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Considering the high demand for air quality, the development of biomass-based air filtration membranes with high air filtration efficiency and good stability is an urgent task. In this work, polyvinyl alcohol (PVA), gelatin (GA), and cellulose nanocrystals (CNC) were mixed and prepared into a membrane through an electrospinning method for air filtration. After a hydrophobic modification, the modified PVA/GA/CNC composite membrane showed excellent filtration efficiency for PM2.5 (97.65%) through the internal three-dimensional structure barrier and the electrostatic capture effect of the CNC with a negative charge, as well as a low-pressure drop (only 50 Pa). In addition, the modified PVA/GA/CNC composite membrane had good mechanical properties (maximum tensile fracture rate of 78.3%) and high stability (air filtration efficiency of above 90% after five wash-filter cycles and a high-temperature treatment at 200 °C). It is worth noting that the whole preparation process is completed without organic solvents, putting forward a new strategy for the construction of green air filtration membranes. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is an essential macronutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat (Triticum aestivum L.) is one of the main food crops, and its yield has increased from approximately 4000 kg ha−1 to 6000 kg ha−1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha−1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017–2018 and 2018–2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertilizer-utilization efficiency and on the crop growth and yield of winter wheat under sprinkler fertigation conditions. The results showed that in the N application range of 40–200 kg ha−1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate (from N200 to N250) did not have additional benefits. The N uptake after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the recommended optimal N application rate was 200 kg ha−1, and the best topdressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions. [ABSTRACT FROM AUTHOR]

Timely and accurate estimation of Above-Ground-Biomass (AGB) in cotton is essential for precise production monitoring. The study was conducted in Shaya County, Aksu Region, Xinjiang, China. It employed an unmanned aerial vehicle (UAV) as a low-altitude monitoring platform to capture multispectral images of the cotton canopy. Subsequently, spectral features and textural features were extracted, and feature selection was conducted using Pearson's correlation (P), Principal Component Analysis (PCA), Multivariate Stepwise Regression (MSR), and the ReliefF algorithm (RfF), combined with the machine learning algorithm to construct an estimation model of cotton AGB. The results indicate a high consistency between the mean (MEA) and the corresponding spectral bands in textural features with the AGB correlation. Moreover, spectral and textural feature fusion proved to be more stable than models utilizing single spectral features or textural features alone. Both the RfF algorithm and ANN model demonstrated optimization effects on features, and their combination effectively reduced the data redundancy while improving the model performance. The RfF-ANN-AGB model constructed based on the spectral and textural features fusion worked better, and using the features SIPI2, RESR, G_COR, and RE_DIS, exhibited the best performance, achieving a test sets R2 of 0.86, RMSE of 0.23 kg·m−2, MAE of 0.16 kg·m−2, and nRMSE of 0.39. The findings offer a comprehensive modeling strategy for the precise and rapid estimation of cotton AGB. [ABSTRACT FROM AUTHOR]

Skeleton-based human interaction recognition is a challenging task in the field of vision and image processing. Graph Convolutional Networks (GCNs) achieved remarkable performance by modeling the human skeleton as a topology. However, existing GCN-based methods have two problems: (1) Existing frameworks cannot effectively take advantage of the complementary features of different skeletal modalities. There is no information transfer channel between various specific modalities. (2) Limited by the structure of the skeleton topology, it is hard to capture and learn the information about two-person interactions. To solve these problems, inspired by the human visual neural network, we propose a multi-modal enhancement transformer (ME-Former) network for skeleton-based human interaction recognition. ME-Former includes a multi-modal enhancement module (ME) and a context progressive fusion block (CPF). More specifically, each ME module consists of a multi-head cross-modal attention block (MH-CA) and a two-person hypergraph self-attention block (TH-SA), which are responsible for enhancing the skeleton features of a specific modality from other skeletal modalities and modeling spatial dependencies between joints using the specific modality, respectively. In addition, we propose a two-person skeleton topology and a two-person hypergraph representation. The TH-SA block can embed their structural information into the self-attention to better learn two-person interaction. The CPF block is capable of progressively transforming the features of different skeletal modalities from low-level features to higher-order global contexts, making the enhancement process more efficient. Extensive experiments on benchmark NTU-RGB+D 60 and NTU-RGB+D 120 datasets consistently verify the effectiveness of our proposed ME-Former by outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches. [ABSTRACT FROM AUTHOR]

Network quantization, which strives to reduce the precision of model parameters and/or features, is one of the most efficient ways to accelerate model inference and reduce memory consumption, particularly for deep models when performing a variety of real-time vision tasks on edge platforms with constrained resources. Existing quantization approaches function well when using relatively high bit widths but suffer from a decline in accuracy at ultra-low precision. In this paper, we propose a bit-weight adjustment (BWA) module to bridge uniform and non-uniform quantization, successfully quantizing the model to ultra-low bit widths without bringing about noticeable performance degradation. Given uniformly quantized data, the BWA module adaptively transforms these data into non-uniformly quantized data by simply introducing trainable scaling factors. With the BWA module, we combine uniform and non-uniform quantization in a single network, allowing low-precision networks to benefit from both the hardware friendliness of uniform quantization and the high performance of non-uniform quantization. We optimize the proposed BWA module by directly minimizing the classification loss through end-to-end training. Numerous experiments on the ImageNet and CIFAR-10 datasets reveal that the proposed approach outperforms state-of-the-art approaches across various bit-width settings and can even produce low-precision quantized models that are competitive with their full-precision counterparts. [ABSTRACT FROM AUTHOR]

The spaceborne Electric Field Detector (EFD) is one of the payloads of the China Seismo-Electromagnetic Satellite (CSES-01), which can measure electric field data at near-Earth orbit for investigating fundamental scientific topics such as the dynamics of the top-side ionosphere, lithosphere–atmosphere–ionosphere coupling, and electromagnetic field emissions possibly associated with earthquake occurrence. The Extremely Low-Frequency (ELF) waveform shows anomalous step variations, and this work proposes an automatic detection algorithm to identify steps and analyze their characteristics using a convolutional neural network. The experimental results show that the developed detection method is effective, and the identification performance reaches over 90% in terms of both accuracy and area under the curve index. We also analyze the rate of the occurrence of steps in the three components of the electric field. Finally, we discuss the stability of the statistical results on steps and their relevance to the probe's function. The research results provide a guideline for improving the quality of EFD data, and further applications in monitoring the low-Earth electromagnetic environment. [ABSTRACT FROM AUTHOR]

