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Abstract Background Antimicrobial resistance has emerged as a major global health threat, necessitating the urgent development of new antimicrobials through innovative methods to combat the rising prevalence of resistant microbes. With this view, we developed three novel nanoconjugates using microbial natural pigment for effective application against certain pathogenic microbes. Results A natural red pigment (RP) extracted from the endophyte Monascus ruber and gamma rays were applied to synthesize RP-ZnO, RP-CuO, and RP-MgO nanoconjugates. The synthesized nanoconjugates were characterized by different techniques to study their properties. The antimicrobial potential of these nanoconjugates was evaluated. Moreover, the antibiofilm, protein leakage, growth curve, and UV light irradiation effect of the synthesized nanoconjugates were also studied. Our results confirmed the nano-size, shape, and stability of the prepared conjugates. RP-ZnO, RP-CuO, and RP-MgO nanoconjugates showed broad antimicrobial potential against the tested bacterial and fungal pathogens. Furthermore, the RP-ZnO nanoconjugate possessed the highest activity, followed by the RP-CuO against the tested microbes. The highest % inhibition of biofilm formation by the RP-ZnO nanoconjugate. Membrane leakage of E. coli and S. aureus by RP-ZnO nanoconjugate was more effective than RP-MgO and RP-CuO nanoconjugates. Finally, UV light irradiation intensified the antibiotic action of the three nanoconjugates and RP-ZnO potential was greater than that of the RP-MgO, and RP-CuO nanoconjugates. Conclusion These findings pave the way for exploiting the synthesized nanoconjugates as potential materials in biomedical applications, promoting natural, green, and eco-friendly approaches.

Abstract Recently, nano-manufactured materials have been used to treat many diseases, such as healing wounds and other modern biological applications. This study investigates the positive effect of Punica granatum seeds extract on kidney and testicular toxicities induced by iron oxide nanoparticles. Forty mice were randomly divided into four groups; the 1st group was the control group. The 2nd group was dosed daily with PSE at 100 g per kg. The 3rd group was dosed with 10 doses of iron oxide nanoparticles at 30 mg/kg b.wt of a mouse per day, 10 times only, then this toxic substance was withdrawn for the rest of the experimental period (30 days). The 4th group was dosed with the same doses as the second and third groups. In this research, we focused on the possibility of using the positive curative effects of PSE, which were estimated at the level of blood chemistry biomarkers, as well as histological and histochemical examinations for the kidney and testis after exposure of mice to iron oxide nanoparticles. These aim to clarify the effect of iron oxide nanoparticles on kidney and testicular morphology and their functions, as well as the potential ameliorative effects of PSE.

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 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 The research objective in the context of the study relates to the major concern of corrosion affecting the wind turbines in operation to find materials with high durability in relation to environmental conditions of operation, strength, and cost. A method is an integration of the Analytical Hierarchy Process (AHP) and VIKOR Multi-Criteria Decision Making (MCDM) techniques that will assess seven different material options on sixteen criteria that comprise corrosion resistance, mechanical properties, cost, and a negative environmental impact. From this result, the AHP method calculated the weights for the indicators and chose potential materials, and finally, the VIKOR method used these materials and compared and ranked them to obtain a compromise solution. The research novelty integrates the AHP and VIKOR MCDM methods to address corrosion in wind turbines. By evaluating seven materials against challenging sixteen criteria—including corrosion resistance, mechanical properties, cost, and toxicity, AHP ranks and weights the criteria, while VIKOR identifies the optimal material choice. This dual approach enhances the selection process, ensuring the chosen material improves turbine performance and durability, offering a significant advancement in the sustainable development of wind energy technology. In conclusion, by integrating AHP and VIKOR, it comprehensively evaluates multiple material options based on corrosion resistance, mechanical properties, cost, and environmental impact. This methodology effectively identifies materials that enhance wind turbine performance and extend their lifespan, addressing a critical industry challenge. The alternative exhibits a similarity to the positive ideal solution (Si) of 0.3704 and a relative closeness to the ideal solution (Ri) of 0.0750. Additionally, its priority ranking (Qi) is 0.001, placing it in the first rank for Carbon Fiber Reinforced Polymers (CFRP) within the selection methodology.

Abstract Background Accumulation of biofilm over composite resin restorations is one of the principal causes of recurrent caries. Therefore, this study aimed to develop antibacterial composite resins by crystalline selenium nanoparticles (SeNPs), assessing the antibacterial, mechanical, and physical properties of the composite resin after SeNPs incorporation. Methods SeNPs were synthesized via a green method. The nanoparticles were characterized by UV-Vis spectroscopy, fourier transform infrared (FT-IR) spectroscopy and transmission electron microscopy (TEM). The nano-filled composite (Filtek™ Z350XT ) was considered as a control group (G0). Two concentrations of SeNPs (0.005 wt% and 0.01 wt%.) were added to the tested resin composite (G1& G2), respectively. The physical/mechanical and antibacterial properties of the composite specimens (n = 10/group) were characterized. A one-way ANOVA was conducted to analyze these data followed by Bonferroni post hoc test for pairwise comparison. Results Modified composites with SeNPs showed antibacterial activity against E. coli and S. mutans. Mechanical properties including diametral tensile strength, compressive strength, or surface roughness were not affected by nano-incorporation compared to control. Furthermore, the degree of conversion showed no statistical difference. However, SeNPs incorporation into resin composite produces color change that can be visually perceived. Conclusions The green synthesized SeNPs significantly improved the antimicrobial properties of the dental composite without compromising mechanical performance. However, it shows color change after SeNPs incorporation.

Abstract The classification of chronic diseases has been a prominent research focus in public health, extensively leveraging machine learning algorithms. One of these chronic diseases that has significant rates of occurrence all around the world is diabetes, which is a disease by itself. Many academics are working to construct robust machine-learning algorithms for accurate categorization, given the prevalence of this chronic disease. A revolutionary methodology that can accurately categorize diabetic disease is the focus of this study, which aims to provide new methods. The proposed technique in this work is based on developing a novel feature selection method, DWWPA, which stands for dynamic waterwheel plant algorithm. The DWWPA algorithm is utilized in the process of optimizing the K-nearest neighbors (KNN) model in order to improve the accuracy of its classification. In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings demonstrate the superiority and success of the proposed method. Furthermore, several different statistical analysis techniques, such as Analyses of variance (ANOVA) and Wilcoxon signed-rank test, are carried out to investigate the statistical difference and importance of the suggested strategy in contrast to the other ways at the same level of competition. The conclusions of these tests were consistent with what was anticipated they would be. Based on the suggested feature selection and the optimization of the KNN model, the proposed method has an accuracy of 98.9% when taken as an entire. The suggested method was useful in accurately classifying diabetic disease, as evidenced by the fact that it achieved a higher level of accuracy than the contemporary approaches.

