Search Results (52)

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

Long-term participation in marathon running involves complex psychological processes, yet existing research predominantly focuses on static, single-time-point analyses. This study addresses the gap by longitudinally examining the psychological evolution of an elite Chinese marathon runner (119 marathons completed) to uncover dynamic shifts from novice to master(s) level athlete stages. A longitudinal single-case study was conducted using inductive thematic analysis. Data included in-depth interviews, observational records, and archival materials spanning three life stages (youth, middle age, maturity). Five experts validated the credibility and validity of the findings. The results show that the runner’s psychological trajectory followed a three-phase model: competitive drive (youth: external achievement motivation), reflective transformation (middle age: health prioritization and identity reconfiguration), and value reconstruction (maturity: legacy mission and lifelong running). These stages were shaped by the interplay of achievement motivation, social roles, and physiological changes. Notably, the transition mirrored China’s marathon culture shift from elitism to mass participation. This study proposes a novel “motivation-physicality-society” interaction model, challenging static theories of sports psychology. It highlights how long-term runners dynamically balance extrinsic and intrinsic motivations while embedding personal growth within socio-cultural transformations. The findings offer theoretical foundations for optimizing psychological support systems and promoting sustainable marathon engagement. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

This study introduces a novel Gaussian process (GP) regression framework that probabilistically enforces physical constraints, with a particular focus on equality conditions. The GP model is trained using the quantum-inspired Hamiltonian Monte Carlo (QHMC) algorithm, which efficiently samples from a wide range of distributions by allowing a particle’s mass matrix to vary according to a probability distribution. By integrating QHMC into the GP regression with probabilistic handling of the constraints, this approach balances the computational cost and accuracy in the resulting GP model, as the probabilistic nature of the method contributes to shorter execution times compared with existing GP-based approaches. Additionally, we introduce an adaptive learning algorithm to optimize the selection of constraint locations to further enhance the flexibility of the method. We demonstrate the effectiveness and robustness of our algorithm on synthetic examples, including 2-dimensional and 10-dimensional GP models under noisy conditions, as well as a practical application involving the reconstruction of a sparsely observed steady-state heat transport problem. The proposed approach reduces the posterior variance in the resulting model, achieving stable and accurate sampling results across all test cases while maintaining computational efficiency. Full article

Amasi, a traditional fermented milk produced in Southern Africa, is associated with several health benefits, such as probiotic activities, immune system modulation, and pharmacological (antimicrobial, antitumor and antioxidant) potential. This study investigated the microbial diversity in Amasi (produced from cow’s and goat’s milk) through targeted metagenomic bacterial 16S rRNA and fungal ITS sequencing, the metabolic functional prediction of Amasi samples using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and profiled amino acids constituents using Liquid Chromatographic-Mass Spectrophotometry (LC-MS). The results obtained revealed Firmicutes, Bacteroidetes, and Proteobacteria as the most prevalent bacterial phyla, with Lactococcus and Lactobacillus being the most abundant genera. On the other hand, Ascomycota, Basidiomycota, and Mucoromycota were the main fungal phyla, while Aspergillus, Kazachstania, and Debaryomyces spp. dominated the fungal genera. Also, Pseudomonas spp., Bacillus spp., Clostridium spp., Cronobacter spp., Alternaria spp., Diaporthe spp., and Penicillium spp. were the probable pathogenic bacteria and fungi genera found, respectively. Atopobium, Synechococcus, and Parabacteroides were found less often as rare genera. It was found that the amino acid and drug metabolism pathway prediction values in Amasi samples were significantly higher (p < 0.05) than in raw cow and goat milk, according to the inferred analysis (PICRUSt). The amino acid validation revealed glutamine and asparagine values as the most significant (p < 0.05) for Amasi cow milk (ACM) and Amasi goat milk (AGM), respectively. Comparatively, ACM showed more microbial diversity than AGM, though there were relative similarities in their microbiome composition. PICRUSt analysis revealed significant metabolites in the two Amasi samples. Overall, data from this study showed heterogeneity in microbial diversity, abundance distributions, metabolites, and amino acid balance between raw cow/goat milk and Amasi samples. Full article

Accurate estimation of landslide depth is essential for practical hazard assessment and risk mitigation. This work addresses the problem of determining landslide depth from satellite-derived elevation data. Using the principle of mass conservation, this problem can be formulated as a linear inverse problem. To solve the inverse problem, we present a regularization approach that computes approximate solutions and regularization parameters using the Balancing Principle. Synthetic data were carefully designed and generated to evaluate the method under controlled conditions, allowing for precise validation of its performance. Through comprehensive testing with this synthetic dataset, we demonstrate the method’s robustness across varying noise levels. When applied to real-world data from the Fels landslide in Alaska, the proposed method proved its practical value in reconstructing landslide thickness patterns. These reconstructions showed good agreement with existing geological interpretations, validating the method’s effectiveness in real-world scenarios. Full article

