Search Results (7,662)

High-resolution remote sensing image change detection holds significant application value in the fields of urban planning, disaster assessment, and others. However, it faces the dual challenge of pseudo-change interference and loss of detailed information. To address these issues, a frequency-domain-aware Siamese detail recovery network (FSD-Net) is designed in this paper. Firstly, from the perspective of frequency domain analysis, a theory on the dual roles of frequency domain components is introduced to reveal the robustness of low-frequency components to pseudo-changes and the dual semantic noise attributes of high-frequency components. Based on this theory, a frequency-aware context-guided difference (FCGD) module is designed. By explicitly decoupling the difference features into low-frequency global components and high-frequency residual components, it utilizes the prior low-frequency scene as a semantic gate to adaptively modulate the high-frequency differences, which effectively suppress pseudo-change interference. Subsequently, a detail recovery block (DRB), based on sub-pixel convolution, is constructed. This achieves unbiased spatial rearrangement through the semantic redundancy of channel dimensions, which avoids the checkerboard artifacts of traditional upsampling, and by employing a progressive multi-stage upsampling strategy to integrate shallow detail features from the encoder. The experimental results on the three public datasets of LEVIR-CD, WHU-CD, and CDD-CD demonstrate that the FSD-Net outperforms current mainstream methods (e.g., ChangeFormer, BAN, and so on) in core metrics such as F1 score and IoU, with a particularly significant improvement in recall. The ablation experiments validate the effectiveness and complementarity of the FCGD and DRB. Parameter sensitivity analysis indicates that the auxiliary loss weight λ is dataset dependent, with λ = 0.1 serving as a robust default choice. This study provides an efficient and reliable solution for change detection in high-resolution remote sensing imagery. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

Climate change poses significant challenges to the operation and safety of dam and reservoir (D&R) systems, particularly in regions characterized by water scarcity and high climate variability. This study presents a structured methodology for climate risk assessment that integrates regional climate projections, system-specific thresholds, and a semi-quantitative risk matrix approach. A key innovation is the explicit linkage between climate indicators and system performance through physically based thresholds, combined with empirically derived exceedance probabilities from high-resolution climate projections. The methodology is applied to the Almopeos D&R system in northern Greece, using an ensemble of statistically downscaled CMIP6 simulations under two emission scenarios (SSP2-4.5 and SSP5-8.5) and two future periods (2041–2060 and 2081–2100). Three climate indicators are analyzed: TX35 (temperature extremes), CDD (consecutive dry days), and Rx1day (extreme precipitation). Results indicate that temperature increase is the dominant climate risk hazard, leading to increased irrigation demand and reduced system reliability, with risks classified as high to very high. Drought conditions represent a secondary but important risk, becoming critical during prolonged dry periods affecting reservoir storage, while extreme precipitation events exhibit low likelihood but potentially high consequences for dam safety. Adaptation measures are prioritized using a qualitative multi-criteria approach, highlighting the effectiveness of operational measures, while structural and monitoring interventions remain essential for ensuring system safety. The proposed methodology provides a transparent and transferable framework for climate-resilient planning of water infrastructure systems. Full article

To address the lack of traversable region awareness in conventional path planning algorithms for obstacle-crossing robots, an adaptive path planning method is proposed. First, a traversal-aware environment model is constructed by introducing graded traversable regions with associated physical traversal costs. To effectively navigate this complex model, a hybrid Ant Colony Optimization (ACO) framework integrating Jump Point Search (JPS) and the Genetic Algorithm (GA) is developed. Specifically, a JPS-inspired pruning strategy is incorporated into the state transition process to significantly reduce redundant node expansion. Crucially, genetic operators—namely crossover and mutation—are embedded within the main ACO iterative loop to dynamically sustain population diversity and effectively mitigate stagnation in local optima. Correspondingly, the pheromone initialization, state transition mechanisms, and update rules are redesigned to incorporate the robot’s obstacle traversal capabilities. The framework is further complemented by path optimization operations that reduce unnecessary turning points. Extensive simulation experiments demonstrate that the proposed method outperforms conventional ACO-based and classical path planning algorithms. In particular, it achieves an average reduction of 11.1% in path length and 65.5% in the number of waypoints, while ensuring effective coordination with the robot’s physical traversal capabilities. These results validate the superior search efficiency, robustness, and practical applicability of the proposed approach. Full article

