Search Results (255)

In recent years, the advancement of various applications, data mining, technologies, and socio-technical systems has led to the development of interactive platforms that enhance user experiences through personalization. In the sports domain, users can access training plans, routes and healthy habits, all in a personalized way thanks to sports recommender systems. These recommendation engines are fueled by rich datasets that are collected through continuous monitoring of users’ activities. However, their potential to address user profiling is limited to single users and not to the dynamics of groups of sportsmen. This paper introduces GoSS-Rec, a Group-oriented Segment Sequence Recommender System, which is designed for groups of cyclists who participate in fitness activities. The system analyzes collective preferences and activity records to provide personalized route recommendations that encourage exploration of diverse cycling paths and also enhance group activities. Our experiments show that GoSS-Rec, which is based on Prod2vec, consistently outperforms other models on diversity and novelty, regardless of the group size. This indicates the potential of our model to provide unique and customized suggestions, making GoSS-Rec a remarkable innovation in the field of sports recommender systems. It also expands the possibilities of personalized experiences beyond traditional areas. Full article

Schizophrenia (SCZ) is a debilitating disorder with substantial societal and economic impacts. The clinical high risk of psychosis (CHR-P) state generally precedes the onset of SCZ, offering a window for early intervention. However, treatment guidelines for CHR-P individuals remain contentious, particularly regarding antipsychotic (AP) medications. Although several studies have examined the effects of APs on reducing the risk of conversion to psychosis, the novelty of this narrative review lies in its focus on differentiating APs’ effects on positive and negative symptoms, as well as cognitive functioning, in CHR-P individuals. Evidence suggests that APs may be cautiously recommended for attenuated positive symptoms to stabilize individuals for psychological interventions, but their use in treating negative symptoms is generally discouraged due to limited efficacy and potential side effects. Similarly, the effects of APs on cognitive abilities remain underexplored, with results indicating a lack of significant neurocognitive outcomes. In conclusion, APs’ use in CHR-P patients requires careful consideration due to limited evidence and potential adverse effects. Future research should focus on individual symptom domains and treatment modalities to optimize outcomes in this critical population. Until then, a cautious approach emphasizing non-pharmacological interventions is advisable. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article

Pile foundations are critical structural elements, and their integrity is essential for ensuring the stability and safety of construction projects. Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. A dataset of 328 reflectograms collected from real construction sites, including 246 intact and 82 defective samples, was used to train and evaluate the model. To address class imbalance, oversampling techniques were applied. Several state-of-the-art pretrained CNN architectures were compared, with ConvNeXtLarge achieving the highest accuracy of 98.2%. The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. The proposed novelty includes adapting pre-trained CNN architectures specifically for real-world pile integrity testing, addressing practical challenges such as data imbalance and limited dataset size through targeted oversampling techniques. The proposed approach demonstrates significant improvements in accuracy and efficiency compared to manual interpretation methods, making it a promising solution for practical applications in the construction industry. The proposed method demonstrates potential for generalization across varying pile lengths and geological conditions, though further validation with broader datasets is recommended. Full article

Critical components in support structures for wind turbines, flange joints, are fundamental to ensure the structural integrity of mechanical assemblies under varying operational conditions. This paper investigates the structural performance of L-type flange joints, focusing on the influence of bolt grades and bolt pretension through a finite element analysis (FEA) study of its key performance indicators, including stress distribution, deformation, and force–displacement behaviors. This paper studies two high-strength bolt grades, Grade 10.9 and Grade 12.9, and two main steps—first, bolt pretension and, second, external loading (tower shell tensile load)—to investigate the influence on joint reliability and safety margins. The novelty of this study lies in its specific focus on static axial loading conditions, unlike the existing literature that emphasizes fatigue or dynamic loads. Results show that the specimen carrying a higher bolt grade (12.9) has 18% more ultimate load carrying capacity than the specimen with a lower bolt grade (10.9). Increased pretension increases the stability of the joint and reduces the micro-movements between A and B (on model specimen), but could result in material fatigue if over-pretensioned. Comparative analysis of the different bolt grades has provided practical guidance on material selection and bolt pretension in L-type flange joints for wind turbine support structures. The findings of this work offer insights into the proper design of robust flange connections for high-demand applications by highlighting a balance among material properties, bolt pretension, and operational conditions, while also proposing optimized pretension and material recommendations validated against classical analytical models. Full article

