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Cordyceps militaris (C. militaris) is a valued medicinal fungus that can be traced back thousands of years in traditional Chinese medicine (TCM). Both TCM and modern scientific research have confirmed the positive effects of C. militaris on human health. In recent years, C. militaris has gained wide popularity; unfortunately, strains often degrade during cultivation, resulting in a decline in fruiting bodies and active components that negatively impacts the development of C. militaris in the health food and medicine industries. This review summarizes the current progresses in research on the genomic, transcriptomic, proteomic, and genetic manipulation of C. militaris and discusses its primary metabolites and strain degradation mechanisms. The current challenges and future prospects of C. militaris research are also discussed. [ABSTRACT FROM AUTHOR]

Introduction: The characteristics of intermittency and volatility brought by a high proportion of renewable energy impose higher requirements on load forecasting in modern power system. Currently, load forecasting methods mainly include statistical models and machine learning methods, but they exhibit relative rigidity in handling the uncertainty, volatility, and nonlinear relationships of new energy, making it difficult to adapt to instantaneous load changes and the complex impact of meteorological factors. The Transformer model, as an algorithm used in natural language processing, with its self-attention mechanism and powerful nonlinear modeling capability, can help address the aforementioned issues. Methods: However, its current performance in time series processing is suboptimal. Therefore, this paper improves the Transformer model through two steps, namely, Data-Slicing and Channel-independence, enhancing its adaptability in load forecasting. Results: By using load data from Northern Ireland as an example, we compared GRU, CNN, and traditional Transformer models. We validated the effectiveness of this algorithm in short-term load forecasting using MAPE and MSE as indicators. Discussion: The results indicate that, in short-term load forecasting, the MDS method, compared to GRU, CNN, and traditional Transformer methods, has generally reduced the MSE by over 48%, and achieved a reduction of over 47.6% in MAPE, demonstrating excellent performance. [ABSTRACT FROM AUTHOR]

With the popularity of electric vehicles (EVs), vehicle-to-grid (V2G) technology is attracting increasing attention due to its crucial merit of enabling bidirectional power flows between EVs and grid, so as to enhance the grid security and stability by regulated dispatching. However, the existing V2G approaches are confronted with several unrealizable challenges because of high computational complexity for large-scale EVs and impracticality for future power data acquisition. In this article, an edge computing framework is proposed in a distributed manner to ensure the dispatching efficiently and provide the raw dataset flexibly. Meanwhile, the long short-term memory network is applied to prediction merely by the past and present power data. Moreover, attention mechanism and data clustering are utilized to improve the prediction accuracy and operation robustness. Experiments involving real dataset demonstrated that the proposed V2G scheme is able to achieve very satisfactory dispatching performance with the prediction accuracy up to 98.89%. [ABSTRACT FROM AUTHOR]

In recent years, selective laser melting (SLM) has gained an important place in fabrication due to their strong individualization which cannot be manufactured using conventional processes such as casting or forging. By proper control of the SLM processing parameters, characteristics of the alloy can be optimized. In the present work, 316L stainless steel (SS), as a widely used biomedical material, is investigated in terms of the effects of scanning speed on in vitro biocompatibility during SLM process. Cytotoxicity assay is adopted to assess the in vitro biocompatibility. The results show the scanning speed strongly affects the in vitro biocompatibility of 316L SS parts and with prolongs of incubation time, the cytotoxicity increase and the in vitro biocompatibility gets worse. The optimal parameters are determined as follows: scanning speed of 900 mm/s, laser power of 195 W, hatch spacing of 0.09 mm and layer thickness of 0.02 mm. The processing parameters lead to the change of surface morphology and microstructures of samples, which can affect the amount of toxic ions release, such as Cr, Mo and Co, that can increase risks to patient health and reduce the biocompatibility.

An effective approach is proposed to adjust the surface morphology induced by using a femtosecond laser, including the area and period of rippled structures. The effect of the processing steps and laser polarization on the surface morphology of rippled structures on a titanium surface was experimentally investigated in this study. A processing sequence was designed for two series of femtosecond laser pulses that irradiate a titanium surface, for example, N = 50(0°) + 50(90°). The experimental results show that the area and period of rippled structures can be simultaneously adjusted by following a two-step method. Due to the enhancement of energy absorption and SP-laser coupling of the initial rippled structures, large area surface structures with small periods are fabricated using two series of femtosecond laser pulses with the same polarization direction. By changing the polarization direction of the two series of femtosecond laser pulses, the recording, erasing, and rewriting of subwavelength ripples is achieved. During the rewriting process, material removal and the formation of new ripples simultaneously occur. [ABSTRACT FROM AUTHOR]

Summary: In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users' privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently. [ABSTRACT FROM AUTHOR]

