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"Li, Wenjun"
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Crowdsourcing : cloud-based software development
\"This book presents the latest research on the software crowdsourcing approach to develop large and complex software in a cloud-based platform. It develops the fundamental principles, management organization and processes, and a cloud-based infrastructure to support this new software development approach. The book examines a variety of issues in software crowdsourcing processes, including software quality, costs, diversity of solutions, and the competitive nature of crowdsourcing processes. Furthermore, the book outlines a research roadmap of this emerging field, including all the key technology and management issues for the foreseeable future. Crowdsourcing, as demonstrated by Wikipedia and Facebook for online web applications, has shown promising results for a variety of applications, including healthcare, business, gold mining exploration, education, and software development. Software crowdsourcing is emerging as a promising solution to designing, developing and maintaining software. Preliminary software crowdsourcing practices and platforms, including Apple's App Store and TopCoder, demonstrate the advantages of crowdsourcing in terms of software ecosystem expansion and product quality improvement.\"--Back cover.
Book
Structural stability of the evolving developer collaboration network in the OSS community
The structural stability of the developer collaboration network is critical to the success of the OSS (Open Source Software) community. However, research on the structural stability of the evolving developer collaboration network in OSS communities is relatively insufficient. In this paper, according to the software version sequence, we construct the corresponding developer collaboration network of the Angular OSS community and then analyse this network’s structural stability during network evolution. The results show that the network always presents an economical modular small-world structure during its evolution. The maintenance of the structure is related to a cohesive core, which is composed of two types of nodes (i.e., hubs and connectors). The hubs organize noncore nodes to form modules, while connectors facilitate the formation of inter-module connections. The overall results highlight the important role of core developers in the sustainable development of OSS communities and may provide a reference for community initiators to implement protection strategies for core developers.
Journal Article
تطوير إقتصاد دائري في الصين
هذا العمل الجماعي المشترك، كما يشير عنوانه : (التاريخ المديني الحضري للصين) هو كتاب حضاري بامتياز. كتاب يستعرض جوانب أساسية من حضارة الشعب الصيني القديمة-المتجددة الراسخة، الضاربة جذورها عميقا في تربة التاريخ البشري الشامل. يعد الكتاب سفر حضاري قيم وبحث متصل رصين حول التطور التاريخي العمراني في المدن الصينية وهو إذ يتناول بشكل أساسي، فن العمارة والبناء والتصميم وتخطيط المدن والشوارع والأحياء والأسواق والمقار الحكومية والحدائق والمعابد والساحات ومسالك الملاحة النهرية والري، فإنه لا يغفل في الوقت نفسه عن ما يتصل به هذا الموضوع، من عشرات المواضيع الحضارية والثقافية والمعيشية الأخرى، مثل : العبادات والاعتقادات والتقاليد والعادات والآداب والطقوس والحرف والفنون والنظم الاجتماعية والصراع السياسي والغزو الخارجي والتنظيم الحكومي والقبلي والاجتماعي والطبقي والعسكري والإداري والإنمائي والعمراني والإسكاني والتعليمي والتجاري والأدبي والفني والموسيقي والفولكلوري. إنه كتاب جميل يقدم العديد من اللوحات الرشيقة الرحبة المتقابلة المتلاحقة المتكاملة التي ترحل بنا بين القصور والأنهار والأسوار والأحياء والمعاهد والمعابد وهياكل عبادة الأجداد ومواقع التنقيب والساحات والحدائق والميادين ودواوين الشعر وسجلات التاريخ ولا يخلو كل ذلك من جولات من البحث المقارن في غير أمر من هذه الأمور ولن نطيل عليكم في الإضاءة على مواضيع هذا الكتاب لأكثر من ذلك، آملين لكم سلاسة القراءة ومتعة الاكتشاف.
