Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization.

Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO's ability to discover refined solutions, reducing average surface roughness to 4.63 µm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

Analysis of thermal decomposition kinetics of chicken feather fiber reinforced Poly-lactic acid composites filament.

Derivative Thermogravimetric analysis under air was used to observe the thermal decomposition process of Chicken feather fiber (CFF) reinforced Poly-lactic acid (PLA) composite filament of 2.2 mm diameter. The thermal degradation of the sample was initiated at 140 Ö¯C. Approximately 75 % of the thermal degradation occurred between the temperature of 357 Ö¯C and 399 Ö¯C. The composite's activation energy was established using the Coats-Redfern method. The results showed that the activation energy of 112.06 kJ/mol is utilized for the sample throughout the temperature range of 23 Ö¯C to 398 Ö¯C. A low activation energy is indicative of rapid chemical reactions between the CFF and PLA molecules. The results from TGA and DTGA indicate that the addition of CFF in the PLA matrix enhanced the thermal stability.

Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques.

Air writing is one of the essential fields that the world is turning to, which can benefit from the world of the metaverse, as well as the ease of communication between humans and machines. The research literature on air writing and its applications shows significant work in English and Chinese, while little research is conducted in other languages, such as Arabic. To fill this gap, we propose a hybrid model that combines feature extraction with deep learning models and then uses machine learning (ML) and optical character recognition (OCR) methods and applies grid and random search optimization algorithms to obtain the best model parameters and outcomes. Several machine learning methods (e.g., neural networks (NNs), random forest (RF), K-nearest neighbours (KNN), and support vector machine (SVM)) are applied to deep features extracted from deep convolutional neural networks (CNNs), such as VGG16, VGG19, and SqueezeNet. Our study uses the AHAWP dataset, which consists of diverse writing styles and hand sign variations, to train and evaluate the models. Prepossessing schemes are applied to improve data quality by reducing bias. Furthermore, OCR character (OCR) methods are integrated into our model to isolate individual letters from continuous air-written gestures and improve recognition results. The results of this study showed that the proposed model achieved the best accuracy of 88.8% using NN with VGG16.

Antecedents for online food delivery platform leading to continuance usage intention via e-word-of-mouth review adoption.

The focus of hospitality initially was on ambience and novelty to attract customers. With the rise of the digital revolution, the hospitality industry has also undergone significant change. Long-distance travel at the workplace, odd working hours, and a variety of food options have driven people staying in Indian metropolises towards online food delivery (OFD) services. The popularity of OFD services has risen because of their practicality, simplicity, and a rise in consumer confidence in digital payments. Specifically, for the food industry, digitalization has opened new horizons to capture customers. The competition is not among the big brands, but big brands are competing with homemakers who run tiffin services, and street food hawkers who claim to provide traditional Dhaba-style food and fast food. The customers are loaded with unlimited options to choose the food in terms of price, cuisine, quality, etc. The present research examines the associations between service quality of OFD services, perceived ease of use, and word-of-mouth review adoption, leading to expectation confirmation modeling. The path analysis was carried out using data from 500 Indian respondents residing in Tier-I cities who have been using OFD services regularly. The research outcome shows that servqual has a positive influence on perceived ease of use and confirmation. Additionally, it encourages continued usage intentions because of its favorable impact on the adoption of e-word-of-mouth reviews.

Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques.

The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.

Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils.

Pipelines are widely used to transport water, wastewater, and energy products. However, the recently published American Society of Civil Engineers report revealed that the USA drinking water infrastructure is deficient, where 12,000 miles of pipelines have deteriorated. This would require substantial financial investment to rebuild. Furthermore, the current pipeline design practice lacks the guideline to obtain the optimum steel reinforcement and pipeline geometry. Therefore, the current study aimed to fill this gap and help the pipeline designers and practitioners select the most economical reinforced concrete pipelines with optimum steel reinforcement while satisfying the shear stresses demand and serviceability limitations. Experimental testing is considered uneconomical and impractical for measuring the performance of pipelines under a high soil fill depth. Therefore, a parametric study was carried out for reinforced concrete pipes with various diameters buried under soil fill depths using a reliable finite element analysis to execute this investigation. The deflection range of the investigated reinforced concrete pipelines was between 0.5 to 13 mm. This indicates that the finite element analysis carefully selected the pipeline thickness, required flexural steel reinforcement, and concrete crack width while the pipeline does not undergo excessive deformation. This study revealed that the recommended optimum reinforced concrete pipeline diameter-to-thickness ratio, which is highly sensitive to the soil fill depth, is 6.0, 4.6, 4.2, and 3.8 for soil fill depths of 9.1, 12.2, 15.2, and 18.3 m, respectively. Moreover, the parametric study results offered an equation to estimate the optimum pipeline diameter-to-thickness ratio via a design example. The current research outcomes are imperative for decision-makers to accurately evaluate the structural performance of buried reinforced concrete pipelines.