Search Results (44,214)

Cultural Heritage (CH) is becoming increasingly vulnerable to the impacts of climate change, aging, and environmental decay, necessitating advanced preventive conservation strategies. This study presents the results of the SPIDER project, focused on Marquis’s Palace in Botrugno, a small but representative case study in Southern Italy of a municipality overwhelmed with the management of valuable CH sites. The approach integrates multi-sensor surveys, subsurface diagnostics, HBIM modeling, and IoT microclimatic monitoring into a lightweight information model designed for operational flexibility. In addition to that,, the possibility of producing new, eco-friendly filaments for Fused Filament Fabrication (FFF) printing from industrial stone dust waste was explored through a preliminary morphological, structural, and chemical–physical investigation of the stone material historically used in construction, with the aim of identifying materials similar to the original using a simplified, low-cost process. The findings highlight that economic and social factors such as limited resources and the “digital divide” hinder effective technology transfer. Consequently, this study investigates whether a ”lightweight” Asset Information Model (AIM) can provide a more sustainable alternative to complex Digital Twins for small municipalities and other public bodies. For this reason, this research proposes a scalable, wide but basic framework of information management tools and methods aimed at enhancing territorial capacity building, fostering technology integration and social inclusion, and valorizing multidisciplinary approaches to address the challenges affecting CH. Full article

Precise identification and monitoring of small agricultural water bodies are essential for sustainable water resources management in arid and semi-arid regions, where even limited water losses can significantly affect agricultural productivity and local water security. However, the accurate detection of small ponds remains a major challenge in remote sensing, to address this challenge, this study proposes an integrated three-step framework that combines high-resolution remote sensing imagery, machine and deep learning techniques, and hydrological analysis to identify agricultural ponds and quantify their evaporation losses in Bastam, Iran. In the first step, a dedicated annotated dataset comprising 1061 RGB satellite images, each with a spatial size of 256 × 256 pixels and a ground resolution of 0.5 m, was developed for model training and evaluation. Using this dataset, three deep learning models BiSeNet, UNet3+, and SegNet and four traditional supervised classifiers Maximum Likelihood, Neural Network, Mahalanobis Distance, and Minimum Distance were implemented and compared for pond detection. The results demonstrated that deep learning models consistently outperformed conventional classifiers in delineating small agricultural ponds. Among all evaluated methods, BiSeNet achieved the highest segmentation performance, with an IoU of 82.08%, an F1-score of 90.15%, a precision of 91.86%, and a recall of 88.50%. Among the conventional classifiers, Maximum Likelihood combined with a 5 × 5 spatial kernel produced the best performance, achieving an IoU of 76.90%, an F1-score of 86.93%, a precision of 90.87%, and a recall of 83.32%, whereas simpler classifiers such as Minimum Distance showed only marginal improvements after kernelization. In the final step, the detected ponds were used to estimate evaporation losses through the Meyer method. The hydrological analysis revealed a clear periodic pattern in evaporation and a cumulative water loss of 388,636.7 m³ over a nine-month period, highlighting the considerable impact of evaporation on the efficiency of small agricultural water storage systems in dry environments. Based on these findings, practical mitigation strategies, including evaporation-reducing chemical surface films and floating covers, are discussed as potential options for reducing water loss. Overall, the proposed framework demonstrates the clear advantage of deep learning for the accurate identification of small agricultural ponds and provides an integrated methodological basis for monitoring water bodies and evaluating associated evaporation losses. The study offers a practical and transferable approach for supporting agricultural water management and improving water-use efficiency in arid and semi-arid regions. Full article

