Search Results (319)

In an era of exponential data growth, ensuring high data quality has become essential for effective, evidence-based decision making. This study presents a structured and comparative review of the field by integrating data classifications, quality dimensions, assessment methodologies, and modern software tools. Unlike earlier reviews that focus narrowly on individual aspects, this work synthesizes foundational concepts with formal frameworks, including the Findable, Accessible, Interoperable, and Reusable (FAIR) principles and the ISO/IEC 25000 series on software and data quality. It further examines well-established assessment models, such as Total Data Quality Management (TDQM), Data Warehouse Quality (DWQ), and High-Quality Data Management (HDQM), and critically evaluates commercial platforms in terms of functionality, AI integration, and adaptability. A key contribution lies in the development of conceptual mappings that link data quality dimensions with FAIR indicators and maturity levels, offering a practical reference model. The findings also identify gaps in current tools and approaches, particularly around cost-awareness, explainability, and process adaptability. By bridging theory and practice, the study contributes to the academic literature while offering actionable insights for building scalable, standards-aligned, and context-sensitive data quality management strategies. Full article

The sharp decline in the population along the northeastern border poses a significant threat to the security of the region, the prosperity of border areas, and the stability of the social economy in our country. Effective management of human and land resources is crucial for the high-quality development of border areas. Taking Changbai County on the northeastern border as an example, based on multi-source data such as land use, the natural environment, climate conditions, transportation location, and social economy from 2000 to 2020, the land use transfer matrix, spatial kernel density, and PLUS model were used to analyze the spatio-temporal evolution characteristics of land use and explore simulation scenarios and optimization strategies under different planning concepts. This study reveals the following: (1) During the study period, the construction land continued to increase, but the growth rate slowed down, mainly transferred from cultivated land and forest land, and the spatial structure evolved from a single center to a double center, with the core always concentrated along the border. (2) The distance to the port (transportation location), night light (social economy), slope (natural environment), and average annual temperature (climate conditions) are the main driving factors for the change in construction land, and the PLUS model can effectively simulate the land use trend under population contraction. (3) In the reduction scenario, the construction land decreased by 1.67 km², the scale of Changbai Town slightly reduced, and the contraction around Malugou Town and Badagou Town was more significant. The study shows that the reduction scenario is more conducive to the population aggregation and industrial carrying capacity improvement of shrinking county towns, which is in line with the high-quality development needs of border areas in our country. Full article

This study presents a new equation, the dynamic recovery factor (DRF), for evaluating the recovery factor (RF) in homogeneous and heterogeneous reservoirs. The DRF method’s outcomes are validated and compared using the decline curve analysis (DCA) method. Real measured field data from 15 wells in a homogenous sandstone reservoir and 10 wells in a heterogeneous carbonate reservoir are utilized for this study. The concept of the DRF approach is based on the material balance principle, which integrates several components (weighted average cumulative pressure drop (ΔP_cum), total compressibility (C_t), and oil saturation (S_o)) for predicting RF. The motivation for this study stems from the practical restrictions of conventional RF valuation techniques, which often involve extensive datasets and use simplifying assumptions that are not applicable in complex heterogeneous reservoirs. For the homogenous reservoir, the DRF approach predicts an RF of 8%, whereas the DCA method predicted 9.2%. In the heterogeneous reservoir, the DRF approach produces an RF of 6% compared with 5% for the DCA technique. Sensitivity analysis shows that RF is very sensitive to variations in C_t, ΔP_cum, and S_o, with values that vary from 6.00% to 10.71% for homogeneous reservoirs and 4.43% to 7.91% for heterogeneous reservoirs. Uncertainty calculation indicates that errors in C_t, ΔP_cum, and S_o propagate to RF, with weighting factor (W_i) uncertainties causing changes of ±3.7% and ±4.4% in RF for homogeneous and heterogeneous reservoirs, respectively. This study shows the new DRF approach’s ability to provide reliable RF estimations via pressure dynamics, while DCA is used as a validation and comparison baseline. The sensitivity analyses and uncertainty analyses provide a strong foundation for RF estimation that helps to select well-informed decisions in reservoir management with reliable RF values. The novelty of the new DRF equation lies in its capability to correctly estimate RFs using limited available historical data, making it appropriate for early-stage development and data-scarce situations. Hence, the new DRF equation is applied to various reservoir qualities, and the results show a strong alignment with those obtained from DCA, demonstrating high accuracy. This agreement validates the applicability of the DRF equation in estimating recovery factors through different reservoir qualities. Full article

