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Abstract The California bearing ratio (CBR) of a granular materials are influence by the soil particle distribution indices such as D10, D30, D50, and D60 and also the compaction properties such as the maximum dry density (MDD) and the optimum moisture content (OMC). For this reason, the particle packing and compactibility of the soil play a big role in the design and construction of subbases and landfills. In this research paper, experimental data entries have been collected reflecting the CBR behavior of granular soil used to construct landfill and subbase. The database was utilized in the ratio of 78–22% to predict the CBR behavior considering the artificial neural network (ANN), the evolutionary polynomial regression (EPR), the genetic programming (GP), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and the response surface methodology (RSM) intelligent learning and symbolic abilities. The relative importance values for each input parameter were carried out, which indicated that the (CBR) value depends mainly on the average particle size (D30, 50 & 60). They showed a combined influence index of 66% of the considered parameters in the model exercise. This further shows the importance and structural influence of the particles within the D50 and D60 range in a granular material consistency in the design and construction purposes. Performance indices were also used to study the ability of the models. The ANN model showed the best performance with accuracy of 88%, then GP, EPR and RF with almost the same accuracies of 85% and lastly the XGBoost with accuracy of 81%. Also, the RSM produced an R2 of 0.9464 with a p-value of less than 0.0001. These values show that the ANN produced the decisive model with the superior performance indices in the forecast of CBR of granular material used as subbase and waste compacted earth liner material. The results further show that optimal performance of the CBR depended on D50 and D60 for the design of subgrade, subbase, and liner purposes and also during the performance monitoring phase of the constructed flexible pavement foundations and compacted earth liners.

Abstract Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

Background: Thyroid cancer, the most common endocrine neoplasm, has a strong female predominance. There has been a steady increase in the incidence of thyroid cancer worldwide. This study aimed to investigate the prevalence of thyroid cancer and identify its predominant histological subtypes in the Aljuf region of Saudi Arabia. The study findings and new data obtained will contribute to advancing knowledge about thyroid cancer at the national level. Methods and Results: This retrospective, cross-sectional descriptive study was conducted in Sakaka, Aljouf region, Saudi Arabia. Data from thyroid specimens collected at King Abdul-Aziz Specialist Hospital and Prince Mutaib Hospital between January and December 2022 was analyzed. Among 3102 histopathological samples and 298 cytology samples received in both hospitals, 181 were thyroid specimens: histopathology samples [63(34.8%)] and fine-needle aspiration (FNA) samples for cytological diagnosis [118 (65.2%)]. The age of patients with thyroid disorders ranged from 17 to 69, with a mean age of 38.4 ± (SD)1.2 years. The highest percentage, 76(42%), were in the age group over 40. The majority of patients with thyroid disorders, 139 (76.8%), were women compared to 42(23.2%) men, giving a female-to-male ratio of 3.3: 1. 165(91.2%) of the study population were Saudi patients, while the non-Saudis represented only 16(8.8%) cases. Among 181 thyroid specimens, thyroid cancer was diagnosed in 33 patients (18.2%), benign tumors in 43 patients (23.8%) [follicular adenoma 39 (90.7%) and Hürthle cell adenoma 4 (9.3%)], goiter in 96 (53.0% cases), Hashimoto's thyroiditis in 6 cases (3.3%), and chronic lymphocytic thyroiditis in 3 cases (1.7%). The age of patients with thyroid cancer ranged from 20 to 57 years, with a mean age of 36.9 years. There were 25 (75.8%) females versus 8 (24.2%) males, with a ratio of 3.1:1. Papillary thyroid carcinoma was the most frequent histological subtype among patients with thyroid cancer, accounting for 75.8%, while follicular thyroid carcinoma represented 24.2%. Among 155 cancer cases, thyroid cancer ranked third (21.3%) after breast cancer (26.4%) and colon adenocarcinoma (23.9%). Conclusion: Thyroid pathology, including cancer, is notably prevalent among females and older age groups in the Al-Jouf region. Thyroid carcinoma is the third most diagnosed cancer type after breast carcinoma and colon adenocarcinoma in Al-Jouf in 2022. Papillary thyroid carcinoma is the most frequent histological subtype among Saudi patients with thyroid cancer.

Abstract Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three—price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively—using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness.

Abstract Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts’ models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas.

Abstract Brainstem cerebral cavernous malformations (CCM) are clinically more aggressive compared to superficial CCMs. Due to their location, resection can be challenging, making stereotactic radiosurgery (SRS) an attractive alternative for symptomatic patient. Brainstem CCM patients (n = 170) were treated with Gamma Knife SRS at 11 radiosurgical centers. Hemorrhagic risk reduction, risk factors of post-SRS hemorrhage, and clinical outcomes were retrospectively analyzed. Most patients had a single (165/170 patients) brainstem CCMs treated; the majority of CCMs (165/181) presented with bleeding. Single-session SRS decreased the risk of repeat hemorrhage in patients with hemorrhagic brainstem CCM (HR: 0.17, p 13 Gy (HR = 2.57, p = 0.044). Adverse radiation effect (ARE) occurred in 9 patients (5.3%) and was symptomatic in four (2.4%). At a median follow-up of 3.4 years (Inter-quartile range: 5.4), 13 patients (8.0%) had a worsened clinical status, with the treated CCM being the cause in 5.6% (10) of the patients. Single-session SRS decreased the risk of repeat hemorrhage in patients with hemorrhagic brainstem CCM and conveyed this benefit with a low risk of advrse radiation effects (ARE) and worsening clinical status.

Abstract The control of multi-robot formations is a crucial aspect in various applications, such as transport, surveillance and monitoring environments. Maintaining robots in a specific formation pose or performing a cooperative task is a significant challenge when a fault occurs among any of the robots. This work presents a Decentralized Fault-Tolerant Control (DFTC) scheme that addresses lidar sensor faults within a system of multiple differential wheeled mobile robots. The robots change the formation shape according to the number of available robots within the formation. A Graph theory is implemented to represent the multi-robot formation and communication. Each mobile robot is equipped with three sensors: a wheel encoder, an Inertial Measurement Unit (IMU), and a lidar sensor. Sensor fault detection and isolation (FDI) is implemented at two levels. The pose estimation obtained from the wheel encoder and IMU is fused using an extended Kalman filter (EKF), and this pose estimation is utilized at the local level of lidar sensor FDI. At the system level, the FDI of the lidar sensor involves computing a residual by comparing the pose estimation with other lidar sensors mounted on other mobile robots within the formation. The presented FTC scheme is simulated in Simulink multi-robot environments.

