بحوث سكوبس — مصدق ماهر عبد الزهرة

Accurate methods for early detection are required since heart disease is still a major problem in world health. In order to accurately forecast the occurrence of heart illness using electrocardiogram data, this research introduces a hybrid model called MLP-FRCNN (Multi-Layer Perceptron-Faster Region-Based Convolutional Neural Network). The suggested method uses the DNLMS algorithm to remove baseline fluctuations and motion artifacts from ECG signals before processing them. By utilizing Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT), we are able to extract features, with a particular emphasis on important components such the QRS complex. To improve the Faster R-CNN, the Honey Badger Algorithm (HBA) takes into account factors including computing efficiency and overlapped detecting boxes. Results from further tests show that, in comparison to contemporary methods, we achieve better accuracy, sensitivity, specificity, and F1 score. Machine learning, which began with data modification and accumulation, has evolved into a powerful tool for driving transformative change and remains a central component of the ongoing pursuit of artificial intelligence. Accurate detection and treatment for coronary heart disease patients are greatly enhanced by the suggested model’s higher speed of convergence and enhanced predictive capabilities. To improve accuracy, an FFNN combiner takes the estimates from both the Faster R-CNN and MLP and applies them to patient’s demographics information and low-order characteristics. With a 98% accuracy rate, the hybrid model outperforms both MLP (94% accuracy rate) and HBA-FRCNN (96% accuracy rate). © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

This article discusses a novel strategy to make AI-based healthcare informed consent clearer, simpler to obtain, and more equitable. The recommended solution uses natural language processing and user input to process permission forms faster and make them easier to understand for patients. We verify the shorter documents for clarity, user comprehension, and fairness, ensuring the agreement process is effective for numerous individuals. Dynamic feedback allows the consent process to improve depending on user behavior and group-specific issues. The strategy uses continual growth and monitoring to eliminate bias, clarify materials, and ensure compliance. Performance measuring metrics improve reading scores, user comprehension, validation accuracy, and fairness measures compared to previous techniques. The proposed method simplifies health care and makes consumers happy. These modifications ensure that informed consent in healthcare is lawful, fair, and adaptable, enabling a patient-centered strategy that adjusts to changing healthcare requirements. This study suggests that employing modern technology in informed consent might make patients more confident and participating, creating a more inclusive healthcare system. © 2025 IEEE.

This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need 100-150ms. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves. © 2025 IEEE.

This paper introduces graph-based deep learning for functional connectivity mapping and brain network analysis that improves accuracy, reliability, and efficiency. The method creates a link network from functional MRI data, with nodes indicating brain areas and lines exhibiting statistical connectivity. The model strengthens neural linkages through spectrum decomposition, graph convolution layers, and attention processes to improve categorization. It outperforms CNNs (89.2%), RNNs (85.6%), SVM (80.4%), and RF (78.1%) with 95.8% accuracy. It is fast (120 ms), scalable, and easy to grasp (9/10), making it ideal for large-scale scanning. The approach also detects abnormalities in neuron joining, which may aid neurological disorder diagnosis. Precision (94.5%), recall (96.2%), and F1-score (95.3%) demonstrate its dependability. It utilizes less memory than CNNs (400 MB) and RNNs (450 MB) at 250 MB. This study shows that graph-based deep learning works effectively in functional connectivity research. They help us understand brain-computer interfaces, cognitive function, and neurological diseases. © 2025 IEEE.

The objective of this research work is to evaluate the influence of v-shaped corrugated basin integrated with nanoenhanced phase change material (NePCM) on the freshwater productivity. Experiments were conducted using different nanoparticles such as graphene oxide (GO), Al2O3, TiO2 and CuO as well as phase change material (PCM) namely fatty acids and graphene oxide. The conventional single slope solar still (CSSS) was combined with individual nanoparticles (CSSS + nanoparticle) and PCM (CSSS + PCM) and also with their combination (CSSS + nanoparticle + PCM) for performance analysis. The consideration of nanoparticle and PCM either individually or in combination increased the daily yield in comparison to the CSSS. The highest productivity was obtained in the case of CSSS + Paraffin wax + Al2O3 + TiO2 + GO (5.21 L/m2/day) as compared to the CSSS (3.15 L/m2/day), increasing the daily yield by 65%. The daily yield in case of CSSS + Fatty acid + Al2O3 + TiO2 + GO is 5.09 L/m2/day over the CSSS, thereby resulting in productivity improvement by 61%. The findings of the study present the potentiality of the developed v-shaped corrugated solar still with different combination of nanoparticles and PCM. © Copyright (c) 2025 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The mobility and open access of wireless communication channels expose sensitive electronic health information to unauthorized access. In Wireless Body Sensor Networks (WBSNs), securing this data is critical. To address these security concerns, the proposed system introduces a Modified-Enhanced Lattice-Based Cryptography with Message Authentication Code (MAC-MELBC) as a hybrid authentication solution. This system integrates both symmetric and asymmetric cryptographic techniques, creating a robust ciphering mechanism. The Message Authentication Code (MAC) ensures message integrity and authentication, while symmetric key cryptography supports efficient key management and validation of communication parties. The MAC-MELBC method employs symmetric cryptography for the authentication process and asymmetric lattice-based cryptography for secure key generation and data verification. The asymmetric component, MELBC, utilizes advanced lattice-based algorithms to generate encryption keys and perform cryptographic operations for verifying physiological data collected from the human body. The system splits plaintext and applies dual encryption for enhanced security and authentication. Its encryption and decryption mechanisms offer strong protection against various security threats within WBSNs. Performance is evaluated using key generation time, encryption/decryption time, memory usage, total execution time, energy consumption, and resilience against attacks. Compared to conventional algorithms like RSA and Elliptic Curve Cryptography (ECC), the proposed MAC-MELBC demonstrates superior performance, achieving a 96% accuracy rate and offering enhanced security and efficiency for WBSN implementations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

This review paper aims to evaluate the impact of defects on the reliability and degradation of photovoltaic (PV) modules during outdoor exposure. A comprehensive analysis of existing literature was conducted to identify the primary causes of degradation and failure modes in PV modules, with a particular focus on the effect of defects. Based on a risk priority number (RPN) analysis of previous studies, dust accumulation on the PV surface (severity = 9), module shading (severity = 8) and humidity (severity = 7) were found to be the most significant causes of degradation. Furthermore, the degree of performance and degradation of PV modules were analyzed based on the identified failure mechanisms and modes. The analysis revealed that the decrease in efficiency ranged from 0.2 to 3%. The dust accumulation reduced the module efficiency by 3%, while corrosion in the module decreased efficiency by 1.9%. These findings highlight the importance of addressing specific defects to maintain optimal PV module performance in outdoor conditions. This review paper provides valuable insights into the effect of defects on the performance of PV modules, and critical defects occur during outdoor exposure to PV modules which depend on the type of PV technology and outdoor environment conditions and are able to mitigate the further performance of PV modules. The present study will help manufacturers improve the design and maintenance strategies of PV systems. © The Author(s) 2024.

The powder mixed electrical discharge machining (PMEDM) process was simulated via finite element analysis in the current study to assess heat behavior and material removal rate. The goal of this paper is to conduct a thorough experimental and thermal examination of electrical discharge machining (EDM) in order to forecast its cutting characteristics and subsequently optimize the output variables using a response surface methodology for simulations and choosing the most suitable set of process variables related to the PMEDM process. This study’s objective is to design a 2D axisymmetrical transient thermal model that might also describe the physics of material removal in a single spark PMEDM operation on a Titanium Zirconium Molybdenum (TZM) superalloy. ANSYS (version 9.1) software is used to perform transient heat transfer simulations to determine the temperature profile with the amount of material removal at different current, pulse on and off times, gap voltages, and fraction of heat that enters the specimen. The PMEDM process produced craters with a lower diameter and depth, which increased the material removal rate and enhanced the surfacing quality. Compared to the conventional EDM process, the inclusion of powder raised the heat flux given to the work material by 10%–12%. It has been determined that with the single spark modeling technique, the temperature significantly dropped in both the radial and depth directions. The computational results are compared with experimental observations for similar machining conditions, and both results agree satisfactorily. © 2024 American Institute of Physics Inc.. All rights reserved.

