The objective of this study was to determine the ultrasonication-assisted extraction conditions that maximize the DPPH radical scavenging activity of extracts obtained from the stems of Lespedeza bicolor Turcz through the application of the Response Surface Methodology (RSM). Before delving into the analysis of extraction conditions using the RSM model, we conducted efficiency validation of ultrasonication-assisted extraction and executed single-factor experiments for ethanol concentration, extraction time, and extraction temperature. The data obtained from these single-factor experiments were employed to construct the Box-Behnken Design (BBD). In these results, in the single-factor experiments, it was evident that the parameters for ethanol concentration, extraction time, and extraction temperature exhibited quadratic trends. The single-factor experiments allowed us to discern the trends for each parameter leading to the maximum antioxidant capacity, and this data was subsequently applied to the BBD. Following the completion of initial experiments, a Response Surface Methodology (RSM) model was constructed based on Box-Behnken Design (BBD). According to the predictive model developed in this study, it was anticipated that performing ultrasonic-assisted extraction for 85.0412 minutes at an ethanol concentration of 32.573% and an extraction temperature of 51.5608°C will result in a DPPH radical scavenging activity of 79.7146%. The predictive results were statistically verified through a comparative analysis with actual measurements and ANOVA analysis, confirming the statistical significance of the model. The finding of this study underscore the significance of optimizing extraction conditions in the precise quantification of the antioxidant potential for economic advantages in both experimental and industrial contexts.

수염풍뎅이(Polyphylla laticollis manchurica)는 과거에는 흔히 발견되었으나, 1970년대 이후 한반도 내 개체수 가 급격히 감소하여 2005년 환경부에 의해 멸종위기 야생생물 Ⅰ급으로 지정되었다. 또한 해당종의 분자생물학적 연구는 멸종위기종이라는 특성으로 인해 제한적으로 진행되었다. 그로 인해 NCBI 등 공공 데이터베이스에서 제공되는 서열정보들 또한 부족한 실정이다. 이 연구는 이러한 한계를 극복하고 수염풍뎅이의 유전적 특성을 규명하기 위해 생물정보학적 기술을 활용하여 전사체 분석을 진행하였다. Illumina HiSeq 2500 플랫폼을 사용하여 53,433,048개의 RNA reads를 얻었으며, Trinity와 TGICL을 이용한 De novo 어셈블리 분석을 통해 18,172개의 unigenes를 생성하였다. 생성된 unigenes는 GO, KOG, KEGG, PANM DB를 활용하여 annotation을 진행하였다. 그 결과, GO 분석에서는 ‘binding and catalytic activities’와 관련된 항목이 높은 발현을 보였으며, KOG 분석의 경우 ‘Cellular Processes and Signals’ 범주가 높은 비율을 나타내었다. KEGG 분석을 통해 2,118개의 unigenes가 metabolic 카테고리에 annotation된 것을 확인하였다. SSR 모티프 분석에서는 AT/AT (42.90%) 모티프, AAT/ATT (13.13%) 모티프 순으로 많이 나타나는 것을 확인하였다. 이 연구를 통해 분석한 결과 들을 이용하여 유전자원 및 종 정보를 실시간 제공 및 정보 공유가 가능하도록 Database 및 web-interface를 구축하 였으며, 이러한 자료들은 국내 멸종위기종인 수염풍뎅이의 고유한 유전적 특성을 발굴 및 확보할 수 있는 기반자 료로써 활용될 수 있을 것으로 사료된다.

장내 미생물 군집은 소화 과정, 면역 시스템, 질병 발생 등 숙주의 다양한 면에 광범위한 영향을 주는 것으로 알려져 있으며, 주요 장내 미생물 종은 숙주의 생리 기능에 핵심적인 역할을 수행한다고 발표된 바 있다. 곤충의 장내 미생물 군집에 관한 연구가 최근 활발히 이루어지고 있으며, 이들 연구는 주로 장내 미생물 군집과 기생충, 병원체 간의 상호작용, 종간의 신호 전달 네트워크, 먹이의 소화 과정 등을 중심으로 이루어지고 있다. 이러한 연구들은 대부분 Illumina MiSeq을 활용하여 16S rRNA 유전자의 V1부터 V9 영역 중 선택된 특정 부분을 대상으로 짧은 서열 정보를 대상으로 진행되었다. 그러나, 최근에는 PacBio HiFi 기술이 상용화되면서 16S rRNA의 전장 분석이 가능할 수 있게 되었다. 이번 연구는 장수말벌(Vespa mandarinia)의 해부를 통해 gut과 carcass 부분을 분리한 뒤, 각 샘플을 Illumina MiSeq과 PacBio HiFi 기술을 활용하여 미생물 군집 간의 차이점을 확인하기 위하여 수행되었다.