Book
Intelligent Recommendation System Based on the Infusion Algorithms with Deep Learning, Attention Network and Clustering
The creation and use of big data have driven the intelligent development of e-commerce. The information generated in e-commerce provides a good means to analyze the behavior of users. How to use this information to give customer recommendations, improve the accuracy of recommendations and protect information security is a topic worth studying. For improving the accuracy of recommendations, analysis of users and tagging of resources are key. The current popular session recommendation algorithms face many problems, such as user interest drift which is difficult to be handled by these algorithms, thus affecting the recommendation accuracy. Based on these problems, this paper proposes a recommendation model based on deep learning, applies it to the clustering analysis of user tagging system, and designs a personalized recommendation algorithm for the tagging system. The model proposed in this paper can effectively analyze not only the interests exhibited by users in the current session, but also their potential long-term interests. By comparing the different performances of different datasets, the experimental results of this paper show that the proposed algorithmic model in this paper helps to dig the interests of different users, thus improving the quality of the recommendation system.
Journal Article
Comparison of deep learning models for predictive maintenance in industrial manufacturing systems using sensor data
This paper presents a comprehensive comparison of deep learning models for predictive maintenance (PdM) in industrial manufacturing systems using sensor data. We propose a framework that encompasses data acquisition, preprocessing, and model construction using various deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and their hybrid variants. Experiments conducted on three industrial datasets demonstrate the effectiveness of these models in predicting equipment failures and estimating remaining useful life. The CNN-LSTM hybrid model achieves the best performance with 96.1% accuracy and 95.2% F1-score, outperforming standalone CNN and LSTM architectures. Through ablation studies and feature importance analysis, we identify critical components and parameters that influence model performance. The results highlight the potential of deep learning approaches in revolutionizing predictive maintenance practices by enabling more accurate and reliable fault prediction in industrial manufacturing systems. Our findings provide valuable insights for implementing data-driven predictive maintenance strategies in real-world industrial applications.
Journal Article
Neuroinflammation in epileptogenesis: from pathophysiology to therapeutic strategies
Epilepsy is a group of enduring neurological disorder characterized by spontaneous and recurrent seizures with heterogeneous etiology, clinical expression, severity, and prognosis. Growing body of research investigates that epileptic seizures are originated from neuronal synchronized and excessive electrical activity. However, the underlying molecular mechanisms of epileptogenesis have not yet been fully elucidated and 30% of epileptic patients still are resistant to the currently available pharmacological treatments with recurrent seizures throughout life. Over the past two decades years accumulated evidences provide strong support to the hypothesis that neuroinflammation, including microglia and astrocytes activation, a cascade of inflammatory mediator releasing, and peripheral immune cells infiltration from blood into brain, is associated with epileptogenesis. Meanwhile, an increasing body of preclinical researches reveal that the anti-inflammatory therapeutics targeting crucial inflammatory components are effective and promising in the treatment of epilepsy. The aim of the present study is to highlight the current understanding of the potential neuroinflammatory mechanisms in epileptogenesis and the potential therapeutic targets against epileptic seizures.
Journal Article
Rebuilding pastoral social-ecological resilience on the Qinghai-Tibetan Plateau in response to changes in policy, economics, and climate
Economic, policy, and climate changes have profoundly influenced pastoral social-ecological systems on the Tibetan Plateau. Climate change is believed to be leading to increasing extreme weather conditions such as snow disasters and droughts, putting a strain on the rangeland resources herders must have to increase income. Market-based economic reforms and interrelated development policies such as the Rangeland Household Contract Policy, the Ecological Construction Project, and herder settlement Initiatives have increased integration of pastoral regions into modern markets with promotion of tourism, expanded livestock markets, and marketing opportunities for rangeland resources. Although allocating common rangelands to households is the foundation of current rangeland management strategies to achieve these goals, it removes important technologies for coping with high variability in rangeland forage production from the traditional rangeland management portfolio on the Tibetan Plateau. These include shared risk, shared labor, seasonal and yearly herd mobility, and access to diverse areas of rangelands and multiple water sources. Field study of two villages in Guinan County of Qinghai Province, and Ruoergai County of Sichuan Province from 2011 to 2014 found that the villages responded to externally driven policy, economic, and climate changes with an innovative locally adapted quota-based grazing management system that preserves valuable management technologies, conserves rangeland resources, and provides individual opportunities for financial gain. In this way the village social-ecological system has exhibited considerable resiliency, maintaining a form of community governance that functions to manage the rangelands, improve well-being as indicated by livestock productivity, and, according to local perceptions, maintain rangeland condition. The community-based grazing quota system devised by the villages occupies a middle ground between common and individual models for resource use because it focuses more on how to equitably distribute services and utilities from rangelands, instead of how to distribute rangelands.
