Search Results (322)

Background/Objectives: Children with cancer admitted to the pediatric intensive care unit (PICU) are at increased risk for post-intensive care syndrome (PICS-p) due to prolonged immobility, deep sedation, and severe illness. The ABCDEF bundle offers a framework for enhancing ICU care and patient recovery, but implementing all components in pediatric oncology patients is challenging. This study assesses the development and implementation of the BRAVE (Beginning Restorative Activities Very Early) initiative, specifically BRAVE2, to integrate the comprehensive ABCDEF bundle and a nurse-led mobility program, in collaboration with rehabilitation specialists, within a pediatric oncology intensive care unit. Methods: BRAVE2 was a quality improvement project conducted in a single pediatric ICU from 2022 to 2023. We analyzed ICU data to assess patient demographics, frequency of physical and occupational therapy (PT/OT) consultations, time to initial mobilization, and delirium screening rates (CAPD score of 9 or higher) for patients with ICU stays over 48 h. BRAVE2 addressed all elements of the ABCDEF bundle, including regular pain assessments, evaluation of spontaneous breathing readiness, sedation adjustments, delirium screening, early mobilization, and family engagement. Outcomes were monitored using statistical process control methods. Results: Of 140 patients, 117 (84%) remained in the ICU for more than 48 h. The delirium screening rate was 15.5%, consistently below the target of 30%. PT/OT consultations within 72 h occurred in 80.7% of patients, and early mobilization in 49.7% of patients, both below the 80% goal. However, 90.4% of patients with tracked mobility were able to ambulate during their ICU stay. No mobility-related safety incidents were reported. Conclusions: Rolling out a full ICU liberation plan in a pediatric oncology ICU is possible, and implementing a comprehensive one is feasible and sustainable despite challenges. Although therapist-led early mobilization did not meet targets, incorporating nurse-led mobility strategies and routine delirium screening has established a scalable model to enhance ICU care and support long-term recovery for these patients. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

Extraintestinal pathogenic Escherichia coli (ExPEC) causes severe infections in humans and animals, yet bovine isolates remain poorly characterized. Here, we report the first complete genome of a bovine ExPEC strain, BN01 (serotype O101:H9-ST10-phylogroup A), isolated from calf meningoencephalitis. Unlike classical ExPEC that typically belong to B2/D phylogroups and O1/O2/O18 serotypes, BN01 represents the A-ST10-O101 sublineage that has emerged as predominant among bovine ExPEC populations. The genome comprises a chromosome encoding 197 virulence factors, with cdiA uniquely identified in BN01 compared to six other representative ExPEC genomes—a contact-dependent growth inhibition system, and three distinct plasmids: a conjugative ESBL carrier (blaCTX-M-164, IncI1), a bovine-associated multidrug resistance island (IncY), and a mobilization-ready vector (IncFII). Animal virulence assays demonstrated that intraperitoneal challenge with E. coli BN01 caused 80% mortality (8/10 mice) within the observation period and yielded an LD₅₀ of 10^6.3 CFU/mouse. These findings demonstrate that high-risk ExPEC can occur in phylogroups typically associated with commensal strains, expanding the conventional understanding of phylogroup–virulence associations. They highlight the need to assess the zoonotic potential of livestock-associated atypical lineages and support integrated genomic surveillance under the One Health framework. Full article

Electric vehicles (EVs) are increasingly regarded as a key component of low-carbon mobility and the sustainable energy transition. However, their large-scale deployment raises challenges that extend beyond vehicle technologies and require a system-level understanding of interactions with power networks, energy resources and users. This paper presents a critical review of the literature published since 2012, examining EV development from an integrated energy perspective that includes vehicle technologies, charging infrastructure, power electronics, grid integration, renewable energy coupling and environmental implications. A structured methodology is used to identify and analyze peer-reviewed studies, with particular emphasis on high-impact review articles that consolidate knowledge across disciplines. The analysis shows that, despite significant technological progress, large-scale EV deployment remains constrained by infrastructure limitations, distribution grid readiness, charging coordination strategies, material availability and socio-technical factors. Simulation-based studies play a central role in anticipating these impacts and informing deployment strategies before real-world implementation. Rather than addressing individual components in isolation, this review highlights interdependencies between technologies, control approaches and energy systems. Based on this synthesis, key research priorities and high-level challenges are identified, providing guidance for future research and policy aimed at enabling EVs to effectively support sustainable ambient energy and mobility systems worldwide deployment. Full article

