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With the large-scale integration of wind power into the grid, the inertia and disturbance resistance of the power system are gradually declining, leading to an increasingly prominent frequency stability issue. An adaptive frequency support control strategy for wind farms based on rotor kinetic energy is proposed. Firstly, a participation factor is proposed, which determines the degree of wind farm participation in frequency support based on the average rotor speed of the wind farm and adaptively adjusts the inertia response parameters and frequency droop control parameters of wind farms. Based on the real-time available rotor kinetic energy of wind turbines, the frequency modulation power of wind farms is dynamically allocated. The frequency support ability of wind turbines with more rotor kinetic energy is fully utilized and excessive participation of low-speed wind turbines is avoided in frequency modulation. After the frequency support is completed, a staged rotor speed recovery method is used to restore the rotor speed of the wind turbines. Simulation results show that the proposed strategy can adaptively adjust the active output power based on the operating status of the wind farms and the available rotor kinetic energy of the wind turbines while achieving effective support for the system frequency and avoiding secondary frequency drop.

Aiming at the problem that traditional fixed-weight multi-objective reactive power optimization is unable to make the most suitable control decisions for real-time working conditions when dealing with the complex and changing working conditions of new power systems, an adaptive multi-objective reactive power optimization control strategy is proposed, which takes the weighted minimum of the deviation of the system active network loss and the voltage of the grid connection points as the objective function, and the weighting coefficients of the objective function are adaptively adjusted according to the deviation of the voltage of the grid connection points. The strategy takes the minimization of active network loss and the deviation of grid voltage as the objective function. Firstly, the relationship between voltage fluctuation at the grid-connected points of offshore wind farms and the active and reactive power outputs is analyzed to establish the corresponding reactive power allocation model, and the corresponding reactive power control model is established with respect to the input and output characteristics of the wind turbine and the static var generator (SVG). In addition, considering the power constraints and safe operation constraints of offshore operation, the variable inertia weight particle swarm optimization algorithm is used to solve the reactive power control strategy. Finally, the offshore wind farm model is built in MATLAB for simulation verification, and the simulation example shows that, compared with the traditional fixed-weight multi-objective reactive power optimization, the adaptive multi-objective reactive power optimization control strategy can quickly adjust the priority of each optimization objective according to the real-time working conditions of the grid, which can achieve the coordinated optimization of the active network loss and grid-connected point voltage.

Polymer-based heat exchangers can offer a promising solution for environmental sustainability due to their low energy consumption. The incorporation of microchannels and nanofluids further enhances the heat transfer performance of these heat exchangersIn this study, a polymer-based microchannel heat exchanger combined with nanofluid is simulated through the integration of an artificial neural network predictive model and a three-dimensional computational fluid dynamics model. This study unveils an advanced calculation that integrates artificial intelligence and readily-available computational software provided as the advanced calculation system. A statistical mathematics response surface method which data is used for correlating the calculation model is applied to obtain the design parameters between operating conditions and for optimal performance. The optimized results reveal that polymer-based microchannel heat exchanger combined with nanofluid is a promising innovation. The heat transfer improvement achieved a 12 % increase in the overall heat transfer coefficient by using TiO2/Water compared to Water. Moreover, a 1.03 performance index is obtained when CuO/Water nanofluid is used, a 66 horizontal parallel connecting of the polymer-based microchannel heat exchanger shows that the equipment can afford the same heat transfer performance of the metal-based microchannel heat exchanger in TiO2/Water nanofluid usage and implying a balance between heat transfer enhancement and energy consumption.

Abstract The increasing integration between biological and digital interfaces has led to heightened interest in utilizing biological materials to store digital data, with the most promising one involving the storage of data within defined sequences of DNA that are created by de novo DNA synthesis. However, there is a lack of methods that can obviate the need for de novo DNA synthesis, which tends to be costly and inefficient. Here, in this work, we detail a method of capturing 2-dimensional light patterns into DNA, by utilizing optogenetic circuits to record light exposure into DNA, encoding spatial locations with barcoding, and retrieving stored images via high-throughput next-generation sequencing. We demonstrate the encoding of multiple images into DNA, totaling 1152 bits, selective image retrieval, as well as robustness to drying, heat and UV. We also demonstrate successful multiplexing using multiple wavelengths of light, capturing 2 different images simultaneously using red and blue light. This work thus establishes a ‘living digital camera’, paving the way towards integrating biological systems with digital devices.