Journal Article
Advances in the Understanding of Reactive Oxygen Species-Dependent Regulation on Seed Dormancy, Germination, and Deterioration in Crops
Reactive oxygen species (ROS) play an essential role in the regulation of seed dormancy, germination, and deterioration in plants. The low level of ROS as signaling particles promotes dormancy release and triggers seed germination. Excessive ROS accumulation causes seed deterioration during seed storage. Maintaining ROS homeostasis plays a central role in the regulation of seed dormancy, germination, and deterioration in crops. This study highlights the current advances in the regulation of ROS homeostasis in dry and hydrated seeds of crops. The research progress in the crosstalk between ROS and hormones involved in the regulation of seed dormancy and germination in crops is mainly summarized. The current understandings of ROS-induced seed deterioration are reviewed. These understandings of ROS-dependent regulation on seed dormancy, germination, and deterioration contribute to the improvement of seed quality of crops in the future.
Journal Article
Enhancing teacher recruitment and retention through decision-making models in education systems
Teacher recruitment and retention remain critical challenges for education systems worldwide, with far-reaching implications for educational quality and institutional sustainability. Traditional approaches often fail to address the complexity of these issues, neglecting the interplay of multiple conflicting criteria and the inherent uncertainty in decision-making. This gap necessitates advanced decision-making frameworks that can effectively evaluate and prioritize strategies for improving teacher recruitment and retention. To bridge this gap, this study introduces a novel decision-making framework integrating intuitionistic fuzzy sets (IFSs) to handle uncertainty more effectively. The Entropy method is employed to compute objective weights, while the ranking comparison (RANCOM) method determines subjective weights, ensuring a balanced consideration of qualitative and quantitative factors. The weighted aggregated sum product assessment (WASPAS) method is then applied. The framework is validated through sensitivity analysis to assess its robustness and comparative analysis to establish its superiority over traditional methods. The results identify the Golden Ticket Salary Plan
as the optimal strategy, achieving the highest ranking (0.3654), followed by
(0.3487),
(0.3485),
(0.3400),
(0.2976) and
(0.2707). The ranking order for the strategies is as follows:
. These findings highlight the significance of structured decision-making in optimizing teacher workforce management. This study provides valuable insights for policymakers and administrators, ensuring sustainable advancements in teacher workforce management.
Journal Article
Serial mediating role of future time perspective and grit in the relationship between growth mindset and academic engagement
This study examined the effect of growth mindset on academic engagement and the parallel and serial mediating roles of future time perspective and grit in the relationship between growth mindset and academic engagement. The participants were 565 Chinese university students. They were asked to complete the Growth Mindset Scale, the General Future Time Perspective Scale for College Students, the Grit Scale, and the Chinese College Student Academic Engagement Questionnaire. The results showed that growth mindset positively predicted academic engagement; Future time perspective and grit can act as parallel mediators in the effects of growth mindset on academic engagement; Future time perspective and grit form serial mediators in the effect of growth mindset on academic engagement. Educators can enhance the academic engagement of university students by fostering the growth mindset, establishing the future time perspective and shaping the grit to stimulate their willingness to proactively set up future development goals and internal motivation for learning.
Journal Article