This study introduces a new methodology for the configuration and optimization of algorithm-driven strategies in the digital economy. It puts forward a hybrid optimization algorithm for the efficient handling of complex resource allocation problems. The proposed approach combines adaptive learning mechanisms with traditional optimization methods, showing significant improvement in convergence speed, solution accuracy, and stability of the system. Through extensive experimental validation conducted on a range of benchmark functions and real-world contexts, this algorithm proves to be outstanding at a 48.7% reduction in convergence time, as well as a solution quality enhancement by 66.4% compared with the traditional methods. Robustness analysis confirms consistent effectiveness under all diverse noise conditions and retains high success rates, even in demanding environments. This result greatly contributes to advancing algorithmic optimization approaches for digital economic systems and paves the way toward concrete applicative implementations.

With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes. Personalized learning has become a key method to enhance teaching quality and learning outcomes. This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students. This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN). The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features. Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information. Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information. These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement. Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations. Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.

The rapid development of digital technology and artificial intelligence has made the improvement and optimization of intelligent warehousing and automated distribution systems important topics for research in modern logistics management. With this as the background, the current study uses a systematic approach to explore critical factors, innovative ways, and implementation strategies related to these factors and their role in improving the effectiveness of intelligent warehousing systems. The study adopts a mixed-methodological approach, establishing a comprehensive evaluation index system including operational efficiency, technical performance, and economic benefits, and simultaneously verifying the implementation of the system through empirical analysis. According to the findings, the intelligent warehousing system increased the efficiency of operations in relation to order processing time and had reduced it by 71.7%, and enhanced the accuracy of picking to 99.8%. The intelligent warehouse system by use of machine learning and meta-heuristic algorithms had greatly improved the efficiency in resources utilization and energy as storage utilization increased by 19.3% while energy consumption dropped by 31.4%. A cost-benefit analysis shows that, despite the significant up-front financial investment, the system achieved a 186% return on investment over three years. This research deepens the theoretical understanding of intelligent warehousing and, at the same time, provides optimization strategies applicable to industry practice. Future research directions should focus on exploring the applications of multi-agent digital twin technology and researching how intelligent warehousing systems contribute to supply chain resilience and sustainability.

Power metering system is directly related to the production and operation level and benefit of power supply enterprises, and even has a close relationship with the national economic development and people’s life. Numerous scholars have applied deep learning to the field of fault diagnosis. Accordingly, this paper proposes a fault diagnosis method for power metering system based on stacked autoencoder (SAE) algorithm. The deep learning data samples are formed by comprehensively collecting the historical operation data of the system and the feature data provided by the third-party manufacturers. And the fault diagnosis model is designed with the SAE algorithm, and the training and optimization fine-tuning of the algorithm model is realized by BP neural network. Finally, the model is trained using explicit test data samples, and the BP neural network can reach the set accuracy after 3804 training sessions with the output error. Compared to Elman neural network iterations are less and converge faster. Using the trained fault detection model of power metering system for fault diagnosis, the model can successfully classify the faults and achieve the expected diagnostic effect.

This paper presents an AdaBoost-DNN (Adaptive Boosting-Deep Neural Network) model for the detection of anomalous electricity consumption in power grid users. Initially, the k-means SMOTE (Synthetic Minority Oversampling Technique) technique is employed to enhance the sample set of the original anomalous consumption data to address the issue of data imbalance. Subsequently, an ensemble learning model based on AdaBoost-DNN is designed for the detection of anomalous consumption. To validate the effectiveness and superiority of the proposed AdaBoost-DNN model, comparative experiments are conducted with three traditional algorithms.

This study aims to construct a corporate demand model of brand design for financial central enterprises based on grounded theory, providing a systematic theoretical framework to help financial central enterprises effectively meet their brand design needs. The method of grounded theory is used to extract relevant information from public information and in-depth interviews, and to demonstrate the rationality of the corporate demand model by combining quantitative research. Through an in-depth analysis of the subsystems of demand motivation generation, demand factor analysis and demand design realisation, a feasible theoretical framework is provided for financial central enterprises to achieve effective satisfaction of brand design corporate demands. The problem of accurately grasping requirements in the practice of brand design for financial central enterprises is solved. Useful reference and support are provided for the brand design of financial central enterprises. The innovative practice of brand design is promoted for financial central enterprises.

