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Typhoon is an important factor leading to large-scale off-grid of offshore wind power， which is easy to cause a serious imbalance of supply and demand in the affected power grid， thus affecting the safe and stable operation of the regional power system， especially the coastal power grid with large-scale offshore wind power access. Based on this， this paper first establishes a typhoon mathematical model based on Rankine vortex model for the actual typhoon path. The method to slow down the effect of sudden drop of wind is proposed. Then， taking full account of the operation safety requirements of the receiving end power grid， a collaborative operation strategy of offshore wind farm including energy storage was proposed. Aiming at system safety， stability and operation economy， factors such as air abandonment volume and energy storage regulation capacity were comprehensively considered to optimize the wind abandonment volume of offshore wind power. Finally， a simulation analysis is carried out on the receiving end grid system of offshore wind power with energy storage. The results show that the strategy can minimize the operating cost of offshore wind power farm while ensuring the safety and stability of the receiving end system.

Semantic understanding enhancement methods and deep learning are popular areas of artificial intelligence research and have significant potential in natural language processing. The English translation is one of the typical application scenarios combining these two technologies. In order to thoroughly analyze the information contained in English texts and improve the accuracy of English text translation, this study proposes an unbalanced Bi-LSTM model. Firstly, the BERT model is used to vectorize the original English corpus and extract the preliminary semantic features. Then, the unbalanced Bi-LSTM network is used to increase the weight of the textual information containing important semantics to further improve the effect of the key features on the recognition of the English text and, at the same time, an attention mechanism that introduces the word vectors is used to widen the gap between the key textual information and the non-key information, so as to improve the effect of the English translation. The accuracy of English text translation can be significantly enhanced by comparing the classification effect with various models, as shown by the results. The accuracy of the model can reach over 90% in about 60 pieces of translation training, and the mean square average is only 1.52. Its translation effect has won the recognition of more than 50% of professionals. The model's ability to translate English is evident.

ObjectiveTo verify the main quality indexes of coenzyme Q10， and study the quality consistency of coenzyme Q10 produced by different processes.MethodsAccording to the Chinese Pharmacopoeia （2020 version） and national food safety standards， the main quality indexes such as coenzyme Q10 components， moisture， related substances， isomers， contaminant residues and microbial indexes of 40 batches of raw materials were detected.ResultsThe content of coenzyme Q10 in all collected materials was above 99.2%； the maximum single impurity ranged from 0.27% to 0.37%； the total impurity ranged from 0.45% to 0.85%. The total number of colonies in all samples was less than 10 CFU/g， Escherichia coli， Staphylococcus aureus and Salmonella were not detected， and the total number of yeasts and molds were less than 10 CFU/g. The results of moisture and contaminant residues all met the requirements.ConclusionAll coenzyme Q10 collected as health food raw materials met the requirements of Chinese Pharmacopoeia （2020 version） and GB 16740-2014 National Food Safety Standard Health Food.