Chloramphenicol (CAM), a well-known broad-spectrum antibiotic, inhibits peptide bond formation in bacterial ribosomes. It has been reported to affect ribosome assembly mainly through disrupting the balance of ribosomal proteins. The present study investigates the multifaceted effects of CAM on the maturation of the 50S ribosomal subunit in Escherichia coli (E. coli). Using label-free quantitative mass spectrometry (LFQ-MS), we observed that CAM treatment also leads to the upregulation of assembly factors. Further cryo-electron microscopy (cryo-EM) analysis of the ribosomal precursors characterized the CAM-treatment-accumulated pre-50S intermediates. Heterogeneous reconstruction identified 26 distinct pre-50S intermediates, which were categorized into nine main states based on their structural features. Our structural analysis highlighted that CAM severely impedes the formation of the central protuberance (CP), H89, and H58 during 50S ribosomal subunit maturation. The ELISA assay further demonstrated the direct binding of CAM to the ribosomal precursors, suggesting that the interference with 50S maturation occurs through a combination of direct and indirect mechanisms. These findings provide new insights into the mechanism of the action of CAM and provide a foundation for a better understanding of the assembly landscapes of the ribosome. Full article

Assessing the regional mass balance of European glaciers presents significant challenges due to limited measurements. While various albedo methods have been explored for individual glaciers, a comprehensive analysis of aggregated albedo methods is lacking. Addressing this gap, in our study, we examined five MODIS aggregated albedos (raw average, minimum average, average minimum, interpolated average, and cumulative) versus the annual mass balance for 12 Austrian and Norwegian glaciers from 2001 to 2020 to establish connections between them. We find that the raw average albedo is strongly correlated with the annual mass balance of Austrian glaciers (r = 0.91), while the interpolated average albedo is significantly correlated with the annual mass balance of Norwegian glaciers (r = 0.90). Moreover, we observe that high-elevation glaciers experience fewer cloud cover days, allowing the raw average albedo to reliably estimate the annual mass balance, whereas low-elevation glaciers are often obscured by clouds, potentially masking the true minimum albedo. Additionally, traditional indicators, such as the equilibrium-line altitude and accumulation area ratio, exhibit significant correlations with the annual mass balance of Norwegian and Austrian glaciers (r = 0.90 and 0.87, respectively), yet albedo demonstrates higher robustness. These findings provide a reference for selecting appropriate aggregation methods to reconstruct glacier mass balance from albedo observations. Full article

The global prevalence of obesity continues to rise, contributing to an increased frequency of abdominal wall reconstruction procedures, particularly ventral hernia repairs, in individuals with elevated body mass indexes. Undertaking these operations in obese patients poses inherent challenges. This review focuses on the current literature in this area, with special attention to the impact of concomitant panniculectomy. Obese individuals undergoing abdominal wall reconstruction face elevated rates of wound healing complications and hernia recurrence. The inclusion of concurrent panniculectomy heightens the risk of surgical site occurrences but does not significantly influence hernia recurrence rates. While this combined approach can be executed in obese patients, caution is warranted, due to the higher risk of complications. Physicians should carefully balance and communicate the potential risks, especially regarding the increased likelihood of wound healing complications. Acknowledging these factors is crucial in shared decision making and ensuring optimal patient outcomes in the context of abdominal wall reconstruction and related procedures in the obese population. Full article

Maritime glaciers in the southeastern Tibetan Plateau (TP) have experienced important changes in mass and dynamics over the past decades, challenging the regional water supply and glacier-related hazards. However, knowledge about long-term variations in the surface velocity and mass balance of maritime glaciers remains incomplete due to the lack of representative observations in the southeastern TP. In this study, offset tracking is employed to measure spatiotemporal variation in the surface velocity of the Hailuogou Glacier (HLG) in Mount Gongga of the southeastern TP using Sentinel-1A imagery, while the time series of the HLG mass balance is reconstructed since 1950 by a physically based energy–mass balance model. Our satellite-based results find that HLG surface velocity shows significant spatial heterogeneity with a double-peak pattern along the flow line, and sustained slowdown below the icefall zone has been observed during the past nearly 40 years, although the icefall zone and the area above it have become relatively active. Our modeling indicates a persistent increase in mass loss over the last seven decades with an average rate of −0.58 m water equivalent (w.e.) year⁻¹, which has accelerated in the past two decades. Sustained slowdown on the glacier is concomitant with pronounced negative mass balance, thereby enhancing glacier wastage in recent decades. The long-term trend in HLG mass loss is mainly driven by an increase in positive air temperature that decreases surface albedo and solid precipitation ratio and increases longwave incoming radiation, besides the influence of supraglacial debris cover. Large-scale atmospheric circulation patterns in the Eurasian region provide important implications for regional-to-local climate variability, unsustainably intensifying the trend of the negative mass balance of the HLG in the southeastern TP in the past two decades. Full article

