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Bioisosteric design has become an essential approach in the development of drug molecules. Recent advancements in synthetic methodologies have enabled the rapid adoption of this strategy into drug discovery programs. Consequently, conceptionally innovative practices would be appreciated by the medicinal chemistry community. Here we report an expeditous synthetic method for synthesizing aryl difluoromethyl bicyclopentane (ADB) as a bioisostere of the benzophenone core. This approach involves the merger of light-driven C−F bond activation and strain-release chemistry under the catalysis of a newly designed N -anionic-based organic photocatalyst. This defluorinative coupling methodology enables the direct conversion of a wide variety of commercially available trifluoromethylaromatic C−F bonds (more than 70 examples) into the corresponding difluoromethyl bicyclo[1.1.1]pentanes (BCP) arenes/difluoromethyl BCP boronates in a single step. The strategy can also be applied to [3.1.1]and [4.1.1]propellane systems, providing access to analogues with different geometries. Moreover, we have successfully used this protocol to rapidly prepare ADB-substituted analogues of the bioactive molecule Adiporon. Biological testing has shown that the ADB scaffold has the potential to enhance the pharmacological properties of benzophenone-type drug candidates. Bioisosteric design has become an essential approach during drug molecule discovery process. Here, the authors describe a strategy for defluorinative couplings of propellanes, enabling the synthesis of aryl difluoromethyl bicyclopentanes as benzophenone-type bioisosteres.

In the human–exoskeleton interaction process, accurately recognizing gait phases is crucial for effectively assessing the assistance provided by the exoskeleton. However, due to the similarity in muscle activation patterns between adjacent gait phases, the recognition accuracy is often low, which can easily lead to confusion in surface electromyography (sEMG) feature extraction. This paper proposes a real-time recognition method based on multi-scale fuzzy approximate root mean entropy (MFAREn) and an Efficient Multi-Scale Attention Convolutional Neural Network (EMACNN), building upon the concept of fuzzy approximate entropy. MFAREn is used to extract the dynamic complexity and energy intensity features of sEMG signals, serving as the input matrix for EMACNN to achieve fast and accurate gait phase recognition. This study collected sEMG signals from 10 subjects performing continuous lower limb gait movements in five common motion scenarios for experimental validation. The results show that the proposed method achieves an average recognition accuracy of 95.72%, outperforming the other comparison methods. The method proposed in this paper is significantly different compared to other methods (p < 0.001). Notably, the recognition accuracy for walking in level walking, stairs ascending, and ramp ascending exceeds 95.5%. This method demonstrates a high recognition accuracy, enabling sEMG-based gait phase recognition and meeting the requirements for effective human–exoskeleton interaction.

With the rapid development of economic globalization, the trade of various countries has become increasingly close and thus the rapid growth of container transportation business. The rational choice of transportation routes and intermodal transportation methods has become a hot topic in this field. In order to improve the efficiency of intermodal transportation and reduce the cost of intermodal transportation, this paper studies the evaluation of the synergy effect of container multimodal transportation based on the BP neural network algorithm. First, it is determined that the multi-attribute decision-making method is used to comprehensively evaluate the synergy effect of container multimodal transportation, and the data are normalized. Select the appropriate hidden layer nodes, use Matlab to determine the learning rate, select the logsig function for the network transfer function, select Traingdx as the training function, and establish a container multimodal transport synergy evaluation model based on BP neural network. Secondly, the model is solved, and the problem is transformed into finding the shortest route from Q to P without exceeding the cost and time constraints. Then carry on the simulation experiment, use Matlab to solve the transportation time and total cost between cities. Experimental data show that when the number of iterations is 800, the algorithm begins to converge; at the fifth training, the error between the actual output and the expected output is only 0.0004; in route B, the time of multimodal transportation is 48.18% less than that of single transportation, and the cost is saved by 50.02%. This shows that the container multimodal transport synergy evaluation model based on BP neural network can accurately evaluate the effects, and container multimodal transport can indeed improve transportation efficiency and save costs.

With the rapid integration and wide application of the enterprise Internet of Things, big data and 5G-class large networks, traditional enterprise cloud computing systems cannot timely process various mass information data generated by connecting with network edge electronic devices. There are obvious technical disadvantages. In order to effectively solve this complex problem, edge mobile computing came into being. The purpose of this article is to study the protection methods of commercial confidential information, using the security relationship between data information in edge technology computing and the technical characteristics of data privacy information protection. This paper proposes a theoretical system and technical architecture centered on the use of data security technology. The three key technologies of access control, identity authentication and information privacy security protection are researched on the privacy protection processing methods of commercial information security physical systems. The experimental data show that the recognition errors mainly occur in identifying non-anomalous data as abnormal data. The analysis and identification of abnormal information data are basically accurate, and it can quickly complete various processing tasks to eliminate abnormal information data to meet the requirements. The experimental data show that the abnormal data can be used to monitor the security of the commercial information physical system, and it is also a good security protection method for commercial confidential information. It has guiding significance for the confidential information protection of commercial information physical systems. In the next few years, more than 50% of major data applications will need to be analyzed, processed and data stored at the edge of the network. Cloud computing technologies at the edge are widely used.

In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and summarized the methods and steps of logistics network optimization in the whole country. First, we established the logistics network structure.The definition of network level, node function and connection between nodes is carried out. Then, through the improved urban agglomeration order gravity model, the scope of self logistics city is screened. Finally, the link of logistics network structure is scheduled for completion with the goal of cost optimization.

