SACM - China
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9652
Browse
30 results
Search Results
Item Restricted The Impact of Corporate Governance on Risk Management for the Insurance Industry in Saudi Arabia Firms(Saudi Digital Library, 2025) ASSIRI, AMER; Baorong, YuThis study examines the relationship between corporate governance and risk management in the Saudi Arabian insurance sector, focusing on how governance mechanisms influence such key risk dimensions as insurance, credit, and liquidity. Despite significant regulatory reforms, the sector faces persistent challenges, including insufficient board independence, limited diversity, and inadequate risk management expertise, which hinder its ability to achieve sustainable growth and financial stability. The research addresses critical gaps in the literature in particular, the lack of empirical studies on governance and risk management in the Saudi context, the under-exploration of advanced analytical techniques, and the insufficient examination of such governance dynamics as board diversity, CEO characteristics, and risk committee expertise. The study leverages a robust dataset comprising 270 annual reports and 4,620 observations from publicly listed insurance firms in Saudi Arabia, spanning a 10-year period (2014–2023). Advanced machine learning models including generalized linear models, random forests, gradient boosting machines, and deep neural networks are employed to analyze the interplay between governance attributes and risk outcomes. Feature importance analyses, including Shapley additive explanations (SHAP) values and force plots, are used to interpret model predictions and identify the most influential governance factors. These techniques provide granular insights into how individual features, such as board independence, risk committee expertise, and firm characteristics, contribute to risk predictions. Key findings reveal that board independence, risk committee expertise, and composite corporate governance are the most influential factors in mitigating insurance, credit, and liquidity risks. Firms with higher proportions of independent directors and well-qualified risk committees exhibit stronger risk oversight and lower risk exposure. Additionally, such firm characteristics as size, profitability, and leverage play a significant role in shaping risk profiles, while CEO duality and tenure have minimal direct impacts on risk outcomes. The study also highlights the moderate influence of gender diversity on governance effectiveness, although nationality diversity and board shareholding show limited direct effects. The research makes several significant contributions. First, it advances the existing literature by providing empirical evidence on the relationship between governance mechanisms ii and risk management in the Saudi insurance sector a context that has received limited scholarly attention. Second, it offers practical insights for industry stakeholders, emphasizing the importance of strengthening board oversight, enhancing risk committee expertise, and fostering diversity to improve governance frameworks. Third, it provides actionable recommendations for policymakers and regulators to align governance practices with international standards, thus helping ensure the sector’s resilience and sustainable growth. Finally, the study aligns with Saudi Arabia’s Vision 2030 objectives by exploring how governance and risk management practices can support economic diversification, financial stability, and sustainable development. In sum, this thesis bridges critical gaps in the literature and provides a roadmap for enhancing governance and risk management in the Saudi Arabian insurance sector, ensuring its competitiveness and alignment with global best practices. The use of advanced analytical techniques, including SHAP values and force plots, underscores the methodological rigor and practical relevance of the study.16 0Item Restricted Research on Active Vibration Reduction of Wind Tunnel Tail Support Rod Based on BP Neural Network PID Algorithm(Saudi Digital Library, 2024) Alzahrani, Faisal; Shen, XingThe antitumor activity of tallysomycins A and B was determined in five experimental tumor systems in mice. Tallysomycins A and B were highly active against B16 melanoma, sarcoma 180 ascites tumor and Lewis lung carcinoma, and moderately active against P388 leukemia but were without effect on lymphoid leukemia L1210. The antitumor activity of tallysomycin A was 2 to 3 times that of tallysomycin B and 3 to 17 times that of bleomycin. Tallysomycin A was about 1.5 and 4 times more toxic for mice than tallysomycin B and bleomycin, respectively, in terms of subacute LD50 values.7 0Item Restricted DDos Attack Detection Method Based on Information Entropy and Naive Bayes(Saudi Digital Library, 2024) Alshmlan, Abdullah Salem; Songfeng, LuWith the advancement of cloud computing technology, the threshold and cost of using cloud computing are gradually decreasing. Meanwhile, an increasing number of distributed denial of service attacks have shifted to cloud environments, posing a serious threat to the security of the entire cloud network space. These attacks consume a large amount of cloud computing resources and have an impact on the normal use of cloud tenants. In response to the difficulty in detecting DDoS attacks with multiple levels of attacks coexisting in the cloud, and considering the cost of the cloud environment. This article introduces a cloud based DDoS attack detection method, which is based on information entropy and naive Bayesian algorithm. This method detects suspected attack flows by calculating the virtual machine traffic entropy of relevant proxy nodes in the cloud and combining it with naive Bayesian classification methods. Design experiments to compare the effectiveness of other machine learning classification algorithms and traditional information entropy