Search Results (20,828)

Cervical cancer (CC), which continues to be a major public health concern that causes cancer deaths among women worldwide, is mostly caused by persistent human papillomavirus (HPV) infection. This study suggests a dual-delay model of HPV-C infection dynamics that takes into account both cancerous delay and the immune response delay. We identify disease-free and diseased equilibria, investigate their local asymptotic stability, and show that the system is non-negative and bounded. We prove the global asymptotic stability of the equilibria by building Lyapunov functions and using the basic reproduction number $R_0$, and look into the existence of Hopf bifurcations. Additionally, we use forward sensitivity analysis to determine important control parameters. Lastly, the theoretical results were confirmed by numerical simulations. The study demonstrates that time delays play a crucial role in viral transmission and carcinogenesis. The process from HPV infection to the formation of cervical cancer is more correctly simulated by this model, which offers a theoretical mathematical basis for researching the pathophysiology of cervical cancer and developing clinical prevention and control measures. Full article

Understanding Farmer households’ subjective flood risk cognition is important for effectively mitigating the impacts of flood, and adequate disaster preparedness reduces the impact of floods on the sustainability of farmers’ livelihoods. The existing literature focuses on objective flood risk assessment and subjective–objective risk consistency and less systematically explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. Therefore, this study aims to explores the correlation between Farmer households’ subjective flood risk cognition and disaster preparedness. This study employed a random sampling method to conduct a survey among 540 households in Gaoxian County, Jiajiang County, and Yuechi County, which are flood-prone areas in Southwest China. Based on the survey results, this research framework can be used to evaluate systems of subjective flood risk cognition and farmers’ disaster preparedness. We chose the Tobit Regression Model to empirically explore the correlation between subjective flood risk cognition and farmers’ disaster preparedness. The results showed that among the 540 surveyed farmers, their overall subjective flood risk cognition was at a medium-high level (3.58), with self-efficacy more than response efficacy, more than threat, and more than probability. Further, the overall disaster preparedness of farmers was at a medium level (0.5), with physical disaster preparedness more than emergency disaster preparedness and more than knowledge and skills preparedness. The regression analysis showed that the probability of flooding and the threat in Farmer households’ subjective flood risk cognition were positively related to disaster preparedness, whereas self-efficacy, response efficacy, and overall risk cognition in Farmer households’ subjective flood risk cognition were negatively related to disaster preparedness. This study is representative of or may serve as a reference for building governance systems and disaster prevention in other flood risk areas in Southwest China. Full article

This study introduces a novel space feasibility assessment process and evaluates its effectiveness through a comparative analysis with a conventional manual process. The proposed method is designed to enhance spatial comprehension and integrate building performance analysis, thereby supporting budgetary considerations during the early design phase. By providing a more intuitive and interactive environment, the system enables stakeholders—such as building owners—to communicate their spatial requirements to architects and professionals more clearly and efficiently. To validate the effectiveness of the proposed approach, participants completed two distinct scenarios: (1) a manual space feasibility assessment, and (2) a system-supported space feasibility assessment utilizing the proposed method. Participant performance was measured in terms of speed and accuracy in each scenario. Additionally, a user satisfaction survey was conducted to evaluate the usability of the system’s functionality. The experimental results provide an empirical basis for comparing the proposed process with the manual approach. Findings demonstrate that the proposed process enables more efficient and accurate space feasibility assessments, thereby validating its effectiveness as a user-centered decision-support tool during early-stage architectural planning. Full article

