Search

Abstract The induction motor is known to be the most reliable motor in the industry and is also the most energy-consuming load worldwide. It is noticeable in some production areas that the use of a high-capacity induction motor is not required at some operating times, and therefore it is important to control the speed of the induction motor to suit the operating requirements and obtain highly efficient performance. This paper presents a case study of a drinking water purification plant that uses a variable speed drive (VSD) as a speed controller for one of its induction motors to drive the pump. The VSD is connected to the motor in question to regulate its speed and maintain the external pressure of the station at an appropriate value that ensures water delivery to all branches of the network. 24-hour power consumption measurements of the respective engine with and without the use of a VSD are studied and analyzed to determine the potential for energy savings. Finally, a case study simulation is presented to compare the simulation results with the actual results.

Abstract The anticancer, antimicrobial, and insecticidal activities of sarcotrocheliol (1) and cholesterol (2) obtained from the soft coral Sarcophyton trocheliophorum (S. trocheliophorum) were intensively studied. According to this study, both compounds 1 and 2 showed potential cytotoxicity towards the human colorectal carcinomaHCT-116 (IC50 10.4, 11.8 µg/mL) and human liver carcinoma HepG2 cell lines (IC50 8.8, 12.0 µg/mL), respectively. Compounds 1 and 2 were evaluated as potential inhibitors of caspase-3, a member of the cysteine protease family, which is considered a key enzyme in inducing cell apoptosis. Results showed that compounds 1 and 2 have induced apoptosis via up-regulation of caspase-3. Sarcotrocheliol (1) displayed antimicrobial activity against P. aeruginosa (15 mm), B. subtilis (15 mm), M. luteus (14 mm) and C. albicans (15 mm), with a MIC of 1.5 µg/mL against the reported test microorganisms. On the other hand, cholesterol (2) showed less activity towards P. aeruginosa (10 mm), B. subtilis (14 mm), S. aureus (12 mm) and C. albicans (10 mm) with MICs of 3.0, 1.5, 1.5 and 3.0 µg/mL against the tested microorganisms, respectively. Larvicidal activity revealed that compounds 1 and 2 induced remarkable toxicity against the third instar larvae of the mosquito, Culex pipiens even at concentration of 2 ppm. Adulticidal activity data showed that tested compounds are distinctly potent toxicants against the housefly, Musca domestica adult females. Overall, compound 2induced much more insecticidal activity than 1, and M. domestica adult females were more sensitive to tested compounds than C. pipiens larvae. Computationally, Density Functional Theory (DFT) analyses revealed that compound 2 had a higher dipole moment and lower band gap energy when compared to compound 1. So, compounds 2 is chemically more reactive and less stable than compound 1. According to the molecular docking study against PDB IDs: 3KJF, 5UHF and 1ACJ, compounds 1 and 2 demonstrated their activity mode as anticancer, antimicrobial, and insecticidal agents. The compounds exerted many interactions and showed high binding to the proteins, recognizing their potential as drug candidates with broad bioactivities.

Abstract Brucellosis in dromedary camel bulls leads to either temporary or permanent loss of fertility. Camel brucellosis is associated with both orchitis and epididymitis. However, the clinical signs of camel brucellosis are not clear as those in cattle. Therefore, this study aimed to diagnose camel brucellosis based on a serological screening using Rose Bengal plate test (RBPT) followed by competitive ELISA. To understand the impact of brucellosis on camel bull fertility, this study aimed to examine the semen characteristics, evaluate the testicular histopathology, examine hormonal profile, antioxidants and acute phase proteins (APP). A total of 150 mature bulls were used in this study. Blood samples were collected for serological, hormonal, and biochemical analysis. This study revealed that 6.6% and 7.3% of the examined bulls were Brucella-seropositive using RBPT and competitive ELISA, respectively. The Brucella-seropositive dromedary bulls showed poor semen quality, pathological changes orchitis, and lower testosterone. Moreover, our findings showed a higher cortisol level, and significant impairments in the measured APP and antioxidants in Brucella-seropositive bulls. In conclusion, the Brucella-seropositive dromedary bulls showed lower fertility due to poor semen quality and lower testosterone levels. Such lower fertility is likely mediated by high cortisol levels, and impaired APP and antioxidants’ defense response.

Abstract Water scarcity poses a significant challenge to agricultural production, prompting the development of sustainable technologies to optimize water resource utilization. This study focuses on the synthesis and application of a multifunctional poly (vinylpyrrolidone); PVP-based superabsorbent hydrogel (SAH) for controlled release of essential fertilizers (nitrogen, phosphorus, and potassium) and enhanced water retention in soil. The hydrogel was prepared via a facile one-step approach and compared to a control soil without hydrogel amendment. The reaction was initiated in the presence of poly(vinylpyrrolidone) (PVP) to produce a PVP-based copolymer hydrogel. The hydrogel was then subjected to a basic treatment using either sodium hydroxide (hydrogel #1) or potassium hydroxide (hydrogel #2). The PVP-based SAH exhibited excellent swelling capacity, water retention, and fertilizer release properties. When applied to Pisum sativum plants under drought stress, the hydrogel significantly improved soil moisture levels, nutrient availability, and plant growth parameters compared to the control. The hydrogel #2-amended plants demonstrated enhanced biomass, chlorophyll content, and photosynthetic efficiency, highlighting the hydrogel’s effectiveness in mitigating the adverse effects of drought stress. These findings demonstrate the potential of the PVP-based SAH as a promising strategy for sustainable agriculture, offering using readily available and inexpensive raw materials, suggesting a relatively low-cost and scalable production process. Furthermore, the hydrogel facilitates water conservation, controlled nutrient delivery, and improved plant performance under drought stress conditions.

