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This paper reports the development and switching controller design of an inverted pendulum system (IPS) platform. The euler-lagrange approach is first used to model the dynamics of the IPS which takes into account the impact of friction forces during its movements. The paper then derives a switching control method to swing the pendulum rod into the neighborhood of and stabilizing it at the equilibrium point. The implemented switching controller consists of: i) a nonlinear swing up control which brings the pendulum to the vertical position and ii) a linear stabilizing control which maintains the pendulum rod to remain in a vertical position around the neighborhood of the vertical axis. The nonlinear controller is constructed using lyapunov’s method while the linear controller is designed using linear quadratic regulator (LQR) method framework. Simulation and experimental results are presented to show the effectiveness of the proposed switching controller. [ABSTRACT FROM AUTHOR]

In software defect prediction, noisy attributes and high-dimensional data remain to be a critical challenge. This paper introduces a novel approach known as multi correlation-based feature selection (MCFS), which seeks to address these challenges. MCFS integrates two feature selection techniques, namely correlation-based feature selection (CFS) and correlation matrix-based feature selection (CMFS), intending to reduce data dimensionality and eliminate noisy attributes. To accomplish this, CFS and CMFS are applied independently to filter the datasets, and a weighted average of their outcomes is computed to determine the optimal feature selection. This approach not only reduces data dimensionality but also mitigates the impact of noisy attributes. To further enhance predictive performance, this paper leverages the particle swarm optimization (PSO) algorithm as a feature selection mechanism, specifically targeting improvements in the area under the curve (AUC). The evaluation of the proposed method is conducted on 12 benchmark datasets sourced from the NASA metrics data program (MDP) corpus, renowned for their noisy attributes, high dimensionality, and imbalanced class records. The research findings demonstrate that MCFS outperforms CFS and CMFS, yielding an average AUC value of 0.891, thereby emphasizing it is efficacy in advancing classification performance in the context of software defect prediction using k-nearest neighbors (KNN) classification. [ABSTRACT FROM AUTHOR]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]

The rise of social media platforms has led to an increase in the flow and dissemination of information, but it has also made generating and spreading rumors easier. Rumor detection requires understanding the context and semantics of text, dealing with the evolving nature of rumors, and processing vast amounts of data in real-time. Deep learning (DL)-based techniques exhibit a higher accuracy in detecting rumors on social media compared to many traditional machine learning approaches. This study presents a systematic review of DL approaches in rumor detection, analyzing datasets, pre-processing methods, feature taxonomy, and frequently used DL methods. In the context of feature selection, we categorize features into three areas: text-based, user-based, and propagation-based. Besides, we surveyed the trends in DL models for rumor detection and classified them into convolutional neural networks (CNN), recurrent neural networks (RNN), graph neural networks (GNN), and other methods based on the model structure. It offers insights into effective algorithms and strategies, aiming to guide researchers, developers, social media users, and governments in detecting and preventing the spread of false information. The study contributes to enhancing research in this field and identifies potential areas for future exploration. [ABSTRACT FROM AUTHOR]

This paper discusses software metrics and their impact on software defect prediction values in the NASA metric data program (MDP) dataset. The NASA MDP dataset consists of four categories of software metrics: halstead, McCabe, LoC, and misc. However, there is no study showing which metrics participate in increasing the area under the curve (AUC) value of the NASA MDP dataset. This study utilizes 12 modules from the NASA MDP dataset, where these 12 modules are being tested into 14 relationships of software metrics derived from the four existing metric categories. Subsequently, classification is performed using the k-nearest neighbor (kNN) method. The research concludes that software metrics have a significant impact on the AUC value, with the LoC+McCabe+misc metrics relationship influencing the improvement of the AUC value. However, the metrics relationship that has the most impact on achieving less optimal AUC values is McCabe. Halstead metric also plays a role in decreasing the performance of other metrics. [ABSTRACT FROM AUTHOR]

The security of wireless communication systems has been the focal subject of research for more than a century. It is a topic of vital importance due to its effectiveness in many fields. Besides, the importance of sending data from one point to another without intercepting by unwanted third-party has become an essential requirement in conjunction with the widespread hacking technologies. This research paper is set out to explore a new technique used in internet of things applications that will ensure a secure wireless communication system. Additionally, it clarifies five distinct cryptographic methods: advanced encryption standard, elliptic curve cryptography, transposition, substitution, and Rivest, Shamir, and Adleman. In addition, the secure wireless communication system combines four of the prementioned techniques to build a highly secure system. Moreover, we managed to elucidate the critical points of our work on the long-range systems into a short yet informative and detailed summary. This paper will extensively cover the following topics: cryptography, cryptographic methods and techniques, software implementation, applications, and advantages of the system. [ABSTRACT FROM AUTHOR]

