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Carbon fiber-reinforced polymer (CFRP) reinforcement technology has been widely used in the reinforcement of reinforced concrete (RC) beams. At this stage, high prestressed CFRP board reinforcement is often used in actual reinforcement. However, most reinforced bridges are designed for a long time, and the design value of the protective layer is low, and it is impossible to achieve a large prestressed tension. Therefore, this paper proposes the reverse-arch method to paste the CFRP board and apply low prestress to strengthen the symmetrical RC beam. Through the three-point forward loading test, the cracking load, ultimate load, crack width, mid-span deflection, strain and failure mode of a reverse-arch method-pasted CFRP board-reinforced beam, a directly pasted CFRP board-reinforced beam and an unreinforced beam are compared. The results show that the load-bearing capacity and stiffness of the test beam can be improved by pasting CFRP plates with anti-arch method, but the ductility of the test beam is reduced. Compared with the unreinforced beam, the maximum cracking load and ultimate load are increased by 56% and 63% respectively. The reverse-arch method can produce low prestress, improve the stiffness and bearing capacity of members, and has a good prospect of engineering application. [ABSTRACT FROM AUTHOR]

Land degradation poses a critical threat to the stability and security of ecosystems, especially in salinized areas. Monitoring the land degradation of salinized areas facilitates land management and ecological restoration. In this research, we integrated the salinization index (SI), albedo, normalized difference vegetation index (NDVI) and land surface soil moisture index (LSM) through the principal component analysis (PCA) method to establish a salinized land degradation index (SDI). Based on the SDI, the land degradation of a typical salinized area in the Central Asia Amu Darya delta (ADD) was analysed for the period 1990–2019. The results showed that the proposed SDI had a high positive correlation (R2 = 0.89, p < 0.001) with the soil salt content based on field sampling, indicating that the SDI can reveal the land degradation characteristics of the ADD. The SDI indicated that the extreme and strong land degradation areas increased from 1990 to 2019, mainly in the downstream and peripheral regions of the ADD. From 1990 to 2000, land degradation improvement over a larger area than developed, conversely, from 2000 to 2019, and especially, from 2000 to 2010, the proportion of land degradation developed was 32%, which was mainly concentrated in the downstream region of the ADD. The spatial autocorrelation analysis indicated that the SDI values of Moran's I in 1990, 2000, 2010 and 2019 were 0.82, 0.78, 0.82 and 0.77, respectively, suggesting that the SDI was notably clustered in space rather than randomly distributed. The expansion of unused land due to land use change, water withdrawal from the Amu Darya River and the discharge of salt downstream all contributed to land degradation in the ADD. This study provides several valuable insights into the land degradation monitoring and management of this salinized delta and similar settings worldwide. [ABSTRACT FROM AUTHOR]

Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy. [ABSTRACT FROM AUTHOR]

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation. [ABSTRACT FROM AUTHOR]

The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km2, whereas FATA showed the least increase at 0.2 ha/km2 from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols affect climate change, air quality, and human health. The aerosol optical depth (AOD) is a widely utilized parameter for estimating the concentration of atmospheric aerosols. Consequently, continuous AOD monitoring is crucial for environmental studies. However, a method to continuously monitor the AOD throughout the day or night remains a challenge. This study introduces a method for estimating the All-Day AOD using ground air quality and meteorological data. This method allows for the hourly estimation of the AOD throughout the day in the Beijing–Tianjin–Hebei (BTH) region and addresses the lack of high temporal resolution monitoring of the AOD during the nighttime. The results of the proposed All-Day AOD estimation method were validated against AOD measurements from Advanced Himawari Imager (AHI) and Aerosol Robotic Network (AERONET). The R2 between the estimated AOD and AHI was 0.855, with a root mean square error of 0.134. Two AERONET sites in BTH were selected for analysis. The results indicated that the absolute error between the estimated AOD and AERONET was within acceptable limits. The estimated AOD showed spatial and temporal trends comparable to those of AERONET and AHI. In addition, the hourly mean AOD was analyzed for each city in BTH. The hourly mean AOD in each city exhibits a smooth change at night. In conclusion, the proposed AOD estimation method offers valuable data for investigating the impact of aerosol radiative forcing and assessing its influence on climate change. [ABSTRACT FROM AUTHOR]

Unreasonable cultivation methods and management measures have led to widespread obstacles in tobacco continuous cropping in planting areas, resulting in reduced tobacco yield and soil degradation. Therefore, intercropping tobacco with other crops is an effective strategy to improve continuous cropping barriers. In this study, flue-cured tobacco NC102 and conventional planting varieties of Salvia miltiorrhiza were used as materials, and four treatments of flue-cured tobacco monoculture (CK), flue-cured tobacco, and Salvia miltiorrhiza at a ratio of 1:1 (TS11), 2:2 (TS22), and 2:3 (TS23), respectively, were set up to study their effects on soil microorganisms, physical and chemical properties, and yield and quality of flue-cured tobacco. The results showed that intercropping Salvia miltiorrhiza increased the number of soil bacteria and actinomycetes, decreased the number of fungi, and increased the activity of urease and sucrase. The content of available nitrogen and available phosphorus in intercropping Salvia miltiorrhiza soil was significantly higher than that of the flue-cured tobacco monoculture, while the content of available potassium was lower than that of the flue-cured tobacco monoculture. The soil environment was more conducive to the growth of flue-cured tobacco. Compared with the flue-cured tobacco monoculture, the proportion of superior tobacco in intercropping Salvia miltiorrhiza increased by 2.2–3.4%, and the ratio of potassium to chlorine in leaves of different parts of flue-cured tobacco increased by 12.3–75.0%. The content of total sugar and soluble sugar in middle and upper leaves of intercropping flue-cured tobacco was higher than that of the flue-cured tobacco monoculture, which improved the quality of flue-cured tobacco. From the analysis of the chemical composition of tobacco leaves, TS11 (flue-cured tobacco and Salvia miltiorrhiza intercropping row ratio of 1:1) had the best treatment effect, potassium content, total sugar, reducing sugar content, and potassium chloride ratio of flue-cured tobacco were the highest, the chlorine content was the lowest, and the quality was better than other treatments. From the analysis of total output value, the total output value of TS22 (flue-cured tobacco and Salvia miltiorrhiza intercropping row ratio of 2:2) was higher than that of other intercropping treatments. In 2018 and 2019, the total output value increased by 21.3% and 22.4%, respectively, compared with the flue-cured tobacco monoculture. The intercropping advantage was obvious, and the treatment effect was the best. [ABSTRACT FROM AUTHOR]