Abstract Artificial intelligence (AI) integration in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates university students’ perceptions of using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants’ views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied to compare variables. Two hundred seventy-nine (279) students completed the questionnaire. More than half of the participants (52%, n = 145) expressed their belief in AI’s potential to reduce treatment errors. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the “Benefits and Positivity Toward AI in Healthcare” and “Concerns and Fears” domains (p = 0.024) and (p = 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.

Abstract The kidney is considered the main site for the net release of Tyrosine (Tyr) to the circulation through hydroxylation of Phenylalanine (Phe) using phenylalanine hydroxylase enzyme. In chronic kidney disease (CKD) patients the enzyme activity is impaired, therefore the serum concentration ratio of Tyr/Phe is reduced compared to healthy individuals. This motivates us to develop a cost effective, green, simple, sensitive, and selective HPTLC method to measure the levels of Tyr and Phe in serum samples. The analysis was carried out using acetonitrile: ethanol: 25% ammonia solution: ethyl acetate (6.5: 1.5: 1: 0.5) as a mobile phase. Rf values were 0.55 ± 0.05 for Phe and 0.39 ± 0.05 for Tyr. Densitometry scanning was performed using UV detector and dual wavelengths of 210 and 225 nm were obtained. A linear correlation was observed between the levels of Phe and Tyr, ranging from 50 to 700 ng band− 1 and 50 to 600 ng band− 1, respectively, under the optimum conditions. The method selectivity, linearity, precision, accuracy, and robustness were all confirmed in accordance with ICH recommendations. Calculations of the separation and resolution factors, number of theoretical plates, and height equivalent to theoretical plates prove to the chromatographic system accuracy and high separation efficiency. The developed method exhibits an acceptable eco-scale when measuring the method greenness using AGREE and GAPI softwares. It was applied for the determination of Phe and Tyr concentrations in human serum samples.

Abstract ZnAl2O4 and CuAl2O4 spinel nanoparticles were prepared by a modified Pechini method and used with the natural chitosan (CS) and shellac (SH) polymers to form novel composite membranes as promising food packaging materials. The selection of ZnAl2O4 and CuAl2O4 spinel nanoparticles was based on their antibacterial characteristics, availability, and economy. Using a straightforward and adaptable solution mixing and casting method, the bio-composites were created. The mechanical, physical, antibacterial, homogeneity and air permeability properties of composite films were investigated. The film structure was evaluated in terms of component interactions using FTIR spectra. The addition of 10% SH increased the tensile strength, percentage strain at maximum load, Young’s modulus, and burst strength by 114–101%, 3.6–8.4, 103–119, and 179–153% for low and middle M.wt./CS respectively. Chitosan/shellac-CuAl2O4 composite has superior properties compared to ZnAl2O4 composite. In general, 0.05% spinel provides a composite having better qualities than that of 0.1 additions. Middle M.wt. chitosan provides a composite with superior properties compared to that of low M.wt. The incorporation of ZnAl2O4 or CuAl2O4 enhanced the thermal stability of the SH/CS composite. ZnAl2O4 provides superior thermal stability than CuAl2O4. When shellac/CS film structure is treated with the previously indicated ZnAl2O4 or CuAl2O4 formulation, the % swelling decreases along with an increasing in the gel fraction. The antimicrobial assessment using inhibition zone diameter and shake flask methods showed that a composite of 1:9 shellac/chitosan/0.05% of CuAl2O4 exerted the highest Gram-positive antibacterial activity against B. mycoides (21 mm), and C. albicans (22 mm). So, these enhancements make chitosan/shellac/ZnAl2O4 or CuAl2O4 composite films a good alternative to producing food packaging materials.