detection methods. In the verification phase, the detection method proposed in this article demonstrated good performance in detecting DDoS attacks of different attack intensities in cloud environments.7 0Item Restricted Machine Learning-based Detection Strategies for DDoS Attacks(Saudi Digital Library, 2025) Alshmlan, Abdullah Salem A; Songfeng, LuWith the rapid development of information technology, Distributed Denial-of-Service (DDoS) attacks have become a major threat to network security, posing severe challenges to the online services of enterprises and individuals. Traditional defense methods are often inefficient against complex, evolving attack patterns and fail to provide better detection and response. To address these limitations, this study focuses on developing and evaluating machine learning-based models for detecting Distributed Denial-of-Service (DDoS) attacks. A hybrid model combining lightweight Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks is developed to leverage CNN’s spatial feature extraction and BiLSTM’s temporal dependency modeling. The CIDDS-001 dataset is used after rigorous preprocessing, including cleaning, feature selection, normalization, and sliding-window segmentation. Several architectures are trained and compared, including the proposed CNN-BiLSTM and an enhanced Self-Attention BiLSTM variant that dynamically emphasizes critical traffic patterns. Experimental evaluation using metrics such as accuracy, precision, recall, and F1-score demonstrates that the hybrid and attention-based models achieve superior performance and effectively reduce false alarm rates. Overall, the study provides a practical and adaptable approach for DDoS attack detection, enhancing the responsiveness and reliability of network defense systems. Future work will focus on extending this framework to larger and more diverse datasets to further improve its generalization in real-world scenarios.10 0Item Restricted Research on photovolatic dust accumulation degree evaluation method based on PSO-RF algorthim(Saudi Digital Library, 2025) Madkhali, mohammed ibraheem h; jianbo, yi随着全球能源转向可再生能源，光伏发电的高效运行与维护成为研究重点。在沙特阿拉伯等干旱多尘地区，组件积灰严重影响发电效率，现有模型在复杂气候下泛化能力不足。 本文以沙特 Abhur Al Janubiyah 地区为研究对象，提出了一种基于 粒子群优化-随机森林（PSO-RF） 的光伏阵列积灰评估方法，主要研究成果如下： 1. 利用 单二极管模型（SDM） 在 Matlab/Simulink 中建立光伏阵列模型，分析光照与温度对输出特性的影响，并通过 扰动观察法（P&O） 实现最大功率点跟踪（MPPT）。 2. 构建 PSO-RF 积灰评估模型，通过粒子群优化随机森林超参数，提升分类精度与泛化能力。在不同积灰程度（0、15、30 g/m²）下，模型在典型天气条件中的准确率最高达 92.31%，优于传统 RF 模型。 3. 结合沙特气象数据，利用 PVsyst 软件进行区域化仿真，结果表明：PSO-RF 模型在积灰分类中准确率达 94.8%，优于 SVM、XGBoost 等模型，验证了其在复杂气候下的环境适应性。28 0Item Restricted The Dilemma of Simulated Contracts: Navigating the Evolving Landscape of Fraud Against the Backdrop of Legislative Inertia in Common and Civil Law Jurisdictions.(Peking University, 2025-06-01) Algunisi, Abdulaziz; Feldman, MarkThis study aims to explain the legal status of simulated contracts in Islamic law as applied in Saudi Arabia and the Civil law used in Egypt and the Common law used in USA, and their impact, as well as to outline the judiciary's position in these jurisdictions regarding these contracts. The study uses an inductive and analytical approach to explain most of the research comparing the legal status legal status and judicial applications in these three jurisdictions. The research concludes that, in a general sense, simulation in contracts involve both parties colluding to hide their true intentions under a false appearance for mutual benefit. This concept of simulation is similar to the concept of legal stratagems known in Islamic law. If simulated contracts do not undermine legitimacy or contradict a legal interest, they are permissible. However, if they do the opposite, they are prohibited and forbidden. Relying on the Fiqh rule stating that “The essence of contracts is determined by their intended purposes and meanings, not by their wording and structure.” meaning that the essence of contracts lies in their intended purposes, not in their outward appearance, surprisingly, an equivalent principle is found in the USA legal system “Substance over form”. It is important to note that the U.S. prioritizes economic reality, while the Middle Eastern systems emphasize the subjective intent and potential for deception, which will be further explained later. Moreover, the Saudi judiciary does not consider the outward appearance of a contract if it is proven that the contract's reality is different. The Egyptian law, by which the Saudi new legislations are greatly affected, provides for the same rule as the true intent of the parties prevails over the form of the simulated contract. This rule has been applied by the Egyptian judiciary in application to the Egyptian Law.22 0Item Restricted A Comprehensive Cause Analysis of Human Errors in Saudi Arabia’s Oil and Gas Industry(Saudi Digital Library, 2025) AL RASHED, HUSSAIN; Zhizhong, LiThe study investigates the role of human error in accidents within the oil and gas sector of Saudi Arabia, integrating both qualitative and quantitative analyses to offer a comprehensive understanding of the issue. This study first examined the nature and causes of human errors within the oil and gas industry in Saudi Arabia by semi-structured interviews with industrial professionals. Thematic analysis revealed critical themes around the assessment of current safety measures, environmental and operational challenges, contributing factors, types of errors, and strategies for improvement. The qualitative approach also highlighted how adverse weather conditions, ergonomic issues, and organizational culture contribute to operation errors. Morever, the study involves a detailed human error cause analysis using the Human Factors Analysis and Classification Systems (HFACS) model. The findings emphasized that training deficiencies, ineffective supervision, and weak safety culture significantly contribute to human error in the Saudi oil and