Application of optimized large surface area date stone (Phoenix dactylifera ) activated carbon for rhodamin B removal from aqueous solution: Box-Behnken design approach.

Box-Behnken model of response surface methodology was used to study the effect of adsorption process parameters for Rhodamine B (RhB) removal from aqueous solution through optimized large surface area date stone activated carbon. The set experiments with three input parameters such as time (10-600min), adsorbent dosage (0.5-10g/L) and temperature (25-50°C) were considered for statistical significance. The adequate relation was found between the input variables and response (removal percentage of RhB) and Fisher values (F- values) along with P-values suggesting the significance of various term coefficients. At an optimum adsorbent dose of 0.53g/L, time 593min and temperature 46.20°C, the adsorption capacity of 210mg/g was attained with maximum desirability. The negative values of Gibb's free energy (ΔG) predicted spontaneity and feasibility of adsorption; whereas, positive Enthalpy change (ΔH) confirmed endothermic adsorption of RhB onto optimized large surface area date stone activated carbons (OLSADS-AC). The adsorption data were found to be the best fit on the Langmuir model supporting monolayer type of adsorption of RhB with maximum monolayer layer adsorption capacity of 196.08mg/g.

Enhancing sustainability in the production of palm oil: creative monitoring methods using YOLOv7 and YOLOv8 for effective plantation management.

The You Only Look Once (YOLO) deep learning model iterations-YOLOv7-YOLOv8-were put through a rigorous evaluation process to see how well they could recognize oil palm plants. Precision, recall, F1-score, and detection time metrics are analyzed for a variety of configurations, including YOLOv7x, YOLOv7-W6, YOLOv7-D6, YOLOv8s, YOLOv8n, YOLOv8m, YOLOv8l, and YOLOv8x. YOLO label v1.2.1 was used to label a dataset of 80,486 images for training, and 482 drone-captured images, including 5,233 images of oil palms, were used for testing the models. The YOLOv8 series showed notable advancements; with 99.31 %, YOLOv8m obtained the greatest F1-score, signifying the highest detection accuracy. Furthermore, YOLOv8s showed a notable decrease in detection times, improving its suitability for comprehensive environmental surveys and in-the-moment monitoring. Precise identification of oil palm trees is beneficial for improved resource management and less environmental effect; this supports the use of these models in conjunction with drone and satellite imaging technologies for agricultural economic sustainability and optimal crop management.

Corrosion image classification method based on EfficientNetV2.

Corrosion is one of the key factors leading to material failure, which can occur in facilities and equipment closely related to people's lives, causing structural damage and thus affecting the safety of people's lives and property. To identify corrosion more effectively across multiple facilities and equipment, this paper utilizes a corrosion binary classification dataset containing various materials to develop a CNN classification model for better detection and distinction of material corrosion, using a methodological paradigm of transfer learning and fine-tuning. The proposed model implementation initially uses data augmentation to enhance the dataset and employs different sizes of EfficientNetV2 for training, evaluated using Confusion Matrix, ROC curve, and the values of Precision, Recall, and F1-score. To further enhance the testing results, this paper focuses on the impact of using the Global Average Pooling layer versus the Global Max Pooling layer, as well as the number of fine-tuning layers. The results show that the Global Average Pooling layer performs better, and EfficientNetV2B0 with a fine-tuning rate of 20 %, and EfficientNetV2S with a fine-tuning rate of 15 %, achieve the highest testing accuracy of 0.9176, an ROC-AUC value of 0.97, and Precision, Recall, and F1-Score values exceeding 0.9. These findings can be served as a reference for other corrosion classification models which uses EfficientNetV2.

Optimization of Process Parameters in CNC Turning of Aluminum 7075 Alloy Using L27 Array-Based Taguchi Method.

With the advent of the industrial revolution 4.0, the goal of the manufacturing industry is to produce a large number of products in relatively less time. This study applies the Taguchi L27 orthogonal array methodological paradigm along with response surface design. This work optimizes the process parameters in the turning of Aluminum Alloy 7075 using a Computer Numerical Control (CNC) machine. The optimal parameters influenced the rate of metal removal, the roughness of the machined surface, and the force of cutting. This experimental investigation deals with the optimization of speed (800 rpm, 1200 rpm, and 1600 rpm) and feed (0.15, 0.20, and 0.25 mm/rev) in addition to cutting depth (1.0, 1.5, and 2.0 mm) on the turning of Aluminum 7075 alloy in a CNC machine. The outcome in terms of results such as the removal rate of material (maximum), roughness on the machined surface (minimum), along with cutting force (least amount) were improved by the L27 array Taguchi method. There were 27 specimens of Al7075 alloy produced as per the array, and the corresponding responses were measured with the help of various direct contact and indirect contact sensors. Results were concluded all the way through diagrams of main effects in favor of signal-to-noise ratios and diagrams of surfaces with contour diagrams for various combinations of responses.

Determination of Non-Recrystallization Temperature for Niobium Microalloyed Steel.

In the present investigation, the non-recrystallization temperature (TNR) of niobium-microalloyed steel is determined to plan rolling schedules for obtaining the desired properties of steel. The value of TNR is based on both alloying elements and deformation parameters. In the literature, TNR equations have been developed and utilized. However, each equation has certain limitations which constrain its applicability. This study was completed using laboratory-grade low-carbon Nb-microalloyed steels designed to meet the API X-70 specification. Nb- microalloyed steel is processed by the melting and casting process, and the composition is found by optical emission spectroscopy (OES). Multiple-hit deformation tests were carried out on a Gleeble® 3500 system in the standard pocket-jaw configuration to determine TNR. Cuboidal specimens (10 (L) × 20 (W) × 20 (T) mm3) were taken for compression test (multiple-hit deformation tests) in gleeble. Microstructure evolutions were carried out by using OM (optical microscopy) and SEM (scanning electron microscopy). The value of TNR determined for 0.1 wt.% niobium bearing microalloyed steel is ~ 951 °C. Nb- microalloyed steel rolled at TNR produce partially recrystallized grain with ferrite nucleation. Hence, to verify the TNR value, a rolling process is applied with the finishing rolling temperature near TNR (~951 °C). The microstructure is also revealed in the pancake shape, which confirms TNR.

Analysis using image segmentation for the elemental composition of activated carbon.

This article encompasses the method related to image segmentation of the Field Emission Scanning Electron Microscope (FESEM) images of Acacia Mangium Wood derived Activated Carbons under different conditions. Image segmentation using Hue-Saturation-Value (HSV) thresholding method was adapted to identify the different pattern composition in the grayscale images by varying the intensity Value (V) and keeping Hue (H) and Saturation (S) to zero, and each pattern was considered as one type of element that constituted the Activated Carbon. The algorithm was developed to compute the percentage of each pattern using non-zero pixels, and on the basis of different patterns, different elements having certain percentage of composition were recorded. Later, these results were compared with the Energy Dispersive X-ray Spectroscopy (EDS) to cross check the difference in percentage of each element present at the surface of the Activated Carbon. Part of this result is published in the article [1], "Comparison of surface properties of wood biomass Activated Carbons and their application against rhodamine B and methylene blue dye" Surfaces and Interfaces vol. 11 (2018) pp1-13.•The methods involved will be useful for characterization of Activated Carbon materials.•Image segmentation using HSV thresholding will inspire other researchers to apply similar concept on other materials.•Different patterns obtained for FESEM images using HSV thresholding was able to determine the presence of multiple elements present in the prepared Activated Carbon samples.