Sustainable biofuel production from renewable feedstocks remains a key challenge in the development of low-carbon transportation fuels. This paper outlines an effective approach to biofuel production using marine macroalgal oil produced from Ulva fasciata as a sustainable non-edible feedstock, with focus on environmental suitability and efficiency of the process. The lipids that were obtained after the macroalgal biomass were subjected to proximate and physicochemical analyses to determine the suitability of the feedstock. Zn-doped CaO nano catalyst was developed and systematically characterized to elucidate its structural, morphological and textural properties. Biofuel production was systematically modeled and optimized using response surface methodology (RSM), artificial neural networks (ANN), and a genetic algorithm (GA) to understand process interactions and maximize conversion efficiency. Under optimized conditions, a biofuel yield of 80.78
The Metal Additive Manufacturing (AM) has become a revolutionary manufacturing paradigm which facilitates the production of complex, lightweight and high-performance parts which are more design flexible compared to the traditional subtractive manufacturing. The review discusses the leading metal 3D printing technologies and they include Selective Laser Melting (SLM), Direct Metal Laser Sintering (DMLS), Electron Beam Melting (EBM), Directed Energy Deposition (DED), and Binder Jetting. The experiment examines how the feedstock properties, especially powder morphology and wire based materials affect the process stability and quality of the part. Moreover, the most important process parameters, including laser power, scanning speed, and thermal conditions are addressed in connection with microstructure development, defect formation and residual stresses. Mechanical performance such as tensile strength, hardness, fatigue resistance, and anisotropy are measured and methods of post-processing are measured through heat treatment, hot isostatic pressing, and surface finishing. The review also shows new industrial uses and explains the existing issues in terms of cost, reproducibility, certification, and sustainability, as well as describes the future research opportunities in multi-material printing, in-situ monitoring, and AI-based optimization of processes.
Titanium (Ti) alloys are widely used in industrial manufacturing, healthcare, transportation, and other sectors. However, due to significant variations in their physical and chemical properties when welded with other alloys, cracks are likely to develop in the joints, making it difficult to achieve stable welds. Key aspects of these processes include residual stresses, processing windows, temperature, material flow, welding tool wear, and design considerations. Particular emphasis is placed on the relationship between microstructure and resulting properties. This review aims to present current research and applications while providing a comprehensive overview of recent advancements in the welding and joining of titanium and light alloys. Various welding techniques such as fusion welding, brazing, friction welding, and reactive joining have been explored for titanium and light alloys. Among these, friction stir welding (FSW) of titanium alloys is the primary focus of this study, with special attention given to tool design, welding parameters, and weld strength.
Melanoma is highly dangerous and can spread rapidly to other parts of the body. It has an increasing fatality rate among different types of cancer. Timely detection of skin malignancies can reduce overall mortality. Therefore, clinical screening methods require more time and accuracy for diagnosis. An automated, computer-aided system would facilitate earlier melanoma detection, thereby increasing patient survival rates. This paper identifies melanoma images using a Convolutional Neural Network. Skin images are preprocessed using Histogram Equalization and Gabor transforms. A Gabor filter-based Convolutional Neural Network (CNN) classifier trains and classifies the extracted features. We adopt Gabor filters because they are bandpass filters that transform a pixel into a multi-resolution kernel matrix, providing detailed information about the image. This study suggests a method with accuracy, sensitivity, and specificity of 98.58%, 98.66%, and 98.75%, respectively. This research supports SDGs 3 and 4 by facilitating early melanoma detection and enhancing AI-driven medical education.
Nowadays, the Internet of Things (IoT) is proliferating, bringing worldwide alarm for the security of interconnected networks, which requires an effective threat detection model to lessen cyber threats. Traditional threat detection models often struggle to identify complex attacks happening in an IoT network due to heterogeneous traffic features, data imbalance, and limited feature learning. This study aims to design an accurate threat detection in an IoT network using an attention-based deep learning (DL) algorithm that enhances spatial and temporal feature learning. Also, data-balancing and feature selection methods are employed in the research to improve accuracy and robustness. The proposed model was tested on two widely used datasets: IoT-23 and N-BaIoT, which provide realistic, diverse data on IoT network traffic and botnet attacks that are crucial for developing robust security systems. The experimental findings demonstrated that our model shows remarkable performance for threat detection compared to existing techniques by reaching a maximum accuracy of 99.93