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 Background Studying insect succession on carcasses is important in estimating the postmortem interval. This study aims to identify the decomposition stages of decomposing rabbit carcasses and to find out the relationship between seasonal variations and abundance of insects colonizing rabbit carcasses at El-Sharkia Governorate, Egypt. Methods Three domestic rabbits (weighing 1300 g each) were killed by a sharp knife. The carcasses were exposed to the sun, left to decompose and inspected twice daily at 6-h intervals to collect insects. Maggots were collected and reared. Results The rabbit carcasses underwent four decomposition stages: fresh, bloat, decay and dry stages. The identified families and their respective collected species included three dipteran families: Calliphoridae (Lucilia sericata, Chrysomya megacephala, and Chrysomya albiceps), Sarcophagidae (Sarcophaga argyrostoma) and Muscidae (Musca domestica and Synthesiomyia nudiseta), three coleopteran families. Histeridae (Saprinus semistriatus), Cleridae (Necrobia rufipes) and Dermestidae (Dermestes frischii and Attagenus gloriosus) and three hymenopteran families: Chalcididae (Brachymeria femorata), Vespidae (Vespa orientalis) and Formicidae (Monomorium sp.). Carrion fauna was dominated by dipteran and coleopteran species, with calliphorid and sarcophagid flies found to play a significant role in carrion consumption process. Conclusion The succession pattern and decomposition rate were season dependent. The information collected may help establish the basic database for entomological forensic investigations in the future. Graphical abstract

Abstract This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability.

Abstract The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Abstract Curcumin, the main polyphenol component of turmeric powder, has garnered increasing attention as an effective supplement in fish diets. A comparative trial was conducted to evaluate the impacts of dietary supplementation with different forms of curcumin (free, in combination, or nanoparticles) on hemato-biochemical parameters, reproductive capacity, and related gene expressions of red tilapia (Oreochromis niloticus x O. mossambicus) broodstock. Fish (n = 168) were fed an isonitrogenous (30% CP), isocaloric (18.72 MJ kg − 1) diet containing basal diet (Control), 60 mg kg-1 of either free curcumin (Cur), curcumin/nano-curcumin blend (Cur/NCur), or nano-curcumin (NCur) for 56 days. Red tilapia broodstock (155 ± 5.65 g) were stocked at a male: female ratio of 1:3. Blood samples and gonads were collected to assess hemato-biochemical parameters, reproductive capacity, and related gene expression at the end of the feeding trial. The results indicated that the values of hematological parameters (RBCs, WBCs, hemoglobin), total protein, albumin values, and reproductive hormones (T, LH, and FSH) were significantly increased, while liver function enzymes were decreased in the NCur group (P

Abstract Purpose Acute decompensated heart failure (ADHF) patients with a poor renal function have a worse prognosis. The urea-to-creatinine ratio (UCR) may be a valuable tool for assessing kidney function and stratifying patients with ADHF. We evaluated the value of an admission and follow-up UCR in predicting AKI, 28-day mortality, and rehospitalization in patients with ADHF. Methods This prospective research comprised sixty patients with ADHF. Based on the mean admission UCR, patients are classified into two groups: the high UCR group (UCR ≥ 42.33) and the low UCR group (UCR 34.85 (p

Abstract The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

Polyurethane coatings were synthesized through polyaddition polymerization using various polyols. These polyols included polypropylene glycol with different molecular weights, such as PPG-2000, and PPG-1200, as well as 1,4- butanediol. Additionally different contents of bisphenol F; namely 4.5, 11.5, and 24.2%, as well as methylene diphenyl diisocyanate at an NCO/OH ratio of 2.0. The prepolymer polyurethane films were analyzed to study their characteristics using techniques such as Fourier transform infrared spectroscopy, viscosity measurements, thixotropic index analysis, and mechanical property evaluations. The pot life and dry times of polyurethane coating containing 11.5% bisphenol F were 90 minutes and 10 hours (time to touch), respectively, with full hardening achieved at 28 hours. Furthermore, the coating exhibited excellent mechanical properties: tensile strength of 80 N/m², elongation of 260%, adhesion strength of 10 MPa (cross-hatch), hardness of 95, and a contact angle of 118°. These properties, coupled with its strong adhesion (10 MPa) to metal surfaces, contributed to its outstanding chemical and corrosion resistance. The coatings obtained from the prepolymer polyurethane based on 11.5% bisphenol F exhibited the highest mechanical properties and water, chemical, and corrosion resistance followed by 4.5%, and 24.2%, respectively.

Abstract A green building (GB) is a design idea that integrates environmentally conscious technology and sustainable procedures throughout the building’s life cycle. However, because different green requirements and performances are integrated into the building design, the GB design procedure typically takes longer than conventional structures. Machine learning (ML) and other advanced artificial intelligence (AI), such as DL techniques, are frequently utilized to assist designers in completing their work more quickly and precisely. Therefore, this study aims to develop a GB design predictive model utilizing ML and DL techniques to optimize resource consumption, improve occupant comfort, and lessen the environmental effect of the built environment of the GB design process. A dataset ASHARE-884 is applied to the suggested models. An Exploratory Data Analysis (EDA) is applied, which involves cleaning, sorting, and converting the category data into numerical values utilizing label encoding. In data preprocessing, the Z-Score normalization technique is applied to normalize the data. After data analysis and preprocessing, preprocessed data is used as input for Machine learning (ML) such as RF, DT, and Extreme GB, and Stacking and Deep Learning (DL) such as GNN, LSTM, and RNN techniques for green building design to enhance environmental sustainability by addressing different criteria of the GB design process. The performance of the proposed models is assessed using different evaluation metrics such as accuracy, precision, recall and F1-score. The experiment results indicate that the proposed GNN and LSTM models function more accurately and efficiently than conventional DL techniques for environmental sustainability in green buildings.

