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Introduction: This study aimed to explore the impact of COVID-19 on strength performance in highly trained athletes. Method: A force plate was employed to measure squat jump height (SJH), counter-movement jump height (CMJH), and drop jump reactive strength index (DJRSI) in 27 highly trained athletes before infection, and at one week, two weeks, and four weeks post-recovery. Additionally, an Isometric Mid-thigh Pull (IMTP) test was conducted to record maximum isometric strength (MIS) and the rate of force development of the initial phase (RFD 0–50; RFD 0–100). Repeated measures analysis of variance was utilized to compare variations in these indicators across different time points. Results: One week post-recovery, SJH (-7.71%, P = 0.005), CMJH (-9.08%, P < 0.001), DJRSI (-28.88%, P < 0.001), MIS (-18.95%, P < 0.001), RFD 0–50 (-64.98%, P < 0.001), and RFD 0–100 (-53.65%, P < 0.001) were significantly lower than pre-infection levels. Four weeks post-recovery, SJH (-2.08%, P = 0.236), CMJH (-3.28%, P = 0.277), and MIS (-3.32%, P = 0.174) did not differ significantly from pre-infection levels. However, DJRSI (-11.24%, P = 0.013), RFD 0–50 (-31.37%, P = 0.002), and RFD 0–100 (-18.99%, P = 0.001) remained significantly lower than pre-infection levels. Conclusion: After COVID-19, highly trained athletes exhibited a significant reduction in maximum strength, explosive strength, reactive strength, and initial phase force generation capability. By four weeks post-recovery, their maximum and explosive strength had returned to near pre-infection levels, yet their reactive strength and initial phase force generation capability remained significantly impaired. [ABSTRACT FROM AUTHOR]

The integration of high-penetration renewable energy sources is playing an increasingly important role in the frequency regulation of the power system. However, due to the varying output characteristics and response speeds of different renewable distributed energy resources (DERs), the overall response from the collective output of these distributed energy resources may not meet expected requirements and could have adverse effects on the grid. One drawback of traditional distributed coordinated control is its high communication requirements. This paper proposes using digital twin (DT) technology for the coordinated control of distributed energy resources, which can minimize the communication needs between various distributed energy resources while achieving coordinated control. Verification of the frequency support coordinated control strategy based on digital twin technology shows that it can effectively enhance the individual output characteristics and overall response of each distributed energy resource, providing effective support for grid frequency. [ABSTRACT FROM AUTHOR]

The fluctuations brought by the renewable energy access to the distribution network make it difficult to accurately describe the state space model of the distribution network's dynamic process, which is the basis of the existing dynamic state estimation methods such as the Kalman filter. The inaccurate state space model directly causes an error of dynamic state estimation results. This paper proposed a new dynamic state estimation method which can mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Firstly, the proposed method mitigates the impact of renewable energy fluctuation by considering PV power prediction in establishing distribution network state space model. Secondly, SVSF filter is introduced to achieve more accurate estimation under noise. The case study and evaluations are carried out based on MATLAB simulation. The results prove that the smooth variable structure filter with photovoltaic power prediction has a better dynamic state estimation effect under the fluctuation of the distribution network compared with the existing Kalman filter. [ABSTRACT FROM AUTHOR]

BioLadder is an online data analysis platform designed for proteomics research. It offers a variety of proteomics analysis modules, as well as modules for other omics data analysis. The platform includes experimental data analysis modules and common data analysis modules. It provides functions for data visualization, preprocessing, quantitative analysis, sequence analysis, abundance mapping, classification analysis, and function analysis. The platform is user-friendly, with simplified input formats, specialized default parameters for proteomics, diverse adjustment methods, powerful color schemes, comprehensive help information, and real-time assistance. The platform is developed using the Vue.js and Laravel frameworks, with data persistence managed by MySQL. The authors of the platform are affiliated with Qinglian Biotech Co., Ltd. The work was supported by the National Key Research Program of China. [Extracted from the article]

