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Experimental demonstration of logical magic state distillation
β-sitosterol alleviated HFD-induced atherosclerosis by regulating the MAPK/Nrf2/NLRP3 pathway in ApoE−/− mice
Background Atherosclerosis (AS), driven by chronic inflammation and oxidative stress, remains a leading cause of cardiovascular morbidity. While β-sitosterol, a dietary phytosterol, shows therapeutic potential for AS, its mechanisms remain unclear. This study aimed to explore whether β-sitosterol alleviates AS by modulating the MAPK/Nrf2/NLRP3 pathway. Methods ApoE−/− mice fed a high-fat diet (HFD) were treated with β-sitosterol for 8 weeks. Lipid profiles, aortic plaque area, oxidative stress markers, and inflammatory mediators were analyzed. Nrf2 pathway activity and NLRP3 inflammasome components were assessed using ELISA, qRT-PCR, and histochemical assays. Results β-sitosterol significantly reduced serum total cholesterol, LDL-C, and aortic plaque area in HFD-fed mice. It suppressed the MAPK pathway and NLRP3 inflammasome activation while downregulating MMP-2/9 expression. Additionally, β-sitosterol activated the Nrf2 pathway, increasing catalase protein (CAT) activity and reducing oxidative stress in liver tissue. However, it showed limited effects on NF-κB, IL-6, IL-10, and certain antioxidants. Conclusion β-sitosterol ameliorates AS by attenuating lipid accumulation, inflammation, and oxidative stress via coordinated regulation of the MAPK/Nrf2/NLRP3 pathways. These findings highlight its potential as a therapeutic agent, though clinical studies are warranted to confirm efficacy and safety in humans.
Structural and functional characterization of human sweet taste receptor
Optimizing Ghanaian Postgraduate students’ job performance: The impact of polychronicity, work-school facilitation, and organisational support
This study examined the influence of polychronicity, work-school facilitation, and organizational support on the job performance of postgraduate students in Ghana. Guided by the Conservation of Resources (COR) theory, the research explored how personal and organizational resources affected job performance outcomes. A quantitative, explanatory research design was used. A sample of 341 distance education postgraduate students from three Ghanaian universities was selected using convenience sampling. Data were collected via a three-time lagged survey method to minimize common method bias. Structural Equation Modeling (SEM) using SMART PLS software (version 3.98) was employed for data analysis. The findings revealed that polychronicity had a significant positive effect on job performance. Organizational support moderated this relationship, strengthening the effect of polychronicity on job performance. Additionally, work-school facilitation mediated the relationship between polychronicity and job performance, suggesting that students who effectively balanced academic and work roles performed better. Polychronicity also directly influenced work-school facilitation. The study provided both theoretical and practical insights, highlighting the importance of individual time-use preferences and institutional support in enhancing the performance and well-being of postgraduate students.
Deep reinforcement learning-based multi-lane mixed traffic ramp merging strategy
Due to concentrated conflicts, on-ramp merging is an important scenario in the study of new hybrid traffic control. Current research mainly focuses on optimizing the vehicle passage sequence of ramp vehicles merging with mainline vehicles in single-lane scenarios, neglecting the coordination problem of vehicles in multiple mainline lanes. Therefore, an Improved Dueling Double DQN (D3QN) On-ramp Merging Strategy (IDS stands for the initials of Improved, D3QN, and Strategy) combined with a sine function is proposed, establishing a Vehicle Coordination System (VCS) to guide the merging of vehicles in multi-lane mainline traffic. This strategy uses the improved D3QN algorithm combined with the excellent smoothness of the sine function to evaluate driving safety, helping vehicles find suitable gaps in traffic flow. An action masking mechanism was deployed during the strategy exploration phase to prevent unsafe actions. The proposed VCS + IDS strategy was tested in SUMO simulations of on-ramp merging under different density of vehicle flow. Under a traffic flow of 1200 vehicles per lane per hour, the on-ramp merging completion rate of VCS + IDS reached 98.62%, and the task completion rate was 98.11%, which increased by 11.08% and 10.79% compared to traditional D3QN, respectively, validating the effectiveness of this method.