Paecilomyces tenuipes (P. tenuipes) is a fungus cultivated artificially by South Korean researchers, utilizing rice bran as its substrate. The increased demand for this fungus has not been met with successful cultivation methods for fruiting body production in natural environments. Therefore, we tested the effect on the growth of P. tenuipes using a Solid media based on pests. In this results, the Solid media based on M.alternatus was effective in increasing the growth of P. tenuipes and the content of cordycepin. Moreover, we confirmed the conditions for manufacturing a Solid media based on M.alternatus for P. tenuipes growth. We suggested that the growth-promoting compounds offers valuable insights for optimizing fungal cultivation conditions, thereby enhancing productivity and contributing to a broader understanding of fungal physiology in varying nutritional environments.

Plants synthesize antioxidant compounds as a defense mechanism against reactive oxygen species. Recently, plant-derived antioxidant compounds have attracted attention due to the increasing consumer awareness in the heath industry. However, traditional methods for measuring the antioxidant activity of these compounds are time-consuming and costly. Therefore, our study constructed a quantitative structure-activity relationship (QSAR) model that can predict antioxidant activity using graph convolutional networks (GCN) from plant structural data. The accuracy (Acc) of the model reached 0.6 and the loss reached 0.03. Although with lower accuracy than previously reported QSAR models, our model showed the possibility of predicting DPPH antioxidant activity in a wide range of plant compounds (phenolics, polyphenols, vitamins, etc.) based on their graph structure.

Ship collision accidents not only endanger the safety of ships and personnel, but also may cause serious marine environmental pollution. To solve this problem, advanced technologies have been developed and applied in the field of intelligent ships in recent years. In this paper, a novel path planning algorithm is proposed based on particle swarm optimization (PSO) to construct a decision-making system for ship's autonomous collision avoidance using the process analysis which combines with the ship encounter situation and the decision-making method based on ship collision avoidance responsibility. This algorithm is designed to avoid both static and dynamic obstacles by judging the collision risk considering bad weather conditions by using BP neural network. When the two ships enter a certain distance, the optimal collision avoidance course and speed of the ship are obtained through the improved collision avoidance decision-making method. Finally, through MATLAB and Visual C++ platform simulations, the results show that the ship collision avoidance decision-making scheme can obtain reasonable optimal collision avoidance speed and course, which can ensure the safety of ship path planning and reduce energy consumption.

In this study, a bipolar visible light responsive photocatalytic fuel cell (PFC) was constructed by loading a Z-scheme g-C3N4/ carbon black/BiOBr and a Ti3C2/ MoS2 Schottky heterojunction on the carbon brush to prepare the photoanode and photocathode, respectively. It greatly improved the electron transfer and achieved efficient degradation of organic pollutants such as antibiotics and dyes simultaneously in two chambers of the PFC system. The Z-scheme g-C3N4/carbon black/BiOBr formed by adding highly conductive carbon black to g-C3N4/BiOBr not only effectively separates the photogenerated carriers, but also simultaneously retains the high reduction of the conduction band of g-C3N4 and the high oxidation of the valence band of BiOBr, improving the photocatalytic performance. The exceptional performance of Ti3C2/ MoS2 Schottky heterojunction originated from the superior electrical conductivity of Ti3C2 MXene, which facilitated the separation of photogenerated electron–hole pairs. Meanwhile, the synergistic effect of the two photoelectrodes further improved the photocatalytic performance of the PFC system, with degradation rates of 90.9% and 99.9% for 50 mg L− 1 tetracycline hydrochloride (TCH) and 50 mg L− 1 rhodamine-B (RhB), respectively, within 180 min. In addition, it was found that the PFC also exhibited excellent pollutant degradation rates under dark conditions (79.7%, TCH and 97.9%, RhB). This novel pollutant degradation system is expected to provide a new idea for efficient degradation of multiple pollutant simultaneously even in the dark.

