Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
35,082
result(s) for
"Kai Wang"
Sort by:
Infographic design in media
With more than 100 creative infographic projects, this title provides an updated perspective of the media landscape that examines contemporary visualization techniques in design. In each case there's an informative theoretical introduction, where the authors adopt succinct summaries of the works based on sound expertise, so that the readers can quickly catch the main ideas of each profile, appreciate the author's creativity, and can apply these concepts to drive their own inspirations.--Amazon.com.
Book
Interpretable ensemble learning model with shapley additive explanations for predicting anxiety symptoms risk in Chinese older adults with body shape index abnormality
This study aimed to construct and validate an interpretable risk prediction model for anxiety symptoms in Chinese older adults with abnormal body shape, explore the association between A Body Shape Index (ABSI) and anxiety symptoms, and identify key predictive factors via explainable methods. Data were from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008–2014 (n = 1,844/2,663/3,058 for 2008/2011/2014). The 2008 data (80% training, 20% internal validation) and 2011/2014 data (external validation) were used. Feature selection, data balancing, ensemble learning (Boosting/Stacking/Voting), and Shapley Additive exPlanations (SHAP) were applied. ABSI was positively associated with anxiety symptoms (P = 0.038), with stronger effects in males (trend slope = 0.03) than females (0.02); female anxiety prevalence (39.37%) was higher than males (20.79%). The Boosting-ADASYN model performed best (internal AUC = 0.814, external AUC = 0.766–0.772). SHAP identified marital status, age, self-reported health, education, and happiness as top predictors. ABSI outperformed BMI in capturing abnormal body fat distribution. This study provides an interpretable tool for early anxiety identification in this population, supporting precise interventions combining ABSI and psychosocial strategies.
Journal Article
Dynamic prediction of adolescent sports injury probability based on the improved ARIMA model in the background of digital physical education teaching
This study aims to solve the problem of dynamic prediction of sports injuries in adolescent physical education teaching. By integrating multi-source heterogeneous data and improving time series modeling, a real-time early warning system suitable for digital education scenarios is constructed. In view of the limitations of traditional ARIMA models in capturing the suddenness and multi-frequency characteristics coordination of injury events, this study innovatively integrates Time-Varying Filtering Empirical Mode Decomposition (TVF-EMD) and Mixed-Frequency Data Sampling (MIDAS) techniques to achieve separate modeling of injury trend and fluctuation terms, and establish cross-scale correlation between second-level sports data and quarterly physical fitness assessment. In an empirical study involving 217 middle school student athletes, the model significantly improved prediction accuracy (multi-scenario average RMSE reduced by > 40%), with an accuracy rate of 86.2% in high-intensity basketball training, a false alarm rate of 5.2% for ACL injuries in football, and the critical threshold of fracture risk was determined when the monthly running distance exceeded 280 km in track and field training. According to the research, Key technical breakthroughs include dynamically optimizing model parameters through particle filtering to adapt to sudden changes in teaching load, using LightGBM residual correction mechanism to improve the ability to capture sudden injury events, and verifying the reliability of three-dimensional motion trajectory monitoring in virtual reality teaching (error < 12.3 mm). The research results provide core methodological support for constructing a real-time and personalized campus sports health protection system, and will be further validated through multi-center cohort studies to promote clinical translational applications.
