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Found 6374 publications
A data-driven approach to tackling academic stress-coping and mental health issues in college students using spherical fuzzy MARCOS methodology
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 185
Issue: Unknown
Page Range: Unknown
Description:
The drastically developing nature of the knowledge economy and the rising need for top-notch expertise have placed tremendous pressure on college students. As higher education becomes more accessible, masses of students are enrolling in colleges, which puts additional pressure on colleges and institutions; as a result, they cannot provide adequate resources to the students. As the class size increases, many students require mental health assistance, academic guidance, and financial aid, which then puts pressure on the teachers and the facilities. This flood of students overloads the facilities, resulting in it becoming more challenging to provide attention and concern, leading many students to feel overlooked and affecting their mental health. Due to not getting timely support, students may find it challenging to handle their academic responsibilities. Moreover, the students face a heavy workload, unclear guidance, and limited resource access. The objective of this study is to develop a structured, data-driven decision-making framework for systematically evaluating and improving student mental health and academic stress-coping strategies in a college setting. To address this, a comprehensive decision-making structure, measurement of alternatives, and ranking according to the compromised solution (MARCOS) within the spherical fuzzy (SF) environment, has been applied, which evaluates the key factors causing mental health issues by comparing the ideal and anti-ideal alternatives. The novelty of the proposed approach lies in leveraging the SF framework's explicit ability to model hesitation (abstinence) alongside truth and falsity degrees, enabling more accurate representation of subjective psychological assessments compared to traditional fuzzy models. Furthermore, the method calculates utility functions corresponding to each alternative (coping technique), prioritizes the strategies, and selects the most effective intervention. The results reveal that personalized mental health plans emerged as the top-ranked coping strategy, highlighting the importance of tailored support in culturally and contextually diverse academic environments.
Open Access: Yes
Bioinformatics analysis of Rickettsia typhi autoimmune associations and screening of Streptomyces-derived inhibitors
Publication Name: Biodata Mining
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Background: Rickettsia typhi is the causative agent of epidemic murine typhus and Rocky Mountain spotted fever. The infection can affect multiple vital organs, including the heart, lungs, kidneys, and brain. Doxycycline is the recommended treatment but inflammation, mal-response, and drug resistance may arise. No natural product inhibitors have been reported against this bacterium. Aim: The objective of this study was to establish a potential connection between autoimmune disorders triggered by R. typhi, identify therapeutic targets within its core proteome, and explore novel natural product inhibitors from Streptomyces spp. that could potentially inhibit it. Methodology: Complete proteomes of four publicly available R. typhi strains were used for pan-proteomic analysis. The fni gene product (Isopentenyl pyrophosphate isomerase) was selected as the potential drug target. Molecular docking of 607 Streptomyces-derived metabolites was performed, with top hits validated using DiffDock and Vinardo scoring. Additionally, the Absorption, Distribution, Metabolism, Excretion, and Toxicity properties of the leading compounds were assessed via pkCSM, and formulation characteristics optimized using FormulationAI. Results: Out of the 803 core proteins, associations between 14 proteins were mined for autoimmune diseases (including psoriasis, rheumatoid arthritis, optic atrophy, uveitis, even-plus syndrome, Sjogren syndrome, inflammatory bowel disease, allergic rhinitis, systemic lupus erythematosus, sclerosis, Stevens-Johnson syndrome, toxic epidermal necrolysis, colitis etc.). 17 core proteins were predicted as druggable. ZINC01482946 demonstrated the strongest inhibitory potential, as confirmed by DiffDock scoring, convolutional neural network-based ranking, and Vinardo scoring. It demonstrated a stable configuration and exhibited a favorable pharmacokinetic profile, with bioavailability enhanced through cyclodextrin complexation. Conclusion: To the best of our knowledge, this is the first report identifying human autoimmune associations with R. typhi and natural product inhibitors targeting the pathogen. ZINC01482946 shows potential as an effective inhibitor of R. typhi, while SBE-β-CD appears to be a promising cyclodextrin for improving its solubility and bioavailability. Clinical trial number: Not applicable.
Open Access: Yes
Scenario-driven decision models for rare element waste management by integrating koch snowflake fuzzy sets and euclidean expert weighting
Publication Name: Sustainable Futures
Publication Date: 2025-12-01
Volume: 10
Issue: Unknown
Page Range: Unknown
Description:
The most critical factors must be determined to effectively manage environmental wastes generated during the extraction of rare elements. Otherwise, businesses may not be able to effectively manage their limited financial and human resources. This situation negatively affects the financial performance of the projects. The limited number of existing studies in the literature causes environmental risks to be insufficiently managed and recycling processes to be unoptimized. This study aims to determine priority strategies to increase the effectiveness of rare element waste management processes. Comprehensive and original decision-making models are created under three different scenarios. Koch Snowflake fuzzy sets, Euclidean based expert weighting and cognitive information modelling and analysis system (CIMAS) approaches are integrated in this model. The main contribution of this study is that a new type of fuzzy numbers called Koch Snowflake fuzzy sets is developed by considering the concept of fractal numbers. Fractal geometry is a powerful tool for modelling complex and dynamic systems. Hence, these new sets provide more flexible and more detailed uncertainty modelling. Moreover, considering different scenarios dynamic strategies can be developed that can adapt to changing conditions, such as pandemics or trade wars. The findings denote that technological developments are determined as the most critical factor under normal conditions. In the scenario where trade wars occur, it is revealed that political and regulatory measures should be addressed as a priority. In the event of a new epidemic disease such as COVID-19, it is concluded that more importance should be given to long-term storage strategies.