Background and Aim: Mango (Mangifera indica) is a globally important fruit crop whose productivity is repeatedly threatened by foliar diseases such as anthracnose, bacterial canker, powdery mildew, die-back, and sooty mould. Although deep learning has rapidly advanced automated leaf-disease diagnosis, recently reported accuracies on the MangoLeafBD benchmark are approaching saturation, and many studies still rely on random data splits that may inflate performance through leakage of duplicate or near-duplicate images. This study aimed to develop and rigorously evaluate a leakage-aware, deployment-oriented deep learning framework for the complete eight-class MangoLeafBD task. Methods: Three modern transfer-learning backbones—EfficientNetB0, MobileNetV3Large, and ConvNeXtTiny—were fine-tuned using a two-stage training strategy on a group-aware data partition. Duplicate and near-duplicate images were detected with cleaned filename stems, average hashing (aHash), and difference hashing (dHash) and grouped before splitting to guarantee zero cross-partition overlap. The strongest models were combined through probability-level soft voting. Robustness was further assessed using 5-fold StratifiedGroupKFold cross-validation. Explainability was examined with Gradient-weighted Class Activation Mapping (Grad-CAM), deployment suitability was characterized through CPU latency benchmarking, and the framework was operationalized as a publicly accessible web-based diagnostic system. Results: EfficientNetB0 achieved the highest performance under the leakage-controlled protocol, reaching 99.50% accuracy and 99.50% weighted F1-score on the 599-image group-aware test set. The heterogeneous soft-voting ensemble matched EfficientNetB0 but did not exceed it, indicating that the ensemble gains reported in earlier studies may partly reflect optimistic split conditions. Five-fold grouped cross-validation confirmed stability, yielding a mean accuracy of 99.63% (SD = 0.20%) and a mean weighted F1 of 99.62% (SD = 0.20%). Grad-CAM visualizations showed that the model attended to biologically meaningful lesion regions across all eight classes, and CPU benchmarking produced a mean single-image latency of 25.3 ms (p95 = 27.6 ms) with batch throughput scaling up to 166.5 images per second. Conclusions: The proposed framework demonstrates that a compact, leakage-aware EfficientNetB0 model can match the accuracy of more complex hybrid CNN–transformer architectures while remaining interpretable and deployable on commodity CPU hardware. By coupling group-aware evaluation, explainable AI, latency benchmarking, and a publicly accessible web application, the study advances reproducible, deployment-ready precision agriculture research and offers a more rigorous benchmarking protocol for future mango leaf disease classification studies. Full article

Mobile applications are increasingly promoted as instruments for improving agricultural information access, advisory delivery, market participation, and decision support, particularly for smallholder farmers in developing nations. Research, however, has been dominated by general technology-acceptance and diffusion constructs, while the design-sensitive and infrastructural mechanisms studied in human–computer interaction (HCI) have received comparatively little attention. In this paper we develop a parsimonious HCI model of mobile application use in agriculture. Drawing on the technology acceptance model, the unified theory of acceptance and use of technology, diffusion of innovations, socio-technical systems theory, and human–computer interaction for development (HCI4D), the model proposes that agricultural application use is driven by five antecedent domains: perceived agronomic value, inclusive usability and accessibility, contextual and cultural fit, trust and transparency, and social and institutional embeddedness. Each plays a distinct role across three use stages: adoption intention, sustained use, and decision impact. Contextual constraints (infrastructure and farmer characteristics) moderate these relationships. We develop six testable propositions from the model. The model is conceptual: it is offered as a framework for empirical testing rather than as a validated account of farmer behavior. The paper contributes an HCI-sensitive specification of mobile application use under agricultural field conditions: a “field-ready” conception of mobile HCI. Full article

