Search Results (503)

Air quality is a vital factor for safeguarding public and environmental health. Particulate matter (i.e., PM2.5 and PM10) and nitrogen dioxide are among the most harmful air pollutants leading to severe health risks such as respiratory and cardiovascular diseases, while also affecting the environment negatively by contributing to the formation of acid rains and ground level ozone. The European Union has introduced new thresholds on those pollutants to be met by the year 2030, taking into consideration the guidelines set by the World Health Organization, aiming for a healthier environment for humans and living species. Cyprus is an island that is vulnerable to those pollutants mostly due to its geographic location, facilitating shipping activities and dust transport from Sahara Desert, and the methods used to produce electricity which primarily rely on petroleum products. Furthermore, the country suffers from heavy traffic conditions, making it susceptible to high levels of nitrogen dioxide. Thus, the projection of air pollutants according to different scenarios based on regulations and policies of the European Union are necessary towards clean air and better practices. The Screening for High Emission Reduction Potential on Air (SHERPA) is a tool developed by the European Commission which allows the simulation of emission reduction scenarios and their effect on the following key pollutants: NO, NO₂, O₃, PM2.5, PM10. This study aims to assess the potential of the SHERPA simulation tool to support air quality related decision and policy planning in Cyprus to ensure that the country will remain within the thresholds that will be applicable in 2030. Full article

To maintain product reliability and stabilize performance, it is essential to prioritize the identification and resolution of latent defects. Advanced products such as high-precision electronic devices and semiconductors are susceptible to performance degradation over time due to environmental factors and electrical stress. However, conventional performance testing methods typically evaluate products based solely on predefined acceptable ranges, making it difficult to predict long-term degradation, even for products that pass initial testing. In particular, products exhibiting borderline values close to the threshold during initial inspections are at a higher risk of exceeding permissible limits as time progresses. Therefore, to ensure long-term product stability and quality, a novel approach is required that enables the early prediction of potential defects based on test data. In this context, the present study proposes a machine learning-based framework for predicting latent defects in products that are initially classified as normal. Specifically, we introduce the Sigma Deviation Count Labeling (SDCL) method, which utilizes a Gaussian distribution-based approach. This method involves preprocessing the dataset consisting of initially passed test samples by removing redundant features and handling missing values, thereby constructing a more robust input for defect prediction models. Subsequently, outlier counting and labeling are performed based on statistical thresholds defined by 2σ and 3σ, which represent potential anomalies outside the critical boundaries. This process enables the identification of statistically significant outliers, which are then used for training machine learning models. The experiments were conducted using two distinct datasets. Although both datasets share fundamental information such as time, user data, and temperature, they differ in the specific characteristics of the test parameters. By utilizing these two distinct test datasets, the proposed method aims to validate its general applicability as a Predictive Anomaly Testing (PAT) approach. Experimental results demonstrate that most models achieved high accuracy and geometric mean (GM) at the 3σ level, with maximum values of 1.0 for both metrics. Among the tested models, the Support Vector Machine (SVM) exhibited the most stable classification performance. Moreover, the consistency of results across different models further supports the robustness of the proposed method. These findings suggest that the SDCL-based PAT approach is not only stable but also highly adaptable across various datasets and testing environments. Ultimately, the proposed framework offers a promising solution for enhancing product quality and reliability by enabling the early detection and prevention of latent defects. Full article

As the pediatric COVID-19 landscape evolves, it is essential to evaluate whether SARS-CoV-2 infection predisposes children to allergic disorders. This narrative review synthesizes current epidemiological and immunological evidence linking pediatric COVID-19 with new-onset atopy. Epidemiological data remain heterogeneous: large Korean and multinational cohorts report increased risks of asthma and allergic rhinitis following COVID-19, whereas U.S. cohorts show neutral or protective associations, highlighting geographic and methodological variability. Mechanistic insights provide biological plausibility: epithelial injury and the release of alarmin cytokines (IL-33, IL-25, TSLP) promote Th2 polarization and ILC2 expansion, while epigenetic “scars” (e.g., LMAN2 methylation changes) and hematopoietic stem cell reprogramming may sustain long-term Th2 bias. Cytokine memory involving IL-7 and IL-15 contributes to altered T- and B-cell homeostasis, whereas disrupted regulatory T-cell function may reduce tolerance thresholds. Paradoxical trade-offs exist, such as ACE2 downregulation in allergic airways, which may lower viral entry but simultaneously amplify type-2 inflammation. Together, these processes suggest that SARS-CoV-2 infection could foster a pro-allergic milieu in susceptible children. Although current evidence is inconclusive, integrating epidemiological surveillance with mechanistic studies is crucial for predicting and alleviating post-COVID allergic outcomes. Longitudinal pediatric cohorts and interventions targeting epithelial alarmins or microbiome restoration may hold promise for prevention. Full article

