Search Results (2,614)

Background/Objectives: Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools. Methods: This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015–2024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography(EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models. Results: The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models. Conclusions: This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis. Full article

The work concerns the fight against the decline and depopulation in the Inner Peripheries (IPs), a phenomenon taking place globally and throughout Europe, where it has reached an alarming dimension, causing regional disparities that threaten the achievement of Sustainable Development Goals. In Italy, where the term Inner Areas (IAs) is used as a synonym, more than half of the municipalities have the typical problems of IPs. As the latter have a supramunicipal nature, provincial strategic spatial planning is considered adequate to counter these critical issues, assuring a balanced and sustainable development in the social, economic, and environmental domains. To this end, after the state-of-the-art review, this paper proposes a methodology for the construction and mapping of a Decline Index, useful for spatially identifying the most critical municipalities which provide priority strategies to counteract decline through the Provincial Territorial Coordination Plan. The method is based on a multicriteria analysis approach in which Principal Component Analysis (PCA) is integrated and applied to the case study of the Avellino Province in the Campania Region (Italy), which contains many municipalities belonging to IAs. The proposed method is also useful in supporting regional planning and programming for territorial cohesion and sustainable regional growth. Full article

The traditional mechanical transmission system motion accuracy allocation process has the following problems: the error modeling process can not reflect the error formation mechanism of the system, and the influence of maintenance costs and the motion accuracy robustness of the system are ignored in the process of establishing the optimal allocation model of motion accuracy. In this paper, firstly, meta-action theory is introduced and the meta-action unit is taken as the basic analysis unit, the error modeling of mechanical transmission systems is studied, and the formation mechanism of the motion error is correctly analyzed. Secondly, the comprehensive cost of a mechanical transmission system, considering the part manufacturing cost, assembly cost and maintenance cost per unit, is accurately evaluated using the multi-criteria decision-making (MCDM) method. Thirdly, based on the motion error model, a robust model for system motion accuracy is obtained by analyzing the sensitivity of each motion pair. Then, a multi-objective optimal allocation model of motion accuracy is established. The model is solved by an intelligent algorithm to obtain the Pareto non-dominated solution set, and the optimal solution is selected by the fuzzy set method. Finally, the method described in this paper is illustrated by an engineering example. Full article

This research is motivated by the arbitrary nature of decision-making environments and the dynamic changes in decision patterns, particularly in international dispute resolution. These challenges introduce uncertainties that could be effectively managed by fuzzy logic, which provides a robust framework for evaluating alternatives under multiple criteria. In this study, an Interval-Valued Type-2 Fuzzy TOPSIS approach is proposed to assess various dispute resolution methods, including negotiation, good offices, mediation, international inquiry, conciliation, international organization, arbitration, and international jurisdiction. Common criteria are determined by examining academic literature and by interviewing relevant experts.—cost-efficiency, duration, impartiality, binding nature, and generalizability are considered essential in determining the best resolution method. The proposed method allows for a nuanced evaluation by incorporating both primary and secondary levels of uncertainty, enabling decision-makers to determine the best alternative solution more reliably. This method’s application extends not only to the international law field but also to industrial engineering, where complex, uncertain decision environments require similarly sophisticated multicriteria decision-making tools. By systematically analyzing these resolution methods, this study aims to provide a structured, quantifiable approach that enhances the decision-making process for both international legal practitioners and engineers working with uncertain and dynamic systems. The results of this study ultimately contribute to improved decision-making outcomes and greater efficiency in multidisciplinary problem solving. The assessments of experts in international law, international relations, and political science in their respective fields of expertise have been gathered to form a consensus. This study contributes to the literature as it is the pioneering application of fuzzy multicriteria decision-making techniques in the field of international law. The results of this study imply that the best option from the different decision-maker evaluations is international jurisdiction. Consequently, the utilization of multicriteria decision-making tools can result in more informed and effective decisions in complex and uncertain situations, which is advantageous to both legal practitioners and engineers. Additionally, incorporating different disciplines can help streamline the decision-making process and improve overall efficiency in solving multidisciplinary problems. Full article

In Latin America, initiatives have been advocated for developing rural roads that facilitate optimal conditions free from dust, mud, and noise. The criteria for assessing public investment do not align with the requirements of rural infrastructure. Indeed, in rural areas, the territorial conditions such as openness to rural–urban markets, access to education and health, environmental protection, culture, and identity are more important than transportation times or traffic volume. Hence, a multicriteria evaluation method is proposed to prioritize the rural road improvements and maximize their contribution to sustainable territorial development. The roads with the highest sustainable contribution are optimized using a multi-objective decision-making analysis and prioritized based on a Manhattan distance. In addition, a fuzzy cognitive map analyzes the dynamic behavior of the optimal roads. Based on this proposal, a case study is applied where fifteen roads are selected from a sample of 101 in the Araucanía Region, Chile. For this, 16 evaluation criteria, 27 indicators, and sustainability’s social, environmental, technical, and economic dimensions are considered. The results detect reduced one-dimensional contributions despite identifying 15 optimal roads that collectively enhance sustainability. Two roads stand out for their long-term sustainability contribution, which are influenced by economic criteria of zonal productivity, tourism, and road maintenance. Thus, this method can help public agencies rank the roads that must be the subject of development projects. Full article

