The spread of COVID-19 in the beginning of 2019 has raised many challenges affecting agile team members. One of the major characteristics of Agile Project Management (APM) teams is co-location. Co-location enhances collaboration among team members who are self-organized and self-managed. Due to the pandemic, agile team members were obliged to start working remotely facing many challenges that compromised team productivity and efficiency. This paper proposes remedies that could help agile teams improve their collaboration and overcome the challenges imposed by the pandemic when working remotely. The information is based mainly on insights, suggestions and recommendations from agile management practitioners and subject matter experts.
As the demand for solar renewable energies grows globally, researchers' goal has always been to develop low-cost, high-efficiency cells, knowing that higher panel temperatures lead to poor conversion performance and decreased long-term reliability, posing a well-known challenge in the field of photovoltaics. This study employed both theoretical and computational methodologies to investigate the relationship between temperature and efficiency. To model the performance of the solar cell under varying temperatures, theoretical equations relating the temperature of the cell to the cell's efficiency were developed, and MATLAB SIMULINK was used to develop a computational model showing the output power of a solar cell under varying temperatures. Our findings reveal that the efficiency of an LR5-72HPH 545 Watts solar cell decreases from 21.3% at 25°C to 17.41% at 70°C. The results of the theoretical and computational studies were compared and found to have a small error of 1.05%, proving that computational modeling can be relied on to accurately predict solar cell performance, where this model is valuable since it allows a to analyze the effect of temperature on solar cells in a way that is simpler and faster to achieve than theoretical methods. However, by employing suitable cooling systems, it is possible to limit the impacts of temperature, which is critical for the development of more efficient and dependable solar cells, particularly in high-temperature conditions.
Deep learning has been evolving recently which allowed it to handle complex problems like big data, computer vision, and human-level control. One of the deep learning-powered applications recently emerged is called “deepfake”. Deepfake algorithms have recently been a controversial development in Artificial Intelligence, because they use deep learning to generate fake yet realistic content based on an input dataset. As a result, many are concerned with the potential risks in terms of cyber-security as it causes threats to privacy, democracy, and national security. Multiple techniques were proposed to detect deepfake videos, however most cannot cope with the variety of the deepfake generation techniques. Therefore, in this study, we optimize one of the best existing deepfake detection methods based on Xception model. In particular, our proposed optimization scheme consists of a pre-processing phase performing advanced image enhancement on the videos in hand for highlighting the face features for better feature extraction as well fake content detection, which is preceded by a close-up dataset cleansing. Our experiments show that the proposed pre-processing optimization scheme had improvemes the performance of the Xception Binary Classifier- Inference model from 94% to 96%.
Biometric authentication is an important aspect of security systems that identify individuals based on unique biological traits. Unimodal biometric systems do, however, have security and accuracy issues. In order to increase accuracy, this article suggests a multimodal biometric authentication method that combines fingerprint, voice, face, and iris data. By minimizing the false acceptance rate (FAR) and false rejection rate (FRR), as well as by its higher accuracy when compared to existing techniques, the performance of the proposed technique is assessed. The article emphasizes the value of multimodal approaches to biometric authentication as well as how they could improve the precision and security of identification systems. The development of more dependable and robust biometric authentication systems is significantly impacted by the proposed technique.
End Life Tires (ELTs) in Lebanon represent a significant environmental problem, the longer they are left without treatment, the more it threatens humans' health and safety especially when living in a country classified among the top 25 countries with the highest car ownership. Dealing with this problem is feasible by using proper treatment technique called pyrolysis, where shredded tires are heated in a reactor vessel containing an oxygen-free atmosphere to produce pyrolysis oil, and this oil is further heated in distillation reactor to produce diesel (670 L from 330 ELTs) and many other valuable and profitable products like steel wires, carbon black, solvent thinner, and grease. Protecting the environment for our future and future generations is a basic need, the public and government share major responsibly and are expected to do more to safeguard the environment.