The medical imaging domain frequently encounters the dual challenges of annotation scarcity and class imbalance. A critical issue lies in effectively extracting information from limited labeled data while mitigating the dominance of head classes. The existing approaches often overlook in-depth modeling of sample relationships in low-dimensional spaces, while rigid or suboptimal dynamic thresholding strategies in pseudo-label generation are susceptible to noisy label interference, leading to cumulative bias amplification during the early training phases. To address these issues, we propose a semi-supervised medical image classification framework combining labeled data-contrastive learning with class-adaptive pseudo-labeling (CLCP-MT), comprising two key components: the semantic discrimination enhancement (SDE) module and the class-adaptive pseudo-label refinement (CAPR) module. The former incorporates supervised contrastive learning on limited labeled data to fully exploit discriminative information in latent structural spaces, thereby significantly amplifying the value of sparse annotations. The latter dynamically calibrates pseudo-label confidence thresholds according to real-time learning progress across different classes, effectively reducing head-class dominance while enhancing tail-class recognition performance. These synergistic modules collectively achieve breakthroughs in both information utilization efficiency and model robustness, demonstrating superior performance in class-imbalanced scenarios. Extensive experiments on the ISIC2018 skin lesion dataset and Chest X-ray14 thoracic disease dataset validate CLCP-MT’s efficacy. With only 20% labeled and 80% unlabeled data, our framework achieves a 10.38% F1-score improvement on ISIC2018 and a 2.64% AUC increase on Chest X-ray14 compared to the baselines, confirming its effectiveness and superiority under annotation-deficient and class-imbalanced conditions. Full article

Unaccustomed eccentric exercise is well established to induce exercise-induced muscle damage (EIMD), characterized by transient strength loss, delayed onset muscle soreness (DOMS), reduced range of motion, and proprioceptive disturbances. While limb dominance has been proposed as a potential modulator of susceptibility to EIMD, evidence remains inconclusive. This exploratory study aimed to compare alterations in muscle damage indices between dominant and non-dominant knee extensors 48 h after eccentric isokinetic exercise. Eighteen physically active young women (23 ± 2 years) completed two eccentric exercise sessions (5 × 15 maximal contractions at 60°/s), one per limb, with sessions separated by 24–30 days. For all participants, testing was conducted during the early follicular phase. Muscle strength (isometric and eccentric peak torque), DOMS (palpation and pain pressure threshold), range of motion, fatigue index, and position sense were assessed pre- and 48 h post-exercise. Significant reductions in isometric and eccentric peak torque, increased DOMS, impaired position sense, and altered fatigue index were observed 48 h post-exercise in the exercised limb (p < 0.001), with no differences between dominant and non-dominant limbs across all indices. These findings demonstrate that limb dominance does not influence the magnitude of EIMD in knee extensors of young women. Practical implications include equal consideration of both limbs in eccentric training, rehabilitation, and injury prevention programs. Full article

Electromagnetic compatibility (EMC) impacts of arc discharge from medium-low speed maglev trains on the VHF Data Broadcast (VDB) link of the Ground-Based Augmentation System (GBAS) are systematically investigated. Considering that a single VDB station serves multiple runways, this study evaluates how different placements of the VDB antenna relative to runway thresholds affect its susceptibility to maglev interference. Field measurements of maglev arc radiation under various operational conditions revealed a maximum radiation field strength of 57.76 dBμV/m at 113 MHz. Laboratory experiments further identified a minimum required signal-to-interference ratio (SIR) of 26 dB for reliable VDB signal decoding. Theoretical analyses demonstrate that maintaining a separation greater than 5.73 km between the maglev arc source and runway threshold is necessary to ensure signal integrity. The findings offer practical guidance for airport planning and electromagnetic protection design. Full article

