Search

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models. [ABSTRACT FROM AUTHOR]

In order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system. [ABSTRACT FROM AUTHOR]

Faced with the challenges of intelligently managing the entire product lifecycle, traditional management techniques no longer meet the complex demands of modern enterprises. To address it, we employ an ontology modeling method based on a multidimensional view of product information for a knowledge graph, culminating in a comprehensive PLM knowledge system. To better serve PLM users, we propose an intelligent recommendation model based on context and user behavior for knowledge recommendations. This model seamlessly integrates the knowledge storage structure, user preference attributes, and the rich semantics of the knowledge graph. To improve the recommendation capability of the model, we design an improved multi-hop neighbor node sampling algorithm and use a graph convolutional neural network to quantify the target user preference attributes layer by layer. After a series of rigorous validation experiments, our method consistently outperformed competing models across key performance metrics, such as accuracy and recall, underscoring its efficacy and practicality in knowledge recommendation within PLM systems. [ABSTRACT FROM AUTHOR]

With the advancement of large-scale omics technologies, particularly transcriptomics data sets on drug and treatment response repositories available in public domain, toxicogenomics has emerged as a key field in safety pharmacology and chemical risk assessment. Traditional statistics-based bioinformatics analysis poses challenges in its application across multidimensional toxicogenomic data, including administration time, dosage, and gene expression levels. Motivated by the visual inspection workflow of field experts to augment their efficiency of screening significant genes to derive meaningful insights, together with the ability of deep neural architectures to learn the image signals, we developed DTox, a deep neural network-based in visio approach. Using the Percellome toxicogenomics database, instead of utilizing the numerical gene expression values of the transcripts (gene probes of the microarray) for dose-time combinations, DTox learned the image representation of 3D surface plots of distinct time and dosage data points to train the classifier on the experts' labels of gene probe significance. DTox outperformed statistical threshold-based bioinformatics and machine learning approaches based on numerical expression values. This result shows the ability of image-driven neural networks to overcome the limitations of classical numeric value-based approaches. Further, by augmenting the model with explainability modules, our study showed the potential to reveal the visual analysis process of human experts in toxicogenomics through the model weights. While the current work demonstrates the application of the DTox model in toxicogenomic studies, it can be further generalized as an in visio approach for multi-dimensional numeric data with applications in various fields in medical data sciences. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of intelligent technology, information security and privacy issues have become increasingly prominent. Epidemiological survey data (ESD) research plays a vital role in understanding the laws and trends of disease transmission. However, epidemiological investigations (EI) involve a large amount of privacy-sensitive data which, once leaked, will cause serious harm to individuals and society. Collecting EI data is also a huge task. To solve these problems and meet personalized privacy protection requirements in EIs, we improve the uOUE protocol based on utility-optimized local differential privacy to improve the efficiency and accuracy of data coding. At the same time, aiming at the collection and processing of ESD, a multidimensional epidemiological survey data aggregation scheme based on uOUE is designed. By using Paillier homomorphic encryption and an identity-based signature scheme to further prevent differential attacks and achieve multidimensional data aggregation, the safe, efficient, and accurate aggregation processing of ESD is executed. Through security proof and performance comparison, it is verified that our algorithm meets the requirements of local differential privacy and unbiased estimation. The experimental evaluation results on two data sets show that the algorithm has good practicability and accuracy in ESD collection and provides reliable and effective privacy protection. [ABSTRACT FROM AUTHOR]

The quaternion windowed linear canonical transform is a tool for processing multidimensional data and enhancing the quality and efficiency of signal and image processing; however, it has disadvantages due to the noncommutativity of quaternion multiplication. In contrast, reduced biquaternions, as a special case of four-dimensional algebra, possess unique advantages in computation because they satisfy the multiplicative exchange rule. This paper proposes the reduced biquaternion windowed linear canonical transform (RBWLCT) by combining the reduced biquaternion signal and the windowed linear canonical transform that has computational efficiency thanks to the commutative property. Firstly, we introduce the concept of a RBWLCT, which can extract the time local features of an image and has the advantages of both time-frequency analysis and feature extraction; moreover, we also provide some fundamental properties. Secondly, we propose convolution and correlation operations for RBWLCT along with their corresponding generalized convolution, correlation, and product theorems. Thirdly, we present a fast algorithm for RBWLCT and analyze its computational complexity based on two dimensional Fourier transform (2D FTs). Finally, simulations and examples are provided to demonstrate that the proposed transform effectively captures the local RBWLCT-frequency components with enhanced degrees of freedom and exhibits significant concentrations. [ABSTRACT FROM AUTHOR]