Emotional adjustment and peer relationships: The role of behavioural reputation and classroom social climate
Internalising and externalising difficulties are linked with poor peer relationships, but there are gaps in our understanding of the reputational and contextual factors that connect emotional adjustment, social behaviour, and social acceptance in the peer group. A sample of 1439 children aged 7–13 years from 60 classrooms provided self-report and peer-nomination data at two time points over one school year. Children completed a self-report measure of anger, anxiety, and depressive symptoms, alongside ratings of positive and negative peer experiences. They also provided responses to a sociometric survey, which generated scores of social preference as well as behavioural reputation for cooperative, disruptive, aggressive, shy, and leadership qualities. Anger predicted lower social preference via a less cooperative reputation and a more disruptive reputation. Results also showed that anger had a weak connection with greater social preference via a less shy reputation. Anxiety predicted lower social preference via a more shy reputation. Moreover, a more problematic classroom social climate, in terms of peer experiences, was found to attenuate the negative peer consequences of the disruptive reputation associated with anger. Results clarify the value of locating internalising and externalising difficulties in the peer context. They advance theoretical conceptions of the social consequences of emotional maladjustment, and indicate the relevance of classroom-level social factors in peer socialisation processes.
Lightweight design based on automotive drive axle housing
To address the limitations in the cruising range and improve the overall efficiency of Pure Electric Vehicles (PEVs), this study focuses on the lightweight design of PEV drive axles. Aligning with the current trend toward lightweight and integrated development in electric vehicles, the research is based on the concept of an integrated electric drive axle housing. A three-dimensional model of the electric vehicle axle was developed using three-dimensional modeling software(3D modeling software), taking into account the working principles and load characteristics of the electric drive axle. Subsequently, Finite Element Analysis (FEA) was performed using finite element analysis software to evaluate the stiffness, strength, and modal characteristics of the integrated electric drive axle housing. The study analyzed stress distribution, deformation patterns, and natural frequency ranges under various operating conditions. Based on the FEA results, the axle housing structure was optimized by reducing the wall thickness and modifying the material of components subjected to lower stress levels, with the goal of minimizing mass. The optimal solution involved adjusting the housing thickness to achieve a more uniform and efficient thickness distribution, thereby meeting the lightweight design objectives. Simulation verification of the optimized axle structure confirmed that the weight reduction was achieved without compromising the required strength and stiffness. The findings demonstrate that, while ensuring safety, the optimized axle structure achieved a 12% reduction in weight. This study provides a feasible solution and a solid theoretical foundation for advancing the development of lightweight electric vehicles.
Seismic prediction of shale oil lithofacies associations based on sedimentary facies patterns: A case study of the shahejie formation in the Huanghekou Sag
The lithofacies play a pivotal role in studying development patterns, reservoir characteristics, and sweet spot predictions of shale oil. Lithofacies classification typically relies on core observations and conventional well logging analyses, whereas seismic attribute extraction is often employed in regions with sparse or absent wells. However, seismic attribute extraction entails considerable computation and time, and exclusive reliance on seismic attribute analysis can result in multiple interpretations. This paper emphasizes predicting shale oil lithofacies associations based on seismic reflection characteristics and sedimentary facies patterns which can can help avoid these issues. The lithofacies classification scheme has identified seven lithofacies and associations by means of core observations, testing data, and logging curve analysis of the Shahejie Formation in the Huanghekou Sag. Through well-seismic calibration, the seismic reflections and sedimentary patterns of different lithofacies associations were examined to formulate a seismic facies identification chart and propose six models. For areas without wells, based on the distribution of sedimentary facies and in combination with seismic reflection characteristics, identification and delineation are conducted on a planar scale to analyze the distribution features of lithofacies associations. The results of predicting the distribution of shale oil lithofacies associations in the Shahejie Formation indicate that the development pattern of lithofacies associations is basically consistent with that of sedimentary facies units. The primary models developed in the study area encompass delta, sublacustrine fan, and shore-shallow lake. The approach of identifying shale oil lithofacies associations based on seismic reflection and sedimentary backgrounds offers a novel means for discerning lithofacies and associations in sections devoid of cores and specialized well logging data.