Tibial spine avulsion injuries represent a distinct pattern of anterior cruciate ligament (ACL) injury in children and adolescents, reflecting the unique anatomical and biomechanical properties of the skeletally immature knee. Unlike midsubstance ACL ruptures, these injuries involve avulsion of the tibial insertion and pose specific diagnostic and therapeutic challenges. Management strategies remain heterogeneous, particularly for partially displaced fractures, with variation in surgical indications, fixation techniques, and rehabilitation protocols. This narrative review provides a structured synthesis of current evidence on the anatomy, biomechanics, clinical presentation, and management of pediatric tibial spine avulsion injuries. Nonoperative and operative approaches are compared, with attention to fixation strategies, complications, physeal considerations, and rehabilitation principles. Return-to-sport (RTS) outcomes are examined, with available evidence suggesting that RTS rates may be comparable between operative and nonoperative management in selected patients, although interpretation is limited by heterogeneous and predominantly retrospective data. Early mobilization appears important for reducing arthrofibrosis risk, while rehabilitation should be individualized. RTS decision-making remains inconsistent, with commonly used criteria largely extrapolated from ACL reconstruction literature and lacking validation in pediatric populations. Multifactorial assessment incorporating functional testing and patient-reported outcomes is increasingly advocated, although evidence for psychological readiness remains limited. Overall, the current literature is characterized by methodological heterogeneity and limited comparative data, restricting definitive conclusions. This review provides a clinically oriented synthesis while highlighting key uncertainties and priorities for future research. Full article

Phishing remains a widespread and evolving cyber threat that targets human and technical vulnerabilities across email, web, mobile, and social media. Meanwhile, digital forensics has developed into a standards-driven discipline dedicated to identifying, preserving, analysing, and presenting digital evidence. Despite overlapping goals, phishing detection research and digital forensics typically operate separately. Detection efforts emphasise classification accuracy and rapid mitigation, while forensic practices prioritise evidential integrity and incident reconstruction. The analysis suggests that incorporating forensic-quality artefacts, such as Simple Mail Transfer Protocol (SMTP) headers, Domain Name System (DNS) and Transport Layer Security (TLS) traces, memory dumps, behavioural logs, metadata, and provenance records, may support attribution analysis, interpretability, and more evidentially robust incident reporting. It covers email, network, endpoint, behavioural, and legal areas to identify common shortcomings in forensic readiness, provenance preservation, and reproducibility. Based on these insights, we propose a conceptual framework that redefines digital forensics as a proactive, ongoing capability integrated into operational phishing defences. The review highlights gaps in research, such as the limited availability and validation of AI-generated phishing datasets, privacy-aware evidence management and deanonymization risks in evidence correlation, and automated workflows for handling evidence. It also suggests future directions for integrating forensic reasoning into advanced phishing mitigation systems. Full article