Background/Objectives: Diclofenac (DCF) and other nonsteroidal anti-inflammatory drugs (NSAIDs) are widely used for pain and inflammation management; however, their clinical significance is limited by poor aqueous solubility, short biological half-life, and dose-dependent gastrointestinal, renal, and cardiovascular adverse effects. Nanocarrier-based delivery systems have been extensively explored because they can enhance the apparent solubility of poorly water-soluble NSAIDs, provide controlled and sustained drug release, prolong systemic circulation, and improve drug localization at the site of action. By reducing peak plasma concentrations and off-target exposure, these systems may decrease dose-dependent gastrointestinal and systemic adverse effects while maintaining therapeutic efficacy. Most studies focus on optimizing formulation composition, while the manufacturing process is often treated as a secondary parameter. The research critically evaluates conventional and emerging fabrication methods for NSAID nanocarriers, using DCF as the principal reference compound, with emphasis on their impact on physicochemical characteristics, reproducibility, scalability, and translational potential. Methods: A structured literature search was performed in PubMed/MEDLINE, Scopus, and Web of Science (2015–2026, with emphasis on 2022–2026) for DCF and NSAID-loaded submicron delivery systems reporting quantitative formulation data and clearly defined fabrication methods, resulting in a narrative review of approximately 375–395 eligible studies, comprising 75 DCF-specific studies and approximately 300–320 studies involving other NSAIDs that were included as representative surrogate systems when DCF-specific evidence was unavailable for particular fabrication approaches. The review followed Scale for the Assessment of Narrative Review Articles (SANRA) recommendations. Studies were analyzed using a standardized seven-parameter framework including encapsulation efficiency, release profile, particle size control, polydispersity, scalability, reproducibility, and process complexity. Results: Batch-based techniques, such as thin-film hydration for chitosan-coated liposomal systems, consistently provide high encapsulation efficiency, sustained drug release, and good biocompatibility. However, these methods are often associated with batch-to-batch variability, operator dependence, and limited scalability. In contrast, continuous manufacturing approaches, including microfluidic mixing, nanostructured lipid carriers, and Quality-by-Design (QbD)–guided processes, demonstrate improved control over particle size distribution and polydispersity, enhanced reproducibility, and better scalability potential. Conclusions: Manufacturing methodology is an important determinant of DCF and NSAID nanocarrier performance alongside formulation composition. Continuous manufacturing approaches offer promising improvements in reproducibility, process control, and scalability, but current evidence remains uneven across different nanocarrier classes. Further standardized comparative studies are needed to support their broader translation into clinical applications Full article

Under the dual pressures of freshwater scarcity and intensifying soil salinization in arid regions, the efficient use of brackish water has become a critical pathway for alleviating regional water stress. This narrative review seeks to synthesize a new paradigm characterized by deep integration of smart agriculture with brackish water irrigation. Further review shows that smart agriculture can open a new pathway for precision regulation of brackish water irrigation. Through reviewing existing studies on how brackish water irrigation affects soil properties and crop growth, we summarized the issues that emerged and proposed an integrated framework for sustainable brackish water application combined with smart agricultural management. Conventional brackish water irrigation increases the risks of deterioration in soil physicochemical properties, disruption of microbial community structure, and inhibition of crop growth and yield. On this basis, we propose a paradigm framework for smart brackish water irrigation, consisting of paradigm foundations, key technologies, application scenarios, and long-term goals. This framework clarifies the core connotations of intelligent water-salt coordination, dynamic threshold management, and multi-source data-driven decision-making, and promotes the transition of brackish water irrigation toward greater precision, intelligence, and system integration. This review can establish a technical system for smart brackish water utilization and provide both theoretical and practical support for the high-quality, efficient, and sustainable use of brackish water resources in arid regions. Full article

As critical infrastructure supporting sustainable urban development globally, the seismic resilience evaluation of tunnels is paramount to metropolitan safety. This paper proposes a probabilistic seismic resilience analysis framework for tunnel structures in soft soils. By integrating vulnerability models and functionality functions, resilience metrics for shallow, medium-depth, and deep tunnels are obtained, revealing how these metrics evolve with seismic intensity. The research further investigates the impact of different vulnerability models on tunnel resilience, treating occurrence probabilities of different damage states as random variables. Resilience metrics incorporating multiple vulnerability models are derived based on the Dirichlet distribution. The results of the study show that tunnel resilience metrics increase progressively with greater burial depth. At PGA = 0.8 g, for example, the resilience metrics calculated using a single model are 0.907, 0.962, and 0.985 for shallow, medium-depth, and deep tunnels, respectively. When accounting for multiple vulnerability models, the composite resilience metric tends to align with results from models carrying higher weights. This work establishes a scientific decision-making basis for optimizing seismic tunnel design, post-disaster functional recovery, and urban infrastructure resilience management. Full article

