Perspectives and experiences of gender inclusion for STEM programs through an intersectional lens

Background: STEM is becoming increasingly important in determining a country's economic and social progress. Despite decades of research and advocacy, women remain underrepresented in STEM education and professions. Such STEM gender gaps, with certain regional and subject differentiations, nevertheless remain as global issues further exacerbated by the COVID-19 pandemic. Informal educational initiatives on GIFSTEM (gender inclusion for STEM) emerged as part of the solution. In recent years, they also have attracted increasing partnership and investment interests from the public and private sectors. However, relatively little evidence exists to demonstrate how the impacts of GIFSTEM initiatives are experienced by different participants, particularly those outside the US. There are also increasing questions about the monolithic framing of gender in these programs. Research Design: This research evaluates the experiences of learners and project leaders of GIFSTEM organizations in a range of geographical settings through an intersectional lens. In this qualitative study, data is collected through 13 individual online semi-structured interviews. Participants represent two groups, those who are learners (both past and present), and those who are project organizers and leaders of different GIFSTEM organizations. Findings: Data from interviews show that learners find GIFSTEM programs helpful in three ways: community, networking-mentoring, as well as a broadened understanding of possible paths in STEM education and professions. Depending on their intersectional identities, learners also experience two barriers, heightened visibility and feelings of exclusion due to identity metrics other than gender, that make them feel uncertain about remaining in STEM. Furthermore, learner participants feel that GIFSTEM programs do little, sometimes even the opposite, in mitigating these issues. Project leader interviews demonstrate that, depending on the specific programming goals, different numerical metrics are used, in combination with qualitative data from individual participations, for impact measurements of their affiliated GIFSTEM organization. Project leaders also have to make a series of pragmatic considerations in the process of developing and implementing a sustainable GIFSTEM organization. For instance, decisions regarding target learner demographics, program contextualization, and navigating relationships with commercial partners. In the end, individual GIFSTEM organizations must make strategic and difficult decisions depending on their operational contexts to reach their respective end goals. Acronyms: GIFSTEM: gender inclusion for STEM GIFT: gender inclusion for Tech (a specific subset of GIFSTEM initiatives) CS: Computer Science EE: Electrical Engineering FAANG: An acronym describing five prominent American technology companies: Facebook(Meta), Amazon, Apple, Netflix, and Google (Hobbs, 2022)

YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6

We develop an all-in-one computer vision toolbox named EasyCV to facilitate the use of various SOTA computer vision methods. Recently, we add YOLOX-PAI, an improved version of YOLOX, into EasyCV. We conduct ablation studies to investigate the influence of some detection methods on YOLOX. We also provide an easy use for PAI-Blade which is used to accelerate the inference process based on BladeDISC and TensorRT. Finally, we receive 42.8 mAP on COCO dateset within 1.0 ms on a single NVIDIA V100 GPU, which is a bit faster than YOLOv6. A simple but efficient predictor api is also designed in EasyCV to conduct end2end object detection. Codes and models are now available at: https://github.com/alibaba/EasyCV.Comment: 5 pages, 5 figure

Learn-to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition

Most existing compound facial expression recognition (FER) methods rely on large-scale labeled compound expression data for training. However, collecting such data is labor-intensive and time-consuming. In this paper, we address the compound FER task in the cross-domain few-shot learning (FSL) setting, which requires only a few samples of compound expressions in the target domain. Specifically, we propose a novel cascaded decomposition network (CDNet), which cascades several learn-to-decompose modules with shared parameters based on a sequential decomposition mechanism, to obtain a transferable feature space. To alleviate the overfitting problem caused by limited base classes in our task, a partial regularization strategy is designed to effectively exploit the best of both episodic training and batch training. By training across similar tasks on multiple basic expression datasets, CDNet learns the ability of learn-to-decompose that can be easily adapted to identify unseen compound expressions. Extensive experiments on both in-the-lab and in-the-wild compound expression datasets demonstrate the superiority of our proposed CDNet against several state-of-the-art FSL methods

Magnetic resonance angiography signal intensity as a marker of hemodynamic impairment in intracranial arterial stenosis.