Abstract This research explores the design of an infrared (IR) photodetector using mercury cadmium telluride (Hg1–x Cd x Te). It proposes two- and three-dimensional homojunction models based on p+-Hg0.7783Cd0.2217Te/n–-Hg0.7783Cd0.2217Te, focusing on applications in the long-wavelength infrared range. The photodetector’s performance is analyzed using Silvaco ATLAS TCAD software and compared with analytical calculations based on drift-diffusion, tunneling, and Chu’s approximation techniques. Optimized for operation at 10.6 μm wavelength under liquid nitrogen temperature, the proposed photodetector demonstrates promising optoelectronic characteristics including the dark current density of 0.20 mA/cm2, photocurrent density of 4.98 A/cm2, and photocurrent density-to-dark current density ratio of 2.46 × 104, a 3-dB cut-off frequency of 104 GHz, a rise time of 0.8 ps, quantum efficiency of 58.30 %, peak photocurrent responsivity of 4.98 A/W, specific detectivity of 3.96 × 1011 cmHz1/2/W, and noise equivalent power of 2.52 × 10–16 W/Hz1/2 indicating its potential for low-noise, high-frequency and fast-switching applications. The study also incorporates machine learning regression models to validate simulation results and provide a predictive framework for performance optimization, evaluating these models using various statistical metrics. This comprehensive approach demonstrates the synergy between advanced materials science and computational techniques in developing next-generation optoelectronic devices. By combining theoretical modeling, simulation, and machine learning, the research highlights the potential to accelerate progress in IR detection technology and enhance device performance and efficiency. This multidisciplinary methodology could serve as a model for future studies in optoelectronics, illustrating how advanced materials and computational methods can be utilized to enhance device capabilities.

Abstract The aim of the present study is to assess the shielding performance of a novel lead-free epoxide material against ionizing radiation. The effect of variation in particle size and concentration of tin oxide (SnO), which was added to epoxy resin polymer (ER), on its radiation shielding properties has been investigated in this research. Ten samples of ER samples incorporated with different concentrations (0%,20%,40%,60%) of SnO microparticles, nanoparticles, and both sizes combined were prepared and assessed. The linear attenuation coefficients (LAC) were measured experimentally through the collimated gamma-ray beam at 0.0595 MeV, 0.6617 MeV, 1.1730 MeV, and 1.330 MeV emitted from Am-241, Cs-137 and Co-60, respectively (to cover all energy range of gamma rays) for all samples with various concentrations and particle sizes of SnO. The other radiological shielding parameters such as half value layer (HVL), tenth value layer (TVL), and radiation protection efficiency (RPE) were estimated and compared for all different samples. The results prove that the increasing of the concentration and reducing the particle size of SnO leads to the enhancement of the radiation protection properties of the ER polymer. Moreover, it was observed that the incorporation of SnO micro- and nanoparticles together improves the radiation shielding properties of ER samples. Conclusively, the reinforcing of ER polymer material matrix by micro/nanoparticles of SnO as composite with enhanced radiation shielding specifications was highlighted.

Abstract Nanostructured materials are advantageous within numerous fields of medicine owing to their intriguing qualities, which include their size, reactive surface, bioactivity, potential for modification, and optical characteristics. Cassia javanica flower extract was used as a chelating agent in an environmentally friendly process to create SeNPs FTIR, XRD, and TEM, SAED were utilized to analyze and characterize the synthesized. The findings showed that the MIC of Se NPs against B. subtilis and S. aureus was 500 µg/ml. Conversely, the MIC for P. aeruginosa, E. coli, and C. albicans were 125, 250, and 62.5 µg/ml, respectively. Hence, SeNPs considerably reduced the activity; the inhibition peaked at 77.6% at 250 µg/ml to reach 49.04% at 7.8 µg/ml. Which showed the greatest suppression of MRSA biofilm formation without affecting bacterial growth. SeNPs showed an intriguing antioxidant capacity, achieving an IC50 of 53.34 µg/ml. This study looked how soaking seeds before sowing them with Se NPs at 50, 100, and 200 ppm affected the plants’ development in different parameters, as well as their yield of Vicia faba L. The growth conditions were effectively increased by soaking application of various quantities of Se NPs. The highest values of dry weight/pod (g), number of seeds/plant, weight of 100 seeds (g), and number of pods/plant were caused by high concentrations of Se NPs, by 28.43, 89.60, 18.20, and 94.11%, respectively.

Abstract Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To achieve optimal results, this paper utilizes diffusion models as the primary engine for these tasks. This paper presents two main contributions: (1) “Speaking the Language of Faces” (SLF): a flexible, modular, fusion-less and architecturally simple multimodal system. (2) A Scalability scheme and a sensitivity analysis which can assist practitioners in system parameter estimation and feature selection. SLF consists of two main components: a feature vector generator (encoder), and an image generator (decoder) utilizing a conditional diffusion model. SLF can accept various inputs, including low-resolution images, speech signals, person attributes (age, gender, ethnicity), or any combination of these. Moreover, Scalability based on conditional scale values is utilized. The implementation of SLF has confirmed its versatility (e.g., speech to face image generation, conditioned face super-resolution). We trained multiple system versions to conduct a sensitivity analysis and to determine the influence of each individual feature on the output image. Consequently, speaker embeddings have proven to be sufficient audio features for our task. It was also found that the effects of audio signals are profound and are more pronounced than those of the low resolution images (8 × 8), whose effects are still significant. The effect of gender, ethnicity and age were found to be moderate. On another note, conditional scale values significantly impact the system’s behavior and performance.

