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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 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 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 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.

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.

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.

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.

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.

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.

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.

Myocardial involvement was shown to be associated with an unfavorable prognosis in patients with COVID-19, which could lead to fatal outcomes as in myocardial injury-induced arrhythmias and sudden cardiac death. We hypothesized that magnetic resonance imaging (MRI) myocardial strain parameters are sensitive markers for identifying subclinical cardiac dysfunction associated with myocardial involvement in the post-acute sequelae of COVID-19 (PASC). This study evaluated 115 subjects, including 65 consecutive COVID-19 patients, using MRI for the assessment of either post-COVID-19 myocarditis or other cardiomyopathies. Subjects were categorized, based on the results of the MRI exams, as having either ‘suspected’ or ‘excluded’ myocarditis. A control group of 50 matched individuals was studied. Along with parameters of global cardiac function, the MRI images were analyzed for measurements of the myocardial T1, T2, extracellular volume (ECV), strain, and strain rate. Based on the MRI late gadolinium enhancement and T1/T2/ECV mappings, myocarditis was suspected in 7 out of 22 patients referred due to concern of myocarditis and in 9 out of 43 patients referred due to concern of cardiomyopathies. The myocardial global longitudinal, circumferential, and radial strains and strain rates in the suspected myocarditis group were significantly smaller than those in the excluded myocarditis group, which in turn were significantly smaller than those in the control group. The results showed significant correlations between the strain, strain rate, and global cardiac function parameters. In conclusion, this study emphasizes the value of multiparametric MRI for differentiating patients with myocardial involvement in the PASC based on changes in the myocardial contractility pattern and tissue structure.

Abstract The main aim of this work is to study the effect of different nutrient supply systems and their effect on the performance of the Nile Tilapia (Oreochromis niloticus) and Lettuce (Lactuca sativa var. crispa) plant integration system. To achieve that, five treatments having different culture systems (T1: Aquaculture (control), T2: Hydroponics (standard requirement: N = 210, P = 31, K = 234, Mg = 48, Ca = 200, S = 64, Fe = 14, Mn = 0.5, Zn = 0.05, B = 0.5, Cu = 0.02 and Mo = 0.01 ppm), T3: Aquaponics without nutrients addition, T4: Aquaponics with supplementary nutrients (KNO3, 101 g L−1, KH2PO4, 136 g L−1, Ca(NO3)2, 236 g L−1, MgSO4, 246 g L−1, K2SO4, 115 g L−1 and chelates for trace elements) in water (EC is 800 ppm) and T5: Aquaponics with supplementary nutrients spray on plants) were carried out. The previous systems were operated at three flow rates, namely, 1.0, 1.5 and 2.0 L h−1 plant−1. The various water quality parameters, plant growth and fish growth were studied. The result indicated that the highest values of N, P, k, Ca and Mg consumption rate were found with T2 and 1.5 L h−1 plant−1 of flow rate. The root length, fresh and dry of shoot and root for lettuce plants grown in T2 system was better than those grown in different culture system (T3, T4 and T5). Different culture systems showed significant effect on fish growth in terms of weight gain, specific growth rate and feed efficiency ratio. Higher growth rate was observed in treatment T3 as compared to other treatments. The production costs ranged from 2820.5 to 4885.4 LE ($ = 30.92 LE) for all culture systems.

Abstract The main aim of this study is to determine the physical and chemical properties of biochar synthesized from different materials (straw rice, sawdust, sugar cane, and tree leaves) at different pyrolysis temperatures (400, 600, and 800 °C). The physical and chemical properties such as moisture content, water holding capacity, bulk density, and porosity; and pH, electrical conductivity (EC), organic matter, organic carbon, total nitrogen, potassium, phosphorus, calcium, magnesium, sodium, and sulfur were determined, respectively. The results show that the biochar yield decreased with increasing pyrolysis temperature, and the values of the analyzed properties varied depending on the type of biochar and pyrolysis temperature. The moisture content ranged from 1.11 to 4.18%, and the water holding capacity ranged from 12.9 to 27.6 g water g−1 dry sample. The highest value of bulk density (211.9 kg m−3) was obtained from sawdust at a pyrolysis temperature of 800 °C. The porosity values ranged from 45.9 to 63.7%. The highest values of pH and EC (10.4 and 3.46 dS m−1) were obtained from tree leaves at a pyrolysis temperature of 800 °C. Total organic matter ranged from 66.0 to 98.1%, total organic carbon ranged from 38.3 to 56.9%, and total nitrogen ranged from 0.4 to 1.9%. The highest values of phosphorus and calcium content (134.6 and 649.0 mg kg−1) were obtained from sugar cane at a pyrolysis temperature of 800 °C. The magnesium, sodium and sulfur content had ranges of 10.9–51.7, 1124–1703 and 3568–12,060 mg kg−1, respectively.