This review examines South Africa’s escalating flood vulnerability through a synthesis of over 80 peer-reviewed articles, historical records, policy reports, and case studies. Using a PRISMA-guided analysis, the study identifies key climatic drivers, including extreme rainfall from tropical–temperate interactions, cut-off lows, and La Niña conditions that interact with structural weaknesses such as inadequate drainage, poorly maintained stormwater systems, and rapid urban expansion. Apartheid-era spatial planning has further entrenched risk by locating marginalised communities in floodplains. Governance failures like weak disaster risk reduction (DRR) policies, fragmented institutional coordination, and insufficient early warning systems intensify flood vulnerabilities. Catastrophic events in KwaZulu-Natal (KZN) and the Western Cape (WC) illustrate the consequences exemplified by the April 2022 KZN floods alone, which caused over 450 deaths, displaced more than 40,000 people, and generated damages exceeding ZAR 17 billion. Nationally, more than 1500 flood-related fatalities have been documented in the past two decades. Emerging resilience pathways include ecosystem-based adaptation, green infrastructure, participatory governance, integration of Indigenous knowledge, improved hydrological forecasting, and stricter land-use enforcement. These approaches can simultaneously reduce physical risks and address entrenched socio-economic inequalities. However, significant gaps remain in spatial flood modelling, gender-sensitive responses, urban–rural disparities, and policy implementation. The review concludes that South Africa urgently requires integrated, multi-scalar strategies that combine scientific innovation, policy reform, and community-based action. Embedding these insights into disaster management policy and planning is essential to curb escalating losses and build long-term resilience in the face of climate change. Full article

Indoor sports facilities face distinctive indoor air quality (IAQ) challenges due to high occupant density, elevated metabolic emissions, and diverse pollutant sources associated with physical activity. This review presents a narrative synthesis of multidisciplinary evidence concerning IAQ in sports environments. It explores major pollutant categories, including carbon dioxide (CO₂), particulate matter (PM), volatile organic compounds (VOCs), and airborne microbial agents, highlighting their sources, behavior during exercise, and associated health risks. Research shows that physical activity can increase PM concentrations by up to 300%, and CO₂ levels frequently exceed 1000 ppm in inadequately ventilated spaces. The presence of semi-volatile organics and bioaerosols further complicates pollutant dynamics, especially in humid and densely occupied areas. Measurement technologies such as optical sensors, chromatographic methods, and molecular techniques are reviewed and compared for their applicability to dynamic indoor settings. Existing IAQ standards across China, the USA, the EU, the UK, and WHO are examined, revealing a lack of activity-specific thresholds and insufficient responsiveness to real-time conditions. Mitigation strategies (e.g., including demand-controlled ventilation, use of low-emission materials, liquid chalk substitutes, and integrated HEPA-UVGI purification systems) are evaluated, many demonstrating pollutant removal efficiencies over 80%. The integration of intelligent building management systems is emphasized for enabling real-time monitoring and adaptive control. This review concludes by identifying research priorities, including the development of activity-sensitive IAQ control frameworks and long-term health impact assessments for athletes and vulnerable users. Full article

Steel corrosion plays a major role in the geochemical evolution at the canister/bentonite interface of the engineered barrier systems of geological radioactive waste repositories. The interactions between corrosion products and bentonite can significantly affect bentonite properties and performance. These interactions have been investigated by resorting to in situ tests conducted in underground laboratories, such as the FEBEX (Full-scale Engineered Barrier Experiment) test. The FEBEX in situ test, which was conducted at the Grimsel underground research laboratory in Switzerland from 1997 to 2015, demonstrated substantial corrosion of the steel liner in areas without a heater, primarily due to the presence of O₂. Here we report a reactive transport model that simulates steel corrosion products and their interactions with bentonite. The model builds on a previously published conceptual geochemical model and addresses its limitations by integrating a more detailed representation of temperature and unsaturated flow conditions, leveraging prior thermo–hydrodynamic–mechanical–chemical (THMC) models. Given the prevailing uncertainties in O₂ and redox conditions during the test and the limited data on liner corrosion and gas conditions at the liner–bentonite interface, liner corrosion was modeled by using a prescribed time-dependent function for the corrosion rate. Goethite, hematite, and magnetite were the Fe minerals allowed to precipitate in the model. The corrosion rate and the specific surface area of the hematite and magnetite were calibrated based on the profiles of goethite, hematite, and total Fe (including dissolved, exchanged and sorbed forms) observed at the post mortem analysis of the FEBEX in situ test. The model reproduces the observed goethite and hematite precipitation near the liner but underestimates the measured values at greater distances from the liner. The pattern of total calculated Fe concentrations reproduce the measured values except at a distance between 15 and 50 mm from the liner. Goethite is the predominant corrosion product in the model results, even under reducing conditions, owing to kinetic constraints on magnetite and hematite precipitation and to the enhanced stability of goethite driven by pH increase and thermal evolution. Full article