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

Graphite felt is a felt-like porous material made of high-temperature carbonized polymers. It is widely used in electrode materials because of its good temperature resistance, corrosion resistance, large surface area and excellent electrical conductivity. In this paper, the surface functional group modification is of graphite felt electrodes (mainly nitrogen doping modification, nitrogen–sulfur or nitrogen–boron co-doping modification) and surface catalytic modification (metal/ion surface modification and metal oxide surface modification as Main). There are two main methods and research progresses to improve the performance of graphite felt electrodes, and the comprehensive performance of surface functional group-modified graphite felt electrodes and surface catalytically modified graphite felt electrodes are compared respectively. The results show that both surface functional group modification and surface catalytic modification can improve the comprehensive performance of graphite felt electrodes. In this paper, the future development direction of graphite felt activation modification is also prospected.

Coal-based graphite has become the main material of emerging industries. The microstructure of coal-based graphite plays an important role in its applications in many fields. In this paper, the effect of carbon disulfide/N-methyl-2-pyrrolidone solvent mixture extraction on the microstructure of bituminous coal-based graphite was systematically studied through preliminary extraction coupled with high-temperature graphitization. The graphitization degree g (75.65%) of the coal residue-based graphite was significantly higher than that of the raw coal-based graphite. The crystallite size La of the coal residue-based graphite was reduced by 47.06% compared with the raw coal-based graphite. The ID/ IG value of the coal residue-based graphite is smaller than that of the raw coal-based graphite. The specific surface area (16.72 m2/ g) and total pore volume (0.0567 m3/ g) of the coal residue-based graphite are increased in varying degrees compared with the raw coal-based graphite. This study found a carbon source that can be used to prepare coal-based graphite with high graphitization degree. The results are expected to provide a theoretical basis for further clean and efficient utilization of the coal residue resources.

To improve the pyrolytic carbon (PyC) deposition rate of Carbon/Carbon (C/C) composites prepared by the traditional chemical vapor infiltration (CVI) method, the 3D Ni/wood-carbon (3D Ni/C) catalyst was introduced into the CVI process. The effects of catalyst on the density of C/C composites were studied, and the deposition rate and morphologies of PyC were investigated after catalytic CVI. The morphologies of catalyst and PyC were characterized by scanning electron microscope and polarized light microscopy. The catalytic deposition mechanism of PyC was studied by density functional theory. The experimental results show that the initial carbon deposition efficiency of the catalytic pyrolysis process was 3–4 times that of the noncatalytic process. The catalyst reduced the energy barrier in the first step of deposition reaction from 382.55 to 171.67 kJ/mol according to simulation results. The pyrolysis reaction energy with Ni catalyst is reduced by 54% than that without the catalyst.

COVID-19로 인해 큰 혼란이 야기되었는데, 중국은 데이터를 이용하여 방역을 하였고 중국내 감염병 발생이 어느 정도 억제되었다. 하지만 데이터화 대응 상황은 양날의 검처럼 중대한 공중위생사건이 발생하여 대응 상황을 완화하는 동시에, 개인정보의 안전에 대한 위험도 내포하고 있다. 최근 2년동안 COVID-19의 방역과정에서 생기는 개인정보 보호 문제에 대응하기 위하여, 중국은 <개인정보보호법>을 제정하여 공포하였다. 중국에서는 과거부터 개인정보 보호입법에 관한 논의가 수차례 있었으나 입법에 이르지 못하다가 최근 COVID-19의 방역과정에서 생기는 개인정보 보호의 문제에 대한 대응 필요성이 대두된 가운데 중국은 20 21년 8월 20일 개인정보보호법을 제정하여 공포되었으며 동법은 같은 해 11월 1일부터 시행되고 있다. 이 연구는 중국에서의 데이터화 대응 상황 중 발생하는 개인정보 보호의 문제점을 검토한다.