Abstract The main aim of this work is to study the main factors affecting the quality of the dried product and the energy requirements through optimizing these factors. To achieve that different drying systems (solar, hybrid solar and oven dryings), layers thickness (1, 2 and 3 cm) and drying temperatures (50, 60 and 70 °C) were used. The obtained results indicated that, the accumulated weight loss of marjoram leaves ranged from 73.22 to 76.9%, for all treatments under study. The moisture content of marjoram leaves ranged from 273.39 to 333.17, 258.02 to 333.04 and 269.38 to 324.90% d.b. for hybrid solar, oven and solar drying systems, respectively. The highest value of the drying rate of marjoram leaves (223.73 gwater kg−1 h−1) was obtained when the marjoram dried by oven drying system at 70 °C at 1 cm layer thickness. The highest values of the basil and marjoram oil content (2.91%) was obtained when the marjoram dried under sun drying system. The energy consumption for drying marjoram decreases with increasing drying temperature and layer thickness for hybrid solar and oven drying systems. The cost of dried marjoram dried under hybrid solar drying system was lower than those of oven drying system, the highest cost (13.48 LE kg−1) was obtained at a temperature of 50 °C and a layer thickness of 1 cm.

AbstractThe cyanobacteria may contribute significantly to sustainable agriculture by enhancing plant growth, productivity and environmental quality. Tuberose is ranked among the ten most significant cut flowers in the world. In this regard, this study intends to improve the tuberose plant’s growth, flowering, corms, and concrete oil under sandy soil conditions by employing a sufficient amount of N fertilizer or/and Spirulina platensis extract. N was added to the pot at a rate of 2, 4 g and the control treatment (without adding; 0). S. platensis extract was foliar sprayed at concentrations of 10, 15 %, and distilled water. Results showed that N or S. platensis increased the leaves dry weight, no. of florets/spike, spike dry weight, corms and cormels dry weight, total chlorophyll, and concrete oil. Ultimately, a combination of 2 g N/plant with a 15% concentration of S. platensis extract proved to be the most successful treatment for all the features under investigation.

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.

In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country’s higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model.

Diabetes is a chronic disease that is usually caused when the pancreas fails to produce sufficient insulin or when the body is unable to effectively utilize the insulin produced by the pancreas. Early detection of diabetes enables the implementation of a suitable treatment method, which can lead to a healthy lifestyle. A necessity arises for an automated system capable of diagnosing diabetes using clinical and physical data in cases when the conventional approach to detecting diabetes proves to be arduous. In this paper, a new diabetes classification model based on optimized long short-term memory (LSTM) is presented and evaluated on the Pima Indians Diabetes Database (PIDD). To improve the LSTM model, a novel hybrid waterwheel plant and stochastic fractal search (WWPASFS) is proposed for optimizing its parameters. To confirm the performance superiority of the proposed WWPASFS + LSTM model, it is compared to various machine learning models and metaheuristic optimization methods. In addition, the binary WWPASFS is proposed to extract the relevant features in the PIDD dataset, with the aim of improving the accurate classification of diabetes patients. The WWPASFS + LSTM model attained the highest accuracy of 98.2% in classifying diabetes patients on the dataset in hand. The WWPASFS + LSTM model exhibited superior performance compared to the other five models, namely decision tree, K-nearest neighbors, neural networks, random forest, and support vector machines. On the other hand, the statistical analysis of the proposed approach is studied and the results prove its difference and significance.

This study evaluated the effects of whole flaxseed (WFS), fish oil (FO), and different sources of Se, Zn, and Fe (inorganic, organic, and nano-source) on egg production, quality, sensory attributes, and serum protein metabolites in laying hens. A total of 144 hens were divided into six groups with six replicates of four hens each. Hens were fed six diets as follows: 1) control diet; 2) 7.5%WFS+1.5%FO; 3) 7.5%WFS+1.5%FO+175 mg/kg vitamin E (VE); 4) 7.5%WFS+1.5%FO+175 mg/kg VE + inorganic sources of Se, Zn, and Fe (ISeZnFe); 5) 7.5%WFS+1.5%FO+175 mg/kg VE + organic sources of Se, Zn, and Fe (OSeZnFe); 6) 7.5%WFS+1.5%FO+175 mg/kg VE + nano-source of Se, Zn, and Fe (NSeZnFe) from 40–50 weeks of age. Laying hens fed 7.5% WFS, 1.5% FO, and different sources of trace elements in their diets had no negative effects on laying rate, egg weight, egg mass, feed intake, feed conversion ratio, body weight change, or survival rate compared to that of hens fed the control diet. Dietary treatments did not negatively affect the external and internal egg characteristics or egg sensory attributes. Feeding 7.5%WFS+1.5%FO+VE+ISeZnFe positively influenced yolk color in fresh eggs. Dietary treatments had a significant impact on egg nutritional composition, with the highest levels of macronutrients found in eggs from hens fed the 7.5%WFS+1.5%FO+VE+NSeZnFe treatment. The highest plasma globulin concentrations were observed in hens fed organic and nano-source trace elements. The same diets reduced plasma uric acid levels. Based on these findings, the inclusion of organic or nano-source trace minerals in diets containing WFS and FO positively affected egg quality and hen protein metabolites.

Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

The effect of different contents of fullerene on the properties of polyurethane resins (PUs), including rheology and thermal properties, was investigated. Polyurethane resins were prepared through polyaddition reactions using different isocyanate monomers such as isophorone diisocyanate (IPDI), methylene diphenyl diisocyanate (MDI), hexamethylene diisocyanate (HDI), and different polyols, such as poly(oxytetramethylene) glycol (PTMG), the triol trade name FA-703, and polypropylene glycols (PPG), at an NCO/OH ratio 0.94 and a temperature of 100 °C. IR spectroscopy was used to control the polymerization of PUs through the shifting of NCO peaks. The results showed that the rheology and thermal properties of the prepared PU resins depend on the type of isocyanates and fullerene used. Based on the type of isocyanates, the PU resin prepared by MDI has the highest viscosity and thermal stability compared to the other isocyanates investigated. On the other hand, the PU resins prepared by IPDI mixed with fullerene had the highest viscosity and thermal stability. However, the initial decomposition temperature (T onset) of the PUs decreased with the addition of fullerene without affecting the maximum decomposition temperature (PDTmax) of the PU resin.

Due to climate change, expansion of salt-affected arable lands has emerged as a major threat to global food security and agricultural sustainability. Conventional crop breeding programs have proven insufficient for mitigating the risks of salt stress in soil and crop productivity. Research on improving soil health and crop tolerance to boost yield performance under salt stress by plant-associated microbiomes has gained considerable attention over the past few decades. In addition to the rehabilitation of salt-degraded soils, halotolerant plant growth-promoting rhizobacteria (HT-PGPR) are effective in stimulating growth, improving nutritional values and yields, and increasing tolerance/resistance to abiotic/biotic stress conditions of plants. To alleviate salinity stress, HT-PGPR employ a wide range of mechanisms, including production of secondary metabolites and siderophores, synthesis of phytohormones and enzymes, maintenance of ion homeostasis, and increasing nutrient availability in crop plants. Because our goal is to continuously improve our understanding of HT-PGPR and their impact on agriculture, the current review addresses the challenges associated with salinity, and updates the scientific community with effective solutions (e.g., HT-PGPR) for sustainable agriculture and farming practices.

Bimatoprost (BIM) is a prostaglandin F2α analogs originally approved for the treatment of glaucoma and ocular hypertension. Recent studies have highlighted its potential to boost hair growth. The objective of this investigation is to challenge the potential of spanlastics (SLs) as a surfactant-based vesicular system for promoting the cutaneous delivery of BIM for the management of alopecia. BIM-loaded spanlastics (BIM-SLs), composed of Span as the main vesicle component and Tween as the edge activator, were fabricated by ethanol injection method. The formulated BIM-SLs were optimized by 23 full factorial design. The optimized formula (F1) was characterized for entrapment efficiency, surface charge, vesicle size, and drug release after 12 h (Q12h). The optimized formula (F1) exhibited high drug entrapment efficiency (83.1 ± 2.1%), appropriate zeta potential (−19.9 ± 2.1 mV), Q12h of 71.3 ± 5.3%, and a vesicle size of 364.2 ± 15.8 nm, which favored their cutaneous accumulation. In addition, ex-vivo skin deposition studies revealed that entrapping BIM within spanlastic-based nanogel (BIM-SLG) augmented the dermal deposition of BIM, compared to naïve BIM gel. Furthermore, in vivo studies verified the efficacy of spanlastic vesicles to boost the cutaneous accumulation of BIM compared to naive BIM gel; the AUC0-12h of BIM-SLG was 888.05 ± 72.31 μg/mL.h, which was twice as high as that of naïve BIM gel (AUC0-12h 382.86 ± 41.12 μg/mL.h). Intriguingly, BIM-SLG outperforms both naïve BIM gel and commercial minoxidil formulations in stimulating hair regrowth in an androgenetic alopecia mouse model. Collectively, spanlastic vesicles might be a potential platform for promoting the dermal delivery of BIM in managing alopecia.

