Search Results (1,215)

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

Background: Heart failure with preserved ejection fraction (HFpEF) is increasingly prevalent worldwide due to ageing and comorbidities. Emerging evidence suggests that Philadelphia-negative chronic myeloproliferative neoplasms (MPNs), particularly those with JAK2 mutations, may contribute to the development of HFpEF, especially by promoting inflammation and increasing thrombotic risk. Methods: This prospective case–control study assessed 58 patients with Philadelphia-negative MPNs and 41 controls, by clinical, paraclinical, and echocardiographic evaluation, to diagnose diastolic dysfunction and HFpEF according to the ESC guideline criteria. Results: Patients with MPNs had a significantly higher prevalence of HFpEF compared to controls (p = 0.008), higher H₂FPEF scores (median 5 vs. 3, p < 0.001), and significant echocardiographic abnormalities, including a higher left ventricular mass index (LVMI) (100.1 vs. 76.6 g/m², p < 0.001), E/e’ (11.00 vs. 7.00, p < 0.001), and pulmonary artery systolic pressure (PASP) (26.0 vs. 7.42 mmHg, p < 0.001). Multivariable logistic regression models identified male sex (OR = 8.993, p = 0.001) and the presence of JAK2 mutation (OR = 5.021, p = 0.002) as independent risk factors for HFpEF in this population. Conclusions: Patients with chronic MPNs, particularly males and those with JAK2 mutations, are at an increased risk of HFpEF, highlighting the importance of routine cardiologic assessment to improve outcomes in this patient population. Full article

This study examines the performance of a GaN HEMT with a 150 nm gate length, fabricated on silicon carbide, across various operational modes, including direct current (DC), radio frequency (RF), and small-signal parameters. The evaluation of DC, RF, and small-signal performance under diverse bias conditions remains a relatively unexplored area of study for this specific technology. The DC characteristics revealed relatively little I_ds at zero gate and drain voltages, and the current grew as V_gs increased. Essential measurements include I_dss at 109 mA and I_dssm at 26 mA, while the peak g_m was 62 mS. Because transconductance is sensitive to variations in V_gs and V_ds, it shows “Vth _roll-off,” where Vth decreases as V_ds increases. The transfer characteristics corroborated this trend, illustrating the impact of drain-induced barrier lowering (DIBL) on threshold voltage (Vth) values, which spanned from −5.06 V to −5.71 V across varying drain-source voltages (V_ds). The equivalent-circuit technique revealed substantial non-linear behaviors in capacitances such as C_gs and C_gd concerning V_gs and V_ds, while also identifying extrinsic factors including parasitic capacitances and resistances. Series resistances (R_gs and R_gd) decreased as V_gs increased, thereby enhancing device conductivity. As V_gs approached neutrality, particularly at elevated V_ds levels, the intrinsic transconductance (g_mo) and time constants (τ_gm, τ_gs, and τ_gd) exhibited enhanced performance. f_t and f_max, which are essential for high-frequency applications, rose with decreasing V_gs and increasing V_ds. When V_gs approached −3 V, the S₂₁ and Y₂₁ readings demonstrated improved signal transmission, with peak S₂₁ values of approximately 11.2 dB. The stability factor (K), which increased with V_ds, highlighted the device’s operational limits. The robust correlation between simulation and experimental data validated the equivalent-circuit model, which is essential for enhancing design and creating RF circuits. Further examination of bias conditions would enhance understanding of the device’s performance. Full article

One of the important tasks in sand liquefaction assessment is to evaluate the likelihood of soil liquefaction. However, most liquefaction assessment methods are deterministic for influencing factors and fail to calculate the liquefaction probability by systematically considering the probability distributions of soil factors. Based on field liquefaction investigation cases, probability distribution fitting and a hypothesis test were carried out. For the variables that failed to pass the fitting and test, the kernel density estimation was conducted. Methods for calculating the liquefaction probability using a Monte Carlo simulation with the probability distribution were then proposed. The results indicated that for (N₁)₆₀, SM, S, and GM followed a Gaussian distribution, while CL and ML followed a lognormal distribution; for FC, SM and GM followed a lognormal distribution; and for d₅₀, ML and S followed a Gaussian and lognormal distribution, respectively. The other factors’ distribution curves can be calculated by kernel density estimation. It is feasible to calculate the liquefaction probability based on a Monte Carlo simulation of the variable distribution. The result of the liquefaction probability calculation in this case was similar to that of the existing probability model and was consistent with actual observations. Regional sample differences were considered by introducing the normal distribution error term, and the liquefaction probability accuracy could be improved to a certain extent. The liquefaction probability at a specific seismic level or the total probability within a certain period in the future can be calculated with the method proposed in this paper. It provides a data-driven basis for realistically estimating the likelihood of soil liquefaction under seismic loading and contributes to site classification, liquefaction potential zoning, and ground improvements in seismic design decisions. The practical value of seismic hazard mapping and performance-based design in earthquake-prone regions was also demonstrated. Full article

Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation of geological features. However, in areas with dense vegetation coverage, the information directly extracted from single-source optical imagery is limited, thereby constraining interpretation accuracy. Supplementary inputs such as synthetic aperture radar (SAR), topographic features, and texture information—collectively referred to as sensitive features and prior knowledge—can improve interpretation, but their effectiveness varies significantly across time and space. This variability often leads to inconsistent performance in general-purpose models, thus limiting their practical applicability. To address these challenges, we construct a geological element interpretation dataset for Northwest China by incorporating multi-source data, including Sentinel-1 SAR imagery, Sentinel-2 multispectral imagery, sensitive features (such as the digital elevation model (DEM), texture features based on the gray-level co-occurrence matrix (GLCM), geological maps (GMs), and the normalized difference vegetation index (NDVI)), as well as prior knowledge (such as base geological maps). Using five mainstream deep learning models, we systematically evaluate the performance improvement brought by various sensitive features and prior knowledge in remote sensing-based geological interpretation. To handle disparities in spatial resolution, temporal acquisition, and noise characteristics across sensors, we further develop a multi-source complement-driven network (MCDNet) that integrates an improved feature rectification module (IFRM) and an attention-enhanced fusion module (AFM) to achieve effective cross-modal alignment and noise suppression. Experimental results demonstrate that the integration of multi-source sensitive features and prior knowledge leads to a 2.32–6.69% improvement in mIoU for geological elements interpretation, with base geological maps and topographic features contributing most significantly to accuracy gains. Full article

China’s carbon peak and neutrality transition is critically constrained by the severe talent shortage and structural inefficiencies in higher education. This study systematically investigates the current status of “dual-carbon” talent cultivation and demand in China, leveraging annual “dual-carbon” talent cultivation data from universities nationwide. By applying the GM(1,1)-ARIMA hybrid forecasting model, it projects future national “dual-carbon” talent demand. Key findings reveal significant regional disparities in talent cultivation, with a pronounced mismatch between industrial demands and academic supply, particularly in interdisciplinary roles pivotal to decarbonization processes. Forecast results indicate an exponential growth in postgraduate talent demand, outpacing undergraduate demand, thereby underscoring the urgency of advancing high-end technological research and development. Through empirical analysis and innovative modeling, this study uncovers the structural contradictions between “dual-carbon” talent cultivation and market demands in China, providing critical decision-making insights to address the bottleneck of carbon-neutral talent development. Full article

Growing ozone (O₃) pollution in industrial cities urgently requires in-depth mechanistic research. This study utilized multi-year observational data from Datong City, China, from 2020 to 2024, integrating time trend diagnostics, correlation dynamics analysis, Environmental Protection Agency Positive Matrix Factorization 5.0 (EPA PMF 5.0) model simulations, and a grey prediction model (GM (1,1)) projection method to reveal the coupling mechanisms among O₃ precursors. Key breakthroughs include the following: (1) A ratio of volatile organic compounds (VOCs) to nitrogen oxides (NO_x) of 1.5 clearly distinguishes between NO_x-constrained (winter) and VOC-sensitive (summer) modes, a conclusion validated by the strong negative correlation between O₃ and NO_x (r = −0.80, p < 0.01) and the dominant role of NO titration. (2) Aromatic compounds (toluene, xylene) used as solvents in industrial emissions, despite accounting for only 7.9% of VOC mass, drove 37.1% of ozone formation potential (OFP), while petrochemical and paint production (accounting for 12.2% of VOC mass) contributed only 0.3% of OFP. (3) Quantitative analysis of OFP using PMF identified natural gas/fuel gas use and leakage (accounting for 34.9% of OFP) and solvent use (accounting for 37.1% of OFP) as key control targets. (4) The GM (1,1) model predicts that, despite a decrease in VOC concentrations (−15.7%) and an increase in NO_x concentrations (+2.4%), O₃ concentrations will rise to 169.7 μg m⁻³ by 2025 (an increase of 7.4% compared to 2024), indicating an improvement in photochemical efficiency. We have established an activity-oriented prioritization framework targeting high-OFP species from key sources. This provides a scientific basis for precise O₃ emission reductions consistent with China’s 15th Five-Year Plan for synergistic pollution/carbon governance. Full article