The design of an Active Reflective Surface Control System (ARCS) is a complex engineering task involving multidimensional and multi-criteria constraints. This paper proposes a novel methodological approach for ARCS design and optimization by integrating Axiomatic Design (AD) and Multi-Criteria Decision Making (MCDM) techniques. Initially, a structured design plan is formulated within the axiomatic design framework. Subsequently, four MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Entropy Weight Method (EWM), Multi-Criteria Optimization and Compromise Solution (VIKOR), and the integrated TOPSIS–Grey Relational Analysis (GRA) approach—are used to evaluate and compare the alternative solutions. Additionally, fuzzy information axioms are used to calculate the total information content for each alternative to identify the optimal design. A case study is conducted, selecting the optimal actuator for a 5 m diameter scaled model of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), followed by digital control experiments on the chosen actuator. Based on the optimal design scheme, an ARCS prototype is constructed, which accelerates project completion and substantially reduces trial-and-error costs. Full article

This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities. Full article

The Blue Economic Zone of the Shandong Peninsula is located in the transitional zone between land and sea, with a complex ecological environment. The determination of hot and cold spots in various ecosystem services is crucial for the coordinated development of ecosystem services and the optimization of the spatial pattern of the ecological environment. This study, based on natural and socio-economic data, utilizes various ecological models to simulate water yield (provisioning service), carbon sequestration (regulating service), biodiversity (supporting service), and aesthetic and scientific research values (cultural service). Using a multi-criteria decision-making approach, it identifies hot and cold spots of ecosystem services in different development–conservation scenarios. Combining the protection efficiency of different areas, it proposes a spatial pattern promotion scheme. The research indicates significant spatial differences in ecosystem services without clear trade-offs and synergies. Changes in the weights of ecosystem services in 11 scenarios result in significant differences in hot and cold spots. Compared to the neutral scenario (S6), the distribution of hot and cold spots in protection scenarios (S1–S5) is relatively scattered, while in development scenarios (S7–S11), hot spots show an increasing trend of concentration in the southeast, with cold spots scattered in the west and northwest. Four spatial pattern promotion schemes are proposed based on protection efficiency and policy preferences. Promotion areas should focus on ecological restoration and improvement to raise local ecosystem service levels. Protection areas should emphasize maintaining their existing high-level ecosystem services to achieve a synergistic enhancement of various ecosystem services. Full article

Remote agricultural regions in desert areas, such as Ghardaïa in southern Algeria, face significant challenges in energy supply due to their isolated locations and harsh climatic conditions. Harnessing solar energy through photovoltaic (PV) systems offers a sustainable solution to these energy needs. This study aims to identify suitable areas for PV power installations in Ghardaïa, utilizing a geographic information system (GIS) combined with the fuzzy analytical hierarchy process (AHP). Various environmental, economic, and technical factors, such as solar radiation, land use, and proximity to infrastructure, are incorporated into the analysis to create a multi-criteria decision-making framework. The integration of fuzzy logic into AHP enables a more flexible evaluation of these factors. The results revealed the presence of ideal locations for installing photovoltaic stations, with 346,673.30 hectares identified as highly suitable, 977,606.84 hectares as very suitable, and 937,385.97 hectares as suitable. These areas are characterized by high levels of solar radiation and suitable infrastructure availability, contributing to reduced implementation costs and facilitating logistical operations. Additionally, the proximity of these locations to agricultural areas enhances the efficiency of electricity delivery to farmers. The study emphasizes the need for well-considered strategic planning to achieve sustainable development in remote rural areas. Full article

This study supports Jamaica’s renewable energy implementation strategies by providing updated wind atlases and identifying suitable locations for future wind farms. Using a GIS-based Analytic Hierarchy Process with multi-criteria decision-making (AHP-MCDM), this research integrates stakeholders’ opinions, environmental considerations, and technical factors to assess land suitability for wind energy development. The analysis reveals that Jamaica has the potential to increase its wind power output by 8.99% compared to the current production of 99 MW. This expansion could significantly contribute to offsetting fossil fuel-based energy consumption and reducing carbon dioxide emissions. It identifies sites across several parishes, including Westmoreland, Clarendon, St. Mary, and St. James, as highly suitable for utility-scale wind farm development. By providing detailed spatial information and estimated energy outputs, this research offers valuable insights for energy planners, investors, and policymakers to create sustainable energy policies and advance Jamaica’s 50% renewable energy goal by 2030. Full article