Halide perovskite light‐emitting diodes (LEDs) have made remarkable advancements in both efficiency and stability. However, when compared to green and red LEDs, blue perovskite LEDs still lag behind in terms of efficiency and stability. Presently, efficient blue LEDs primarily rely on lead‐based perovskites, which goes against the environmentally friendly practices and restricts potential industrial applications. Consequently, there has been a surge of interest in academic research toward developing blue LEDs using lead‐free perovskites. This paper provides a comprehensive review of the structural diversity of lead‐free perovskite derivatives used for blue emission, encompassing Sn‐, Zr‐, Bi‐, Sb‐, Cu‐, Zn‐based halides as well as double perovskites. Various material synthesis techniques are discussed, such as nanocrystal preparation and film preparation. Additionally, the photoluminescence properties for different energy band structures are outlined, along with the luminescence mechanism involving energy transfer and strategies for enhancing luminous efficiency. The advancement in efficiency and stability of lead‐free perovskite LEDs is also examined in this review. Finally, the current challenges and future opportunities in advancing research on blue lead‐free perovskite derivatives and their potential application in LED technology is emphasized. [ABSTRACT FROM AUTHOR]

Objective measures are recommended. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterized by progressive damage to both upper and lower motor neurons. Genetic factors are known to play a crucial role in ALS, as genetic studies not only advance our comprehension of disease mechanisms but also help unravel the complex phenotypes exhibited by patients. To gain further insights into the genetic landscape of ALS in the Chinese population and explore genotype–phenotype correlations among individuals, we conducted whole-genome sequencing to screen genes in 34 Chinese familial ALS (FALS) probands lacking the most common ALS-associated genes. Within this cohort, we identified a rare heterozygous missense mutation in the N-terminal domain of KIF5A (c.86A>G) in one of the probands. This finding is significant as mutations in the KIF5A gene have been implicated in ALS in European cohorts since 2018, predominantly characterized by C-terminal mutations. Analysis of the clinical phenotype within this familial lineage revealed a delayed onset of symptoms, an extended survival duration, and initial manifestations in both upper limbs. These observations underscore the clinical heterogeneity observed in ALS patients harboring KIF5A mutations. In conclusion, our study contributes to the growing body of evidence linking KIF5A to ALS and enhances our understanding of the intricate genetic landscape of this disease. [ABSTRACT FROM AUTHOR]

With the continuous improvement in production efficiency and quality of life, the requirements of electrical equipment for power quality are also increasing. Accurate detection of various power quality disturbances is an effective measure to improve power quality. However, in practical applications, the dataset is often contaminated by noise, and when the dataset is not sufficient, the computational complexity is too high. Similarly, in the recognition process of artificial neural networks, the local optimum often occurs, which ultimately leads to low recognition accuracy for the trained model. Therefore, this article proposes a power quality disturbance classification strategy based on the variational mode decomposition (VMD) and improved support vector machine (SVM) algorithms. Firstly, the VMD algorithm is used for preprocessing disturbance denoising. Next, based on the analysis of typical fault characteristics, a multi-SVM model is used for disturbance classification identification. In order to improve the recognition accuracy, the improved Grey Wolf Optimization (IGWO) algorithm is used to optimize the penalty factor and kernel function parameters of the SVM model. The results of the final case study show that the classification accuracy of the proposed method can reach over 98%, and the recognition accuracy is higher than that of the other models. [ABSTRACT FROM AUTHOR]

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users' ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism's ability to capture feature information over long time periods. To validate the model's decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models' test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. [ABSTRACT FROM AUTHOR]

Background: Chronotype has gained recognition as a significant factor in enhancing athletic performance. This study aimed to deepen our understanding of athletes' sleep chronotypes and provide a foundation for developing evidence-based training and competition programs. By comprehensively describing and analyzing the chronotype characteristics of Chinese professional athletes, considering individual and sports factors, sleep quality and habits, and mental energy, this research aimed to contribute valuable insights to the field. Methods: A sample of 1069 professional athletes from sports teams in Shanghai completed the Athlete Sleep Screening Questionnaire and the Athlete Mental Energy Scale to assess chronotype, sleep quality, sleep-influencing habits, and mental energy. Results: Among athletes, sleep typology fell within the intermediate range, slightly leaning toward morningness. Male athletes and those who engaged in static sports displayed a greater propensity for morningness. Age correlated with a preference for eveningness. High-level athletes exhibited a stronger inclination toward eveningness. Sleep quality issues were associated with an inclination toward eveningness. Daily caffeine intake and the habit of using electronic devices before bedtime are also linked to eveningness. Chronotype demonstrated the ability to predict various dimensions of athletes' mental energy. It was the strongest predictor of vigor, but the loadings were smaller than those of sleep quality. Conclusion: Chinese athletes' chronotypes primarily exhibit distinct characteristics related to individual factors such as gender, sports discipline, and ranking, as well as habits like caffeine consumption and electronic device use. Moreover, these sleep patterns demonstrate predictive capabilities across all dimensions of athletes' mental energy. This study sheds light on Chinese athletes' unique sleep chronotype attributes, enriching our understanding of sleep patterns among professional athletes under various systems. These insights offer an initial basis for enhancing the effectiveness of athlete scheduling and training management. [ABSTRACT FROM AUTHOR]