Climate change and water resource shortages have become important problems limiting winter wheat production in the North China Plain (NCP). Understanding the physiological characteristics of different wheat varieties and their relationship with crop growth and yield is of great importance for addressing climate change through a scientific approach, adopting reliable wheat varieties, and ensuring food production. This study was conducted throughout three winter wheat seasons from 2018 to 2021. The crop growth, physiological indicators, crop yields, and water productivity (WP) of 12 wheat varieties widely cultivated in the NCP were measured to investigate the relationships between physiological characteristics and crop yield. The results showed that among the three wheat seasons, the maximum plant height of each wheat variety was relatively stable, while the changes in maximum plant density (PDm), maximum leaf area index (LAIm), and maximum dry matter (DMm) were highly variable. The gas exchange parameters and fluorescence parameters of wheat flag leaves varied with growth stage, and certain varieties were sensitive to water stress. The wheat grain yield, seasonal crop evapotranspiration (ETa), and WP of the 12 varieties were similar in the 2019–2020 and 2020–2011 seasons and were approximately 25%, 7%, and 19% higher than those in the 2018–2019 season, respectively, in which there were consecutive cool events in the winter and spring. Generally, the grain yields were positively correlated with PDm, LAIm, DMm, ETa, the harvest index, and WP, at a significance level of 0.01. The high-yield wheat varieties had higher photosynthetic rates in the middle and late grain-filling stages. Based on these relationships, high-yield wheat varieties may be selected to address climate change in the NCP as well as other regions in the world. Considering the variations in yield and WP in the three different climatic conditions, we recommend Yingbo700, Lunxuan103, Shimai26, Shinong086, Han6172, and Hanong1412 as high-yield and drought-resistant wheat varieties to be used in the NCP. [ABSTRACT FROM AUTHOR]

The electric field detector of the CSES satellite has captured a vast number of lightning whistler events. To recognize them effectively from the massive amount of electric field detector data, a recognition algorithm based on speech technology has attracted attention. However, this approach has failed to recognize the lightning whistler events which are contaminated by other low-frequency electromagnetic disturbances. To overcome this limitation, we apply the single-channel blind source separation method and audio recognition approach to develop a novel model, which consists of two stages. (1) The training stage: Firstly, we preprocess the electric field detector wave data into the audio fragment. Then, for each audio fragment, mel-frequency cepstral coefficients are extracted and input into the long short-term memory network for training the novel lightning whistler recognition model. (2) The inference stage: Firstly, we process each audio fragment with the single-channel blind source to generate two different sub-signals. Then, for each sub-signal, the mel-frequency cepstral coefficient features are extracted and input into the lightning whistler recognition model to recognize the lightning whistler. Finally, the two results above are processed by decision fusion to obtain the final recognition result. Experimental results based on the electric field detector data of the CSES satellite demonstrate the effectiveness of the algorithm. Compared with classical methods, the accuracy, recall, and F1-score of this algorithm can be increased by 17%, 62.2%, and 50%, respectively. However, the time cost only increases by 0.41 s. [ABSTRACT FROM AUTHOR]

The influence of artificial aging time on the microstructures and mechanical properties of the 6063 aluminum alloy profile extruded by porthole die was investigated through hardness testing, expansion testing, scanning electron microscope (SEM), and transmission electron microscope (TEM). The results showed that the artificial aging time had a significant impact on the size, morphology, distribution of precipitated phases, and mechanical properties of the porthole die extruded 6063 aluminum alloy profiles. As the artificial aging time increased, the second phase particles gradually precipitated, and the precipitation strengthening gradually enhanced, resulting in an increase in the hardness of the profile. The hardness of the welding zone was lower than that of the matrix zone. Compared with the precipitation in the matrix zone, the size and distribution of the precipitates were uneven, and the time for the precipitation was long in the welding zone due to the influence of grain size. The width of the precipitate free zone (PFZ) in the welding zone was greater than that in the matrix zone. The expansion ratio decreased with the increase of aging time, which indicated that the artificial aging treatment was adverse to the plastic deformation ability of the profiles. [ABSTRACT FROM AUTHOR]

Hetao Irrigation District (HID) is one of the main regions for maize and sunflower production in North China. However, water resource shortages and soil salinization greatly limit maize and sunflower production. The surface irrigation method is the main irrigation method in HID; however, now, the plastic mulching and drip irrigation area is increasing to reduce irrigation water and enhance crop yield. In this study, the soil water, salt, and nitrogen contents at the 0–100 cm soil layer under plastic mulching and non-mulching conditions with the surface irrigation—fertilizer broadcast practice and drip fertigation method were investigated at the maize elongation and maturation stages in the 2021 and 2022 seasons. The results show that the mulching practice and irrigation methods greatly influenced the soil salt and ionic nitrogen (NO3− and NH4+) distributions and, ultimately, the maize yield. Mulching reduced the soil salt content in the 0–20 cm soil layer by a mean of 35.7% under surface irrigation and by 18.6% under the drip irrigation condition. The NO3− content in the 0–20 cm soil layer with the drip fertigation system was approximately 8 times higher in mulching soil than that out of mulching. However under the surface irrigation condition, the NO3− content was 8–10 times lower under mulching than that out of mulching. The soil salt and NO3− contents were distributed uniformly at each soil layer deeper than a 40 cm depth, indicating minor effects of mulching. The soil NH4+ content decreased as the soil depth increased and distributed uniformly at each soil layer, indicating the insignificant influence of the mulching practice. As a result, the maize yield under the drip-mulching condition was approximately 11% (10.6~11.4%) higher than that under the surface-mulching condition in the two maize seasons. Given that surface irrigation is currently the primary irrigation method in the Hetao Irrigation District (HID), we have proposed three approaches aimed at enhancing maize production through the improvement of nitrogen levels in surface-mulching practices. [ABSTRACT FROM AUTHOR]

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. [ABSTRACT FROM AUTHOR]