Fast growth rate is associated with musculoskeletal biomechanical imbalance and dorsal cranial myopathy in broiler chickens
Dorsal cranial myopathy is a degenerative lesion that affects the anterior Latissimus dorsi muscle in broiler chickens, with an etiology that remains unknown. The objective was to investigate the influence of musculoskeletal biomechanical balance and gait on the prevalence of dorsal cranial myopathy in three broiler chicken strains with differing growth potential. Three-hundred and ninety-six broiler chickens from three genetic strains with differing growth potential (fast, intermediate, and slow, 132 birds/strain) were housed in 18 pens with 22 birds/pen. Five birds/pen (n = 30 birds/genetic strain) were randomly wing- or leg-banded to assess gait and musculoskeletal biomechanical balance (by calculating body angulation) at 1, 2, 3, and 3.7 kg weight sampling points. Dorsal cranial myopathy was assessed one day after birds reached final body weight. Gait and musculoskeletal balance were both negatively impacted by body weight in fast- and slow-growing strains but not in the intermediate-growing strain. Dorsal cranial myopathy was more prevalent in fast-growing broilers compared to other strains, with no case observed in the slow-growing strain. Impaired gait negatively affected musculoskeletal biomechanical balance and increased the prevalence of dorsal cranial myopathy. Our results suggest that genetic strain, musculoskeletal biomechanical imbalance, poor gait, and high body weight are all associated with the prevalence of dorsal cranial myopathy in broiler chickens. We successfully simplified a non-invasive body posture methodology to quantify the musculoskeletal biomechanical balance in broiler chickens.
Brief report: Older adolescents and young adults may be at higher risk for changes to menstrual cycle length with COVID-19 vaccination
Existing research has consistently demonstrated that adult women experience a small temporary increase in menstrual cycle length following COVID-19 vaccination; however, less is known about whether these changes differ depending on age. The purpose of this study was to assess differences in menstrual cycle length in older adolescent and adult women following an initial COVID-19 vaccine dose compared to an unvaccinated control group, with all analyses conducted across four separate age groups (18–24, 25–29, 30–34, and 35–45). Participants with menstrual cycles averaging 24–38 days prior to vaccination prospectively tracked menstrual cycles using the fertility awareness application Natural Cycles. We compared the within-individual change in menstrual cycle length pre- to post-vaccination in the vaccination group and across four consecutive menstrual cycles in the unvaccinated group, by age. Results demonstrated a 1.11 day adjusted increase in menstrual cycle length for adolescents and young adults (AYAs; ages 18–24), relative to unvaccinated individuals in that age range, while the three older groups all experienced less than a one-day post-vaccination adjusted increase in cycle length compared to their unvaccinated counterparts. These data show that, while COVID-19 vaccination was associated with longer menstrual cycle length for all groups overall, greater increases in menstrual cycle length were observed in AYAs. This suggests that younger individuals are more susceptible to menstrual cycle changes following COVID-19 vaccination.
