Earticle

Industries are increasingly turning to green chemistry, with laccase enzymes garnering attention due to their ability to use oxygen to produce water as a by-product while catalyzing a wide range of reactions. Bacterial laccases, though less explored, offer stability at high temperatures and pH levels. However, they require mediator systems to handle complex molecules. Structure-based virtual screening aids in identifying suitable mediators, potentially reducing costs, and accelerating discovery. These techniques rely solely on the target protein three-dimensional structure and do not require knowledge of the unique bioactivity of the protein. As a result, structure-based approaches are theoretically more adaptive to unknown protein and ligand compounds than ligand-based ap-proaches. Therefore, we aimed to screen and compare the efficacy of natural mediators over the artificial mediators for laccase oxidation. Here, we predicted that natural mediators like coumaric acid and ferulic acid showed promise comparable to artificial ones, such as N-hydroxythalimide and N-hydroxyacetanilide respectively, in computational assessments (binding energy), suggesting virtual screening’s potential in mediator discovery.

In recent years, there has been a growing interest in research suggesting that the spacetime of the universe is formed by quantum entanglement. This paper aims to predict the future of the universe based on this quantum entanglement. Quantum entanglement follows the area law that it is proportional to the area of the boundary surface between two regions. Assuming that this quantum entanglement fundamentally controls the volume of the universe through the Holo-graphic Principle, known as the Ryu-Takayanagi formula, this paper explores the implications. Fur-thermore, it is hypothesized that the area law breaks at extremely low temperatures, where quan-tum entanglement becomes proportional to the inverse power of the temperature T, ranging from T-1/5 to T-2/3. Applying this to the quantum entanglement in the universe, it is assumed that the break-down of the area law occurs at these extremely low temperatures. Therefore, as the universe ex-pands and the temperature becomes extremely low, where the area law breaks, quantum entangle-ment decreases, and the expansion of the universe comes to a halt. At this moment, there is a possi-bility that an energy gap emerges. This paper discusses the cessation of the universe's expansion, leaving the exploration of what follows as a future research topic.

Positivity, negativity, and wave-particle duality are fundamental properties in existence. Assuming the existence of a complex point charge with these properties, a spherical standing wave model is derived. This spherical standing wave is a spherically symmetric standing wave that reso-nates between the inner and outer radii. The inner radius does not become zero; therefore, no self-energy divergence problem exists. Since it has complex amplitude corresponding to voltage (scalar potential) and current (vector potential), it is compatible with quantum theory that also has complex amplitude. An electron model is assumed to be a spherical standing wave with an electron classical radius as the inner radius, a Bohr radius as the outer radius, and an elementary charge as the size of wave source. This study obtains energy and resonance conditions from an equivalent resonance circuit. The derived formulas include the Compton wavelength, electron mass, ionization energy, and Rydberg constant. Thus, the calculated values were consistent with existing physical constants.

This study examines the step-skew design of cogging torque reduction for SPMSM (Sur-face Permanent Magnet Synchronous Motor) in EPS (Electric Power Steering) System. In this paper, a cycloid curve on the magnet shape is proposed to reduce the cogging torque for rotor step skew design. Based on the same rotor step-skew design, an evaluation index is used and determined to compare the proposed and conventional magnet shape design. The proposed and conventional de-sign methods are compared using numerical method such as FEM (Finite Element Method).

Emotion recognition is rapidly advancing within the realm of artificial intelligence (AI), spurred by progress in deep learning, multimodal data handling, and broader availability of large datasets. This paper offers an in-depth overview of emotion recognition strategies, focusing on bio-metric signals (e.g., audio, visual, physiological, and brain signals), conventional machine learning methods, and the latest deep learning architectures, including Transformers, two-dimensional Con-volution Neural Networks (CNNs) 2D CNNs, and three-dimensional CNNs (3D CNNs). We further examine multimodal systems and the role of Large Language Models (LLMs) in merging textual, audio, and video information for more precise emotion assessments. Key challenges such as real-time implementation, data biases, and cultural diversity are also highlighted. Ethical issues related to privacy and the potential misuse of emotion recognition technologies receive attention, as does a discussion of emerging applications in healthcare, human-computer interaction (HCI), mental health monitoring, and education. In sum, this review aims to contribute to the scholarly conversa-tion around evolving emotion recognition methodologies and their applications in practical systems.

The pursuit of extending and restoring the fatigue life of rolling bearings represents a critical challenge in mechanical engineering, with significant implications for sustainability and in-dustrial efficiency. This study focuses on enhancing the fatigue life of both new and remanufactured rolling bearings through innovative surface modification technologies, particularly ultrasonic nano-crystal surface modification (UNSM). By optimizing the efficiency and effectiveness of the UNSM, this research delves into the scientific mechanisms underlying these technologies to maximize their performance. Furthermore, this work aligns with circular economy principles by enabling the re-peated reuse of remanufactured bearings. While current technologies permit only one additional reuse cycle, this study aims to surpass this limitation, targeting more than three reuse cycles through advanced surface modification techniques. By pushing the boundaries of current knowledge and technology, this research contributes to more sustainable industrial practices, fostering a circular economy and reducing resource consumption in mechanical industries.

In the resting state, EEG and fMRI have functional correlations in the low-frequency band, and integrating the two modalities can provide a more comprehensive understanding of brain activity. However, multimodal imaging faces challenges such as high cost and complexity of data fusion. In this study, we developed a Transformer-CNN to generate fMRI data from EEG signals and introduced spatial normalization to compensate for differences in brain structures between sub-jects. Our results showed that the brain structures were normalized to the same extent, so that the model could focus only on predicting the signal values of fMRI, and compared with actual fMRI scans, we obtained PSNR of 25.92 and SSIM of 0.56, which were quantitatively and qualitatively evaluated. Although there are some qualitative limitations for medical device utilization, our ap-proach opens new avenues in neuroscience, especially in environments where simultaneous EEG-fMRI acquisition is not possible. This study highlights the potential of deep learning in advancing multimodal imaging and provides enhanced insights into brain function.

Recently, as electric vehicles operate using battery voltages, issues such as thermal run-away or fire in batteries have been reported. The primary cause of this thermal runaway is the volt-age difference between cells that occurs during charging and discharging processes. Therefore, it is essential to accurately balance the battery using a battery management system (BMS). This paper proposes an isolated balancing circuit designed to stably maintain the balance of each unit cell in a high-voltage battery pack structure. The control circuit signal is transmitted to a photo diode, and the balancing operation is controlled using signals from an optical detector. The low-voltage oper-ation control circuit is isolated from the high-voltage circuit, enabling reliable balancing even under high voltages of thousands of volts. The cell-by-cell register operation method allows simultaneous balancing of multiple cells. A balancing current of 80 mA was achieved, and within a temperature range from -20°C to 80°C, the balancing operation showed a variation within 81 mA ± 1 mA, owing to the temperature-compensated balancing mechanism of the isolation circuit. The proposed bal-ancing circuit is designed to operate at an ultra-low power of 180 μW, minimizing the battery energy consumption required for the balancing operation.