Background/Objectives: Phenotypic antimicrobial susceptibility testing (AST) is essential for guiding timely and effective antibiotic therapy. Rapid and accurate reporting of AST results enables earlier optimization of treatment and supports antimicrobial stewardship by minimizing unnecessary use of broad-spectrum antibiotics. This study aimed to evaluate the performance of the Selux DX Next-Generation Phenotyping AST system in comparison with the standard-of-care MicroScan WalkAway Plus system and broth microdilution reference results. Methods: A total of 332 clinical isolates and 97 Antimicrobial Resistance (AR) Bank reference isolates were tested using the Selux DX and MicroScan systems. Performance was assessed by categorical agreement (CA), error rates [very major errors (VMEs), major errors (MEs), minor errors (mEs)], and turnaround time. Results: The Selux DX system demonstrated ≥90% CA for most drug–organism combinations, consistent with Clinical and Laboratory Standards Institute (CLSI) acceptance thresholds, although elevated error rates were noted for erythromycin, aztreonam, cefazolin, minocycline, and ampicillin/sulbactam. Across 5124 drug–bug combinations, 55 VMEs (1.1%), 42 MEs (0.8%), and 203 mEs (4.0%) were identified. The Selux DX system achieved a markedly shorter average turnaround time of 5.5 h compared with 16 h for the MicroScan system, though at the cost of a longer setup time. Conclusions: The Selux DX system provides rapid and reliable phenotypic AST results, supporting earlier clinical decision-making and antimicrobial stewardship. However, discrepancies with certain antimicrobial agents, particularly among highly resistant reference isolates, highlight the need for further validation in larger, multicenter studies. Full article

Hybrid computer-aided design and computer-aided manufacturing (CAD/CAM) materials have gained prominence in restorative dentistry due to their advantageous mechanical and esthetic properties; however, their long-term performance may be adversely affected by acidic oral environments, such as those associated with gastroesophageal reflux disease (GERD). This in vitro study aimed to investigate the effects of simulated gastric acid exposure on the surface roughness, gloss, color stability, and microhardness of two hybrid CAD/CAM materials: Vita Enamic and Cerasmart. Standardized rectangular specimens (2 mm thickness) were prepared and polished using a clinically relevant intraoral protocol. Baseline measurements were obtained for surface roughness, gloss, color change (ΔE), and Vickers microhardness. All specimens were then immersed in hydrochloric acid (pH 1.2) for 24 h to simulate prolonged gastric acid exposure, after which the same properties were re-evaluated. Post-immersion analysis revealed significant increases in surface roughness and reductions in gloss and microhardness for both materials (p < 0.05), with Vita Enamic demonstrating greater susceptibility to degradation. Color changes remained below the clinically perceptible threshold, with no significant differences between materials. These findings highlight the potential vulnerability of hybrid CAD/CAM materials to acidic environments and underscore the importance of careful material selection in patients predisposed to acid-related challenges. Full article

Largescale vegetation reconstruction projects in the western and northern parts of China, along with climate change and increased humidity, have significantly boosted fractional vegetation cover (FVC) in the Mu Us Desert. However, this increase may impact the area’s vulnerability to drought stress. Here, we assessed the area’s susceptibility to hydrometeorological drought by analyzing the maximum correlation coefficients (MCC) derived from the spatiotemporal relationships between FVC and estimates of standardized precipitation evapotranspiration index (SPEI) for the area. The results of the study were as follows: (1) FVC exhibited an increasing trend throughout the growing seasons from 2003 to 2022. Although the region experienced an overall wetting trend, drought events still occurred in some years. MCC-values were predominantly positive across all timescales, suggesting that vegetation generally responded favorably to drought conditions. (2) The order of response of land covertype to drought, from greatest to lowest, was grassland, cultivated land, forestland, and sand land. Cultivated land and grassland exhibited heightened sensitivity to short-term drought; forestland and sand land showed greater sensitivity to long-term drought. (3) With a high FVC, the response of grassland and sand land to drought was significantly enhanced, whereas the response of cultivated land and forestland was less noticeable. (4) Low FVC grassland and sand land have not yet reached the VCCSW threshold and can support moderate vegetation restoration. In contrast, forestland and cultivated land exhibit drought sensitivity regardless of FVC levels, indicating that increasing vegetation should be approached with caution. This research offers a method to evaluate the impact of drought stress on ecosystem stability, with findings applicable to planning and managing vegetation cover in arid and semiarid regions globally. Full article

