Search Results (1,668)

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

This article examines how the role of digital platforms is reshaping political communication and consensus-building in contemporary societies. It questions how algorithmic architectures are transforming the relationship between leadership, audiences, and power. Drawing on an empirical analysis of online interaction data, the study analyses Donald Trump’s political communication during the August 2025 summit with Putin in Alaska, presenting it as a paradigmatic example of networked leadership. The study focuses on the dynamics of mobilisation, polarisation, and identity construction within digital ecologies. The findings show that the leader’s centrality derives not only from traditional party structures, but also from the ability to coordinate heterogeneous communication flows as well as activate processes of affective and symbolic resonance. The article proposes a theoretical model that conceptualises Trump’s audience as a cognitive and emotional power device, highlighting the convergence of post-organisational populism, algorithmic mediatisation, and communicative governance. This leadership expresses forms of “algorithmic charisma” that redefine the modalities of political legitimacy. Methodologically, the study highlights the value of data-driven interpretive approaches, while also addressing their limitations related to algorithmic transparency and replicability. In conclusion, the article offers a critical reflection on emerging ecologies of consensus and the democratic implications of the ongoing “platformisation” of the public sphere. Full article

Background/Objectives: Emerging evidence suggests that multiple micronutrient supplements (MMS) provide additional benefits for maternal and neonatal health compared with iron and folic acid (IFA) supplements. To achieve effective coverage, acceptability, and adherence—and to inform a nationwide rollout of MMS—it is essential to understand the context-specific factors that shape implementation. This study evaluated the pilot implementation of MMS in Rwanda to identify key enablers, areas for improvement, and challenges related to antenatal care (ANC) attendance and MMS use. Methods: Data were collected through a survey of 3257 women who attended ANC services, seven focus group discussions with 35 ANC attendees, and key informant interviews with 20 ANC nurses and 21 community health workers. Results: Pregnant women reported high ANC attendance (74%) and MMS consumption (79%), largely driven by strong motivation and awareness of MMS benefits. Strategies to remember daily intake and to manage side effects supported adherence, as did reminders, motivation, and information from family members and healthcare providers. Limited patient-centered counselling, financial constraints, barriers to accessing ANC services, and product stock-outs were key areas for strengthening service delivery in Rwanda. Conclusions: Sustaining high ANC attendance and MMS adherence as the program transitions from the pilot phase to national scale-up is essential. Improving counseling quality and strengthening supply chains may reinforce ANC services and support sustained MMS adherence, with benefits for maternal and child health. Full article

This mixed-methods study investigates the strategic management of digital transformation in Bulgarian schools by analysing principals’ self-reported leadership practices and styles. Using data from a nationally representative sample (N = 349) gathered through the SELFIE tool, complemented by 30 in-depth interviews, the research examines how school leaders understand and enact their roles as digital leaders within a context of fragmented policies and uneven digital capacity. Quantitative results reveal a central paradox: although 89.7% of principals claim to actively support teachers’ digital innovation, only about half report having a formalised digital strategy. This imbalance between strong operational support and weak institutionalisation reflects the dominant approach to school digitalisation in Bulgaria. Qualitative cluster analysis identifies three leadership profiles: (1) a strategic–collaborative profile, characterised by long-term planning, partnerships, and data-driven decisions; (2) a supportive–collaborative profile focused on teacher communities and context-specific professional development but lacking strategic vision; and (3) a balanced–pragmatic profile oriented toward measurable improvements and adaptive responses. Triangulation with national assessment data shows that leadership styles align with institutional contexts: high-performing schools tend to apply strategic–collaborative leadership, while lower-performing schools adopt pragmatic, adaptive approaches. The study argues that digital transformation requires context-sensitive frameworks recognising multiple developmental trajectories, highlighting the need for differentiated policies that support strategic institutionalisation of existing digital innovations while addressing structural inequalities. Full article

