Multi-source Evidence Research Articles

Regional Fault Location of Distribution Network Based on Distributed Observation and Fusion of Multi-Source Evidence

Regional Fault Location of Distribution Network Based on Distributed Observation and Fusion of Multi-Source Evidence

A novel framework for evidence-based assessment of flood resilience integrating multi-source evidence: A case study of the Yangtze River Economic Belt, China

Floods are extremely destructive to the society, economy and nature, and severely restrict sustainable urban development. There is an urgent need to assess the level of flood resilience, analyze its evolutionary characteristics and improve the flood resilience of the region. However, few previous studies have integrated multi-source evidence into a framework for assessing flood resilience over a longer time span. This study integrated multi-source evidence to analyze the Yangtze River Economic Belt, including literature-based evidence, policy texts and social media data. Subsequently, the Kruskal algorithm and Jaccard coefficient were used to construct collinear networks and identify the clustering dimensions of flood resilience. After evidence judgment, a flood resilience indicator system was constructed. The flood resilience index was calculated using the improved Critic method. The dynamic evolution characteristics and key influencing factors were illustrated by the Kernel density and Grey correlation methods, respectively. The principal contribution is to integrate multi-source evidence to assess flood resilience, with a newly constructed indicator system that systematically considers local public demand, policy priorities and common indicators from previous literature. The results show that: the flood resilience index of the Yangtze River Economic Belt is in the range of [0.16, 0.75] from 2000 to 2022, with higher values in the downstream compared to those in the midstream and upstream. The social dimension dominating the overall flood resilience. The kernel density curve of the YREB is characterized by a right trailing. The inter-regional differences tend to widen and have a polarization effect. The special funds for water conservancy finances and the length of flood prevention levees are key factors influencing the level of flood resilience. Finally, strategies for enhancing flood resilience were proposed. This study provides a feasible framework for constructing an indicator system and assessing flood resilience by integrating multi-source data. It is important for flood risk management and helps to the development of urban flood prevention and resilience strategies.

The daily costs of workaholism: A within-individual investigation on blood pressure, emotional exhaustion, and sleep disturbances.

Workaholism literature has been so far focused on individual differences in workaholic tendencies, considering the construct as a stable individual trait and highlighting its health and well-being consequences. Only recently, research has started inspecting the daily dynamics and potential consequences of state workaholism. In this preregistered study, we aimed at systematically investigating the within-individual fluctuations in workaholism levels and their potential short-term and delayed psychophysiological responses as captured by ambulatory assessment integrating subjective and objective data. Using an intensive longitudinal design over 10 workdays with 114 workers from various occupations (2,534 measurement occasions), we found higher systolic and diastolic blood pressure, emotional exhaustion, and sleep disturbances in workdays characterized by higher-than-usual workaholism symptoms. Moreover, the reactivity to state workaholism, as indexed by afternoon blood pressure, was found as a mediator of the subsequent prolonged activation indexed by bedtime blood pressure. Finally, we found evidence of a buffering effect of evening psychological detachment on the relationship between state workaholism and sleep disturbances. Overall, our results support the conceptualization of workaholism as a multilevel phenomenon that acts as an internal job-related demand by showing the typical strain reactions triggered by well-characterized external demands. This study contributes to the literature by highlighting that transient workaholism symptoms can result in significant short-term stress responses at different levels, providing new, robust, and multisource evidence that underlies the importance of effectively preventing and managing dysfunctional work investment since its early manifestation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adopting a cell-based method to identify mineralized geological structures in support of mineral prospectivity mapping at the Kuh–Lakht epithermal gold deposit, Central Iran