Open Access: Yes
Evaluating the Impact of Aggregation Operators on Fuzzy Signatures for Robot Path Planning
Publication Name: Sensors
Publication Date: 2025-12-01
Volume: 25
Issue: 23
Page Range: Unknown
Description:
This study investigates the impact of different aggregation operators (commonly referred to as fuzzy operators) on the application of fuzzy signatures. Fuzzy signatures are specialized multidimensional data structures that symbolically represent data. As a use case, the study focuses on robot environment representation and path planning, presenting the results obtained by applying various aggregation operators including (Formula presented.), (Formula presented.), (Formula presented.) (Formula presented.) and (Formula presented.) (Formula presented.) on the normalized values obtained from the robot sensors. The comparison highlights their effects on the computational load and path lengths of the path planning task. The findings reveal that the most efficient aggregation operator, in terms of computational load and the path length, is the (Formula presented.) (Formula presented.) aggregation operator. Specifically, the (Formula presented.) (Formula presented.) consistently yielded the shortest paths (as low as 22 nodes) and the lowest execution times (down to 0.0913 s), demonstrating superior efficiency compared to the (Formula presented.) operator, which resulted in path lengths up to 34 nodes and execution times reaching 0.1923 s. This represents an improvement of up to 35.3% reduction in path length and 52.5% reduction in execution time when comparing the (Formula presented.) (Formula presented.) to the (Formula presented.) operator based on observed extreme values. Furthermore, this work provides the first empirical comparison of fuzzy aggregation operators specifically for fuzzy signature-based mobile robot path planning.
Open Access: Yes
DOI: 10.3390/s25237342
A comprehensive narrative review on precision medicine approach to hypertension: exploring the role of genetics, epigenetics, microbiome, and artificial intelligence
Publication Name: Journal of Health Population and Nutrition
Publication Date: 2025-12-01
Volume: 44
Issue: 1
Page Range: Unknown
Description:
Background: Hypertension (HTN) impacts approximately 1.28 billion individuals globally and poses a great burden of disease. The objectives of this study are to explore the role of genetics, epigenetics, microbiome, and artificial intelligence (AI) in the management of HTN. A thorough literature search was conducted across various databases including PubMed, Google Scholar, Web of Science (WoS), and Medline to retrieve articles related to the role of genetics, epigenetics, microbiome, and AI in the precision medicine of HTN. Genes—including ACE, NOS3, ADD1, CYP11B2, NPPA, and NPPB—have a profound impact on blood pressure (BP) regulation in our body and polymorphism in these key genes can lead to HTN. Up or down-regulation of genes by epigenetic factors such as miRNA-155, miRNA-210, and miRNA-122 can significantly contribute to the development of HTN. These genetic and epigenetic factors can also be used as specific targets for gene editing and gene therapy for long-term management of HTN. However, the implementation of these techniques has not been possible in clinical settings due to lack of human studies and safety concerns related to unpredictable DNA alterations, nucleotide deletions, and loss of allele-specific chromosomes. Modulation of gut microbiome through oral supplements, fecal microbiota transplant (FMT), and dietary interventions has emerged as one the most effective and safe techniques for managing HTN in human models. AI-based cutting-edge models have helped curate personalized diet plans based on an individual’s unique microbiome, genomic information, and physiological conditions leading to a reduction in BMI, fat, BP, and heart rate while improving overall cardiac health and gut microbial diversity. Despite the significant advantages offered by AI-based medicine, ethical concerns—related to data privacy, bias, and discrimination—and ineffective models have led to limited integration of AI in precision medicine of HTN. The integration of genetics, epigenetics, microbiome, and AI-based models can play a key role in improving the current landscape of precision medicine of HTN. These cutting-edge techniques can lead to a shift from the current one-size-fits all approach to more personalized treatment plan however further research in human models is needed to determine the safety and true efficacy of these techniques. Additionally, new AI-models need to be developed that address ethical concerns and are effective in real-world clinical settings.
