Research on Boost Soft-switching Converter

In order to improve the circuit working efficiency of zero-voltage zero-current transition Boost converter, an improved modulation strategy was proposed. The main switch can  only  achieve  zero-current  turn-on under traditional modulation strategy. However, the change to the turn-on time of main switch and the turn-off time of auxiliary switch can make the main switch achieve zero-voltage  zero-current  turn-on  and  the  auxiliary switch realize zero-voltage zero-current turn-off in advance as well. The simulation results show that the main switch can achieve zero-voltage zero-current turn-on and the auxiliary switch could realize zero- voltage zero-current turn-off, the circuit working efficiency was improved

An Implementation of Multimodal Fusion System for Intelligent Digital Human Generation

With the rapid development of artificial intelligence (AI), digital humans have attracted more and more attention and are expected to achieve a wide range of applications in several industries. Then, most of the existing digital humans still rely on manual modeling by designers, which is a cumbersome process and has a long development cycle. Therefore, facing the rise of digital humans, there is an urgent need for a digital human generation system combined with AI to improve development efficiency. In this paper, an implementation scheme of an intelligent digital human generation system with multimodal fusion is proposed. Specifically, text, speech and image are taken as inputs, and interactive speech is synthesized using large language model (LLM), voiceprint extraction, and text-to-speech conversion techniques. Then the input image is age-transformed and a suitable image is selected as the driving image. Then, the modification and generation of digital human video content is realized by digital human driving, novel view synthesis, and intelligent dressing techniques. Finally, we enhance the user experience through style transfer, super-resolution, and quality evaluation. Experimental results show that the system can effectively realize digital human generation. The related code is released at https://github.com/zyj-2000/CUMT_2D_PhotoSpeaker

The nonlinear time lag multivariable grey prediction model based on interval grey numbers and its application

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open access article.The linear relationship of the original grey prediction model is too single, and the original grey prediction model does not consider the time delay of the effect of the current input parameters on the output parameters. In order to solve these problems, the interval grey number sequence is taken as the modelling sequence of the model, and the nonlinear parameter γ and the time-delay parameter τ are introduced into the multivariate grey prediction model, so as to construct the nonlinear time-delay multivariable grey prediction model for interval grey number. In view of the uncertain characteristics of the smog index data, this paper applies the improved model to the simulation and prediction of the smog index data. Compared with the original model, the results show that the prediction effect of the model proposed in this paper is superior to the original model in terms of its effectiveness and feasibility

A Hybrid Algorithm Based on Comprehensive Search Mechanisms for Job Shop Scheduling Problem

The research on complex workshop scheduling methods has important academic significance and has wide applications in industrial manufacturing. Aiming at the job shop scheduling problem, a hybrid algorithm based on comprehensive search mechanisms (HACSM) is proposed to optimize the maximum completion time. HACSM combines three search methods with different optimization scales, including fireworks algorithm (FW), extended Akers graphical method (LS1+_AKERS_EXT), and tabu search algorithm (TS). FW realizes global search through information interaction and resource allocation, ensuring the diversity of the population. LS1+_AKERS_EXT realizes compound movement with Akers graphical method, so it has advanced global and local search capabilities. In LS1+_AKERS_EXT, the shortest path is the core of the algorithm, which directly affects the encoding and decoding of scheduling. In order to find the shortest path, an effective node expansion method is designed to improve the node expansion efficiency. In the part of centralized search, TS based on the neighborhood structure is used. Finally, the effectiveness and superiority of HACSM are verified by testing the relevant instances in the literature

Forecasting smog in Beijing using a novel time-lag GM (1, N) model based on interval grey number sequences

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Purpose Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction. Design/methodology/approach This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness. Findings In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies. Practical implications The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective. Originality/value Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model

Effect and mechanism of chlorogenic acid on cognitive dysfunction in mice by lipopolysaccharide-induced neuroinflammation

BackgroundNeuroinflammation is an important factor causing numerous neurodegenerative pathologies. Inflammation can lead to abnormal neuronal structure and function and even death, followed by cognitive dysfunction. There is growing evidence that chlorogenic acid has anti-inflammatory effects and immunomodulatory activity.PurposeThe aim of this study was to elucidate the potential targets and molecular mechanisms of chlorogenic acid in the treatment of neuroinflammation.MethodsWe used the lipopolysaccharide-induced neuroinflammation mouse model and the lipopolysaccharide-stimulated BV-2 cells in vitro model. Behavioral scores and experiments were used to assess cognitive dysfunction in mice. HE staining and immunohistochemistry were used to assess neuronal damage in the mouse brain. Immunofluorescence detected microglia polarization in mouse brain. Western blot and flow cytometry detected the polarization of BV-2 cells. The migration of BV-2 cells was detected by wound healing assay and transwell assay. Potential targets for chlorogenic acid to exert protective effects were predicted by network pharmacology. These targets were then validated using molecular docking and experiments.ResultsThe results of in vivo experiments showed that chlorogenic acid had an obvious ameliorating effect on neuroinflammation-induced cognitive dysfunction. We found that chlorogenic acid was able to inhibit BV-2 cells M1 polarization and promote BV-2 cells M2 polarization in vitro while also inhibiting the abnormal migration of BV-2 cells. Based on the network pharmacology results, we identified the TNF signaling pathway as a key signaling pathway in which chlorogenic acid exerts anti-neuroinflammatory effects. Among them, Akt1, TNF, MMP9, PTGS2, MAPK1, MAPK14, and RELA are the core targets for chlorogenic acid to function.ConclusionChlorogenic acid can inhibit microglial polarization toward the M1 phenotype and improve neuroinflammation-induced cognitive dysfunction in mice by modulating these key targets in the TNF signaling pathway

