Jiangsu Vocational Institute of Commerce

To find the optimal path for distribution, the experiment proposes a low-carbon logistics and distribution path optimisation method for smart cities based on the internet of things (IoT) technology and ant colony genetic hybrid algorithm. On dataset A, when iterating up to 62 times, the average running time of this method (5.54 s) is less than other algorithms. When the full load factor was only 30%, the research method started to get the minimum total cost. The overall cost did not increase noticeably with the tax when the carbon tax was between 0 and 100 yuan/ton. It indicated that the businesses in this range found the carbon tax to be acceptable. The research technique yielded a shorter optimal distribution path distance of 18.69 kilometres, as demonstrated by the application comparison. These results can provide a new theoretical basis for the low-carbon logistics and distribution in the smart city.

Abstract Power transformer fault diagnosis is crucial for ensuring the reliability and safety of power systems. However, challenges such as feature redundancy in dissolved gas analysis data and the sensitivity of support vector machines (SVM) to hyperparameter selection significantly affect diagnostic accuracy. To address these issues, this paper proposes a novel MIC-INGO-SVM model. First, the Maximal Information Coefficient (MIC) is employed to evaluate nonlinear correlations between features and fault types, enabling effective dimensionality reduction by retaining only the most informative gas components. Second, an Improved Northern Goshawk Optimization (INGO) algorithm is developed to optimize SVM hyperparameters. Derived from the original Northern Goshawk Optimization (NGO), the INGO incorporates three key improvements: best-point-set initialization, stochastic reverse learning, and an adaptive Gauss-Cauchy hybrid mutation strategy. These strategies significantly enhance the algorithm’s convergence speed and global search capability. The optimized SVM model is then applied for fault classification. Experimental results on a real-world dataset demonstrate that the proposed method achieves a diagnosis accuracy of 0.9467, outperforming several state-of-the-art approaches. An ablation study further validates the effectiveness of both MIC-based feature selection and INGO-based parameter optimization.

Path selection in tourism route planning involves optimizing travel routes to maximize tourist satisfaction and minimize travel time, cost, or other constraints. This task can be complex due to factors like visitor preferences, attraction availability, and travel schedules. Tourism planners employ algorithms, such as Genetic Algorithms (GA) or probability-based approaches, to identify efficient routes by analyzing large datasets. These algorithms evaluate potential paths based on criteria like distance, attraction variety, and user satisfaction, often adapting based on real-time data and user feedback to ensure optimal results. Dynamic programming and probabilistic models can further enhance path selection by consideSring changing conditions and transitional probabilities between destinations, providing tourists with tailored, flexible routes that meet their preferences while adhering to practical constraints. This paper investigates the application of Weighted Ranking Ant Colony Optimization (WRACO) in tourism route planning, aiming to enhance travel experiences by efficiently navigating the complexities of tourism landscapes. WRACO integrates a weighted ranking scheme into the Ant Colony Optimization (ACO) framework, biasing ant decision-making towards more attractive paths. Through a comprehensive analysis of simulation results, WRACO demonstrates its efficacy in iteratively refining travel itineraries, minimizing travel distances while ensuring convergence to optimal or near-optimal solutions. Through simulation, WRACO achieves a significant reduction in travel distances, with the best tour length minimized to 200 units over ten iterations. Comparative analysis with other optimization algorithms reveals WRACO's superiority, showcasing a notably low best tour length and high solution quality.

In recent years, network attacks have occurred frequently, and Trojan horses, worms, and ransomware have emerged endlessly on the Internet. This poses a serious threat to cyber security and even national security. Therefore, a new three-branch decision soft increment K-nearest neighbor algorithm is proposed, representing the class cluster as an interval set. The interval set’s upper, boundary, and lower bound correspond to the positive, boundary, and negative domains generated by the three-branch decision. An initial K-nearest neighbor algorithm based on representative points is proposed. The new datasets are pre-clustered in the same way to eliminate the influence of data processing order on the final clustering results. The results showed that the improved K-nearest algorithm could accurately classify Normal, Dos, and Probing, and the classification accuracy was above 85%. The accuracy of U2R was lower, with an average of 57.32%. The proposed method had the highest classification accuracy on the dataset, and the detection rate of all types of attacks reached more than 98%. This proposal is of great significance for the protection of network security.

