Search Results (2,660)

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

The digital transformation of tourist facilities requires careful selection of technologies that can provide secure, stable and scalable network infrastructure. Due to the possibility of application in different sectors with different specificities, the focus of the research was placed on the implementation of smart tourist services. A hybrid multi-criteria decision-making model based on PIPRECIA and MVA models was applied for the research. Based on the literature and the opinions of experts in the field, evaluation criteria such as bandwidth, latency, energy efficiency, security and privacy, scalability, costs and interoperability were defined, and internet technologies such as Li-Fi, Wi-Fi 7, Wi-Fi 6, private 5G networks, Ethernet-over-Power (EoP), NB-IoT and LoRaWAN were defined. The results obtained put the security and privacy criterion at the top (0.2253), followed by scalability (0.1952) and bandwidth (0.1624). The obtained results indicate that Wi-Fi 7 achieved the highest weighted score (4.2247), followed closely by Li-Fi (4.2177) and Wi-Fi 6 (4.0771). Wi-Fi 7 demonstrated particularly strong performance in scalability, interoperability and bandwidth, making it highly suitable for environments with high user density. Li-Fi achieved very high scores in security and latency, which makes it particularly appropriate for security-sensitive smart tourism environments. Lower-ranked technologies such as NB-IoT and LoRaWAN proved valuable for supporting IoT and monitoring functions, rather than as primary communication infrastructure. The proposed model has proven to be a flexible, transparent and practical tool for strategic decision-making in the field of smart tourism. In addition to the basic application presented in the paper, the model has the potential to be adapted to different contexts and expanded with additional criteria or new technologies. The proposed hybrid approach can serve as a useful decision-making tool for tourism managers, system engineers and urban planners who are looking for optimal solutions for the development of digital infrastructure. Full article

This study examines the relationship between emotional intelligence and enterprise risk management maturity among risk leaders in Indonesia’s financial services sector, adopting a workplace accountability perspective to explain how leadership behavioural competencies support effective risk ownership, risk communication, and accountable risk decision-making. Drawing on survey data from 280 board-level executives holding the Qualified Risk Governance Professional credential, the study measures emotional intelligence using the Bar-On EQ-i and enterprise risk management maturity using the RIMS Risk Maturity Model. The findings reveal a strong and positive association between emotional intelligence and enterprise risk management maturity, with interpersonal competence and adaptability exhibiting the strongest associations with ERM maturity, while no significant differences are observed across job roles or organisational size. By empirically examining the association between leadership emotional capabilities and the institutionalisation of risk governance, the study contributes to global management and the literature on risk by extending enterprise risk management research beyond technical frameworks and compliance models, particularly within emerging market contexts. The results suggest that emotional intelligence may represent a transferable governance capability that is relevant to organisations operating in complex, uncertain, and globally interconnected environments. Practically, the study suggests that emotional intelligence development may represent a useful complement to leadership and risk capability programmes aimed at supporting risk culture, cross-functional engagement, and accountability. Full article

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. Full article

Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems’ success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study examines the hypothesised relationships between service quality, system quality, information quality, data quality, and public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality showed the strongest statistical association with public trust, followed by system quality. Service quality was also significantly associated with system, information, and data quality. In addition, system, information, and data quality showed significant indirect statistical relationships in the association between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation. Full article

Runway systems serve as the critical interface between airports and terminal airspace, and their efficient operation is essential for balancing air traffic demand and airport capacity. With the continuous growth of air traffic, intelligent runway demand and capacity management has become increasingly important for mitigating congestion and delays. This paper presents a comprehensive review of runway capacity–demand management from both supply-side and demand-side perspectives. On the supply side, runway configuration selection is reviewed, including runway configuration capacity envelopes, influencing factors, and existing optimization methodologies, such as prescriptive models, descriptive models, and reinforcement learning approaches. On the demand side, flight runway sequencing for arrivals, departures, and integrated arrival–departure operations is systematically analyzed. Problem analogies, operational characteristics, optimization objectives, and solution algorithms are discussed in detail. A critical comparison of existing methodologies is conducted from the perspectives of solution quality, real-time capability, human interpretability, technology readiness, trust requirements, and human–AI collaboration. Finally, future research directions are identified, including integrated runway management, multi-airport coordination, uncertainty-aware optimization, human–AI decision support, AI-enabled runway management, and integrated manned–unmanned operations. The review provides a reference for researchers, airport operators, air navigation service providers, and decision-support system developers seeking to improve runway operational efficiency and safety. Full article