This article provides a detailed evaluation of cutting-edge artificial intelligence (AI) approaches and metaheuristic algorithms for optimizing wind turbine location inside wind farms. The growing need for renewable energy sources has fueled an increase in research towards efficient and sustainable wind farm designs. To address this challenge, various AI techniques, including genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing, artificial neural networks (ANNs), convolutional neural networks (CNNs), and reinforcement learning, have been explored in combination with metaheuristic algorithms. The goal is to discover optimal sites for turbine placement based on a variety of parameters such as energy output, cost-effectiveness, environmental impact, and geographical restrictions. The paper examines the advantages and disadvantages of each strategy and highlights current breakthroughs in the area. This assessment adds to continuing efforts to optimize wind farm design and promote the use of clean and sustainable energy sources by offering significant insights into current advances. [ABSTRACT FROM AUTHOR]

In this paper, a proposal is made for a cryptographic algorithm designed for passive ultra-high-frequency (UHF) radio frequency identification systems. The algorithm relies on the advanced encryption standard (AES) as its fundamental encryption technique, augmented by two supplementary steps: the initial step involves generating a random key and the second is the randomization of data, this introduces an extra level of security to encryption process against attacks. The developed architecture has been optimized to minimize hardware resource consumption with faster execution speed. The algorithm has been simulated, synthesized and implemented in an xtreme digital signal processing (DSP) starter kit equipped with xilinx’s spartan-3A DSP 1800A edition and it serves the purpose of encrypting and decrypting user data on a radio frequency identification (RFID) passive tag. The main objective is to make it difficult to break the algorithm because of its multiple steps. The experimental results showed that the speed, functionality and cost of encryption and decryption make this a perfectly practical solution, providing a satisfactory level of security for today’s communications systems, or other electronic data transfer processes where security is required. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes. [ABSTRACT FROM AUTHOR]

Health monitoring systems based on the internet of things (IoT) improve patient well-being and reduce mortality risks. Machine learning techniques are most helpful in early fall prediction and detection. In this paper, fall prediction analysis and decision-making are done with existing benchmark clinical records. Classification techniques are incorporated to track the consistency and precision of data acquired by the IoT-based remote health monitoring for elderly people, especially those who are living alone. This work undertakes two approaches to early predicting a patient’s acute illness. The first approach has analyzed the existing benchmark patient activity data with different features. This approach builds the classification model for fall incidence with the help of machine learning models. In second approach, we collect real-time sensor data such as blood pressure and heart rate from IoT sensor gadgets which are transmitted to the prediction model for early prediction. Experimental results prove that the random forest (RF) classifiers and XGBoost provides the maximum accuracy. [ABSTRACT FROM AUTHOR]

This effort develops and uses a smart stick to help blind individuals walk more safely and avoid hazards. This research paper involved to develop smart stick for blind people to help them to walk safely by avoiding obstructions, and at emergency conditions sent their location to person in charge like a doctor or relatives to help them accordingly. Three ultrasonic sensors are used in the proposed device’s architecture to detect obstructions at three different heights, low, mid, and high obstructions using speaker to alert the blind person. An emergency message with the location of the blind person’s phone is sent through a mobile app to the doctor or person in charge whenever the designed smart stick’s emergency button is touched, or the stick is dropped down. The Arduino Uno platform, Bluetooth model, MPU-6050 3-axis gyroscope as a position sensor, and microSd card module has been used to effectively implement the stick. At the testing stage, the device gave good results. The user received notifications in the form of voice messages and vibrations from the smart stick when it detects things or obstacles in front of them. Additionally, even though they cannot directly activate it, the automated emergency condition has been detection and activated. [ABSTRACT FROM AUTHOR]

Smart cities today can utilize vehicular delay tolerant networks (VDTN) to collect data from connected-objects in the environment for various delay-tolerant applications. They can take advantage of the available intelligent transportation systems (ITS) infrastructures to deliver data to the central server. The system can also exploit multiple and diverse mobility patterns found in cities, such as privately owned cars, taxis, public buses, and trams, along with their vehicle-toeverything (V2X) communications capabilities. In the envisioned convergence between the ITS and V2X, we believe that a simple and efficient routing protocol can be deployed for the delay-tolerant data delivery, contrary to the implementation of optimized solutions that might be resource-demanding and difficult to standardize. In this paper, we analyzed the performances of four baseline VDTN routing protocols, namely: direct delivery, first contact, epidemic, and spray and wait, to understand their strengths and weaknesses. Our simulation results highlighted the trade-off between distinct approaches used by those protocols and pointed out some gaps that can be refined. This study provides new interesting ideas and arguments towards developing a simple, efficient, and high-performing routing protocol for data collection in smart cities. [ABSTRACT FROM AUTHOR]