Small-scale Darrieus wind turbines have a wide scope in areas that are isolated from the power grid for such small-scale household applications. Applications of wind turbines on house roofs are one potential way to generate electricity from wind energy harvesting in low-wind urban locations. This work studies the aerodynamic behavior of a vertical axis wind turbine based on a MATLAB programming mathematical model. The NACA0021 airfoil profile blade was used in this present research investigation. The turbine was fabricated with dimensions such as chord length, c = 95 mm, blade height, h = 600 mm, and turbine diameter, D = 600 mm. The experimental results of the turbine for air velocity from 1 to 12 ms−1 were used in this paper and compared with analytical results. It has been observed that the fixed-pitch turbine does not start by itself at a low air velocity of 1 to 5 m/s due to a minimum and negative torque. © 2024 Author(s).

Demand for energy is increasing as the world's population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun's rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel's performance was analyzed using the linear regression method. © 2024 Institute of Advanced Engineering and Science. All rights reserved.

Lighting systems are among the most energy-intensive systems in the home because they consume a lot of electricity. Nighttime blackouts are currently being caused by an urgent need for lighting systems, especially emergency lighting. PLN and the battery provide electrical energy for emergency lighting. DC-DC converters that use the bidirectional method regulate electrical energy sources. PLN bidirectional converters have two functions, namely buck mode and boost mode, which work whenever the primary source of PLN is out, the battery will automatically supply the lighting system. In this research, the bidirectional converter can switch from buck mode to boost mode depending on the voltage of the power supply When the power supply voltage is low, the converter will switch to buck mode to reduce the voltage, and when the power supply voltage is high, the converter will switch to boost mode to increase the voltage. This allows the battery to be used as a backup power source when the main source fails. A lamp load of 10 W is used in the boost mode of the discharging process. Analytical proportional integral control methods are used to control the charging and discharging processes. © 2023 Taylor & Francis Group, LLC.

In this study, preparation of azo dyes as appropriate functional azo groups. Then pointed out best chemical properties of azo dyes by Mechanism preparation of Azo dye derived from Cephalosporin (Ceftazidime and Cefotaxime) and application in Pure Pharmaceutical dosage. Also in this study, synthesized poly (AM-co-AC) hydrogel by free radical copolymerization, was utilized as an initiator for the free radical reaction in the presence of a catalyst, potassium persulfate (KPS), and N,N-methylene-bis-acrylamide (MBA) as crosslinking agent. The overlay nanopolymer was diagnosed utilized techniques, like FESEM, TEM and XRD measurements, this surface have a properties could be applied for future work of water treatment. Precision, selective, rapid, sensitive, inexpensive, and accurate spectrophotometric method has been developed for the study of cefotaxime in pure pharmaceutical dosage. The oxidative coupling reaction of the cefotaxime drug with 2,4-dinitrophenyl hydrazine in potassium periodate as a chromogenic reagent in alkaline medium to preparation of azo dye form a color-stable orang product soluble in water with a maximum λmax of 580 nm for two drug (Ceftazidime and Cefotaxime). The best conditions for the estimation were established, like the effect of volume of the reagent, the order of additions, the effect of volume of sodium hydroxide, the effect of temperature, the effect of solvent, and the effect of oxidation time. That obeyed law lambert beer in linearity of the concentration (1–10 mg/L) of cefotaxime, correlation coefficient of R2 (0.9979), (0.9689) and LOD( 1.2×10-4 μg/ml), ( 1.4×10-3 μg/ml),and LOQ (9.2×10-4 μg/ml), LOQ (8.3×10-3μg/ml), for two drug (Ceftazidime and Cefotaxime) respectively. The value of recovery% was in the range of 99.16–100.7 (n = 3), which indicates the precision of the developed method. This method is useful successfully for the determination of for two drug (Ceftazidime and Cefotaxime) in pharmaceuticals (injection). © This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

As different pollutants are deposited on the high voltage bushings, a dry band forms, which causes a flashover. The bushing's contaminated layer will weaken its insulation and have an impact on its electrical characteristics. The performance of bushings in dry band conditions of various lengths was investigated in this proposed piece of work, and a dynamic arc model is presented for the arc process in polluted bushings. It shows satisfactory performance in modelling the arc variables for various dry band positions. The developed dynamic open model for contaminated bushings with and without RTV coating predicted the flashover voltage and dry band positions. Any type of contamination, such as sea salt, road salt, and industrial pollutants prevalent in several sites, can be studied using the established model. Ultimately, it was discovered that there was good agreement between the model's results and the outcomes of the experiments. • Mathematical modeling of 22 kV bushing is conceded out for diverse polluted dry band location at lead-in, lead-out and middle region of bushing surface. • Dynamic arc modeling involved in bushing flashover process for different dry band location is done and flashover voltage is predicted • Experimental work is carried out to find FOV for the bushing with different dry location and compared with predicted FOV. © 2024

The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy. © 2022

In this study, the mechanical properties of concrete mortars have been predicted using machine learning tools, Response Surface Methodology (RSM), and Artificial Neural Network (ANN) approach. This study focused on mortar, in which cement has been partially replaced by 20% fly ash (FA) and 20% hydrated lime. In the experiment, the compressive strength (CS) of mortar has determined after curing the mix for 7 and 28 days, respectively. Glass fiber was added in the proportions of 0%, 0.2%, 0.4%, 0.6%, 0.8%, and 1% by weight of concrete to the mortar accordingly. The compressive strength of mortar incorporated with glassfiber increases according to an increase in the proportion of the glass fiber. Results indicates that the optimal fiber proportion of the glass fiber in the mortar had been observed to be 0.6%. The predicted compressive strength at day 28 has been modeled using RSM and ANN. The RSM model has been used to predict mechanical properties (R2 ≥ 0.7534) accurately. Furthermore, the appropriate R threshold (R > 0.999) for training, testing, and validation demonstrates that the ANN model has successfully captured the variability in the data. The results show that with the high correlation between the experimental and prediction results in data, more accuracy has been observed in the ANN model than in the RSM model. © 2023 Elsevier Ltd

In this paper, the study and simulation of turbulent flow inside a parabolic solar collector tube equipped with two spring insert samples with two different pitch ratios (P/D = 0.22, 0.44) and a specific cross-section are investigated. Heat transfer and properties of Cu-Fe3O4/Water hybrid nanofluid with volume fractions of φ = 1%, 3%, and 5% for the range of Reynolds numbers of 7000, 9000, and 11000 are selected and investigated using the single-phase method. The tube is used as the adsorbent pipe of a parabolic solar collector to investigate the performance of the parameters such as volume fraction, Nusselt number, pressure drop, parabolic solar collector efficiency, performance evaluation criterion, and field synergy principle. The results show that with decreasing the pitch ratio, Nu and solar collector efficiency increase. The maximum efficiency is 2.53 for a tube with P/D = 0.22 (Re = 7000, φ = 1%). The maximum efficiency is obtained under the same conditions in a tube containing the spring insert with P/D = 0.44 is equal to 2.39. At the same Reynolds numbers, the average of these velocities is significantly higher than a plain tube. Field Synergy Principle is a reliable criterion to investigate the mechanism of heat transfer increase that this coefficient value in the tube equipped with a spring insert is much higher than in a plain tube. This increase indicates the positive performance of the spring insert on the heat transfer rate and solar collector efficiency improvements. Therefore, spring inserts with a lower P/D are more desirable to achieve the highest efficiency of solar collectors as a source of renewable energy. © 2022 The Authors.

