Support System Research Articles

"Aging Amplifies Challenges": A Descriptive Phenomenological Exploration of the Lived Experiences andNeeds of Elderly Individuals With Schizophrenia.

Older adults diagnosed with schizophrenia face unique public health challenges, with specific treatment needs, complex care demands, accelerated aging, and increased susceptibility to health issues. This phenomenological study explored the existential realities and needs of older adults diagnosed with schizophrenia. Fifteen participants, with a mean age of 69.47 (SD ± 9.47) years, ranging from 62 to 79 years old and hailing from rural regions, participated in the study. Four main themes and eight sub-themes emerge: compounding the burden (challenges in symptom management, comorbidities); the abyss of a life filled with emptiness (loss of mental pillars, living in agony); living on the margins of society (the vicious cycle of social and self-isolation, unattainable social welfare); and glimmer of light in the darkness (support systems, self-adjustment). The study calls for healthcare professionals to improve follow-up care efficiency, strengthen engagement, understand patients' living conditions and needs, enforce existing welfare policies for older mentally ill patients, and enhance their mental health and quality of life.

An Ensemble Framework Augmenting Surveillance Cameras for Detecting Intruder Clusters as Potential Mobs

<p>Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a Machine Learning (ML) 3-tiers Ensemble framework, which integrates Gray Level Co-occurrence Matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.</p>

Identification of thresholds and key drivers on Water Use Efficiency in different maize ecoregions in Yellow River Basin of China

Identifying the constrains on water use efficiency (WUE) of crops along a wet-to-dry gradient is important due to irrigation water scarcity, as well as the increasing drought risk under climate change in China. This study coupled five high-resolution climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) with the Decision Support System for Agrotechnology Transfer (DSSAT)-CERES-Maize model to quantify drought risk and the drivers affecting WUE in five major maize ecoregions of the Yellow River Basin (YRB) under three future scenarios (SSP126, SSP370, and SSP585) for both the historical baseline (1985-2014) and three future periods: 2021-2040 (2030s), 2041-2070 (2050s), and 2071-2100 (2080s). And a bias correction method was implemented for the crop model to analyze optimal WUE thresholds for maize across varying dry-wet gradients. The results indicated that future drought risk will likely persist in the YRB under all scenarios, but with regional differences in drought severity and frequency. The southwestern region (V) experienced the highest frequency of drought (62.50%-SSP126), while the northwestern region (III) exhibited the lowest frequency (33.00%-SSP585) in 2030s, and 83.30% of areas in the southwestern (V) showed significant wetting in the 2080s under SSP126. The bias-corrected CERES-Maize model effectively simulated crop yield and evapotranspiration (ET), resulting in an average reduction of 4.00% and 9.73% in normalized root mean square error (nRMSE) respectively. Distinct WUE thresholds ranging from 1.96 to 8.41 kg ha−1 mm-1 were observed across various scenarios-periods in the maize regions, mostly under slight and moderate dry/wet conditions. Notably, all SSP585 scenarios demonstrated a decrease in WUE thresholds compared to the baseline. Across all scenarios and periods, WUE was mainly driven by yield in the eastern regions (I and II) but by ET in the western regions (III, IV, and V). These findings suggest that regions experiencing varying degrees of drought severity should undergo differentiated management and optimization of agricultural practices to improve WUE under future climate scenarios.

Discussion of “Effects of Movement Induced by Ground Improvement on the Performance of an Excavation Support System in Underconsolidated Clay”

Discussion of “Effects of Movement Induced by Ground Improvement on the Performance of an Excavation Support System in Underconsolidated Clay”

Extending multi-criteria coastal vulnerability assessment to low-lying inland areas: examples from Estonia, eastern Baltic Sea

