Sujito

Mn-doped Fe3O4/reduced graphene oxide (rGO) nanocomposites derived from corncob biomass were successfully synthesized as sustainable magnetic nanomaterials for engineering applications. Biomass-derived rGO was prepared from corncob waste, while Mn-doped Fe3O4 nanoparticles were synthesized using a coprecipitation method and subsequently integrated with the rGO matrix. The structural, morphological, chemical, and magnetic properties of the synthesized nanocomposites were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDS), and vibrating sample magnetometry (VSM). XRD analysis confirmed the formation of a single-phase cubic spinel Fe3O4 structure with an average crystallite size of 11.5 nm and no detectable secondary phases. SEM observations revealed agglomerated ferrite particles uniformly distributed on layered rGO sheets, while EDS confirmed the presence of Fe, Mn, C, and O within the composite. The complementary results obtained from XRD, FTIR, SEM–EDS, and VSM collectively confirmed the successful formation of the Mn-doped Fe3O4/rGO nanocomposite. The synthesized material exhibited soft magnetic behavior with a saturation magnetization (Ms) of 32.83 emu g−1. The narrow hysteresis loop and magnetic response are consistent with the characteristics commonly observed in nanoscale ferrite nanocomposites, demonstrating its potential for electromagnetic interference shielding, magnetic separation, sensing technologies, and other sustainable magnetic engineering applications.

This research proposes the Queen Honey Bee Migration (QHBM) algorithm to optimize IoT data transmission in hostage rescue operations, with a primary focus on energy efficiency and reliability. The methodology employs comprehensive simulation techniques to compare QHBM performance against two established optimization algorithms—Fuzzy BT and PSO—across diverse network configurations and operational scenarios. Simulation results demonstrate that QHBM significantly outperforms both alternative approaches. The algorithm extends network lifetime by 25% compared to PSO and 15% compared to Fuzzy BT, addressing a critical requirement for prolonged operation during rescue missions. Additionally, QHBM enhances network throughput by 30%, maintaining a consistent data transmission ratio of 98%, while simultaneously reducing computational overhead by 20%. The QHBM algorithm demonstrates particularly robust performance in challenging environments characterized by high node density and dynamic mobility patterns, which closely resemble real-world hostage rescue scenarios. The algorithm achieves this by dynamically balancing energy consumption across the network while maintaining reliable data transmission pathways, even when network topology changes rapidly. The bio-inspired approach of QHBM leverages the efficient decision-making patterns observed in honey bee colonies, specifically the migration behaviors of queen bees, to create adaptive routing protocols that respond effectively to changing network conditions. This research makes a significant contribution to the development of nature-inspired optimization methods that can enhance the performance and resilience of tactical communication systems deployed in high-stakes rescue operations. The findings suggest promising applications for similar bio-inspired algorithms in other mission-critical IoT deployments where energy efficiency and transmission reliability are paramount concerns.

This study designs a Dynamic Voltage Restorer (DVR) model with PID controller based on the Ant Colony Optimization (ACO) Algorithm, which is simulated to compen

The Direct On-Line (DOL) process of starting a high-power 3-phase induction motor causes voltage sags in the distribution system that is connected to one point of common coupling (PCC). Voltage sag can cause damage and failure of sensitive loads. This article analyzes and proposes a simulation of voltage sag recovery using a Dynamic Voltage Restorer (DVR) based on an Artificial Neural Network (ANN). ANN is used as a detector and regulator of the voltage compensation value. In this study, a 3-phase induction motor will be connected to a sensitive load, and the DVR will be placed in series with a voltage source or PCC with a sensitive load. The simulation test system uses Simulink-Matlab R2016a with different configurations of induction motor parameters. Based on the simulation results show that the parameters of the 3-phase induction motor cause the depth and duration of the voltage sag. DVR with ANN control can detect and compensate for a voltage sag of 0.5 pu so that the voltage will be normal to 1 pu.

In this research, IoT Lora technology is used to monitor the operation of the solar powered automatic pump. The advantage of the Lora system is that it is a much better range than using conventional WIFI connection. Using a Point to Point topology, there are two devices, a sending unit and a receiving unit. The sender is in charge of sending sensor reading data in the form of current values and the condition of the water pump electrical wire connection, while the receiver is in charge of receiving data from the sender and then translating it into digital data which is then displayed on the OLED for monitoring purposes. The receiver is also connected to the mobile app via Bluetooth connection. Data displayed on smartphone by using mobile app. Based on the test results, the average value of delivery delay is 26664.9 and an average Received Signal Strength Indication (RSSI) of -111.7. This test is done by sending 10 experimental data where the distance between the transmitter and receiver is 200 meters. It can be concluded that the system is able to work in accordance with the design that has been done. The system can work properly and detect abnormalities in the electric pump, then send it as a notification to residents, thus facilitating the pump maintenance process.

An improved symbolic analysis procedure to enhance the analytic efficiency of the reported symbolic nodal analysis is presented. Two techniques are adopted in the proposed method to reduce the order of the system of equations when performing symbolic analysis. The first one uses voltage signal sources directly to perform symbolic analysis without replacing them with their nullor equivalences. The second one uses the nullor, grounded mirror, and floating mirror elements to model the active devices that involve differential or multiple single-ended signals. Practical examples are given which demonstrate the feasibility of the proposed methods.