Multi-modal feature fusion and effectively exploiting high-level semantic information are critical in salient object detection (SOD). However, the depth maps complementing RGB image fusion strategies cannot supply effective semantic information when the object is not salient in the depth maps. Furthermore, most existing (UNet-based) methods cannot fully exploit high-level abstract features to guide low-level features in a coarse-to-fine fashion. In this paper, we propose a compensated attention feature fusion and hierarchical multiplication decoder network (CAF-HMNet) for RGB-D SOD. Specifically, we first propose a compensated attention feature fusion module to fuse multi-modal features based on the complementarity between depth and RGB features. Then, we propose a hierarchical multiplication decoder to refine the multi-level features from top down. Additionally, a contour-aware module is applied to enhance object contour. Experimental results show that our model achieves satisfactory performance on five challenging SOD datasets, including NJU2K, NLPR, STERE, DES, and SIP, which verifies the effectiveness of the proposed CAF-HMNet. [ABSTRACT FROM AUTHOR]

The electromagnetic data observed with the CSES (China Seismo-Electromagnetic Satellite, also known as Zhangheng-1 satellite) contain numerous spatial disturbances. These disturbances exhibit various shapes on the spectrogram, and constant frequency electromagnetic disturbances (CFEDs), such as artificially transmitted very-low-frequency (VLF) radio waves, power line harmonics, and interference from the satellite platform itself, appear as horizontal lines. To exploit this feature, we proposed an algorithm based on computer vision technology that automatically recognizes these lines on the spectrogram and extracts the frequencies from the CFEDs. First, the VLF waveform data collected with the CSES electric field detector (EFD) are converted into a time–frequency spectrogram using short-time Fourier Transform (STFT). Next, the CFED automatic recognition algorithm is used to identify horizontal lines on the spectrogram. The third step is to determine the line frequency range based on the proportional relationship between the frequency domain of the satellite's VLF and the height of the time–frequency spectrogram. Finally, we used the CSES power spectrogram to confirm the presence of CFEDs in the line frequency range and extract their true frequencies. We statistically analyzed 1034 orbit time–frequency spectrograms and power spectrograms from 8 periods (5 days per period) and identified approximately 200 CFEDs. Among them, two CFEDs with strong signals persisted throughout an entire orbit. This study establishes a foundation for detecting anomalies due to artificial sources, particularly in the study of short-term strong earthquake prediction. Additionally, it contributes to research on other aspects of spatial electromagnetic interference and the suppression and cleaning of electromagnetic waves. [ABSTRACT FROM AUTHOR]

With the increasing demand for vegetable fruits, vegetable plants are moved to protected structures for achieving high production and economic revenue, especially in undesirable seasons. In North China, tomato crops, as widely consumed vegetables, are now increasingly planted in solar greenhouses (GH), especially in the winter period. To improve the microclimate inside GH in winter, a sunken solar greenhouse was used recently. This study was to evaluate the photosynthetic characteristics of tomato plants and its responses to the inside microclimate in this new GH. In this experiment, the plant transpiration (E) and photosynthesis (Pn) rates of healthy and diseased plants were measured from July to December for three growth seasons in a commercial GH in North China. Results show both E and Pn were positively related to inside radiation and vapor pressure deficit. The stomata conductance to E (gsw) and Pn (gtc) performed relatively constant during daytime, and weakly related to inside microclimate. The parameters of E, Pn, gsw and gtc were greatly reduced for diseased plants in summer because of the heat shock. The water use efficiency at the leaf level, the ratio of Pn to E, was higher for solar radiation of 400–500 W m−2, temperature of 20–30 °C, relative humidity of higher than 80%, and vapor pressure deficit of less than 2.0 kPa. The results of this study could help farmers in the region of 30 to 40 degrees north latitude to enhance the growth of tomato crops in winter by using this sunken solar greenhouse. [ABSTRACT FROM AUTHOR]

Tomato crops are increasingly cultivated in winter in solar greenhouses to achieve high economic benefit in the North China Plain (NCP). Accurate predictions of crop transpiration (Tr) are of great significance for formulating a scientific irrigation system and increasing water productivity in this water shortage region. In this study, tomato transpiration at daily and hourly scales were estimated using Penman-Monteith (PM), Shuttleworth-Wallace (SW), and Priestley-Taylor (PT) models, and results were compared to the measured sap flow data (SF) in three tomato growth seasons in winter from 1 November 2018 to 9 December 2020. Results showed that both PM and SW models could perfectly estimate daily tomato Tr, with a determination coefficient R2 of 0.96 and 0.94 and slopes of 0.99 and 0.98, respectively, when all three seasons' data were pooled together. The estimated daily Tr by the original PT model with a coefficient (α) of 1.26 was also linearly related to the SF with R2 of 0.92; however, the Tr was underestimated by 33%. Then α was calibrated using the data in the 2018 winter season. When the calibrated α was used in the 2019 and 2020 seasons, the estimated daily Tr showed comparable results with the PM and SW models. At hourly scales, the PM model performed best with an error of 3.0%, followed by the PT model (7.8%); the SW model underestimated Tr by 18.2%. In conclusion, all three models could be used to estimate daily Tr, and the PM and calculated PT models can be used to estimate hourly Tr. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea syndrome (OSAS) has become a major worldwide public health concern, given its global prevalence. It has clear links with multiple comorbidities and mortality. Cognitive impairment is one related comorbidity causing great pressure on individuals and society. The clinical manifestations of cognitive impairment in OSAS include decline in attention/vigilance, verbal–visual memory loss, visuospatial/structural ability impairment, and executive dysfunction. It has been proven that chronic intermittent hypoxia (CIH) may be a main cause of cognitive impairment in OSAS. Inflammation plays important roles in CIH-induced cognitive dysfunction. Furthermore, the nuclear factor kappa B and hypoxia-inducible factor 1 alpha pathways play significant roles in this inflammatory mechanism. Continuous positive airway pressure is an effective therapy for OSAS; however, its effect on cognitive impairment is suboptimal. Therefore, in this review, we address the role inflammation plays in the development of neuro-impairment in OSAS and the association between OSAS and cognitive impairment to provide an overview of its pathophysiology. We believe that furthering the understanding of the inflammatory mechanisms involved in OSAS-associated cognitive impairment could lead to the development of appropriate and effective therapy. [ABSTRACT FROM AUTHOR]

