Search Results (9,768)

The form of a city evolves as the complexity of its systems increases. This study discusses how urban growth challenges have contributed to the deterioration of built environments and cultural heritage assets. It investigates how smart sustainable city (SSC) strategies have become significant policy instruments in regenerating Baghdad’s future built heritage and advancing the conservation of the city’s architectural heritage, infrastructure systems, and quality of life. The study aims to investigate how SSC methods can serve as the main element for managing complex urban data and advancing heritage, socio-economic, and environmental sustainability. The research employs mixed methods such as mapping, serial vision, and walking tools to survey Baghdad’s heritage centre (Old Rusafa) natural and built environment and cultural heritage condition. Together, these methods provide a comprehensive understanding of the heritage area’s physical and socio-cultural dimensions. It is argued that achieving smart urban heritage requires the adoption of sustainable strategies that promote the conservation of architectural heritage. Accordingly, the research outcomes enhance understanding of the smart sustainable city concept (SSC) impact on Baghdad city’s cultural heritage regeneration and allow for the creation of an Index Wheel, which provides city stakeholders with a range of strategies and indicators to conserve Baghdad’s built heritage sustainably. Full article

Background: Dementia is one of the most common diseases of the elderly worldwide. Sharing experiences of caring for patients with dementia with other carers is essential to improve the quality of care, promote better outcomes, and learn from others. Aim: to explore nurses’ experiences of working with patients with dementia in Lithuanian supportive treatment and nursing hospitals. Methods: A qualitative descriptive design was employed in this study, with data collected through semi-structured interviews. Nurses with direct experience caring for patients with dementia in supportive treatment and nursing hospitals were recruited through purposive sampling. This sampling strategy was chosen to ensure that participants could provide rich, contextual, and experience-based insights into the phenomenon under investigation. Open-ended questions were divided into three themes: 1. Identifying nursing needs. 2. Care for people with dementia. 3. Patient behavior management and situation management. To ensure methodological rigor and transparency, the Consolidated Criteria for Reporting Qualitative Research (COREQ) were applied throughout the study’s planning, data collection, and analysis processes. Results: Nine nurses working in three different Lithuanian hospitals participated in the study. Theme 1: respondents reported that the needs of patients with dementia depend on their previous lifestyle and hobbies, as well as on essential physiological needs such as eating and drinking, bathing and personal hygiene, and the absence of pain. Theme 2: All participants emphasized that ensuring a safe environment is crucial for people with dementia. Theme 3: When faced with inappropriate patient behaviour, nurses attempt to calm the patient, speak gently, provide distraction, or, when necessary, temporarily separate the patient from others. Additional actions include administering medication and stabilizing the patient. Overall, these findings illustrate that dementia care requires continuous emotional presence, situational judgment, and adaptation to each patient’s individual needs. Conclusions: Patients with dementia require highly individualized care focused on nutrition, hygiene, pain control, and communication. Nurses’ daily activities centered on essential bodily care, medication management, and mobility support to maintain safety and prevent complications. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

Data centers are critical digital infrastructure supporting cloud computing, artificial intelligence, and global information services. Despite their high-reliability design, they remain vulnerable to fire incidents due to continuous operation, high electrical loads, dense power systems, and the increasing use of lithium-ion batteries. Although such events are rare, their consequences can be severe, including service disruption, equipment damage, financial loss, and risks to data integrity. This study presents a systematic literature review of fire safety risk management frameworks in data centers, following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2025 were retrieved from Scopus and Web of Science, screened, and appraised using structured quality criteria. Twelve empirical studies were synthesized and benchmarked against NFPA 75 and NFPA 76 standards. The findings are organized into three domains: Strategic Management, Fire Risk, and Fire Preparedness. The results show a strong focus on technical prevention and electrical hazards, while organizational readiness, emergency response, and recovery remain underexplored. Benchmarking indicates that industry standards adopt a more comprehensive lifecycle approach than the academic literature. This study reframes data center fire safety as a socio-technical reliability system and highlights critical gaps, providing a foundation for future research and improved fire safety governance and resilience. Full article

