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Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is highly prevalent worldwide and poses a significant threat to men’s health, particularly affecting young men. However, the exact causes and mechanisms behind CP/CPPS remain unclear, leading to challenges in its treatment. In this research, a CP/CPPS rat model was established with complete Freund’s adjuvant (CFA), and berberine hydrochloride was administered through daily gavage to assess its therapeutic effects. The alterations in the gut microbiome induced by CP/CPPS and berberine hydrochloride were investigated through 16S ribosomal RNA sequencing of cecum content and colonic epithelial cells. To investigate the impact of the gut microbiome on CP/CPPS, a pseudo germ-free rat model was established, and fecal microbiome transplantation (FMT) was performed on these rats. In all, berberine hydrochloride demonstrated effective reduction of inflammation and oxidative stress in the prostate, offering significant therapeutic advantages for CP/CPPS. Through analysis of the gut microbiome using 16S ribosome RNA sequencing, distinct differences were observed between CP/CPPS rats and control rats, and Clostridium butyricum was identified as a key bacteria. Pseudo germ-free rats that underwent FMT from CP/CPPS rats or rats treated with berberine hydrochloride displayed varying levels of inflammatory cytokine production, oxidative stress, and activity of associated signaling pathways. In conclusion, the therapeutic potential of berberine hydrochloride in addressing CP/CPPS is highly significant. The gut microbiome has emerged as a critical factor in the development of CP/CPPS and plays a pivotal role in mediating the therapeutic effects of berberine hydrochloride.

Recent years, there have been several high-frequency oscillation events in modular multilevel converter (MMC) based DC current projects. Considering the wideband oscillation characteristics of this issue, passive MMC impedance shaping methods are superior solutions for their advantages over the active methods in enhancing the broadband damping level. However, the optimization of parallel passive devices mainly focused on reactive power compensation and the suppression of characteristic harmonics generated by low-level inverters, which is not applicable to MMC systems. To address the optimization design problem of passive damping devices, this paper proposes an optimized design method for passive filters by analyzing the relationship between the passive damping device and the impedance shaping effect of the inverter. The proposed method reduces the capacity of the passive damping device while ensuring system stability margin. Finally, the effectiveness of the proposed passive impedance shaping strategy is verified through time-domain simulations considering changes in AC side operating conditions and power reduction operation of the MMC.

Background: The functions of the ELOVLs are mainly involved in the elongation of saturated and polyunsaturated fatty acids, thus influencing the metabolism of fatty acids. Abnormal lipid metabolism may result in NAFLD and NASH, which may lead to cirrhosis and liver cancer. These results suggest that ELOVLs-mediated metabolism might be involved in the development of HCC. The purpose of this study was to study the expression and function of ELOVL1 in human liver cancer. Method: Using TCGA, GEPIA and other databases, we analyzed the relationship between the expression of ELOVL1 and liver cancer. The expression of ELOVL1 was detected by immunohistochemical method and Western blot method in hepatic carcinoma and hepatic carcinoma cells. Then, the effects of ELOVL1 on proliferation, apoptosis and invasion in vitro and in vivo were investigated by means of different methods. Result: Our results indicate that ELOVL1 is more highly expressed in liver cancer than in normal tissues. Survival analysis showed that OS and DSS were shorter in patients with high ELOVL1 expression than in those with low expression. Multivariate Cox analysis further demonstrated that over-expression of ELOVL1 was an independent risk factor for overall survival in HCC. The results of ROC also confirmed the value of ELOVL1 in the diagnosis of liver cancer. The results of KEGG enrichment and GSEA indicate that ELOVL1 is associated with lipid metabolism and NAFLD, as well as PPAR, PI3K-AKT-mTOR. Compared with the control group, it was found that silencing ELOVL1 in Huh7 and HepG2 cells could inhibit the growth of cells, promote the apoptosis and decrease the metastasis and invasion. Changes in ELOVL1 induced cell proliferation and metastasis may be related to PI3K/AKT/mTOR. Low expression of ELOVL1 inhibited the growth of xenograft tumors in hepatocellular carcinoma xenograft model. Conclusion: Our data indicate that the activation of PI3K/AKT/mTOR pathway in HCC may contribute to the promotion of cancer. Thus, ELOVL1 may be a promising therapeutic target for HCC.