Conventional hazard and operability (HAZOP) studies remain central to process safety management, but their periodic, document-centered implementation and dependence on expert judgment limit their ability to track dynamic operational risk across the full plant lifecycle. This critical literature review, drawing on 113 sources, examines how digital twin (DT) and artificial intelligence (AI) technologies may augment HAZOP practice, advancing an augmentation principle: DT–AI should strengthen expert-led hazard analysis as a decision-support layer, not replace human judgment. The review maps conventional HAZOP limitations to four complementary technology pathways: AI-assisted knowledge capture and deviation reasoning; DT-based hazard monitoring and early warning; hybrid physics–data modeling for predictive safety; and explainable AI for operator trust and regulatory acceptance. Four representative industrial case studies illustrate current practice, and a structured barrier analysis identifies why many pilots stall before reaching sustained deployment. These pathways offer strong potential to improve HAZOP coverage, traceability, operational relevance, and lifecycle learning, but a study-level evidence assessment finds that no reviewed study demonstrates long-term, multi-site validation of an integrated DT–AI–HAZOP system; most implementations remain at conceptual, prototype, or limited pilot levels. Key failure modes, including model drift, sensor faults, large language model hallucination, and automation complacency, are examined alongside a thirteen-item implementation risk register spanning technical, organizational, economic, security, and scalability concerns. The review proposes a phased industrial implementation roadmap, a lifecycle-oriented safety-management framework, and a three-horizon research agenda for advancing toward reliable, validated HAZOP-informed digital twin systems. Full article

The global packaging industry is highly energy intensive, with liquid carton packaging facing growing pressure to improve sustainability through energy efficiency. The objective of this review is to synthesize and critically evaluate existing literature on energy performance metrics, energy auditing practices, optimization frameworks, and renewable energy integration in liquid carton packaging manufacturing. Unlike previous studies that focus on individual aspects of industrial energy management, the review adopts an integrated system-level perspective that combines process-level energy analysis, auxiliary utility systems, energy performance indicators, digital monitoring approaches, optimization tools, and renewable energy integration. This holistic approach provides a more comprehensive understanding of energy performance improvement opportunities within liquid carton packaging manufacturing. The study examines global energy trends, system inefficiencies, and best practices in implementing energy management systems, modeling tools, and solar photovoltaic adoption. A qualitative approach was applied, analyzing peer-reviewed articles, industry reports, and case studies to identify key themes and comparative strategies. Findings indicate that energy-intensive processes such as extrusion coating and flexographic printing dominate consumption, while auxiliary systems contribute significantly to non-process energy use. Despite advancements in monitoring and renewable integration, gaps persist in standardized performance metrics, real-time data utilization, and regional representation, particularly in Africa, Latin America, and other developing regions where packaging manufacturing systems remain underrepresented in the literature. The findings provide a practical reference guide for energy managers, manufacturing engineers, and sustainability practitioners seeking to implement ISO 50001-based energy management systems, real-time energy monitoring frameworks, and renewable energy integration strategies within packaging manufacturing facilities. The review further highlights the need for standardized performance metrics and region-specific studies to support sustainable and energy-efficient packaging operations. Full article