In temperate freshwater ecosystems, zooplankton play a crucial role in the pelagic food web and act as sensitive indicators of environmental change. They respond to shifts in water temperature, hydrodynamic mixing, and short-term meteorological events. This study investigated the epilimnetic zooplankton fauna of Lake Karamurat (Bolu, Türkiye), a small tectonic temperate lake, with a specific focus on the influence of rainfall events and wind speed on community structure. The samples were taken seasonally and horizontally using a plankton net (55 µm mesh size) and were analyzed alongside in situ physico-chemical measurements and meteorological data. In total, 74 zooplankton taxa were identified, comprising 54 rotifer species and 20 crustacean species (16 Cladocera and 4 Copepoda). Testudinella greeni was recorded for the first time in Türkiye, representing a new addition to the Turkish Rotifera fauna. Multivariate analyses revealed that electrical conductivity, water temperature, dissolved oxygen, precipitation, and wind speed were key drivers shaping community composition. The findings suggest that wind-driven surface mixing and episodic rainfall events enhanced vertical redistribution, leading to dominance of rotifers and small-bodied cladocerans in the epilimnion. These findings underscore the critical role of sampling strategy in shallow lakes under dynamic conditions and provide new faunistic insights into the zooplankton diversity of Anatolian lakes. Full article

Plastic pollution poses severe threats to ecosystems, human health, and economies as plastics fragment into macro- and microplastics that accumulate across marine and terrestrial environments. Conventional monitoring is constrained by scale, cost, and resources, particularly in under-resourced regions, whereas citizen science provides an inclusive, community-driven alternative for data collection, analysis, and remediation to support evidence-based policy. This systematic review advances the field through three novel contributions: a refined participatory typology that explicitly prioritizes co-creative models for equitable engagement in the Global South; the first comprehensive synthesis of direct citizen involvement in plastic bioremediation, including community microbial isolation, household biodegradation trials, and real-world testing of biodegradable materials; and a new conceptual framework positioning citizen science as the central nexus linking upstream prevention, technological innovation, bioremediation, and global governance. Findings highlight large-scale geotagged datasets, behavioral change, and policy influence, while persistent challenges include data standardization, digital exclusion, and Global North bias. We therefore advocate institutional mainstreaming through dedicated policy offices, decolonial integration of indigenous knowledge, and hybrid citizen–lab validation pipelines, especially in underrepresented regions such as Africa, establishing citizen science as a transformative mechanism for participatory and equitable responses to escalating plastic pollution. Full article

The gut microbiome is a modifiable factor in cancer survivorship. Diet represents the most practical intervention for modulating the gut microbiome. However, diet–microbiome relationships in prostate-cancer survivors remain poorly characterized. We conducted a comprehensive analysis of diet–microbiome associations in 79 prostate-cancer survivors (ages 62–81) enrolled in a randomized exercise intervention trial, 59.5% of whom still have active metastatic disease. Dietary intake was assessed using the Diet History Questionnaire (201 variables) and analyzed using three validated dietary pattern scores: Mediterranean Diet Adherence Score (MEDAS), Healthy Eating Index-2015 (HEI-2015), and the Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) diet score. Gut microbiome composition was characterized via 16S rRNA sequencing. Dimensionality reduction strategies, including theory-driven diet scores and data-driven machine learning (Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO)), were used. Statistical analyses included beta regression for alpha diversity, Permutational Multivariate Analysis of Variance (PERMANOVA) for beta diversity (both Bray–Curtis and Sørensen metrics), and Microbiome Multivariable Associations with Linear Models (MaAsLin2) with negative binomial regression for taxa-level associations. All models tested interactions with exercise intervention, APOLIPOPROTEIN E (APOE) genotype, and testosterone levels. There was an interaction between MEDAS and exercise type on gut alpha diversity (Shannon: p = 0.0022), with stronger diet–diversity associations in strength training and Tai Chi groups than flexibility controls. All three diet-quality scores predicted beta diversity (HEI p = 0.002; MIND p = 0.025; MEDAS p = 0.034) but not Bray–Curtis (abundance-weighted) distance, suggesting diet shapes community membership rather than relative abundances. Taxa-level analysis revealed 129 genera with diet associations or diet × host factor interactions. Among 297 dietary variables tested for cognitive outcomes, only caffeine significantly predicted Montreal Cognitive Assessment (MoCA) scores after False Discovery Rate (FDR) correction (p = 0.0009, q = 0.014) through direct pathways beneficial to cognitive performance without notable gut microbiome modulation. In cancer survivors, dietary recommendations should be tailored to exercise habits, genetic background, and hormonal status. Full article

