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Abstract This paper proposes a total fusion of blockchain and AI tech for tomorrow’s viable, rich in diversity and transparent fisheries. It outlines the main goal of tackling overfishing challenges due to lack of transparency and biodiversity depletion in the fisheries sector. With the use of blockchain technology, we can ensure that all fishery products are safely traced from their harvest up to when they get to the market— at the same time, AI algorithms are used in monitoring fish populations and predicting them plus decision-making processes which should be enhanced thus promoting bio-diversity and ensuring sustainability of fish stocks. Results show promise on using both technologies together: improving sustainability plus transparency in fisheries which would promote more fish biodiversity, while others including using an artificial intelligence system have not been confirmed yet by observations. The conclusion underscores the transformative nature of these technologies as having great implications towards fisheries management; this implies that there is a need for future observational studies aimed at validating such other findings.

Satellite communication has reached a turning point in transmitting data quickly and receiving signals clearly in faraway places. This paper presents a metamaterial-based antenna for distant and military applications that overcomes restrictions and performs unmatched. This project explores metamaterials to create a unique antenna. Expect to be astounded by its power to bend electromagnetic waves, miniaturize, and change our relationship with the universe. The feeding mechanism of this antenna is a modified co-planar waveguide. At 5.6 GHz, the proposed antenna unit cell measures 0.076λ0×0.2λ0, making it small thanks to its MTM characteristic. CP radiation is made when two orthogonally polarized modes are stimulated at the same time and two composite right- and left-handed transmission line unit cells are placed orthogonally. Reduced size, increased bandwidth, and improved Learn how this discovery opens new doors for high-resolution photography, broadband internet, and space exploration. One nanostructure that makes up a metamaterial is the Split Ring Resonator (SRR). SRR dimensions must be smaller than the resonance wavelength, making them crucial for near-infrared and optical responses. This effort examined nanoscale SRR characteristics in the infrared and visible ranges. SRRs composed of aluminum (Al) and gold (Au) were produced on silicon and silica substrates using electron beam lithography (EBL). The proposed structure consists of a microstrip-line-supplied SRR, a parasitic patch that is perpendicular to the ground plane, and two via holes connected by two split rings that feed the patch indirectly. Choosing the right material parameter for all such antennas depends on the planned structure. Fabrication and testing of the antenna matched simulations. Dimensions: 23.7 mm × 16.2 mm × 1.6 mm; substrate dielectric constant: 4.4. Experimental data are detailed and compared to analytical calculations. Simulation results show that metamaterials have negative permittivity and permeability in a certain frequency range, which can be predicted by an analytical method. Simulations indicated eight BRS, WiMAX, radar, and mobile phone resonance frequencies. Radiation is dipole-like in the omnidirectional in the H-plane and E-plane. 10 dB return loss and 3 dB axial ratio bandwidths are 38.6.7% and 8.1%, respectively.

Abstract This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain’s transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain’s consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper.

Abstract This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model’s effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Abstract Biofiltration is a method of pollution management that utilizes a bioreactor containing live material to absorb and destroy pollutants biologically. In this paper, we investigate mathematical models of biofiltration for mixing volatile organic compounds (VOCs) for instance hydrophilic (methanol) and hydrophobic ( $$\alpha$$ α -pinene). The system of nonlinear diffusion equations describes the Michaelis-Menten kinetics of the enzymic chemical reaction. These models represent the chemical oxidation in the gas phase and mass transmission within the air-biofilm junction. Furthermore, for the numerical study of the saturation of $$\alpha$$ α -pinene and methanol in the biofilm and gas state, we have developed an efficient supervised machine learning algorithm based on the architecture of Elman neural networks (ENN). Moreover, the Levenberg-Marquardt (LM) optimization paradigm is used to find the parameters/ neurons involved in the ENN architecture. The approximation to a solutions found by the ENN-LM technique for methanol saturation and $$\alpha$$ α -pinene under variations in different physical parameters are allegorized with the numerical results computed by state-of-the-art techniques. The graphical and statistical illustration of indications of performance relative to the terms of absolute errors, mean absolute deviations, computational complexity, and mean square error validates that our results perfectly describe the real-life situation and can further be used for problems arising in chemical engineering.

