Search Results (4,852)

Background: Seasonal influenza prevention in young adults is influenced by access, trust, and vaccine information exposure, but local evidence linking vaccination uptake with illness and economic burden is limited. Methods: We conducted a non-probability, cross-sectional electronic survey of adults aged 18–49 years who lived, worked, or studied in the San Francisco Bay Area during the 2025 to 2026 influenza season. Measures included vaccination uptake, influenza-like illness, recovery, functional and economic burden, vaccination sites, and vaccine information exposure. Multivariable logistic regression examined factors associated with vaccination uptake; Kaplan–Meier and Cox models examined time to recovery. Results: Of 554 responses, 463 were included. Vaccination uptake was 86.2% (n = 399; 95% confidence interval [CI], 82.7–89.2%), likely reflecting a health-engaged convenience sample. Influenza-like illness was reported by 38.4%; median recovery time was 5 days, median missed work or school was 2 days, and median direct out-of-pocket cost was US$20. Prior season vaccination (adjusted odds ratio [aOR], 2.24; 95% CI, 1.15–4.34) and greater trust in Centers for Disease Control and Prevention (CDC) or public health agencies (aOR, 1.46; 95% CI, 1.05–2.02) were associated with vaccination. Pharmacies were the second most common vaccination site and preferred future site. Conclusions: Influenza prevention for young adults may benefit from pharmacy-inclusive, multichannel access paired with trusted communication. Findings should be interpreted in light of non-probability recruitment and likely overrepresentation of health-engaged respondents. Full article

To determine the factors influencing juvenile recruitment of the kelp Eisenia bicyclis, a seven-year monitoring survey was conducted in an area affected by seismic subsidence caused by the 2011 earthquake and subsequent breakwater restoration. Juvenile recruitment was high in September 2011 and in May to June of 2013–2015, but low in 2012, 2016 and 2017. Analysis of the relationship between environmental factors and juvenile recruitment revealed that recruitment was associated with light intensity, with lower water temperature two months prior, and an increase in nutrients four months prior. The seasonal increase in nutrient concentrations during winter may have been influenced by the seasonal northwestward coastal current. In contrast, despite elevated nutrient concentrations, recruitment was relatively poor in 2016–2017. This might be attributed to the unstable seabed environment associated with the breakwater construction. Our monthly monitoring of both the number of E. bicyclis juvenile recruitments and environmental factors at the same site demonstrated that a time-lagged increase in nutrient concentrations and a decrease in water temperature are associated with Eisenia bicyclis juvenile recruitment. This study provides fundamental information on kelp recruitment that will contribute to predicting the recovery of kelp communities following disturbances and recruitment dynamics under environmental change. Full article

This article analyzes how martyrdom progressively diverged from heroism to become an autonomous category of political discourse in nineteenth-century southern European Catholic societies. It argues that the secularization and politicization of Catholic asceticism played a central role in this process, which culminated in the martyr’s moment of agony and raised the question of the voluntary dimension of martyrdom. The analysis draws on a wide range of political martyrologies, funeral orations, and press reports, and focuses mostly on the Spanish and Italian cases, where martyrdom gained greatest prominence in the political sphere and gave rise to the most explicit theoretical expositions. It highlights the extent to which martyrdom was transformed into a powerful tool of legitimization and mobilization centered on willing self-denial and self-sacrifice for a cause or community. By reassessing secular martyrdom in relation to its religious substrate, this article seeks to reconsider sacrifice, holiness, and memory within Southern European Romanticism, shedding new light on the dynamics of secularization in post-revolutionary Europe. Full article

