Search Results (132)

Composite materials are increasingly required to operate in cryogenic environments, including liquid hydrogen and oxygen storage, deep-space structures, and polar infrastructures, where long-term strength, toughness, and reliability are essential. This review provides a unique contribution by systematically integrating recent advances in understanding cryogenic behaviour into a unified multi-scale framework. This framework synthesises four critical and interconnected aspects: constituent response, composite performance, enhancement mechanisms, and modelling strategies. At the constituent level, fibres retain stiffness, polymer matrices stiffen but embrittle, and nanoparticles offer tunable thermal and mechanical functions, which collectively define the system-level performance where thermal expansion mismatch, matrix embrittlement, and interfacial degradation dominate failure. The review further details toughening strategies achieved through nano-addition, hybrid fibre architectures, and thin-ply laminates. Modelling strategies, from molecular dynamics to multiscale finite element analysis, are discussed as predictive tools that link these scales, supported by the critical need for in situ experimental validation. The primary objective of this synthesis is to establish a coherent perspective that bridges fundamental material behaviour to structural reliability. Despite these advances, remaining challenges include consistent property characterisation at low temperature, physics-informed interface and damage models, and standardised testing protocols. Future progress will depend on integrated frameworks linking high-fidelity data, cross-scale modelling, and validation to enable safe deployment of next-generation cryogenic composites. Full article

Beetroot juice (BRJ), a concentrated dietary source of nitrate alongside betalains and polyphenols, influences physiology through enhanced nitrate–nitrite–NO bioavailability, antioxidant activity, and interactions with oral and gut nitrate-reducing microbiota. The efficiency of these mechanisms depends on dose, timing, and preservation of oral bacteria, with antibacterial mouthwash or thiocyanate-rich foods potentially blunting NO₂⁻ generation. Acute BRJ ingestion consistently elevates circulating nitrate and nitrite, yet its impact on glucose, insulin, and lipid regulation is modest; chronic intake may reinforce nitrate-reduction capacity, improve redox balance, and shift microbial composition, though long-term metabolic outcomes remain variable. Cardiovascular adaptations appear more coherent, with acute reductions in systolic blood pressure and improved endothelial function complemented in some cases by microvascular enhancements during multi-week supplementation. Neuromuscular and cognitive effects are less uniform; BRJ does not reliably increase maximal strength or global cognition but may support electrophysiological recovery after muscle-damaging exercise and improve executive performance under fatigue. In exercise settings, dose and timing are critical, as BRJ most consistently benefits endurance performance by reducing oxygen cost, improving exercise economy, and enhancing time-trial or time-to-exhaustion outcomes, whereas effects on sprint, power, and team-sport tasks are more sensitive to contraction duration, recovery intervals, and athlete training status. Overall, available evidence supports a role for NO-mediated vascular and metabolic pathways in the physiological effects of BRJ, although marked inter-individual variability highlights the need for responder-focused dosing strategies and further mechanistic investigation integrating metabolic, microbial, and performance-related outcomes. Full article

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment. Full article

Coastal sandbars play a crucial role in shoreline protection, yet monitoring their dynamics remains challenging due to the cost and limited temporal coverage of traditional surveys. This study assesses the feasibility of using Sentinel-2 multispectral imagery combined with the logarithmic band ratio method to automatically detect submerged sandbar crests along three morphologically distinct beaches on the northwestern Mediterranean coast. Pseudo-bathymetry was derived from log-transformed band ratios of blue-green and blue-red reflectance used to extract the sandbar crest and validated against high-resolution in situ bathymetry. The blue-green band ratio achieved higher accuracy than the blue-red band ratio, which performed slightly better in very shallow waters. Its application across single, single/double, and double shore-parallel bar systems demonstrated the robustness and transferability of the approach. However, the method requires relatively clear or calm water conditions, and breaking-wave foam, sunglint, or cloud cover conditions limit the number of usable satellite images. A temporal analysis at a dissipative beach further revealed coherent bar migration patterns associated with storm events, consistent with observed hydrodynamic forcing. The proposed method is cost-free, computationally efficient, and broadly applicable for large-scale and long-term sandbar monitoring where optical water clarity permits. Its simplicity enables integration into coastal management frameworks, supporting sediment-budget assessment and resilience evaluation in data-limited regions. Full article