Abstract The local stability of a circus truss antenna on the condition of thermal excitation is investigated. Based on the derived four‐dimensional averaged equation, we consider two kinds of eigenvalues of the bifurcation response equation in the case of 1:2 internal resonance. For each eigenvalue, the stable conditions and the numerical simulations are all obtained, which prove the correctness of theoretical analysis. The amplitude of thermal excitation plays an important role in stability analysis.

Given that lactoferrin (LF) exerts an excellent protection of intestinal homeostasis, the underlying mechanisms, especially epigenetic regulations, are still unknown. This study aimed to investigate the effects of dietary LF epigenetically modulates the oxidative genes by histone modifications to ameliorate ileum inflammation of mice exposed to DON contaminated diet. As expected, we found in the morphology analysis that DON exposure increased ileum crypt depth (CD) and villus width (VW) but reduced villus height (VH) and VH: CD ratio compared to those of the vehicle group. Consistently, the elevated ROS and MDA, along with the decreased ATP, SOD, CAT, GSH, and complex I, III, V were observed in the DON-exposed mice ileum. In contrast, LF markedly ameliorated the impairments of morphological and biochemical indexes. Next, we conducted transcriptome analysis to explore the changed signaling pathways using the ileum RNA of the mice treated with DON or LF. Firstly, the cell cycle pathway genes were significantly downregulated in the DON-exposed mice, and LF improved the cell cycle profile. Again, gene ontology analysis showed that inflammation and oxidative stress were significantly activated by DON exposure, and these were recovered when the DON-exposed mice were supplemented with an LF diet. Consistent with these findings, the signaling pathways of the reduced oxidative phosphorylation and elevated TNFα were also observed to be ameliorated by LF treatment. Importantly, histone modifications, including acetylation, methylation, and lactylation were suggested to be the vital players involved in the DON or LF treatment, in which LF significantly increased the loss of histone modifications on these genes. With a bioinformatics analysis and validation by qRT-PCR, the nuclear receptor NR5A2 was selected as a key master in the ileum of mice stimulated by DON. LF performed the benefit function on the NR5A2-mediated oxidative stress genes Ncoa4 and Prdx3 in the DON-exposed mice. Moreover, a ChIP-qPCR was used to verify that histone marks involving H3K9ac, H3K18ac, H3k27ac, H3K4me1, H3K9la, and H3K18la facilitated the epigenetic regulation of NR5A2-modulated actions. We conclude that dietary LF effectively ameliorated ileum lesions induced by DON in mice by modulating oxidative genes Ncoa4 and Prdx3 through histone modifications.

Transformer fault diagnosis is crucial for ensuring the safe and stable operation of power systems. However, traditional diagnostic methods suffer from issues such as insufficient feature utilization and poor model interpretability. To address these challenges, this paper proposes a hybrid fault diagnosis model. First, the model utilizes SHapley Additive exPlanations values for feature selection. Then, the improved Harris Hawks optimization is proposed to optimize the hyperparameters of eXtreme gradient boosting model (Xgboost), further improving the model’s classification performance. Experimental results demonstrate that the proposed model outperforms existing methods across multiple performance indicators, achieving an accuracy of 0.9509, which represents a 2.68% improvement compared to the Xgboost model. This model fully leverages the advantages of each method, effectively solving problems like insufficient feature utilization, poor model interpretability, and limited performance of optimization algorithms in traditional fault diagnosis methods. It provides a more reliable and efficient solution for transformer fault diagnosis.

ABSTRACT The impact of digital transformation on export product quality is a critical issue in the context of the rapidly advancing global digital economy. Although prior studies have examined the relationship between digitalization and firm performance, limited attention has been paid to its specific effect on export product quality. Using data from 6184 Chinese manufacturing firms over the period from 2007 to 2015, this study investigates the link between digital transformation and export product quality. We find a U‐shaped relationship where the initial stages of digitalization lead to a decline in quality due to high transformation costs, but as digitalization progresses, firms experience significant improvements in product quality. Our findings highlight two key mechanisms: improved production efficiency and the alleviation of financing constraints. This study underscores the importance of digital transformation in enhancing international competitiveness, with particularly strong effects in foreign‐owned firms, capital‐intensive industries, and coastal regions.