Abstract Bacterial therapy for colorectal cancer (CRC) represents a burgeoning frontier. The probiotic Limosilactobacillus fermentum GR-3, derived from traditional food “Jiangshui”, exhibited superior antioxidant capacity by producing indole derivatives ICA and IPA. In an AOM/DSS-induced CRC mouse model, GR-3 treatment alleviated weight loss, colon shortening, rectal bleeding and intestinal barrier disruption by reducing oxidative stress and inflammation. GR-3 colonization in distant colon induced apoptosis and reduced tumor incidence by 51.2%, outperforming the control strain and vitamin C. The beneficial effect of GR-3 on CRC was associated with gut microbiome modulation, increasing SCFA producer Lachnospiraceae NK4A136 group and suppressing pro-inflammatory strain Bacteroides. Metagenomic and metabolic analyses revealed that GR-3 intervention upregulated antioxidant genes (xseA, ALDH) and butyrate synthesis gene (bcd), while increasing beneficial metabolites (SCFAs, ICA, IPA, VB12 and VD3) and reducing harmful secondary bile acids. Overall, GR-3 emerges as a promising candidate in CRC therapy, offering effective gut microbiome remediation.

Metal-Ceramic Functionally Graded Materials (FGM) find extensive applications in nuclear power generators, spacecraft systems, and energy conversion systems. This study aims to investigate the behavior of FGMs under transient thermal loads and time-dependent mechanical loads through finite element modeling. A 2D model is developed using COMSOL Multiphysics to simulate the behavior of a metal-ceramic FGM thick cylinder. The model considers Titanium-Zirconium-Molybdenum (TZM) alloy for metals and Silicon carbide (SiC) for ceramic. It accounts for the effects of transient thermal loads and internal time-dependent pressure exerted on the cylinder's inner surface. Thermo-mechanical properties, assumed to follow an exponential function, are incorporated into the model. Numerical analysis results are compared with established analytical solutions to validate the model. Graphical comparisons between numerical and analytical results demonstrate significant agreement, confirming the reliability of the numerical approach. This study provides insights into the behavior of metal-ceramic FGMs under transient thermal and mechanical loads. The findings underscore the effectiveness of finite element modeling in understanding complex interactions within FGMs. Future research directions include exploring more complex geometrical models, incorporating additional loading scenarios, and performing experimental validation to enhance understanding of FGM performance in real-world conditions.

Ti-10%Zr-2%Sn-8%Mo alloy and Ti-10%Zr-2%Sn-8%Ta alloy were manufactured using the technique of powder metallurgy. The corrosion of the two alloys was performed after 1 h, 24 h, and 48 h immersion in 3.5% NaCl solution using electrochemical impedance spectroscopy (EIS), cyclic polarization (CPP), and the measurement of current over time. EIS (Nyquist and Bode) data indicated that the Ti-10%Zr-2%Sn-8%Ta alloy has higher polarization resistance compared to the Ti-10%Zr-2%Sn-8%Mo alloy. CPP results revealed that the Ti-10%Zr-2%Sn-8%Ta alloy has higher corrosion resistance and lower corrosion currents. The experiments of measuring the change of current over time at 500 mV showed that both the Ti-10%Zr-2%Sn-8%Mo alloy and the Ti-10%Zr-2%Sn-8%Ta alloy do not show pitting corrosion, which confirms the CPP results. Extending the time to 24 h and 48 h increased the corrosion resistance for both alloys. The surface of the alloys after being corroded in NaCl solution were investigated using scanning electron microscopy and energy dispersive spectroscopy. All investigations confirmed that the Ti-10%Zr-2%Sn-8%Ta alloy is more passivated than the Ti-10%Zr-2%Sn-8%Mo alloy and the increase of exposure time to 48 h enhances the passivation of both alloys, which recommends that these alloys are best choice in the biomedical applications.

Abstract Considerable attention has been paid to exploring the biotechnological applications of several Monascus sp. for pigment production. In this study, our focus is on enhancing the bioprocessing of red pigment (RP) derived from the endophytic fungus Monascus ruber SRZ112. To achieve this, we developed a stable mutant strain with improved productivity through gamma irradiation. This mutant was then employed in the immobilization technique using various entrapment carriers. Subsequently, we optimized the culture medium for maximal RP production using the Response Surface Methodology. Finally, these immobilized cultures were successfully utilized for RP production using a semi-continuous mode of fermentation. After eight cycles of fermentation, the highest RP yield by immobilized mycelia reached 309.17 CV mL−1, a significant increase compared to the original titer. Importantly, this study marks the first report on the successful production of Monascus RP in a semi-continuous mode using gamma rays’ mutant strain, offering prospects for commercial production.

Abstract Probiotics are becoming increasingly popular as eco-friendly alternatives in aquaculture. However, there is limited research on their impacts on the reproductive efficiency of Red Tilapia (Oreochromis niloticus x O. mossambicus) broodstock. Therefore, this experiment aimed to explore the combined effects of selective probiotics Bacillus subtilis and B. licheniformis (BSL; 1:1) added to water on blood hematology, serum metabolites, gonadal histology, reproductive performance, and reproductive associated genes in Red Tilapia broodstock. Tilapia broodfish weighing 140–160 g were stocked in four treatment groups: control (T0), and the other three groups were added different levels of BSL to the water as follows: T1 (0.01 g/m3), T2 (0.02 g/m3), and T3 (0.03 g/m3), respectively. Results indicate that BSL administration significantly improved RBCs, hemoglobin, hematocrit, MCH, and MCHC, with the highest improvement seen in the T3 group (P

Abstract There is an urgent need for new bioactive molecules with unique mechanisms of action and chemistry to address the issue of incorrect use of chemical fertilizers and pesticides, which hurts both the environment and the health of humans. In light of this, research was done for this work to isolate, identify, and evaluate the germination-promoting potential of various plant species’ fungal endophytes. Zea mays L. (maize) seed germination was examined using spore suspension of 75 different endophytic strains that were identified. Three promising strains were identified through screening to possess the ability mentioned above. These strains Alternaria alternate, Aspergilus flavus, and Aspergillus terreus were isolated from the stem of Tecoma stans, Delonix regia, and Ricinus communis, respectively. The ability of the three endophytic fungal strains to produce siderophore and indole acetic acid (IAA) was also examined. Compared to both Aspergillus flavus as well as Aspergillus terreus, Alternaria alternata recorded the greatest rates of IAA, according to the data that was gathered. On CAS agar versus blue media, all three strains failed to produce siderophores. Moreover, the antioxidant and antifungal potentials of extracts from these fungi were tested against different plant pathogens. The obtained results indicated the antioxidant and antifungal activities of the three fungal strains. GC-Mass studies were carried out to determine the principal components in extracts of all three strains of fungi. The three strains’ fungus extracts included both well-known and previously unidentified bioactive compounds. These results may aid in the development of novel plant growth promoters by suggesting three different fungal strains as sources of compounds that may improve seed germination. According to the study that has been given, as unexplored sources of bioactive compounds, fungal endophytes have great potential.