Culturally responsive assessments have been proposed as potential tools to ensure equity and fairness for examinees from all backgrounds including those from traditionally underserved or minoritized groups. However, these assessments are relatively new and, with few exceptions, are yet to be implemented in large scale. Consequently, there is a lack of guidance on how one can compute comparable scores on various versions of these assessments. In this paper, the multigroup multidimensional Rasch model is repurposed for modeling data originating from various versions of a culturally responsive assessment and for analyzing such data to compute comparable scores. Two simulation studies are performed to evaluate the performance of the model for data simulated from hypothetical culturally responsive assessments and to find the conditions under which the computed scores are accurate. Recommendations are made for measurement practitioners interested in culturally responsive assessments. [ABSTRACT FROM AUTHOR]

In this paper, we develop a mixed stochastic approximation expectation‐maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four‐parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of the stochastic approximation expectation‐maximization (SAEM) algorithm for multidimensional data, but it also alleviates the potential instability caused by label‐switching, and then improved the estimation accuracy. Simulation studies are conducted to illustrate the good performance of the proposed MSAEM method, where MSAEM consistently performs better than SAEM and some other existing methods in multidimensional item response theory. Moreover, the proposed method is applied to a real data set from the 2018 Programme for International Student Assessment (PISA) to demonstrate the usefulness of the 4PNO model as well as MSAEM in practice. [ABSTRACT FROM AUTHOR]

We argue theoretically and demonstrate empirically that to understand the electoral fortunes of far-right parties in Western Europe, we need to consider the advantages and disadvantages these parties encounter in the multidimensional political issue space. We argue that salience changes among the electorate benefit far-right parties more than shifting far-right parties' policy positions. We further posit that changes in the public salience of European integration are more important for far-right success than other issues -- including immigration. Utilizing similar survey questions from the Chapel Hill Expert Survey (CHES) about parties' positions and the European Election Studies (EES) about voters' policy preferences, we estimate multidimensional voting models in 12 West European countries. We then use mathematical simulations to show that the issue that matters most for far-right success is European integration. This research has important implications for the study of electoral competition, parties' campaign strategies, and voting behavior. [ABSTRACT FROM AUTHOR]

In recent years the field of gaming addiction has experienced an upsurge in empirical studies on various treatment approaches. Despite the advances in our understanding of how gaming addiction can be treated, the wider concept of recovery continues to be under-researched. The purpose of this review was to explore how individuals addicted to video games experience the process of recovery. Seven databases were systematically searched for qualitative studies. Eight studies representing the views of 225 participants were included in the review. Study findings were exported into NVivo software and analysed using Thematic Synthesis. Six themes were constructed: “developing awareness”, “deciding to change”, “the process of quitting”, “the challenges of quitting”, “recovery never stops” and “treatment for gaming addiction”. Except for the last, themes represent processes that most participants had gone through during recovery, though significant variation was found in how each process was experienced. In addition to overcoming addiction symptoms, recovery involved management of concomitant problems and various negative consequences of excessive gaming. Regarding practice implications, current findings suggest that treatment programs should adopt a multidimensional approach, providing evidence-based treatments, help for co-occurring problems, as well as pre- and post-treatment support to accommodate individuals at different stages of recovery. Further research is needed to expand our understanding of recovery, for instance, the impact of gender differences or how recovery experiences change based on different recovery goals (i.e., abstinence or reduced play time). [ABSTRACT FROM AUTHOR]

This article presents a standardized alternative to the traditional phase cycling approach employed by the overwhelming majority of contemporary Nuclear Magnetic Resonance (NMR) research. On well-tested, stable NMR systems running well-tested pulse sequences in highly optimized, homogeneous magnetic fields, the hardware and/or software responsible for traditional phase cycling quickly isolate a meaningful subset of data by averaging and discarding between 3/4 and 127/128 of the digitized data. In contrast, the new domain colored coherence transfer (DCCT) approach enables the use of all the information acquired from all transients. This approach proves to be particularly useful where multiple coherence pathways are required, or for improving the signal when the magnetic fields are inhomogeneous and unstable. For example, the authors' interest in the nanoscale heterogeneities of hydration dynamics demands increasingly sophisticated and automated measurements deploying Overhauser Dynamic Nuclear Polarization (ODNP) in low-field electromagnets, where phase cycling and signal averaging perform suboptimally. This article demonstrates the capabilities of DCCT on ODNP data and with a collection of algorithms that provide robust phasing, avoidance of baseline distortion, and the ability to realize relatively weak signals amid background noise through signal-averaged correlation alignment. The DCCT schema works by combining a multidimensional organization of phase cycled data with a specific methodology for visualizing the resulting complex-valued data. It could be extended to other forms of coherent spectroscopy seeking to analyze multiple coherence transfer pathways. [ABSTRACT FROM AUTHOR]