Abstract Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people’s security and promote their well-being.

Abstract Direct AC–AC converters are strong candidates in the power converting system to regulate grid voltage against the perturbation in the line voltage and to acquire frequency regulation at discrete step levels in variable speed drivers for industrial systems. All such applications require the inverted and non-inverted form of the input voltage across the output with voltage-regulating capabilities. The required value of the output frequency is gained with the proper arrangement of the number of positive and negative pulses of the input voltage across the output terminals. The period of each such pulse for low-frequency operation is almost the same as the half period of the input grid or utility voltage. These output pulses are generated by converting the positive and negative input half cycles in noninverting and inverting forms as per requirement. There is no control complication to generate control signals used to adjust the load frequency as the operating period of the switching devices is normally greater than the period of the source voltage. However, high-frequency pulse width modulated (PWM) control signals are used to regulate the output voltage. The size of the inductor and capacitor is inversely related to the value of the switching frequency. Similarly, the ripple contents of voltage and currents in these filtering components are also inversely linked with PWM frequency. These constraints motivate the circuit designer to select high PWM frequency. However, the alignment of the high-frequency control input with the variation in the input source voltage is a big challenge for a design engineer as the switching period of a high-frequency signal normally lies in the microsecond. It is also required to operate some high-frequency devices for various half cycles of the source voltage, creating control complications as the polarities of the half cycles are continuously changing. This requires at least the generation of two high-frequency signals for different intervals. The interruption of the filtering inductor current is a big source of high voltage surges in circuits where the high-frequency transistors operate in a complementary way. This may be due to internal defects in the switching transistors or some unnecessary inherent delay in their control signals. In this research work, a simplified AC–AC converter is developed that does not need alignment of high-frequency control with the polarity of the source voltage. With this approach, high-frequency signals can be generated with the help of any analog or digital control system. By applying this technique, only one high-frequency control signal is generated and applied in AC circuits, as in a DC converter, without applying a highly sensitive polarity sensing circuit. So, controlling complications is drastically simplified. The circuit and configuration always avoid the current interruption problem of filtering the inductor. The proposed control and circuit topology are tested both in computer-based simulation and practically developed circuits. The results obtained from these platforms endorse the effectiveness and validation of the proposed work.

Abstract Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality’s strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEG’s resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signature’s image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

Abstract This article proposes a dual mode dual-polarized antenna configuration for IRNSS and fifth generation (5G) applications, operating at a frequency of 3.5 GHz based on characteristic mode analysis (CMA), and aims to provide broadband dual-polarized functionality. The original design of the antenna is a traditional patch antenna, and its dual-polarized features are determined using characteristic mode analysis. The full-wave method is used to stimulate both orthogonal modes using a 50 Ω coaxial input line at 3.5 GHz. In this design, the circular patch has been extended into an elliptical patch through a process of mode separation. The circular patch exhibits resonance at a frequency of 2.5 GHz, whereas the extended elliptical radiator demonstrates two resonance modes at 2.5 GHz and 3.5 GHz. The operational mechanism is elucidated by modal analysis and characteristic angle. This antenna operates on two different frequencies at the 2.5 GHz IRNSS band with horizontal polarization and the 3.5 GHz 5G service with vertical polarization. The maximum gain achieved with these frequency ranges is 5.31 dBi and 4.72 dBi, respectively. A ring resonator is chosen to improve the axial ratio and impedance bandwidth of the suggested prototype. The antenna's ground plane is shaped like a rectangle and features a V-shaped slot in the radiating patch. The antenna's physical footprint is 50 mm × 50 mm × 1.6 mm and an FR4 dielectric substrate serves as its foundation. Through its interaction with a PIN diode, the diode modifies the polarization of the antenna. The antenna functions as a right-handed circular polarization (RHCP), when the diode is operational. The bandwidth from 4.3 to 7.5 GHz is covered. On the other hand, it generates linear polarization (LP) between 4.2 and 5.3 GHz. The experimental antenna is evaluated and examined for its performance characteristics. The simulations are carried out utilizing the CST simulator. A prototype antenna has been manufactured and its performance has been validated against simulated findings.