Abstract Future inductive charging ports must possess the capability to charge any electric vehicle (EV), irrespective of the specific coil architecture it is equipped with. This study examines the misalignment scenarios of the global circular pad at transmitter side (CirPT) with circular receiver pad (CirPR) and a double-D receiver pad (DDPR). The CirPT, CirPR, and DDPR configurations for WPT3 (11.1 kW) with ground clearance meeting the Z2-class specifications and above ground surface installation are built by utilizing circuit analysis and 3D-finite element simulations, as outlined by the Society of Automotive Engineering (SAE) J2954 standard. The simulated designs are employed to determine the frequency (f) and the compensating network components (CNCs) required to achieve optimal power transfer efficiency while maintaining nominal power levels. The analysis of misalignment scenarios involves examining various performance factors, including coupling coefficient (k), transmission power (P o ), efficiency (η), and leakage electromagnetic fields (EMFs). These factors are assessed under conditions of ideal alignment, as well as various linear and angular misalignments within the inductive charging system. The results demonstrate that both the CirPR and DDPR configurations can successfully interface with the CirPT to provide the required P o to the EV battery with commendable efficiency. In perfect alignment, the efficiencies are 95.10% for the CirPT-CirPR model and 91.60% for the CirPT-DDPR model. In maximum misalignment, the efficiencies are 87.10% for the CirPT-CirPR model and 89.50% for the CirPT-DDPR model, all exceeding the acceptable threshold of 80%.

Abstract Social media has emerged as a dominant platform where individuals freely share opinions and communicate globally. Its role in disseminating news worldwide is significant due to its easy accessibility. However, the increase in the use of these platforms presents severe risks for potentially misleading people. Our research aims to investigate different techniques within machine learning, deep learning, and ensemble learning frameworks in Arabic fake news detection. We integrated FastText word embeddings with various machine learning and deep learning methods. We then leveraged advanced transformer-based models, including BERT, XLNet, and RoBERTa, optimizing their performance through careful hyperparameter tuning. The research methodology involves utilizing two Arabic news article datasets, AFND and ARABICFAKETWEETS datasets, categorized into fake and real subsets and applying comprehensive preprocessing techniques to the text data. Four hybrid deep learning models are presented: CNN-LSTM, RNN-CNN, RNN-LSTM, and Bi-GRU-Bi-LSTM. The Bi-GRU-Bi-LSTM model demonstrated superior performance regarding the F1 score, accuracy, and loss metrics. The precision, recall, F1 score, and accuracy of the hybrid Bi-GRU-Bi-LSTM model on the AFND Dataset are 0.97, 0.97, 0.98, and 0.98, and on the ARABICFAKETWEETS dataset are 0.98, 0.98, 0.99, and 0.99 respectively. The study’s primary conclusion is that when spotting fake news in Arabic, the Bi-GRU-Bi-LSTM model outperforms other models by a significant margin. It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.

Abstract This work investigates the efficiency of green-synthesized WO3 nanoflakes for the removal of methylene blue dye. The synthesis of WO3 nanoflakes using Hyphaene thebaica fruit extract results in a material with a specific surface area of 13 m2/g and an average pore size of 19.3 nm. A combined theoretical and experimental study exhibits a complete understanding of the MB adsorption mechanism onto WO3 nanoflakes. Adsorption studies revealed a maximum methylene blue adsorption capacity of 78.14 mg/g. The pseudo-second-order model was the best to describe the adsorption kinetics with a correlation coefficient (R2) of 0.99, suggesting chemisorption. The intra-particle diffusion study supported a two-stage process involving surface adsorption and intra-particle diffusion. Molecular dynamic simulations confirmes the electrostatic attraction mechanism between MB and the (002) WO3 surface, with the most favorable adsorption energy calculated as -0.68 eV. The electrokinetic study confirmed that the WO3 nanoflakes have a strongly negative zeta potential of -31.5 mV and a uniform particle size of around 510 nm. The analysis of adsorption isotherms exhibits a complex adsorption mechanism between WO3 and MB, involving both electrostatic attraction and physical adsorption. The WO3 nanoflakes maintained 90% of their adsorption efficiency after five cycles, according to the reusability tests.

Abstract This article is concerned with a thermoelastic response in a non-local micro-stretch completely covered in the endless non-viscous fluid under temperature dependent, the medium is investigated employing the theory of Green-Naghdi (G-N III) and the model of three-phase-lag (3PHL).Basic equations are derived based on these models. The normal mode technique is employed to achieve an analysis solution to the problem. The study used a magnesium crystal element to compare non-local measurements of thermo-micro-stretch elasticity in water using the (G-N III) theory and the (3PHL) model. The non-local effect has been discovered to have a considerable impact on all physical quantities. Furthermore, comparisons are made between three different frequency values.