Scientific-grade cameras are frequently employed in industries such as spectral imaging technology, aircraft, medical detection, and astronomy, and are characterized by high precision, high quality, fast speed, and high sensitivity. Especially in the field of astronomy, obtaining information about faint light often requires long exposure with high-resolution cameras, which means that any external factors can cause the camera to become unstable and result in increased errors in the detection results. This paper aims to investigate the effect of displacement introduced by various vibration factors on the imaging of an astronomical camera during long exposure. The sources of vibration are divided into external vibration and internal vibration. External vibration mainly includes environmental vibration and resonance effects, while internal vibration mainly refers to the vibration caused by the force generated by the refrigeration module inside the camera during the working process of the camera. The cooling module is divided into water-cooled and air-cooled modes. Through the displacement and vibration experiments conducted on the camera, it is proven that the air-cooled mode will cause the camera to produce greater displacement changes relative to the water-cooled mode, leading to blurring of the imaging results and lowering the accuracy of astronomical detection. This paper compares the effects of displacement produced by two methods, fan cooling and water-circulation cooling, and proposes improvements to minimize the displacement variations in the camera and improve the imaging quality. This study provides a reference basis for the design of astronomical detection instruments and for determining the vibration source of cameras, which helps to promote the further development of astronomical detection. [ABSTRACT FROM AUTHOR]

An inertial sensor is a crucial payload in China's Taiji program for space gravitational wave detection. The performance of the capacitive displacement sensing circuit in the low-frequency band (0.1 mHz to 1 Hz) is extremely important because it directly determines the sensitivity of the space gravitational wave detection missions. Therefore, significant, yet challenging, tasks include decreasing the low-frequency noise in capacitive displacement sensing circuits and improving the capacitive sensing resolution. This study analyzes the noise characteristics of the pre-amplifier circuit within the capacitive sensing circuit, achieves precise tuning of the transformer bridge, and examines how transformer parameters affect noise. In addition, this study introduces a method using a discrete JFET to reduce the operational amplifier current noise and analyzes how feedback resistance and capacitance in TIA circuits affect the overall circuit noise. The proportional relationship between different transformer noises and TIA noise before and after optimization was analyzed and experimentally verified. Finally, an optimized TIA circuit and a superior transformer were utilized to achieve an increase in the capacitive sensing resolution from 1.095 aF/rtHz @ 10 mHz to 0.84 aF/rtHz @ 10 mHz, while improving the performance by 23%. These findings provide valuable insights into further decreasing circuit noise and increasing the capacitive sensing resolution. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration (ET) with high spatiotemporal resolution is very important for the research on agricultural water resource management and the water cycle processes, and it is helpful to realize precision agriculture and smart agriculture, and provides critical references for agricultural layout planning. Due to the impact of the clouds, weather environment, and the orbital period of optical satellite, there are difficulties in providing daily remote sensing data that are not contaminated by clouds for estimating daily ET with high spatial-temporal resolution. By improving the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), this manuscript proposes the method to fuse high temporal and low spatial resolution Weather Research and Forecasting (WRF) model surface skin temperature (TSK) with the low temporal and high spatial resolution remote sensing surface temperature for obtaining high spatiotemporal resolution daily surface temperature to be used in the estimation of the high spatial resolution daily ET (ET_WRFHR). The distinction of this study from the previous literatures can be summarized as the novel application of the fusion of WRF-simulated TSK and remote sensing surface temperature, giving full play to the availability of model surface skin temperature data at any time and region, making up for the shortcomings of the remote sensing data, and combining the high spatial resolution of remote sensing data to obtain ET with high spatial (Landsat-like scale) and temporal (daily) resolution. The ET_WRFHR were cross-validated and quantitatively verified with MODIS ET products (MOD16) and observations (ET_Obs) from eddy covariance system. Results showed that ET_WRFHR not only better reflects the difference and dynamic evolution process of ET for different land types but also better identifies the details of various fine geographical objects. It also represented a high correlation with the ET_Obs by the R2 amount reaching 0.9186. Besides, the RMSE and BIAS between ET_WRFHR and the ET_Obs are obtained as 0.77 mm/d and −0.08 mm/d respectively. High R2, as well as the small RMSE and BIAS amounts, indicate that ET_WRFHR has achieved a very good performance. [ABSTRACT FROM AUTHOR]

In the proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor influencing the output characteristics of PEMFC, and there is a coordination problem between their flow controls. Thus, the integrated controller of the PEMFC gas supply system based on distributed deep reinforcement learning (DDRL) is proposed to solve this problem, it combines the original airflow controller and hydrogen flow controller into one. Besides, edge-cloud collaborative multiple tricks distributed deep deterministic policy gradient (ECMTD-DDPG) algorithm is presented. In this algorithm, an edge exploration policy is adopted, suggesting that the edge explores including DDPG, soft actor-critic (SAC), and conventional control algorithm are employed to realize distributed exploration in the environment, and a classified experience replay mechanism is introduced to improve exploration efficiency. Moreover, various tricks are combined with the cloud centralized training policy to address the overestimation of Q-value in DDPG. Ultimately, a model-free integrated controller of the PEMFC gas supply system with better global searching ability and training efficiency is obtained. The simulation verifies that the controller enables the flows of air and hydrogen to respond more rapidly to the changing load. [ABSTRACT FROM AUTHOR]

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a significant role in the fine classification of vegetation types. However, it is not clear how the ultrahigh resolution UAV hyperspectral images react in the fine classification of vegetation types in highly fragmented planting areas, and how the spatial resolution variation of UAV images will affect the classification accuracy. Based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (S185) onboard a UAV platform, this paper examines the impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas in southern China by aggregating 0.025 m hyperspectral image to relatively coarse spatial resolutions (0.05, 0.1, 0.25, 0.5, 1, 2.5 m). The object-based image analysis (OBIA) method was used and the effects of several segmentation scale parameters and different number of features were discussed. Finally, the classification accuracies from 84.3% to 91.3% were obtained successfully for multi-scale images. The results show that with the decrease of spatial resolution, the classification accuracies show a stable and slight fluctuation and then gradually decrease since the 0.5 m spatial resolution. The best classification accuracy does not occur in the original image, but at an intermediate level of resolution. The study also proves that the appropriate feature parameters vary at different scales. With the decrease of spatial resolution, the importance of vegetation index features has increased, and that of textural features shows an opposite trend; the appropriate segmentation scale has gradually decreased, and the appropriate number of features is 30 to 40. Therefore, it is of vital importance to select appropriate feature parameters for images in different scales so as to ensure the accuracy of classification. [ABSTRACT FROM AUTHOR]

