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With the development of the industrial internet of things, an increasing number of intelligent terminal devices are being deployed in mining operations. However, due to the surge in network traffic and the limited availability of computational resources, these terminal devices face challenges in meeting high-performance requirements such as low transmission latency and low energy consumption. To address this issue, this paper proposes a method that combines partial offloading with collaborative mobile edge computing (MEC). This approach leverages device-to-device communication to partition computational tasks into multiple subtasks, offloading some of them to collaborative devices or MEC servers for execution. This not only alleviates the computational burden on MEC servers but also makes full use of the idle computing resources of terminal devices, thereby enhancing resource utilization efficiency. Given the limited computational capacity of terminal devices, this paper optimizes the offloading decision-making process between terminal devices and MEC servers. By introducing weighted coefficients for latency and energy consumption, the proposed method ensures that task completion latency does not exceed a predefined threshold while minimizing the overall system cost. The problem is formulated as a multi-objective optimization problem, which is solved using a two-layer alternating optimization framework. In the upper layer, an improved genetic algorithm (IGA) based on heuristic rules is employed to generate an offloading decision population, while the lower layer utilizes the deep deterministic policy gradient (DDPG) algorithm to optimize the offloading strategy and the weighted coefficients for latency and energy consumption. To evaluate the effectiveness of the proposed method, we compare it with five baseline algorithms: the improved grey wolf optimizer metaheuristic algorithm, the traditional genetic algorithm, the binary offloading decision mechanism, the partial non-cooperative mechanism, and the fully local execution mechanism. Simulation results demonstrate that the proposed IGA-DDPG algorithm achieves significant improvements over these baseline methods. Specifically, under various experimental scenarios, IGA-DDPG reduces latency by an average of 24.5%, decreases energy consumption by 26.3%, and lowers overall system cost by 44.6%. Moreover, the algorithm consistently ensures a 100% task completion rate under different system configurations.

The increasing reliance on satellite constellations for global connectivity, particularly in remote and underserved areas, poses significant challenges in task scheduling within these networks. As satellite systems become integral components of edge computing architectures, optimizing task allocation and timing becomes crucial to ensure efficient operation and service delivery. In response to these challenges, this article introduces an innovative approach to task scheduling within satellite constellations, which are increasingly functioning as edge computing platforms. By leveraging Inter-Satellite Links and focusing on the intricate dependencies among satellite tasks, we have developed the SA-DCoSA algorithm, specifically tailored to address the unique timing constraints inherent in satellite applications. The effectiveness of this algorithm is highlighted by substantial improvements in application on-time completion rates and reductions in overall task completion times, as validated through rigorous simulations using real Iridium constellation Two-Line Element set data. This work not only advances the operational efficiency of satellite networks but also enhances their capacity to meet the growing demands of global connectivity.

With the improvement in communication network density, inter-cell interference has become severe. Due to the blurring of boundaries, cell-free networks are considered as a solution. However, it faces some challenges, such as high energy consumption due to the deployment of a large number of base stations, and security issues in complex communication scenarios. To tackle these issues, we propose a novel modeling scheme involving symbol-level precoding and reconfigurable intelligent surfaces (RIS). This solution can reduce the base station transmission power while ensuring communication layer security. Then, we decompose the non-convex problem of modeling into two sub-problems and solve them iteratively. The first sub-problem is to design symbol-level precoding which can be realized by an efficient gradient descent algorithm. The second sub-problem is about solving the reflection coefficients of RIS, which can be obtained by a Riemann conjugate gradient algorithm. In simulations, our proposed method outperforms benchmark methods. While ensuring physical layer security, the power consumption of cell-free networks has been reduced by 6 dBm.

With the development of 5 G communication and Internet of Things (IoT) technology, increasing data is generated by a large number of IoT devices at edge networks. Therefore, increasing need for distributed Data Centers (DCs) are seen from enterprises and building elastic applications upon DCs deployed over decentralized edge infrastructures is becoming popular. Nevertheless, it remains a great difficulty to effectively schedule computational tasks to appropriate DCs at the edge end with low energy consumption and satisfactory user-perceived Quality of Service. It is especially true when DCs deployed over an edge environment, which can be highly inhomogeneous in terms of resource configurations and computing capabilities. To this end, we develop an edge task scheduling method by synthesizing a M/G/1/PR queuing model for characterizing the workload distribution and a Deep Deterministic Policy Gradient algorithm for yielding high-quality schedules with low energy cost. We conduct extensive numerical analysis as well and show that our proposed method outperforms state-of-the-art methods in terms of average task response time and energy consumption.