Diagnosing and treating newborn seizures accurately and promptly is crucial for providing the best possible care for these patients. For the purpose of intelligently identifying newborn seizures, this work introduced a unique method that uses spectral and spatial graph neural networks (SSGNNs) optimized with the Aquila algorithm. Using electroencephalogram (EEG) recordings, the suggested methodology takes advantage of the complex spatial and spectral characteristics of infant brain activity. Spatial and spectral GNNs were used to extract significant spatiotemporal patterns suggestive of seizure episodes by organizing the brain activity data as a graph, with nodes representing various brain regions and edges signifying functional relationships. By combining spectral and spatial data, the depiction of newborn brain dynamics was improved and made it possible to distinguish between seizure and non-seizure phases with greater accuracy. Moreover, the introduction of the Aquila algorithm improved the GNNs' performance in seizure identification tasks by streamlining the training process. A large dataset of EEG recordings from newborns with and without seizures was used to assess the effectiveness of the suggested method. Higher accuracy, sensitivity, and specificity in seizure detection were achieved in the experimental results, which showed greater performance when compared to conventional methods. This work offered an automated, data-driven method for identifying newborn seizures, which is a major development in the treatment of newborns. By combining spectral and spatial GNNs and optimizing the results using the Aquila method, it is possible to enhance seizure detection accuracy and potentially prevent neurological consequences in affected children by intervening early. This method has the potential to completely change the way neonatal care is provided by giving medical professionals a strong tool for accurate and prompt seizure monitoring in neonatal intensive care units (NICU).

Abstract COVID-19 has dramatically affected financial markets, economies, and societies worldwide and exposed pre-existing inequalities in cities. This work aims to understand the inequalities in some cities worldwide, their reasons and circumstances, and impacts to drive lessons for future prevention, intervention, and post-catastrophe/ hazard plans such as COVID-19 that would raise resilience and decrease damages. Six major cities were included in the analysis and contrasted based on specific assessment criteria. The study included the impact of the pandemic on the economy and the government's responses global crisis. Additionally, newfound measures and technologies developed to control the hazard, including the community's response and cooperation to solving the issue were explored. The outcomes of this work shed a light on problems to be addressed in the future towards enhances the resilience of cities pre- and post- global crisis. Through the comparisons made in this paper, conclusions regarding the cities' successful combat against COVID-19 were drawn. According to the comparative analysis, it became apparent that poverty, culture, and governance are primary factors that control the success of states under emergency. Moreover, it is understood that technology is a significant factor in combating pandemics and health emergencies as large as COVID-19, especially for tracking and monitoring.

Abstract Background Recognizing and identifying latent safety threats (LSTs) before patient care commences is crucial, aiding leaders in ensuring hospital readiness and extending its impact beyond patient safety alone. This study evaluated the effectiveness of a combination of Simulation-based Clinical Systems Testing (SbCST) with Healthcare Failure Mode and Effect Analysis (HFMEA) with regard to mitigating LSTs within a newly constructed hospital. Methods Three phases of the combined SbCST and HFMEA approach were implemented across all hospital settings. The scenarios tested system functionalities, team responses, and resource availability. The threats thus identified were categorized into system-related issues, human issues, and resource issues, after which they were prioritized and addressed using mitigation strategies. Reassessment confirmed the effectiveness of these strategies before hospital commissioning. Results More than 76% of the LSTs were mitigated through the combined approach. System-related issues, such as nonfunctional communication devices and faulty elevators, were addressed by leadership. Human issues such as miscommunication and nonadherence to hospital policy led to improvements in interprofessional communication and teamwork. Resource issues, including missing equipment and risks of oxygen explosion, were addressed through procurement, maintenance, and staff training for equipment preparation. Conclusion The SbCST and HFMEA were highly effective with regard to proactively identifying and mitigating LSTs across all aspects of hospital preparedness. This systematic and comprehensive approach offers a valuable tool for enhancing patient safety in new healthcare facilities, thereby potentially setting a new standard for proactive hazard identification and risk management in the context of healthcare construction and commissioning.

