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.
This paper reports the development of PID controller for real-time measurement of tissue thickness in bariatric surgery. The measured thickness is utilized to instantly modify stapling and cutting depths. Bariatric surgery is intended to help people with obesity to lose excess weight and reduce your risk of potentially life-threatening weight-related health problems. This surgery must be safe and completed quickly in order to reduce the risk of developing and experiencing possible side-effects that can sometimes be fatal. Due to this, the linear stapler endocutter, which is the surgical tool used to conduct the surgery, must accurately measure the height of the tissues to be sleeved to ensure a perfect b-shape staple has been put in place. This helps to prevent any dangerous and risky blood leakage, thus allowing a short recovery stay at the hospital. In this paper, we report a PID model of a digital caliper to be embedded in linear staplers currently available in the market to ensure a correct measurement of tissue thickness is taken and consequently an appropriate staple fitting the height of the tissue has been used. The digital caliper will work to stop the firing of the stapler in case the measurements are not correct fit thus preventing any further damage to the tissues from the extra staples and preventing the need for additional firing with the staples.
The development of an efficient medical image processing technique to detect Alzheimer's disease in the early stages will be an important medical method practitioners. Image processing, machine learning and deep learning are necessary to assist in prevent the progression of Alzheimer's disease (AD). It can be detected when processing a Magnetic Resonance Image (MRI) for the brain, and analyzing its structure. In this paper, a review of different types of innovative techniques is presented. Starting with preprocessing techniques then segmentation image techniques for several brain regions or tissues. This identifiable section or tissue assists the doctor in determining if the patient is either normal or has a disease, and as well as assists in the improvement of computer aided diagnosis efficiency.
Intimate Partner Violence (IPV) is a wide social problem in Canada and abroad. Survivors of IPV are likely to experience mental health challenges. Detecting the experience of mental health challenges is paramount to address them as early as possible. Using a Statistic Canada survey (General Health survey, 2014), we have built a machine learning approach to predict the experience of poor mental health among IPV survivors. Multi-Layer Perceptron (MLP) provide the best accuracy score of 94.88 for a 14-feature model, and 94.21 % for a 24-feature model. The use of a more detailed dataset from Statistics Canada is recommended. Multidisciplinary research has a great potential in this emerging field.
Localization in harsh and complex environments, such as industrial, confined or underground mines, is paramount. This paper presents a comparative localization study of wireless sensors in a complex environment, an underground mine, based on the received signal strength (RSS). Three estimation algorithms are tested to localize sensors in this harsh environment; the maximum likelihood estimation (ML), the two-step weighted least squares estimation (TWLS), and the generalized total least squares estimation (GTLS). The propagation model used in this work is obtained from conducted experimentation in a real mine environment. To the best of our knowledge, no comparative study was done between different localization algorithms in a mine environment. The aim of this work is to show the importance of the suitable choice of localization technique in difficult propagation conditions, allowing afterwards use and enhancing it depending on propagation parameters.
This paper discusses the development of an advanced robotic system equipped with a computer vision system for use in greenhouses to identify and harvest ripe, unripe, and diseased tomatoes. The system operates in two modes, harvesting and pruning, and is implemented using a raspberry pi and free software libraries such as Python, OpenCV, and PyTorch. The vision system utilizes YOLOv3 deep neural network for classification and MiDaS for depth estimation, and a graphical user interface is included for communication with the farmer.
The current challenges posed to engineers by the many consequences of climate change and the aim to achieve sustainable development goals drive the need to model both natural and artificial phenomena of increasing complexity in ways that remain tractable, interpretable, computationally efficient, and suitable to iterative optimization and design schemes. This work briefly reviews the techniques that are most commonly used for model simplification in the field of engineering. It mainly covers closed-form analytical models, numerical model simplification approaches, regression-based models, machine learning models, and prominent model order reduction techniques.
This paper presents a study on a 5G communication system using Quadrature Phase Shift Keying (QPSK) modulation, focusing on the effects of impulsive noise based on the Bernoulli distribution, and the impact of applying a Finite Impulse Response (FIR) Low-Pass Filter (LPF) on the system's performance. The main goal is to analyze the system's resilience to impulsive noise and assess the effectiveness of the FIR LPF in reducing its negative effects while maintaining overall communication quality. The LPF used in this study is a complex Finite Impulse Response (FIR) filter with a predetermined order ( $\mathrm{N}=100$ ) and cutoff frequency. The LPF considers various factors to effectively reduce the impact of impulsive noise on the communication system. The proposed approach is evaluated under Additive White Gaussian Noise (AWGN) with and without impulsive noise, as well as Rayleigh fading channel conditions, using key performance metrics such as Bit Error Rate (BER), Symbol Error Rate (SER), and Error Vector Magnitude (EVM). The results show that the QPSK modulation is reliable in the presence of impulsive noise and that the LPF effectively recovers its harmful effects. The performance with and without impulsive noise is evaluated to demonstrate the robustness of the proposed LPF approach.