In recent years, the energy performance of public buildings has attracted substantial attention due to the significant energy-saving potential. As a semi-open high-space building, the high-speed railway station is obviously different from other public buildings and even traditional stations in terms of energy consumption and internal environment. This paper investigates the current energy consumption situation and environmental quality of 15 high-speed railway passenger stations in China. Results show that the energy consumption of the high-speed railway station is between 117–470 kWh/(m2·a). The energy consumption of the station is related to the area and the passenger flow. The energy use of the station using district heating is higher than that of the station without district heating in the same region. The higher glazing ratio induces good natural lighting in the station, but the uniformity of the lighting in the station is not good. The acceptable temperature range of passengers in winter is larger than that in summer. The average air change rate of the high-speed railway station is 3.2 h−1 in winter and 1.8 h−1 in summer, which is the main reason of high energy consumption of the HVAC (Heating Ventilation Air Conditioning) system in this kind of building. [ABSTRACT FROM AUTHOR]

Psychrotrophic foodborne pathogens, such as enteropathogenic Yersinia, which are able to survive and multiply at low temperatures, require cold shock proteins (Csps). The Csp superfamily consists of a diverse group of homologous proteins, which have been found throughout the eubacteria. They are related to cold shock tolerance and other cellular processes. Csps are mainly named following the convention of those in Escherichia coli. However, the nomenclature of certain Csps reflects neither their sequences nor functions, which can be confusing. Here, we performed phylogenetic analyses on Csp sequences in psychrotrophic enteropathogenic Yersinia and E. coli. We found that representative Csps in enteropathogenic Yersinia and E. coli can be clustered into six phylogenetic groups. When we extended the analysis to cover Enterobacteriales, the same major groups formed. Moreover, we investigated the evolutionary and structural relationships and the origin time of Csp superfamily members in eubacteria using nucleotide-level comparisons. Csps in eubacteria were classified into five clades and 12 subclades. The most recent common ancestor of Csp genes was estimated to have existed 3585 million years ago, indicating that Csps have been important since the beginning of evolution and have enabled bacterial growth in unfavorable conditions. [ABSTRACT FROM AUTHOR]

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation. [ABSTRACT FROM AUTHOR]

Clouds are diverse and complex, making accurate cloud type identification vital in improving the accuracy of weather forecasting and the effectiveness of climate monitoring. However, current cloud classification research has largely focused on daytime data. The lack of visible light data at night presents challenges in characterizing nocturnal cloud attributes, leading to difficulties in achieving continuous all-day cloud classification results. This study proposed an all-day infrared cloud classification model (AInfraredCCM) based on XGBoost. Initially, the latitude/longitude, 10 infrared channels, and 5 brightness temperature differences of the Himawari-8 satellite were selected as input features. Then, 1,314,275 samples were collected from the Himawari-8 full-disk data and cloud classification was conducted using the CPR/CALIOP merged cloud type product as training data. The key cloud types included cirrus, deep convective, altostratus, altocumulus, nimbostratus, stratocumulus, stratus, and cumulus. The cloud classification model achieved an overall accuracy of 86.22%, along with precision, recall, and F1-score values of 0.88, 0.84, and 0.86, respectively. The practicality of this model was validated across all-day temporal, daytime/nighttime, and seasonal scenarios. The results showed that the AInfraredCCM consistently performed well across various time periods and seasons, confirming its temporal applicability. In conclusion, this study presents an all-day cloud classification approach to obtain comprehensive cloud information for continuous weather monitoring, ultimately enhancing weather prediction accuracy and climate monitoring. [ABSTRACT FROM AUTHOR]

Cigar tobacco, an economically important cash crop, holds a substantial role within the fiscal framework of the national economy. This crop, however, is characterized by a marked vulnerability to pathogenic bacteria, culminating in consequential financial loss throughout its cultivation phase. Plant growth-promoting rhizobacteria (PGPR), a salient class of advantageous bacterial flora, are recognized for their ability to enhance plant growth, inhibit deleterious pathogens, and synthesize compounds that either have a direct impact on plant morphogenesis or activate otherwise ineffectual soil components. Through these mechanisms, PGPR augments the soil's nutritional profile, making it more receptive to plant uptake, thus stimulating vegetative growth. The Bacillus subtilis microbial fertilizer, the prime exemplar of PGPR, demonstrates not only a pathogen-suppressive effect but also an induction of the plant's innate disease resistance mechanism. This bolsters the plant's resilience to disease fosters a probiotic milieu within the soil, and catalyzes the formation of agglomerate structures, all of which contribute to enhanced soil fertility and moisture retention, increased soil friability, and the facilitation of root expansion. In this study, a controlled pot experiment was conducted to elucidate the mechanism through which inter-root probiotics rehabilitate the soil's ecosystem and foster crop growth in cigar tobacco seedlings afflicted with root black rot bacteria. Four treatments were instituted, including CK: a blank control (no microbial application); A: probiotic only (Bacillus subtilis microbial fertilizer); B: both pathogenic and probiotic (the Bacillus subtilis microbial fertilizer together with root black rot pathogen); C: pathogenic only (the root black rot pathogen). Our empirical findings delineate that the presence of pathogenic bacteria deteriorates the soil environment, thereby constraining the transmutation of soil nutrients and their subsequent assimilation by plants. This severely impedes the vegetative development of cigar plants. By contrast, the application of a PGPR microbial fertilizer modified the soil microbial community structure, exhibiting an antagonistic interaction with the indigenous pathogenic bacterial species. Relative to the CK treatment, the application of the Bacillus subtilis microbial fertilizer was found to invigorate the catalytic conversion of soil enzymes, incrementing the peroxidase, acid phosphatase, urease, and sucrase activity by 12.98%, 19.55%, 13.57%, and 17.91%, respectively. Meanwhile, it was observed to ameliorate the soil's physicochemical attributes, enhancing the available content of nitrogen, phosphorus, and potassium by 4.52%, 6.52%, and 15.14%, respectively, along with the augmentation of soil organic matter content by 17.33%. The fortification of soil physicochemical properties and the enrichment of soil fertility, as a result of the PGPR microbial fertilizer application, translated into a robust 57.23% enhancement of root vigor and a 60.47% extension of the root length of cigar tobacco seedlings. These soil amendments subsequently fueled an uptick in the growth parameters of cigar plants, including increases in plant height, stem girth, leaf count, maximal leaf dimensions, and both the fresh and dry weight of cigar tobacco. [ABSTRACT FROM AUTHOR]