Phosphorus (P) is considered as an essential element in crop production. Besides, the study of P from the elemental and bio sources impacts on productivity and quality of orange trees could emphasize its utilization importance. This study aimed to evaluate the chemical implication of three treatments of P fertilization (Triple phosphate (TP; 620 and 900 g/tree/year), phosphate rock (PR; 1820 and 2742 g/tree/year) and 1:1 TP:PR on Valencia orange trees (Citrus sinensis L. Osbeckunder). Where, all these treatments were equipped with bio-phosphorus fertilizer (phosphorin) (0 and 5 g/tree/year added twice in the year, Feb. and Jun.). The measurements included leaves nitrogen (N), phosphorus (P) and Potassium (K), number of fruits (NoF), juice weight/volume, total soluble solids (T.S.S.), vitamin C (V.C), and acidity. The results showed that the treatment (50 % TP +50 % PR at 450 + 1371g/tree/year + phosphourin at 5g/tree/year) gave the highest values for all characters i.e. N % in tree leaves 2.61 and 2.69 %, P% in tree leaves 0.16 and 0.17 %, K in tree leaves 1.79 and 1.86 %, number of fruits/tree 412.0 and 420.0, fruit weight 267.0 and 280.0g, fruit weight/tree 110.0 and 117.60 kg, fruit size 260.41 and 272.37 cm3, juice weight 114.1 and 126.15g, fruit juice 44.33 and 44.99 %, juice volume 110.15 and 114.90 cm3, peel thickness 0.58 and 0.60 cm, T.S.S. 12.14 and 12.21 %, T.S.S./Acid ratio 9.07 and 9.41, and V.C 70.15 and 72.44 (mg/100 ml) as well as the lowest total acidity 0.93 and 0.91 %, under both the 1st season and the 2nd one, respectively. In conclusions, as the application of bio-fertilization has improved the quality charcatistics over seasons. Thus, this study opens the way towards the advanced application of bio-P element to understand the biogeochemical cycles, dynamics and function of natural ecosystems elements in agricultural and food security program.

There is a lack of literature on the usefulness of ultrasonography in the diagnosis and prognosis of thoracic and abdominal neoplasia in domestic ruminants, such as cattle, camels, sheep, and goats. This review aims to shed light on the current applications and prospects of ultrasonography in the diagnosis of thoracic and abdominal neoplasia in domestic ruminants. The scientific literature on ultrasonographic evaluation of the thoracic and abdominal neoplasia in domestic ruminants has been systematically reviewed to verify the potential role of ultrasonography in diagnosing such neoplasia. Based on the literature results, cattle (71.03%) were the most affected animals by thoracic and abdominal neoplasia, followed by sheep (11.86%), goats (11.57%), and camels (5.54%). In all included species, the abdominal neoplasia was more frequent (6.18%) than the thoracic neoplasia (2.97%), and the most frequent neoplasms were forestomach neoplasia. It is concluded that ultrasonography is not widely used in diagnosing thoracic and abdominal neoplasia in ruminant practice. Using ultrasonography in conjunction with guided needle aspiration and biopsy can offer veterinarians the opportunity for more precise diagnosis and treatment decisions guidance of thoracic and abdominal neoplasia in domesticated ruminants. [Open Vet J 2024; 14(8.000): 1751-1760]

Abstract Green building (GB) techniques are essential for reducing energy waste in the construction sector, which accounts for almost 40% of global energy consumption. Despite their importance, challenges such as occupant behavior and energy management gaps often result in GBs consuming up to 2.5 times more energy than intended. To address this, Building Automation Systems (BAS) play a crucial role in enhancing energy efficiency. This research develops a predictive model for GB design using machine learning to minimize energy consumption and improve indoor sustainability. The dataset is utilized to predict cooling and heating individually, with data visualization by graphically illustrating dataset features and preprocessing through Z-Score normalization and dataset splitting. The proposed model, based on active learning and utilizing ML regressors such as Random Forest (RF), Decision Tree (DT), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), Light Gradient Boosting Machine (LGBM), K-Nearest Neighbor (KNN), and Logistic Regressor (LR), shows significant performance improvements. The CBR-AL model achieves impressive results with values of 0.9975 for cooling (Y1) and 0.9883 for heating (Y2), indicating a high level of accuracy. The model’s success in reducing energy consumption and improving sustainability has potential ripple effects, including substantial cost savings, reduced carbon footprints, and improved operational efficiency in green buildings. This approach not only enhances environmental sustainability but also sets a benchmark for future advancements in predictive modelling for energy management.

Maha Abdullah Alwaili,1 Abdallah H Elhoby,2 Norhan M El-Sayed,2 Islam Z Mahmoud,3 Afaf Alharthi,4 Mohammad El-Nablaway,5,6 Dina M Khodeer2 1Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia; 2Department of Pharmacology and Toxicology, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt; 3Department of Cardiovascular Medicine, Faculty of Medicine, Suez Canal University, Ismailia, Egypt; 4Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; 5Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Diriyah, Riyadh, 13713, Saudi Arabia; 6Department of Medical Biochemistry, Faculty of Medicine, Mansoura University, Mansoura, 35516, EgyptCorrespondence: Dina M Khodeer, Email dina_khoudaer@pharm.suez.edu.egIntroduction: This comprehensive study investigated the therapeutic potential of α-asarone in mitigating myocardial oxidative damage, primarily induced by hexavalent chromium (Cr(VI)) exposure in mice.Methods: In this experiment, 24 mice were divided into four groups to assess the cardioprotective role of α-asarone. The study focused on two treatment groups, receiving 25 mg and 50 mg of α-asarone, respectively. These groups were compared against a control group subjected to Cr(VI) without α-asarone treatment, and a normal control negative group. The key biochemical parameters evaluated included serum levels of Creatine Kinase-MB (CK-MB) and Troponin I, markers indicative of myocardial damage. Additionally, the levels of Malondialdehyde (MDA) were measured to assess lipid peroxidation, alongside the evaluation of key inflammatory biomarkers in cardiac tissue homogenates, such as Tumor Necrosis Factor-α (TNF-α) and Interleukin-1β (IL-1β). Results Remarkably, α-asarone treatment resulted in a significant reduction in these markers compared to the control group. The treatment also elevated the activity of cardinal antioxidant enzymes like catalase (CAT) and superoxide dismutase (SOD), and reduced the glutathione (GSH). Furthermore, a notable upregulation of Peroxisome Proliferator-Activated Receptor Gamma (PPAR-γ) in cardiac tissue homogenates was observed, highlighting a potential pathway through which α-asarone exerts its protective effects. Histopathological analysis of cardiac tissues revealed that α-asarone ameliorated the structural lesions induced by Cr(VI). The study thus provides substantial evidence that α-asarone ameliorates Cr(VI)-induced cardiotoxicity through a multifaceted approach. It enhances cardiac enzyme function, modulates free radical generation, improves antioxidant status, and mitigates histopathological damage in cardiac tissues. Given these findings, α-asarone emerges as a promising agent against Cr(VI)-induced myocardial injury.Purpose: This study paves the way for further research into the cardioprotective properties of α-asarone and its potential application in clinical settings by specifically exploring the protective efficacy of α-asarone against Cr(VI)-induced cardiotoxicity and delineating the underlying biochemical and molecular mechanisms involved.Keywords: α-asarone, chromium, mice, cardiotoxicity, oxidative stress, histopathological studies