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts. Full article

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of chemotherapy. CIPN can lead to a dose reduction and/or the interruption of chemotherapy, limiting its effectiveness, while chronic CIPN decreases patients’ quality of life. Improvements in cancer treatment and patients’ survival have increased the number of patients living with CIPN. The only evidence-based treatment for CIPN-related pain, duloxetine, provides only modest clinical benefit, and there is no effective clinical management option for numbness and tingling. Several experimental studies and clinical reports suggest that adjuvant ozone treatment may be beneficial in managing CIPN. Methods: This narrative review aims to provide an overview of current knowledge regarding CIPN and ozone therapy. Specifically, it summarizes experimental studies (18) and clinical reports (27) published between 1995 and 2025 that offer preliminary evidence supporting the potential role of ozone treatment in managing CIPN, highlighting the need for ongoing randomized clinical trials to establish its efficacy. Additionally, this review highlights existing gaps in the literature and proposes directions for future research. Results: The hypothesized mechanisms of action and experimental findings suggest that ozone therapy may be a valuable intervention for CIPN, a concept supported by preliminary clinical observations. Conclusions: Clinically relevant approaches for established CIPN are currently unavailable. While preliminary data suggest a potential role of ozone therapy, clinical evidence remains limited. Further high-quality randomized controlled trials are needed to confirm its efficacy and safety in this context; several trials are currently ongoing. Full article

Under the context of global climate change, sea-level rise and frequent storm surge events pose significant challenges to coastal areas. Protecting coastlines from erosion, mitigating socio-economic losses, and maintaining ecosystem balance are critical for the sustainable development of coastal zones. The concept of “living shorelines” based on Nature-based Solutions (NbS) employs near-natural ecological restoration and protection measures. In low-energy coastal segments, natural materials are prioritized, while high-energy segments are supplemented with artificial structures. This approach not only enhances disaster resilience but also preserves coastal ecosystem stability and ecological functionality. This study constructs a coastal vitality evaluation system for Fujian Province, China, using the entropy weight method, integrating three dimensions: protective safety, ecological resilience, and economic vitality. Data from 2010 and 2020 were analyzed to assess the spatiotemporal evolution of coastal vitality. Results indicate that coastal vitality initially exhibited a spatial pattern of “low in the north, high in the center, and low in the south,” with vitality values ranging from 0.20 to 0.67 (higher values indicate stronger vitality). Over the past decade, ecological restoration projects have significantly improved coastal vitality, particularly in central and southern regions, where high-vitality segments increased markedly. Key factors influencing coastal vitality include water quality, cyclone intensity, biological shoreline length, and wetland area. NbS-aligned coastal management strategies and soft revetment practices have generated substantial ecological and economic benefits. To further enhance coastal vitality, region-specific approaches are recommended, emphasizing rational resource utilization, optimization of ecological and economic values, and the establishment of a sustainable evaluation framework. This study provides scientific insights for improving coastal protection capacity, ecological resilience, and economic potential. Full article

Artificial intelligence (AI) has the potential to significantly advance the management of nonpoint source pollution (NPSP), a critical environmental issue characterized by diffuse sources and complex transport mechanisms. This study systematically examines current AI applications addressing NPSP through bibliometric and systematic analyses. A total of 124 studies were included after rigorous identification, screening, and eligibility assessments based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Key findings from the bibliometric analysis include publication trends, regional research contributions, author and journal contributions, and core concepts in NPSP. The systematic analysis further provided: (a) a comprehensive synthesis of NPSP characterization, covering pollution sources, key drivers, pollutants, transport pathways, and environmental impacts; (b) identification of emerging AI technologies such as the Internet of Things, unmanned aerial vehicles, and geographic information systems, and their potential applications in NPSP contexts; (c) a detailed classification of AI models used in NPSP assessment, highlighting predictors, predictands, and performance metrics specifically in water quality prediction and monitoring, groundwater vulnerability mapping, and pollutant-specific modeling; and (d) a critical assessment of knowledge gaps categorized into AI model development and validation, data constraints, governance and policy challenges, and system integration, alongside proposed targeted future research directions emphasizing adaptive governance, transparent AI modeling, and interdisciplinary collaboration. The findings from this study provide essential insights for researchers, policymakers, environmental managers, and communities aiming to implement AI-driven strategies to mitigate NPSP. Full article