• An V2G scheme concerning on edge computing is proposed. • The architecture of scheme is scalable, reliable and information secure. • The algorithm of scheme aims at PV self-consumption and load flattening. • The proposed scheme provides very high computational performance. • Case studies are verified in terms of the short-time scale and the long-time scale. Plug-in electric vehicles (PEVs) and renewable energy sources (RESs) can relief the stress on air pollution. Particularly, using RES for PEV energy requirement can integrate more RESs on the grid. In this paper, a vehicle-to-grid (V2G) scheme concerning on RES and edge computing, i.e. the internet of smart charging points with photovoltaics integration, is presented. Within the architecture of the scheme, each charging point equips computing and storage units, so as to store PEV sensitive information locally and conduct "burn after scheduling". Besides, this architecture can transform the traditional large-scale V2G problem into several sub-problems, which are small enough to optimize. Based on the architecture of the scheme, an associated high-efficiency algorithm is designed. Six typical scenarios of PEV charging are elaborated and two indexes are presented to facilitate 1) the self-consumption of photovoltaics energy by PEV charging and 2) the peak-shaving and valley-filling of net load. Additionally, voltage regulation and real-time control are applied to ensure the security of the distribution grid and mitigate the uncertain conditions. Finally, compared with uncoordinated charging, the short-time scale simulation realizes the peak-shaving and valley-filling by 17.54% and 12.42%, respectively; and the amount of self-consumption of photovoltaics energy increases by 258.74%. Furthermore, the long-time scale simulations also present a satisfying performance for the grid energy saving and the load factor. Particularly, the proposed scheme offers high computational efficiency compared with different architecture and algorithm, and the execution time for scheduling one PEV at one-time interval shows a microsecond basis. [ABSTRACT FROM AUTHOR]

With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods. [ABSTRACT FROM AUTHOR]

Sterol 14α-demethylase catalyzes lanosterol hydroxylation, which is one of the key reactions in the biosynthetic pathway of sterols. There is only one sterol 14α-demethylases gene named Erg11 in Saccharomyces cerevisiae genome. In this study, three sterol 14α-demethylases genes named AoErg11A, AoErg11B and AoErg11C were identified in Aspergillus oryzae genome through bioinformatics analysis. The function of these three genes were studied by yeast complementation, and the expression pattern/subcellular localization of these genes/proteins were detected. The results showed that the three AoErg11s were expressed differently at different growth times and under different abiotic stresses. All of the three proteins were located in endoplasmic reticulum. The AoErg11s could not restore the temperature-sensitive phenotype of S. cerevisiae erg11 mutant. Overexpression of the three AoErg11s affected both growth and sporulation, which may be due to the effect of AoErg11s on ergosterol content. Therefore, this study revealed the functions of three AoErg11s and their effects on the growth and ergosterol biosynthesis of A. oryzae , which may contribute to the further understanding of the ergosterol biosynthesis and regulation mechanism in this important filamentous fungus, A. oryzae ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Jin, Li, Qin, Shang, Yan, Liu, Zeng and Hu.)

Thiolase plays important roles in lipid metabolism. It can be divided into degradative thiolases (thioase I) and biosynthetic thiolases (thiolases II), which are involved in fatty acid β-oxidation and acetoacetyl-CoA biosynthesis, respectively. The Saccharomyces cerevisiae genome harbors only one gene each for thioase I and thiolase II, namely, Pot1 and Erg10 , respectively. In this study, six thiolases (named AoErg10A to AoErg10F) were identified in Aspergillus oryzae genome using bioinformatics analysis. Quantitative reverse transcription-PCR (qRT-PCR) indicated that the expression of these six thiolases varied at different growth times and under different forms of abiotic stress. Subcellular localization analysis showed that AoErg10A was located in the cytoplasm, AoErg10B and AoErg10C were in the mitochondria, and AoErg10D, AoErg10E, and AoErg10F were in the peroxisome. Yeast heterologous complementation assays revealed that AoErg10A, AoErg10D, AoErg10E, AoErg10F, and cytoplasmic AoErg10B (AoErg10B ΔMTS ) recovered the phenotypes of S. cerevisiae erg10 weak and lethal mutants and that only AoErg10D, AoErg10E, and AoErg10F recovered the phenotype of the pot1 mutant that cannot use oleic acid as the carbon source. Overexpression of AoErg10s affected either the growth speed or the sporulation of the transgenic strains. In addition, the fatty acid and ergosterol content changed in all the AoErg10-overexpressing strains. This study revealed the function of six thiolases in A. oryzae and their effect on growth and fatty acid and ergosterol biosynthesis, which may lay the foundation for genetic engineering for lipid metabolism in A. oryzae or other fungi. IMPORTANCE Thiolases, including thioase I and thiolase II, play important roles in lipid metabolism. Aspergillus oryzae, one of the most industrially important filamentous fungi, has been widely used for manufacturing oriental fermented food such as sauce, miso, and sake for a long time. In addition, A. oryzae has a high capability in production of high lipid content and has been used for lipid production. Thus, it is very important to investigate the function of thiolases in A. oryzae. In this study, six thiolase (named AoErg10A to AoErg10F) were identified by bioinformatics analysis. Unlike other reported thiolases in fungi, three of the six thiolases showed dual functions of thioase I and thiolase II in S. cerevisiae, indicating that the lipid metabolism is more complex in A. oryzae. The reveal of function of these thiolases in A. oryzae can lay the foundation for genetic engineering for lipid metabolism in A. oryzae or other fungi.

This study proposes an efficient approach that uses a 1064 nm continuous fiber laser to achieve nanoscale welding of crossed multiwalled carbon nanotubes (MWCNTs). We investigate the effects of laser irradiation time (from 1 to 6 s) on the structure changes and the welding quality of crossed MWCNTs using a scanning electron microscope, transmission electron microscope, and Raman spectroscopy. The experimental results demonstrate that (1) after 2 or 3 s laser irradiation, moderate temperature of MWCNTs can be formed and can cause a higher degree of graphitization and (2) the degree of graphitization and effective contact of nanowelded MWCNT junctions strongly affects its electrical properties.