The objective of this article is to investigate how queuing models can be used to improve supply chain and manufacturing processes. By utilizing queuing theory, production and distribution systems can become more efficient overall, reduce bottlenecks, and minimize waiting periods. In order to model and analyses different phases of manufacturing and supply chain processes, this study uses queuing theory. The suggested models are validated using a commercial optimization software. The results show that using queuing models strategically lowers waiting times and optimizes resource allocation, which boosts operational effectiveness and lowers expenses. By establishing a thorough framework for using queuing models in manufacturing and supply chain operations, this study adds to the body of material already in existence while also giving managers useful advice on how to maximize efficiency and gain a competitive edge.

The failure of the implant-abutment connection is a critical concern in dental implantology, as it directly impacts the durability and function of dental restorations. Key issues such as micro-movement, inadequate sealing, and mechanical fatigue are identified as primary causes of loosening, corrosion, and eventual failure at the abutment interface. By analyzing clinical studies and biomechanical models, the review provides a comprehensive understanding of the vulnerabilities in implant-abutment systems and offers suggestions for improving connection integrity. These recommendations include selecting advanced materials, improving surface treatments, and optimizing prosthetic designs to enhance the longevity and performance of dental implants. Additionally, it is noted that modifications in implant placement during the initial surgery can lead to significant challenges during second-stage surgery, with the choice of an inappropriate abutment further complicating the situation. Despite the progress made in implant systems, the mechanical aspects of implant-supported prostheses should remain a top priority. From an engineering perspective, implants with an internal hexagon connection paired with a Morse taper are considered superior to external hex implants, as they offer a stronger connection, better load distribution, and less micro-movement. This review consolidates current knowledge by exploring the key factors that contribute to the failure of these connections, emphasizing the mechanical, material, and design aspects involved. The review examines how implant design, surface treatments, and occlusal forces contribute to these failures.

This paper presents an approach that utilizes waste heat from the exhaust of Biomass Cookstoves (BCS), which is dominantly used for food grilling in tropical Asian countries, Thailand's fish grilling stoves were used as a case study. The study involves designing and comparing computationally the exergy quantities from four types of spiral heat exchangers— (H01, H02, H03, and H04) - using Computational Fluid Dynamics (CFD) within a flow rate range of 0.5-2.5 l/min with heat transfer oil as the working fluid. Subsequently, the economic feasibility was analyzed using the Net Present Value (NPV) and Internal Rate of Return (IRR) methods, and the carbon dioxide emissions to the atmosphere were evaluated across four helical coil configurations, C01, C02, C03, and C04. The results from the CFD analysis under specified conditions demonstrate that the helical heat exchanger can reclaim up to 2900W of exergy from BCS exhaust, which is transferred to water used by coffee machines for commercial use. This system can reduce electricity consumption for water heating by 38.48%, equating to a cost savings of 22,311.81 THB (621.50 USD) per year, with a maximum NPV of 20,195.49 THB (562.55 USD). The investment in upgrading BCS can be recouped within 4.6 years. On the environmental conservation front, using waste heat from BCS exhaust can also reduce carbon dioxide emissions to the atmosphere by up to 3,606.04 kg CO2e per year per stove. The findings from this analysis can help restaurant operators make informed decisions on investing in BCS improvements and aligning business operations with environmental conservation efforts.

Now, the emergence of image-based data, remote sensing (which can practically guide us to collect better image data) turned into one of the best supporters of environmental monitoring. These techniques offer an effective way to monitor wide-area environmental events, like deforestation, climate change, and urban expansion. The key benefit of such techniques is the collection of data in near real-time over extensive areas, allowing scientists and managers to keep track of operational environments quickly and accurately. Nevertheless, noise, low-resolution, and distortion from the atmospheric condition or sensors would cause problems to the raw data that are collected from these sources (Wang et al., 2022). Heap over the above circumstances, the extraction of useful information from environmental images needs to be enhanced and reconstructed, especially in the field of high-resolution, such as land-use mapping and disaster monitoring.

It is essential to have a solid understanding of the non-linear solution known as optimal power flow (OPF) in order to comprehend how the power system operates. In order to solve the problem of achieving optimal power flow in power systems, the purpose of this study is to provide an examination of a variety of metaheuristic methodologies. These strategies can solve nonlinear issues. The performance and fundamental elements that are used to compare various metaheuristic search algorithms are discussed here. These metaheuristic search approaches are compared to one another. Various optimization techniques, such as the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), Fire Fly Algorithm (FFA), and Artificial Bee Colony (ABC) algorithms, are examined here. The objective functions of these algorithms vary, but they all aim to minimize fuel cost, active power losses, and voltage fluctuations. The use and analysis of these optimization algorithms on standard IEEE 14-bus test systems in MATLAB is the subject of this study. The purpose of this study is to explore the fundamental variables that need to be taken into consideration when selecting metaheuristic approaches in order to address the OPF problem that arises during the operation of power systems.

In the present study, we investigate first-principles calculations to determine the structural, electronic and optical properties of spinel-type oxides Zn2MO4 (M = Ti, Si, Pd) using the pseudo-potential plane wave method within density functional theory (DFT), as implemented in the BIOVIA Material Studio and the CASTEP code. This study aims to evaluate the optical properties of three spinels with identical crystalline structures under ambient conditions and determine which is best suited for optoelectronic applications. We began by proving their mechanical stability, then calculated their gap energy, as this is closely related to their optical properties. The exchange and correlation functionals used are generalized gradient approximation with the Perdew-Burke-Ernzerhof (GGA-PBEsol), for structural and mechanical properties. The results obtained show that there is a good analogy with previous studies and also that with its semiconductor behavior (a moderate gap Eg=3.33 eV) and its high absorption in the UV-visible range makes it the best candidate for opto-electronic applications.

This paper presents a novel approach for lane detection using a Convolutional Neural Network with Line Detection (CNN-LD) methodology, aimed at enhancing the accuracy and efficiency of road lane recognition. The proposed model leverages advanced pre-processing techniques, including distortion correction, color space transformation, and noise reduction, to prepare input images for effective feature extraction. The methodology incorporates edge detection using Sobel filters and the Hough transform for precise lane identification. A comprehensive dataset of 4,000 annotated images captured under diverse lighting conditions—daytime, low light, and night-time—was utilized to train and evaluate the model. The CNN-LD framework demonstrated superior performance, achieving an accuracy of 98.92% and an F1-Score of 97.90%, significantly outperforming traditional methods. The integration of the Kanade–Lucas–Tomasi (KLT) tracker ensures robust lane tracking, even in challenging environments. Experimental results indicate that the proposed approach effectively addresses common issues in lane detection, such as variations in visibility and road conditions. This research contributes to the development of intelligent transportation systems, providing a reliable solution for autonomous driving applications. Future work will focus on improving model robustness against adverse weather conditions and integrating multimodal data for enhanced lane detection capabilities.