The assessment of vulnerability to coastal hazards is a significant coastal management problem in regions with complicated shoreline, such as Estonia. This study implements the vulnerability assessment based on the multi-criteria decision analysis using fuzzy logic, analytical hierarchy process, and weighted linear combination (including input from experts) integrated with a geographical information system, to map the coastal vulnerability index (CVI) of the Estonian coasts at high resolution based on 16 parameters. The novelty of our approach is that we expand this assessment to a 2 km wide inland area that is an intrinsic but often overlooked part of coastal vulnerability estimates. The Estonian shores have mostly low and moderate vulnerability. Short segments with high vulnerability are impacted by severe waves and highly elevated water levels. The CVI also characterizes low-lying areas, such as large river valleys, reasonably well. Estimates of coastal vulnerability based on the three most important parameters according to experts’ judgements provide a reasonable approximation of the 16-parameter CVI in mostly homogeneous coastal regions, but less so elsewhere where its value is questioned. The results show that the application of the developed integrated decision support system, applied to a 2 km wide coastal strip, provides more information than single tools to assist coastal managers and stakeholders in planning, preparing for and responding to hazards.

Closure to “Effects of Movement Induced by Ground Improvement on the Performance of an Excavation Support System in Underconsolidated Clay”

Closure to “Effects of Movement Induced by Ground Improvement on the Performance of an Excavation Support System in Underconsolidated Clay”

Evaluating agriculture 4.0 decision support systems based on hyperbolic fuzzy-weighted zero-inconsistency combined with combinative distance-based assessment

Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision-Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including “accessibility,” “re-planning,” “expert knowledge,” “interoperability,” “scalability,” “uncertainty and dynamic factors,” “prediction and forecast,” and “historical data analysis”. Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight “re-planning” (0.143) and “prediction and forecast” (0.140) as the most significant criteria, while “expert knowledge” ranked lowest (0.113). In the combinative distance-based assessment, the system labelled “OCCASION” achieved the highest score (3.843), positioning it as the most favourable ADSS, whereas the “MOLP-based beef supply chain” system scored lowest (−3.519). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.

Weighted ensemble deep learning approach for classification of gastrointestinal diseases in colonoscopy images Aided by explainable AI

Gastrointestinal diseases are significant health issues worldwide, requiring early diagnosis due to their serious health implications. Therefore, detecting these diseases using artificial intelligence-based medical decision support systems through colonoscopy images plays a critical role in early diagnosis. In this study, a deep learning-based method is proposed for the classification of gastrointestinal diseases and colon anatomical landmarks using colonoscopy images. For this purpose, five different Convolutional Neural Network (CNN) models, namely Xception, ResNet-101, NASNet-Large, EfficientNet, and NASNet-Mobile, were trained. An ensemble model was created using class-based recall values derived from the validation performances of the top three models (Xception, ResNet-101, NASNet-Large). A user-friendly Graphical User Interface (GUI) was developed, allowing users to perform classification tasks and use Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI tool, to visualize the regions from which the model derives information. Grad-CAM visualizations contribute to a better understanding of the model’s decision-making processes and play an important role in the application of explainable AI. In the study, eight labels, including anatomical markers such as z-line, pylorus, and cecum, as well as pathological findings like esophagitis, polyps, and ulcerative colitis, were classified using the KVASIR V2 dataset. The proposed ensemble model achieved a 94.125% accuracy on the KVASIR V2 dataset, demonstrating competitive performance compared to similar studies in the literature. Additionally, the precision and F1 score values ​​of this model are equal to 94.168% and 94.125%, respectively. These results suggest that the proposed method provides an effective solution for the diagnosis of GI diseases and can be beneficial for medical education.

Comparative analysis of deep learning models for various nonalcoholic fatty liver disease datasets

Fatty liver disease is caused by increased liver buildup or weight above 5-10%. This disorder is widespread in people with diabetes, overweight persons, and metabolic syndrome patients. Clinical decision support systems can improve liver failure diagnosis and prediction to reduce this situation. Many liver failure models have drawbacks, and liver failure prediction is still a problem. This work uses four large open-access critical care patient datasets to create and verify liver failure risk prediction models. This study aims to construct a clinically applicable diagnostic and predictive model that evaluates the probability or risk of liver failure in intensive care unit (ICU) patients using extreme gradient boosting (XGBoost), artificial neural networks (ANN), multi-layer perceptron (MLP), Modular Neural Network (MNN), and generalized feed forward (GFF). We evaluated performance metrics using these models: accuracy, sensitivity, specificity, and predictive accuracy.

