Search Results (7,722)

Accurate land use/land cover (LULC) maps generated through cloud computing can support large-scale land management. Leveraging the rich resources of Google Earth Engine (GEE) is essential for developing historical maps that facilitate the analysis of regional LULC dynamics. We implemented the best-performing scheme on GEE to produce 30 m LULC maps for the Shandong Peninsula urban agglomeration (SPUA) and to detect LULC changes, while closely observing the spatio-temporal trends of landscape patterns during 2004–2024 using the Shannon Diversity Index, Patch Density, and other metrics. The results indicate that (a) Gradient Tree Boost (GTB) marginally outperformed Random Forest (RF) under identical feature combinations, with overall accuracies consistently exceeding 90.30%; (b) integrating topographic features, remote sensing indices, spectral bands, land surface temperature, and nighttime light data into the GTB classifier yielded the highest accuracy (OA = 93.68%, Kappa = 0.92); (c) over the 20-year period, cultivated land experienced the most substantial reduction (11,128.09 km²), accompanied by impressive growth in built-up land (9677.21 km²); and (d) landscape patterns in central and eastern SPUA changed most noticeably, with diversity, fragmentation, and complexity increasing, and connectivity decreasing. These results underscore the strong potential of GEE for LULC mapping at the urban agglomeration scale, providing a robust basis for long-term dynamic process analysis. Full article

We propose a novel numerical test to evaluate the reliability of numerical propagations, leveraging the fiber bundle structure of phase space typically induced by Lie symmetries, though not exclusively. This geometric test simultaneously verifies two properties: (i) preservation of conservation principles, and (ii) faithfulness to the symmetry-induced fiber bundle structure. To generalize the approach to systems lacking inherent symmetries, we construct an associated collective system endowed with an artificial G-symmetry. The original system then emerges as the G-reduced version of this collective system. By integrating the collective system and monitoring G-fiber bundle conservation, our test quantifies numerical precision loss and detects geometric structure violations more effectively than classical integral-based checks. Numerical experiments demonstrate the superior performance of this method, particularly in long-term simulations of rigid body dynamics and perturbed Keplerian systems. Full article

Background/Objective: Sleep is a vital determinant of human health, where both its quantity and quality directly impact physical and mental well-being. Thermoregulation plays a pivotal role in sleep quality, as the body’s ability to regulate temperature varies across different sleep stages. This study examines the effects of a novel real-time temperature adjustment (RTA) mattress, which dynamically modulates temperature to align with sleep stage transitions, compared to constant temperature control (CTC). Through polysomnographic (PSG) assessments, this study evaluates how adaptive thermal regulation influences sleep architecture, aiming to identify its potential for optimizing restorative sleep. Methods: A prospective longitudinal cohort study involving 25 participants (13 males and 12 females; mean age: 39.7 years) evaluated sleep quality across three conditions: natural sleep (Control), CTC (33 °C constant mattress temperature), and RTA (temperature dynamically adjusted: 30 °C during REM sleep; 33 °C during non-REM sleep). Each participant completed three polysomnography (PSG) sessions. Sleep metrics, including total sleep time (TST), sleep efficiency, wake after sleep onset (WASO), and sleep stage percentages, were assessed. Repeated-measures ANOVA and post hoc analyses were performed. Results: RTA significantly improved sleep quality metrics compared to Control and CTC. TST increased from 356.2 min (Control) to 383.2 min (RTA, p = 0.030), with sleep efficiency rising from 82.8% to 87.3% (p = 0.030). WASO decreased from 58.2 min (Control) and 64.6 min (CTC) to 49.0 min (RTA, p = 0.067). REM latency was notably reduced under RTA (110.4 min) compared to Control (141.8 min, p = 0.002). The REM sleep percentage increased under RTA (20.8%, p = 0.006), with significant subgroup-specific enhancements in males (p = 0.010). Females showed significant increases in deep sleep percentage under RTA (12.3%, p = 0.011). Conclusions: Adaptive thermal regulation enhances sleep quality by aligning mattress temperature with physiological needs during different sleep stages. These findings highlight the potential of RTA as a non-invasive intervention to optimize restorative sleep and promote overall well-being. Further research could explore long-term health benefits and broader applications. Full article