The integration of nanotechnology into drug repurposing strategies is redefining the development landscape for diagnostic, therapeutic, and theranostic agents. In radiopharmacy, nanoplatforms are increasingly being explored to enhance or extend the use of existing radiopharmaceuticals, complementing earlier applications in other biomedical fields. Many of these nanoplatforms evolve into multifunctional systems by incorporating additional imaging modalities (e.g., MRI, fluorescence) or non-radioactive therapies (e.g., photodynamic therapy, chemotherapy). These hybrid constructs often emerge from the reformulation, repositioning, or revival of previously approved or abandoned compounds, generating entities with novel pharmacological, pharmacokinetic, and biodistribution profiles. However, their translational potential faces significant regulatory hurdles. Existing frameworks—typically designed for single-modality drugs or devices—struggle to accommodate the combined complexity of nanoengineering, radioactive components, and integrated functionalities. This review examines how these systems challenge current norms in classification, safety assessment, preclinical modeling, and regulatory coordination. It also addresses emerging concerns around digital adjuncts such as AI-assisted dosimetry and software-based therapy planning. Finally, the article outlines international initiatives aimed at closing regulatory gaps and provides future directions for building harmonized, risk-adapted frameworks that support innovation while ensuring safety and efficacy. Full article

Accurately predicting building heating and cooling loads is crucial for optimizing HVAC systems and enhancing energy efficiency. However, data-driven models often face overfitting issues due to scarce training data, a common challenge for new constructions or under data privacy constraints. Transfer learning (TL) offers a solution, but its effectiveness heavily depends on selecting an appropriate source domain through effective similarity measurement. This study systematically evaluates the performance of 20 prevalent similarity metrics in TL for building heating load forecasting to identify the most robust metrics for mitigating data scarcity. Experiments were conducted on data from 500 buildings, with seven distinct low-data target scenarios established for a single target building. The Relative Error Gap (REG) was employed to assess the efficacy of transfer learning facilitated by each metric. The results demonstrate that distance-based metrics, particularly Euclidean, normalized Euclidean, and Manhattan distances, consistently yielded lower REG values and higher stability across scenarios. In contrast, probabilistic measures such as the Bhattacharyya coefficient and Bray–Curtis similarity exhibited poorer and less stable performance. This research provides a validated guideline for selecting similarity metrics in TL applications for building energy forecasting. Full article

With the booming development of the low-altitude economy and the widespread application of Unmanned Aerial Vehicles (UAVs), integrated sensing and communication (ISAC) technology plays an increasingly pivotal role in intelligent communication networks. However, low-altitude platforms supporting ISAC, such as UAV swarms, are highly vulnerable to deception jamming in complex electromagnetic environments. Existing multistatic radar systems face challenges in processing slowly fluctuating targets (like low-altitude UAVs) and adapting to complex electromagnetic environments when fusing multiple pulse echoes. To address this issue, targeting the protection needs of low-altitude targets like UAVs, this paper leverages the characteristic of rapid amplitude fluctuation in frequency-agile radar echoes to analyze the differences between true and false targets in multistatic frequency-agile radar systems, particularly for slowly fluctuating UAV targets, demonstrating the feasibility of discrimination. Building on this, we introduce a neural network approach to deeply extract discriminative features from true and false target echoes and propose a neural network-based variable-length processing method for deception jamming discrimination in multistatic frequency-agile radar. The simulation results show that the proposed method effectively exploits deep-level echo features, significantly improving the discrimination probability between true and false targets, especially for slowly fluctuating UAV targets. Crucially, even when trained on a fixed number of pulses, the model can process input data with varying pulse counts, greatly enhancing its practical deployment capability in dynamic UAV mission scenarios. Full article

