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The rational and effective application of digital technology to the field of agricultural production, so as to promote the digital transformation of agricultural production, is a key measure for China to move from a large agricultural country to an agricultural power. However, China is still facing many difficulties and obstacles in the process of implementing this initiative, and it is urgent to study and develop targeted solutions to overcome difficulties and overcome obstacles. By studying the current situation of agricultural digitalization development in Anhui Province, this paper finds the problems existing in the process of agricultural digitalization in Anhui Province. Based on this premise, this paper further studies the implementation logic of agricultural digital development and transformation in Anhui Province, so as to put forward the theoretical implementation logic of the digital transformation of agricultural production in Anhui Province, and provide effective suggestions for the digital transformation of agricultural production in Anhui Province.

The objective of this study was to investigate the effect of sedan wheelbase size on the kinematics and injury severity of left rear-seat occupants by using the finite element (FE) modeling method. A total of 270 cases with detailed accidental information records were analyzed to define the influence laws of wheelbase size and impact speed on the injury of left rear-seat occupants. First, the THUMS (Ver. 4.0.2) FE model was used to reconstruct two small offset collisions with different wheelbases size and unbelted left rear-seat occupants, and the effectiveness of the accident model was verified. Then, seatbelts were added to the left rear-seat occupant models. Finally, LS-DYNA software was used to study the correlation among head and chest injury and five sedan wheelbases sizes (2300, 2450, 2600, 2750, and 2905 mm) at three impact velocities (54, 64, and 74 km/h). The results showed that the occupants’ chest injuries showed an upward trend at the impact velocity of 64 and 74 km/h when the wheelbases sizes was reduced to 2300 mm. This research illustrated that at higher impact velocities, excessively small wheelbases might increase the chest injury severity of left rear-seat occupants.

Pyriproxyfen is a juvenile hormone-like pesticide. Once intake occurs, it leads to a series of poisoning characters consequences in silkworm, Bombyx mori (ID: 7091, Lepidoptera), such as non- cocooning, non-pupation, production of low-active eggs, and extended stages. However, the poisoning mechanism is still unclear. Here, silkworms were fed mulberry leaves soaked with different pyriproxyfen concentrations, and the heads were dissected for transcriptome analysis, while the hemolymph was used for determinations of ecdysone and juvenile hormone titers. As a result, after conjoint analysis of 3 feeding groups and a control group, 555 differentially expressed genes (DEGs) were obtained, which were mainly involved in hormone metabolism, glycometabolism and protein metabolism. Meanwhile, 119 genes were significantly correlated with the pyriproxyfen concentrations, and they were mainly involved in drug metabolism and glycometabolism. The ecdysone titers in several feeding groups were significantly lower than those of the control group, while juvenile hormone was not detected in all groups, including the control and feeding groups. Correspondingly, due to activation of the juvenile hormone signaling pathway by pyriproxyfen, key genes in the ecdysone synthesis pathway were downregulated, and a large number of downstream genes were up- or downregulated. In addition, nearly all genes in the detoxification pathway were upregulated. These results suggested that, affected by the juvenile hormone signaling pathway, ecdysone titers decreased and further affected a series of downstream processes, and this was the key reason for pyriproxyfen poisoning in silkworm, B. mori, which could lay a foundation for the study of pyriproxyfen resistance in silkworm.

A combination of airbag, seatbelt, and other restraint systems greatly reduces injury to drivers in small offset collisions. However, the airbag causes accidental injury to the driver in the deployment process. To maximize the protection effect of the restraint system on the driver, this study proposes a pre-tensioned force-limiting seatbelt. A small offset collision accident with video information was simulated by using a Neon sedan and the THUMS (v.4.0.2) finite element model. The effectiveness of the accident model and the matching use of a pre-tensioned force-limiting seatbelt and airbag for driver protection were verified. To obtain the best parameter matching of protection effect, first, the seatbelt force-limiting A, pre-tensioned force B, pre-tensioned time C, airbag ignition time D, and mass flow coefficient E were selected as influencing factors, and orthogonal tests with different factor levels were designed. Then, the direct analysis method was applied to analyze the influence laws of each factor on driver dynamic response and injury. In addition, the radial basis function surrogate model was constructed by synthesizing each kind of critical injury value to the human body. Combined with NSGA-II multi-objective genetic algorithm, the structural performance parameters of the restraint system were optimized and matched. Results showed that the optimal protection matching parameters of the restraint system were 4933.5 N−2499.9 N−16 ms−15.3 ms−0.5 (A−B−C−D−E). Finally, the best matching parameters were input into the accident model for verification. After optimization, the WIC and Nij of drivers were reduced by 37.9% and 45.3%, respectively. The results show that the optimized restraint system can protect the driver the most.

Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasive technique to probe the complex micro-architecture of the tissue being imaged. The diffusional properties of the tissue at the imaged resolution are well captured by the ensemble average propagator (EAP), which is a probability density function characterizing the probability of water molecule diffusion. Many properties in the form of imaging 'stains' can then be computed from the EAP that can serve as bio-markers for a variety of diseases. This motivates the development of methods for the accurate estimation of the EAPs from dMRI, which is an actively researched area in dMRI analysis. To this end, in the recent past, dictionary learning (DL) techniques have been applied by many researchers for accurate reconstruction of the EAP fields from dMRI scans of the central nervous system (CNS). However, most of the DL-based methods did not exploit the geometry of the space of the EAPs, which are probability density functions. By exploiting the geometry of the space of probability density functions, it is possible to reconstruct EAPs that satisfy the mathematical properties of a density function and hence yield better accuracy in the EAP field reconstruction. Using a square root density parameterization, the EAPs can be mapped to a unit Hilbert sphere, which is a smooth manifold with well known geometry that we will exploit in our formulation of the DL problem. Thus, in this paper, we present a general formulation of the DL problem for data residing on smooth manifolds and in particular the manifold of EAPs, along with a numerical solution using an alternating minimization method. We then showcase the properties and the performance of our algorithm on the reconstruction of the EAP field in a patch-wise manner from the dMRI data. Through several synthetic, phantom and real data examples, we demonstrate that our non-linear DL-based approach produces accurate and spatially smooth estimates of the EAP field from dMRI in comparison to the state-of-the-art EAP reconstruction method called the MAPL method, as well as the linear DL-based EAP reconstruction approaches. To further demonstrate the accuracy and utility of our approach, we compute an entropic anisotropy measure (HA), that is a function of the well known Rényi entropy, from the EAP fields of control and injured rat spinal cords respectively. We demonstrate its utility as an imaging 'stain' via a quantitative comparison of HA maps computed from EAP fields estimated using our method and competing methods. The quantitative comparison is achieved using a two sample t-test and the results of significance are displayed for a visualization of regions of the spinal cord affected most by the injury.

Deep neural networks (DNNs) play an important role in machine learning due to its outstanding performance compared to other alternatives. However, DNNs are usually not suitable for safety-critical applications since DNNs can be easily fooled by well-crafted adversarial examples. To address this issue, spectral normalization (SN) technique was proposed to counter adversarial attacks, which ensures that the trained model has low sensitivity towards the disturbance of input samples. Unfortunately, this strategy requires exact computation of spectral norm, which is computation intensive and impractical for large-scale networks. In this paper, we introduce an acceleration technique for spectral normalization based on Fourier transform and layer separation. The proposed method provides DNNs with promising security protection while maintaining minimized time cost, which turns SN from a theoretically feasible approach to a practically useful framework. Experimental evaluation using autonomous systems demonstrates that our acceleration method is able to significantly improve both time efficiency (up to 60%) and model robustness (61% on average) compared with the state-of-the-art spectral normalization in real-world applications.

This study investigated compound nerve action potential (CNAP) changes of nerve trunk subjected to stretch at various strains and strain rates. An animal model of rapid stretch injury to nerve based on traffic accidents was developed in order to study CNAP changes of nerve trunk subjected to acute stretch. 18 bullfrogs were dissected and the sciatic nerves’ trunks were obtained. The nerve trunk was stretched by 6%, 12% or 18% of its length and the tensile strain rates were 30s-1 and 50s-1 respectively. The results showed that at 6% strain and 30s-1 strain rate, the amplitude, action potential duration and conduction velocity of CNAP had a slight change. When the strain and strain rate continued to be increased, the amplitude, conduction velocity and area under the curve would be decreased while the action potential duration would be increased. At 18% strain and 50s-1 strain rate, CNAP conduction was almost completely blocked. The different strains or strain rates had a significant effect on nerve signal conduction. Under the same strain, a higher strain rate would increase the probability of nerve conduction block. These findings are of great significance to study the injury mechanism of rapid nerve stretch and to evaluate the nerve functional damage level in traffic accidents.

The traditional elevator building room electric vehicle monitoring system adopts manual monitoring, which has disadvantages such as easy fatigue of personnel and difficulty in monitoring each video channel; to address this situation, we propose an online detection system for elevator building room electric vehicles based on the improved YOLOv4-tiny algorithm, which first crawls pictures of electric vehicles as positive samples, and crawls pictures with relevant backgrounds or similarity in order to reduce the error detection rate and leakage detection rate of the system. The experimental results show that the map of the online detection system based on the improved YOLOv4-tiny algorithm is 94.07%, which is about 2 percentage points higher than the original algorithm.