Invasive species can alter community composition and ecosystem functioning. In the subtidal rocky shores of Arraial do Cabo Bay, southeastern Brazil, the invasive coral Tubastraea spp. has established populations, raising concerns about long-term impacts on native benthic communities. This study investigates temporal shifts in β-diversity across 44 fixed plots containing Tubastraea spp., monitored over 383 days. Underwater photographic surveys and multivariate analyses identified nine distinct benthic community types, each forming mosaic structures of sessile organisms. Temporal β-diversity analyses revealed that only the group characterized by Tubastraea, crustose calcareous algae and the zoantharian Palythoa caribaeorum showed significant differences between species gains and losses over time, suggesting temporal-scale dependency. Key contributors to community dissimilarity included P. caribaeorum, crustose calcareous algae, turf, the sponge genus Darwinella, and Tubastraea. This study highlights the importance of considering both spatial and temporal heterogeneity when assessing the ecological impact of marine invasive species. Our findings underscore the need for multi-scale monitoring to fully understand the dynamics of tropical subtidal ecosystems under biological invasion. While numerous studies report a correlation between Tubastraea abundance and shifts in ecological diversity, this relationship may be weak, as critical drivers such as the complexity of community organization are rarely accounted for. Full article

This study reconstructs seven decades (1949–2019) of morphodynamic changes and sediment dynamics in the Diable River (Québec, Canada) using nine series of aerial photographs, a high-resolution LiDAR Digital Elevation Model (2021), and grain-size analysis. The objectives were to document long-term river evolution, quantify erosion and deposition, and evaluate sediment connectivity between eroding sandy bluffs and depositional zones. Planform analysis and sediment budgets derived from DEMs of Difference (DoD) reveal an oscillatory trajectory characterized by alternating phases of sediment export and temporary stabilization, rather than a simple trend of degradation or aggradation. The most dynamic interval (1980–2001) was marked by widespread meander migration and the largest net export (−142.5 m³/km/year), whereas the 2001–2007 interval showed net storage (+70.8 m³/km/year) and short-term geomorphic recovery. More recent floods (2017, 2019; 20–50-year return periods) induced localized but persistent sediment loss, underlining the structuring role of extreme events. Grain-size results indicate partial connectivity: coarse fractions tend to remain in local depositional features, while finer sediments are preferentially exported downstream. These findings emphasize the geomorphic value of temporary sediment sinks (bars, beaches) and highlight the need for adaptive river management strategies that integrate sediment budgets and local knowledge into floodplain governance. Full article

In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of $1\times {10}^{-3}$ by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article

In the face of ever-evolving cyber threats, modern intrusion detection systems (IDS) must achieve long-term adaptability without sacrificing performance on previously encountered attacks. Traditional IDS approaches often rely on static training assumptions, making them prone to forgetting old patterns, underperforming in label-scarce conditions, and struggling with imbalanced class distributions as new attacks emerge. To overcome these limitations, we present a continual learning framework tailored for adaptive intrusion detection. Unlike prior methods, our approach is designed to operate under real-world network conditions characterized by high-dimensional, sparse traffic data and task-agnostic learning sequences. The framework combines three core components: a clustering-based memory strategy that selectively retains informative historical samples using DP-Means; multi-level knowledge distillation that aligns current and previous model states at output and intermediate feature levels; and a meta-learning-driven class reweighting mechanism that dynamically adjusts to shifting attack distributions. Empirical evaluations on benchmark intrusion detection datasets demonstrate the framework’s ability to maintain high detection accuracy while effectively mitigating forgetting. Notably, it delivers reliable performance in continually changing environments where the availability of labeled data is limited, making it well-suited for real-world cybersecurity systems. Full article