Glaciers play a vital role in the Asian mountain water towers and have significant downstream impacts on domestic, agricultural, and industrial water usage. The rate of glacier mass loss in the Southeastern Tibetan Plateau (SETP) is among the highest in Asia and has intensified in recent decades. However, a comprehensive quantification that considers both spatial and temporal aspects of glacier mass loss across the entire SETP is still insufficient. This study aimed to address this gap by utilizing geodetic datasets specific to each glacier by calibrating the Open Global Glacier Model (OGGM) driven by HAR v2 and reconstructing the glacier mass balance of 7756 glaciers in the SETP from 1980 to 2019 while examining their spatial variability. The findings reveal that the average mass balance during this period was −0.50 ± 0.28 m w.e. a⁻¹, with an accelerated loss observed in the 2000s (average: 0.62 ± 0.24 m w.e. a⁻¹). Notably, central glaciers in the SETP exhibited relatively smaller mass loss, indicating a gradient effect of increased loss from the central region toward the eastern and western sides. By the end of this century, the area, length, and volume of glaciers in the entire SETP region are projected to decrease by 83.57 ± 4.91%, 90.25 ± 4.23%, and 88.04 ± 4.52%, respectively. Moreover, the SETP glacier melt runoff is estimated to decrease by 62.63 ± 6.16% toward the end of the century, with the “peak water” point of glacier melt runoff predicted to occur in 2023 under the SSP585 scenario. Sensitivity experiments demonstrated that the SETP glaciers are more than three times more sensitive to temperature changes than to precipitation variations, and the observed decrease in monsoon precipitation indicates the weakening magnitude of the Indian summer monsoon in recent years. The spatially refined and high-temporal-resolution characteristics of glacier mass loss presented in this study contribute to a better understanding of specific glacier changes in the SETP. Additionally, the prediction results provide valuable references for future water resources management and policy formulation in the SETP region. Full article

At present, blast furnace (BF) ironmaking is still the main process for producing hot metal in China and around the world. Under the constraint of the global goal of “double carbon”, it is urgent to carry out hydrogen metallurgical innovation for the existing BF ironmaking process with higher carbon emissions. In recent years, BF technology with hydrogen enrichment and pure oxygen has made some progress, effectively reducing carbon emissions of hot metal per tons, but it is still unable to break through the technical bottleneck of emission reduction of more than 30%. In view of this, the authors put forward an ironmaking technology of a reduction smelting furnace (RSF) that is hydrogen-rich and utilizes pure oxygen and carbon recycle (Hy-O-CR), which breaks through the technical defect of traditional BF emission reduction of less than 30% by reshaping the furnace. Firstly, the construction process of the mass and energy balance model for two main unit modules in the new process (RSF with Hy-O-CR and top gas cycle) is introduced, and then the parameter optimization under specific scenario conditions is analyzed, and the influence mechanism of several key variables on the parameters in the furnace is obtained. Finally, the emission of CO₂ in the whole process is explored in the case of two typical operating parameters. The results show that after using CCUS technology, the minimum value of direct CO₂ emission is 215.93 kg/tHM, which is as high as 84.58% compared with the traditional BF process. Even if the removed CO₂ is counted in carbon emissions, the minimum value of direct or indirect carbon emissions is 729.85 kg/tHM, and the proportion of emission reduction can reach 47.87%. The research results show that the reconstruction of Hy-O-CR technology can change the ratio of direct reduction and indirect reduction, which greatly breaks through the emission limit of the traditional BF and provides a new reference for hydrogen metallurgy technology and a basis for further study of the optimization of RSF size. Full article

The study of the Peña Negra paleoglacier during the Last Glacial Maximum reveals its sensitivity to paleoclimatic variations. The evolutionary phases of the paleoglacier are correlated with the evolutionary models proposed for the Sierra de Béjar-Candelario and the Central Iberian System. To recognize the mechanisms of ice advance/retreat and the response of the glacier to paleoclimatic variations, modeling is carried out based on a geographic information system tool. This model is key to establishing the spatial extent of the ice and the estimation of the Equilibrium line altitudeequilibrium line altitudes at each moment, which makes it easier to infer the approximate climatic conditions of each phase (temperature and precipitation) and allows us to improve the understanding of the glacial dynamics versus variations in paleoenvironmental conditions and paleoglacial morphometry. The spatial reconstruction data show that the paleoglacier had 0.526 km³ of ice during the phase of maximum extension, while the paleoclimatic data reflect an increase in precipitation and a slight decrease in average summer temperatures compared to today. The stability phases are associated with the periods of greatest precipitation when the mass balance was positive. Full article

Due to the scarcity of observational data and the intricate precipitation–runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate. Full article