Search Results (177)

The Terziali is a shear-hosted orogenic gold prospect located in the Central Anatolian Crystalline Complex, Türkiye. This study focuses on soil geochemistry, element correlations, background and threshold values, and evaluates exploration implications over a survey area of 35.5 km². A total of 1826 soil samples were collected from the B horizon using a grid of 100 × 50 m and were analyzed using ICP-AES, ICP-MS, and fire assay techniques. Statistical techniques of median + 2MAD threshold calculations, descriptive statistics, Kolmogorov–Smirnov tests, correlation analysis, hierarchical clustering, and Q–Q plots were carried out to identify geochemical anomalies. The data demonstrate Au threshold (28 ppb) and peak concentration (460 ppb), non-normal distributions characterized by strong positive skewness, revealing the outliers linked to mineralization. Soil geochemistry indicates a moderate association between Au and As in the four-acid dataset (r = 0.465), although the correlations between Au and Sb and Ag and W are relatively weak. The spatial analysis indicates that Au anomalies are predominantly linked to the NW–SE-oriented Demirli Thrust Fault. As displays extensive dispersion halos surrounding the gold anomalies; it establishes itself as an efficient pathfinder element. Conversely, Sb and W exhibit unique anomaly patterns, whereas Ag patterns are weak and dispersed. The Terziali prospect provides a substantial geochemical framework for identifying structurally controlled orogenic gold systems in Central Anatolia and the western Tethyan metallogenic belt. Full article

Background/Objectives: Household food insecurity is a modifiable social determinant of health with important implications for diet quality and health outcomes. Dietitians are well positioned to identify and respond to food insecurity; however, little is known about how this is addressed in routine dietetic practice across Europe. This exploratory study examined food insecurity screening and response practices among dietitians practicing in Europe and examined associated correlates using the Capability, Opportunity, and Motivation Model of Behaviour (COM-B). Methods: An online cross-sectional survey informed by the COM-B framework was distributed through the European Federation of Associations of Dietitians’ institutional communication channels between February and June 2024. Participants were recruited using a voluntary, convenience-based sampling strategy through professional networks and social media dissemination. A total of 148 dietitians practicing in European countries responded. The questionnaire assessed routine food insecurity screening practices, COM-B correlates, perceived barriers, actions taken following identification, and learning needs. Quantitative data were analysed descriptively, and open-ended responses were used illustratively to contextualise reported practices. Results: Food insecurity screening was not systematically embedded in routine dietetic practice, with 11.6% of respondents reporting routine screening and 30.2% not asking about food insecurity. Identification of food insecurity did not consistently translate into follow-up action, and responses were predominantly referral-based rather than involving direct material support. While capability and motivation to address food insecurity were generally high, opportunity-related factors—such as time constraints, limited organisational support, and unclear referral pathways—emerged as the main barriers shaping professional behaviour. Substantial heterogeneity was observed across practice settings. Conclusions: In this sample of dietitians practicing in Europe, food insecurity screening and response were variable and often constrained by organisational and contextual factors. These findings highlight the need for system-level support and practice-oriented training to facilitate the integration of food insecurity into routine dietetic care. Full article