Introduction: Fluoride varnishes are widely used in caries prevention, but the rate and duration of fluoride ion release differ depending on material composition and environmental factors. Objectives: This systematic review synthesized evidence from in vitro studies on human teeth to identify key factors influencing fluoride release. Methods: A systematic literature search was conducted in July 2025 in PubMed, Scopus, Web of Science, Embase, and the Cochrane Library using the terms “fluoride release” AND “varnish” in titles and abstracts. Study selection followed PRISMA 2020 guidelines, predefined eligibility criteria, and was structured according to the PICO framework. Of 484 retrieved records, 15 studies met the inclusion criteria and were analyzed qualitatively. Results: The primary outcome was the magnitude and duration of fluoride release from varnishes. Most studies reported peak release within the first 24 h, followed by a marked decline, although some formulations (e.g., Clinpro XT and Duraphat) maintained more stable long-term release. Substantial methodological heterogeneity was observed across studies, including differences in sample type, storage medium, pH, temperature, and measurement protocols, which influenced fluoride release dynamics. Reported secondary outcomes included enamel remineralization, changes in surface properties, and antibacterial activity, with bioactive additives such as CPP–ACP and TCP enhancing preventive effects. Acidic conditions consistently increased fluoride release. Conclusions: The magnitude and persistence of fluoride release from varnishes depend on both intrinsic material properties and external environmental conditions. Bioactive additives may prolong fluoride availability and provide additional preventive benefits. Full article

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. Full article

Bone immunity represents a dynamic interface where skeletal homeostasis intersects with systemic immune regulation. We synthesize emerging paradigms by contrasting two functionally distinct microenvironments: the marrow cavity, a hematopoietic and immune cell reservoir, and cancellous bone, a metabolically active hub orchestrating osteoimmune interactions. The marrow cavity not only generates innate and adaptive immune cells but also preserves long-term immune memory through stromal-derived chemokines and survival factors, while cancellous bone regulates bone remodeling via macrophage-osteoclast crosstalk and cytokine gradients. Breakthroughs in lymphatic vasculature identification challenge traditional views, revealing cortical and lymphatic networks in cancellous bone that mediate immune surveillance and pathological processes such as cancer metastasis. Central to bone immunity is the neuro–immune–endocrine axis, where sympathetic and parasympathetic signaling bidirectionally modulate osteoclastogenesis and macrophage polarization. Gut microbiota-derived metabolites, including short-chain fatty acids and polyamines, reshape bone immunity through epigenetic and receptor-mediated pathways, bridging systemic metabolism with local immune responses. In disease contexts, dysregulated immune dynamics drive osteoporosis via RANKL/IL-17 hyperactivity and promote leukemic evasion through microenvironmental immunosuppression. We further propose the “brain–gut–bone axis” as a systemic regulatory framework, wherein vagus nerve-mediated gut signaling enhances osteogenic pathways, while leptin and adipokine circuits link marrow adiposity to inflammatory bone loss. These insights redefine bone as a multidimensional immunometabolic organ, integrating neural, endocrine, and microbial inputs to maintain homeostasis. By elucidating the mechanisms of immune-driven bone pathologies, this work highlights therapeutic opportunities through biomaterial-mediated immunomodulation and microbiota-targeted interventions, paving the way for next-generation treatments in osteoimmune disorders. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

Football-themed eSports, combining entertainment and learning elements, are booming, offering benefits in terms of cognitive and motor skill development. Despite this, with the increasing use of eSports and their impact on cognitive and motor skills, there is still a paucity of empirical studies that systematically explore how cognitive stimulation from eSports can translate into psychomotor performance on the field, particularly with regard to linear speed. The aim of this study was to investigate the effects of an ecological–dynamic training protocol, integrated with football eSports, on the development of linear sprint speed in young soccer players. Thirty-two male youth football players (age range: 12–16 years) participated in the study. Participants were divided into an experimental group, which followed a combined ecological–dynamic training protocol including football eSports, and a control group, which performed standard training sessions. Pre- and post-intervention assessments of 30 m sprint performance were conducted using electronic timing gates. Statistical analysis using repeated-measures ANOVA indicated a marked improvement in 30 m sprint performance within the experimental group, decreasing from 4.908 s to 4.651 s. A significant time × group interaction was observed (F = 74.076, p < 0.001). Moreover, a robust main effect of time emerged (F = 141.12, p < 0.001), confirming consistent gains in linear sprint speed. Post hoc comparisons revealed significant differences across all assessment points (p < 0.001). The findings suggest that embedding football eSports into an ecologically grounded training framework may enhance the development of linear speed in young soccer players. This integrated approach shows potential as an innovative tool for performance enhancement, although further investigations are needed to confirm long-term efficacy and generalizability to other sporting populations. Full article