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

Abstract The problem of submodule switching frequency reduction in half‐bridge modular multilevel converters (HB‐MMCs) with capacitor voltage self‐balancing control is considered and explored in this paper. A selection principle of submodule switching state vectors is proposed based on the voltage self‐balancing switching state matrix, aiming to lower submodule switching frequency and device losses. The relationship between system stability and submodule switching signals is revealed according to the capacitor voltage self‐balancing characteristics, and the full‐rank constraints on the voltage self‐balancing switching state matrix are proposed. Considering the tradeoff between switching loss and capacitor voltage fluctuation, the evaluation indexes of voltage self‐balancing control effect are determined. The selection principle of submodule switching state vectors and the optimized construction method of switching state matrix are presented. Voltage self‐balancing HB‐MMC models are built in MATLAB/Simulink, and it is verified that the submodule switching state vector selection principle proposed in this study can effectively reduce switching frequency while meeting the evaluation requirements of practical engineering projects, so as to achieve the balance between switching loss and steady‐state capacitor voltage fluctuation.

The growing use of glass in architecture has driven research into reducing its energy consumption. Thermochromic (TC) glass technology shows promise for enhancing building energy efficiency by regulating solar heat dynamically. Although TC glass helps reduce heat radiation, additional solutions like Low-E or vacuum glass are needed to control heat convection and conduction. Low-E glass, while effective in lowering heat transfer, may increase surface temperature. Thermo-sensitive hydrogels, known for their light-scattering properties at high temperatures, have been explored to complement TC glass. However, their stability at elevated temperatures remains a challenge, especially for applications requiring durability under varying weather conditions. This study proposes enhancing the adhesion between hydrogel and glass by introducing silica–oxygen bonds. As a result, TC glass demonstrates stable performance over 100 cycles within temperature ranges from 85 °C to 30 °C in summer and 40 °C to −20 °C in winter. Furthermore, by incorporating ethylene glycol, the freezing point of TC glass is reduced to −26 °C, rendering it suitable for use in colder regions. The implementation of TC glass effectively addresses the dual requirements of summer shading and winter heating in areas with both cold winters and hot summers, significantly reducing building energy consumption. This study contributes substantially to developing advanced intelligent building materials, paving the way for more sustainable architectural designs.

A converter valve is the core equipment of HVDC transmission system, whose operating temperature threshold is strictly constrained. This paper proposes a novel prediction method for outlet water temperature of converter valve based on F-BP network, which aims to accurately predict the outlet water temperature and assists the operation and maintenance personnel to take measures in time so that the temperature of the converter valve will not exceed its preset threshold when the operation condition has changed. Firstly, the principle and method of the construction of typical operation databases of converter valve is stated, including data standardization, the calculation of the optimal clustering category number and the final clustering process. Then, the steps of using the typical operation databases and BP neural network to make predictions are presented. Using MATLAB, we predicted the outlet water temperature of a converter valve in Chuxiong Converter Station with F-BP method and two other existing methods in comparison. The results indicate that the proposed approach’s prediction accuracy increases by 0.9141 °C and 0.9938 °C respectively compared with the simple BP neural network and linear regression, which contributes to the prediction application of the outlet water of a converter valve.

Abstract The transition from deep dormancy to seed germination is essential for the life cycle of plants, but how this process occurs in the gymnosperm Chinese yew (Taxus chinensis var mairei), the natural source of the anticancer drug paclitaxel, remains unclear. Herein, we analyse the transcriptome, proteome, spatial metabolome, and spatial lipidome of the Chinese yew and present the multi-omics profiles of dormant and germinating seeds. Our results show that abscisic acid and gibberellic acid 12 homoeostasis is closely associated with gene transcription and protein translation, and the balance between these phytohormones thereby determines if seeds remain dormant or germinate. We find that an energy supply of carbohydrates from glycolysis and the TCA cycle feed into the pentose phosphate pathway during seed germination, and energy supplied from lipids are mainly derived from the lipolysis of triacylglycerols. Using mass spectrometry imaging, we demonstrate that the spatial distribution of plant hormones and phospholipids has a remarkable influence on embryo development. We also provide an atlas of the spatial distribution of paclitaxel C in Chinese yew seeds for the first time. The data from this study enable exploration of the germination mechanism of Chinese yew seeds across several omics levels.

