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Study on the bending deformation properties of microcracked R-ECC road–bridge link slabs

Long-term systemic androgen deprivation partially modulates neuroinflammation in male AppNL−G−F/NL−G−F mice

Valosin-containing protein modulator KUS121 protects retinal neurons in a rat model of anterior ischemic optic neuropathy

Investigation of the properties of polyester blended knitted fabric dyed with luminescent dyes as a highly visible fabric

Abstract Recently, many challenges have emerged in the dyes and textile fields to keep pace with the needs of the modern era while achieving safety and comfort during usage. Where the fluorescent dye caused a major boom in the field of dyes and was used to paint many surfaces for exciting and to attract attention purpose. They were utilized on textiles, particularly those composed of synthetic materials employed in hazardous areas such as traffic roads, where they become more visible upon exposure to light. Therefore, this research aims to investigate the color fastness, physio-mechanical properties, and UV resistance of fabrics produced by knitting techniques from polyester blended with cotton or bamboo and dyed with fluorescence dyes as dispersing yellow and red dyes. The evaluation results of dyed fabrics pointed to the dispersed red dyes improved the physio-mechanical, comfortable and colorfastness properties of polyester/cotton samples compared to polyester/bamboo samples except bursting resistance, while the dispersed yellow dyes considerably enrich the ultraviolet protections of polyester/bamboo samples compared to polyester/cotton.

Comparison of mini-percutaneous nephrolithotomy and standard percutaneous nephrolithotomy in the treatment of renal calculi with renal insufficiency

Construction of a multiscale feature fusion model for indoor scene recognition and semantic segmentation

GRB2 promotes brain metastasis in HER2-positive breast cancer by regulating the Ras/MAPK pathway

Modified grey wolf optimization for energy-efficient internet of things task scheduling in fog computing

Investigating emission characteristics and combustion performance of a CRDI engine utilizing novel biodiesel derived from waste cooking oil and pentanol blends

The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning

Design, synthesis and simulation of fractional-order element using MOS transistors as distributed resistive capacitive devices

Application of ophthalmic magnets in the removal of magnetic intraocular foreign bodies

Metabolic profiling and antibacterial activity of secondary metabolites extracted from the endophytic bacteria of Combretum erythrophyllum

Prevalence, clinical reasons and associated factors of extended treatment duration for drug susceptible tuberculosis – a real-world experience

Neural processing of laughter in depression

Abstract Laughter can convey social intent ranging from acceptance (friendly inclusive laughter) to rejection (malign taunting laughter). We investigated perception of auditory and visual laughter in patients with major depressive disorder (MDD) versus healthy controls (HC). 48 MDD patients and 52 HC rated 60 laughter recordings presented auditorily or visually regarding the expressed social intent during an fMRI experiment at 3T. Depression severity was assessed based on questionnaires. MDD patients rated the perceived social intent of the laughter significantly more negative than HC across both modalities. The individual magnitude of this negativity bias of social intent attribution significantly correlated with both depression severity as well as activation in anteromedial prefrontal cortex (AMPFC) during perception of auditory laughter. MDD patients also exhibited a significantly reduced activation in AMPFC and depression severity partially mediated effects on rating of auditory laughter as evidenced by mediation analysis. Our results demonstrate altered perception of social intent expressed by laughter in MDD. Neuroimaging data point to the AMPFC for mediation of this effect as its activity was correlated with both depression severity and a negative attribution bias during perception of auditory laughter. Furthermore, at group level activity in this area was reduced in MDD patients.

Mixed adverse ergonomic factors exposure in relation to work-related musculoskeletal disorders: a multicenter cross-sectional study of Chinese medical personnel

Chacma baboon natural anticoagulants and factor VIII activities

Implementation of pooled testing to increase access to routine viral load monitoring for people living with HIV on antiretroviral therapy

A multi-scale remote sensing semantic segmentation model with boundary enhancement based on UNetFormer

Abstract The precise execution of semantic segmentation on remote sensing data is a pivotal factor. It determines the achievements and impact of geoscience endeavors and their applications. However, challenges caused by target edge blurring and scale variability in high-resolution remote sensing imagery hinder the improvement of segmentation accuracy. In this work, to address these issues, a Boundary-Enhanced Multi-Scale Semantic Segmentation Network (BEMS-UNetFormer) based on UNetFormer is proposed for remote sensing data. Firstly, an improved Boundary Awareness Module (BAM) is used to extract the edge information of the target from the low-level features to enhance the recognition of the target edges. Secondly, the improved Boundary-Guided Fusion Module (BFM) incorporates the edge information from BAM into subsequent decoding, further refining the precise representation of boundary regions. Finally, at the pivotal junction between the encoder and decoder, the Multi-Scale Cascaded Atrous Spatial Pyramid Pooling (MSC-ASPP) is designed, capable of deeply mining and integrating multi-scale deep features. The method was tested on two mainstream datasets, Potsdam and Vaihingen, achieving 86.12% and 83.10% MIoU, respectively, improving by 1.38% and 1.79% over the baseline model. Notably, the IoU and F1 Score for the small-scale target “Car” in the Potsdam dataset reached 91.20% and 95.57%, respectively, while the “Building” and “LowVeg” categories in the Vaihingen dataset achieved the highest IoU and F1 Score. The experimental results indicate that the proposed method demonstrates higher precision in segmenting small-scale targets and target boundaries, surpassing mainstream methods overall.

Hydrogen can both move or pin dislocations in body-centered cubic metals