With the rapid growth of Wireless Sensor Network (WSN) applications across domains such as environmental monitoring, smart cities, and healthcare, ensuring energy efficiency has become paramount to extending the network’s operational lifetime. WSN nodes are typically resource-constrained, making the design of energy-efficient routing protocols crucial to maintaining stability and functionality. Among the existing protocols, the Stable Election Protocol (SEP) has proven effective in heterogeneous networks due to its ability to manage nodes with varying energy levels. Over time, numerous modifications of SEP have emerged to address its limitations and further enhance network performance. This paper proposes a new energy efficient version of the extended SEP protocol, considering the threshold sensitivity. The heterogeneous node distribution and network energy are modified, and factors are optimally tuned to improve the overall network lifetime. Additionally, the modified absolute distance is proposed for the clustering process. The half ratio of the node scaling is considered for higher-energy nodes. The paper evaluated three cases of the proposed routing protocol, showing a continuous improvement in the stability period.

Surface hardness is a critical quality attribute in machined components, directly influencing wear resistance, fatigue strength, and overall performance. Optimizing surface hardness during turning operations is essential for achieving superior product quality and extending component lifespan. This study reviews advancements in optimizing surface hardness through controllable factors such as cutting parameters, tool material and geometry, workpiece properties, cooling and lubrication techniques, and machining environments. Experimental design methodologies, including Taguchi methods and Response Surface Methodology (RSM), alongside modeling techniques like Finite Element Analysis (FEA) and machine learning, are explored. The review highlights significant findings, identifies gaps in existing research, and emphasizes the importance of sustainable and hybrid approaches for achieving optimal surface hardness. Emerging trends, such as the integration of AI and IoT in machining, are also discussed, offering insights into future directions for improving surface quality and operational efficiency in turning processes.

This study investigates the effects of heat treatment and erosive wear on the hardness and performance of ductile iron. Various heat treatment processes, including annealing, normalizing, and quenching, were applied to modify the microstructure and enhance material properties. Hardness measurements revealed significant improvements, particularly in quenched samples, due to the formation of martensite. Erosive wear tests demonstrated that heat-treated ductile iron exhibited superior resistance to wear compared to untreated specimens. The findings underscore the critical role of heat treatment in optimizing the durability and functional properties of ductile iron for applications in abrasive and erosive wear environments.

Metal Inert Gas (MIG) welding, also known as Gas Metal Arc Welding (GMAW), is a versatile and widely used welding process that employs a continuous consumable wire electrode and an inert shielding gas to join metals. The process is known for its efficiency, precision, and adaptability to various materials, including steel, aluminum, and stainless steel. MIG welding is widely utilized in industries such as automotive, construction, and manufacturing due to its high-speed operation and ability to produce strong, clean welds with minimal post-weld cleanup. Its ease of automation and suitability for diverse applications make it a preferred choice for modern fabrication needs.

Today all business is totally depending upon Data. At Every second Petabytes of data are generation from different domains or areas. We all Knows that utilization of data is useful data is all around. Today’s scenario many Top-Notch Companies are depends upon data. They are growing day by day in their business area based upon the net profit. Data mining is really a important tools for processing the data from its Raw format to useful data from last many years. Recently data mining is migrated to Machine Learning. Machine Learning is a branch or sub domain of Artificial Intelligence and Mathematical Statistics. Machine learning can be divided into supervised, unsupervised and reinforcement learning. Here Researcher’s wants to find such types of solution which can find for the solution for People who want to buy a new house are more conservative with their budget and market strategies. In the many available current system, the calculation of house price is calculating without the necessary forecasting about future market business trends. The goal of this Paper is to predict efficient house pricing for real estate customers in terms of their budgets and requirements the trends and price limits of the previous market, and near coming future developments. The outcome of this Paper considers client specifications and then combines the application of several Machine Learning Algorithms like regression algorithms for finding our Analysis. This will help customers to invest in an asset without an agent and it also reduces the risk involved in the transaction.