Optimizing Shearing Characteristics of Sugarcane Leaves for Efficient Biomass Utilization and Machinery Design in the Sugar Industry

Sugarcane leaves, which are significant biomass residues from the globally important industrial crop, have potential as fuel sources for electricity generation. This study aimed to investigate the influence of moisture content, leaf region, and loading rate on shear strength and specific shearing energy of sugarcane leaves, focusing on the Khon Kaen 3 (KK3) cultivar. Experimental factors included four levels of moisture content (48.17%, 30.22%, 23.10%, and 8.90% w.b.), three leaf regions (lower, middle, and upper), and four loading rates (150, 250, 350, and 450 mm/min). Results showed significant impacts of moisture content, leaf region, and loading rate on shear strength and specific shearing energy (P < 0.01). The lower leaf region exhibited the highest shear strength (1.380 N/mm²) and specific shearing energy (12.184 mJ/mm²) at a moisture content of 48.17% w.b. and a loading rate of 150 mm/min. Conversely, the upper leaf region showed the lowest shear strength (0.372 N/mm²) and specific shearing energy (2.651 mJ/mm²) at a moisture content of 8.90% w.b. and a loading rate of 450 mm/min. To enhance cutting efficiency and minimize energy consumption during cutting leaves, it is recommended to sun-dry the leaves for 20-30 days before cutting to achieve a moisture content below 20% w.b. These findings could optimize cutting processes, machinery design, and agricultural practices in sugarcane harvesting and biomass utilization. This study is expected to contribute to understanding plant mechanical properties and provide insights for cutting devices and biomass processing systems. Further research should explore additional factors to advance efficiency and sustainability in the sugar industry and biomass utilization

Micromechanical Behavior of Granular Materials Caused by Particle Shapes under Triaxial Loading

The mechanical behavior was investigated of 3 different particle shapes under different stress paths by setting various intermediate stress ratios. The method of 3D discrete element was applied to research the effect of particle shapes with aspect ratios of ellipsoids and spheres, with both specified by height/width values of 0.4, 0.6, and 1 under different intermediate stress ratios or b values. Each one was used with a single particle shape based on 16 different sizes and random rotation angles. All 3 samples were subjected to a limited isotropic pressure of 100 kPa prior to shearing and constant mean stress using a stress controller. Macro behavior was evaluated based on the stress and strain responses. Micro mechanisms were reported based on the coordination number together with the sliding contract fraction. The fabric tensor of the contact normal, normal contact, and tangential contact forces were examined for the various sample shapes during intermediate loadings of different stress ratios. It was found that anisotropic fabrics and the b values relative to the normal contact force were higher than for the contact normal for all shapes. Furthermore, at the peak stress of each stress path, the specific behavior of normal contact forces varied with the particle shape

Electronic Tracking Device of Mass Public Transport Vehicle for Evaluating Driving Performance in Thailand

Thailand Mass Public Transport has a location tracking device along with a vehicle speed limiter attached to each public transport vehicle combined with Mass Public Transport Policy that aims to prevent dangers caused by improper driving behavior. These actions result in the troubles caused by inappropriate driving behavior decreasing drastically. However, risks from driving have more cases that the vehicle’s position and speed cannot determine and analyze. Thus, this research aims to develop a data-collecting device that collects linear acceleration, angular velocity, and magnetic field while transmitting data to an online database. The collected data enables the creation of a three-dimensional simulation from ten different public transport vehicle routes. The two main goals of developing a data-collecting device are to maximize the data collection frequency and evaluate its effectiveness in a real environment while the public transport vehicle is on duty. From ten vehicle routes, the device was able to collect stable data at a frequency of 50 Hz, with a reliability rate of over 50%. However, the device encounters various problems from external factors and bus layout diversity while testing in real environments on the road, which have been solved and are ready for real usage and statistical data analysis in the future

Effects of Weave Pattern on Filtration Performance of Woven Filter Cloths by Computational Fluid Dynamic Modeling

Outdoor physical activities are essential for maintaining a healthy lifestyle, but they can also expose individuals to harmful air pollutants such as particulate matter. Particulate matter, especially those with a diameter of less than 10 µm (PM10), can penetrate deep into the lungs and cause adverse health effects such as respiratory diseases, cardiovascular diseases, and even premature deaths. Consequently, masks are essential while outside with high PM pollution. Additionally, the COVID-19 pandemic has made it necessary for people to wear masks as a protective measure against the virus while engaging in outdoor activities. However, not all masks provide adequate protection against both the virus and particulate matters. This study aimed to investigate the effect of weave patterns on the filtration performance of woven filter cloths using Computational Fluid Dynamics (CFD) simulations. A laminar-flow model was applied due to low Reynolds number of the face velocity. Specifically, the study focused on PM10. The filtration process was examined in a relation to three weave patterns: plain weave, twill weave, and satin weave, using the CFD model. The Discrete Phase Model (DPM) was used for simulating the particulate matter trajectories. The numerical model was validated with the data from Konda et al (2020) [19]. The results showed that the twill and satin weaves had higher filtration efficiencies than the plain weave. Finally, the findings of this study will be used to guide the manufacturing of masks that are suitable for protecting individuals from the dust and viruses while exercising

Effect of Sintering and Various Fillers in Zirconia Composite Coating for High Temperature Application