This research designs, analyzes, and studies a 2.45 GHz rectangular microstrip patch antenna (RMPA). The antenna design uses Rogers RT5880 (lossy) substrate material with 2.2 dielectric permittivity, 1.5 mm thickness, and 0.0009 loss tangent. Additionally, the antenna was designed and simulated using computer simulation technology (CST) studio 2019 software. Plot designs were again created using Origin Pro Software. The simulation results showed that the return loss (S11), voltage standing wave ratio (VSWR), gain, directivity, bandwidth, efficiency, and surface current were -45.992 dB, 1.0101, 6.115 dBi, 6.534 dBi, 70.8 MHz, 93.59%, and 49.9 A/m, respectively. This paper aims to increase return loss to a typical VSWR value near 1. Besides boosting antenna gain, directivity, and efficiency, it can be used in future wireless applications, including mobile phones and wireless LANs. The proposed antenna design outperforms earlier experiments, demonstrating that the research has increased performance. [ABSTRACT FROM AUTHOR]

Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain. [ABSTRACT FROM AUTHOR]

Within the rapidly evolving landscape of wireless communication technologies, this scientific paper delves into an innovative approach by exploring the integration of the Bluetooth frequency band into a circular antenna design. Leveraging the capabilities of metamaterials. The Bluetooth frequency band a cornerstone for short-range communications, takes center stage in our study. Traditionally associated with wireless connectivity in diverse applications, its integration into a circular antenna structure opens up new avenues for optimizing performance and functionality. Our research aims to not only enhance the efficiency of short-range communication systems but also contribute to the broader discourse on antenna design evolution. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. By scrutinizing the symbiotic relationship between the Bluetooth frequency band and the circular antenna augmented with metamaterial, our study endeavors to push the boundaries of current design paradigms. We believe that this exploration will not only address the contemporary challenges associated with wireless communication but also pave the way for the development of more sophisticated, adaptable, and compact devices in the ever-expanding realm of telecommunications. [ABSTRACT FROM AUTHOR]

In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers (HOs) between cells. Ping-pong handover (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints. In addition, the proposed algorithm will have immunity to RLF and will significantly minimize the amount of PPHO compared to other workers. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the user mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the user mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover between overlapped small cells decreased and will not exceed one PPHO compared to another literature model. [ABSTRACT FROM AUTHOR]

Radiological imaging generates large data volumes for diagnosing. These data volumes still constitute an immense challenge for individual radiology practices, either when complying with the archive, hacker manipulation or swiftly distributing images among specialists as part of the diagnostic process. Analyzing images through illegal distortions may lead to inaccurate medical decisions. Watermarking techniques can be used to authenticate images, detect, and recover illegal changes made to teleradiology images as well. In this paper, a new blind medical image watermarking joint cryptocompression system is proposed to simultaneously achieve watermarking and compression. A discrete wavelet transform is applied to the medical image to locate different frequency sub-bands. The watermark is scrambled using one-dimensional chaotic map generator with uniform distribution modulo-one transformations for generating highly independent and uniformly distributed random chaotic sequences. A particle cryptocompression algorithm is applied for the insertion step: the scrambled watermark is inserted into the highest singular value derived from the compressed low-frequency sub-band, ensuring a delicate balance between imperceptibility and robustness. From the conducted tests and comparison of the obtained results with other techniques, we concluded that the robustness of the algorithm under various attacks was improved by the excellent value of the NC parameter, especially in the case of compression and geometric attacks. [ABSTRACT FROM AUTHOR]

