Advancing Open Science

Growth and carcass traits are key economic traits in beef cattle production, and identifying their associated genetic markers is crucial for improving breeding efficiency. Charolais cattle, as a superior beef breed, exhibit excellent performance in growth rate and meat production. The aim of this study was to utilize the preferred high-coverage whole-genome resequencing (hcWGS) as a replacement for single nucleotide polymorphism (SNP) chips to identify significant SNPs and candidate genes associated with growth (body weight, body height, cross height, body length, and chest measurement across different growth stages) and carcass traits (live backfat thickness and eye muscle area at 18 months) in 240 Charolais cattle, thereby providing guidance for beef cattle breeding. Through hcWGS (approximately 13× coverage) and quality control, 4,088,633 SNPs were identified and subsequently used for genetic analyses. Through FarmCPU-based genome-wide association studies, 196 potentially significant SNPs associated with growth traits and 29 SNPs with carcass traits were identified. Annotation analyses revealed 353 candidate genes (such as RBM33, KCTD17, PTHLH, RAC2, CHD6, TRDN, WBP1L, TLL2, CH25H, and ST13) linked to growth traits and 26 candidate genes linked to carcass traits (such as CHST11, LRRK2, RIOK2, and INTS10). Additionally, three SNPs (g.8674692C>G, g.54418624G>T, and g.71085551G>A) were validated via polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP), enabling efficient marker-assisted selection. Furthermore, eight SNPs in the Acyl-CoA oxidase 1 (ACOX1) gene were found to be associated with growth and backfat thickness traits. These findings provide valuable preliminary insights into the genetic mechanisms underlying growth and carcass traits in Charolais cattle, facilitating genome-assisted breeding.

In order to investigate the current status of soil heavy metal pollution, ecological risk, and risk sources in the black soil area of the Eastern Inner Mongolia Province, topsoil (0–20 cm) samples from farmland in the black soil area (N = 163) were collected to determine the contents of seven heavy metals. The levels of soil heavy metal pollution and ecological risk in the study area were evaluated by combining the geo-accumulation index, potential ecological risk index, and static environmental carrying capacity; the positive matrix factorization (PMF) model was used to identify the pollution sources and contributions of heavy metals in the soil and analyze the risk levels to adults and children. The soil was predominantly weakly acidic, with mean values of Cr, Ni, Cu, As, Cd, Pb, and Zn of 61.77, 26.77, 17.07, 12.11, 0.08, 12.61, and 85.71 mg·kg⁻¹. The mean concentrations of heavy metals exceeded the background values, except for Pb, the mean concentration of which was lower than the soil background. Ni concentrations of 6.21% at the sampling sites exceeded the risk screening value for agricultural soils. The geo-accumulation index showed that Cr (55.15%) and As (54.00%) were mainly mild pollutants; the static environmental carrying capacity indicated that the soils were slightly polluted by Ni, As, and Zn; and the potential ecological risk indices of Cd, Ni, and As were at moderate levels. The PMF model analyzed three pollution sources: mixed agricultural practice–transportation sources (39.46%), mineral-related activity sources (27.01%), and pesticide–fertilizer agricultural practices (33.53%). The human health risk assessment indicated that 46.58% of sampling sites posed a carcinogenic risk to children, with Ni as the main carcinogenic element. In conclusion, the potential contamination of As, Cd, Ni, Cr, and Zn in the Eastern Inner Mongolia farmland black soil area should be further studied.

With the continuous deepening of Mars exploration missions, the Mars helicopter has become a key platform for acquiring high-resolution near-ground imagery. However, accurate semantic segmentation of the Martian surface remains challenging due to complex terrain morphology, sandstorm interference, and the limited onboard computational resources that restrict real-time processing. Existing models either introduce high computational overhead unsuitable for deployment on Mars aerial platforms or fail to jointly capture fine-grained local texture and global contextual structure information. To address these limitations, we propose LisseMars, a lightweight semantic segmentation network designed for efficient onboard perception. The model integrates a Window Movable Attention (WMA) module for enhanced global context extraction and a multi-convolutional feedforward module (CFFN) to strengthen local detail representation. A Dynamic Polygon Convolution (DPC) module is further introduced to improve segmentation performance on geometrically heterogeneous objects, while a Group Fusion Module (GFM) enables effective multi-scale semantic integration. Extensive experiments are conducted on both real Tianwen-1 Mars helicopter imagery and synthetic datasets. The results show that our method achieved a mean IoU of 78.56% with only 0.12 MB of model parameters, validating the effectiveness of the proposed framework. The real-time performance of proposed method on edge device deployment further demonstrate potential application for real Mars airborne missions.

Although women have participated in mining activities across the world for centuries, the industry continues to be perceived as predominantly male-oriented. This perception persists largely due to the male-dominated workforce and the physically demanding nature of mining operations. This paper examines the ergonomic impacts of mining machinery on female mineworkers. The study was conducted in three underground coal mining operations located in Mpumalanga, South Africa, using a quantitative research approach. To evaluate the ergonomic demands placed on women working underground, the researchers employed the Rapid Entire Body Assessment (REBA) in combination with direct observation techniques. The findings revealed that female mineworkers experience considerable challenges when performing tasks requiring significant physical strength and endurance. The observed female mineworker recorded a final REBA score of seven, indicating a medium-risk level. Ergonomic challenges in underground coal mining are further intensified for female mineworkers due to the absence of gender-specific considerations in equipment design, task allocation, and the overall working environment. Although the risk classification was moderate, the results underscore the need for further investigation and the timely implementation of corrective measures. Addressing these issues will require the integration of inclusive ergonomic principles that account for gender diversity within the mining workforce.