In natural environments, the connecting bolts of overhead lines and power towers are prone to loosening and missing, posing potential risks to the safe and stable operation of the power system. This paper reviews the challenges in bolt defect detection using power vision technology, with a particular focus on unmanned aerial vehicle (UAV) images. These UAV images offer a cost-effective and flexible solution for detecting bolt defects. However, challenges remain, including missed detection due to the small size of bolts, false detection caused by dense and occluded bolts, and underfitting resulting from imbalanced bolt defect datasets. To address these issues, this paper summarizes solutions that leverage deep learning algorithms. An experimental analysis is conducted on a dataset derived from UAV inspections, comparing the detection characteristics and visualizing the results of various algorithms. The paper also discusses future trends in the application of UAV-based power vision technology for bolt defect detection, providing insights for the advancement of intelligent power inspection.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators’ profit, while the lower-level objective is to minimize the user’s power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

The journal retracts the article “Inhibition of SDF-1α/CXCR4 Signalling in Subchondral Bone Attenuates Post-Traumatic Osteoarthritis” [...]

To achieve a hierarchical and quantitative evaluation of grid-wide power quality in the distribution network, reflecting the overall power quality level of the distribution network, a comprehensive evaluation method for power quality in a grid-wide system based on complex network theory is proposed. Firstly, based on the propagation characteristics of power quality disturbances, a power quality evaluation index system is constructed. Secondly, to reflect the constraint effect of the local power quality level of nodes on the overall power quality level of the distribution system, corresponding indices such as improved node degree, improved node electrical betweenness, and node self-healing capability are proposed based on complex network theory, and the power quality influence degree of nodes is calculated. Then, the GRA-ANP (Grey Relational Analysis–Analytic Network Process) subjective weight calculation method is improved by introducing grey relational analysis to address the impact of differences in different decision-making results. Based on power quality monitoring data, the entropy weight method is used for objective weighting. To avoid the partiality of a single weight evaluation result, the game equilibrium algorithm is employed to calculate the comprehensive weight of each power quality index. Subsequently, considering the correlation and dependency among indices, the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method is used to obtain the power quality grade of each node. Combining this with the calculation of the power quality influence degree of nodes, the overall power quality grade of the distribution network is determined, achieving a hierarchical and quantitative evaluation of power quality in the entire distribution system. Finally, through a case study analysis of an improved 13-node distribution network, it is verified that the proposed method can fully extract data information and produce comprehensive and accurate power quality assessment results by comparing it with other methods. This provides strong support for the safe and stable operation of the distribution system and the subsequent optimization and management of power quality.

Plastic film mulch systems are used widely in arid areas, and the associated tillage measures affect soil properties, root and crop growth, and nutrient uptake. However, much debate surrounds the most suitable tillage method for plastic film mulch systems. We conducted a two-year field experiment to explore the impact of three tillage treatments - rotary tillage before ridge–furrow plastic film mulch (MR), no-tillage before ridge–furrow plastic film mulch (MZ), and plow tillage before ridge–furrow plastic film mulch (MP) - on soil total nitrogen, available nitrogen, root stratification structure, nitrogen transfer and utilization, and maize yield. The results showed that MP had better soil quality than either MR or MZ over 2019 and 2020, with higher nitrate-nitrogen and total nitrogen in the 0–40 cm soil layer. MP improved the soil physicochemical properties more than the other treatments, producing significantly higher root numbers and root biomass for the aerial and underground nodal roots than MR and MZ. At harvest, MP had the highest root biomass density, root length density, and root surface area density in the different soil layers (0–20, 20–40, and 0–40 cm). Significant correlations occurred between root biomass and aboveground nitrogen accumulation during maize growth. During grain filling, MP had the greatest nitrogen transfer amount, significantly increasing root and aboveground nitrogen transfer by 19.63–45.82% and 11.15–24.56%, respectively, relative to the other treatments. MP significantly produced 1.36–26.73% higher grain yields and a higher grain crude protein content at harvest than MR and MZ. MP also had higher values for the nitrogen harvest index, nitrogen uptake efficiency, and partial factor productivity of nitrogen fertilizer than MR and MZ. In conclusion, plow tillage combined with a ridge–furrow plastic film mulch system facilitated maize root development and improved nitrogen utilization, thereby increasing maize yield more than the other treatments.

