Search Results (1,565)

Adiabatic demagnetization refrigeration (ADR) has attracted considerable attention as an effective approach to reach ultra-low temperatures required for fundamental physics and quantum technologies. Here we directly characterize the cryogenic magnetocaloric performance of the rare-earth-based double-perovskite oxide Gd₂CuTiO₆ (GCTO) through quasi-adiabatic demagnetization measurements. Magnetization measurements show no long-range magnetic transition above 1.8 K and indicate dominant antiferromagnetic (AFM) interactions, consistent with an AFM ordering temperature of $T_{\mathrm{N}}\approx 1.15$ K reported previously. Notably, the isothermal magnetization $M\left(H\right)$ at 1.8 K deviates from an ideal single-ion Brillouin response and is better described by a molecular-field correction for the Gd sublattice, suggesting correlated paramagnetism persisting above $T_{\mathrm{N}}$. In contrast to previous studies that inferred cooling performance from thermodynamic estimates, we directly validate the achievable sub-Kelvin cooling in GCTO through quasi-adiabatic measurements. In the quasi-ADR process starting from $T_0$∼2 K, demagnetization fields of 4, 6, and 9 T yield minimum temperatures of $T_{\min }=761.5$, 452.4, and 289.2 mK, respectively, well below $T_{\mathrm{N}}$. After complete removal of the magnetic field, the sample temperature remains highly stable for at least several tens of minutes, demonstrating a long hold time under quasi-adiabatic conditions. Moreover, the $T\left(H\right)$ curves reveal a characteristic field scale around $H_{\mathrm{c}}$∼1 T, implying a field-induced modification of the low-temperature magnetic-entropy landscape that is relevant to the cooling behavior during demagnetization. These results highlight GCTO as a promising magnetic refrigerant for sub-Kelvin ADR applications and underscore the role of correlated magnetism in optimizing cryogenic magnetocaloric performance. Full article

Ventilation systems in agricultural settings are designed to deliver specific air exchange rates, which are often not achievable using natural ventilation. In this study, we analyzed 105 agricultural ventilation fans tested between 2015 and 2025 at the Bioenvironmental and Structural Systems (BESS) Laboratory, including 0.6, 0.9, 1.2, and 1.5 m diameter fans operating at static pressures ranging from 0 to 75 Pa. The main objective of the study is to develop and introduce guidelines to help select the most suitable ventilation fans to improve the sustainability of agricultural operations. Two web-based interactive calculators were developed to visualize fan performance relative to low- and high-performing fans of the same diameter. Our findings indicated that the ventilation efficiency ratio (VER) of the fans ranged from 2 to 50 m³ h⁻¹ W⁻¹, and larger fans consistently showed higher efficiency at typical operating pressures of 12.5 to 37.5 Pa. In general, variable-speed fans operated at 85%, rather than full capacity, achieved higher efficiency. Two cost comparison scenarios were examined. In the first scenario, the fan with a higher purchasing cost but also 35% higher efficiency resulted in a payback period of 4.1 years. In the second scenario, the difference in fan efficiencies was less than 3.5%, which did not help with recovering higher purchase costs during the 10-year analysis period. It was concluded that selecting fans solely based on purchase price can lead to higher long-term costs. To improve the sustainability of agricultural fans, VER and operating conditions need to be evaluated together and integrated into automated control strategies. Future studies can focus on integrating fans with high efficiencies into sensor-based automated ventilation control systems to quantify long-term energy savings in livestock buildings and other agricultural operations. Full article

Vibrations have long been a critical subject of investigation across engineering disciplines. With the expansion of major manufacturing sectors such as shipbuilding, automotive engineering, aerospace, and railway transport, the challenges associated with noise, environmental impact, and geotechnical stability have become increasingly complex. Mechanical systems inherently dissipate energy through vibration, and this dissipation can significantly influence structural performance, durability, and operational efficiency. Since the early foundational studies on vibration control in the 1980s, substantial progress has been made in developing innovative mitigation techniques. Among these, the acoustic black hole (ABH) concept has emerged as a promising passive method for reducing vibrational energy without adding significant mass. Over the years, researchers have further enhanced ABH structures by incorporating damping layers, which improve their ability to dissipate energy and control structural vibrations. More recently, scientific interest has shifted toward understanding the role of embedded or dispersed particles in vibration attenuation. Particle-based approaches have shown potential for improving energy dissipation mechanisms through micro-scale interactions, yet the underlying physical processes and their influence on vibration behavior remain active topics of research. In this study, we examine the influence of particles on vibration reduction through combined experimental and numerical investigations. The system is subjected to repeated excitation forces of 1 V, 2 V, and 3 V across frequency ranges of 10–1000 Hz and 10–2000 Hz. Two structural models, ABH-ABH and ABH, were considered, with particles embedded at the mid-plane of each configuration. Additionally, sinusoidal translational motion was analyzed at frequencies between 550 and 625 Hz, with a displacement velocity of 0.5 m/s, to determine the loss factor damping. The numerical results show consistent trends with experimental measurements, reinforcing the effectiveness of particle-enhanced ABH structures in vibration control. Full article