Urban marketplaces are increasingly recognised in European policy debates as multifunctional public spaces that extend beyond their traditional economic role. Across the European Union, emerging trends point to a shift towards market models that integrate social interaction, cultural activities, tourism, sustainable food production and place-based urban development, thereby contributing to more inclusive and liveable cities. However, the extent to which such transformations align with local actors’ perspectives and existing governance arrangements remains underexplored, particularly in Central European contexts. This paper evaluates the transformative potential of urban marketplaces as multifunctional public spaces from the perspective of key stakeholders in the City of Zagreb. The study adopts a mixed-methods approach, combining a desk analysis of European policy and practice trends with empirical research conducted at the local level. Data were collected through surveys of marketplace users (citizens) and vendors (tenants), complemented by focus groups with other relevant local actors. This approach provides insights into everyday practices, perceived barriers, governance challenges, and future expectations related to marketplace development. The findings reveal a strong alignment between stakeholder perspectives and European policy trends promoting people-centred and multifunctional marketplaces, while also identifying tensions with existing management and governance frameworks. Citizens emphasise the cultural, social, and tourism value of marketplace interactions, whereas vendors and governance actors highlight infrastructure needs and the importance of more participatory governance arrangements. The paper argues that stakeholder-based evaluation of transformative potential is essential for advancing inclusive, resilient, and sustainable urban regeneration through urban marketplaces. Full article

Globally, approximately 1.4 billion hectares of land are affected by soil salinity, necessitating effective and field-applicable remediation strategies. This study aimed to evaluate the growth performance, ion regulation, physiological responses, and phytodesalination potential of ice plant (Mesembryanthemum crystallinum L.) under field conditions with varying salinity levels in coastal Taiwan across winter and spring seasons. Soil salinity and sodicity across the three experimental fields (low, moderate, and high) were characterized using electrical conductivity (EC), exchangeable Na, the Na/K ratio, and exchangeable sodium percentage (ESP). Plant performance and salt removal efficiency were evaluated through field cultivation and biochemical analysis. Ice plant could successfully grow and produce harvestable biomass across all salinity levels, although growth and biomass declined with increasing soil salinity. Pronounced seasonal differences were observed in both plant performance and salt removal efficiency. Winter conditions, characterized by lower temperature and reduced evaporative demand, favored biomass accumulation and Na uptake, resulting in substantially higher phytodesalination capacity, with estimated NaCl removal of approximately 1256–1826 kg ha⁻¹ per cropping cycle. In contrast, spring conditions with higher temperatures, longer sunshine duration, and increased evapotranspiration significantly reduced biomass production and Na removal efficiency. Correspondingly, osmolytes such as proline, ononitol, and D-pinitol, along with total phenolic content (TPC), were significantly elevated in spring-grown plants, suggesting enhanced osmotic adjustment and antioxidant defense. However, the variation in the half-maximal inhibitory concentration (IC₅₀) values suggested that the plant’s antioxidant capacity was not solely a function of total phenolic content but resulted from complex interactions among different antioxidant compounds under heightened environmental stress. Overall, this field study provides strong empirical evidence that M. crystallinum is a highly salt-tolerant species with significant, season-dependent phytodesalination potential. Winter cultivation is particularly effective for the sustainable management and amelioration of coastal salt-affected agricultural soils. Full article

The middle–lower Huaihe River Basin faces persistent flood and waterlogging threats, with river–lake interactions being a critical yet underexplored factor constraining flood discharge capacity. This study investigates the flood discharge capacity and erosion–deposition dynamics in the Bengbu–Hongze Lake reach and proposes sustainable management solutions. By analyzing long-term hydrological data (1954–2020) and cross-sectional measurements (1971–2025), we quantified changes in channel morphology and flood behavior. The results reveal that while upstream inflow has remained stable (annual runoff 20.5–33.3 billion m³), sediment concentration has continuously declined by approximately 80%—from 0.474 kg/m³ in the 1950s to 0.094 kg/m³ in the 2020s. The main channel exhibits persistent incision totaling 135.7 × 10⁶ m³, while floodplains have undergone progressive aggradation of 35.1 × 10⁶ m³, reflecting a sediment-starved river system in geomorphic disequilibrium. Critically, the riverbed leading to Hongze Lake exhibits an adverse slope, rising from –10 m at Fushan to over +9 m at Laozishan, while the lake’s sedimentation has reduced its storage capacity by 29% since the 1980s (from 31.27 × 10⁸ m³ to 22.15 × 10⁸ m³). Despite extensive engineering interventions, significant issues persist—including the backwater effect of Hongze Lake, prolonged high water levels during moderate floods (in 2020, water level at Fushan reached 18.34 m at only 61% of the design discharge), and exacerbated waterlogging in riparian lowlands. Therefore, we advocate for a paradigm shift towards a river–lake separation scheme, specifically, an inner-lake embankment approach. This nature-based solution aims to restore the river’s physical structure and harness its self-shaping morphological function for long-term flood management and ecological sustainability. Our findings provide a quantitative basis for re-evaluating the river–lake relationship and offer a strategic direction for sustainable flood management in highly altered alluvial river systems. Full article

