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This research paper presents an efficient data collection scheme for Wireless Sensor Networks (WSNs) that simultaneously compresses and encrypts sensor data to extend network lifespan. To address WSN resource limitations, the scheme combines Compressive Sensing (CS) with Elliptic Curve Cryptography (ECC) and Elliptic Curve Diffie–Hellman (ECDH) key exchange. Sensor data is securely compressed and encrypted using ECC-based public key mechanisms, mitigating CS-related attacks during aggregation and transmission. The measurement matrix seed serves as a private key that is exchangeed between sensor nodes and the base station, enhancing both security and efficiency. A prime-number-based Tree Path Identifier (TPID) routing and Cluster Head (CH) selection strategy is employed to optimize communication. Seven CS algorithms—including Orthogonal Matching Pursuit (OMP), Binary Compressive Sensing (BCS), Subspace Pursuits (SP), Approximate Message Passing (AMP), Split Bregman Iterative (SBI), Basis Pursuit (BP) and Compressive Sampling Matching Pursuit (CoSaMP) algorithms—are evaluated across various data sparsity levels. Results show that SP, AMP, and SBI algorithms outperform others in preserving energy, extending network life, and delaying the First Dead Node (FDN) appearance. Performance metrics include residual energy, network lifetime, total energy dissipation, and throughput. Energy savings confirm the superiority of the proposed hybrid scheme over traditional CS algorithms.

This manuscript introduces a novel algorithm tailored to investigate the strong convergence of sequences under specific conditions. Notably, the framework incorporates a finite family of generalized demimetric operators within real Hilbert spaces, broadening existing operator theory. The proposed algorithm efficiently establishes strong convergence and demonstrates its versatility through successful applications, including proving the existence of solutions to split minimization and feasibility problems, thereby showcasing its potential in optimization and numerical analysis.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

Bladder cancer (BC) poses a significant health challenge in Egypt, constituting 16% of male cancers and resulting in over 7,900 deaths annually. Globally, BC stands as the tenth most prevalent cancer with an anticipated 72.8% rise in new cases by 2040. As BC is an important health problem, early detection and prevention are crucial for improving outcomes. This could be done by increasing the disease's awareness through structured campaigns for the community, increasing their insights about the risk factors of the disease; most commonly, smoking as well as other risk factors as exposure to certain chemicals, alcohol consumption, diet, obesity and schistosomiasis. The awareness includes not only the risk factors but also the most common early symptoms for detection. To compare the effect of our awareness campaign about BC in Mansoura, Egypt on the index of awareness measured by our locality-generated simplified questionnaire. The campaign featured symposiums, awareness booths and online initiatives that gathered substantial participation. We administered a purpose-designed survey (in either Arabic or English) comprising questions regarding the following: perception of BC as common cancer, BC's risk factors acknowledgement, knowledge about screening and diagnosis, perception of the methods of treatment and the importance of screening. We used face-to-face surveys targeting the public in Mansoura to compare between the index of awareness about BC pre-awareness and post-awareness. In addition, online surveys targeting Egyptian medical students and medical staff were conducted to evaluate their knowledge about BC. This study was conducted on 1,673 people ( = 304 in-person surveys and = 1,369 online surveys). Of the 1,673 participants, 52.45% were females; the age of the responders ranged from 18 to 40 years. The mean index of awareness before our awareness in the campaign in the face-to-face phase was 58.52% while the index of awareness after our campaign increased to 94.28%. The public's post-awareness survey responses revealed an enhancement in awareness of BC and highlighted the campaign's effectiveness in disseminating critical information. Prospective long-term research is proposed to assess such awareness campaigns' impact on patient survival and community burden.

