Search Results (11,708)

Background: Parental practices that support autonomy, provide structure, and foster warm relationships are associated with greater satisfaction of students’ basic psychological needs for autonomy, competence, and relatedness. In minority educational contexts, however, students’ psychological need satisfaction is also shaped by broader sociocultural conditions that may create additional pressures and sources of chronic stress. Within such environments, parental support may function as a protective factor that helps students cope with educational and cultural demands. Objective: The aim of this study was to explore how parental support contributes to the satisfaction of students’ basic psychological needs within a minority educational context where students from the Greek minority attend a bilingual school operating within a Turkish educational framework. Methods: A qualitative design was employed using three focus groups conducted in a minority school located in Gökçeada, Türkiye: one with parents (N = 5), one with lower secondary school students (N = 6), and one with upper secondary school students (N = 6). Interview questions were developed on the basis of Basic Psychological Needs Theory. Data were analyzed thematically by five members of the research team. Results: Findings indicated that parental support influenced students’ need satisfaction through practices related to autonomy (e.g., trust, space for mistakes), competence (e.g., encouragement, comparison), and relatedness (e.g., emotional presence, empathy). However, these practices were not experienced in a uniform way. Rather, their meaning and impact were shaped by contextual conditions associated with minority status, including bilingual educational demands, limited resources, and close-knit community dynamics. Conclusions: The study suggests that in minority school settings, parental support operates not simply as a general interpersonal resource but as a contextually mediated protective process. By showing how sociocultural and institutional conditions shape the enactment and experience of autonomy, competence, and relatedness, the findings extend existing BPNT research beyond majority settings and offer a more context-sensitive understanding of students’ psychological need satisfaction. Full article

Time-reversible synchronization (TRS) of nonlinear oscillators is a recently proposed technique that ensures super-exponential convergence of dynamics between master and slave systems, which is beneficial in many real-time applications. Nevertheless, this approach has not been demonstrated in any real-time embedded system to practically verify it and quantitatively estimate its advantages. Furthermore, previous studies did not consider the application of time-reversible synchronization to a wide, practically relevant class of chaotic systems with piecewise-linear nonlinearity. To fill these gaps, in this work, we developed an FPGA-based time-reversible synchronization controller for the analog Chua circuit and its digital counterpart. To achieve complete synchronization, we first reconstructed dynamical equations of the circuit. Then, we performed a rigorous theoretical analysis of synchronization possibility between analog and digital systems by each single variable. Next, we implemented the digital model of the Chua circuit in the MyRIO-1900 FPGA using the reconstructed dynamical model and showed its capability of digital-to-analog and analog-to-digital conventional Pecora–Carroll (PC) synchronization. Then, an algorithm of time-reversible synchronization on MyRIO-1900 was tested, achieving complete synchronization at the predefined normalized RMSE level of 0.01, requiring an average of 8.0 fewer points and a median of 10.1 fewer points than the PC synchronization. Finally, we implemented a proof-of-concept version of a capacitive sensor based on the analog Chua circuit with an FPGA-based observer using PC synchronization or the TRS algorithm with a heuristic selection of a starting point. Our experiments reveal that when using the TRS algorithm, the time needed to detect a pre-selected 3% level of capacitance change is reduced by a mean factor of 4 and a median factor of 4.9 in comparison with the conventional PC synchronization. This allows for using the developed solution in applications where the synchronization rate is crucial, including chaos-based sensing, communication, and monitoring. Full article

Rumen microbial fermentation plays a central role in nutrient utilization and milk production in dairy cows. This study evaluated the effects of supplementation with exogenous fibrolytic enzymes, alone or in combination with live yeast on rumen microbiota, fermentation characteristics, nitrogen-related metabolites, and production performance during the transition from outdoor grazing to indoor housing. Thirty Lithuanian Red dairy cows were assigned to control (CTR), enzyme (E), or enzyme plus yeast (YE) treatments across outdoor (OD) and transit (T) periods, while nine cows (three per group) were selected for rumen and microbiota analysis. Rumen bacterial communities were characterized using 16S rRNA gene sequencing, and functional parameters were evaluated using linear mixed-effects models. Supplementation resulted in selective changes in several bacterial genera, including Blautia spp., WPS-2, Ruminococcus spp., Erysipelotrichaceae UCG-009, Sharpea spp., uncultured Bacteroidales, and Prevotellaceae UCG-003, and was associated with alterations in fermentation patterns, particularly propionate concentration, and in nitrogen metabolism, including putrescine dynamics. The transition period significantly influenced microbial diversity and total bacterial abundance across treatments. Cows in the YE group maintained higher milk yield during the transition period. Overall, dietary supplementation modulated specific rumen metabolic responses and contributed to production stability without causing large-scale changes in overall microbial structure. Full article