Improving the filtration efficiency of air filter materials is an ongoing research goal. This study conducted in-depth research on a new reduced graphene oxide air filter material, and the differences in its performance and conductivity durability before and after eliminating static electricity were tested and analyzed. The results showed that the filtration efficiency of the reduced graphene oxide air filter material significantly decreased after eliminating static electricity. The maximum decrease in filtration efficiency was observed at a filtration velocity of 0.8 m/s, with PM10 > PM1.0 > PM2.5. In this case, the filtration efficiency decreased by 11.8%, 7.98%, and 7.17%, respectively. The maximum difference in filtration efficiency of 0.29 μm particulates was about 12.7%. Eliminating static electricity slightly increased the resistance (2.5~15.5 Pa). In addition, the new reduced graphene oxide air filter material exhibited good conductivity and stability after continuous testing. This study provides data support for the application of subsequent electrification sterilization, reference values for multi-angle applications, and the development of new composite air filter materials. [ABSTRACT FROM AUTHOR]

To quantify the uncertainties propagating from the fuel depletion calculation to the criticality calculation in the burnup credit system, this paper evaluates the effects of the nuclide concentration uncertainty on the criticality calculation based on Monte Carlo uncertainty sampling methods, and analyzes the assumption that the measured-to-calculated nuclide concentration ratio obeys a normal distribution with uncorrelation among isotopes in the Monte Carlo uncertainty sampling method by using the sensitivity and uncertainty analysis method and the Latin hypercube sampling method. The results indicated that the Monte Carlo uncertainty sampling method could effectively quantify the uncertainties with a calculation accuracy within 3%, and the criticality uncertainty calculation for the assumption that the measured-to-calculated concentration ratios obey normal distributions was more conservative than that of the samples according to their actual distributions. Thus, the assumption of a normal distribution is reasonable in the sampling process. Moreover, the uncertainty results of the criticality calculation considering the correlations among important isotopes presented a decrease of approximately 5% over those without the isotopic correlations. Therefore, introducing the correlations of significant isotopes could reduce the uncertainty of the criticality calculation for spent-nuclear-fuel storage systems. [ABSTRACT FROM AUTHOR]

Mobile crowdsourcing has been exploited to collect enough fingerprints for fingerprinting-based localization. Since the construction of a fingerprint database is time consuming, mobile users should be well motivated to participate in fingerprint collection task. To this end, a Walrasian equilibrium-based incentive mechanism is proposed in this paper to motivate mobile users. The proposed mechanism can eliminate the monopoly of the crowdsourcer, balance the supply and demand of fingerprint data, and maximize the benefit of all participators. In order to reach the Walrasian equilibrium, firstly, the social welfare maximization problem is constructed. To solve the original optimization problem, a dual decomposition method is employed. The maximization of social welfare is decomposed into the triple benefit optimization among the crowdsourcer, mobile users, and the whole system. Accordingly, a distributed iterative algorithm is designed. Through the simulation, the performance of the proposed incentive scheme is verified and analyzed. Simulation results demonstrated that the proposed iterative algorithm satisfies the convergence and optimality. Moreover, the self-reconstruction ability of the proposed incentive scheme was also verified, indicating that the system has strong robustness and scalability. [ABSTRACT FROM AUTHOR]

Kinesins play an important role in many physiological functions including intracellular vesicle transport and mitosis. The emerging role of kinesins in different cancers led us to investigate the expression and functional role of kinesins in meningioma. Therefore, we re-analyzed our previous microarray dataset of benign, atypical, and anaplastic meningiomas (n = 62) and got evidence for differential expression of five kinesins (KIFC1, KIF4A, KIF11, KIF14 and KIF20A). Further validation in an extended study sample (n = 208) revealed a significant upregulation of these genes in WHO°I to °III meningiomas (WHO°I n = 61, WHO°II n = 88, and WHO°III n = 59), which was most pronounced in clinically more aggressive tumors of the same WHO grade. Immunohistochemical staining confirmed a WHO grade-associated upregulated protein expression in meningioma tissues. Furthermore, high mRNA expression levels of KIFC1, KIF11, KIF14 and KIF20A were associated with shorter progression-free survival. On a functional level, knockdown of kinesins in Ben-Men-1 cells and in the newly established anaplastic meningioma cell line NCH93 resulted in a significantly inhibited tumor cell proliferation upon siRNA-mediated downregulation of KIF11 in both cell lines by up to 95% and 71%, respectively. Taken together, in this study we were able to identify the prognostic and functional role of several kinesin family members of which KIF11 exhibits the most promising properties as a novel prognostic marker and therapeutic target, which may offer new treatment options for aggressive meningiomas. [ABSTRACT FROM AUTHOR]

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%. [ABSTRACT FROM AUTHOR]

Regular polytopes (RPs) are an extension of 2D (two-dimensional) regular polygons and 3D regular polyhedra in n-dimensional ( n ≥ 4 ) space. The high abstraction and perfect symmetry are their most prominent features. The traditional projections only show vertex and edge information. Although such projections can preserve the highest degree of symmetry of the RPs, they can not transmit their metric or topological information. Based on the generalized stereographic projection, this paper establishes visualization methods for 5D RPs, which can preserve symmetries and convey general metric and topological data. It is a general strategy that can be extended to visualize n-dimensional RPs ( n > 5 ). [ABSTRACT FROM AUTHOR]

Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP. [ABSTRACT FROM AUTHOR]

Oil is the "blood" and economic lifeblood of modern industry, but traditional light crude oil has been over-consumed, and it has been difficult to meet human demand for energy, so the exploitation of heavy oil is particularly important. In this paper, an oil-soluble catalyst was synthesized to catalyze the pyrolysis reaction of heavy oil in collaboration with reservoir minerals, so as to achieve efficient viscosity reduction of heavy oil and reduce production costs. The experimental results showed that Zn(II)O + K had the best synergistic viscosity reduction effect after the aquathermolysis of No. 1 oil sample under the reaction conditions of 180 °C, 4 h, 30% of water, and 0.2% of catalyst, respectively, and the viscosity reduction rate was 61.74%. Under the catalysis of the isopropanol system, the viscosity reduction rate was increased to 91.22%. A series of characterizations such as freezing point, thermogravimetric analysis, DSC analysis, component analysis, gas chromatography, wax crystal morphology analysis, and GC-MS analysis of aqueous organic matter were carried out on heavy oil after reaction by different reaction systems, and it could be verified that the viscosity of heavy oil was reduced. Finally, through the study of the reaction mechanism of the model compound, combined with the aqueous phase analysis, it can be clearly found that the depolymerization between macromolecules, the breaking of heteroatom chains, hydrogenation, ring opening, and other effects mainly occur during the reaction, thereby weakening the van der Waals force and hydrogen bond of the recombinant interval, inhibiting the formation of grid structure in heavy oil and effectively reducing the viscosity of heavy oil. [ABSTRACT FROM AUTHOR]

