Antonine University (UA) is a Lebanese Catholic university committed to offer quality education, to promote inter-disciplinary and contextualized research, and to enhance the sustainable wellbeing of its local and global communities. Its graduates are proactive citizens prepared to embrace an ever-growing knowledge, improve it collaboratively, and apply it responsibly..
Cet article documente et examine les chants de lamentation funèbre (nadba) des femmes de la communauté monothéiste druze du district du Chouf (gouvernorat du Mont-Liban) au Liban. Il propose une analyse sémiotique grammatologique modale, socio-culturellement contextualisée, d’une nadba enregistrée lors d’une enquête de terrain auprès de femmes détentrices de cette tradition. Cet examen, qui comporte une double réécriture morphophonologique transformationnelle musicale et syntaxique musicale de la lamentation, souligne le lien organique entre ce répertoire et les pratiques élégiaques des autres communautés du Liban, tout en mettant en exergue les spécificités liées au système de croyance druze.
Cet article s’intéresse à l’effet sédatif de l’écoute de Mašriq (monodie modale instrumentale improvisative et concertante, issue de la tradition musicale artistique du Mašriq) sur la perception douloureuse et anxiogène de la ponction de la fistule artérioveineuse de patients libanais en situation d’hémodialyse, et ce, en comparaison avec l’écoute du K 448 de Mozart et l’isolement silencieux. La première hypothèse est grammatologique musicale consistant à associer la vertu antalgique au contenu sémiosique de la musique écoutée, lequel découle de la conjonction d’une complexité syntaxique de la structure mélodique profonde, susceptible de détourner l’attention du patient, avec une fluidité attractive de la structure mélodique et rythmique de surface, susceptible d’induire une gratification plaisante et relaxante. Quant à la deuxième hypothèse, elle est contextuelle culturelle et suppose que cette écoute antalgique serait optimisée par la plus grande adéquation de la musique écoutée avec la culture autochtone des patients et leurs affinités musicales. L’étude clinique expérimentale, réalisée en 2019-2020 auprès de 88 patients libanais en situation d’hémodialyse, permet de confirmer statistiquement ces deux hypothèses.
This paper presents a comparative study on reinforcement learning-based PID tuning for a classical mass-spring-damper system using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The primary objective is to evaluate how different observation structures influence the controller's ability to minimize tracking error, overshoot, and settling time. Three custom Gymnasium environments were developed for experimentation, each differing in the observation space: (1) a baseline model using error, previous error, and integral error; (2) an extended model that includes percent overshoot; and (3) a further extended model incorporating both overshoot and settling time. All experiments used the same agent architecture, training conditions, and evaluation protocols. Results demonstrate that augmenting the state representation with additional performance metrics can lead to more stable and responsive control behavior, with notable improvements in transient performance. The findings emphasize the role of informative observations in enhancing the performance of reinforcement learning-tuned PID controllers for continuous control systems.
We present a simulation-based approach to increase throughput and resource utilization of Printed Circuit Board (PCB) assembly lines. We use ARENA to simulate and optimize production, ultimately reducing overall costs and maximizing throughput and resource utilization. A multifaceted simulation approach is used to assess different alternatives, providing insights into critical production parameters. Using ARENA to produce real-time simulations has proven to be an excellent, cost-effective approach for studying industrial supply chains, and the impact of various control parameters. For over a decade, Printed Circuit Board (PCB) assembly lines have been providing solutions, developing new technologies, and pioneering advancements in the semiconductor industry. Through more efficient resource allocation, production line reconfigurations, and long-term throughput analysis, PCB assembly lines can further enhance production processes and reduce costs through long-run simulations, “what-if” and uncertainty analysis. Simulation-based modeling using ARENA (by Rockwell Automation) provides invaluable insight into entire production lines from raw materials through assembly and quality control. This approach determines the root causes of downtimes, seamlessly identifying bottlenecks, and various forms waste and delays. The simulation model is also used to understand the effect of changes in operational plans, machine breakdown and downtime occurrences, resource allocation, as well as other production line disruptions. PCB assembly lines are always focused on continuous improvement, maintaining a lean supply chain, and optimizing its production lines without relying on simulations. We show that through a simulation-based approach, PCB assembly lines can redesign production schedules and resource allocation by reevaluating production processes, increasing PCB availability and production throughput, and reducing costs and defect rates.
In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.
