Search Results (347)

Historical small towns (HSTs) embody irreplaceable cultural heritage and territorial identity, facing depopulation, economic marginalization, and infrastructure decay. Improving their liveability and attractiveness is essential to reverse these trends and boost sustainable development. In this context, HSTs are potential drivers of circular and sustainable socio-technical systems, where the circular economy (CE) offers a framework for local sustainability. However, HSTs lack adequate sustainable CE implementation tools. This study, the culmination of the H-SMA-CE project, develops a Decision Support System (DSS) to assist local policymakers in planning CE transitions in Italian HSTs. The DSS integrates three building blocks: context analysis (metabolic flows, stakeholder networks), an intervention library with cost–benefit data, and a composite Municipal Circular Economy Index (MCEI). The tool enables users to assess baseline circularity, simulate scenarios, and identify optimal investment portfolios through multi-objective optimization. This approach allows for the simultaneous evaluation of the benefits of each sustainability aspect, i.e., environmental, economic and social. Tested on the municipality of Taurasi (Italy), an HST with a wine-based economy, the results show that balanced intervention strategies yield greater circularity improvements than single-objective approaches. The paper contributes to the discourse on digital tools for sustainability transitions, offering a replicable model for evidence-based CE governance in heritage-rich territorial contexts. Full article

Background: Medical emergencies occur at varying rates across the globe. Given the significant effort invested in identifying them and assessing dentists’ preparedness to deliver treatment in these life-threatening conditions, a global overview was needed. Materials and Methods: In this scoping review, data from PubMed, Cochrane Library, and Google Scholar databases were examined to identify all relevant studies reporting on the impact of medical emergencies on dentists and determine their operational preparedness at a national or regional level. Operational preparedness was determined in accordance with existing emergency operational preparedness frameworks across six domains: Anticipate, Assess, Prevent, Prepare, Respond, and Recover. Significant Findings: Global data show that dentists will invariably encounter medical emergencies across their careers. However, our investigation found that in countries where there is strong foundational training and regular refresher training, fewer frequent emergencies and stronger operational preparedness are reported. Governmental regulation emerged as a key facilitator of operational preparedness. Still, barriers exist, primarily limited access to medical emergency courses, shortages of office supplies for emergency drugs and materials, and the absence of medical emergency registries. Conclusions: A reassessment of the medical emergency training courses’ content appropriateness is paramount. Training interventions should also focus on raising awareness about the importance of preventive measures and office optimization through planning. Further research is needed to identify any overlooked facilitators and barriers to operational preparedness in medical emergencies. This will help identify opportunities for improvement and minimize the impact of emergencies on dental practices. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Heritage Building Information Modeling (HBIM) is accelerating the transition from reactive restoration to preventive conservation in architectural heritage management. Nevertheless, research at the heritage-cluster scale remains limited, particularly in terms of multi-source data integration, dynamic value–risk coupling, and lifecycle-oriented decision support. This study proposes an intelligent HBIM-based framework designed to support integrated data processing, automated value–risk assessment, and preventive intervention planning for masonry heritage clusters. The framework is validated through its application to the Suopo Ancient Watchtower Complex in Danba, Sichuan, consisting of 84 polygonal stepped-in stone towers. By integrating 3D laser scanning, unmanned aerial vehicle (UAV) oblique photogrammetry, and historical archival data, a closed-loop workflow is established, spanning data acquisition, parametric semantic modeling, and intervention prioritization. A dedicated parametric component library and hierarchical semantic database tailored to irregular polygonal masonry significantly enhance modeling consistency, semantic coherence, and cross-building reusability. Leveraging the Revit Application Programming Interface (API) and Dynamo, the framework embeds a value–risk model (P = V × R), enabling automated component-level evaluation, real-time visualization of conservation priorities, and one-click generation of intervention lists. Results demonstrate improved modeling accuracy, efficiency, and decision reliability compared with conventional manual workflows. The framework offers a scalable and replicable pathway for sustainable conservation of masonry heritage clusters in high-seismic regions and provides a foundation for future integration with IoT-enabled digital twin systems. Full article