This study conducted a tribological investigation of base oil (PAO6 and 5W 40) and ionic liquids (IL)-modified lubricants through a four-ball tribometer for 30 min. The lubricants were fabricated via a two-step method using stirring magnetic and ultrasonic dispersion. IL, base oil, and lubricants were, respectively, characterized by XRD and FTIR analysis. In addition, multiple characterizations such as EDS, 3D morphology, and SEM were carried out to evaluate the wear and friction performance of steel balls. Ultimately, the results showed that the coefficient of friction (COF) and wear scar diameter (WSD) of wear scar lubricated by IL-modified lubricants were greatly decreased than that by base oil. IL can well improve the tribological properties of PAO 6 oil and 5W-40 oil due to the tribo-film appearance on the friction surface of wear scar by the effective role of IL. Fascinatingly, this investigation comprehensively and elaborately put a new sight into the lubrication mechanism of how IL reacted with a base oil and enhanced the tribological characteristics. [ABSTRACT FROM AUTHOR]

Simple Summary: The use of antibiotics as a growth promoter in food-producing animals has been restricted throughout the world. As a result, many antibiotic alternatives have been evaluated in the poultry industry. Aspergillus niger is one of the probiotics of fungal origin that has shown promising effects in broiler production; however, few studies have been conducted on laying hens. Therefore, this study investigated the role of Aspergillus niger on performance, egg quality, and cecal microbial load of pathogenic bacteria in laying hens. The results showed that supplementation of Probioist®, a product containing the probiotic Aspergillus niger, at a rate of 220 mg/kg of diet, improved egg production and Haugh unit, and numerically decreased the cecal microbial load of Clostridium perfringens, Salmonella spp., and Escherichia coli. This study aimed to investigate the role of the probiotic Aspergillus niger on the production performance, egg quality, and cecal microbial load of Clostridium perfringens, Salmonella spp., and Escherichia coli in Hy-Line W-36 laying hens. A total of 72, 45-week-old Hy-Line W-36 laying hens were randomly allocated to one of the three dietary treatments with six replicates, and each replicate had four individually caged laying hens (n = 6 and 4 hens/replicate). The hens in each treatment group were fed a corn and soybean meal diet (Control), a diet supplemented with bacitracin methylene disalicylate (BMD) at a rate of 495 mg/kg of feed (Positive Control), or a diet supplemented with Aspergillus niger (Probioist®) at a rate of 220 mg/kg of feed (Probiotic). Supplementing probiotics in the laying hen diet significantly increased egg production at weeks 3 and 6 compared with the Positive Control. Haugh unit, a measure of egg quality, was significantly higher in laying hens fed the probiotic diet compared with the Control or Positive Control at week 10. Furthermore, the Probiotic group had numerically lower cecal microbial loads of pathogenic bacteria (Clostridium perfringens, Salmonella spp., and Escherichia coli) compared with the Control and Positive Control groups. The results suggest that Aspergillus niger could be used as a probiotic to improve laying hen performance and egg quality. [ABSTRACT FROM AUTHOR]

In the Northern China Plain (NCP), sunken solar greenhouses (SSG) are now increasingly being used for vegetable cultivation in winter due to low winter temperatures. Investigating plant transpiration and determining crop coefficients are helpful when developing irrigation scheduling and improving crop growth. In this study, a three-season experiment was conducted in a commercial tomato SSG to investigate changes in microclimate, sap flow (SF), photosynthesis traits and plant physiological parameters, and to calculate the crop coefficient and evaluate the irrigation efficiency using current irrigation management practices. Results show that the average transmissivity from top plastic covers was 0.69, and the inside temperature increased by approximately 10 °C in November and 15–18 °C in December, which guaranteed the growth of tomatoes in winter. The leaf photosynthesis rate (Pn) is linearly related to radiation, however, a concave quadratic function is a better fit for Pn and VPD, with the highest Pn at approximately 1.0 kPa VPD; leaf transpiration is positively and linearly related to both radiation and VPD. Therefore, increasing greenhouse transmissivity and maintaining an internal VPD of approximately 1 kPa could produce a high leaf Pn and low transpiration concurrently. Daily total SF was linearly correlated with solar radiation, VPD and temperature with determination coefficients of 0.87–0.96, 0.89–0.91 and 0.62–0.84, respectively. Correcting the slope of SF to radiation with VPD (SF = (0.12 + 0.14VPD)Rs), R2 increased by 0.08, and the root mean square error and relative error decreased by 0.047 mm day−1 and 6.53%, respectively. Therefore, this integrated equation is recommended to estimate daily tomato transpiration when plant height is approximately 1.5 m, and the leaf area index (LAI) is between 2 and 2.5. During the fruit expansion and ripening period, the average basal crop coefficients (Kcb) for greenhouse tomatoes in winter was between 0.99 and 1.11. The irrigation efficiency increased from 0.3 in the first season to 0.6–0.69 in the second and third seasons when the tensiometer method was used. Therefore, using the tensiometer method to guide tomato irrigation could markedly improve irrigation efficiency in greenhouses. [ABSTRACT FROM AUTHOR]

The sprinkler irrigation method has been widely used in agricultural fields due to its high water productivity and microclimate regulation traits. Quantitative analysis of the water distribution of sprinkler irrigation water by considering canopy influence is critical to evaluate crop growth and water use efficiency. In this study, stemflow was measured by collecting the water flowing down along stems using a high-adsorption sheet, throughfall water was measured by contains placed between wheat rows, and canopy interception was measured by the mass difference of plants between before and after sprinkler irrigation during wheat anthesis and grain-filling stages in the North China Plain. The results showed that the canopy interception water was between 0.6 and 1.3 mm, with a mean of 0.9 mm per sprinkler irrigation event for a leaf area index of approximately 4. Stemflow water was linearly related to the irrigation water and approximately 30% of the irrigation water. The throughfall water was also linearly related to the irrigation water above the canopy and accounted for approximately 60% of the irrigation water. The three components of sprinkler water are weakly influenced by the plant leaf area index, wind conditions and sprinkler irrigation system layouts in this study. [ABSTRACT FROM AUTHOR]

