In this article, a UWB metasurface absorber is designed for a frequency range of 6.75GHz-14.5GHz. The absorber consists of a simple unit cell size of 0.265λ ₀ ×0.265λ ₀ ×0.103λ ₀ on a single-layer FR-4 substrate with a thickness of 3.1mm. It has greater than 90.0% absorbance. Next, an X-band circularly polarized MIMO antenna is developed on an FR-4 substrate with a thickness of 1.0mm. The antenna has dimensions of 0.966λ ₀ ×0.766λ ₀ ×0.053λ ₀ . The MIMO antenna’s S-parameters, radiating efficiency, gain, and diversity parameters are further indicators of its usefulness. The designed metasurface is also employed as a tool to enhance the peak gain of the proposed MIMO antenna up to 8.8dBi and reduce radar cross-section by 18dBm ² , by placing the metasurface array superstrate and down correspondingly. The simulated and measured results for the metasurface and antenna, both individually and collectively, are more harmonious, demonstrating the applicability of the suggested designs for IoT over satellite, military applications, and identifying blood clots in CT-scan applications.

The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ₁ = 64.5), tool wear (Ψ₂ = 22.3) and metal removal rate (Ψ₃ = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm³/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

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.

Intelligent models are showing an uprise in industry and academia to optimize the system’s outcome and adaptability to predict challenges. In machining, there is difficulty of unpredictability to the part performance especially in super alloys. The aim of this research is to propose an intelligent machining model using contemporary techniques, namely, combinative distance-based assessment (CODAS), artificial neural network (ANN), adaptive neuro-fuzzy inference systems, and particle swarm optimization (ANFIS-PSO) approach for minimizing resultant force, specific cutting energy, and maximizing metal removal rate. Resultant force response has shown to be affected by feed rate and cutting speed with a contribution of 54.72% and 41.67%, respectively. Feed rate and depth of cut were statistically significant on metal removal rate contributing with the same value of 38.88%. Specific cutting energy response resulted to be statistically significant toward feed rate with 43.04% contribution and 47.81% contribution by depth of cut. For the CODAS approach, the optimum parameters are cutting speed of 70 m/min, feed of 0.33 mm/rev, and depth of cut of 0.6 mm for the seventh experiment. The estimated values predicted by the ANN and ANFIS method were close to the measured values compared to the regression model. The ANFIS model performed better than the ANN model for predicting turning of the Inconel 825 alloy. As per quantitative analysis, these two models are reliable and robust, and their potential as better forecasting tools can be used for hard-to-machine materials. For hybrid ANFIS-PSO, the optimum parameters for minimizing resulting force were (82, 0.11, 0.15), for minimizing specific cutting energy (45, 0.44 and 0.6) and maximizing metal removal rate (101, 0.43, 0.54). The hybrid model ANFIS-PSO has proven to be a better approach and has good computational efficiency and a lower discrepancy in assessment.

A nickel-based copper alloy known as Monel-400 is extensively applied in many industries including aerospace, marine engineering, and nuclear power generation, owing to its exceptional characteristics such as extreme tensile strength and toughness, excellent corrosion resistance, and the ability to retain shape even at extremely high temperatures. Traditional methods of drilling Monel-400 alloy are difficult due to quick tool wear and poor surface polishing, resulting in expensive machining costs. In this study, a technique called heat annealing was implemented to externally heat-treat the Monel-400 alloy material before the drilling process. Cutting force, surface roughness, and tool wear were used as the responses to investigate the effect of heat annealing and the drilling parameters on the machinability of Monel-400. The results revealed that the cutting force (Fz) and surface roughness (Ra and Rt) could be reduced by 33%, 31%, and 25%, respectively, after annealing at 700 °C compared to the results of the drilled Monel-400 at room temperature. It can be observed that the maximum improvement can reach 42% of Fz, 35% of Ra, and 59% of Rt while annealing Monel-400 at 1000 °C. A significant reduction was observed in the tool wear for machining the annealed material, which minimized the tooling and overall machining cost. Regarding the effects of the drilling process on the considered responses, the results revealed that the spindle speed has a greater effect on the cutting force, whereas the feed rate has the most significant effect on Ra. The significance of the drilling input parameters on the outputs is determined by analysis of the main effect plots and surface plots. Subsequently, the multi-objective genetic algorithm (MOGA) is used to identify the optimal parametric conditions for minimizing the cutting force and surface roughness of the drilled holes. The optimized values achieved via multi-objective optimization are the cutting force, Fz = 388–466 N, and the surface roughness, R_a = 0.17–0.19 μm and Rt = 3–3.5 μm, respectively.

AXL kinase is a promising target in novel drug discovery for cancer. A ligand-based pharmacophore model was generated with the Pharmit web server. Its inbuilt PubChem molecule database was screened and led to 408 candidate molecules. Docking of the AXL kinase active sites with the identified list of candidate molecules was carried out with Autodock Vina docking software. This resulted in four compounds selected for further investigation. Molecular dynamics simulation of two ligands (PubChem-122421875 and PubChem-78160848) showed considerable binding with AXL kinase. From the MM-PBSA binding free energies investigation, the PubChem-122421875 (G = −179.3 kJ/mol) and PubChem-78160848 (G = −208.3 kJ/mol) ligands had favorable protein-ligand complex stability and binding free energy. Hence, PubChem-122421875 and PubChem-78160848 molecules identified in this work could be a potent starting point for developing novel AXL kinase inhibitor molecules.