Agriculture is a major global source of methane (CH₄), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH₄ emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH₄ flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine learning (AutoML) framework to compare their performance and process-level interpretability. The daily model demonstrated high predictive accuracy with correlation coefficients (CC) of 0.897 in 5-fold cross-validation and 0.819 in Leave-One-Year-Out (LOYO) cross-validation. Shapley Additive Explanations (SHAP) analysis revealed that while soil temperature is the dominant predictor for daily emissions (explaining ~50% of the variance), variable importance shifts significantly at finer resolutions. The hourly model exhibited a more complex multivariate structure. In this high-resolution context, although Normalized Difference Vegetation Index (NDVI) remains constant diurnally, its importance strengthens as a critical regulator of emission sensitivity, interacting with hourly meteorological fluctuations to capture short-term dynamics. The resulting 500 m daily gridded maps provide a robust foundation for national inventory refinement and spatially targeted mitigation planning. Our findings suggest that while the daily model offers optimal computational efficiency for long-term monitoring, the hourly model is superior for mechanistic understanding and detecting episodic emission events. This multi-resolution framework establishes an empirical basis for selecting appropriate temporal scales in operational greenhouse gas monitoring systems. Full article

Deep-learning-based methods have achieved remarkable success in hyperspectral image (HSI) classification tasks due to their promising ability. However, the high dimensionality and spectral–spatial correlations of HSIs usually lead to information redundancy and feature entanglement, limiting the classification performance. To address these issues, we propose a novel high–low frequency interaction Mamba network, called HL-Mamba, which achieves effective decoupling and interaction between global structures and edge details of HSIs in the frequency domain, thereby improving spectral–spatial representation for HSI classification. Specifically, a high–low frequency decomposition Mamba module is designed to decompose the HSI into low-frequency structural and high-frequency edge detail components, which allows the model to learn global structures and fine-grained details, enhancing classification performance. By employing two parallel Mamba branches to model long-range dependencies across different frequency components, the network achieves efficient global modeling while mitigating information redundancy. Furthermore, a cross-frequency interaction module is designed to establish complementary information flow between high- and low-frequency features through a dynamic attention mechanism. In this way, low-frequency structural features guide the aggregation of high-frequency details, whereas high-frequency textures refine global structural representations, yielding more discriminative spectral–spatial features for HSI classification. In addition, a frequency alignment loss is designed to enhance the consistency and complementarity between high- and low-frequency features, further improving classification performance. Extensive experiments on four public benchmark datasets (i.e., Indian Pines, Pavia University, WHU-Hi-HanChuan, and Houston datasets) demonstrate that the proposed HL-Mamba significantly outperforms eight comparison methods, achieving an overall accuracy of 94.07%, 93.82%, 95.28%, and 87.32%, respectively. Ablation studies further verify the effectiveness of core component within the network. Full article

Despite the success of Geometric Graph Neural Networks (GGNNs), their reliance on local message passing inherently limits the receptive field, leading to the over-squashing of distant information. Current solutions—ranging from graph rewiring to global attention—address this as a purely topological bottleneck, often neglecting the explicit electronic degrees of freedom (e.g., charge transfer and electrostatics) that physically govern long-range couplings. To resolve this disconnect, we propose HMP-Net, a framework that integrates over-squashing remedies with chemically meaningful interaction channels. Our approach introduces a differentiable hierarchy that routes information through learned “master nodes”, enabling direct global reasoning while preserving local chemical fidelity. Crucially, we clarify the theoretical ambiguity between topological and physical long-range interactions, establishing practical boundaries for when explicit physical modeling suffices and when architectural interventions are strictly necessary to capture global electronic states. Full article

