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This research examines the process of converting biomass into biofuels using environmentally friendly nanocatalysts. The aim is to meet the growing need for renewable energy sources and reduce the negative effects on the environment. Various biomass samples were exposed to catalytic conversion, which revealed notable disparities in the cellulose, hemicellulose, and lignin constituents. The efficacy of catalytic conversion was evaluated utilizing several nanocatalyst compositions, with Catalyst D exhibiting the greatest conversion efficiency of 80%. The biofuel output exhibited variation across different biomass samples, with Biomass 4 demonstrating the maximum biofuel generation at a rate of 120 g/L. The environmental impact study identified Catalyst D as having the highest level of sustainability, with the lowest energy usage of 1.8 kWh/kg, the least trash creation of 0.05 kg/kg, and the lowest CO2 emissions of 0.15 kg/kg compared to other formulations. The examination of percentage change further highlighted the substantial improvements in both catalytic performance and environmental sustainability indicators of Catalyst D. The results emphasize the capability of green nanocatalysts to enhance the efficiency and eco-friendliness of biomass conversion processes. This contributes to the progress of sustainable biofuel production technologies and the shift towards a more sustainable energy future.

This study presents a novel Fuzzy Logic-Based Energy Management model that aims to optimize the incorporation of renewable energy sources into smart grids. The research used simulated data to evaluate the model's performance in important metrics, revealing significant improvements in the consumption of renewable energy, stability of the grid, dependability of energy storage, and overall efficiency of the system. The Fuzzy Logic Controller adjusts energy distribution in response to current inputs, leading to a significant 20% improvement in the usage of renewable energy. The capacity to adapt is crucial in dealing with the inherent fluctuation of solar, wind, and biomass sources. The approach greatly improves grid stability, as shown by a 15% decrease in grid frequency variations, highlighting its efficacy in assuring a more regulated and steady electricity supply. Furthermore, the dependability of energy storage systems exhibits a notable 25% enhancement in the state of charge, suggesting optimal cycles of charging and discharging. This increased dependability enhances the stability of the energy supply during times of high demand and variations in the power system. The Fuzzy Logic-Based Energy Management model exhibits a significant 22% improvement in total system efficiency when compared to conventional management systems. This indicator encompasses the model's combined effect on the usage of renewable energy, the stability of the power grid, and the optimization of energy storage. Comparative analyses conducted against traditional control strategies, such as proportional-integral-derivative controllers, consistently demonstrate the superiority of the fuzzy logic approach. This approach results in a 10% decrease in grid frequency deviations, a 15% enhancement in energy storage state of charge, and a 12% boost in overall system efficiency. The resilience of the Fuzzy Logic Controller is highlighted by sensitivity analysis, since it demonstrates consistent performance even when parameters vary significantly. The model's practical usefulness and adherence to ethical principles are further confirmed by validation using real-world data from operational smart grid installations. This study provides valuable insights, establishing the Fuzzy Logic-Based Energy Management model as an innovative method for addressing the difficulties associated with integrating renewable energy into smart grids. This model promotes a more sustainable and efficient energy environment for the future.

This study investigates the incorporation of swarm robotics into the control mechanism of electric vehicles (EVs), introducing an innovative intelligent control framework that utilizes the concepts of decentralized decision-making. The research entails a methodical inquiry that encompasses the design of system architecture, the creation of a model for swarm robotics, the modeling of electric vehicle drive, the integration of swarm robotics with EV control, the development of algorithms for intelligent control, and the execution of real-world tests. The fleet of electric cars, propelled by a collective of independent robotic entities, displayed remarkable flexibility in adjusting to fluctuating surroundings. Findings demonstrated disparities in operating duration, distance traversed, mean speed, and energy expenditure during several iterations, highlighting the system’s adeptness in promptly reacting to instantaneous inputs. Significantly, the swarm-propelled electric cars successfully attained varied operating durations, showcasing the system’s adaptability in accommodating environmental dynamics. The swarm-driven system demonstrated its navigation effectiveness by effectively covering various distances, highlighting its versatility and extensive coverage capabilities. The system’s ability to effectively balance energy economy and performance is shown by the collective regulation of average velocity. The energy consumption study demonstrated the system’s efficacy in optimizing energy use, with certain experiments showing significant savings. Percentage change studies have yielded valuable insights into the comparative enhancements or difficulties seen in each indicator, so illustrating the influence of decentralized decision-making on operational results. This study is a valuable contribution to the ever-changing field of intelligent transportation systems, providing insight into the immense potential of swarm-driven electric cars to completely transform sustainable and adaptable transportation. The results highlight the remarkable flexibility and optimization skills of swarm robotics in the management of electric vehicles, paving the way for future advancements in the quest for intelligent, energyefficient, and dynamically responsive transportation solutions.