Currently, research on the small-signal stability of grid-forming converters under two control modes, grid-following and grid-forming, mainly focuses on low-level converters. However, studies on the differences between grid-following modular multilevel converters (MMC) and grid-forming MMC are limited. A comprehensive analysis of the wideband oscillation risk differences in MMC-HVDC systems under these two control modes from the perspective of impedance characteristics is necessary. Therefore, based on the harmonic state-space (HSS) modeling theory, this article establishes wideband sequence impedance models for grid-forming and grid-following MMC. Subsequently, this article identifies key control parameters affecting the impedance characteristics of MMC under two control modes. The wideband oscillation risks of MMC-HVDC systems under both control modes are compared. The study indicates that when the grid strength weakens, grid-following MMC faces instability risks, while grid-forming MMC can maintain stability. In instances where high-frequency resonance peaks exist in the impedance of the grid, both control modes of MMC may face instability risks. Furthermore, by adjusting the obtained key control parameters of MMC, this article effectively suppresses the oscillation risk discussed above. Finally, the analysis results are verified through an electromagnetic transient simulation.

In this study, an optimized dual-layer configuration model is proposed to address voltages that exceed their limits following substantial integration of photovoltaic systems into distribution networks. Initially, the model involved segmenting the distribution network’s voltage zones based on distributed photovoltaic governance resources, thereby elucidating the characteristics and governance requisites for voltages across distinct regions. Subsequently, a governance model for voltage limit exceedances, grounded in optimizing energy storage configurations, was formulated to mitigate photovoltaic power fluctuations by deploying energy storage systems. This model coordinates the reactive power output of photovoltaic installations with the active power consumption of energy storage systems, thereby augmenting voltage autonomy in the power grid. This study leveraged Karush–Kuhn–Tucker (KKT) conditions and the Big-M method to transform the dual-layer model into a single-layer linear model, thereby enhancing solution efficiency and precision. Finally, a simulation was carried out to demonstrate that the strategy proposed from this research not only achieves commendable economic efficiency, but also significantly improves the regional voltage effect by 28.7% compared to the optical storage capacity optimization model.

This paper focus on direct current (DC) filter grounding faults to propose a novel dilated normalized residual convolutional neural network (DRNCNN) fault diagnosis model for high-voltage direct current (HVDC) transmission systems. To address the insufficiency of the traditional model’s receptive field in dealing with high-dimensional and nonlinear data, this paper incorporates dilated convolution and batch normalization (BN), significantly enhancing the CNN’s capability to capture complex spatial features. Furthermore, this paper integrates residual connections and parameter rectified linear units (PReLU) to optimize gradient propagation and mitigate the issue of gradient vanishing during training. These innovative improvements, embodied in the DRNCNN model, substantially increase the accuracy of fault detection, achieving a diagnostic accuracy rate of 99.28%.

Introduction: A multi-degree-of-freedom ankle rehabilitation robot with an adjustable workspace has been designed to facilitate ankle joint rehabilitation training. It features a rotation center adapted to the human body, making it suitable for patients with ankle dysfunction following a stroke.Method: In this study, a multi-degree-of-freedom reconfigurable ankle rehabilitation robot (RARR) with adaptable features, based on the principles of ergonomics, has been proposed to cater to the varying needs of patients. This robot offers an adjustable workspace, allowing for different types of ankle joint rehabilitation exercises to be performed. By adjusting the assembly of the RARR, personalized and targeted training can be provided to patients, circumventing issues of redundancy in degrees of freedom during its use. A kinematic model of the robot has been established, and finite element simulation has been employed to analyze the strength of critical components, ensuring the safety of the robot. An experimental platform has been set up to assess the smoothness of the rehabilitation process with RARR, with angle measurements conducted using an Inertial Measurement Unit (IMU).Results and discussion: In conclusion, both simulation and experimental results demonstrate that the robot offers an adjustable workspace and exhibits relatively smooth motion, thereby confirming the safety and effectiveness of the robot. These outcomes align with the intended design goals, facilitating ankle joint rehabilitation and advancing the field of reconfigurable robotics. The RARR boasts a compact structure and portability, making it suitable for various usage scenarios. It is easily deployable for at-home use by patients and holds practical application value for wider adoption in rehabilitation settings.