Net primary production (NPP) in the ocean is fundamental to marine food webs, supporting oxygen production for heterotrophic respiration and contributing to the long-term sequestration of carbon. Rising ocean temperatures associated with climate change are expected to alter NPP dynamics. However, it remains challenging to understand how different abiotic (especially sea temperature) and biotic factors influence marine NPP due to the complex network of interactions between these factors. This study introduces a flexible machine-learning-based framework for evaluating the sensitivity of NPP to variations in key environmental drivers, particularly sea temperature, by testing and comparing alternative machine learning algorithms. In the case study presented here, Support Vector Machines (SVM) achieved the highest predictive performance among the evaluated models. Variable-importance analysis of the best-performing algorithm, within the scope of this comparative framework, revealed that variables intrinsically linked to NPP, such as chlorophyll-a and solar radiation, play a key role in determining the predictive ability of the models. Meanwhile, sea temperature emerged as the key external factor influencing the performance of the models. The NPP exhibits a correlative sensitivity to increase of 1 °C in sea temperature, with relative changes ranging between 3% and 16%. These projections reflect model-based sensitivities derived from historical co-variation. Therefore, the results represent conditional projections under observed relationships. Although SVM performed best for this case study, the proposed framework is adaptable and can incorporate alternative algorithms, predictor sets and preprocessing strategies, enabling robust and transferable assessments of the sensitivity of regional ocean productivity to climate change. Full article

Background/Objectives: Effective glycemic management from the time of diagnosis is essential in the care of children and adolescents with type 1 diabetes (T1D), as early glycemic patterns can influence long-term health outcomes. Methods: This exploratory study evaluated a one-month interactive, group- and play-based education program designed to enhance food and carbohydrate counting skills among families of children and adolescents with newly diagnosed (ND) T1D (<1 year since diagnosis) or suboptimal glycemic control (SGC) (hemoglobin A1c (HbA1c) > 7.5% (58 mmol/mol)). The intervention included hands-on learning activities in food and carbohydrate counting, and peer interaction to support development of diabetes self-management skills. Data were collected at baseline, post-intervention, and at six-months follow-up through medical records, glucose sensor data, and a questionnaire assessing diabetes self-management skills, dietary practices, and carbohydrate counting. Results: Between September 2022 and April 2024, 55 children and adolescents were enrolled in the ND group and 22 in the SGC group. Post-intervention, carbohydrate counting skills improved, particularly in the ND group. Participants reported greater confidence and independence in carbohydrate counting and insulin dosing, with parents noting sustained benefits at six-months follow-up. No significant changes were observed in glycemic control, including time-in-range and postprandial glucose profiles. Conclusions: In this exploratory study, early interactive and play-based group education was associated with improvements in carbohydrate counting skills and self-care confidence in children and adolescents with newly diagnosed T1D. These improvements were not accompanied by changes in glycemic outcomes. The findings occurred during a complex and transitional phase following diagnosis. Further research is needed to examine sustainability and long-term clinical impact. Full article

Background/Objectives: Recombinant adeno-associated virus (AAV) vectors have revolutionized gene therapy for monogenic diseases such as Duchenne muscular dystrophy (DMD). However, high systemic doses required for muscle transduction cause a spectrum of toxicities ranging from transient hepatic inflammation to fatal multi-organ failure leading to death. These adverse events have reshaped the risk–benefit considerations for gene therapy in DMD. Methods: We conducted a narrative review describing complications associated with AAV-mediated gene therapies in the DMD field. PubMed and Clinicaltrials databases were used to search for peer-reviewed manuscripts published between 1987 and 2025. Publicly available abstracts and press releases were also used to describe AAV-mediated adverse events that have been discovered. Priority was given to large prospective cohorts, meta-analyses, and high-impact publications. Results: We outlined the mechanistic basis of AAV toxicity—spanning innate and adaptive immune activation, vector–host interactions, transgene overexpression, and host vulnerability—and discussed their therapeutic implications for DMD. We also highlighted ongoing strategies for vector re-design, immune modulation, patient selection, and regulatory adaptation, aiming to improve efficacy with safety in the next generation of muscular dystrophy gene therapies. Conclusions: Patient safety remains the number one priority in the AAV-mediated gene therapies field. Achieving long-term benefits requires continued optimization of existing vectors, implementation of strict criteria for patient selection, and regulation of immune responses, with close collaboration and transparent dialog among scientists, clinicians, and regulatory agencies, informed by both successful cases as well as tragic deaths reported in the fields of neuromuscular diseases. Full article