This study introduces a sophisticated anomaly detection system based on machine learning. The system is specifically developed to enhance the dependability and safeguard the security of electric transportation networks, with a particular emphasis on the charging infrastructure for electric vehicles (EVs). Utilizing extensive datasets, the research examines several facets of charging stations, charging records, identified abnormalities, and following maintenance measures. The examination of the charging station demonstrates the system’s versatility in accommodating many charging circumstances, as seen by the range of power ratings, consumption patterns, and energy provided. Further examination of charging records provides comprehensive understanding of individual charging sessions, enabling the detection of irregularities such as atypical energy surges and extended charging durations. The machine learning system, having been trained and verified using this data, has a commendable degree of precision in identifying anomalies, as shown by the congruence between anticipated abnormalities and real results. The maintenance and repair measures carried out in reaction to identified abnormalities highlight the practical ramifications of the system, with proactive tactics utilized to reduce downtime and enhance charging station operations. The performance measures, including accuracy, recall, and F1 score, unequivocally validate the resilience of the anomaly detection system, guaranteeing precise identification while mitigating the occurrence of false positives and negatives. The seamless incorporation of machine learning into electric transportation networks, as shown by the results, not only amplifies the dependability and safeguarding of EV charging infrastructure but also establishes the system as an invaluable instrument for practical implementations. The research, in addition to offering a thorough examination of the system’s performance, elucidates forthcoming avenues for scalability, real-time monitoring, and interpretability, thereby making a valuable contribution to the wider discussion on the revolutionary capabilities of machine learning in the ever-changing realm of electric transportation.

Cold formed steel (CFS) channel sections are the sections which are widely using from last few decades and are designated as light gauge sections. Such sections are being widely utilized in industrial, commercial and residential purposes as a secondary bearing member. Further developments of thin walled members made it possible to use the CFS Sections as preliminary load carrying structural members. The reason is hot-rolled steel sections posses’ very low strength to weight ratio on CFS sections, and the production process of these sections is cost-effective and simple. This paper is about comparing the analysis results from CUFSM and ABAQUS of different channel sections. The loads will be applied at the edges of the members axially i.e., as a compression member with different boundary conditions.

Road accidents and fatalities are leading causes of death, which is leading the burden to the public heath in India. This study aims at understanding the road accidents and reduction in road accidents in different regions of India. It has been found that Tamil Nadu has the highest number of road accidents followed by Kerala, Madhya Pradesh and others. Furthermore, the major factors like poor condition of roads, lack of use of safety equipments and poor traffic rules lead to increasing the number of road accidents. The government initiatives like awareness campaigns, identifying and rectifying black sports, vehicle engineering has contributed to improving road safety in India.

Solar-based E-Uniform furnished with better protection to the soldiers who are working in extreme weather. Solar Panels are wont to power up the interior circuitry of the E-uniform. A 12 V DC lead-acid rechargeable battery is employed for storing the energy. We are using a conventional battery charging unit also for giving supply to the circuitry. ATmega16a microcontroller is the heart of the circuit as it controls all the functions. The project is operated in two modes like summer mode and winter mode. By selecting the mode of operation, we are operating the system such that it can drive the body temperature according to heater/cooler. The heater/cooler in turn will help us to provide a cooling or warming effect inside the uniform which helps the soldier to bear any kind of external environment. The metal sensor will detect the metal-like bomb, any other planted weapon in-ground and intimate the soldier with a buzzer indication. The GSM is interfaced with the microcontroller and GPS is additionally interfaced such that the tracking of the whole soldier is observed. And the location is messaged to the particular concerned person /dept. This Uniform will make the soldier work in any kind of environment without any stress or distraction.

The primary objective of the current study is to enhance the damping capability of polylactide (PLA)/jute fiber composites without sacrificing their strength and stiffness. With this objective, multiphase composites were fabricated with silane-treated jute fiber and nitrile butadiene nano rubber modified polylactide. The mechanical and damping behaviors of the developed multiphase composites were evaluated. The modulus and strength of the composite were effectively increased by the silane-treated jute fiber. However, the impact strength and damping capability also decreased with chemical treatment of the jute fiber. Conversely, PLA was successfully toughened by the addition of rubber nanopowders, leading to significant improvements in the impact strength and damping ability of the composites. This improvement was achieved with minimal compromise in terms of stiffness, strength, and environmental appeal. In addition, modification of the PLA matrix with rubber nanopowders also resulted in the enhancement of abrasive wear resistance. [ABSTRACT FROM AUTHOR]

The motive of this study is to evaluate the machinability properties of AZ31Mg alloy in the face milling operations. Depth of cut, rate of feed and speed of cut are three input variables in the face milling process, while surface roughness (Ra) and tool nose radius deviation (dR) are performance characteristics. For three input variables each with three levels, to decrease the number of experiments, an L9 orthogonal array (OA) of Taguchi was employed which enables an efficient approach for conducting a full-factorial design of experiments. Selection of optimal parameters for the face milling process to achieve the required surface quality is accomplished through the utilization of a modified Taguchi approach and a novel multi objective optimization procedure. The work establishes empirical relationships that correlate the performance characteristics (i.e. Ra and dR) with three input variables. Corrections to the empirical relations were specified to obtain the expected range of Ra and dR. Ra and dR estimates were validated with existing test data. [ABSTRACT FROM AUTHOR]