This study investigates the application of artificial intelligence techniques in coal mine gas monitoring and prediction, aiming to construct more efficient and accurate gas concentration prediction models to reduce the risk of gas explosion in coal mine production. Due to the limited performance of traditional prediction methods in dealing with high-dimensional and dynamic three-dimensional mining environments, this study employs a fusion model based on temporal convolutional network (TCN) and temporal generative adversarial network (TimeGAN), TCN-TimeGAN, to predict the gas concentration. The model combines the interval sampling advantage of TCN and the time series characteristics of TimeGAN, and through four processes of embedding, recovering, by generating and discriminating gas concentration time sequences, the time-dependent features of gas concentration data can be effectively captured, thus improving the prediction accuracy and timeliness. In this study, gas concentration data from September 2020 through December 2021 were used as the basis, through data cleaning and outlier processing, it is found that the gas concentration data has obvious time-dependence, which is suitable for using time series modeling. Embedding and Recovery Networks via TCN-TimeGAN Modeling, the gas concentration data are mapped to a low-dimensional feature space, a generative network is then used to generate new time series data from random noise, and the model parameters are optimized by combining the discriminative network in order to improve the quality and consistency of the generated data. In particular, to cope with the problem of gradient instability of generative adversarial networks during training, In this paper, Wasserstein distance is introduced to optimize the loss function and a gradient penalty term is added during the training process to improve the stability of model training and the realism in the samples generated. In addition, this study also explores the prediction performance of combining LSTM networks for gas concentration. The standard recurrent neural network (RNN) faces the problem of gradient vanishing in the processing of long time-dependent data, whereas the improved LSTM overcomes this problem through memory cells and gating mechanism for real-time prediction task of gas concentration. In this study, the LSTM is further extended to three-dimensional spatial input data, experiments demonstrate the prediction accuracy of the improved LSTM. To verify the validity of the model, this paper adopts a hierarchical K-fold cross-validation method, which divides the data into a training set and a validation set to ensure that the model can be generalized. Experimental results indicate that TCN-TimeGAN and improved LSTM significantly outperform traditional methods in gas concentration prediction. By analyzing the training and validation accuracies, the models showed high prediction accuracy (89.1% to 93.8%) after 20 epochs, verifying the stability and applicability of the models.
In conclusion, this study shows that the gas prediction model based on TCN-TimeGAN and improved LSTM can more accurately predict the gas concentration in coal mines, improve the intelligence level of coal mine gas monitoring, and provide technical support for safe production in coal mines. Meanwhile, the methods and models in this study also provide new thoughts and methods for time series data prediction in other fields.

This paper presents an innovative optimization framework aimed at data mining in social networks, guaranteeing solutions for some of the basic challenges of computational efficiency, scalability, and accuracy. This work presents a precise approach that integrates state-of-the-art algorithmic enhancements with dynamic resource management techniques. Extensive experimental validation using real and synthetic datasets has marked the significant performance gains achieved within the framework. These results point to a 70.2% reduction in processing time and a 71.2% saving in memory consumption, all while maintaining accuracy rates above 95%. This optimization framework is very stable under different operation conditions, since its responses have always remained below 85 ms under peak loads of up to 245,000 requests per second. The empirical evaluation of the framework across diverse social networking platforms bears testimony to the fact of practical efficacy and has emerged strongly while dealing with dynamic network architecture with extensive data processing needs. The application results in significant improvement in resource utilization efficiency, providing sub-linear increase in memory consumption for maintaining consistent performance under fluctuating load scenarios. The present study extends the scope of social network analysis by proposing a scalable, efficient, and reliable optimization framework that might be of vital importance in both research and practical implementation contexts.

With the unprecedented growth of technological advancement, effective technological transfer has become increasingly important in all dimensions of human lives. Technological transfer is a multi-level and complex ecosystem network with complicated inter-relational elements and effective fac-tors. This complexity raises the question of how to rearrange the elements of the technology transfer to improve its positive performance. To address this issue, this study aims to compare the perfor-mance and gaps of the three modes of technology transfer, which are technology entrepreneurship, technology licensing, and technology shareholding, by evaluating the three participants, which are universities/research institutes, corporations, and intermediary agencies, using related attributes. This study applies a hybrid multiple attribute decision-making (HMADM) model including the DE-MATEL for constructing the INRM, DANP for computing influence weights, modified VIKOR for evalu-ating the performances and gaps among the three technology transfer modes so that to develop sus-tainable and systemic improvement strategies. At the macro level, the results show that, the technol-ogy transfers modes receive an overall positive effect, especially universities/research institutions. At the micro level, the technology licensing has not only the highest performance but also the largest gap. According to this finding, technology licensing is the most feasible way to cater to technology transfer at the macro level from the micro level. The findings suggest decision makers pay attention to the role of universities/research institutes as the main factor influencing technology transfer ef-fectiveness. Also, they should focus on influential attributes such as researcher participation and technical collaboration ability for reducing the gap.

With the reforms in competition rules and equipment by the International Table Tennis Federation (ITTF), the number of rounds in table tennis matches has increased, placing higher demands on athletes’ abilities to transition between technical and tactical offensive and defensive strategies, as well as on their physical and psychological qualities. Therefore, this study employs methods such as the strength difference evaluation, competition performance (CP), and multiple regression. Using 48 international important matches in which Player W (anonymous) participated as case studies, the study evaluates and predicts the competitive performance of W when facing athletes of different world rankings. The results indicate that in matches against athletes with different skill rankings, the phases where the technical strength difference significantly affects competition performance (CP) are the attack-after-serve phase and attack-after-receive phase, followed by the rally phase, and finally, the rally phase Ⅱ. The competitive level in serve rounds is superior to that in receive rounds. The Kruskal-Wallis test results reveal significant fluctuations in the competition performance (CP) during the rally phase Ⅱ, demonstrating highly significant differences (P < 0.01). In matches, Player W has a very low probability of winning when not holding an absolute advantage in key techniques (the first four strokes) – particularly pronounced when facing athletes ranked in the world top 20. The multiple regression model for the technical strength difference in table tennis matches plays a certain role in predicting the performance of athletes in terms of technical and tactical indicators during matches, offering a clear reflection of the effectiveness of these indicators.