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The palm and date sector is one of the most important sectors in Saudi Arabia. The total number of fertile palm trees in Saudi Arabia is about 31 million. In the production of pitted dates, date molasses, date paste, and date confectionery, a considerable number of date kernels are usually discarded as waste. This study reports experimental investigations conducted to evaluate the potential of waste date kernel ash (DKA), obtained by the calcination of date pits at 800 °C, as a partial cement replacement in concrete. DKA has low silica oxide and does not qualify as a pozzolanic material. The effect of DKA partially replacing the cement and acting as a filler material in concrete was investigated, and its properties were compared with two pozzolanic materials, fly ash (FA) and natural pozzolan (NP). Twelve concrete mixes in which cement was replaced with different proportions of calcined DKA (5%, 10%, 15%, 20%, and 30%), NP (10%, 20%, and 30%), and FA (10%, 20%, and 30%) were investigated in the experimental program. The properties of DKA, FA, and NP concrete mixes were evaluated in fresh and hardened states, including the heat of hydration, mechanical characteristics, and thermal properties. The results show that replacing cement with 5% date kernel ash increases the compressive strength by 0.42%, 3.2%, and 2.5% at 3, 7, and 28 days, respectively, while the 28-day compressive strength decreases by 2.4%, 5.4%, 16.3%, and 26.69% when the cement is replaced with 10%, 15%, 20%, and 30% DKA, respectively. Date kernel ash concrete mixes with 10%, 20%, and 30% replacement levels demonstrated higher compressive and tensile strengths and lower thermal conductivity, density, and workability when compared to natural pozzolan and fly ash. DKA is a promising partial cement replacement material; nevertheless, additional research is required to assess the durability of DKA in concrete.

Incidences of dengue-virus infection are increasing globally. Around 3.90 billion population is at high risk of infection. There is no cure/ prevention available. Hence aim of the current review was the evaluation of current development and prospects for the production of safe anti-dengue drugs. The data of review was synthesized by PRISMA. Grading of Recommendation Assessment, Development, and Evaluation criteria were employed to establish the quality of the literature. Many drugs/compounds have shown promising prospects. Target site of these drugs on dengue virus were inhibition of replication (N-sulfonyl peptide-hybrids, milk-exosomes, sunitinib, schisandrin-A, cavinafungin, minocycline, 4-hydroxyphenyl retinamide, Goniothalamus umbrosus, Dryopteris crassirhizoma, Morus alba, statins), protease-enzyme (Thiazolidinone-peptide, nelfinavir, carnosine, 1,2-benzisothiazol-3(2H)- one and 1,3,4-oxadiazole), capsid-protein (Juglanin and silymarin), NS4B-gene (Desatinib), entry-in-host-cell (Duramycin, glycodendrimers, curdlan sulfate), RNA-capping (Lanatoside-C), fusion (Chloroquine) and NS3-helicase (Suramina). Many compounds/drugs are found effective by targeting the structural proteins of the dengue virus to use as a therapy in the future.

Zirconia ceramics are versatile materials with remarkable properties such as a high thermal resistance, high fracture strength, and low thermal conductivity. They are chemically inert and highly wear- and corrosion-resistant, making them ideal for a wide range of applications in the aerospace, automotive, and biomedical fields. In dentistry, zirconia ceramics are used for veneers, crowns, bridges, and implants because of their biocompatibility. Despite the various benefits of zirconia ceramics, they are difficult to process because of their high hardness and brittleness. Additive manufacturing (AM) has proven to be a viable alternative to conventional fabrication processes, particularly for the processing of difficult-to-cut materials. AM of ceramics has gained significant attention in recent years because of its flexibility and ability to produce customized geometries rapidly and economically. In this study, the digital light processing (DLP) technique was employed to 3D print yttria-stabilized zirconia. The fabricated zirconia was evaluated and characterized for use in dental applications. Thermogravimetric analysis (TGA) and differential thermogravimetry (DTG) were performed on the green body to assess the decomposition of the additives in the slurry and determine the debinding temperatures. The as-built parts were subjected to debinding and sintering to obtain fully dense zirconia parts. The parts tended to shrink after sintering; therefore, the shrinkage ratios were evaluated and found to be 1.2817, 1.2900, and 1.3388 in the x-, y-, and z-directions, respectively. The average density after sintering was 6.031 g/cc. The flexural strength determined using four-point bending tests was 451.876 MPa, and the tensile and compressive strengths were 143 MPa and 298.4 MPa, respectively.