Understanding the genetic diversity and structure of regional cacao and its close relatives is essential for strengthening conservation strategies and enhancing the resilience of Amazonian agroforestry systems. This study evaluated the genetic diversity, population structure, and varietal relationships of 48 sexually derived regional accessions of Theobroma cacao, T. grandiflorum, and T. bicolor with desirable morpho-agronomic traits, together with eight universal T. cacao reference clones, all cultivated in farmer-managed agroforests of the northwestern Colombian Amazon, using a panel of 15 SSR markers. The loci exhibited substantial allelic richness (mean Na = 8.53) and consistently high expected heterozygosity (Hexp = 0.74), with numerous private alleles indicating species- and lineage-specific divergence. Bayesian clustering, ΔK inference, and minimum spanning networks identified four genetically coherent subpopulations corresponding to the three species and a distinct lineage within T. cacao, strongly aligned with the discriminant analysis of principal components (DAPC) results. Analysis of Molecular Variance (AMOVA) revealed that most genetic variation occurred among subpopulations (56.68%), while pairwise FST (Wright’s fixation index) values confirmed strong interspecific differentiation and significant divergence within T. cacao. No isolation-by-distance pattern was detected. These findings demonstrate that regional Theobroma germplasm maintained in smallholder agroforests constitutes a valuable reservoir of genetic diversity that complements universal reference clones. By documenting species-level divergence and lineage-specific variation, this study supports the integration of farmer-managed genetic resources into conservation planning and highlights their importance for the long-term resilience of Amazonian cacao-based agroforestry landscapes. Full article

Background and Objectives: Keratoconus is a progressive corneal ectatic disorder and a leading cause of corneal transplantation in developed countries. Early detection is critical for initiating timely interventions such as corneal cross-linking, which can halt disease progression and preserve long-term visual function. This review aims to synthesize current diagnostic approaches for early keratoconus detection and assess their clinical impact on visual outcomes. Materials and Methods: A comprehensive literature search was conducted across PubMed/MEDLINE, Web of Science, Google Scholar, Scopus and the Cochrane Library through September 2025. Search terms included “early keratoconus,” “subclinical keratoconus,” “forme fruste keratoconus,” “keratoconus detection,” “corneal topography,” “corneal tomography,” “anterior segment optical coherence tomography (AS-OCT),” “corneal biomechanics,” “artificial intelligence,” “genetic risk, “environmental factors”, and “machine learning.” Two independent reviewers analyzed the data. Studies were included if they investigated diagnostic modalities for early-stage keratoconus and discussed their relevance to visual outcomes. Results: One hundred and seven studies were included in the final review. Four diagnostic modalities demonstrated consistent clinical value: 1. corneal topography for assessing anterior surface irregularities; 2. corneal tomography, currently regarded as the gold standard due to its ability to detect early posterior elevation and pachymetric changes; 3. AS-OCT for epithelial and stromal profiling; and 4. biomechanical assessments, which evaluate corneal tissue stability prior to structural alterations. Artificial intelligence, when integrated with imaging data, enhances diagnostic sensitivity and standardizes interpretation across clinical settings. Conclusions: Early keratoconus detection is crucial for preserving vision; and integrating multimodal, AI-supported diagnostics into routine care—especially for high-risk groups—enhances accuracy, improves outcomes, and reduces progression rates of disease. Full article