The torsional, lateral, and axial vibrations that occur during drilling operations have negative effects on the drilling equipment. These negative effects can cause huge economic impacts, as the failure of drilling tools results in wasted materials, non-productive time, and substantial expenses for equipment repairs. Many researchers have tried to reduce these vibrations and have tested several models in their studies. In most of these models, the drill string used in oil wells behaves like a rotating torsion pendulum (mass spring), represented by different discs. The top drive (with the rotary table) and the BHA (with the drill pipes) have been considered together as a linear spring with constant torsional stiffness and torsional damping coefficients. In this article, three models with different degrees of freedom are considered, with the aim of analyzing the effect of variations in the stiffness and damping coefficients on the severity of torsional vibrations. A comparative study has been conducted between the three models for dynamic responses to parametric variation effects. To ensure the relevance of the considered models, the field data of torsional vibrations while drilling were used to support the modeling assumption and the designed simulation scenarios. The main novelty of this work is its rigorous comparative analysis of how the stiffness and damping coefficients influence the severity of torsional vibrations based on field measurements, which has a direct application in operational energy efficiency and equipment reliability. The results demonstrated that the variation of the damping coefficient does not significantly affect the severity of the torsional vibrations. However, it is highly recommended to consider all existing frictions in the tool string to obtain a reliable torsional vibration model that can reproduce the physical phenomenon of stick–slip. Furthermore, this study contributes to the improvement of operational energy efficiency and equipment reliability in fossil energy extraction processes. Full article

This study addresses the multifaceted challenges inherent in implementing effective smart-city waste-management systems. Recent global trends indicate increased adoption of Industry 4.0 technologies—such as the Internet of Things (IoT), artificial intelligence (AI), and data analytics—to optimize waste collection and processing. The central research question investigates the role of innovative business models and sustainable decision-making frameworks in advancing smart waste management within urban environments. This research integrates three interrelated domains: business-model innovation, smart-city paradigms, and sustainability in waste management. Its novelty lies in synthesizing these domains, conducting a comparative analysis of best practices from leading European smart cities, and proposing a conceptual framework to guide sustainable decision-making. Methodologically, the study employs a systematic literature review, case-study analyses, and the synthesis of theoretical and empirical data. Key findings demonstrate that innovative business models—such as product-as-a-service, circular-economy approaches, and waste-as-a-service—substantially enhance the sustainability and operational efficiency of urban waste systems. However, many cities lack comprehensive strategies for integrating these models, highlighting the necessity for deliberate planning and active stakeholder engagement. Based on these insights, the study offers actionable recommendations for policymakers and urban managers to embed sustainable business models into smart-city waste infrastructures. These contributions aim to promote the development of resilient, efficient, and environmentally responsible waste-management systems in smart cities. Full article

This study investigates the convergence of blockchain, artificial intelligence (AI), near-field communication (NFC), and mobile technologies in electronic payment (e-payment) systems, proposing an innovative integrative framework to deconstruct the systemic innovations and transformative impacts driven by such technological synergy. Unlike prior research, which often focuses on single-technology adoption, this study uniquely adopts a cross-technology convergence perspective. To our knowledge, this is the first study to empirically map the multi-technology convergence landscape in e-payment using scientometric techniques. By employing bibliometric and thematic network analysis methods, the research maps the intellectual evolution and key research themes of technology convergence in e-payment systems. Findings reveal that while the integration of these technologies holds significant promise, improving transparency, scalability, and responsiveness, it also presents challenges, including interoperability barriers, privacy concerns, and regulatory complexity. Furthermore, this study highlights the potential for convergent technologies to unintentionally deepen the digital divide if not inclusively designed. The novelty of this study is threefold: (1) theoretical contribution—this study expands existing frameworks of technology adoption and digital governance by introducing an integrated perspective on cross-technology adoption and regulatory responsiveness; (2) practical relevance—it offers actionable, stakeholder-specific recommendations for policymakers, financial institutions, developers, and end-users; (3) methodological innovation—it leverages scientometric and topic modeling techniques to capture the macro-level trajectory of technology convergence, complementing traditional qualitative insights. In conclusion, this study advances the theoretical foundations of digital finance and provides forward-looking policy and managerial implications, paving the way for a more secure, inclusive, and innovation-driven digital payment ecosystem. Full article