COVID-19 gains from the research and technology component's establishment of information science, artificial intelligence, and computer understanding. The article aims to discuss the numerous facets of today's modern technology utilized to combat COVID-19 emergencies on various scales, such as medicinal picture handling, illness tracking, expected outcomes, computational science, and medications. Techniques: A complex search of the knowledge base associated with existing COVID-19 innovation is conducted. Furthermore, a concise survey of the excluded data is conducted, analyzing the various aspects of current developments for dealing with the COVID-19 pandemic. The below are the outcomes: We have a window of musings on the audit of the tech propellers used to mitigate and mask the significant impact of the upheaval. Even though several investigations into current innovation in COVID-19 have surfaced, there are still required implementations and contributions of innovation in this war. Consequently, a thorough presentation of the available data is given, and several modern technology implementations for combating the pandemic of COVID-19. Continuous advancements of advanced technologies have aided in improving the public's lives, and there is a strong belief that proven study plans utilizing AI would be of great benefit in assisting people in combating this infection. © 2021

One of the environmental pollution is happened by the discharge of industrial wastewater that needs to be adequately filtered. Given that the effluent from the leather industry contains high levels of chromium, heavy metals, lipids, and Sulphur, it is one of the wastewater disposals that are most damaging. This experimental study focuses on reverse osmosis and hybrid organic polyimide membrane for nanofiltration for sustainable wastewater treatment. In the RO and organic polyamide Nano-porous membranes, a thin film of polyamide membrane was used for efficient filtration. Taguchi analysis optimized process parameters such as pressure, temperature, pH, and volume reduction factor. The outcome shows an 89% reduction in total wastewater hardness, an 88% reduction in sulfate, and an 89% efficiency reduction in COD. As a result, the proposed technology significantly increased filtration efficiency. © 2023 Elsevier Ltd

Graphene is one of the most important two-dimensional carbon allotropes with a plate structure similar to honeycomb nets. Due to its high thermal conductivity (TC), it is an excellent material for the thermal management of electronic nano-components. Investigating and obtaining thermal attributes of graphene for use and replacement in electronic components to study the cooling of parts is one of the important topics discussed by researchers. This research investigates monolayer graphene (MG) and helical graphene (HG). First, the TC of MG is calculated and compared using the equilibrium method (Green-Kubo) and the non-equilibrium method. The produced graphene sheets are usually not perfect and have various defects affecting graphene's thermal and mechanical attributes. Then, the impact of nitrogen doping defect on the TC of MG is investigated. In addition, three samples of HG in different dimensions are simulated using the non-equilibrium method. The TC for each of these samples is obtained and compared. Finally, as an innovation of this research, simulated graphene coated with hydrogen atoms and TC are calculated for this model. The results show that nitrogen doping in the graphene structure reduces the amount of TC. The TC of HG depends on the effective length of the structure and the cross-sectional area of the structure. Also, the TC was reduced by hydrogenating the HG structure. © 2022 Elsevier B.V.

Researchers have carried out the kinetics of various agro products for open sun drying, but research articles still need to address such analysis for cotton seeds. Open sun drying of cotton seeds has been experimentally investigated and presented in this paper. Shorting of cotton seeds was carried out to collect appropriate samples in current research work. Cotton seeds were found to have a nearly ovoid shape with an average radius of 2 to 2.5 mm. The initial moisture content of cotton seeds was estimated to be 14.65% wet-basis using the hot air oven method. During drying, the reduction in the mass of cotton seeds was measured at every one-hour time interval. From this data, it was observed that drying occurred with a falling rate period. Drying data were fitted with 10 mathematical models available in the literature. Multi-regression analysis in Excel-solver equation was performed to obtain values of constants and coefficients of these models. Coefficient of determination (R2), reduced chi-square (χ2) and root mean square error were taken as criteria for the selection of the best drying model. The diffusion approach and models by Verma et al. were chosen as the most suitable drying models for open sun drying of cotton seeds. Effective diffusivity was estimated and found within the range suggested in the literature. © The Author(s) 2023.

This study focuses on distributed generation (photovoltaic power plant). We evaluated material theories and solar energy distribution difficulties. The 100-kilowatt photovoltaic power plant's technical and economic features were then determined. Growing global population, finite energy supplies, and the negative environmental effects of irresponsible fossil fuel consumption have pushed renewable energy to the forefront of global concern. These factors have influenced the global trend toward renewable energy. This article introduces photovoltaic systems as a new energy source and calculates their technical and economic characteristics. Promoting the use of these systems, especially in areas remote from the electricity distribution network, while mitigating network development and fuel supply problems could reduce fossil fuel consumption. This method works in rural areas without electrical distribution. During the summer, the deviation angle is 15 to 20 degrees less than the latitude, and vice versa during the rest of the year. It reduces greenhouse gas emissions significantly, and in the near future, it will be economically feasible to do so if production of these systems is increased and construction costs are reduced © 2023, Mathematical Modelling of Engineering Problems.All Rights Reserved.

This research aims to investigate the effect of different amounts of doping elements (magnesium and yttrium ions) on the hardness, elastic modulus, flexural strength, and transparency of alumina ceramics. For this purpose, different amounts of Mg2+ and Y3+ doped α-Al2O3 nanoparticles were synthesized via the co-precipitation method. The results revealed that the majority of Mg2+ and Y3+ doped α-Al2O3 nanoparticles have a particle size of 300–400 nm. Furthermore, the density and transparency (60% in-line transmittance at a wavelength of 5 μm, with a sample thickness of 2.4 mm) of the bulk materials prepared with doping of 100 ppm Mg2+and 400 ppm Y3+ (100M400Y) presented the best performance compared with other samples. Furthermore, the hardness and Young modulus of this sample were 28 GPa and 349 GPa, respectively. The flexural strength of the 100M400Y sample reached the highest value, 193 MPa, due to the smaller grain size and minimal porosity. © 2023 The Author(s)

The objective of this paper is to study the algebraic properties of weak fuzzy complex matrices, where many elementary properties will be obtained such as the invertibility, the determinants, and the eigen values and vectors. In addition, a full solution of linear systems of weak fuzzy complex equations will be provided as an effective and easy algorithm. Also, many examples to clarify the validity of our approach. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

The Internet of Things (IoTs) has accelerated with the introduction of powerful biomedical sensors, telemedicine services and population ageing are concerns that can be solved by smart healthcare systems. However, the security of medical signal data that collected from sensors of IoTs technology, while it is being transmitted over public channels has grown to be a serious problem that has limited the adoption of intelligent healthcare systems. This suggests using the technology of blockchain to create a safe and reliable heart sound signal (PCG) that can communicate with wireless body area networks. The security plan offers a totally dependable and safe environment for every data flowing from the back end to front-end. Also in this paper, to classify heart sound signals, we suggested a one-dimensional convolutional neural network (1D-CNN) model. The denoising autoencoder extracted the heart sounds' deep features as an input feature of 1D-CNN. To extract the detailed characteristics from the PCG signals, a Data Denoising Auto Encoder (DDAE) was used instead of the standard MFCC, the suggested model shows significant improvement. The system's benefits include a less difficult encryption algorithm and a more capable and effective blockchain-based data transfer and storage system. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

Solar panels are designed to convert solar energy into electrical energy. This electrical energy is then sent to a battery or an inverter, which converts it into usable power. The power produced by the panels cannot be monitored directly as it is being generated. This system typically consists of a solar panel monitoring device that measures the voltage, current and temperature of the solar panel. This data is then used to determine the efficiency of the solar panel and identify any potential problems that need to be addressed. Monitoring the performance of the solar panel, it helps to ensure it is operating at its peak efficiency and reducing the risk of potential damage. A 100 Wp panel and a 12V 45 AH battery are used in the solar power plant battery charging process. The voltage sensor needs to be calibrated so that it can accurately measure the voltage from the solar panel and the battery. This is important because the voltage must be within certain parameters in order for the battery to charge safely and efficiently. By monitoring the performance of the solar panel and the voltage sensor, potential risks of damage can be minimized. Calibration is followed by determining the programme and low and high values. Using the Arduino IDE software, the programme is then input into Arduino. The results of the DC voltage sensor measurement and the programme used were then compared. A 3-day monitoring process is carried out for PLTS battery charging. The average voltage that rises during charging from 08.00 to 15.00 is 0.341 V after the monitoring process. © 2023, International Journal of Renewable Energy Research. All Rights Reserved.