Abstract A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Abstract The proliferation of smart conurbations entails an efficient system design for managing all the crowds in public places. Multitude controlling procedures are carried out for controlling compact areas where more number of peoples is present at several groups. Therefore for controlling purpose the proposed method aims to design a pictorial representation using Internet of Things (IoT). The process is carried out by taking images and then organizing it using switching techniques in the presence of square boxes where entire populace is identified on real time experimentations. For processing and controlling the occurrence a separate architecture is designed with analytical equivalences where all data set is stored in cloud platform. Further the incorporation of system model is carried out using Switching Based Algorithm (SBA) which adds more number of columns even for high population cases. In order to verify the effectiveness of proposed model five scenarios are considered with performance evaluation metrics for SBA and all the test results provides best optimal results. Moreover the projected model is improved with an average percentage of 83 as compared to existing models.

Mahmoud Izraiq,1 Raed Ibrahim Alawaisheh Snr,1 Ismail Hamam,2 Mohammad Hajjiri,3 Ibrahim K Jarrad,2 Qutaiba Albustanji,1 Yaman B Ahmed,4 Omran A Abu-Dhaim,1 Ibrahim Zuraik,5 Ahmad A Toubasi,5 Mohammad Ali Dmour,1 Hadi Abu-Hantash6 1Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan; 2Department of Cardiology, King Hussein Cancer Center Amman, Amman, Jordan; 3Department of Cardiology, Abdali Hospital, Amman, Jordan; 4Cardiology Section, Internal Medicine Department, King Abdullah University Hospital, Irbid, Jordan; 5Cardiology Section, Internal Medicine Department, Jordan University Hospital, Amman, Jordan; 6Department of Cardiology, Amman Surgical Hospital, Amman, JordanCorrespondence: Mahmoud Izraiq, Cardiology Section, Internal Medicine Department, Specialty Hospital, Amman, Jordan, Tel +962795652260, Email izraiq@yahoo.comIntroduction: Heart failure (HF) and atrial fibrillation (AF) are constantly linked together as predictors of a substantial increase in morbidity and mortality. In this study, we investigated the effects of atrial fibrillation in patients with heart failure.Methods: This study was a prospective observational multicenter national registry encompassing 21 health institutes in Jordan, comprising university hospitals, private hospitals, and private clinics. Patients visiting the cardiology clinic or inpatients admitted due to acute decompensated HF were included. The collected variables included age, sex, BMI, comorbidities, HDL, LDL, triglycerides, BNP, Sodium, potassium, hemoglobin, and creatinine.Results: Our study of 1571 patients showed significant differences between those with and without atrial fibrillation (AF). AF patients included more females (49.4% vs 34.0%), had a higher prevalence of hypertension (88.0% vs 78.5%), and were older (57.8% aged ≥ 70 years). Smoking rates were lower in patients with AF (22.3% vs 37.0%), while dyslipidemia was less common (54.5% vs 65.3%). Patients with AF also had more hospital admissions than those without AF (16% vs 11.6%). In addition, triglyceride levels were notably lower, hemoglobin levels were < 10 g/dL, and eGFR was reduced in patients with AF. In predicting death, the Random Forest Classifier had the highest accuracy (93.02%) and AUC (92.51%), whereas Logistic Regression had higher sensitivity (72.09%). Creatinine, Length of Hospital Stay, and other factors influenced the predictions, with creatinine levels being a strong predictor of patient outcomes.Conclusion: Atrial fibrillation patients were older and had a higher proportion of females compared than non-atrial fibrillation patients. Hypertension, a family history of premature coronary artery disease, and structural heart disease were notably higher in the atrial fibrillation group. Patients with atrial fibrillation had higher rates of hospital admissions than those without atrial fibrillation.Keywords: atrial fibrillation, heart failure, Jordan, registry, machine learning

