. Global warming has become a critical constraint on global sustainable development, with manufacturing widely regarded as the core entity undertaking the "zerocarbon" mission. Amid accelerating green and low-carbon transformation, enterprises must simultaneously deploy emission reduction strategies at both production and channel levels while navigating the interplay among government regulations, consumer preferences, and carbon market mechanisms. To address this challenge, this study constructs a two-level supply chain game model comprising a manufacturer and a retailer, where consumers make product choices based on their low-carbon preferences. Employing game theory and comparative static analysis, this research examines how carbon quota, carbon trading prices, and consumers' green preferences influence manufacturers' carbon reduction and channel decisions. Our analysis reveals the following findings. First, dualchannel operations combined with emission reduction investments generate the highest profits for manufacturers by enabling broader market coverage and capturing the demand premium from low-carbon preferences. Second, retailers' profits also increase with carbon prices, quotas, or consumer preferences under both single and dual-channel emission reduction scenarios. Third, the emission reduction rate remains higher in the dual-channel model than in the single-channel model under identical conditions, amplifying incentives for low-carbon investment.
To enhance the error-correction capability of neural network decoders for polar codes and minimize the required memory overhead, this paper proposes a low-precision successive cancellation (SC) decoding algorithm. The algorithm is assisted by deep neural networks and incorporates quantization and partitioning techniques. Specifically, it quantizes the weights of the deep neural network decoder to a limited number of bits, utilizing an 8-bit quantized input and output in the Q8.4 fixed-point digital format for all arithmetic operations, thereby achieving int8 computations. With quantization-aware training, quantization errors are accounted for within the training loss, allowing the deep neural network to effectively learn how to decode with reduced precision. The proposed scheme, by employing fewer floating-point weights, reduces both memory overhead and the number of floating-point operations. Simulation results indicate that the proposed decoder decreases memory usage and decoding latency while maintaining a slightly improved bit error rate (BER) performance.
. This article presents an open-source intrusion detection and notification (IDN) framework designed to detect attacks targeting Siemens S7-series programmable logic controllers (PLCs) in operational technology (OT) environments. The framework is validated using a simulated weight-based sorting system developed in Factory I/O, with TIA Portal control programs deployed on S7-300, S7-1200, and S7-1500 PLCs. Suricata serves as the core intrusion detection engine, while the Elasticsearch, Logstash, and Kibana (ELK) stack and LINE Notify are integrated to visualize alerts and provide realtime operator notifications. Simulated attacks are carried out using Snap7 to interact with each PLC during live process operations. The experimental results show that the framework reliably detects intrusions across all tested S7 PLC models and delivers timely alerts, demonstrating its effectiveness for real-time monitoring and incident response. In contrast to previous studies that focus primarily on protocol analysis or on individual PLC types, this work offers a practical and scalable intrusion detection solution validated on real hardware and designed to accommodate the coexistence of legacy and modern controllers within OT systems.
This article shows a model to determine the minimum contact area with the soil for trapezoidal combined footings considering that the area is partially compressed, i.e., a part of the contact area under the footing with the soil is subject to compression and the other part has zero pressure. Some works present the minimum contact area for trapezoidal combined footings, but the contact area of the footing with the soil is completely compressed. The methodology is developed by integration to determine the equations of the resultant force and the moments on the X and Y axes for the five cases of biaxial bending, and four cases of uniaxial bending (two on the X axis and two on the Y axis). Several studies are developed to determine the minimum contact area with the soil for trapezoidal combined footings subjected to biaxial bending and uniaxial bending in each column. The proposed model shows a significant reduction in the minimum contact area with the soil of up to 65.30% for biaxial bending and up to 48.68% for uniaxial bending compared to other studies. The model can be used as a review to minimize the allowable load capacity of the soil (objective function) and the same constraint functions for biaxial or uniaxial bending.
. Existing studies on the evaluation benchmarks and approaches of large language models (LLMs) primarily concentrate on accuracy, robustness, bias and security. As the application of LLMs in the metro domain intensifies, it is imperative to construct a specific LLM evaluation framework and benchmark for evaluating LLMs. We collected the official information released via metro companies' official websites, WeChat official accounts, Weibo, Douyin, as well as other professional sources in the metro domain. After data cleaning, the original data were categorized into four types: passenger service, line operation and maintenance, emergency management, and safety operation, and the training dataset, evaluation benchmark and criteria were constructed. Then, multiple agents are constructed through fine-tuning the Qwen-7B model and prompt engineering, and MetroEval framework is established by multi-agent collaboration to evaluate the ty, and ethics of LLMs in the metro domain. By evaluating and comparing the indicators of six models, it is discovered that retrieval-augmented generation (RAG) and fine-tuning can effectively enhance the performance indicators of the base models. It is expected that MetroEval can provide ideas and assistance for the future research and application of LLMs in the metro domain.