Mineral prospectivity mapping (MPM) is a new tool in mineral exploration with the objective of gathering and integrating multi-source evidence to provide decision-making criteria for exploration targeting. Ideally, the MPM approach accounts for the uncertainties associated with different predictive modeling techniques. The cell-based association (CBA) is a computational workflow that addresses the prediction-related bias of the MPM protocols. In this contribution, the CBA technique was employed to prioritize grid cells according to the importance of mineralized geological features and to identify geological settings favorable for ore deposition. The Kuh-Lakht epithermal gold deposit in Central Iran was adopted as a case study to examine the relevance of the proposed workflow and define target areas for brownfield exploration. To assess the significance of the proposed technique in dealing with evidence layers rather than training locations (i.e., mineralized/non-mineralized sites), two geochemical evidence layers, single-element and multi-element signatures, were used to train the CBA technique. An appraisal analysis through prediction-area plots and success-rate curves indicated that multi-element geochemical signatures are more relevant for delineating mineralized geological structures and vectoring for concealed gold mineralization. Notably, the input of single-element geochemical data into the CBA workflow enabled the prediction of additional exploration targets, whereas multi-element geochemical data failed to achieve the same results. The results indicated that the CBA technique can efficiently cope with the uncertainty of insufficient data input and permit the delineation of exploration targets. Additionally, our results suggest that the incorporation of a ranking system of critical geological factors that facilitate the gold mineralization into conventional MPM protocols can improve the robustness of mineral exploration targeting.

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

A two-level fusion model of vibro-acoustic signals for centrifugal fan blade crack detection

Blade crack detection is the key to ensuring the smooth and safe operation of centrifugal fans. However, a single vibration signal is difficult to fully reflect the health state of the blade and is susceptible to noise interference in the industrial field, which makes it difficult to detect blade cracks. Therefore, a two-level fusion model of vibro-acoustic signals is proposed for blade crack detection of centrifugal fans. Firstly, based on the designed correlated degree of cyclostationarity fusion rule, the data-level fusion of multi-source homogeneous vibro-acoustic signals is completed, and the fused signals with more obvious fault features are obtained. Then, a one-dimensional feature pyramid network is proposed to extract the vibro-acoustic features and generate initial decisions. Finally, a basic probability assignment acquisition method based on the precision of the initial classifiers and a weighted average method based on the Pignistic probability distance are proposed to minimize the conflict between multi-source evidence, and the Dempster-Shafer evidence theory method is used to obtain the blade crack detection results. The effectiveness of the proposed method is verified by the centrifugal fan blade crack detection experiments. Compared with other related methods, the proposed method has better detection performance and stronger robustness.

ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.

Background: Multi-source evidence fusion aims to process and combine evidence from different sources to support rational and reliable decision-making. The evidential reasoning (ER) approach is a helpful method to deal with information from multiple sources with uncertainty. It has been widely used in business analytics, healthcare management, and other fields for optimal decision-making. However, computerized implementation of the ER approach usually requires much expertise and effort. At present, some ER-based computerized tools, such as the intelligent decision system (IDS), have been developed by professionals to provide decision support. Nevertheless, IDS is not open source, and the user interfaces are a bit complicated for non-professional users. The lack of a free-to-access and easy-to-use computerized tool limits the application of ER. Methods: We designed and developed a Python package that could efficiently implement the ER approach for multi-source evidence fusion. Further, based on it, we built an online web-based system, providing not only real-time evidence fusion but also visualized illustrations of combined results. Finally, a comparison study between the Python package and IDS was conducted. Results: A Python package, ERTool, was developed to implement the ER approach automatically and efficiently. The online version of the ERTool provides a more convenient way to handle evidence fusion tasks. Conclusions: ERTool, compatible with Python 3 and can be installed through the Python Package Index at https://pypi.org/project/ERTool/, was developed to implement the ER approach. The ERTool has advantages in easy accessibility, clean interfaces, and high computing efficiency, making it a key tool for researchers and practitioners in multiple evidence-based decision-making. It helps bridge the gap between the algorithmic ER and its practical application and facilitates its widespread adoption in general decision-making contexts.

Risk assessment approach for tunnel collapse based on improved multi-source evidence information fusion

Risk assessment approach for tunnel collapse based on improved multi-source evidence information fusion

A vibro-acoustic signals hybrid fusion model for blade crack detection

Blade crack detection is the key to ensuring the smooth and safe operation of centrifugal fans. However, a single vibration signal is difficult to fully reflect the health state of the blade and is susceptible to noise interference in the industrial field, which makes it difficult to detect blade cracks. Therefore, a vibro-acoustic signals hybrid fusion model (VAS-HFM) is proposed for blade crack detection of centrifugal fans. Firstly, the kernel cosine similarity is designed to exploit the nonlinear relationship between multi-source vibro-acoustic signals and complete the data-level fusion of homogeneous vibro-acoustic signals. Then, a one-dimensional feature pyramid network (1D-FPN) is proposed to extract the vibro-acoustic features and generate initial decisions. Finally, a basic probability assignment (BPA) acquisition method based on the precision of the initial classifiers and a weighted average method based on the Jousselme evidence distance (JED) are proposed to minimize the conflict between multi-source evidence, and the Dempster-Shafer theory of Evidence (DSE) method is used to obtain the blade crack detection results. The effectiveness of the proposed method is verified by two centrifugal fan blade crack detection experiments. Compared with other related methods, the proposed method has better detection performance and stronger robustness.