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a critical challenge. Traditional error control strategies, such as Forward Error Correction (FEC) and Automatic Repeat Request (ARQ), often introduce excessive latency or bandwidth overhead. Meanwhile, receiver-side concealment methods struggle under high motion or significant packet loss, motivating the exploration of predictive models. SimVP-v2, with its efficient convolutional architecture and Gated Spatiotemporal Attention (GSTA) mechanism, provides a strong baseline by reducing complexity and achieving competitive prediction performance. Despite its merits, SimVP-v2’s reliance on 2D convolutions for implicit temporal aggregation limits its capacity to capture complex motion trajectories and long-term dependencies. This often results in artifacts such as motion blur, detail loss, and accumulated errors. Furthermore, its single-modality design ignores the complementary contextual cues embedded in the audio stream. To overcome these issues, we propose A3DSimVP (Audio- and 3D-Enhanced SimVP-v2), which integrates explicit spatio-temporal modeling with multimodal feature fusion. Architecturally, we replace the 2D depthwise separable convolutions within the GSTA module with their 3D counterparts, introducing a redesigned GSTA-3D module that significantly improves motion coherence across frames. Additionally, an efficient audio–visual fusion strategy supplements visual features with contextual audio guidance, thereby enhancing the model’s robustness and perceptual realism. We validate the effectiveness of A3DSimVP’s improvements through extensive experiments on the KTH dataset. Our model achieves a PSNR of 27.35 dB, surpassing the 27.04 of the SimVP-v2 baseline. Concurrently, our improved A3DSimVP model reduces the loss metrics on the KTH dataset, achieving an MSE of 43.82 and an MAE of 385.73, both lower than the baseline. Crucially, our LPIPS metric is substantially lowered to 0.22. These data tangibly confirm that A3DSimVP significantly enhances both structural fidelity and perceptual quality while maintaining high predictive accuracy. Notably, A3DSimVP attains faster inference speeds than the baseline with only a marginal increase in computational overhead. These results establish A3DSimVP as an efficient and robust solution for latency-critical video applications. Full article

Regional water systems face growing pressure from climate variability, water scarcity, and increasingly complex wastewater pollution. These challenges require governance models that integrate institutional coordination with effective technological solutions. This review is based on a structured analysis of peer-reviewed literature indexed in Scopus, Web of Science, and ScienceDirect, covering publications from approximately 2014 to 2025. The findings show that clearly defined institutional roles, basin-level coordination, stable financing mechanisms, and active stakeholder participation significantly improve governance outcomes. Technological advances such as membrane filtration, advanced oxidation processes, nature-based treatment systems, and digital monitoring platforms enhance treatment efficiency, resilience, and opportunities for resource recovery. Regions differ widely in their ability to adopt these solutions, mainly due to variations in governance coherence, investment capacity, and climate-adaptation readiness. The review highlights the need for policy frameworks that align institutional reforms with technological modernization, including the adoption of basin-based planning, digital decision-support systems, and circular water-economy principles. These measures provide actionable guidance for policymakers and regional authorities seeking to strengthen long-term water security and wastewater management performance. Full article

In the context of a high proportion of renewable energy integration, active splitting section search—one of the “three defense lines” of a power system—is crucial for the security, stability, and long-term sustainability of islanded grids. Addressing the random fluctuations of high-penetration wind power and the weakened voltage support capability caused by multi-infeed HVDC, this paper proposes a slow-coherency-based active splitting section optimization model that explicitly accounts for wind power uncertainty and multi-infeed DC stability constraints. First, a GMM-K-means method is applied to historical wind data to model, sample, and cluster scenarios, efficiently generating and reducing a representative set of typical wind outputs; this accurately captures wind uncertainty while lowering computational burden. Subsequently, an improved particle swarm optimizer enhanced by genetic operators is used to optimize a multi-dimensional coherency fitness function that incorporates a refined equivalent power index, frequency constraints, and connectivity requirements. Simulations on a modified New England 39-bus system demonstrate that the proposed model markedly enlarges the post-split voltage stability margin and effectively reduces power-flow shocks and power imbalance compared with existing methods. This research contributes to enhancing the sustainability and operational resilience of power systems under energy transition. Full article

