Search Results (13,026)

Cryotherapy and radiofrequency (RF) treatments modulate tissue temperature to induce therapeutic effects; however, improper application can result in thermal injury. Traditional temperature-based monitoring methods rely on multiple thermal sensors whose accuracy strongly depends on their number and spatial positioning, often failing to detect early tissue crystallization. This study introduces a fractional order bioimpedance modelling framework for the early detection of tissue freezing during cryogenic and thermal medical treatments, with the feasibility and effectiveness of this approach having been reported in our prior publications. While bioimpedance spectroscopy itself is a well-est. The corresponablished technique in biomedical engineering, its novel application to predict and identify premature freezing events provides a new pathway for safe and efficient energy-based therapies. Fractional-order models derived from the Cole family accurately reproduce the complex electrical behavior of biological tissues using fewer parameters than classical integer-order models, thus reducing both hardware requirements and computational cost. Experimental impedance data from human abdominal, gluteal, and femoral regions were modelled to extract fractional parameters that serve as sensitive indicators of phase-transition onset. The results demonstrate that the proposed approach enables real-time identification of freezing-induced electrical transitions, offering a physiologically grounded alternative to conventional temperature-based monitoring. Furthermore, the fractional order bioimpedance method exhibits high reproducibility and selectivity, and its analytical figures of merit, including the limits of detection and quantification, support its use for reliable real-time tissue monitoring and early injury detection. Overall, the proposed fractional order bioimpedance framework enhances both safety and control precision in cryogenic and thermal medical applications. Full article

Urban street greening structure plays a crucial role in promoting environmental justice and enhancing residents’ daily well-being, yet existing studies have primarily focused on vegetation quantity while neglecting how perception and governance interact to shape fairness. This study develops an integrated analytical framework that combines deep learning, machine learning, and spatial analysis to examine the impact of perceptual experience and socio-economic indicators on the equity of greening structure distribution in urban streets, and to reveal the underlying mechanisms driving this equity. Using DeepLabV3+ semantic segmentation, perception indices derived from street-view imagery, and population-weighted Gini coefficients, the study quantifies both the structural and perceptual dimensions of greening equity. XGBoost regression, SHAP interpretation, and Partial Dependence Plot analysis were applied to reveal the influence mechanism of the “Matthew effect” of perception and the Site governance responsiveness on the fairness of the green structure. The results identify two key findings: (1) perception has a positive driving effect and a negative vicious cycle effect on the formation of fairness, where positive perceptions such as beauty and safety gradually enhance fairness, while negative perceptions such as depression and boredom rapidly intensify inequality; (2) Site management with environmental sensitivity and dynamic mutual feedback to a certain extent determines whether the fairness of urban green structure can persist under pressure, as diverse Tree–Bush–Grass configurations reflect coordinated management and lead to more balanced outcomes. Policy strategies should therefore emphasize perceptual monitoring, flexible maintenance systems, and transparent public participation to achieve resilient and equitable urban street greening structures. Full article

This paper systematically investigated the mechanical behavior of welded-plate lifting lugs subjected to dynamic and eccentric loadings in steel structure hoisting applications. By integrating on-site stress monitoring throughout the hoisting process with finite element numerical simulations, the dynamic response characteristics of the lugs were comprehensively analyzed. The results indicated that the stress response followed a three-stage evolution comprising elastic growth stage, peak fluctuation stage, and gradual decay stage. Non-uniform loading significantly intensified stress concentrations at the edges of the lifting holes and in the lug–stiffener transition region, with local impact parameters ranging from 1.02 to 1.12 and exhibiting a distinctly non-uniform spatial distribution. A refined finite element model was established, and comparisons with experimental data confirmed that static and dynamic prediction errors were controlled within 5 MPa and 5%, respectively. The optimal lifting angle of 75° was identified, resulting in a significant reduction in dynamic amplification. Furthermore, a small-sample Bootstrap method was introduced to probabilistically correct the dynamic parameter, enhancing design reliability by approximately 10%. Overall, this research provided a more rigorous theoretical foundation and practical design tool for evaluating the safety of lifting lugs subjected to dynamic loading. Full article