Prediction of rock burst intensity based on multi-source evidence weight and error-eliminating theory.

Rock burst is the main geological hazard in deep underground engineering. For the prediction of the intensity of rock burst, a model for prediction of rock burst intensity on the basis of multi-source evidence weight and error-eliminating theory was established. Four indexes including the ratio of rock's compressive-tensile strength [Formula: see text], the stress coefficient of rock [Formula: see text], the elastic energy index of rock Wet, and integrality coefficient Kv were chosen as the prediction variables of rock burst; the index weights are calculated by different weighting methods and fused with evidence theory to determine the final weight of each index. According to the theory of error-eliminating, taking "no rock burst" (I in classification standards of rock burst intensity) as the objective and using the error function to process 18 sets of typical rock burst data and the weight of evidence fusion as the normalized index limit loss value, a model for prediction of rock burst intensity was built. It is verified by the actual situation and three other models. Finally, the model has been applied to rock burst prediction of Zhongnanshan tunnel ventilation shaft. The results show that evidence theory fuses multi-source index weights and improves the method of determining index weights. The index value is processed by error-eliminating theory, and the limit value problem of index value normalization is optimized. The predicted results of the proposed model are consistent with the situation of Zhongnanshan tunnel. It improves the objectivity of the rock burst prediction process and provides a research idea for rock burst intensity prediction index.

Human Autoantigen Atlas: Searching for the Hallmarks of Autoantigens.

Understanding autoimmunity to endogenous proteins is crucial in diagnosing and treating autoimmune diseases. In this work, we developed a user-friendly AAgAtlas portal (http://biokb.ncpsb.org.cn/aagatlas_portal/index.php#), which can be used to search for 8045 non-redundant autoantigens (AAgs) and 47 post-translationally modified AAgs against 1073 human diseases that are prioritized by a credential score developed by multisource evidence. Using AAgAtlas, the immunogenic properties of human AAgs was systematically elucidated according to their genetic, biophysical, cytological, expression profile, and evolutionary characteristics. The results indicated that human AAgs are evolutionally conserved in protein sequence and enriched in three hydrophilic and polar amino acid residues (K, D, and E) that are located at the protein surface. AAgs are enriched in proteins that are involved in nucleic acid binding, transferase, and the cytoskeleton. Genome, transcriptome, and proteome analyses further indicated that AAb production is associated with gene variance and abnormal protein expression related to the pathological activities of different tumors. Collectively, our data outlines the hallmarks of human AAgs that facilitate the understanding of humoral autoimmunity and the identification of biomarkers of human diseases.

Research on mechanical fault diagnosis based on MADS evidence fusion theory

In machine intelligence fault diagnostic and health status decision-making systems, rich, complex, and fuzzy feature information cannot facilitate fault decision-making merely on a single data source. This requires utilizing the heterogeneity of information gathered from multiple sources to diminish the system’s uncertainty and improve the accuracy of decision-making. In this work, a novel neural network-based multi-source fusion classification model is proposed to diagnose the pump mechanical faults. The multi-head attention Dempster–Shafer (D–S) evidence fusion (MADS) system extends the model’s ability to focus on rich features. Furthermore, the uncertain values throwing mechanism can effectively eliminate samples from uncertain categories and increase the model’s ability to distinguish diagnostic results with low confidence. Compared with a single sensor, our multi-sensor joint decision based on seven sensors considerably improved the fault diagnostic accuracy of MADS system, which has increased by at least 12.34%. Experimental validation demonstrates that utilizing the improved combination rules provided for multi-source evidence fusion fault diagnosis can significantly improve the efficacy of conventional D–S fusion and reduce the probability of misjudgment; combining the multi-head attention mechanism can dramatically increase the precision of model fault diagnosis. The proposed method has the potential to substantially accelerate research in the field of multi-source sensor joint fault diagnosis.