Multi-spectral semantic segmentation has shown great advantages under poor illumination conditions, especially for remote scene understanding of autonomous vehicles, since the thermal image can provide complementary information for RGB image. However, methods to fuse the information from RGB image and thermal image are still under-explored. In this paper, we propose a simple but effective module, add–multiply fusion (AMFuse) for RGB and thermal information fusion, consisting of two simple math operations—addition and multiplication. The addition operation focuses on extracting cross-modal complementary features, while the multiplication operation concentrates on the cross-modal common features. Moreover, the attention module and atrous spatial pyramid pooling (ASPP) modules are also incorporated into our proposed AMFuse modules, to enhance the multi-scale context information. Finally, in the UNet-style encoder–decoder framework, the ResNet model is adopted as the encoder. As for the decoder part, the multi-scale information obtained from our proposed AMFuse modules is hierarchically merged layer-by-layer to restore the feature map resolution for semantic segmentation. The experiments of RGBT multi-spectral semantic segmentation and salient object detection demonstrate the effectiveness of our proposed AMFuse module for fusing the RGB and thermal information. [ABSTRACT FROM AUTHOR]

Many studies indicated that ionospheric total electron content (TEC) prediction is vital for terrestrial and space-based radio-communication systems. In previous TEC prediction schemes based on RNN, they learn TEC representations from previous time steps, and each time-step made an equal contribution to a prediction. To overcome these drawbacks, we propose two improvements in our study: (1) To predict TEC with both past and future time-step, Bidirectional Gate Recurrent Unit (BiGRU) was presented to improve the capabilities. (2) To highlight critical time-step information, attention mechanism was used to provide weights to each time-step. The proposed attentional BiGRU TEC predicting method was evaluated on the publicly available data set from the Centre for Orbit Determination in Europe. We chose three geographical locations in low latitude, middle latitude, and high latitude to verify the performance of our proposed model. Comparative experiments were conducted using Deep Neural Network (DNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Term memory (BiLSTM), and Gated Recurrent Unit (GRU). Experimental results show that the proposed Attentional BiGRU model is superior to the other models in the selected nine regions. In addition, the paper discussed the effects of latitudes and solar activities on the performance of Attentional BiGRU model. Experimental results show that the higher the latitude, the higher the prediction accuracy of our proposed model. Experimental results also show that in the middle latitude, the prediction accuracy of the model is less affected by solar activity, and in other areas, the model is greatly affected by solar activity. [ABSTRACT FROM AUTHOR]

Understanding the impact of sprinkler irrigation on field energy balance, microclimate, and crop evapotranspiration is of great importance for optimizing irrigation scheduling and enhancing crop growth. In this study, the microclimate variables, energy, and water flux were measured using an eddy covariance system during four wheat (Triticum aestivum L.) growing seasons in a sprinkler-irrigated field of North China Plain. The variation patterns of microclimate, energy balance (net solar radiation Rn, soil heat flux G, latent heat LE, and sensible heat H) and crop evapotranspiration (ET) were analyzed during and after sprinkler irrigation events. A novel quantitative model using a stepwise regression method was developed to predict the change in microclimate after sprinkler irrigation by considering irrigation, weather, meteorology, and crop traits. The results showed that the reflectance rate of the wheat canopy decreased by 0.01, and the daily LE/Rn increased by 0.19–0.23 in the 1–3 days after sprinkler irrigation with 40–50 mm water, which finally resulted in crop ET increased by 1.8–4.7 mm during irrigation interval, and seasonal total ET could increase by 9–24 mm when five normal sprinkler irrigations were implemented in a wheat season. The mean daily H/Rn decreased by 0.06–0.17, indicating weak energy exchange between canopy and environment. The measured daily minimum (Tmin), maximum temperatures (Tmax) and daily mean vapor pressure deficit (VPD) decreased by approximately 0.8 °C, 0.9 °C, and 0.25 kPa, respectively, and daily mean relative humidity increased by approximately 7.5% on the first 3 days after sprinkler irrigation; and these changes decreased and were negligible on the 5th–7th days. The decreases in daily Tmin, Tmax, and mean VPD after sprinkler irrigation could change more under higher irrigation amounts and sunny days with a larger crop leaf area index based on the fitted models. [ABSTRACT FROM AUTHOR]

The nonlinear load-settlement behaviours of vertically loaded pile groups containing various numbers of piles (up to a few hundred piles in a group) are analysed using the method proposed by Lee and Xiao. This back-analysis method assumes the "local shear displacement" of a thin layer of disturbed soil along the pile–soil interface and the soil outside the interface is assumed to be linear elastic. Parametric studies are conducted to examine the load-displacement behaviours of the rigidly capped pile groups. Factors such as the number of piles in a group, pile slenderness (L/D), and pile spacing (S/D) are examined to study the effects on the performance of pile groups up to the failure state. Some phenomenological features of large pile groups under nonlinear conditions, which are difficult to obtain through an elastic analysis, are revealed in the present analysis. [ABSTRACT FROM AUTHOR]

In order to explore the abnormal disturbance of the space electric field caused by earthquakes using the electric field data of the ULF and VLF frequency bands of the electric field observed by the ZH-1 satellite, and taking the Mw7.7 earthquake in the Caribbean Sea in the southern sea area of Cuba on 29 January 2020 as an example, the signal-to-noise ratio of the NAA and NLK artificial source VLF transmitting stations in the Northern Hemisphere and the height of the lower ionosphere was calculated. The disturbance of the electric field in the ULF band was extracted using the S-G filtering method. The results indicate that: (1) The ionospheric anomaly caused by this earthquake appeared 20 days before the earthquake, and before the earthquake, there were significant anomalous changes in all parameters within the pregnant seismic zone. The signal-to-noise ratios of the NAA and NLK artificial source transmitter stations decreased by 30%, and the height of the low ionosphere decreased by 5–10 km, while there were anomalous perturbations in several orbits of the ULF electric field, and the magnitude of the perturbations exceeded three times the standard deviation. (2) The SNR of the artificial source transmitting stations before and after the earthquake was significantly reduced in the third period before the earthquake and recovered after the earthquake. (3) The low ionospheric height appears to be reduced before the earthquake and recovers after the earthquake. (4) The decrease in the S/N ratio occurred simultaneously with the decrease in ionospheric height 15 days–10 days before the earthquake. This provides a reference for extracting pre-earthquake ionospheric precursor anomalies. [ABSTRACT FROM AUTHOR]