Massive coal debris and dust are generated in the process of coal production and transportation, greatly affecting coal mine working environment and local residents. This study is aimed at promoting the dust suppression efficiency of dust suppressors and improving the working space and surrounding environment following the principle of waste reuse. Specifically, a new type of green superabsorbent dust suppressor (AA-DWF-PAM/GD) was prepared by treating the raw material, i.e., China fir sawdust, with solutions such as NaOH and Na2SO3 through microwave-assisted in-situ synthesis. The following findings were obtained. The microscopic morphology, reaction mechanism, thermal stability and water absorption performance of AA-SW-PAM/GD were investigated by means of SEM, FTIR, TG-DTG and swelling kinetics experiments. The results show that AA-SW-PAM/GD presents a network structure and boasts high stability. DWF reacts well with acrylic acid (AA) and polyacrylamide (PAM), and no activity damage occurs before 73 ℃. Besides, the dust suppressor has the highest water absorption rate of 325 times, and its highest salt water absorption rate is about 100 when the solubility of NaCl, MgCl2 and CaCl2 salt solutions is 1%. In the dust suppression performance test, AA-DWF-PAM/GD reaches a dust suppression rate of about 90% when the airflow velocity is 14 m/s. After AA-DWF-PAM/GD is mixed with coal for combustion, the heat release rate is increased by 4.53 kW/m2, and the combustion time is prolonged by about 15 s. That is, after being sprayed during its use, AA-DWF-PAM/GD will not pose a negative impact on the calorific value of coal. According to the results of molecular dynamics simulation, AA-DWF-PAM/GD primarily acts on the coal-water interface, raising the thickness of the coal-water interface to 28×10−6 mm (13-41×10−6 mm), enhancing the diffusion of water molecules on the coal surface and attracting water molecules to migrate toward the coal seam. Moreover, after its addition, the self-diffusion coefficient (D) of water molecules grows by 4.3×10−9 m2/s, which means that the wettability of the coal seam surface is promoted. Overall, considering its low cost, environmental friendliness and remarkable dust suppression performance, AA-DWF-PAM/GD proposed in this study is a promising dust suppressor that can be widely used in coal mines.

The Internet of Things, as an important part of important data aggregation, forwarding and control, is often subject to risks such as eavesdropping or data loss due to the huge amount of received data. Based on this, this paper introduces the GA-LM-BP algorithm, BP network, and LM-BP algorithm deep learning to optimize the data received by the Internet of Things, and selects the most suitable communication mode optimization algorithm. The experimental results show that the accuracy error of GA-LM-BP, BP and LM-BP algorithms shows a downward trend, from 0.029 to 0.011; the training time is reduced by 208 mins, and the training speed is increased to 74%, indicating that GA-LM-BP deep learning Excellent performance in the security and confidentiality of data transmission in the Internet of Things. In addition, we further analyzed GA-LM-BP from COP, SOP and STP to verify its reliability and safety.

In the scenarios of traffic diagnostic analysis and prediction in optical network based on deep learning, the work of traffic data collection and storage for optical link is limited due to security and other reasons. Aiming at the problem that the amount of traffic data is too small to support deep learning training, this paper proposes an optical network traffic data synthesis algorithm based on topology link recognition. The core idea is to combine the conditional generation model based on optical network topology and the data synthesis model based on optical network traffic in the framework of generative adversarial networks. The traffic data of the given optical link is synthesized in a self-supervised way. The simulation results show that the auto-correlation coefficients of the traffic data synthesized by the proposed algorithm is close to the real data, and the accuracy of the traffic prediction model based on fully connected neural network is more than 95%.

【Objective】 Canopy resistance (rc) is an important parameter to calculate evapotranspiration (ET) from natural and planted fields, and its value varies between plants. Different models have been proposed to calculate rc and the objective of this paper is to propose a method to optimize these models. 【Method】 We took summer maize as the model plant, and the optimized canopy resistance model was used to estimate the evapotranspiration from the maize field using both meteorological data and eddy covariance data measured from a weather station at Huailai in China. Data and results measured and calculated in 2013 were used to calibrate the coefficients in the Jarvis (JA) model and the coupled surface resistance model (CO), using the ant colony optimization (ACO) and least square method (LSM) respectively; the backpropagation neural network model was used to analyze the sensitivity of rc to different factors. The ET calculated by the optimized model was tested against the ET measured from fields using an eddy covariance system in 2014. 【Result】 The meteorological factors to which rc was sensitive were ranked in the order radiation >leave area index>humidity >temperature > wind speed. The CO model optimized by the ACO gave the best calibrated rc model with R2=0.89, RMSE=410.90 s/m and d=0.88, and the ET estimated by it was also most accurate with R2=0.72, RMSE=1.07 mm and d=0.75. 【Conclusion】 Radiation and leave area index are the factors affecting rc the most. The CO model optimized by ACO was most accurate for calibrating the rc model and calculating ET of the maize field.