Abstract Potential uses for electromagnetic launchers in defense systems, space exploration, and transportation have recently emerged. In addition, this accelerator has many applications, such as deploying small satellites into low-earth orbit and accelerating high-speed trains (e.g., bullet trains and Hyperloop) with a low-cost propulsion system instead of expensive linear motors, particularly in space applications. Therefore, the full capability and optimization of these launchers’ efficiency are still required. Therefore, this paper focuses on presenting a new design to decrease the coil’s magnetic circuit reluctance and boost the magnetic flux lines by adding a laminated iron yoke surrounding the coil. This design makes the inductance value of the iron-yoked accelerator twice the inductance in case of the absence of the iron-yoke at its peak. Additionally, the initial inductance of the iron-yoked accelerator is approximately 65% higher than that of the coil without the iron yoke. Consequently, the modified design proposed an efficiency of 17.5%, which represents a 60% improvement over the efficiency of the regular accelerator. In addition, the introduced design eliminates the suck-back force using a fast-switching device (IGBT) to switch the coil off when the projectile reaches half of the coil. Moreover, a mathematical model for the iron-yoked accelerator is built on MATLAB Simulink and validated experimentally. An artificial intelligence optimization technique, the gravitational search algorithm (GSA), is used to optimize the accelerator parameters, such as the number of turns, capacitor value, and capacitor voltage. Finally, the experimental evaluation of the GSA-optimized system demonstrated an additional 15% enhancement in efficiency, bringing the total efficiency to 20%.

Abstract This study investigates the role of dietary Bacillus subtilis and oregano essential oil in mitigating the effects of high stocking density on growth performance, carcass traits, physiological stress indicators, gene expression, and intestinal integrity in broiler chickens. A total of, 1250 one-day-old Ross 308 male broiler chicks were randomly allocated to five experimental groups, where each group had five replicates of 50 chicks. Group 1 (control, LSD): 15 chicks/m2 fed a basal diet without feed additive, group 2 (HSD): 20 chicks/m2 fed a basal diet without feed additive, group 3 (BHSD): 20 chicks/m2 fed a basal diet supplemented with B. subtilis (500 mg/kg diet), group 4 (OHSD): 20 chicks/m2 fed a basal diet supplemented with oregano essential oil (300 mg/kg diet), group 5 (CHSD): 20 chicks/m2 fed a basal diet supplemented with oregano essential oil and B. subtilis. At 35 days of age, there was a noticeable improvement in the growth performance of broilers fed CHSD under high stocking density through the increase in body weight gain, dressing percentage, and crude protein digestibility with a decrease in feed conversion rate compared to other groups. Adding CHSD enhanced the state of oxidation and immunity through increasing superoxide dismutase, glutathione peroxidase, and the relative weight of bursa of Fabricius, while decreasing malondialdehyde, in addition to increasing plasma triiodothyronine levels. The microbial structure and morphometric parameters improved in the group that received the CHSD compared to the other groups, where villus height and Lactobacillus population increased, whereas Escherichia coli and Clostridium perfringens population decreased. Glucose transporter 2 (GLUT2), fatty acid transporter 1 (FABP1), and amino acid transferase 1 (CAT1) gene expression levels significantly increased when feeding on oregano essential oil with B. subtilis. In conclusion, combining oregano essential oil and B. subtilis supplements mitigated the effects of high stocking density by enhancing growth performance, antioxidative status, and intestinal integrity, in addition to modifying the genetic expression of genes related to nutrient absorption.

Abstract Producing hydrogen efficiently through water electrolysis could greatly reduce fossil fuel consumption. As well as this renewable energy source will also help combat global warming and boost economic investment opportunities. This paper studied some factors affecting the performance of oxy-hydrogen/hydroxy (HHO) gas generator, such as applied voltage (from 10.5 to 13.0 V) and electrolyte solution concentration (from 0.05 to 0.20 M), using a dry fuel cell based on the electrolyzing technique of water. The results revealed that the HHO gas production rate, power consumption, and temperature change of electrolyte solution increased significantly with increasing the tested applied voltage and electrolyte concentration. This study concluded that the optimum conditions for producing HHO gas ranged from 11.5 to 12.0 V for applied voltage and from 0.05 to 0.10 M for KOH concentration according to the lowest specific energy and highest HHO gas generator efficiency. Under the previous optimum conditions, the highest productivity, specific energy, and efficiency of the HHO gas generator were 343.9 cm3 min−1, 3.43 kW h m−3, and 53.79%, respectively, using 12.0 V for applied voltage and 0.10 M for electrolyte solution concentration. These findings provide an unambiguous direction for adjusting the operational factors (applied voltage and electrolyte concentration) for efficient HHO gas production and use in different applications. Furthermore, the required energy to operate the HHO gas generator can be obtained from renewable sources.

Abstract The sonication method was used to prepare a new set of CuO and CuO/Ag nanocomposites. The particle size was estimated using XRD and HR-TEM while the morphology of the nanoparticles was investigated with SEM. The average particle sizes of CuO and Ag falls in the 29.32–35.80 nm and 35.13–45.95 nm ranges, respectively. XRD declared that CuO has the space groups C 1 c 1 (9) and C 1 2/c 1 (15), while silver has space group F m -3 m (225). XPS analysis indicated the presence of Ag as Ag0 and Cu as Cu2+. Nano-oxide and nanocomposites were used to synthesis CuO and CuO/Ag doped lithium–zinc borosilicate glass. Physical parameters of the glass samples were calculated including density, $$\hbox {V}_{m}$$ V m , $$\hbox {V}_{o}$$ V o , $$\hbox {V}_{m}^{B}$$ V m B , OPD, $$\hbox {d}_{B-B}$$ d B - B , $$\hbox {n}_{b}$$ n b , and N, $$\hbox {R}_{p}$$ R p , and $$\hbox {R}_{i}$$ R i depending on Ag and CuO mole fractions. The physical properties of glass indicated an increase in density and an initial expansion in glass structural network with the addition of silver metal due to its larger size followed by a compression as its molar ratio increase due to its higher $$\hbox {C}_{no}$$ C no . XRD measurements were reported for the glass samples doped with nanoparticles, proving the amorphous phase. ESR measurements were determined for all glass samples to detect the nature of the doped nanoparticles when incorporated inside a glassy matrix where CuO was found as tetragonal in octahedral sites and silver can be transformed after melting inside the glass matrix into Ag+ to form more stable $$\hbox {Ag}^{x+}_{y}$$ Ag y x + clusters.The fabricated sample of CuAgB-4 with significant nano silver doping (7.32% mol) has the maximum LAc and effective atomic number. Nano silver content increases the $$\gamma$$ γ -RdSg in the lithium–zinc borosilicate glasses.

