Search Results (1,986)

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction. Full article

Urban soundscape management is a central challenge to the livability and sustainability of cities and requires approaches that complement level indicators with frameworks capable of integrating context, use and experience. In this framework, the present work applies a multilayer methodology to the Old Town of Bilbao, understood as a useful case study to explore the applicability of soundscape reading in historic centers with intense coexistence of commercial, hospitality and catering uses, pedestrian, logistical and cultural uses. The methodology is organized into two phases. The first focuses on the recording and documentation of control points and routes through sound fieldwork, perceptual descriptions and homogeneous systematization of information. From this corpus, a qualified sound map and a first visual characterization of the sound identity are elaborated. The second phase presented in this article, consists of the interpretative synthesis of the corpus through five analytical dimensions and the preparation of fragments and sound sequences conceived for future application through reactivated listening. The results are presented at three levels: (1) a traceable documentary corpus of records, files and synthetic representations; (2) a comparative reading by dimensions that identifies spatial contrasts between interior, exterior and perimeter, as well as relationships between urban form, uses, persistence, masking and salience; and (3) a set of operational audio materials prepared for subsequent comparison with inhabitants and users. In a transversal way, type–token reading distinguishes between the diversity of sounds and dominance by repetition. The article does not yet carry out participatory validation of these materials; its contribution consists of proposing and applying a traceable analytical protocol as a basis for future phases of social contrast and applied discussion. Full article

A method for integrated brand analysis and strategy is developed in this work. The foundation of this method is market research, through which the relevance of brand attributes, their evaluation for competing brands and the market performance of these brands on the steps of the buying process are determined. On this basis, the overall evaluation of brands and their number of brand attributes with the best evaluation are calculated so that strategic decision guidelines for overall brand positioning can be deduced. These strategic decision guidelines are securing the brand based on the existing identity/image, developing the brand based on the existing identity/image, developing (pivoting to) a new brand identity/image, whilst securing the strengths of the existing identity/image, and developing a new brand identity/image. On the level of brand attributes, the weighted relevance of attributes and their evaluation difference to the best competitor are calculated so that, again, strategic decision guidelines can be deduced. The strategic decision guidelines on brand attribute level are securing the attributes as the core brand identity (first priority), selecting and developing the attributes to the core brand identity (second priority), securing the attributes as the extended brand identity (third priority), and selecting and developing the attributes as the extended brand identity (fourth priority). Based on the market performance of brands across the stages of the buying process, the conversions between these steps are determined. On this basis, strategic decision guidelines for market cultivation are deduced, i.e., awareness, image, sales, and loyalty strategies. To gain first indications of the validity of the method for integrated brand analysis and strategy, it is applied to food retail and chocolate brands in the German market. Future research should focus on further validating the method and enhancing it by integrating segmenting and targeting processes and, potentially, marketing measures on an operational level. Full article

Digital transformation has profoundly reshaped caregiving practices, yet its influence on family cohesion within disability contexts remains underexplored, particularly in Arab societies. This qualitative phenomenological study examines how digital technologies shape family cohesion among Jordanian caregivers of children with disabilities. In-depth, semi-structured interviews were conducted with 22 primary caregivers, and data were analyzed using reflexive thematic analysis. The findings reveal a central tension of being “between connectivity and care,” articulated through four interrelated themes: (1) a digital double-bind in which online support networks function as a vital “virtual village” while simultaneously contributing to intra-familial fragmentation; (2) the reconfiguration of care labor, whereby digital management emerges as an invisible and gendered form of caregiving work, often positioning mothers as primary digital coordinators; (3) the translation of traditional social capital (wasta) into digital spaces to navigate systemic resource constraints, producing new moral and emotional burdens; and (4) the strategic use of digital platforms to preserve cultural, religious, and familial identity in the face of stigma, thereby reinforcing internal cohesion. These findings suggest that digital technologies do not merely facilitate connection but actively reconfigure family dynamics through ongoing negotiation between support and strain. The study underscores the need for family-centered digital inclusion policies and support interventions that mitigate digital burdens while harnessing technology’s potential to strengthen culturally grounded resilience among families of children with disabilities. Full article