Lakes represent valuable natural resources with significant potential for sustainable tourism development. Their tourism valorization requires a comprehensive assessment framework that integrates environmental, infrastructural, socio-economic, and governance dimensions. This study applies the Fuzzy TOPSIS method to evaluate the sustainable tourism potential of ten selected lakes in Serbia. A multi-criteria framework was developed based on ten indicators, including water quality, tourism pressure and carrying capacity, biodiversity, environmental conservation, tourism infrastructure, accessibility, economic effects, and community involvement. Expert evaluations, supported by available evidence, were transformed into triangular fuzzy numbers in order to account for uncertainty and subjectivity in the assessment process. The Fuzzy TOPSIS model was used to calculate the relative closeness of each lake to the ideal solution for sustainable tourism valorization. The results reveal significant differences among the analyzed lakes. Lake Đerdap achieved the highest ranking (Ci = 0.818), followed by Lake Zaovine (Ci = 0.723), Lake Perućac (Ci = 0.644), and Lake Vlasina (Ci = 0.640), reflecting their favorable overall performance across the selected environmental, infrastructural, accessibility, and tourism-related criteria. Lower-ranked lakes were characterized by infrastructural limitations and less favorable performance in selected tourism-related criteria. The study illustrates the applicability of the Fuzzy TOPSIS approach for sustainable tourism evaluation and provides a practical framework for tourism planning, destination management, and policy-making aimed at supporting sustainable lake tourism development in Serbia. Full article

Water leakage in urban water distribution networks (WDNs) poses significant challenges for sustainable resource management and infrastructure reliability. Traditional detection methods are often reactive and difficult to scale in modern sensor-rich environments. This paper proposes a hybrid data-driven framework for early leak detection that integrates physics-informed simulation with machine learning and explainable analytics. A region-aware EPANET-style simulator is developed to generate realistic hydraulic data under varying demand patterns, environmental conditions, and pressure-dependent leak scenarios. To enhance generalizability, the synthetic dataset is combined with a BATADAL-inspired benchmark, enabling both in-domain and cross-domain evaluation. A feature engineering pipeline is introduced to capture temporal, spatial, and hydraulic relationships, expanding raw sensor signals into a high-dimensional representation. Six machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression, Isolation Forest, and a PCA-Based Autoencoder, are systematically evaluated under constrained false-positive requirements. The results show that tree-based ensemble models achieve strong detection performance while maintaining low false-alarm rates (FPR ≤ 0.05). Importantly, cross-domain experiments demonstrate that models trained on simulated data retain competitive performance when applied to benchmark datasets, indicating robust transferability. Finally, explainability analysis reveals that pressure-based temporal statistics and spatial gradients are key indicators of leakage, providing interpretable insights for system monitoring. The proposed framework offers a scalable and generalizable approach for intelligent leak detection in modern water distribution systems. Full article

