This study aims to explore the challenges and opportunities associated with developing healthcare infrastructure in Saudi Arabia through the implementation of smart technologies. The healthcare sector in Saudi Arabia is undergoing significant transformation, and the integration of smart technologies has the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance the overall healthcare experience. However, several challenges need to be addressed in order to fully leverage the benefits of smart technologies in healthcare infrastructure development. This research identifies and analyzes these challenges while also highlighting the opportunities that arise from the adoption of smart technologies in the Saudi Arabian healthcare system. The findings contribute to the understanding of the current state of healthcare infrastructure in Saudi Arabia and provide insights into the strategies and policies required to overcome challenges and maximize the benefits of smart technologies in healthcare.
Social distancing among people is vital in minimizing spread of COVID-19 within community and can be effective in flattening the outbreak. This research work focuses on developing a close contact proximity detection system among smartphone users, particularly of COVID-19 patient, using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone users in their surroundings. Covering a physical space of six feet, a mandatory safety measure in shopping centers, schools, and other crowded areas, is a social solution advocated by World Health Organization (WHO) officials in this COVID-19. Everyone is concerned about their safety in the COVID-19 environment, so we came up with the concept of producing this new equipment. Most of the time, our attention is drawn to those in front of us and to our sides, but we are unable to keep track of those behind us. The major goal of this project is to keep individuals at a safe distance from one another. PIR sensor is used in this proposed work. Why did the World Health Organization (WHO) put 6 feet as a social distancing? When someone coughs or sneezes, small droplets are spread from the cough or sneeze. If you are in close proximity, you can breathe in those droplets, which may contain the COVID-19 virus, according to the World Health Organization. Vanderbilt University infectious disease expert Dr. William Schaffner said the "6 feet distance" rule comes from studies of respiratory physiology. Schaffner explains that even "without a cough or sneeze, the exhaled air mixes with the surrounding air within a distance of 3 to 6 feet, which is known as the breathing zone." Schaffner continues: "If you are standing 3 to 6 feet away from me, you may inhale droplets that spread through coughing or sneezing. Of course, if I am infected with the virus, these droplets will contain the virus."
Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.
By the year 2026, it is estimated that the number of smartphone users in Mexico will be approximately 118.1 million. Each smartphone has the functionality of sending and receiving SMS (Short Message Service) messages, which pose a significant threat to all users, as it makes any device vulnerable to a malware attack. In particular, worm-type malware takes advantage of this means of communication in order to spread. Studying the dynamics of malware propagation can help understand and prevent massive contagion between mobile devices. In this work, a model based on Network Automata and compartmental epidemiological models is presented, aiming to simulate, analyze and study the spread of worm-like malware through sending SMS on smartphones.
A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%; thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
This material is aimed to attract attention to the "incoherent approach for power NOMA-RIS-MIMO transmission in wireless channels".Such kind of approach might be successfully applied in future dense networks formed by High-Speed Vehicles (HSV networks, etc.).Those scenarios take place in doubly selective communication channels typical for such kind of radio networks.The proposal for the presented hereafter incoherent view ("paradigm") is based on several basic principles: 1) Shift from the "coherent "ideology", i.e. rejection of the application of any type of Channel State Information (CSI, CSIT); 2) Application of the so-called "invariant" to the communication channel's features (distortions) modulation technique together with its incoherent demodulation; 3) Orthogonal channel decomposition by means of "universal" eigen basis (in the form of Prolate Spheroidal Wave Functions, PSWF) as "artificial trajectories" of wave propagation; 4) Chaotic filtering (chaos parameter settings as UE signatures) together with sequential multiuser parallel detection algorithms for users' identification (classification).It is shown that the proposed approach might provide an effective use of the radio resource and it is relatively simple for implementation.
In this study, we thoroughly examined the impact of heat treatments and hole count (p) on the properties of LnSrBaCu3O6+z (Ln = Eu, Sm, Nd) compounds. We focused on preparation, X-ray diffraction with Rietveld refinement, AC susceptibility, DC resistivity measurements, and heat treatment effects. Two heat treatment types were applied: oxygen annealing [O] and argon annealing followed by oxygen annealing [AO]. As the rare earth Ln’s ionic radius increased, certain parameters notably changed. Specifically, c parameter, surface area S, and volume V increased, while critical temperature Tc and holes (p) in the CuO2 plane decreased. The evolution of these parameters with rare earth Ln’s ionic radius in [AO] heat treatment is linear. Regardless of the treatment, the structure is orthorhombic for Ln = Eu, tetragonal for Ln = Nd, orthorhombic for Ln = Sm [AO], and pseudo-tetragonal for Sm [O]. The highest critical temperature is reached with Ln = Eu (Tc [AO] = 87.1 K). Notably, for each sample, Tc [AO] surpasses Tc [O]. Observed data stems from factors including rare earth ionic size, improved cationic and oxygen chain order, holes count p in Cu(2)O2 planes, and in-phase purity of [AO] samples. Our research strives to clearly demonstrate that the density of holes (p) within the copper plane stands as a determinant impacting the structural, electrical, and superconducting properties of these samples. Meanwhile, the other aforementioned parameters contribute to shaping this density (p).
The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide.However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation.With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA.One way to improve aircraft and operational safety is through trajectory prediction.Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety.This paper proposes a meta-learning approach to predict short-to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft.The proposed solution brings together multiple models to improve prediction accuracy.In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models.This approach gives our model an edge over single-model predictions.We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics.The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used.The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.
Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs).However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances.In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference.The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm.The data of various flights are collected and cleaned in an initial stage.The clean data is divided randomly into training and test sets.Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset.The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity.The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry.It is worth noting that the proposed algorithm is not dependent on conventional algorithms' prerequisites.