Soil physicochemical and biochemical properties are fundamental to soil processes and ecosystem functioning in forest environments, yet their responses to dominant tree species in humid montane regions remain largely ununderstood. This study examined the effects of three widespread broadleaf species—Quercus pontica, Quercus petraea, and Fagus orientalis—on soil physical, chemical, and biochemical properties in natural forests in the Eastern Black Sea region, where these species play key ecological roles in structuring forest composition and biogeochemical processes. A total of 15 soil samples (5 per forest type) were collected under comparable climatic and geological conditions and analyzed for particle-size distribution, pH, electrical conductivity (EC), soil organic carbon, and key microbial activity indicators. Significant differences in soil properties were detected among forest types. Soils under Q. pontica were characterized by the lowest silt content and pH, but the highest sand content, soil organic carbon, microbial biomass carbon (C_mic), and microbial respiration. In contrast, soils under Q. petraea exhibited the highest clay content and pH, whereas F. orientalis soils showed lower sand content, EC, soil organic carbon, microbial biomass nitrogen (N_mic), and basal respiration. Multivariate analyses revealed that soil texture, pH, and C_mic are key factors driving soil differentiation across forest types. These patterns indicate that species-specific litter inputs and belowground processes regulate soil biochemical functioning by altering resource availability and habitat conditions. Crucially, this study sheds light on the soil-forming responses of these ecologically dominant species and their impacts on carbon cycle pathways and microbial dynamics at the regional scale. Overall, the study shows that tree species identity is a critical factor influencing soil function, with significant consequences for forest management, carbon sequestration strategies, and ecosystem resilience to changing environmental conditions. Full article

Asset discovery is a fundamental but inherently flawed capability in cybersecurity, as current methodologies frequently confuse preliminary discovery observations with definitive asset inventories, thereby obscuring uncertainty, restricting auditability, and eroding trust in security-critical decision-making. This work addresses the issue of inconsistent asset identification in dynamic IT settings by presenting an evidence-based architectural paradigm that clearly distinguishes observation, identity resolution, and inventory representation. The principal research aim is to develop and authenticate an architecture that maintains discovery evidence, facilitates deterministic, verifiable identity resolution, and supports interpretable inventory derivation. In contrast to state-centric and model-driven methodologies, the proposed architecture enhances (i) traceability through the preservation of time-scoped, method-attributed observations, (ii) identity continuity amidst dynamic conditions such as IP reassignment and infrastructure modifications, and (iii) auditability by facilitating the reconstruction of inventory claims from foundational evidence. An examined proof-of-concept implementation in a controlled yet realistic network environment shows superior identity stability, greater discovery traceability, and retention of historical context relative to traditional inventory models. The results validate the practicality and architectural benefits of the strategy; nevertheless, the evaluation is constrained by a lack of formalised performance indicators and adversarial robustness, which are recognised as priorities for further investigation. Full article

Fatal intraspecific aggression in brown bears (Ursus arctos) remains poorly documented, yet elucidating its dynamics is critical in order to understand species’ physiology, informing management strategies, and advancing wildlife forensic science applications, which are useful in cases where a natural or illegal cause of death needs to be discerned. In this study, we reported four confirmed cases of lethal aggression (two yearlings and two adults) in the Italian Alps. Comprehensive autopsies were performed to characterize lesion patterns and infer the aggressor identity. Claw-induced lacerations, bite marks and the aspect of hemorrhages suggested the attack sequence. Aggressor identity was investigated by using forensic odontology through inter-canine distance (I-CD) and genetic analysis of peri-lesional saliva. I-CD allowed us to plausibly hypothesize the aggressor’s species and, in the cases where it was possible, to classify the sex and/or age group of the aggressors. While genetic analysis allowed the identification of the four brown bear victims, it did not provide informative results on the aggressors. The cause and manner of death were coded according to international criteria (International Classification of Diseases 11th Revision [ICD-11], WHO). Adult fatalities, supported by gastric content analysis, reflect trophic competition regardless of the mating context and highlight the role of anthropogenic food sources in conflict emergence. These findings underscore the value of integrated approaches in wildlife investigations and provide new insights into ecophysiological factors driving lethal intraspecific aggression. Full article

