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SignificanceOil crops play a significant role in the food supply, as well as the important source of edible vegetable oils and plant proteins. Real-time, dynamic and large-scale monitoring of oil crop growth is essential in guiding agricultural production, stabilizing markets, and maintaining health. Previous studies have made a considerable progress in the yield simulation of staple crops in regional scale based on remote sensing methods, but the yield simulation of oil crops in regional scale is still poor as its complexity of the plant traits and structural characteristics. Therefore, it is urgently needed to study regional oil crop yield estimation based on remote sensing technology.ProgressThis paper summarized the content of remote sensing technology in oil crop monitoring from three aspects: backgrounds, progressions, opportunities and challenges. Firstly, significances and advantages of using remote sensing technology to estimate the of oil crops have been expounded. It is pointed out that both parameter inversion and crop area monitoring were the vital components of yield estimation. Secondly, the current situation of oil crop monitoring was summarized based on remote sensing technology from three aspects of remote sensing parameter inversion, crop area monitoring and yield estimation. For parameter inversion, it is specified that optical remote sensors were used more than other sensors in oil crops inversion in previous studies. Then, advantages and disadvantages of the empirical model and physical model inversion methods were analyzed. In addition, advantages and disadvantages of optical and microwave data were further illustrated from the aspect of oil crops structure and traits characteristics. At last, optimal choice on the data and methods were given in oil crop parameter inversion. For crop area monitoring, this paper mainly elaborated from two parts of optical and microwave remote sensing data. Combined with the structure of oil crops and the characteristics of planting areas, the researches on area monitoring of oil crops based on different types of remote sensing data sources were reviewed, including the advantages and limitations of different data sources in area monitoring. Then, two yield estimation methods were introduced: remote sensing yield estimation and data assimilation yield estimation. The phenological period of oil crop yield estimation, remote sensing data source and modeling method were summarized. Next, data assimilation technology was introduced, and it was proposed that data assimilation technology has great potential in oil crop yield estimation, and the assimilation research of oil crops was expounded from the aspects of assimilation method and grid selection. All of them indicate that data assimilation technology could improve the accuracy of regional yield estimation of oil crops. Thirdly, this paper pointed out the opportunities of remote sensing technology in oil crop monitoring, put forward some problems and challenges in crop feature selection, spatial scale determination and remote sensing data source selection of oil crop yield, and forecasted the development trend of oil crop yield estimation research in the future.Conclusions and ProspectsThe paper puts forward the following suggestions for the three aspects: (1) Regarding crop feature selection, when estimating yields for oil crops such as rapeseed and soybeans, which have active photosynthesis in siliques or pods, relying solely on canopy leaf area index (LAI) as the assimilation state variable for crop yield estimation may result in significant underestimation of yields, thereby impacting the accuracy of regional crop yield simulation. Therefore, it is necessary to consider the crop plant characteristics and the agronomic mechanism of yield formation through siliques or pods when estimating yields for oil crops. (2) In determining the spatial scale, some oil crops are distributed in hilly and mountainous areas with mixed land cover. Using regularized yield simulation grids may result in the confusion of numerous background objects, introducing additional errors and affecting the assimilation accuracy of yield estimation. This poses a challenge to yield estimation research. Thus, it is necessary to choose appropriate methods to divide irregular unit grids and determine the optimal scale for yield estimation, thereby improving the accuracy of yield estimation. (3) In terms of remote sensing data selection, the monitoring of oil crops can be influenced by crop structure and meteorological conditions. Depending solely on spectral data monitoring may have a certain impact on yield estimation results. It is important to incorporate radar off-nadir remote sensing measurement techniques to perceive the response relationship between crop leaves and siliques or pods and remote sensing data parameters. This can bridge the gap between crop characteristics and remote sensing information for crop yield simulation. This paper can serve as a valuable reference and stimulus for further research on regional yield estimation and growth monitoring of oil crops. It supplements existing knowledge and provides insightful considerations for enhancing the accuracy and efficiency of oil crop production monitoring and management.