Probiotics and postbiotics have gained increasing attention in microbiome research due to their potential roles in maintaining gut homeostasis and supporting human health. While probiotics are defined as live microorganisms that confer health benefits when administered in adequate amounts, postbiotics represent preparations of inanimate microorganisms and/or their components that also exert biological effects on the host. This narrative review provides an updated overview of the current knowledge on probiotics and postbiotics, with a particular focus on their mechanisms of action, production strategies, and emerging applications in human health. The review discusses key mechanisms through which probiotics and postbiotics interact with the host, including modulation of the gut microbiota, enhancement of epithelial barrier integrity, immune system regulation, metabolic modulation, and systemic signaling pathways. Advances in production technologies, ranging from conventional fermentation to innovative inactivation and stabilization approaches, are also examined, alongside challenges related to yield optimization, stability, safety, and standardization. Although a growing body of evidence suggests potential benefits of probiotics and postbiotics in metabolic, inflammatory, gastrointestinal, and immune-related conditions, much of the available data is derived from preclinical studies or small-scale clinical trials. Consequently, their clinical efficacy, optimal dosing, and long-term safety require further validation. By integrating current findings and highlighting existing gaps in the literature, this review aims to clarify the therapeutic potential of probiotics and postbiotics and to support the development of more robust, evidence-based strategies for their application in functional foods, supplements, and future biotherapeutic interventions. Full article

Accurate quantification of spatial distances between fluorescent signals in multi-channel 3D microscopy is essential for understanding genomic organization and gene regulation. However, chromatic aberration introduces systematic spatial offsets between channels that significantly bias distance measurements, particularly at short genomic distances. We present FISH-Dist, an automated computational pipeline for quantitative distance measurements in 3D fluorescence in situ hybridization (FISH) experiments acquired on standard confocal microscopes. Our method combines deep learning-based spot segmentation, 3D Gaussian fitting for sub-pixel localization, and two complementary chromatic aberration correction approaches: affine (ACC) and linear (LCC). We validated the pipeline by measuring the lengths of DNA origami nanorulers and systematically evaluated FISH probe design parameters, including probe spacing, density, and target sequence length. FISH-Dist achieves sub-pixel accuracy in signal detection and substantially reduces inter-channel distance measurement errors. This enables a reproducible quantification of spatial relationships in 3D FISH datasets. Unlike existing tools optimized for long-range chromosomal interactions or requiring super-resolution microscopy, FISH-Dist specifically addresses the technical challenges of standard confocal imaging at short genomic distances, where chromatic aberration has a proportionally greater impact on measurement accuracy. Full article

The prevalence of ground fissures in deformation-affected areas has intensified, presenting serious risks to both operational safety and the local natural environment. Fissures in these disturbed terrains are typically characterized by elongated morphologies and large-scale variations, which pose substantial challenges to accurate feature extraction. To address these complexities, this paper proposes a semantic segmentation network termed MGF-UNet. In the shallow layers, we integrate multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) to sharpen anisotropic textures and boundary details under high-resolution representations. For the deeper layers, a Token-Selective Context Transformer (TSCT) is designed to perform selective global modeling on high-level semantic features, effectively capturing long-range dependencies while preserving the structural integrity of elongated fissures. Meanwhile, we employ feature-wise linear modulation (FiLM) to derive pixel-wise affine parameters from shallow structures, which pre-modulate deep features and strengthen cross-level interactions. In the decoder, a Fourier transform-based adaptive feature fusion (AFF) module suppresses background noise and enhances boundary contrast, followed by cross-scale aggregation for final prediction.Benchmark tests conducted on the mining-area fissure dataset (MFD) and road-based datasets demonstrate that MGF-UNet achieves an accuracy of 78.2%, a Dice score of 81.4%, and an IoU of 68.6%, outperforming existing mainstream networks. The results confirm that MGF-UNet provides an effective solution for automatic fissure extraction in deformation-prone environments, offering significant potential for geohazard monitoring and ecological restoration. Full article

Brake pedals and wheel braking units are mechanically decoupled in brake-by-wire systems. This causes the driver to lose the familiar pedal feel. To address this issue, this paper designed an active braking pedal simulator based on the long-travel Halbach-array linear motor. Firstly, this paper conducted both qualitative and quantitative analyses on the pedal characteristics of a traditional hydraulic braking system and used them as a reference. A dual-coil independent control strategy was designed in order to overcome the thrust instability at the junction of the Halbach-array magnetic field. This enables the linear motor to achieve smooth and continuous thrust output throughout the entire travel range. Secondly, this paper also designed a “linear motor + spring” solution to reduce energy consumption and peak motor thrust. By conducting a quantitative analysis of the relationship between the spring stiffness, motor work and peak thrust, the spring stiffness was optimized. The results show that when the spring stiffness is 3.73 N/mm, the motor work can be reduced to 5.92 Joules while significantly reducing the peak thrust. Finally, this paper also established a testing platform. It was used to verify the performance of the proposed pedal simulator under low-intensity, medium-intensity, and high-intensity braking conditions as well as an anti-lock braking system intervention. The testing results show that the pedal simulator can actively adjust the pedal characteristics according to the braking intensity, and it can provide clear vibration feedback during the anti-lock braking system intervention. Therefore, the proposed pedal simulator effectively simulates the pedal feel of hydraulic braking systems while improving energy efficiency and operational stability. It provides a feasible solution for enhancing the driver–vehicle interaction and the driving comfort of brake-by-wire systems. Full article

