In this work, gas metal arc welding of AISI 304 stainless steel at varying compositions of argon-CO₂ shielding environment was performed using an established optimum parametric combination. Thereafter, investigations on the microstructure of the welded joints and mechanical properties of the weldments were carried out. Weldments of excellent surface quality that are void of spatters and pores were obtained when the shielding gas composition (wt.%) range is between 100% argon and 75% argon - 25% CO₂. Increasing percentage composition of CO₂ beyond 25% resulted in irregular bead formation characterized with spatters and pores. The hardness of the welded joint became significantly high as the CO₂ composition in the shielding gas increased. The highest value of 310 HV was obtained when the shielding gas composition was 5% argon- 95% CO₂. The least (220 HV) was obtained when the shielding gas was 100% argon. High ultimate tensile strength (596 - 378 MPa) was achieved when the shielding gas composition range is between 100% argon and 75% argon-25% CO_2. The UTS dropped significantly as the CO₂ composition in the shielding gas increased beyond 25%. It decreased from 336 MPa at 70% argon-30% CO₂ shielding gas composition to 133 MPa when 100% CO₂ was utilized as the shielding gas. At the end, the effects of the CO₂ addition and suitable composition of CO₂ addition to argon shielding environment during GMAW of AISI 304 stainless steel have been established.

The rapid emergence of new disease variants, ongoing health issues, various types of accidents, and the continuous challenges associated with aging pose significant burdens that the healthcare sector must address daily. Historically, these challenges were managed through manual methods until the advent of smart technology. The integration of smart technologies, such as AI-driven robotics, has created a paradigm shift in healthcare. However, the growth and global acceptance of these innovative systems face several challenges, including interpretability, uncertainty, and clinical trust; human-robot interaction in clinical settings; robustness, adversarial safety, and cybersecurity; hardware limitations; effective sensing and multimodal data integration; and the need for outcome-oriented, long-term clinical trials, among others. This review presents enhanced approaches to tackle these challenges through the application of advanced AI-powered robotics. Key strategies include the development of sophisticated AI models, replacing single-sensor systems with sensor fusion (incorporating vision, force, and biosignals) to prevent failures, secure model execution through encrypted inference and hardware enclaves, implementing shared and adjustable autonomy frameworks, enhancing anomaly detection and input validation, and utilizing adversarial training and robust optimization with certified bounds.

Integrating cyber-physical systems (CPS) into gas metal arc welding (GMAW) has transformed the welding process and improved the quality of welded parts. GMAW is a widely used welding method due to its simplicity, high deposition rate, and ability to be automated. However, current GMAW faces several challenges, including porosity, poor fusion or penetration, spatter, wire feed issues, heat distortion, and lack of fusion. To address this gap, CPS can be effectively introduced to overcome GMAW challenges by combining sensing, computation, and control to create a smart, adaptive welding process. By integrating CPS, GMAW technology can be further improved by enabling sensors to detect shielding gas flow rate, humidity, and contamination; artificial intelligence algorithms to predict porosity risk based on sensor data; CPS to monitor torque, motor speed, and wire tension; and real-time feedback control to adjust wire feed rate or notify maintenance if issues occur. Thermal imaging and arc sensors can monitor heat input and penetration depth, while CPS uses predictive models (such as digital twins) to simulate heat input and stress distribution. CPS can also develop self-learning welding systems that improve over time. Cloud-based CPS platforms enable supervisors to track weld quality in real-time, remotely adjust process settings, and receive predictive maintenance alerts. With this understanding, this review is structured to discuss how CPS can be effectively utilized to address GMAW challenges and enhance welded product quality. With the adoption of CPS in the GMAW process, achieving defect-free welds with better mechanical and microstructural properties is now realistically attainable. Finally, this work presents a conceptual design framework for enhancing GMAW systems, along with potential solutions and conclusions.

Crude palm kernel oil (CPKO) is a vital industrial and domestic commodity, yet its high level of impurities limits its direct use without refinement. Degumming is a fundamental step in refining, requiring efficient mixing, heating, and removal of phospholipids. In this study, parametric design principles and computational fluid dynamics (CFD) were applied to optimise the geometry and performance of a degumming reactor. Different height-to-diameter ratios (R _h/d = 1.0, 1.05, 1.10, 1.15, and 1.20) were analysed to determine their effect on fluid dynamics, velocity distribution, and thermal uniformity. CFD simulations were conducted using ANSYS Fluent, supported by heat transfer analysis and descriptive statistics, to identify the most efficient configuration. The findings demonstrate that an Rh/d ratio of 1.05 offers the best hydrodynamic stability and thermal performance, making it suitable for small and medium-scale refinery operations. This integration of CFD and parametric modelling highlights the role of digital engineering in improving food process equipment for resource-constrained industries.