Temperate broadleaved forests support diverse arthropod communities, but canopy-dwelling insects in European lime (Tilia cordata Mill.) stands are still poorly known. We surveyed light-attracted canopy insects in six T. cordata Genetic Conservation Units and related protected stands across Lithuania. One modified, solar-powered UV light trap was installed in the canopy (10–15 m) at each site and operated twice per month from June to August in 2023 and 2024. We used diversity metrics, similarity indices, multiple regression, and non-metric multidimensional scaling (NMDS) together with PERMANOVA to examine the structure of insect communities and assess the influence of environmental factors. In total, 6031 individuals representing 295 insect species were recorded, with higher abundance, species richness and Shannon diversity in 2024 than in 2023. Across both years and all sites, Shannon H diversity index ranged from 3.21 to 3.92. Sørensen indices indicated moderate species similarity among sites and distinct species composition at the Ukmergė genetic reserve. The 20 most abundant taxa comprised over 60% of all individuals, and dominance structure changed markedly between years: Serica brunnea dominated in 2023 but was nearly absent in 2024. Regression revealed a significant positive effect of air temperature on insect abundance (about a 31% increase per 1 °C), while precipitation had no significant effect on insect abundance. NMDS and PERMANOVA showed strong spatial structuring, with sites explaining most of the variation, and weaker but significant temporal and site-by-year effects. Overall, insect diversity metrics showed non-significant correlations with T. cordata genetic diversity parameters. Results demonstrate that mature T. cordata forest stands are important reservoirs of canopy insect diversity and highlight pronounced spatial heterogeneity, interannual dynamics, and temperature sensitivity of canopy assemblages in Lithuanian forests. Full article

Understory vegetation communities in chestnut (Castanea sativa L.) forests feature unique biodiversity patterns and high conservation value, yet the complex drivers of these communities remain poorly quantified. This study investigates the combined effects of structural, microclimatic, and topographic parameters on understory biodiversity in the mountainous region of Chalkidiki, Northern Greece. Using a nested plot design (n = 30), we integrated analytical in situ microclimatic monitoring with hemispherical photography (HemiView canopy image analysis system) to accurately quantify canopy architecture (canopy cover and solar radiation parameters), while a detailed vegetation inventory of vascular plants was performed to determine plant community structure and composition. Generalized Additive Models (GAMs) were employed to model Shannon Diversity (H’) and a weighted rarity index (RSR) representing complementary aspects of understory biodiversity. Our results reveal that the tree slenderness of the dominant stand serves as a robust proxy for stand competition and compactness. Lower slenderness values, reflecting reduced overstory competition, were significantly associated with enhanced light availability and potentially with microclimatic stability, which in turn supported higher levels of species diversity and rarity. Distinct ecological trends were observed between diversity and rarity. Shannon diversity was highest in closed forest environments characterized by lower temperatures, low stand slenderness values, southern aspects, and lower elevations, with the final model explaining 66.1% of the variance (n = 27). In contrast, species rarity was primarily driven by stand slenderness and low disturbance levels (explaining 54.6% of the variance), with the majority of rare species occurring in undisturbed stands (n = 30). These findings suggest that targeted, low-intensity management for competition promotes structurally stable stands and microclimatic buffering, facilitating the preservation of understory biodiversity. Full article

Low-cost optical sensors have emerged as promising tools for in situ freshwater quality monitoring, offering the potential to expand spatial and temporal data coverage, particularly in community-based monitoring projects. However, despite rapid technological development of low-cost optical sensors, analytical validation practices of these devices remain poorly studied. This study aims to systematically and critically assess analytical validation practices applied to low-cost optical sensors based on absorbance, fluorescence, colorimetry, and light scattering, potentially designed for community-based freshwater monitoring. A total of 40 studies were analysed to evaluate how key analytical performance parameters, including sensitivity, accuracy, precision, and repeatability, as well as comparison with reference methods or benchtop instruments, were assessed and reported in relation to established validation guidelines. The analysis revealed substantial heterogeneity and critical gaps in validation approaches. While most studies report sensitivity metrics such as limits of detection and quantification, comprehensive evaluation of key analytical parameters such as accuracy, precision, and reproducibility was often limited. The reliance on single calibration experiments and high determination coefficients ($R^2$) frequently overestimates sensor performance. The lack of open-source materials further limits reproducibility and deployment: essential information such as design files, calibration procedures, and open-source resources is often incomplete or unavailable. To address these limitations, we propose a structured framework for validation and reporting that integrates established analytical guidelines with the practicalities of low-cost sensor development. Adoption of this approach would enable more consistent performance evaluation, improving reproducibility and facilitating comparison across studies and devices. Overall, strengthening analytical validation and reporting practices is essential to support the transition of low-cost optical sensors from proof-of-concept systems to reliable analytical devices for freshwater quality monitoring. Full article