Heat transfer enhancement is required for numerous situations, i.e., gas turbines, nuclear power plants, micro and macro scale heat transfer, airfoil cooling, electronic cooling, semiconductors, biomedical and combustion chamber lines, etc. One of the prominent ways of increasing the heat transfer coefficient from the surface of a heat exchanger is by moving the position of the thermal boundary layer to make it either thinner or break the same partially. It requires making use of an increased surface area/fins. Accordingly, the research progressed in heat transfer enhancement by using concavities/dimples of the heat exchanger surfaces to improve the heat transfer coefficient and heat transfer rate. These impregnations are made on the internal flow tubes/surfaces of the heat exchanger surfaces. The present research work aims at the experimental investigation of a heat exchanger to determine the airflow pattern and computation of heat transfer rate on the dimpled surfaces. This research work will be beneficial and applicable to heat transfer enhancement applications like micro heat transfer, where space constraint is considered. The geometries considered for the experiment include flat plates and dimpled surfaces. The parameters like Reynolds number (varied from 20,000 to 50,000), dimple depth to imprint diameter ratio (varied from 0.2 to 0.4), and heater input to the test plates (varied from 75 to 120 W) are considered for the comparisons. The results with dimpled surfaces are compared with the flat plate surfaces having no dimples. The Reynolds and Nusselt numbers rise in direct proportion to the heater input. For pin fin and dimpled plate, the ratio of Nusselt number to area average Nusselt number drops for 75 W and 100 W input. The dimpled plate with a ratio of 0.3 between imprint diameter to dimple depth had the highest ratio of Nusselt number to Nusselt number value for the entire group. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

The operation of the electrical systems is a major problem for electrical companies' subject to uncertainties threatening. In this study, the optimal management of the energy demand in the electrical distribution grid is done by interval optimization approach under electrical price uncertainty. The management of the energy demand is implemented via incentive-based modelling of the demand response programs (DRPs). The incentive-based modelling as reserve, and based on bid price for reduction of the electrical demand at peak hours is proposed. The interval optimization approach is used for the minimization of the electrical price uncertainty effects. The main objective in the proposed approach is minimizing operation cost; epsilon-constraint method is utilized to solve the problem. Finally, an electrical distribution grid has been used at various case studies to numerical simulation results and positive effects of the proposed modelling under uncertainties. © 2023 Ngakan Ketut Acwin Dwijendra et al., published by Sciendo.

The bending behavior of beams with a square cross-section containing holes under a three-point bending load was investigated in the present study. As aluminum alloys are lighter than steel and there are many automobile companies that use this material, 6063 aluminum alloy was chosen for this study. The present research was carried out both experimentally and numerically. In the numerical section, aluminum beams were simulated with the finite element software LS-DYNA. These beams contained holes with a diameter of 21 mm, which were placed at different distances on the length of the beam. In this research, 36 perforated beams were simulated in three thicknesses: 1, 1.5, and 2, and the holes were placed at 0, 3.5, 7, 10.5, 14, 17.5, 21, 24.5, 28, 35, 42, and 49 mm in relation to the middle of the beam. In addition, a beam without holes was studied for each thickness. Numerical simulations were validated with experiments and good results were observed. The obtained results showed that as the hole moves from the center to the sides, the absorption of energy and maximum force increases. In the final section, the most optimal beams were determined in terms of thickness and hole location, and based on these a car bumper was proposed. © IMechE 2022.

Within this work, to promote the efficiency of organic-based solar cells, a series of novel A-π-D type small molecules were scrutinised. The acceptors which we designed had a moiety of N, N-dimethylaniline as the donor and catechol moiety as the acceptor linked through various conjugated π-linkers. We performed DFT (B3LYP) as well as TD-DFT (CAM-B3LYP) computations using 6-31G (d,p) for scrutinising the impact of various π-linkers upon optoelectronic characteristics, stability, and rate of charge transport. In comparison with the reference molecule, various π-linkers led to a smaller HOMO–LUMO energy gap. Compared to the reference molecule, there was a considerable red shift in the molecules under study (A1–A4). Therefore, based on the analysis of energy level, A4 and A3 were shown to be promising non-fullerene acceptors with the designed donors for applications in solar cells. It is hoped that the current study would provide theoretical insights into the design and amplification of optoelectronic characteristics of suggested frameworks on a grand scale in comparison with the reference molecules. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Abstract—In innovative research, human footsteps are harnessed as a backup energy source. Researchers have developed shoe insoles equipped with a mini AC generator, which efficiently converts mechanical energy into electrical energy. This generator incorporates gears, a diode, capacitors, and an integrated circuit to regulate the output voltage, maintaining a steady 5 V. The electrical energy is stored in a battery via a DC step-up mechanism. When activated, this setup produces 4.99 V, 9.1 A, and 36.37 W of power. The study employed a three-cell storage battery with a capacity of 1800mAh for temporary energy storage. Charging the battery required 40 pumps, consuming 45.46 watts. Renewable energy sources, such as this innovative use of human footsteps, offer sustainability, environmental friendliness, and long-term cost-effectiveness. By converting mechanical energy into electrical energy, the mini AC generator embedded in the shoe insoles maximizes the utilization of human movement. This electrical energy is efficiently stored in a battery for future use through the DC step-up mechanism. The study’s findings highlight the potential of tapping into renewable energy sources to meet our energy needs sustainably. © 2023 Taylor & Francis Group, LLC.

Using massive multi-input multi-output (massive MIMO) techniques in the modern wireless transmission links offers highest performance and best spectral efficiency among all the recent techniques. On the other hand, one of the most attractive modern modulation techniques is orthogonal time frequency space (OTFS) technology which is considered as a new modulation's generation that overcome the challenges of fifth generation (5G).Third proposed system implemented using a downlink massive MIMO transmission based on OTFS modulation had been proposed. An efficient channel estimation named 3D-SOMP technique had been proposed for mMIMO-OTFS system with highest performance among all related systems. To obtain better performance by using massive MIMO systems, there are different precoding techniques proposed. ZF and MMSE precoding methods demonstrated to show the best method that can be used in massive MIMO systems. Zero forcing precoding method offers the best performance with less bit error rate among the proposed methods. © 2023 IEEE.

The random forest algorithm under the MapReduce framework has too many redundant and irrelevant features, low training feature information, and low parallelization efficiency when dealing with multihoming big data network problems, so parallelism is based on information theory, and norms is proposed for random forest algorithm (PRFITN). In this paper, the technique used first builds a hybrid dimensional reduction approach (DRIGFN) focused on information gain and the Frobenius norm, successfully reducing the number of redundant and irrelevant features; then, an information theory feature is offered. This results in the dimensionality-reduced dataset. Finally, a technique is suggested in the Reduce stage. The features are grouped in the FGSIT strategy, and the stratified sampling approach is employed to assure the information quantity of the training features in the building of the decision tree in the random forest. When datasets are provided as key/value pairs, it is common to want to aggregate statistics across all objects with the same key. To acquire global classification results and achieve a rapid and equal distribution of key-value pairs, a key-value pair redistribution method (RSKP) is used, which improves the cluster's parallel efficiency. The approach provides a superior classification impact in multihoming large data networks, particularly for datasets with numerous characteristics, according to the experimental findings. We can utilize feature selection and feature extraction together. In addition to minimizing overfitting and redundancy, lowering dimensionality contributes to improved human interpretation and cheaper computing costs through model simplicity. © 2023 Musaddak Maher Abdul Zahra et al.

A great deal of attention must be paid to the development of NRE, because electricity demand is growing rapidly. For this reason, it is important to continue developing ideas for how to generate electricity in various ways. A piezoelectric material such as PVDF can be used to generate electricity. PVDF material is a piezoelectric material, meaning it is able to convert mechanical energy into electrical energy. This is done by creating an electric field when mechanical stress is applied to the material. This makes PVDF a great option for generating electricity from renewable sources, such as wind or wave energy. By using this technology, we can reduce our dependence on fossil fuels and protect the environment. An electric current rectifier rectifies the electric current released by the PVDF. Current and voltage sensors are used to measure voltage and current values, which are then translated by an Arduino board. Micro SD cards are used to store voltage and current measurements. This research shows that PVDF converts waves' mechanical energy into electrical energy. An impact on a wave will result in an increase in voltage and current generated. It is estimated that on average 2.29 mW of power is generated. © 2023, International Journal of Renewable Energy Research. All Rights Reserved.

