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Coke production is an important source of environmental polycyclic aromatic compounds (PACs), including parent polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The focus near coking plants has primarily been on parent-PAH contamination, with less attention given to highly toxic derivatives. In this study, soil samples were collected from both within and outside of a coking plant. The concentrations of parent-PAHs and their derivatives, including methylated-PAHs, oxygenated-PAHs, and nitrated-PAHs, were examined. Spatial interpolation was employed to determine their spatial distribution patterns. Methods for identifying potential sources and conducting incremental lifetime cancer risk analysis were used. This could achieve a comprehensive understanding of the status of PAC pollution and the associated health risks caused by coke production. The concentrations of total PACs inside the plant ranged from 7.4 to 115.8 mg/kg, higher than those outside (in the range of 0.2 to 65.7 mg/kg). The spatial distribution of parent-PAH concentration and their derivatives consistently decreased with increasing distance from the plant. A significant positive correlation (p < 0.05) among parent-PAHs and their derivatives was observed, indicating relatively consistent sources. Based on diagnostic ratios, the potential emission sources of soil PACs could be attributed to coal combustion and vehicle emissions, while principal component analysis–multiple linear regression further indicated that primary emissions and secondary formation jointly influenced the PAC content, accounting for 60.4% and 39.6%, respectively. The exposure risk of soil PACs was dominated by 16 priority control PAHs; the non-priority PAHs’ contribution to the exposure risk was only 6.4%.

In today’s business environment, reducing costs is crucial due to the variety of Internet of Things (IoT) devices and security infrastructure. However, applying security measures to complex business scenarios can lead to performance degradation, making it a challenging task. To overcome this problem, we propose a novel algorithm based on deep reinforcement learning (DRL) for optimizing cost in multi-party computation software-defined security middle platforms (MPC-SDSmp) in real-time. To accomplish this, we first integrate fragmented security requirements and infrastructure into the MPC-SDSmp cloud model with privacy protection capabilities to reduce deployment costs. By leveraging the power of DRL and cloud computing technology, we enhance the real-time matching and dynamic adaptation capabilities of the security middle platform (Smp). This enables us to generate a real-time scheduling strategy for Smp resources that meet low-cost goals to reduce operating costs. Our experimental results demonstrate that the proposed method not only reduces the costs by 13.6% but also ensures load balancing, improves the quality-of-service (QoS) satisfaction by 18.7%, and reduces the average response time by 34.2%. Moreover, our solution is highly robust and better suited for real-time environments compared to the existing methods.

How to generate the path planning of mobile robots quickly is a problem in the field of robotics. The Q-learning(QL) algorithm has recently become increasingly used in the field of mobile robot path planning. However, its selection policy is blind in most cases in the early search process, which slows down the convergence of optimal solutions, especially in a complex environment. Therefore, in this paper, we propose a continuous local search Q-Learning (CLSQL) algorithm to solve these problems and ensure the quality of the planned path. First, the global environment is gradually divided into independent local environments. Then, the intermediate points are searched in each local environment with prior knowledge. After that, the search between each intermediate point is realized to reach the destination point. At last, by comparing other RL-based algorithms, the proposed method improves the convergence speed and computation time while ensuring the optimal path.

In recent years, various types of power theft incidents have occurred frequently, and the training of the power-stealing detection model is susceptible to the influence of the imbalanced data set and the data noise, which leads to errors in power-stealing detection. Therefore, a power-stealing detection model is proposed, which is based on Improved Conditional Generation Adversarial Network (CWGAN), Stacked Convolution Noise Reduction Autoencoder (SCDAE) and Lightweight Gradient Boosting Decision Machine (LightGBM). The model performs Generation- Adversarial operations on the original unbalanced power consumption data to achieve the balance of electricity data, and avoids the interference of the imbalanced data set on classifier training. In addition, the convolution method is used to stack the noise reduction auto-encoder to achieve dimension reduction of power consumption data, extract data features and reduce the impact of random noise. Finally, LightGBM is used for power theft detection. The experiments show that CWGAN can effectively balance the distribution of power consumption data. Comparing the detection indicators of the power-stealing model with various advanced power-stealing models on the same data set, it is finally proved that the proposed model is superior to other models in the detection of power stealing.

Neural networks are very vulnerable to adversarial examples, which threaten their application in security systems, such as face recognition, and autopilot. In response to this problem, we propose a new defensive strategy. In our strategy, we propose a new deep denoising neural network, which is called UDDN, to remove the noise on adversarial samples. The standard denoiser suffers from the amplification effect, in which the small residual adversarial noise gradually increases and leads to misclassification. The proposed denoiser overcomes this problem by using a special loss function, which is defined as the difference between the model outputs activated by the original image and denoised image. At the same time, we propose a new model training algorithm based on knowledge transfer, which can resist slight image disturbance and make the model generalize better around the training samples. Our proposed defensive strategy is robust against both white-box or black-box attacks. Meanwhile, the strategy is applicable to any deep neural network-based model. In the experiment, we apply the defensive strategy to a face recognition model. The experimental results show that our algorithm can effectively resist adversarial attacks and improve the accuracy of the model.