Internal and civil wars affect the lives of religious and ethnic minorities the most. For Syrian citizens of Armenian origin, the Republic of Armenia represented one of the most accessible and meaningful destinations to relocate to, shaped by shared ethnicity, collective memory, and historical ties. When the Syrian war erupted in 2011, thousands opted to resettle in Armenia, yet they and host institutions struggled to categorize them as immigrants, refugees, or repatriates. This ambiguous status has received little scholarly attention. To explore these complexities, the study employed a survey-based research design involving 124 participants, supplemented by an open-ended question intended to capture personal narratives and nuanced identity negotiations. The manuscript examines how the labels immigrant, refugee, and repatriate carry distinct legal, social, and emotional implications, especially against the backdrop of the 1915 Armenian Genocide’s enduring memory and the particularly negative connotations of “immigrant” and “refugee” in Western Armenian and Arabic languages. Within this contested semantic and policy terrain, repatriation appears not merely as a bureaucratic category but as a culturally resonant and sometimes preferred pathway for some Diaspora Armenians, informed by lifelong exposure to repatriation narratives through formal education (language textbooks) and informal communal practices. The case sheds light on the broader conception of stakeholders, including how they self-identify, how they understand their status in Armenia, and the factors shaping their choices, particularly in the context of contemporary geopolitics and the role of education in influencing external perceptions of them. Full article

The main objective of this study is to investigate smoking dynamics, identify the most influential factors governing smoking behavior, and develop effective intervention strategies through the integration of fractional-order modeling, sensitivity analysis, optimal control theory, and artificial neural networks (ANNs). A nonlinear fractional-order compartmental model is formulated by dividing the population into potential smokers, light smokers, heavy smokers, and quit smokers. The smoking reproduction number is derived to characterize the transmission and persistence of smoking behavior within the population. To determine the impact of model parameters on smoking dynamics, both normalized forward sensitivity analysis and global sensitivity analysis based on Latin Hypercube Sampling (LHS) with Partial Rank Correlation Coefficient (PRCC) are performed. The obtained results identify the most sensitive transmission and progression parameters and demonstrate their important role in shaping smoking prevalence within the community. Furthermore, the classical integer-order model is compared with the fractional-order formulation, where the fractional model provides a more realistic description due to its ability to incorporate memory and hereditary effects associated with smoking behavior. An optimal control framework involving awareness and treatment strategies is further introduced to investigate effective smoking reduction policies. The numerical results demonstrate that awareness campaigns reduce smoking initiation, while treatment interventions increase smoking cessation, and the combined implementation of both strategies produces the most significant reduction in smoking prevalence. The consistency between the sensitivity analysis and optimal control results further supports the reliability of the proposed framework. Numerical simulations are carried out to analyze the qualitative and quantitative behavior of the system under different epidemiological scenarios. In addition, an ANN-based computational framework is employed as an efficient numerical tool to accurately approximate the complex dynamics of the proposed fractional-order smoking model with very low prediction error. Overall, the present study provides a comprehensive mathematical and computational framework for understanding, analyzing, and controlling smoking behavior within a population. Full article

Biofilms are structured communities of microorganisms embedded in a self-produced extracellular polymeric substance (EPS) matrix, whose development significantly enhances microbial resistance to antibiotics, disinfectants, and host immune defenses, posing major challenges in clinical, industrial, and environmental settings. Compared with planktonic cells, biofilm-associated microorganisms can exhibit up to 10- to 1000-fold increased tolerance to antimicrobial agents, contributing to the persistence of biofilm-associated infections (BAIs). These infections remain difficult to eradicate due to reduced penetration, altered metabolic states, and the presence of dormant or persister cells. Anti-biofilm strategies can be broadly classified into physical approaches (e.g., ultrasound, mechanical stress, and light-based approaches) that target biofilm structure; chemical and enzymatic methods (e.g., EPS-degrading enzymes) that destabilize the matrix; and biological and molecular strategies (e.g., quorum-sensing (QS) inhibitors, anti-virulence agents, bacteriophages, phage-derived antimicrobial molecules, antimicrobial peptides, and natural bioactive compounds) that modulate biofilm development and integrity by targeting regulatory pathways and matrix stability through distinct mechanisms of action. Natural compounds, including lactoferrin, lactoferrin-derived peptides, and probiotic and postbiotic fractions of lactic acid bacteria (LAB), as well as plant-derived metabolites, have shown promising anti-biofilm effects, with efficacy often enhanced through complementary or potentially synergistic interactions. However, despite these advancements, clinical translation remains limited. For example, BAIs account for approximately 80% of chronic infections, with high recurrence rates and therapeutic failure reported in device-associated infections and chronic wounds. These limitations highlight the need for clinically translatable, multimodal approaches that integrate structural biofilm disruption, antimicrobial targeting, and host response modulation to design more effective and sustainable anti-biofilm strategies. Full article