Recent technological breakthroughs enable academics to suggest automating hydroponic farms to increase productivity and reduce workforce requests, hence improving agricultural profitability and output. Traditional deep learning models for detecting plant diseases may pose challenges in terms of their compatibility with embedded systems and only a few research works show their implementation on low-resource Internet of Things (IoT) devices like smartphones or Raspberry Pi. The current models have several shortcomings, including a substantial power bill, high computing power demands, quick energy dissipation, and significant storage space demands because of their intricate structures. Hence, in this paper, an intelligent hydroponic lettuce deficiency classification method is presented by employing deep learning. Initially, collected images are taken to the developed Ensemble Multi-Dilated Adaptive Networks (E-MD-ANet)-based deficiency classification network. In this network, techniques such as Visual Geometry Group 16 (VGG16), Residual Attention Network (RAN), and Convolutional Neural Network (CNN) are adopted for the extraction of the feature stage. Further, the extracted features from these networks are given to the Long Short-Term Memory (LSTM) for the deficiency classification. The parameters presented in the E-MD-ANet network are tuned using the proposed Improvised Honey Badger Algorithm (I-HBA). Thus, the hydroponic lettuce deficiency classified image is attained in the end. Here, the experiments are conducted based on the Lettuce NPK dataset which contains the, Fully Nutritional, Potassium Deficient, and Nitrogen Deficient, and Phosphorus Deficient whereas the total count of images is gathered as 12, 58, 66, and 72. Also, various evaluation parameters are taken for the analysis like precision, NPV, specificity, F1-score, FPR, FDR, accuracy, MCC, FNR, and sensitivity. In the end, the efficacy validation of the suggested task is conducted and contrasted with numerous conventional classification mechanisms to showcase the system’s efficacy. The offered approach shows elevated performance regarding accuracy and specificity rate which attains 96% and 95%. From the result analysis, the offered approach achieves greater performance while combining with the baseline techniques.

The soil quality plays a crucial role in providing essential nutrients for crop growth and ensuring a bountiful yield. Identifying the soil composition, which includes sand, silt particles, and the mixture of clay in specific proportions, is vital for making informed decisions about crop selection and managing weed growth. Furthermore, soil pollution from emerging contaminants presents a substantial risk to water resource management and food production. Developing numerical models to comprehensively describe the transport and reactions of chemicals within both the plants and soil is of utmost importance in crafting effective mitigation strategies. To address the limitations of traditional models, this paper devises an innovative approach that leverages deep learning to predict hydroponic and soil compound dynamics during plant growth. This method not only enhances the understanding of how plants interact with their environment but also aids in making more informed decisions about agriculture, ultimately contributing to more sustainable and efficient crop production. The data needed to perform the developed hydroponic and soil compound prediction model is acquired from online resources. After that, this data is forwarded to the feature extraction phase. The weighted features, deep belief network (DBN) features, and the original features are achieved in the feature extraction stage. To get the weighted features, the weights are optimally obtained using the Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolution Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for hydroponic and soil compound prediction. Thus, the hydroponic and soil compound prediction data is attained in the end. Finally, the performance evaluation of the suggested work is conducted and contrasted with numerous conventional models to showcase the system’s efficacy.