This paper presents a novel data fusion framework that integrates Automatic Identification System (AIS) data with radar surveillance for real-time vessel monitoring in inland restricted waterways. The approach exploits the complementarity between heterogeneous sensors: AIS provides semantic information with temporal sparsity, while radar offers high-frequency observations without vessel identity. The proposed solution combines Kalman filtering and Newton interpolation (K-N) for high-resolution AIS resampling, followed by optimal data association using the Kuhn-Munkres (KM) algorithm. By formulating data association as a global optimization problem, the framework achieves globally optimal sensor fusion while effectively handling data imbalance through virtual point augmentation. Experimental validation using real-world data demonstrates a matching accuracy of 94.2% in low-density scenarios and 80.1% in high-traffic conditions, with computational efficiency suitable for real-time deployment. The system performs consistently across different waterway geometries, although performance varies slightly between curved and straight channels. By fusing the high temporal resolution of radar data with the rich identity information from AIS, this framework enables more accurate and reliable vessel tracking, providing waterway authorities with enhanced situational awareness for improved traffic management and scheduling in restricted waterways. Full article

Background: Severe hypoglycemia is a major reason for emergency medical service (EMS) activation among patients with diabetes. However, real-world epidemiology, including onset location, timing, caller identity, and prehospital management, remains insufficiently described. This study aimed to characterize these cases and assess prehospital interventions and patient outcomes. Methods: We conducted a retrospective, descriptive study using EMS transport records and emergency department (ED) data from two core hospitals and their regional EMS systems in Japan between January 2018 and December 2023. Included patients were those transported by EMS for hypoglycemia with a corresponding ED diagnosis. Extracted data included patient characteristics, episode location and time, EMS caller identity, prehospital interventions, and clinical outcomes. Results: Among 237 episodes, the median age was 74 years and 59.9% were male. Most events occurred at home (78.1%) and during evening or nighttime hours (51.9%). Family members were the most frequent EMS callers (67.5%), yet 12.5% of patients received bystander medical intervention. EMS teams performed most prehospital interventions (68.8%), primarily intravenous glucose administration (65.2%). At EMS arrival, 16.0% were fully conscious and 21.1% were comatose. Hospitalization occurred in 44.3%. The hospitalization rate was 34.2% among patients who received prehospital intervention and 53.2% among those who did not. Conclusions: Most hypoglycemia episodes were discovered by family members, but bystander intervention was uncommon. Differences in hospitalization rates were observed according to the presence and timing of prehospital intervention. Full article

Short food supply chains (SFSCs) for artisan dairy products promote rural development, cultural preservation, and consumer trust but face challenges not found in mainstream chains. This study focuses on queso Tenate, a traditional cow-milk cheese from central Mexico, and examines how its SFSC organisational structure influences its capacity to ensure food safety, quality consistency, market delivery, and viability. Using a single-case exploratory design, the study applies the Viable System Model (VSM) as a diagnostic framework to map systemic functions within an artisan dairy enterprise. Data were collected through VSM-informed interviews and observations of production and retail practices. The findings show that food safety, quality performance, and market delivery reliability are structurally mediated by systemic coherence, not product characteristics alone. While strong relational coordination and shared identity sustain viability, several functions—particularly coordination, audit, and intelligence—remain person-dependent. This study identifies structural implications for strengthening regulatory coordination and monitoring practices without undermining relational management or artisan identity. The primary contributions are as follows: (i) extending SFSC research through a systemic diagnosis of an artisan dairy chain in an emerging economy; (ii) linking VSM-based organisational study to food safety, quality consistency, and market performance; and (iii) positioning VSM as a conversational tool for SFSC viability. Limitations include the single-case design, reliance on qualitative data, and absence of longitudinal measurements. Future research should compare VSM applications across multiple SFSCs, integrate quantitative analyses, and explore its use as a management tool. The study highlights the role of systemic coherence in ensuring SFSC sustainability and cultural embeddedness. Full article

Accurate, non-contact monitoring of rumination behavior is essential for assessing dairy cow health and welfare, as well as for optimizing feeding strategies and herd management in modern precision livestock farming. However, practical deployment in commercial barns faces challenges such as occlusions, variable lighting, and dynamic cow movements. To address this, we developed a robust, automated vision-based framework for continuous rumination monitoring. The core of our system integrates an enhanced object detection algorithm with a robust tracking module, specifically improved to capture subtle behavioral features and maintain identity under complex conditions. Evaluated on a comprehensive dataset collected from commercial settings under various lighting and occlusion scenarios, our framework achieved high detection accuracy (mAP of 96.26%) and reliable tracking performance (multi-object tracking accuracy of 99.2%). This demonstrates its suitability for real-time, on-farm deployment. The study provides a practical, end-to-end solution for fine-grained behavioral analysis in complex environments, offering a tool that can enhance welfare assessment and support decision-making in dairy farm management. The methodological approach is also adaptable to other precision livestock monitoring tasks. Full article