The next frontier in postmenopausal osteoporosis management lies not in novel pharmacological agents, but in the systematic integration of mechanism-guided drug selection, fall prevention, and long-term adherence strategies into a unified patient-centered care model. This review is intended for clinicians and clinical researchers involved in the diagnosis, treatment, and long-term management of postmenopausal osteoporosis. We provide a mechanism-to-practice framework that explicitly maps each therapeutic class to the specific molecular pathway it targets: bisphosphonates inhibit osteoclast function downstream of RANKL activation; denosumab blocks RANKL directly at the cytokine level; romosozumab inhibits sclerostin to restore Wnt-mediated bone formation. This mechanistic foundation supports a risk-stratified treatment paradigm in which antiresorptives address accelerated remodeling in moderate-risk patients, while patients at very high fracture risk—characterized by severe bone deficit or recent fragility fractures—benefit from an anabolic-first approach followed by consolidation. Beyond drug selection, we examine the persistent treatment gap in which fewer than 20% of post-fracture patients receive therapy, arguing that fall prevention—responsible for >90% of hip fractures—and medication adherence deserve equal priority in clinical practice. We further analyze key controversies, including T-score- versus FRAX-based intervention thresholds, limitations of the trabecular bone score, cost-effectiveness constraints on anabolic-first sequencing, and evidence gaps in post-denosumab transition strategies. By synthesizing mechanistic insights, guideline recommendations, and critical appraisal of current limitations, this review offers not only an overview of existing knowledge but a coherent decision-support model aimed at improving fracture prevention through comprehensive, individualized care. Full article

Understanding the spatial and temporal structure of crustal deformation is essential for identifying tectonic blocks, assessing seismic hazard, and detecting precursory deformation associated with major megathrust earthquakes. In this study, we analyze twenty years of continuous GNSS observations from the Japanese Islands to identify coherent deformation domains and anomalous regions using an integrated time-dependent clustering framework. The workflow combines six machine learning algorithms (Hierarchical Agglomerative Clustering, K-means, Gaussian Mixture Models, Spectral Clustering, HDBSCAN and consensus clustering) and constructs a set of deformation-related features including steady-state velocities, strain rates, co-seismic and post-seismic displacements, and spatial distance metrics. Optimal cluster numbers are determined by validity metrics, and the most robust segmentation is obtained using a consensus approach. The resulting spatiotemporal domains reveal clear segmentation associated with major geological structures such as the Fossa Magna graben, the Median Tectonic Line, and deformation belts related to Pacific Plate subduction. The method also highlights deformation patterns potentially associated with the preparation stages of megathrust earthquakes. Our results demonstrate that machine learning-based clustering of long-term GNSS time series provides a powerful data-driven tool for quantifying deformation heterogeneity and improving the understanding of active geodynamic processes in subduction zones. Full article

Thought processes in the brain occur as it continually modifies its use of energy. This review integrates research findings from molecular neurology, vascular physiology and non-equilibrium thermodynamics to create a comprehensive perspective on thinking as a coordinated energy process. Data shows that there is a relationship between the processing of information and metabolism throughout all scales, from the mitochondria’s electron transport chain to the rhythmic changes in the microvasculature. Through the cellular level of organization, mitochondrial networks, calcium (Ca²⁺) signals from astrocytes and the adaptive control of capillaries work together to maintain a state of balance between order and dissipation that maintains function while also maintaining the ability to be flexible. The longer-term regulatory mechanisms including redox plasticity, epigenetic programs and organelle remodeling may convert short-lived states of metabolism into long-lasting physiological “memory”. As well, data indicates that the cortical networks of the brain appear to be operating close to their critical regimes, which will allow them to respond to stimuli but prevent the brain from reaching an unstable energetic state. It is suggested that cognition occurs as the result of the brain’s ability to coordinate energy supply with neural activity over both time and space. Providing a perspective of the functional aspects of neurons as a continuous thermodynamic process creates a framework for making predictive statements that will guide future studies to measure coherence as a key link between energy flow, perception, memory and cognition. Full article