Open Access: Yes
Integrated Rough AHP and Neural Network Model for Mobile Phone Selection with Big Data Under Uncertainty
Publication Name: Journal of Intelligent and Fuzzy Systems
Publication Date: 2025-12-01
Volume: 49
Issue: 6
Page Range: 1414-1427
Description:
This paper applied an integrated approach to Multi-Attribute Decision Making (MADM) by combining the Rough Analytic Hierarchy Process (RAHP) and Neural Network, specifically a Multi-Layer Perceptron (MLP) for a specific problem of smartphone selection. The Rough Analytic Hierarchy Process, grounded in rough set theory, proves adept at handling uncertainties in decision-making processes. Through the integration of RAHP and MLP, this study provides a comprehensive framework for ranking mobile phone criteria, focusing on camera quality, selfie capabilities, audio performance, display features, battery life, and pricing. The practical example employed demonstrates the applicability of the proposed methodology in real-world decision-making scenarios, the fusion of RAHP and MLP emerges as a potent solution for Multiple Attribute Decision Making (MADM) problems, offering decision-makers confidence in navigating intricate scenarios. This integrated approach signifies a new era of robust decision-making, enhancing outcomes across diverse domains by synergizing structured prioritization and uncertainty management. The paper proceeds with a literature review, outlining existing approaches in decision-making scenarios. The methods section details the operations with rough numbers, the Rough Analytic Hierarchy Process, and the Multi-Layer Perceptron. A numerical example of mobile phone selection is presented, illustrating the application of the integrated approach. In the presented numerical example, two scenarios are provided: one without a price criterion and another with a price criterion. In the price-less scenario, the Honor Magic5 Pro is chosen, while in the scenario considering price, the Oppo Find X6 Pro is selected as the best option.
Open Access: Yes
Reconstructing the Definition of Space Debris
Publication Name: Air and Space Law
Publication Date: 2025-12-01
Volume: 50
Issue: 6
Page Range: 537-558
Description:
The international community has realized that space debris is a global concern that should be addressed with a sense of urgency. Keeping in mind the limitations of the prevailing normative framework, the optimal solution would be to address the problem by way of universal lawmaking. However, any universal lawmaking effort presupposes the creation of a uniform definition of space debris. Non-binding international standards and national documents currently contain a variety of definitions. The coexistence of divergent definitions may lead to fragmentation and legal uncertainty, and their restrictive approach may become untenable as humankind ventures deeper into outer space. Based upon these considerations and adopting a proactive mindset, the present paper aims to re-evaluate the prevailing definitions with a view to proposing a comprehensive, feasible and durable definition covering all extant and future forms of space debris. The analysis commences with an overview of selected universal, regional and national definitions. This is followed by the identification, reassessment and possible revision of shared elements of the definitions concerned. The reassessed and revised elements are then assembled to propose a new definition to contribute to the ongoing discussion on space debris.
Open Access: Yes
DOI: 10.54648/aila2025059
Strategizing for Sustainability: Examining the Dynamic Interplay of the Circular Economy, Green Technology Innovation, and Green Performance
Publication Name: Global Journal of Flexible Systems Management
Publication Date: 2025-12-01
Volume: 26
Issue: 4
Page Range: 935-961
Description:
Environmental challenges critically affect manufacturing firms which face numerous concerns regarding their sustainable operations. These operations aim to operationalize the dimensions of circular economy capabilities (CEC) and green technology innovation (GTI) to strengthen competitiveness in fragile environments. This research validates a holistic understanding of green performance by integrating theories and dimensions to identify effects that predict sustainable green performance. Drawing from the green dynamic capability view (GDCV), which is a contextual extension of the DCV and flexible systems management (FSM) paradigm, this study investigates how CEC and GTI predict green performance (GP). Survey data of 301 senior professionals from manufacturing firms acquired from a developing country, such as Bangladesh, were used. To assess the survey data, the study used a multimethodological approach using Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the suggested tie in the midst of the CEC and GTI on the GP. The findings reveal that all the antecedents of the circular economy are necessary conditions except absorptive capacity to predict green performance, as reported in the NCA. The fsQCA results show that combinations of CEC and GTI are sufficient conditions to predict high green performance. This research uses a unique combination of CEC and GTI to predict high GP via the supplementary method of fsQCA. Therefore, the findings should also motivate professionals of manufacturing firms to focus even more on the necessity effects of a single condition to predict GP and the asymmetric effects of combinations of CEC and GTI to produce multiple configurations to predict high green performance.
Open Access: Yes
Monocular Curb Edge Detection via Robust Geometric Correspondences
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-12-01
Volume: 15
Issue: 24
Page Range: Unknown
Description:
Advanced driver-assistance and autonomous systems require perception that is both robust and affordable. Monocular cameras are promising due to their ubiquity and low cost, yet detecting abrupt road surface irregularities such as curbs and bumps remains challenging. These sudden road gradient changes are often only a few centimeters high, making them difficult to detect and resolve from a single moving camera. We hypothesize that stable image-based homography, derived from robust geometric correspondences, is a viable method for predicting sudden road surface gradient changes. To this end, we propose a monocular, geometry-driven pipeline that combines transformer-based feature matching, homography decomposition, temporal filtering, and late-stage IMU fusion. In addition, we introduce a dedicated dataset with synchronized camera and ground-truth measurements for reproducible evaluation under diverse urban conditions. We conduct a targeted feasibility study on six scenarios specifically recorded for small, safety-relevant discontinuities (four curb approaches, two speed bumps). Homography-based cues provide reliable early signatures for curbs (3/4 curb sequences detected at a 5 cm threshold). These results establish feasibility for monocular, geometric curb detection and motivate larger-scale validation. The code and the collected data will be made publicly available.
Open Access: Yes
DOI: 10.3390/app152412922