The suitable placement and power of distributed generation (DG) can bring technical benefits to the distribution network. This paper applies the Coot optimization algorithm to the DG placement problem with the goal of minimizing power loss and feeder balancing load (LBF). The weight method is used to combine the membership objectives. The evaluation results on the network of 70 nodes for different weight values of the objective function show that the optimal power and installation location of DGs significantly reduces power loss and improves LBF index. In this study, eleven cases were considered. As the weight of power loss part W1 increases from 0 to 1, the power loss gradually decreases, the LBF index gradually increases, the maximum current gradually decreases, and the minimum voltage amplitude gradually improves. Comparing the results of Coot with particle swarm optimization (PSO) shows that the indicators are improved. In the three cases where W1 is 0, 0.5, and 1, power loss gained by Coot compared to PSO is less than 47.2663 kW, 73.2725 kW, 30.8708 kW, respectively. LBF index of Coot compared to PSO is less than 98.2%, 88.2%, 81.9%, respectively. The maximum, minimum, average, standard deviation, and CPU time of Coot are smaller than those of PSO. So, Coot is one of the promising methods for this problem. [ABSTRACT FROM AUTHOR]

Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for imagelevel classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread. [ABSTRACT FROM AUTHOR]

Since cloud computing has an abundance of users, the system has to execute a wide range of tasks. Task scheduler methods that are both robust and efficient while delivering the best outcomes are required. The task volume and runtime in the cloud vary rapidly, making task assessment and resource mapping difficult. Security issues, communication delays, and data loss are substantial barriers to scheduling. Furthermore, optimization techniques can be utilized to reduce load and assign tasks so that the user can finish tasks faster. This paper offers a hybrid job scheduling technique for cloud computing using adaptive particle swarm optimization and ant colony optimization particle swarm optimization-ant colony optimization (adaptive PSO-ACO). After rapidly finding the initial solution via particle swarm optimization, the ant colony optimization approach establishes its first pheromone distribution. The suggested hybrid algorithm is compared to standalone PSO and ACO algorithms. Compared to ACO, the percentage decrease is 7.9%. Hybrid method has the lowest total cost, 55% less compared to PSO. Tasks vary when virtual machines (VMs) are constant and VMs vary when tasks are constant. Parameters like final cost, makespan, fitness value, computation time and weighted time are assessed to evaluate the performance of the hybrid task scheduling algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we design and simulate a 28/38 GHz dual-band multiple-input multiple-output (MIMO) patch antenna array that operates in the FR2 frequency band (28 GHz and 38 GHz). This antenna array comprises four similar patch antennas with a rectangular "L minus two slots" shape. In addition, it applies to 5G electronic components such as a smartphone. We used high-frequency structure simulator (HFSS) software to perform the design and simulation for this antenna. In addition, this proposed antenna array provides better performance, such as; bandwidth around 28 GHz is equal to 0.69 GHz and 38 GHz is equal to 0.86 GHz, equal gains 5. 9 dB at 28 GHz and 9 dB at 38 GHz, directivities are 6.3 dB at 28 GHz and 9.4 dB at 38 GHz, the efficiency of 95.38% at 28 GHz, and efficiency of 96.53% at 38 GHz. [ABSTRACT FROM AUTHOR]

This paper presents the design of model predictive control (MPC) combined reinforcement learning (RL) applied in an intelligent transportation system (ITS). The car is to follow the reference path by the MPC control, and its parks in the parking by RL has been trained. The MPC controller constantly moves the vehicle along the reference path while the MPC algorithm searches for an empty parking spot. Meanwhile, the reinforcement learning-proximal policy optimization (RL-PPO) control will perform parking on demand if the MPC finds a parking position. This hybrid controller can quickly implement programming on MATLAB software by writing code. Furthermore, this hybrid controller simultaneously performs precise detection and avoidance of obstacles in tight parking spaces. The correctness of the theory is demonstrated through MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Microstrip patch antennas are becoming increasingly popular because they are small, have low profiles, are easy to integrate, are very cheap, and work well. For this reason, this antenna could be used for wireless communication systems. This research paper reviews and studies 28 GHz microstrip patch antenna for wireless applications. Different substrate materials have been used to make these antennas, such as FR-4 (loss), FR-4 Epoxy, Rogers RT/droid 5880, TLC-30, Rogers RT/droid 5880 LZ, and others. Different substrate materials and shapes were used to make microstrip patch antennas with a frequency of 28 GHz. This article discusses the different sizes of antennas, the other geometric shapes antennas can take, the different ways antennas' properties can be analyzed, and the different types of antennas. It will also talk about the material, thickness, loss tangent, return loss, bandwidth, voltage standing wave ratio (VSWR), gain, efficiency, and directivity of the substrate. This antenna is used for super-high-frequency (SHF), radars, commercial wireless local area networks (LANs), cell phones, and other wireless communications systems. [ABSTRACT FROM AUTHOR]