Molnupiravir (MPV) is the first direct-acting oral antiviral drug that effectively decreases nasopharyngeal infections with SARS-CoV-2 virus. The stability of MPV was tested by subjecting the drug to various stress conditions. The drug is liable to oxidative, acidic, and alkaline degradation and showed significant stability against thermal degradation. Mass spectrometry identified the degradation products and guided suggestion of the degradation patterns. Interestingly, while inspecting the UV-absorption spectra, we observed no absorbance at 270 nm for the products of the three degradation pathways (c.f. intact MPV). Direct spectrophotometry seemed a solution that perfectly fit the purpose of the stability assay method in our case. It avoids sophisticated instrumentation and complex mathematical data manipulation. The method determined MPV accurately (100.32% ± 1.62) and selectively (99.49% ± 1.63) within the linear range of 1.50 × 10 –5 –4.0 × 10 –4  M and down to a detection limit of 0.48 × 10 –5  M. The proposed method is simple and does not require any preliminary separation or derivatization steps. The procedure proved its validity as per the ICH recommendations. The specificity was assessed in the presence of up to 90% degradation products. The study evaluated the greenness profile of the proposed analytical procedure using the National Environmental Methods Index (NEMI), the Analytical Eco-Scale, and the Green Analytical Procedure Index (GAPI). The three metrics unanimously agreed that the developed procedure results in a greener profile than the reported method. The method investigated the degradation reactions' kinetics and evaluated the reaction order, rate constant, and half-life time for each degradation process.

With the rapidly increasing usage of renewable sources, especially wind power, maximizing the power produced from wind energy conversion system (WECS) has become a major concern. Various methods are utilized in the domain of wind turbine performance enhancement for tracking the maximum power point (MPP). Among them, the perturb and observe (P&O) approach is widely applied because of its straightforward implementation. Nevertheless, the primary drawback of this approach is the imprecision caused by variations at the peak power point. Consequently, due to wind’s arbitrary and complicated characteristics, using an intelligent optimization technique is compulsory as it can give effective tracking performance. In this study, a recently developed nature-inspired metaheuristic, termed the Greater Cane Rat Algorithm (GCRA), which emulates the cognitive foraging behavior of greater cane rats during and after the breeding season. The GCRA approach seeks to regulate the boost converter by computing the duty cycle value using the voltage and current variables. The Wind Energy Conversion System (WECS) incorporates a wind turbine, a Permanent Magnet Synchronous Generator (PMSG), a rectifier, and a DC/DC boost converter that is linked to a load. The wind system can track the maximum power via a mechanical sensorless tracker system without the need to connect an additional mechanical sensor. The suggested strategy is compared to various tracking methodologies, including the classical Perturb & Observe (P&O), Particle Swarm Optimization (PSO), and Gray Wolf Optimization (GWO). The obtained results, which have been executed in the environment of MATLAB/SIMULINK R2022b, illustrate that the proposed approach improves the performance of the tracking system under different wind profiles step, realistic, and ramp variation of the wind velocity. The proposed strategy outperforms a tracking efficiency that exceeds 99%, surpassing other considered tracking approaches, which are at 95.5%, 94.7%, and 91.4% with the least error ratio and the best tracking for the power coefficient ratio.

Ensuring stability of both voltage and frequency in linked power networks (LPNs) is a critical challenge, primarily due to their nonlinear dynamics and load variability. Due to the high penetration of intermittent renewable energy sources, traditional Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) schemes often struggle to ensure fast, robust, and coordinated regulation in modern multi-area hybrid grids. To address these limitations, this research introduces a novel cascaded control architecture developed for Load Frequency Control (LFC) and Automatic Voltage Regulation (AVR) within a three-area hybrid LPN comprising thermal, wind, hydro, photovoltaic, and diesel generation sources. The proposed framework integrates three cascaded regulators: FOPI, TIDμ, and PIDA. Combining strengths of the three controllers provides better transient response, higher robustness against system uncertainties, and Improved steady-state accuracy. Also, for better performance, the parameters of the proposed controller are optimally selected using a recent developed optimization algorithm called Differential Creative Search (DCS). All simulations were carried out in MATLAB/Simulink environment. The obtained results are comprehensively compared to results obtained by utilizing other algorithms, Artificial Ecosystem-based Optimization (AEO), Dandelion Optimizer (DO), and the Runge–Kutta Optimization (RUN) algorithm. Results indicate that the DCS algorithm achieved the superior outcome, attaining the lowest objective function value of 0.0507, surpassing AEO, DO, and RUN with values of 0.0706, 0.0789, and 0.0649, respectively. Furthermore, the proposed controller was benchmarked against advanced control strategies such as FOPI–PI, TFOIDFF, and FOPI–PIDD2, yielding improvements in objective function values by 28.89%, 54.89%, and 26.42%, respectively. The simulation findings demonstrate that the FOPI–TIDμ–PIDA controller ensures significantly reduced overshoot by less than 0.12 Hz, faster settling times by less than 9.4 s, and enhanced voltage–frequency regulation even under ±25% variations in system parameters. Collectively, these results Proves the robustness, adaptability, and effectiveness of the proposed controller in advancing the stability and resilience of sustainable hybrid linked power networks.

