Search Results (22,500)

Existing thermal sensation prediction models typically examine the relationship between skin temperature and thermal sensation during cooling or heating seasons. However, due to significant fluctuations in indoor thermal environments during shoulder seasons and considerable individual variation in clothing preferences, traditional thermal sensation prediction models demonstrate poor predictive performance during shoulder seasons. This study aims to investigate the relationship between facial skin temperature and clothing insulation versus thermal sensation under shoulder seasonal conditions and to establish a predictive model for human thermal sensation influenced by clothing insulation. First, facial temperature data under different clothing conditions are collected online using infrared thermal imaging equipment. Subjective thermal sensations are obtained through questionnaires, enabling analysis of the influence of relationships among clothing insulation, facial temperature, and thermal sensation. Subsequently, correlation analysis is used to identify the facial temperature zones closely related to human thermal sensation. Finally, a random forest algorithm is employed to establish a thermal sensation prediction model. Research findings indicate that during shoulder seasons, the left and right cheeks and lips exhibit a higher correlation with thermal sensation. Due to variations in clothing insulation, thermal sensation models based solely on facial temperature characteristics demonstrate lower predictive accuracy and struggle to overcome interference caused by individual clothing differences. After incorporating clothing insulation as a key input feature parameter, the model’s Root Mean Square Error decreased from 0.869 to 0.533, representing a 38.7% improvement in prediction accuracy. This demonstrates that the clothing insulation parameter plays a crucial role in enhancing the precision of human thermal sensation prediction models during shoulder seasons. Full article

Sensors are an established driver of diagnostics and prevention in the medical field, including orthopaedics. Today, the subclass of ion-selective sensors (ISSs) is on the leading edge due to its advantages, enabled by technological advancements in manufacturing, such as miniaturization, precision, accuracy, specificity, a wide measuring scale, ease of use, flexible operating conditions, and measuring speed. While ISSs’ impact on environmental and health fields is already the subject of investigation, it still needs to be analysed specifically in orthopaedics, which is the aim of this Review. A PubMed and Scopus search was performed using the keywords “ion”, “sensor”, “electrodes”, “selective”, “musculoskeletal”, “implant”, “joint replacement”, and “orthopaedic”; after systematic screening, 44 studies were included in the synthesis. First, studies were classified based on the target ion. Only a few papers treated applications specifically in orthopaedics, confirming that ISSs are still largely an unexplored frontier here. However, all of the studies targeted ions with a role also in musculoskeletal pathophysiology, thus relative ISSs could have a potential impact on orthopaedic diagnosis and treatment. Then, when described by the papers, ISSs’ technological solutions were systematically evaluated. Finally, the main ISSs development targets for reaching orthopaedic clinical application were highlighted, including biocompatibility (e.g., implantability), long-term stability, calibration, and validation. Overcoming these challenges will enable ISSs to progress from laboratory prototypes to clinically viable tools, supporting the advancement of next-generation sensorised prostheses, fixation devices, and surgical instruments, and paving the way for predictive and personalised orthopaedic medicine. Full article

Rural land art festivals serve as an important practical vehicle for integrating urban and rural culture and tourism. They constitute a crucial component of rural tourism in China and play a key role in the sustainable development of rural areas. However, in practice, these festivals are generally confronted with the dilemma of superficial tourist participation and insufficient sustainability. This study aims to uncover the intrinsic psychological evolution mechanism underlying tourists’ responses to external stimuli and their value co-creation. The S-O-R model and the two-factor theory are integrated to construct an analytical framework: “external stimulus–psychological sequence–behavioral response.” Using “Modern Fields” as the case study and 437 valid data points, an empirical analysis is conducted with PLS-SEM and artificial neural networks (ANNs). The results indicate that tourist participation is directly driven by destination quality. Content stickiness exerts an indirect influence through perceived value. Perceived value facilitates value co-creation only when it is fully mediated by tourist participation. The path from participation to co-creation is significantly strengthened by restorative environmental perception. A multi-group analysis further reveals that inexperienced tourists exhibit a “stimulus-driven” characteristic, whereas experienced tourists follow a “value internalization” path. The ANN analysis further shows that the strongest nonlinear predictive power for co-creation behavior is held by restorative environmental perception. A significant direct nonlinear effect is also exerted by destination quality. The evolutionary nodes and boundary conditions of tourists’ psychological sequence during this process are revealed. The boundary effect of restorative environmental perception as a catalyst for rural art tourism is demonstrated. A theoretical basis and practical insights are thereby provided for the segmented operation and sustainable development of these activities. Full article