Zirconia is a ceramic material that is relatively cheap and easy to purify from mineral form. Zirconia powder has stable properties under high temperature conditions making it suitable for use as a coating for steel substrates. Ceramic composite coating is one option that can be used to increase its durability by adding filler which has lubricant properties. In this research, hBN, MoS2 and graphite were used as filler coatings. The coating method used is slurry spray, which is a simple method and there is a subsequent sintering process so that the resistance of the coating to the substrate is better. The effect of the coating is seen before and after the sintering process on the surface and thickness. And to see the adhesion of the coating to the substrate, a thermal shock test was carried out. From the test results, it was found that sintering had a significant effect on the coating surface, where the defects on the coating surface became fewer and more even. The optimum temperature for sintering is 600oC where the least porosity is obtained

Potato Leaves Blight Disease Recognition and Categorization Using Deep Learning

Potato cultivation is vital in numerous countries, contributing to food security and economic value. However, crop diseases, particularly early and late blight, pose significant challenges to potato production. The accurate diagnosis of these diseases remains unclear to many individuals. This study leverages the increasing penetration of smartphones and recent advancements in deep learning to develop a Convolutional Neural Network (CNN) model for real-time detection of early and late blight in potatoes. The dataset was pre-processed by normalizing, dividing, and extracting images using the Python data processing library. The approach incorporates slight variations in the network layers to optimize the model's performance. The method was evaluated using classification optimizers, metrics, and loss functions and further refined using layer-by-layer TensorBoard analysis. Hyperparameters such as features, labels, validation split, batch size, and training epochs were carefully selected. The final model demonstrated promising results, achieving an accuracy of 96.09% on the survey dataset. Experimental findings highlight the approach's potential for automatically detecting both early, late blight and healthy, thereby significantly improving the accuracy of disease diagnosis

Prediction of the Mechanical Behaviour of HDPE Pipes Using the Artificial Neural Network Technique

Actual statistics show that in recent years, more than 90% of the water distribution pipes installed in the world are made of plastic, exclusively polyethylene (PE). Due to the extensive use of these materials, it is necessary to have a good understanding of the mechanical properties of HDPE used for distribution system piping. For our study, we selected HDPE pipes as the material of choice. We then took a new approach to the analysis and prediction of mechanical properties, using new models based on Artificial Intelligence. In this paper, experimental tensile tests were conducted to obtain the mechanical properties of pipes. The first part of this work focuses on the mechanical tests, specifically tensile tests, while the second part centers on the numerical procedure for predicting the mechanical characteristics, a deep learning model was developed for prediction. The model was trained using a large dataset, including information on pipes. Specially designed deep learning architectures capture complex relationships and patterns in the data, enabling accurate predictions, Several ANN models were created to predict mechanical behaviour based on experimental data. We analyzed Bayesian regularization using MATLAB, an advantage of BR artificial neural networks is their ability to reveal potentially complex relationships. The results showed that the constructed prediction model is satisfactory since the M.S.E. value is nearly 0 (0.00023) and the  value is close to 1 (0.99934).  This study evaluates the advantages of our methodology by demonstrating the predictive power of an AI-based method and how well it predicts HDPE pipe behavior. The paper study will have significant effects on the water distribution and plastics industries

A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement

This article introduces an extension of the improved minima-controlled recursive averaging noise estimation from single to multi-channel speech enhancement systems. With the spatial information of microphone array signals being fully exploited, more accurate estimate of the noise spectrum can be obtained over the single-channel counterpart. Computer simulation demonstrates superior performance of the proposed noise estimator in terms of noise tracking performance and noise estimation error. Furthermore, the use of the proposed technique with the multi-channel Wiener filter yields improved signal-to-noise ratio and speech distortion

MorphoNet: A Novel Bivalve Images Classification Framework with Convolutional Neural Network

The bivalves' morphometric analysis of the freshwater shell characteristics is based on the shell size, shape, tooth, scars, and texture. We experimented and compared the accuracies of the following popular convolutional neural network architectures: ResNeSt, MobileNet, VGG16, Transfer Learning, and EfficientNet, whose model trainings are based on the bivalve image dataset obtained from a biology laboratory. The MobileNet model that gives the highest accuracy rate by 72% is selected to be a classification model of our framework named MorphoNet. We also applied the YOLO4 object detection in the MorphoNet to detect the teeth and scars on the bivalve image. The framework can identify the bivalve class labels and detect the interesting features on the bivalve images automatically. It is an alternative tool to help the biologists in a preliminary class label identification and support the land-marking creation and morphometric analysis instead of doing it by hand

Thin Layer Drying Kinetics and Mathematical Modeling of Moisture Diffusivity in Cocoa Pod Husk (CPH)

Cocoa pod husk (CPH), an agricultural by-product of the cocoa separation process, contains an average moisture content of 5.40 ±0.05 kgwater/kgdry matter. Drying characteristics of CPH were examined in hot air at 50, 60 and 70°C using a laboratory oven with air ventilation at 3 L/min and a load cell sensor (HX-711) was used for weight loss tracking. Twelve mathematical models simulated the drying rate from a drying curve at each operating temperature by comparing four statistically calculated parameters. Levels of variation were investigated by plotting experimental data against the predicted moisture ratios to identify the sum of residuals and obtain a good fit. The Midilli et al. model provided the best drying characteristics with optimized statistical parameters. Using an Arrhenius type relationship, the effective diffusivity coefficient of moisture transfer varied from 7.979 x 10-10 to 13.298 x 10-10 m2/s, with operating temperature set at 50, 60 and 70°C and activation energy for moisture diffusion 70.48 kJ/mol