Abstract This paper introduces a novel insulated cable designed to enhance the distribution system’s capabilities. Accordingly, high-density polyethylene loaded with varying concentrations of zinc oxide (ZnO) nanoparticles (NPs) ranging from 0.0 to 5 wt% was prepared using the melt-blending technique. Zinc oxide (NPs) is synthesized by using sol–gel technique and their microstructure was examined by X-ray diffraction. The new insulated cable, HDPE nanocomposite loaded with 1 wt% of ZnO, demonstrates a 43% reduction in the relative dielectric constant and a 16.5% improvement in breakdown strength compared to pure HDPE. The observed changes in both the dielectric constant and breakdown strength offer several advantages in electrical applications. These benefits include a decrease in feeder current at the same loading level, mitigation of inrush transients during load switching, and a reduction in earth fault current values, particularly in unearthed distribution networks with ungrounded cables. A comparative study is conducted between the conventional insulating cable based on the original material (HDPE) and the new insulated cable incorporating ZnO nanomaterial at a ratio of 1.0 wt% of the total cable mass per unit length. This comparison utilizes data from two actual medium-voltage distribution feeders. Both actual feeders are simulated using the EMTP/ATP package. The obtained results prove the efficacy of the developed material cable (polymer doped with ZnO NPs) compared to the base material. The peak and duration of inrush current can be cut to 77.3% and 67% of their original values, respectively. The earth fault current can be reduced to 56.5% in ungrounded networks, while substation current under the normal operation can be cut to 84.3% with the same load currents.

Abstract Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Abstract The grading of non-muscle invasive bladder cancer (NMIBC) continues to face challenges due to subjective interpretations, which affect the assessment of its severity. To address this challenge, we are developing an innovative artificial intelligence (AI) system aimed at objectively grading NMIBC. This system uses a novel convolutional neural network (CNN) architecture called the multi-scale pyramidal pretrained CNN to analyze both local and global pathology markers extracted from digital pathology images. The proposed CNN structure takes as input three levels of patches, ranging from small patches (e.g., $$128 \times 128$$ 128 × 128 ) to the largest size patches ( $$512 \times 512$$ 512 × 512 ). These levels are then fused by random forest (RF) to estimate the severity grade of NMIBC. The optimal patch sizes and other model hyperparameters are determined using a grid search algorithm. For each patch size, the proposed system has been trained on 32K patches (comprising 16K low-grade and 16K high-grade samples) and subsequently tested on 8K patches (consisting of 4K low-grade and 4K high-grade samples), all annotated by two pathologists. Incorporating light and efficient processing, defining new benchmarks in the application of AI to histopathology, the ShuffleNet-based AI system achieved notable metrics on the testing data, including 94.25% ± 0.70% accuracy, 94.47% ± 0.93% sensitivity, 94.03% ± 0.95% specificity, and a 94.29% ± 0.70% F1-score. These results highlight its superior performance over traditional models like ResNet-18. The proposed system’s robustness in accurately grading pathology demonstrates its potential as an advanced AI tool for diagnosing human diseases in the domain of digital pathology.

Abstract Floods accompanied by thunderstorms in developed cities are hazardous, causing damage to infrastructure. To secure infrastructure, it is important to employ an integrated approach, combining remote sensing, GIS and precipitation data. The model was developed based on the estimation of event-based runoff and investigated the relationship between runoff and impervious surfaces. The novel approach of combining Hydrologic Engineering Center’s River Analysis System (HEC-GeoRAS) along with satellite imagery was utilized, where spatial data was combined with real-time values to run the model. As a first step, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model was fed with information about precipitation, slope, soil type, as well as land use and land cover. The results reveal that the subbasins of Deira, Nief and Jumeirah have the largest impervious area and, thus, a higher probability of flood occurrence. The model was calibrated and validated using previous runoff events and by comparing observed and simulated streak flow and peak discharge against those reported in previous studies. It was found that the model is efficient and can be used in similar regions.

Abstract Dental disorders are common worldwide, causing pain or infections and limiting mouth opening, so dental conditions impact productivity, work capability, and quality of life. Manual detection and classification of oral diseases is time-consuming and requires dentists’ evaluation and examination. The dental disease detection and classification system based on machine learning and deep learning will aid in early dental disease diagnosis. Hence, this paper proposes a new diagnosis system for dental diseases using X-ray imaging. The framework includes a robust pre-processing phase that uses image normalization and adaptive histogram equalization to improve image quality and reduce variation. A dual-stream approach is used for feature extraction, utilizing the advantages of Swin Transformer for capturing long-range dependencies and global context and MobileNetV2 for effective local feature extraction. A thorough representation of dental anomalies is produced by fusing the extracted features. To obtain reliable and broadly applicable classification results, a bagging ensemble classifier is utilized in the end. We evaluate our model on a benchmark dental radiography dataset. The experimental results and comparisons show the superiority of the proposed system with 95.7% for precision, 95.4% for sensitivity, 95.7% for specificity, 95.5% for Dice similarity coefficient, and 95.6% for accuracy. The results demonstrate the effectiveness of our hybrid model integrating MoileNetv2 and Swin Transformer architectures, outperforming state-of-the-art techniques in classifying dental diseases using dental panoramic X-ray imaging. This framework presents a promising method for robustly and accurately diagnosing dental diseases automatically, which may help dentists plan treatments and identify dental diseases early on.