Monkeypox (Mpox) is a re-emerging infectious disease representing a new global challenge. It poses a substantial threat to countries, particularly those with a low number of cases. Due to its popularity as a tourist destination and its proximity to many African refugees, Egypt is potentially at risk of Mpox importation. Therefore, effective disease management necessitates healthcare workers (HCWs) to possess adept knowledge, along with a positive attitude and behavior. The study aimed to assess the knowledge, attitude, and perception of Egyptian HCWs and medical students towards human Mpox. The present cross-sectional study data was collected from participants between October and December 2022 via a questionnaire. The questionnaire comprised 31 questions in the knowledge section, 11 questions in the attitude section, and 14 in the perception section. The present study involved a total of 1,034 HCWs and medical students. It was found that 55.3% of the participants demonstrated adequate knowledge about Mpox, whereas 44.5% and 39.8% of the respondents exhibited favorable attitudes and perceptions towards the disease, respectively. Binary logistic regression analysis revealed that adequate knowledge was significantly observed in ages older than 40 years ( < 0.001), married participants ( < 0.001), and doctors ( < 0.001). The positive attitude was significantly observed among the male sex ( = 0.045), urban residents ( = 0.002), and nurses ( = 0.002). Conversely, married participants (p = 0.013), doctors ( < 0.001), and individuals employed in pharmacy and laboratory departments ( < 0.001) experienced an increase in positive perception. Knowledge, attitude, and perception towards Mpox among Egyptian HCWs and medical students exhibit suboptimal levels. Addressing these gaps is crucial to controlling and effectively preventing disease transmission.

Hydroxychloroquine (HCQ) is prescribed to treat malaria and certain autoimmune diseases. Recent studies questioned its efficiency in relieving COVID-19 symptoms and improving clinical outcomes. This work presents a quality-by-design approach to develop, optimize, and validate a potentiometric sensor for the selective analysis of HCQ in the presence of its toxic impurities (key starting materials), namely 4,7-Dichloroquinoline (DCQ) and hydroxynovaldiamine (HND). The study employed a custom experimental design of 16 sensors with different ion exchangers, plasticizers, and ionophores. We observed the Nernstian slopes, correlation coefficients, quantification limit, response time, and selectivity coefficient for DCQ and HND. The computer software constructed a prediction model for each response. The predicted responses strongly correlate to the experimental ones, indicating model fitness. The optimized sensor achieved 93.8% desirability. It proved a slope of 30.57 mV/decade, a correlation coefficient of 0.9931, a quantification limit of 1.07 × 10 –6  M, a detection limit of 2.18 × 10 –7  M, and a fast response of 6.5 s within the pH range of 2.5–8.5. The sensor was successfully used to determine HCQ purity in its raw material. The sensor represents a potential tool for rapid, sensitive, and selective monitoring of HCQ purity during industrial production from its starting materials.

Salt-cemented soils are problematic soils and known for high compressibility and inadequate shear strength, pose significant challenges to structural stability. This paper investigates stabilizing such soils using deep soil mixing (DSM) technique, a cost-effective alternative to traditional deep foundations. A series of laboratory-scale models were employed to evaluate the performance of stabilized salt-cemented soils under various soil states. A specially designed deep soil mixing apparatus was employed to prepare a DSM column within a steel tank, with instrumentation for measuring induced stresses and settlements under various soil conditions. The results demonstrated that DSM significantly enhances bearing capacity and reduces settlement, with effectiveness influenced by factors such as salt content and total water content (TWC). The TWC, which includes initial water content and water/binder ratio, is critical for mixing efficiency and should be critically designed prior to the treatment to achieve the maximum possible improvement. TWC is equal to 41% contributing to the highest column strength and hence the ultimate bearing capacity. Scanning electron microscope images revealed denser structures in treated columns, indicating improved soil contact and strength. While the ultimate bearing capacity decreases with higher salt content, improvements ranged from 125 to 150% depending on salt concentration. Chemical interactions between soil salts and cementitious materials were also found to enhance shear strength over time. The study’s findings provide valuable guidelines for optimizing DSM treatment parameters to effectively stabilize salt-cemented soils. The developed models were validated against past design equations and real-world tests, confirming their reliability and effectiveness.