Convolutional neural networks (CNN) have transformed visual recognition, yet robust geometric reasoning, reliable out-of-distribution generalization, and recognition from limited data remain substantially unsolved. CNNs draw their architectural inspiration from the mammalian visual cortex, but the translation from biology to engineering was selective and, in places, imprecise, and those imprecisions have consequences that are well documented. This paper examines where the biological fidelity holds and where it gives way, grounding the analysis in formal results that predate deep learning and in recent empirical findings on CNN failure modes. We identify three diagnosable architectural limitations. First, CNNs conflate visual modalities that the biological system separates structurally at the lateral geniculate nucleus, feeding raw RGB pixels into a single undifferentiated filter bank and entangling orientation, color, and texture signals from the first layer onward. Second, CNNs repeat a spatial subsampling operation across the full depth of the network, far beyond the early visual cortex stages where it has biological warrant. Barnard and Casasent established formally in 1990 that this operation discards positional information irreversibly at every layer where it is applied, and repeating it into regions that correspond to V4 and inferotemporal cortex compounds this loss without the compensating transition to qualitatively different computations that the biological hierarchy performs. Third, the pooling-as-complex-cell analogy that motivated this design reflects a misreading of what complex cells compute. The spatiotemporal energy model formalizes complex cell behavior as geometry extraction: detecting the presence and orientation of a local edge structure robustly, abstracting over photometric accidents of contrast polarity and sub-wavelength phase that are not geometrically meaningful. Pooling is a tolerable first-stage approximation of this behavior, but as a general-purpose invariance mechanism repeated across the full depth of the network, it is attempting something categorically different, namely object-level position invariance through spatial subsampling, which achieves its goal by discarding exactly the geometric information that the energy model preserves. Treating pooling as a scalable, indefinitely repeatable implementation of complex cell behavior—rather than as a first-stage approximation with a natural biological endpoint at V3—conflates two operations that differ not in degree but in kind, and crucially it removed the principled criterion for confining the S-C operation to early visual cortex: because pooling was understood as a general-purpose invariance mechanism, the field had no architectural reason to stop repeating it. We survey how capsule networks, group-equivariant CNNs, PDE-based networks, and vision transformers each address one or two of these limitations while leaving the others intact. We propose six desiderata that a more biologically complete architecture would need to satisfy and argue that satisfying them requires treating the visual cortex’s solution as a coherent package in which each component depends on the others working correctly, rather than as a menu of independently selectable principles. Full article

Combine harvesters are evolving from machines equipped with isolated monitoring devices into distributed sensing platforms for process supervision, machine diagnosis, and adaptive control. This review summarizes representative research on six major sensing tasks in combine harvesters: grain loss, grain breakage, cleaning load, feed rate, grain-bin state, and grain quality. The reviewed studies are compared within a unified engineering framework that considers sensing target, installation position, signal path, disturbance source, calibration transferability, field robustness, and control relevance. Rather than evaluating sensors only as individual devices, this review emphasizes the coupled design of transducers, structural anti-interference measures, sampling paths, signal processing, and field-oriented validation under vibration-dominated and dust-laden harvesting conditions. The analysis shows that loss-rate and feed-rate sensing are currently the most mature and control-relevant categories, whereas breakage-rate, grain-bin, and integrated quality sensing remain constrained by representative sampling, disturbance resistance, and cross-condition generalization. Future progress will depend on multi-sensor fusion, realistic benchmark protocols, crop-aware calibration transfer, and tighter integration among onboard sensing, machine control, and digital harvesting systems. By clarifying the engineering value of these sensing routes, the review also supports loss reduction, quality preservation, labor-saving operation, and more reliable adaptive control in commercial grain harvesting. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools. Full article

The growing sophistication of the internet and web space has spawned highly dynamic, multi-vector cyber threats that cannot be handled by automated detectives and hence the necessity to introduce analyst-oriented, cognitively powerful security analysis apparatus. The character of current visualization-based security frameworks is that they are inclined to deliver data unproactively, fail to engage the dynamic setting, and fail to comprehend the evolving motive of assailants, resulting in subsequent identification and a fractured understanding of coordinated web attacks. The paper introduces a new model of interactive security visualization known as Context-Oriented Visual Exploration of Resilient Threats (COVERT), a hybrid of behavioral context modeling, adaptive visual storytelling, and intent-sensitive interaction. COVERT is dynamically rearranged to the development of threats, patterns of interaction between analysts, and objectives of the possible attacks, which helps in releasing relevant security capabilities gradually. The framework integrates graphical threat flows, attention-directed visual cues, and real-time feedback loops to align system responses to the thinking processes of the analysts. The evaluation of high-scale web traffic and attack simulation dataset indicates that COVERT is much more effective in the multi-stage detection of attacks, false-positive interpretation is minimized, and the investigation period is reduced compared to the visualization infrastructure of the static and semi-interactive infrastructure. According to user studies, there is higher situation awareness, enhanced correlation of distributed events, and enhanced decision-making in complex web intrusion situations, such as advanced persistent threats and web exploitation coordination. Combining contextual intelligence with adaptive interaction and visualization of security, COVERT reveals that intent-based visual analytics may greatly improve internet and web threat detection and analysis systems to support more agile and resilient cyber defense procedures. The proposed COVERT strategy achieved 93% threat-detection rate, the false positives were reduced to 6%, the response time of the analysts was reduced to 140 s, and the situational awareness was increased to 88%. Full article