Journal Article
Toward Hydrogen‐Free and Dendrite‐Free Aqueous Zinc Batteries: Formation of Zincophilic Protective Layer on Zn Anodes
Rechargeable aqueous Zn‐ion batteries (ZIBs) are regarded as one of the most promising devices for the next‐generation energy storage system. However, the uncontrolled dendrite growth on Zn metal anodes and the side hydrogen evolution reaction, which has not yet been well considered, hinder the practical application of these batteries. Herein, a uniform and robust metallic Sb protective layer is designed based on the theoretic calculation and decorated on Zn plate via in situ replacement reaction. Compared with the bare Zn plate, the as‐prepared Zn@Sb electrode provides abundant zincophilic sites for Zn nucleation, and homogenizes the electric field around the Zn anode surface, both of which promote the uniform Zn deposition to achieve a dendrite‐free morphology. Moreover, the Gibbs free energy (∆GH) calculation and in situ characterization demonstrate that hydrogen evolution reaction can be effectively suppressed by the Sb layer. Consequently, Sb‐modified Zn anodes exhibit an ultralow voltage hysteresis of 34 mV and achieve excellent cycling stability over 1000 h with hydrogen‐ and dendrite‐free behaviors. This work provides a facile and effective strategy to suppress both hydrogen evolution reaction and dendrite growth. A uniform and robust metallic Sb protective layer is decorated on Zn plate via in situ replacement reaction. With rich zincophilic sites for Zn nucleation, improved electrolyte wettability and homogenized electric field, the Sb layer promotes the uniform Zn deposition with a dendrite‐free morphology. Moreover, the Gibbs free energy calculation and in situ characterization demonstrate that hydrogen evolution reaction can be effectively suppressed by the Sb layer.
Journal Article
التخفيف من حدة الفقر في الصين المعاصرة
استنادا إلى نظرة عامة على أوضاع الفقر، يقدم هذا الكتاب مسار التخفيف من حدة الفقر والتنمية في الصين، ويشرح نموذج التنمية والتخفي من حدة الفقر بخصائص صينية والتمسك بمباديء (سيطرة الحكومة ومشاركة المجتمع والاعتماد على الذات والتنمية الموجهة والتنمية الشاملة) كما يقدم الكتاب تلخيصا شاملا لإنجازات الصين العظيمة وخبراتها الهامة وإسهاماتها الرئيسية في قضية التخفيف من حدة الفقر في العالم، ويعرض بإيجاز نظرات وممارسات التخفيف المستهدف من الفقر في العصر الجديد من أجل توفير مراجع لكسب المعركة ضد الفقر في الصين وقضية التخفيف من حدة الفقر في العالم.
Book
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
This paper presents a comprehensive survey on vision-based robotic grasping. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the target object in the input data. The object pose estimation task mainly refers to estimating the 6D object pose and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses for known objects. The grasp estimation task includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. These three tasks could accomplish the robotic grasping with different combinations. Lots of object pose estimation methods need not object localization, and they conduct object localization and object pose estimation jointly. Lots of grasp estimation methods need not object localization and object pose estimation, and they conduct grasp estimation in an end-to-end manner. Both traditional methods and latest deep learning-based methods based on the RGB-D image inputs are reviewed elaborately in this survey. Related datasets and comparisons between state-of-the-art methods are summarized as well. In addition, challenges about vision-based robotic grasping and future directions in addressing these challenges are also pointed out.
Journal Article
Infection of bat and human intestinal organoids by SARS-CoV-2
A novel coronavirus—severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—emerged in humans in Wuhan, China, in December 2019 and has since disseminated globally
1
,
2
. As of April 16, 2020, the confirmed case count of coronavirus disease 2019 (COVID-19) had surpassed 2 million. Based on full-genome sequence analysis, SARS-CoV-2 shows high homology to SARS-related coronaviruses identified in horseshoe bats
1
,
2
. Here we show the establishment and characterization of expandable intestinal organoids derived from horseshoe bats of the
Rhinolophus sinicus
species that can recapitulate bat intestinal epithelium. These bat enteroids are fully susceptible to SARS-CoV-2 infection and sustain robust viral replication. Development of gastrointestinal symptoms in some patients with COVID-19 and detection of viral RNA in fecal specimens suggest that SARS-CoV-2 might cause enteric, in addition to respiratory, infection
3
,
4
. Here we demonstrate active replication of SARS-CoV-2 in human intestinal organoids and isolation of infectious virus from the stool specimen of a patient with diarrheal COVID-19. Collectively, we established the first expandable organoid culture system of bat intestinal epithelium and present evidence that SARS-CoV-2 can infect bat intestinal cells. The robust SARS-CoV-2 replication in human intestinal organoids suggests that the human intestinal tract might be a transmission route of SARS-CoV-2.
Bat and human intestinal organoids can support replication of SARS-CoV-2, enabling further characterization of the virus lifecycle and investigation of potential mechanisms of enteric infection in COVID-19.
Journal Article