In this paper, considering the characteristics of e-commerce logistics and the problems arising from the construction of logistics network, based on the full investigation and research of the B2C electricity supplier operation mode, we have studied and summarized the research results of scholars at home and abroad aiming at the related problems, and summarized and summarized the methods and steps of logistics network optimization in the whole country. First, we established the logistics network structure.The definition of network level, node function and connection between nodes is carried out. Then, through the improved urban agglomeration order gravity model, the scope of self logistics city is screened. Finally, the link of logistics network structure is scheduled for completion with the goal of cost optimization.

In recent years, China’s cross-border e-commerce has flourished, and the transaction volume has increased year by year. Cross-border e-commerce has become a favorable breakthrough point for China's foreign trade. This article mainly studies the action mechanism and model of cross-border e-commerce green supply chain based on customer behavior. Green supply chain partners select 24 secondary indicators of the evaluation system as the input vector. The historical data of each index is collected by field investigation as sample data and brought into the neural network for training. The output vector of the only output layer of the network is used as the evaluation result of the supplier. This paper divides the operation mode of green supply chain into four stages and puts forward improvement tools for the functional modules in each stage. Enterprises can use the tools in the modules to improve the operation efficiency of green supply chain. According to the green level evaluation demand of green supply chain, this paper uses the hierarchical method to evaluate it. According to the survey results, this paper uses arithmetic average method to deal with the operation and establishes a secondary index after decomposition. Finally, this paper uses confirmatory factor analysis to test the measurement model and further uses analysis of variance to test the relationship between the two types of social cues and behavioral willingness. The data shows that the orderly fluctuation range of the east, middle, and west cross-border logistics subsystems basically remains at around 0.2 to 0.6. The results show that the establishment of the green supply chain model has a very positive significance for the implementation and development of the green supply chain in China’s manufacturing industry. Through the research of profit and profit distribution in the green supply chain, it provides guidance for the green supply chain to effectively select the supply chain members to cooperate and calculate and distribute the profit reasonably, so that the green supply chain management can be widely used in reality.

Over the past three decades, considerable progress has been made in the development of methods to construct sp 2 carbon–nitrogen (C–N) bonds using palladium, copper or nickel catalysis 1 , 2 . However, the incorporation of alkyl substrates to form sp 3 C–N bonds remains one of the major challenges in the field of cross-coupling chemistry. Here we demonstrate that the synergistic combination of copper catalysis and photoredox catalysis can provide a general platform from which to address this challenge. This cross-coupling system uses naturally abundant alkyl carboxylic acids and commercially available nitrogen nucleophiles as coupling partners. It is applicable to a wide variety of primary, secondary and tertiary alkyl carboxylic acids (through iodonium activation), as well as a vast array of nitrogen nucleophiles: nitrogen heterocycles, amides, sulfonamides and anilines can undergo C–N coupling to provide N -alkyl products in good to excellent efficiency, at room temperature and on short timescales (five minutes to one hour). We demonstrate that this C–N coupling protocol proceeds with high regioselectivity using substrates that contain several amine groups, and can also be applied to complex drug molecules, enabling the rapid construction of molecular complexity and the late-stage functionalization of bioactive pharmaceuticals. The synergistic combination of copper catalysis and photoredox catalysis forms sp 3 C–N bonds in a rapid, room-temperature coupling protocol with high efficiencies and regioselectivities and a broad substrate scope.

With the advent of the crossborder e-commerce (CE) boom, more and more traditional enterprises have transformed into the field of crossborder e-commerce. It has become a new outlet for enterprises in the international trade environment, and at the same time, it has some problems in the utilization of resources and the maximization of benefits. In response to this problem, it is very important to optimize the export operation model of crossborder e-commerce enterprises (CEE). With the development of intelligent algorithms, research on the application of intelligent algorithms to the economic field has gradually been carried out. Its characteristics and advantages are of great significance to the optimization of CEE export operation mode. The purpose of this paper is to study the CEE export operation mode and optimization strategy based on data mining algorithm (DMA). Through the analysis and research of DMA, it can be applied to the optimization of CEE export operation mode to cope with the world trade in the new environment. This paper explains the basic theory of DMA and CEE export operation mode. Its effect is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results show that the comprehensive trade quality index of the CEE export operation mode integrating DMA is higher, and the adaptability is stronger. Compared with the traditional operating model, the difference between the two indices is 3.175 index points. It has guiding significance in terms of CEE export operation mode and optimization strategy.

Based on the SBM model including unexpected output, this paper studies the water resource utilization efficiency of 30 provinces in China from 2003 to 2019. The study found that China’s water resource utilization efficiency showed obvious provincial differences. The water resource utilization efficiency of most eastern coastal provinces was relatively high, and that of most central and western inland provinces was not high. There are also significant differences among the three regions of the East, the middle, and the West. The utilization efficiency of water resources in the East is the highest, followed by the middle, and the West is the lowest. The redundancy of input factors, such as labor, capital, and water consumption, is the main reason for the low efficiency of water resource utilization, and the redundancy of wastewater discharge also affects the efficiency of water resource utilization. The clustering results show that the utilization efficiency of water resources in most provinces of China is located in medium efficiency area and low efficiency area, and the efficiency needs to be improved. There are relatively few provinces in high-efficiency areas, highlighting that China’s water resource utilization still faces severe challenges.