This study examines research trends and identifies key gaps relevant to the field of structural safety and resilience; additionally, a systematic literature review (SLR) guided by the PRISMA methodology was conducted, analyzing 4188 documents ranging from 1975 to 2025. The research revealed key trends, including a focus on various aspects of the structural stability and resilience of buildings affected by earthquakes through analysis of various innovative methods and materials. The present study encompasses work describing the use of steel–wood composite columns to improve building stability, assessment of the impact of wood accumulation on bridges during floods, and the effect of debris flow on the stability of check dams. In addition, this study also evaluates the seismic performance of school buildings in Mexico, a method of diagnosing cracks in concrete dams, and the application of recycled materials from old tires for seismic disaster mitigation. Acoustic emission monitoring methods in medieval towers and the design of seismic isolation systems with variable damping are also discussed. Bibliometric analysis highlighted increased collaboration and a thematic shift towards green and data-driven approaches. However, significant gaps were identified. The findings explain that the use of innovative materials and methods can improve the stability and resistance of building structures with respect to dynamic loads, such as those associated with earthquakes and floods. The findings provide guidance for the design and maintenance of safer and more sustainable infrastructure in the future. Full article

Alzheimer’s disease is a growing global health concern, demanding innovative solutions for early detection, continuous monitoring, and patient support. This article reviews recent advances in Smart Wearable Medical Devices (SWMDs), Internet of Things (IoT) systems, and mobile applications used to monitor physiological, behavioral, and cognitive changes in Alzheimer’s patients. We highlight the role of wearable sensors in detecting vital signs, falls, and geolocation data, alongside IoT architectures that enable real-time alerts and remote caregiver access. Building on these technologies, we present LumiCare, a conceptual, context-aware mobile system that integrates multimodal sensor data, chatbot-based interaction, and emerging 6G network capabilities. LumiCare uses machine learning for behavioral analysis, delivers personalized cognitive prompts, and enables emergency response through adaptive alerts and caregiver notifications. The system includes the LumiCare Companion, an interactive mobile app designed to support daily routines, cognitive engagement, and safety monitoring. By combining local AI processing with scalable edge-cloud architectures, LumiCare balances latency, privacy, and computational load. While promising, this work remains at the design stage and has not yet undergone clinical validation. Our analysis underscores the potential of wearable, IoT, and mobile technologies to improve the quality of life for Alzheimer’s patients, support caregivers, and reduce healthcare burdens. Full article

The increasing frequency and severity of forest fires, driven by climate change and intensified human activities, pose substantial threats to ecological security and sustainable development. However, most assessments remain centered on occurrence risk, lack a resilience-oriented perspective and comprehensive indicator systems, and therefore offer limited guidance for building system resilience. This study developed a forest fire resilience (FFR) assessment framework with 25 indicators in three levels and six domains across four resilience dimensions. Balancing expert judgment and data, we obtained indicator weights by integrating the Analytic Hierarchy Process (AHP) and the Criteria Importance Through Intercriteria Correlation (CRITIC) via a game-theoretic scheme. The analysis revealed that, among the level-2 indicators, climate factors, infrastructure, and vegetation characteristics exert the greatest influence on FFR. At the level-3 indicator scale, monthly minimum relative humidity, fine fuel load per unit area, and the deployment of smart monitoring systems were critical. Among the four resilience dimensions, absorption capacity plays the predominant role in shaping disaster response. Building on these findings, the study proposes targeted strategies to enhance FFR and applies the assessment framework to twelve administrative divisions of Baise City, China, highlighting marked spatial variability in resilience levels. The results offer valuable theoretical insights and practical guidance for strengthening FFR. Full article