In power cloud environment, the existing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) access control schemes, do not consider the generation of access structure and the existence of malicious users. To tackle these problems, a power cloud access control (PCAC) scheme is proposed, which improves the traditional CP-ABE access control model. Considering the heavy time consumption of CP-ABE, PCAC encrypts the symmetric key, instead of the raw data. PCAC combines the access tree and linear secret-sharing scheme (LSSS) to achieve the automatic generation and efficient operation of access structure. Additionally, an action audit phase, based on zero-knowledge verification was designed to defend against malicious users. The experiments proved that PCAC meets the requirement of fine-grained access control, in a power cloud. Compared with existing CP-ABE schemes, the PCAC scheme reduced about half of the time consumption, in the action audit phase and costs about one-third the time, in the data obtainment stage.

Vehicle-to-grid (V2G) is an important component of smart grids and plays a significant role in improving grid stability, reducing energy consumption and generating cost. However, while electric vehicles are being charged, it is possible to expose the location and movement trajectories of the electric vehicles, thereby triggering a series of privacy and security issues. In response to this problem, we propose a new quadtree-based spatial decomposition algorithm to protect the location privacy of electric vehicles. First of all, we use a random sampling algorithm, which is based on differential privacy, to obtain enough spatial data to achieve the balance between large-scale spatial data and the amount of noise. Secondly, in order to overcome the shortcomings of using tree height to control Laplacian noise in the quadtree, we use sparse vector technology to control the noise added to the tree nodes. Finally, according to the vehicle-to-grid network structure in the smart grid, we propose a location privacy protection model based on distributed differential privacy technology for EVs in vehicle-to-grid networks. We demonstrate application of the proposed model in real spatial data and show that it can achieve the best effect on the security of the algorithm and the availability of data.

The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

Nowadays, the challenges of energy depletion, environmental pollution and food security caused by extensive agriculture development are attracting global attention. In China, the construction of ecological farms is a key initiative to effectuate the goal of peaking carbon dioxide emissions and achieving carbon neutrality, contributing to high-quality agricultural development. Based on this, this study selects the national-level ecological farms directories issued by the Ministry of Agriculture and Rural Affairs (MARA) of China in 2021 and 2022, and collects the corresponding economic, social and physical geographic data for GIS spatial analysis and Geodetector. The results are as follows: (1) The distribution of ecological farms in various provinces of China is uneven and spatially clustered. It generally presents a ‘high in the east and low in the west with concentrated cores’ pattern. The construction scope significantly expanded over time, and the high-value areas of nuclear density are concentrated in East China, with the development core transitioned from East China to Central China. (2) Environmental conditions, industrial foundation, economic and social development level, science and technology level and financial support all significantly affect the spatial distribution of ecological farms in China, among which the science and technology level has the most significant enhancement effect on other factors. (3) Environmental conditions provide the construction basis for ecological farms, while economic and social development level and financial support determine the number of ecological farms. The industrial foundation affects the scale of ecological farms in China, while the level of science and technology eliminates the restrictions of other factors to a certain extent. This study provides a reference for optimizing the spatial distribution pattern of ecological farms in China and promoting ecological agriculture. In addition, it presents a viable approach to safeguarding food security.

Industrialization and urbanization have led to continuous urban development. The rapid change in land-use type and extent has a significant impact on the capacity of ecosystem services. Changes in the landscape pattern of roads, rivers, railway stations, and expressway entrances and exits have evident geographical proximity effects. We used landscape pattern indices and ecosystem service value (ESV) to evaluate the landscape pattern and ESV spatial differentiation of the Pearl River Delta region and its typical transportation infrastructure and rivers in 1990, 2000, and 2017. The results show that rapid urbanization and industrialization have led to changes in urban land use along the Pearl River Estuary. Urban land changes on the east bank of the Pearl River are greater than urban land changes on the west bank of the Pearl River; the landscape diversity of the Pearl River Delta has increased, the connectivity of the landscape has decreased, and the degree of fragmentation has increased. Second, the city size of the Pearl River Delta was negatively correlated with the ESVs. The ESVs in the core areas of the Pearl River Delta urban agglomeration were smaller than those in the fringe areas. With the gradient change in urban land use, ESVs showed a growing trend from the city center to the surrounding areas. The key areas for ecological protection and restoration should be central urban areas and suburbs. Third, the siphoning effect of the buffer zones of railway stations and expressway entrances and exits was very strong and drove the development and utilization of the surrounding land. As the degree of land development in the buffer zone decreased, the ESVs increased. Fourth, different grades of roads in the Pearl River Delta had different impacts on the regional landscape and ESVs. County roads had a greater interference effect than expressways, national roads, and provincial roads, and the riverside plains of the Pearl River Delta have a large development space, low urban development costs, and multiple land-use activities that have profoundly changed the landscape of the river buffer zone.

Analysis of the change of overall energy efficiency performance of an energy system is a fundamental work for the energy-saving policymaking. However, previous studies seldom focus on energy stages from useful energy to final service, while most attention are paid on stages from energy source to useful energy. In this paper, we develop a high-resolution the Societal Exergy Analysis and Logarithmic Mean Divisia Index (SEA-LMDI) method to analysis changes and driving factors of the aggregate exergy efficiency, in which the boundary of the SEA is extended to passive systems and final services, and a LMDI decomposition method is developed to quantify contributions of efficiency factors and structure factors of all six stages on the aggregate exergy efficiency. A case study of China during 2005–2015 reveals that: (a) the aggregate exergy efficiency from energy source to final service is only from 3.7% to 4.8% during 2005–2015, showing a huge theoretical potential of efficiency improvement. (b) Large passive losses are identified in passive systems and nearly 2/3 of useful energy can be theoretically saved by improving passive systems. (c) Deep analysis of industrial coal-fired boilers indicates that the internal structural adjustments are also important for the aggregate improvement.