Terahertz (THz) technology is the utilization of electromagnetic waves with frequencies in the range of 0.1 to 10 terahertz. This frequency range is also known as the submillimeter range, lying between the microwave and infrared regions of the electromagnetic spectrum. Terahertz technology has numerous applications in various fields, such as communications, spectroscopy, imaging, and sensing. In communications, terahertz waves can transmit large amounts of data over short distances and potentially revolutionize wireless communication networks. Recently, scientists and researchers have been concentrating on terahertz technology as it continues to evolve. In this paper, we design and simulate a rectangular-shaped patch nanoantenna with a line-feeding technique; the proposed antenna is based on three layers: a perfect electric conductor (PEC) patch, a silicon substrate layer, and a fully PEC ground plane layer. The main aim is to study the impact of altering the nanoantenna's parameters, including shape, size, ground plane, feed line, and patch nanostructures, on its behavior. The simulated antenna operates in triple-bands of frequrncywhich are 571.85, 715.66, and 905.05 THz. As a result, the applications of this terahertz frequency band are within the visible range. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for calculating the DC source amplitude in an extended multilevel inverter (MLI) structure so that the maximum number of levels and the output voltage waveform are as close as possible to the sinusoidal wave with minimum total harmonic distortion (THD). For the developed structure, three algorithms are suggested to determine the amounts of DC voltage resources. The first important point about choosing the right amounts for the DC resources is that the number of levels should be as large as possible, and the second important point is that the intervals between the levels should be the same throughout the waveform. By observing these two points, the output voltage waveform can be as near as possible to the sinusoidal wave that we want. In this study, we used iteration-based methods to find suitable values for DC sources. Simulation results are offered to confirm the capability of the extended multilevel converter. After we solved the problem through calculation and analysis, a code was written in MATLAB with the aim that this time the code will tell us for what values of DC sources we will have the largest number of levels and as we expected, the output of the MATLAB code confirmed the correctness of the calculations. [ABSTRACT FROM AUTHOR]

In this paper, the results of making a reduced graphene oxide cathode electrode with Cu2S nanoparticles are shown so that it can be used as a counter electrode in quantum dot solar cells to replace other counter electrodes. An rGO-Cu2S paste obtained by hydrolysis was scanned onto the surface of the fluorine-doped tin oxide (FTO) conductive substrate when bound to Cu2S nano by a screen-printing process, then calcined at 350 °C to crystallize the film. Following calcination, the film was examined for structure using energy-dispersive X-ray (EDX) and X-ray diffraction (XRD) spectroscopy, as well as for type and particle size using scanning and transmission electron microscopy and transmission electron microscopy, respectively. Mott-schottky measurement is used to determine the semiconductor and carrier concentrations in the film, and an electrochemical device is used to assess the electrodes redox capacity in a polysulfide electrolyte solution. The operability of the rGO-Cu2S cathode at the peak of the current density in the C-V curve was 24 mA/cm², a 30-fold increase compared to that of the Cu2S electrode. This result shows that the efficiency, Voc, FF, Jsc are 4.92%, 0.525 V, 0.418, and 22.4 mA/cm2, respectively. [ABSTRACT FROM AUTHOR]

With continuous development of wireless communication and internet of things (IoT) technology enables many terminals to be interconnected with each other through the external environment by wireless communication. Along with that, 5G/6G technology provides services with enhanced quality and high data transmission speeds, requiring the implementation of IoT in the 5G/6G architecture. Free space optical (FSO) communication is considered as a promising technique that can connect IoT devices by high-speed communication link, so for wireless networks to fully realize the potential of 5G/6G technology providing speeds of 100 Gb/s or more, FSO is the optimal choice. By implementing FSO features in IoT, the performance and convegare of IoT will be improved and enhanced by using techniquaes for FSO systems. Therefore, this paper proposed and investigated the FSO communication system using reconfigurable intelligent surfaces (RISs) over Weibull distribution. The performance of system is evaluated according to the average channel capacity (ACC) parameters in the case with and without RIS, while the the different of the model is changed. The average spectral efficiency (ASE) parameters can also be represented ACC. [ABSTRACT FROM AUTHOR]

This paper presents new techniques for designing a simple and reliable multifeatured biometric system based on a single trait source. First, a one-toone relationship between the feature’s edge and its associated angle is utilized after extracting the contrast feature using the gray-level co-occurring matrix (GLCM) method. Secondly, the classifying stage is modified to process one-dimensional vectors rather than the whole feature’s template. That means whatever the template size is, the matching operation is always processing a one-dimensional vector called a mean-feature vector which requires low storage and less computation complexity. Finally, for comparison purposes, the performances of the three biometric systems are calculated for recognizing 170 subjects taken from four facial databases. These comparisons are made using three error distance measurements. The recognition rates of the angle-based feature were very competitive to the regular edge-based results; however, the overall recognition accuracy is highly improved after fusing the decision of the two unibiometric systems using the Logic-OR operator. The fused system performance was satisfactory and it shows that the decision fusion of the single source trait based multifeatured system has promising performance represented by accuracy improvement, low storage, and low matching time. [ABSTRACT FROM AUTHOR]

Following the standard methods proposed by Polak-Ribiere-Polyak (P-R), in this work we introduce two new non-linear conjugate gradient methods for solving unconstraint optimization problem, our new methods based on P-R. Standard method (P-R) have performance well in numerical result but does not satisfy global convergency condition. In this paper we modified double attractive and powerful parameters that have better performance and good numerical result than P-R method, also each of our robust method can satisfies the descent condition and global convergency condition by using wolf condition. More over the second method modified by logistic mapping form, the main novelty is their numerical results and demonstrate performance well with compare to a standard method. [ABSTRACT FROM AUTHOR]

This paper presented the performance of physical layer security of permutation index (PI) with modulating bits-based orthogonal frequency division multiplexing differential chaos shift keying system (PIM-OFDMDCSK), the objective behind PIM-OFDM-DCSK is to improve and increase the information rate, spectral, and energy efficiencies. In PIM-OFDM-DCSK, the (M) subcarriers, each carry (n+1) bits. The n bits are index bits utilized to choose one permutation of the chaotic reference. On the receiver, after OFDM demodulation, the correlation is made between the permuted versions of the chaotic reference and the received bearing sequence of data. Then the received bits are assessed by determining the maximum correlator outputs via which the n bits are detected. Then the maximum output correlator sign is utilized to detect the modulating bit. The performance of the PIM-OFDM-DCSK is tested under additive white gaussian noise and multipath fading channel. Furthermore, the bit error rate is derived based on the wiretap system for the proposed PIM-OFDM-DCSK and compared with other DCSK modulations. The results show that the proposed PIM-OFDM-DCSK scheme is promising and competitive compared with other DCSK system, and the best BER performance is as an increase in the value of n and a decrease in the value of β. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). One promising application of computer vision techniques in ITS is in the development of lowcomputing and accurate vehicle counting systems that can be used to eliminate dependence on external cloud computing resources. This paper proposes a compact, reliable and real-time vehicle counting solution which can be deployed on low-computational requirement edge computing devices. The system makes use of a custom-built vehicle detection algorithm based on the you only look once version 8 nano (YOLOv8n), combined with a deep association metric (DeepSORT) object tracking algorithm and an efficient vehicle counting method for accurate counting of vehicles in highway scenes. The system is trained to detect, track and count four distinct vehicle classeses, namely: car, motorcycle, bus, and truck. The proposed system was able to achieve an average vehicle detection mean average precision (mAP) score of 97.5%, a vehicle counting accuracy score of 96.8% and an average speed of 19.4 frames per second (FPS), all while being deployed on a compact Nvidia Jetson Nano edge-computing device. The proposed system outperforms other previously proposed tools in terms of both accuracy and speed. [ABSTRACT FROM AUTHOR]

In this paper, a bit error rate (BER) performance is evaluated for power domain non-orthogonal multiple access (PD-NOMA) system. The performance test is examined considering; additive white gaussian noise (AWGN), flat and long-term evolution (LTE)-multipath selective channels such as; pedestrian channel model (EPA), vehicular channel model (EVA), and typical urban model (ETU). The proposed system considering two user equipment’s (UE1 and UE2) with a single base station (BS) for downlink channel. Two different powers were allocated to each user according to their positions from the BS. The superposition coding process is performed with successive-interference-cancelation (SIC) at both transmitter/receiver sides respectively to distinguish the desired received signal. The performance evaluations proves that the degree of power allocated to each user plays a significant rule in frequency selection environments. UE1 has a better BER performance than UE 2 by about 9 dB in EPA, 6 dB in EVA, and 7 dB in ETU environments respectively at a BER of 10-3 . However, in flat fading environment, the distance for each user represents a significant factor affecting the BER performance, where, UE 2 has a better BER performance than UE 1 by about 10 dB at a BER of 10-3 . [ABSTRACT FROM AUTHOR]

