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The rapid economic development is inseparable from the support of industrial production and technological progress. Although the material living standard of human beings has been improved, at the same time, the environment on which human beings lives has been destroyed, and physical health is also threatened. Environmental problems are the product of unreasonable use of resources and economic growth. In recent years, my country's environmental protection measures and laws and regulations have been increasingly improved, but the environmental situation is still not optimistic. Manufacturing enterprises are part of the pollution problem and should play an important role in the disclosure of environmental information. This paper analyzes the problems of environmental accounting information disclosure of listed companies in my country's manufacturing industry, and puts forward relevant suggestions and countermeasures accordingly.

Objective: This study aimed to compare whole brain network between herpes zoster (HZ) patients and post-herpetic neuralgia (PHN) patients, as well as to investigate the associations between whole brain network changes and pain intensity and the accuracy of classifying between different types of pain. Methods: PHN patients (n = 50) and HZ patients (n = 50) and healthy controls (HCs) (n = 50) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Functional connectivity and global and local graph theory metrics were calculated by using Dosenbach-160 atlas. The relationship between neuroimaging indicators and clinical scales was evaluated using correlation analysis, and receiver operating characteristic (ROC) curves evaluated the feasibility of classifying PHN and HZ patients using specific neuroimaging indicators. Results: (1) 10 greater average connectivities were found in HZ group among the default mode, frontoparietal, cingulo-opercular, sensorimotor, occipital networks (ONs), and cerebellum (p < 0.001). (2) HZ patients exhibited higher global efficiency than those in the PHN and HCs (t = 2.178, p = 0.038). (3) Multiple linear regression analyses indicated that functional connectivity between the ventral frontal cortex in the cingulo-opercular network and the occipital gyrus in the ON influenced the visual analog score pain scores (β = 4.273; p = 0.004). Conclusion: The variation of functional connectivity between ventral frontal cortex in the cingulo-opercular network and occipital gyrus in the ON may be a robust neuroimaging marker of the transition from HZ to PHN patients. Advances in knowledge: Whole-brain network analysis may be effective in distinguishing HZ and PHN patients and predicting pain intensity.

We present a diode-pumped tunable continuous wave (CW) Yb:Ca4GdO(BO3)3 (Yb:GdCOB) laser using an electro-optic crystal based on the 2 F 5/2−2 F 7/2 transition. The highest output power of 3.21 W was achieved at an emission wavelength of 1030 nm with a slope efficiency of 27.2% with respect to the absorbed pump power. Combined with polarization components Yb:GdCOB, the electro-optical crystal KH2PO4 (KDP) formed a Lyot filter in the cavity and compressed the available gain bandwidth. With an adjustable voltage applied to the KDP crystal, the laser wavelength could be tuned from 1024 to 1034 nm. To our knowledge, it is the first study that has realized the tuning in Yb:GdCOB laser.

Dopamine (DA) regulates reproductive behavior and promotes maturation of the reproductive system in insects. In Drosophila , male courtship behavior is regulated by the protocerebral posteriolateral dopaminergic cluster neuron 2ab (PPL2ab). The ability to synthesize DA in the PPL2ab affects male-male and male-female courtship behavior. The DA level in the brain of adult male Drosophila influences sexual orientation; although, male-male courtship is induced when the DA level is too high or too low. DA also regulates changes in reproductive behavior and post-copulation behavior. As a neurohormone, DA regulates taste and smell perception in insects. In the central nervous system, DA regulation of reproductive behavior involves the DA/MH (molting hormone) receptor (DopEcR) and synergy between DA and ecdysone, with the tritocerebrum P1 neurons playing an important role in the male courtship information loop. DA can promote development in the eggs and regulate the reproductive state in social insects. The promotional effect of DA on egg development involves developmental stage differences in DA receptor expression and interactions among DA, juvenile hormone (JH), MH, and octopamine (OA). For example, the expression level of dopamine D2-like receptor (DD2R) in the corpora allata of D. melanogaster young females is far lower than that in sexually mature females. Meanwhile, the expression level of dopamine D1-like receptor (DopR) in young females is much higher than that in mature females. By contrast, the expression of DopR and DD2R in the fat body is the opposite to that in the corpora allata. The interactions among DA, JH, MH, and OA depend on the expression of certain genes and regulation of metabolic enzyme activity. In addition, the interactions among different hormones also involve the insulin-like signaling (ILPs) pathway. As to reproductive state regulation in social insects, it has been found that the reproductive state of worker bees is regulated by queen mandibular pheromone (QMP), larvae pheromones, and the ovarian DA D2 receptor gene. Research on DA regulation of insect reproduction will provide us with references for the development of new drugs, and for research on mechanisms in human brain science and nervous system diseases.

In this paper, a robust dual-channel disturbance rejection control based on an inner-outer loop control framework is proposed for the attitude control of the quadrotor under modeling uncertainties and unknown disturbances. In the outer loop, a tracking differentiator is introduced to obtain smooth tracking signals and their derivatives. In addition, an outer loop controller is developed with the tracking signals set as the desired signals. In the inner loop, a robust dual-channel disturbance rejection controller based on a sliding mode observer is constructed, which contains an inner disturbance rejection channel (IDRC) and an outer disturbance rejection channel (ODRC). In the IDRC, a disturbance rejection compensator is designed to obtain the disturbance compensation values and to compensate a part of the lumped disturbances in the attitude dynamic model. In the ODRC, an inner loop controller with variable-gain switching terms and constant-gain switching terms is designed, whose switching terms are used to compensate the remaining part of the lumped disturbances in the attitude dynamic model. By using the proposed control scheme, the robustness is guaranteed, and the chattering phenomenon caused by the variable-gain switching terms is greatly reduced. The stability of the proposed scheme is analyzed by using the Lyapunov theory. Finally, the effectiveness is tested by the platform experiments.