The performance of PVDF pyroelectric films for electrical power production has been improved by adding very small amounts of nanostructured ZnO prepared by sol-gel method which is a cheap, simple, and easy method. The XRD spectrum of the prepared ZnO indicates that it is a nanostructure. Pyroelectric films are prepared from PVDF and different percentages of prepared nano ZnO. The Pyroelectric coefficient, Dielectric Constant, and Conductivity of prepared films are measured to be used as inputs for the mathematical model that is created to obtain the optimal percentage of added nano ZnO to PVDF, determine the optimal thickness of the pyroelectric film, and study the effect of the film surface area on energy harvesting performance. It is found that the addition of nano ZnO to PVDF pyroelectric film has two effects on energy harvesting: positive and negative. The positive effect is an increasing pyroelectric coefficient, but the negative effect is an increasing dielectric constant. So the optimal percentage of adding nano ZnO is defined. The optimal value of the thickness of PVDF film, which has an optimal added ZnO percentage, is determined. The reduced surface area of the pyroelectric film increases energy conversion efficiency.
This work proposes a design for non-catalytic isothermal synthesis gas production from natural gas using two different types of reactors: equilibrium reactor and plug-flow reactor (PFR). The process design was developed using Aspen HYSYS simulations. The obtained results showed that a significant hydrogen purity of 66 mol% can be obtained using equilibrium reactor with recycle compared to 50 mol% using plug-flow reactor. The proposed PFR design was also compared to a similar work in literature involving catalytic operations. As a result, the effectiveness of conventional reactors in the non-catalytic production of synthesis gas with significant hydrogen purity has been proven, presenting an advantage over the catalytic processes represented by the avoidance of the challenges associated with catalytic reactions, such as pressure drop and catalysts deactivation.
A type of power plant known as a solar tower power plant or central tower plant uses numerous mirrors, often called heliostats, to concentrate sunlight and direct it towards a central tower. The orientation of the heliostat mirrors is calculated based on various factors including the location of the mirror and tower, the time, and the date. The tracking system for the heliostats is modeled and implemented on an ARM Cortex-M3 microcontroller using Simulink, with the controller board interfaced for real-time deployment of equations. Results from simulation and experimentation are presented in the paper.
The employment of digital technologies is essential for creating new comfort levels, security, smartness, and efficiency in buildings. Buildings will shift from being a static, reactive element in the energy sector to being a proactive important component with the capacity to intelligently adapt to changes in the dynamic environment and interact efficiently with other structures and networks in smart communities and grids by possessing an array of sensors and meters everywhere, as well as a vast database of digital information. To capitalize on the benefits of building sector digitization and respond to rising demands for energy efficiency, comfort, and safety, we propose merging historical, real-world, and forecast data into a context information model to characterize and better comprehend the building. This is accomplished by digitally representing real-world assets as Digital Twins, enabling for integrated data-driven decision making across the building life cycle. The proposed holistic Digital Twin platform ‘Twin4Build’ substitutes the conventional static Building Information Model with a living deployable model that combines dynamic energy models and real-time data to provide a variety of operational services such as gathering and handling data, real-time performance monitoring and analysis, continuous commissioning, and strategic planning.
In this short note, we explain a complex-analytic numerical algorithm using the Schwarz-Christoffel mapping, for estimating the fields in physical objects of a linear material with polygonal boundary and finite constitutive parameter inside an applied field, by numerically solving the corresponding matching conditions. Examples of such linear materials are linear magnetic or dielectric materials and conductive materials. The formalism of linear magnetic problems is used to develop the methodology.
Machine learning is becoming an increasingly critical tool in next-generation telecommunications ecosystems. Effective anomaly detection tools are more necessary than ever as mobile solutions continue to become more complex, with an array of features designed to enhance network capabilities. This article presents a novel approach to detect anomalies and forecast traffic for 5G core networks, aiming to prevent severe outages and reduce traffic impact, especially for mission-critical services. By collecting 5G network functions (NFs) metrics, we developed an unsupervised learning model utilizing Autoencoder architecture with bidirectional LSTMs. Our experiments demonstrate the effectiveness of this technique on a 5G network, showing promising potential for future applications.
This paper presents a new methodology for developing a low-cost wireless ECG transmission and monitoring system based on IoT technology, designed for real-time detection and classification of heart diseases. The study focuses on using ECG data for heart disease classification, which is an area of growing interest in recent years. The study collected data from 1000 subjects using our designed system to collect the normal data (300 patients) and a Biopac MP160 data acquisition system for the collection of 10 diseases abnormal data, where all the data are acquired from lead 1. The aim of this study is to develop an accurate and reliable classification model for heart diseases using ECG data. Pre-processing steps were taken to prepare the data for feature extraction, including the use of Empirical Mode Decomposition (EMD) and digital filters such as low pass, high pass, and derivative pass filters. A new feature extraction steps based on a new ECG peak detection, segmentation, and wave modeling for each segment is also presented. Two classification methods were used: Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF). The results showed that MLP had a much higher accuracy of 99.1% compared to RBF, which had an accuracy of 97.4%. The study emphasizes the potential of using ECG data for accurate classification of heart diseases. The results demonstrate that proper pre-processing and feature extraction techniques are crucial for improving accuracy. This study is significant for remote patient monitoring and telemedicine applications, as it provides a low-cost, non-invasive method for detecting and classifying heart diseases using ECG data.
Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, “Smart Shoes for Blind and Visually Impaired People”, and “Human posture monitoring device”. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy.