This paper proposes a deep-learning-based scheduling approach for community microgrids that explicitly accounts for building thermal dynamics and customer comfort preferences. Traditional heating, ventilation, and air-conditioning (HVAC) scheduling models are NP-hard and scale poorly, especially for large systems with many buildings. To address this challenge, we develop a dual-encoder deep learning model that predicts building-level HVAC ON/OFF schedules using temporal load and temperature profiles, along with static building thermal parameters. The proposed model is trained in a supervised manner using solutions generated by an optimization-based HVAC scheduling framework, thereby serving as a computationally efficient surrogate for predicting HVAC schedules within a microgrid. The model is trained on samples generated by the optimization-based HVAC scheduling framework and evaluated using precision, recall, and F1-score. The results indicate strong predictive performance. Full article

Multipath propagation presents a major challenge to acoustic communication, causing signal distortion, delay spread, and inter-symbol interference, which degrade data integrity. This study investigates the use of Orthogonal Frequency Division Multiplexing (OFDM) as a robust modulation strategy for communication in complex acoustic environments where radio frequency (RF) propagation is severely attenuated. Using a software-defined radio (SDR) platform implemented in GNU Radio, OFDM performance was experimentally evaluated against Binary Frequency Shift Keying (BFSK) and Binary Phase Shift Keying (BPSK) under simulated and real multipath conditions in materials including air, water, and steel. The results show that OFDM achieves consistently lower bit error rates (BERs) and greater resilience to multipath interference due to its sub-carrier orthogonality and cyclic-prefix structure. The research also highlights how the frequency selectivity and coherence bandwidth of acoustic channels influence modulation performance across different media. By implementing custom transducers and real-time baseband processing, the study demonstrates how software-defined acoustics can be adapted for highly reflective and frequency-dependent environments. The observed improvements in BER and signal stability validate OFDM’s effectiveness in maintaining data integrity despite time and frequency dispersion effects. These findings demonstrate that OFDM enables reliable acoustic data transmission across heterogeneous media and is well suited to sensor-network applications in RF-hostile environments such as railway infrastructure, sealed containers, and submerged systems. Future work will include quantitative channel characterisation—specifically measuring delay spread, coherence bandwidth, and impulse response profiles—to further optimise OFDM parameters and provide a generalisable framework for adaptive modulation in dynamic acoustic channels. Full article

This paper develops a unified finite-bit distributed optimization framework for UAV swarms operating over bandwidth-limited communication graphs. We consider strongly convex and smooth global objectives decomposed over local UAV cost functions and study three communication-efficient algorithmic regimes. First, we design a quantized distributed gradient-tracking descent scheme with fixed finite-bit communication and show that, under bounded quantization errors, the method converges R-linearly to a quantization-dependent neighborhood of the global optimizer. Second, we introduce an adaptive quantization strategy that dynamically adjusts the number of transmitted bits according to the current convergence stage. By forcing the quantization distortion to decay proportionally to the optimization error, the proposed adaptive scheme recovers exact linear convergence to the optimal solution while substantially reducing the cumulative communication load. Third, we develop a fully distributed 1-bit communication mode in which UAVs exchange only sign information and use coordinate-wise majority voting to aggregate both descent and consensus directions. The robust linear-contraction property is proved to a small neighborhood under a sign-Polyak–Lojasiewicz condition and a probabilistic majority-correctness assumption. Full article

Particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) are two operationally defined fractions frequently used in studies related to soil organic carbon (SOC) dynamics. However, the changes and governing mechanisms of these fractions, particularly along a restoration chronosequence, remain poorly understood. Here, we investigated changes in SOC fractions, soil properties, and microbial communities across a restoration chronosequence (1, 5, 7, 13, and 20 years) of alpine meadows using a space-for-time substitution approach on the Qinghai–Tibet Plateau. We quantified the contributions of biotic and abiotic drivers using Spearman correlation analysis, linear regression and random forest analysis. The results revealed a unimodal pattern in SOC, POC, and MAOC contents, peaking at 7, 5, and 7 years, respectively, with no further increase thereafter. Restoration duration strongly shaped microbial community structure and observed species richness, but had no significant effect on Shannon index and Pielou index. Random forest analysis identified soil water content (SWC) and total nitrogen (TN) as the primary predictors of SOC. The microbial community composition dominated the variation in POC while enzyme activity was the key driver of MAOC. Our findings highlight that soil carbon accumulation during alpine meadow restoration is a nonlinear process with a temporal threshold, and POC and MAOC are regulated by distinct biotic and abiotic mechanisms. This study provides a theoretical basis for understanding carbon sequestration mechanisms during alpine meadow restoration and developing sustainable grassland management strategies. Full article

