A novel multi-modal rehabilitation monitoring over human motion intention recognition.

Human Motion Intention Recognition (HMIR) plays a vital role in advancing medical rehabilitation and assistive technologies by enabling the early detection of pain-indicative actions such as sneezing, coughing, or back discomfort. However, existing systems struggle with recognizing such subtle movements due to complex postural variations and environmental noise. This paper presents a novel multi-modal framework that integrates RGB and depth data to extract high-resolution spatial-temporal and anatomical features for accurate HMIR.

Relaxation behavior near the first-order phase transition line.

Using the Metropolis algorithm, we simulate the relaxation process of the three-dimensional kinetic Ising model. Starting from a random initial configuration, we first present the average equilibration time across the entire phase boundary. It is observed that the average equilibration time increases significantly as the temperature decreases far from the critical temperature T_{c}.

Does an improved HDI trigger tourism outflows for China? New evidence from the ARDL cointegration approach.

Extant studies have predominantly focused on understanding the effects of inbound tourism on economic growth. At the same time, it ignores the key factors that promote outbound tourism in a country. Outbound tourism not only plays a crucial role in the sustainable development of the host country but also helps foster an understanding of cross-cultural similarities and differences, promoting goodwill towards the home country.

Dominant Classifier-assisted Hybrid Evolutionary Multi-objective Neural Architecture Search.

Neural Architecture Search (NAS) automates the design of deep neural networks but remains computationally expensive, particularly in multi-objective settings. Existing predictor-assisted evolutionary NAS methods suffer from slow convergence and rank disorder, which undermines prediction accuracy. To overcome these limitations, we propose CHENAS: a Classifier-assisted multi-objective Hybrid Evolutionary NAS framework.

Energy-Efficient Resource Allocation for Near-Field MIMO Communication Networks.

With the rapid development of sixth-generation (6G) wireless networks and large-scale multiple-input multiple-output (MIMO) technology, the number of antennas deployed at base stations (BSs) has increased significantly, resulting in a high probability that users are in the near-field region. Note that it is difficult for the traditional far-field plane-wave model to meet the demand for high-precision beamforming in the near-field region. In this paper, we jointly optimize the power and the number of antennas to achieve the maximum energy efficiency for the users located in the near-field region.

A Hybrid Framework Integrating Traditional Models and Deep Learning for Multi-Scale Time Series Forecasting.

Time series forecasting is critical for decision-making in numerous domains, yet achieving high accuracy across both short-term and long-term horizons remains challenging. In this paper, we propose a general hybrid forecasting framework that integrates a traditional statistical model (ARIMA) with modern deep learning models (such as LSTM and Transformer). The core of our approach is a novel multi-scale prediction mechanism that combines the strengths of both model types to better capture short-range patterns and long-range dependencies.

-Palmitoleic Acid Regulates Lipid Metabolism via Diacylglycerol Metabolic Shunting.

Obesity and related metabolic disorders are closely linked to dysregulated lipid metabolism, where the metabolic balance of diacylglycerol (DAG) played a pivotal role. Although -palmitoleic acid (POA) exhibits anti-obesity effects, its efficacy varies across dietary conditions, and its molecular mechanisms remains unclear. In this study, we investigated the dose-dependent regulatory effects of POA on DAG metabolic shunting in db/db mice, employing lipidomics, pathway analysis, and gene/protein expression assays.

Does coal mining exert a greater influence than vegetation restoration on streamflow variation? Evidence from the Loess Plateau, China.

The streamflow change within the Loess Plateau is of great importance, given its complex driving mechanisms and diverse human activities. While it is widely recognized that human activities play a crucial role in impacting streamflow in this region, the specific contributions of each sort of human activity remain poorly understood. This is especially true for the coal mining since this process is particularly difficult to quantify, and thus, it remains unknown whether it is more important than another widely studied process-vegetation restoration-in streamflow changes.

Spatiotemporal variability of terrestrial water storage over the Tibetan Plateau from the joint inversion of GNSS and GRACE observations.

The Tibetan Plateau (TP), characterized by its unique regional features and geographical landscape, is a critical area for studying changes in terrestrial water storage (TWS), which are significantly influenced by global warming. In this study, we integrate data from the Global Navigation Satellite System (GNSS) and the Gravity Recovery and Climate Experiment (GRACE) to jointly estimate TWS variations in the TP and examine their spatiotemporal fluctuations in relation to large-scale climate patterns. To evaluate our approach, we conducted two synthetic tests, which showed that the root mean square errors (RMSEs) for the joint inversion were 23-37% lower than those for GNSS inversion, confirming the effectiveness of our method.

FOXO is involved in anti-WSSV immunity by regulating autophagy-related genes.

Forkhead box O (FOXO) interacts with autophagy related (ATG) genes to regulate autophagic activity. However, whether FOXO can regulate ATG genes to participate in crustacean infection with white spot syndrome virus (WSSV) is unknown. In this study, we identified seven ATG genes (EsGABARAP, EsBeclin-1, EsATG12, EsATG5, EsATG7, EsLC3, and EsATG9) in Eriocheir sinensis.

Divergent risk gene profiles in smallholder and large-scale paddy farms.

The proliferation of microbial resistance and virulence genes in agricultural soils poses an increasing threat to soil quality, food security, and public health. Smallholder and large-scale farming systems represent the two dominant modes of rice cultivation in many developing countries, yet the impact of their contrasting management practices on soil microbial risk gene reservoirs remains poorly understood. In this study, we systematically compared the profiles and ecological dynamics of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), metal resistance genes (MRGs), and virulence factor genes (VFGs) in paddy soils from paired smallholder and large-scale farms in Jiangsu, China.