In the steelmaking process, ensuring stable and reliable furnace plays a vital role for guaranteeing production quality of steel products. Traditional methods for detecting furnace anomalies in blast furnaces rely on operator judgment models built upon expert knowledge that can be limited by human experience. Moreover, data generated in blast furnace ironmaking process can be multidimensional, non-Gaussian distributed, and periodical, which can be easily affected by environmental and human factors and thus resulting in low accuracy of anomaly detection. Therefore, an online intelligent framework for detecting furnace anomalies is in high need. In this paper, the authors propose a novel anomaly detection method based on a furnace condition parameter-characterization model, a mining model of periodic patterns in the ironmaking process, and a multi-domain adaptive anomaly detection algorithm. They conduct extensive numerical analysis based on real-world production datasets as well to evaluate the effectiveness and accuracy of the method.

As computer image processing and digital technologies advance, creating an efficient method for classifying sports images is crucial for the rapid retrieval and management of large image datasets. Traditional manual methods for classifying sports images are impractical for large-scale data and often inaccurate when distinguishing similar images. This paper introduces an SE module that adaptively adjusts the weights of input feature mapping channels, and a Res module that excels in deep feature extraction, preventing gradient vanishing, multi-scale processing, and enhancing generalization in image recognition. Through extensive experimentation on network structure adjustments, the SE-RES-CNN neural network model is applied to sports image classification. The model is trained on a sports image classification dataset from Kaggle, alongside VGG-16 and ResNet50 models. Training results show that the proposed SE-RES-CNN model improves classification accuracy by approximately 5% compared to VGG-16 and ResNet50 models. Testing revealed that the SE-RES-CNN model classifies 100 out of 500 sports images in 6 s, achieving an accuracy rate of up to 98% and a single prediction time of 0.012 s. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency.

Tropospheric delay is the main error source that affects the further improvement of the accuracy of space geodesy. High-precision zenith tropospheric delay (ZTD) can be used as a prior value for precise point positioning (PPP) in global navigation satellite systems (GNSSs) to enhance the speed and accuracy of real-time PPP solutions. Using the Saastamoinen ZTD model, we computed ZTDs using different meteorological elements. One ZTD was termed MZTD and was obtained from 80 reference sites in the China Mainland Crustal Movement Observation Network (CMONOC), the other was termed HZTD and was obtained from elements acquired from the improved version of the hourly global pressure and temperature atmospheric model (HGPT2). The results indicate that the accuracy of the MZTD was 12.94% higher than that of the HZTD, with the ZTDs estimated by post-processing GNSS values as the reference values. Additionally, the MZTD and HZTD were both applied as constraints to the PPP solution. The application of the MZTD constraints to the PPP floating-point solution resulted in a 28.9% improvement in accuracy and a 36.4% decrease in convergence time in the U-direction as a maximum, compared with the application of the HZTD constraints.

The rotor temperature monitoring is crucial for reliability and performance of permanent magnet synchronous motor. Overheating is the main reason for motor demagnetization, which may lead to power output interruption. However, direct measurement of rotor temperature is difficult to be widely applied in electric drive products due to expensive equipment and complicated integration. This paper introduces an innovative approach for estimating rotor temperature of permanent magnet synchronous motor based on BP neural network with big data-driven. The architecture of BP neural network is constructed by adopting Bayesian optimization theory, meanwhile related control parameters of rotor temperature estimation are picked out by Pearson correlation analysis. And then, BP neural network is trained forward and backward by using sufficient experimental data to obtain optimized weights and biases. Finally, wireless temperature measurement equipment is integrated into electric drive system on whole vehicle. Experimental results show that the maximal error is within 6.2°C under different operation environment and road conditions. The proposed method in this paper can meet requirements of rotor temperature estimation precision without extra cost and be extend to other motor application fields.

