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Breast cancer is a pressing public health issue globally and in Morocco, with rising cases among women. This study aims to evaluate breast cancer awareness and self-examination practices among female university students, informing future educational interventions. A cross-sectional study surveyed 437 students at Ibn Zohr University, Agadir, using a questionnaire covering demographics, knowledge of breast cancer, risk factors, symptoms, and breast self-examination (BSE). Results showed high awareness of breast cancer (95.3%), with social networks and media being primary information sources. However, only 48.25% had intermediate knowledge levels, and BSE awareness was moderate (60.8%) with low practical skills (28.0%). Reasons for not performing BSE included lack of knowledge and discomfort. Significant associations were found between knowledge levels and age, year of study, study options, and information sources. Despite high awareness, there is a crucial need to enhance knowledge about breast cancer risk factors, symptoms, and BSE practices among young women in Morocco. Educational programs targeting university students are essential for promoting early detection and improving attitudes toward breast health.

This article addresses the dynamic landscape of smart supply chain management, characterized by the integration of cutting-edge technologies. It proposes an IoT-Blockchain system for monitoring equipment status in a smart supply chain environment. The system utilizes IoT sensors to collect temperature and humidity data from the equipment. This collected data is then processed and stored in the cloud using the InfluxDB database. To further enhance security and transparency in the monitoring process, the system incorporates blockchain technology. This ensures the tamper-proof nature of collected data through the deployment of smart contracts. The monitoring platform developed on Grafana provides users with an intuitive dashboard for accessing real-time information about the equipment’s status. The proposed system offers a comprehensive monitoring solution for sensitive supply chain operations, allowing stakeholders to track the equipment’s journey along the supply chain and monitor its status in real-time. This system has the potential to revolutionize supply chain monitoring, providing an efficient and secure way to optimize equipment performance and improve overall supply chain efficiency.

In the rapidly evolving domain of e-commerce, effective warehouse management emerges as a critical factor for ensuring timely deliveries. This paper addresses the Storage Location Assignment Problem (SLAP) in e-commerce warehouses, a challenge intensified by varying product volumes and unpredictable demands. We introduce a novel Intelligent Storage Location Assignment (ISLA) method that utilizes advanced time series clustering algorithms specifically, Self-Organizing maps, dynamic time warping-Based k-means, and Agglomerative Hierarchical Clustering (AHC), to optimize order fulfillment and enhance warehouse efficiency. By clustering and positioning items with similar demand patterns, our approach minimizes order preparation time, reduces unnecessary warehouse movements, and improves operational flows. Our empirical evaluation, based on a real-world dataset from Kaggle, demonstrates the superiority of AHC in efficiently grouping high-turnover items, as evidenced by higher silhouette scores. Applying this method in simulations across various picking strategies such as s-shape, mid-point, discrete order picking, zone picking, and batch picking, we achieve significant efficiency improvements. Notably, our ISLA method results in up to 61% and 69% efficiency gains under s-shape and midpoint routing policies, respectively, outperforming traditional random and ABC storage assignments. These results not only highlight the significant potential of Artificial Intelligence (AI) in revolutionizing warehouse operations but also bridge the existing knowledge gap by showcasing a practical and impactful application of AI in SLAP. Our research advances the field of smart logistics, emphasizing the critical role of AI-driven intelligent storage location assignment in optimizing warehouse processes and enhancing the efficiency of the e-commerce supply chain.

Task scheduling in distributed computing architectures has attracted considerable research interest, leading to the development of numerous algorithms aiming to approach optimal solutions. However, most of these algorithms remain confined to simulation environments and are rarely applied in real-world settings. In a previous study, we introduced the MFHS framework, which facilitates the transition of scheduling algorithms from simulation to practical deployment. Unfortunately, MFHS currently offers a limited selection of scheduling heuristics. In this work, we address this limitation by presenting the MFHS_jMetal framework, integrating the extensive task scheduling algorithms available in the well-established jMetal framework. Our implementation demonstrates the successful expansion of available scheduling algorithms while preserving the core characteristics of MFHS, bridging the gap between theoretical models and real-world deployment.