Background: Adolescence and young adulthood are critical periods during which psycho-emotional factors can significantly influence disease management and increase the risk of complications. This systematic review aims to examine the impact of body image, self-image, self-perception, and other psycho-emotional variables on the management of type 1 diabetes mellitus (T1DM) in this population. Methods: This review follows the Cochrane Handbook, PRISMA 2020 guidelines and the JBI Checklist for Systematic Reviews and Research Syntheses. A comprehensive search was conducted across both general and discipline-specific databases (PubMed, Web of Science, Scopus, Embase, CINAHL, APA PsycInfo, APA PsycArticles) between March and April 2025. The inclusion criteria focused on studies involving adolescents with T1DM that addressed relevant emotional or psychological aspects. Methodological quality was assessed using JBI tools. Data extraction was performed independently by four reviewers, with discrepancies resolved by consensus. A total of 25 studies met the inclusion criteria. Results: Body image concerns were found to be highly prevalent among adolescents and young adults with T1DM, and were associated with adverse outcomes such as disordered eating behaviors and suboptimal glycemic control. Gender differences were consistently reported, with adolescent girls and young women displaying greater body dissatisfaction and engaging more frequently in risky weight management practices, including insulin omission. Other factors, such as self-perception, diabetes-specific stress, and identity formation, also played significant roles in treatment adherence and psychosocial adaptation. Notably, this review reveals a lack of interventions specifically designed to address the psychological dimensions of T1DM. Conclusions: Body image and self-concept exert a substantial influence on T1DM management in adolescents and young adults, affecting both glycemic outcomes and psychosocial well-being. There is a pressing need for gender-sensitive and developmentally appropriate interventions that address body image, self-concept, and disease acceptance. Future research should prioritize longitudinal designs and the development and evaluation of targeted psycho-emotional support strategies. Full article

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world. Full article

Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs). Full article

Background/Objectives: Healthcare-associated infections (HAIs), including sepsis, represent a major challenge in clinical practice owing to their impact on patient outcomes and healthcare systems. Large language models (LLMs) offer a potential solution by analyzing clinical documentation and providing guideline-based recommendations for infection management. This study aimed to evaluate the performance of LLMs in extracting and assessing clinical data for appropriateness in infection prevention and management practices of patients admitted to an infectious disease ward. Methods: This retrospective proof-of-concept study analyzed the clinical documentation of seven patients diagnosed with sepsis and admitted to the Infectious Disease Unit of San Bortolo Hospital, ULSS 8, in the Veneto region (Italy). The following five domains were assessed: antibiotic therapy, isolation measures, urinary catheter management, infusion line management, and pressure ulcer care. The records, written in Italian, were anonymized and paired with international guidelines to evaluate the ability of LLMs (ChatGPT-4o) to extract relevant data and determine appropriateness. Results: The model demonstrated strengths in antibiotic therapy, urinary catheter management, the accurate identification of indications, de-escalation timing, and removal protocols. However, errors occurred in isolation measures, with incorrect recommendations for contact precautions, and in pressure ulcer management, where non-existent lesions were identified. Conclusions: The findings underscore the potential of LLMs not merely as computational tools but also as valuable allies in advancing evidence-based practice and supporting healthcare professionals in delivering high-quality care. Full article

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time agricultural environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) and data-driven insights, optimizing water usage. In this study, from an initial pool of 48 papers retrieved from well-known databases such as Scopus and Web of Science, etc., a rigorous eligibility criterion was applied, narrowing the focus to 11 pertinent studies. This review highlights major disciplines where DT technology is being applied: hydroponics, aquaponics, vertical farming, and irrigation. Additionally, the literature identifies two key sub-applications within these disciplines: the simulation and prediction of water quality and soil water. This review also explores the types and maturity levels of DT technology and key concepts within these applications. Based on their current implementation, DTs in agriculture can be categorized into two functional types: monitoring DTs, which emphasize real-time response and environmental control, and predictive DTs, which enable proactive irrigation management through environmental forecasting. AI techniques used within the DT framework were also identified based on their applications. These findings underscore the transformative role that DT technology can play in enhancing efficiency and sustainability in agricultural water management. Despite technological advancements, challenges remain, including data integration, scalability, and cost barriers. Further studies should be conducted to explore these issues within practical farming environments. Full article