The interaction between tumor cells and stroma in urological malignancies is governed by secreted and damage-associated factors that promote angiogenesis, immune modulation, and metastasis. This review synthesizes current evidence on six biomarkers—GDF15, VEGF, TGF-β1, HSP90, HMGB1, and S100A9—detailing their biological functions and clinical implications. We discuss GDF15’s roles in metabolic stress and immune regulation, VEGF’s central role in neovascularization, and TGF-β1’s dualistic tumor-suppressive and promotive effects. We then examine damage-associated molecular patterns, highlighting HSP90’s extracellular immunomodulation, HMGB1’s signaling via pattern-recognition receptors, and S100A9’s pro-inflammatory activity through RAGE and Toll-like receptors. Comparative analyses across renal cell carcinoma and bladder cancer cohorts elucidate each marker’s diagnostic accuracy, prognostic value, and predictive capacity for targeted therapies. Notably, GDF15 and HSP90 correlate with ferroptosis susceptibility in RCC and urinary VEGF with HMGB1 increases the chances of non-invasive bladder cancer detection. We suggest that multiplexed biomarker panels could enhance early detection, risk stratification, and personalized treatment in urological oncology. We advocate for prospective studies to validate thresholds, clarify interactions, and improve clinical integration. Full article

Hepatitis C virus (HCV) remains a critical public health concern globally, including in Bangladesh. In this study, we employed a mathematical modeling framework to analyze the national dynamics of HCV infections and associated mortality in Bangladesh. Utilizing surveillance data from the Directorate General of Health Services, we examined the epidemiological trajectory of HCV and assessed the impact of various intervention strategies. The Next Generation Matrix approach was employed to derive basic reproduction numbers, and the model was calibrated with observed HCV incidence data to estimate some model parameter values. We conducted sensitivity analysis to assess how variations in model parameters affect HCV prevalence, revealing that transmission rates of both drug-susceptible and drug-resistant strains have the greatest impact. Additionally, bifurcation analysis was performed to examine parameter thresholds and assess the stability of the system. We then used the model to estimate the impacts of various intervention policies on reducing HCV cases and deaths. Among single interventions, increased effective treatment for drug-susceptible cases proved to be the most rapid and effective strategy for reducing the total number of both drug-susceptible and drug-resistant HCV cases, as well as mortality. Additionally, our results imply that combining interventions increases their overall effectiveness, achieving substantial reductions in cases and deaths with relatively modest investment. However, complete eradication of HCV in Bangladesh would require significantly greater resources. Full article