Liver injury and disease caused by alcohol is a common complication to human health worldwide. Chamazulene is a natural proazulene with antioxidant and anti-inflammatory properties. This study aims to investigate the hepatoprotective effects of chamazulene against ethanol-induced liver injury in rat models. Adult Wistar rats were orally treated with 50% v/v ethanol (8–12 mL/kg body weight [b.w.]) for 6 weeks to induce alcoholic liver injury. Chamazulene was administered orally to rats 1 h prior to ethanol administration at the doses of 25 and 50 mg/kg b.w. for 6 weeks. Silymarin, a commercial drug for hepatoprotection, was orally administered (50 mg/kg b.w.) for the positive control group. Chamazulene significantly reduced (p < 0.05) the levels of serum alkaline phosphatase, aspartate aminotransferase, alanine aminotransferase, and malondialdehyde, whereas the levels of antioxidant enzymes (glutathione peroxidase, catalase, and superoxide dismutase) and reduced glutathione were significantly restored (p < 0.05) in contrast to the ethanol model group. The levels of pro-inflammatory cytokines (tumour necrosis factor-α and interleukin-6) were suppressed by chamazulene (p < 0.05) with relevance to ethanol-induced liver injury. Histopathological alterations were convincing in the chamazulene-treated groups, which showed protective effects against alcoholic liver injury. Chamazulene has a significant hepatoprotective effect against ethanol-induced liver injury through alleviation of oxidative stress and prevention of inflammation.

In view of problems of low positioning accuracy of underground personnel positioning system and incomplete functions of underground personnel vital signs monitoring system, a wearable device with functions of personnel positioning, falling or lying posture monitoring and heart rate monitoring was studied. The device adopts combination of RSSI ranging and step calculation to improve positioning accuracy of underground personnel. Three-axis acceleration sensor is used to detect abnormal movement of person, and heart rate sensor is used to obtain heart rate data. The test results show that the device can achieve accurate positioning of underground personnel, can accurately determine falling or lying posture of underground personnel, and alarm abnormal state.

Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and the intrinsic GPU heterogeneity combine to limit accuracy and increase the time to convergence. We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging. Instead of statically partitioning batches to GPUs, batches are routed based on the relative processing speed. Batch size scaling assigns larger batches to the faster GPUs and smaller batches to the slower ones, with the goal to arrive at a steady state in which all the GPUs perform the same number of model updates. Normalized model merging computes optimal weights for every GPU based on the assigned batches such that the combined model achieves better accuracy. We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy and is scalable with the number of GPUs.

This study investigates the relationship between corporate site visits (CSVs) and firms' real earnings management. Using a unique dataset of site visits to Chinese firms listed on the Shenzhen Stock Exchange from 2009 to 2016, we find that such visits are negatively associated with firms' real earnings management. The results are robust to using alternative CSV measures, controlling for alternative communication channels, and using the propensity score matching method. In cross-sectional analyses, we find that the negative association between site visits and real earnings management is stronger for more complex firms and firms with greater information asymmetry. In addition, we find that CSVs are negatively associated with both management and corporate misconduct but not with accrual-based earnings management or restatements. [ABSTRACT FROM AUTHOR]

The widely-adopted practice is to train deep learning models with specialized hardware accelerators, e.g., GPUs or TPUs, due to their superior performance on linear algebra operations. However, this strategy does not employ effectively the extensive CPU and memory resources - which are used only for preprocessing, data transfer, and scheduling - available by default on the accelerated servers. In this paper, we study training algorithms for deep learning on heterogeneous CPU+GPU architectures. Our two-fold objective - maximize convergence rate and resource utilization simultaneously - makes the problem challenging. In order to allow for a principled exploration of the design space, we first introduce a generic deep learning framework that exploits the difference in computational power and memory hierarchy between CPU and GPU through asynchronous message passing. Based on insights gained through experimentation with the framework, we design two heterogeneous asynchronous stochastic gradient descent (SGD) algorithms. The first algorithm - CPU+GPU Hogbatch - combines small batches on CPU with large batches on GPU in order to maximize the utilization of both resources. However, this generates an unbalanced model update distribution which hinders the statistical convergence. The second algorithm - Adaptive Hogbatch - assigns batches with continuously evolving size based on the relative speed of CPU and GPU. This balances the model updates ratio at the expense of a customizable decrease in utilization. We show that the implementation of these algorithms in the proposed CPU+GPU framework achieves both faster convergence and higher resource utilization than TensorFlow on several real datasets.