Steel and polypropylene fibers result in a significant enhancement in strength, ductility, and toughness of concrete. This paper investigates experimentally and numerically the effect of these fibers on heat of hydration and early‐age cracking in mass concrete structures. Experimental investigations were conducted on evolution of strength, Young's modulus, and adiabatic heat rise in fly ash concrete mixes with steel and polypropylene fibers. Concrete mixes with ordinary Portland cement, 40% fly ash replacing the cement, 40% fly ash with 0.3 and 0.5% steel fibers, and 40% fly ash with 0.3 and 0.5% propylene fibers were investigated. The evolution of compressive and tensile strength, modulus of elasticity, and adiabatic heat rise using semi‐adiabatic calorimeter for all mixes was measured. The evolution of temperature at several locations in two full‐scale mass concrete blocks with normal concrete and fly ash concrete was measured. A multiphysics‐based finite element simulation of the two field mass concrete blocks was carried out using the software DIANA FEA BV (South Holland, Netherlands), which predicted the heat generation, temperature field, stresses developed in the blocks, and the associated cracking with good accuracy. The effect of steel and polypropylene fibers on heat, temperature, and stress generation in mass concrete blocks was investigated using the experimentally obtained parameters. The polypropylene fibers result in significant performance enhancement for early‐age cracking compared with the steel fibers. [ABSTRACT FROM AUTHOR]

The growing demand for Magnesium in the automotive and aviation industries has enticed the need to improve its corrosive properties. In this study, the WE43 magnesium alloys were friction stir welded (FSW) by varying the traverse speed. FSW eliminates defects such as liquefication cracking, expulsion, and voids in joints encountered frequently in fusion welding of magnesium alloys. The microstructural properties were scrutinized using light microscopy (LM) and scanning electron microscopy (SEM). Additionally, the elemental makeup of precipitates was studied using electron dispersive X-ray spectroscopy (EDS). The electrochemical behavior of specimens was evaluated by employing potentiodynamic polarization tests and was correlated with the microstructural properties. A defect-free weldment was obtained at a traverse and rotational speed of 100 mm/min and 710 rpm, respectively. All weldments significantly improved corrosion resistance compared to the base alloy. Moreover, a highly refined microstructure with redistribution/dissolution of precipitates was obtained. The grain size was reduced from 256 µm to around 13 µm. The corrosion resistance of the welded sample was enhanced by 22 times as compared to the base alloy. Hence, the reduction in grain size and the dissolution/distribution of secondary-phase particles within the Mg matrix are the primary factors for the enhancement of anti-corrosion properties.

Laser processing of materials finds application in micro-nano devices mainly because of its accuracy, flexibility, and ability to machine almost any material. Although it offers numerous advantages, it is a complex process involving a large number of factors. The quality of machining often depends on the appropriate selection of parameters. Moreover, the output responses in machining processes have conflicting nature; some are to be minimized, and others have to be maximized. This work uses grey relationship analysis coupled with principal component analysis for multi-response optimization of conflicting responses during laser machining of micro-channels. Micro-channels with a cross-sectional size of 200 × 100 µm were created using Nd:YAG laser beam micro-milling in steel alloy (AISI 1045). The scan speed, layer thickness, and scan strategy were found to have a significant effect on the dimensional accuracy of the microchannel. At the same time, the material removal rate was mostly influenced by layer thickness. Multi-response optimization results suggest low pulse frequency, high scan speed, low layer thickness, and S3 scan strategy for accurately fabricating micro-channels.

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.

Support structures are required for overhanging surfaces in additive manufacturing to prevent part deformation. However, these support structures are undesirable because they are removed during postprocessing after the part is built. It is critical to remove metal support structures because they can occasionally leave residue on the part. Among the various support-removal techniques, manual removal and wire electrical discharge machining (EDM) are the most frequently used. While most manufacturers recommend the manual method due to its economic benefits, it may be challenging for thin walls, small-scale components, and brittle materials. The purpose of this study is to evaluate and compare these two methods, taking into account both their advantages and disadvantages. The results indicate that while manual support-removal is quick, it results in a degraded surface topography and increased surface roughness due to support tooth remnants and residue. The wire EDM method of removing support resulted in a more uniform surface topography and a superior surface finish. However, as a result of thermal damage, a heat-affected zone developed on the machined surface.

Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristic-based scheduling method for FMSs.