Understanding Etiologies of Cardiac Arrest: Seeking Definitional Clarity.

Cardiac arrest leading to death and sudden cardiac death (SCD) may refer implicitly to situations in which death is unexpected and primarily of cardiac cause. National and international societies have published differing definitions for the various terms relating to cardiac arrest and SCD. We highlight the controversies in defining SCD, including the lack of a universal definition, the heterogeneity in the operationalization of the term "sudden," and limitations of time-based systems of SCD classification. We discuss the importance of a standardized methodology for classifying cardiac arrest as recommended by the World Health Organization (WHO) that should include use of multisource evidence (eg, coroner, autopsy, and toxicology reports) for confirming or refuting a cardiac cause of arrest. We reveal how a universal definition of SCD has been incorrectly attributed to the WHO and how this has been perpetuated in the literature. We make the case that definitional clarity is essential to understanding epidemiology, evaluating novel treatments, forming international collaboration, and innovating public health prevention strategies. We propose a practical schema to categorize cardiac arrest events to describe and study this population more accurately.

Data sharpening and linguistic theorizing: a case study of the causative derivation of Urdu change-of-state verbs

AbstractThis study examines the evidential value of corpus and introspective data from the perspective of morphological operations involved in the causative derivation of Urdu change-of-state verbs. By analyzing multi-source data on the transitivity status of causative variants of 112 Urdu verbs, it discovers a crucial relation between the type of evidence and the aspect of linguistic competence it addresses. More specifically, it compares data from lexical translation, Urdu WordNet and Urdu Lughat on the one hand, and data from a judgment task coupled with dialogical introspection on the other hand, and finds that the former can reveal the gradient nature of morphological productivity, but not its dynamic nature; the latter, however, can help explore the dynamic nature of productivity in the causative alternation. Such an observation confirms that both corpus and introspection complement each other, and that a particular source-specific piece of evidence may limit the coverage and generality of analysis. Thus, the study shows the importance of sharpening data by multi-source evidence for examining how various components of a phenomenon interrelate in the context of the overall grammatical organization of a language.

Universities and SDGs: evidence of engagement and contributions, and pathways for development

ABSTRACT Sustainable development has become a momentous global concern since the end of the twentieth century. The 2015 adoption of the SDGs represents a significant challenge for higher education globally as it compels widescale consideration of how the sector will address the SDGs and contribute to the 2030 Agenda. Seven years after the adoption of the SDGs, therefore, it is important to investigate the extent of engagement and clarify opportunities for growth. This paper reports research into university engagement with and contributions to the SDGs, what has happened and been documented so far, and what plans university leaders have for future engagement. From the analysis of multi-source evidence, it is reasonable to conclude that contributions vary across and are fragmented within universities, not resourced or reported in systematic ways. It calls for a much broader research agenda in this area that focuses not just on specific substantive issues but looks broadly across a suite of countries, universities, evidence, and issues and articulates a comprehensive view on what has been achieved as well as areas for development.

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evidence</i> received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. A case study of several real-world distribution systems is presented to validate the proposed method.

Clinical comprehensive evaluation of Wenxin Granules in treatment of arrhythmia with deficiency of Qi and Yin

This study evaluated the safety, effectiveness, economy, innovation, suitability, accessibility, and characteristics of traditional Chinese medicine of Wenxin Granules in the treatment of arrhythmia(ventricular premature beat and atrial premature beat) with deficiency of Qi and Yin. The multi-criteria decision analysis(MCDA) model was adopted, and the criterion layer and index layer were weighted by experts. CSC v2.0 was used for clinical comprehensive evaluation. This study embodies the clinical value of Wenxin Granules, promotes its safe, effective and rational use, and provides a basis for national medical decision-making. The multi-source evidence shows that the major adverse reaction of Wenxin Granules is gastrointestinal damage. According to the available studies, Wenxin Granules has controllable risk and thus is rated as grade B in terms of safety. The systematic evaluation of effectiveness shows that compared with antiarrhythmic western medicine, Wenxin Granules demonstrates improved clinical efficacy and electrocardiogram efficacy, which is supported by high-quality evidence, and thus the effectiveness of Wenxin Granules is evaluated as grade A. Economic research shows that Wenxin Granules is more economical than antiarrhythmic western medicine, which is supported by sufficient evidence and clear results, and thus the economy of this preparation is rated as grade B. The indications and contraindications of Wenxin Granules are clear and detailed to different types of arrhythmia, which, together with the precise positioning and prominent clinical innovation and industrial innovation, rates it as grade A in terms of innovation. The suitability of drug storage, prescription circulation, dosage form and course of treatment basically meet the clinical medication needs of doctors and patients, and thus the suitability of Wenxin Granules is evaluated as grade B. Because of the few restrictions and the sustainable resources of medicinal materials, the accessibility of Wenxin Granules is evaluated as grade A. The prescription compatibility focuses on the pathogenesis characteristics of deficiency of Qi and Yin, and there are more than 3 000 cases studied. Therefore, the characteristics of traditional Chinese medicine of Wenxin Granules are evaluated as grade B. Based on the evidence from all the above dimensions, Wenxin Granules has the clinical comprehensive value of class A and prominent characteristics of traditional Chinese medicine. It is suggested to include Wenxin Granules into the policy results related to basic clinical medication management according to the procedure.