Study protocol for an observational panel study of heat strain in the general adult population in Basse Santa Su, The Gambia
Heat is among the most hazardous environmental factors for human health, but humidity’s role in heat-related health effects remains unclear. This study will assess the effect of humid heat and other environmental conditions on health in a representative population in Basse Santa Su, The Gambia, a region at high risk of humid heat exposure. We will examine the association between humid heat exposure and physiological heat strain, identify vulnerable sub-groups, and evaluate adaptive behaviours. We will recruit 60–90 healthy adults from Basse Santa Su and surrounding areas. Participants will be monitored for four non-consecutive weeks across dry (November–May) and rainy (June–October) seasons. Daily questionnaires will assess activities, thermal comfort, adaptation behaviours, heat strain symptoms, mood, and sleep quality. Wearables will collect time-resolved personal and indoor exposure (temperature and humidity), heat strain, and further physiological covariates. A fixed monitoring network will measure outdoor air temperature, humidity, air quality, and environmental noise. Descriptive analyses will assess baseline characteristics, heat stress and heat strain. Case-time series analysis with distributed non-linear lagged models will estimate immediate and delayed associations between exposure to humid heat and physiological heat strain. Stratified analyses by individual characteristics will explore possible vulnerability groups. Multiple exposure models and interaction terms will explore cumulative effects of multiple environmental factors. Multilinear land use regression modelling will develop high-resolution maps of temperature, humidity, and heat stress. This study will provide new insights into humid heat’s effect on health, particularly in low-income, high-exposure settings. This study addresses limitations in prior epidemiological research on heat, humidity, and health, including lack of high-resolution and individual-level data, and limited focus on humidity as a heat-health driver, on non-mortality outcomes and on climate-vulnerable populations. This study combines high-resolution microclimate mapping and individual-level measurements which may inform future epidemiological studies and heat-health interventions.
Dual-model approach for concurrent forecasting of electricity prices and loads in smart grids: Comparison of sparse encoder NAR and GA-optimized LSTM
Accurate forecasting of electricity prices and loads is challenging in smart grids due to the strong interdependence between load and price. To address this, we propose two deep recurrent neural network models that forecast both load and price concurrently. The first model, Sparse Encoder Nonlinear Autoregressive Network (SENARX), introduces a sparse encoder for enhanced feature extraction and nonlinear autoregression with exogenous inputs. The second, GA-LSTM, integrates Long Short-Term Memory with genetic algorithm-based optimization to improve forecasting accuracy and robustness. Both models were evaluated using ISO New England data and outperformed benchmark models. The NARX model achieves MAPE values of 0.03 for load and 0.08 for price forecasting, while LSTM shows MAPE values of 1.53 and 1.91, respectively. The models demonstrate promising potential for real-time forecasting in smart grids. This paper presents a comparative study of SENARX and GA-LSTM against traditional methods such as ARIMA, SVM, and Bayesian Networks using market data from EPEX (Europe), IEX (India), and JEPX (Japan). SENARX achieved a MAPE of 3.82% (EPEX) and 4.13% (IEX), while GA-LSTM reached RMSE of 27.02 MW (EPEX) and 29.33 MW (JEPX). Compared to ARIMA (MAPE: 6.57%−7.21%, RMSE: up to 48.74 MW), the proposed models improved accuracy by over 40%. SENARX also trained faster (2385s vs 3100s for ARIMA). GA-LSTM showed faster convergence and lower error rates, and SENARX was robust against data noise. These characteristics make the models suitable for short-term load forecasting in dynamic and uncertain markets. Future work will test their performance under extreme events like peak demand and climate anomalies.
Visual edge feature enhancement of product appearance design images based on improved retinex algorithm
Under the influence of complex factors such as lighting, color distortion, and suspended solids, there is a problem of losing edge feature information and blurring edges in product appearance design images. In order to improve the clarity and visual effect of product appearance design, a visual edge feature enhancement method for product appearance design images based on an improved Retinex algorithm is proposed. By using a color correction method based on depth of field estimation, the blue tone of the product appearance design image is removed, and color correction and contrast are applied to the product appearance design image. Improve the Gray Wold algorithm and design an edge attenuation compensation method to solve the problem of edge color attenuation under noise interference, and obtain clearer product appearance design images. On the basis of clarity processing, convert the original RGB image into HSV. On the basis of the Retinex model, multi-level decomposition of brightness is carried out, and different filtering parameters are set to obtain multiple illumination and reflection images with different scale information; Using exponential function and Sigmoid function to process reflection images and illumination images separately, reducing external interference on images of different scales, and solving the difficulty of enhancing images with uneven illumination, high noise, low illumination, and loss of details. At the same time, adaptive nonlinear correction is applied to the saturation component, and the corrected saturation, brightness, and hue are fused and converted into RGB, expanding the edge grayscale feature information in various spatial domains. Improve the weights of traditional bilateral filtering methods, reduce the depth difference between information at different scales, and enhance the visual edge features of product appearance design images. The experimental results show that the proposed method enhances the image with a PCQI of 1.033, an IQE of 0.610, an IQM of 1.830, and an information entropy higher than 0.7. The above data proves that this method has a high richness of edge feature information after image enhancement, significantly improving the visual edge feature enhancement effect of product appearance design images.