Many ribosomal proteins are highly expressed in tumors and are closely related to their diagnosis, prognosis and pathological characteristics. However, few studies are available on the correlation between ribosomal proteins and chemoresistance. RRS1 (human regulator of ribosome synthesis 1), a critical nuclear protein involved in ribosome biogenesis, also plays a key role in the genesis and development of breast cancer by protecting cancer cells from apoptosis. Given that apoptosis resistance is one of the causes of the cisplatin resistance of tumor cells, our aim was to determine the relationship between RRS1 and cisplatin resistance in breast cancer cells. Here, we report that RRS1 is associated with cisplatin resistance in breast cancer cells. RRS1 silencing increased the sensitivity of MCF-7/DDP cells to cisplatin and inhibited cancer cell proliferation by blocking cell cycle distribution and enhancing apoptosis. AEG-1 (astrocyte elevated gene-1) promotes drug resistance by interfering with the ubiquitination and proteasomal degradation of MDR1 (multidrug resistance gene 1), thereby enhancing drug efflux. We found that RRS1 binds to and stabilizes AEG-1 by inhibiting ubiquitination and subsequent proteasomal degradation, which then promotes drug efflux by upregulating MDR1. Furthermore, RRS1 also induces apoptosis resistance in breast cancer cells through the ERK/Bcl-2/BAX signaling pathway. Our study is the first to show that RRS1 sensitizes breast cancer cells to cisplatin by binding to AEG-1, and it provides a theoretical basis to improve the efficacy of cisplatin-based chemotherapy.

Depicting Secondary Eyewall Formation (SEF) and Eyewall Replacement Cycle (ERC) in a numerical model is important for tropical cyclone (TC) forecasting. However, there is no consensus about what resolutions are appropriate to describe SEF/ERC within a full‐physics mesoscale model. In this study, numerical experiments are conducted to examine the impact of the horizontal and vertical resolutions on SEF/ERC. The mesoscale model is configured through nesting to the horizontal grid spacings of 6, 4, 2, 1.33, 0.67‐km, and with 27‐ and 54‐levels on an f‐plane in a quiescent environment. In addition, there are more levels below 1.5‐km to better describe the TC boundary layer (TCBL). The simulations with 6 and 4‐km grid spacings show no obvious SEF/ERC regardless of the number of vertical levels. When the horizontal grid spacings decrease to 2‐km or smaller, the simulations manifest SEF/ERC. These results are supported by a few simulations with the ARW model using similar configurations. Furthermore, the spectra of kinetic energy and vertical velocity from various resolutions confirm that the grid spacings should be smaller than 4‐km to resolve SEF/ERC. The impact of doubling vertical levels on the SEF/ERC is not as significant as doubling the horizontal resolutions. Finally, we discuss the coupling between the balanced/unbalanced flows (above/in the TCBL), and their effect on SEF. It is proposed that the coupled balanced/unbalanced processes that generate the quasi‐steady cooling zone in the primary eyewall and two warming regions inside and beyond the cooling zone are essential for SEF. Plain Language Summary Secondary Eyewall Formation (SEF) and Eyewall Replacement Cycles (ERC) are common phenomena in a mature tropical cyclone. A SEF is one in which a new eyewall develops beyond the old eyewall. An ERC is that the new eyewall gradually decreases in diameter and replaces the old eyewall. SEF/ERC often produce an oscillation of the TC's maximum intensity, while also serving as a mechanism for storm growth, that is, increasing the radius of gale‐force winds. An expansion of gale‐force winds near landfall can impact a larger coastal area while also reducing preparation time. Horizontal and vertical resolutions of the computer model are integral issues in TC simulation. In this study, we conduct numerical simulations to examine the impact of the model resolutions on SEF/ERC. The simulations with 6 and 4‐km grid spacings show no obvious SEF/ERC. When horizontal grid spacings decrease to 2‐km, the simulations show SEF/ERC. As grid spacings decrease to 1.33 and 0.67‐km, the SEF/ERC are more remarkable. The sensitivity tests confirm that a cooling (or less warming) zone appears near the primary eyewall, and two warming regions inside and beyond the cooling zone appear before SEF. Furthermore, the “warming‐cooling‐warming” structure can only be better resolved with 2‐km or smaller grid spacings. Key Points Secondary eyewall formation (SEF) and eyewall replacement cycle (ERC) can significantly change the intensity and structure of the tropical cyclone The grid spacings should be at least 2‐km to resolve SEF and ERC The dynamical processes which generate the quasi‐steady cooling zone in the primary eyewall is essential for SEF