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation. [ABSTRACT FROM AUTHOR]

In nature, some fish can adhere tightly to the surface of stones, aquatic plants, and even other fish bodies. This adhesion behavior allows these fish to fix, eat, hide, and migrate in complex and variable aquatic environments. The adhesion function is realized by the special mouth and sucker tissue of fish. Inspired by adhesion fish, extensive research has recently been carried out. Therefore, this paper presents a brief overview to better explore underwater adhesion mechanisms and provide bionic applications. Firstly, the adhesion organs and structures of biological prototypes (e.g., clingfish, remora, Garra, suckermouth catfish, hill stream loach, and goby) are presented separately, and the underwater adhesion mechanisms are analyzed. Then, based on bionics, it is explained that the adhesion structures and components are designed and created for applications (e.g., flexible gripping adhesive discs and adhesive motion devices). Furthermore, we offer our perspectives on the limitations and future directions. [ABSTRACT FROM AUTHOR]

With the increase of nonlinear loads (such as electric arc furnace, electrified railway, etc.) in power system, the harmonic distortion and unbalance of voltage and current are becoming more and more serious. The energy flow in the system is more complex and variable, and the power factor calculation method is controversial. This paper summarizes the influence factors and calculation methods of power factor for nonsinusoidal and unbalanced systems. The calculation methods of arithmetic power factor, vector power factor and effective power factor are compared first, and then the power factor calculation method recommended by IEEE Std 1459‐2010 and its improvement method under three‐phase balance user, active power calculation and metering error are analyzed, and the recommended calculation methods for different situations are given. Finally, future research perspectives and challenges for power factor calculation are pointed out. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Water electrolysis is a clean, non-polluting way of producing hydrogen that has seen rapid development in recent years. It offers the possibility of resolving the issue of excessive carbon emissions in conventional hydrogen production methods. In addition, waste heat recovery in hydrogen fuel cells can significantly increase the efficiency of energy use. Thus, to combine the electric power system, the hydrogen energy system, and the district heating system, this research suggests a novel optimal multi-energy complementary electricity–hydrogen–heat model. Rooftop photovoltaics, energy storage batteries, electric boilers, and hydrogen energy systems made up of hydrogen generation, hydrogen storage, and hydrogen fuel cells are all included in the suggested model. Furthermore, the electricity–hydrogen–heat system can be connected successfully using waste heat recovery in hydrogen fuel cells to create a coordinated supply of heat and power. In this work, the waste heat of hydrogen fuel cells is taken into account to increase the efficiency of energy use. To show the effectiveness of the suggested optimal multi-energy complementary model, many case studies have been conducted. [ABSTRACT FROM AUTHOR]

With the wide application of the gas-power system, gas-power coupling equipment such as gas turbines are gradually becoming widely used, and the problem of insufficient system reserve capacity needs to be solved. In order to improve the operational flexibility of the gas-power system, this paper combines the source side, the load side, and the energy storage side to propose a multi-type backup system, and constructs the source-load-storage multiple reserve capacity system of the gas-power system. Through the participation of gas turbine, steam turbine, interruptible load, and energy storage battery to provide reserve capacity, it can fully cope with the output fluctuation of the load side and source side and realize the coordinated operation of multiple resources to provide reserve capacity. In addition, the coordination of gas turbines and steam turbines can further improve the operation flexibility of the gas-power system. Through the example analysis, it was found that the proposed method reduced the total operating cost of the system by 10.6%. [ABSTRACT FROM AUTHOR]