The capacity of the brain to adapt to experience has long been associated with synaptic plasticity; however, recent evidence demonstrates that experience-driven neural activity also modulates white matter organization through dynamic regulation of oligodendrocyte lineage cells and myelination. Activity-dependent myelination has emerged as a complementary form of neuroplasticity that contributes to circuit efficiency, temporal coordination, and cognitive function. This review aims to examine the neurobiological mechanisms linking cognitive stimulation and activity-dependent neuronal signaling with oligodendroglial dynamics and adaptive myelination. A narrative review of experimental and translational studies was conducted, focusing on evidence from animal models and human research exploring neuron–oligodendroglia interactions, neurotransmitter-mediated signaling, learning paradigms, physical exercise, and neuromodulatory interventions relevant to myelination and brain plasticity. Accumulating evidence indicates that cognitive stimulation, learning, and physical activity modulate neuronal firing patterns and neurotransmitter release, influencing oligodendrocyte precursor cell proliferation, differentiation, and myelin remodeling. Neurotransmitters such as glutamate, GABA, dopamine, and acetylcholine play key roles in neuron–oligodendroglia communication, largely through calcium-dependent intracellular signaling pathways. These mechanisms have been associated with experience-dependent circuit refinement across motor, cognitive, and stress-related paradigms. Rather than implying direct clinical effects, this review highlights oligodendroglial plasticity as a biologically plausible substrate through which cognitive and behavioral experiences may influence adaptive myelination and white matter integrity. Understanding these mechanisms provides a conceptual framework for future research exploring non-pharmacological approaches to modulate brain plasticity at the level of myelin. Full article

We investigated the biodiversity and spatiotemporal dynamics of benthic hydroids at two contrasting eastern South Adriatic sites: exposed, oligotrophic Lokrum Island and sheltered, nutrient-enriched Bistrina Bay. A total of 54 hydroid taxa were recorded, with substantially higher richness at Lokrum (42 taxa) than at Bistrina (24 taxa). Assemblage composition differed markedly between sites, confirming that local environmental conditions are a primary determinant of community structure, while shallow sublittoral assemblages showed the greatest temporal variability due to seasonally short-lived athecate species. The shared seasonal partitioning at both sites suggests that temperature-mediated life-cycle timing is a key structuring mechanism, and the sharp summer decline in richness underscores the need for multi-seasonal sampling. Laboratory observations of Stylactis inermis from Torre del Serpe near Otranto revealed notable life-cycle plasticity, with detached short-lived eumedusoids reverting to a sessile stolonal stage. This trait may promote persistence under fluctuating conditions while reducing field detectability. Together, these results provide the first seasonal, depth-stratified ecological baseline for monitoring eastern South Adriatic benthic communities under environmental and anthropogenic change. Full article

This study investigated phase-dependent changes in gut microbiome composition, predicted functional potential, and lipid metabolism in intensively reared broiler chickens and ducks across the starter, grower, and finisher phases (from day-old to 42 days of age), over six production cycles (four chicken and two duck cycles), using 16S rRNA sequencing and blood lipids profiling. A total of 70 pooled manure samples were collected (46 from chickens and 24 from ducks), along with 34 blood samples (22 from chickens and 12 from ducks), all obtained under standard production conditions. Microbial diversity remained stable across growth phases within each species, whereas clear interspecies differences were observed (p < 0.01). Microbiome maturation involved a shift from early facultative and environmentally associated taxa during the starter phase (day-old to 14 days of age), including Acinetobacter (p < 0.01) and Enterococcus (p < 0.001), toward a more stable, host-adapted community. At the level of predicted functional pathways, shifts between growth phases were more pronounced in ducks. Predicted gene-level profiles showed phase-specific differentiation in chickens, with starter-associated genes linked to core carbon and nitrogen metabolism and finisher-associated genes related to structural and transport functions, whereas ducks exhibited a more balanced reorganization involving carbohydrate, energy, and nitrogen metabolism. Host lipid profiles between adjacent growth phases showed dynamic shifts in ducks (p < 0.05). These species-specific lipid patterns were mirrored by microbiome–lipid associations, as demonstrated by correlation analyses between dominant bacterial genera and blood lipid parameters, revealing more coordinated relationships in chickens and more heterogeneous patterns in ducks. Overall, these findings demonstrate species-specific organization of gut microbiome changes and their integration with blood lipid profiles under intensive production conditions. Full article