Tibetan monks have a special way of life and food habits, however, little is known about their dyslipidemia. This study aimed to investigate the prevalence of dyslipidemia and risk factors of this population. A cross-sectional study of dyslipidemia was conducted in 876 Tibetan monks and 912 local residents in the same area. All subjects underwent interviews and physical examinations. The total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) of the subjects were analyzed. Compared to local residents, the overall prevalence of dyslipidemia in monks was 29.5%, which was significantly lower (p

On-load tap changer is an important part of transformer. It is playing an important role in voltage regulation, and its safe and stable operation affects the reliability of power supply of the power grid. The tap changer has to maintenance after a certain number of operations. In the maintenance process, there are problems such as long time consumption and low efficiency. On this basis, this paper proposes a development on the main transformer on-load tap changer hoisting equipment. It has been verified in practice that the equipment can significantly shorten the working time and improve the working efficiency under the premise of ensuring the work safety.

Mitochondrial homeostasis is vital for the progression of lung cancer. Nurr1 has been identified as a novel mediator of mitochondrial homeostasis in several types of cancers. The aim of our study was to investigate whether Nurr1 modulates the viability of A549 lung cancer cells by inducing mitochondrial dysfunction, with a focus on the p53-Drp1 signaling pathway.

In recent years, artificial intelligence technologies represented by deep learning and natural language processing have made huge breakthroughs and have begun to emerge in the field of crowdfunding project analysis. Natural language processing technology enables machines to understand and analyze the text of crowdfunding projects, and classify them based on the summary description of the project, which can help companies and individuals improve the project pass rate, so it has received widespread attention. However, most of the current researches are mostly applied to topic modeling of project texts. Few studies have proposed effective solutions for classification prediction based on abstracts of crowdfunding projects. Therefore, this paper proposes a sequence-enhanced capsule network model for this problem. Specifically, based on the work of the capsule network, we propose to connect BiGRU and CapsNet in order to achieve the effect of considering both the sequence semantic information and spatial location information of the text. We apply the proposed method to the kickstarter-NLP dataset, and the experimental results prove that our model has a good classification effect in this case.

To investigate the effect of stacking structure on the wettability of coal dust,ten selected coal samples from China's different regions with different metamorphic grades were analyzed by X-ray diffraction. In combination with the dynamic contact angle determination results of the liquid-solid interface,the relationship between microcrystalline parameters and the wettability of coal dust was deduced. The results show that the inorganic mineral types gradually decrease with increasing metamorphic grade,and the more the primary mineral types are,the better the wettability of coal dust is,but the secondary minerals have no significant influence on the wettability of coal dust. For different coal types,the higher the metamorphic grade of coal is,the more the crystalline component is,the bigger the structural unit of aromatic microcrystals is,and the higher the aromatic ring condensation degree will be. While the coals with low-medium metamorphic grades have more amorphous components,which include hydrophilic alkyl side chains of small molecules,oxygen-containing functional groups,etc. With increasing stack degree and spread degree,the plane spacing decreases gradually,and the wettability of coal dust becomes worse. When the stack degree is 18.5 nm,the spread degree is 41.7 nm and the plane spacing is 0.898 nm,the wettability of coal dust will be the best. The closer the X-ray diffraction microcrystalline parameters are to the above combinations,the better the wettability of coal dust is.

The mandibular canine usually has a single root with a single canal. It is rarely to see a mandibular ca⁃ nine with two roots and two toot canals. A case of a patient who has a right mandibular canine with two roots and two root canals was reported. Cone beam computed tomography (CBCT) comprehensively demonstrated the root canal system and especially provided the information of canal varieties and complex canal before operation. CBCT can improve the discovery rate of canal varieties, guide root canal treatment and prevent missed roots in tooth extraction.

This is paper is taking the design of wildlife corridor from Mingshui to Hami section of national expressway from Beijing to Urumqi as an example. From January 2015 to May 2018, the project team monitored and studied the wildlife utilization of 13 corridors along the route from Mingshui to Hami. High utilization rate was detected with 3 channels.