gas industry. Additionally, a questionnaire was developed and used to survey 90 professionals, highlighting a notable correlation between error frequency and inadequate safety practices, indicating that over 70% of accidents are human-related. This quantitative analysis introduced a risk evaluation of Performance Shaping Factors (PSFs), identifying key factors such as fatigue, inadequate training, and environmental factors that influence error rates. The findings predicted that targeted interventions, including enhanced training, improved communication, and stronger safety management systems, are crucial for mitigating risks. Thus, this study is summarized by emphasizing that human error remains a significant factor in accidents and operational disruptions. This study provides valuable insights into the human error challenges within Saudi Arabia's oil and gas industry and offers actionable recommendations. The recommendations include a focus on enhanced training, strengthened leadership commitment to safety, and the integration of advanced technologies to reduce human errors and improve safety outcomes.69 0Item Restricted RESEARCH ON ELECTRICITY CONSUMPTION PREDICTION BASED ON DEEP INTEGRATION MODELS(Harbin Institute of Technology, 2024) Alkhattabi, Moayad; Chen, YingWith the development of power system and the improvement of intelligence level, power consumption forecast plays a vital role in power system operation management, energy dispatching and market trading. Effective power demand forecasting is the key to power system planning and operation, and is of great significance to achieve safe, efficient and sustainable energy supply. However, the traditional deep learning model has the problem of falling into local optimal in the optimization process, which leads to the unstable performance of the model. To overcome this problem, the particle swarm optimization (PSO) algorithm is improved in this study to improve the performance of deep learning models in power consumption prediction tasks. This study collates and summarizes the challenges encountered when dealing with nonlinear, non-stationary, and high-dimensional data. To overcome these challenges, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of deep learning models, thereby enhancing the model's fitting ability and generalization performance. The improved PSO algorithm in this study adopts dynamic weight adjustment and multi-stage optimization strategy, which effectively realizes the balance between global search and local search, and greatly improves the performance of the model in complex power systems. In the process of model construction, the stack ensemble learning method is adopted, and five machine learning methods including long short-term memory network (LSTM) are used to build a deeply integrated power consumption model prediction model. To verify the validity and applicability of the model, extensive experimental tests are carried out on real world power system data sets. The experimental results show that the PSO-Stacking model in this study has a root-mean-square error (RMSE) of 0.095, a mean absolute error (MAE) of 0.074, and an R square (R²) of 0.862, which are robust performance indicators. These results demonstrate the effectiveness of improved particle swarm optimization algorithm and stacked ensemble learning model in power consumption prediction tasks. Compared with the traditional deep learning model, the optimized deep learning model using the improved PSO algorithm shows considerable improvement in accuracy, stability and response speed.Item Restricted The Impact of Total Quality Management Practice on The Operational Performance of Saudi Arabian Petrochemical Firms(Hunan University, 2024) Alkhaibari , Abdulmuttalib Mohammed A; Yuan, ShanmiThis study comprehensively examines the impact of Total Quality Management (TQM) practices on the operational performance of petrochemical firms in Saudi Arabia, integrating both qualitative and quantitative research methods. It delves into the unique cultural, economic, and industrial landscape of the region to assess how TQM practices are tailored and implemented within these firms and their influence on operational outcomes. The research is anchored in an extensive literature review that lays the foundation for a theoretical model, addressing the integration of TQM within the specific context of Saudi Arabia's petrochemical sector. Utilizing a structured survey method, the study gathers data from 250 companies, achieving 200 valid responses, which are then rigorously analyzed. The empirical phase of the research employs statistical techniques to explore the relationships between various TQM elements and operational performance. Key findings reveal that process management and measurement, analysis, and improvement are critical TQM elements exerting the most significant direct effects on corporate performance. In contrast, customer and market factors have a substantial indirect impact, followed by strategic elements. The study highlights the essential role of human resource management, process control, and quality breakthroughs in enhancing the quality systems of petrochemical enterprises. This research contributes to the theoretical understanding of TQM in a specific industrial context, highlighting the importance of leadership support, strategic planning, and customer-centric approaches for effective TQM implementation. It provides actionable insights for practitioners in the petrochemical industry, emphasizing the need for comprehensive and scientifically grounded TQM projects. Keywords: Total Quality Management; Petrochemical Industry; Operational Performance; Saudi Arabian Firms; Process Management; Quality ImprovementItem Restricted 《针对沙特阿拉伯中高级汉语水平学习者的中国文化教学研究》(UNIVERSITY OF INTERNATIONAL BUSINESS AND ECONOMICS, 2024) ALTAMIMI, TAHANI; 导师是韩潇老师针对沙特阿拉伯中高级汉语水平学习者的中国文化教学研究56 0
- «
- 1 (current)
- 2
- 3
- »