Electrochemical-oxidative dualism: Decoupling the acute effects of lake water-aged tire wear particles on periphytic biofilm-mediated denitrification.

As emerging microplastic pollutants, tire wear particles (TWPs) have unclear photochemical impacts on aquatic nitrogen cycles. This study investigated how three types of TWPs-mechanically generated via rolling (R-TWPs), sliding (S-TWPs), and low-temperature crushing (C-TWPs)-and their aged counterparts (AC-, AR-, AS-TWPs) influenced nitrate reduction in periphytic biofilms. Aging in lake water altered the surface properties of TWPs: AC- and AR-TWPs accumulated inorganic ions and organic coatings, while AS-TWPs facilitated microbial colonization.

Record-breaking 2023 marine heatwaves.

The year 2023 witnessed an extraordinary surge in marine heatwaves (MHWs) across Earth's oceans, setting new records in duration, extent, and intensity, with MHW activity totaling 53.6 billion °C days square kilometer-more than three standard deviations above the historical norm since 1982. Notable events include the North Atlantic MHW (276-year return period) and the Southwest Pacific (141 years).

Intelligent biosensors for human movement rehabilitation and intention recognition.

Introduction: Advancements in sensing technologies have enabled the integration of inertial sensors, such as accelerometers and gyroscopes, into everyday devices like smartphones and wearables. These sensors, initially intended to enhance device functionality, are now pivotal in applications such as Human Locomotion Recognition (HLR), with relevance in sports, healthcare, rehabilitation, and context-aware systems. This study presents a robust system for accurately recognizing human movement and localization characteristics using sensor data.

Ongoing uncoordinated anthropogenic emission abatement promotes atmospheric new particle growth in a Chinese megacity.

Atmospheric new particle growth in diameter is the crucial process determining air quality effects raised by secondary aerosols. However, uncertain mechanisms and long-term trends of new particle growth limit the assessments of urban air quality evolution. Here we report an increasing trend of new particle growth rate in responds to anthropogenic emission abatement in urban Beijing during autumn from 2017 to 2021.

Effects of inorganic and organic fertilization on abundance and community structure of Candidatus Methanoperedens-related archaea in paddy soils.

Aim: Candidatus Methanoperedens-related archaea have recently been identified as anaerobic methane oxidizers in paddy soils. Fertilization practices, including the application of inorganic and organic fertilizers (e.g.

A data-to-forecast machine learning system for global weather.

Weather forecasting traditionally relies on numerical weather prediction (NWP) systems that integrate global observations, data assimilation (DA), and physics-based models. However, further advances are increasingly constrained by high computational costs, the underutilization of vast observational datasets, and challenges in obtaining finer resolution. Recent advances in machine learning present a promising alternative, but still depend on the initial conditions generated by NWP systems.

Pixel adaptive deep-unfolding neural network with state space model for image deraining.

Rain streaks affects the visual quality and interfere with high-level vision tasks on rainy days. Removing raindrops from captured rainy images becomes important in computer vision applications. Recently, deep-unfolding neural networks (DUNs) are shown their effectiveness on image deraining.

Classifying retinal diseases via pyramid vision graph convolutional network for optical coherence tomography images.

Recent advancements have seen a significant focus on using deep neural networks for classifying retinal diseases in optical coherence tomography (OCT) images. However, traditional deep neural networks treat images as grid or sequential structures, limiting their flexibility in capturing irregular and complex objects, resulting in suboptimal performance in practical applications. To address this issue, we propose a novel visual neural network model with a pyramid structure, called pyramid vision graph convolutional networks (PVGCN).

Association between depression and health care expenditure in middle-aged and older patients with heart disease.

Objective: Patients with heart disease frequently experience a heightened incidence of depression, thereby affecting their overall medical expenditures. Nevertheless, earlier investigations have focused on the healthcare costs incurred by individual patients with heart disease or those with depression. The present study assessed the correlation between depression and the medical expenditures of patients with heart disease, highlighting the importance of mental health care for patients with with heart disease.

High-Entropy Solid-State Electrolytes for Rechargeable Batteries: Mechanism, Structural Designs, Characterizations, and Applications.

Rechargeable batteries achieve remarkable advancements over the past few decades. Conventional electrolytes suffer from a range of challenges, such as low conductivity and poor thermal stability. Recent studies show that various properties of high-entropy solid-state electrolytes effectively solve these challenges.

Enclosed-terrain urbanized areas require vertically stratified air quality management: Insights from particulate matter vertical observations in China's Sichuan Basin.

Urbanized basins are widely recognized as hotspots‌ of particulate matter (PM) pollution. Characterizing the vertical stratification of PM in these regions is essential to elucidate the influence of regional air pollutant transport, dynamics of planetary boundary layer, and pollution-meteorology feedbacks. Using Mount Emei (500-3100 m a.

A high-sensitivity ECL biosensor for single-cell analysis: Integrating CRISPR/Cas12a and entropy-driven amplification.

This study presents a homogeneous electrochemiluminescence (ECL) biosensor for ultrasensitive detection of HEK293 cells by targeting the hERG potassium channel at the single-cell level. The biosensor integrates multiple signal amplification steps, including photocleavable DNA-antibody conjugates, entropy-driven strand displacement, T7 RNA polymerase-mediated transcription, and CRISPR/Cas12a-mediated trans-cleavage. This cascade enables precise and robust signal enhancement.

Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture.

Background: Parkinson's disease (PD), a progressive neurodegenerative disorder prevalent in aging populations, manifests clinically through characteristic motor impairments including bradykinesia, rigidity, and resting tremor. Early detection and timely intervention may delay disease progression. Spiral drawing tasks have been established as effective auxiliary diagnostic tools.