affecting rice households’ vulnerability in Nghe An province. The findings suggest the need to strengthen the social network between farmers, agricultural cooperatives, and local governments to enhance farmers’ capacity to cope with climate-induced issues, especially floods and droughts in the coastal area. Findings also imply the need to provide farmers with input subsidies, effective irrigation systems, training, and consultant services to reduce households’ vulnerability. Furthermore, this study also highlights the need to promote official financial services with low interest rates in adopting adaptation strategies for sustainable development of the whole region.

An Assessment to Solid Waste Management System in the Rajshahi City Vodra Railway Slum Through Community Participation

Better life is the first requirement of every people and for this a healthier environment is needed. Municipal solid waste is a serious environmental hazard and social problem in Bangladesh. But the Rajshahi city dwellers are not lucky for this because Municipal authorities acknowledge the importance of adequate solid waste collection and disposal as well as resource recovery. Therefore solid waste is indiscriminate by dumped on dumped in open spaces, streets, and drains, clogging the drainage system which causes serious health risks and degradation of the living environment. It has been found that about 170 tons of solid wastes of different types are being generated daily in Rajshahi. This paper aims to review the recent solid waste scenario in Rajshahi City Corporation. Finally, this paper will propose some probable measures for its management. Participatory Rural Appraisal approach (PRA) is applied to involve community people in solid waste management. This study to identify the existing condition of the solid waste management system by Resource map, Mobility map PRA tools and find out the problem by pairwise ranking matrix, cause-effect diagram PRA tools. There is also finding out possible solutions to a solid waste management system by SWOT analysis and Dream map. Findings from the study show that the Municipality, which is the sole provider of the solid waste management services neither performed this service efficiently nor fully involved the community to support it. This study thus underscores the importance of community participation by bringing together the Municipality and the local community in the participatory forum to discuss solid waste management problems and suggest appropriate solutions.

A Study of Parallelizable Alternatives to Dynamic Time Warping for Aligning Long Sequences

This article investigates several parallelizable alternatives to DTW for estimating the alignment between two long sequences. Whereas most previous work has focused on reducing the total computation and/or memory costs of DTW, our focus is instead on reducing wall clock time by utilizing common hardware like GPUs that are optimized for parallel processing. We propose and study four different parallelizable alignment algorithms: the first three algorithms compute approximations of DTW by breaking the pairwise cost matrix into rectangular regions and processing the regions in parallel, and the fourth algorithm computes an exact DTW alignment by processing the cost matrix along diagonals rather than rows or columns. We characterize the performance of our proposed alignment algorithms on an audio-audio alignment task, and we develop GPU-based implementations for the two best-performing algorithms, which we call weakly-ordered Segmental DTW (WSDTW) and Parallelized Diagonal DTW (ParDTW). Our experiments indicate that ParDTW is the most practical and useful of the four algorithms: it computes an exact DTW alignment and reduces runtime by 1.5 to 2 orders of magnitude on long sequences compared to current alternatives. We present a comprehensive evaluation and study of the alignment accuracy, runtime, and practical limitations of the proposed alignment algorithms.

MCDM and Various Prioritization Methods in AHP for CSS: A Comprehensive Review

Availability and diversity of cloud service providers (CSPs) had put the users into confusion for its selection of the appropriate service providers. Some cloud service providers are good at some services while others are good at offering other services. The selection of an appropriate cloud service is one of the multi-criteria decision analysis (MCDA) problems that became a critical issue of public concern in the uncertain cloud industry. Based on multiple criteria, various multi-criteria decision-making (MCDM) methods can be used for the selection of the best CSPs. Researchers considered MCDM techniques as the best methodology for deciding cloud rank. The paper presents a set of decision criteria and their sub-criteria required for evaluating CSPs. The main goal of this paper is to present a review of various MCDM methods for decision-making. Furthermore, the strengths and weaknesses of various MDCM techniques are discussed to help the researchers about the current trends in the field of decision making. An overview of MCDM techniques used for Cloud service selection (CSS) is presented. Several methods used for deriving priority vectors from a Pairwise Consistency Matrix (PCM) in Analytic Hierarchy Process (AHP) technique, used in recent years are discussed in this research paper.

Multi-criteria prioritization of the renewable power plants in Australia using the fuzzy logic in decision-making method (FMCDM)

Abstract The presented study focused on developing an innovative decision-making framework to select the best renewable-power-plant technologies, considering comprehensive techno-economic and environmental variables. Due to the favourable conditions, Australia was selected as the case study. A fuzzy-logic method and analytical hierarchy process were applied to prioritize different renewable-energy power plants. The techno-economic factors included levelized cost of energy, initial cost, simple payback time, and operation and maintenance costs along with environmental factors including carbon payback time, energy payback time and greenhouse-gas emissions were used to rank the power plants. The results showed that the capital cost and simple payback time had the highest priority from an economic point of view. In comparison, greenhouse-gas emissions and carbon payback time were the dominant environmental factors. The analysis results provided economic and environmental priority tables for developing different power plants in the current state and a future scenario by 2030. The fuzzy results and pairwise composite matrix of alternatives indicated that the onshore wind, offshore wind, single-axis tracker polycrystalline photovoltaic, single-axis tracker monocrystalline photovoltaic, fix-tilted polycrystalline photovoltaic and fix-tilted monocrystalline photovoltaic scored the highest in the current state. In contrast, by 2030, the single-axis tracker photovoltaic power plants will be the best choice in the future scenario in Australia. Finally, the results were used and analysed to recommend and suggest several policy implementations and future research studies.

