Volume 9 | Issue 3 |

 Abstract

Data quality poses a significant challenge for organizations deploying Customer Relationship Management (CRM) systems like Salesforce. Despite progress in data cleansing techniques, existing methodologies are prone to be dependent heavily on manual intervention or rule-based systems lacking the flexibility to learn from changing data patterns. Past research has focused mainly on static algorithms and traditional machine learning models for cleansing, which lack the ability to generalize to the dynamic and complex datasets prevalent in Salesforce environments. This limitation highlights the need for intelligent, autonomous agents with continuous learning and real-time adaptation. Autonomous AI agents with reinforcement learning and adaptive feedback capabilities hold immense potential to address these limitations by cleansing data autonomously with little or no human intervention. However, utilization of such agents specifically for Salesforce datasets has remained under-explored. This paper attempts to bridge this gap by developing a new framework where AI agents systematically find, fix, and remove data anomalies leveraging contextual knowledge of Salesforce data structures and business rules. The framework employs unsupervised learning to detect inconsistencies and reinforcement learning to improve cleansing strategies based on outcomes, promoting scalability and robustness in complex CRM datasets. Experimental testing with real-world Salesforce datasets shows spectacular improvements in accuracy, efficiency, and minimized manual effort compared to existing methodologies. This research makes contributions towards the development of autonomous data management for CRM systems towards more intelligent, self-sustaining data ecosystems that fuel decision-making and operational efficiency.

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 Keywords

Autonomous self-learning AI agents, automated data cleaning, Salesforce data sets, reinforcement learning, data quality, CRM data handling, unsupervised anomaly detection, adaptive data correction.

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 Abstract

Advanced manufacturing techniques within mechanical engineering, driven by the growing need for precision, efficiency, and adaptability in modern production systems. Key research areas included Additive Manufacturing (AM), non-traditional machining, advanced material processing, and the early integration of smart manufacturing technologies. Extensive work was conducted on optimizing AM processes such as Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), and Direct Metal Laser Sintering (DMLS), with a focus on improving part accuracy, surface quality, and mechanical performance. Non-traditional machining techniques like Electrical Discharge Machining (EDM), Laser Beam Machining (LBM), and Ultrasonic Machining were studied for their effectiveness in processing hard and composite materials. Parallel efforts in advanced material processing--including powder metallurgy, cryogenic machining, and composite fabrication--enabled the development of high-strength, lightweight components suitable for aerospace, automotive, and biomedical applications. The emergence of smart manufacturing concepts, such as sensor integration, process automation, and early-stage digital twins, marked a transition toward intelligent and connected manufacturing systems aligned with Industry 4.0 principles. Sustainability also became a focus, with research exploring energy-efficient processes and environmentally friendly practices.

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 Keywords

Additive Manufacturing, Selective Laser Sintering, Electrical Discharge Machining, Laser Beam Machining, Ultrasonic Machining.

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 Abstract

This study examines the bending behavior of single-layer armature-type graphene nanoplates of varied diameters due to variable-distribution atomic vacancy defects. This study is novel because it thoroughly examines the effects of aspect ratio, defect concentration, clamped-free (CF), clamped-clamped (CC), and simply-supported (SS) boundary conditions on graphene nanoplate buckling. Novel space frame structures were employed to characterize nanoplates to preserve their discreteness. The finite element approach anticipated buckling. The simulation results for the critical buckling load (Pcr) were confirmed by comparing them to accessible literature data. Results show that reducing this factor significantly decreases Pcr at low aspect ratios, whereas the loss becomes slow at higher aspect ratios. Research indicates that atomic-scale errors considerably reduce Pcr, with a 10% concentration resulting in a 40% drop in critical buckling stress, highlighting the significance of defects in nanostructure stability.

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 Keywords

Defective Nanoplates; Critical Buckling Load; Boundary Conditions.

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In this work, a sectional model of the exhaust manifold was investigated with the goal of reducing the amount of heat that is lost via pipe insulation by making use of a variety of different insulating materials. The thermal resistance of the exhaust manifold was increased by using four different types of insulating materials in order to decrease the amount of heat that was lost inside the manifold. A discretization and modeling process was performed on the insulating material model. The thermal and structural features of the discretized model have been prepared for evaluation, and they have been presented to the model. An input that is provided to the analytical model is the heat flux from the exhaust gas. The difference in temperature and the amount of heat lost were both considered outputs. The present analysis allows for the conclusion that the use of these four materials will result in a reduction in the amount of heat that is lost via the exhaust manifold.

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 Keywords

Thermal Property, Discretized Model, Exhaust Manifold, Automobile Element.