Introduction: Biofilms are communities of microorganisms on wet or moist surfaces that are difficult to remove and can repeatedly release microorganisms. This causes significant problems in many areas, such as food technology and medicine, as biofilms can cause fatal infections in patients. One approach that has not yet been extensively researched is to prevent biofilm formation on surfaces by irradiating them with visible blue or violet light, which is known for its antimicrobial effect on planktonic bacteria. Methods: Various bacterial suspensions (Bacillus subtilis, Pseudomonas stutzeri, Streptococcus cristatus and Pseudomonas syringae) were placed in 24-well MTPs (microtiter plates) and irradiated with violet or blue light for up to 6 days. During this time, they were shaken at room temperature at a frequency of 100 rpm. The irradiation intensities were 0, 10 or 20 mW/cm². After each day, the bacterial suspension was aspirated and the biofilm at the bottom of the well was assessed by absorption measurement and microscopy images. Results: Besides B. subtilis, thin biofilms were found on all well bottoms after 24 h, even on the irradiated microtiter plates. In general, however, the biofilm absorption/scattering was lower on the irradiated MTPs than on the non-irradiated ones. For B. subtilis, irradiation prevented biofilm formation during the observation period. Surprisingly, in most experiments, blue light had a stronger effect than violet light, which does not correspond to the experiences with planktonic bacteria. Conclusion: Violet and blue light exhibits a positive effect on suppressing the formation of new biofilm, but the applied maximum irradiance of 20 mW/cm² was unable to prevent the formation of new biofilm for most of the examined bacteria. Further investigations with higher irradiance levels are necessary to determine whether it is possible to inhibit the formation of new biofilm altogether. Full article

Urban street lighting remains a significant source of energy consumption in cities, largely due to static operation and limited responsiveness to real-time conditions. This inefficiency increases operational costs and environmental impact, especially in rapidly urbanizing regions. To address this issue, this study investigates IoT-enabled smart street lighting as an adaptive and data-driven solution within smart city frameworks. The work focuses on the growing body of research in this domain and examines its evolution, technical structure, and emerging environmental role. The study aims to provide a structured synthesis that connects research trends with system-level design, while highlighting the transition from energy-focused systems to multifunctional urban platforms. A bibliometric-driven and thematic review approach is adopted. A dataset of 151 publications was analyzed using Bibliometrix and Biblioshiny tools to extract trends, collaboration patterns, and research themes. This analysis is complemented by a qualitative evaluation of system architectures, sensing technologies, communication models, and control strategies. The findings indicate a sustained annual growth rate of 14.87% and a highly collaborative research landscape, with an average of 3.97 authors per study. The results also reveal that energy efficiency remains the dominant focus, while environmental integration is emerging but still underrepresented. The study further identifies key gaps related to scalability, sensor reliability, and the lack of standardized evaluation metrics. The outcomes provide a comprehensive roadmap for future research and support the development of scalable, intelligent, and sustainable lighting systems. The proposed insights are applicable to urban environments globally, particularly in regions seeking cost-effective and energy-efficient infrastructure solutions. Full article