The cocoa production in Ghana, largely reliant on rainfall and undertaken by smallholder farmers, is increasingly endangered by climate change-induced water scarcity. Although supplemental irrigation has been posited as an adaptive measure, its hydrological impacts remain understudied. This current study seeks to bridge this knowledge gap by employing the Soil and Water Assessment Tool (SWAT) to evaluate the hydrological and water resource implications of supplemental irrigation within the Upper Offin sub-basin of Ghana. High-resolution spatial data and field survey inputs were used to model dry period baseline and irrigation scenarios for cocoa farms with gentle slopes (2%). The results reveal that supplemental irrigation from the shallow aquifer can sustainably support irrigation for up to 5% of the cocoa area (4760 ha) without adversely affecting groundwater flow. Extending irrigation to 30% of the cocoa area (28,540 ha) is feasible with minimal reduction in catchment water yield. This study’s novelty lies in integrating high-resolution data with localized management practices to provide actionable insights for balancing cocoa productivity and water sustainability. The findings offer practical recommendations for policymakers, emphasizing that through solar-powered irrigation the shallow groundwater is a pathway to enhance climate resilience of cocoa productivity. Full article

The formulation of biosimilar products critically determines their stability, safety, immunogenicity, and market accessibility. This article presents a novel integrative framework for biosimilar formulation that balances scientific, regulatory, and intellectual property dimensions, offering a holistic perspective rarely unified in the literature. It highlights the growing trend toward buffer-free, high-concentration systems that leverage protein self-buffering to improve patient comfort and formulation stability. The article also addresses regulatory flexibility from the FDA and EMA, which allows scientifically justified deviations from reference formulations to ensure pharmaceutical equivalence and minimize immunogenicity. A novelty of this article is its comprehensive analysis of how digital innovations, such as Quality-by-Design, Process-Analytical-Technology, and AI-based in silico simulations, are transforming formulation design and bioprocess optimization to reduce immunogenic risks and enhance bioequivalence. Two important key takeaways emerge: (1) strategic innovation in formulation, especially using buffer-free and high concentration systems, improve product stability and patient tolerability while complying with regulatory standards; and (2) intellectual property challenges, including patent thickets, strongly influence formulation decisions, making early legal-strategic alignment essential for market entry. The article confirms that practical recommendations for the selection of recombinant therapeutic protein formulations can effectively guide developers and regulators toward safer, more efficient, and commercially viable biosimilar products. Full article

This study addresses the challenge of rapidly and accurately distinguishing zones of microbial activity from antibiotic inhibition zones in Petri dishes. We propose a laser speckle imaging technique enhanced with subpixel correlation analysis to monitor dynamic changes in the inhibition zone surrounding an antibiotic disc. This method provides faster results compared to the standard disk diffusion assay recommended by EUCAST. To enable automated analysis, we used machine learning algorithms for classifying areas of bacterial or fungal activity versus inhibited growth. Classification is performed over short time windows (e.g., 1 h), supporting near-real-time assessment. To further improve accuracy, we introduce a correction method based on the known spatial dynamics of inhibition zone formation. The novelty of the study lies in combining a speckle imaging subpixel correlation algorithm with ML classification and with pre- and post-processing. This approach enables early automated assessment of antimicrobial effects with potential applications in rapid drug susceptibility testing and microbiological research. Full article

This paper analyzes the occupational risks associated with the activities of transporting and spreading bituminous emulsion, focusing on a specific technological process used in a company in Romania. This study aims to identify risk factors, systematically evaluate them, and propose preventive measures aimed at reducing occupational accidents and diseases. The main hazards identified include exposure to hazardous chemicals, mechanical risks generated by the equipment used, and ergonomic factors that may affect workers’ health. Given the specificity of the activity analyzed, there is currently a lack of relevant studies specifically addressing the occupational safety and health of this category of workers, which further highlights the novelty and importance of the present research. Based on the results obtained, recommendations are formulated for optimizing work conditions, including the use of appropriate protective equipment, improving operational procedures, and implementing effective technical and organizational measures. This study contributes to the development of a solid preventive framework in the field of transporting and applying bituminous emulsion, thus supporting the improvement of occupational safety and health in the road construction industry. The results obtained can be used to develop more effective policies in the field of occupational safety and to raise awareness among decision-makers about the need for proactive measures in preventing occupational risks. Full article