Numerical simulation is an effective method for studying groundwater flow and heat transfer in geothermal energy projects. Describing the characteristics of thermal plumes is important for operational planning of geothermal energy projects. In contrast to shallow geothermal system, the injection temperature differs significantly from the natural temperature of thermal reservoir in high-temperature geothermal projects, which leads to changes in fluid density and dynamics viscosity. The purpose of this paper is to investigate the impacts of temperature-induced changes in density and dynamics viscosity on simulation. The Enhanced Geothermal System (EGS) in North Jiangsu Basin, China, is taken as a case project. Based on the theory of groundwater flow and heat transfer in porous-fracture dual medium, a numerical model of EGS is established to predict the thermal performance. The density and the dynamics viscosity in the model were set as either constant or temperature-dependent to simulate the hydraulic head and temperature of the production well. The influence of temperature-induced changes in density and dynamics viscosity on the simulation was quantitatively studied. The results show that temperature-induced change in dynamics viscosity has a greater impact on the simulation, with deviation in hydraulic head exceeding 20% if the dynamics viscosity is assumed constant. The temperature-dependent variation in viscosity should be incorporated into the simulation process to improve the accuracy of the calculation. In practice, EGS projects should maximize the temperature differential between produced and injected water. The increased viscosity of lower-temperature circulation water extends its residence time within the system, thereby facilitating more thorough heat extraction. This research enhances our understanding of the role of the temperature in groundwater flow and heat transfer within EGS. Full article

Biological control fungi play an important role in the management of plant-parasitic nematodes; however, the molecular basis underlying their diverse biocontrol strategies remains incompletely understood. In this study, a comparative genomic analysis was performed on four representative biocontrol fungi: Purpureocillium lilacinum PLFJ-1, Trichoderma harzianum CBS 226.95, Pochonia chlamydosporia 170, and Aspergillus niger CBS 513.88. Genome comparison revealed substantial variation: genome size ranged from 34.0 Mb (A. niger) to 44.2 Mb (P. chlamydosporia), GC content from 47.5% (T. harzianum) to 58.5% (P. lilacinum), and predicted gene models also differed markedly among the four fungi. Phylogenetic analysis based on the Internal Transcribed Spacer divided these fungi into two major clades corresponding to distinct evolutionary lineages. Orthogroup analysis identified both a conserved core gene set and species-specific gene repertoires. Functional annotation using KEGG, KOG, and GO indicated a high degree of conservation across core metabolic processes, catalytic activities, and cellular components, with distinct differences within specific functional categories. Further comparative analyses demonstrated pronounced variation in the composition and abundance of carbohydrate-active enzymes (CAZymes) and peptidases, as well as a notable expansion and enrichment of S8 subtilisin-like serine peptidases in the nematode-parasitic fungi P. lilacinum and P. chlamydosporia. Secondary metabolite analysis revealed lineage-specific biosynthetic gene clusters (BGCs). Notably, P. lilacinum and P. chlamydosporia carried PKS/NRPS clusters potentially linked to nematicidal activity, while A. niger and T. harzianum displayed broader but less infection-specific metabolic profiles. Together, these findings suggest that distinct enzymatic and metabolic gene repertoires, particularly expansions of S8 serine peptidases and specific CAZyme families, may contribute to the biocontrol potential of these fungi. Full article

Background/Objectives: Cytochrome P450 enzymes (CYP2D6, CYP2C19, CYP3A4) play a key role in interindividual variability in cardiovascular drug metabolism. This study aimed to describe metabolizer phenotype distributions in a Turkish emergency cardiac cohort and across diagnostic categories. Methods: This retrospective descriptive pharmacogenomic study included 250 patients. Genotyping was performed using TaqMan assays for CYP2D6 (*2, *4, *10, *41), CYP2C19 (*2, *17), and CYP3A4 (*22, *1B). Phenotypes were assigned according to CPIC guidelines. CYP2D6 copy-number variation was not assessed. Results: Non-normal metabolizer phenotypes were observed in 55.6% (CYP2D6), 84.4% (CYP2C19), and 30.4% (CYP3A4) of patients. For CYP2D6, normal (44.4%) and intermediate (42.0%) metabolizers predominated. For CYP2C19, intermediate metabolizers were most frequent (36.0%), followed by normal (22.8%), rapid (17.2%), poor (14.8%), and ultra-rapid metabolizers (9.2%). CYP3A4 showed predominantly normal activity (69.6%). Phenotype distributions varied across diagnoses without clear clustering. Conclusions: A high prevalence of CYP2D6 and CYP2C19 variability with predicted functional relevance based on CPIC was observed, whereas CYP3A4 activity was more stable. These findings provide descriptive pharmacogenomic data to support future genotype-guided cardiovascular therapy studies. Full article