Web service classification plays an important role in software reuse, service discovery, and automatic metadata organization. Although recent deep learning approaches have improved classification performance by using service names and natural-language descriptions, most existing methods still operate as black-box models and offer limited insight into how different metadata sources influence classification decisions. This lack of transparency reduces their practical usefulness for developers who need to verify predicted categories, analyze incorrect classifications, and improve service metadata quality. A well-trained interpretable model can not only help developers choose more appropriate and reliable categories for each web service, but also help write a more reasonable service name and description. In this paper, we present xServeNet, an explainability-oriented extension of ServeNet for transparent web service classification. xServeNet preserves the BERT-based representation and CNN–BiLSTM feature extractor of ServeNet and introduces (i) an instance-wise dynamic source-fusion mechanism that adaptively combines service-name and service-description features according to their semantic contribution, and (ii) model-internal importance indicators at both the source and word levels that support inspection of classification decisions without introducing additional trainable parameters. We benchmark xServeNet against eleven machine learning baselines on two real-world ProgrammableWeb datasets of 10,943 and 14,086 services covering 50 categories. xServeNet reaches 71.08% Top-1/91.35% Top-5 accuracy on the original dataset and 74.10% Top-1/92.95% Top-5 accuracy on the updated dataset, consistently improving Top-1 accuracy over ServeNet while remaining competitive on Top-5, and achieving the lowest per-category Top-5 standard deviation among all twelve compared methods. In practice, the importance indicators support three concrete activities at the service registry: helping developers verify predicted categories at registration time, iterating on description wording when the predicted category looks wrong, and supporting registry curators in flagging likely mislabelled services for review. Full article

Hatay experienced severe spatial, economic, and social losses following the earthquakes on 6 and 20 February 2023. Beyond the scale of physical destruction, the post-disaster period has brought deep transformations in everyday life, access to services, and the governance of space. This study examines the reconstruction process in Hatay from a perspective of just recovery and evaluates how the discourses of justice highlighted in policy documents are reflected in planning practice. Furthermore, the study offers empirical contributions on how justice is produced through spatial planning tools such as reserve area decisions, rubble management, expropriations, and access to services. Within the scope of the research, post-disaster policy documents, municipal reports, and media content were examined using qualitative content analysis, and the findings were supported by field-based spatial observations. The analyses show that, although the discourse of participation is frequently emphasized, it remains limited in decision-making processes; and issues related to the needs of vulnerable groups and equal access to services are more weakly represented. Spatial examples highlight the gap between normative discourses and practice through reserve area decisions, debris dumping management, and environmental risks. Overall, the study reveals that the principles of just recovery have been only partially implemented in the reconstruction process in Hatay, and that, for long-term resilience, participation, spatial equality, and the recognition of diverse lifestyles need to be strengthened at the institutional level. Full article