Twisting is an important part in the production process of large package E-glass yarn. To derive the twisting process parameters of large package E-glass yarn, a method from the physical properties of bobbin yarn is proposed based on the study of the twisting principle of E-glass yarn. After unwinding the bobbin yarn and measuring its twist, loop pitch, and corresponding diameter, the calculation is completed according to the steps of first determining the descending speed of the traveler, then fitting the spindle speed data, and finally calculating the elevating speed of the traveler. The result is that the twisting spindle speed of D450 E-glass yarn with 31.8 T/m decreases from 5,015 to 4,550 rpm, the elevating speed of the traveler is 0.91 m/min, and the descending speed of the traveler is 1.50 m/min. The accuracy of the calculation is verified by monitoring the rotating speed of the roving cake and measuring the loop pitch of bobbin yarn in the twisting experiment. The result shows that the prediction error of this method is less than 1.1%, which can effectively derive the twisting process parameters of large package E-glass yarn, and provide a reference for the twisting process design scheme of glass fiber strand manufacturers.

The development of machine learning provides a new path for translating fuzzy semantics into English and literature. In this paper, we propose translation points for fuzzy semantics based on the fuzziness of language by studying the linguistic features in English literature. The hierarchical PY process under the Bayesian nonparametric model is adopted to achieve word alignment in English translation, and the clustering process of English words is controlled by introducing discount and intensity parameters. Based on this, the parameter estimation of the word probability model is completed using Gibbs sampling, and the Moses decoder extracts the translation features. Based on monolingual word classification, this model reduces 6.85% in the average time consumed per sentence compared to the Beam algorithm and 9.57% compared to the A* algorithm.

Oxidized coal is a coal body that has undergone the process of warming up and cooling down, and has been widely used in mine fire area start-up, stratified mining, and re-mining of residual coal, etc. The coal dust generated from coal development and utilization in these areas is oxidized coal dust. Due to the different degrees of oxidation of the coal body, the internal structure is affected, and the explosion characteristics of the resulting oxidized coal dust are changed. For the non-stick coal, this paper used a programmed heating chamber to pretreat the samples at different temperatures(25 ℃, 75 ℃ and 115 ℃) to explore the change in the explosion characteristics of the oxidized coal dust under the conditions of different variables (oxidized coal dust cloud concentration, oxidized coal dust particle size). The results show that the explosion process of oxidized coal dust can be divided into four stages, namely, the initial negative pressure stage, high pressure dust injection stage, explosion boost stage and pressure decay stage; the explosion characteristics of oxidized coal dust and coal dust concentration conform to the quadratic polynomial law, which increases and then decreases with increasing concentration, and the optimal coal dust concentrations of T25, T75 and T115 coal dust are concentrated at 300 g/m3 and 200 g/m3; in the coal dust cloud concentration is constant, the maximum explosion pressure value of oxidized coal dust decreases with the increase of particle size.

Waste heat utilization has been prioritized especially in various industries and sectors. Many researchers have developed heat recovery processes by designing suitable waste heat recovery units (WRU), such as heat exchangers, using water as a coolants to receive heat from the waste heat fluid in the production process. The conventional heat exchanger has limitations such as its equipment size, space for installation, and flexibility. The microchannel heat exchanger is one of many ideas for resolving these limitations. Moreover, the coolant on the cold side can be upgraded by adding nanometer-sized solid particles which is called “Nanofluid”. To reduce the high investigation cost and time, a new efficient and cost-effective simulation method was selected to use for investigating the performance of a microchannel heat exchanger with nanofluids in this study. To analyze the heat recovery at low temperature, i.e. around 100–200 °C, nanofluid property predictive models were developed using an artificial neural network (ANN). Then, the predictive models were embedded and integrated into computational fluid dynamics to design a microchannel heat exchanger. It is found that the use of nanofluids improved the heat transfer efficiency of this heat exchanger. The suitable nanofluid types and concentrations were selected based on the thermal–hydraulic​ performance. Here, the 3% weight TiO2/Water fluid with a 1.03 thermal–hydraulic​ performance ratio was found to be the most promising nanofluid for using in this condition.

This paper discussed a CQFN shell in 7 mm×7 mm×1.2 mm which was based on HTCC technology for the miniaturization needs of millimeter wave microwave device packaging. In order to solve the problem of electromagnetic leakage during high frequency signal transmission of ceramic shell, the shielding ground hole is designed and optimized from different directions of signal transmission by using HFSS. Through simulation comparison, the shielding relationship between ground hole and electromagnetic signal in different areas is discussed and summarized. The results show that the RF pin could be used in the band from 0.1 GHz～40 GHz. The insertion loss of RF ports is less than 0.65 dB. The VSWR of the package is less than 1.50.