Severe wintertime particulate pollution (PM₁₀ and PM_2.5) affects the Santiago Metropolitan Region in Chile and is intensified by basin topography and frequent thermal inversions. Local authorities rely on the Critical Episodes Management (CEM) forecasting system, yet its predictive performance is variable. This study assesses CEM to identify operational vulnerabilities and propose data-driven improvements for urban air-quality governance. About ~1.2 million hourly meteorological and air-quality records (2017–2022) were analyzed using Generalized Additive Models (GAMs) to characterize key nonlinear relationships, and we evaluated the operational skill of the Cassmassi-1 PM₁₀ model and the WRF-Chem-based PM_2.5 forecasting component used by the system. Cassmassi-1 missed more than 50% of critical episodes and showed a false-alarm rate above 60%, consistent with limitations associated with static or incomplete emission representations. By contrast, the WRF-Chem-based component achieved episode prediction accuracy above 70%. GAM results indicate that wind speeds below 2 m s⁻¹, high diurnal temperature range, and relative humidity below 65% are strongly associated with extreme events. Considering the results, we recommend transitioning to nonlinear forecasting approaches that explicitly incorporate these meteorological thresholds and vertical stability indicators to improve alert reliability, strengthen urban resilience, and reduce population exposure. Full article

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

Today’s global and competitive environment forces companies to revise their competitive strategies and assess their operations’ performance. Customers are demanding new products and services, and organizations should adapt to the changing requirements of the customers. Companies may achieve excellence in their operations with cost reduction, by reducing time-to-market, and through improvements in delivery and quality. The main contribution of this study is assessing the linkages among operational performance (OP), environmental dynamism (ED), and competitive strategy (CS) in an emerging economy, Turkey. This study also aims to define the dimensions used to assess the operational performance, which are called the competitive manufacturing priorities in the operations management literature. To test the linkages between environmental dynamism, operational performance, and competitive strategy, a structural model is proposed. Analyses are conducted in SPSS 28.0 and AMOS 24.0 programs using the data gathered from Turkish manufacturing companies. Since 99.8% of firms operating in Türkiye are SMEs, most of the companies participating in this study (124 of 211) are also SMEs, and another contribution of this study is understanding the dimensions affecting the operational performance of SMEs According to the results, environmental dynamism has a significant relation to operational performance, and operational performance has a positive linkage with competitive strategy as well. The results also indicate that the most important dimensions used in assessing operational performance are customer satisfaction and supplier performance, as expected for manufacturing companies. Furthermore, the results of this study are expected to support organizations in developing and implementing effective strategies that integrate new capabilities and environmental considerations into their competitive strategy. As expected in SMEs, the most used competitive strategy is found to be “cost leadership,” because they can achieve operational performance by efficiently using resources, and by minimizing the production and transaction costs, they can enhance their competitiveness in the market. Full article

Artificial intelligence (AI) is increasingly being adopted for decision support in sustainable building risk management, yet the trustworthiness of AI-supported sustainability risk decisions depends as much on data quality as on analytical capability. Poor data conditions can amplify sustainability risks by producing unreliable decision support, yet existing studies provide limited insights into which data quality dimensions should be prioritized to enable trustworthy AI outcomes. This study identifies and prioritizes the critical data quality dimensions for trustworthy AI-supported decisions in sustainable building risk management. A questionnaire survey was conducted of accredited sustainable building professionals and their expert judgements were analyzed through an Analytic Hierarchy Process (AHP). The findings reveal that system-dependent dimensions, particularly traceability and interoperability, are prioritized over intrinsic dimensions like accuracy and consistency. The findings suggest that trustworthy AI-supported sustainability decisions depend strongly on a verifiable data provenance, cross-system integration and interpretable outputs rather than data correctness alone. This study reframes data quality from a general prerequisite to a prioritized, context-sensitive construct underpinning trustworthy AI applications, extending data-driven decision theory in the sustainable building domain. Ultimately, a phased data governance approach is recommended to prioritize traceability and interoperability as the foundational conditions for construction organizations implementing trustworthy AI in sustainable building risk management. Full article