Most crops accumulate above-ground biomass (AGB) through photosynthesis, inspiring the development of the Photosynthetic Accumulation Model (PAM) and Simplified PAM (SPAM). Both models estimate AGB based on time-series optical vegetation indices (VIs) and canopy height. To further enhance the model performance and evaluate its applicability across different crop types, an improved PAM model (IPAM) is proposed with three strategies. They are as follows: (i) using numerical integration to reduce reliance on dense observations, ($ii$) introduction of Fibonacci sequence-based structural correction to improve model accuracy, and ($iii$) non-photosynthetic area masking to reduce overestimation. Results from both soybean and cotton demonstrate the strong performance of the PAM-series models. Among them, the proposed IPAM model achieved higher accuracy, with mean $R^2$ and RMSE values of 0.89 and 207 g/m² for soybean and 0.84 and 251 g/m² for cotton, respectively. Among the vegetation indices tested, the recently proposed Near-Infrared Reflectance of vegetation (NIRv) and Kernel-based normalized difference vegetation index (Kndvi) yielded the most accurate results. Both Monte Carlo simulations and theoretical error propagation analyses indicate a maximum deviation percentage of approximately 20% for both crops, which is considered acceptable given the expected inter-annual variation in model transferability. In addition, this paper discusses alternatives to height measurements and evaluates the feasibility of incorporating synthetic aperture radar (SAR) VIs, providing practical insights into the model’s adaptability across diverse data conditions. Full article

Neuroinflammation driven by microglial activation and α-synuclein (αSyn) aggregation is one of the central features driving Parkinson’s disease (PD) pathogenesis. GM1 ganglioside’s oligosaccharide moiety (OligoGM1) has shown neuroprotective potential in PD neuronal models, but its direct effects on inflammation remain poorly defined. This study investigated the ability of OligoGM1 to modulate microglial activation and αSyn handling in a human in vitro model. Human embryonic microglial (HMC3) cells were exposed to αSyn pre-formed fibrils (PFFs) in the presence or absence of OligoGM1. Microglial activation markers, intracellular αSyn accumulation, and cytokine release were assessed by immunofluorescence and ELISA. OligoGM1 had no effect on microglial morphology or cytokine release under basal conditions. Upon αSyn challenge, cells exhibited increased amounts of ionized calcium-binding adaptor molecule 1 (Iba1), triggered receptor expressed on myeloid cells 2 (TREM2), elevated αSyn accumulation, and secreted pro-inflammatory cytokines. OligoGM1 pre-treatment significantly reduced the number and area of Iba1(+) cells, the intracellular αSyn burden in TREM2(+) microglia, and the release of interleukin 6 (IL-6). OligoGM1 selectively attenuated αSyn-induced microglial activation and enhanced αSyn clearance without compromising basal immune function. These findings confirm and support the potential of OligoGM1 as a multitarget therapeutic candidate for PD that is capable of modulating glial reactivity and neuroinflammatory responses. Full article

The size of the gluteus medius muscle (GM) in swine significantly impacts both hindlimb conformation and carcass yield, while little is known about the genetic architecture of this trait. This study aims to estimate genetic parameters and identify candidate genes associated with this trait through a genome-wide association study (GWAS). A total of 439 commercial crossbred pigs, possessing both Landrace and Yorkshire ancestry, were genotyped using the Porcine 50K chip. The length and width of the GM were directly measured, and the area was then calculated from these values. The heritabilities were estimated by HIBLUP (V1.5.0) software, and the GWAS was conducted employing the BLINK model implemented in GAPIT3. The heritability estimates for the length, width, and area of the GM were 0.43, 0.40, and 0.46, respectively. The GWAS identified four genome-wide significant SNPs (rs81381267, rs697734475, rs81298447, and rs81458910) associated with the gluteus medius muscle area. The PDE4D gene was identified as a promising candidate gene potentially involved in the regulation of gluteus medius muscle development. Our analysis revealed moderate heritability estimates for gluteus medius muscle size traits. These findings enhance our understanding of the genetic architecture underlying porcine muscle development. Full article