Background: Workplace violence (WPV) is a pervasive occupational hazard in healthcare that undermines staff safety and quality of care. In Bulgaria, WPV remains widespread and underreported, despite recent legislative initiatives. This study assessed healthcare professionals’ perceptions of the effectiveness of WPV prevention strategies and examined how prior exposure shapes these perceptions. Methods: A nationwide cross-sectional online survey was conducted in December 2024 with 944 healthcare professionals from multiple sectors. Participants rated the perceived effectiveness of 11 prevention strategies, including environmental/security measures, organizational, and national-level interventions, on a three-point scale. Friedman ANOVA with Kendall’s W assessed overall strategy rankings, while Mann–Whitney U tests with rank-biserial correlations compared specific effectiveness ratings between subgroups defined by WPV exposure (experienced or witnessed vs. not exposed in the previous 12 months). Results: In the previous 12 months, 34.7% of respondents reported direct WPV, and 43.4% had either experienced or witnessed incidents. Friedman ANOVA indicated significant differences in perceived effectiveness across strategies (Kendall’s W = 0.13), with stronger differentiation among violence-exposed respondents (W = 0.37) than among non-exposed respondents (W = 0.09). National-level interventions and security/response measures were consistently ranked the highest. Mann–Whitney tests showed significantly higher endorsement of most strategies among violence-exposed professionals, with large effect sizes for security measures and enforcement of sanctions. Conclusions: Bulgarian healthcare professionals view WPV prevention as requiring a multicomponent approach that integrates robust national policy with organizational and environmental measures. Direct exposure to violence is associated with stronger support for security-focused and national interventions. These findings inform context-specific, evidence-based WPV prevention programs for Bulgarian healthcare facilities. Full article

With China’s rapidly aging population, enhancing the safety and age-friendliness of existing residential communities has become a pressing need in the context of urban renewal. Based on empirical analysis of 146 questionnaires collected from aging communities in Jiangsu Province, this study examines how built environment factors influence safety risks and perceived security among older adults. The results show that public seating (F3), pedestrian pathways (F11), staircases (F1), lighting (F5), landscaping (F10), and outdoor animals (F12) significantly affect both actual safety risks and perceived safety. Insufficient lighting, uneven pathways, unstable seating, and unsafe staircases are the primary causes of falls, collisions, and abrasions, while issues such as standing water, overgrown vegetation, and stray animals further reduce residents’ sense of security. The findings indicate that improving elderly safety relies more on environmental visibility, accessibility, and spatial maintenance than on compensating for individual physical limitations. Therefore, interventions such as enhancing lighting, maintaining pedestrian routes, providing stable seating, and strengthening community management can effectively reduce risks and enhance perceived security. This study offers empirical evidence to guide age-friendly community renewal and provides policy insights for promoting safe, inclusive, and sustainable development in aging cities. Full article

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies. Full article

Background: Teamwork and effective communication are widely recognized as essential pillars for the safety and quality of healthcare. However, in Portugal, no validated instrument had previously been available to assess healthcare professionals’ attitudes toward teamwork. This study aimed to translate, culturally adapt, and validate the TeamSTEPPS^® Teamwork Attitudes Questionnaire (T-TAQ) for the Portuguese context, resulting in the Portuguese version of the instrument. Methods: A methodological study with a quantitative approach was developed. The translation and cultural adaptation process followed internationally recognized guidelines. The sample consisted of 162 healthcare professionals (136 nurses and 26 physicians) from a hospital in Lisbon. Exploratory and confirmatory factor analysis techniques were used to assess construct validity. The internal consistency of the scale was analyzed using Cronbach’s alpha coefficient. Results: The Portuguese version comprises 30 items distributed across five dimensions: Effective Leadership Support, Team Functional Performance, Teamwork Coordination, Willingness to Engage in Teamwork, and Team Functioning Supervision. The scale demonstrated a total explained variance of 53.9% and an overall internal consistency coefficient (α) of 0.86, indicating good reliability. Confirmatory factor analysis supported the five-factor structure of the scale (χ²/df = 1.461; CFI = 0.900; GFI = 0.821; RMSEA = 0.054; MECVI = 4.731). Conclusions: The T-TAQ-PT proved to be a valid, reliable, and robust instrument for assessing healthcare professionals’ individual attitudes toward teamwork, contributing to the development of research and clinical practice in the Portuguese context. Full article