A length-scale insensitive degradation function is applied to extend the cracks during hydraulic fracturing under stress boundary conditions in this study. The phase field method is an effective modeling technique that has great potential for use in hydraulic fracturing. Nonetheless, current hydraulic fracturing research is still concentrated on small scales. The phase field model employs a degradation function that is insensitive to length scale, allowing for the decoupling of the phase field length scale from the physical length scale. This facilitates the simulation of hydraulic fracturing crack extensions in larger structures with a consistent mesh density. The correctness of the phase field method is verified firstly by comparing with the experimental results, and the accuracy and efficiency of the proposed method are further verified through a series of numerical calculations. [ABSTRACT FROM AUTHOR]

To better promote the research and development of regional crop yield simulations, we review related research on regional crop yield simulations over the past ten years, summarize the research progress on regional crop yield simulations at home and abroad from the three aspects of crop growth models, remote sensing technology, and data assimilation technology, and propose three future development directions for regional crop yield simulations: (1) Based on the agronomic mechanism of crop yield estimation, it is necessary to fully consider crop growth characteristics and yield formation mechanisms. (2) With respect to the remote sensing mechanism of crop yield estimation, we can consider combining radar stereo lateral view measurement technology to determine crop characteristics and remote sensing information. (3) From the perspective of combining agronomy and remote sensing, the crop yield spatiotemporal simulation assimilation algorithm should be optimized, and the yield simulation unit should be extended. It is expected that these considerations can provide new ideas for regional crop yield simulations with high accuracy, a large scale, and full coverage. [ABSTRACT FROM AUTHOR]

This study proposes an uncertainty quantification method based on deep neural networks and Catmull–Clark subdivision surfaces for vibroacoustic problems. The deep neural networks are utilized as a surrogate model to efficiently generate samples for stochastic analysis. The training data are obtained from numerical simulation by coupling the isogeometric finite element method and the isogeometric boundary element method. In the simulation, the geometric models are constructed with Catmull–Clark subdivision surfaces, and meantime, the physical fields are discretized with the same spline functions as used in geometric modelling. Multiple deep neural networks are trained to predict the sound pressure response for various parameters with different numbers and dimensions in vibroacoustic problems. Numerical examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Breast cancer mainly affects women and is the second leading cause of cancer‐related deaths worldwide. Breast cancer affects women aged 15–59. The current study explored periplocin's anticancer activities against breast cancer MDA‐MB‐231 cells by down‐regulating the PI3K/Akt/mTOR pathway. The MTT assay assessed control‐treated and periplocin (2.5–50 μM) treated MDA‐MB‐231 cell viability. ROS accumulation and apoptosis levels in periplocin‐treated cells were examined using DAPI, dual staining, and Annexin V‐FITC/PI assays. Caspase enzymes were studied using assay kits. Flow cytometry was used to measure cell cycle distributions. Periplocin‐treated cells were analyzed using RT‐PCR assays and insilico analyses for the expression of PI3K/Akt/mTOR molecules. The periplocin treatment remarkably reduced the viability of the MDA‐MB‐231 cells, with an IC50 concentration of 7.5 μM. The fluorescent staining assays revealed a substantial increase in ROS levels and apoptotic events in the periplocin‐treated cells. The flow cytometry analysis revealed that periplocin triggered apoptosis and arrested the cell cycle in G0/G1 phases. Periplocin increased the caspase‐3, ‐8, and ‐9 enzyme activities. In MDA‐MB‐231 cells, Periplocin decreased PI3K/Akt/mTOR activity, and in silico analysis, Periplocin was inhibited by CDK8‐Cyclin C interactions. Periplocin has anticancer properties against breast cancer and may be an effective therapeutic agent for treating breast cancer. [ABSTRACT FROM AUTHOR]

Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on‐site food safety testing an important necessity. As a result, DNA‐fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA‐fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected. [ABSTRACT FROM AUTHOR]

We examine the causal effect of stakeholder orientation on firms’ cost of debt. Our test exploits the staggered state-level adoption of constituency statutes, which allows directors to consider stakeholders’ interests when making business decisions. We find a significant drop in loan spreads for firms incorporated in states that adopted such statutes relative to firms incorporated elsewhere. We further show that constituency statutes reduce the cost of debt through the channels of mitigating conflicts of interest between residual and fixed claimants and between holders of liquid claims and holders of liquid claims, limiting legal liability and lowering takeover threats. [ABSTRACT FROM AUTHOR]