Abstract A new group of thiazolidine-2,4-dione derivatives of ciprofloxacin having butyryl linker 3a-l was synthesized via an alkylation of thiazolidine-2,4-diones with butyryl ciprofloxacin with yield range 48–77% andfully characterized by various spectroscopic and analytical tools. Anti-cancer screening outcomes indicated that 3a and 3i possess antiproliferative activities against human melanoma LOX IMVI cancer cell line with IC50 values of 26.7 ± 1.50 and 25.4 ± 1.43 µM, respectively, using doxorubicin and cisplatin as positive controls with an IC50 of 7.03 ± 0.40 and 5.07 ± 0.29 µM, respectively. Additionally, compound 3j showed promising anticancer activity against human renal cancer A498 cell line with IC50 value of 33.9 ± 1.91 µM while doxorubicin and cisplatin showed IC50 values of 3.59 ± 0.20 and 7.92 ± 0.45, respectively. On the other hand, compound 3i did not show considerable anti-bacterial activity against S. aureus, E. coli and P. aeruginosa, and only moderate activity against K. pneumoniae with only a tenth of the activity of ciprofloxacin, confirming the cytotoxicity observed. Mechanistically, compound 3i inhibited both topoisomerase I and II with IC50 of 4.77 ± 0.26 and 15 ± 0.81 µM. Furthermore, it induced cell cycle arrest at S phase in melanoma LOX IMVI cells. Moreover, 3i provoked substantial levels of early, late apoptosis and necrosis in melanoma LOX IMVI cell line comparable to that induced by doxorubicin. Furthermore, compound 3i increased the expression level of active caspase-3 by 49 folds higher in LOX IMVI cell, increased protein expression level of Bax more than the control by 3 folds and inhibited PARP-1by 33% in LOX IMVI. All results were supported by theoretical docking studies on both tested enzymes confirming potential cytotoxicity for the synthesized hybrids.

Abstract Lead borate glass is the best radiation shielding glass when lead is in high concentration. However, it has low transparency after radiation exposure. Radiation decreases transparency due to chemical and physical changes in the glass matrix, such as creating or healing defects in the glass network. The addition of rare earth elements like cerium and dysprosium oxides to lead borate glasses can improve their transparency and durability as radiation shielding barriers. The newly manufactured glasses’ optical absorption, structural, and radiation shielding properties were measured. The optical characteristics of the generated samples were examined to determine the effect of the cerium/dysprosium ratio on the structural alterations, specifically in the presence of bridging oxygen (BO) and non-bridging oxygen (NBO). Incorporating Ce3+ results in peaks at 195 nm for borate units, 225 nm for Ce3+, and a broadened peak at 393 nm due to overlapping peaks for Ce3+ and Ce4+ in the UV region. By adding Dy, multiple peaks are observed at 825, 902, 1095, 1275, and 1684 nm, corresponding to the transition from 6H15/2 ground state to 6F5/2, 6F7/2, 6F9/2, 6F11/2, and 6H11/2. The samples were also tested before and after exposure to gamma irradiation from a 60Co source at a dose of 75 kGy to assess their stability against radiation. The energy gap value during irradiation shows decreased non-bridging oxygen. The energy gap difference before and after irradiation for the M4 sample shows higher NBO to BO conversion, reducing radiation damage and improving structural stability. Furthermore, X-ray photoelectron spectroscopy was utilized to get insight into the coordination chemistry of the created glass samples. The half-value layer (HVL), radiation protection efficiency (RPE), neutron removal cross-section (FRNCS), mean free path (MFP), mass attenuation coefficients (MAC), and effective atomic numbers (Zef) of the glassy structure were calculated theoretically to assess its radiation shielding qualities. The linear attenuation coefficient order for the prepared samples was M1 > M2 > M3 > M4. The FRNCS values were 0.090, 0.083, 0.081, and 0.079 cm−1 for samples M1, M2, M3, and M4, respectively.

Abstract This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm’s binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm.

Abstract Software reuse has emerged as a crucial practice in the software industry, offering significant benefits in time-to-market, and resources management. This is particularly pertinent in web systems development, where the integration of diverse technologies and the varied backgrounds of technical teams pose substantial challenges. The rapid expansion of web systems underscores the urgent need to adopt best practices and methodologies for web system reuse to streamline the development process, reducing effort, cost, and time. This paper aims to identify the key challenges of web system reuse in the context of small and medium-sized software companies in Egypt and Saudi Arabia. Using qualitative research methods, including interviews, focus groups, and participant observations, an empirical study was conducted to examine current reuse practices and understand the root causes of common challenges. Based on the results of the empirical study, a systematic approach was developed to enhance web system reuse during the development process in the context of small and medium-sized software companies in Egypt and Saudi Arabia. The proposed approach addresses critical gaps in current practices, offering practical guidelines to improve efficiency, reduce development time, and enhance overall software quality. This research contributes to the broader discourse on software reuse by providing context-specific insights and adaptable solutions that are relevant to similar markets worldwide.

Abstract Lung cancer is an important global health problem, and it is defined by abnormal growth of the cells in the tissues of the lung, mostly leading to significant morbidity and mortality. Its timely identification and correct staging are very important for proper therapy and prognosis. Different computational methods have been used to enhance the precision of lung cancer classification, among which optimization algorithms such as Greylag Goose Optimization (GGO) are employed. These algorithms have the purpose of improving the performance of machine learning models that are presented with a large amount of complex data, selecting the most important features. As per lung cancer classification, data preparation is one of the most important steps, which contains the operations of scaling, normalization, and handling gap factor to ensure reasonable and reliable input data. In this domain, the use of GGO includes refining feature selection, which mainly focuses on enhancing the classification accuracy compared to other binary format optimization algorithms, like bSC, bMVO, bPSO, bWOA, bGWO, and bFOA. The efficiency of the bGGO algorithm in choosing the optimal features for improved classification accuracy is an indicator of the possible application of this method in the field of lung cancer diagnosis. The GGO achieved the highest accuracy with MLP model performance at 98.4%. The feature selection and classification results were assessed using statistical analysis, which utilized the Wilcoxon signed-rank test and ANOVA. The results were also accompanied by a set of graphical illustrations that ensured the adequacy and efficiency of the adopted hybrid method (GGO + MLP).

Abstract Since the beginning of humanity, people have used wild medicinal plants for the treatment of various diseases. Nowadays, researchers and scientists pay attention to exploring new tools to maximize the efficacy of natural products from natural resources, and among these tools formulation of nanoparticles is very promising. The green synthesis of metal/metal oxide nano-solutions using the Deverra tortuosa extract has still not been explored well. This study aims to prepare many metal/metal oxide nanoparticle solutions such as ZnO-NPs, SeO2-MNPs, and Ag-NPs via the methanol extract of D. tortuosa as safe, easy, green, and economic approach as well as characterize the chemical components of D. tortuosa plant by GC-MS spectroscopy analysis. The synthesized M-NPs solutions were characterized by UV–visible (UV–Vis) spectroscopy, Transmission Electron Microscope (TEM), zeta potential, Fourier Transform Infrared (FT-IR) spectroscopy, and phytochemical analyses. The potential antioxidant capacity was estimated for the methanol extract of this plant along with the metal/metal oxide nanoparticles solutions by DPPH assay. In addition, the cytotoxic activity was in vitro assessed for the inspected samples against various tumor and normal cell lines applying MTT assay. The application of the D. tortuosa methanolic extract resulted in NPs with a range of 11.79–85.9 nm which was characterized by UV-Vis spectroscopy, TEM, zeta potential, and FT-IR spectroscopy that revealed various functional groups of the tested samples. The GC-MS analysis of the D. tortuosa methanolic extract showed the presence of 31 chemical compounds with 2-methyl-3-oxocyclopent-1-ene-1-carboxylic acid, methyl oleate, and 6-allyl-4,5-dimethoxybenzo[d][1,3]-dioxole as major constituents. The extract showed considerable antioxidant activity as well as antimicrobial activity that was assessed also against varied bacterial and fungal species. Remarkable potencies for the investigated metal/metal oxide nanoparticle solutions hinder the growth of the tumor cell lines, in addition to the growth of the microbial species. Based on the determined biological activities of the produced NPs, future study is recommended to characterize the pure authentic compounds that are identified within the D. tortuosa as major compounds as well as evaluate their modes of action.