The goal of this study was to detect communicative sequences in mother–baby dyads according to the degree of prematurity, comparing them in three corrected ages (two, four and six months). The observational methodology was used from the mixed methods perspective, and the design is nomothetic/follow-up/multidimensional. Fifteen mother–baby dyads with extreme prematurity and 15 mother–baby dyads with moderate prematurity participated. An observation instrument was constructed combining an observation instrument and category systems to code the dyads' behaviours. Fifteen minutes of free interaction were recorded and Type IV data were logged. The programs HOISAN and SDIS-GSEQ/GSEQ were used to analyse the results. The results detected different behavioural patterns related to the interaction in both the expression of communicative behaviours and in the time when they were deployed in the ages evaluated. [ABSTRACT FROM AUTHOR]

The renowned 2D invertible Hénon map turns into 1D noninvertible quadratic map when its leading parameter b becomes zero. This well-known link was studied by Mira who demonstrated that the bifurcation set of Hénon diffeomorphism is similar to his "box-within-a-box" bifurcation structure of 1D endomorphism. In general, such similarity has not been strictly established, especially in multidimensional cases. In this paper, we proved that the Mira bifurcation structure of a quadratic noninvertible map persists when the parameter increases from zero and the map turns into an invertible multidimensional generalized Hénon map. The changes of periodic and homoclinic orbits and chaotic attractors at this transition are described. We proved the existence of Newhouse regions is different from those Mira boxes that accumulate to the homoclinic bifurcations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role in extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data to 2-D image data to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results. [ABSTRACT FROM AUTHOR]

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits' metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors' behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations. [ABSTRACT FROM AUTHOR]

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

Private information retrieval (PIR) allows a client to retrieve data from a database without the database server learning what data are being retrieved. Although many PIR schemes have been proposed in the literature, almost all of these focus on retrieval of a single database element, and do not consider more flexible retrieval queries such as basic range queries. Furthermore, while practically-oriented database schemes aiming at providing flexible and privacy-preserving queries have been proposed, to the best of our knowledge, no formal treatment of range queries has been considered for these. In this paper, we firstly highlight that a simple extension of the standard PIR security notion to range queries is insufficient in many usage scenarios, and propose a stronger security notion aimed at addressing this. We then show a simple generic construction of a PIR scheme meeting our stronger security notion, and propose a more efficient direct construction based on function secret sharing—while the former has a round complexity logarithmic in the size of the database, the round complexity of the latter is constant. After that, we report on the practical performance of our direct construction. Finally, we extend the results to the case of multi-dimensional databases and show the construction of PIR scheme supporting multi-dimensional range queries. The communication round complexity of our scheme is O (k log n) in worst case, where n is the size of database and k is the number of elements retrieved by the query. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Gibbs‐INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and has low computing memory, high computational efficiency with much fewer iterations, and still achieve higher estimation accuracy. Therefore, it has the ability to handle large amount of multidimensional response data with different item responses. Simulation studies are conducted to compare with the Metroplis‐Hastings Robbins‐Monro (MH‐RM) algorithm and an application to the study of the IPIP‐NEO personality inventory data is given to assess the performance of the new algorithm. Extensions of the proposed algorithm for application on more complicated models and different data types are also discussed. [ABSTRACT FROM AUTHOR]

Open card sorting is a widely used method in HCI for the design of user-centered Information Architectures (IAs). This article proposes a new algorithm that combines the best merge method (BMM), category validity technique (CVT), and multidimensional scaling (MDS) to explore, analyze and visualize open card sort data. A study involving 20 participants and 41 cards explored the IA redesign of a university's website. The collected data were analyzed using two popular methods employed in the quantitative analysis of open card sort data (i.e., hierarchical clustering, K-means) and the proposed algorithm. It was found that the latter provides increased IA insights compared to the existing methods. Specifically, the proposed algorithm can expose hidden patterns and relationships amongst cards and identify complexities. We also found that the proposed algorithm produces better initial clusters, which have a direct effect on the final clustering quality. [ABSTRACT FROM AUTHOR]