BackgroundIntracranial arterial stenosis (ICAS) is the predominant cause of ischemic stroke and transient ischemic attack in Asia. Change of signal intensities (SI) across an ICAS on magnetic resonance angiography (MRA) may reflect its hemodynamic severity.MethodsIn-patients with a symptomatic single ICAS detected on 3D time-of-flight MRA were recruited from 2 hospitals. Baseline and 1-year follow-up data were collected. Signal intensity ratio (SIR) [ =  (mean post-stenotic SI -mean background SI)/(mean pre-stenotic SI - mean background SI)] was evaluated on baseline MRA to represent change of SIs across an ICAS. Acute infarct volume was measured on baseline diffusion-weighted images (DWI). Relationships between SIR and baseline characteristics as well as 1y outcomes were evaluated.ResultsThirty-six subjects (86.1% males, mean age 55.0) were recruited. Overall, mean SIR was 0.84±0.23. Mean SIRs were not significantly different between the 23 (63.9%) anatomically severe stenoses and the 13 (36.1%) anatomically moderate stenoses (0.80±0.23 versus 0.92±0.21, p = 0.126). SIR was significantly, linearly and negatively correlated to acute infarct volume on DWI (Spearman correlation coefficient -0.471, p = 0.011). Two patients (5.6%) had recurrent ischemic strokes at 1y, not related to SIR values.ConclusionsChange of signal intensities across an ICAS on MRA may reflect its hemodynamic and functional severity. Future studies are warranted to further verify the relationships between this index and prognosis of patients with symptomatic ICAS

Research on the Mechanism of Entrepreneurship Education on College Students’ Entrepreneurial Willingness and Its Future Prediction

The strength of college students’ entrepreneurial willingness is a barometer for measuring the effectiveness of entrepreneurship education. It is also an important avenue for college students to expand their employment opportunities and enhance the quality of their employment in the face of new employment trends. Comprehensive universities offer a wide range of disciplines and great professional specialization. It is of great significance to explore the diversity results in college students’ entrepreneurship education indicators. According to the data on the relationship between entrepreneurial education and entrepreneurship willingness in comprehensive universities in Jiangsu province, various factors such as subject characteristics, work experience, educational background, and family environment significantly impact college students’ willingness to become entrepreneurs. The implementation of entrepreneurship education, including the awakening of entrepreneurial consciousness, the cultivation of entrepreneurial abilities, and the improvement of entrepreneurial willingness, has a direct impact on college students’ willingness to start their own businesses

A New Measurement Method of Relative Volume Wear Ratio Based on Discharge Debris Composition Analysis in Micro-EDM

In microelectrical discharge machining (micro-EDM) milling process, due to the unavoidability of electrode wear, selection of electrode with high electrical erosion resistance and accurate electrode compensation is entitled to be conducted to ensure high precision and high quality. The RVWR is used as criterion for electrode wear characteristics and is fundamental to achieve accurate electrode compensation; however, it is hardly measured accurately with conventional methods. In this paper, firstly, the error of RVWR measured by conventional measurement method is analyzed. Thereafter, for accurately measuring RVWR, a new measurement method is proposed based on electrical debris composition analysis. The RVWR of widely used tungsten, molybdenum, and copper electrode in machining different materials is measured, respectively, and the optimum electrode is selected based on the measuring results. Finally, microgrooves on different materials are machined with tungsten electrode, and the experiment results show that the microstructures have good bottom surface profiles, which indicates that the proposed method is effective to precisely measure the RVWR and guarantee accurate electrode compensation in micro-EDM process

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud

Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships. While existing diffusion models have shown promise in generating images from textual descriptions, they often neglect domain-specific contexts and lack robustness in handling the Chinese language. This paper introduces PAI-Diffusion, a comprehensive framework that addresses these limitations. PAI-Diffusion incorporates both general and domain-specific Chinese diffusion models, enabling the generation of contextually relevant images. It explores the potential of using LoRA and ControlNet for fine-grained image style transfer and image editing, empowering users with enhanced control over image generation. Moreover, PAI-Diffusion seamlessly integrates with Alibaba Cloud's Machine Learning Platform for AI, providing accessible and scalable solutions. All the Chinese diffusion model checkpoints, LoRAs, and ControlNets, including domain-specific ones, are publicly available. A user-friendly Chinese WebUI and the diffusers-api elastic inference toolkit, also open-sourced, further facilitate the easy deployment of PAI-Diffusion models in various environments, making it a valuable resource for Chinese text-to-image synthesis

