Gongcheng Kexue Yu Jishu/Advanced Engineering Science

In Wireless Sensor Networks (WSNs), efficient and reliable data transmission is essential for improving network longevity and efficiency. This paper proposes a Cluster Based Fuzzy Decision-Making approach for traffic-free(FDM-FT) data transmission in WSNs. The system employs intelligent clustering to organize nodes into energy-efficient groups and utilizes fuzzy logic to dynamically evaluate factors like remaining energy and proximity to the base station and node congestion level. Based on these inputs, optimal cluster heads and routing paths are selected to minimize traffic congestion and energy consumption. Simulation results demonstrate improved packet delivery ratio, reduced latency, and enhanced network longevity compared to traditional routing protocols.in This model Cluster Based Fuzzy Decision-Making approach for traffic-free(FDM-FT)offers a scalable and adaptive solution for congestion-aware and energy-efficient communication in resource-constrained sensor networks The FDM-FT mechanism aims to reduce data loss, energy loss, and increase throughput. It is primarily focused on avoiding network traffic interruptions and ensuring continuous data flow source from the node (cluster head), to the base station. The performance of this mechanism is evaluated using the Network Simulator (NS2) and demonstrates impressive levels for the indices, indicating its suitability for real-world applications.

Power transformers need to be continuously monitored in order to identify emerging issues before they become catastrophic failures. In order to assess transformer condition in real time, this paper proposes a fuzzy logic-based transformer health monitoring system that combines several sensor inputs (voltage, current, oil temperature, oil level, etc.). A Mamdani fuzzy inference engine is implemented by an ATmega328 microcontroller, which also uses GSM to notify maintenance staff of faults. To tune the fuzzy logic controller and simulate different fault scenarios (overloads, over-temperature, undervoltage, etc.), a comprehensive MATLAB/Simulink model was created. According to simulation results, the fuzzy system can identify the transformer's health status as Normal, Alert, or Critical and initiate the proper control measures (such as load reduction or emergency trip) well before serious faults occur. The behavior of a hardware prototype that was constructed and tested on a laboratory transformer closely matched the predictions of the simulation. When handling ambiguous sensor data and integrating several fault indicators into a single health index, the fuzzy logic approach demonstrates strong capabilities. In general, this work offers a thorough framework for fuzzy logic-based intelligent transformer condition monitoring, enhancing the sensitivity of fault detection and operational dependability of transformers.

The increasing demand for energy-efficient and high-performance digital systems has made the design of low-power arithmetic circuits a critical area of research. Full adders, being core components of arithmetic logic units (ALUs), play a vital role in determining the overall efficiency of digital architectures. This paper presents a novel Low-Power Hybrid 4-bit Full Adder design utilizing 14nm FinFET technology to address the challenges of power consumption and speed in modern computing applications. The proposed architecture combines Gate Diffusion Input (GDI) logic with Energy-Efficient Diode-Connected DC Biased Positive Feedback Adiabatic Logic (EE-DC-DB PFAL), leveraging both logic minimization and adiabatic switching to enhance power performance. FinFET devices are employed to exploit their superior electrostatic control and reduced leakage characteristics at the nanoscale. Comprehensive simulations conducted using industry-standard tools demonstrate significant improvements in power dissipation, delay, and energy efficiency compared to traditional CMOS-based full adder designs. The results validate the effectiveness of the proposed hybrid approach, making it a promising candidate for integration into low-power digital processing units and future ultra-scaled VLSI systems.

A double-sided linear induction motor (DSLIM) optimised for high-thrust and high-efficiency applications in automation and transportation is designed, analysed, and simulated in this work. With its symmetrical double-sided design, the DSLIM offers balanced magnetic flux and increased thrust density, which makes it appropriate for automated material handling and high-speed urban transit systems like Maglev trains. Finite Element Analysis (FEA) is used to analyse performance and calculate important parameters such as thrust force, synchronous speed, and slip. According to simulation results, the DSLIM design is effective. It produces a lot of thrust, is around 70% efficient at the ideal slip levels, and significantly reduces end effects, which are major problems in LIM applications. The design's precision and dependability are confirmed by the high degree of agreement between theoretical calculations and simulation results.

Concentrated Photovoltaic (CPV) systems are gaining interest due to their excessive overall performance and capacity for sustainable sun electricity era. However, their performance is substantially laid low with actual-global running variability, collectively with fluctuations in sun irradiance, temperature versions, and optical misalignment. This examine develops a MATLAB and Simulink-based completely simulation framework to assess CPV tool normal overall performance underneath dynamic environmental conditions. Key overall performance indicators which encompass electricity output, performance, and thermal stability are analyzed to perceive barriers and areas for optimization. Advanced control techniques, which include advanced Maximum Power Point Tracking (MPPT) algorithms and thermal law techniques, are implemented to beautify gadget reliability. The simulation results advocate an performance development of about X percentage, with optimized manage mechanisms mitigating actual-time fluctuations efficiently. Findings endorse that adaptive optimization techniques play a critical function in retaining CPV typical overall performance stability under numerous situations. This research offers valuable insights for the layout and operation of CPV structures, making sure higher strength yield and prolonged-time period overall performance. The proposed optimization strategies contribute to the advancement of CPV era, promoting its integration into large-scale renewable electricity infrastructures.

In this paper characterizes the Ɱ* quasi paranormal composition operator and Ɱ* quasi paranormal weighted composition operator on L^2 spaces and investigates their various properties.

Post-harvest diseases in mangoes significantly impact their quality, market value, and supply chain efficiency. Identifying and tracking defective mangoes caused by these diseases is essential for reducing losses and ensuring food safety. This study presents the development of a database system designed to record, monitor, and analyse defective mangoes affected by post-harvest diseases such as anthracnose, stem-end rot, and soft rot. The database incorporates key features, including disease classification, severity levels, visual symptoms, geographical origins, storage conditions, and time since harvest. Advanced query functionalities allow stakeholderssuch as farmers, distributors, and researchersto access actionable insights and trends, enabling better decision-making in disease management, transportation, and storage protocols. By integrating data visualization and predictive analytics, the database facilitates early detection of disease patterns, contributing to improved post-harvest handling practices. This initiative aims to enhance the efficiency of mango supply chains, minimize waste, and ensure the delivery of high-quality mangoes to consumers while promoting sustainable agricultural practices.