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Stabilization of Human Serum Albumin against Urea Denaturation by Diazepam and Ketoprofen

Manoharan P, Wong YH, Tayyab S

Protein Pept Lett, 2015;22(7):611-7.
PMID: 25961707

Stabilizing effect of diazepam and ketoprofen, Sudlow's site II markers on human serum albumin (HSA) against urea denaturation was studied using fluorescence spectroscopy. The two-step, three-state urea transition of HSA was transformed into a single-step, two-state transition with the abolishment of the intermediate state along with a shift of the transition curve towards higher urea concentrations in the presence of diazepam or ketoprofen. Interestingly, a greater shift in the transition curve of HSA was observed in the presence of ketoprofen compared to diazepam. A comparison of the intrinsic fluorescence and three-dimensional fluorescence spectra of HSA and partially-denatured HSAs, obtained in the absence and the presence of diazepam or ketoprofen suggested significant retention of native-like conformation in the partially-denatured states of HSA in the presence of Sudlow's site II markers. Taken together, all these results suggested stabilization of HSA in the presence of diazepam or ketoprofen, being greater in the presence of ketoprofen.
An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties

Manoharan P, Chandrasekaran K, Chandran R, Ravichandran S, Mohammad S, Jangir P

Environ Sci Pollut Res Int, 2024 Feb;31(7):11037-11080.
PMID: 38217814 DOI: 10.1007/s11356-023-31608-z

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
Fulltext The effects of musical feedback training on metacognition and self-directed learning

Li W, Manoharan P, Cui X, Liu F, Liu K, Dai L

Front Hum Neurosci, 2023;17:1304929.
PMID: 38173798 DOI: 10.3389/fnhum.2023.1304929

INTRODUCTION: Metacognition and self-directed learning are key components in educational research, recognized for their potential to enhance learning efficiency and problem-solving skills. This study explores the effects of musical feedback training on these competencies.
METHODS: The study involved 84 preservice teachers aged 18 to 21. Participants were randomly assigned to either an experimental group, which received musical feedback training, or a control group.
RESULTS: The findings indicate that musical feedback training effectively improved metacognitive abilities. However, its impact on the readiness for self-directed learning was inconclusive. A notable difference in metacognition and self-directed learning readiness was observed between the experimental and control groups during the session, indicating a significant interaction effect. Furthermore, a positive correlation was identified between metacognition and self-directed learning.
DISCUSSION: These results contribute to educational discourse by providing empirical evidence on the utility of musical feedback training in fostering metacognition. They also highlight the importance of consistent and long-term engagement in self-directed learning practices. The significance of these findings advocates for incorporating music feedback training into music education curricula to enhance metacognition and improve overall learning efficiency.
Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

Manoharan P, Ravichandran S, Kavitha S, Tengku Hashim TJ, Alsoud AR, Sin TC

Sci Rep, 2024 Sep 09;14(1):20979.
PMID: 39251720 DOI: 10.1038/s41598-024-71223-7

In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ξ 1 , ξ 2 , ξ 3 , ξ 4 , R C , λ , and b . The fuel cells (FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs.