Search Results (2,488)

People with intellectual disabilities are disproportionately affected by diet-related health inequalities. This Perspective outlines a dual challenge: (1) intrinsic vulnerabilities—cognitive limitations, health-literacy constraints, and comorbidities—that impair individuals’ ability to make healthy dietary choices, and (2) extrinsic neglect—insufficient support in care environments, inadequate nutrition-related training among informal caregivers and support staff, and structural gaps in policy and services. We argue that this “double jeopardy” undermines nutritional equity and proposes strategies for person-centered nutrition education, caregiver empowerment, supportive food environments, and inclusive policy frameworks. Greater interdisciplinary collaboration and tailored research are urgently needed to ensure nutritional health as a right for people with intellectual disabilities. Full article

As shale gas development extends into deeper formations, the unclear brittle-ductile transition (BDT) mechanism and low fracturing efficiency have emerged as critical bottlenecks, posing challenges to the sustainable and economical utilization of this clean energy resource. This study, focusing on the Liangshang Formation shale of Sichuan Basin’s Pingye-1 Well, pioneers a paradigm shift by identifying Poisson’s ratio ($\mathsf{\nu }$) as the master variable governing this transition. Triaxial tests reveal that $\mathsf{\nu }$ systematically increases with depth, directly regulating the failure mode shift from brittle fracture to ductile flow. Building on this, we innovatively propose the Poisson’s Ratio-regulated Energy-based Brittleness Index (PNE-BI) model. This model achieves a decoupled diagnosis of BDT by quantifying how ν intrinsically orchestrates the energy redistribution between elastic storage and plastic dissipation, utilizing ν as the sole governing variable to regulate energy weighting for rapid and accurate distinction between brittle, transitional, and ductile states. Experiments confirm the $\mathsf{\nu }$-dominated energy evolution: Low $\mathsf{\nu }$ rocks favor elastic energy accumulation, while high $\mathsf{\nu }$ rocks (>0.22) exhibit a dramatic 1520% surge in plastic dissipation, dominating energy consumption (35.9%) and confirming that $\mathsf{\nu }$ enhances ductility by reducing intergranular sliding barriers. Compared to traditional multi-variable models, the PNE-BI model utilizes $\mathsf{\nu }$ values readily obtained from conventional well logs, providing a transformative field-ready tool that significantly reduces the experimental footprint and promotes resource efficiency. It guides toughened fracturing fluid design in ductile zones to suppress premature closure and optimizes injection rates in brittle zones to prevent fracture runaway, thereby enhancing operational longevity and minimizing environmental impact. This work offers a groundbreaking and sustainable solution for boosting the efficiency of mid-deep shale gas development, contributing directly to more responsible and cleaner energy extraction. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

Alkaline water electrolysis remains one of the leading and most mature technologies for large-scale hydrogen production. Its advantages stem from the use of inexpensive, earth-abundant materials and well-established industrial deployment, yet the technology continues to face challenges, including sluggish hydrogen evolution reaction (HER) kinetics and energy-efficiency limitations compared with acidic electrolysis systems. This review provides a comprehensive overview of the fundamental principles governing alkaline electrolysis, encompassing electrolyte chemistry, electrode materials, electrochemical mechanisms, and the roles of overpotentials, cell resistances, and surface morphology in determining system performance. Key developments in catalytic materials are discussed, highlighting both noble-metal and non-noble-metal electrocatalysts, as well as advanced approaches to surface modification and nanostructuring designed to enhance catalytic activity and long-term stability. Particular emphasis is placed on the emerging strategy of in situ ionic activation, wherein transition-metal ions and oxyanions are introduced directly into the operating electrolyte. These species dynamically interact with electrode surfaces under polarization, inducing real-time surface reconstruction, improving water dissociation kinetics, tuning hydrogen adsorption energies, and extending electrode durability. Results derived from polarization measurements, electrochemical impedance spectroscopy, and surface morphology analyses consistently demonstrate that ionic activators, such as Ni–Co–Mo systems, significantly increase the HER performance through substantial increase in surface roughness and increased intrinsic electrocatalytic activity through synergy of d-metals. By integrating both historical context and recent research findings, this review underscores the potential of ionic activation as a scalable and cost-effective way toward improving the efficiency of alkaline water electrolysis and accelerating progress toward sustainable, large-scale green hydrogen production. Full article

Pt-rare earth metal (Pt-RE) alloys are considered to be one of the most promising electrocatalysts for producing oxygen reduction reactions (ORRs) due to their compressively strained Pt overlayer and their exceptional negative-alloy formation energies, which result in excellent activity and stability. However, there are still great challenges in the chemical synthesis of Pt-RE nanoalloys. Herein, we report a simple method employing the nanopores of porous carbon as nanoreactors to synthesize a Pt₅La nanoalloy. The Pt₅La alloy nanoparticles are embedded in porous carbon (Pt₅La@C) with a particle size of around 1–3 nm and also exhibit a very narrow size distribution because of the confined-space effect. The as-prepared Pt₅La@C nanoalloy exhibits highly efficient ORR performance with a half-wave potential of 0.912 V in 0.1 M HClO₄, which is 56 mV higher than that of a commercial Pt/C catalyst. Moreover, it achieves an improved intrinsic activity of 0.69 mA cm⁻² and, a mass activity of 0.42 A mg_Pt⁻¹ at 0.90 V. In addition, it also delivers a very stable lifespan performance, with negligible decay in half-wave potential after accelerated stress testing for 10,000 cycles. This work also provides a new method for the development of promising Pt-RE nanoalloys with ultrasmall nanoparticles with a very narrow size distribution for various efficient energy-conversion devices. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

Autologous fat grafting is the main surgical technique for soft tissue reconstruction. However, its clinical use with more extended volumes is limited by repeated procedures due to the little possibility of banking tissue, donor-site morbidity and unpredictable graft resorption rates. To overcome these problems, adipose tissue engineering has focused on developing injectable scaffolds. Most of them are hydrogels that closely mimic the biological, structural and mechanical characteristics of native adipose tissue. This review provides an overview of current injectable scaffolds designed to restore soft tissue volume defects, emphasizing their translational potential and future directions. Natural injectable scaffolds exhibit excellent biocompatibility but degrade rapidly and lack mechanical strength. Synthetic injectable scaffolds provide tunable elasticity and degradation rates but require biofunctionalization to support cell adhesion and tissue integration. Adipose extracellular matrix-derived injectable scaffolds are fabricated by decellularization of adipose tissue. Accordingly, they combine bio-mimetic structure with intrinsic biological cues that stimulate host-driven adipogenesis and angiogenesis, thus representing a translatable “off-the-shelf” alternative to autologous fat grafting. However, despite this broad spectrum of available injectable scaffolds, the establishment of clinically reliable soft tissue substitutes capable of supporting large-volume and long-lasting soft tissue reconstruction still remains an open challenge. Full article

Atherosclerosis remains a leading cause of cardiovascular mortality despite advances in pharmacological and interventional therapies. Current treatment approaches face limitations including systemic side effects, inadequate local drug delivery, and restenosis following vascular interventions. Gel-based technologies offer unique advantages through tunable mechanical properties, controlled degradation kinetics, high drug-loading capacity, and potential for stimuli-responsive therapeutic release. This review examines gel platforms across multiple scales and applications in atherosclerosis research and intervention. First, gel-based in vitro models are discussed. These include hydrogel matrices simulating plaque microenvironments, three-dimensional cellular culture platforms, and microfluidic organ-on-chip devices. These devices incorporate physiological flow to investigate disease mechanisms under controlled conditions. Second, therapeutic strategies are addressed through macroscopic gels for localized treatment. These encompass natural polymer-based, synthetic polymer-based, and composite formulations. Applications include stent coatings, adventitial injections, and catheter-delivered depots. Natural polymers often possess intrinsic biological activities including anti-inflammatory and immunomodulatory properties that may contribute to therapeutic effects. Third, nano- and microgels for systemic delivery are examined. These include polymer-based nanogels with stimuli-responsive drug release responding to oxidative stress, pH changes, and enzymatic activity characteristic of atherosclerotic lesions. Inorganic–organic composite nanogels incorporating paramagnetic contrast agents enable theranostic applications by combining therapy with imaging-guided treatment monitoring. Current challenges include manufacturing consistency, mechanical stability under physiological flow, long-term safety assessment, and regulatory pathway definition. Future opportunities are discussed in multi-functional integration, artificial intelligence-guided design, personalized formulations, and biomimetic approaches. Gel technologies demonstrate substantial potential to advance atherosclerosis management through improved spatial and temporal control over therapeutic interventions. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

Polyurethanes (PUs) are indispensable polymeric materials widely employed across diverse industrial sectors due to their excellent thermal stability, chemical resistance, adhesion, and mechanical durability. However, the intrinsic three-dimensional crosslinked network that underpins their performance also presents a fundamental barrier to reprocessing and recycling. Consequently, most end-of-life PU waste is currently managed through landfilling or incineration, resulting in significant resource loss and environmental impact. To address these challenges, this review presents an integrated perspective on sustainable PU systems by unifying green synthesis strategies with closed-loop recovery approaches. First, recent advances in bio-based polyols and phosgene-free isocyanate synthesis derived from renewable resources—such as plant oils, carbohydrates, and lignin—are discussed as viable means to reduce dependence on petrochemical feedstocks and mitigate toxicity concerns. Next, emerging chemical recycling methodologies, including acidolysis and aminolysis, are reviewed with a focus on the selective recovery of high-purity monomers. Finally, PU vitrimers and dynamic covalent polymer networks (DCPNs) based on urethane bond exchange reactions are examined as reprocessable architectures that combine thermoplastic-like processability with the mechanical robustness of thermosets. By integrating synthesis, recovery, and reuse within a unified framework, this review aims to outline a coherent pathway toward establishing a sustainable circular economy for PU materials. Full article

Membrane proteins remain the most challenging targets for structural characterization, yet their elucidation provides valuable insights into protein function, disease mechanisms, and drug specificity. Structural biology platforms have advanced rapidly in recent years, notably through the development and implementation of nanodiscs—discoidal lipid–protein complexes that encapsulate and solubilize membrane proteins within a controlled, native-like environment. While nanodiscs have become powerful tools for studying membrane proteins, faithfully reconstituting the compositional asymmetry intrinsic to nearly all biological membranes has not yet been achieved. Proper membrane leaflet lipid distribution is critical for accurate protein folding, stability, and insertion. Here, we share a protocol for reconstituting tailored compositional asymmetry within nanodiscs through membrane extraction from giant unilamellar vesicles (GUVs) treated with a leaflet-specific methyl-β-cyclodextrin (mβCD) lipid exchange. Nanodisc asymmetry is verified through a geometric approach: biotin-DPPE-preloaded mβCD engages in lipid exchange with the outer leaflet of POPC GUVs solubilized by the lipid-free membrane scaffold protein (MSP) Δ49ApoA-I to form nanodisc structures. Once isolated, nanodiscs are introduced to the biotin-binding bacterial protein streptavidin. High-speed atomic force microscopy imaging depicts nanodisc–dimer complexes, indicating that biotin-DPPE was successfully reconstituted into a single leaflet of the nanodiscs. This finding outlines the first step toward engineering tailored nanodisc asymmetry and mimicking the native environment of integral proteins—a potentially powerful tool for accurately reconstituting and structurally analyzing integral membrane proteins whose functions are modulated by lipid asymmetry. Full article

Chiral interactions pose significant challenges for regularization due to the ${\gamma }_5$ Dirac matrix, which is intrinsically four-dimensional. Dimensional regularizations, while widely employed in gauge theories, encounter challenges when treating ${\gamma }_5$ in $d\ne 4$ dimensions, potentially leading to violations of chiral symmetry and the emergence of spurious anomalies. In this work, we examine aspects of Implicit Regularization, a framework formulated to operate in the physical dimension, thereby potentially avoiding ambiguities associated with ${\gamma }_5$. We discuss its implementation and implications for symmetry preservation in chiral gauge theories. Full article