Abstract Using plant extracts in the green synthesis of nanoparticles has become an environmentally acceptable approach. In our study, copper oxide nanoparticles (CuO NPs) were synthesized using ethanolic extracts of Azadirachta indica and Simmondsia chinensis. CuO NP formation was confirmed by the change in color and by UV‒visible spectroscopy (CuO NPs peaked at a wavelength of 344 nm). TEM images confirmed the semispherical shape of the CuO NPs, with particle sizes ranging from 30.9 to 10.7 nm. The antibacterial activity of these NPs was evaluated by using the agar diffusion method against clinical isolates, including methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli, Pseudomonas aeruginosa, Acinetobacter spp., Klebsiella pneumoniae, and Stenotrophomonas maltophilia. The minimum inhibitory concentration (MIC) of CuO NPs ranged from 62.5 to 125 µg/ml. In contrast, the antioxidant activity and antibiofilm activity of CuO NPs ranged from 31.1 to 92.2% at 125–500 µg/ml and 62.2–95%, respectively, at 125 –62.5 µg/ml. Our results confirmed that CuO NPs had IC50s of 383.41 ± 3.4 and 402.73 ± 1.86 at 250 µg/mL against the HBF4 cell line. Molecular docking studies with CuO NPs suggested that penicillin-binding protein 4 (PBP4) and beta-lactamase proteins (OXA-48) strongly bind to S. aureus and K. pneumoniae, respectively, with CuO NPs. Our study confirms the promising use of CuO NPs in treating pathogenic bacteria and that CuO NPs could be possible alternative antibiotics. This study supports the pharmaceutical and healthcare sectors in Egypt and worldwide.

Abstract A mathematical analysis was conducted to predict the power requirements of the olive trunk shaker. The predicted required power of the olive trunk shaker is influenced by shaking displacement, fruit detachment force, fruit mass, stem length, damping ratio, unbalanced mass of the tree shaker, and exciting shaking frequency. The Olive trunk shaker was tested in the field at five attachment heights, 400, 500, 600, 700, and 800 mm, and six levels of olive trunk diameter: 78, 84, 101, 143, 158, and 186 mm. The results indicated that the theoretical shaking power was 12.8 kW while the actual required shaking power ranged from 7.1 to 17.8 kW, with an average of 12.6 kW due to variations in tree trunk diameters and different attachment heights. Multiple regression using the least squares method was employed to establish a statistical relation between the theoretical equation and the actual required power as a function of the clamp attachment height and trunk diameter. The regression coefficient of the predicted equation was 93.6% for attachment height values between 400 and 800 mm and tree trunk diameter values between 78 and 186 mm, with a mean absolute error of the prediction model was 0.5009 kW.

Abstract The development of efficient and environmentally sustainable materials for white light-emitting diodes (WLEDs) is of paramount importance in the field of lighting technology. In this study, we present a solvent-modulated synthesis approach for the fabrication of multicolor narrow-bandwidth emissive carbon quantum dots (CQDs) as a promising solution for constructing WLEDs. The synthesis method involves the controlled reaction of organic precursors in different solvent environments, leading to the formation of CQDs with distinct emission wavelengths with a relatively small full width at half maximum, ranging from 28 to 42 nm. Moreover, these synthesized multicolor CQDs demonstrate a remarkably high fluorescence quantum yield of up to 65%, indicating their potential for constructing efficient WLED when incorporated in polymer matrix and coated on the surface of blue light-emitting diode (LED).

Abstract Waste and energy sectors have significant contributions to the greenhouse gas (GHG) emissions caused primarily by the population expansion. Waste-to-Energy (WtE) is introduced to address the issue raised by both sectors simultaneously through utilization of the potential energy stored in municipal solid waste (MSW) as well as offsetting GHG emissions. Limited research have been conducted in Egypt to assess the current situation of MSW management and associated methane emissions. The current study focused on estimating the baseline methane emissions for six Egyptian governorates and determining the energy production potential from WtE projects. To achieve this aim, three scenarios have been assessed: Baseline, Landfill Gas to Energy (LFGE), and Incineration scenarios. Key results revealed that a total of 3.7 million tonnes of methane would be emitted from all studied governorates generated over 50 years. Incineration also found to be more favorable in all governorates in terms of energy production, quantity of avoided GHG emissions, and in terms of economic viability over LFGE. Implementing incineration in all governorates would generate about 5.6 TWh energy annually and could avoid about 5 Mt CO2 eq annually in comparison to LFGE that would generate about 0.6 TWh annually and could avoid about 2.5 Mt CO2 eq annually. In terms of economic viability of WtE projects, while they were generally not economically viable under the assumptions made in the current study, incineration technology deemed promising, but policy adjustments, such as competitive Feed-in Tariff (FiT) rates and the inclusion of gate fees, are necessary. Specific minimum gate fees and FiT were identified for each governorate, providing essential guidance for decision makers to ensure the viability of WtE implementation. This study would support the decision makers in assessing technically and financially feasible options for WtE technologies in the selected governorates.