Abstract Background Toxoplasmosis is a zoonotic parasitic disease caused by Toxoplasma gondii. Toxoplasma gondii infection of the lungs can lead to severe pneumonia. However, few studies have reported Toxoplasma pneumonia. Most reports were clinical cases due to the lack of a good disease model. Therefore, the molecular mechanisms, development, and pathological damage of Toxoplasma pneumonia remain unclear. Methods A mouse model of Toxoplasma pneumonia was established by nasal infection with T. gondii. The model was evaluated using survival statistics, lung morphological observation, and lung pathology examination by hematoxylin and eosin (H&E) and Evans blue staining at 5 days post-infection (dpi). Total RNA was extracted from the lung tissues of C57BL/6 mice infected with T. gondii RH and TGME49 strains at 5 dpi. Total RNA was subjected to transcriptome analysis by RNA sequencing (RNA-seq) followed by quantitative real-time polymerase chain reaction (qRT–PCR) validation. Transcript enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to assess the biological relevance of differentially expressed transcripts (DETs). Results C57BL/6 mice infected with T. gondii via nasal delivery exhibited weight loss, ruffled fur, and respiratory crackles at 5 dpi. The clinical manifestations and lethality of RH strains were more evident than those of TGME49. H&E staining of lung tissue sections from mice infected with T. gondii at 5 dpi showed severe lymphocytic infiltration, pulmonary edema, and typical symptoms of pneumonia. We identified 3167 DETs and 1880 DETs in mice infected with the T. gondii RH and TGME49 strains, respectively, compared with the phosphate-buffered saline (PBS) control group at 5 dpi. GO and KEGG enrichment analyses of DETs showed that they were associated with the immune system and microbial infections. The innate immune, inflammatory signaling, cytokine-mediated signaling, and chemokine signaling pathways displayed high gene enrichment. Conclusion In this study, we developed a new mouse model for Toxoplasma pneumonia. Transcriptome analysis helped to better understand the molecular mechanisms of the disease. These results provided DETs during acute T. gondii lung infection, which expanded our knowledge of host immune defenses and the pathogenesis of Toxoplasma pneumonia. Graphical Abstract

Abstract With the increasing proportion of renewable power generations in the power system, the power grid impedance may fluctuate greatly, and it is difficult for the virtual synchronous generator (VSG) with a single control structure to meet the stability requirements. Thus, the control principles of the two types of VSGs are compared to conclude that there are similarities in the control structure of the two VSGs. And the small signal models of the two types of VSGs are established to analyze the stability boundaries, coming to the conclusion that the stability region is complementary. Based on the conclusions, the paper puts forward the idea of switching operation modes: switch to the U‐VSG mode when the grid strength weakens and switch to the PQ‐VSG mode when greater, so that the inverter can keep stable in a wider range of grid strength. Therefore, the characterization method of the switching boundary with hysteresis properties is proposed. Then the improved recursive least squares (RLS) algorithm is introduced to identify the power grid impedance online without additional injected disturbance, based on which the adaptive multi‐mode smooth switching control strategy is proposed. Finally, the effectiveness of the analysis and the control strategy are verified by simulations.

Rice noodle is traditional food in China, with a soft, flexible and smooth texture, which is loved by the general public. However, there is a wide variety of rice noodles and has a strong regional character. At present, in the rice noodle quality evaluation indicators and the selection of raw material for rice noodles, there is no unified national standards for rice noodles class. Therefore, this paper reviewed the classification of rice noodles based on the existing relevant studies, and discussed the quality evaluation indexes of rice noodles from three aspects: sensory evaluation, physicochemical characteristics and texture characteristics of rice noodle. We also analyzed and compared the indicators and suitable range of rice suitable for rice noodle production, and discussed the influence of the production process of rice noodle on the quality of rice noodle from five aspects: fermentation, milling, pasting, aging and drying. Finally, the main points of controlling the risk of microbial contamination during the production of rice noodle were discussed, order to provide reference for establishing unified evaluation standard for rice noodles and principles of rice noodles selection, optimizing rice noodles process technology, and minimizing the risk of microbial contamination during the production of rice noodles.