The Open Research Knowledge Graph (ORKG) is a digital library for machine-actionable scholarly knowledge, with a focus on structured research comparisons obtained through expert crowdsourcing. While the ORKG has attracted a community of more than 1,000 users, the curated data has not been subject to an in-depth quality assessment so far. Here, proposed as a first exemplary step, within a team of domain experts, we evaluate the quality of six selected ORKG Comparisons based on three criteria, namely: 1) the quality of semantic modelling, 2) the maturity of the Comparisons in terms of their completeness, syntactic representation, identifier stability, and their linkability mechanisms ensuring the interoperability and discoverability. Finally, 3) the informative usefulness of the Comparisons to expert and lay users. We have found that each criterion addresses a unique and independent aspect of quality. Backed by the observations of our quality evaluations presented in this paper, a fitting model of knowledge graph quality appears one that is indeed multidimensional as ours. [ABSTRACT FROM AUTHOR]

• The Notre Dame fire highlighted the crucial role of digital data in heritage science, necessitating comprehensive multidisciplinary data interpretation. • State-of-the-art analysis identified gaps in semantics, memory, data correlation, and the balance between technology and human activity. • An initiative was launched to establish a digital ecosystem that represents both the tangible object and the evolving knowledge surrounding it. • The initiative integrates digital humanities, heritage science, and computer science, placing emphasis on an ecosystem tailored for human-led data collection and multi-dimensional data integration. • The Digital Data Working Group tackled challenges in digitisation and modelling, with a primary focus on studies of the wooden roof and collapsed vaults. The 2019 fire of Notre-Dame de Paris was not only a significant event in its history, but also provided a unique platform for multidisciplinary studies and digital data analysis. At the initiative of the CNRS and the Ministry of Culture, a large-scale collaboration is currently underway to manage and analyse the extensive data generated in connection with the restoration of the cathedral. The role of digital data is crucial for understanding complex multidisciplinary studies, especially in the field of cultural heritage. A digital data working group, composed of thirty scientists from 12 laboratories, is focused on documenting and organising this collective experience to serve both the immediate needs of restoration and the long-term goals of cultural heritage research. Key challenges include data traceability, knowledge modelling, multidimensional analysis and the need to address gaps in semantic understanding, memory, data correlation and technology. To address these challenges, a novel method is proposed that combines the digital representation of material heritage with the evolving knowledge about it. By collaborating with the wider scholarly community involved in Notre Dame's scientific action, the aim is to introduce new methods in heritage research and bridge the gap between the humanities and digitally-driven scholarship. This initiative also intersects with two primary scientific vectors: the technological approach to data collection and the generation and analysis of semantically enriched datasets. [ABSTRACT FROM AUTHOR]

The objective of this case study is to present the issues, methods and experiments underway to enable the acquisition of data and the creation of digital models of the timber structures, also known as the Forest , of Notre-Dame de Paris at different temporal states. Among the different models created, a restitution was developed from all the data collected and produced. This interpretation is the result of collecting, collating, and synthesizing heterogeneous data on pre- and post-fire structures. Indeed, the constitution of this model is also based on new data, obtained after the accident, which required the development of ad-hoc solutions to meet the various constraints of the site. These different resources have made it possible to produce the first univocal and three-dimensional restitution of the frameworks before - and after - the fire and the way to gather in particular all the partial data having been used to create it. This experimentation proposes the necessary qualities to enrich the future ecosystem of analysis and multidimensional memorization for the restoration and conservation of Notre-Dame. [ABSTRACT FROM AUTHOR]

Cadastral databases have been used for over 20 years, but most contain 2D data. The increasing presence of high-rise buildings with modern architecture complicates the process of determining property rights, restrictions, and responsibilities. It is, therefore, necessary to develop an efficient system for storing and managing multidimensional cadastral data. While there have been attempts to develop 3D cadastral database schemas, a comprehensive solution that meets all the requirements for effective data storage, manipulation, and retrieval has not yet been presented. This study aims to analyse the literature on 3D cadastral databases to identify approaches and technologies for storing and managing these data. Based on a systematic literature review integrated with a snowballing methodology, 108 documents were identified. During the analysis of the related documents, different parameters were extracted, including the conceptual data model, query type, and evaluation metrics, as well as the database management system (DBMS) used and technologies for visualisation, data preparation, data transformation, and the ETL (extract, transform, and load) process. The study emphasised the importance of adhering to database design principles and identified challenges associated with conceptual design, DBMS selection, logical design, and physical design. The study results provide insights for selecting the appropriate standards, technologies, and DBMSs for designing a 3D cadastral database system. [ABSTRACT FROM AUTHOR]

Differential item functioning (DIF) often arises from multiple sources. Within the context of multidimensional item response theory, this study examined DIF items with varying secondary dimensions using the three DIF methods: SIBTEST, Mantel-Haenszel, and logistic regression. The effect of the number of secondary dimensions on DIF detection rates was investigated with several test conditions such as the percentage of DIF items, item loadings, the correlation between primary and secondary dimensions, and DIF magnitude. Results indicate that both the Mantel–Haenszel and logistic regression procedures performed effectively in detecting DIF when multiple secondary dimensions were present. [ABSTRACT FROM AUTHOR]