Digital Twin (DT) technologies are increasingly adopted in the automotive domain to support real-time monitoring, predictive analytics, and connected decision-making across vehicles, drivers, and road infrastructure. However, research on Vehicle, Driver, and Road Digital Twins (VDTs, DrDTs, and RDTs) remains fragmented, with heterogeneous definitions, architectural assumptions, and integration strategies. This paper presents a critical review of seventy-six studies published between 2008 and 2025, examining how these three DT domains are modeled, evaluated, and connected within intelligent mobility scenarios. The review synthesizes recurring architectural patterns, communication and computing choices, and the role of interoperability and standardization in multi-twin systems. It also highlights open challenges involving distributed coordination, semantic alignment, real-time operation, and driver-aware adaptation. Based on this analysis, the paper presents a unified conceptual framework for connected automotive digital twins and discusses key directions for building scalable and safety-aware mobility services. Full article

This paper proposes a Pulse Train Control (PTC) strategy for the Series Capacitor Buck (SCB) converter operating in Discontinuous Conduction Mode (DCM). Instead of synthesizing a continuous duty ratio, the controller selects between two preset duty ratios in each switching period, and the same binary decision is applied to the two interleaved phases with a ${180}^{\circ }$ phase shift. A reduced one-dimensional control-oriented discrete-time map is derived from output charge balance to describe the control cycle-scale regulation dynamics. Based on this map, the bounded-regulation condition is established and the design roles of the pulse pair $(D_H,D_L)$ are clarified. The regulated steady state is shown to be a bounded threshold-crossing periodic motion rather than a static equilibrium, and the evolution of pulse patterns with operating condition is interpreted through border collision transitions. Full switching model and experimental results from a 12-V-to-1-V prototype support the predicted high-pulse fraction trend, the multiplier-based local attraction assessment of annotated periodic pulse patterns, the input voltage-dependent ripple estimate, and the fast large-signal response under representative load step conditions. Full article

Background: Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) depends on the fusion of multiple complementary modalities. However, clinical practice often faces incomplete modality sets due to acquisition failures, patient contraindications, or protocol variations. Current methods either treat each modality feature extractor in isolation or depend on computationally expensive teacher networks for cross-modal knowledge transfer. Objective: This paper presents Hierarchical Adaptive Group Self-Support Learning with Boundary-Aware Calibration (HAGSS), a framework that overcomes three key limitations of existing group self-support methods: static group formation that ignores temporal prediction quality, uniform treatment of boundary and interior voxels, and distribution mismatch across heterogeneous modality logits. Methods: We propose a hierarchical adaptive group formation mechanism that reassigns group leader roles at each epoch based on voxel-level prediction confidence scores instead of fixed sensitivity priors. We also introduce a boundary-aware calibration module that applies spatially varied distillation weights with greater emphasis on tumor boundary regions. In addition, we design a cross-scale consistency regularization term that enforces agreement between multi-resolution predictions to stabilize the self-support target. Results: Experiments on BraTS2020, BraTS2018, and BraTS2021 datasets show that HAGSS achieves consistent improvements over state-of-the-art baselines. The average Dice gains across the whole tumor, tumor core, and enhancing tumor regions reach 1.30% on BraTS2020 and 1.61% on BraTS2021 compared to existing methods. All improvements are statistically significant ($p<0.05$). Conclusions: HAGSS operates exclusively during training, adds no parameters or inference cost, and can be applied as a plug-in module to any multi-encoder incomplete multi-modal segmentation architecture. Code is publicly available at GitHub. Full article