Digital transformation is increasingly reshaping how tourism destinations enhance service quality and strengthen competitive positioning, particularly in developing economies characterized by heterogeneous digital maturity and structural constraints. This study develops and empirically tests a conceptual model examining the relationship between destination digital transformation, tourism supply quality, and destination competitiveness, with a specific focus on the mediating role of quality-oriented tourism supply. Survey data were collected from 242 tourism stakeholders and analyzed using hierarchical regression and bootstrapped mediation analysis (PROCESS Model 4, 5000 samples). The results show that digital transformation has a significant positive total effect on destination competitiveness (β = 0.48, p < 0.001), explaining 56% of the variance in competitiveness (R² = 0.56). However, a substantial portion of this effect is transmitted indirectly through tourism supply quality. The mediation analysis confirms a statistically significant partial mediation effect, with approximately 41% of the total effect operating through quality-oriented mechanisms. The findings demonstrate that digital transformation enhances competitiveness primarily when embedded within structured quality management, online reputation management, and smart governance practices, rather than through technological adoption alone. The study contributes to the literature by integrating digital transformation and tourism supply quality into a unified competitiveness framework tailored to developing economy contexts and provides practical guidance for policymakers and destination managers seeking inclusive and sustainable growth through quality-oriented digital strategies. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Background/Objectives: Metabolic syndrome (MetS) causes significant oral manifestations that negatively impact oral health-related quality of life (OHRQoL). This randomized controlled trial evaluated the effects of combined dental interventions and lifestyle guidance on OHRQoL in patients with MetS. Methods: In total, 82 participants with MetS were randomized into an intervention group (IG; n = 39), receiving dental treatment plus lifestyle guidance, or a control group (CG; n = 43), receiving lifestyle guidance only. OHRQoL was assessed using GOHAI and OHIP-14 at baseline, 1 month, and 3 months. Data were analyzed using repeated-measures ANOVA and multivariable ANCOVA, adjusting for age, sex, baseline OHRQoL, and waist circumference. Pearson correlations examined the relationship between metabolic changes (Δ) and OHRQoL. Results: At 3 months, the IG demonstrated significantly superior OHIP-14 scores (p = 0.020) and a large effect size in social disability (${\eta }_p^2$ = 0.148, p < 0.001) compared to the CG. Within-group analysis showed the IG achieved highly significant longitudinal improvements in pain and psychological discomfort (all p < 0.001). Subgroup analysis confirmed these gains were primarily driven by participants with missing teeth (${\eta }_p^2$ = 0.447, p < 0.001), whereas the periodontitis-only subgroup showed non-significant shifts. Multivariable analysis identified age and baseline scores as primary predictors. Notably, OHRQoL improvements significantly correlated with reductions in body weight (r = 0.355, p = 0.001) and waist circumference (r = 0.238, p = 0.031). Conclusions: Integrated dental and lifestyle interventions significantly improved OHRQoL in MetS patients by enhancing psychosocial well-being and social reintegration. Gains were functionally driven by systemic metabolic success. Addressing “nutritional barriers” through dental rehabilitation, while targeting weight loss goals, was essential for holistic MetS management. Full article

Background: Occupational exposure to cytotoxic drugs remains a major concern for pharmacy personnel, due to their well-established, carcinogenic, mutagenic and organ-specific effects. Despite the existence of robust international guidelines, evidence suggests substantial variability in compliance, training quality and operational conditions across healthcare settings. Objective: This study aimed to characterise current handling practices, assess working conditions, training, safety procedures, exposure patterns, and perceived risk factors among pharmacy technicians involved in the preparation of cytotoxic drugs in Portugal. Methods: A cross-sectional descriptive study was conducted using a structured questionnaire grounded in international standards (ISOPP, NIOSH, ASHP, USP <800>). The instrument was developed through literature review, expert panel validation (n = 42), and pre-testing. Data were collected electronically between April and May 2025 from pharmacy technicians actively handling cytotoxic drugs in Portugal. Results: A total of 124 valid responses were analysed. Most participants were female (78%) and under 50 years, with nearly one-third having less than one year of experience. Prolonged daily exposure (31.5% participants worked ≥ 5 h/day) extended uninterrupted handling periods (28.2% worked > 120 min), and high preparation workloads were common. While adherence to core protective measures—such as reinforced gowns, double gloves, and Class II B2 biological safety cabinets—was high, important gaps were identified, including incomplete use of closed system transfer devices, inconsistent respiratory and foot protection, limited automation, and insufficient environmental monitoring. Structured competency assessment, periodic training, and formal documentation were frequently absent. Institutional policies on reproductive risk showed strong protection for women but less clarity for male workers. Conclusions: Cytotoxic drug handling practices in Portugal demonstrate satisfactory adherence to fundamental protective measures but reveal significant structural and organisational gaps related to workload management, environmental monitoring, and continuous training. The absence of unified national guidance contributes to variability across institutions. These findings highlight the need for greater standardisation of occupational safety practices. Full article

Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha⁻¹) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference (p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly (p < 0.05) during the fruit expansion stage (FES); AGNA differed significantly among treatments at VGS and FES (p < 0.05). The two-step screening yielded NDSI _{(732, 879)} and NDSI _{(560, 690)} as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI _{(711, 986)} and NDSI _{(515, 736)} were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R² = 0.93–0.98, RMSE = 0.87–1.02 g kg⁻¹) and AGNA (R² = 0.95–0.97, RMSE = 1.92–2.55 g plant⁻¹), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops. Full article