Phthalate esters (PAEs), ubiquitous plastic additives, have emerged as persistent contaminants in aquatic ecosystems, yet their propagation from molecular initiating events to ecosystem-level collapse remains poorly integrated. This review synthesizes current knowledge on the source-to-sink dynamics of PAEs, revealing a critical paradox in their bioaccumulation patterns: unlike classical persistent organic pollutants, high molecular weight PAEs exhibit distinct trophic dilution rather than biomagnification along food webs, driven by metabolic biotransformation in higher trophic organisms. Despite this dilution, PAEs trigger a bottom-up toxicity cascade. Driven by molecular initiating events, PAEs induce a range of adverse effects at the individual level, including immunotoxicity, neurotoxicity, endocrine disruption, metabolic dysfunction, and trans-trophic oxidative stress. Crucially, prolonged exposure drives epigenetic reprogramming, which reduces reproductive output, thereby threatening long-term population recruitment. These individual and population deficits could escalate into higher ecological consequences, specifically by diminishing benthic biological control over phytoplankton, dampening energy transfer efficiency, and simplifying community structure, thereby posing a potential threat to primary productivity and aquatic ecosystem sustainability. Despite recent advances, critical knowledge gaps remain, particularly regarding their cascading impacts on ecosystem services, as well as synergistic interactions between PAEs and other contaminants. In order to validate laboratory results with actual ecological risk assessments, future research should incorporate multi-scale models and quantitative adverse outcome Pathways as well as their synergistic interactions between PAEs and other contaminants, and advanced in vitro systems such as organoids. Resolving these issues is essential to reducing the risks that PAEs pose to aquatic environments. Full article

Background: Hydrolyzed collagen peptides (HCP) are widely used as bioactive ingredients in anti-aging and skin rejuvenation formulations due to their role in supporting skin hydration, elasticity, and extracellular matrix integrity. However, their high hydrophilicity limits effective incorporation into lipid-based systems, and restricts controlled release from formulations. Objective: In this study, ethosomal nanocarriers were designed as a phospholipid–ethanol-based system to promote favorable molecular interactions with hydrophilic peptides, aiming to enhance the encapsulation, stability, and controlled release of HCP for dermocosmetic applications. Methods: HCP-loaded ethosomes were prepared using phospholipid (Lipoid P75) and ethanol and optimized by varying high-pressure homogenization cycles. Physicochemical properties, including vesicle size, distribution uniformity, zeta potential, pH, and long-term stability, were monitored for up to 180 days. Vesicle morphology and peptide–lipid interactions were characterized using cryo-scanning electron microscopy and FTIR spectroscopy. Encapsulation efficiency was determined by ultrafiltration, while cytocompatibility was assessed in HaCaT keratinocyte cells. In vitro release behavior was investigated using Franz diffusion cells and compared with aqueous HCP solutions. Results: All formulations exhibited nanoscale size distribution and high colloidal stability, with negative zeta potentials ranging from −42.9 to −76.7 mV. The optimized formulation demonstrated sustained encapsulation efficiency (73% after 180 days) and preservation of peptide structure, as confirmed by FTIR, indicating effective chemical stabilization within the ethosomal matrix. Cytotoxicity studies confirmed good skin cell compatibility. In vitro release studies revealed a controlled and prolonged release profile from ethosomal carriers compared with free HCP solutions, suggesting improved topical bioavailability of collagen peptides. Conclusions: To the best of our knowledge, this work provides one of the first systematic investigations of optimized ethosomal systems for the stabilization of hydrophilic collagen peptides as anti-aging dermocosmetic ingredients. These findings demonstrate that optimized HCP-loaded ethosomes represent a promising ingredient formulation platform enabling bioactive preservation, formulation stability, and controlled topical performance for collagen-based skin rejuvenation applications. Full article