Design of a Trajectory Tracking Controller for Coreless Tubular Linear Motor Using Model Predictive Controller

This paper presents a cascaded control structure for a coreless tubular linear motor. The system includes position and speed loops employing PI controllers, and a current loop using Finite Control Set Model Predictive Control (FCS-MPC). This structure addresses challenges associated with low stator inductance, specifically its impact on current control. A simulation model was developed using MATLAB/Simulink. The simulation results demonstrate the effectiveness of the proposed solution in tracking the desired trajectory and minimizing the negative effects of low stator inductance on the current loop

Optimization of FDM Process Parameters for Minimizing Specific Wear Rate Using a GA- ANFIS Hybrid Model

This study investigates the optimization of tribological performance in Fused Deposition Modeling (FDM) fabricated components by focusing on the specific wear rate (SWR) of Polylactic Acid (PLA) specimens. A total of 30 samples were fabricated using a MakerBot Method X 3D printer following ASTM G99 standards, considering four key process parameters: nozzle temperature, infill density, layer height, and printing speed. Wear behavior was evaluated using a Pin-on-Disc apparatus under dry sliding conditions. To predict and minimize SWR, a hybrid GA-ANFIS (Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System) model was employed. The ANFIS framework effectively captured nonlinear relationships among input variables, while GA optimized membership functions to improve prediction accuracy. Experimental results demonstrated that nozzle temperature and layer height had the most significant influence on SWR. The optimized parameter combination achieved a minimum SWR of 8.26 × 10⁻⁴ mm³/N·m, representing a 25.12% reduction compared to non-optimized settings. The proposed hybrid approach proved to be a robust tool for process parameter optimization, enabling enhanced wear resistance and mechanical integrity in FDM-printed parts

Scenic Impressions and Lasting Experiences: A Study on the Impact of Destination Image on Tourist Satisfaction in the Nilgiris

The image of a destination plays a pivotal role in shaping tourist satisfaction and influencing their choice of travel. This study explores the dynamic relationship between the perceived image of the Nilgiris – a scenic and culturally rich hill district in Tamil Nadu – and the level of satisfaction experienced by its visitors. With growing competition among tourist destinations, understanding what truly matters to travelers is crucial for sustainable tourism development.To uncover these insights, data was collected from 600 tourists visiting various parts of the Nilgiris, including Ooty, Coonoor, and Kotagiri. The study employed the Garret Ranking Technique, a robust statistical tool that enabled the prioritization of various factors influencing destination image such as natural beauty, hospitality, cleanliness, accessibility, local culture, food, and safety. Respondents were asked to rank these attributes based on their travel experience. The Garret score conversion allowed the identification of key dimensions most valued by tourists, thereby revealing the strongest contributors to their overall satisfaction. Findings highlight that natural scenery and pleasant climate ranked highest among tourist preferences, followed closely by local hospitality and cultural richness. On the other hand, aspects like infrastructure and traffic management were ranked lower, indicating areas needing improvement. The study underscores the importance of enhancing destination image holistically, as even one weak link can affect tourist perception and repeat visits.This research offers practical insights for tourism planners, local authorities, and hospitality stakeholders in the Nilgiris to strategically strengthen and promote destination elements that elevate tourist satisfaction and foster long-term loyalty

Performance Assessment of Concrete Using Paper and Wastewater Sludge to Replace Part of the Cement

Along with deforestation and the use of fossil fuels, the cement manufacturing sector contributes significantly to carbon dioxide (CO₂) emissions. Additionally, the concrete industry is one of the major consumers of natural raw resources, which has an impact on environmental sustainability. In order to tackle these issues, this study examines the effects of partially substituting paper mill and wastewater sludge for cement in weight percentages of 5%, 10%, and 15% on the compressive, split tensile, and flexural strengths of concrete at 7 and 28 days of curing. According to experimental data, the 5% replacement mix showed better mechanical qualities than the control mix (0% replacement), suggesting that it could be a sustainable option in the manufacturing of concrete, even though higher replacement levels resulted in a decrease in strength. Strength was shown to decrease after 5%, underscoring the drawbacks of adding too much sludge. The viability of using industrial by-products in concrete to lessen reliance on cement and CO₂ emissions while preserving structural integrity is clarified by this study. By encouraging the use of waste materials in cement-based composites, the findings support the continuous efforts towards sustainable construction methods

African Oil Bean Seed Oil Biodiesel Optimization Production via the Technique of Response Surface Methodology-Genetic Algorithm (RSM-GA) and RSM

This article focuses on optimized production of biodiesel from African Oil Bean Seed Oil, an indigenous African  tropical tree of the leguminosea family, using response surface methodology (RSM) and response surface methodology-genetic algorithm (RSM-GA). Transeterification method was adopted using sodium hydroxide (NaOH) catalyst and methanol (alcohol). The extracted oil was pre-treated due to its high free fatty acid FFA contentFrom the research findings, the physiochemical properties of AOBSO are within ASTM ranges. The process parameters investigated were agitation speed, methanol/oil molar ratio, reaction time, reaction temperature, and catalyst concentration. RSM and RSM-GA gave nearly identical optimal results, with RSM-GA producing the better yield. Agitation speed of 225 rpm, methanol/oil molar ratio of 6.2:1, reaction time of 60 minutes,  reaction temperature of  60oC and catalyst concentration of 0.775%wt were therefore the optimal parameters for RSM-GA. The yield of methyl esters (FAAE) under these optimal process parameters was 99.75%

Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design

The research analyzes the wear characteristics of MWCNT-reinforced AA7075 metal matrix composites under different combinations of MWCNT volume fraction (2–6 wt%), operating temperature (80–120°C) and applied force (40–60 N). The wear resistance of composites produced by stir-casting fabrication received analysis through ANOVA combined with regression modeling after testing their wear resistance properties. A combination of 6% reinforcement with 100°C temperature under 40 N load proved to be the optimal conditions according to the desirability function approach which led to a wear rate of 3.349 Nm/mm³ and 0.826 in desirability. The studies reveal that reinforcement percentage served as the key variable (p = 0.004) which decreased wear by 25% when using 2% MWCNTs. Performance outcomes were most significantly improved through moderation of temperature conditions at 100°C combined with loading at 40 N. A developed regression model demonstrated the capability to predict wear rates with less than 5% error accuracy following validation through experimental confirmation. The obtained results can directly help engineers build high-wear-resistant composites for industries focused on aerospace and automotive manufacturing

Gesture Control Revolution: Enhancing Automotive Infotainment through Advanced Hand Gesture Recognition

In the ever-developing industry of automobiles, a focus should be made on the innovation of the car’s user experience while keeping the driver safe. The following paper therefore aims at proposing a new hand gesture recognition system to be implemented in car infotainment, which employs a modified CNN model enhanced with KNN for enhanced gesture mapping. The efficiency of the system was tested on a data of samples consisting of 10000 images of 10 different gestures performed by different users under different lighting conditions. The results obtained for the experimental evaluation proved that the used CNN reached the accuracy of 92,5% with the validation set and the further use of KNN for post-processing increased the classification accuracy up to 95,2%. Resource consumption was low, the CNN occupied roughly 50 MB of memory, that is why it is possible to use it for the in-vehicle system. A similar survey that targeted users showed that 85% of them were comfortable with the system as it was easy to learn and did not interfere with the control of infotainment functions. This research discusses the possibility of using gesture recognition technology to improve the user experience in vehicles making infotainment systems safer and more efficient

Streamlining Network Operations: Combining Meraki MX with Cisco DNA Center for Automation and Assurance

This research explores the integration of Meraki MX with Cisco DNA Center to better operate networks, automate management processes, and ensure network performance and reliability. Modern network environments are becoming increasingly complex, and organizations are seeking solutions that make it possible to enhance automation, reduce manual work, and improve operating efficiency. The study primarily evaluates the level of automation through the Meraki MX device, scrutinizes the implementation and performance assured through Cisco DNA Center, and investigates the positive impacts of merging them on performance efficiency and efficacy of the operations in the networks. This exploratory qualitative piece synthesizes qualitative case studies based on secondary research on expert views and technical manuals regarding the aspects and best practice that could or are being accomplished in this mergence. The results indicate major network performance improvement, which include a 6.5% increase in uptime, a 62.5% reduction in troubleshooting time, and a 20% increase in network health score. On the other hand, the issues included compatibility with legacy systems, initial setup costs, and training of staff were identified. Based on the research, the conclusion is that integration offers great advantages in terms of automation and efficiency in operation; however, it has to address these challenges to be successfully implemented. It seems the research helps develop a better understanding of how the union of Meraki MX with Cisco DNA Center could optimize network management by giving practical insights into surmounting integration hurdles and maximum performance

Leveraging Microservices and Serverless Architectures for Enhanced Enterprise Agility

This study investigates the impact of serverless and microservice architectures on the agility, scalability, and cost efficiency of an enterprise. Modern digital enterprises can no longer rely on traditional monolithic architectures due to constraints on deployment velocity, flexibility, and scalability. The research underlines the far-reaching benefits of microservices in modularity, resource consumption, and development cycle time optimization, while also noting the benefits of serverless computing in infrastructure expenditure, auto-scaling capabilities, and performance enhancement. Moreover, the integration of services was analyzed with a focus on security hygiene through policy enforcement, authentication, and workload distribution techniques. It is indisputable that the shift towards microservices and serverless structures provides enterprises with the ability to rapidly achieve innovations, operational agility, and scalability. This study has proven that the adoption of cloud-native architectures is imperative for enterprise modernization and attaining competitiveness within the ever-evolving realm of information technologies

Comparative Analysis of Experimental -Based Wear Rate Investigation of Different Coatings on Nitrided AISI H13 Tool Steel

The research investigates the tribological behavior of Titanium Carbide (TiC) and Chromium Nitride (CrN) and Aluminum Titanium Nitride (AlTiN) coatings used on gas-nitrided AISI H13 tool steel when operating under multiple conditions. The Taguchi L9 orthogonal design evaluated how coating type together with temperature (40–50 °C) and load (5–15 N) affect wear rate measurements. A tribometer tester performed the wear tests and  were determined by applying the circular segment method to assess the cross-sectional area of the tracks formed during testing.. The multiple linear regression prediction model for wear rate performance exhibited an error margin of less than 10% throughout every experimental trial. The statistical results from analysis of variance (ANOVA) showed coating type to be the main contributor to wear variation (p = 0.002). Within the set of tested coatings AlTiN established the highest degree of wear resistance during optimized conditions. The verification tests confirmed the accurate forecasting capabilities of the predictive model for regression while showing that duplex surface modifications work properly. Results show that using AlTiN-coated nitrided AISI H13 steel makes it possible to deploy these tools in demanding high-temperature applications which need exceptional wear protection