Abstract The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135–210 min and winter drying took 145–225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10− 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945–15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

Abstract Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Abstract Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic bacterium that is widely distributed in aquatic environments and causes major economic losses in fish and public health hazards.This study aimed to identify the occurrence of P. aeruginosa in samples collected from fish and fish handlers, and to investigate the antimicrobial susceptibility, virulence determinants, and biofilm genes of P. aeruginosa isolates. A total of 276 samples were cross-sectionally collected from Nile tilapia (53), Golden grey mullet (52), Mediterranean horse mackerel (50), Striped red mullet (71), and fish handlers (50) at five different retail fish markets in Damietta Governorate, Egypt. Pseudomonas species (spp.) were biochemically identified in 57.9% of the total examined samples. Peudomonas aeruginosa were the most prevalent species isolated from the fish and human samples via PCR technique. Peudomonas aeruginosa isolates exhibited full resistance (100%) to tobramycin (TOB), gentamicin (CN), and colistin (CL), with a high level of susceptibility (88.5%) to imipenem (IPM) using the disk diffusion method. Most P. aeruginosa isolates (84.6%) exhibited drug resistance, with 61.5% were multidrug resistance (MDR) and 23.1% were extensive drug resistance (XDR). Most isolates had at least four virulence-associated genes (lasB, toxA, exoU, and oprL) and three biofilm genes (psIA, peIA, and lasR) by using uniplex PCR. The lasI, and rhlR Quorum Sensing (QS) genes were identified in 84.6% and 61.5% in the examined P. aeruginosa isolates, respectively. The highest mortality rate in Nile tilapia experimentally infected with P. aeruginosa isolate encoding most of virulent genes. Multivariate analyses revealed high heterogeneity among the examined isolates. This study revealed the emergence of virulent and drug resistant P. aeruginosa isolates in fish, poses high risks to consumers and food. Thus, strict hygienic measures should be considered when catching, handling, and storing fish, in addition to the routine application of antimicrobial susceptibility testing.

Abstract This study was aimed to compare the clinical and radiographic outcomes of patients with intercondylar fractures of the humerus treated with orthogonal and parallel plating methods via precontoured plates. This was a retrospective comparative study conducted on 50 adult patients with intercondylar humerus fractures that were surgically treated over an eleven-year period. The patients were divided into two groups: Group A underwent internal fixation via parallel plating, whereas Group B received orthogonal plating. Clinical outcomes were evaluated via the Mayo Elbow Performance Score (MEPS), and the radiographic assessments included time to consolidation, pseudoarthrosis, malunion and hardware removal. Both groups presented similar demographic and preoperative characteristics. The functional outcomes assessed by the MEPS were not significantly different between the groups. Radiographically, comparable healing times and rates of complications, including pseudoarthrosis, malunion and hardware removal, were observed. The study findings suggest that both orthogonal and parallel plating methods yield comparable clinical and radiographic outcomes in the treatment of intercondylar humerus fractures. These results underscore the effectiveness of both techniques and emphasize the importance of further research to elucidate the optimal plating method for specific fracture patterns.

Abstract This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R2) of 0.9779. These metrics surpass those obtained from baseline models including linear regression and recurrent neural networks, highlighting the potential of accurate imputation to significantly enhance the efficiency and output of renewable energy systems. The findings advocate for the integration of robust data imputation methods in the design and operation of photovoltaic systems, contributing to the reliability and sustainability of energy resource management. Furthermore, this research makes significant contributions by showcasing the comparative performance of traditional machine learning models in handling data gaps, emphasizing the practical implications of data imputation on optimizing hydrogen production systems. By providing a detailed analysis and validation of the imputation models, this work offers valuable insights for future advancements in renewable energy technology.

Abstract Dust accumulation on photovoltaic (PV) panels in arid regions diminishes solar energy absorption and panel efficiency. In this study, the effectiveness of a self-cleaning nano-coating thin film is evaluated in reducing dust accumulation and improving PV Panel efficiency. Surface morphology and elemental analysis of the nano-coating and dust are conducted. Continuous measurements of solar irradiances and ambient temperature have been recorded. SEM analysis of dust revealed irregularly shaped micron-sized particles with potential adhesive properties, causing shading effects on the PV panel surface. Conversely, the coating particles exhibited a uniform, spherical shape, suggesting effective prevention of dust adhesion. Solar irradiance ranged from 120 W/m² to a peak of 720 W/m² at noon. Application of the self-cleaning nano-coating thin film consistently increased short circuit current (I sc ), with the coated panel averaging 2.8 A, which is 64.7% higher than the uncoated panel’s 1.7 A. The power output of the coated panel ranged from 7 W to 38 W, with an average of approximately 24.75 W, whereas the uncoated panel exhibited a power output between 3 W and 23 W, averaging around 14 W. These findings highlight the substantial potential of nano-coating for effective dust mitigation, particularly in dusty environments, thus enhancing PV system reliability.

