With the increasing number of decommissioned charging piles, efficient reuse of their components is essential for sustainable resource utilization and intelligent grid management. To address the challenges in recycling and scheduling retired charging pile components, this study proposes a cost-optimization approach for delivery planning in smart grid logistics. An Electric Vehicle Routing Problem with Time Windows (EDVRP-TW) model is formulated that considers vehicle capacity and time constraints. To solve it, an Improved Chicken Swarm Optimization Algorithm (ICOOT) is developed, integrating Circle chaotic mapping, spiral search strategy, and normal cloud mutation to enhance convergence speed and solution quality. Simulation experiments using real-world datasets demonstrate that the proposed method significantly reduces operation and maintenance costs, achieving up to an 11.77% cost reduction. The results validate the effectiveness and applicability of the model and algorithm in intelligent recycling and scheduling of grid materials.

To address the limitations of hallucinated responses in large language models (LLMs), an artificial intelligence (AI) chatbot featuring a retrieval-augmented generation system is designed to assist with subject-based certification instruction. Focusing on the Level B certification curriculum for computer hardware repair as an example, this study develops six distinct knowledge base structures (Type0–Type5) and integrates them into two open-source 7B-parameter LLMs (LLaMA2 and Qwen2) with a custom-built question and answer system. Response accuracy to 10 standardized questions is evaluated by domain experts. Knowledge structure significantly affects performance, with the enriched Type5 base yielding the highest accuracy (Qwen2: 98 points; LLaMA2: 73 points). Statistical tests confirm significant improvements with knowledge base enhancement across knowledge types and between models. These findings highlight the critical role of knowledge representation and LLM selection in domain-specific AI applications, proffering practical design guidelines for intelligent teaching assistants in technical education.

Secondary aluminum dross (SAD), produced by small and medium-sized enterprises in Jombang Regency, Indonesia, is a hazardous waste with high ammonia content that threatens the environment and human health. Although SAD has potential as an alumina source for cement production, ammonia emissions restrict its safe use. This study applies a simultaneous heat-stirred alkaline leaching method to optimize ammonia removal for use as raw material in cement manufacturing. It addresses gaps in single-factor studies by optimizing multiple factors (NaOH concentration, temperature, reaction time, and stirring speed) using the Box–Behnken Design within Response Surface Methodology. Temperature and reaction time are the most influential, while interactions between NaOH and temperature, and between temperature and stirring speed, are critical for maximizing removal. The optimized process removed 98.81% ammonia, while an alternative yielded 98.34% with lower chemical and energy inputs. It enables safe SAD reuse and promotes the circular economy through waste valorization.

Precast concrete infill walls are widely applied to enhance the lateral stiffness and seismic performance of reinforced concrete frames. This study aims to establish a quantitative understanding of how key design parameters influence the mechanical behavior of precast concrete infill wall systems. To achieve this objective, nonlinear finite element analyses validated against ATENA-based experimental results were conducted to examine the effects of wall aspect ratio, thickness, and tie reinforcement configuration on system-level stiffness, strength, and ductility. Results show that decreasing the aspect ratio from 0.67 to 0.47 increases lateral stiffness by approximately 15-20% but reduces ductility by about 10%. Increasing wall thickness from 100 mm to 200 mm enhances peak load capacity by up to 30% while shifting damage from the infill wall to the frame. Denser wall-column ties improve residual load capacity by 18-25%, whereas wider wall–beam tie spacing slightly reduces ductility without significantly affecting peak load.

This study presents a cost-effective automation solution for preparing polymerase chain reaction (PCR) reagent cartridges used in automated nucleic acid analyzers. Manual preparation is labor-intensive and error-prone, often causing inaccurate volumes and reagent mismatches. To address this, a dispensing system based on open-source 3D printer technology is developed. It incorporates a motion platform and a syringe-based pump, and precisely dispenses reagents into cartridge chambers designed for magnetic DNA extraction and real-time PCR. The system is evaluated for manual inefficiency and error. Dispensing accuracy, assessed gravimetrically using 500 μL of distilled water, shows a relative accuracy of 0.30% and a coefficient of variation (CV) of 2.64%, both within ISO 8655 limits. In terms of efficiency, the system fills a single cartridge chamber in 13.57 seconds, much faster than the approximately 3 minutes required for manual reagent injection. These results highlight the system’s potential to improve throughput and precision in cartridge preparation.