Sugarcane is a high-value crop in Egypt, yet weed communities in the understudied Upper Egypt region have not been systematically characterized. This study provides a comprehensive analysis of weed floristic composition, phytogeographical affinities, and the edaphic and canopy light factors governing vegetation structure across contrasting Nile Valley clay and reclaimed desert lands in Qena Governorate. Fourteen stands were surveyed during the 2024/2025 sugarcane growing season, recording 110 species from 33 families (68 annuals and 42 perennials), which were dominated by Poaceae, Asteraceae, Fabaceae, Euphorbiaceae, and Amaranthaceae (54.6% of the flora recorded). Therophytes were the most abundant life form (60.9%), and 51.8% of species belonged to Neotropical, Palaeotropical, Cosmopolitan, and Pantropical chorotypes. Diversity indices showed high and balanced species diversity, with no dominance by any single species. Seasonal variation showed that species richness peaked in spring, decreased through summer and autumn, and correlated with light intensity under the canopy. TWINSPAN identified four vegetation groups, which were merged into three primary vegetation groups (A, B, and C) via DCA and CCA ordinations and linked to microhabitats shaped by elevation and soil physicochemical properties. CCA revealed that Group C (stands in the Nile Riverbank lands) had the highest diversity, which was associated with organic matter, clay, and field capacity. In contrast, Group A (stands of reclaimed desert land) had low richness linked to high levels of Total Dissolved Solids (TDS), Electrical Conductivity (EC), Na, K, Mg, CaCO₃, and sandy soils. Group B (stands of Nile clay lands) was an intermediate transitional community between groups A and C. These findings establish edaphic factors as the primary determinant of weed community structure, with salinity as the critical constraint in reclaimed lands and seasonal light variation as a secondary diversity filter. Full article

The oral cavity harbors a complex microbial community where pathogens implicated in dental caries and periodontitis can heavily colonize toothbrushes, transforming them into persistent sources of contamination that threaten both oral and systemic health. Consequently, this study evaluated the bactericidal efficacy of 275 nm ultraviolet light-emitting diode (UV-LED) irradiation against common oral bacteria in vitro and its practical utility for extraoral toothbrush sanitization. Suspensions of Streptococcus mutans, Streptococcus sanguinis, Porphyromonas gingivalis, and Fusobacterium nucleatum were irradiated for 3 min, 6 min, and 9 min. Bacterial growth and bactericidal effects were measured using growth curve and colony-forming unit assays, respectively. LIVE/DEAD staining and crystal violet staining were used to evaluate the bacterial viability and multispecies biofilm formation after irradiation. Additionally, the sanitization effects of a 275 nm UVC-based portable device on used toothbrushes were investigated. Direct UVC irradiation at 275 nm exhibited strong bactericidal effects against common oral bacteria in vitro. UVC irradiation also showed great sanitization effects on used toothbrushes. In summary, the vulnerability of common oral bacteria to 275 nm UVC, combined with its sanitizing efficacy on used toothbrushes, establishes a solid basis for extraoral sanitization, offering a reliable strategy to mitigate the risk of oral pathogen transmission from contaminated toothbrushes. Full article

Autonomous unmanned aerial vehicles (UAVs) must decide when to trust onboard perception, when to request edge support, and when to avoid acting under poor visual or communication conditions. This study develops a risk-aware edge-assisted UAV perception framework that combines calibrated visual confidence with next-window service-level agreement (SLA) feasibility. The local branch uses MobileNetV3-Small for fast onboard color recognition, while the edge branch uses ResNet-18 for stronger remote inference. Low-confidence samples are offloaded only when the SLA predictor estimates that the wireless link is feasible; otherwise, the system enters fallback, meaning that the current prediction is not treated as immediately actionable. The evaluation follows a hard cross-illumination split: indoor and fluorescent light samples are used for training and validation, and indoor night and sunlight samples are reserved for testing. Under this setting, the local model achieves 76.89% accuracy and 73.25% macro-F1, while the edge model achieves 81.26% accuracy and 77.58% macro-F1. The SLA predictor, trained on enhanced telemetry features while preserving the original target label, achieves 85.74% accuracy, 85.57% macro-F1, 0.9420 ROC-AUC, and 0.9585 PR-AUC on temporally held-out records. The joint policy achieves 93.23% coverage and 79.90% success over active decisions, using local inference for 82.76% of the samples, edge offloading for 10.47%, and fallback for 6.77%. These results indicate that the framework is best understood as a tunable risk management layer for UAV perception rather than a pure accuracy maximization classifier. It avoids blind offloading and reduces forced decisions when both visual confidence and communication feasibility are weak. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article