Studying the combination of convection and chemical processes in blood flow can have significant applications like understanding physiological processes, drug delivery, biomedical devices, and cardiovascular diseases, and implications for various fields can lead to developing new treatments, devices, and models. This research paper investigates the combined effect of convection, heterogeneous-homogeneous chemical processes, and shear rate on the flow behavior of a ternary hybrid Carreau bio-nanofluid passing through a stenosed artery. The ternary hybrid Carreau bio-nanofluid consists of three different types of nanoparticles dispersed in a Carreau fluid model, miming the non-Newtonian behavior of blood. This assumed study generates a system of PDEs that are processed with similarity transformation and converted into ODEs. Furthermore, these ODEs are solved with bvp4c. The results show that the convection, heterogeneous-homogeneous chemical processes, and shear rate significantly impact the bio-nano fluid’s flow behavior and the stenosed artery’s heat transfer characteristics.

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

Objective: To analyze the static and dynamic urodynamic parameters of reservoirs and continent conduits in continent cutaneous urinary diversion with catheterizable stoma. Materials and methods: 76 patients had augmented ileocystoplasty or continent urinary diversion with catheterizable urinary stoma based on Mitrofanoff principle and Yang-Monti procedure using subserous tunnel as continence mechanism. They were followed up for at least 6 months post-operatively for continence through stoma and divided into two groups (continents vs non-continent) according to stomal continence. Both groups had urodynamic assessment performed via the stoma to assess reservoir capacity, pressure and contractions, efferent limb functional length, reservoir overactivity, static and dynamic maximal closure pressures and leak point pressure. Results: Continence rate was 87%. Continent group included 66 patients and incontinent group included 10 patients. In both groups at rest, the reservoir pressure after filling did not exceed 25 cm H2O. During peristaltic contraction, the pressure did not exceed 30 cm H2O and the duct remained continent. After Valsalva maneuver, the reservoir pressure increased up to 34 (+ 7.4) cm H2O and leakage occur in 10 patients (13%). Reservoir (wall) overactivity was recorded in 54 patients, with insignificant rise in intraluminal pressure during the contractions. In both groups, the efferent tract closing pressure was always higher than the reservoir pressure. The mean of maximal closing pressure at Valsalva was 82.5 (+ 4.18) cm H2O in the continent group and 61.66 (+ 8.16) cm H2O in the incontinent group. The mean functional length of the conduit was 4.95 + 1.62 in the continent group and 2.80 + 1.50 cm in the incontinent group. Conclusions: Urodynamic evaluation of continent catheterizable cutaneous stoma after Yang-Monti procedure has a practical significance. Functional length of the conduit seems to be the most influential factor for continence reflecting static & dynamic maximal closure pressure. Higher conduit closing pressure is associated with better continence. Contractions of the pouch and peristaltic contraction of the conduit has no effect on continence mechanism.

The rise of antibiotic resistance in bacteria is becoming a global concern, particularly due to the dwindling supply of new antibiotics. This situation mandates the discovery of new antimicrobial candidates. Plant-derived natural compounds have historically played a crucial role in the development of antibiotics, serving as a rich source of substances possessing antimicrobial properties. Numerous studies have supported the reputation of 6-gingerol, a prominent compound found in the ginger family, for its antibacterial properties. In this study, the antibacterial activities of 6-gingerol were evaluated against Gram-negative bacteria, Acinetobacter baumannii and Klebsiella pneumoniae, with a particular focus on the clinically significant Gram-negative Pseudomonas aeruginosa and Gram-positive bacteria Staphylococcus aureus. Furthermore, the anti-virulence activities were assessed in vitro, in vivo, and in silico. The current findings showed that 6-gingerol’s antibacterial activity is due to its significant effect on the disruption of the bacterial cell membrane and efflux pumps, as it significantly decreased the efflux and disrupted the cell membrane of S. aureus and P. aeruginosa. Furthermore, 6-gingerol significantly decreased the biofilm formation and production of virulence factors in S. aureus and P. aeruginosa in concentrations below MICs. The anti-virulence properties of 6-gingerol could be attributed to its capacity to disrupt bacterial virulence-regulating systems; quorum sensing (QS). 6-Gingerol was found to interact with QS receptors and downregulate the genes responsible for QS. In addition, molecular docking, and molecular dynamics (MD) simulation results indicated that 6-gingerol showed a comparable binding affinity to the co-crystalized ligands of different P. aeruginosa QS targets as well as stable interactions during 100 ns MD simulations. These findings suggest that 6-gingerol holds promise as an anti-virulence agent that can be combined with antibiotics for the treatment of severe infections.