Artificial intelligence (AI), building information modeling (BIM), and digital twins are increasingly transforming construction sites into smart, data-driven environments that support safer, more efficient, and more sustainable building and urban infrastructure delivery. However, site-level spatial decision-making related to site layout optimization (SLO) remains constrained by fragmented data environments, limited interoperability, and weak integration between planning, monitoring, and adaptive decision-making. This study presents a systematic literature review of how AI, BIM, and enabling digital technologies are being applied to support smart construction site management, site-level spatial decision-making, and SLO-related applications. A Scopus-based search conducted in October 2025 identified 169 records, of which 63 studies were retained following PRISMA-guided screening. Because explicit SLO studies remain limited, the review synthesizes both directly relevant SLO studies and contextually relevant enabling studies with clear implications for smart and sustainable construction operations. The review combines bibliometric analysis, thematic content analysis, and cross-functional technology mapping to examine the intellectual structure of the field, the main operational domains addressed, and the dominant technological convergences supporting intelligent site decision-making. The findings show that the field is expanding rapidly but remains unevenly consolidated, with greater evidence concentration and practical readiness in real-time digital twin and spatial data management, automated monitoring, and proactive safety intelligence than in closed-loop logistics coordination and autonomous mobility. Across application domains, the dominant technology convergences combine machine learning and deep learning with multidimensional BIM, frequently extended through digital twins, sensors, cloud platforms, UAVs, simulation tools, and GIS-related infrastructures. The review further shows that the main barriers to deployment are not merely algorithmic, but also relate to interoperability, data quality, implementation complexity, human oversight, and limited field validation. Overall, this study provides a structured synthesis of evidence concentration, practical readiness, dominant patterns, and unresolved gaps of AI-BIM-enabled smart construction site management, and outlines directions for more interoperable, human-centered, and field-validated systems that support sustainable smart building and urban infrastructure delivery. Full article

Urban mobility planning in smart cities requires sophisticated simulation tools, yet their complexity often creates a technical barrier for non-expert stakeholders. This paper presents a novel architecture that integrates generative artificial intelligence with digital twin technology to create an accessible and decision-support prototype. The framework employs a conversational AI agent based on Gemini 2.5 Flash Lite to interpret natural language intentions and translate them into validated simulation parameters. A critical safety layer, built using Pydantic, ensures that the agent’s stochastic outputs adhere to strict technical schemas and predefined logical bounds before execution. The underlying digital twin, developed with SimPy, NetworkX, and OSMnx, features a multi-source data integration strategy that includes demographic density (INE), tourism activity (ISTAC), and high-resolution traffic statistics (TomTom) to calibrate vehicle behavior. The architecture was technically demonstrated through a Technology Readiness Level (TRL) 4 proof-of-concept in Las Palmas de Gran Canaria, simulating multimodal scenarios including buses, the future MetroGuagua (BRT), and pedestrian flows. Results demonstrate a 96% success rate in intent recognition and configuration mapping, with end-to-end execution times under 20 min for a 19 h simulated day. This study demonstrates that LLM-driven orchestration, coupled with automated data pipelines and a decoupled microservice architecture, can lower technical barriers to urban simulation, which could support broader participation in future smart city deployments. Full article

Smart city mobility is increasingly governed by a techno-solutionist logic that prizes data, automation, and efficiency, often at the expense of public trust, social legitimacy, and lived experience. This article argues that the fate of a mobility transition appears to depend less on the sophistication of the technology than on the pace and posture of change. Building on the CalmMobility framework and on Weiser and Brown’s concept of calm technology, it develops the idea of calm smart mobility—a human-paced, options-first approach in which innovation enters everyday life gradually and with credible alternatives already in place, so that residents are not asked to continuously adapt. The framework’s three pillars (Comprehensiveness; Pacing–Sequencing–Inclusion; Future-Readiness) are mapped onto four recurring challenges of smart mobility (Policy Layering, Affective Mismatch, Governance Silos, and the Future-Readiness Gap) and then used as a descriptive analytical lens to characterize seven documented implementations across economic, spatial, mass-transit, service, and platform interventions and four world regions: the Stockholm congestion charge, the London ULEZ expansion, the Barcelona superblocks, Bogotá’s TransMilenio bus rapid transit and Ciclovía, Seoul’s Cheonggyecheon restoration and bus reform, Helsinki’s Whim Mobility-as-a-Service, and Sidewalk Toronto. Presented through a comparison table, a positioning map, and adoption trajectories rather than rankings, the characterization suggests that the provision of alternatives, the sequencing and pace of change, and the genuineness of co-creation are more closely associated with smooth adoption than the type of instrument deployed. The article is conceptual and framework-building. The cases illustrate and probe the framework instead of validating it, and a testable central hypothesis is specified for future empirical work. Calm smart mobility is offered as a transferable, citizen-centred logic for guiding smart city mobility transitions at a human pace. Full article