Medical support systems for childbirth in space: Equipment, drugs, and practical challenges

Spacefaring childbirth represents a crucial step towards humanity's expansion into a multi-planet species. This review examines the medical equipment, drugs, and support systems necessary for childbirth in space. It highlights the importance of selecting efficient equipment, addressing potential challenges, and utilizing NASA and SpaceX's technological advancements.

Consumer Attitudes towards AI-based Financial Advice: Insights for Decision Support Systems (DSS) and Technology Integration

As financial technology (fintech) rapidly transforms the financial services landscape, the integration of artificial intelligence (AI) in providing financial advice becomes a focal point. This study delves into the intricate dynamics of AI-based financial advice adoption, focusing on technology integration, decision support systems, and the mediating role of perceived utility. The primary purpose of this research is to unravel the complex relationships between technology integration, Decision Support System (DSS), perceived utility, and their collective influence on consumer attitudes toward AI-based financial advice and technology adoption. Adopting a cross-sectional design, we target the Chinese population and utilize an online questionnaire for data collection. A sample size of 259 participants is determined using the rule of thumb technique, with random sampling ensuring representativeness. Data analysis will be conducted using the AMOS software, allowing for an in-depth examination of the relationships between variables. The study findings demonstrate how integrating technology enhances customers' perceptions and acceptance of it, especially when it comes to data security and compatibility. The study also demonstrates how customer attitudes and the uptake of AI-driven financial aid are significantly impacted by DSS aspects like decision transparency and predictive analytics precision. The study is unique because of its cross-cultural approach and perceived value as a mediating component. While this study focuses on the Chinese population, the researchers acknowledge the importance of cultural differences in shaping user perceptions and behaviors. While the findings may offer valuable insights into the Chinese context, caution is advised in generalizing the results to other cultural contexts.

Decision Support Systems for Lifelong Learning: Leveraging Information Systems to Enhance Learning Quality in Higher Education

This study investigates the impact of technology adoption—specifically AI Tools, Decision Support Systems (DSS), and Learning Management Systems (LMS)—on higher education. As these technologies reshape educational paradigms, understanding their effects on learning performance, satisfaction, and the adoption and usage of these tools is critical. The research aims to empirically examine the relationships between technology adoption, self-efficacy, and key educational outcomes. It explores the direct effects of AI Tools, DSS, and LMS on learning performance and satisfaction, as well as the role of self-efficacy as a mediator. Utilizing a quantitative approach, the study collected data from 356 students via a distributed questionnaire. Variables measured include technology adoption, self-efficacy, learning performance, satisfaction, and adoption and usage of educational tools. Data analysis was conducted using SPSS and Origin, incorporating regression, mediation, and moderation analyses. The study found significant positive effects of technology adoption on learning performance (β = 0.45, p < 0.01), satisfaction (β = 0.40, p < 0.01), and adoption and usage (β = 0.50, p < 0.01). Self-efficacy significantly mediated these relationships, indicating that higher confidence in using technology enhances its benefits. This research extends Bandura's social cognitive theory by empirically validating the mediating role of self-efficacy in technology adoption within educational contexts. The findings provide actionable insights for educators and policymakers, suggesting that boosting students' confidence in using technology can amplify its positive effects on learning outcomes.