Background: Family and friend caregivers provide essential support across health and social care systems but remain inconsistently identified, assessed, and supported in routine practice. Although numerous caregiver needs’ assessment instruments exist, many focus on burden, distress, or preparedness rather than explicitly eliciting caregiver-defined support needs, limiting their utility for care planning, care transitions, and system integration. Methods: We conducted a rapid scoping review to identify and characterize caregiver needs’ assessment tools developed for family and friend caregivers. Searches were conducted in MEDLINE, PsycINFO, CINAHL, Web of Science, Health and Psychosocial Instruments, and the Cochrane Library. Eligible studies described the development, validation, or implementation of instruments designed to assess caregiver needs. Data were extracted on tool characteristics, domains assessed, administration methods, and implementation-relevant features. Item-level content analysis distinguished caregiver-defined support needs from related constructs, including burden, strain, preparedness, and care-recipient monitoring. Results: Forty-three studies describing caregiver needs’ assessment instruments were included (19 instruments; 17 instrument families). Tools varied widely in length, administration, and conceptual framing. Seven domains of caregiver-defined support needs were identified: caregiver health and self-care; emotional and psychological support; information, communication, and navigation; practical and instrumental support; social and relational support; autonomy and life participation; and spiritual, cultural, and existential support. Information and navigation needs were most frequently assessed, while autonomy and spiritual domains were least consistently represented. Many instruments demonstrated construct drift, assessing stressors or impacts rather than explicitly eliciting caregiver-defined support needs. Few tools were designed for longitudinal reassessment, workflow integration, or documentation within electronic medical records. Conclusions: Existing caregiver needs’ assessment tools inadequately support routine, system-integrated caregiver-centered care. Advancing caregiver-centered practice requires tools that explicitly elicit caregiver-defined support needs and are designed for workflow integration, longitudinal use, and interdisciplinary care pathways. Full article

Background: Embodied artificial intelligence (EAI), integrating advanced AI algorithms with robotic platforms capable of sensing, planning, and acting, has emerged as a transformative approach in healthcare delivery. This systematic review synthesizes evidence on robotic perception, decision-making, and clinical impact of EAI systems in healthcare settings. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library for studies published between January 2020 and August 2025. Seventeen studies met eligibility criteria, spanning four domains: surgical assistance, rehabilitation, hospital logistics, and telepresence. The protocol was prospectively registered in PROSPERO under ID: CRD420261285936. Results: Perception architectures predominantly employed multimodal sensor fusion, combining vision with force/torque, depth, and physiological signals. Decision-making approaches included imitation learning, reinforcement learning, and hybrid symbolic-neural control. Key findings indicate that surgical robots demonstrated consistency advantages in specific experimental tasks, rehabilitation robotics produced statistically significant improvements (SMD = 0.29) across 396 randomized controlled trials, and both logistics and telepresence systems achieved very high operational success levels. Nonetheless, important barriers remain, including limited external validation, small sample sizes, and insufficient cost-effectiveness data. Conclusions: Future research should prioritize standardized benchmarks, prospective multicenter trials, and patient-centered outcome measures to facilitate clinical translation of EAI technologies. Full article