Urban fire prevention is shifting from reactive response to proactive risk governance, yet current approaches often overlook risk-type heterogeneity, spatial dependencies, and underlying behavioral mechanisms, especially equitable risk distribution among vulnerable groups. To address this, this study integrates the Pressure–State–Response (PSR) model with environmental criminology theories (Routine Activity Theory (RAT) and Crime Pattern Theory (CPT)) to couple macro social causal chains with micro behavioral–spatial mechanisms. Using data from the digital urban management system of Shenzhen’s Guangming District in 2019, four fire risk event types are examined: electric bike charging violations (EB), unauthorized power wiring (PW), water heater misuse (WH), and aging gas pipelines (GP). Spatial error models explain 82–89% of the variance across fire risk event types, and spatial 5-fold cross-validation shows minimal performance decline (ΔR² = 0.03–0.08), confirming robust prediction without overfitting. Key findings include: (1) elderly proportion is significantly positively associated with WH and PW (coefficients = 2.64 and 3.06, p < 0.01); (2) restaurant density has a consistently positive association with all four risk types (coefficients = 0.24–0.60, p < 0.01); (3) functional diversity and connectivity exhibit dual patterns, showing negative associations with more visible, easily detectable violations (PW, GP) but positive relationships with relatively concealed behaviors (EB); (4) reported safety deficiencies display strong positive associations with all fire risk event types and can therefore serve as an effective early-warning indicator for broader fire risk. These results support risk-specific, equity-oriented prevention strategies that prioritize vulnerable groups and high-risk environments. The validated PSR–RAT/CPT framework provides a novel theoretical basis for targeted fire risk governance and advances safe, resilient, inclusive cities aligned with Sustainable Development Goal 11. Full article

Background: Poor adherence and monotony in home-based pelvic floor muscle training (PFMT) often lead to suboptimal rehabilitation outcomes. Serious games using virtual reality (VR) may improve training motivation and precision. This study aimed to explore user demands for a VR pelvic floor rehabilitation training system with game-based features. Methods: A Kano model-based questionnaire was developed and distributed to patients receiving PFMT. The survey assessed 20 demand items spanning five dimensions: system operation, exercise guidance, personalization, device use, and interaction. Traditional Kano categorization and an optimized mixed-method classification were used to identify core demand attributes. Satisfaction and dissatisfaction indices were also calculated. Results: A total of 112 valid questionnaires were analyzed. Using the Kano model, 20 demand items were classified as attractive (n = 7), one-dimensional (n = 5), must-be (n = 6), or indifferent (n = 2). Personalization-related demands were mainly identified as attractive attributes, whereas exercise guidance-related demands were primarily classified as must-be or one-dimensional attributes. Satisfaction Index (SI) values ranged from 0.27 to 0.64, and absolute Dissatisfaction Index (DSI) values ranged from 0.34 to 0.71. Optimized Kano analysis identified nine mixed attributes. The questionnaire demonstrated excellent internal consistency (Cronbach’s α = 0.96). Conclusions: Participants demonstrated positive willingness to adopt a game-based VR system for PFMT, with diverse needs identified across functional and motivational dimensions. These findings suggest that integrating immersive, personalized, and gamified design features may hold promise for enhancing user engagement and anticipated training adherence, though direct evaluation of clinical effectiveness awaits future prototype-based studies. The identified demand priorities provide structured, evidence-informed guidance for the user-centered design of serious game–oriented VR pelvic floor rehabilitation systems. Full article