Clustering algorithms has emerged as an important data mining technique for pattern recognition, data analysis and dimensionality reduction. Clustering is usually used in all fields to merge the same feature objects into a single group. The clustering method is incorporated with search algorithms to search the dataset from the databases. For large databases, there is a need of good clustering algorithm with high accuracy. Despite its high performance, the existing methods show some limitations. This paper focused on optimize the clustering method with a search structure for large multidimensional databases with dynamic indexes for the effective validation of construction projects. In the construction project validation to improve the clustering methods using Evolutionary Constrained Differential Optimization (ECDO) and Distributed Weighted Fuzzy C-Means algorithm to improve clustering performance than the centralized clustering approaches. The proposed methodology includes ECDO which presents new cluster tree indexing approach with cluster speed improvement in the validation of the construction projects. Distributed Fuzzy Possibilistic C-Means algorithm is proposed in improvement of cluster centre to overcome coincidence cluster problems. The comparative analysis stated that proposed ECDO model exhibits ~6% reduced memory utilization ~11% reduced scalability and ~8% minimized selectivity than the conventional WPCM model. [ABSTRACT FROM AUTHOR]

This paper aims to present and compare contemporary concepts of the common good formulated by economists with reference to the understanding of the common good by the great men of prayer: Augustine of Hippo; Thomas Aquinas; Jacques Maritain; and Popes John XXIII, John Paul II, and Francis. It seeks to determine in what direction the economic theory of the common good can develop, taking into account inspiration drawn from Catholic social teaching (CST). Given the interdisciplinary nature of the common good, a historical and interdisciplinary approach, along with the descriptive method, was adopted. The paper highlights the tendency of economic theory toward one-dimensional and relativistic concepts of the common good and suggests a search for economic ideas of the common good that are simultaneously multidimensional and universalistic. It recognizes the achievements of CST, created by the great men of prayer, in enhancing the understanding of the category of the common good and posits that these teachings can serve as research inspiration for economists. [ABSTRACT FROM AUTHOR]

The nature of ethical responsibility as an individual's critical ability or the basic values and principles for corporate ethical actions has not been confirmed. There are several instruments for measuring ethical actions in finance, technology, and information systems domains, however, most of them have limitations to assess the ethical responsibility of the fintech professionals. The purpose of this study is to develop an instrument for measuring fintech professionals' ethical responsibility. Based on an ethical responsibility conceptual framework, a set of ethical responsibility measurements has been developed through a systematic process. With a questionnaire, data were collected from 480 fintech professionals working in Indonesia. A two-tier approach was used to validate the ethical responsibility measurements, including exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The results of data analysis showed a structure of six dimensions for fintech ethical responsibility. The multi-dimensional instrument was constructed and validated, namely the Scale of Ethical Responsibility (SER), as an effective instrument to assess fintech professionals' ethical responsibility. Finally, the applications of SER and its future development in the fintech sector are discussed. [ABSTRACT FROM AUTHOR]

Dedicated bus lane (DBL) is one of the effective measures to improve transit system efficiency and further promote transit priority. Then, the performance assessment of DBLs turns out to be an essential task for better reflecting the interests of all stakeholders. To this end, this paper presents a data-driven and multidimensional framework to objectively and accurately assess the detailed performance of DBLs from the perspective of passengers, government, and operators, respectively, in which multiple data sources (i.e., IC card and GPS data) and corresponding mining technologies are adopted to establish a series of criteria, enabling a before-and-after comparison for DBL opening. The proposed model has been applied to evaluate the efficiency of the DBL system in the city of Chongqing, China. The results reveal the benefits of opening DBLs, especially for government interests, in which DBLs use less public resources to expand more public welfare. But its role in strengthening transit system competitiveness is diminished, because the opening of DBLs did not significantly increase speed and volume. [ABSTRACT FROM AUTHOR]