Attraction–Repulsion Optimisation Algorithm (AROA) is a newly proposed metaheuristic algorithm for solving global optimisation problems, which simulates the equilibrium relating to the attraction and repulsion phenomenon that occurs in the natural world, and aims to achieve a good balance between the development exploration phases. Although AROA has a more significant performance compared to other classical algorithms on complex realistic constrained issues, it still has drawbacks in terms of diversity of solutions, convergence precision, and susceptibility to local stagnation. To further improve the global optimisation search and application ability of the AROA algorithm, this work puts forward an Improved Attraction–Repulsion Optimisation Algorithm based on multiple strategies, denoted as IAROA. Firstly, the elite dynamic opposite (EDO) learning strategy is used in the initialisation phase to enrich the information of the initial solution and obtain high-quality candidate solutions. Secondly, the dimension learning-based hunting (DLH) exploration tactics is imported to increase the candidate solution diversity and enhance the trade-off between local and global exploration. Next, the pheromone adjustment strategy (PAS) is used for some of the solutions according to the threshold value, which extends the search range of the algorithm and also accelerates the convergence process of the algorithm. Finally, the introduction of the Cauchy distribution inverse cumulative perturbation strategy (CDICP) improves the local search ability of the algorithm, avoids falling into the local optimum, and improves the convergence and accuracy of the algorithm. To validate the performance of IAROA, algorithms are solved by optimisation with the original AROA and 13 classical highly cited algorithms on the CEC2017 test functions, among six engineering design problems of varying complexity. The experimental results indicate that the proposed IAROA algorithm is superior in terms of optimisation precision, solution stability, convergence, and applicability and effectiveness on different problems, and is highly competitive in solving complex engineering design problems with constraints. Full article

In this study, we formulate and analyze a susceptible–infected–susceptible (SIS) dynamic on metapopulation networks, where each node has a finite carrying capacity and the motion of individuals is modulated by vacant space at the destination. We obtain that the vacancy-dependent mobility pattern results in various asymptotic population distributions on heterogeneous metapopulation networks. The resulting population distributions have remarkable impact on the behavior of SIS dynamics. We show that, for the given total number of individuals, higher heterogeneity in population distributions facilitates epidemic spreading in terms of both a smaller epidemic threshold and larger macroscopic incidence. Moreover, we analytically obtain a sufficient condition that the disease-free equilibrium becomes unstable and an endemic state arises. Contrary to the absence of an epidemic threshold in the standard diffusion case without excluded-volume effects, the finite carrying capacity induces a nonzero epidemic threshold under certain conditions in the limit of infinite network sizes with an unbounded maximum degree. Our analytical results agree well with numerical simulations. Full article

This study investigated the coupling mechanism between interlayer twist angle and projectile size on the ballistic performance of bilayer phosphorene membranes, a topic essential for designing efficient nano-protective materials, yet still poorly understood. Using coarse-grained molecular dynamic simulations, we systematically explored how twist angles (0–90°) and projectile radii (2–10 nm) jointly influence impact response for membranes with a radius equal to 48 nm. We found that the effect of twist angle becomes significant only beyond a critical projectile size (~8 nm). Below this threshold, deformation remains local and twist-independent. However, for larger projectiles, the twist angle drastically alters wave propagation and failure modes. Specifically, a 90° twist induces severe wave reflection and interference, leading to a dramatic force amplification (up to 82%) and a 28% reduction in ballistic limit velocity, making it the most susceptible configuration. These results underline the critical role of twist–boundary–wave interaction in governing impact resistance and provide practical insights for the design of phosphorene-based nano-armor systems tailored to specific impact conditions. Full article

Air pollution remains one of the most urgent global challenges, affecting both public health and environmental integrity, with its severity escalating in parallel with industrialization and urban expansion. Defined as the presence of harmful substances in the atmosphere, air pollution poses risks to human health and disrupts the development of plant and animal life. Urban areas, particularly large cities, frequently exhibit pollutant concentrations that exceed safety thresholds established by the World Health Organization (WHO). This study presents a comprehensive analysis of airborne fungal microbiota in two distinct districts of Sofia, Bulgaria: the highly urbanized city center (Orlov Most) and a less urbanized southwestern area (New Bulgarian University, Ovcha Kupel). Weekly fluctuations in mold spore abundance were monitored, revealing elevated contamination levels on Fridays, likely due to intensified vehicular traffic preceding weekends and public holidays. Taxonomic identification of dominant mold species was conducted using both classical and molecular genetic methods. The isolated fungal strains predominantly belonged to the phylum Ascomycota (80%), with Talaromyces and Alternaria emerging as the most prevalent genera. Additionally, antifungal susceptibility testing indicated that most isolates were sensitive to commonly used antifungal agents, although resistance was observed in two strains of Talaromyces wortmannii. These findings underscore the significance of fungal bioaerosols in urban air quality assessments and highlight the need for targeted monitoring and mitigation strategies. Full article