Abstract The primary aim of the article is to analyze the response of the human immune system when it encounters the hepatitis B virus. This is done using a mathematical system of differential equations. The differential equation system has six components, likely representing various aspects of the immune response or virus dynamics. A Bayesian regularization neural network has been presented in the process of training. These networks are employed to find solutions for different categories or scenarios related to hepatitis B infection. The Adams method is used to generate reference data sets. The back-propagated artificial neural network, based on Bayesian regularization, is trained and validated using the generated data. The data is divided into three sets: 90% for training and 5% each for testing and validation. The correctness and effectiveness of the proposed neural network model have been assessed using various evaluation metrics. The metrics have been used in this study are Mean Square Error (MSE), histogram errors, and regression plots. These measures provide support to the neural network to approximate the immune response to the hepatitis B virus.

Abstract This study helps in managing waste glass and greening the environment by incorporating laboratory waste glass into mortar production to make an eco-friendly shielding material against gamma rays. The efficiency of using waste glass powder as a cement replacement or addition in mortar production was studied by using two waste glass sizes: micro glass (particle size range from 10.09 to 24.73 μm) and nano glass (particle size range from 10.57 to 26.42 nm) to design different mortar specimens with varying percentages of fine glass powder from 0 to 30%. Compressive strength and flexure strength were evaluated to determine mechanical properties. The results indicated that adding WGP to mortar positively affects the characteristics of cementitious composites. The linear and mass attenuation coefficients of the samples were experimentally determined using a NaI detector and various radioactive sources (Am-241, Ba-133, Eu-152, Cs-137, and Co-60) with gamma energies ranging from 59.53 to 1332 keV. The obtained coefficients were then compared to the theoretical values of the composites using XCOM software to verify their accuracy. Additionally, the half-value layer, tenth-value layer, mean free path, and effective atomic number were computed. Furthermore, the results revealed that the mortar sample with 30% nano additive glass was the most effective in reducing gamma radiation.

Abstract This study aims to investigate the effectiveness of iron oxide (Fe2O3) and carbon black in micro and nanoscales incorporated into an epoxy adhesive matrix for gamma-ray attenuation. The composites were prepared via a simple and cost-effective synthesis method. The grain size of powder NPs was measured using a transmission electron microscope (TEM), and the particle size was about 20 ± 5 nm and 31.46 ± 2 nm for carbon and Fe2O3, respectively. The morphological properties were characterized by a scanning electron microscope, which indicated the excellent dispersion of NPs, which blocked almost all pores of the composite and increased the capability of radiation attenuation. In addition, the chemical composition of samples using energy dispersive X-rays (EDX) and the compressive strength were measured. Furthermore, the linear and mass attenuation coefficients were determined experimentally for incident photon energies of 59.51–1408.01 keV emitted from γ-ray sources using the sodium iodide scintillation detector NaI. A comparison was conducted between the experimental data and theoretical results that obtained from XCOM software, examined the validity of the experimental results. The relation deviation rate was found to vary between 0.0008 and 2.83%. Furthermore, the measurement of the relation deviation rate between the linear attenuation coefficients of micro and nano composites revealed a range of values between 1 and 25%. Also, shielding parameters such as half-value layer (HVL), tenth-value layer (TVL), mean free path (MFP), and effective atomic number (Zeff) were measured. Moreover, the equivalent atomic number (Zeq), absorption, and exposure buildup factors for prepared samples were calculated. The results showed that the incorporation of Fe2O3 NPs enhanced the shielding capability of the composites, as evidenced by the significant reduction in gamma-ray transmission. The composite materials exhibited excellent mechanical strength, making them suitable for practical applications in radiation shielding. Furthermore, it was determined that the elevation in N-Fe2O3 concentration resulted in a direct increase in the linear attenuation coefficient, from 0.314 to 0.519 cm−1 at 0.5951 MeV and from 0.099 to 0.124 cm−1 at 0.662 MeV. Nevertheless, a slight increase was discerned in the identified mass attenuation coefficients at 0.1332 and 0.1408 MeV. The experimental data for MFP, HVL, and TVL demonstrate that the EFeC4 sample exhibits optimal performance, with values of 1.9, 1.3, and 4.4 cm at 0.5951 MeV, and at 0.661 MeV, the values are 8.04, 5.57, and 18.52 cm, while at 0.1408 MeV, the values are 12.06, 8.36, and 27.78 cm, respectively. Overall, this research highlights the potential of iron oxide-carbon/epoxy composites as efficient and reliable materials for gamma-ray protection in various fields, including nuclear power plants, medical facilities, and space exploration.

Abstract The aim of this work is to develop and evaluate self-nanoemulsifying drug delivery systems (SNEDDS) containing simvastatin to increase its oral bioavailability. Formulation EO 5 (Ethyl oleate 9.3% w/w: Tween 80 49.4% w/w: Propylene glycol 39.3% w/w) and Formulation CL 14 (Clove oil 54.3% w/w: Tween 80 34.4% w/w: Transcutol-P 9.3% w/w) were thoroughly studied. They showed emulsification time less than 1 min, droplet size in the nanometric range, and almost a complete drug release after 2 h. The in-vitro dissolution profile of both formulations was found to be significant in comparison to the pure drug in pH 1.2 and 7.4 buffers (P

Abstract Three types of synthetic coal-derived adsorbents were characterized as potential enhanced structurers during the removal of chlorpyrifos pesticide. The raw coal (CA) was activated into porous graphitic carbon (AC), and both CA and AC were blended with polyaniline polymers (PANI/CA and PANI/AC) forming two advanced composites. The adsorption performances of the modified structures in comparison with CA were evaluated based on both the steric and energetic parameters of the applied advanced isotherm model (the monolayer model of one energy). The uptake performances reflected higher capacities for the PANI hybridized form (235.8 mg/g (PANI/CA) and 309.75 mg/g (PANI/AC) as compared to AC (156.9 mg/g) and raw coal (135.8 mg/g). This signifies the impact of activation step and PANI blending on the surface and textural properties of coal. The steric investigation determined the saturation of the coal surface with extra active sites after the activation step (Nm(AC) = 62.05 mg/g) and the PANI integration (Nm(PANI/CA) = 113.5 mg/g and Nm(PANI/AC) = 169.7 mg/g) as compared to raw coal (Nm(CA) = 39.6 mg/g). This illustrated the reported uptake efficiencies of the modified samples, which can be attributed to the enhancement in the surface area and the incorporation of additional chemical groups. The results also reflect that each site can be loaded with 3–4 molecules of chlorpyrifos, which are arranged vertically and adsorbed by multi-molecular mechanisms. The energetic studies (