Hepatic encephalopathy (HE) is a major neuropsychological condition that occursas a result of impaired liver function. It is frequently observed in patients with advanced liver disease or cirrhosis. Memory impairment is among the symptoms of HE; the pathophysiologic mechanism for this enervating condition remains unclear. However, it is possible that neuroinflammation may be involved, as recent studies have emphasized such phenomena. Therefore, the aim of the present study is to assess short working memory (SWM) and examine the involvement of microglia in a chronic model of HE. The study was carried out with male Wistar rats that were induced by repeated thioacetamide (TAA) administration (100 mg/kg i.p injection for 10 days). SWM function was assessed through Y-maze, T-Maze, and novel object recognition (NOR) tests, together with an immunofluorescence study of microglia activation within the hippocampal areas. Our data showed impaired SWM in TAA-treated rats that was associated with microglial activation in the three hippocampal regions, and which contributed to cognitive impairment.

Nowadays, because of increasing use of Internet connection, security becomes a huge challenge for individuals as well as governments and organizations. Therefore, in the last decade, the world is moving toward green computing in the purpose either to store energy or to decrease operational costs. So, this new technology uses web servers to provide web applications to end user. Generally, these web servers become unavailable because of HyperText Transfer Protocol (HTTP) flood Denial of Service (DoS) attack, especially HTTP-Get DoS attack. This paper proposes a novel approach based on ontology to detect slow HTTP-Get DoS attack as an intelligent system at application layer. For testing our ontological model, Canadian Institute for Cybersecurity Intrusion Detection System (CIC-IDS2017) dataset test tool is used to test our model. Results show that our ontological model detects HTTP-Get DoS attack with detection accuracy of 100%. For more illustration, a comparison study with other classic existing approaches is done so that our ontological model performs better than HADM and NetFPGA, which have an accuracy of 92.63% and 93%, respectively. [ABSTRACT FROM AUTHOR]

Breast cancer is a pressing public health issue globally and in Morocco, with rising cases among women. This study aims to evaluate breast cancer awareness and self-examination practices among female university students, informing future educational interventions. A cross-sectional study surveyed 437 students at Ibn Zohr University, Agadir, using a questionnaire covering demographics, knowledge of breast cancer, risk factors, symptoms, and breast self-examination (BSE). Results showed high awareness of breast cancer (95.3%), with social networks and media being primary information sources. However, only 48.25% had intermediate knowledge levels, and BSE awareness was moderate (60.8%) with low practical skills (28.0%). Reasons for not performing BSE included lack of knowledge and discomfort. Significant associations were found between knowledge levels and age, year of study, study options, and information sources. Despite high awareness, there is a crucial need to enhance knowledge about breast cancer risk factors, symptoms, and BSE practices among young women in Morocco. Educational programs targeting university students are essential for promoting early detection and improving attitudes toward breast health. [ABSTRACT FROM AUTHOR]

Cutaneous leishmaniasis is a significant public health concern globally. This study aims to evaluate the impact of cutaneous leishmaniasis on the quality of life of patients in the Draa-Tafilalet region of Morocco. In this cross-sectional study, data were collected from 87 patients between December 2022 and July 2023 using the Skindex-16 questionnaire. The results revealed that cutaneous leishmaniasis has a mild to moderate impact on health-related quality of life, with 26.4 % of participants reporting a low impact and 73.6 % reporting a moderate impact. A significant gender difference was observed in Skindex-16 scores, with moderate impact being more prevalent among females (60.90 % vs. 30.10 %, p = 0.002). Furthermore, facial lesions were associated with a statistically significant reduction in quality of life, particularly in the emotional (p < 0.001) and functioning (p = 0.01) domains. These findings highlight the need for targeted management strategies that address the substantial impact of cutaneous leishmaniasis on patients' quality of life. Future studies with larger sample sizes and extended follow-up periods are warranted to further elucidate the effects of cutaneous leishmaniasis on patients' well-being., (Copyright © 2024 Elsevier B.V. All rights reserved.)