Abstract Background This study aims to determine the smear layer removal from the root canals using different irrigant solutions with diode laser. Methods A set of sixty human upper incisor teeth were chosen. Access cavity preparation was done followed by canal instrumentation. Samples were divided into six groups (Group A: sodium hypochlorite NaOCl, Group B: NaOCl + diode laser, group C: NaOCl + EDTA ethylene diamine tetra-acetic acid solution, Group D: NaOCl + EDTA + diode laser, Group E: NaOCl + garlic extract, Group F: NaOCl + garlic extract + diode laser. Examining the smear layer removal at the coronal, middle and apical thirds of the root canals was done by using electron microscope analysis. Using an energy-dispersive X-ray, the calcium/phosphorus Ca/P ratio was measured. Results Among the examined groups, there were not any significant differences regarding the smear layer removal. The elimination of smear layers at the middle and coronal thirds was greater in all examined groups than in the apical third. EDX analysis revealed no statistically significant variation between the tested groups. Conclusions Using diode laser with sodium hypochlorite and ethylene diamine tetra-acetic acid irrigant solution was more effective for the elimination of smear layers. Garlic extract could be used as an alternative irrigant solution in debris removal.

Anticorrosion coatings are specialized products designed to protect metal surfaces from costly and dangerous corrosion, acting as barriers against corrosive organisms. Therefore, this study examined the physical and mechanical properties of epoxy resin in the presence of reactive diluents. Reactive diluents were synthesized based on styrene (St) and glycidyl methacrylate (GMA) through the free radical method using benzoyl peroxide (BPO) as a catalyst with various feed ratios of St and GMA. Diluents were characterized by Fourier Transform Infrared (FTIR) spectroscopy and Thermogravimetric Analyzer (TGA). Epoxy resins and hardener were at a ratio of 1:1, 1:0.5, and 1:0.3, respectively. The results showed that reactive diluents [Poly(St-co-GMA)] had an excellent dilution effect on epoxy resin with a decrease in epoxy resin viscosity from 8592 mPa-s to 1656, 680, and 430 mPa-s. Furthermore, the adhesion, tensile, and hardness properties contained 70% GMA and 30% St at a hardener ratio of 1:0.3. The increase in the mechanical properties was attributed to the reaction between modified epoxy resins with hardener.

Abstract In light of the multitude of olive trees cultivated and the lack of the genetic diversity of available genotypes to select varieties and lines that are characterized by high diversity and better performance under the corresponding conditions, A comparison analysis of the genotyping and morphological characteristics of eight olive cultivars growing in Saudi Arabia’s Al-Jouf region was conducted and analyzed. Morpho-anatomical and chemical characteristics along with both inter-simple-sequence repeats (ISSRs) and start-codon-targeted (SCoT) markers were used to evaluate the genetic diversity among eight olive varieties in Al-Jouf, Saudi Arabia. Analyses of 27 morphological, chemical, and anatomical characteristics concluded the existence of genetic differences among the studied varieties. Moreover, six ISSR and eight SCoT primer combinations produced a total of 48 loci, of which 18 (10 ISSR and 8 SCoT) were polymorphic. The average polymorphism information content (PIC values of 0.48 and 0.44, respectively) and marker index (MI of 0.79 and 0.48, respectively) detected for ISSR and SCoT markers revealed the prevalence of high genetic diversity among the studied olive varieties. Based on chemical and anatomical characteristics and the selected molecular markers, the eight olive cultivars were grouped into two distinct clusters. Clusters in the adjacent joint dendrogram produced using ISSR, SCoT and combined data were similar, and grouped all individuals into two groups. However, the dendrogram generated on the basis of SCoT separated individuals into subgroups containing at least two varieties. The findings showed that both methods were effective in assessing diversity, and that SCoT markers can be used as a reliable and informative method for assessing genetic diversity and relationships among olive varieties and can serve as a complementary tool to provide a more complete understanding of the genetic diversity available in Olea europaea populations in Saudi Arabia.

The global aquaculture industry is increasingly seeking sustainable alternatives to fishmeal (FM) due to its high price and shortfall in supply. In this context, fermented aquatic weed meal (FAWM) could emerge as a viable plant protein source for aquafeed. Four isoproteic diets [30 % crude protein (CP)] were formulated, incorporating 50 % total protein from FAWM comprising fermented Azolla diet (D1), Pistia diet (D2), and Eichhornia diet (D3). The control diet (D0) did not contain FAWM. At the end of the 90 days feeding trial, their growth performance, whole-body proximate composition, gut microbial load, haemato-biochemical indices, liver and gut health, and economic efficiency were determined. Fish fed with D0 had significantly (p < 0.05) improved growth performance and feed utilization compared to other treatment groups. Meanwhile, the fish supplemented with D1 diet exhibited significantly (p < 0.05) higher final weight (g), specific growth rate (%/day), weight gain (%), total biomass (g), and protein efficiency ratio among the FAWM dietary groups. The D1 group also demonstrated the significantly (p < 0.05) highest whole-body CP (64.27 ± 0.40 %) and lower crude lipid (8.24 ± 0.28 %) compared to other test diets. The total bacteria (TB) and lactic acid bacteria (LAB) in the fish gut were found to be significantly (p < 0.05) higher in D1 group. Furthermore, most of the hemato-biochemical indices of fish were significantly (p < 0.05) affected by FAWM inclusions, with few exceptions. The histological findings indicated that amongst the FAWM groups, D1 fish exhibited improved intestinal health. Nonetheless, the gut of the control fish demonstrated substantially (p < 0.05) lower villi width and crypt depth than other treatments. The D1 and D2 diet groups had significantly improved liver health. Moreover, FAWM inclusion enhanced economic efficiency by considerably reducing farm feed cost (US$/kg) and increasing return on investment (%). In summary, dietary inclusion of fermented Azolla pinnata (D1) protein at 50 % in aquafeed promoted feed utilization, growth, health, and farm economics of Asian catfish fingerlings compared to other FAWM diets.