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Cloud computing has increased its service area and user experience above traditional platforms through virtualization and resource integration, resulting in substantial economic and societal advantages. Cloud computing is experiencing a significant security and trust dilemma, requiring a trust-enabled transaction environment. The typical cloud trust model is centralized, resulting in high maintenance costs, network congestion, and even single-point failure. Also, due to a lack of openness and traceability, trust rating findings are not universally acknowledged. "Blockchain is a novel, decentralised computing system. Its unique operational principles and record traceability assure the transaction data's integrity, undeniability, and security. So, blockchain is ideal for building a distributed and decentralised trust infrastructure. This study addresses the difficulty of transferring data and related permission policies from the cloud to the distributed file systems (DFS). Our aims include moving the data files from the cloud to the distributed file system and developing a cloud policy. This study addresses the difficulty of transferring data and related permission policies from the cloud to the DFS. In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing. The data files are moved from Amazon S3 buckets to the interplanetary file system (IPFS). In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing. © 2022 Rajit Nair et al.

The rational design and fabrication of a high performance, durable, and cost effective bifunctional electrocatalyst employed for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is of remarkably great importance for the commercialization of rechargeable metal–air batteries. While electrocatalysts designed based on spinel oxides have been deemed as promising catalyst materials for the ORR and OER, their catalytic performance still must be considerably enhanced to satisfy the prerequisites of practical applications. In this work, a heterostructure (MnCo2O4/N-GQD/MXene) of Ti3C2Tx (MXene) nanosheets decorated with spinel manganese-cobalt oxide (MnCo2O4) nanoparticles and N-doped-graphene quantum dots(N-GQD) fabricated via facile hydrothermal method. The presence of plentiful active reaction sites, high surface area and distinctive electronic structure, resulting from robust interfacial interaction, lead to the higher electron conductivity and faster ORR/OER kinetics in Zn-air batteries. Therefore, the obtained MnCo2O4/N-GQD/MXene electrocatalyst presenting an outstanding ORR performance with a low over potential of only 22 mV, half-wave potential (E1/2) of 0.87 V (vs RHE), and remarkable long-term durability, strikingly outperforms commercial Pt/C (20 % w/w). Concerning OER, the MnCo2O4/ N-GQD/MXene exhibiting an onset potential of 1.54 V(vs RHE) and a Tafel slope of 65 mV dec−1 displays better performance than IrO2. The zinc-air battery with MnCo2O4/ N-GQD/MXene cathode shows superior peak power density (152.8 mW cm−2), tremendous capacity efficiency up to 753 mAh g−1 and substantial stability, proving that MnCo2O4/ N-GQD/MXene could feasibly be used in metal–air batteries. © 2022 Elsevier B.V.

Among several complex hydrological process elements, Evapotranspiration (ET) is the most complex one. Estimation of ET is very challenging compared to other hydrological variables as it depends on complex interactions of several hydrometeorological variables. In the current research, the estimation of daily ET from maximum and minimum temperature was established. For this purpose, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Multivariate Adaptive Regression Spline (MARS) were hybridized with two advanced metaheuristic optimization algorithms [i.e., Whale Optimization Algorithm (WOA) and Bat Algorithm (BA)]. Daily ET and temperature data estimated at 3 locations in the coastal region of southwest Bangladesh for the period 2005–2016 were used to develop and validate the models. The results showed a good performance of DENFIS-WOA model with minimum values of normalized root mean square error (NRMSE = 0.35–0.54) in estimating ET using only temperature in the complex climatic setup of southwest Bangladesh. DENFIS-BA also showed reasonable performance (NRMSE = 0.43–0.62), while the performance of MARS–WOA (NRMSE = 0.54–0.97) and MARS-BA (0.60–1.13) was found satisfactory in terms of most of the statistical indices. Obtained results were also evaluated using innovative visual presentations of model outputs, which revealed the better capability of only DENFIS-WOA in estimating mean, variability and distribution of ET for all the months and locations. The results indicate the potential of DENFIS-WOA to be used for reliable estimation of daily ET from the temperature in a tropical humid coastal region. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

By 2050, the world's population will have increased by 34%, to more than 9 billion people, needing a 70% increase in food production. Prepare more dishes with fewer ingredients. Therefore, the critical goal of manufacturers is to increase production while being ecologically benign. Supply chain systems that do not enable direct farmer-to-consumer connection and rising input costs influence data collection, security, and sharing. Constraints on data security, manipulation, and single-point failure are unfulfilled due to a lack of centralized IoT agricultural infrastructure. To address these issues, the article proposes a blockchain-based IoT model. This study also shows one-of-a-kind energy savings. The decentralization of data storage improves the supply chain's transparency and quality through blockchain technology, thus farmers can engage more efficiently. Blockchain technology improves supply chain traceability and security. This article provides a transparent, decentralized blockchain tracking solution and proposes an intelligent model protocol for several Internet of Things (IoT) devices that monitor crop development and the agricultural environment. A new approach has resolved the bulk of the supply chain difficulties. Smart contracts were utilized to organize all transactions in decentralized supply networks. The use of blockchain technology improves transaction quality, and customers may verify the legitimacy of an item's authenticity and legality by using the system. A total of 100 IoT nodes were distributed randomly to each 500 m2 cluster farm. The Internet of Things nodes were used to assess soil moisture, temperature, and crop disease. Network stability period and network life of the proposed method show 90.4% accuracy. The food supply chain will be more efficient and trustworthy with an intelligent model. The immutability of ledger technology and smart contract support further increases supply chain security, privacy, transparency, and trust among all stakeholders in the multi-party system. By 2050, the world's population will need a 70% increase in food production. The food supply chain will be more efficient and trustworthy with an intelligent model. This article provides a transparent, decentralized, and intelligent model protocol for several Internet of Things (IoT) devices. © 2022 Anass Hamadelneel Adow et al.

Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR (τα)) of the T-Gr nanofluids were increased significantly. © 2022 The Authors

Microgrids need optimization to reduce economic problems and human losses. Scattered resources in power systems and microgrids have led to many environmental, economic and human, and animal losses. The most important part of these problems is related to voltage and frequency fluctuations when possible occurrences such as extreme load changes or errors in microgrids. These problems lead to microgrid collapse. Therefore, providing optimal solutions that can solve these challenges is essential. For this purpose, the present study has tried to provide a high-performance control structure in the time of internal and external disturbances based on short-term planning. The proposed approach is the use of an evolutionary neuro-fuzzy network. Perhaps the main reason for using this approach can be due to uncertainty in the distribution and distribution of loads in microgrids and power systems. Simulation has been performed in MATLAB and Simulink environments, and the results show that the optimal load distribution has been done evolution in microgrids © 2022 Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta. All right reserved.