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This paper focuses on designing and building a passive solar tracking system to improve the performance and efficiency of solar power systems. The proposed system utilizes passive techniques that eliminate the need for active control mechanisms or external power sources, thereby reducing complexity and energy consumption. The paper encompasses the conceptualization, design, prototyping, and testing of the passive tracking system to optimize the angle at which sunlight strikes the solar panels. The design process begins with an in-depth exploration of solar tracking principles, including the Sun's movement, geographical factors, and how the incident angle affects energy capture. Various passive tracking methods, such as bimetallic strips, compressed gas systems, and shape memory alloys, are examined to identify the most appropriate approach for the paper. Based on the analysis, a new passive tracking mechanism is developed, considering aspects like simplicity, reliability, cost-effectiveness, and adaptability to various solar panel configurations. The design features mechanical components like linkages, gears, and pivots that allow the system to automatically follow the Sun's path throughout the day. The expected outcome of this paper is to demonstrate the practicality and advantages of a passive solar tracking system in boosting energy production and maximizing the use of solar resources. The results could contribute to the development of more efficient and cost-effective solar power systems, especially in situations where active tracking systems may not be feasible or economically viable.

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 Keywords

Passive solar tracking system, solar power systems, incident angle optimization, mechanical design, manufacturing, energy efficiency.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Samvargeshti Houtra Vivaranam

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Samvargeshti Houtra Vivaranam

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 Abstract

Studies on Earthworm biodiversity, population Composition, population diversity and seasonal fluctuation at study site covering Forest ecosystem and adjoining of forest cropland ecosystem in northern part of Gulbarga District, Karnataka State, Earthworm found in these localities are Eudichogaster indicus and Eudichogaster poonensis, high earthworm population density and persistant population for an extended period upto November in chandrampalli forest due to high rainfall occurring in this site. The earthworm population become zero in the study sites between 0-20 cm soil depth from December onwards due to declining soil moisture content.

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 Keywords

Population, Earthworm, Density, Seasons, Moisture

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's business landscape, companies often focus on a consumer-centric approach, leveraging digital and social media marketing. Advertising on social media tends to be subtle, operating at a subconscious level, with influencers serving as a prime example. When executed effectively, this type of marketing is interactive, engaging, and fosters a connection with consumers, rather than merely appealing to their rational side. The lifestyle and electronics sectors are particularly influential in shaping the buying behavior of youth in Patiala. Lifestyle brands excel in creating compelling visuals through influencers for products related to food, fitness, and organic goods, reflecting the latest trends. Electronics, meanwhile, attract tech-savvy youth and are frequently promoted by influencers across various platforms. This research paper aims to integrate these factors by utilizing both primary and secondary data. Its objective is to examine the relationship between the trust that average consumers place in influencers and the subsequent consumer behavior of Patiala's youth aged 15-25. Primary data will be gathered through a structured questionnaire, and the findings will be analyzed and presented based on these observations.

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 Keywords

Social media, Youth, Consumer behavior, Lifestyle, Electronics, Influencers

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Artificial Intelligence (AI) has revolutionized IT service delivery, enabling unprecedented optimization. This study examines AI-enhanced service delivery methods to boost efficiency, save costs, and improve customer satisfaction. AI in IT service management (ITSM) has enabled automation, predictive analytics, and intelligent decision-making, which are essential to high-quality IT services. AI has greatly impacted predictive maintenance and incident management. AI systems may forecast system problems by studying past data and finding trends, enabling IT teams to intervene early. Today's always-on business environment requires less downtime and constant service availability. AI-driven incident management automation categorizes, prioritizes, and resolves problems without human interaction, streamlining the resolution process. This improves reaction times and frees up IT staff to handle more difficult jobs. AI also improves customer support in IT service delivery. Chatbots and virtual assistants are growing more intelligent and can handle a variety of consumer difficulties. These technologies provide 24/7 assistance, improving client satisfaction. AI systems improve over time by learning from each encounter. More precise and individualized replies boost client satisfaction. AI helps IT teams optimize resource allocation. AI can evaluate data and allocate staff and gear to satisfy service requests using machine learning techniques. It reduces waste and operating expenses by optimizing resource consumption. AI can forecast peak periods and adapt personnel levels to suit demand, helping workforce management. The use of AI in IT service delivery is not without hurdles. Data privacy, high initial investment, and specialist AI system management skills might hinder adoption. Over-reliance on AI may weaken human supervision and distort decision-making. Therefore, firms must carefully analyze these issues and establish risk mitigation techniques. In conclusion, AI-driven service delivery optimization may alter IT companies. IT departments may increase service delivery, efficiency, cost, and customer pleasure by using AI. AI will become more important in IT service management, therefore enterprises must adapt and embrace these advances

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 Keywords

AI-driven optimization, IT service delivery, predictive maintenance, incident management, customer support, resource allocation, automation, ITSM, machine learning, workforce management.

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Creative Commons Attribution 4.0 and The Open Definition

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This conceptual paper explores the intricate relationships between consumer identity, advertisement, and health consciousness in shaping healthy food consumption behavior among millennials. As a generation that prioritizes health, wellness, and authenticity, millennials are particularly susceptible to the influences of digital advertising and social media. The study posits that consumer identity, defined by personal values and lifestyle choices, mediates the impact of advertising on healthy food consumption. Simultaneously, health consciousness moderates the extent to which these identities influence purchasing decisions. By understanding these dynamics, marketers, health advocates, and policymakers can develop more targeted strategies to promote healthier eating habits among millennials, thereby fostering long-term positive health outcomes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Identity, Advertisement, Health Consciousness, Consumer Behaviour, Healthy Food Consumption, Millennials

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Creative Commons Attribution 4.0 and The Open Definition