Abstract In this paper, a simple and efficient approach is presented to classify different power materials based on a one port microwave sensor in X-band. This classification focuses on powder materials, unlike prior studies that focused on liquids (castor oil, neem oil, sunflower oil, sesame oil, and mahua oil), this classification represents a shift towards powdered materials. The response of the proposed sensor is enhanced by adding a metamaterial (MTM) unit cell of F-shape to focus electromagnetic waves on the sample under test. This metamaterial-based sensor is designed to differentiate between different types of materials based on the corresponding reflection coefficient. The sample under test is included inside a dielectric box inserted inside a rectangular waveguide. The MTM unit cell is added on the front face of this box towards the direction of the incident wave. The resonance frequency depends on the characteristics of the powder material inside the box. The MTM unit cell enhances this resonance to simplify the process of classification of different materials. The measured results show that the proposed sensor can detect a wide range of powder materials, including clay, cement, sand, and mixtures: cement & sand, and clay & sand. The designed sensor can be used in various applications, including detection and classification of different powder materials in industrial applications.

Abstract The global transportation electrification commerce sector is now booming. Stakeholders are paying an increased attention to the integration of electric vehicles and electric buses into the transportation networks. As a result, there is an urgent need to invest in public charging infrastructure, particularly for fast charging facilities. Consequently, and to complete the portfolio of the green environment, these fast-charging stations (FCSs) are designed using 100% of renewable energy sources (RESs). Thus, this paper proposes an optimization model for the techno-economic assessment of FCSs comprising photovoltaic and wind turbines with various energy storage devices (ESDs). In this regard, the FCS performance is evaluated using flywheels and super capacitors due to their high-power density and charging/discharging cycles and rates. Then, optimal sizing of these distributed generators is attained considering diverse technical and economical key performance indicators. Afterwards, the problem gets more sophisticated by investigating the effect of RES’s uncertainties on the selection criterion of the FCS’s components, design and capacity. Eventually, as an effort dedicated to an online energy management approach, a deep learning methodology based on radial basis network (RBN) is implemented, validated, and carried out. In stark contrast to conventional optimization approaches, RBN demonstrates its superiority by obtaining the optimum solutions in a relatively short amount of time.

Abstract The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Background: Nanocomposite resins have been widely used in modern denture manufacturing. However, their long-term color stability is a concern for both dental professionals and patients. Purpose: to evaluate the effect of different beverages on the color stability of 3D-printed denture base resins modified with zirconium dioxide nanoparticles (ZrO2NPs). Methods: A total of 440 specimens were fabricated and distributed into 11 groups (n = 40/group). The control group of heat polymerized (PMMA) and five groups of two different 3D-printed resins (NextDent and ASIGA) as experimental groups with various concentrations of ZrO2NPs added to the 3D-printed resins (0.5 wt%, 1 wt%, 3 wt%, 5 wt%) in addition to one unmodified group per resin. Specimens per group are sorted into four subgroups (n = 10) according to tested beverages, as follows: coffee, tea, cola, and mineral water. Before immersion, all the specimens were exposed to 5000 thermal cycles. Color changes (ΔE00) were assessed prior (T0) and following immersion for 6 days (T1) and 12 days (T2) using a spectrophotometer. Color difference values were calculated by using CIEDE2000 color difference. Data was analyzed by ANOVA and post hoc Tukey test with a significant level of less than 0.05. Results: Tea produced the highest color change for both NextDent and ASIGA materials, whereas water caused the least color change on PMMA at T2. Increasing the immersion time resulted in more color changes, with tea and coffee showing significant differences. PMMA had considerably less color change than 3D-printed resins. The color change of 3D-printed increased after adding ZrO2NPs. Conclusions: Beverage type and immersion time have an impact on the color stability of unmodified and ZrO2NP-modified denture base resins with significant change after immersion in tea and coffee.

Background/Objectives: Deep hypothermic circulatory arrest (DHCA) is safe, but subtle neurodevelopmental deficits may persist far beyond the perioperative period. We aimed to investigate the relationship between DHCA duration and neurodevelopmental outcomes in young children undergoing cardiac surgery with DHCA. Methods: Children aged < 42 months, including neonates who underwent cardiac surgery using DHCA without regional perfusion techniques, were included as the DHCA group. Children in the same age range who underwent cardiac surgery without DHCA were included as the control group. All enrolled patients underwent neurodevelopmental assessment using the Bayley Scales of Infant and Toddler Development (BSTID) by a trained pediatrician, and 17 DHCA patients and 6 control patients completed the BSTID assessment. Results: Both groups showed no significant preoperative, operative, or postoperative differences. Adjusted multivariable analysis revealed that prematurity and age at assessment were significant changing predictors of each of the BSTID components (p < 0.001), except for the gross motor component, where only age at assessment was a significant adjusting predictor. Longer DHCA was associated with lower fine and gross motor BSTID components; however, the association was not statistically significant (p = 0.06). Conclusions: Long-duration DHCA without regional perfusion techniques may be associated with less optimal neurodevelopmental outcomes.

Abstract This paper seeks to progress the field of topological photonic crystals (TPC) as a promising tool in face of construction flaws. In particular, the structure can be used as a novel temperature sensor. In this regard, the considered TPC structure comprising two different PC designs named PC1 and PC2. PC1 is designed from a stack of multilayers containing Silicon (Si) and Silicon dioxide (SiO2), while layers of SiO2 and composite layer named hyperbolic metamaterial (HMM) are considered in designing PC2. The HMM layer is engineered using subwavelength layers of Si and Bismuth Germinate, or BGO ( $${\text{Bi}}_{4}{\text{Ge}}_{3}{\text{O}}_{12}$$ Bi 4 Ge 3 O 12 ). The mainstay of our suggested temperature sensor is mainly based on the emergence of some resonant modes inside the transmittance spectrum that provide the stability in the presence of the geometrical changes. Meanwhile, our theoretical framework has been introduced in the vicinity of transfer matrix method (TMM), effective medium theory (EMT) and the thermo-optic characteristics of the considered materials. The numerical findings have extensively introduced the role of some topological parameters such as layers’ thicknesses, filling ratio through HMM layers and the periodicity of HMM on the stability or the topological features of the introduced sensor. Meanwhile, the numerical results reveal that the considered design provides some topological edge states (TESs) of a promising robustness and stability against certain disturbances or geometrical changes in the constituent materials. In addition, our sensing tool offers a relatively high sensitivity of 0.27 nm/°C.

Abstract This research introduces a novel two-parameter distribution, the power-modified XLindley distribution, developed through the application of power transformation techniques to the existing modified XLindley distribution. This new distribution enhances flexibility and adaptability in statistical modeling. We conduct a thorough examination of its statistical properties, exploring its potential to improve data fitting and modeling accuracy. To assess the effectiveness of the model, we employ multiple estimation techniques and evaluate their performance through extensive simulation experiments. Our findings indicate that the maximum product of the spacings method is particularly effective for parameter estimation. To demonstrate the practical utility of the proposed model, we apply it to two real-world datasets: one related to flood data and the other to reliability engineering. The results underscore the distribution’s superior ability to capture the characteristics of these datasets compared to existing models, highlighting its significance for applications in natural disaster analysis and reliability studies.