In millimeter wave V2I communication systems, accurate beam prediction is crucial for optimizing network performance and improving signal transmission efficiency. Traditional beam prediction methods mainly rely on single-modal data, which often fails to capture the comprehensive environmental information required for high accuracy prediction. In contrast, multi-modal approaches leverage complementary information from different data sources and offer a more promising solution. However, many existing fusion methods primarily depend on real-time sensory inputs and do not fully exploit stable environmental features in V2I scenarios, limiting the effective use of each modality. To address these limitations, this paper proposes a environment-aware proactive beam prediction method based on a multi-modal prior mask map (MMPMM), which integrates offline mapping with an online beam prediction network. Specifically, the method fuses information from images, point clouds, positions, and the MMPMM to predict the optimal beam index. The MMPMM provides channel-related prior information by extracting static V2I scene features offline without incurring any additional online measurement overhead. Experimental results on real-world datasets demonstrate that the proposed method achieves a Top-3 beam prediction accuracy of up to 71.23% while maintaining stable performance under the evaluated dynamic and degraded conditions, demonstrating its effectiveness in the considered scenarios. Full article

Rework continues to be a critical issue in construction projects, contributing to cost escalation, schedule delays, and compromised quality. While earlier studies have identified isolated causes such as design deficiencies, communication failures, and inadequate workmanship, the structural relationships among these factors have not been sufficiently examined. This study investigates the interdependencies among major rework causation domains using Structural Equation Modelling (SEM) based on survey responses from 200 construction professionals. A total of 43 observed variables, identified through an extensive literature review, were grouped into four latent constructs: contractor-related, owner-related, design-related, and resource/workforce-related factors. Confirmatory Factor Analysis (CFA) was conducted to validate the measurement model, followed by structural path analysis to examine causal linkages. The findings reveal that design-related and owner-related factors exert the most significant direct and indirect influence on rework, followed by contractor- and workforce-related factors. The proposed model demonstrates satisfactory goodness-of-fit indices, confirming its reliability and applicability. Compared to conventional ranking and fuzzy-based approaches, SEM provides a more systematic and comprehensive understanding of rework dynamics. The findings provide practical guidance for project managers and decision-makers by identifying the most critical drivers of rework, enabling targeted mitigation strategies and improved resource allocation to enhance overall construction project performance. Full article

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations. Full article

Although treated wastewater (TWW) is increasingly being used to irrigate urban landscapes in arid regions like Qatar to preserve scarce freshwater resources, little is known about its long-term ecological impacts. The effects of extended irrigation with TWW on the composition of weed communities and soil characteristics in urban turfgrass systems were assessed in this study for a full year period. Three turfgrass fields in public parks in Doha that are not distant and similar in turf species and type of management were chosen. One of them has received regular tap water, and the other two had received a period of two years or a period of seven years irrigation with TWW. Due to nutrient availability in TWW, long-term irrigation improved turfgrass performance but drastically changed the structure and composition of the weed communities. More weed diversity and abundance were observed under irrigation with TWW, coinciding with cumulative increases in soil salinity [from 265 µS/cm for soil irrigated with regular tap water to about 1799 µS/cm for soil long-term irrigated with treated wastewater] and nutrient levels. Dactyloctenium aristatum and Euphorbia prostrata were dominating the field under TWW irrigation, while Cyperus rotundus prevailed better under regular tap-water irrigation. Crucially, build-up of toxic elements was found in the turfgrass, but not harmful. Overall, the findings showed that although TWW is a useful source for maintaining urban green spaces in arid regions, its long-term use necessitates cautious management to reduce weed growth and adaptation. Maintaining sustainable and healthy urban landscapes may be aided by using salt-leaching irrigation techniques and seasonal blending with freshwater. Full article

This paper presents a reproducible system innovation for processing Digital Imaging and Communications in Medicine (DICOM) mammography images based on pixel-monotonic dynamic range management and engineering-verifiable intensity transformations. Standard DICOM conversion schemes to 8-bit representation often result in irreversible luminance-range compression, locality-dependent contrast distortions, and reduced robustness of deep learning models. The proposed framework preserves the physical consistency of the Modality LUT and photometric polarity, performs breast-aware robust Winsor normalization, and applies strictly monotonic global tone mapping while preserving the 16-bit depth of the training data. System validation was performed using architecture-independent metrics. Compared to standard processing, the median value of normalized mutual information increased from 0.878 to 0.892, the effective number of bits increased from 7.88 to 10.11 (+2.25), the representation entropy increased by 1.42 bits, and the clipping rate was reduced to almost zero. Experiments with the Faster R-CNN detector showed stable or improved calcification localization at Intersection over Union (IoU) ≥ 0.5 under controlled augmentation conditions. The results confirm that pixel-monotonic dynamic range control provides a reproducible, engineering-verifiable basis for AI-based mammography analysis within the evaluated dataset and experimental setting. Full article