In intensive broiler production, rapid environmental fluctuations can induce heat stress, adversely affecting flock welfare and productivity. Apparent temperature (AT), integrating temperature, humidity, and wind speed, provides a comprehensive thermal index, guiding predictive climate control. This study develops a multi-step AT forecasting model based on a hybrid SE-TCN–Transformer architecture enhanced with Kalman filtering. The temporal convolutional network with SE attention extracts short-term local trends, the Transformer captures long-range dependencies, and Kalman smoothing reduces prediction noise, collectively improving robustness and accuracy. The model was trained on multi-source time-series data from a commercial broiler house and evaluated for 5, 15, and 30 min horizons against LSTM, GRU, Autoformer, and Informer benchmarks. Results indicate that the proposed model achieves substantially lower prediction errors and higher determination coefficients. By combining multi-variable feature integration, local–global temporal modeling, and dynamic smoothing, the model offers a precise and reliable tool for intelligent ventilation control and heat stress management. These findings provide both scientific insight into multi-step thermal environment prediction and practical guidance for optimizing broiler welfare and production performance. Full article

Background/Objectives: Gestational diabetes mellitus (GDM) is one of the most common metabolic complications of pregnancy and is linked to long-term metabolic and cardiovascular risks for both mother and child. Its pathophysiology includes increased generation of reactive oxygen species (ROS) and/or decreased antioxidant defenses; nonetheless, the redox dynamics between mother and fetus are still poorly understood. Our goal was to assess oxidative stress (via derivatives of reactive oxygen metabolites, d-ROMs) and antioxidant capacity (via biological antioxidant potential, BAP) in maternal, umbilical cord, and neonatal blood from women with GDM compared to normoglycemic controls, and to investigate potential associations with clinical and neonatal outcomes. Methods: In this single-center cross-sectional study, 56 women with GDM and 52 matched controls provided maternal venous, umbilical cord, and neonatal blood samples at delivery. Plasma d-ROMs and BAP were measured using colorimetric assays. Clinical and neonatal outcome data were collected. Results: Women with GDM had considerably higher maternal d-ROM levels compared to both the umbilical cord and neonatal compartments. BAP measurements revealed that maternal blood had the lowest antioxidant capacity, while cord and newborn samples had higher levels. GDM mothers had significantly greater maternal d-ROMs and lower BAP compared to controls (both p < 0.05). There were no differences in cord blood d-ROMs or BAP between the GDM and the control group. The maternal BAP/d-ROM ratio decreased significantly in the GDM group (p < 0.01), but the cord ratio remained constant. Notably, neither maternal nor neonatal redox indicators were related to perinatal outcomes, indicating a limited prognostic potential for unfavorable neonatal occurrences. Conclusions: GDM is associated with increased maternal oxidative stress and decreased antioxidant capacity, with no substantial changes in newborn redox status. Redox indicators did not predict perinatal issues across this group. These findings demonstrate the need for larger prospective research to determine whether early changes in redox balance can predict the development of GDM or unfavorable outcomes. Full article

In the field of wave energy, multi-axis wave energy converters (WECs) have emerged as a research priority owing to their enhanced energy absorption, leading to increased computational complexity. Conventional model predictive control (MPC) approaches have demonstrated limitations in the trade-off between real-time requirements and control performance. This paper proposes a bi-layer MPC strategy, including a long-term energy maximization layer and a short-term trajectory-tracking layer. First, a multi-axis underactuated WEC (MU-WEC) is proposed, which incorporates an inertial cable-driven parallel mechanism to absorb energy from multiple directions. In addition, a control-oriented dynamic model of a MU-WEC is established. Then, a bi-layer MPC strategy is proposed, which decouples computationally intensive optimization processes from time-sensitive real-time control, alleviating the computational burden significantly. Therefore, the upper layer achieves enhanced control performance by enabling extended prediction horizons, whereas the lower layer serves to ensure real-time requirements. Moreover, numerical simulations under irregular wave conditions demonstrate the performance of the proposed bi-layer MPC: under different waves, bi-layer MPC improves energy absorption by 127–311% over conventional MPC. This performance enhancement stems from the 5 times extension of the prediction horizon enabled by the reduced computational burden. Full article