Amatoxin poisoning induces delayed-onset acute liver failure, which are responsible for more than 90% of deaths in mushroom poisoning. It has been postulated from animal and human studies that biliary drainage interrupting enterohepatic amatoxin circulation may affect amatoxin poisoning. Dogs were randomly divided into four groups of six animals each. In 20 mg/kg and 60 mg/kg with biliary drainage groups, after accepting bile drainage operation, beagles were fed Amanita exitialis powder (20 or 60 mg/kg) in starch capsules. In control and bile drainage groups, the beagle dogs were fed with empty capsules. They were assessed for toxicity signs, biochemical and pathological changes, and peptide toxins in plasma, urine and bile. The data were directly compared with those from our published studies on Amanita exitialis-exposed beagles without biliary drainage. Amatoxins were rapidly absorbed and eliminated from plasma after Amanita exitialis ingestion. Amatoxins in 0–1-day urine accounted for more than 90% of the total urine excretion, and amatoxins in bile accounted for less than 20% of the total urine and bile excretion. The dogs with biliary drainage showed less severe toxicity signs and biochemical and pathological changes and much lower internal exposure than dogs without biliary drainage. Biliary drainage caused a more than 70% reduction in intestinal amatoxin absorption and could reduce amatoxin absorption from the gastrointestinal tract. [ABSTRACT FROM AUTHOR]

Bacteriorhodopsin protein extracted from Halobacterium salinarum is widely used in many biohybrid electronic devices and forms a research subject known as bioelectronics, which merges biology with electronic technique. The specific molecule structure and components of bR lead to its unique photocycle characteristic, which consists of several intermediates (bR, K, L, M, N, and O) and results in proton pump function. In this review, working principles and properties of bacteriorhodopsin are briefly introduced, as well as bR layer preparation method. After that, different bR-based devices divided into photochemical and photoelectric applications are shown. Finally, outlook and conclusions are drawn to inspire new design of high-performance bR-based biohybrid electronic devices. [ABSTRACT FROM AUTHOR]

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests. [ABSTRACT FROM AUTHOR]

Indoor environment quality is currently a hot research topic. In this study, composite air filter materials were synthesized using different impregnated porous medium materials, and their filtration performance and structural parameters were analyzed. The results showed that composite filter materials' structures changed at the fibers' surfaces when synthesized using different porous medium material layers. The filtration efficiency of composite filter materials synthesized using different porous media reached a maximum 0.8 m/s filtration velocity, and PM10, PM2.5, and PM1.0 increased by 1.67~26.07, 1.19~26.96, and 1.10~21.98%, respectively. The filtration efficiencies of reduced graphene oxide composite for PM10, PM2.5, and PM1.0 were 21.26, 20.22, and 18.50% higher, respectively, than those of carbon black composite. In addition, the filtration efficiency of the composite material synthesized by reducing graphene oxide improved for 0 to 1.0 μm particulates and was more effective by comparison. Filtration efficiency and resistance were comprehensively considered during air filter use to provide useful values for the selection and preparation of composite filter materials in the future. [ABSTRACT FROM AUTHOR]

Phosphorylation of the serine 139 of the histone variant H2AX (γH2AX) is a DNA damage marker that regulates DNA damage response and various diseases. However, whether γH2AX is involved in neuropathic pain is still unclear. We found the expression of γH2AX and H2AX decreased in mice dorsal root ganglion (DRG) after spared nerve injury (SNI). Ataxia telangiectasia mutated (ATM), which promotes γH2AX, was also down-regulated in DRG after peripheral nerve injury. ATM inhibitor KU55933 decreased the level of γH2AX in ND7/23 cells. The intrathecal injection of KU55933 down-regulated DRG γH2AX expression and significantly induced mechanical allodynia and thermal hyperalgesia in a dose-dependent manner. The inhibition of ATM by siRNA could also decrease the pain threshold. The inhibition of dephosphorylation of γH2AX by protein phosphatase 2A (PP2A) siRNA partially suppressed the down-regulation of γH2AX after SNI and relieved pain behavior. Further exploration of the mechanism revealed that inhibiting ATM by KU55933 up-regulated extracellular-signal regulated kinase (ERK) phosphorylation and down-regulated potassium ion channel genes, such as potassium voltage-gated channel subfamily Q member 2 (Kcnq2) and potassium voltage-gated channel subfamily D member 2 (Kcnd2) in vivo, and KU559333 enhanced sensory neuron excitability in vitro. These preliminary findings imply that the down-regulation of γH2AX may contribute to neuropathic pain. [ABSTRACT FROM AUTHOR]

With the normalization of epidemic prevention and control, air filters are being used and replaced more frequently. How to efficiently utilize air filter materials and determining whether they have regenerative properties have become current research hotspots. This paper discusses the regeneration performance of reduced graphite oxide filter materials, which were studied in depth using water cleaning and the relevant parameters, including the cleaning times. The results showed that water cleaning was most effective using a 20 L/(s·m2) water flow velocity with a 17 s cleaning time. The filtration efficiency decreased as the number of cleanings increased. Compared to the blank group, the filter material's PM10 filtration efficiency decreased by 0.8%, 19.4%, 26.5%, and 32.4% after the first to fourth cleanings, respectively. The filter material's PM2.5 filtration efficiency increased by 12.5% after the first cleaning, and decreased by 12.9%, 17.6%, and 30.2% after the second to fourth cleanings, respectively. The filter material's PM1.0 filtration efficiency increased by 22.7% after the first cleaning, and decreased by 8.1%, 13.8%, and 24.5% after the second to fourth cleanings, respectively. Water cleaning mainly affected the filtration efficiency of particulates sized 0.3–2.5 μm. Reduced graphite oxide air filter materials could be water washed twice and maintain cleanliness equal to 90% of the original filter material. Water washing more than twice could not achieve the standard cleanliness equal to 85% of the original filter material. These data provide useful reference values for the evaluation of the filter materials' regeneration performance. [ABSTRACT FROM AUTHOR]

