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Green development is inseparable from financial support. The impact of digital inclusive finance (DIF), an emerging financial format, on the green development efficiency (GDE) needs to be studied. Using the panel data of the Provincial Digital Financial Inclusion Index from 2011 to 2019, this paper examines the impact of DIF and its coverage breadth (CB), usage depth (UD), and digitalization level (DL) on GDE, and analyzes the regional heterogeneity of the impact of DIF on GDE. The research also explores the mechanism by which DIF affects the efficiency of provincial green development, including the moderating role of environmental regulation (ER) and the mediating role of industrial structure upgrade (ISU). The results show that DIF, UD, and DL can significantly improve provincial GDE, and the effect of coverage breadth is not obvious. From a regional perspective, DIF can promote GDE in the eastern and central regions, whereas it has no obvious effect on the western region. Moreover, ER has played a moderation role in the process of DIF affecting GDE. ISU has played a partial mediation role in the process of DIF affecting GDE. The research conclusions can provide relevant suggestions for the green development of China’s provinces.

Changes in physicochemical characteristics, chemical structures and maturity of swine, cattle and chicken manures and composts during 70-day composting without addition of bulking agents were investigated. Physicochemical characteristics were measured by routine analyses and chemical structures by solid-state 13C NMR and FT-IR. Three manures were of distinct properties. Their changes in physicochemical characteristics, chemical structures, and maturity were different not only from each other but also from those with addition of bulking agents during composting. Aromaticity in chicken manure composts decreased at first, and then increased whereas that in cattle and swine manure composts increased. Enhanced ammonia volatilization occurred without addition of bulking agents. NMR structural information indicated that cattle and chicken composts were relatively stable at day 36 and 56, respectively, but swine manure composts were not mature up to day 70. Finally, the days required for three manures to reach the threshold values of different maturity indices were different.

Transmission line detection is the basic task of using UAVs for transmission line inspection and other related tasks. However, the detection results based on traditional methods are vulnerable to noise, and the results may not meet the requirements. The deep learning method based on segmentation may cause a lack of vector information and cannot be applied to subsequent high-level tasks, such as distance estimation, location, and so on. In this paper, the characteristics of transmission lines in UAV images are summarized and utilized, and a lightweight powerline detection network is proposed. In addition, due to the reason that powerlines often run through the whole image and are sparse compared to the background, the FPN structure with Hough transform and the neck structure with multi-scale output are introduced. The former can make better use of edge information in a deep neural network as well as reduce the training time. The latter can reduce the error caused by the imbalance between positive and negative samples, make it easier to detect the lines running through the whole image, and finally improve the network performance. This paper also constructs a powerline detection dataset. While the net this paper proposes can achieve real-time detection, the f-score of the detection dataset reaches 85.6%. This method improves the effect of the powerline extraction task and lays the groundwork for subsequent possible high-level tasks.

In light of the proliferation of cybercrimes, the effective identification and mitigation of such online criminal activities has emerged as a significant challenge within the domain of network security. Therefore, this study introduces dilated convolution technology, self-attention mechanism, convolutional neural network and long short-term memory network, and proposes an overlapping traffic recognition model based on improved convolutional neural network and an online crime recognition model with long short-term memory network. In the traffic segmentation model test, the recall rate, F1 value, and error rate of the model under normal traffic conditions were 91.43%, 93.46%, and 92.43%, respectively. The error rate was 4.15%. The accuracy of the online crime recognition model for malware propagation and illegal transactions was 96.54% and 92.87% respectively. In the concept drift test, when the training time and test time interval was 60 days, the accuracy of the model was 48.67% higher than that of the long short-term memory network. Compared with the mainstream framework and traditional methods, its accuracy in high traffic scenarios was 94.78%, the error rate was 3.89%, and the P-value was < 0.05. In the final simulation test, the model could effectively identify illegal software transactions. The results show that the proposed model has high accuracy and strong generalization ability in identifying overlapping traffic and website fingerprint crimes, and effectively improves the detection ability of criminal activities in anonymous networks.

In light of the proliferation of cybercrimes, the effective identification and mitigation of such online criminal activities has emerged as a significant challenge within the domain of network security. Therefore, this study introduces dilated convolution technology, self-attention mechanism, convolutional neural network and long short-term memory network, and proposes an overlapping traffic recognition model based on improved convolutional neural network and an online crime recognition model with long short-term memory network. In the traffic segmentation model test, the recall rate, Fl value, and error rate of the model under normal traffic conditions were 91.43%, 93.46%, and 92.43%>, respectively. The error rate was 4.15%. The accuracy of the online crime recognition model for malware propagation and illegal transactions was 96.54% and 92.87% respectively. In the concept drift test, when the training time and test time interval was 60 days, the accuracy of the model was 48.67% higher than that of the long short-term memory network. Compared with the mainstream framework and traditional methods, its accuracy in high traffic scenarios was 94.78%, the error rate was 3.89%, and the P-value was < 0.05. In the final simulation test, the model could effectively identify illegal software transactions. The results show that the proposed model has high accuracy and strong generalization ability in identifying overlapping traffic and website fingerprint crimes, and effectively improves the detection ability of criminal activities in anonymous networks.