Recently, correlation filters and deep convolutional network show good performance for visual tracking. Many real-time and high accuracy tracking algorithms are realized; however, there are still some challenges to build a robust tracker. In this paper, we present a novel tracking framework named multi-attention filter (MAF) to solve some challenges for tracking like object drift in a long time, lack of training samples and fast motion. Our framework consists of two components, a basic CNN network to extract feature maps and a set of classifiers to distinguish between the target and the background. First, to solve the problem of object drift in a long time, a simple but effective evaluation mechanism is proposed to the framework, the evaluation mechanism checks the tracking results and corrects it when needed. In addition, the results from different classifiers are fused to predict the object location according to intersection over union. Second, to overcome the lack of training samples, MAF stores some positive and negative samples in two queues, one named long-term queue and another named short-term queue. Third, to deal with fast motion of the target, attention mechanisms including channel attention and location attention are added to the tracker. In our experiments on the popular benchmarks including OTB-2013 and OTB-2015. MFA achieves state of the art among trackers, and as a correlation filter framework, MAF is very flexible and has great rooms for improvement and generalization.

The high entropy alloy (HEA) is a potential material for coating because of its high hardness and wear resistance. In order to study the wear performance and protection ability of FeNiCrCoCu HEA coating on Cu substrate, the friction behavior of abrasive particle and material wear and damage were studied by molecular dynamics (MD). The cutting of depth and velocity of abrasive particle were used as variables for simulation. By analyzing the mechanical data of results, it was found that the friction coefficient of abrasive particle decreased gradually with the increase of the cutting of velocity. Dislocation extraction analysis (DXA) and Common neighbor analysis (CNA) were used to analyze the lattice structure and dislocations. By analyzing the above data, the wear and damage of the material was obtained, and the inhibition behavior of Hirth dislocation lock on dislocation nucleation was found. Cu substrate damage results showed that HEA coatings could effectively improve the wear resistance of the material surface. In the case of small cutting of depth, the high stress region in the material was concentrated below the abrasive particle, and the material subsurface damage would be intensified. This research can provide a theoretical basis for the application of HEA coatings.

Recently, single image super-resolution (SISR) based on deep convolutional neural networks (CNNs) has been widely studied and achieved remarkable results. However, most of these methods mainly widen or deepen the network to get better results, ignoring the self-similarity in natural images. Some works have successfully leveraged this self-similarity by exploring non-local attention modules, but they only use non-local attention modules at one scale or two scales. And they need to calculate the self-similarity in each residual module, which makes the network more complex and takes a long time to train. We propose a multiple-scale self-similarity module (MSSS) that can be flexibly inserted into existing various super-resolution networks. MSSS uses convolution with different kernels and the trailing dilated convolution to obtain feature maps at different scales, and then calculates the correlation between multiple feature maps at different scales through two-head region-level recurrent criss-cross attention modules (TRRCCA), finally gets fusion output through channel attention. So MSSS can mine multiple-scale self-similarities. We only insert a MSSS module into the basic residual SISR network, and the result is significantly improved.

Noise is one of the most challenging tasks in medical images which is happening during transmission and acquisition, and as a result, it complicates a subsequent medical image analysis task. This paper proposes a novel local receptive field smoothing network (FS-Net) for medical image denoising by introducing smoothing priors of receptive field of intermediate CNN layers. First, for a receptive field of a CNN, each of its values smoothing properties are retained by weighting their local neighborhoods (i.e., after smoothing, each new neurons are the weighted average of its neighborhoods). The weights are calculated using the similarity of their corresponding receptive field (i.e., the similarity between the receptive field used to calculate a receptive fields value and the receptive field used to calculate their corresponding neighborhoods) and this keeps the smoothing properties. Second, by applying this smoothing prior on receptive fields of intermediate CNN layers, an end to end FSNet which directly approximate the latent clean images from its noisy version is designed. The receptive fields smoothing helps to train deeper architecture and improves the reconstructed image quality. Experimental results on Breast Mammography images reveal that the proposed method outperforms other states of the art denoising methods.

As a fundamental technique for cruise of unmanned aerial vehicles, track planning is critical for these vehicles to reach their destinations safely without any collision and along tracks within the minimum time. In this paper, a track planning method based on double-rolling windows that integrates global and local planning is proposed for unmanned aerial vehicles under dynamic and uncertain environment. For global planning, roadmaps are modeled according to known global environmental information to search the optimal tracks. Concerning local planning, unmanned aerial vehicles fly along searched global track points, while their tracks are constantly corrected and optimized with fast algorithms according to detailed environmental information that is detected on a real-time basis. By making full use of global priori information and detected real-time information, the track planning method based on double-rolling windows may not only guarantee optimal tracks for unmanned aerial vehicles, but also adapts to dynamic changes to environment and tasks for the purpose of rolling optimization. After simulation and analysis, this method is proven to be effective and feasible for track planning under predetermined environment.

A new analog logarithmic(Log.) operating method based on lambert-Bill's law in a biochemical analyzer is researched in this paper. We use the relationship ofthe voltage across the capacitor Log. to the time of discharging when the capacitor is discharged, change the relationship of transmittance and the concentration of a solution to the relationship of the voltage across the capacitor and the time of discharging, the concentration of the solution is gotten easily. This operator exists between pure analog and pure logic Log. operator, which avoids theory error produced by logic Log. operator fully, absorbs the merits of low cost and fast working speed of analog Log. operator, uses a large dynamic program controlled gain amplifier to improve adaptation range of input signal. thus a high operating accuracy and speed are obtained . At the same time correlate measuring experiments are done and error analyzing are produced.