To achieve real-time safety management of on-site power system work, the object detection algorithm of the cloud platform is transplanted into the edge computing device. However, due to the limited capacity and arithmetic power of the AI chip carried in the edge computing device, the algorithm needs to be studied in a lightweight manner. Therefore, this paper proposes an improved edge-side lightweight YOLOv4 (You Only Look Once version 4) algorithm, which is compressed and accelerated by improving the network structure and the confidence loss function of the algorithm without losing too much detection accuracy. Firstly, a lightweight convolutional neural network based on depthwise separable convolution and mobile inverted bottleneck convolution is constructed as the backbone network of YOLOv4, and the depthwise separable convolution is used instead of the conventional convolution, which can greatly reduce the parameters number and computation of the algorithm. Secondly, an improved bidirectional feature fusion network is constructed as the neck network of YOLOv4 algorithm to improve the information availability of multi-semantic features and enhance the extraction capability of small-scale features. Then, the confidence loss function of the algorithm is improved to alleviate the problems of uneven distribution of positive samples and negative samples and uneven distribution of easy-to-classify samples and hard-to-classify samples. Finally, the improved YOLOv4 algorithm is tested on an edge computing device equipped with an NVIDIA Jetson Xavier NX chip, and the dataset based on the compliance of on-site dress code in the electric power system field is tested. The experimental results show that, compared with the original YOLOv4 algorithm, the improved YOLOv4 algorithm reduces the parameters number by 93.11%, and the real-time video stream detection speed on the edge computing device is increased by 22.00%, reaching 15.05 FPS. The lightweight improved YOLOv4 algorithm is suitable for deployment at the edge end and can achieve real-time accurate identification of risks in the on-site work.

The existing photovoltaic frequency regulation strategies do not take into account the differences in the frequency regulation capability. So they cannot fully exploit the support capacity to the grid frequency. For the purpose of improving the control precision and regulation effect of PV, a comprehensive control strategy for photovoltaic virtual synchronous generator (PV-VSG) is proposed, which accurately considers the frequency regulation capability. First, the inertia regulation margin, power regulation speed and power regulation margin are taken as the evaluation index of frequency regulation capability of PV. Then, the frequent deviation-free control strategy with integral link and the multiple control modes switching strategy are proposed, whose control parameters are designed according to the frequency regulation capability. In the simulation verification, the proposed method reduces the frequency deviation by 0.11 Hz compared to existing strategies, and can quickly recover to the normal value. At the same time, the frequency regulation utilizes the capability of the PVs, which also validates the effectiveness of the proposed strategy.

The thermal decomposition process of 1,7-diacetoxy-2,4,6-trinitro-2,4,6-triazaheptane (BSX) was studied by TG-DSC, TG-MS, TG-FTIR technology and ReaxFF molecular dynamics (ReaxFF MD) simulation. The decomposition activation energy of BSX was calculated according to the test results of TG-DSC. The main thermal decomposition products of BSX were determined by combining experiments and simulations, and the possible thermal decomposition pathways were analyzed. The results showed that the decomposition activation energy of BSX is 161 ± 2.6 kJ·mol−1; The thermal decomposition of BSX may generate the main intermediate products C8H14N5O8, C4H7NO2, C2H3O2, HNO2, NO2 and NO, etc., and the intermediate products continued to decompose to generate the final products CH2O, CO2, CO, H2O and N2; The reaction rate constants of each stage of BSX were calculated, and the possible thermal decomposition pathways of BSX were obtained: BSX → C8H14N5O8 + NO2 → C5H9N3O4 + C4H7N4O6 + C4H7NO2 + C3H5N2O4 → C2H3O2 + C2H3O + CH2O + CO2 + CO + H2O + HNO2 + N2O2 + NO2 + NO + H + OH + … → CH2O + CO2 + CO + H2O + N2 + … This study contributes to a deeper understanding of the decomposition process of BSX after heating and has specific significance for reducing the thermal hazard of the BSX pyrolysis process.