To achieve the sustainable development goal of "no poverty", many attempts have been made to measure poverty so that policy intervention can target the right people with the correct intensity. Since the traditional method of a unidimensional approach using monetary indicators, such as income and consumption, is now considered insufficient, a multidimensional approach has been employed using non-monetary indicators. The latter approach encompasses the different poverty aspects affecting an individual's capabilities and functioning. This study aimed to calculate multidimensional poverty in Pakistan using the Alkire & Foster method and Pakistan Social and Living Standards Measurement (PSLM) published by the Pakistan Bureau of Statistics data for 2018–19. To further complete the research, a binary logistic regression has been run to measure the effects of a deviation in income status on multidimensional poverty to see the impact of monetary income on non-monetary measures. Results show that the national global Multidimensional Poverty Index (MPI) is 0.24, meaning that multidimensionally poor people in Pakistan experience 24% of the total deprivations. The most deprived dimension of the three is education (44.7%), which needs special attention. Furthermore, if a household's total annual income increases and becomes more significant than the mean income of the sample, the household's probability of being multidimensionally poor will decrease. This implies that with the increase in average national income, the national poverty will reduce not only in absolute terms but also multidimensionally. This study's findings have several implications for policymakers, and the results of the MPI should align the allocation of public sector resources calculated to give a geographical and sectoral image of the poverty situation, which will guide policy designs and allocation of budget and resources. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel anomaly detection method for data centers based on a combination of graph structure and abnormal attention mechanism. The method leverages the sensor monitoring data from target power substations to construct multidimensional time series. These time series are subsequently transformed into graph structures, and corresponding adjacency matrices are obtained. By incorporating the adjacency matrices and additional weights associated with the graph structure, an aggregation matrix is derived. The aggregation matrix is then fed into a pre-trained graph convolutional neural network (GCN) to extract graph structure features. Moreover, both the multidimensional time series segments and the graph structure features are inputted into a pretrained anomaly detectionmodel, resulting in corresponding anomaly detection results that help identify abnormal data. The anomaly detection model consists of a multi-level encoder-decoder module, wherein each level includes a transformer encoder and decoder based on correlation differences. The attention module in the encoding layer adopts an abnormal attention module with a dual-branch structure. Experimental results demonstrate that our proposed method significantly improves the accuracy and stability of anomaly detection. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of healthcare technology, the critical need for robust privacy safeguards is undeniable. Local Differential Privacy (LDP) offers a potential solution to address privacy concerns in data-rich industries. However, challenges such as the curse of dimensionality arise when dealing with multidimensional data. This is particularly pronounced in k-way joint probability estimation, where higher values of k lead to decreased accuracy. To overcome these challenges, we propose the integration of Bayesian Ridge Regression (BRR), known for its effectiveness in handling multicollinearity. Our approach demonstrates robustness, manifesting a noteworthy reduction in average variant distance when compared to baseline algorithms such as LOPUB and LOCOP. Additionally, we leverage the R-squared metric to highlight BRR's advantages, illustrating its performance relative to LASSO, as LOPUB and LOCOP are based on it. This paper addresses a relevant concern related to datasets exhibiting high correlation between attributes, potentially allowing the extraction of information from one attribute to another. We convincingly show the superior performance of BRR over LOPUB and LOCOP across 15 datasets with varying average correlation attributes. Healthcare takes center stage in this collection of datasets. Moreover, the datasets explore diverse fields such as finance, travel, and social science. In summary, our proposed approach consistently outperforms the LOPUB and LOCOP algorithms, particularly when operating under smaller privacy budgets and with datasets characterized by lower average correlation attributes. This signifies the efficacy of Bayesian Ridge Regression in enhancing privacy safeguards in healthcare technology. [ABSTRACT FROM AUTHOR]

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti‐infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice. [ABSTRACT FROM AUTHOR]

The quest for technological development as a solution for sustainable development is the genesis of the multidimensional conflict between sustainability, competitive innovation and technological progress. Albeit persistent cognisance for appropriate technology solutions for a specific issue, the debate on the choice between short-run solutions and sustainability, local or global development, awareness and consumption, political gains, and citizens' benefits remains inconclusive. This paper argues that the debate stems from misunderstanding the term sustainability and the strategic misrepresentation of technology for political gain. Concurrently, the inability of the market failure rationale to choose appropriate technology to minimise the divergence between the private and social benefit makes the policy formulation problem more intricate. Furthermore, despite increasing awareness regarding the environmental status, failure to alter their consumption pattern to reduce the energy requirement exacerbates the issues. These multidimensional aspects can obscure possibilities for true sustainability and socially deliberate technological futures. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. Many metabolites have been identified as biomarkers of specific traits. However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six machine learning models, logistic regression (LR) model exhibited the best prediction performance. The value of area under the receiver operating characteristic curve (AUC) was 0.92 when 62 features were incorporated in the external validation set for the LR model. In this model, LAA could be well predicted by clinical risk factors including body mass index, smoking, and medications for controlling diabetes, hypertension, and hyperlipidemia as well as metabolites involved in aminoacyl-tRNA biosynthesis and lipid metabolism. In addition, we found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. Moreover, we have shown that using shared features can yield more reliable correlations than either model, which can be valuable for future identification of LAA. [ABSTRACT FROM AUTHOR]