Resilience assessment of regional areas against earthquakes using multi-source information fusion

This paper proposes a hybrid information fusion approach that integrates the Dempster-Shafer (D-S) evidence theory and cluster analysis, in order to assess the resilience of regional areas against earthquakes at an early stage. The developed resilience assessment framework involves indicators within geological, building, and social dimensions. Basic probability assignments (BPAs) of evidence are determined based on mass functions (MFs) firstly. Subsequently, these BPAs are fused by a new evidence fusion method incorporating the K-means clustering. The proposed evidence fusion method comprises two steps, namely intra-evidence fusion and inter-evidence fusion, to mitigate the adverse effects of conflicts in the multi-source evidence. Defuzzification is implemented on the fused BPA to obtain a representative value for the quantitative evaluation of seismic resilience. A global sensitivity analysis (GSA) is employed to investigate the impacts of various indicators on the resilience of regional areas. A case study in Nepal is used to examine the practicability and validity of the proposed approach. In the case study, the seismic resilience of Nepal at the district level is assessed in a quantitative manner. The research results imply that: (1) There are 84.4% of districts that possess a relatively low overall seismic resilience within the country of Nepal. (2) Building foundation type, internal wall type, and population density are the most sensitive factors contributing to the degree of resilience. (3) The proposed approach tends to provide more rational assessment results with conflicting evidence, compared with the conventional fusion method. The developed approach can be used as a decision tool to estimate regional resilience and provide insights into proactive control and damage mitigation.

RELIGIOUS BEHAVIOR OF AGRARIAN COMMUNITY IN LINGSAR VILLAGE, LOMBOK BARAT

research aimed to analyze work ethos and Sasak Islam religious behavior of agrarian community in Lingsar Village of Lombok Barat, Nusa Tenggara Barat. Sasak Islam religious behavior and work ethos of agrarian community in farming practice was the unit of analysis in this qualitative research with case study approach. Data collection was carried out through observation, in-depth interview, and documentation. To validate data, multisource evidence was used and analyzed with Weber’s social action theory. The result of research showed that religious behavior of Lingsar village’s farmer community included nerimaq (grateful), cukup (feeling enough), and ikhlas (sincere). Work ethos could be seen in social action such as working hard and diligently and farming processing land well. There was a relationship between farmer’s work ethos and Sasak Islam religious understanding of agrarian community in Lingsar Village.

Multisource evidence theory‐based fraud risk assessment of China's listed companies

AbstractFinancial fraud has great negative impact on capital markets. It misleads investors and the government and will hurt the development of the capital market in China. To detect corporate fraud, an artificial intelligence‐based method is proposed to evaluate the fraud risk. A new model for assessing the fraud risk of listed companies in China is put forward. The proposed approach collects multisource evidence from inside and outside listed companies. The internal evidence for fraud risk assessment is gathered by a machine‐learning method. The external evidence for fraud risk assessment is obtained by a web crawler. An evidence theory‐based is employed for integrating the multisource evidence. The Fraud Risk Assessment Model based on Multisource Evidence Theory is established. The proposed model is applied to assess the fraud risk of the listed companies in China. The results indicate that the proposed model is effective at evaluating the fraud risk of listed companies, and it also has a higher precision, recall, and F‐1 measure than the traditional probability model.