Climate change is reshaping the epidemiology of vector-borne diseases in Europe by altering the ecological conditions that determine vector survival, seasonal activity and pathogen transmission. Rising temperatures, milder winters, prolonged warm seasons and changing precipitation patterns are increasing the suitability of parts of Europe for competent mosquito, tick and sandfly vectors. These changes, combined with human mobility and land-use change, increase the probability that imported pathogens encounter permissive conditions for local transmission. This Opinion article examines autochthonous vector-borne disease transmission in Europe, using dengue as a sentinel example of a wider climate-sensitive transition. We discuss how imported viraemic cases, established competent vectors, vector–host contact and delayed clinical recognition can converge to enable local outbreaks. Beyond dengue, we consider West Nile virus, chikungunya, tick-borne encephalitis, leishmaniasis and Crimean–Congo haemorrhagic fever as examples of a broader and increasingly heterogeneous European risk landscape. We argue that the public-health impact of this transition is shaped not only by vector expansion, but also by gaps in surveillance integration, diagnostic readiness, workforce preparedness and One Health coordination. Strengthening climate-informed surveillance, rapid laboratory capacity, frontline clinical awareness and cross-sectoral response systems will be essential to prevent repeated introductions from becoming sustained public-health challenges. Full article

This research represents an advancement in smartphone-based image acquisition methodology, building upon a previous study to estimate the essential oil content of bergamot fruits in situ using a deep learning approach. To overcome an operational constraint due to a bulky portable dark box to standardise illumination, this study proposes a more versatile solution: a mobile application based on a colour card reference. By replacing physical shielding with digital compensation, the app functions as a local colourimetric sensor, enabling real-time correction of images acquired directly in the orchard, regardless of environmental variables such as direct sunlight or shadows. Workflow relies on an automated calibration procedure. Upon image acquisition, the application utilises ArUco Markers to autonomously detect and extract both the colour card and the fruit surface. The core of the innovation lies in the colour calibration algorithm based on RGB histogram matching logic, which calculates the precise chromatic transformation required to align the field data with the reference card data (acquired under controlled conditions). These calculated parameters are then dynamically mapped onto the fruit’s image. The final output is a normalised high-fidelity image, ready for the calculation of chromatic indices, such as the citrus colour index, or for seamless integration into predictive models. The results show that the application is a valid tool for colour calibration, thanks to the good agreement with the values obtained using the inspection chamber. The latter can therefore be replaced by the app, which allows reliable results to be obtained even when used on its own. Full article

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation. Full article

Aqueous precipitation and crystallization are central to impurity removal, product formation, and resource recovery in mineral and chemical processing, but robust inline monitoring remains challenging because supersaturation is not measured directly and conductivity signals are affected by temperature, composition drift, bubbles, solids, polarization, and fouling. Electrical conductivity (EC) is attractive as a low-cost, rugged process analytical tool, yet its usefulness depends on mechanistic interpretation: EC reflects charge-carrier concentration and mobility rather than supersaturation itself. This review organizes the literature into a layered framework covering (i) measurement integrity and deployment, (ii) bulk-signal extraction in multiphase media, (iii) estimation of latent variables such as dissolved concentration or supersaturation proxies, and (iv) control readiness based on conductivity-derived targets. Frequency-aware conductivity extraction, event-anchored verification, and observer-based estimation are treated as optional, complementary modules. A Ca-carbonate/CaCO₃ system is used as an illustrative case because its coupling among conductivity, pH/speciation, supersaturation, and precipitation is especially transparent, although the framework is intended for broader processing systems, including complex liquors and slurries. Opportunities are also highlighted for nanomaterials to improve both precipitation control and EC information content. Full article