The Q-matrix is commonly used in diagnostic classification models and has recently been incorporated into the multidimensional item response theory (MIRT) models to add information about the relationship between items and dimensions of the latent trait. The reformulation of the MIRT models with Q-matrix (MIRT-Q) has presented to improve the precision of the parameters of these models and to provide a simple and intuitive method for users to define the item-trait relationship. This paper aims to explore the incorporation of the Q-matrix in the formulation of MIRT models for polytomous item responses. Specifically, we introduce the incorporation of the Q-matrix into two of the polytomous MIRT models most known and used: the multidimensional graded response (MGR) model, hereinafter called MGR-Q, and the multidimensional generalized partial credit (MGPC) model, hereinafter called MGPC-Q. We provide readers the code of the MGR-Q and MGPC-Q models in Stan, a Bayesian estimation software, and we conduct a simulation study in order to evaluate the parameter recovery of the estimation method. To illustrate the use of both models in practice, we fit them to an operational dataset from 2400 individuals on 13 items and demonstrate the estimation of MGR-Q and MGPC-Q using the Stan program. [ABSTRACT FROM AUTHOR]

This article, based on literature review and interviews with key informants in 2021-22, reflects on the degree to which social science and social science specialists were involved in the Thai government's handling of the multidimensional effects of the COVID-19 pandemic. The article juxtaposes the marginal role of social scientists in official structures with their multiple engagements outside of such structures. The findings show that pandemic-specific social research agenda of social scientists were generally technocratic in nature, being geared to the perceived needs of policymakers, even if formal mechanisms to feed inputs into the policymaking process were restricted. Using public debates and the media, a smaller group of social scientists were able to make an impact by highlighting neglected aspects of the pandemic, advocating for the rights of the underserved and challenging policy decisions. [ABSTRACT FROM AUTHOR]

The results of the application of the methods of the synergetic control theory to a high-dimensional immunology object with uncertainty in its descriptions are reported. The control here is the therapy treated as a problem for constructing an optimal cure program. The control object is presented in continuous and discrete forms, i.e., mathematical models given by a system of ordinary differential equations with a bounded disturbance and a system of stochastic difference equations, respectively. Two algorithms for deriving robust regulators applicable to a 10-dimensional nonlinear multi-loop system with unstable limit states, which models an immune response to the hepatitis B infection, are obtained. Analytical control design for a continuous model relies on the method of nonlinear adaptation on the target manifold. The second algorithm represents a stochastic extension of the method of analytical design of aggregated discrete regulators minimizing the variance of the target macro variable. The numerical simulation of the developed control systems indicates the performance of the designed control algorithms. The results of this study can be used as a component part of the mathematical tools of expert systems and decision support systems. [ABSTRACT FROM AUTHOR]

The use of multidimensional forced‐choice (MFC) items to assess non‐cognitive traits such as personality, interests and values in psychological tests has a long history, because MFC items show strengths in preventing response bias. Recently, there has been a surge of interest in developing item response theory (IRT) models for MFC items. However, nearly all of the existing IRT models have been developed for MFC items with binary scores. Real tests use MFC items with more than two categories; such items are more informative than their binary counterparts. This study developed a new IRT model for polytomous MFC items based on the cognitive model of choice, which describes the cognitive processes underlying humans' preferential choice behaviours. The new model is unique in its ability to account for the ipsative nature of polytomous MFC items, to assess individual psychological differentiation in interests, values and emotions, and to compare the differentiation levels of latent traits between individuals. Simulation studies were conducted to examine the parameter recovery of the new model with existing computer programs. The results showed that both statement parameters and person parameters were well recovered when the sample size was sufficient. The more complete the linking of the statements was, the more accurate the parameter estimation was. This paper provides an empirical example of a career interest test using four‐category MFC items. Although some aspects of the model (e.g., the nature of the person parameters) require additional validation, our approach appears promising. [ABSTRACT FROM AUTHOR]

There is a scarcity of scholarship that sheds light on international doctoral students' identity construction in quotidian encounters beyond the formal curriculum. In this autoethnographic study, based on my diary entries, via a socio-constructivist lens, I teased out my multidimensional identity construction by referring to situations, activities and relations embedded in daily experiences during my overseas study sojourn. My autoethnography reveals that how I make sense of my becoming and being as a Chinese sojourning in Australia for doctoral education transcends the experientiality of doing research alone, but incorporates gendered, sociocultural and professional facets within my past-present-future life trajectory. As I navigated these encounters, strategically mobilising my agency and utilising structural contexts towards the aim of achieving ontological security, I engaged in negotiating a transformative identity. The research calls for more studies in the future that explore the complexities and nuances of international doctoral students' identity construction in quotidian realities. [ABSTRACT FROM AUTHOR]