Reliable beam management in Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing and Communication (ISAC) systems needs accurate trajectory prediction and a clear sense of prediction risk. Most existing methods use deterministic future positions or raw uncalibrated uncertainty. Under high mobility and uncertainty, this leads to unreliable beam decisions. We design a control-oriented probabilistic trajectory prediction framework. It uses calibrated trajectory uncertainty as a risk signal for adaptive beam management. The framework first combines motion history and visual context to predict trajectory distributions. Split conformal calibration turns raw Gaussian uncertainty into statistically reliable risk bounds. A codebook-constrained beam management strategy adjusts beamwidth based on the calibrated spatial risk. This balances beamforming gain, coverage robustness, and switching stability. Tests on UAV data show better prediction accuracy than representative probabilistic baselines. The raw uncertainty remains under-calibrated, and conformal calibration is therefore applied to improve its reliability before beam-control decisions. Using the calibrated uncertainty for beam control improves communication coverage and cuts outages and severe misalignment in high-risk situations. Calibrated predictive uncertainty can serve as an actionable control variable for robust beam management in dynamic UAV-assisted ISAC environments. Full article

Fluoride contamination in groundwater poses a significant public-health concern in most semi-arid areas such as the Punjab alluvial aquifers of Pakistan, with local concentrations exceeding the WHO guideline. Reliable fluoride dynamics prediction and mechanistic interpretation of fluoride is key for targeted monitoring and risk mitigation. This paper built an integrated hydrogeochemical machine learning model to predict the fluoride concentration and classify exceedance risk in the Rechna Doab aquifer Tehsil Jaranwala, Punjab, Pakistan. Nested cross-validation and independent test evaluation were performed on conventional models (linear regression, random forest, XGBoost) and a deep tabular model (FT-Transformer). Model reliability was evaluated using discrimination and probability-calibration metrics, while Shapley Additive Explanations (SHAP) and permutation importance were applied to identify the main hydrogeochemical controls on fluoride prediction. Moreover, the robustness was tested by noise sensitivity experiments. Fluoride concentrations showed a positive skewed distribution with some local exceedances related to the geogenic and hydrochemical influences. Nonlinear models greatly outperformed the linear baseline; XGBoost showed robust regression performance (test R² = 0.878; RMSE ≈ 0.190 mg/L). The FT-Transformer showed strong exceedance-classification performance, with high sensitivity (recall = 0.875) and good probability calibration (Brier ≈ 0.021). Interpretability analyses identified EC/TDS, Mg²⁺, and Ca²⁺ as important predictors, linking fluoride enrichment to chemically evolved groundwater with reduced calcium activity, sodium enrichment, and alkalinity buffering. The proposed framework provides accurate, interpretable, and risk-oriented support for groundwater fluoride monitoring in alluvial aquifer systems. Full article

This paper addresses a persistent divide in water governance: critical frameworks reveal power dynamics and ontological diversity but lack operational guidance, while operational frameworks prioritize technical management at the expense of ontological plurality and social legitimacy. We introduce Water-Important Socioecological Systems (WISe) as a prescriptive framework that integrates political ontology with hydrosocial territory analysis to inform more reflexive and inclusive water governance. WISe designates specific zones where ecological functions for water sustainability are concentrated and where social practices, productive livelihoods, and symbolic meanings coexist inseparably with biophysical processes. Unlike Integrated Water Resources Management (IWRM), which treats social and ecological dimensions as separate pillars, and the Ostrom Social-Ecological Systems framework, which undertheorizes ontological plurality, WISe explicitly centers the coexistence of multiple ways of understanding and relating to water as a governance principle. The framework was developed through a five-phase mixed-methods conceptual inquiry combining a systematic literature review (202 documents), an exploratory stakeholder survey of 223 participants across six Colombian hydrographic basins, and an analysis of designated water-strategic ecosystems. The findings reveal that ontological diversity is distributed across all stakeholder groups: hydrological supply framings predominate (36.4–45.8%), yet territorial-integrated perspectives appear in all groups, with government actors (22.9%) showing the highest proportion. The majority (56.1%) perceive WISe as exclusively state-managed, revealing a dominant ontological position that reduces socioecological territories to objects of administrative control. This article presents WISe as a conceptual and prescriptive framework informed by exploratory empirical evidence. Rather than offering a definitive empirical validation of the model, this study provides initial analytical grounding for its development and identifies indicative patterns that warrant further testing across other geographical and institutional contexts. WISe offers a framework comprising six defining characteristics and five operational dimensions that bridge theoretical understandings with governance-oriented analysis, treating ontological difference not as an obstacle but as essential knowledge for more reflexive and equitable water governance. Full article