Combining Electrochemical Nitrate Reduction and Anammox for Treatment of Nitrate-Rich Wastewater: A Short Review

Treatment of nitrate-rich wastewater is important but challenging for the conventional biological denitrification process. Here, we propose combining the electrochemical reduction and anaerobic ammonium oxidation (anammox) processes together for treatment of nitrate-rich wastewater. This article reviews the mechanism and current research status of electrochemical reduction of nitrate to ammonium as well as the mechanism and applicability of the anammox process. This article discusses the principles, superiorities, and challenges of this combined process. The feasibility of the combined process depends on the efficiency of electrochemical nitrate reduction to ammonium and the conditions in the anammox process to use the reduced ammonium as the substrate to achieve deep nitrogen removal. The article provides a feasible strategy for using the electrochemical reduction and anammox combined process to treat nitrate-rich wastewater

Quantitative identification of complexity of microscopic fracture structure of coal seam floor composite rock layer

Under mining conditions, the micro crack structure of the composite strata of the coal seam floor affects the uplift and seepage of the groundwater in the underlying high water pressure aquifer, and its complexity is of great significance for the prediction and early warning of the water disaster in the coal seam floor. In this paper, a total of 57 groups of rock samples extracted from three adjacent mines in the east of Pingdingshan Coalfield are taken as the object. Based on the comprehensive information of SEM image acquisition and N2 adsorption test, the fissure ratio of rock stratum area, equivalent crack diameter of rock stratum, fractal dimension value of fissure area, specific surface area, crevice volume and its fractal dimension are the main controlling factors. Using entropy weighted TOPSIS mathematical model, the complexity of the micro fracture structure of the composite rock stratum between the Ji16-17 coal seam and the Cambrian limestone top interface is quantitatively evaluated. The results show that the micro crack structure of the composite strata in the floor of the Ji16-17 coal seam is very simple, simple, medium, complex and extremely complex, accounting for 25.00%, 21.43%, 39.29%, 7.14% and 7.14% respectively. The micro crack structure of the composite strata in the study section is relatively uncomplicated; Compared with the four rock sections, the order of the quantitative mean value of the complexity of the rock stratum micro fracture structure is: L1−L2 limestone section>L5−L7 limestone section>sandstone section>bauxite mudstone section. The complexity of the rock stratum micro fracture structure better reflects the development of the rock stratum fracture. The paper considers the coupling effect of multiple factors on the composite rock stratum, realizes the quantitative classification and zoning discrimination of the micro crack structure in the vertical section of the composite rock stratum, which is of great significance for accurately describing the aquifer performance of the composite rock stratum in the coal seam floor and scientifically guiding the prevention and control of the water hazard in the coal seam floor

Social avoidance and social adjustment in Chinese preschool migrant children: the moderating role of teacher–child relationships

ObjectivesThis study aimed to explore the moderating role of teacher–child relationships in the relations between social avoidance and social adjustment (i.e., prosocial behavior, peer exclusion, and anxious-fearful behavior) in Chinese migrant preschoolers.MethodsParticipants were 148 migrant children aged 4–6 years (82 boys, Mage = 62.32, SD = 6.67) attending kindergartens in Shanghai, People's Republic of China. Mothers reported children's social avoidance, and teachers rated teacher–child relationships and children's social adjustment.ResultsResults indicated that social avoidance was positively related to peer exclusion and negatively related to prosocial behavior. Teacher–child relationships moderated those associations. Specifically, teacher–child closeness buffered the relationship between social avoidance and peer exclusion, whereas teacher–child conflict exacerbated the relations between social avoidance and peer exclusion and anxious-fearful behavior.ConclusionThe current finding informs us of the importance of improving teacher–child closeness and reducing teacher–child conflict to buffer the negative adjustment among socially avoidant young children who migrated from rural-to-urban China. The findings also highlight the importance of considering the meaning and implication of social avoidance for migrant preschoolers in Chinese culture