Background: As acute stroke remains an important leading cause of morbidity and mortality in the world, this study sought to survey the general public in four different countries, in four different continents to ascertain the level of knowledge of the public for the risk factors, and the signs and symptoms of stroke. Methods: This was a cross-sectional survey of the general public that was conducted via a self-administered online survey using the SurveyMonkey® platform in the United States, the United Kingdom, the Kingdom of Saudi Arabia, and Egypt. Questions on identifying the risk factors, as well as the signs and symptoms of stroke were asked. A score was given to all subjects, and they were then asked to identify the life-style changes that they thought would reduce the risk of getting a stroke. Results: A total of 2000 subjects were recruited via the electronic search engine at the SurveyMonkey®, 500 from each country. The data showed that subjects in Western countries agreed on dyslipidemia and lack of exercise being the top two risk factors for stroke. While the Middle Eastern subjects mostly identified smoking and having a previous stroke as the top risk factors. Regarding the signs and symptoms, Western subjects agreed perfectly that the top three were hemiparesis, heavy tongue and facial asymmetry. Conclusion: It was clear from the data collected in this study that the knowledge level of signs and symptoms of stroke in the general public, irrespective of the country was poor. [SJEMed 2024; 5(1.000): 001-009]

Detection of aerial activities, including drones and birds, has practical implications for automating bird surveys and developing radar systems for aerial object collision detection. Convolutional neural networks (CNNs) have been extensively utilized for image recognition and classification tasks, albeit prior research predominantly focuses on single-class 'drone' classification. However, a gap persists in achieving high accuracy for multi-class classification. To address the limitations of traditional CNNs, such as vanishing gradients and the necessity for numerous layers, this study introduces a novel model termed ''MobVGG.” This model combines the architectures of MobileNetV2 and VGG16 to accurately classify images as either 'bird' or 'drone'. The dataset comprises 4212 images for each category of 'bird' and 'drone'. The stringent methodology was applied for dataset preparation and model training to ensure the reliability of the findings. Comparative analysis with previous research demonstrates that the proposed MobVGG model, trained on both 'bird' and 'drone' images, achieves superior accuracy (96 %) compared to benchmark studies. Our paper targets researchers and graduate students as its primary audience.

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk and averting blindness. The optic nerve head is harmed by the neurodegenerative condition known as glaucoma. Machine learning and deep learning systems for glaucoma and cataract detection have recently received much attention in research. The automatic detection of these diseases also depends on deep learning transfer learning platforms like VeggNet, ResNet, and MobilNet. The authors proposed MobileNetV1 and MobileNetV2 based on an optimized architecture building lightweight deep neural networks using depth-wise separable convolutions. The experiments used publicly available data sets with both cataract & normal and glaucoma & normal images, and the results showed that the proposed model had the highest accuracy compared to the other models.