Simple Summary: The history of oyster culture consists of a succession of developmental phases using different species, followed by collapses associated with infectious diseases. The Pacific oyster (Crassostrea gigas), native to the northwest Pacific Ocean, was characterized by wide adaptability, and translocated to support major aquaculture industries in many countries around the world. A deadly herpesvirus, Ostreid herpesvirus-1 (OsHV-1), has hit the superior oyster since 1991. OsHV-1 infection, combined with subsequent bacteraemia by opportunistic bacteria, has brought heavy losses to the main production regions of the Pacific oyster. Because of the considerable economic importance of Pacific oysters, there is a wealth of information available on diseases that affect them around the world. Little comprehensive information is available about the epidemiology status of major disease-causing agents in China. On the other hand, the scale and yields of Pacific oyster increased rapidly in recent years, as a result of the introduction and popularization of triploid oysters. In the present study, a comprehensive survey of potential pathogens associated with mortality events of Pacific oysters was carried out. Our results highlight the potential risks of OsHV-1, Vibrio natriegens, and Vibrio alginolyticus to the aquaculture industry of the Pacific oyster in China. The Pacific oyster (Crassostrea gigas) aquaculture industry increased rapidly in China with the introduction and promotion of triploid oysters in recent years. Mass mortalities affecting different life stages of Pacific oysters emerged periodically in several important production areas of Northern China. During 2020 and 2021, we conducted a passive two-year investigation of infectious pathogens linked to mass mortality. Ostreid herpesvirus-1 (OsHV-1) was detected to be associated with mass mortalities of hatchery larvae, but not juveniles and adults in the open sea. Protozoan parasites, such as Marteilia spp., Perkinsus spp. and Bonamia spp. were not detected. Bacterial isolation and identification revealed that Vibrio natriegens and Vibrio alginolyticus were the most frequently (9 out of 13) identified two dominant bacteria associated with mass mortalities. Pseudoalteromonas spp. was identified as the dominant bacteria in three mortality events that occurred during the cold season. Further bacteriological analysis was conducted on two representative isolates of V. natriegens and V. alginolyticus, designated as CgA1-1 and CgA1-2. Multisequence analysis (MLSA) showed that CgA1-1 and CgA1-2 were closely related to each other and nested within the Harveyi clade. Bacteriological investigation revealed faster growth, and more remarkable haemolytic activity and siderophore production capacity at 25 °C than at 15 °C for both CgA1-1 and CgA1-2. The accumulative mortalities of experimental immersion infections were also higher at 25 °C (90% and 63.33%) than at 15 °C (43.33% and 33.33%) using both CgA1-1 and CgA1-2, respectively. Similar clinical and pathological features were identified in samples collected during both naturally and experimentally occurring mortalities, such as thin visceral mass, discolouration, and connective tissue and digestive tube lesions. The results presented here highlight the potential risk of OsHV-1 to hatchery production of larvae, and the pathogenic role of V. natriegens and V. alginolyticus during mass mortalities of all life stages of Pacific oysters in Northern China. [ABSTRACT FROM AUTHOR]

For the sake of understanding the mechanism of deep subcriticality and high heterogeneity of neutron fluence rate in time–space on the neutron kinetics of the Accelerator-driven Subcritical Reactor Subcritical Reactor (ADSR) under varied beam transients and neutron spectra. A Monte Carlo homogenization approach for the neutron time–space kinetics of the ADSR is proposed in this study, and the influence mechanism on the kinetic parameters of the ADSR under varied neutron spectra, subcriticality, and beam transients is examined. The results show that the Monte Carlo homogenization for the α eigenvalue mode is more adaptable to the subcriticality characteristics under varied subcriticality; under beam transients, the relative differences in the kinetic parameters of the different modes of the ADSR with fast/thermal spectra increase with the depth of subcriticality, and the differences in neutron generation time for varied modes are larger than those of effective fraction of delayed neutron. Thus, it is recommended to use a more adaptable Monte Carlo homogenization method for the time–space kinetics of ADSR, and the effects of the high heterogeneity of neutron fluence rate and deep subcriticality in time–space on the neutron generation time should be considered. [ABSTRACT FROM AUTHOR]

The KLT-40S is a small modular reactor developed by Russia based on the KLT-40 reactor with two fuel assembly designs: a four-ring and a five-ring. Few studies have been published on fuel assembly and power-flattening designs for the KLT-40S. In this paper, the effects of different fuel assembly designs on burnup and power flattening are investigated. This paper compares the effects of the two fuel assembly designs of the KLT-40S on its burnup characteristics, analyzes the effects of fuel rod diameter on burnup characteristics, and conducts a computational study on the ideal power-flattening design. The results show that the five-ring fuel assembly design has better burnup characteristics than the four-ring fuel assembly design. At a fuel rod diameter of 0.62 cm, the optimal burnup lattice is obtained. The 15.84% + 19.75% power-flattening design (uranium enrichment in the innermost and outermost rings + uranium enrichment in inner rings) reduces the local power peaking factor of the five-ring fuel assembly below 1.11 throughout the lifetime. Therefore, the KLT-40S five-ring fuel assembly has better burnup characteristics, and its optimal burnup lattice is at the 0.62 cm fuel rod diameter. The use of power-flattening designs can effectively reduce the local power peaking factor. [ABSTRACT FROM AUTHOR]

Nuclear power is a type of clean and green energy; however, there is a risk of radioactive material leakage when accidents occur. When radioactive material leaks from nuclear power plants, it has a great impact on the environment and personnel safety. In order to enhance the safety of nuclear power plants and support the operator's decisions under accidental circumstances, this paper proposes a fault diagnosis method for nuclear power plants based on the sparrow search algorithm (SSA) optimized by the CNN-LSTM network. Firstly, the convolutional neural network (CNN) was used to extract features from the data before they were then combined with the long short-term memory (LSTM) neural network to process time series data and form a CNN-LSTM model. Some of the parameters in the LSTM neural network need to be manually tuned based on experience, and the settings of these parameters have a great impact on the overall model results. Therefore, this paper selected the sparrow search algorithm with a strong search capability and fast convergence to automatically search for the hand-tuned parameters in the CNN-LSTM model, and finally obtain the SSA-CNN-LSTM model. This model can classify the types of accidents that occur in nuclear power plants to reduce the nuclear safety hazards caused by human error. The experimental data are from a personal computer transient analyzer (PCTRAN). The results show that the classification accuracy of the SSA-CNN-LSTM model for the nuclear power plant fault classification problem is as high as 98.24%, which is 4.80% and 3.14% higher compared with the LSTM neural network and CNN-LSTM model, respectively. The superiority of the sparrow search algorithm for optimizing model parameters and the feasibility and accuracy of the SSA-CNN-LSTM model for nuclear power plant fault diagnosis were verified. [ABSTRACT FROM AUTHOR]

Reliable measures of nighttime atmospheric fine particulate matter (PM2.5) concentrations are essential for monitoring their continuous diurnal variation. Here, we proposed a night PM2.5 concentration estimation (NightPMES) model based on the random forest model. This model integrates the radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB), moon phase angle, and meteorological data. We collected 13486 samples from the Beijing Tianjin–Hebei (BTH) region. The determination coefficient (R2) of the NightPMES model was 0.82, the root mean square error (RMSE) was 16.67 µg/m3, and the mean absolute error (MAE) was 10.20 µg/m3. The applicability analysis of the moon phase angles indicated that the amount of data available increased by 60% while the accuracy remained relatively unchanged. In the seasonal model, the meteorological factors and DNB radiance were found to be the primary factors affecting the PM2.5 concentration in different seasons. In conclusion, this study provided a method for estimating nighttime PM2.5 concentration that will improve our understanding of air pollution and associated trends in PM2.5 variation. [ABSTRACT FROM AUTHOR]