Background: The acceptance of new vaccines under uncertainty—such as during the COVID-19 pandemic—poses a major public health challenge because efficacy and safety information is still evolving. Methods: We propose an integrative analytical framework that combines a theory-based model of vaccine acceptance—the cognitive–affective–normative (CAN) model—with machine-learning techniques (decision tree regression, random forest, and Extreme Gradient Boosting) and SHapley Additive exPlanations (SHAP) integrated into an importance–performance map (IPM) to prioritize determinants of vaccination intention. Using survey data collected in Spain in September 2020 (N = 600), when the AstraZeneca vaccine had not yet been approved, we examine the roles of perceived efficacy (EF), fear of COVID-19 (FC), fear of the vaccine (FV), and social influence (SI). Results: EF and SI consistently emerged as the most influential determinants across modelling approaches. Ensemble learners (random forest and Extreme Gradient Boosting) achieved stronger out-of-sample predictive performance than the single decision tree, while decision tree regression provided an interpretable, rule-based representation of the main decision pathways. Exploiting the local nature of SHAP values, we also constructed SHAP-based IPMs for the full sample and for the low-acceptance segment, enhancing the policy relevance of the prioritization exercise. Conclusions: By combining theory-driven structural modelling with predictive and explainable machine learning, the proposed framework offers a transparent and replicable tool to support the design of vaccination communication strategies and can be transferred to other settings involving emerging health technologies. Full article

Climate fluctuations are expected to drive a decline in the growth of many conifer and broadleaf species, especially in the Mediterranean region, where these species grow at or very near the southern limits of their distribution. Such trends have important implications not only for forest productivity but also for plant diversity, as shifts in species performance may alter competitive interactions and long-term community composition. Using tree-ring data sourced from two Abies cephalonica stands with different elevation in Mount Parnassus in Central Greece, we evaluate the growth responses of the species to climatic variability employing a dendroecological approach. We hypothesize that radial growth at higher elevations is more strongly influenced by climate variability than at lower elevations. Despite the moderate to relatively good common signal indicated by the expressed population signal (EPS: 0.645 for the high-altitude stand and 0.782 for the low-altitude stand), the chronologies for both sites preserve crucial stand-level growth patterns, providing an important basis for ecological insights. The calculation of the Average Tree-Ring Width Index (ARWI) for both sites revealed that fir in both altitudes exhibited a decline in growth rates from the late 1980s to the early 1990s, followed by a general recovery and increase throughout the late 1990s. They also both experienced a significant decline in growth between approximately 2018 and 2022. The best-fit model for annual ring-width variation at lower elevations was a simple autoregressive model of order one (AR1), where growth was driven exclusively by the previous year’s growth (p < 0.001). At the higher elevation, a more complex model emerged: while previous year’s growth remained significant (p < 0.001), other variables such as maximum growing season temperature (p = 0.041), annual temperature (inverse effect, p = 0.039), annual precipitation (p = 0.017), and evapotranspiration (p = 0.039) also had a statistically significant impact on tree growth. Our results emphasize the prominent role of carry-over effects in shaping their annual growth patterns. Full article