Abstract It is well-established that general anxiety associates with the lower use of adaptive emotion regulation and the higher use of maladaptive emotion regulation. However, no study has previously investigated the impact of cognitive emotion regulation on academic anxieties. Using a sample of secondary school students (N = 391), this study examined the impact of cognitive emotion regulation on math and science anxieties. Math anxiety showed stronger correlations with adaptive than maladaptive emotion regulation, whereas general anxiety showed stronger correlations with maladaptive than adaptive emotion regulation. Hierarchical regression analyses showed that math anxiety was associated with the high uses of acceptance, rumination and other-blame and the low uses of positive reappraisal and putting into perspective. However, with controlling science and general anxieties, math anxiety was associated with the high use of rumination and the low use of positive reappraisal. In contrast, science anxiety was associated with the high uses of acceptance and other-blame and the low use of positive reappraisal. Importantly, however, with controlling math and general anxieties, those science anxiety associations did not remain. Accordingly, these results might provide important insights for the specificity, etiology, and intervention of math anxiety.

Abstract The current study introduces groundbreaking insights into how organic matter (OM) of the black phosphate (RB-Ph) uniquely influences phosphorus (P) solubility during acetic acid (AA) leaching, expanding our understanding in this crucial area. To highlight such role, the OM of the RB-Ph was treated separately by different procedures including calcination at 550 ℃/4 h (CB-Ph), 30% hydrogen peroxide (HB-Ph) and intensive grinding to nano-sizes (NB-Ph). The mineralogical, chemical and morphological characteristics of phosphatic and non-phosphatic components of these phosphatic materials were carefully examined pre- and post-treatment via different techniques. The P dissolution of the precursor RB-Ph and its modified derivatives all over the applied experimental parameters traced the following trend: NB-Ph > RB-Ph > CB-Ph > HB-Ph. Intensive grinding to nanoscale resulted in amorphous components with conspicuous OM content (TOC, 0.410%), significantly enhanced P dissolution rate of NB-Ph (730–980 ppm), despite the noticeable reduction in its P2O5 content to 22.34 wt.%. The precursor RB-Ph, thanks to its high OM content (TOC, 0.543%), also displayed a sufficient P dissolution rate (470–750 ppm) compared to the two other modified derivatives, CB-Ph (410–700 ppm) and HB-Ph (130–610 ppm). Such deep and conspicuous impact of OM on P solubility can be tied to their decomposition, releasing not only organic acids but also the adsorbed P by the OM’s surficial binding sites to the solution. Finally, the optimum conditions of P leaching were attained at 2:1 acid/solid (w/w) ratio and 2 h of retention time of all investigated samples.

Abstract This paper investigates a novel category of probability distributions and a specific member within this category. We have formulated a new family of trigonometric distributions by utilizing the odds ratio derived from the distribution function of a base distribution. This newly devised distribution family termed the “Sine pie-power odd-G family” of distributions, is constructed through a transformation involving the sine function. The paper presents an overview of the fundamental characteristics inherent to this proposed distribution family. Using the Weibull distribution as a base reference, we have introduced a member belonging to the proposed distribution family. This member demonstrates various hazard functions such as j, reverse-j, increasing, decreasing, or bathtub shapes. The paper examines essential statistical attributes of this newly introduced distribution. The estimation of the distribution’s parameters is carried out via the maximum likelihood estimation method. The accuracy of the parameter estimation procedure is validated through Monte Carlo simulations. The outcomes of these simulations reveal a reduction in biases and mean square errors as sample sizes increase, even for small samples. Two sets of real-engineering data are considered to demonstrate the proposed distribution’s applicability. The performance of the suggested distribution is evaluated using some model selection criteria and goodness-of-fit test statistics. Empirical evidence from these evaluations substantiates that the proposed model outperforms six existing models.

Abstract Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conducting statistical, correlation and sensitivity analysis, and then the database was used to train and validate the three models. All the models used local and global slenderness ratios (λ, λc, λt) and relative eccentricities (ex/D, ey/B) as inputs and (F/Fy) as output. The performances of the developed models were compared with the predicted capacities from two design codes (AISI and EC3). The results showed that both design codes have prediction error of 33% while the three developed models showed better performance with error percent of 6%, and the (EPR) model is the simplest one. Also, both correlation and sensitivity analysis showed that the global slenderness ratio (λ) has the main influence on the strength, then the relative eccentricities (ex/D, ey/B) and finally the local slenderness ratios (λc, λt).

Abstract The aim of this study was to compare the changes in the sagittal pharyngeal airway dimension (SPAD) in adolescents with Class II mandibular retrusion treated with Invisalign Mandibular Advancement (IMA), prefabricated Myobrace (MB), and Twin block (TB). For this retrospective study, the pre-treatment and post-treatment lateral cephalograms of 60 patients who underwent myofunctional treatment, using either one of the tested appliances were gathered from the files of treated patients. Changes in the SPAD were measured in each group, and comparisons were carried out between the three study groups. Additionally, sagittal skeletal measurements were carried out. Comparisons of the study variables at T0 and T1 between the three groups were performed using one-way ANOVA, while comparisons of the difference (T1–T0) were performed using Kruskal Wallis test. A significant SPAD increase has been reported using the three tested appliances (p 0.05). The IMA, MB, and TB generated significant SPAD and sagittal changes, with both IMA and TB surpassing MB in the airway area improvement post-treatment. Moreover, the three tested Class II functional appliances did not affect the vertical dimension.

Abstract This study evaluated the impact of differential sowing windows and improved weed management strategies on weed dynamics, productivity, and economic viability of direct drum seeded rice (Oryza sativa L.) in the temperate agro-ecosystem of Kashmir. A two-year field experiment was conducted utilizing a split-plot design with two sowing dates (May 10 and June 3) as main plots and six weed management practices as sub-plots. The earlier sowing date (May 10) resulted in significantly enhanced leaf area index, crop growth rate, relative growth rate, net assimilation rate, and grain and straw yields compared to the later sowing (June 3). Among weed management treatments, four mechanized conoweedings (equivalent to weed-free conditions) and sequential application of bensulfuron methyl + pretilachlor (60 and 600 g a.i. ha−1) as pre-emergence followed by 2,4-D (0.75 kg a.i. ha−1) as post-emergence demonstrated superior efficacy in weed suppression and augmentation of crop growth parameters and yield attributes. These treatments also exhibited the lowest weed index and highest benefit–cost ratio. The May 10 sowing, coupled with efficacious weed control measures, significantly reduced weed density and biomass while concomitantly improving nutrient uptake and economic returns. The results indicate that adopting a May 10 sowing date for direct seeded rice, in conjunction with either four conoweedings or the aforementioned sequential herbicide application, can optimize agronomic productivity and economic profitability under the temperate conditions of Kashmir. The study aided in choosing the best sowing window and efficient weed management strategy for attaining higher productivity and profitability of direct seeded rice in temperate conditions.