Introduction: Promoting healthy habits in childhood is fundamental for fostering long-term well-being. This study aimed to develop and psychometrically validate an app-integrated instrument to assess knowledge, habits, and attitudes related to health in children aged 8–11, within the context of the MHealth intervention Healthy Jeart. Methods: A quantitative, cross-sectional design was used. An initial item pool underwent expert content validation before being administered to a sample of 623 children from primary education centers in Andalusia, Spain. Construct validity was examined through exploratory and confirmatory factor analyses. Results: The analyses supported a coherent four-factor structure comprising 21 items: (1) Use of technologies, (2) diet and growth, (3) psychological well-being, and (4) physical activity and well-being. The instrument demonstrated satisfactory model fit and internal consistency, providing a multidimensional assessment of children’s health-related behaviors. The sample was recruited from primary schools in Andalusia (Spain), which may limit the generalizability of the findings to other regions and cultural contexts. Conclusions: The validated instrument offers a reliable and efficient means of evaluating healthy habits in children aged 8–11, particularly when embedded within digital interventions such as Healthy Jeart. It represents a valuable tool for educators and pediatric nursing professionals working in school settings, enabling early identification of gaps in health literacy and supporting targeted interventions that promote holistic child well-being. Full article

As a form of long-term asset allocation, pension fund investment necessitates accurate estimation of both asset returns and associated risks over extended time horizons. However, long-term asset returns are significantly influenced by macroeconomic factors, whereas variance-based risk measures cannot account for the directional nature of deviations from expected returns. To address these issues, we propose a novel CVaR-based Black–Litterman model incorporating macroeconomic cycle views (CVaR-BL-MCV) for optimal asset allocation of pension funds. This approach integrates macroeconomic cycle dynamics to quantify their impact on asset returns and utilizes Conditional Value-at-Risk (CVaR) as a coherent measure of downside risk. We employ a Markov-switching model to identify and forecast the phases of economic and monetary cycles. By analyzing the economic cycle with PMI and CPI, economic conditions are categorized into three distinct phases: stable, transitional, and overheating. Similarly, by analyzing the monetary cycle with M2 and SHIBOR, monetary conditions are classified into expansionary and contractionary phases. Based on historical asset return data across these cycles, view matrices are constructed for each cycle state. CVaR is used as the risk measure, and the posterior distribution of the Black–Litterman (BL) model is derived via generalized least squares (GLS), thereby extending the traditional BL framework to a CVaR-based approach. The experimental results demonstrate that the proposed CVaR-BL-MCV model outperforms the benchmark models. When the risk aversion coefficient is 1, 1.5, and 3, the Sharpe ratio of pension asset allocation using the CVaR-BL-MCV model is 21.7%, 18.4%, and 20.5% higher than that of the benchmark models, respectively. Moreover, the BL model incorporating CVaR improves the Sharpe ratio of pension asset allocation by an average of 19.7%, while the BL model with MCV achieves an average improvement of 14.4%. Full article

Treatment adherence in chronic diseases is addressed here as a measurable construct that can be formally defined and computed from heterogeneous IoT data streams. The central contribution of this work lies in establishing a mathematical formulation of adherence that integrates both explicit treatment-related activities and behavioural indicators derived from sensor observations. The methodology specifies how raw data from wearables, BLE beacons, and ambient devices can be transformed into clinically meaningful activities through fuzzy logic, enabling the representation of uncertainty, temporal variability, and partial evidence. This framework also accommodates activity labels generated by machine learning models, providing a mechanism to adapt their outputs—originally expressed as probabilistic or categorical predictions—into fuzzy memberships suitable for adherence computation. By unifying sensor-driven activity extraction and model-based activity recognition under a common fuzzy representation, the proposed formulation delivers a coherent pathway for calculating adherence across multiple dimensions and contexts, thereby supporting robust and interpretable evaluation of patient behaviour. By integrating these elements, the methodology provides a comprehensive and interpretable profile of adherence, moving from isolated measures to a unified characterisation of patient behaviour. The framework enables healthcare professionals and patients to better monitor progress, anticipate risks, and support long-term disease management. Full article