Objectives: The aim of this study was to analyze current trends in screen exposure and to provide a deeper understanding of the relationships between temperament, screen exposure, and psychological adjustment in preschoolers. Methods: The study was conducted in kindergartens and one health center in the city of Zagreb, using a convenience sample of 115 mothers who assessed their preschool children’s screen exposure, temperament, and psychological adjustment. Results: Descriptive data analysis indicated that children’s screen time generally fell within the American Academy of Child and Adolescent Psychiatry’s recommended guidelines. Correlation analysis indicated that externalizing problems were significantly positively correlated with impulsivity, activity, emotionality, and weekend screen time. Conversely, prosocial behavior was negatively correlated with impulsivity and weekend screen exposure. Moderation analyses revealed that weekend screen time significantly altered the associations between temperament and externalizing problems. Specifically, longer weekend screen exposure weakened the relationships between Impulsivity and externalizing problem and between Activity and externalizing problems, suggesting that screen time may buffer the impact of high-risk temperament profiles on behavioral difficulties. Weekend screen time did not moderate the relationship between Emotionality and externalizing problems. Similarly, longer screen exposure weakened the negative association between Impulsivity and prosocial behavior, indicating that screen time may reduce the extent to which impulsive temperament undermines prosocial functioning in preschool children. Conclusions: These findings provide deeper insight into the role of temperament and screen time exposure in predicting both maladaptive and prosocial behaviors among preschool-aged children. Full article

Peptide therapeutics have emerged as a versatile class of biomolecules bridging the gap between small-molecule drugs and large biologics. Advantages of such molecules include high target specificity, potent bioactivity and reduced off-target toxicity. Despite these, broader clinical translation remains constrained by inherent limitations like poor metabolic stability, rapid renal clearance, limited membrane permeability and scalable synthesis. This review aims to systematically integrate advances in peptide science across natural discovery, synthetic methodologies, structural engineering, and translational delivery systems, while identifying critical research gaps hindering clinical adoption. We highlight diverse natural sources of bioactive peptides, including plant- (lunasin), animal- (Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP)), microbial- (nisin and cyclosporine), marine- (dolastatins) and venom-derived (chlorotoxin and ω-conotoxin MVIIA (ziconotide)) agents. Advances in solid-phase peptide synthesis (SPPS), green chemistry, and catalytic strategies are discussed alongside emerging in silico approaches, including artificial intelligence-driven sequence design and molecular modeling. Structural modifications such as cyclization, hydrocarbon stapling, PEGylation, and lipidation are critically evaluated for their role in enhancing pharmacokinetic and pharmacodynamic properties. Furthermore, nanoformulation strategies, including self-assembling peptides and cell-penetrating systems, are examined for their potential to overcome biological barriers. Importantly, this review identifies key unresolved challenges, including the lack of predictive models for peptide delivery systems, safety concerns associated with long-term modifications, and limited in vivo validation of naturally derived peptides. Addressing these gaps through integrated computational and experimental approaches will be essential for advancing next-generation peptide therapeutics. Collectively, this work provides a comprehensive framework for the rational design and translation of peptide-based precision medicines. Full article

Generation Z, a cohort defined by digital connectivity, sensitivity to social influence, and environmental awareness, has attracted considerable scholarly attention in sustainable consumption research. Yet a persistent gap between their expressed pro-sustainability attitudes and actual purchasing decisions remains well-documented. This study examines whether Gen Z characteristics help bridge that gap by directly influencing sustainable purchase behavior and by moderating the role of purchase intention in that process. A quantitative design was employed using survey responses from 302 Gen Z consumers. The findings suggest that while Gen Z characteristics significantly predicted actual sustainable purchasing and purchase intention exerted a positive direct effect, the interaction between the two was negative and statistically significant. Conditional effects analysis further revealed that the influence of generational characteristics on purchasing behavior is stronger at lower levels of purchase intention and progressively weaker as intention increases. These results suggest that traits such as digital responsiveness, social embeddedness, and environmental orientation do not merely reinforce existing intentions but appear to compensate for their absence, activating sustainability-aligned behavior even when motivational commitment is limited. The study repositions the intention–behavior gap among Gen Z as something modulated by generational characteristics that drive purchasing behavior when intention alone falls short. Full article

