Applied Science and Engineering Journal for Advanced Research
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146 research outputs found
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Intent-Based Networking with AI: Towards Fully Autonomous Network Operations
Get PDFThis research explored the integration of Artificial Intelligence (AI) into Intent-Based Networking (IBN) systems with the goal of enabling fully autonomous network operations. Utilizing a qualitative research design, the study involved expert interviews and system behavior observations within simulated environments. The AI-driven IBN prototype was developed with components for natural language intent recognition, policy translation, and autonomous fault management. The findings indicated high accuracy in interpreting user intents (93.7%), efficient policy deployment, and significant reductions in both configuration and recovery times compared to traditional Software-Defined Networking (SDN) systems. Experts validated the system\u27s operational advantages while also noting challenges in handling ambiguous inputs and adapting to diverse network configurations. Overall, the research highlighted the feasibility and benefits of AI-enhanced IBN while recommending further real-world testing and security considerations to achieve truly autonomous network infrastructures
Importance of Dependency on Local Area for Backbone Regular Courses - A Case Study
Get PDFThis paper shows the importance of dependency on local area for admissions. The paper is a case study on one of the premium institute of Punjab. Different technical and non technical courses available in the Institute are divided into 04 categories A, B, C & D depends on the present demand. In order to achieve desirable admission target in local area, the Institute teams have to covered 70 km distance by radius from the location of the Institute. Further, distance of the Institute from various feeder areas is calculated and admission target given to teams not only to cover courses in category C & D but also in Category A & B. This paper is not having more reference because it is based on the true data collected on various parameters
Improving Wear Resistance of Epoxy Composites via Ceramic Nanoparticle Reinforcements by using Taguchi Technique
Get PDFIn this study, the weight percentages of various composites are added to epoxy resin coating material with a fixed amount of ceramic particles in order to improve the coating\u27s mechanical and tribological qualities. Prepared were test specimens of pure epoxy resins with varying reinforcement weight percentages (4%, 6%, and 8%). The configuration of a tribometer is used to examine the material\u27s wear properties by conducting various tests on a polymer matrix composite. The results show that adding TiO2 (titanium dioxide) and creamic Al2O3 (aluminum oxide) reinforcement material significantly improves the mechanical and tribological characteristics of the newly developed epoxy paint composite. On a tribometer with variable load and temperature, the wear resistance of a specimen was examined. According to the data, both reinforcements in epoxy resins result in a lower wear rate than pure epoxy. An analysis of variance, also known as an ANOVA, was carried out in order to ascertain the relevance of the operating parameters to the performance qualities that were being taken into account. Further experimentation has been conducted to validate the performance of optimal parameters. Finally, the confirmation test to compare the projected value of the wear rate to the experimental value has been conducted
Optimizing Makespan and Buffer Allocation for Enhanced Job Shop Scheduling Efficiency: A Hybrid Metaheuristic Approach
Get PDFIn today’s highly competitive manufacturing landscape, optimizing job shop scheduling has become vital for maximizing operational efficiency and minimizing production delays. This study explores the dual challenge of makespan minimization and efficient buffer management within job shop environments. By integrating a hybrid metaheuristic approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), we propose an intelligent scheduling framework that not only reduces the overall makespan but also enhances the utilization of intermediate buffers across machines. The experimental results, validated through benchmark datasets and real-time shop floor simulations, demonstrate significant improvements in throughput, machine utilization, and flow consistency. Our approach outperforms traditional heuristics by dynamically adjusting buffer capacities and job sequencing based on system feedback, paving the way for more resilient and adaptive manufacturing operations
Experimental and Regression-Based Wear Analysis of MWCNT Reinforced AA7075 Using Box-Behnken Design
Get PDFThe 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
Scenic Impressions and Lasting Experiences: A Study on the Impact of Destination Image on Tourist Satisfaction in the Nilgiris
Get PDFThe 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
Predictive SLA Management: Leveraging Machine Learning to Improve Upstream Feed Reliability