Antennas with higher gain and efficiency deliver superior performance across a wide frequency range. Achieving these characteristics at high frequencies while keeping a compact size necessitates sophisticated design approaches. This research presents a substrate-integrated waveguide (SIW) cavity-backed slotted patch antenna (SPA) tailored for the 28 GHz and 34 GHz frequency bands. Additionally, a linear tapered slot antenna is designed with a compact profile of 27.5 mm × 7.5 mm × 0.254 mm. The SIWs are implemented using vias on the outer profile of the antenna, and circular and rectangular slots are etched on the radiating surface. The goal of optimizing the antenna geometry is to enhance return loss within the desired frequency bandwidth, which means the Genetic Algorithm (GA) will determine the optimal antenna shape to achieve lower return loss than the original design within this bandwidth. The antenna exhibits dual resonance at 28 GHz and 38 GHz in the millimeter-wave range, providing an impedance bandwidth of 211 MHz (27.72 GHz–27.94 GHz) at 28 GHz and 127 MHz (37.88 GHz–37.98 GHz) centered at 38 GHz. The proposed antenna demonstrates gains of 8.04 dBi and 9.72 dBi at these operating bands. A prototype of the antenna is fabricated on RT/duroid 5880 and its characteristics are measured. The overall VSWR of the antenna ranges from 1 to 2, with a radiation efficiency of 94 %. The proposed antenna achieves dual-band performance with increased directivity and stable gain, exhibiting enhanced electric field distribution, radiation patterns, and reflection coefficient (S11), all of which contribute to a comprehensive understanding of the antenna's performance. This study compares the designed antenna's performance to that of the fabricated prototype. The proposed antenna is ideal for 5G applications due to its small size, broad spectral coverage, and excellent gain.

The concept of the Internet of Things (IoT) threat surface refers to the overall susceptibility of smart devices to potential security risks. This vulnerability includes the combined impact of security weaknesses, gaps in protective measures, and potential vulnerabilities within the device OS, installed libraries, and applications, as well as the infrastructure involved. This comprises both identified and unforeseen risks that could potentially compromise the device’s integrity, data, logs, and hosted applications. By minimizing the extent to which the device’s components are exposed, it becomes possible to reduce the vulnerabilities inherent in the device, thereby decreasing its overall threat surface area. This research introduces an innovative framework for assessing Smart IoT cameras within the ecosystem. This framework involves the identification and categorization of webcam devices, followed by an analysis of potential threats based on various exposure indicators present within each layer. Subsequently, this information is used to determine the possible paths through which a device might be compromised, allowing for the evaluation of severity and both maturity levels. The authors present metrics that aid in reevaluating and recalibrating the security levels, considering the discovered threat surface elements. These refined metrics offer a fresh perspective on security, offering valuable insights for stakeholders who are engaged in the development, deployment, and evaluation of the security aspects of such devices.

Persistence behavior is a tactic advanced adversaries use to maintain unauthorized access and control of compromised assets over extended periods. Organizations can efficiently detect persistent adversaries and reduce the growing risks posed by highly skilled cyber threats by embracing creative techniques and utilizing sophisticated tools. By taking a proactive stance, businesses may increase their entire cybersecurity posture by anticipating and mitigating possible risks before they escalate. Security analysts perform thorough investigations and extract meaningful insights from large datasets with greater technical advantage by using Elasticsearch in conjunction with a variety of linguistic tools. This research presents a novel methodology for proactive threat intelligence to identify and mitigate advanced adversaries that use persistent behaviors. The authors designed and set up an Elasticsearch-based advanced Security Information and Event Management platform to offer a proactive threat-hunting strategy. This enables comprehensive analysis and detection by integrating Lucene, Kibana, and domain-specific languages. The goal of this research is to locate hidden advanced enemies who exhibit persistent behavior during cyberattacks. The framework can help improve the organization’s resilience to identify and respond to threats by closely examining activities like boot or logon auto-start execution in registry keys, tampering with system processes and services, and unauthorized creation of local accounts on compromised assets. This study emphasizes proactive actions over reactive reactions, which advances danger detection techniques. This technical study provides security practitioners seeking to improve defenses against new advanced attacks to stay ahead in a dynamic threat landscape.