Population growth and urbanization cause developing-country cities to create energy-intensive buildings. Building energy efficiency can be improved through active and passive solar design to reduce energy consumption, increase equipment efficiency, and utilize renewable energy, converting renewable energy into thermal energy or electricity. In this study, passive architecture was evaluated for both urban block and building energy usage. When reliable information and analysis of signs and parameters impacting energy consumption are available, designers and architects can evaluate and passively design a building with higher precision and an accurate picture of its energy consumption in the early stages of the design process. This article compares the location of Baku’s building mass to six climate-related scenarios. Three methodologies are used to determine how much solar energy the models utilize and the difference between annual heating and cooling energy consumption. The structure’s rotation has little effect on the energy utilized in most forms. Only east-west linear designs employ 6 to 4 kWh/m2of area and are common. Most important is the building’s increased energy consumption, which can take several forms. The building’s westward rotation may be its most important feature. Any westward revolution requires more energy. Building collections together offers many benefits, including the attention designers and investors provide to all places. Having an integrated collection and a sense of community affects inhabitants’ later connections. Dictionary and encyclopedia entries include typology discoveries. These findings will inform future research and investigations. An architect must know a variety of qualities and organizations to define and segregate the environment because architecture relies heavily on the environment. This research involves analyzing the current situation to gain knowledge for future estimations. The present will determine the future. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Background: Today, pool boiling heat transfer (PBHT) is always one of the most important challenges in engineering. One way to increase the PBHT of fluids is using of nanoparticles. Methods: In the present study, the effect of nanoparticle type (Cu, Ag, and Fe) and size (Fe radius of 10, 15, and 30 Å) and porosity (1, 2.5, and 5%) on PBHT of water/Fe nanofluid are examined using molecular dynamics (MD) method. To investigate the PBHT of simulated structures, the change in heat flux and phase change duration is calculated. Outcomes display that the heat flux has an increase-decrease-increase process, which indicates the pool boiling process in the simulated samples, and it has reached the maximum value for nanofluids with Fe nanoparticles. The addition of Fe, Cu, and Ag nanoparticles decreases the phase change duration to 0.33, 0.24, and 0.28 ns, respectively. While the radius of the Fe nanoparticles enhances, the heat flux increases, and the phase change duration reduces from 0.33 ns to 0.27 ns. Significant findings: As the porosity increases from 1 to 5%, the heat flux increases, and the phase change duration reduces from 0.30 to 0.21 ns. The results of this simulation are expected to be effective in the PBHT of nanofluids. © 2022 Taiwan Institute of Chemical Engineers

Over the years, the privacy of a biomedical signal processing is protected using the encryption techniques design and meta-heuristic algorithms which are significant domain and it will be more significant shortly. Present biomedical signal processing research contained security because of their critical role in any developing technology that contains applications of cryptography and health deployment. Furthermore, implementing public-key cryptography in biomedical signal processing sequence testing equipment needs a high level of skill. Whatever key is being broken with enough computing capabilities using brute-force attack. As a result, developing a biomedical signal processing cryptography model is critical for improving the connection between existing and emerging technology. Furthermore, public-key cryptography implementation for meta-heuristic-based bio medical signal processing sequence test equipment necessitates a high level of skill. The suggested novel technique can be used to develop a secure algorithm of artificial bee colony, which depend on the advanced encryption standard (AES). AES can be used to reduce the encryption time and to increase the protection capacity for health systems. The novel secure can protect the biomedical signal processing against plain text attacks © The Author 2022. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal

This work is dedicated to classifying the general n-refined neutrosophic ring by building a ring isomorphism and the direct product of the corresponding classical rings with itself. On the other hand, we use the classification property to solve the problem of n-refined neutrosophic computing of Eigen values and Eigen vectors of an n-refined neutrosophic matrix. Also, it will be applied to solve n-refined linear systems and models. © 2022, American Scientific Publishing Group (ASPG). All rights reserved.

This paper is dedicated to studying the neutrosophic formula of some famous matrix equations used in theoretical data mining algorithms and control systems by using neutrosophic matrices and refined neutrosophic matrices over neutrosophic real fields. On the other hand, we concentrate on the neutrosophic formula of the Sylvester equation, and Lyapunov equation, where we study their formulas and properties in terms of theorems in the neutrosophic real number field and refined real number field. Also, we illustrate many different examples to clarify the validity of our work. © 2022, American Scientific Publishing Group (ASPG). All rights reserved.

For growing transmitting capability over fixed bandwidth, there are many potential techniques that had been developed in the recent years. There are several possible methods for increasing transmission capacity over fixed bandwidth. One of the most effective methods that can offer achieving more information is orthogonal frequency division multiplexing (OFDM). We designed a powerful simulation framework for a backhaul RoF-MMW optical transmission system between main base station and remote antenna unit based on multi-carrier OFDM modulation. The powerful simulation of next generation optical MC-OFDM transportation along RoF-MMW long transmission system had been successfully implemented. By using 50 Gb/s default bit rate and 128 QAM modulation format for OFDM-RoF-MMW system along 50 km, the best achievement of overall bit rate obtained (11.2 Tb/s) with accepted BER to get ultra-high-capacity transmission system. Thanks to the python DSP and the losses compensators used in the simulation framework, the PAPR losses and drift in the receiver section had been compensated and many values of OFDM sub-carriers (16 and 32 OFDM sub-carriers) achieved for ultra-high-capacity transmission system. © 2022, Innovative Information Science and Technology Research Group. All rights reserved.

The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data © 2022, Authors. This is an open access article under the Creative Commons CC BY license

A short time ago Internet of Things (IoTs) is being applied in many fields like healthcare systems, disease forecasting, etc. Even though the IoTs has enormous promise in a variety of applications, there are several areas where it may be improved. In the present work, we have concentrated on improvement of the performance of IoT by adding two technologies such as machine learning algorithms (Naïve Bayes (NB), Random Forest (RF)) and Ant Colony Meta-Heuristic (ACMH) algorithm to select best features from data. The efficient proposed framework applied on the data of SARS-Co V2 for disease prediction to minimize the time consumption and improve the accuracy of forecasting COVID disease. Thus, the lifetime network of IoT will lead to an increase. The performance of proposed work evaluated using reliable metrics such as precision, accuracy, running time, balance accuracy, recall, and F-Measure. We conclude from the results of evaluating, that ML algorithms in IoT achieved best performance than without using ACMH algorithm; RF with ACMH in IoT framework achieved best performance that NB with ACMH algorithm. But NB is best from RF in running time with and without ACMH algorithm. © 2022 IEEE.

Everything can be connected in the Internet of Things (IoTs) technology that enables efficient communication between connected objects. IoTs industry-based meta-heuristic and mining algorithms, which are considered an important field of Artificial Intelligence will be used to construct a healthcare application in this study for lowering costs, increasing efficiency, accurate analysis of data and better care for patient. Meta-heuristic algorithms are now effective modelling and optimisation tools. The proposed framework offers hybrid meta-heuristic and mining algorithms to solve the optimisation and analysis issues. Grey Wolf Optimisation (GWO) applies a spiral-shaped path to assure diversity and convergence. To encourage convergence, the Genetic Algorithm (GA) is introduced. Also, we applied a support vector machine and Naïve Bayes for extracting and analysing important information for heart collected from sensors. The goals are to build Electronic Healthcare (E-Health), which includes establishing a link between patients and health providers to monitor, diagnose and save useful information. It is the foundation for accomplishing efficient and robust monitoring. The results offer that the IoTs framework is optimised and enhanced using the hybrid algorithm, which outperforms the GWO and GA, and the mining algorithms are more accurate with a hybrid algorithm than used mining with only GWO or GA. © 2022 The Authors. IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

3D scanning is rapidly becoming a key maintenance tool. Aerospace was a pioneer in adopting 3D scanning technology because aircraft manufacture and maintenance require precision. Monitoring deterioration, removing components for maintenance, and verifying covert operations are not practical or helpful without technology. Ultra-precise 3D scanning is needed to ensure aeroplane airworthiness. The Royal Malaysian Air Force is facing issues maintaining the structural integrity and time-consuming manual inspections, which exposes technicians to error and delays aircraft maintenance. Furthermore, the Sukhoi Su-30MKM fighter jet has updated avionics, missiles, and bombs. Sukhoi's structural integrity is under jeopardy after a decade of service. This study describes the process of scanning aircraft in 3D and creating 3D representations in Su-30MKM maintenance. The study also covers the digital transformation of aircraft construction into a model. As a result, the proposed 3D Scanning Maintenance System has assisted the maintenance team to accelerate reverse engineering, aircraft maintenance operations. Also, the 3D scanning application increases the effectiveness of repairing and maintaining Su-30MKM in line with the Royal Malaysian Air Force's mission readiness to embrace Industrial Revolution 4.0. © 2022 The Authors. IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

The frighteningly high levels of power consumption at present are caused mainly by the expanding global population and the accessibility of energy-hungry smart technologies. So far, various simulation tools, engineering and AI-based methodologies have been utilized to anticipate power consumption effectively. While engineering approaches forecast using dynamic equations, AI-based methods forecast using historical data. The modeling of nonlinear electrical demand patterns is still lacking for durable solutions, however, the available approaches are only effective for resolving transient dependencies. Furthermore, because they are only based on historical data, the current methodologies are static in nature. In this research, we present a system based on deep learning to anticipate power consumption while accounting for longterm historical relationships. In our approach, a transformer-based model is used for the prediction of electricity demand on data collected from the regional facilities in Iraq. According to the conducted experiments, our approach claims competitive performance, achieving an error rate of 2.0 in predicting 1-day-ahead of electricity demand in the test samples © 2022, Majlesi Journal of Electrical Engineering.All Rights Reserved.

