The objective of the present work is to evaluate the performance of a low-cost tractor equipped with a parallel hybrid engine, which was simulated using AMESim software. The tractor was evaluated with three different farming implements attached to the tractor, and each implement requires a different type of power. The first simulation was executed without any implements attached. The tractor was able to run for 170 s with the electric motor only, which resulted in fuel savings during this period. The first implement, a moldboard plow, was attached for the second round of evaluation, and the electric motor ran by itself for 150 s, which also led to fuel savings during operation. During the third simulation, the tractor was attached to a Bette Harvest, which has a very high-power demand. The obtained results show that both engines were engaged to provide the required energy. During the final round of evaluation, simulations were run for a straw tub grinder. In this simulation, the electric motor ran alone until the battery was fully discharged. Thereafter, the combustion engine was activated in order to facilitate operations and to charge the battery. The results show that the parallel hybrid architecture employed for the low-cost tractor significantly decreased the CO₂ emissions and minimized the consumption of fuel.

This paper examines the rapid advancements and integration of emerging technologies in the medical field, particularly focusing on Artificial Intelligence (AI), the Internet of Medical Things (IoMT), Augmented Reality (AR), and cybersecurity. The study leverages data from Scopus and Web of Science databases to highlight the trends, challenges, and future directions in the development of medical devices. Significant progress has been made in enhancing patient care through the integration of AI and IoMT, which facilitate predictive analytics, personalized treatment plans, and real-time data monitoring. AR is transforming medical training and surgical precision, while cybersecurity measures are becoming increasingly vital to protect sensitive health data. Despite these advancements, the field faces challenges such as data privacy concerns, infrastructure limitations, and interoperability issues. The study also explores Africa’s contributions, with a particular emphasis on Morocco’s emerging role in this technological landscape. Three major research clusters identified include AI and AR, IoT and cybersecurity, and embedded systems, each playing an important role in the evolution of medical technologies. By analyzing publications from 2010 to 2024, the paper provides insights into the current state and future potential of advanced medical technologies, aiming to offer a foundation for further research and innovation in this rapidly evolving field.

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing.

The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.

Cardiovascular diseases remain a major global health challenge, highlighting the need for accessible, intelligent, and real-time cardiac monitoring solutions. This paper presents Moroccan VitaTrack GeoMed, a patented Made-in-Morocco 12-lead ECG monitoring prototype integrating embedded signal acquisition, artificial intelligence, real-time visualization, and screening support. The proposed framework combines ECG preprocessing, a new hybrid CNN–Transformer architecture for local morphological feature extraction and long-range temporal dependency modeling, and an ensemble-based meta-classification module designed to improve prediction robustness under practical operating conditions. The proposed classification framework was retrospectively evaluated using 7536 12-lead ECG recordings from a publicly available dataset, covering nine ECG categories. Across ten experimental runs, the framework achieved a micro-F1 of 91.76 ± 0.23%, a macro-F1 of 87.35 ± 0.19%, and a micro-AUPRC of 96.46 ± 0.13%. The highest F1-scores were obtained for SR (95.40 ± 0.22%) and SB (95.14 ± 0.23%). AFL, ST, AF, and TWC achieved F1-scores between 86.72 ± 0.39% and 90.80 ± 0.42%, while MI, SA, and MCA showed more moderate performance at 79.42 ± 0.46%, 80.37 ± 0.48%, and 79.94 ± 0.67%, respectively, possibly due to overlapping ECG patterns and class heterogeneity. Separately from this retrospective algorithmic evaluation, the framework was integrated into the Moroccan VitaTrack GeoMed prototype and assessed through a one-month preliminary technical feasibility study involving a single healthy volunteer (the principal author). A total of 42 repeated ECG recording sessions were conducted under six monitoring conditions, of which 38 produced analyzable recordings, corresponding to a technical session consistency rate of 90.5%. Comparison with a conventional 12-lead ECG system demonstrated relative measurement differences below 3% and a signal correlation coefficient of 0.93. These findings support the preliminary engineering feasibility of the integrated platform for real-time ECG acquisition, processing, visualization, and screening-support functionality. Importantly, the reported classification metrics were obtained from retrospective database recordings and do not represent diagnostic performance on pathological ECG signals acquired directly using the prototype. Further prospective clinical studies involving independent participants and patients with documented cardiac abnormalities are required to establish diagnostic validity, clinical utility, and generalizability before routine clinical use.

Background The integration of Artificial Intelligence (AI) in electrocardiographic (ECG) devices has become a pivotal area of research, particularly during the COVID-19 pandemic. These technologies are essential for enhancing cardiac diagnosis and monitoring. Methods This study assesses current trends, key contributors, and collaborative networks in the field of AI-enhanced ECG devices. We utilized a comprehensive analysis, using the Biblioshiny library from Bibliometrix for data exploration of data extracted from the Scopus database and VOSViewer for creating and visualizing maps. These tools were played an important role in conducting an in-depth analysis of the relationships and developments within the field. Results The analysis shows a significant increase in publications related to AI-enhanced ECG devices, with a marked surge during the COVID-19 pandemic. Despite the growing interest and technological advancements, the study exposes a notable disparity in the geographical distribution of research contributions, highlighting substantial under-representation of African researchers. This gap is attributed to infrastructural, financial constraints, and limited collaborative networks within the continent. Conclusion The rapid evolution and increasing importance of AI in ECG devices underscore the need for more inclusive research practices. There is a critical need to integrate and promote contributions from under-represented regions, particularly Africa, to ensure a globally diverse perspective in tackling health challenges. This study calls for enhanced participation and support for African researchers to bridge the existing research gap and foster global health equity.