Introduction: Intensive chemotherapy protocols and hematopoietic stem cell transplantation (HSCT) in children with cancer frequently lead to severe complications, such as mucositis and immune dysfunction. A growing body of evidence indicates that these complications are closely associated with the patient’s nutritional status and the composition of the gut microbiome, which becomes profoundly destabilized as a result of cytotoxic therapy and antibiotic use. Background: The aim of this review is to critically evaluate the current state of knowledge on the interplay between gut dysbiosis, metabolomic profiles—with particular emphasis on short-chain fatty acids (SCFAs)—and treatment-related toxicity in pediatric patients, as well as to delineate pathways toward personalized nutritional therapy. Methods: A narrative review was conducted, including clinical and preclinical studies published between January 2015 and October 2025. PubMed/MEDLINE, Embase, Cochrane Library, and other databases were searched, focusing on changes in microbiome composition, correlations between gut-derived metabolites and the severity of complications (sepsis, graft-versus-host disease [GvHD], mucositis), and the effects of targeted nutritional interventions (probiotics, prebiotics, postbiotics, and fecal microbiota transplantation [FMT]) on microbiome modulation during anticancer therapy. Results: The analysis demonstrates that pediatric oncologic treatment leads to a marked reduction in microbial diversity, including the loss of protective Clostridiales taxa (e.g., Faecalibacterium), accompanied by an overgrowth of Proteobacteria pathobionts. Metabolomic profiling indicates that low SCFA levels (e.g., butyrate < 20–50 µmol/g) are a strong predictor of severe mucositis, prolonged neutropenia, and an increased risk of sepsis. Interventions aimed at restoring eubiosis and enhancing SCFA production show potential in strengthening the intestinal barrier, modulating immune responses, and enabling maintenance of the planned relative dose intensity (RDI) of chemotherapy by reducing treatment-related toxicity. Conclusions: Gut microbiome profiling and fecal metabolomics represent promising prognostic tools in pediatric oncology. There is an urgent need for further research employing “omics”-based approaches to develop precise, individually tailored nutritional protocols. Such strategies, including postbiotics and FMT, may minimize treatment-related adverse effects and improve long-term clinical outcomes in pediatric patients. Full article

Background and Objectives: This systematic review investigates the relationship between preoperative psychological disorders and postoperative satisfaction among patients undergoing aesthetic surgery. While aesthetic procedures can enhance self-image, growing evidence indicates that underlying mental health conditions, particularly BDD, depression, and anxiety, may compromise surgical outcomes. Materials and Methods: A comprehensive literature search was performed in PubMed, the Cochrane Library, and Google Scholar between January 2010 and December 2024. Eligible observational studies assessed preoperative psychological conditions—primarily body dysmorphic disorder, depression, and anxiety—using validated instruments, such as the Body Dysmorphic Disorder Questionnaire (BDDQ), Body Dysmorphic Disorder Examination (BDDE), Hospital Anxiety and Depression Scale (HADS), Beck Depression Inventory (BDI), and structured clinical interviews, and reported postoperative patient-reported satisfaction following aesthetic surgery. Study quality was evaluated using an adapted QUIPS framework. Results: Across the 13 included studies, six reported a negative association between moderate-to-severe preoperative psychological symptoms and postoperative satisfaction, five found no significant association, and two described positive or conditional associations. Methodological heterogeneity in psychological assessment tools and satisfaction measures was a major source of divergence across studies. Despite these differences, the evidence underscores the need for standardized, validated psychological evaluation protocols in aesthetic surgery. Incorporating mental health screening into routine surgical planning can enhance ethical practice, reduce dissatisfaction, and improve long-term patient outcomes. Conclusions: These findings advocate for a multidisciplinary approach that includes psychological assessment as an essential component of patient care in aesthetic medicine. Full article