Sliding mode control (SMC) is a promising robust control approach for the abrupt variations in complex System. Energy internet (El), as a dynamic nonlinear strong coupling System, has different operating states under different operating conditions (system connection, load levels and faults, etc.). We-Energy (WE), as a basic energy unit in El, is proposed for describing the essential characteristics and operating state of EL According the operating states of El, four operating modes of WE presented in this paper can be randomly converted, which correspond the stochastic markov process. Then, based on the coupling characteristics of El, a set of state equations is established to embody the markovian jumping System (MJS) of WE. Moreover, sliding surface and contorl scheme for WE based on MJS is designed. Finally, a numerical simulation is shown to illustrate the proposed method.

At present, integrated energy systems have received extensive attention, but there is no basic framework for stability analysis of coupled systems. The injection of a large amount of renewable energy also has a great impact on the stability of the system. This paper focuses on how to analyze the static stability of the coupling system with uncertainty, which mainly considers the uncertainty of wind power generation and photovoltaic power generation and also considers the influence of P2G technology on the whole system. Firstly, this paper analyzes the principles of wind power generation and photovoltaic power generation and constructs the probability model of renewable energy power generation power. Then, the three-point estimation method is used to process the data, and the probability distribution of the unknown quantity is obtained by probabilistic power flow analysis. Finally, the probability distribution of each eigenvalue is obtained by analyzing the sensitivity of the characteristic roots to the voltage. Thus, the static stability of the system is judged. The applicability of proposed methodology is demonstrated by analyzing an integrated IEEE 14-bus power system and a Belgian 20-node gas system in this paper.

The annual electricity trade scheduling is the basis of long-term power generation scheduling. In recent decades, the ratio of new energy generation in China has increased annually, and the electricity market has operated under the “market electricity” and “planned electricity” double track mode in recent years. However, the existing annual electricity trade scheduling methods are extensive and cannot adapt to the new situation of “market electricity” and large-scale new energy generation. The annual scheduled energy of the power units is set as a decision variable, and a novel annual energy scheduling optimization model based on Gini coefficient of fairness is presented in this paper. In this model, “market electricity” capacity is conversed monthly, considering peaking reserve demand and monthly characteristics of new energy generation. The fairness constraint set based on Gini coefficient is introduced into the optimization model to solve various fairness problems. Simulation results show that the introduction of the Gini coefficient and the optimization model considering the monthly conversion of marketing electricity capacity can obtain more accurate and reasonable electricity distribution results, and the peaking demand can be considered more fairly and effectively. The proposed method provides a feasible solution to the annual electric energy scheduling for dual-track operation country such as China.

Generally, energy trading in smart grid is realized by microgrids. Correspondingly, energy trading in energy internet relies on small-scale energy systems, named as Weenergies (WEs). Previous works on the distributed energy trading focused on the trading platform or trading mechanism based on distributed communication. However, most ignored the fact that there is no express delivery of energy trading, and the transmission of energy depends on a fixed physical topology. Energy transactions without considering the transmission distance will increase the difficulty of energy scheduling and the transmission cost of energy. Aiming at this problem, an aggregation game among WEs is proposed for two-way multi-energy trading, and a distributed algorithm is designed to solve the Nash equilibrium. Since each WE only needs to communicate with its neighbors to exchange information, this distributed process reduces communication burden and improves information security. Furthermore, a multi-energy transmission optimization model is established to determine the transmission path of the transmission energy, which can minimize the transmission cost. Subsequently, to reduce the influence of real-time fluctuations of renewable energy and load, a receding horizon control algorithm is designed to improve the reliability of the proposed game. Finally, the effectiveness in dealing with two-way multi-energy trading of the proposed strategy is verified through simulations on the five connected WEs.

Background There is ongoing debate about the safety and efficacy of epicutaneous immunotherapy (EPIT) in treating food allergies. The systematic review and meta-analysis aimed to evaluate the safety and efficacy of EPIT. Methods We systematically searched international trial registers (ClinicalTrials.gov), PubMed, Embase, the Cochrane Central of Controlled Trials (CENTRAL), and Web of Science from the inception of the database until June 25, 2023. Two authors independently screened potential studies based on the following criteria: food allergy, epidermal immunotherapy, and randomized controlled trials(RCTs). The risk-of-bias assessment was performed using the Cochrane risk-of-bias 2 (ROB 2) tool. The primary outcomes included desensitization, local adverse events, systemic adverse events, and quality of life. Secondary outcomes included epinephrine utilization, topical medication utilization, and severe adverse events. We assessed certainty of evidence by the GRADE approach. Results Ten studies involving 1970 participants were included. Ten high-quality RCTs focusing on peanut allergy and cow’s milk allergy were included in the analysis. The meta-analysis revealed that EPIT promoted desensitization in patients with food allergy ( RR 2.11, 95% CI 1.72–2.58; I 2  = 0%, high certainty), particularly in aged ≤ 11 years ( RR 3.84, 95% CI 2.39–6.26; I 2  = 34%). Additionally, treatment duration ≥ 52 weeks was found to increase immune tolerance ( RR 3.37, 95% CI 2.39–4.75; I 2  = 13%). Patients who undergo EPIT treatment not only raised the local adverse reactions ( RR 1.63, 95% CI 1.10–2.41; I 2  = 82%, low certainty) but also raised systemic adverse reactions ( RR 1.52, 95% CI 1.01–2.28; I 2  = 0%, high certainty). Conclusion After EPIT treatment, patients with food allergy can effectively increase their immune tolerance to food. However, it also significantly increases mild-to-moderate anaphylaxis. There is limited data on the impact of EPIT on quality of life and other food allergic diseases, indicating a need for further research.