Recognizing vehicles under difficult environmental conditions is an enduring challenge to the effective implementation of intelligent transportation systems and autonomous driving technologies. In everyday life, many factors, including poor weather (rain), night-time illumination & darkness, and partial occlusion will greatly reduce the reliability of typical deep learning-based vehicle identifiers. This paper proposes a robust framework for vehicle recognition using 3 complementary strategies: data augmentation; domain adaptation using a Domain- Adversarial Neural Network (DANN); and applying an image enhancement module based on Zero-Reference Deep Curve Estimation (Zero-DCE). The UA-DETRAC dataset was used to develop a robust evaluation protocol based on 5 different scene types: sunny, rainy, night-time, low-light and occluded. At the validation stage, both ResNet50 and EfficientNet-B0 were used as baseline models. Experiments were carried out to compare each of the 3 strategies both separately and in combination. The results showed that using the image enhancement module produced the greatest improvement (8.3% & 7.9%) in coordination with the low-light & night-time scene types respectively. Domain adaptation has improved recognition performance (i.e., the accuracy of recognition) for almost every scene category (particularly those with occlusions), where the use of domain adaptation has increased recognition performance by 6.1%. In addition, combining all three approaches resulted in a very large overall performance increase from an average of 73.4% (baseline) to 87.6%, an improvement of 14.2%. The paper also looks at the time that each method takes to make a prediction and how they can be used together to improve vehicle recognition in applications.

As a classic strategy confrontation board game, chess features both complex spatial layout characteristics and long-term sequential dependency, making it an important research scenario in the fields of artificial intelligence and machine learning. This study is based on 3,196 endgame match data and 35 board state features, constructing a multi-model comparison framework including decision trees, logistic regression, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), to verify the performance differences of different algorithms in the task of predicting the outcome of chess moves. Through feature encoding, dataset division, and multi-model training and evaluation, the strengths and weaknesses of each model in terms of accuracy, generalization ability, and inference efficiency are analyzed. Preliminary experiments show that logistic regression as a baseline model can effectively learn simple chess game patterns. Subsequently, the introduction of LSTM and CNN revealed that the dataset is relatively weak in sequence modeling and spatial feature extraction, resulting in low accuracy. Further improvement in prediction accuracy is needed. This research provides a reference basis for the model selection of the intelligent analysis system for board games, and also offers practical ideas for the prediction task that integrates temporal and spatial features.

An escalation in sophisticated, encrypted botnet attacks has accompanied the rapid expansion of the Internet of Things (IoT) ecosystem. Conventional security measures are increasingly ineffective due to the rise of encrypted communication channels, while standard machine learning and deep learning models are computationally expensive for local edge deployment. This paper first proposes a hybrid, dual-branch deep learning framework designed for high-fidelity botnet detection within containerized edge environments. Experimental evaluations are then conducted using a curated dataset derived from IoT-23 and tested within a Docker-based simulation. The proposed framework in the resource-constrained setting can achieve high accuracy and a low False Positive Rate while maintaining superior computational efficiency and outperforming traditional baseline classifiers. These findings demonstrate strong capabilities for robust detection at the network edge with negligible memory overhead, offering a scalable solution for modern IoT infrastructures. Finally, the paper provides a critical assessment of the framework's limitations and future research directions of botnet detection in edge deployment scenarios.

Artificial intelligence has become a key tool in quantitative trading, while the multi-factor model remains a common framework for stock selection and asset allocation. Existing reviews, however, usually discuss multi-factor models or AI-based quantitative strategies separately. They rarely provide a systematic account of how the two approaches are integrated. This paper aims to fill this gap by reviewing recent Chinese and international studies and examining the integration of multi-factor models with artificial intelligence in quantitative trading. The review shows that traditional multi-factor models provide interpretable variables and economic logic, but they have limitations in factor construction, nonlinear modelling and market regime adaptation. Machine learning improves factor screening and return prediction; deep learning extends the use of time-series and textual information; reinforcement learning further links prediction results with portfolio adjustment and trading decisions. These developments increase the flexibility of quantitative strategies, but they also bring problems such as overfitting, weak interpretability, unstable generalization and regulatory risk. Future research should give more attention to explainable AI, dynamic factor selection, multi-source data fusion and risk-constrained trading systems. Such work can help connect economic meaning with data-driven modelling and improve the practical value of AI-enhanced multi-factor strategies.

With quantum computing now widely treated as a credible adversary to conventional public-key assumptions—most notably discrete logarithms and integer factorization—post-quantum cryptography has moved from a speculative research topic to an active engineering agenda. Within that broader landscape, lattice-based cryptography is routinely singled out as the leading candidate for next-generation digital signatures, largely because its security can be reduced to well-studied lattice problems believed resistant to quantum attacks. Aggregate signatures compress multiple individual signatures into a single compact object, translating into improved batch-verification throughput in settings such as blockchain and IoT. Against this backdrop, the present investigation offers a detailed analysis of lattice-based aggregate signature constructions. Core lattice notions are reviewed alongside security models, followed by an examination of two aggregation paradigms: unordered and ordered. Representative schemes are analyzed with respect to their construction logic and structural bottlenecks. A cross-scheme comparison is then used to motivate future research directions. By pulling together recent progress while isolating persistent obstacles, this investigation characterizes an ongoing shift from feasibility-oriented prototypes toward deployment-minded designs, offering a focused reference for standardization efforts targeting post-quantum-secure aggregate signatures.