Industrial areas, which represent a specific type of urbanised area with an extremely high concentration of material reserves, can be considered key anthropogenic raw material reservoirs in the context of urban mining. Industrial areas, characterised by a high material density and a specific composition of structural systems, show extraordinary potential for providing secondary raw materials with high material and energy value. This increases the need for their systematic evaluation. The aim of the present study was to define the role of the selected industrial area as a strategic node for secondary raw material extraction, to identify the structure and quality of “urban deposits” in the selected location of the Ostrava–Karviná region (CZ), and to provide an analytical framework for its integration into circular planning processes. The methodological approach is based on a combination of pre-demolition audit, material flow mapping, spatial analysis, and structural element characterisation. It is becoming apparent that industrial areas have a high material density and contain significant amounts of recyclable metals, reinforced concrete elements, etc. These stocks are often concentrated in structural systems with predictable geometries, such as serial assembly prefabricated and steel frames, allowing for more accurate estimates of recoverable volumes. The results show that the incorporation of industrial areas into the process of urban mining can significantly reduce the consumption of primary raw materials, mitigate the environmental impacts associated with the extraction of raw materials, and, at the same time, promote the regeneration of industrial areas (or brownfields) through the planned decomposition of structures. The inclusion of urban mining in urban development strategies and the regeneration of industrial sites leads to the prediction that urban mining is one of the key elements for achieving a material-efficient and low-carbon urban environment. Full article

Background/Objectives: Coronary slow flow phenomenon (CSFP) is considered a condition identified during coronary angiography (CAG), associated with recurrent ischemic symptoms and adverse cardiovascular outcomes while no significant epicardial coronary obstruction is present. The combined predictive role of metabolic and nutritional indices in CSFP has not been fully elucidated. Methods: This analysis, based on a retrospective observational design at a single center, included 214 patients aged < 65 years undergoing CAG who had either normal coronary arteries (NCA) (n = 100) or CSFP (n = 114). CSFP was defined using the TIMI frame count criteria. The triglyceride-glucose (TyG) index, Prognostic Nutritional Index (PNI), Controlling Nutritional Status (CONUT) score, Naples prognostic score (NPS), and C-reactive protein–albumin–lymphocyte (CALLY) index were calculated. Logistic regression was employed to assess independent contributors to CSFP. Results: In comparison with the NCA group, patients with CSFP were more frequently male (73.7% vs. 43.0%, p < 0.001) and active smokers (33.3% vs. 19.0%, p = 0.018). Among these calculated indices, higher TyG index values were observed; in contrast, the CSFP group exhibited lower PNI and CALLY index values. In multivariable analysis, male sex (OR = 5.187, 95% CI: 2.520–10.674, p < 0.001), the TyG index (OR = 1.811, 95% confidence interval [CI]: 1.251–2.622, p = 0.002), and PNI (OR = 0.544, 95% CI: 0.362–0.817, p = 0.003) retained their predictive value for CSFP. Conclusions: Coronary slow flow phenomenon in a non-elderly cohort appears to be linked to metabolic dysfunction, immunonutritional imbalance, and sex-specific differences, with the combined evaluation of the TyG index, PNI, and male sex potentially enhancing risk stratification. Full article

Background/Objectives: Artificial intelligence (AI) tools are increasingly integrated into acute stroke imaging workflows, but real-world performance for ischemia detection on non-contrast CT (NCCT) remains incompletely validated by investigators independent of the developer. This study externally validated the BrainScan AI system in an unselected, consecutively enrolled emergency cohort. Methods: Consecutive adult patients undergoing NCCT under the routine acute stroke protocol at a single tertiary centre between January and December 2025 were prospectively enrolled. The reference standard was the post-consensus radiological diagnosis, supplemented where available by follow-up imaging and clinical course. Primary outcomes were diagnostic accuracy for ischemia and intracranial haemorrhage detection, assessed by sensitivity, specificity, predictive values, likelihood ratios, and area under the ROC curve (AUC; DeLong). Pre-specified secondary analyses included regional sensitivity, confidence-score behaviour, artefact robustness, threshold sensitivity, a cluster-robust bootstrap for within-patient correlation, and a quantitative bias analysis under non-differential reference-standard misclassification. Sample size adequacy was assessed using a precision-based framework. Results: A total of 1419 NCCT examinations from 1260 patients were analysed. Ischemia sensitivity was 59.2% (95% CI 52.1–66.1) and specificity was 99.8% (99.4–100), with an AUC of 0.930 (0.906–0.954). The Youden-optimal threshold (0.055) recovered sensitivity to 86.1% with negligible specificity loss, reflecting a markedly bimodal score distribution. Regional sensitivity was lower in infratentorial structures. Bias-corrected estimates were stable across all reference-standard parameters consistent with the data. Haemorrhage detection performed substantially better (sensitivity 96.7%; AUC 0.983). Conclusions: The system shows excellent specificity and strong discrimination but moderate sensitivity for ischemia, supporting its role as a rule-in adjunct rather than a stand-alone tool, pending multicentre validation and site-specific threshold recalibration. Full article