The thermal evolution process of organic matter is associated with the complete hydrocarbon generation and expulsion process in shale, however, the thermal evolution of organic matter is a long process and cannot be realized without experimental simulations. Although several scholars have substantially studied the thermal evolution of organic matter, it remains a challenging and much debated issue in the studies of organic geochemistry. Volcanic events are crucial in the enrichment of organic matter, and appropriate heating accelerates the thermal evolution of organic matter. However, how strong-rock baking restricts the evolution of organic matter in shale has not been specifically studied. The South Qilian Basin in China is a typical superimposed basin where complex tectonic movements have induced multiple volcanic events, which makes it a favorable location to perform the aforementioned research. This study used the Galedesi Formation shale in the Hala Lake Depression of the South Qilian Basin as an example for investigating the constraints of the volcanic events related to the thermal evolution of organic matter by integrating the results obtained using the geochemical and petrological methods. Our results demonstrate that the lacustrine Galedesi Formation shale of the Hala Lake Depression in the Late Triassic is a typical deep-lake facies deposit with good hydrocarbon generation potential. However, because of the influence of regional tectonic evolution, the burial depth of shale is not deep and the thermal evolution of organic matter is insufficient. Due to the influence of multiple volcanic thermal events in the later stages, the thermal maturity of organic matter in the Galedesi Formation shale generally exceeds 3.0%, which is abnormally high. The apparent carbonization of organic matter can be observed via scanning electron microscopy. Rapid magma baking typically cannot effectively promote the hydrocarbon generation of shale organic matter. Finally, the burial depth of lacustrine shale of the Galedesi Formation in the Hala Lake Depression of South Qilian Basin is too shallow. Organic matter hydrocarbon generation and later shale preservation conditions are not conducive to the enrichment, accumulation, exploration, and development of shale gas. [ABSTRACT FROM AUTHOR]

This paper focuses on the high-resolution (HR) remote sensing images semantic segmentation task, whose goal is to predict semantic labels in a pixel-wise manner. Due to the rich complexity and heterogeneity of information in HR remote sensing images, the ability to extract spatial details (boundary information) and semantic context information dominates the performance in segmentation. In this paper, based on the frequently used fully convolutional network framework, we propose a boundary enhancing semantic context network (BES-Net) to explicitly use the boundary to enhance semantic context extraction. BES-Net mainly consists of three modules: (1) a boundary extraction module for extracting the semantic boundary information, (2) a multi-scale semantic context fusion module for fusing semantic features containing objects with multiple scales, and (3) a boundary enhancing semantic context module for explicitly enhancing the fused semantic features with the extracted boundary information to improve the intra-class semantic consistency, especially in those pixels containing boundaries. Extensive experimental evaluations and comprehensive ablation studies on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of BES-Net, yielding an overall improvement of 1.28/2.36/0.72 percent in mF1/mIoU/OA over FCN_8s when the BE and MSF modules are combined by the BES module. In particular, our BES-Net achieves a state-of-the-art performance of 91.4% OA on the ISPRS Vaihingen dataset and 92.9%/91.5% mF1/OA on the ISPRS Potsdam dataset. [ABSTRACT FROM AUTHOR]

Binary neural networks (BNNs) have been proposed to reduce the heavy memory and computation burdens in deep neural networks. However, the binarized weights and activations in BNNs cause huge information loss, which leads to a severe accuracy decrease, and hinders the real-world applications of BNNs. To solve this problem, in this paper, we propose the information-enhanced network (IE-Net) to improve the performance of BNNs. Firstly, we design an information-enhanced binary convolution (IE-BC), which enriches the information of binary activations and boosts the representational power of the binary convolution. Secondly, we propose an information-enhanced estimator (IEE) to gradually approximate the sign function, which not only reduces the information loss caused by quantization error, but also retains the information of binary weights. Furthermore, by reducing the information loss in binary representations, the novel binary convolution and estimator gain large information compared with the previous work. The experimental results show that the IE-Net achieves accuracies of 88.5% (ResNet-20) and 61.4% (ResNet-18) on CIFAR-10 and ImageNet datasets respectively, which outperforms other SOTA methods. In conclusion, the performance of BNNs could be improved significantly with information enhancement on both weights and activations. [ABSTRACT FROM AUTHOR]

Cumulus cells of pre-pubertal domestic animals are dysfunctional, perhaps due to age-specific epigenetic events. This study was designed to determine effects of melatonin treatment of donors on methylation modification of pre-pubertal cumulus cells. Cumulus cells from germinal vesicle stage cumulus oocyte complexes (COCs) were collected from eighteen lambs which were randomly divided into control group (C) and melatonin group given an 18 mg melatonin implant subcutaneous (M). Compared to the C group, the M group had higher concentrations of melatonin in plasma and follicular fluid (p < 0.05), greater superovulation, a higher proportion of fully expanded COCs, and a lower proportion of apoptotic cumulus cells (p < 0.05). Real-time PCR results showed that melatonin up-regulated expression of genes MT1, Bcl2, DNMT1, DNMT3a and DNMT3b, but down-regulated expression of genes p53, Caspase 3 and Bax (p < 0.05). Furthermore, melatonin increased FI of FITC (global methylation level) on cumulus cells (p < 0.05). To understand the regulation mechanism, the DNMTs promoter methylation sequence were analyzed. Compared to the C group, although there was less methylation at two CpG sites of DNMT1 (p < 0.05) and higher methylation at two CpG sites of DNMT3a (p < 0.05), there were no significant differences in methylation of the detected DNMT1 and DNMT3a promoter regions. However, there were lower methylation levels at five CpG sites of DNMT3b, which decreased methylation of detected DNMT3b promoter region on M group (p < 0.05). In conclusion, alterations of methylation regulated by melatonin may mediate development of cumulus cells in lambs. [ABSTRACT FROM AUTHOR]

A novel ternary Ag3PO4/g-C3N4/hydroxyapatite photocatalyst was prepared, and its morphology, composition and structure were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, high-resolution transmission electron microscopy, and electron spin resonance, etc. The results show that g-C3N4 is evenly dispersed in the interior of hydroxyapatite, forming a homogeneous composite, and significantly improves the band gap structure of the material as a whole. Ag3PO4/g-C3N4/hydroxyapatite has good electron transfer ability and an appropriate energy band structure, which shows that the material has a good degradation effect and stability. Finally, based on the characterization and experimental results, a possible Z-scheme mechanism was proposed, and the active species involved in the reaction are mainly ·O2− and h+. [ABSTRACT FROM AUTHOR]