Multi-scale error-driven dense residual network for image super-resolution reconstruction
Image super-resolution reconstructs high-resolution images from low-resolution inputs. However, current single-image super-resolution techniques often struggle to capture multi-scale information and extract high-frequency details, which compromises reconstruction quality. Moreover, the prevalent feed-forward network architectures lack robust feedback mechanisms for iterative refinement and enhanced acquisition of high-frequency information. To overcome these limitations, this research develops advanced strategies for multi-scale feature extraction, fusion, and feedback in single-image super-resolution. We propose an innovative error-driven, multi-scale dense residual network (EMDN) that retains a feed-forward structure while integrating error-driven feedback. Specifically, our approach utilizes dual multi-scale features: one derived from convolutional kernels of varying sizes and another extracted from diverse inputs, both processed concurrently. Comparative evaluations across different scaling factors demonstrate that our method outperforms existing approaches in both subjective and objective assessments. In particular, compared to the baseline feed-forward network, our model achieves improvements of up to 0.385% in peak signal-to-noise ratio and 0.191% in structural similarity index measure. The experimental results validate the effectiveness and practical significance of our proposed method in enhancing image resolution and restoration quality.
Long-range PM2.5 pollution and health impacts from the 2023 Canadian wildfires
High-resolution ex vivo nanoCT reveals 3D architecture of the adult male mouse lower urogenital tract
Benign prostatic hyperplasia (BPH) is the most common cause of lower urinary tract symptoms/dysfunction (LUTS/LUTD) in aging men. Over the past 30 years, the prevalence of BPH has increased by 122%, rising from 50.7 million cases in 1990 to 112.5 million in 2021. It is expected that this number will continue to rise over the next 15 years with the global aging population. Although mouse models are invaluable for studying human disease, gross anatomical differences between human and murine prostates complicate their translational relevance for BPH/LUTS research. The purpose of this study was to develop and validate a nanoCT-based imaging approach to enable detailed anatomical analysis of the murine lower urinary tract, with the dual goals of advancing tools for LUTD research and improving the translational relevance of mouse models to human prostate disease. Advancements in nano-computed tomography (nanoCT) have enabled high-resolution characterization of murine organ anatomy, providing new insights into how morphological differences contribute to pathology. To accomplish this, whole lower urogenital tracts from 8-week-old healthy male C57BL/6J mice and microdissected prostatic urethras were imaged on a v|tome|x M or nanotom M nano-CT system, respectively. Images were processed for 2D segmentation and subsequent 3D reconstruction. Our approach enabled high-resolution visualization and characterization of the murine lower urogenital tract, including the urinary bladder, prostate lobes, seminal vesicles, and ductus deferens, as well as the microscopic ductal architecture of the prostatic urethra. The resulting 3D reconstructions preserved native anatomical relationships and allowed for comparisons between murine and human prostate anatomy; however, our findings are limited in that nanoCT is inherently an ex vivo analysis and only healthy, young C57BL/6J mice were used and pathology was not assessed in the current study. Together, these findings establish a method that can be used for assessing anatomical and morphological changes associated with LUTD development, while also highlighting key anatomical similarities that enhance the translational relevance of mouse models for human prostate disease.