Search

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.

Practising self-regulated learning is essential in one's lifelong learning journey. As educators, we can assist our students to regulate their learning effectively, whether this is in an online learning environment or any other. However, many factors affect how well learners regulate their learning. Research reveals that self-regulated learning practices vary and warrant further exploration. This study specifically investigates how the guided practice of self-regulated learning behaviour affects learning in the online learning environment. To guide students in managing their learning more effectively, an automated self-regulated learning management system was developed. The system assists students in practising the cognitive, meta-cognitive and motivational aspects of self-regulated learning. 155 postgraduate students in two online healthcare-related courses were randomly selected to be involved in the study. Trace data from the self-regulated learning management system was used to triangulate the students' self-reports in relation to their self-regulated learning behaviours. Non-parametric statistical tests were used in the analysis. Findings indicate that the use of the self-regulated learning management system facilitated and aided students in practising more effective self-regulated learning behaviours thus impacting positively on learning motivation and metacognitive reflection. However, its effect is inconclusive in relation to academic performance. In summary, positive changes were made to their self-regulated learning behaviours and these subsequently improved their self-regulation and related outcomes. [Note: The page range (43-59) shown on the PDF is incorrect. The correct page range is 43-48.]

Introduction: Musculoskeletal disorders (MSDs) are a significant occupational health concern worldwide, affecting individuals in various professions, including healthcare practitioners. This study aimed to determine the 12-month prevalence of MSDs and identify associated factors among healthcare practitioners in Ouarzazate, in the southeastern region of Morocco. Methods: A cross-sectional study was conducted, collecting data from 120 participants. The Nordic questionnaire was used to identify MSD prevalence and locations. Results: The overall prevalence of MSDs was found to be 89.2%. Lower back pain was the most common reported site of pain, accounting for 63.3% of cases over the past 12 months. Females had a higher prevalence of MSDs (95.0%) compared to males (77.5%) (p-value = 0.009). Midwives had the highest prevalence (93.8%), followed by physiotherapists (87.5%) and general nurses (84%). Professionals working in the shift work system had a higher prevalence of MSDs (92%). Discussion: This study provides important insights into the prevalence and factors associated with MSDs among healthcare practitioners at the Provincial Hospital Center in Ouarzazate. The results underscore the need for targeted interventions and preventive measures to address the high prevalence of MSDs in this population. Such initiatives can contribute to healthcare professionals' well-being and occupational health, ultimately enhancing patient care quality.

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.

In order to stay up-to-date in the nursing field, nurses must pursue continuous education. This study examined nurses' motivations to take part in continuing education activities by conducting a survey amongst a sample of nurses employed in two hospitals in south-east Morocco. The survey was carried out using a two-part self-administered questionnaire. Out of 144 nurses who received the questionnaire, 118 (84%) replied. The results showed that the majority of nurses have never participated in a continuing education session in the last 3 years. Respondents' motivation for continuing education was mainly intrinsic, with the emphasis on factors related to professional competence and knowledge seeking. The use of such results can therefore be of great use to both managers and practitioners in planning continuing education programmes. However, it is desirable that more studies take place to investigate further the issue of continuing education with the aim of promoting and developing professional nursing skills.

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.

Effective time management is essential for us all, whether students or anyone else. There are many factors which affect how well students manage their time and in what ways. As with everything, some are excellent at managing their time and others are not. As faculty, we can assist our learners to better manage their time, whether this is in the online learning environment or any other. However, studies reveal that the effect of time management training on time management practices varies, and there is therefore a need to explore this further. This study investigates how the practice of time management, an important self-regulated learning enabler, affects learning in the online learning environment. An automated adaptive time management enabling system was used to guide students in managing their time more effectively. The system assisted students in their time management through visual reinforcement, adaptive release, learning monitors and learning motivators. The findings showed that the use of the time management enabling system facilitated and guided the students in studying the course in a consistent manner and aided students in practising more effective time management thus impacting performance. In summary, positive changes were made to their time management behaviours and these subsequently improved their self-regulation.

Abstract Background Despite negative neuroimaging findings many athletes display neurophysiological alterations and post-concussion symptoms that may be attributable to neurometabolic alterations. Methods The present study investigated the effects of sports concussion on brain metabolism using 1H-MR Spectroscopy by comparing a group of 10 non-concussed athletes with a group of 10 concussed athletes of the same age (mean: 22.5 years) and education (mean: 16 years) within both the acute and chronic post-injury phases. All athletes were scanned 1-6 days post-concussion and again 6-months later in a 3T Siemens MRI. Results Concussed athletes demonstrated neurometabolic impairment in prefrontal and motor (M1) cortices in the acute phase where NAA:Cr levels remained depressed relative to controls. There was some recovery observed in the chronic phase where Glu:Cr levels returned to those of control athletes; however, there was a pathological increase of m-I:Cr levels in M1 that was only present in the chronic phase. Conclusions These results confirm cortical neurometabolic changes in the acute post-concussion phase as well as recovery and continued metabolic abnormalities in the chronic phase. The results indicate that complex pathophysiological processes differ depending on the post-injury phase and the neurometabolite in question.

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.

With growing evidence of advanced technologies-assisted smart processes, it is fundamental to comprehend whether manufacturing systems are adequate to manage flexibility and complexity to enhance the monitoring of smart factories. Smart manufacturing (SM) is evolving as a new version of traditional manufacturing, revealing the magnitude and power of smart technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). The wide applicability of these technologies is allowing important innovations across all industries. As the manufacturing industry has gained benefits, the current boosts of Smart Manufacturing are experiencing exceptional levels of interest. However, providing suitable SM systems and identifying the priority of requirements may vary according to different scenarios. To this end, this study presents a systematic survey of the current SM research trends. Furthermore, this paper aims to present a consistent and comprehensive vision of existing efforts in smart manufacturing and discussed the remaining open issues.

Smart Logistics (SL) offers a competitive advantage for e-commerce by utilizing Information and Communication Technologies (ICT) such as IoT, AI, Blockchain, Cloud computing, 5G, etc. This technology automates, optimizes, and enables real-time tracking and monitoring of shipments, predicts, and prevents delays, and optimizes delivery routes and schedules. It also provides greater visibility and control, allowing e-commerce businesses to react quickly and efficiently to changes in demand or supply. The purpose of this study is to investigate the impact of digitalization on trade logistics in e-commerce, emphasizing the significance of smart logistics for the e-commerce industry. We reviewed 288 articles published in the last decade in the Scopus database to assess the maturity of research in this area. For researchers, this study provides a better understanding of smart e-commerce logistics and identifies research gaps in the literature. For e-commerce professionals, it can help them adopt the latest technological trends in their logistics. Through a systematic literature review and network analysis approach, the study has contributed by identifying 5 clusters related to ICT application fields in e-commerce and 5 clusters related to important ICT enablers in smart logistics. We also identified several research gaps and areas for future study, including the underutilization of computer vision technology and the need for further research on product quality inspection and accessibility for people with disabilities. Additionally, we suggest exploring the power of deep learning to solve Vehicle Routing Problems (VRP) and optimizing sensing data volume for minimizing costs associated with data storage and transfer. This study provides a comprehensive overview of the state of the art in smart logistics for e-commerce and serves as a guide for future research in this field.

Today with the technology development, the learning activity takes some different methods and approaches. Among these approaches, we can talk about virtual learning using the learning management systems and the flipped classroom concept. Moodle platform represents one of these systems. The limitation of this Platform is the lack of an integrated adaptive mechanism to manage the students' learning process. In this paper, we present a model that can be combined with the Moodle platform to provide students with an adapted content according to their knowledge level and skills. Also, offer the instructors the ability to manage the learning mechanism of their students in the context of flipped learning. The model was validated by the implementation of the Smart Adaptive Management for Flipped Learning (SAM-FL) plugin. The proposed model is based on several hierarchical agents allowing modularity in the development of course content and tests' thresholds. The evaluation of the model was carried out objectively by comparing learner output levels against entry levels, and subjectively by performing a quality of experience with the learners. The obtained results showed the model effectiveness in terms of learners' improvement levels and acceptance of our solution towards them.