Purpose—The reconstruction of worn-out urban fabrics poses a significant challenge in sustainable urban development, as such places, due to their decay and infrastructural inefficiencies, diminish residents’ quality of life and generate many environmental, social, and economic issues. Meanwhile, green building techniques have emerged as a novel option because they focus on environmental sustainability and resource efficiency. Nonetheless, effectively executing these strategies in worn-out urban fabrics necessitates a thorough feasibility evaluation to identify the associated obstacles and implementation prerequisites. The current study aimed to identify critical indicators for the feasibility of employing contemporary green building techniques in the repair of worn-out urban fabrics in Iran. The revitalization of worn-out urban fabrics is essential to enhancing the quality of life of urban inhabitants. Regarding this matter, the concept of green buildings, which emphasizes environmental sustainability, deserves significant attention. Meanwhile, feasibility assessments can help to successfully implement these changes in worn-out urban fabrics. Accordingly, the current study seeks to determine the essential indicators for the feasibility assessment of using initiative green building methods in the revitalization of worn-out urban fabric. Design/methodology/approach—In this vein, two rounds of the Delphi survey technique were carried out to identify and consolidate the indicators for the feasibility assessment of using initiative green building methods in the revitalization of the worn-out urban fabric in Iran. A research questionnaire was developed after reviewing the literature. It consists of four main dimensions (i.e., environmental, cultural–social, management–legal, and technical–technological) containing a total of 26 distinct indicators. The questionnaire was distributed among 123 experienced specialists. Eventually, the collected data were analyzed using the SPSS and Smart PLS programs. Findings—The results revealed that identified dimensions and indicators can be considered significant and essential indices in evaluating the use of initiative green building methods in the revitalization of worn-out urban fabric. Furthermore, the sequence of importance of the dimensions was environmental, followed by technical and technological, cultural and social, and managerial and legal. The environment, with an average rating of 3.33, ranked first; technical–technology, with an average rating of 2.45, ranked second; cultural–social, with an average rating of 2.15, ranked third; and management–legal, with an average rating of 2.07, ranked fourth. Furthermore, among the ranked indicators, the utilization of natural plants as a source of inspiration for living design in communal areas, aimed at toxin absorption and gas mitigation while achieving thermal equilibrium, received the highest average rating of 18.22, securing the first position. Conversely, the indicator assessing residents’ financial capacity, and the establishment of executive assurances and governmental support for the revitalization of the neighborhoods’ fabric garnered the lowest average rating of 10.98, placing it 26th and final. Originality/value—This research’s findings can significantly influence public policy and urban planning initiatives, aiding in the sustainable repair of worn-out urban fabrics in Iran by offering a systematic framework for evaluating the viability of innovative green building techniques. Full article

This study aims to explore the mechanisms through which corporate social responsibility (CSR) impacts customer loyalty in the banking sector, focusing on the mediating effects of consumer moral identity (CMI), perceived service quality (PSQ), and relationship quality (RQ). Based on Social Identity Theory and Stakeholder Theory, a theoretical model integrating CSR, CMI, PSQ, RQ, and customer loyalty was constructed and empirically tested using the PLS-SEM method. Data were collected through an online survey, yielding 338 valid samples. Analysis of the data revealed that CSR significantly positively affected CMI, PSQ, and RQ, indicating that the fulfillment of social responsibilities by banks enhances consumers’ moral identity, perceived service quality, and relationship quality. Additionally, CMI, PSQ, and RQ significantly positively influenced customer loyalty, with RQ showing the most prominent effect. Furthermore, CSR also had a significant indirect effect on customer loyalty through CMI, PSQ, and RQ. In terms of practical implications, this study suggests that the banking industry should regard CSR as a crucial strategy for winning customer loyalty, actively engage in CSR activities, and integrate CSR concepts into branding, service, and customer relationship management. Moreover, banks should also focus on enhancing CMI, PSQ, and RQ as critical pathways through which CSR influences customer loyalty. The theoretical significance of this research lies in: (1) expanding the theoretical perspectives on how CSR affects customer responses, addressing the limitations of previous studies that focused predominantly on direct effects or a single mediator; (2) examining the role of CMI in the banking context, enriching the research on CSR and consumer moral identity; and (3) revealing the mechanisms of CSR’s effect in the unique service context of the banking industry. Full article