In this study, a novel crude oil degrading bacterium was isolated from an oil-contaminated freshwater lake using crude oil as the sole carbon source. The strain was named Pseudomonas aeruginosa AQNU-1 based on the analyses of its morphological characteristics and 16S rRNA gene sequencing. Gas chromatography-mass spectrometry (GC-MS) was carried out to investigate the degradation of crude oil fractions under dynamic (37 °C, 180 r/min) and static (37 °C, 0 r/min) cultivation over three months of continuous enrichment in the laboratory. It was found that strain AQNU-1 exhibited stronger biodegradation efficiency for n-alkanes of C13–C35 under dynamic cultivation with degradation ratios of 87–100% compared to ratios of 74–100% under static cultivation. Furthermore, this strain could fully utilize alkylcyclohexane (M/Z 82), alkylbenzene (M/Z 92) and alkyltoluene (M/Z 106) in crude oil under both conditions. It also had better biodegradability of partial aromatic compounds in the crude oil, showing differences within compound families of aromatic hydrocarbons. Further, the potential degradation ability of this isolated strain decreased with increasing molecular weight, with the dynamic condition performing better in general. These results suggest that the isolated strain has great potential to assimilate indigestible crude-oil contaminants under different hydrological conditions, providing a valuable microbiological resource for in situ remediation of natural wetlands. [ABSTRACT FROM AUTHOR]

The solar greenhouse is a common protected structure for crop production when ambient temperatures are low. In the North China Plain (NCP) winter temperatures are very low and an improved solar greenhouse with a lowered soil surface (0.5–1.5 m deep), referred to as a sunken solar greenhouse (SSG), is used. A four-season experiment was conducted in a commercial SSG with tomato crops to characterize internal microclimate, sap flow (SF) and crop coefficients. Results show that temperature inside the SSG could be more than 20 °C higher than outside in winter, which favors tomato growth and resulted in acceptable yields. Daily total SF was related to solar radiation, vapor pressure deficit (VPD) and temperature, in that order, both in winter and summer. The decoupling coefficient (which is the ratio of radiative to aerodynamic influences on evapotranspiration) in daytime was 0.76 in winter and 0.84 in summer, indicating strong decoupling (i.e., predominance of radiative influences) of the internal environment where wind speed was low. Basal crop coefficients at the mid stage of crop growth averaged 1.15–1.43 in winter and 0.91–0.92 in spring and summer. Thus, in the SSG, for similar climatic conditions sap flow in winter was higher than that in summer, which should be considered in irrigation scheduling. [ABSTRACT FROM AUTHOR]

Currently, an increasing number of convolutional neural networks (CNNs) focus specifically on capturing contextual features (con. feat) to improve performance in semantic segmentation tasks. However, high-level con. feat are biased towards encoding features of large objects, disregard spatial details, and have a limited capacity to discriminate between easily confused classes (e.g., trees and grasses). As a result, we incorporate low-level features (low. feat) and class-specific discriminative features (dis. feat) to boost model performance further, with low. feat helping the model in recovering spatial information and dis. feat effectively reducing class confusion during segmentation. To this end, we propose a novel deep multi-feature learning framework for the semantic segmentation of VHR RSIs, dubbed MFNet. The proposed MFNet adopts a multi-feature learning mechanism to learn more complete features, including con. feat, low. feat, and dis. feat. More specifically, aside from a widely used context aggregation module for capturing con. feat, we additionally append two branches for learning low. feat and dis. feat. One focuses on learning low. feat at a shallow layer in the backbone network through local contrast processing, while the other groups con. feat and then optimizes each class individually to generate dis. feat with better inter-class discriminative capability. Extensive quantitative and qualitative evaluations demonstrate that the proposed MFNet outperforms most state-of-the-art models on the ISPRS Vaihingen and Potsdam datasets. In particular, thanks to the mechanism of multi-feature learning, our model achieves an overall accuracy score of 91.91% on the Potsdam test set with VGG16 as a backbone, performing favorably against advanced models with ResNet101. [ABSTRACT FROM AUTHOR]

Precision machining (e.g., fine grinding, polishing) induced residual stress is very small and often not constant across the wafer and it is difficult to be directly obtained by stress testing equipment or Stoney equation. The residual stress could be obtained theoretically based on the principle of superposition in which the entire wafer deformation is taken as the sum of all deformations induced by the residual stresses of different positions on the wafer surface. However, the solved residual stress is affected greatly by deformation measurement errors and fluctuates greatly across the wafer surface. To solve the problem, a regularization method with continuity constraints was proposed in this study. The mechanisms for the discontinuity of the residual stress distribution and the sensitivity of calculation results to the measurement errors were studied. The influences of the number of subareas of the silicon wafer were investigated and the continuity constraint term was constructed based on the positional relationship of different subareas. Stable and continuous residual stress distribution was successfully obtained after using the proposed regularization method. The method may also be applied to estimate the residual stress from surface deformation for thin substrate plates of other materials. [ABSTRACT FROM AUTHOR]

In the silicone material extrusion (MEX) process, product profile error and performance defects are common problems due to changes in strand shape. A process optimization method considering strand morphology, denoted as SMO, which allows adjustment of the strand shape by adjusting process parameters during the printing process is presented. The relation between process parameters (extrusion speed, moving speed, nozzle height, and nozzle radius) and the geometric parameters (strand width and strand height) of the cross-section, as well as the relationship between strand spacing, layer height, and process parameters in no void constraint is discussed and verified. SMO was utilized to produce specimens with tunable strand width and strand height. Tensile tests and profile scans were performed to compare SMO with other methods to verify its feasibility. Specimens fabricated using the SMO method have up to a 7% increase in tensile strength, up to a 10% reduction in processing time, and about a 60% reduction in strand height error over unused ones. The results show that the SMO method with adjustable strand width can effectively balance efficiency and mechanical properties compared to uniform infill, and the SMO method with adjustable strand height can provide higher accuracy compared to uniform strand height. The proposed method is validated and improves the efficiency and accuracy of silicone MEX. [ABSTRACT FROM AUTHOR]