Colon cancer (COAD) is a common malignancy, yet its etiology is not fully elucidated. This study gathered endocytosis-related genes, using gene expression profiles from TCGA databases to categorize molecular subtypes of COAD into Cluster1 and Cluster2 based on genes related to endocytosis, and further explored the connection between the two molecular subtypes and prognostic characteristics. Differential expression analysis of the two subtypes revealed 3412 differentially expressed genes (DEGs), whose functions were remarkably enriched in the cellular metastasis and oxidative phosphorylation in Cluster1 by fGSEA. Additionally, Cluster1 exhibited higher expression levels of DEGs associated with immune infiltration and metabolism in comparison to Cluster2 by GSVA, and TIDE scores indicated patients with Cluster2 may benefit more from immunotherapy. Based on the DEGs, we utilized univariate Cox regression to identify 759 prognostic genes, which were then screened by three machine learning models (Lasso, RF, SVM-RFE) simultaneously, resulting in four feature genes: NEK4, MED13, OXSR1, and SLAIN2. Moreover, in Cluter1, these feature genes displayed consistent positive or negative correlations with immune escape-related and metabolic reprogramming-related pathways and genes in Pearson heatmap. There results suggesting that there are significant differences in immune escape and metabolic reprogramming between colon cancer subtypes Cluster1 and Cluster2 as determined by genes according to endocytosis.

The determination of Initial Track Tension (ITT) is an important issue for the moving performance of tracked vehicle under soft ground. At the present time, however, the selection of ITT only according to a moving performance or an operating condition under hard ground. Focus on this, a novel approach for determination of ITT without changing the basic vehicle parameters is proposed in this paper, which can improve the moving performance under soft ground comprehensively. Analytical models for predicting ground pressure under tracked vehicle on soft ground including ITT were used based on the track-soil coupling relationship, movement characteristics and turning characteristics of an articulated track vehicle, which applied to the straight driving condition, steering-driving condition and climbing condition. The intrinsic mechanisms between the ITT and the Mean Maximum Pressure (MMP) under roadwheels were revealed. The stresses produced by the action of track and sprocket, roadwheels, idle wheel, soil respectively were studied in consideration of the vehicle-soil mechanics theory and strength theory. The stress state and changes for an element of the track were investigated. What’s more, the relationship between the ITT and fatigue life of the track was explained in detail based on the Miner’s rule. On this basis, an optimal study for the ITT was performed taking advantage of NSGAII, which used the MMP under roadwheels and fatigue life of track as optimization objectives. And then, the determination principles of the ITT were presented, and it was identified by an articulated track vehicle. The results are significant to the prediction and control of the moving performance of track vehicle under soft ground.

Bad weather such as rainy days will seriously affect the image quality and the accuracy of visual processing algorithm. In order to improve the image deraining quality, a multi-scale fusion self attention generation adversarial network (MSFSA-GAN) is proposed. This network uses different scales to extract input characteristics of rain lines. First, Gaussian pyramid rain maps with different scales are generated by Gaussian algorithm. Then, in order to extract the features of rain lines with different scales, the coarse fusion module and fine fusion module are designed respectively. Next, the extracted features are fused at different scales. In this process, the self attention mechanism is introduced to make the network focus on the extracted features of different scales. And before the fusion, the rain pattern reconstruction operation is also carried out, so that the network can reproduce the input image more perfectly. Finally, it is input into the discriminator network with dense blocks to obtain the image that removes the rain lines. We used R100H and R100L datasets to train and test our network. The results show that our method as high as 27.79 in PSNR and UQI is 0.94, which is superior to the existing methods in performance. Meanwhile, we also compared the cost of time, the result of our network is only 0.02s.

BackgroundThe Shashlik electromagnetic calorimeter (ECAL) is used in the Nuclotron-based Ion Collider Facility's MultiPurpose detector (NICA-MPD) of the Russian heavy ion superconducting synchrotron. It is designed as a cylindrical barrel divided into 32 sectors with a total of 43 008 towers. Each tower is composed of 220 layers of lead and 221 layers of scintillator alternately.PurposeThis study aims to simulate multiple parameters of Shashlik ECAL for NICA-MPD that affect the performance of ECAL.MethodsFirst of all, the module of ECAL tower was built based on the principle of Shashlik ECAL. Then, the GEENT4 software was employed to simulate the performance of every single tower of ECAL according to its structure composition. Considering the influence of the design parameters of the scintillator and absorber on energy deposition, energy resolution, and energy leakage after electrons were incident on the tower and the subsequent scintillator luminescence process, the influence of the reflective layer on the number of photons was in the wavelength shifted fiber and detector. Finally, factors that affected energy deposition, energy leakage, energy resolution, and photon number in the wavelength shift (WLS) and sensor, and the influence of the thickness, number of layers, and size of the scintillator and absorber in the module on energy deposition, energy leakage, and energy resolution were simulated and analyzed.ResultsThe simulation results show that the best side length of cross-section of each tower is 4 cm. When the total thickness of absorber and scintillator is 1.8 mm and the thickness of lead absorber is 0.3 mm, the energy resolution meets the experimental requirements of 5%@3 GeV. When the radiation length reaches 13X0, maximum energy deposition and obtains better energy resolution can be achieved. Adding a reflective layer to the tower side can increase the number of photons in the fiber by 10%~19% whereas adding a reflective coating to the fiber end can effectively increase the number of photons reaching the detector by 50%.ConclusionsThe photon numbers in the wavelength shift fiber and the detector are affected by the reflection layer on both the tower side and the end face of the fiber. The importance and necessity of adding reflector are verified by this study.