In practice, village planning often suffers from an “emphasis on plan preparation but neglect of implementation”, a challenge that is especially evident in Sichuan Province, China, where highly diverse landforms and uneven development foundations make one-size-fits-all evaluation approaches difficult to apply. This study aims to develop a locally adaptable and operational method to quantify village planning implementation effectiveness control, enabling cross-type comparison and bottleneck diagnosis. We construct a three-level indicator system spanning eight domains—baseline control, land-use layout and construction, ecological protection and restoration, industrial development, infrastructure, public service facilities, living environment, and disaster prevention and mitigation—and determine indicator weights using the Analytic Hierarchy Process (AHP). To capture both compliance and progress, a dual-path scoring strategy is employed: constraint-based indicators are assessed using a threshold method by comparing current values (T1) with planning standards/thresholds (T2), while expectation-based indicators adopt a progress-ratio method incorporating baseline values before plan preparation (T0), current status (T1), and targets (T2). Three representative villages—Gaohuai (peri-urban integration), Sanlongchang (agglomeration and upgrading), and Lianmeng (characteristic protection)—are examined. Results show medium-to-high comprehensive scores (81–85) with pronounced type differences: Gaohuai ranks highest (85.37), whereas Sanlongchang is lowest (81.40), and Lianmeng is intermediate (83.71). Comparative diagnosis reveals shared bottlenecks driven by the superposition of “quota–space–ecological constraints”, alongside type-specific weaknesses requiring differentiated control strategies. The proposed framework offers a replicable, multi-source-data-oriented tool for implementation monitoring and adaptive policy adjustment. The novelty lies in reframing village plan implementation evaluation as implementation control effectiveness under a baseline-constrained planning system, while operationalizing a dual-path, unified-scale scoring scheme with a type-screenable indicator library for cross-type comparison and checklist-oriented diagnosis. Full article

Cities worldwide face profound morphological changes due to population growth and urban densification. Coupled with climate change, this exacerbates the Urban Heat Island (UHI) effect and degrades outdoor thermal comfort. This paper introduces a novel simulation framework for climate-resilient urban design, transitioning from static planning standards to dynamic performance optimization. This research utilizes a multi-tiered data acquisition strategy, beginning with a PRISMA-guided Systematic Literature Review of 133 articles to identify key UHI mitigation variables. A high-fidelity, multi-physics Computational Fluid Dynamics (CFD) model was developed using the ANSYS Fluent solver, discretized with a poly-hexacore mesh of over 78 million cells. The simulation environment integrates multiscale data, including 2.5D urban geometry from GIS platforms, high-resolution satellite information (e.g., Copernicus and LiDAR) for surface and soil properties, and EUMETSAT weather files for boundary conditions. The model explicitly resolves aerodynamic and thermodynamic exchanges using Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, with vegetation represented via porous-medium parameterization. The core novelty lies in the development of a parameterized library of “Architectural Elements” (AEs) that introduces standardized material properties, derived from Ansys Granta Selector, directly with GIS-based street designs. This allows for iterative “what-if” scenario analyses over critical 24 h periods to assess the synergistic impact of green infrastructure (GI) and advanced materials. Validation against real-world monitoring data from the Grow-Green project confirmed the model’s accuracy, with a maximum error of only 0.22%. The results demonstrate that interconnecting isolated green areas and utilizing local porous materials can reduce UHI spot temperatures by 2–4 °C while significantly lowering building energy consumption. Full article

This paper introduces LeapNP (Learning and Planning Framework for Numeric Problems), a lightweight, Python-native framework engineered to support both classical and numeric planning tasks. Designed with a fully modular interface, it specifically aims to facilitate the seamless integration of deep learning methodologies. The design philosophy of LeapNP stems from the observation that traditional planners, while highly efficient, lack the necessary flexibility for experimental research, particularly at the intersection of learning and planning. Most state-of-the-art engines are built as highly optimized, rigid executables that are resistant to internal modification. LeapNP disrupts this paradigm by offering a framework where the entire planning stack is accessible and mutable. Users can seamlessly plug in custom implementations for grounding, define novel state representations, or design bespoke search strategies, thereby enabling a level of integration with learning models that is currently impractical with standard tools. By significantly lowering the engineering barrier, our planner fosters rapid experimentation and accelerates research in neuro-symbolic planning. We also present a comprehensive suite of search algorithms, designed to evaluate different properties of learned heuristics. These include two algorithms designed to exploit batching to maximize inference throughput, and a greedy algorithm meant to test the intrinsic robustness of the learned models, running them as general policies. Full article