The optimisation of whiteness of polyester fabric treated with nanoparticles of 2,2′-(vinylenedi-p-phenylene)bis-benzoxazole (OB-1) by the Taguchi method

This study explores a new approach to achieve a whitening effect on polyester fabric by utilizing a ground form of raw OB-1 (OB-1-G) in combination with dispersing agents. The whitening process parameters, such as whitening temperature, OB-1-G mass, and whitening time were optimized using the L16 orthogonal array-based Taguchi methodology. The signal-to-noise ratio was carried out through a larger-is-better approach to augment the parameter responses, specifically the whiteness. The results suggest that the degree of whiteness of polyester fabric is significantly affected by the whitening treatment temperature (P < 0.05), with a contribution percentage of 93.87%. A whiteness index of 94.12 was achieved for the polyester fabric treated at the optimized conditions. The fabric treated with OB-1-G at the optimized conditions was characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD). The investigation also included the correlation between the length of time spent in washing and rubbing, and their efficacy in achieving whitening outcomes. The research demonstrated the effectiveness of OB-1-G nanopowder in combination with dispersing agents as a fluorescent optical brightener for the optical brightening of polyester fiber with potential use in the textile industry on a larger scale

Inhibition of NK1.1 signaling attenuates pressure overload-induced heart failure, and consequent pulmonary inflammation and remodeling

BackgroundInflammation contributes to heart failure (HF) development, the progression from left ventricular failure to pulmonary remodeling, and the consequent right ventricular hypertrophy and failure. NK1.1 plays a critical role in Natural killer (NK) and NK T (NKT) cells, but the role of NK1.1 in HF development and progression is unknown.MethodsWe studied the effects of NK1.1 inhibition on transverse aortic constriction (TAC)-induced cardiopulmonary inflammation, HF development, and HF progression in immunocompetent male mice of C57BL/6J background.ResultsWe found that NK1.1+ cell-derived interferon gamma+ (IFN-γ+) was significantly increased in pulmonary tissues after HF. In addition, anti-NK1.1 antibodies simultaneously abolished both NK1.1+ cells, including the NK1.1+NK and NK1.1+NKT cells in peripheral blood, spleen, and lung tissues, but had no effect on cardiopulmonary structure and function under control conditions. However, systemic inhibition of NK1.1 signaling by anti-NK1.1 antibodies significantly rescued mice from TAC-induced left ventricular inflammation, fibrosis, and failure. Inhibition of NK1.1 signaling also significantly attenuated TAC-induced pulmonary leukocyte infiltration, fibrosis, vessel remodeling, and consequent right ventricular hypertrophy. Moreover, inhibition of NK1.1 signaling significantly reduced TAC-induced pulmonary macrophage and dendritic cell infiltration and activation.ConclusionsOur data suggest that inhibition of NK1.1 signaling is effective in attenuating systolic overload-induced cardiac fibrosis, dysfunction, and consequent pulmonary remodeling in immunocompetent mice through modulating the cardiopulmonary inflammatory response

Precision Detection of Dense Litchi Fruit in UAV Images Based on Improved YOLOv5 Model

The utilization of unmanned aerial vehicles (UAVs) for the precise and convenient detection of litchi fruits, in order to estimate yields and perform statistical analysis, holds significant value in the complex and variable litchi orchard environment. Currently, litchi yield estimation relies predominantly on manual rough counts, which often result in discrepancies between the estimated values and the actual production figures. This study proposes a large-scene and high-density litchi fruit recognition method based on the improved You Only Look Once version 5 (YOLOv5) model. The main objective is to enhance the accuracy and efficiency of yield estimation in natural orchards. First, the PANet in the original YOLOv5 model is replaced with the improved Bi-directional Feature Pyramid Network (BiFPN) to enhance the model’s cross-scale feature fusion. Second, the P2 feature layer is fused into the BiFPN to enhance the learning capability of the model for high-resolution features. After that, the Normalized Gaussian Wasserstein Distance (NWD) metric is introduced into the regression loss function to enhance the learning ability of the model for litchi tiny targets. Finally, the Slicing Aided Hyper Inference (SAHI) is used to enhance the detection of tiny targets without increasing the model’s parameters or computational memory. The experimental results show that the overall AP value of the improved YOLOv5 model has been effectively increased by 22%, compared to the original YOLOv5 model’s AP value of 50.6%. Specifically, the APs value for detecting small targets has increased from 27.8% to 57.3%. The model size is only 3.6% larger than the original YOLOv5 model. Through ablation and comparative experiments, our method has successfully improved accuracy without compromising the model size and inference speed. Therefore, the proposed method in this paper holds practical applicability for detecting litchi fruits in orchards. It can serve as a valuable tool for providing guidance and suggestions for litchi yield estimation and subsequent harvesting processes. In future research, optimization can be continued for the small target detection problem, while it can be extended to study the small target tracking problem in dense scenarios, which is of great significance for litchi yield estimation