Accurate processing of low-probability-of-intercept (LPI) radar signals poses a critical challenge in electronic warfare support (ES). These signals are often transmitted at very low signal-to-noise ratios (SNRs), making reliable analysis difficult. Noise interference can lead to misinterpretation, potentially resulting in strategic errors and jeopardizing the safety of friendly forces. Accordingly, effective noise suppression techniques that preserve the original waveform shape are crucial for reliable analysis and accurate parameter estimation. In this study, we propose the recognize-then-denoise network (RTDNet), which effectively removes noise while minimizing signal distortion. The proposed approach first employs a modulation recognition network to infer the modulation scheme and then feeds the inferred label to an attention-based denoiser to guide feature extraction. By leveraging prior information, the attention mechanism preserves key features and reconstructs challenging patterns such as polytime and polyphase codes. Simulation results indicate that RTDNet more effectively removes noise while maintaining the waveform shape and salient signal structures compared with existing techniques. Furthermore, RTDNet improves modulation classification accuracy and parameter estimation performance. Finally, its compact model size and fast inference meet the performance and efficiency requirements of ES missions. Full article

This case study in Foz do Iguaçu, Brazil, addresses the urban problem of the degradation of road corridors used by public transport, affecting the accessibility, safety, and efficiency of urban mobility. To address this issue, a multi-criteria methodology based on MIVES (Integrated Value Model for Sustainable Assessments) was applied, combined with the AHP (Analytic Hierarchy Process) method, allowing the evaluation of 20 key urban roads using a hierarchical set of indicators linked to infrastructure, accessibility, and mobility. The assessment was operationalized through the Transport Infrastructure Condition Index (TICI), which yielded results ranging from 0.32 to 0.88, reflecting significant contrasts in the road’s upkeep and maintenance conditions. The lowest scores were associated with deficiencies in universal accessibility, cycling infrastructure, signage, and adaptations for people with reduced mobility, highlighting structural limitations in sustainability and urban inclusion. The model facilitates the prioritization of road interventions based on urgency and criticality, becoming a useful tool for guiding public investment decisions. Its comprehensive approach and replicability make it a valuable methodological alternative for other Latin American contexts, where pressure to improve urban services coexists with budgetary constraints, contributing to more efficient and sustainable strategic planning of public transportation. Full article

Accurate wave detection is essential for reliable ship motion prediction and the safety of offshore operations. Wave buoys are widely deployed as key instruments for capturing wave characteristics. However, buoys drift due to the waves and currents, resulting in errors in reconstructed wave elevation. To address this challenge, a fixed-position wave-elevation reconstruction method is proposed in this paper. First, a temporal convolutional network (TCN) module is integrated with a gated recurrent unit (GRU) network to efficiently capture the nonlinear relationship between buoy motion and wave elevation, enabling simultaneous wave elevation reconstruction and dynamic deviation compensation. Second, a static deviation compensation algorithm developed from wave theory is introduced to convert the spatial deviation into temporal misalignment. The proposed method is evaluated in both time and frequency domains across various sea conditions. Results demonstrate that the proposed method effectively compensates for deviations and achieves accurate reconstruction of wave elevation at the target position. In higher sea states, accurate reconstruction is maintained even at large static deviations, with relative errors typically within 10–15%. Frequency-domain analysis shows that coherence approaches 1 near the spectral peak and below 0.3 at higher frequencies, indicating that the dominant wave components are accurately reconstructed and that high-frequency noise has a limited impact on overall accuracy. Full article

As the retrieval of unconventional oil and gas resources extends to the deep and ultra-deep domains, the issue of cement sheath failure in shale oil wellbores seriously endangers wellbore safety, making it imperative to uncover the relevant damage mechanism and develop effective assessment approaches. In response to the limitations of conventional finite element methods in representing mesoscopic damage, in this study, we determined the mesoscopic parameters of cement paste via laboratory calibrations; constructed a 3D casing–cement sheath–formation composite model using the discrete element method; addressed the restriction of the continuum assumption; and numerically simulated the microcrack initiation, propagation, and interface debonding behaviors of cement paste from a mesomechanical viewpoint. The model’s reliability was validated using a full-scale cement sheath sealing integrity assessment apparatus, while the influences of fracturing location, stage count, and internal casing pressure on cement sheath damage were analyzed systematically. Our findings indicate that the DEM model can precisely capture the dynamic evolution features of microcracks under cyclic loading, and the results agree well with the results of the cement sheath sealing integrity evaluation. During the first internal casing pressure loading phase, the microcracks generated account for 84% of the total microcracks formed during the entire loading process. The primary interface (casing–cement sheath interface) is fully debonded after the second internal pressure loading, demonstrating that the initial stage of cyclic internal casing pressure exerts a decisive impact on cement sheath integrity. The cement sheath in the horizontal well section is subjected to high internal casing pressure and high formation stress, resulting in more frequent microcrack coalescence and a rapid rise in the interface debonding rate, whereas the damage progression in the vertical well section is relatively slow. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