Digital transformation in port logistics represents a profound and systemic shift in the way maritime trade and supply chain operations are designed, coordinated, and governed through the pervasive integration of advanced digital technologies and data-driven management practices. It extends beyond the mere digitization of paper-based documents into electronic formats and beyond the digitalization of isolated processes with IT tools. Transformation involves reconfiguring organizational structures, decision-making logics, and value creation models around connectivity, automation, and predictive intelligence. In practice, it includes the adoption of smart port technologies such as the Internet of Things, 5G communication networks, digital twins, blockchain-based trade documentation, and artificial intelligence applied to vessel scheduling and cargo planning. It also encompasses collaborative platforms like port community systems that link shipping companies, terminal operators, freight forwarders, customs, and hinterland transport providers into data-driven ecosystems. The purpose of digital transformation is not only to improve efficiency and reduce operational bottlenecks, but also to enhance resilience against disruptions, ensure sustainability in line with decarbonization goals, and reposition ports as orchestrators of trade networks rather than passive providers of physical infrastructure. Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

Background/Objectives: Seasonal influenza imposes a significant burden on pediatric public health. Despite official recommendations and full insurance coverage, vaccination rates among children in Slovakia remain critically low. This study aims to analyze the attitudes, beliefs, and determinants of parental hesitancy regarding childhood influenza vaccination in the post-pandemic context. Methods: A single-center cross-sectional survey was conducted between February and March 2025 using convenience sampling among parents of children attending a pediatric immunoallergology center. An anonymous questionnaire collected data on demographics, risk perception, and attitudes. Data from 301 parents were analyzed using descriptive statistics, chi-squared tests, and odds ratios (OR) to identify key predictors of hesitancy. Results: Only 27.6% of parents expressed willingness to vaccinate their children, while 42.5% were opposed and 29.9% hesitant. Statistical analysis revealed no significant association between parental university education and vaccination intent (p > 0.05), indicating that vaccine hesitancy in this specific setting was present across all educational backgrounds. However, the source of information proved to be a critical determinant: consulting a pediatrician significantly increased the odds of acceptance (OR = 6.32; 95% CI: 3.54–11.28), whereas reliance on the internet and social media was a significant predictor of refusal (OR = 0.29; 95% CI: 0.17–0.50). The primary reported barrier was fear of adverse effects (70.4%), which significantly outweighed doubts about efficacy (30.2%). Conclusions: Parental hesitancy in Slovakia is a widespread phenomenon pervasive across all educational backgrounds, driven primarily by safety concerns and digital misinformation. The contrast between the protective influence of pediatricians and the negative impact of digital media underscores that clinical encounters are currently the most effective firewall against hesitancy. Public health strategies must therefore pivot from general education to empowering pediatricians with active, presumptive communication strategies. Full article

Semantic Communication (SC), driven by a deep learning (DL)-based “understand-before-transmit” paradigm, transmits lightweight semantic information (SI) instead of raw data. This approach significantly reduces data volume and communication overhead while maintaining performance, making it particularly suitable for UAV communications where the platform is constrained by size, weight, and power (SWAP) limitations. To alleviate the computational burden of semantic extraction (SE) on the UAV, this paper introduces federated learning (FL) as a distributed training framework. By establishing a collaborative architecture with edge users, computationally intensive tasks are offloaded to the edge devices, while the UAV serves as a central coordinator. We first demonstrate the feasibility of integrating FL into SC systems and then propose a novel solution based on Proximal Policy Optimization (PPO) to address the critical challenge of ensuring service fairness in UAV-assisted semantic communications. Specifically, we formulate a joint optimization problem that simultaneously designs the UAV’s flight trajectory and bandwidth allocation strategy. Experimental results validate that our FL-based training framework significantly reduces computational resource consumption, while the PPO-based algorithm approach effectively minimizes both energy consumption and task completion time while ensuring equitable quality-of-service (QoS) across all edge users. Full article