Worldwide, the healthcare industry produces massive quantities of medical waste (MW), most of which is incinerated, releasing large quantities of dioxins, mercury, and other pollutants. Despite this, only a limited number of studies have explored the incorporation of MW into construction materials, with a special focus on cement-based construction materials (CB-CMs). However, to the best of the authors’ knowledge, no existing review formally structures, summarizes, correlates, and discusses the findings of previous studies on MW in CB-CMs to encourage further research and applications of this promising alternative. Therefore, the added value of this study lies in providing an innovative and critical analysis of existing research on the use of MW in CB-CMs, consolidating and evaluating dispersed findings through a systematic literature review, enhancing understanding of the topic, and identifying knowledge gaps to guide future research. A robust systematic literature review was conducted, encompassing 40 peer-reviewed research articles, retrieved from the Web of Science Core Collection database. The methodology involved a three-stage process: a descriptive analysis of the included articles, the identification and synthesis of key thematic areas, and a critical evaluation of the data to ensure a rigorous and systematic report. The selection criteria prioritized peer-reviewed research articles in English with full text availability published in the last 7 years, explicitly excluding conference papers, book chapters, short reports, and articles not meeting the language or accessibility requirements. The results indicate that the influence of MW in CB-CM varies significantly. For example, while the incorporation of face masks as fiber reinforcement in concrete generally enhances its mechanical and durability properties, the use of gloves is less effective and not always recommended. Finally, it was found that further research is needed in this field due to its novelty. Full article

The article emphasises both the advantages and disadvantages of photovoltaic power plant deployment, assessing the current stage of development as well as the deficient characteristic criteria, such as the occupied specific surface area or the associated unpredictability. The authors consider that current technologies related to photovoltaic plants provide a maximum efficiency of approximately 28%. Consequently, management methods must be applied in order to improve efficiency and eliminate the reported deficiencies. When assessing a medium- to high-power PV plant, the initial investment, projected efficiency, and parameters of the desired plant are correlated, and sometimes, a cheaper and less efficient power plant can be recommended. Although solar trackers may represent a viable solution in certain scenarios, their effectiveness is strongly influenced by various factors, including panel orientation, climatic conditions, installed capacity, and the specific technologies. These variables can significantly affect such systems’ overall efficiency and suitability. The present study proposes a statistical approach to assessing the economic efficiency of photovoltaic systems equipped with solar trackers, aiming to enhance energy production performance. The results are correlated and validated using field data obtained from existing literature studies to ensure the reliability and accuracy of the analysis. For a better analysis, the paper presents two methods, ANOVA and STEM, which are derived from quality control. The novelty aspect of this proposal consists of the combination of specific data obtained from the PVGIS platform with a new approach for optimisation of energy production in photovoltaic systems based on geographical coordinates. The STEM statistical method provides a high degree of novelty because, although it is a well-known method, it has not yet been applied to analyse the technical and economic efficiency of photovoltaic systems. One of the main advantages of this method is its ability to incorporate a wide range of technical and economic performance parameters. A case study is provided to evaluate the benefits of implementing the STEM method. Full article

This paper is devoted to the problem of magnetohydrodynamic stability (MHDS) in the energy-intensive process of primary aluminum production by electrolysis. Improving MHDS control is important because of the high costs and reduced efficiency caused by the instability of magnetic and current fields. In this work, a methodological analysis of modern theoretical and numerical methods for studying MHDS was carried out, and approaches to optimizing magnetic fields and control algorithms aimed at stabilizing the process and reducing energy costs were considered. This review identified key challenges and proposed promising directions, including the application of computational methods and artificial intelligence to monitor and control electrolysis in real time. In this paper, it was revealed that wave MHD instability at the metal–electrolyte phase boundary is a key physical obstacle to further reducing specific energy costs and increasing energy stability. The novelty of this paper lies in an integrated approach that combines modeling and practical recommendations. The purpose of this study is to systematically summarize scientific data, analyze the key physical factors affecting the energy stability of electrolyzers, and determine promising directions for their solution. The results of this study can be used to improve the energy efficiency and environmental friendliness of aluminum production. Full article