Urban–climate interactions in a warming climate remain largely uncertain; therefore, it is crucial to realistically evaluate and project these feedbacks to establish effective adaptation strategies. This study investigates projected shifts in summertime urban–climate interactions over eastern North America by employing the GEM regional climate model coupled with the Town Energy Balance (TEB) scheme, driven by RCP4.5 and RCP8.5 scenarios for the 1981–2100 period. Evaluations for the current climate (1981–2010) demonstrate that the model simulates an urban-induced warming of 0.5–0.7 °C and a precipitation reduction of 0.2–0.4 mm/day with high fidelity. By the late 21st century (2071–2100), projections under the RCP8.5 scenario indicate a steady weakening of the summer mean Urban Heat Island (UHI) intensity by approximately 0.10 °C, with a more pronounced nighttime attenuation of 0.15 °C. Physically, this weakening is attributed to an enhanced urban-induced evaporative fraction, which limits solar radiation storage within the urban fabric during the day, thereby reducing the thermal energy available for post-sunset release. This UHI attenuation correlates strongly with localized increases in precipitation, particularly in coastal regions where urban-induced effects contribute 20–40% to the total precipitation rise. While this study intentionally utilizes static urban boundaries to isolate the specific sensitivities of current urban morphologies to global warming, these results emphasize that diverse climatological regions will undergo distinct urban–climate feedback changes, providing essential baseline data for resilient urban planning. Full article

The Aedes aegypti mosquito exhibits a critical behavioral adaptation through its oviposition strategy, laying eggs in dry and wet environments just above the water level, allowing eggs to resist desiccation and hatch only when submerged by rain. To investigate this mechanism, we developed a nonlinear dynamic model incorporating climate-driven parameters affecting egg hatching and adult emergence. Theoretical analysis revealed an imperfect pitchfork bifurcation giving rise to a phenomenon we term basin-mediated hysteresis. Unlike classical hysteresis, which relies on coexisting stable states, this mechanism results from the progressive collapse of the extinction basin boundary. As the control parameter approaches its critical value, the basin of attraction of the trivial equilibrium shrinks. Once the population establishes itself above the threshold, returning the parameter below unity does not restore extinction, leading to an irreversible transition governing population persistence. The model was validated using field data from mosquito traps in a Brazilian city, showing strong agreement with observed seasonal patterns of female captures. Parameters were optimized using the Differential Evolution algorithm, yielding high correlation between model and field data. The results demonstrate that the dual oviposition strategy underlies population persistence and seasonal peaks, providing information for planning interventions amid global arbovirus expansion. Full article

Purpose: This study provides a critical examination of the literature on applying artificial intelligence (AI) and Extended Reality (XR) in restaurant settings and related foodservice operations. It focuses on how AI and XE influence consumer nutrition awareness and decision-making about food choices, and their implications for customer satisfaction, loyalty, and service delivery in foodservice environments. Design/methodology/approach: The study adopts a systematic literature review (SLR) approach following the PRISMA method. An initial search identified over 3900 academic papers published between 2016 and 2025. Studies were selected on the basis of predetermined inclusion and exclusion criteria, and 26 peer-reviewed articles were analyzed. The review provides a conceptual synthesis and develops propositions for practical applications and future research directions. Findings: The review reveals a shift from static systems that rely on optimization, toward adaptive and user-centered solutions that are behavior-oriented. AI applications predominate in the case of calorie tracking, personalized recommendations, and menu planning. Though deployment of XR technologies (e.g., AR and VR) is less prevalent, they offer potential for immersive, and real-time interventions. A key distinction emerges between studies demonstrating empirical effectiveness (e.g., improved understanding and healthier choices) and those focused on technical and/or conceptual developments. To date, there has been limited validation of behavioral impacts in foodservice settings. Originality: This study offers a theory-informed conceptualization of AI and XR applications in restaurant and foodservice contexts by integrating three perspectives: hospitality (menus and dining experience), nutrition (dietary awareness and healthier choices), and human–technology interaction (technology acceptance and user engagement). The study reconceptualizes AI- and XR-enabled systems as behavioral intervention tools and outlines a focused research agenda for advancing nutritional communication in foodservice environments. Full article

In the current labor market, perceived employability is a key resource for university students approaching the transition from university to work, which is often marked by heightened stress, vulnerability, and unhealthy behaviors, particularly in contexts with high youth unemployment rates. Despite prior research documenting the buffering role of perceived employability in the relationships between career-related stressors and well-being, limited evidence exists regarding the roles of faculty support and self-compassion, a fundamental factor for effective emotional regulation, during university years. Consequently, this study aimed to examine the relationships between faculty support, self-compassion, career self-efficacy, career planning, and perceived employability through a self-report questionnaire completed by 186 Italian university students, mainly female, with a mean age of 21.24 (SD = 2.57). Results from a partial least squares model indicated that faculty support was indirectly associated with perceived employability through self-compassion, career self-efficacy, and career planning. These findings could support higher education organizations by suggesting the design of interventions to promote supportive learning environments and to develop training in emotional regulation skills. Such an approach could empower students to effectively cope with career-related stressors and, in turn, engage in adaptive behaviors associated with employability. Full article