Coordinated fault recovery is essential for the resilience enhancement of integrated electric and heating systems (IEHS) following natural catastrophes as the linkage of the power distribution system (PDS) and district heating system becomes tighter. DHS reconfiguration is a viable method for service restoration because it could adjust the energy between energy sources and achieve uninterrupted energy supplies. In this paper, a collaborative service restoration model considering DHS reconfiguration is proposed to achieve better recovery after natural disasters. DHS reconfiguration could guarantee interrupted power supply in non-fault regions by shifting electric loads between power sources and accomplish optimal service restoration by adjusting the power output of combined heat and power units. Numerous case studies are undertaken to demonstrate the performance of coordinated reconfiguration on resilience enhancement and to confirm the efficacy of the proposed paradigm. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy, particularly wind power, and the integration of different energy systems have become two major trends in the development of energy systems. In this context, this paper proposes a robust interval optimization method for combined heat and power dispatch (CHPD) to address the challenges associated with wind power accommodation. To enhance the flexibility of a power system and support the integration of wind power, flexibility resources from a district heating system are introduced in the economic dispatch. To ensure the safety and reliability of the CHPD results, a robust interval optimization method is employed. By considering a range of possible wind power outputs, the robust interval optimization method provides a robust and reliable dispatch plan that can accommodate uncertainties and fluctuations in wind power generation. To verify the effectiveness of the proposed model and method, case studies were conducted on a 6-bus electrical power system connected with a 6-node district heating system. The results demonstrate that the proposed approach can effectively enhance the integration of wind power and improve the overall reliability and flexibility of the energy system. [ABSTRACT FROM AUTHOR]

The harmonic and resonant characteristics of a multi-inverter grid-connected system can negatively affect power quality when weak grid conditions are present. In this paper, firstly, harmonic currents are modeled for the inverter, the correctness of low-frequency harmonics and high-frequency harmonics are verified in closed loop, and the characteristics of harmonic currents are analyzed when the parameters are varied. Secondly, the resonant characteristics of the inverter with feed-forward link are analyzed, and a multi-inverter grid-connected equivalent model based on the triple-decomposition conductance is developed and analyzed in conjunction with the resonant modal analysis method. Then, a harmonic resonance impedance reshaping method is proposed to suppress the background harmonics using improved weighted average current control (WACC), suppress the system resonance based on the point of common coupling (PCC) paralleling virtual conductance method, and improve the stability margin of the system combined with impedance reshaping resonance suppression method. Finally, simulations and comparison of results with different suppression methods verify the superiority and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A combined heat and power virtual power plant (CHP-VPP) can effectively control the distributed resources in an electric–thermal coupling system and solve the problem of lack of flexibility caused by large-scale renewable energy grid connection. Similar to the optimal reconfiguration of distribution network topology by operating switches, the district heating system is also equipped with tie and sectionalizing valves to realize the optimal adjustment of district heating network (DHN) topology, which provides an economical and effective method for improving the power system's flexibility. Based on this, this paper proposes a CHP-VPP economic scheduling model considering reconfigurable DHN. Firstly, the energy flow model is introduced to reduce the computational complexity. Secondly, adaptive robust optimization solved by the column-and-constraint generation algorithm is used to settle the randomness of wind power to ensure that the results are feasible in all worst scenarios. Finally, the feasibility of the proposed model is illustrated by case studies based on an actual CHP-VPP. The results show that compared with the reference case, considering the reconfigurability of DHN in the CHP-VPP optimization scheduling process can reduce the cost by about 2.78%. [ABSTRACT FROM AUTHOR]

With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation. [ABSTRACT FROM AUTHOR]

Closed-loop management of athletes at the training base is a compromise approach that balanced epidemic prevention and sports training during the COVID-19 pandemic. This study investigated the impact of prolonged closed-loop management on athletes' sleep and mood during the 2022 Shanghai Omicron wave. The Pittsburgh Sleep Quality Index and the Profile of Mood States were used to assess the sleep and mood states of 110 professional athletes in "closed-loop management" at the training base after 1 and 2 months of closed-loop management, respectively, to characterize changes in sleep and mood with prolonged closed-loop management. After two months of control, the sleep and mood of 69 athletes and students of the same age were measured using the Pittsburgh Sleep Quality Index and Perceptual Stress Scale, as well as the Warwick-Edinburgh Mental Well-being Scale, to compare the differences in sleep and mood between athletes undergoing closed-loop management and the general population who were managed in the community. Paired sample t-tests and independent sample t-tests were used for comparisons across different time intervals and different management approaches. Results showed that with the time of closed-loop management increased, athletes woke up earlier (p = 0.002), slept less (p = 0.024), and became angrier (p = 0.014); athletes had poorer overall sleep quality (p < 0.001) but lower stress level (p = 0.004) than those who were outside the base. In closed-loop management, the athletes were able to maintain a stable sleep and mood state. Sports team administrators must be aware of the need to improve athletes' sleep quality and help athletes to agree with this approach of management. [ABSTRACT FROM AUTHOR]