The electrical power distribution parts include the distribution transformer responsible for reducing high voltage (HV) 11 Kv to low voltage (LV) 416/240 volts, and the power distribution lines (PDLs), which are the source of electrical power supply for residential neighborhoods. Any malfunction of these parts, result in a power outage. Currently, any faults are reported, detected, and alert in a primitive way, and this mechanism certainly takes a lot of time and effort. In this paper, an automatic approach for detection, report and alert has been suggested. This approach process is carried out by continuously checking the status of the three output feeders (power distribution lines), which is the low voltage side of the distribution transformer, when a malfunction occurs in any one or more of these feeders, it will generate an alert signal in the form of a text message using short message service (SMS). The maintenance center is notified by receiving this SMS via SIM900 modem through the global system for mobile (GSM) communications network. The SMS text message includes feeder number, fault type and location address (street number and distribution transformer number). In this design, time and effort are shortened and the maintenance center enables fully automatic monitoring of electric power distribution parts within the sector of responsibility. [ABSTRACT FROM AUTHOR]

Due to the rapid development of information technology, the process of exchanging information over the web has become simple and fast. Nevertheless, the transmission of information will be at risk due to the nature of the internet, as anyone can access and change the data by hacking it. Therefore, it is necessary for protecting any personal information from being accessed by unlawful individuals. It became necessary to use encryption and steganography techniques for protecting data. Steganography is a manner used for covering hidden information in several other media while preserving the form of the original data and leaving no evidence of hidden data, whereas, encryption changes the original information into unclear or ambiguous information, which is called scrambled. The paper presents a method of video steganography as a powerful and effective tool for data steganography. We used a video as a spreading medium and then used it for concealment of an audio message and a document (in a pdf file) in a method that makes the information invisible using the least significant bit (LSB) method. [ABSTRACT FROM AUTHOR]

The number of studies on COVID-19 has rapidly grown in recent years. The pandemic has caused widespread disruption, including in renewable energy research. This study aims to examine the impact of the COVID-19 pandemic on renewable energy research by conducting a bibliometric analysis from the Scopus indexing database from 2020 to 2023. This study employs a bibliometric approach to analyze the authors, affiliation, publication source, keywords, thematic map, and trend topic. The analysis will focus on the comprehensive overview of the published research and identify areas where future research is needed. The results show that the number of research studies related to COVID-19’s effect on renewable energy will continue growing and a solid collaboration network exists. China has had a significant presence on the impact of COVID-19 on renewable energy studies, including organization and university affiliations, number of single and multiple country publications, funding sponsors, and most cited countries. The finding also shows niche themes, research trends, and thematic evolution shifting. This finding will contribute to a better understanding of the impact of the COVID19 pandemic on renewable energy research and a new direction for future research. [ABSTRACT FROM AUTHOR]

A flood stands as one of the most common natural occurrences, often resulting in substantial financial losses to property and possessions, as well as affecting human lives adversely. Implementing measures to prevent such floods becomes crucial, offering inhabitants ample time to evacuate vulnerable areas before flood events occur. In addressing the flood issue, numerous scholars have put forth various solutions, such as the development of fuzzy system models and the establishment of suitable infrastructure. However, when applying a fuzzy system, it often results in a loss of interpretability of the fuzzy rules. To address this issue effectively, we propose to reframe the optimization problem by incorporating stage costs alongside the terminal cost. Results show the proposed model called hybrid fuzzy logic and neural networks (NNs) can mitigate the loss of interpretability. Results also show that the proposed method was employed in a flood early detection system aligned with integrating into Twitter social media. The proposed concepts are validated through case studies, showcasing their effectiveness in tasks such as XOR-classification problems. [ABSTRACT FROM AUTHOR]

Technology’s increasing role in everyday life has pushed the evolution of the internet of things (IoT), which now permeates industries like information technology, agribusiness, and transportation. Critical concerns in IoT security include platform diversity and issues with authentication and authorization. Critical vulnerabilities identified by researchers contain unencrypted communications, compromised interfaces, and compromised access control processes. A new solution, narrowband IoT (NB-IoT), has responded. Based on the cellular network, this technology is designed for improved security and efficiency, operating within the fourth-generation mobile networks and leveraging essential network components. The current study focuses on NBIoT vulnerabilities, particularly in the radio segment, which is notably vulnerable. The research utilized the open-source tool OpenLTE and hardware like software-defined radio (SDR) in a setting with active NB-IoT sensors on an LTE network. This included deploying a test listening tool and a laboratory-based IMSI catcher to intercept active device communications in a testbed. The results highlight significant vulnerabilities: sensors were deactivated following simulated network attacks with rogue eNodeB and traffic area update (TAU) messages, revealing the technology’s susceptibility to connection failure. [ABSTRACT FROM AUTHOR]