Background: Alterations of the vertical dimension of occlusion may affect masticatory muscle function, which is critical in pre-prosthetic planning, especially in Class II division 2 malocclusions. This study aimed to evaluate the impact of increasing VDO on the myoelectric activity of masticatory muscles using surface electromyography (EMG). The null hypothesis was that a 2–4 mm increase in VDO does not significantly influence muscle activity. Methods: Nine patients with Class II division 2 malocclusion were evaluated. EMG recordings of the masseter and anterior digastric muscles were obtained using the BioEMG II system (BioResearch Asoc., Milwaukee, WI, USA) and Biopak™ software (AcqKnowledge 4.x). VDO was increased using the Dupas universal jig. EMG was recorded for 30 s under six conditions: resting posture, intercuspal position (IM), swallowing, resting posture after VDO increase, IM with increased VDO (IM2), and swallowing with increased VDO (swallowing 2). Results: Most EMG variables showed no statistically significant differences after short-term VDO increase. Significant differences were observed only in the resting activity of both masseter muscles and in the right masseter during maximum intercuspation. No significant changes were identified during swallowing. Conclusions: Within the limitations of this pilot study, a 2–4 mm increase in VDO appears to produce minimal short-term changes in masticatory muscle activity in Class II division 2 patients. These findings should be interpreted with caution due to the small sample size and short observation period, and further studies are required to evaluate long-term neuromuscular adaptation. Full article

Pharmaceutical care provides the conceptual foundation for integrating pharmacogenetics into everyday pharmacy practice. Defined by Hepler and Strand as “the responsible provision of drug therapy for the purpose of achieving specific outcomes that improve a patient’s quality of life”, pharmaceutical care emphasizes a patient-centered approach in which the pharmacist collaborates with the patient, physician, and other healthcare professionals to design, implement, and monitor individualized therapeutic plans. In this context, pharmacogenetics can be regarded as an extension of pharmaceutical care: while the traditional model relies on monitoring patient outcomes and adherence, PGx adds a genetic dimension that allows treatment to be optimized from the very beginning. The pharmacist’s role therefore evolves from not only ensuring safe and effective use of medicines, but also interpreting genetic test results, supporting adherence to genetically guided therapy, and educating patients about the implications of their personal genetic profile. The introduction of pharmacogenetics testing as one of the potential services offered by community pharmacies is a promising proposition that may revolutionize the approach to drug therapy. Pharmacogenetics, a subset of pharmacogenomics, focuses on the study of DNA sequence variations that influence response to drugs. Thanks to advances in the field of genomics, it has become possible to study the genetic basis of variability in drug response. The identification of alleles responsible for the rapid or slow metabolism of xenobiotics has ushered in a new era in pharmacology. The aim of this interdisciplinary field, combining genetics and pharmacology, is to adapt treatment to a specific patient based on the analysis of their genome and gene polymorphism. Throughout the world, pharmacogenetics is gaining importance as a tool for personalizing medicine. In countries such as the United States, Canada, and the United Kingdom, programs integrating pharmacogenetics with healthcare are being developed. Clinical trials and the implementation of genetic tests into medical practice allow for better matching of medications and reducing the risk of side effects. Pharmacists will play a key role in integrating pharmacogenetics into healthcare. As specialists in the field of pharmacotherapy, they will support physicians in interpreting the results of genetic tests and adapting drug therapy to the individual needs of the patient. Additionally, pharmacists can educate patients and healthcare professionals about the benefits of pharmacogenetics and monitor the effects and safety of medications. Their involvement in the process of personalization of treatment may contribute to improving the effectiveness and safety of pharmacological therapies. Full article

This study examined a maker-based approach to sustainable digital education in physical education (PE) through a laser-shooting program implemented over a three-year period (2022–2024). While prior studies have largely focused on short-term maker-based PE interventions, less is known about how such practices are refined, stabilized, and diffused across school contexts over time. Using a qualitative case study design, data were collected from lesson plans, instructional artifacts, implementation records, field notes, and semi-structured interviews with five PE teachers, and analyzed using inductive thematic analysis. The findings suggest that, according to teachers’ accounts and classroom documentation, the program was perceived to reduce barriers to participation, diversify student roles, and improve instructional feasibility in indoor PE settings. Over time, the program evolved into a stable and adaptable instructional approach aligned with sustainable digital education, integrating physical computing into embodied learning environments. Diffusion occurred through teacher agency within informal professional networks and institutional training contexts. These findings highlight the potential of maker-based PE as a sustainable digital education approach that may support context-responsive participation, instructional adaptability, and professionally scalable innovation in school PE, with possible relevance for inclusive physical education contexts. Full article