Abstract Artificial neural networks (ANNs) are biologically inspired algorithms designed to simulate the way in which the human brain processes information. In sample preparation for bioanalysis, liquid–liquid extraction (LLE) represents an important step with the extraction solvent selection is the key laborious step. In the current work, a robust and reliable ANNs model for LLE solvent prediction was generated which could predict the suitable solvent for analyte extraction. The developed ANNs model takes a set of chosen descriptors for the cited analyte as an input and predicts the corresponding Hansen solubility parameters of the suitable extraction solvent as a model output. Then, from the solvent combination’s appendix, the analyst can identify the proposed extraction solvents' combination for the cited analyte easily and efficiently. For the experimental validation of the model prediction capabilities, twenty structurally diverse drugs belonging to different pharmacological classes were extracted from human plasma. The extraction process was performed using the predicted extraction solvent combination for each drug and quantitively estimated by HPLC/UV methods to assess their extraction recovery. The developed LLE solvent prediction model is in- line with the global trend towards green chemistry since it limits the consumption of organic solvents.

Abstract Evaluation of water quality is crucial for managing surface water effectively, ensuring its suitability for human use, and sustaining the environment. In the lower Danube River basin, various methods were employed to assess surface water quality for irrigation, drinking, human health risk purposes and the main mechanism control the surface water chemistry. These methods included water quality indicators (WQIs), complex statistical analyses, geographic information systems (GIS), Monte Carlo simulation, and geochemical modeling. Physicochemical analyses of surface water samples revealed primarily Ca–Mg–HCO3 − is the dominant water types. Principal component analysis (PCA), ionic ratios and piper, chloro alkaline index, Chadha, and Gibbs diagrams identified three distinct water characteristics influenced by water-rocks interaction, evaporation, ions exchange, and human activities. The geochemical modeling showed Danube River water’s strong ability to dissolve gypsum, halite, and anhydrite (SI 1) compared to adults, indicating a 2.1 times higher overall non-carcinogenic risk hazard index. However, Monte Carlo simulation demonstrated negligible iron, manganese, and nitrate health hazards for both age groups. These findings are valuable for water quality management decisions, contributing to long-term resource sustainability.

Abstract Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.

Abstract Breast cancer is among the highest morbidity and mortality rates in women around the world. In the present investigation we aimed to synthesis novel nanosystem combining two naturally important anticancer agents with different mechanism of action namely Moringa oleifera and caffeine. Firstly, chemical analysis of Moringa oleifera extract and caffeine was done by gas chromatography-mass spectroscopy (GC–MS) in order to assess the main chemical compounds present and correlate between them and the possible anticancer effect. The novel nanosystem was characterized through dynamic light scattering techniques which revealed the stability and homogeneity of the prepared M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles, while FTIR and transmission electron microscope (TEM) proved the shape and the successful incorporation of M. oleifera leaves extract/Caffeine onto the nanochitosan carrier. Our initial step was to assess the anticancer effect in vitro in cancer cell line MCF-7 which proved the significant enhanced effect of M. oleifera leaves extract/Caffeine nanosystem compared to M. oleifera leaves extract or caffeine loaded nanoparticles. Further studies were conducted in vivo namely tumor biomarkers, tumor volume, bioluminescence imaging, molecular and histopathological investigations. The present study proved the potent anticancer effect of the synthesized M. oleifera leaves extract/Caffeine loaded chitosan nanoparticles. Mo/Caf/CsNPs exhibited a large number of apoptotic cells within the tumor mass while the adipose tissue regeneration was higher compared to the positive control. The prepared nanoparticles downregulated the expression of Her2, BRCA1 and BRCA2 while mTOR expression was upregulated. The aforementioned data demonstrated the successful synergistic impact of Moringa and caffeine in decreasing the carcinoma grade.

Abstract Rice straw is considered an agricultural waste harmful to the environment, which is abundant in most parts of the world. From this point, the present study is devoted to preparing new composites of two types of glue based on rice straw as a plentiful, low-cost matrix. Straw glue samples were prepared by mixing 20% wt. of rice straw with 80% wt. of animal glue (RS-An) and polyvinyl acetate (RS-PVAC) at different thicknesses of 1, 2, and 3 cm. The chemical composition of the prepared samples was identified by energy dispersive X-ray analysis and their morphology was examined using a scanning electron microscope. The mechanical test explored that RS-An and RS-PVAC respectively required a stress of 25.2 and 25.5 MPa before reaching the breaking point. γ-ray shielding performance was analyzed and determined at numerous photon energies from 0.059 to 1.408 MeV emitted from five-point γ-rays sources using NaI (Tl). Linear attenuation coefficient was calculated by obtaining the area under the peak of the energy spectrum observed from Genie 2000 software in the presence and absence of the sample. The experimental results of mass attenuation coefficient were compared with theoretical data of XCOM software with relative deviation ranging from 0.10 to 2.99%. Geant4 Monte Carlo simulation code was also employed to validate the experimental results. The relative deviation of XCOM and Geant4 outcomes was 0.09–1.77%, which indicates a good agreement between them. Other radiation shielding parameters such as half value layer (HVL), tenth value layer, and mean free path were calculated in three ways: experimentally, theoretically from the XCOM database, and by simulation using Geant4 code. Additionally, effective atomic number (Zeff), effective atomic number (Neff), equivalent atomic number (Zeq), and buildup factors were evaluated. It was confirmed that the γ-ray shielding properties were further boosted by mixing rice straw with the animal glue compared to the synthetic one.

Abstract Melt quenching technique was used to create Bismuth Boro-Silicate nano glasses with compositions of 45SiO2-10CaO- 25Na2O- xBi2O3- (20-x) B2O3 (where x is 0, 5, 10, 15, and 20 mol %). Standard point sources AM-241, Ba-133, Co-60, Cs-137, and Eu-152 were used in the radiation experiment to evaluate the attenuation coefficients spanning the energy range of 59.51 keV to 1048.01 keV. The findings show that adding Bi2O3 in place of B2O3 increases the following: radiation protection efficiency (RPE%), transmission factor (TF%), absorption buildup factor values (ABF), exposure buildup factor values (EBF), mass attenuation coefficients (MACs), linear attenuation coefficients (LACs), and radiation protection efficiency (RPE%). In comparison to lead glass, these findings demonstrate the potential of nano Bismuth Boro-Silicate glass as a radiation shielding material.

Abstract The current research study evaluated the health and environmental risks issues associated with potentially toxic elements (PTEs) in the complex terminal aquifer located in the Algerian desert. The methods used included principal component and cluster (dendrogram) analysis to estimate source of ions and contamination. Various indices such as the Heavy Metal Pollution Index (HPI), Metal Index, hazard quotient, hazard index (HI), and cancer risk (CR) were applied to assess both environmental and human health risks. Furthermore, the Monte Carlo method was applied for probabilistic assessment of carcinogenic and non-carcinogenic risks through oral and dermal exposure routes in both adults and children. The results revealed that approximately 16% of the samples fell within the low pollution category (HPI

Abstract The sample strategy employed in statistical parameter estimation issues has a major impact on the accuracy of the parameter estimates. Ranked set sampling (RSS) is a highly helpful technique for gathering data when it is difficult or impossible to quantify the units in a population. A bounded power logarithmic distribution (PLD) has been proposed recently, and it may be used to describe many real-world bounded data sets. In the current work, the three parameters of the PLD are estimated using the RSS technique. A number of conventional estimators using maximum likelihood, minimum spacing absolute log-distance, minimum spacing square distance, Anderson-Darling, minimum spacing absolute distance, maximum product of spacings, least squares, Cramer-von-Mises, minimum spacing square log distance, and minimum spacing Linex distance are investigated. The different estimates via RSS are compared with their simple random sampling (SRS) counterparts. We found that the maximum product spacing estimate appears to be the best option based on our simulation results for the SRS and RSS data sets. Estimates generated from SRS data sets are less efficient than those derived from RSS data sets. The usefulness of the RSS estimators is also investigated by means of a real data example.

Abstract Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

Abstract Patients with chronic liver disease (CLD) often present with significant frailty, sarcopenia, and impaired immune function. However, the mechanisms driving the development of these age-related phenotypes are not fully understood. To determine whether accelerated biological aging may play a role in CLD, epigenetic, transcriptomic, and phenotypic assessments were performed on the skeletal muscle tissue and immune cells of CLD patients and age-matched healthy controls. Accelerated biological aging of the skeletal muscle tissue of CLD patients was detected, as evidenced by an increase in epigenetic age compared with chronological age (mean +2.2 ± 4.8 years compared with healthy controls at −3.0 ± 3.2 years, p = 0.0001). Considering disease etiology, age acceleration was significantly greater in both the alcohol-related (ArLD) (p = 0.01) and nonalcoholic fatty liver disease (NAFLD) (p = 0.0026) subgroups than in the healthy control subgroup, with no age acceleration observed in the immune-mediated subgroup or healthy control subgroup (p = 0.3). The skeletal muscle transcriptome was also enriched for genes associated with cellular senescence. Similarly, blood cell epigenetic age was significantly greater than that in control individuals, as calculated using the PhenoAge (p

Abstract Today, high-energy applications are devoted to boosting the storage performance of asymmetric supercapacitors. Importantly, boosting the storage performance of the negative electrodes is a crucial topic. Fe3O4-based active materials display a promising theoretical storage performance as a negative electrode. Thus, to get a high storage performance of Fe3O4, it must be tailored to have a higher ionic and electronic conductivity and outstanding stability. Functionalized graphite felt (GF) is an excellent candidate for tailoring Fe3O4 with a facile ionic and electronic pathway. However, the steps of the functionalization of GF are complex and time-consuming as well as the energy loss during this step. Thus, the in-situ functionalization of the GF surface throughout the synthesis of Fe3O4 active materials is proposed herein. Fe3O4 is electrodeposited at the in-situ functionalized GF surface with the crystalline nanowires-like structure as revealed from the various analyses; SEM, TEM, Mapping EDX, XPS, XRD, wettability test, and Raman analysis. Advantageously, the synthetic approach introduces full homogeneous and uniform coverage of the large surface area of the GF. Thus, Fe3O4 nanowires with high ionic and electronic conductivity are characterized by a higher storage performance. Interestingly, Fe3O4/GF possesses a high specific capacity of 1418 mC cm−2 at a potential scan rate of 10 mV s−1 and this value retained to 54% at a potential scan rate of 50 mV s−1 at an extended potential window of 1.45 V. Remarkably, the diffusion-controlled reaction is the main contributor of the storage of Fe3O4/GF electrode as revealed by the mechanistic studies.

Abstract Skin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

Abstract The increase in environmental temperature led to economic losses in the poultry industry, urging the use of feed supplements to mitigate the negative effects on chick's welfare and performance. Therefore, this study aimed to examine the effects of marine microalgae (Nannochloropsis Oculata, N. Oculata) additive on commercial Brown Lohmann laying hen's performance, blood metabolites, and antioxidant status under hot environmental conditions. One hundred and eighty birds (34 weeks old with an initial body weight of 1885 ± 23.5 g) were used till 47 weeks. The birds were divided into three equal groups (birds in each group were distributed into four equal replicates 15 hens/ replicate). The 1st group was the control (CON) and was fed the basal diet, while the 2nd (TR1) and 3rd (TR2) groups were fed the basal diet supplemented with 0.5 and 1.0% of N. Oculata, respectively. The results showed that total protein and globulin concentrations increased (P

Abstract Tick-borne diseases in animals are increasing rapidly worldwide, but there is insufficient information about tick-borne diseases infecting dogs in southern Egypt. Thus, in the current study, we detected the presence of Anaplasma marginale (A. marginale) and Babesia canis vogeli (B. canis vogeli) in the blood of dogs. The results revealed that 4/100 (4%) were positive, and a higher infection rate was found in males (75%), than females (25%). The phylogenetic analysis for the major surface protein 4 (msp4) gene in this study was compared with amplicons separate from other reported isolates with alignment by identity 100% with cattle and camels from Egypt, and the phylogenetic analysis for the B. canis vogeli small subunit ribosomal RNA (SSU rRNA) gene in this study identified identity by 99.89% with dogs from Egypt. This report is considered the first report in southern Egypt about A. marginale in dogs based on the sequence analysis of the msp4 gene, providing new data for the classification and identification of A. marginale in dogs compared to A. marginale isolated from other animals in southern Egypt.

Abstract The relationship between the protozoan communities and environmental variables was studied in the Nile River to evaluate their potential as water quality indicators. Protozoans were sampled monthly at six sampling sites in the Nile's Damietta Branch across a spatial gradient of environmental conditions during a 1-year cycle (February 2016–January 2017). The Protozoa community was comprised of 54 species belonging to six main heterotrophic Protozoa phyla. The abundance (average, 1089 ± 576.18 individuals L−1) and biomass (average, 86.60 ± 106.13 μg L−1) were comparable between sites. Ciliates comprised the majority of protozoan species richness (30 species), abundance (79.72%), and biomass (82.90%). Cluster analysis resulted in the distribution of protozoan species into three groups, with the most dominant species being the omnivorous ciliate Paradileptus elephantinus. Aluminium, fluoride, and turbidity negatively affected abundance and biomass, while dissolved oxygen and potassium positively impacted biomass. Of the dominant species recorded over the study area, the amoebozoa Centropyxis aculeata was associated with runoff variables, while the bacterivorous ciliates Colpidium colpoda, Glaucoma scintillans, and Vorticella convallaria were related to the abundance of heterotrophic bacteria, phytoplankton biomass, and total organic carbon. Total dissolved salts, PO4, NH3, NO2, dissolved oxygen, and total organic carbon were the strongest causative factors for protozoa distribution. The α-Mesosaprobic environment at site VI confirmed a high load of agricultural runoffs compared to other sites. This study demonstrates that protozoans can be a potential bioindicator of water quality status in this subtropical freshwater river system.