Industrial pressure relief valves must function reliably and effectively to protect pressurized systems from excessive pressure conditions. These valves are essential safety devices that act as cushions to protect piping systems, equipment, and vessels from the risks of high pressure, which can cause damage or even explosions. The objectives of this study were to minimize valve failures, decrease the number of rejected valves in the production line, and enhance the overall quality of pressure relief valves. This work introduces an integrated quality improvement methodology known as the hybrid multi-criteria decision-making (MCDM)—failure mode and effects analysis (FMEA) approach. This approach is based on prioritizing crucial factors for any failure modes in the industrial setting. The presented case study demonstrates the application of a hybrid approach for identifying the fundamental causes of industrial pressure relief valve failure modes and malfunctions. This investigation highlights the applicability of FMEA as a methodology for determining causes and executing remedial actions to keep failures from happening again. FMEA helps uncover the underlying causes of industrial pressure relief valve failures, while the integration of the hybrid MCDM methodology enables the application of four integrated MCDM methods to identify crucial factors. The adopted model addresses the shortcomings of the conventional FMEA by accurately analyzing the relationships between the risk factors and by utilizing several MCDM methods to rank failure modes. Following the application of the adopted methodology, it was discovered that the high-risk failure modes for the pressure relief valve included misalignment of wire, normal wear/aging, rejection of machined parts, mismatch of mating parts, and corrosion. Therefore, risk managers should prioritize developing improvement strategies for these five failure modes. Similarly, failures comprising debris, delayed valve opening, internal leakage, premature valve opening, and burr foreign particles were determined as second essential groups for improvement. Full article

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing. Full article

To improve the accuracy of choosing restoration materials for repairing ancient Chinese buildings and to mitigate the risk of decision-making, this paper establishes a novel selection model of compositions and proportions of additive lime mortars for the restoration of ancient Chinese buildings. The selection process is influenced by multi-criteria and determined by a group of experts through comprehensive judgment. Thus, it is a multi-criteria group decision-making (MCGDM) problem. Firstly, considering subjective and objective criteria simultaneously, establish a selection index system for compositions and proportions of additive lime mortars in the restoration of ancient Chinese buildings. Secondly, applying a neutrosophic set to characterize experts’ evaluation information and quantify the evaluation information. Thirdly, the best–worst method (BWM) is implemented to obtain criteria weights, and the entropy weight method is utilized to obtain index weights. Finally, obtaining the priority of each alternative solution by using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ranking technique. The practicality of the proposed model was demonstrated through a specific case of the selection of repair materials for a decorative window in one ancient Chinese building. The comparative analysis was carried out to verify the reliability and validity of the model. Full article

This study proposes a sustainable ecotourism framework for the development of semi-mountain and mountain regions of Paiko in Greece, focusing on the strategic design and ranking of trail paths using the multi-criteria decision-making (MCDM) VIKOR method. Aiming to balance environmental conservation with economic benefits, we designed 19 trails paths and allocated signage for resting and recreation facilities. The trail paths were assessed based on criteria such as length, difficulty, scenic appeal, and accessibility. This approach identified key trails that combine scenic beauty with infrastructure suitable for a broad range of visitors, thereby enhancing sustainable tourism appeal. Stakeholder engagement was integral to shaping the trail network, ensuring that the selected paths reflect local values and priorities. This study highlights how the VIKOR method can optimize resource allocation by ranking trails according to their environmental and visitor-centered attributes, supporting regional economic growth through ecotourism. This framework offers a replicable model for other mountainous regions seeking to harness ecotourism’s potential while preserving natural ecosystems. The findings demonstrate the capacity of well-planned trail networks to attract nature-based tourism, stimulate local economies, and respond to the rising post-pandemic interest in outdoor recreation, while promoting long-term conservation efforts. This approach offers a replicable model for the sustainable development of mountainous and semi-mountainous areas in Greece and beyond. Full article

Identifying groundwater potential zones in a basin and developing a sustainable management plan is becoming more important, especially where surface water is scarce. The main aim of the study is to prepare the groundwater potential maps (GWPMs) considering the bivariate statistical models of frequency ratio (FR), weight of evidence (WoE), and the multi-criteria decision-making (MCDM) model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) hybridized with FR and WoE. Two distance measures, Euclidean and Manhattan, were used in TOPSIS to evaluate their effect on GWPMs. The research focused on the Burdur Lake catchment located in the southwest of Türkiye. In total, 74 wells with high yields were chosen randomly for the analysis, 52 (70%) for training, and 22 (30%) for testing processes. Sixteen groundwater conditioning factors were selected. The area under the receiver operating characteristic (AUROC) and true skill statistics (TSS) were utilized to examine the goodness-of-fit and prediction accuracy of approaches. The TOPSIS-WoE-Manhattan model and the FR and WoE models gave the best AUROC values of 0.915 and 0.944 for the training and testing processes, respectively. The best TSS values of 0.827 and 0.864 were obtained by the TOPSIS-FR-Euclidean and WoE models for the training and testing processes, respectively. Full article