In this paper, a novel electrochemical sensor was constructed for the detection of purine bases. Ultrafine carbide nanocrystals confined within porous nitrogen-doped carbon dodecahedrons (PNCD) were synthesized by adding molybdate to ZIF-8 followed by annealing. With MoC-based PNCDs (MC-PNCDs) as the carrier, gold nanoparticles (AuNPs) were deposited on the electrode surface via potentiostatic deposition as the promoter of electron transfer, forming a AuNPs/MC-PNCDs/activated glassy carbon electrode (AGCE) sensor. MC-PNCDs had a large specific surface area, which combined with the excellent electrocatalytic activity of AuNPs, synergistically improved the electrocatalytic activity. The morphology and structure of the electrode surface modifier were characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray photoelectron spectroscopy, infrared spectroscopy, X-ray diffraction, nitrogen adsorption–desorption analysis, and electrochemical characterization. Under the optimal conditions, the linear detection range of guanine (G) and adenine (A) was 0.5–160.0 μM, and the detection limits (S/N=3) were 72.1 and 69.6 nM, respectively. AuNPs/MC-PNCDs/AGCE was successfully constructed, and was used to simultaneously detect G and A with high sensitivity and selectivity. Moreover, the sensor was successfully used to detect G and A in herring sperm DNA samples. [ABSTRACT FROM AUTHOR]

In the task of text sentiment analysis, the main problem that we face is that the traditional word vectors represent lack of polysemy, the Recurrent Neural Network cannot be trained in parallel, and the classification accuracy is not high. We propose a sentiment classification model based on the proposed Sliced Bidirectional Gated Recurrent Unit (Sliced Bi-GRU), Multi-head Self-Attention mechanism, and Bidirectional Encoder Representations from Transformers embedding. First, the word vector representation obtained by the BERT pre-trained language model is used as the embedding layer of the neural network. Then the input sequence is sliced into subsequences of equal length. And the Bi-sequence Gated Recurrent Unit is applied to extract the subsequent feature information. The relationship between words is learned sequentially via the Multi-head Self-attention mechanism. Finally, the emotional tendency of the text is output by the Softmax function. Experiments show that the classification accuracy of this model on the Yelp 2015 dataset and the Amazon dataset is 74.37% and 62.57%, respectively. And the training speed of the model is better than most existing models, which verifies the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Power quality analysis and governance need the identification of power quality issues. With the use of smart meters and various smart collection devices, more and more power quality data are collected, and the massive data collection brings pressure on communication, storage and computation to the conventional algorithm for identifying and classifying power quality disturbances based on cloud computing. In the paper, a classification algorithm for power quality disturbance identification based on edge computing and fusion model is proposed. The algorithm's key concept is to compress and sense the power quality signals at the edge side, and then transmit the compressed power quality data to the cloud, which uses an improved Dense-Net and LSTM fusion model to identify and classify the compressed power quality data. Through experiments, it is proved that the method can compress the power quality signal to 70% of the original signal size while satisfying the recognition and data on power quality disturbance categorization accuracy, reducing the communication cost of data transmission, lowering the computational pressure and caching pressure on the cloud, and having certain robustness. [ABSTRACT FROM AUTHOR]

Structural colors produced by light manipulating at subwavelength dimensions have been widely studied. In this work, a metasurface-based subtractive color filter (SCF) is demonstrated. The color display of the SCF is confirmed by finding the complementary color of colors filtered by SCF within the color wheel. In addition, two artificial neural network (ANN) models are utilized to accelerate the metasurface forward prediction, and the long short-term memory (LSTM) shows much better performance than traditional multilayer perceptron (MLP). Meanwhile, we train an inverse ANN model established with LSTM to recover the optimal geometric parameter combinations of the meta-atoms. With the variation of the geometric parameters of meta-atoms, versatile color displays of structural colors are realized. The metasurface we propose exhibits good performance of transmissive-type structural color in visible range. The work provides a method for high-efficiency geometric parameter prediction, and paves the way to nanostructure-based color design for display and anticounterfeiting applications. [ABSTRACT FROM AUTHOR]