Contamination of agricultural soil by heavy metals poses a significant threat to soil quality and crop yields. Using plants as a natural remediation approach attracts researchers’ attention around the world. A 16-month pot experiment was conducted using Conocarpus erectus in a randomized complete block design. The growth, enzymatic activity, electrolyte leakage, and remediation potential were estimated under Cd nitrate]40 low (L), 60 medium (M), 80 high (H) mg/kg soil [and Pb nitrate]400 (L), 700 (M), 1,000 (H) mg/kg soil [applied individually and in combination. Conocarpus erectus demonstrated a good tolerance (over 70%) against lower and medium cadmium (Cd) and lead (Pb) levels and a medium resistance against high Cd and Pb levels, with a survival rate of 100% under all the treatments used. The most negative treatment on the growth traits and tolerance of C. erectus was (H) Cd and (H) Pb, which reduced plant height; chlorophyll index; dry weights of the leaves, stems, and roots; root length; and tolerance index of biomass and roots by 25.87%, 48.97%, 50.56%, 47.25%, 58.67%, 50.18%, 51.00%, and 50% in comparison to the respective control, consecutively. Relative to the control, all Cd and Pb applications increased polyphenol oxidase (PPO), peroxidase (POD), and catalase (CAT) activities, and the increment was parallel up to medium Cd and Pb levels and then decreased with their high levels but still higher than the control. Electrolyte leakage (EL) was upheaved by raising the levels of Cd and Pb, and it reached the maximum (52.79%) at the (H) Cd (H) Pb treatment. Cd and Pb in the leaves, stems, and roots were boosted by raising their levels in the treatments. Conocarpus erectus is considered a phytoextractor for the Cd levels used because the bioconcentration factor of the stem (BCFs) and the translocation factor (TF) of Cd were >1, and it is a suitable plant for Pb phytoextraction at (L) Pb, (M) Pb, and (M) Cd (M) Pb levels because its Pb BCFs and bioconcentration factor of the root (BCFr) were 1. On the other hand, C. erectus is considered a phytostabilizator for Pb at (H) Pb, (L) Cd, (L) Pb, and (H) Cd (H) Pb levels because its Pb BCFs, BCFr, and TF were

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences.

Abstract Context Recently, prioritize has been given to using natural phytogenic or nano compounds as growth promoters and immunostimulants in fish diets as an alternative to antibiotics. Aims The main propose of this trial was to determine the impact of supplementing diets with spirulina or curcumin nanoparticles on the performance and health indicators of Nile tilapia fingerlings. Methods In a 56-day feeding trial, 180 tilapia fingerlings were assigned into three main groups, as follows: 1st, control group, 2nd, Spirulina platensis (SP; 5 g kg-1 diet) and 3rd, curcumin nanoparticles (CUR-NPs; 30 mg kg-1 diet). Key results Incorporating tilapia diets with SP or CUR-NPs significantly improved performance, body chemical analysis, blood biochemical and hematological indices, digestive enzyme activities, and antioxidant and immunostimulant features compared to the control. Conclusion Fortified tilapia diets with CUR-NPs or SP efficiently boost the productivity and health of Nile tilapia fingerlings. Implications The research introduces new practical solutions for applying safe feed additives as alternatives to antibiotics in tilapia farming.

This study aimed to investigate synthesized wollastonite based on amorphous silicon dioxide and calcium carbonate as adsorbent for cleaning hydrocarbon feedstock from heavy organometallic compounds and sulfur. During the analysis, physicochemical parameters including the specific surface area and intensity of synthesized wollastonite were determined. The results showed that the optimal temperature zone for demetallization of hydrocarbon raw materials was 653 - 683 K, with a contact time of 60 minutes, and pressure of 8 - 10 atm. Furthermore, the content of vanadium, nickel, iron, and sulfur was found to decrease from 540 to 52 µg/kg, 50 to 26 µg/kg, 50 to 12 µg/kg, and 3.50% to 2.39%, respectively.

Intrusion Detection Systems, specifically Network Anomaly Detection Systems (NADSs) are vital tools in network security. The NADSs are affected by data imbalance issues in classifying minority classes. Also, designing an efficient detection framework is sought after to achieve a higher detection rate for minority classes, especially when utilizing ensemble learning methods. To solve the issue of imbalanced data, a hybrid method of sampling techniques is proposed. This imbalance processing tool integrates the Synthetic Minority Oversampling Technique (SMOTE) and the K-means clustering algorithm (SKM). SMOTE over-samples the minority class, and K-means is used to perform a cluster-based under-sampling. We use Denoising Autoencoder (DAE) to select the top 15 features to reduce data dimensionality based on their higher weights. For anomaly detection, the XGBoost algorithm is deployed and the SHapley Additive exPlanation (SHAP) approach is deployed to provide explanations of the proposed techniques. The performance of the SKM-XGB model is assessed using the NSL-KDD and UNSW-NB15 datasets. A comparative analysis and series of experiments were carried out using several ensemble models with multiple base classifiers. The experimental findings indicate that the model's detection rate for binary classification and multiclass classification using the UNSW-NB15 dataset is 99.01% and 97.49%, respectively. The model achieves a 99.37% detection rate for binary classification and a 99.22% detection rate for multiclass classification on the NSL-KDD dataset. We conducted a comparative analysis of various ensemble models with multiple base classifiers. The results indicate that SKM-XGB outperforms the other investigated models and outperforms the performance of state-of-the-art models.

This article presents studies on the modification of petroleum bitumen with polymer waste in the presence of vermiculite. An increase in temperature leads to an increase in the interaction of components, partial breakage of polyethylene and bitumen macromolecules, and the formation of radical-free valences. As a result, fragments of polyethylene and bitumen molecules react with each other, and the formation of qualitatively new structural formations occurs via the strong connection of polar and amorphous bitumen with nonpolar structurally viscous polyethylene. Domestically produced Kulantau vermiculite was used to ensure stable adhesion over a wide temperature range and increase the specific surface area, which acquired additional energy, resulting in an increase in the degree of adhesion to bitumen and increased durability of the binder with improved rheological characteristics. Because of the increased service life of road asphalt pavements, the use of the latter results in a considerable decrease in the cost of polymer-bitumen binders. Based on improved binder formulations, asphalt concrete exhibits excellent water resistance and strength at 50 °C (4.7 to 5.0 MPa). In summary, the modification of bitumen with polymer waste and vermiculite offers a promising avenue for improving the performance and longevity of asphalt pavements.