This study develops an innovative information security protection system for end devices in smart manufacturing industrial control environments. By employing six key functionalities—lightweight identity authentication, traffic analysis, key management, personnel authorization control, system status monitoring, and an alarm mechanism—the system addresses the limitations of traditional firewalls. Experimental procedures involved testing the system against common threats, including phishing (fraud), physical intrusion, and Denial of Service attacks. Results demonstrate over 90% success in mitigating these attacks while maintaining operational efficiency. Furthermore, real-time monitoring and alert features enhance data protection and ensure reliable factory operations.

To solve the issues of low accuracy and difficulty of online fault identification for Automatic Verification Devices (AVDs) of Electric Energy Meters (EEMs), a method based on Installed Standard Energy Meters (ISEMs) is proposed. ISEMs are tested concurrently with EEMs undergoing verification, and test data from meter positions are collected online without disrupting AVD operation. The features of the meter positions are constructed, and their principal components are extracted to reduce feature dimensionality. Unlabeled samples are categorized into typical fault categories using the K-means clustering algorithm. A Multi-Class Support Vector Machine model is trained and optimized by Bayesian optimization based on the labeled samples. The model is then employed for AVD online fault identification. Enhanced with Monte Carlo samples augmentation, the proposed approach achieves a 0.35% error rate, a 94.40% accuracy improvement compared to the model without sample enhancement. This method provides a reliable and cost-effective solution for online fault identification of AVDs.

This study aims to overcome limitations in traditional natural language processing (NLP) models, particularly in network structure and hyperparameter tuning, which often hinder optimal performance across diverse tasks. To address these issues, the ant colony optimization (ACO) algorithm is introduced. This paper optimizes the layer count and other training hyperparameters of the Bidirectional Long Short-Term Memory (BiLSTM) network, enhancing both its flexibility and classification accuracy. To further enhance BiLSTM’s bidirectional selectivity, a bidirectional attention mechanism (BAM) is incorporated, strengthening the model’s capacity to integrate historical and future contextual information. The proposed ACO-BiLSTM-BAM model is validated on the Internet Movie Database (IMDb) movie review dataset, where it achieves a classification accuracy of 92.74%, marking a significant 12.05% improvement over the base BiLSTM model, particularly in discriminating sentiment at varied levels.

This study examines two different underground metro station typologies in Istanbul to evaluate passenger thermal comfort conditions. Long-term field measurements are conducted, and the relative warmth index (RWI) values are calculated to compare the stations’ thermal comfort conditions. The RWI method is employed due to transient environments in metro stations. Dry-bulb temperature and relative humidity data are simultaneously measured at 19 points in Şişli/Mecidiyeköy and 17 points in Gayrettepe station at one-minute intervals. The measurements are conducted every day throughout spring, summer, and autumn. The results show that the expected thermal comfort conditions at the stations are not met for all three seasons. Passenger thermal comfort is at its lowest level in summer, followed by autumn and spring. The RWI values for the platform and concourse levels at the cut-and-cover type are higher than at the bored-tunnel type station due to the lower train-induced air velocity in cut-and-cover type stations.

Rapid and accurate identification of leaf disease is essential in intelligent agriculture. Current methods often struggle with balancing precision and speed. This research introduces the fusion coordinate attention and residual network (FCA-ResNet) model to improve classification accuracy while maintaining a lightweight structure for both healthy wheat leaves and five common wheat leaf diseases. FCA-ResNet incorporates a coordinate attention (CA) mechanism along with a multi-branch Inception module. The model consists of an Inception-based multi-branch structure and CA mechanism fusion module, which optimizes feature focus and weight allocation. Additionally, a multi-scale fusion module utilizes both channel and spatial attention mechanisms to effectively integrate shallow and deep features, improving the detection accuracy of small lesions. The multi-branch structure is designed to replace traditional multi-layer convolution, resulting in a lightweight model. The model achieves an average accuracy of 91.6% on custom datasets, demonstrating its effectiveness in plant disease detection for agriculture.