This review provides a concise overview of key transformer-based language models, including bidirectional encoder representations from transformers (BERT), generative pre-trained transformer 3 (GPT-3), robustly optimized BERT pretraining approach (RoBERTa), a lite BERT (ALBERT), text-to-text transfer transformer (T5), generative pre-trained transformer 4 (GPT-4), and extra large neural network (XLNet). These models have significantly advanced natural language processing (NLP) capabilities, each bringing unique contributions to the field. We delve into BERT’s bidirectional context understanding, GPT-3’s versatility with 175 billion parameters, and RoBERTa’s optimization of BERT. ALBERT emphasizes model efficiency, T5 introduces a text-to-text framework, and GPT-4, with 170 trillion parameters, excels in multimodal tasks. Safety considerations are highlighted, especially in GPT-4. Additionally, XLNet’s permutation-based training achieves bidirectional context understanding. The motivations, advancements, and challenges of these models are explored, offering insights into the evolving landscape of large-scale language models. [ABSTRACT FROM AUTHOR]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [ABSTRACT FROM AUTHOR]

This study intends to identify research gaps and future trends and provide a framework for the next generation of research to assess how much big data (BD) is employed in hospitality and tourist research. The study is based on a comprehensive quantitative evaluation of the relevant literature: Scopus and Web of Science (WoS)-listed academic works. The following criteria were used to assess the submissions: those who have the following traits an overview of the study’s subject matter, including its theoretical and conceptual framework, data sources, data kind and quantity, data collection methods, and data analysis methodologies. Research shows that the usage of books on hospitality and tourism management has increased in recent years. Massive volumes of data are analyzed using analytical methods. However, the scope of this investigation is really wide. Furthermore, this research contributes to an in-depth and systematic assessment of the extent to which scholars in hospitality/tourism know and work on business intelligence and BD. This is the first complete survey of the literature on the topic of hospitality and tourism. [ABSTRACT FROM AUTHOR]

Computer methods in biomechanics are computer methods for solving biomechanics problems. Rula, Reba, Niosh, Owas is a method of analyzing work risks due to incorrect posture. Each method uses a worksheet on one posture to analyze risk. For dynamic work, a computer method is needed that can quickly calculate every change in posture. Virtual reality and Kinect as dynamic work analysis methods cannot be carried out preventively. Preventive measures are needed to prevent workers from risks resulting from work. For this reason, computer simulation methods are needed to create work stations and work processes so that they can be carried out preventively. The s-task simulation builder (S-TSB) framework provides a dynamic work analysis solution using simulation. The three processes carried out include surveying, creating simulations and processing posture data. Software validation was tested using the black box analysis method and the results were as expected. The dynamic working model is tested showing results in the form of graphs so it is easy to compare each change. The use of simulation also saves design costs because optimization can simply be done by changing the simulated work station data and/or work process. [ABSTRACT FROM AUTHOR]

The field of image and 3-dimensional (3D) data segmentation is growing fast and has many uses, like in medicine, and robotics. In this article, we explain how computers understand and divide images and 3D data. We compare different ways of doing this in 2D and 3D, and look at the computer methods used. We also discuss recent work and what they discovered. This article gives a broad overview of what's happening in this area of computer science. It explains the goals of the research, how they do it, and what they've found out. It's a useful guide for researchers to understand what's happening now and what challenges they might face in the future. [ABSTRACT FROM AUTHOR]

This study introduces a novel e-voting system utilizing blockchain technology to address the challenges inherent to traditional voting methods. Traditional systems often suffer from inaccuracies, susceptibility to manipulation, and elevated costs. Conversely, while e-voting shows potential, issues related to transparency and security have curbed its full adoption. Our research overcomes these hurdles by integrating a system developed through the Kanban methodology, with the blockchain serving as the central repository for all election data. This approach boosts transparency and security, using public-private key pairs for each transaction, and simplifying blockchain access. Organizers initiate elections and define eligible voters; this data is then securely moved to the Ethereum blockchain. Voters can effortlessly use the system, casting votes and accessing real-time, unalterable results. Various communication protocols ensure system stability, with simulated cyberattacks showcasing its security. After exhaustive testing and refinement, areas for further enhancement have been identified. This innovative system offers unmatched transparency and trust in the voting process, marking a considerable leap for trustworthy elections, especially in small to medium-sized settings. [ABSTRACT FROM AUTHOR]