Cardiac metabolic flexibility is a key determinant of myocardial energetic resilience. In heart failure with reduced ejection fraction (HFrEF), intrinsic mitochondrial dysfunction and lipotoxicity compromise oxidative capacity. In contrast, heart failure with preserved ejection fraction (HFpEF) is orchestrated primarily by systemic comorbidities and coronary microvascular dysfunction, which decouple glycolysis from glucose oxidation. This review integrates these distinct pathophysiologies into a comprehensive biomarker framework. Beyond core hemodynamic markers, we detail indices of metabolic flux (ketones, acylcarnitines, branched-chain amino acids), endothelial injury, and fibrosis. We further prose a shift from static, isolated measurements to dynamic functional profiling using standardized challenges (e.g., mixed-meal or exercise tests) to quantify metabolic suppression and recovery kinetics. This structured hierarchy enables phenotype-tailored risk stratification and guides mechanism-based precision therapies in the era of personalized medicine. Full article

Background: Accurate assessment of prolonged disorders of consciousness (pDOC) is a critical clinical challenge. Misdiagnosis in pDOC can occur in up to 40% of cases, highlighting the need for more objective and reproducible biomarkers to support neurophysiological scales, thereby improving diagnosis and guiding therapeutic and prognostic decisions. Electroencephalography (EEG) microstate analysis is a promising, non-invasive method for tracking large-scale brain dynamics, but research in pDOC has predominantly relied on a canonical 4-class model. This methodological constraint may limit the ability to capture the full complexity of neural alterations present in these patients. Objective: This pilot study aimed to offer an objective method for assessing consciousness, complementing and enhancing the existing approaches established in the literature. The classical 4-class and an extended 7-class microstate model were compared to determine which more accurately characterizes the complexity of resting-state brain dynamics across different levels of consciousness in pDOC patients and healthy controls (HCs). Methods: Retrospective resting-state EEG (rsEEG) data from a cohort of pDOC patients and HC subjects were analyzed. Microstate analysis was performed using both 4-class and 7-class templates. The models were evaluated and compared based on three criteria: spatial correspondence with canonical maps (shared variance), the number of significant intra-group correlations between temporal features (Spearman test), and their ability to discriminate between the pDOC and HC groups (Wilcoxon test). Results: The 7-class microstate model provided a more accurate description of brain activity for most participants, with a greater number of microstate classes exceeding the 50% shared variance threshold compared to the 4-class model. In the pDOC group, both the 4-class and 7-class models showed a mean shared variance <50% in class D, which is associated with executive functioning across both templates. For the HC group, a prevalence of classes B and D emerged in both models, indicating higher engagement of executive functions. Furthermore, the 7-class model allowed for a group-specific analysis, which demonstrated that microstates A and F were consistently shared among 86% of pDOC patients. This suggests the potential preservation of specific intrinsic brain networks, particularly the sensory and default networks, even in the presence of severely impaired consciousness. Moreover, the 7-class model yielded a higher number of significant correlations within both groups and identified a broader set of temporal features that were significantly different between pDOC patients and HCs. These results highlight the enhanced sensitivity of the 7-class model in distinguishing subtle brain dynamics and improving the diagnostic capability for pDOC. Conclusions: The 7-class microstate model provides a more fine-grained and sensitive characterization of brain activity in both pDOC patients and healthy individuals. It demonstrated better performance in capturing individual brain dynamics, identifying shared network patterns, and discriminating between clinical populations. These findings suggest that the extended 7-class model holds greater potential for clinical utility and could lead to the development of more robust biomarkers for assessing consciousness. Full article

Meniscal preservation is increasingly recognized as a critical determinant of long-term knee joint health, yet successful repair remains challenging due to the meniscus’s limited intrinsic healing capacity. The adult meniscus is characterized by restricted vascularity, low cellularity, a dense extracellular matrix, complex biomechanical loading, and a hostile post-injury intra-articular inflammatory environment—factors that collectively impair meniscus healing, particularly in the avascular zones. Over the past several decades, a wide range of biologic augmentation strategies have been explored to overcome these barriers, including synovial abrasion, fibrin clot implantation, marrow stimulation, platelet-derived biologics, cell-based therapies, scaffold coverage, and emerging biologic and biophysical interventions. This review summarizes the biological basis of meniscal healing, critically evaluates current and emerging biologic augmentation techniques, and integrates these approaches within a unified framework of vascular, cellular, matrix, biomechanical, and immunologic targets. Understanding and modulating the cellular and molecular mechanisms governing meniscal degeneration and repair may enable the development of more effective, mechanism-driven strategies to improve healing outcomes and reduce the risk of post-traumatic osteoarthritis. Full article