Structural Analysis and Finite Element Methods: Modeling and Simulation in Mechanical Engineering

This research dives into the space of "Structural Analysis and Finite Element Methods: Modeling and Reenactment in Mechanical Designing," utilizing a multifaceted approach to comprehensively get the mechanical behaviour of building structures. Finite Element Analysis (FEA) was utilized to scrutinize a steel structure beneath assorted stacking conditions, uncovering stretch conveyances basic for basic optimization. The study amplified its centre to Fluid-Structure Interaction (FSI), unravelling the complex flow between liquid forces and basic reactions, with suggestions for seaward building applications. Warm recreations of composite materials give bits of knowledge into temperature-induced stresses, directing fabric choice and plan alterations in extraordinarily warm situations. Sensitivity investigations and parametric studies methodically investigated plan impacts on auxiliary execution, helping in optimization endeavours. Approval against experimental information guaranteed the precision of numerical recreations, improving their validity

A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference & IOT

In the modern world, technology is always evolving to replace more human labour with artificial intelligence. Moreover, farmers are under constant pressure to irrigate their farms at regular intervals without even a rudimentary grasp of the rainfall pattern and soil humidity, since it is extremely difficult to cultivate any agricultural food in regions with irregular rainfall patterns and high mean temperatures. This paper proposes a crop predictor and smart irrigation system using Mamdani’s fuzzy inference and IoT. The system aims to optimize water usage and crop yield by considering various factors such as soil moisture, temperature, humidity, rainfall, crop type and season. The system consists of three modules: a crop predictor module that uses fuzzy logic to suggest the best crop for a given location and season, an IOT module that collects and transmits the environmental data from sensors to a cloud server, and a smart irrigation module that uses fuzzy logic to control the water flow to the crops based on the data and the crop predictor module. The system is implemented and tested on a NodeMCU and MATLAB platform and shows promising results in terms of water conservation and crop productivity

Optimizing Agricultural Supply Chains with Machine Learning Algorithms

Agricultural supply chains serve as the vital link between producers and consumers, ensuring the efficient flow of agricultural products. Their optimization is essential to address challenges like seasonal variations, transportation complexities, and quality control. Machine learning, with its predictive modeling, demand forecasting, route optimization, inventory management, quality control, and risk management capabilities, offers a promising solution to revolutionize the agricultural industry. These supply chains consist of various components, including producers, distributors, retailers, and consumers, each contributing to the network that delivers agricultural products. To enhance efficiency and product quality, innovative solutions are required to overcome challenges such as seasonal fluctuations and quality concerns. Machine learning empowers supply chain stakeholders to make data-driven decisions, automate processes, and optimize various aspects of the supply chain. This technology enhances the resilience and efficiency of agricultural supply chains, ensuring the delivery of fresh and safe products to consumers. Effective data collection and preprocessing are essential for leveraging machine learning's potential. Through sourcing, cleaning, and structuring data from diverse sources, stakeholders enable machine learning algorithms to make informed recommendations and predictions. Machine learning's application in agricultural supply chains, exemplified by predictive modeling for crop yield through weather data analysis and disease detection, illustrates the power of data-driven technologies in enhancing crop production, reducing losses, and ensuring a secure global food supply

Emerging Technologies and Management Practices: Navigating the Convergence of Computer Science and Organizational Management

The complex intersection of organizational management and machine learning (ML) is examined in this study, along with adoption opportunities and obstacles. Using interpretivist philosophy as well as a deductive approach, we performed a thorough analysis with secondary data. The quantitative analysis showed rising rates of machine learning adoption in a variety of sectors, most notably finance and healthcare. The main challenges determined by the challenges and opportunities matrix were workforce adaptation, integration complexities, in addition to ethical concerns. Organizational structures are reshaped by ML, which dramatically improves operational efficiency and strategic decision-making. Metrics for organizational adaptability place a strong emphasis on the development of workforce skills, the efficacy of change management, implementation agility, and feedback loop application. Critical analysis emphasizes how important it is to support adaptive organizational cultures and match the adoption of ML with moral values. Proactive ethical concerns, strategic workforce development, alongside cooperative policy frameworks are encouraged in the recommendations. Subsequent research ought to investigate the long-term effects of machine learning on organizational dynamics through looking at real-time case studies