Abstract Onychomycosis, a nail infection caused by dermatophytes, yeast, and molds makes up roughly half of all onychopathies and is the most prevalent nail condition in the world. Clinically, nail psoriasis and onychomycosis can frequently be difficult to distinguish from one another. To assess the prevalence of onychomycosis in patients with psoriasis. Fifty patients with psoriasis associated with nail disease were included in this study. After taking clinical history, nail samples were gathered for dermoscopic inspection, culture, direct microscopy with 20% KOH solution, and nail clipping with PAS stain. Of the 50 patients recruited, 43 were males and 7 were females, with mean age 6–71 years (mean ± SD 44.06 ± 16.2). Eleven patients (22%) tested positive for onychomycosis. Dermatophytes were isolated from 2% of patients, yeast from 14% of patients, and non-dermatophytic mold from 38% of patients. Histopathological results revealed fungal hyphae and spores in 18% of patients. The most prevalent dermoscopic sign in psoriatic patients with onychomycosis was spikes (81.8%) with statistical significance (P-value

Abstract In Egypt, while many studies have focused on the radiometry and mineralogy of black sands, research on their effects on nearby aquatic organisms is rare. This study aimed to assess the combined effects of heat stress (HS) and black sand nanoparticles (BS-NPs) on renal function, antioxidant responses (TAC, SOD, CAT), neuro-stress indicators (AchE, cortisol), and to conduct histopathological investigations in the kidney and spleen tissues of African catfish Clarias gariepinus over a 15-day period to exposure to control, HS (32 °C), BS (6.4 g/kg diet) and HS + BS groups. The outcomes revealed that thermal stress alone showed no significant difference from the control. However, creatinine and uric acid levels were significantly higher in the BS-NPs and HS + BS-NPs groups (p

Abstract Blood transfusion has a hazard of transmission of many pathogens, including Toxoplasma gondii (T. gondii) and other venereal infections. It is crucial to conduct epidemiological surveillance to detect the prevalence of these pathogens. The study aimed to assess the seroprevalence of T. gondii and common transfusable venereal infections among healthy blood donors in Menoufia Province, Egypt, and identify associated risk factors. Four hundred twenty individuals were recruited between January and April 2023 for cross-sectional descriptive research from the blood banks of Menoufia University medical hospitals. Collected blood samples were screened for anti-T. gondii IgM and IgG, HBsAg, anti-HCV antibodies, HIV p24 antigen and anti-HIV antibodies, and anti-Treponema pallidum antibodies. 46 (11.0%) and 22 donors (5.2%) individuals tested positive for anti-T. gondii IgG with a 95% CI (8.3–14.6) and IgM with a 95% CI (3.5–8.1), respectively, while one patient (0.2%) was positive for both antibodies. Regarding venereal infections, 12 (2.9%) were positive for HBV, 6 (1.4%) were positive for HCV, 7 (1.7%) were positive for HIV, and none of the tested population showed positivity for syphilis. Female gender, consumption of raw meat, agricultural environment, poor awareness about T. gondii, and blood group type (especially AB and O groups) were identified as independent risk factors for T. gondii infection. The study highlights the importance of testing blood donors for T. gondii and common transfusable venereal illnesses. Starting health education programs and preventative measures, such as suitable meat handling and cleanliness practices, is critical for minimizing the occurrence of these illnesses. Larger-scale additional study is advised to confirm these results and provide guidance for public health initiatives.

Abstract This study investigated a disease outbreak characterized by caligid copepod infestations and subsequent secondary bacterial infections in European seabass (Dicentrarchus labrax) and flathead grey mullet (Mugil cephalus) cultivated at a private facility in the Deeba Triangle region of Egypt. Moribund fish displayed brown spots on the skin, tongue, and gills, along with lethargy and excess mucus. The fish suffered severe infections, exhibiting external hemorrhages, ulcers, and ascites. The fish had pale, enlarged livers with hemorrhaging. Comprehensive parasitological, bacteriological, molecular, immunity and histopathological analyses were conducted to identify the etiological agents and pathological changes. Caligid copepod infestation was observed in wet mounts from the buccal and branchial cavities of all examined fish, and the caligids were identified as Caligus clemensi through COI gene sequencing and phylogenetic analysis. Vibrio alginolyticus was confirmed as a secondary bacterial infection through biochemical tests, recA gene sequencing, and phylogenetic analyses. Antibiotic susceptibility testing revealed resistance to β-lactams, aminoglycosides, and trimethoprim-sulfamethoxazole in V. alginolyticus isolates. Upregulation of the inflammatory marker IL-1β in gill and skin tissues indicated a robust cell-mediated immune response against the pathogens. Histopathological examination revealed severe tissue damage, hyperplasia, hemorrhage, and congestion in the gills, along with hepatocellular degeneration and steatosis in the liver, providing initial insights into this outbreak. A comprehensive therapeutic regimen was implemented, comprising prolonged hydrogen peroxide immersion baths, followed by the application of the nature-identical plant-based compound Lice-less and probiotic Sanolife Pro-W supplementation. This integrated approach effectively eliminated C. clemensi infestations, controlled secondary bacterial infections, and restored fish health, reducing morbidity and mortality rates to minimal levels.