Glycogen, a stored form of glucose, is an important form of energy for aquatic shellfish, contributing to the flavor and quality of the oyster. The glycogen synthase (GYS) and glycogen synthase kinase 3β (GSK3β) are two major enzymes in the glycogenesis. However, the information of the two genes in the Jinjiang oyster Crassostrea ariakensis remains limited. In this study, we identified the genes of GYS and GSK3β and further explored their function in the glycogen synthesis of C. ariakensis. The GYS and GSK3β were distributed in all tested tissues, and high expression of GYS and glycogen content were detected in the gonad, labial palp, hepatopancreas, and mantle, while the high expression of GSK3β was observed in the gill and adductor muscle. The expression of GYS was positively correlated with the glycogen content, while GSK3β was negatively correlated. Additionally, knockdown of GSK3β using RNAi decreased the GYS expression, revealing the negative regulatory effect of GSK3β on GYS. These findings enrich the research data of GSK3β and GYS involved in glycogen synthesis, providing valuable information for further research on the function of GSK3β and GYS in the glycogen synthesis process of oyster. [ABSTRACT FROM AUTHOR]

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. [ABSTRACT FROM AUTHOR]

The C2H2 zinc finger protein is one of the most common zinc finger proteins, widely exists in eukaryotes, and plays an important role in plant growth and development, as well as in salt, low-temperature, and drought stress and other abiotic stress responses. In this study, C2H2 members were identified and analyzed from the low-temperature tolerant transcriptome sequencing data of maize seedlings. The chromosome position, physical and chemical properties, evolution analysis, gene structure, conservative motifs, promoter cis elements and collinearity relationships of gene the family members were analyzed using bioinformatics, and the expression of the ZmC2H2 gene family under cold stress was analyzed by fluorescent quantitative PCR. The results showed that 150 members of the C2H2 zinc finger protein family were identified, and their protein lengths ranged from 102 to 1223 bp. The maximum molecular weight of the ZmC2H2s was 135,196.34, and the minimum was 10,823.86. The isoelectric point of the ZmC2H2s was between 33.21 and 94.1, and the aliphatic index was 42.07–87.62. The promoter cis element analysis showed that the ZmC2H2 family contains many light-response elements, plant hormone-response elements, and stress-response elements. The analysis of the transcriptome data showed that most of the ZmC2H2 genes responded to cold stress, and most of the ZmC2H2 genes were highly expressed in cold-tolerant materials and lowly expressed in cold-sensitive materials. The real-time quantitative PCR (qRT-PCR) analysis showed that ZmC2H2-69, ZmC2H2-130, and ZmC2H2-76 were significantly upregulated, and that ZmC2H2-149, ZmC2H2-33, and ZmC2H2-38 were significantly downregulated. It is hypothesized that these genes, which function in different metabolic pathways, may play a key role in the maize cold response. These genes could be further studied as candidate genes. This study provides a theoretical reference for further study on the function analysis of the maize C2H2 gene family. [ABSTRACT FROM AUTHOR]

P3b is an event-related potential (ERP) that may be abnormal in patients with tic disorders (TD), but evidence has been inconsistent. Given the possible association between P3b and TD and the need for biomarkers for TD, the primary objective of this meta-analysis was to characterize P3b in patients with TD in comparison to healthy controls (HCs). Methods: By searching PubMed, Embase, Web of Science, SCOPUS, Medline, and Google Scholar, we identified studies that compared P3b between TD patients and HCs. The amplitude and latency of P3b were then analyzed. Subgroup analyses were conducted to investigate the influence of different experimental factors on P3b indices. Result: Overall, 19 articles involving 388 cases and 414 controls were evaluated. There were no significant abnormalities in P3b amplitude and latency in TD patients. The P3b amplitude of the TD patients was significantly decreased during the oddball task, and the P3b amplitude of the adult TD patients was also significantly decreased. Conclusion: TD patients may have an abnormal P3b compared to HCs under specified conditions. [ABSTRACT FROM AUTHOR]

Aims: This study investigates the association between circulating tumor cells (CTCs) and breast cancer metastasis. Methods: A retrospective study was conducted using patients with histologically confirmed breast cancer recruited from the First Affiliated Hospital of Nanjing Medical University during the period of August 2017–October 2020. We used adjusted logistic regression, the random forest algorithm, and sensitivity analysis to study the association between CTC enumeration and tumor metastasis. Further, we performed next-generation sequencing (NGS) on the CTCs obtained from two patients with breast cancer brain metastasis. Results: A total of 41 out of 116 enrolled patients were identified with tumor metastasis. CTC enumeration was significantly higher in patients with liver metastasis than in those without liver metastasis. Patients with CTCs ≥ 5 exhibited a higher risk of tumor metastasis than those with CTCs < 5 in the adjusted model (odds ratios (OR) = 6.25, 95% confidence interval (CI) = 2.63–15.58). The random forest model identified CTC enumeration as a significant metastasis-related variable with the highest mean decrease accuracy and mean decrease Gini score. No significant association was found between CTCs and visceral metastasis with an OR of 1.29 (95% CI = 0.98–2.05, p = 0.232). Upon further investigating organ-specific metastasis, we found that patients with high CTC levels were more likely to develop liver metastasis (OR = 4.87, 95% CI = 1.34–20.17, p = 0.021). The NGS study of CTCs identified a total of 120 indel mutations (e.g., CNGB1, NTSR1, ZG16). The enriched biological processes were mechanoreceptor differentiation and macrophage activation involved in the immune response. The enriched KEGG pathways included focal adhesion, the PI3K-Akt signaling pathway, and microRNAs involved in cancer. Conclusions: Our study revealed that CTCs ≥ 5 are a risk factor for tumor metastasis in breast cancer patients. In addition, we reported that CTCs ≥ 5 might be associated with a higher risk of liver metastasis in patients with metastatic breast cancer. We have provided the mutational profiles of CTCs based on next-generation sequencing. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are a class of complex disorders characterized by brain dysfunction with varying degrees of impairment in cognition, emotion, consciousness and behavior, which has become a serious public health issue. The NGFR gene encodes the p75 neurotrophin receptor, which regulates neuronal growth, survival and plasticity, and was reported to be associated with depression, schizophrenia and antidepressant efficacy in human patient and animal studies. In this study, we investigated its association with schizophrenia and major depression and its role in the behavioral phenotype of adult mice. Four NGFR SNPs were detected based on a study among 1010 schizophrenia patients, 610 patients with major depressive disorders (MDD) and 1034 normal controls, respectively. We then knocked down the expression of NGFR protein in the hippocampal dentate gyrus of the mouse brain by injection of shRNA lentivirus to further investigate its behavioral effect in mice. We found significant associations of s2072446 and rs11466162 for schizophrenia. Ngfr knockdown mice showed social and behavioral abnormalities, suggesting that it is linked to the etiology of neuropsychiatric disorders. We found significant associations between NGFR and schizophrenia and that Ngfr may contribute to the social behavior of adult mice in the functional study, which provided meaningful clues to the pathogenesis of psychiatric disorders. [ABSTRACT FROM AUTHOR]