This study investigates the rapid, often uncontrolled urban expansion in Khenchela, a medium-sized city in Algeria’s eastern High Plains, and its profound environmental repercussions amid semi-arid fragility. Drawing on sustainable urban development and resilience frameworks, it dissects pressures such as green space reduction (from 45 ha in 1998 to 33 ha in 2023, dropping per capita from 6.1 m² to 3 m² below WHO standards), water scarcity with 35% leakage losses waste mismanagement, informal settlements on hazardous lands, air/soil pollution, and climate vulnerabilities like heat waves and flooding. Employing a mixed-methods approach documentary analysis of (MPLUUP, LUP and MDP) plans, GIS cartography of spatial evolution (2000–2025), statistical demographics, field observations, and institutional critiques, the research exposes governance gaps: fragmented coordination, weak ecological integration, and resource shortages. It reveals socio-spatial disparities across functional zones, underscoring the need for adaptive, participatory strategies that promote polycentric and compact urban forms, enhanced biodiversity, efficient infrastructure, and inclusive governance to strengthen urban resilience. Full article

The electrical system is a crucial infrastructure of modern society. It provides the energy needed for society to continue its development. However, this critical infrastructure is increasingly threatened by the extreme weather events driven by the escalating climate crisis, posing significant challenges to sustainable development and energy security. Therefore, it is important to conduct comprehensive risk analyses of the electrical system to prepare for future challenges. This paper presents an electrical risk assessment conducted within the European project ICARIA, aiming to evaluate the effects of global climate change on critical infrastructure resilience. The study improves on the first risk assessment conducted, evaluating the electrical system’s vulnerability to flooding events, such as heavy rains or rising sea levels, in the Metropolitan Area of Barcelona. A key contribution to this research is the integration of direct impact assessments and cascading effect analyses, which identify how localised failures in electrical assets can spread throughout the system, potentially leading to a blackout. The research focuses on modelling various flood projections, using extreme weather scenarios and return periods ranging from 1 to 100 years. These projections are employed to evaluate the risk assessment methodology and quantify potential impacts on the electrical grid, including Expected Annual Damage (EAD) and Energy Not Supplied Cost (ENSC). The results aim to provide policymakers and grid operators with valuable insights, enabling the development of data-driven adaptation strategies and climate-resilient infrastructure planning to mitigate the risks posed by extreme weather events. Full article

Africa’s rainfed agricultural systems are highly exposed to climate change, making shifts in temperature and rainfall a major concern for staple-food crop production. Using a MaxENT ecological niche modelling approach with crop occurrence, elevation, soil and climatic predictors, this study assessed current and future suitability for rainfed maize, millet and sorghum under RCP 4.5 and RCP 8.5. The projections show a notable expansion of 11.1–22.0% in areas suitable for maize cultivation, and a decline of 1.6–7.3% in areas suitable for production of millet and sorghum, indicating likelihood for increased food-security risks in regions dependent on drought-tolerant cereals. These differing shifts highlight the need for targeted adaptation measures, including crop diversification and region-specific planning to help sustain crop production under a changing climate. Full article

This study evaluates the landslide susceptibility of Zonguldak Province, Türkiye, by integrating the Analytical Hierarchy Process (AHP), artificial intelligence (AI) algorithms, and SBAS-InSAR deformation data. Eight environmental and geological parameters—elevation, slope, aspect, lithology, hydrogeology, land use, and distances to rivers and roads—were weighted using AHP and analyzed through 25 AI models. Among them, the Ensemble Bagged Trees (EBT) algorithm achieved the highest predictive accuracy (84%), demonstrating strong adaptability to complex geological datasets. The resulting susceptibility maps were validated using both traditional landslide inventories and InSAR-derived deformation maps, achieving an overall agreement of 83.05%. This dual-validation approach allows for the identification of unrecorded or active slope movements not captured in existing inventories. The combined use of AHP and AI significantly improves model reliability by incorporating both expert judgment and data-driven learning. The study introduces a novel hybrid framework for landslide susceptibility mapping and provides a valuable reference for disaster risk management and spatial planning in regions with complex topography. This study also contributes to sustainability by supporting risk-informed land-use planning, reducing potential economic losses, and enhancing environmental resilience in landslide-prone regions. The proposed framework aligns with sustainable development goals by integrating geospatial technologies and data-driven approaches for long-term hazard mitigation. Full article