Glioma is the most frequent, highly aggressive primary intracranial malignant tumor. Circular RNA (circRNA) circ_0037655 has been reported to be a vital regulator in glioma. The different functional mechanism behind circ_0037655 was investigated in the current study.

Abstract Millimetre wave (mmWave) has great potential in vehicle‐to‐vehicle (V2V) communication to provide wide bandwidth and low latency. However, the biggest challenge for the use of mmWave is the high propagation path loss and susceptibility to blockage among vehicles, especially in urban streets. Vehicle shadow fading is quite common in the actual V2V communication. The non‐line‐of‐sight scenarios are considered and the impacts of blocking vehicles on the characteristics of the V2V wireless channels are studied. The measurements are conducted for two scenarios. For the first scenario, a blocking truck is placed between transmitting and receiving cars. For the second scenario, a car is added as a reflector beside the truck. Based on the measurement results, a path loss model for the V2V communication is developed. In order to further verify the effects of building scattering, a simulation is setup and the results are consistent with the measured results.

Most of the conventional chemotherapeutic agents against cancer have poor targeting and efficacy. With the development of nanomedicine, it is found that the stimulating response strategy based on nanomaterials can promote the gather, ingest and release of chemotherapeutic drugs in tumors through the response conditions of pH, redox, ROS and enzymes of the tumor microenvironment and external light, magnetic and other response conditions, thereby improving the safety and killing effect of the drug. This paper reviews the common nanomaterial-based stimulation response strategies and their role in tumor therapy, to provide some references for clinical application and future research.

Accurate acquisition of canopy structures, sizes and shapes of plants are significant in providing technical data for variable-rate precision spraying and plant growth estimation in modern agriculture. This research work proposes a comparative analysis of two mainstream brands of laser sensor scanners for canopy detection. A Hokuyo UTM-30LX laser sensor and a Sick LMS151-10100 laser sensor was used to detect spray targets including two artificial trees and a cuboid foam box, respectively. Two data acquisition and storage algorithms based on C++ language have been developed to collect real-time data from the given targets based on the sensors. A 3-dimentional image reconstruction algorithm was proposed to construct the detection targets using MATLAB software. In this experiment, the detection distances between laser sensors and targets range from 1.8 to 2.2 m, and the traveling speed of laser sensors ranges from 0.5 to 2.0 m/s, and the size of trees 2.15 × 1.24 × 0.70 m3 have been taken for verification of proposed method. The detection accuracies of both laser sensors have been compared by utilizing the given targets under indoor laboratory conditions, and detection accuracies of 3-dimentional reconstruction images of the target are analyzed by the root-mean-square error (RMSE), the coefficient of variation (CV) and edge similarity score (ESS). The experimental results show that the laser sensors have different detection accuracies under the same experimental conditions, and has different detection accuracy under different experimental conditions. The LMS151-10100 laser sensor is more accurate and more suitable for detection in the case of fast detection speed. However, both sensors have the capability to measure the targets accurately and can be applied for the detection of trees in the area of variable-rate precision spraying. Keywords: Agriculture spraying automation, Crops and tree spraying, Lessor sensors, Canopy detection, Orchard

B1 domain of staphylococcal protein G (GB1) is a widely used model protein for developing in vivo and in vitro protein structural determination methods based on paramagnetic nuclear magnetic resonance (NMR) such as pseudocontact chemical shift (PCS) and paramagnetic relaxation enhancement (PRE). However, few previous studies have investigated the interactions between GB1 and metal ions, especially paramagnetic ions. In this study, the interactions between GB1 and divalent/lanthanide metal ions were studied by NMR spectroscopy. It was found that GB1 weakly bound with paramagnetic lanthanide ions and paramagnetic divalent ions, including Cu2+, Mn2+ and Co2+. In contrast, GB1 did not bind with diamagnetic divalent ions, such as Ca2+, Mg2+ and Zn2+. Furthermore, it was demonstrated that there were two binding sites for Cu2+ in GB1, but only one for lanthanide ions and divalent ions Mn2+ and Co2+. The current study demonstrated that NMR spectroscopy is a powerful tool to study weak binding between protein and metal ions. And the results indicated that care must be taken to avoid possible interference to paramagnetic NMR data when using GB1 as the model protein.