Trajectory tracking is a common application method for manipulators. However, the tracking performance is hard to improve if the manipulators contain flexible joints and mismatched uncertainty, especially when the trajectory is nonholonomic. On the basis of the Udwadia–Kalaba Fundamental Equation (UKFE), the prescribed position or velocity trajectories are creatively transformed into second-order standard differential form. The constraint force generated by the trajectories is obtained in closed form with the help of UKFE. Then, a high-order fractional type robust control with an embedded fictitious signal is proposed to achieve practical stability of the system, even if the mismatched uncertainty exists. Only the bound of uncertainty is indispensable, rather than the exact information. A leakage type of adaptive law is proposed to estimate such bound. By introducing a dead-zone, the control will be simplified when the specific parameter enters a certain area. Validity of the proposed controller is verified by numerical simulation with two-link flexible joint manipulator. [ABSTRACT FROM AUTHOR]

Hydraulic lower limb exoskeletons are wearable robotic systems, which can help people carry heavy loads. Recently, underactuated exoskeletons with some passive joints have been developed in large numbers for the purpose of decreasing the weight and energy consumption of the system. There are many control algorithms for a multi-joint fully actuated exoskeleton, which cannot be applied for underactuated systems due to the reduction in the number of control inputs. Besides, since the hydraulic actuator is not a desired force output source, there exist high order nonlinearities in hydraulic exoskeletons, which makes the controller design more challenging than motor driven exoskeleton systems. This paper proposed a precision interaction force controller for a 3DOF underactuated hydraulic stance leg exoskeleton. First, the control effect of the wearer is considered and the posture of the exoskeleton back is assumed as a desired trajectory under the control of the wearer. Under this assumption, the system dynamics are changed from a 3DOF underactuated system in joint space to a 2DOF fully actuated system in Cartesian space. Then, a three-level interaction force controller is designed in which the high-level controller conducts human motion intent inference, the middle level controller tracks human motion and the low-level controller achieves output force tracking of hydraulic cylinders. The MIMO adaptive robust control algorithm is applied in the controller design to effectively address the high order nonlinearities of the hydraulic system, multi-joint couplings and various model uncertainties. A gain tuning method is also provided to facilitate the controller gains selection for engineers. Comparative simulations are conducted, which demonstrate that the principal human-machine interaction force components can be minimized and good robust performance to load change and modeling errors can be achieved. [ABSTRACT FROM AUTHOR]

In north China, vegetables are always cultivated in conventional solar greenhouses (SG), however, these structures cannot be used during most of the winter due to extremely low temperatures. In this study, a new type of a solar greenhouse named sunken solar greenhouse (SSG), where the inside soil surface is lowered 1–2 m below outside and the back wall is 5–8 m width at the bottom and 1.5–2 m on top, was investigated. Inside climatic variables were recorded and compared with those outside during seven cucumber cultivation seasons. Crop evapotranspiration (ETc) was estimated using the Penman–Monteith method. Results show that inside solar radiation was reduced by approximately 40%, however days with a daily maximum inside temperature higher than 20 °C accounted for 80–90% of the days during the winter, which greatly enhanced cucumber fruit production compared to common SGs. The reference crop evapotranspiration (ETo) inside the SSG was reduced by 27% compared to outside. The estimated ETc was generally lower than 4 mm day−1, which resulted in a basal crop coefficient of 0.83. In conclusion, the SSG is environmental-friendly, preferable for winter vegetable cultivation in north China, and can be useful for other regions of the world with cold winter conditions. [ABSTRACT FROM AUTHOR]

An air-backed diaphragm is the key structure of most dynamic pressure sensors and plays a critical role in determining the sensor performance. Our previous analytical model investigated the influence of air cavity length on the sensitivity and bandwidth. The model found that as the cavity length decreases, the static sensitivity monotonically decreases, and the fundamental natural frequency shows a three-stage trend: increasing in the long-cavity-length range, reaching a plateau value in the medium-cavity-length range, and decreasing in the short-cavity-length range, which cannot be captured by the widely used lumped model. In this study, we conducted the first experimental measurements to validate these findings. Pressure sensors with a circular polyimide diaphragm and a backing air cavity with an adjustable length were designed, fabricated, and characterized, from which the static sensitivities and fundamental natural frequencies were obtained as a function of the cavity length. A further parametric study was conducted by changing the in-plane tension in the diaphragm. A finite element model was developed in COMSOL to investigate the effects of thermoviscous damping and provide validation for the experimental study. Along with the analytical model, this study provides a new understanding and important design guidelines for dynamic pressure sensors with air-backed diaphragms. [ABSTRACT FROM AUTHOR]

In this study, the antioxidant components in co-culture of Chlorella pyrenoidosa and Yarrowia lipolytica (3:1 ratio) were confirmed as trypsin-hydrolyzed peptides (EHPs). The EHPs were composed of 836 different peptides with molecular weights ranging from 639 to 3531 Da and were mainly composed of hydrophobic amino acids (48.1%). These peptides showed remarkable protective effects against oxidative stress in HepG2, which may be attributed to their structures. Furthermore, the mRNA and protein levels of nuclear factor erythroid 2-related factor 2 (Nrf2) were significantly lower in the peptide-treated group than in the control group, suggesting that the antioxidant enzyme-coding genes were not activated. The EC50 value of three peptides in the EHPs were in the order of AGYSPIGFVR (0.04 ± 0.002 mg/mL) > VLDELTLAR (0.09 ± 0.001 mg/mL) > LFDPVYLFDQG (0.41 ± 0.03 mg/mL); these results agreed with the prediction of the model (R2 > 0.9, Q2 > 0.5). Thus, EHPs show potential as potent new antioxidant agents. [ABSTRACT FROM AUTHOR]

Volatile programmable metallization cell is a promising threshold switching selector with excellent characteristics and simple structures. However, the large variation of threshold voltage is a major problem for practical application. In this work, we propose a dual-layer structure to increase selectivity and improve the threshold voltage variation. Compared to single-layer devices, this dual-layer device exhibits higher selectivity (>107) and better threshold voltage uniformity with less than 5% fluctuation during 200 DC switching. The improvement is attributed to good control on the location of the filament formation and rupture after introducing a HfO2 layer. It is deduced that a major factor consists of the difference of Ag ions mobility between SiTe and HfO2 due to the grain boundary quantity. [ABSTRACT FROM AUTHOR]