Cities are significant contributors to climate change, environmental degradation, and resource depletion. To address these challenges, sustainable strategies in building design, construction, and management are essential, and digitalisation through the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) can enable it. However, the environmental benefits of BIM–LCA integration remain underexplored, limiting broader practical adoption. This study systematically reviews 80 case studies (2015–2025) to assess how recent applications address known barriers and to identify enablers of successful BIM–LCA workflows. The analysis highlights a growing alignment between technological, regulatory, and methodological advancements and practical implementation needs, especially as technical barriers are increasingly overcome. Nevertheless, systemic challenges related to institutional, behavioural, and socio-economic factors persist. From a stakeholder perspective, four thematic drivers were identified: material circularity and resource efficiency; integration with complementary assessment tools; energy-performance strategies for comfort and efficiency; and alignment with international certification systems. The study offers a stakeholder-oriented framework that demonstrates the multi-level value of BIM–LCA integration not only for environmental impact assessment but to support informed decision-making and reduce resource consumption. These insights aim to bridge the gap between academic research and practical implementation, contributing to the advancement of sustainable practices in the built environment. Full article

The optimization of thermal performance in buildings is essential for sustainable urban development, yet the high cost and complexity of traditional thermal conductivity measurement methods limit broader research and educational applications. This study developed and validated a low-cost, replicable prototype that determines the thermal conductivity of roof tiles and composites using the Lee Disc method automated with Arduino-based acquisition. Standardized samples of ceramic, fiber–cement, galvanized steel, and steel coated with a castor oil-based polyurethane composite reinforced with miriti fiber (Mauritia flexuosa) were analyzed. The experimental setup incorporated integrated digital thermocouples and strict thermal insulation procedures to ensure measurement precision and reproducibility. Results showed that applying the biocompatible composite layer to metal tiles reduced thermal conductivity by up to 53%, reaching values as low as 0.2004 W·m⁻¹·K⁻¹—well below those of ceramic (0.4290 W·m⁻¹·K⁻¹) and fiber–cement (0.3095 W·m⁻¹·K⁻¹) tiles. The system demonstrated high accuracy (coefficient of variation < 5%) and operational stability across all replicates. These findings confirm the feasibility of open-source, low-cost instrumentation for advanced thermal characterization of building materials. The approach expands access to experimental research, promotes sustainable insulation technologies, and offers practical applications for both scientific studies and engineering education in resource-limited environments. Full article

In the context of retrofitting existing buildings into nearly zero-energy buildings (NZEBs), in situ assessment methods have proven reliable for evaluating the performance of building components, including glazing systems. However, these methods are often time-consuming, intrusive to occupants, and disruptive to building operations. This study investigates the potential of a machine learning approach—multiple linear regression (MLR)—to predict the dynamic performance of an office building’s glazing system by analyzing surface temperature variations and their impact on nearby thermal comfort. The models were trained using in situ data collected over just two weeks—one in September and one in December—but were applied to predict the glazing performance on multiple other dates with diverse weather conditions. Results show that MLR predictions closely matched nighttime measurements, while some discrepancies occurred during the daytime. Nevertheless, the machine learning model achieved a daytime prediction accuracy of approximately 1.5 °C in terms of root mean square error (RMSE), which is lower than the values reported in previous studies. For thermal comfort evaluation, the MLR model identified the periods with thermal discomfort with an overall accuracy of approximately 92%. However, during periods when the difference between predicted and measured operative temperatures exceeded 1 °C, the thermal comfort predictions showed greater deviation from actual measurements. The study concludes by acknowledging its limitations and recommending a future approach that integrates machine learning with laboratory-based techniques (e.g., hot-box setups and solar simulators) and in situ measurements, together with a broader variety of glazing samples, to more effectively evaluate and enhance prediction accuracy, robustness, and generalizability. Full article