Salicylic acid (SA) is a stress hormone synthesized in phenylalanine ammonia-lyase (PAL) and the branching acid pathway. SA has two interconvertible forms in plants: SAG (SA O-β-glucoside) and SA (free form). The molecular mechanism of conversion of SA to SAG had been reported previously. However, which genes regulate SAG to SA remained unknown. Here, we report a cytoplasmic β-glucosidase (β-Glu) which participates in the SA pathway and is involved in the brown hull pigmentation in rice grain. In the current study, an EMS-generated mutant brown hull 1 (bh1) displayed decreased contents of SA in hulls, a lower photosynthesis rate, and high-temperature sensitivity compared to the wild type (WT). A plaque-like phenotype (brown pigmentation) was present on the hulls of bh1, which causes a significant decrease in the seed setting rate. Genetic analysis revealed a mutation in LOC_Os01g67220, which encodes a cytoplasmic Os1βGlu4. The knock-out lines displayed the phenotype of brown pigmentation on hulls and decreased seed setting rate comparable with bh1. Overexpression and complementation lines of Os1βGlu4 restored the phenotype of hulls and normal seed setting rate comparable with WT. Subcellular localization revealed that the protein of Os1βGlu4 was localized in the cytoplasm. In contrast to WT, bh1 could not hydrolyze SAG into SA in vivo. Together, our results revealed the novel role of Os1βGlu4 in the accumulation of flavonoids in hulls by regulating the level of free SA in the cellular pool. [ABSTRACT FROM AUTHOR]

Purpose: Snake-inspired robots are of great significance in many fields because of their great adaptability to the environment. This paper aims to systematically illustrate the research progress of snake-inspired robots according to their application environments. It classifies snake-inspired robots according to the numbers of degrees of freedom in each joint and briefly describes the modeling and control of snake-inspired robots. Finally, the application fields and future development trends of snake-inspired robots are analyzed and discussed. Design/methodology/approach: This paper summarizes the research progress of snake-inspired robots and clarifies the requirements of snake-inspired robots for self-adaptive environments and multi-functional tasks. By equipping various sensors and tool modules, snake-inspired robots are developed from fixed-point operation in a single environment to autonomous operation in an amphibious environment. Finally, it is pointed out that snake-inspired robots will be developed in terms of rigid and flexible deformable structure, long endurance and multi-function and intelligent autonomous control. Findings: Inspired by the modular and reconfigurable concepts of biological snakes, snake-inspired robots are well adapted to unknown and changing environments. Therefore, snake-inspired robots will be widely used in industrial, military, medical, post-disaster search and rescue applications. Snake-inspired robots have become a hot research topic in the field of bionic robots. Originality/value: This paper summarizes the research status of snake-inspired robots, which facilitates the reader to be a comprehensive and systematic understanding of the research progress of snake-inspired robots. This helps the reader to gain inspiration from biological perspectives. [ABSTRACT FROM AUTHOR]

A three-phase three-wire voltage source converter (VSC) can produce third harmonics when it is operated under an unbalanced condition. It is essential to understand the mechanism of the production of this third harmonic and to assess its impact on power systems. Therefore, this paper presents an analytical model of the VSC under the unbalanced condition through mathematical derivations, and the final model is a coupled Thevenin circuit. The proposed model allows for direct computation of the non-characteristic third harmonics through harmonic power flow studies. The results show that VSC under unbalanced conditions emits both positive-sequence and negative-sequence third harmonics, and that the positive-sequence third harmonic is much larger than the negative-sequence third harmonic. It also shows that the unbalanced level and the size of the dc-link capacitor are critical to the level of non-characteristic third harmonics. The correctness of the proposed model and its application on noncharacteristic third harmonic evaluations have been verified using EMT simulations. [ABSTRACT FROM AUTHOR]