Detecting trafficking-related indicators in online advertisements remains a challenging natural language processing task due to ambiguous language, repetitive templates, and evolving euphemistic expressions. This study presents a lightweight transformer-based framework for identifying potential trafficking-related risk indicators in publicly accessible online advertisements using contextual semantic classification and leakage-aware evaluation. The framework combines standardized text preprocessing, duplicate filtering, semantic group-aware dataset partitioning, and DistilBERT-based classification to improve detection reliability while reducing semantic leakage between dataset subsets. The dataset consists of 3000 curated online advertisements collected from escort and service-related platforms, labeled using trafficking-related linguistic indicators derived from prior research, public trafficking typologies, and domain-informed annotation guidelines. On the held-out test set, the framework achieves an accuracy of 88.7% and a macro F1-score of 0.8338 under leakage-aware evaluation conditions, with PR-AUC and ROC-AUC of 0.984 and 0.993, respectively. Same-dataset baseline experiments using TF-IDF logistic regression and TF-IDF SVM classifiers show that while these feature-based models attain higher macro F1-scores on the curated dataset, the proposed framework achieves higher overall accuracy and substantially stronger threshold-independent ranking (ROC-AUC), indicating more reliable probabilistic discrimination across decision thresholds in a recall-sensitive setting. The reported PR-AUC and ROC-AUC values are interpreted as upper-bound estimates within the present evaluation setting, as residual dataset-specific regularities may persist despite leakage-aware partitioning. The framework is computationally efficient, suitable for deployment in resource-constrained environments, and designed as a human-in-the-loop decision-support system rather than an autonomous enforcement tool. Overall, lightweight transformer architectures provide a scalable and operationally realistic approach for identifying trafficking-related risk indicators in online advertisements under leakage-aware evaluation conditions. Full article

LIFE REVIVE aims to restore ecological status and ecosystem services in the Lima and Vouga river basins (NW Iberian Peninsula), where hydromorphological alteration and hydropower-driven flow regulation are major causes of water bodies failing to reach Good Ecological Status under the EU WFD. The project targets key pressures such as longitudinal fragmentation by weirs and dams, artificial flow regimes, degradation of spawning substrates, and the spread of invasive aquatic plants, which strongly affect fish communities, including sea lamprey, salmonids, and other diadromous species. Technically, the project combines barrier removal or eco-adaptation, nature-like fish passes, and spawning-habitat renaturalisation with optimized environmental flow regimes (EFR) downstream of important hydropower systems, explicitly accounting for present and future hydroclimatic scenarios. Multi-scale ecohydrological modelling (species distribution models, habitat suitability models, GLM/GAM approaches) will quantify fish–flow–habitat relationships and support the definition of operational EFR guidelines that balance ecological requirements with hydropower and agricultural constraints through joint work with the main Portuguese hydropower operator, EDP. Impact evaluation is structured around a rigorous BACI monitoring design in intervention and control tributaries, using standard WFD biological indices for fish and aquatic/riparian vegetation, hydromorphological indices (HQA, HMS, RHS), and project-specific Key Performance Indicators for water quality, biodiversity, and habitat. Expected outcomes include the restoration of at least 51 km of rivers towards free-flowing conditions, reduced hydromorphological pressure in more than 20 km of heavily modified river stretches, and measurable increases in the distribution and abundance of fish species and native vegetation. A strong communication and capacity-building programme underpins public engagement, while a decision matrix for barrier prioritization, technical workshops, and pilot replications in additional basins (e.g., Alva, Mouro, Deva, and Tea in Galicia) are designed to maximize transferability, policy uptake, and long-term sustainability of the solutions beyond the project lifetime. Full article

Residential buildings must now be designed and retrofitted as adaptive climate–health–work systems rather than as static housing units. This structured literature review synthesises peer-reviewed journal and conference evidence on residential taxonomy, ventilation, indoor environmental quality, overheating, airborne infection resilience, post-pandemic occupancy changes and future performance benchmarks. The review shows that single-family and multifamily buildings remain the most practical first-order categories because they differ in envelope exposure, ventilation pathways, system ownership, governance, retrofit feasibility and occupant control. Single-family dwellings generally provide greater household autonomy, roof-based renewable potential and room-level intervention flexibility, but can also carry higher envelope losses, lower density and stronger dependence on occupant operation. Multifamily buildings benefit from compactness and shared infrastructure, yet face additional risks from common services, vertical shafts, stack effects, corridor pressurisation, inter-zonal airflow and collective maintenance. Ventilation evidence indicates that natural, exhaust-only, supply, balanced heat-recovery, hybrid, demand-controlled and filtration-based strategies cannot be ranked universally; their effectiveness depends on climate, airtightness, pollutant source, occupancy, maintenance and governance. This review further shows that overheating, cooling-demand growth, airborne infection preparedness and remote work are shifting residential performance from winter-centric energy efficiency toward year-round thermal resilience, clean-air delivery and prolonged-occupancy functionality. A future taxonomy is therefore proposed around adaptive performance attributes, including thermal resilience, clean-air capacity, ventilation controllability, energy flexibility, remote-work readiness, vulnerability and retrofit potential. The core contribution is a hypothesis-generating, decision-support and benchmark-development framework for aligning residential design, retrofit and policy with health, indoor environmental quality, energy efficiency and carbon performance. Full article

The Qinghai Lake Basin, a typical ecologically vulnerable, high-altitude, cold region, requires coordinated ecosystem conservation and socio-economic development to achieve territorial sustainability. Based on the Production–Living–Ecological Space (PLES) framework, this study used land use data from five periods between 2000 and 2020 and integrated the PLUS and InVEST models to examine and simulate the evolution of PLES patterns and carbon stock under four scenarios—natural development, ecological protection, economic development, and sustainable development—in 2035. The results show that the PLES pattern in the Qinghai Lake Basin remained generally stable from 2000 to 2020, with ecological space dominating the landscape, while production and living spaces expanded slowly. Carbon stock increased from 214.73 × 10⁶ Mg to 264.70 × 10⁶ Mg, representing a growth rate of 23.27%. Its spatial distribution is highly consistent with the PLES pattern, with ecological space being the main contributor. By 2035, carbon stock is projected to slightly increase under the natural development scenario; under the ecological protection scenario, the expansion of ecological space leads to an increase in carbon stock; it decreases under the economic development scenario due to the encroachment of ecological space by construction land expansion; and under the sustainable development scenario, which balances economic development and ecological protection, carbon stock increases by 4.87 × 10⁶ Mg, achieving the best overall performance. Therefore, it is essential to properly coordinate the relationships among PLES components to achieve synergistic enhancement of ecosystem services and regional sustainable development. The findings provide methodological references and decision support for sustainable development in the Qinghai–Tibet Plateau and other ecologically vulnerable regions. Full article

The Landsat program has provided over 54 years of multispectral imagery, contributing vital information for agricultural and forestry scientific research and operational activities. Freely available Landsat data have enabled scientists to analyze land use patterns, assess ecological impacts, and develop strategies for sustainable management. We explored Landsat’s pivotal role through the lens of the United States Group on Earth Observations 2023 Earth Observation Assessment (EOA). The EOA included comprehensive surveys of more than 2000 federally supported Earth observation data products. We subsequently analyzed how Landsat satellite data and derived products support agricultural and forestry-related priorities compared to other Earth observation inputs. We evaluated both direct and indirect applications of the data, identifying key users across federal agencies and assessing how Landsat data contribute to critical products, services, and objectives. The results indicate that Landsat provides key information to support diverse activities across agriculture and forestry sectors, such as enhancing food supply, improving resilience to disaster and disturbance events, maximizing ecosystem productivity and conservation, and supporting regulatory requirements and decision-making. The Landsat OLI and TIRS sensors ranked 4th and 10th, respectively, out of 1171 Earth observation inputs identified in the study, underscoring their value to agriculture and forestry. Full article

Disruptions in urban supply chains increase the risk of reduced access to medicines whose continuous availability is important for public health. This article develops an open-data decision support system (DSS) framework for assessing medicine availability under shortage and node-failure scenarios. The empirical application combines redeemed e-prescription data from the Ukrainian reimbursement program for 2022–2025 with the registry of dispensing points under National Health Service of Ukraine contracts and applies a unified scenario design to Kyiv and Lviv. The results show that demand is more concentrated in Lviv: the top 10 dispensing nodes account for 29.7% of redeemed e-prescriptions, compared with 14.2% in Kyiv. The proposed DSS supports the redistribution of limited available volume across spatial zones; it does not generate additional supply. Its value lies in identifying where lower-tail coverage, service coverage gaps, and redistribution-distance constraints should be monitored under explicitly defined stress-test assumptions. The framework is therefore positioned as a scenario-based planning tool rather than as a real-time inventory-management system. Full article