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

Understanding academic achievement requires moving beyond the identification of influential factors toward explaining how these factors are organized into functional learning and cognitive mechanisms. Although prior research has extensively documented the roles of socioeconomic status, student attitudes, and learning behaviors, less attention has been paid to how these elements interact within structured pathways that reflect underlying learning intelligence across educational systems. This study adopts a mechanism-oriented perspective to examine mathematics achievement using data from PISA 2018. Focusing on high-performing regions in East Asia and Western countries, it integrates interpretable artificial intelligence methods with structural modeling to investigate how contextual, psychological, and learning-process factors jointly shape achievement outcomes. The findings show that high achievement is not governed by a single set of dominant predictors, but by distinct organizational mechanisms of learning intelligence. In East Asian systems, achievement follows a chain-like convergent structure, in which socioeconomic background is systematically translated into academic outcomes through sequential psychological and self-regulatory processes. Psychological factors, particularly educational expectations and self-beliefs, function as key mediating mechanisms that organize learning engagement and strategy use. By contrast, high-performing systems in Europe and North America exhibit a parallel configuration, in which multiple cognitive and behavioral factors independently contribute to achievement through more decentralized pathways, reflecting a distributed structure of learning intelligence. Across regions, learning processes such as reading engagement and digital literacy show consistently positive associations with achievement. However, their roles vary depending on how they are embedded within broader system-level structures. These results suggest that self-regulation operates not merely as an associated factor, but as an organizing mechanism of learning intelligence that structures the translation of background resources into performance. By reconceptualizing prediction as a means of revealing the organization of learning intelligence, this study proposes a unified analytical framework that links interpretable artificial intelligence with theory-driven explanation. The findings contribute to a deeper understanding of how achievement systems function and highlight that high performance can emerge through multiple, structurally distinct pathways, with important implications for educational research, cognitive theory, and policy design. Full article

Perception-oriented evaluation of automotive headlamps still relies mainly on human-vision photometric criteria, although forward-facing cameras are increasingly safety-critical sensing elements for night driving. This paper benchmarks 16 measured production headlamp light distributions with a simulation chain that combines headlamp spectra and beam patterns, diffuse scene reflection, an imaging-transfer model, and an EMVA-based camera model. The quantitative chain maps scene radiance to sensor-domain signal-to-noise ratio, derives task-specific required signal-to-noise curves from a six-network object-recognition ensemble, and aggregates local threshold satisfaction as region-of-interest coverage across three target reflectances and five driving speeds using WLTP moving-time weights. For the baseline RGB camera, WLTP-weighted coverage ranges from 18.95% to 53.48% across the evaluated light distributions, corresponding to a factor of 2.82 between the weakest and strongest distribution. The camera-parameter sweeps show that favorable beam placement can deliver comparable benchmark coverage with roughly 60% smaller pixel pitch than the weakest distribution, corresponding to an 84% reduction in pixel area, or at materially shorter exposure times. The WLTP-weighted coverage score correlates positively with the established Headlamp Safety Performance Rating, with Pearson $r=0.68$ for the RGB configuration, indicating partial alignment between human-centric and camera-centric illumination needs while confirming that the metrics are not interchangeable. The results identify headlamp light distribution as a primary design parameter for nighttime camera perception and provide a quantitative basis for co-design of automotive lighting and camera-based systems. Full article