Carbon capture is pivotal for achieving carbon neutrality; however, its high energy consumption severely limits the operational flexibility of power plants and remains a key challenge. This study, targeting a full flue gas carbon capture scenario for a 1000 MW coal-fired power plant, identified the dual-element (“steam” and “power generation”) coupling convergence mechanism. Based on this mechanism, a comprehensive set of mathematical model equations for the “carbon–electricity–heat” coupling process is established. This model quantifies the dynamic relationship between key operational parameters (such as unit load, capture rate, and thermal consumption level) and system performance metrics (such as power output and specific power penalty). To address the challenge of flexible operation, this paper further proposes two innovative coupled modes: steam thermal storage and chemical solvent storage. Model-based quantitative analysis indicated the following: (1) The power generation impact rate under full THA conditions (25.7%) is lower than that under 30% THA conditions (27.7%), with the specific power penalty for carbon capture decreasing from 420.7 kW·h/tCO₂ to 366.7 kW·h/tCO₂. (2) Thermal consumption levels of the capture system are a critical influencing factor; each 0.1 GJ/tCO₂ increase in thermal consumption leads to an approximate 2.83% rise in unit electricity consumption. (3) Steam thermal storage mode effectively reduces peak-period capture energy consumption, while the chemical solvent storage mode almost fully eliminates the impact on peak power generation and provides optimal deep peak-shaving capability and operational safety. Furthermore, these modeling results provide a basis for decision-making in plant operations. Full article

Supervised fine-tuning is key for model alignment, but its mechanisms are debated, with conflicting evidence supporting either a superficial alignment hypothesis or significant task improvements. This paper examines supervised fine-tuning’s impact from the perspective of verbatim memorization. Using the open-source OLMo-2 model series and test datasets (instruction format, safety-sensitive, and factual knowledge) constructed from its pre-training corpus, we analyzed changes across memorization, linguistic styles, and task performance. We found that supervised fine-tuning significantly weakens the model’s verbatim memorization of pre-training data. Simultaneously, it improves generated text in terms of alignment objectives, such as polite expression and structured organization. However, this process also leads to performance degradation on knowledge-intensive downstream tasks. Further representation analysis reveals that these changes are mainly concentrated in the later layers of the model. We conclude that supervised fine-tuning acts as a continuation of the learning process on new data. By adjusting model representations, supervised fine-tuning induces a learning tilt toward the styles and content of the instruction-tuning dataset. This inclination successfully instills alignment objectives while consequently reducing the effective accessibility of previously learned knowledge, which indicates the observed degradation in both pre-training data memorization and factual task performance. The source code is publicly available. Full article

Background/objectives: RSV is a major cause of mortality in infants, and despite recent progress to prevent RSV in the very young, an RSV vaccine for this population is still highly warranted. Clinical studies in infants in the 1960s using formalin-inactivated RSV (FI-RSV) led to life-threatening enhanced respiratory disease (ERD). Therefore, a thorough safety assessment of RSV vaccine candidates intended for RSV seronegative infants is crucial. Methods: Prior to clinical pediatric development of Ad26.RSV.preF, an adenovirus type 26 vector-encoding RSV F protein stabilized in its prefusion conformation, predisposition to ERD was extensively assessed in cotton rat and mouse models. Results: Cotton rats intramuscularly immunized with a wide dose range of Ad26.RSV.preF, including low and sub-protective vaccine doses, and challenged with vaccine homologous RSV A2 or heterologous RSV B Wash 18537, did not show signs of predisposition to ERD. Histopathology scores for alveolitis, peribronchiolitis, interstitial pneumonia, and perivasculitis after challenge were significantly lower for Ad26.RSV.preF-immunized cotton rats compared to FI-RSV-immunized cotton rats and comparable to or lower than scores in cotton rats intranasally pre-exposed to RSV prior to challenge to mimic natural repeated infection. These results were observed in animals with or without viral replication in the lung after RSV challenge, in the presence or absence of vaccine-induced antibodies. Similar results were observed in mice, where more extensive assessment of mono- and polymorphonuclear cell alveolitis, mucus cell hyperplasia, and mucus accumulation was performed. Conclusions: Based on these extensive analyses, we conclude that there are no indications of ERD predisposition after Ad26.RSV.preF vaccination in rodent models, irrespective of the vaccine dose, challenge virus strain, or presence of viral replication in the lung. These results are crucial for the pediatric development of this vaccine. Full article