Malicious software, popularly known as malware, is a serious threat to modern computing systems. A comprehensive cybercrime study by Ponemon Institute highlights that malware is the most expensive attack for organizations, with an average revenue loss of $2.6 million per organization in 2018 (11% increase compared to 2017). Recent high-profile malware attacks coupled with serious economic implications have dramatically changed our perception of threat from malware. Software-based solutions, such as anti-virus programs, are not effective since they rely on matching patterns (signatures) that can be easily fooled by carefully crafted malware with obfuscation or other deviation capabilities. Moreover, software-based solutions are not fast enough for real-time malware detection in safety-critical systems. In this paper, we investigate promising approaches for hardware-assisted malware detection using machine learning. Specifically, we explore how machine learning can be effective for malware detection utilizing hardware performance counters, embedded trace buffer as well as on-chip network traffic analysis.

Reliability and trustworthiness are dominant factors in designing System-on-Chips (SoCs) for a variety of applications. Malicious implants, such as hardware Trojans, can lead to undesired information leakage or system malfunction. To ensure trustworthy computing, it is critical to develop efficient Trojan detection techniques. While existing delay-based side-channel analysis is promising, it is not effective due to two fundamental limitations: (i) The difference in path delay between the golden design and Trojan inserted design is negligible compared with environmental noise and process variations. (ii) Existing approaches rely on manually crafted rules for test generation, and require a large number of simulations, making it impractical for industrial designs. In this paper, we propose a novel test generation method using reinforcement learning for delay-based Trojan detection. This paper makes three important contributions. 1) Unlike existing methods that rely on the delay difference of a few gates, our proposed approach utilizes critical path analysis to generate test vectors that can maximize the side-channel sensitivity. 2) To the best of our knowledge, our approach is the first attempt in applying reinforcement learning for efficient test generation to detect Trojans using delay-based analysis. 3) Our experimental results demonstrate that our method can significantly improve both side-channel sensitivity (59% on average) and test generation time (17x on average) compared to state-of-the-art test generation techniques.

This study presents an interpretation system model of geoheritage to facilitate the understanding of geoscience knowledge by the common lay person. Interpretation system construction is crucial to geoparks and is a shared value. The improvement of the effectiveness of geoheritage interpretation is still under exploration. Therefore, the interpretation systems of two global geoparks in China are outlined and compared with the interpretation system in Zion National Park in the United States. From lessons and experiences, this paper suggests utilizing geotourism as a complete contextual communication system, in which a geopark (source) delivers information about its unique cultural and natural values to target tourists (receivers) through tourism activities (channels). The communication effect of geotourism is monitored by feedback from tourists through the post-travel surveys or activities. We expect that this model will provide a better interpretation of geoheritage with a new perspective.

In view of designing the air conditioning project for a gymnasium successfully, the cooling and heating source schemes are fully studied by analyzing the surrounding environment and energy conditions of the project, as well as the analysis of the initial investment and operating costs, which indicates the air source heat pump air conditioning system is the best choice for the project. The indoor air conditioning schemes are also studied systematically and the optimization of air conditioning schemes is carried out in each area. The principle of operating conditions for the whole year is followed and the quality of indoor air and energy-saving are ensured by the optimized design schemes, which provide references for the air conditioning system design in the same kinds of building.

A microsporidian, CmM2, was isolated from Cnaphalocrocis medinalis. The biological characters, molecular analysis and pathogenicity of CmM2 were studied. The spore of CmM2 is long oval in shape and 3.45 ± 0.25 × 1.68 ± 0.18 µm in size, the life cycle includes meronts, sporonts, sporoblasts, and spores, with typical diplokaryon in each stage, propagated in binary fission. There is positive coagulation reaction between CmM2 and the polyclonal antibody of Nosema bombycis (N.b.). CmM2 spores is binuclear, and has 10-12 polar filament coils. The small subunit ribosomal RNA (SSU rRNA) gene sequence of CmM2 was obtained by PCR amplification and sequencing, the phylogenetic tree based on SSU rRNA sequences had been constructed, and the similarity and genetic distance of SSU rRNA sequences were analyzed, showed that CmM2 was grouped in the Nosema clade. The 50% infectious concentration of CmM2 to Bombyx mori is 4.72 × 10(4) spores ml(-1) , and the germinative infection rate is 12.33%. The results showed that CmM2 is classified into genus Nosema, as Nosema sp. CmM2, and has a heavy infectivity to B. mori. The result indicated as well that it is valuable taxonomic determination for microsporidian isolates based on both biological characters and molecular evidence.

The ventilation scheme design of solar energy utilization combined with heat recovery for air changes in residential building is proposed in the paper, in this way we can lower energy consumption for the fresh air and there are simple equipment and easy operation. This is an energy-efficient ventilation system scheme for air changes in residential building with detailed methods according to the special condition of our own country. The design scheme of energy efficiency fresh air system for residential building can meet fresh air requirements, which can not only improve indoor air quality, but also give technical support to the green residences design.