A This study examines the interplay between environmental, social, and governance (ESG) practices, artificial intelligence (AI) adoption, and financial performance within Saudi Arabia’s financial sector. It investigates whether AI adoption moderates the ESG–performance relationship, reflecting the sector’s ongoing digital transformation under Vision 2030. Drawing on 224 firm-year observations across banks, diversified financials, real estate investment trusts (REITs), and insurance companies, the study employs content analysis of annual reports to identify AI implementation. Panel regression models are used to test the effects of ESG practices on both accounting-based (ROE) and market-based (Tobin’s Q) performance measures, while examining AI’s moderating role. The results reveal that ESG practices significantly enhance accounting-based performance, particularly return on equity, while board size exerts a positive and board independence a negative influence. However, ESG does not significantly affect market-based valuation (Tobin’s Q). Notably, AI adoption negatively moderates the ESG–financial performance link, suggesting short-term challenges in integrating digital transformation with sustainability strategies. This study contributes to literature in three key ways. First, it provides new evidence from financial institutions in a developing economy—Saudi Arabia—where ESG and AI integration remains underexplored. Second, unlike previous research that proxies AI adoption through R&D expenditure, this study captures actual deployment of AI tools in operational activities. Third, it extends the ESG–performance debate by introducing AI adoption as a novel moderating factor. The findings offer actionable insights for managers and policymakers in emerging markets, underscoring the importance of developing organizational capabilities that harmonize AI-driven innovation with ESG principles to foster sustainable long-term value creation. Full article

Prefabricated construction is increasingly adopted worldwide because of its advantages in sustainability and construction efficiency. Nevertheless, the construction phase still faces substantial and multifaceted risks. These risks are characterized by high uncertainty and strong dependence on the subjective judgments of decision-makers, which may limit the wider promotion and application of prefabricated construction. Existing risk assessment methods often struggle to represent neutral and refusal attitudes in expert evaluations with sufficient precision. They also do not fully account for the psychological and behavioral mechanisms of decision-makers under risk. To address these limitations, this study proposes an integrated evaluation framework that combines interval-valued picture fuzzy sets (IVPFSs), cumulative prospect theory (CPT), and TOPSIS. The decision objective is to rank alternative construction-stage risk-control schemes and identify the relatively important risk factors influencing the ranking results. IVPFSs are used to represent support, opposition, neutrality, and refusal degrees in expert evaluations. CPT is incorporated to capture decision-makers’ risk preferences, reference dependence, and loss-aversion behavior. A comprehensive risk indicator system is developed from five dimensions: personnel, management, technology, environment, and materials/equipment. Attribute weights are determined using a coordinated AHP–entropy weighting scheme to balance subjective expert judgment with objective information contained in the evaluation data. The proposed framework is applied to a prefabricated building project in Shanghai. The results indicate that personnel-related factors, especially the technical proficiency of construction workers, are relatively important risk sources in the examined case. The model also provides a structured ranking of alternative risk-control schemes. Overall, this study offers an exploratory case-based decision-support framework for construction-stage risk management in prefabricated building projects and demonstrates the potential value of incorporating behavioral preferences into fuzzy multi-criteria risk assessment. Full article

Environmental, social, and governance (ESG) reports are increasingly important for maritime ports and large service and industrial companies, as they measure sustainability, social responsibility, and integration with society. This study aims to strengthen collaboration between port administrations and academia by identifying differences between industry needs and academic focus. The research analyzes ESG indicators from sustainability reports of 23 port administrations and keywords from 34 SCOPUS publications (2020–2025). Indicators and keywords were grouped into ESG dimensions and thematic clusters, generating two criteria: the number of indicators used by ports and the number of keywords in academic research. Two multicriteria decision analysis methods, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), were applied to rank and compare topics across clusters and criteria. Results show similarities in rankings but also reveal differences in priorities between ports and academia, highlighting the need for stronger collaboration and common ESG indicators. Key priorities identified include data and innovation, ranked 1st and 2nd in the governance pillar, employment and job benefits, ranked 1st in the social pillar, and energy efficiency and management, ranked 1st in the environmental pillar. Differences in some environmental and social topics suggest further efforts are needed to align academic research with port administration priorities. Full article