Nanochannels (NCs) are structures for mass transfer (MT) and heat transfer (HT) procedures in actual usages. Prior reports display the atomic behavior of various fluids inside perfect NCs. This study uses the molecular dynamics simulation (MDS) to examine the impact of number of obstacles (N.Os) on argon flow inside Platinum NCs. Simulation outputs are presented by calculating the physical quantities such as temperature (T), potential energy (PE), density (D)/temperature (T)/velocity (V) profiles, and interaction energy (IE). MDS results show that as the N.O increases, the maximum density increases from 0.093 to 0.099 atom/Å3. The maximum velocity decreases from 0.0031 to 0.0025 Å/ps by increasing the N.Os. The maximum temperature decreases from 329.46 to 318.43 K. By increasing the N.Os, the fluid particles' oscillations (FP) and their temperature also decrease. This mechanism can reduce the temperature in the HT process. In addition, with increasing the N.Oss from 1 to 4, the IE increases from −60.52 to -70.86 eV. This increase in IE can reduce the atomic stability of NCs. This behavior reduces the lifetime of NCs in heat/mass transfer processes. Therefore, it is expected that with the outcomes of the current examination and the control of the N.Os, we can optimize the various processes like MT and HT for industrial purposes. © 2022 Elsevier B.V.

Let us begin with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-Ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. In a nutshell, deep learning is a subset of machine learning that solves problems that machine learning alone cannot. Acquiring RNA secondary spatial relationships has been more significant in RNA and functional genomics studies in recent years. Although some RNA secondary sequences may be discovering approaches, most of the time, quick and accurate computational approaches are utilized to predict the structure of DNA strands. Current methods for determining RNA structure of proteins are generally based on the lowest power storage strategy, which seeks the optimal RNA folded form in vivo and employs an incremental process to satisfy the lowest source of critical energy and related aspects. © 2022 IEEE.

In the era of information technology, users had to send millions of images back and forth daily. It's crucial to secure these photos. It is important to secure image content using digital image encryption. Using secret keys, digital images are transformed into noisy images in image encryption techniques, and the same keys are needed to restore the images to their original form. The majority of image encryption methods rely on two processes: confusion and diffusion. However, previous studies didn’t compare recent techniques in the image encryption field.This research presents an evaluation of three types of image encryption algorithms includinga Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and AES techniques. The Fibonacci Q-matrix in the hyperchaotic technique makes use of a six-dimension hyperchaotic system's randomly generated numbers and confuses the original image to dilute the permuted image. The objectives here areto analyze the image encryption process for the Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and Advanced Encryption Standard (AES), and compare their encryption robustness. The discussed image encryption techniques were examined through histograms, entropy, Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and correlation coefficients. Since the values of the Chi-squared test were less than (293) for the Hyperchaotic System & Fibonacci Q-matrix method, this indicates that this technique has a uniform distribution and is more efficient. The obtained results provide important confirmation that the image encryption using Fibonacci Q-matrix in hyperchaotic algorithm performed better than both the AES and SIT based on the image values of UACI and NPCR © 2022, Authors. This is an open access article under the Creative Commons CC BY license

The most commonly used variable speed wind turbine is based on Doubly Fed Induction Generator (DFIG). To control the reactive power of DFIG-based wind turbines, several methods are suggested that controls the reactive power of the DFIG with slow dynamics and considerable ripples. This paper proposes a new control method based on the adaptive reference model which controls the active and reactive powers of DFIG with high dynamics and low ripples. Given that, the proposed technique has proportional-integral (PI), selecting the proper coefficient for PI controller is significant. To overcome this problem, the grey-wolf algorithm is used to optimize the PI coefficients. The results show that the proposed method gives satisfactory performance with lower overshoots and faster dynamic response. © 2022,Majlesi Journal of Electrical Engineering. All Rights Reserved.

Multirotor Unmanned Aerial Vehicles (UAVs) play an imperative role in many real-world applications in a variety of scenarios characterized by a high density of obstacles with different heights. Due to the complicated operation areas of UAVs and complex constraints associated with the assigned mission, there should be a suitable path to fly. Therefore, the most relevant challenge is how to plan a flyable path for a UAV without collisions with obstacles. This paper demonstrates how a flyable and continuous trajectory was constructed by using any-angle pathfinding algorithms, which are Basic Theta*, Lazy Theta*, and Phi* algorithms for a multirotor UAV in a cluttered environment. The three algorithms were modified by adopting a modified cost function during their implementation that considers the elevation of nodes. First, suitable paths are generated by using a modified version of the three algorithms. After that, four Bézier curves-based approaches are proposed to smooth the generated paths to be converted to flyable paths (trajectories). To determine the most suitable approach, particularly when searching for an optimal and collision-free trajectory design, an innovative evaluation process is proposed and applied in a variety of different size environments. The evaluation process results show high success rates of the four approaches; however, the approach with the highest success rate is adopted. Finally, based on the results of the evaluation process, a novel algorithm is proposed to increase the efficiency of the selected approach to the optimality in the construction process of the trajectory. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bayesian regularized neural networks (BRNNs), Bayesian additive regression trees (BART), extreme gradient boosting (xgBoost), and hybrid neural fuzzy inference system (HNFIS) were used considering the complex relationship of rainfall with sea level pressure. Principle components of SLP domain correlated with daily rainfall were used as predictors. The results revealed that the efficacy of AI models is predicting daily rainfall one day before. The relative performance of the models revealed the higher performance of BRNN with normalized root mean square error (NRMSE) of 0.678 compared with HNFIS (NRMSE = 0.708), BART (NRMSE = 0.784), xgBoost (NRMSE = 0.803), and ELM (NRMSE = 0.915). Visual inspection of predicted rainfall during model validation using densityscatter plot and other novel ways of visual comparison revealed the ability of BRNN to predict daily rainfall one day before reliably. Copyright © 2021 Lu Ye et al.

Todays, networks security of has become the important problem in each distributed system. A lot of attacks are becoming less able to detect with software of antivirus and firewall. For improving the security, intrusion detection systems (IDSs) are utilized for detecting the anomalies in traffic of network. Network anomaly detection issue is determining, if incoming traffic of network is anomalous/ legitimate. The automated system of detection schemed for identifying the incoming anomalous patterns of traffic usually apply widely utilized techniques of machine learning. In the article, we have utilized the Information Gain- based algorithm. The algorithm chooses the features optimal number from dataset of NSL-KDD. Additionally, we have integrated selection of feature with the technique of machine learning namely as Support Vector Machine (SVM) by utilizing the algorithm of artificial bee colony as well as Optimization-Cuckoo Search Algorithm for optimizing SVM hyper parameters for dataset effective classification. Proposed method performance has been assessed on the modern intrusion dataset as NSLKDD. Experimental results show that the proposed method outperforms also achieves high accuracy in comparison to the other modern techniques in NSLKDD. © 2021 Zibeline International Publishing Sdn. Bhd.. All rights reserved.