This work presents and validates an eye-tracking-based visual system for driving the delta robot. A delta robot is tracked by image processing based on vision servo control. The vision servo program is developed in C++ to perform image processing-based object detection. For image processing, Haar classifier-based methods are used. Finally, image processing and motion controller movements are integrated into one system to perform the visual servo-based motion of the end effector of the delta robot. Experiments are performed to validate the proposed method from the perspective of image processing. Moreover, this paper validates the kinematic analysis, which is vital for obtaining 3D information on the end-effector of the delta robot. The presented model can be implemented in eye clinics to facilitate ophthalmologists by replacing manual eye-checking equipment with automatic, unattended, computerized eye checkups.

A ground-based microwave radiometer (MWR) can retrieve temperature and vapor density profiles with a temporal resolution at the minute level, which is significant for studying atmospheric thermodynamic stratification and its evolution. Improving MWR retrieval accuracy is crucial for MWR application research. Based on 9-year observations of MWR and radiosonde in Wuhan, China, this study adopts regression model and artificial neural network (ANN) methods to correct MWR temperature and vapor density deviations against radiosondes in diverse skies. Due to the impacts of solar heating and raindrops, MWR temperature presents a cold bias from radiosondes in clear and cloudy skies, but a warm bias in rainy skies, while the MWR vapor density is generally wetter than radiosondes, especially in rainy skies. The validation results show that both regression and ANN models can reduce the biases of MWR temperature and vapor density against radiosondes to around zero in diverse skies, and the MWR vapor density RMSE in rainy skies shows a marked decrease. After correcting using the regression model, the RMSE of MWR temperature (vapor density) declines by 14% (7%), 7% (4%), and 12% (29%) in clear, cloudy, and rainy skies, respectively, and the correction effect of the ANN model is slightly better than the regression model, with corresponding decreases of 19% (8%), 10% (8%), and 12% (30%), respectively. However, the consistency of MWR retrievals with radiosondes is rarely improved after the corrections of regression and ANN models. These results indicate that the regression and ANN models have a reasonable ability to correct MWR retrieval deviation in diverse skies, and there is remaining room for further improvement in MWR retrieval accuracy.

To address the poor resistance of recycled aggregate concrete (RAC) to chloride ion penetration and freeze–thaw deterioration in cold coastal regions, this study introduces basalt fibers (BFs) as a reinforcement to improve its durability and structural integrity. Rapid freeze–thaw and electric flux tests, combined with scanning electron microscopy (SEM), were employed to systematically evaluate the effects of fiber volume fraction and length configuration on the frost resistance and chloride impermeability of basalt fiber-reinforced RAC (BFRAC). The experimental results demonstrated that the incorporation of basalt fibers markedly enhanced the coupled durability of RAC, with the mixture containing 0.15% fiber volume and a balanced hybrid of short (12 mm) and long (18 mm) fibers achieving the most favorable performance. This mixture effectively reduced mass loss and strength degradation under repeated freeze–thaw cycles while substantially lowering chloride ion penetration compared with plain RAC. Microstructural observations revealed that the hybrid fiber system formed a multi-scale three-dimensional network, in which short fibers restrained microcrack initiation and long fibers bridged macrocracks, jointly refining the pore structure and improving the interfacial bonding between recycled aggregates and the cement matrix. This synergistic mechanism enhanced matrix compactness and obstructed chloride transport, leading to a more stable and durable composite. The findings not only establish an optimal basalt fiber design for improving RAC durability but also elucidate the fundamental mechanism underlying hybrid fiber synergy. These insights provide valuable theoretical guidance and practical strategies for developing sustainable, high-performance concrete suitable for long-term service in cold-region coastal infrastructures.

This study explores, from decolonial economics perspective, how nineteen Zimbabwean banks engage with both Euro-American and indigenous knowledge systems in their sustainable finance practices. Despite growing global interest in sustainability, limited research has examined the relevance of these models within Zimbabwe’s socio-economic context. Addressing this gap, the study employs transformative sequential mixed methods, incorporating 289 structured questionnaires, 30 focus group discussions, and 45 archival documents. Data were subjected to descriptive statistics, narrative analysis, Marxist immanent critique, and decolonial theory. Findings reveal that Zimbabwean banks predominantly adopt Euro-American sustainability frameworks such as the UN Sustainable Development Goals, Paris Accords and accounting standards. However, these frameworks often misalign with local realities, obscuring sustainability colonialism, promoting exclusion of indigenous knowledge, reinforcing Global North dominance, and perpetuating weak sustainability theory. This results in superficial compliance that conceals extractive investments and carbon-intensive practices. Moreover, these models deepen subordinated financialization, commodification, elite capture, resource expropriation, and socio-environmental inequalities. The study calls for a paradigm shift, either rejecting Euro-American models in favor of indigenous approaches or adopting a hybrid model that integrates indigenous knowledge. Such a shift would promote strong sustainability, pluralism, and decolonized institutional frameworks to foster financial inclusion, community resilience, and ecological regeneration in Zimbabwe.