Work Engagement and Performance of Romanian School Teachers During the COVID-19 Pandemic: The Impact of Sociodemographic and Contextual Factors

The pandemic has led to significant changes in teaching environments, which may have affected teacher engagement and performance. The aim of the paper is to investigate the levels of work engagement (WE) and work performance (WP) during the pandemic among teachers in primary and lower secondary schools in Romania and to identify if the dimensions of these two concepts differ according to gender, age, residence, status, teaching degree, and course profile, filling a gap in the specialized literature regarding these variables in Romanian education. We conducted a survey between February and March 2022 on a sample of 1051 teachers using the Utrecht Work Engagement Scale (UWES) and the Individual Work Performance Questionnaire (IWPQ 0.3). Teachers reported a high level of engagement and work performance, higher levels of WE being identified among female teachers and those teaching in rural areas. Younger teachers showed the lowest work engagement, despite their familiarity with the digital tools used during the COVID-19 pandemic. Interestingly, while gender and employment status did not have a significant impact on job performance, contextual performance was higher among tenured teaching staff and women. In addition, the profile of subjects taught significantly affected engagement and performance. The results of this research can contribute to a more dynamic and supportive educational system, ultimately leading to improved outcomes for teachers and students.

Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence

Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, and progress in precision agriculture associated with decision support systems for farmers, the objective is to optimize their use. This review focused on the progress made in utilizing machine learning and remote sensing to detect and identify crop diseases that may help farmers to (i) choose the right treatment, the most adapted to a particular disease, (ii) treat diseases at early stages of contamination, and (iii) maybe in the future treat only where it is necessary or economically profitable. The state of the art has shown significant progress in the detection and identification of disease at the leaf scale in most of the cultivated species, but less progress is done in the detection of diseases at the field scale where the environment is complex and applied only in some field crops.

A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

ПСИХОЛОГІЧНИЙ СУПРОВІД РОЗВИТКУ ДОВІРИ В МІЖОСОБИСТІСНИХ СТОСУНКАХ ЮНАКІВ

The article analyzes the problem of developing trust in interpersonal relationships of young men through psychological support. Its relevance is determined, on the one hand, by the importance of trust as a necessary condition for the strength and effectiveness of such relationships, and on the other hand, by the lack of sufficient experience at this age to build them, which leads to a loss of trust. The specified research task is solved on the basis of understanding interpersonal relations as a natural manifestation of the social nature of a person, which is manifested in the system of subject relationships with other people, established according to mutual conscious and emotionally colored choices; trust - as a special attitude of its subject to himself, another subject or object, which is based on common values ​​and interests, is determined by a sense of community with a group or with another person and is manifested in a positive assessment of reliability and safety. The search for optimal methodological approaches for its substantiation was carried out. With the help of the analysis of scientific works, it was found that such approaches can be: a systematic approach, which is the basis for understanding psychological support as a system of psychological support and assistance to young people in the development of trust; an eco-facilitative approach aimed at greening the educational environment, facilitative assistance to young people in the implementation of individual strategies in interpersonal relationships and the involvement of internal resources to solve psychological problems, a spiritual and moral approach that focuses on the actualization of spiritual potential in the process of their spiritual and moral formation. In combination, the mentioned methodological principles are the basis for developing a positive attitude towards oneself and others, the perception of trust in relationships with people as a value due to the strengthening of internal resources - the highest spiritual and moral formations of the individual. On this basis, the project of the program for the development of trust in interpersonal relationships of young people is proposed, which consists in the step-by-step implementation of work in the direction of the spiritual and moral development of young people and the formation of trusting relationships between them in the form of psychological counseling and psychological training. The application of the specified forms of psychological support makes it possible to promote awareness of the spiritual and moral content of trust and the formation of interpersonal relationships of young people based on a positive attitude towards the world, towards others and themselves.

Mental Health Literacy and School Behavioral Health: Introduction to the Special Issue

ABSTRACT Background Mental health literacy is a promising approach to meeting the growing behavioral health needs of youth, and schools offer an ideal setting for this work. Purpose In the introduction to this special issue, we provide an overview of mental health literacy and discuss the connection between mental health literacy and school behavioral health. Methods Literature on mental health literacy and research on school mental health literacy programs are synthesized, followed by an overview of the six papers included in this issue. Results The key components of mental health literacy are defined and contextualized alongside the importance of mental health literacy for promoting well-being. A brief history of research on mental health literacy programs is provided, noting the growth in application to school settings. Discussion Connections between mental health literacy and school behavioral health are discussed, highlighting opportunities for integration with schools’ multi-tiered systems of support. Translation to Health Education Practice This introduction concludes with a discussion of implications for aligning mental health literacy with Health Education.