New Insight regarding Legionella Non-Pneumophila Species Identification: Comparison between the Traditional mip Gene Classification Scheme and a Newly Proposed Scheme Targeting the rpoB Gene.

ABSTRACTThe identification of Legionella non-pneumophila species (non-Lp) in clinical and environmental samples is based on the mip gene, although several studies suggest its limitations and the need to expand the classification scheme to include other genes. In this study, the development of a new classification scheme targeting the rpoB gene is proposed to obtain a more reliable identification of 135 Legionella environmental isolates. All isolates were sequenced for the mip and rpoB genes, and the results were compared to study the discriminatory power of the proposed rpoB scheme. Complete concordance between the mip and rpoB results based on genomic percent identity was found for 121/135 (89.6%) isolates; in contrast, discordance was found for 14/135 (10.4%) isolates. Additionally, due to the lack of reference values for the rpoB gene, inter- and intraspecies variation intervals were calculated based on a pairwise identity matrix that was built using the entire rpoB gene (∼4,107 bp) and a partial region (329 bp) to better evaluate the genomic identity obtained. The interspecies variation interval found here (4.9% to 26.7%) was then proposed as a useful sequence-based classification scheme for the identification of unknown non-Lp isolates. The results suggest that using both the mip and rpoB genes makes it possible to correctly discriminate between several species, allowing possible new species to be identified, as confirmed by preliminary whole-genome sequencing analyses performed on our isolates. Therefore, starting from a valid and reliable identification approach, the simultaneous use of mip and rpoB associated with other genes, as it occurs with the sequence-based typing (SBT) scheme developed for Legionella pneumophila, could support the development of multilocus sequence typing to improve the knowledge and discovery of Legionella species subtypes.IMPORTANCE Legionella spp. are a widely spread bacteria that cause a fatal form of pneumonia. While traditional laboratory techniques have provided valuable systems for Legionella pneumophila identification, the amplification of the mip gene has been recognized as the only useful tool for Legionella non-pneumophila species identification both in clinical and environmental samples. Several studies focused on the mip gene classification scheme showed its limitations and the need to improve the classification scheme, including other genes. Our study provides significant advantages on Legionella identification, providing a reproducible new rpoB gene classification scheme that seems to be more accurate than mip gene sequencing, bringing out greater genetic variation on Legionella species. In addition, the combined use of both the mip and rpoB genes allowed us to identify presumed new Legionella species, improving epidemiological investigations and acquiring new understanding on Legionella fields.

SPR: Similarity pairwise ranking for personalized recommendation

Bayesian personalized ranking (BPR) has been proposed as an effective method to model pairwise learning, and it is widely used in many personalized recommender systems. However, the effectiveness of BPR can be seriously affected by an imbalanced data distribution because it tends to rank popular items ahead of personalized items. As a result, the personalized needs of users cannot be well met. In this paper, we propose a novel personalized recommendation method called similarity pairwise ranking (SPR) to rank users’ favorite items first. SPR eliminates the differences in the scores between popular and personalized items based on their similarity by using a new penalty. In such a way, the SPR-enhanced recommendation will render meaningful and personalized results that better meet the individual needs of users, and it overcomes the negative impact of imbalanced datasets. We design a model to illustrate the improvement of SPR: similarity pairwise ranking matrix factorization (SPRMF). Experimental results obtained using six datasets indicate the superiority in recommendation quality of SPRMF over the recent state-of-the-art methods.

Water resilience mapping of Chennai, India using analytical hierarchy process

BackgroundThe urban water system is the worst hit in global climate change. Water resilience is the system’s ability to retaliate and recover from various water-related disruptions. The present study aims to delineate the water resilience zones in Chennai city, Tamil Nadu, India, by effectively integrating the geographic information system, remote sensing, and analytical hierarchy process (AHP).MethodsThe methodology incorporated 15 vital factors. A multi-criteria decision analysis technique was adopted to assign a weight to each parameter using the AHP. A pairwise decision matrix was constructed, parameter’s relative importance and the consistency ratio were established. Integration of all maps by weighted overlay analysis technique depicted water resilience intensities of five different classes.ResultsVery low, low and moderate water resilience areas accounted for more than three-fourth of the study area. Area Under Curve score (80.12%) depicted the accuracy of the developed model. Sensitivity analysis determined the significance of the parameters in the delineation. The logical structural approach can be employed in other parts of India or elsewhere with modifications.ConclusionThis study is novel in its approach by holistically analyzing water resilience by integrating disruptions related to flood, drought and the city's water infrastructure system's adequacy and efficiency. Researchers and planners can effectively use the study results to ensure resilience as a new perspective on effective water resource management and climate change mitigation. It becomes a decision aid mechanism identifying where the system is vulnerable to potential water-related risks for employing resilience measures.