Urban disaster management frequently relies on isolated single-hazard assessments and static census data. This conventional approach systematically obscures the highly dynamic, time-varying nature of population exposure to co-located environmental hazards. This study develops an observation-based, time-adaptive multi-hazard exposure prioritization framework to quantify these spatiotemporal variations. We integrate seismic amplification susceptibility, derived from shear-wave velocity estimates, and empirical pluvial flooding footprints with hourly dynamic living population data at a 250 m grid resolution in Seoul, South Korea. Results indicate that multi-hazard integration refines spatial prioritization, with 11% of high-priority areas diverging from single-hazard models, primarily driven by highly amplifiable alluvial deposits. Furthermore, dynamic living population data revealed clear diurnal exposure shifts. Business districts exhibited a daytime-to-nighttime exposure ratio of 3.35, whereas residential areas showed an inverse ratio of 0.69, demonstrating that identical physical conditions generate markedly different exposure patterns depending on the daily urban rhythm. Based on these temporal dynamics, we classified high-priority zones into Persistent (79.4%), Day-peak (10.3%), and Night-peak (10.3%) transition types. These findings suggest that urban exposure must be managed as a time-varying attribute rather than a static feature. The proposed classification supports targeted mitigation: structural improvements for Persistent areas, dynamic crowd management for Day-peak zones, and localized alerts for Night-peak zones. Driven by globally accessible mobile data, this framework provides a transferable foundation for exposure-informed urban resilience planning across diverse metropolitan environments. Full article

This article examines contemporary forms of algorithmic governance through a biopolitical framework grounded in Michel Foucault’s analysis of security, risk, and governmentality. Rather than treating algorithmic systems as a rupture with earlier modes of power, the article argues that they intensify a security-based rationality already oriented toward probabilistic reasoning, anticipatory intervention, and the indirect regulation of conduct. Governance increasingly operates by organizing environments in advance, shaping the conditions under which action becomes possible rather than correcting behavior after the fact. Situating transhumanism within this framework, the article approaches enhancement-oriented projects not as speculative or external developments, but as an extension of biopolitical governance from the regulation of life toward its optimization and redesign. Human capacities become objects of assessment and intervention, shifting the biopolitical subject from a bearer of risk to an upgrade-eligible profile oriented toward projected futures. To conceptualize the form of subjectivity produced at the intersection of algorithmic prediction and transhumanist optimization, the article introduces the heuristic figure of Homo virtualis. This figure describes a form of subjectivity in which individuals are approached through predictive profiles rather than stable identities, and responsibility shifts toward managing expected outcomes rather than accounting for past actions. By examining these shifts, the article contributes to debates on algorithmic governance by clarifying how biopolitics, prediction, and subjectivity are reconfigured as futures become increasingly organized in advance. This article adopts a descriptive and analytical approach rather than a normative one. Full article

This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management. Full article

Rural architectural heritage sites in Korea, such as rice mills, breweries, and granaries, face increasing risks of neglect, deterioration, and demolition. Most of these structures are not recognized within formal heritage designation systems, and no established evaluation framework exists. Consequently, their conservation and management remain challenging. This study proposes a comprehensive evaluation framework for the preservation and utilization of rural architectural heritage. Based on a literature review and expert consultation, 18 evaluation indicators were derived and grouped into six value criteria: historical, architectural/artistic, social/cultural, landscape, economic, and utilitarian values. The Analytic Hierarchy Process (AHP) was employed to determine the relative importance and priority of these indicators. Historical value received the highest weight, followed by architectural/artistic and social/cultural values. Among the 18 indicators, “representativeness of the period” ranked highest, followed by “rarity,” “historicity,” “local identity,” and “architectural excellence.” However, the indicators associated with economic and utilitarian values had relatively low weights. The framework was validated by applying it to 17 rural architectural heritage sites in Buyeo, South Korea. This study presents a systematic and value-based evaluation framework that reflects the regional and industrial characteristics of rural architectural heritage and provides both policy and practical implications for sustainable conservation and adaptive reuse. Full article