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In order to improve the reliability of upstream feeds, this study investigates the use of machine learning approaches for the management of anticipatory Service Level Agreements (SLAs). Random Forest, Support Vector Machine, and Gradient Boosting Machine (GBM) were the three models that were constructed and assessed with the help of historical service level agreement (SLA) and operational data. The GBM model displayed exceptional performance, with an accuracy of 94.1%, which enabled it to accurately predict service level agreement (SLA) breaches and carry out proactive interventions. After using predictive service level agreement management, there was a considerable decrease in the number of feed disruptions (26.7%), the average duration of interruptions (34.2%), and the total amount of downtime (51.8%). In addition, the operations team provided qualitative input that emphasized improvements in maintenance planning, a reduction in the number of emergency interventions, and an increase in the level of satisfaction with feed reliability. These findings provide further evidence that incorporating machine learning-driven predictive analytics into service level agreement management (SLA) management improves operational efficiency, decreases downtime, and strengthens decision-making capability in upstream feed operations
Design and Development of Emergency Based Portable Ventilator
Get PDFPortable ventilators with integrated patient monitoring systems significantly advance respiratory care, offering essential support for individuals with breathing difficulties. These sophisticated, mobile medical devices deliver mechanical ventilation through various modes such as volume- controlled and pressure-controlled ventilation customized to the specific needs of each patient. The integration of comprehensive monitoring capabilities allows for the real-time tracking of vital signs including heart rate, oxygen saturation, and respiratory rate, ensuring continuous and accurate assessment of patient status. The portability of these ventilators enhances their utility across diverse environments, including emergencies, home care, and patient transport, where immediate and adaptable respiratory support is crucial. This paper explores the design, functionality, and clinical applications of portable ventilators with patient monitoring systems, highlighting their role in improving patient outcomes and expanding the scope of respiratory care
Effect of Ammonia on the Formation of THMS in Drinking Water Chlorination - A Case Study
No full textIn a water supply system total Trihalomethanes (THMs) content in drinking water may vary considerably depending on water quality and treatment conditions. Most urban water treatment plants generally use chlorine as disinfectant. The effect of various parameters on the formation of THMs has been widely studied around the world over the past few decades. Almost universally, it has been found that increasing any of these parameters tends to promote the formation of THMs—except for ammonia, which has a negative effect on the process. Surprisingly, this exception has not received the attention it deserves in THM research globally. Given the high concentration of ammonia in Dhaka\u27s drinking water sources—particularly during the dry months—this study aimed to evaluate how ammonia affects the formation of THMs in water samples from the largest water treatment plant in Dhaka, Bangladesh. The water samples were tested for a wide range of parameters including pH, ammonia, UV254, TOC, DOC and bromide following the standard methods of testing. THMs was measured by THM plus Method (Method:10132) using UV-VIS Spectrophotometer DR 6000(HACH, USA). A detailed quantitative study was conducted to examine how ammonia affects the formation of trihalomethanes (THMs) when water is chlorinated under varying conditions. Experiments were carried out using treated water from the supply system, which had a dissolved organic carbon (DOC) content of 6.0 mg/L. Chlorination was performed with a free chlorine residual of 0.89 mg/L and a total chlorine residual of 1.29 mg/L. Different doses of ammonia—0.0, 0.5, 1.0, 5.0, and 10.0 mg/L—were applied. The results showed that the presence of ammonia at various concentrations significantly reduced THM formation at the given chlorine levels, however it did not completely eliminate it. THMs formation decreased continuously with increasing ammonia concentration, and the decline is sharp during relatively low concentration of ammonia up to 3 mg N/L then remained near to flat slope after ammonia exceeded 3 mg N/ L. It is noticed that the formation of THMs significantly reduced with increasing ammonia concentration from 0 to 10 mg N/L in chlorinated drinking water. The suppression of THMs was prominent with increasing ammonia concentration from almost zero to 5 mg N/L. However, the formation of THMs remain low and constant after ammonia addition over 5 mg N /L. A general correlation for predicting THM formation based on ammonia concentration is presented, and its predictions align well with the observed results, although it is specific to this study. Further research using a more diverse dataset is recommended. In water supply systems like Dhaka, where significant amounts of ammonia and other organic pollutants are present in river water—along with the perceived risk of THM formation—comprehensive studies should be undertaken to determine how to manage or utilize ammonia effectively during water treatment. One potential approach could be the controlled use of ammonia to form chloramine, which can act as a disinfectant in the treatment process
Design of a Trajectory Tracking Controller for Coreless Tubular Linear Motor Using Model Predictive Controller
Get PDFThis 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