South African power sector has a total installed capacity of about 58,095 MW. However, in the past five years, the nation's energy sector has been struggling to generate a daily average power of about 23,526 MW. Therefore, the use of machine learning technique is crucial to observing the short-term forecast of South African dispatchable power generation and its effects on the nation's economic growth. The choice of SARIMAX as the forecasting tool in this study is due to its flexibility, simplicity, accuracy, and robustness. This study has carried out time series modeling of the South African dispatchable power generation between the period: 2019 and 2023 by using the seasonal auto-regressive moving average with exogenous variables (SARIMAX). The accuracy of the model was measured by the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). When subjected to ARIMA analysis, the predictive model produced the following metrics: RMSE of 669.05 MW, MAE of 575.07 MW, R2 value of 0.937, MBE of -2.45, and MAPE of 0.023. However, upon employing the SARIMAX method, notable improvements were observed, with the metrics indicating RMSE of 469.92 MW, MAE of 330.80 MW, R2 of 0.969, MBE of -0.09 MW, and MAPE of 0.015. The improved accuracy provided by the SARIMAX predictive method confirms the importance of considering seasonal effects in dispatchable generation prediction models. Dispatchable power generation prediction is an important route to achieving sustainable energy for a nation's development, social well-being, and security of life and properties.

In recent years, various energy sources and methods have been used to heat water in domestic and commercial buildings. Water heating methods for large households or commercial buildings include electrical heating elements, gas heaters, and solar energy (solar concentrators, flat plate collectors, evacuated tube collectors, etc.). In recent decades, the focus of water heating has shifted to solar energy, which is abundantly available in most African countries. The weather condition of a region has a huge impact on the system's performance. South Africa is characterised by four different weather seasons (winter, spring, summer, and autumn), unlike most African countries. Therefore, this study focuses on the impact of weather seasons on the system's performance for water heating through the combined use of solar energy and solar concentrator techniques. The system performance was modelled by using Matlab Simulink®, where historical weather data for Pretoria, South Africa, was fed into the model. Based on the weather data input, the system behaviour varied per season due to the change in solar intensity. The average outlet temperatures of the absorber, in the order of magnitude, were 333, 332, 328, and 325 K during the autumn, summer, spring, and winter seasons, respectively. Similarly, the average storage tank temperatures, in the order of magnitude, were 366, 364, 363, and 360 K in spring, summer, autumn, and winter, respectively. From this study, it is concluded that the different weather seasons in South Africa, have a direct impact on the performance of the system. Irrespective of the season, the system produced the required volume of hot water required throughout the year.

Introduction: The role of Acetyl -11-keto-β-boswellic acid (AKBA) in regulating osteoblast differentiation was recently brought to light. Therefore, the current study was designed to explore the osteogenic differentiation capability of AKBA on bone marrow mesenchymal stem cells (BMMSCs) as a potential therapeutic agent to accelerate the healing process in apicoectomy. Materials and Methods: BMMSCs were characterized by flow cytometry. Cellular viability and proliferation assays were used with different concentrations of AKBA. Cells were divided into 5 groups to test osteogenic differentiation: Group I: negative control, Group II: positive control, Group III: BMMSCs were treated with 1 μM AKBA, Group IV: BMMSCs were treated with 0.1 μM AKBA, and Group V: BMMSCs were treated with 0.01 μM AKBA. Mineralization assays and gene expression analysis were assessed, and the significance difference between groups was established at P < 0.05. Results: The flow cytometry analysis demonstrated that BMMSCs had positive expression for mesenchymal stem cell marker and negative expression for hematopoietic markers. The concentration of 0.01 μM gave significantly higher cell density than the untreated cells after 7 days (P < 0.05). Cells treated with 0.1 and 0.01 μM AKBA revealed a significantly higher ALP activity, alizarin red, and von Kossa staining than control groups (P < 0.05). High expression of osteogenic genes was detected in BMMSCs treated with 0.1 μM AKBA (P < 0.05). Conclusions: It was declared that the concentration of 0.1 μM AKBA has no toxicity on BMMSC viability and proliferation with an impact on BMMSC osteogenic differentiation. Therefore, AKBA (0.01 μM) could be used in bone regeneration during periradicular surgery.

Abstract The proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.