Objectives: The aim of this study was to evaluate the impact of four working heights on lumbar biomechanics during wall construction tasks, focusing on work-related musculoskeletal disorders (WMSDs). Methods: Fifteen young male participants performed simulated mortar-spreading and bricklaying tasks while actual body movements were recorded using Inertial Measurement Unit (IMU) sensors. Muscle activities of the lumbar erector spinae (ES), quadratus lumborum (QL), multifidus (MF), gluteus maximus (GM), and iliopsoas (IL) were estimated using a 3D musculoskeletal (MSK) model and measured via surface electromyography (sEMG). The analysis of variance (ANOVA) test was conducted to identify the significant differences in muscle activities across four working heights (i.e., foot, knee, waist, and shoulder). Results: Findings showed that working at foot-level height resulted in the highest muscle activity (7.6% to 40.6% increase), particularly in the ES and QL muscles, indicating an increased risk of WMSDs. The activities of the ES, MF, and GM muscles were statistically significant across both tasks and all working heights (p < 0.01). Conclusions: Both MSK and sEMG analyses indicated significantly lower muscle activities at knee and waist heights, suggesting these as the best working positions (47 cm to 107 cm) for minimizing the risk of WMSDs. Conversely, working at foot and shoulder heights was identified as a significant risk factor for WMSDs. Additionally, the similar trends observed between MSK simulations and sEMG data suggest that MSK modeling can effectively substitute for sEMG in future studies. These findings provide valuable insights into ergonomic work positioning to reduce WMSD risks among wall construction workers. Full article

Food security is crucial for social stability and economic development. Ensuring food security in the arid region of Northwest China presents unique challenges due to limited water and soil resources. This study addresses these challenges by integrating a comprehensive water and soil resource matching assessment with grain production forecasting. Based on data from 2000 to 2020, this research projects the food security status to 2035 using the GM(1,1) model, incorporating a comprehensive index of soil and water resource matching and regression analysis to inform production forecasts. Key assumptions include continued historical trends in population growth, urbanization, and dietary shifts towards an increased animal protein consumption. The findings revealed a consistent upward trend in grain production from 2000 to 2020, with an average annual growth rate of 3.5%. Corn and wheat emerged as the dominant grain crops. Certain provinces demonstrated comparative advantages for specific crops like rice and wheat. The most significant finding is that despite the projected growth in the total grain output by 2035 compared to 2020, the regional grain self-sufficiency rate is projected to range from 79.6% to 84.1%, falling below critical food security benchmarks set by the FAO and China. This projected shortfall carries significant implications, underscoring a serious challenge to regional food security and highlighting the region’s increasing vulnerability to external food supply fluctuations. The findings strongly signal that current trends are insufficient and necessitate urgent and proactive policy interventions. To address this, practical policy recommendations include promoting water-saving technologies, enhancing regional cooperation, and strategically utilizing the international grain trade to ensure regional food security. Full article

Urban agriculture (UA) is emerging as a promising strategy for sustainable food production in response to growing environmental pressures. Indoor vertical farming (IVF), combining Controlled Environment Agriculture (CEA) with Building-Integrated Agriculture (BIA), enables efficient resource use and year-round crop cultivation in urban settings. This study assesses the environmental performance of a prospective IVF system located on a university campus in Portugal, focusing on the integration of photovoltaic (PV) energy as an alternative to the conventional electricity grid (GM). A Life Cycle Assessment (LCA) was conducted using the Environmental Footprint (EF) method and the LANCA model to account for land use and soil-related impacts. The PV-powered system demonstrated lower overall environmental impacts, with notable reductions across most impact categories, but important trade-offs with decreased soil quality. The LANCA results highlighted cultivation and packaging as key contributors to land occupation and transformation, while also revealing trade-offs associated with upstream material demands. By combining EF and LANCA, the study shows that IVF systems that are not soil-based can still impact soil quality indirectly. These findings contribute to a broader understanding of sustainability in urban farming and underscore the importance of multi-dimensional assessment approaches when evaluating emerging agricultural technologies. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

Accurate oil debris analysis and wear monitoring of a gearbox are essential to ensure its stable and reliable operation. Element types of wear debris and their changes in the lubrication oil of the gearbox can be monitored by spectral analysis. However, it is still difficult to identify wear parts of the gearbox due to the complex composition of elements of wear debris. An improved multi-variable grey prediction model by incorporating a multi-objective genetic algorithm (MOGA-GM(1, N)) is proposed to evaluate weight coefficients of element concentrations of wear debris in the lubrication oil of the gearbox. Moreover, a wear growth rate of each element in the lubrication oil is proposed as an index for oil debris analysis to analyze the multi-variable correlation between the common element of iron (Fe) and other related elements of wear parts of the gearbox. Oil debris analysis of the gearbox is conducted on optimal weight coefficients of related elements to the common element Fe using the MOGA-GM(1, N) model. Wear experiment results verify feasibility of the proposed oil debris analysis method. Full article