Image analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants. [ABSTRACT FROM AUTHOR]

In the present paper, the model of multi-server queueing system with random volume customers, non-identical (heterogeneous) servers and a sectorized memory buffer has been investigated. In such system, the arriving customers deliver some portions of information of a different type which means that they are additionally characterized by some random volume vector. This multidimensional information is stored in some specific sectors of a limited memory buffer until customer ends his service. In analyzed model, the arrival flow is assumed to be Poissonian, customers' service times are independent of their volume vectors and exponentially distributed but the service parameters may be different for every server. Obtained results include general formulae for the steady-state number of customers distribution and loss probability. Special cases analysis and some numerical computations are attached as well. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a numerical integration method for hyperbolic systems problems known as the splitting method, which serves as an effective tool for solving complex multidimensional problems in mathematical physics. The exponential stability of the upwind explicit–implicit difference scheme split into directions is established for the mixed problem of a linear two-dimensional symmetric t-hyperbolic system with variable coefficients and lower-order terms. It is noteworthy that there are control functions in the dissipative boundary conditions. A discrete quadratic Lyapunov function was devised to address this issue. A condition for the problem's boundary data, ensuring the exponential stability of the difference scheme with directional splitting for the mixed problem in the l2 norm, has been identified. [ABSTRACT FROM AUTHOR]

GRASP is a scientific software application designed for the graphical inspection, reduction and analysis of multidetector data produced by the small-angle neutron scattering (SANS) instruments at the Institut Laue-Langevin and other neutron sources around the world. The first developments of GRASP began more than 20 years ago and were written in MATLAB, allowing rapid development of scientific code, with much of the data handling, matrix manipulation, mathematical tools, user interface and graphical tools integrated at a high level in the underlying MATLAB platform. By their very nature, multidimensional data are often best appreciated in graphical form. GRASP deals with many of the diverse requirements for data reduction and analysis of SANS data using a general set of tools and reduction algorithms suited to 2D multidetector data. A further fundamental architectural inclusion is a third dimension of data manipulation, thereby easily allowing parametric analysis and cross referencing of series data such as composition, kinetic measurements, temperature, magnetic field, angle or time of flight, often considered as a single 'measurement'. This article serves as a reference document for users of the software, and outlines the architecture and strategy of the program. An overview of some of the features, capabilities, peripheral user modules and neutron scattering tools is presented. [ABSTRACT FROM AUTHOR]

The scientific task, which is solved in the research, is the cognitive display of the state of the national security system with a complex hierarchical structure. As a rule, images are created individually taking into account a specific application field and interpreted by an expert (a group of experts) based on accumulated knowledge. Cognitive mapping is designed to support decision making by an expert (group of experts), monitoring and managing in real time. The object of research is the system of ensuring national security. The subject of the research is the functioning of the national security system. The research developed a method of visualization of the states of the national security system. An overview of the methods of visual graphic presentation of information about the state of multidimensional objects and systems was carried out. The novelties of the proposed method are: – creation of a visual, multi-level and interconnected description of the national security system; – increasing the efficiency of decision making while assessing the state of the national security system; – solving the problem of falling into global and local extremes while assessing the state of the national security system; – combination of graphic and numerical display of controlled state parameters of the national security system; – avoiding the problem of loops while visualizing the state of the national security system in real time. The specified method should be implemented in specialized software, which is used to analyze the state of the national security system and make management decisions. [ABSTRACT FROM AUTHOR]

The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at 10.5281/zenodo.8002909 (Aslam et al., 2023a) and 10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively. [ABSTRACT FROM AUTHOR]

Next Point-of-Interest (POI) recommendation has shown great value for both users and providers in location-based services. Existing methods mainly rely on partial information in users' check-in sequences, and are brittle to users with few interactions. Moreover, they ignore the impact of multi-dimensional auxiliary information such as user check-in frequency, POI category on user preferences modeling and the impact of dynamic changes in user preferences over different time periods on recommendation performance. To address the above limitations, we propose a novel method for next POI recommendation by modeling long and short term user preferences with multi-dimensional auxiliary information. In particular, the proposed model includes a static LSTM module to capture users' multi-dimensional long term static preferences and a dynamic meta-learning module to capture users' multi-dimensional dynamic preferences. Furthermore, we incorporate a POI category filter into our model to comprehensively simulate users' preferences. Experimental results on two real-world datasets demonstrate that our model outperforms the state-of-the-art baseline methods in two commonly used evaluation metrics. [ABSTRACT FROM AUTHOR]