Quantum computing threatens widely deployed public-key cryptosystems, accelerating the adoption of Post-Quantum Cryptography (PQC) in practical systems. Beyond asymptotic security, the feasibility of PQC deployments depends on measured performance on real hardware and on implementation-level overheads. This paper presents an experimental evaluation of five post-quantum digital signature schemes (CRYSTALS-Dilithium, HAWK, SQISign, SNOVA, and SPHINCS+) and three key encapsulation mechanisms (Kyber, HQC, and BIKE) selected to cover multiple PQC design families and parameterizations used in practice. We implement a TCP client–server testbed in Python that invokes C implementations for each primitive—via standalone executables and, where provided, in-process dynamic libraries—and benchmarks key generation, encapsulation/decapsulation, and signature generation/verification on two Windows 11 commodity processors: an AMD Ryzen 7 4000 (8 cores, 16 threads, 1.8 GHz) and an Intel Core i5-1035G1 (4 cores, 8 threads, 1.0 GHz). Each operation is repeated ten times under a low-interference setup, and results are aggregated as mean (with 95% confidence intervals) timings over repeated runs. Across the evaluated configurations, lattice-based schemes (Kyber, Dilithium, HAWK) show the lowest computational cost, while code-based KEMs (HQC, BIKE), isogeny-based (SQISign), and multivariate (SNOVA) signatures incur higher overhead. Hash-based SPHINCS+ exhibits larger artifacts and higher signing latency depending on the parameterization. The AMD platform consistently outperforms the Intel platform, illustrating the impact of CPU characteristics on observed PQC overheads. These results provide comparative evidence to support primitive selection and capacity planning for quantum-resistant deployments, while motivating future end-to-end validation in protocol and web service settings. Full article

With the advancement of task-oriented reinforcement learning (RL), the capability of quadruped robots for motion generation and complex task completion has significantly improved. However, current control strategies require extensive domain expertise and time-consuming design processes to acquire operational skills and achieve multi-task motion control, often failing to effectively manage complex behaviors composed of multiple coordinated actions. To address these limitations, this paper proposes a motion policy generation method for quadruped robots based on multimodal motion primitives and imitation learning. A multimodal motion library was constructed using 3D engine motion design, motion capture data retargeting, and trajectory planning. A temporal domain-based behavior planner was designed to combine these primitives and generate complex behaviors. We developed a RL-based imitation learning training framework to achieve precise trajectory tracking and rapid policy deployment, ensuring the effective application of actions/behaviors on the quadruped platform. Simulation and physical experiments conducted on the Lite3 quadruped robot validated the efficacy of the proposed approach, offering a new paradigm for the deployment and development of motion strategies for quadruped robots. Full article

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults, with a median overall survival of fewer than 15 months despite standard-of-care treatment. Accurate preoperative prognostication is essential for personalized treatment planning; however, existing approaches rely primarily on magnetic resonance imaging (MRI) and often overlook the rich histopathological information contained in postoperative whole-slide images (WSIs). The inherent spatiotemporal gap between preoperative MRI and postoperative WSIs substantially hinders effective multimodal integration. To address this limitation, we propose a contrastive-learning-based Imaging–Pathology Synergistic Alignment (CL-IPSA) framework that aligns MRI and WSI data within a shared embedding space, thereby establishing robust cross-modal semantic correspondences. We further construct a cross-modal mapping library that enables patients with MRI-only data to obtain proxy pathological representations via nearest-neighbor retrieval for joint survival modeling. Experiments across multiple datasets demonstrate that incorporating proxy WSI features consistently enhances prediction performance: various convolutional neural networks (CNNs) achieve an average AUC improvement of 0.08–0.10 on the validation cohort and two independent test sets, with SEResNet34 yielding the best performance (AUC = 0.836). Our approach enables non-invasive, preoperative integration of radiological and pathological semantics, substantially improving GBM survival prediction without requiring any additional invasive procedures. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article