Abstract Patient-focused healthcare applications are important to patients because they offer a range of advantages that add value and improve the overall healthcare experience. The 5G networks, along with Mobile Edge Computing (MEC), can greatly transform healthcare applications, which in turn improves patient care. MEC plays an important role in the healthcare of patients by bringing computing resources to the edge of the network. It becomes part of an IoT system within healthcare that brings data closer to the core, speeds up decision-making, lowers latency, and improves the overall quality of care. While the usage of MEC and 5G networks is beneficial for healthcare purposes, there are some issues and difficulties that should be solved for the efficient introduction of this technological pair into healthcare. One of the critical issues that blockchain technology can help to overcome is the challenge faced by MEC in realizing the most potential applications involving IoT medical devices. This article presents a comprehensive literature review on IoT-based healthcare devices, which provide real-time solutions to patients, and discusses some major contributions made by MEC and 5G in the healthcare industry. The paper also discusses some of the limitations that 5G and MEC networks have in the IoT medical devices area, especially in the field of decentralized computing solutions. For this reason, the readership intended for this article is not only researchers but also graduate students.

Abstract The energy generation efficiency of photovoltaic (PV) systems is compromised by partial shading conditions (PSCs) of solar irradiance with many maximum power points (MPPs) while tracking output power. Addressing this challenge in the PV system, this article proposes an adapted hybrid control algorithm that tracks the global maximum power point (GMPP) by preventing it from settling at different local maximum power points (LMPPs). The proposed scheme involves the deployment of a 3 × 3 multi-string PV array with a single modified boost converter model and an adapted perturb and observe-based model predictive control (APO-MPC) algorithm. In contrast to traditional strategies, this technique effectively extracts and stabilizes the output power by predicting upcoming future states through the computation of reference current. The boost converter regulates voltage and current levels of the whole PV array, while the proposed algorithm dynamically adjusts the converter's operation to track the GMPP by minimizing the cost function of MPC. Additionally, it reduces hardware costs by eliminating the need for an output current sensor, all while ensuring effective tracking across a variety of climatic profiles. The research illustrates the efficient validation of the proposed method with accurate and stable convergence towards the GMPP with minimal sensors, consequently reducing overall hardware expenses. Simulation and hardware-based outcomes reveal that this approach outperforms classical techniques in terms of both cost-effectiveness and power extraction efficiency, even under PSCs of constant, rapidly changing, and linearly changing irradiances.

Abstract This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Introduction: Nanophthalmos is characterized by a short axial length, a thick choroid, and a thick sclera. Unilateral symptomatic disc swelling in nanophthalmos presents both a diagnostic and a therapeutic challenge. Case Presentation: A healthy 59-year-old man reported a two-week-long abrupt vision reduction in his right eye. 20/100 best spectacle (+17.25 diopter) corrected visual acuity, unilateral widespread disc enlargement, central scotoma, and a slight color vision disruption without an afferent pupillary defect were among the positive findings in the right eye. Workup for neuro-ophthalmology was negative. Numerous consultations did not suggest any form of treatment for the patient. Review of the optical coherence tomography (OCT) indicated a small, crowded optic nerve head and substantial diffuse choroidal thickening with dome-shaped temporal peripapillary area with choroidal expansion. In addition to circumferential anterior four-quadrant 95%-deep sclerectomy from recti insertion to the vortices, radial nasal posterior sclerotomy reaching the optic nerve sheath was performed on the patient. After the procedure, 2 weeks later, the patient’s vision returned, and it persisted until the 6-month follow-up. By OCT, the two eyes were comparable as far as disc contour and nerve fiber layer thickness. Conclusion: This form of sclerectomy, which aims at decompressing the oncotic choroidal pressure, is an effective treatment for compressive optic neuropathy in the context of nanophthalmos. Could sclerectomy assist in treating other optic neuropathies associated with peripapillary pachychoroid?

Let $p$ and $q$ be two different prime integers such that $p\equiv q\equiv3\pmod8$ with $(p/q)=1$, and $l$ a positive odd square-free integer relatively prime to $p$ and $q$. In this paper we investigate the unit groups of number fields $\mathbb L=\mathbb{Q}(\sqrt2, \sqrt{p}, \sqrt{q}, \sqrt{-l})$.