Abstract Background The Convolvulus genus is distributed all over the world and has a long history in traditional medicine. As nanotechnology expands its reach into areas like drug delivery and biomedicine, this study intends to assess the potential of Convolvulus arvensis L. extracts as anti-bacterial, anti-inflammatory and anti-cancer agents, along with chemical profiling of the methanolic (MeOH) extract active ingredients. Methods The chemical composition of an 85% MeOH extract was investigated by liquid chromatography with an electrospray source connected to mass spectrometry (LC-ESI-MS). Both the 85% MeOH extract and n-butanol fraction of C. arvensis were loaded for the first time on alginate/chitosan nanoparticles. The 85% MeOH extract, n-butanol fraction and their loaded nanoparticles were tested for their cytotoxicity, anticancer, anti-inflammatory and antibacterial activity (against pathogenic bacteria, E. coli and S. aureus). Results The chemical investigation of 85% MeOH extract of C. arvensis underwent LC-ESI-MS analysis, revealing twenty-six phenolic substances, of which 16 were phenolic acids, 6 were flavonoids, 1 glycolipid, 1 sesquiterpene and 2 unknown compounds. The FT-IR spectra confirmed the encapsulation of the 85% MeOH extract and n-butanol fraction onto alginate/chitosan nanoparticles and small size obtained by TEM maintained them nontoxic and enhanced their anti-inflammatory activity (the IC50 was decreased from 1050 to 175 µg/ml). The anti-cancer activity against HepG2 was increased and the cell viability was decreased from 28.59 ± 0.52 to 20.80 ± 0.27 at a maximum concentration of 1000 µg/ml. In addition, the MIC of encapsulated extracts was decreased from 31.25 to7.78 µg/ml in E. coli (Gm-ve) and from 15.56 to 7.78 µg/ml in S. aureus (Gm + ve) bacteria. Conclusion Both alginate and chitosan are excellent natural polymers for the encapsulation process, which affects positively on the bioactive constituents of C. arvensis extracts and improves their biological properties.

Abstract The River Nile is the artery of Egypt, as it presents more than 96% of the municipal, industrial, and irrigation necessities of Egypt. This study was dedicated to providing data about the effect of pollution at six stations on the River Nile at the Rosetta branch during the period from August 2019 to April 2020, using heavy metals analysis and zooplankton as biological indicators. It was found that the average of heavy metals concentration follows the descending order Al > Fe > Mn > Zn > Ni > Co. Most of the heavy metals recorded its highest values at El-Rahawy station. Zooplankton community was represented by 32 species in addition to 4 Meroplanktons. Five groups of zooplankton were recorded, viz. Rotifera (1717 org./L), Protozoa (552 org./L), Cladocera (54 org./L), Nematoda (46 org./L), and other Meroplankton (44 org./L), dominated by Rotifera followed by Protozoa, Cladocera, Nematoda, and other Meroplanktons contributing 71%, 23%, 2%, 2%, and 2%, respectively. The highest average density of total zooplankton was recorded during spring while the lowest was recorded during summer. The highest similarity of (79.12%) was observed between (Site 1) and (Site 5). Almost all diversity indices were conducted and showed its highest values in site 6. The principal component analysis conducted between heavy metals, and zooplankton showed a significant negative correlation was shown for the dominant zooplankton with the heavy metals except with cobalt. Nematoda and the rotifer Brachionus angularis recorded a positive correlation with heavy metals except cobalt.

The growing emergence of multi-drug resistant microbial strains has kept the scientific world searching for novel bioactive compounds with specific chemical characteristics. Accordingly, researchers have started exploring the understudied metabolites from endophytes as a new source of bioactive compounds. In this context, the current study was designed to evaluate the bioactive properties of endophytic fungi from the Mokrzański forest in Wrocław, Poland that have not yet been fully researched. Forty-three endophytic fungi were isolated from twelve distinct plants. Following their cultivation, fungal extracts were separately prepared from biomass and cell-free filtrates, and their antibacterial, antifungal (against human and plant pathogens), and antioxidant properties were examined. Five promising fungi after screening were identified to possess all of these activities. These strains and their respective plant hosts were Trichoderma harzianum BUK-T (Fagus sylvatica), Aspergillus ochraceus ROB-L1 (Robinia pseudoacacia), Chaetomium cochliodes KLON-L1, Fusarium tricinctum KLON-L2 (Acer platanoides), and Penicillium chrysogenum SOS-B2 (Pinus sylvestris). Moreover, gamma irradiation at several doses (Gy) was separately applied to the fungal cultures to study their effects on the recorded activities. Finally, compounds after preparative thin-layer chromatography fractionation of the five fungal strains were identified by GC-MS. These findings suggest that the isolated endophytic fungi could serve as novel sources of bioactive metabolites with antibacterial, antifungal, and antioxidant properties, potentially paving the way for future research and the development of new bioactive compounds.

There is a growing need for a more streamlined and automated method of refining biofuels, as there are currently no universally applicable process inspection instruments on the market. All process variables in bio-oil upgrading operations are maintained according to the offline specifications of the products and intermediates. Failure of the process and loss of resources can result from batch-wise monitoring not having real-time product standards. Consequently, in order to cut down on waste and lessen the chances of process failure, a quick and accurate tool for specifying intermediates and products is required. To resolve this issue, we created a model using response surface methodology and an artificial neural network that can increase the bio-oil yield involving parameters, i.e., biomass particle size (mm), temperature (°C), and residence time (min). The maximum bio-oil production (47.0883%) was achieved at 3 mm particle size, 523°C temperature, and 20 min residence time. All results are “better” for root mean squared error (∼1), and the highest coefficient of regression for bio-oil production is in the range of 0.97–0.99. Temperature is the most significant factor in bio-oil yield, followed by particle size and residence time. Based on physicochemical properties, bio-oil has the maximum kinematic viscosity (11.3 Cst) and water content (18.7%). Making bio-oil precious compounds allows it to be used as boiler feedstock and steam generation fuel.