A Study on Alzhemier Disease in Takum, Taraba State, Nigeria: An ARIMA Model Approach

Alzheimer’s disease, a progressive neurodegenerative disorder, represents a significant and growing public health burden, particularly as life expectancy increases worldwide. In sub-Saharan Africa, including Nigeria, the disease's prevalence is rising due to aging populations and urbanized lifestyles that elevate risk factors for cognitive decline. This study investigates the trends and projected incidence of Alzheimer’s disease in Takum, Taraba State, Nigeria. The research utilizes patient records from General Hospital Takum, spanning from 2012 to 2021. Following diagnostic tests and data transformations, the ARIMA(1,2,0) model was selected as the best fit for predicting future case counts. The findings reveal a steady increase in Alzheimer’s cases, consistent with global patterns, highlighting the need for proactive measures in healthcare planning. The study thus recommends the need for increased public awareness, investment in diagnostic infrastructure, and support systems for caregivers.

Artificial Intelligence in Emergency Medicine: A Literature Review

IntroductionArtificial intelligence (AI) is rapidly transforming medical fields, particularly emergency medicine (EM), where timely decision-making is crucial. AI offers potential benefits in diagnostic accuracy, patient care optimization, and workflow efficiency within emergency departments (EDs). Purpose of WorkThis review aims to synthesize recent advancements in AI applications within emergency medicine, focusing on diagnostic support, patient triage, clinical decision support systems (CDSS), and workflow optimization. Additionally, we highlight the potential benefits, challenges, and future directions for AI in EM. Material and MethodsA comprehensive literature search was conducted using PubMed and Google Scholar databases. We reviewed peer-reviewed articles from 2008 to 2024, focusing on AI-driven solutions in EDs. Keywords included "artificial intelligence," "emergency medicine," "machine learning," and "clinical decision support." Studies were selected based on their relevance to AI applications in EM, diagnostic improvements, and operational efficiency. The results highlight the promising role of AI in improving diagnostic accuracy, reducing overcrowding, optimizing triage processes, and addressing clinician workload. However, challenges like ethical concerns, data bias, and the need for clinical validation remain. Further research is necessary to integrate AI more effectively in clinical practice.

Controlling large deformations in soft rock roadways with integrated anchor shotcrete and grouting techniques.

Effective and safe support in soft rock roadways remains a critical challenge in underground coal mining due to the extreme weakness of the rocks and their susceptibility to weathering and water invasion. This paper examines the tailgate of longwall panel 2606, a typical soft rock roadway excavated along the edges of mined-out areas with thin pillars at the Lvtang Coal Mine in Guizhou, China. Despite employing a combination of rock bolts, cable bolts, and surface support systems, significant roadway deformation was observed. The mechanisms behind this deformation were analyzed through in-situ tests and clay mineral composition analysis. These analyses revealed that the primary causes of the roadway's substantial deformation were the low strength and weathering susceptibility of the coal and surrounding rock. A novel synergistic control strategy combining anchoring, shotcreting, and grouting was proposed for supporting soft rock roadways. Detailed numerical simulations were conducted to evaluate the deformation and stability of the tailgate under various support scenarios, validating the effectiveness of the proposed support scheme. The synergistic support strategy was implemented to reinforce the 2606 tailgate, and field monitoring was conducted to assess its effectiveness. Monitoring data indicated that rib deformation with the new support design was reduced by 66% compared to the original design. The results demonstrate that the improved synergistic support system effectively mitigated large deformations and maintained the stability of the tailgate. These findings provide a valuable engineering reference for supporting and reinforcing soft rock roadways under similar geological conditions.