A new stability results for the backward heat equation

In this paper, we regularize the nonlinear inverse time heat problem in the unbounded region by Fourier method. Some new convergence rates are obtained. Meanwhile, some quite sharp error estimates between the approximate solution and exact solution are provided. Especially, the optimal convergence of the approximate solution at t = 0 is also proved. This work extends to many earlier results in (f2,f3, hao1,Quan,tau1, tau2, Trong3,x1).Comment: 13 page

A Survey on Some Parameters of Beef and Buffalo Meat Quality

A survey was carried out on 13 Vietnamese Yellow cattle, 14 LaiSind cattle and 18 buffalos in Hanoi to estimate the quality of longissimus dorsi in terms of pH, color, drip loss, cooking loss and tenderness at 6 different postmortem intervals. It was found that the pH value of longissimus dorsi was not significantly different among the 3 breeds (P>0.05), being reduced rapidly during the first 36 hours postmortem, and then stayed stable. The value was in the range that was considered to be normal. Conversely, the color values L*, a* and b* tended to increase and also stable at 36 hours postmortem, except that for LaiSind cattle at 48 hours. According to L* scale, the meat of Yellow and LaiSind cattle met the normal quality but the buffalo meat was considered to be dark cutters. The tenderness of longissimus dorsi was significantly different among the breeds (P<0.05). The value was highest at 48 hours and then decreased for LaiSind and buffalo, but for Yellow cattle the value decreased continuously after slaughtering In terms of tenderness buffalo meat and Yellow cattle meat were classified as “intermediate”, while LaiSind meat was out of this interval and classified as “tough”. Drip loss ratio was increased with the time of preservation (P<0.05). The cooking loss ratio was lowest at 12 hours and higher at the next period, but there was no significant difference among the periods after 36 hours postmotem.Peer reviewe

GENERATION OF BACILLUS SUBTILIS CLONE DISPLAYING METAL-BINDING POLYHISTIDYL PEPTIDE

Joint Research on Environmental Science and Technology for the Eart

The Role of Reinforcement Learning Control for Optimizing Building Energy Management Systems

Modern energy management systems are increasingly challenged by the complexity, uncertainty, and high-dimensional data generated by advanced power systems. These challenges have driven a growing interest in integrating intelligent techniques such as machine learning (ML) and deep learning (DL) into energy management to improve system adaptability and efficiency. Among these approaches, reinforcement learning (RL) has emerged as a promising solution due to its ability to handle dynamic, sequential decision-making processes under uncertainty. RL has demonstrated its potential not only in energy management but also in related areas such as demand response, operational control, and renewable energy integration. This research delves into the development of RL-based frameworks for intelligent energy management in grid-interactive buildings, with a special focus on the integration of electric vehicles (EVs) as distributed energy resources (DERs). Firstly, the thesis carries out a systematic review of the foundational principles of RL and its diverse applications within the domain of power systems. Subsequently, a data-driven framework leveraging the Soft Actor-Critic RL algorithm is proposed to enable prosumers to reduce energy costs, enhance grid stability, improve renewable energy utilization, and maintain user comfort. Simulation results highlight the effectiveness of the proposed framework, showing significant performance gains over state-of-the-art control strategies in terms of cost efficiency, CO₂ emissions reduction, and grid resilience. Additionally, the study provides a critical evaluation of the practical challenges and opportunities of implementing RL-based systems in real-world scenarios. The insights gained highlight the transformative potential of RL in enabling adaptive and sustainable energy management practices. By addressing both the technical complexities and real-world applications, this research advances the understanding of intelligent energy systems and underscores the importance of RL in meeting the growing demands of modern energy infrastructures while promoting sustainability and economic viability

BIODIESEL PRODUCTION BY ESTERIFICATION OF OLEIC ACID WITH ETHANOL UNDER ULTRASONIC IRRADIATION

Joint Research on Environmental Science and Technology for the Eart