The stability of smart grids is crucial for ensuring reliable and efficient power distribution in modern energy systems. This paper presents an optimized Long Short-Term Memory model for predicting smart grid stability, leveraging the Novel Guide-Waterwheel Plant Algorithm (Guide-WWPA) for enhanced performance. Traditional methods often struggle with the complexity and dynamic nature of smart grids, necessitating advanced approaches for accurate predictions. The proposed LSTM model, optimized using Guide-WWPA, addresses these challenges by effectively capturing temporal dependencies and nonlinear relationships in the data. The proposed approach involves a comprehensive preprocessing pipeline to handle data heterogeneity and noise, followed by the implementation of the LSTM model optimized through Guide-WWPA. The Guide-WWPA combines the strength of the WWPA with a novel guidance mechanism, ensuring efficient exploration and exploitation of the search space. The optimized LSTM is evaluated on a real-world smart grid dataset, demonstrating superior performance compared to traditional optimization techniques. Experimental Results indicate significant improvements in prediction accuracy and computational efficiency, highlighting the potential of the Guide-WWPA optimized LSTM for real-time smart grid stability prediction. This work contributes to the development of intelligent energy management systems, offering a robust tool for maintaining grid stability and enhancing overall energy reliability. On the other hand, statistical evaluations were carried out to prove the stability and difference of the proposed methodology. The results of the experiments demonstrate that the Guide-WWPA + LSTM strategy is superior to the other machine learning approaches.

This article presents the notion about Slow Invariant Manifold (SIM) and their fundamental role in model reduction techniques (MRTs) for challenges encountered in mechanical engineering within dissipative systems of chemical kinetics. Focusing on the reaction routes of complex mechanisms, we construct and compare primary approximations of the SIM through MRTs, including the Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). These methods effectively transform high-dimensional complex problems into lower dimensions, solving them without compromising crucial information about the complex systems modified for homogeneous reactive systems. Employing the sensitivity analysis by using the MATLAB's toolbox, we present the numerical findings in a tabular format obtained through MRTs. This study provides the understanding about the accessible exploration of numerical solutions, improving insights of the complex variation within the system.

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies.

This study investigates the dynamic characteristics of the dual-mode resonant non-linear Schrodinger equation with a Bhom potential. Hydrodynamics, nonlinear optical fibre communication, elastic media, and plasma physics are just a few of the mathematical physics and engineering applications for this model. The study aims to achieve two main objectives: first, to discuss bifurcation analysis, and second, to extract optical soliton solutions using the extended hyperbolic function method. The study successfully derives various wave solutions, including bright, singular, periodic singular and dark solitons, based on the governing model. The findings conferred in this article show a crucial advancement in understanding the propagation of waves in non-linear media. Additionally, bifurcation of phase portraits of ordinary differential equation consistent with the partial differential equation under consideration is conducted. We also highlight specific constraint conditions that ensure the presence of these obtained solutions. The existing literature shows that these methods are first time applied on this model.

Infectious diseases, including vector-borne and antibiotic-resistant infections, present significant global health challenges, necessitating the exploration of natural alternatives for disease control. In this study, we investigated the essential oil (EO) profile as well as larvicidal and antibacterial properties of two wild Apiaceae species used in Algeria: Daucus carota L. (DCEO) and Foeniculum vulgare Mill. (FVEO). EO was extracted from the aerial parts by steam distillation and analyzed using Gas Chromatography Mass Spectrometry (GC/MS). Major constituents identified in DCEO were geranyl acetate (50.07%) and elemicin (10.77%), while FVEO contained estragole (24.93%), fenchone (20.20%), and α-phellandrene (17.96%). Both EOs were highly effective towards Culex pipiens larvae, with low LC50 values of 30.6 ± 1.06 ppm for DCEO and 34.7 ± 1.06 ppm for FVEO, indicating their potential as bioinsecticides due to their bioactivity and bioavailability. Additionally, the two Eos demonstrated moderate antibacterial efficacy against gram-positive bacteria, Staphylococcus aureus ATCC 25923 and Staphylococcus aureus MRSA ATCC 43300, respectively, with DCEO showing MIC values of 10 and 20 mg/mL, respectively, and FVEO exhibiting MIC values > 20 mg/mL. However, both EOs showed limited effectiveness against gram-negative bacteria, Escherichia coli ATCC 25922 and Klebsiella pneumonia ATCC 700603. These results highlight the potential applications of DCEO and FVEO as natural bioinsecticides and antibacterial agents, offering promising avenues for further research and development in pest control and food preservation.

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user’s present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles.

Phytochemicals are used in fish farming to reduce stress and combat diseases during intensification. Recently, nanotechnology has represented a paradigm shift in the aquatic feed industry to improve the solubility, availability, and efficacy of phytochemicals. The purpose of this research was to investigate the effects of dietary supplementation with nano-curcumin (CUNE) on the growth, feed utilization, body composition, blood biochemistry, antioxidant status, disease resistance to Vibrio parahaemolyticus (V. parahaemolyticus), and the expression of insulin-like growth factor 1 (IGF-1), growth hormone (GH), interleukin-10 (IL-10), and interlukin-1β (IL-1β) genes in European Seabass (Dicentrarchus labrax) fingerlings. Fish (6.02±0.01) were randomly divided into four equal groups and fed varying levels of CUNE: 0 (CUNE0), 50 (CUNE1), 60 (CUNE2), and 70 mg/kg (CUNE3) of nano-curcumin for 56 days, respectively. The inclusion of dietary CUNE significantly improved the growth indices (final body weight and weight gain, and specific growth rate) and feed utilization (improved feed intake and lower FCR) of European seabass fingerlings (p0.05). All CUNE groups established an enhancement in blood hematology in a dose-dependent manner, with CUNE3 demonstrating the highest values. CUNE supplementation (70 mg/kg diet) produced the highest levels of total protein, albumin, globulin, and triglycerides, as well as the lowest levels of glucose, and hepatic enzymes (ALT, AST, and ALP; p

In this work, an AISI 4340 high-strength steel alloy was surface machined to have four different grades of roughness (Ra). The impact of changing Ra on the corrosion of the steel alloy in 3.5% NaCl solutions after 40 min and 24 h was performed using various electrochemical techniques. The cyclic polarization experiments showed that an increase in Ra increases the steel corrosion via enhancing the corrosion current of the alloy. The electrochemical impedance plots also indicated that an increase in Ra reduces the corrosion resistance of the alloy by decreasing the diameter of the semicircle obtained by the Nyquist spectra. The change in potentiostatic current vs time measurements, which were obtained at −350 mV (Ag/AgCl), confirmed that pitting attack occurs and its intensity further increases with increasing Ra for all steel samples. An increase in immersion time also reduces the resistance to corrosion due to the iron dissolution from the surface of the steel alloy. After corrosion, the surface was analyzed using a scanning electron microscope and energy dispersive spectroscopy investigations.