Zero-Trust Network Architectures (ZTNA) demand innovative cybersecurity mechanisms capable of adapting to more complex attacks and independently healing system failures with minimal human intervention. Conventional intrusion detection and recovery systems are typically plagued with static trust assumptions, response latencies, limited contextual awareness, and high false positive rates in dynamic threat environments. To mitigate these challenges, this research proposes a new framework consisting of Artificial Intelligence-driven fault detection and autonomous recovery systems, integrated with Autoencoder based anomaly reconstruction, contextual trust scoring, reinforcement learning-driven adaptive recovery, and policy-aware decision making in the context of Zero-Trust environments. The framework considers streaming telemetry data to perform anomaly detection and response orchestration in near real time and at low latency using Apache Kafka and Apache Flink. Trust is evaluated dynamically based on behavioral metrics, consistency and reliability of feedback, and fault history in order to facilitate intelligent node classification and secure adaptive policy. Traffic rerouting, node isolation, adaptive policy, and service redeployments are considered autonomous recovery actions optimized by reinforcement learning. The framework yielded 97% anomaly detection accuracy and 96% autonomous recovery success rate, with a 0.965 F1-Score for DDoS detection and a 93% reduction in operational downtime. The Mean Time to Repair (MTTR) was achieved at 1.8s, with significantly diminished false positive rates in comparison to other memory based frameworks such as Deep SVDD, DAGMM, USAD, TranAD, and the Transformer-based framework and traditional rule-based methods. The proposed framework also achieved a 910 MB memory usage optimization with a high fault simulation of 10,000 nodes. The results indicate the framework provided next generation self-healing Zero-Trust Cybersecurity Infrastructure with scalable, robust, and adaptive intelligent Cybersecurity Solutions.

The emergence of many strains of the coronavirus, including the latest omicron strain, which is spreading at a very high speed, is leading to the World Health Organization’s (WHO) concern about the creation of this new mutation. Therefore, there is a strong motivation for modeling and predicting COVID-19 to control the number of cases of the disease. The proposed system for predicting the number of cases of COVID-19 can help governments take precautions to prevent the spread of the disease. In this paper, a statistical logistic growth model was employed to predict the spread of COVID-19 in Australia and Brazil. The datasets were collected from the surveillance systems in Australia and Brazil from March 13, 2020, to December 12, 2021, for 641 days. This proposed method used a tested logistic growth model for the complex spread of COVID-19 and forecasted future values within a time interval of six days. The results of the predicted, cumulative, confirmed cases indicate the robustness and effectiveness of the proposed system, which was categorized by time-dependent dynamics. The coefficient of determination () metric was used to evaluate the model to predict COVID-19, and the proposed system scored the highest correlation ( ). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19.

This study looks at the changing women’s contribution to formal leadership roles in Saudi Arabia and assesses how their greater presence affects cross-sector organizational performance. By synthesizing available literature, analyzing the latest statistics, and reviewing pertinent case studies, this paper highlights the status of women in leadership positions within Saudi organizations, as well as the performance indicators associated with these organizations. The results indicate that organizations with greater proportions of women in leadership tend to show better results in terms of innovation, financial performance, employee engagement, and organizational culture. This advocacy research adds to the growing literature on women and gender issues, particularly concerning leadership in Saudi Arabia, offering contextual relevance to policymakers and organizational leaders focused on performance improvement through women’s inclusive leadership strategies.

American scientific publishing group

This research paper explores the electronic government adoption and implementation level by local government entities in Sudan. It examines how local governments have embraced and integrated digital technologies, platforms, and services using Layne and Lee four stages model. The study employed a content analysis method to collect data from 18 official websites of Sudanese local government entities. The findings suggest that while local e-government initiatives are still in the informative stages, many governments plan to develop more sophisticated e-government offerings. The key barriers and challenges to adopt and implement e-government identified in the research include technological barriers, lack of resources, digital divide, and legal and regulatory obstacles.

Drawing from a sample of Chinese small-medium organizations (SMEs), this study explored the interplay between stakeholders' pressure (PSP), environmental commitment (EC), circular economy performance (CEP), and CSR for sustainability (PCS). Quantitative data from 137 managers of manufacturing SMEs was collected using convenience sampling and cross-sectional design. PLS-SEM processed the data. The findings revealed that the PSP directly affected EC and CEP but not PCS. Further, we deployed two mediators (EC and CEP) to explore the indirect mechanisms within the research framework. The mediation analysis revealed a significant association between PSP and PCS, as well as between EC and PCS. This study explored the possibility of integrating the stakeholder theory and resource-based views, which could enable policymakers to have an insight into utilizing the nuance liaison between the stakeholder and the reflection within the manufacturing sectors for sustainable development.