Improving catalytic selectivity with high conversion meets big challenge for direct conversion of syngas (CO/H2) to aromatics (STA). Here, a series of LaBO3 (B=Fe, Co, Ni, Mn, Cr) perovskite oxides was synthesized and applied as metal oxide component of OX‐ZEO bifunctional catalysts for STA firstly. By combining with HZSM‐5, LaFeO3, LaMnO3 and LaCrO3 exhibited good performance on syngas conversion. For the bifunctional catalyst of LaFeO3/HZSM‐5, a superior catalytic performance with 82.0 % selectivity to aromatics at 28.5 % CO conversion was achieved. The composition of B‐site metals in perovskites and its stabilization in the structure are essential for activity of STA reaction. In situ DRIFTs study revealed the syngas conversion over perovskite followed the surface formate and methoxy intermediate species transformation pathway. [ABSTRACT FROM AUTHOR]

Background: Panicle is a harvesting organ of rice, and its morphology and development are closely associated with grain yield. The current study was carried on a mutant screened through an EMS (ethyl-methane sulphonate) mutagenized population of a Japonica cultivar Kitaake (WT). Results: A mutant, named as asp-lsl (aberrant spikelet-long sterile lemma), showed a significant decrease in plant height, number of tillers, thousand-grains weight, seed setting rate, spikelet length, kernel length and effective number of grains per panicle as compared to WT. Asp-lsl showed a pleiotropic phenotype coupled with the obvious presence of a long sterile lemma. Cross-sections of lemma showed an increase in the cell volume rather than the number of cells. Genetic segregation analysis revealed its phenotypic trait is controlled by a single recessive nuclear gene. Primary and fine mapping indicated that candidate gene controlling the phenotype of asp-lsl was located in an interval of 212 kb on the short arm of chromosome 8 between RM22445 and RM22453. Further sequencing and indels markers analysis revealed LOC_Os08g06480 harbors a single base substitution (G→A), resulting in a change of 521st amino acid(Gly→Glu. The homology comparison and phylogenetic tree analysis revealed mutation was occurred in a highly conserved domain and had a high degree of similarity in Arabidopsis, corn, and sorghum. The CRISPR/Cas9 mutant line of ASP-LSL produced a similar phenotype as that of asp-lsl. Subcellular localization of ASP-LSL revealed that its protein is localized in the nucleus. Relative expression analysis revealed ASP-LSL was preferentially expressed in panicle, stem, and leaves. The endogenous contents of GA, CTK, and IAA were found significantly decreased in asp-lsl as compared to WT. Conclusions: Current study presents the novel phenotype of asp-lsl and also validate the previously reported function of OsREL2 (ROMOSA ENHANCER LOCI2), / ASP1(ABERRANT SPIKELET AND PANICLE 1). [ABSTRACT FROM AUTHOR]

The branched-chain amino acids (BCAAs) including leucine (Leu), isoleucine (Ile) and valine (Val) play a pivotal role in the human body. Herein, we developed capillary electrophoresis (CE) coupled with conventional UV detector to quantify underivatized BCAAs in two kinds of sport nutritional supplements. For direct UV detection at 195 nm, the BCAAs (Leu, two enantiomers of Ile and Val) were separated in a background electrolyte (BGE) consisting of 40.0 mmol/L sodium tetraborate, and 40.0 mmol/L β-cyclodextrin (β-CD) at pH 10.2. In addition, the indirect UV detection at 264 nm was achieved in a BGE of 2.0 mmol/L Na2HPO4, 10.0 mmol/L p-aminosalicylic acid (PAS) as UV absorbing probe, and 40.0 mmol/L β-CD at pH 12.2. The β-CD significantly benefited the isomeric separation of Leu, L- and D-Ile. The optimal conditions allowed the LODs (limit of detections) of direct and indirect UV absorption detection to be tens μmol/L level, which was comparable to the reported CE inline derivatization method. The RSDs (relative standard deviations) of migration time and peak area were less than 0.91% and 3.66% (n = 6). Finally, CE with indirect UV detection method was applied for the quantitation of BCAAs in two commercial sport nutritional supplements, and good recovery and precision were obtained. Such simple CE method without tedious derivatization process is feasible of quality control and efficacy evaluation of the supplemental proteins. [ABSTRACT FROM AUTHOR]

Examines the preparation of small sized silver (Au) doped gel via sol-gel process, where the Au particles were introduced directly from colloidal Au aqueous solution. Chemical and homogeneous mixing of the aqueous solution of colloidal Au particles with the sol system in the liquid state; Formation of a stable condensed solid with homogeneously dispersive Au particles; Use of a transmission electron microscope to observe the red transparent gels obtained.