Due to the fact that countries are presently dealing with the third wave of COVID-19 pandemic and in present time, the data of vaccines for preventing COVID-19 has triggered massive information, it is vital to create a system that can assist decision-makers and health care practitioners in combating COVID-19 and to combat the problem of vaccine information overload is to provide patients with personalized vaccine recommendations. Because of the ability of recommender systems (RSs) that use Collaborative Filtering (CF) to interpret decision-maker expectations, methodologies, it widely used and direct them towards linked tools that are acceptable to recommend the suitable vaccine for the persons. In this paper, we adopted an Enhanced Vaccine RSs for preventing COVID-19, which is called EVRSs-19. EVRSs-19 face some problems such as sparsity and diversity of vaccines data. To overcome these problems, we adopted two proposals. First, use clustering of K-Means to cluster the persons in several groups according to vaccine types to cope with sparsity of vaccines data. Second, use the K-Nearest Neighbors classifier-depend model of CF to discover neighbors in each vaccine cluster to increase diversity. Evaluating the EVRSs-19 system implemented on vaccines data in two testing using some metrics and the findings of these metrics after running the clustering and classification show that the system of EVRSs-19 has a perfect structure. Such as recall (0.92), precision (0.89), diversity score (9). As the vaccines recommendation list progressed, NDCG and DCG for persons are decreased. © 2021 IEEE.

The optical fiber stands for an exceptionally appealing correspondence medium since it offers an enormous data transmission and low lessening, also, can in this manner encourage requesting administrations like excellent visual communication as well alternatives in PC organizations. In this study, a good strategy simulation based on mathematical equations has been presented for a unique optical channel correspondence. Also, this paper shows the nonlinear analysis phenomenon of fiber scattering, modulator as well recipient reaction periods, coding type of waveform. The light source spectral width have influence to the presentation of the fiber optics correspondence like link length, information rate, BER. Additionally, this paper show the force and rising period, spending plan is utilized to get a good guess of the communication length as well the piece rate utilizing optical framework test scheme. © 2021, Semnan University, Center of Excellence in Nonlinear Analysis and Applications. All rights reserved.

Emotions are a vital and fundamental part of life. Everything we do, say, or do not say, somehow reflects some of our feelings, perhaps not immediately. To analyze a human's most fundamental behavior, we must examine these feelings using emotional data, also known as affect data. Text, voice, and other types of data can be used. Affective Computing, which uses this emotional data to analyze emotions, is a scientific fields. Emotion computation is a difficult task; significant progress has been made, but there is still scope for improvement. With the introduction of social networking sites, it is now possible to connect with people from all over the world. Many people are attracted to examining the text available on these various social websites. Analyzing this data through the Internet means we're exploring the entire continent, taking in all of the communities and cultures along the way. This paper analyze text emotion of Iraqi people about COVID-19 using data collected from twitter, People's opinions can be classified based on lexicon into different separate classifications of feelings (anticipation, anger, trust, fear, sadness, surprise, disgust, and joy) as well as two distinct emotions (positive and negative), which can then be visualized using charts to find the most prevalent emotion using lexicon-based analysis © The Author 2021. This work is licensed under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) that allows others to share and adapt the material for any purpose (even commercially), in any medium with an acknowledgement of the work's authorship and initial publication in this journal

Wireless sensor networks (WSNs) are formed of self-contained nodes of sensors that are connected to one base station or more. WSNs have several primary aims one of them is to transport network node's trustworthy information to another one. As WSNs expand, they become more vulnerable to attacks, necessitating the implementation of strong security systems. The identification of effective cryptography for WSNs is a significant problem because of the limited energy of the sensor nodes, compute capability, and storage resources. Advanced Encryption Standard (AES) is an encryption technique implemented in this paper with three meta-heuristic algorithms which are called Hybrid Genetic Firefly algorithm, Firefly algorithm, and Genetic algorithm to ensure that the data in the WSNs is kept confidential by providing enough degrees of security. We have used hybrid Genetic firefly as a searching operator whose goal is to improve the searchability of the baseline genetic algorithm. The suggested framework's performance that based on the algorithm of hybrid genetic firefly is rated by using Convergence Graphs of the Benchmark Functions. From the graphs we have conclude that hybrid genetic firefly with AES is best from other algorithms. © 2021. The Author. This work is licensed under a Creative Commons Attribution License. All Rights Reserved.

The exploitation of fossil fuels has fueled the modern world's development since the industrial revolution. Other energy sources, such as wood, charcoal, and animal power, were displaced by these fuels, which were relatively easy to obtain, had low cost of production, and were easily transportable. The possibility of these fossil reserves being depleted in the medium term, combined with an increase in environmental awareness and the reality of environmental degradation, has changed the situation, reactivating the search for alternative fuels. Biofuels such as bioethanol, biomethanol, and biodiesel are among the alternative fuels gaining popularity due to their environmental benefits. This research investigates the behaviour of a diesel engine that runs on biodiesel (a fuel made from new and unrefined algae oil), ethanol (an essential raw nanomaterial that is readily available in India), and nanometal additives. © 2021 Mohammad Ibrahim Al Mishlah Alghamdi et al.

Pedestrian safety is a serious problem in transportation systems because pedestrian and vehicle crashes often result in fatalities amongst vulnerable road users. A vehicle-to-pedestrian (V2P) communication system allows data exchange between pedestrians and vehicles to prevent or minimise potential dangers of accidents from happening. This work aimed to analyse and review the previous work associated with information exchange in the V2P communication system and classify the existing technology utilized for this purpose. Motivation, accessible problems confronting researchers, and suggestions posed to researchers to develop this critical area of study have been among the reasons considered to enhance awareness of the field's numerous qualitative facets in reported investigations and properties. All of the papers have been divided into four categories: growth, analysis, and survey, FRAMEWORK, and data exchange in the V2P communication system. V2P communication is an area that necessitates automated solutions, instruments, and techniques that allow pedestrian detection and prediction. Pedestrian identification and data sharing on V2P have been the subject of several experiments in order to support pedestrian protection techniques. The reasons, open barriers that hinder the technology's usefulness, and authors' suggestions have been used to identify the essential characteristics of this evolving sector. This study is intended to provide researchers with new resources and enable them to focus on the holes that have been found. © 2021. All rights reserved.

Home management and controlling have seen a great introduction to network that enabled digital technology, especially in recent decades. For the purpose of home automation, this technique offers an exciting capability to enhance the connectivity of equipment within the home. Also, with the rapid expansion of the Internet, there are potentials that added to the remote control and monitoring of such network-enabled devices. In this paper, we had been designed and implemented a fully manageable and secure smart home automation system based on a cloud computing system with an ESP Arduino system. The security of home had been improved by adding a complete camera system with a GSM communication technique to connect the Arduino output data to an external specified number if there is no internet provider. We used three sensors for temperature, gas, and motion measurements. The ESP8226 Wi-Fi device programmed the sensors to maintain the sensors measurements and transfer them to the cloud server database which is programmed to the web server via Appatshy and Mysql formats. The system implemented with high time response so that all readings updated and appeared spontaneously. The designed system should be effective, a secure, and rapid response real-time smart home system should be achieved. © 2020. All Rights Reserved.

Radio Frequency Identification (RFID) system is a new communication technology identifying one or more objects simultaneously in favor of using electromagnetic waves. In RFID systems, tags and readers communicate together over a shared wireless channel, and their signals may collide during transmission process and collision problem may occur in these systems. When two or more tags want to communicate with the reader, this collision problem may occur. This collision problem will waste the time for identification and it is also energy consuming as well and consequently reduces efficiency of the tag identification process. Therefore, it is required to attempt minimizing collision occurrence and decrease possibility of collision by using RFID anti-collision algorithms. In this project a novel Anti-collision Algorithm was suggested, which is called Enhance Dynamic Tree Slotted Aloha (EDTSA) by combining Tree slotted ALOHA and Advance Dynamic Framed Slotted Aloha Anti-Collision Algorithms and using Cubic Spline-based tag estimation technique. We have designed this algorithm by using C# programming language in visual studio 2008. The final results has simulated and compared with other algorithms. Simulated results also have illustrated that proposed algorithm can improve the efficiency of RFID systems. © 2019, Springer Nature Switzerland AG.