沈阳职业技术学院

A scientific and accurate assessment of soil heavy metal risks is fundamental to developing future control measures. This study developed an integrated probabilistic risk assessment model for soil heavy metals by combining source apportionment, Monte Carlo simulation, the potential ecological risk index, and health risk assessment models. This hybrid framework enabled a refined and source-oriented risk evaluation in a concentrated agate industrial area. The results showed that the soil heavy metals in the agate industry concentration area were primarily derived from industrial sources of agate processing (30.21

Tracking the dynamic behavior of electrons produced by the decay of radioactive nuclides is an important and challenging issue. The time-dependent property of laser fields can be used to track the collision and exchange processes between emitted electrons (generated by 90Sr decay) and electrons in the plasma. It is found that the emitted electrons can contribute to the plasma’s stability. That is to say the emitted electrons can not only serve as supplements to enter the plasma to compensate for the loss of ionized electrons caused by collisions and recombination, but also provide energy to the plasma through collisions with positive ions. This article not only provides the conditions for high-frequency collisions to occur, but also, by analyzing the distribution of collision probability, gives the time when the maximum collision probability occurs, as well as the exact numerical value of plasma energy in a very short period of rapid increase.

Using ambient cold air for seasonal ice storage provides a low-carbon cooling strategy. Most existing seasonal ice storage systems rely on electrically driven heat exchangers or passive heat pipes for charging, whereas fully passive charging based on ambient air has been demonstrated mainly for single-cylinder systems. However, scaling to multi-cylinder arrays may substantially alter the freezing behaviors due to airflow redistribution and thermal coupling, and the charging mechanisms at the array system remain resolved. To address this challenge, a seasonal multi-cylinder array ice storage system is proposed, which enables fully passive charging through direct utilization of ambient cold air. An experimental platform and a three-dimensional unsteady CFD model are established to investigate the dynamic freezing behavior of the array. A normalized solidification index is introduced to quantify instantaneous natural convection phase-change intensity, and entropy-generation analysis is employed to characterize thermodynamic irreversibility. Cold energy losses during the storage period are further quantified for different storage volumes. The numerical model was validated against experimental measurements with a root mean square error of 0.72 K. Results show that complete charging required 12,622 s, while the fastest cylinder reached full solidification in 7137 s. As the freezing process transitioned from convection-dominated to conduction-limited regimes, the system-wide entropy-generation rate continuously declined, consistent with the temporal evolution of R(t). Importantly, inlet proximity did not guarantee higher freezing intensity. Instead, flow redistribution and coupled irreversibility effects reduced the effective heattransfer coefficient of the inlet-adjacent cylinder, leading to non-intuitive freezing sequences at the array scale. Compared with the baseline configuration, the optimized array improved charging efficiency by 10.82%, increased natural convection intensity during freezing by up to 67.1%, reduced entropy generation by 9.59%, and lowered the Gini coefficient from 0.08 to 0.07. This work reveals an array-scale freezing mechanism fundamentally different from single-cylinder behavior, thereby enabling scalable fully passive seasonal ice storage.

Starting from the core connotation of ideological and political education in colleges and universities in the new era, this paper defines relevant concepts, reviews the theoretical basis, and analyzes the current development status and prominent challenges in this field. In the context of digital campuses, online platforms, and wireless information-propagation environments, ideological and political education faces new requirements in value guidance, communication carriers, and feedback mechanisms. This study constructs a four-dimensional education logic system composed of value, subject, content, and method, and explains three operating mechanisms: dynamic drive, transmission, and feedback. In view of problems such as insufficient pertinence, weak coordination, and lagging method innovation, five practical paths are proposed: strengthening theoretical arming, innovating education carriers, building a collaborative system, improving teacher development, and enhancing institutional guarantees. The research aims to deepen the integration between educational logic and practical paths, providing theoretical scaffolding and operational guidance for improving educational effectiveness and supporting moral cultivation in digitally networked higher-education environments.

Objectives: Traditional quality inspection methods in intelligent manufacturing often suffer from low efficiency, limited accuracy, and poor generalization to real manufacturing environments. This study aims to develop an intelligent quality inspection method with high accuracy and robust generalization capability. Methods: A deep learning model that integrates Convolutional Neural Networks (CNNs) with selfsupervised learning is proposed. CNNs are used to extract key visual features from manufacturing processes. Self-supervised learning is applied for pretraining on unlabeled data to capture latent process representations. In addition, a multi-layer architecture is designed to jointly model spatial and temporal features. An ensemble learning strategy is further introduced to fuse multimodal information and enhance overall detection performance. Results: Experiments were conducted on three quality inspection tasks: printed circuit board solder joints, ceramic substrates, and flexible packaging. The proposed method achieved detection accuracies of 98.1%, 97.3%, and 95.8%, respectively. At a mean average precision (mAP) of 0.5, the convergence speed improved by an average of 33.6%. The inference latency on the same hardware platform was approximately 22 ms. After INT8 quantization, the performance retention reached 98.9%. Discussion: The results indicate that self-supervised pretraining and multi-level feature fusion effectively improve feature representation in complex manufacturing scenarios. At the same time, computational cost is reduced without sacrificing detection accuracy. This enhances the practicality of the method in real industrial environments. Conclusions: The proposed quality inspection method combining CNNs and self-supervised learning outperforms ResNet50 and traditional detection algorithms in terms of accuracy, stability, and computational efficiency. It provides a practical and reliable solution for intelligent manufacturing quality inspection.