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi'an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data. [ABSTRACT FROM AUTHOR]

Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897–0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the "severity psychosis" hypothesis. [ABSTRACT FROM AUTHOR]

A large number of metalworking fluids in industrial manufacturing processes generate high-concentrations of oil mist pollution, which is a typical semi-volatile aerosol and is generally composed of liquid particles and volatile gas components. Long-term exposure to oil mist pollution brings a series of occupational diseases to workers. For the semi-volatile aerosol, the traditional filter sampling method will lead to particle volatilization, which underestimates the concentration of particles and overestimates the concentration of gas. Therefore, this study combined the advantages of the electrostatic method and the Tenax tube adsorption method, to develop a more accurate measurement technology. First, a dichotomous sampler that could efficiently separate the gas and liquid phases of aerosols was optimized through a numerical model, which was validated by literature results. Next, a test table for oil mist sampling was built with a sampler which was fabricated by 3D printing, and the performance of the sampler was evaluated. The results show that the sampling technique can separate the gas and particulate phases of the oil mist efficiently and accurately. Compared with the traditional single sampling methods, the new sampler can better determine the true concentration of oil mist. [ABSTRACT FROM AUTHOR]

Forest dynamics, including forest loss and gain, are long-term complex ecological processes affected by nature and human activities. It is particularly important to understand the long-term forest dynamics of protected areas to evaluate their conservation efforts. This study adopted the Landsat tree-canopy cover (TCC) method to derive annual TCC data for the period 1984–2020 for the protected areas of northeast China, where protection policies have been carried out since the end of the 20th century, e.g., the Natural Forest Conversion Program (NFCP). A strong correlation was found between the TCC estimates derived from Landsat and LiDAR observations, suggesting the high accuracy of TCC. Forest loss and gain events were also identified from the time series of TCC estimates. High correlations were reported for both forest loss (Producer's accuracy = 85.21%; User's accuracy = 84.26%) and gain (Producer's accuracy = 87.74%; User's accuracy = 88.31%), suggesting that the approach can be used for monitoring and evaluating the effectiveness of the NFCP and other forest conservation efforts. The results revealed a fluctuating upward trend of the TCC of the protected area from 1986 to 2018. The increased area of TCC was much larger than the decreased area, accounting for 59.68% and 40.34%, respectively, suggesting the effectiveness of protection policies. Since the NFCP was officially implemented in 1998, deforestation was effectively curbed, the area of forest loss was significantly reduced (slope: −14.24%/year), and the area of forest gain significantly increased (slope: 4.13%/year). We found that regional forest changes were mainly manifested in "forest gain after loss (forest recovery)" and "forest gain with no loss (forest newborn)", accounting for 40.29% and 37.28% of the total area of forest change, respectively. Moreover, the forest gain area far exceeds the forest loss area, reaching 11.24 million hectares, suggesting a successful forest recovery due to forest protection. [ABSTRACT FROM AUTHOR]

The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793–0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615–0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context. [ABSTRACT FROM AUTHOR]

The accurate identification of overlapping Agaricus bisporus in a factory environment is one of the challenges faced by automated picking. In order to better segment the complex adhesion between Agaricus bisporus, this paper proposes a segmentation recognition algorithm for overlapping Agaricus bisporus. This algorithm calculates the global gradient threshold and divides the image according to the image edge gradient feature to obtain the binary image. Then, the binary image is filtered and morphologically processed, and the contour of the overlapping Agaricus bisporus area is obtained by edge detection in the Canny operator, the convex hull and concave area are extracted for polygon simplification, and the vertices are extracted using Harris corner detection to determine the segmentation point. After dividing the contour fragments by the dividing point, the branch definition algorithm is used to merge and group all the contours of the same Agaricus bisporus. Finally, the least squares ellipse fitting algorithm and the minimum distance circle fitting algorithm are used to reconstruct the outline of Agaricus bisporus, and the demand information of Agaricus bisporus picking is obtained. The experimental results show that this method can effectively overcome the influence of uneven illumination during image acquisition and be more adaptive to complex planting environments. The recognition rate of Agaricus bisporus in overlapping situations is more than 96%, and the average coordinate deviation rate of the algorithm is less than 1.59%. [ABSTRACT FROM AUTHOR]

Bioelectronics, an emerging discipline formed by the biology and electronic information disciplines, has maintained a state of rapid development since its birth. Amongst the various functional bioelectronics materials, bacteriorhodopsin (bR), with its directional proton pump function and favorable structural stability properties, has drawn wide attention. The main contents of the paper are as follows: Inspired by the capacitive properties of natural protoplast cell membranes, a new bio-capacitor based on bR and artificial nanochannels was constructed. As a point of innovation, microfluidic chips were integrated into our device as an ion transport channel, which made the bio-capacitor more stable. Meanwhile, a single nanopore structure was integrated to improve the accuracy of the device structure. Experiments observed that the size of the nanopore affected the ion transmission rate. Consequently, by making the single nanopore's size change, the photocurrent duration time (PDT) of bR was effectively regulated. By using this specific phenomenon, the original transient photocurrent was successfully transformed into a square-like wave. [ABSTRACT FROM AUTHOR]

In order to fill the research gap of high metal loading of high performance PtNi alloy catalysts, a PtNi/C alloy nano-catalyst with metal loading more than 50 wt.% and core-shell like structure was prepared by ethylene glycol reduction, high temperature annealing, and acid pickling. The electrochemical test results showed that the prepared PtNi alloy catalyst had excellent electrochemical activity: the electrochemical surface area (ECSA) was 63.8 m2·gPt−1, and the mass activity (MA) was 0.574 A·mgPt−1, which is 2.73 times greater than those of the Pt/C JM (Johnson Matthey) catalyst. The durability of the PtNi/C catalyst was further investigated. After 30 K cycles of accelerated durability test, the ECSA and MA of the PtNi/C alloy catalyst decreased by 10.2% and 31.2%, respectively. The PtNi/C alloy catalyst prepared in this study has excellent catalytic activity and overcomes the problem of insufficient durability of traditional alloy catalysts and has the potential for large-scale commercial application. [ABSTRACT FROM AUTHOR]