Science and Technology of Engineering, Chemistry and Environmental Protection
ISSN 2959-6157 (Print) ISSN 2960-1339 (Online) Editor-in-chief: Vinod A. Thale （Zhejiang University） Associate Editor: Syed Murawat Abbas Naqvi （Beijing Institute of Technology） Editorial Board Member: Faisal Rafique （Southern University of Science and Technology） Jian Hu （University of Kent） The Journal of Science and Technology of Engineering, Chemistry and Environmental Protection (JSTECEP) is a multidisciplinary, peer-reviewed academic publication dedicated to fostering the next generation of scientific leaders. By providing a professional platform for college students, young researchers, and early-career innovators, JSTECEP bridges the gap between classroom learning and professional academic publishing. Our mission is to democratize the exchange of scientific knowledge. We aim to support emerging scholars by providing a rigorous yet constructive peer-review process that highlights original research, innovative technical solutions, and sustainable developmental practices. Scope of Subjects The journal welcomes submissions that explore the intersections of theory and application across three primary pillars: Engineering: Advancements in mechanical, civil, electrical, and chemical systems, with an emphasis on modern technological integration. Chemistry: Research spanning organic, inorganic, and analytical chemistry, as well as biochemistry and material sciences. Environmental Protection: Innovative approaches to sustainability, climate mitigation, pollution control, and the preservation of natural resources. Why Publish With Us? Support for Emerging Voices: We prioritize high-quality work from students and young professionals who are often underrepresented in legacy journals. Rigorous Peer Review: Every article undergoes a thorough evaluation to ensure technical accuracy and academic integrity. Interdisciplinary Reach: Our broad scope encourages authors to submit work that solves complex, real-world problems involving multiple scientific disciplines. Commitment to Innovation: We celebrate "outside-the-box" thinking and technical creativity that addresses the environmental and industrial challenges of the 21st century. All articles will be submitted to: Google Scholar, Crossref, U.S. Librarian Database, ProQuest (part of Clarivate’s Web of Science), Ulrich's Periodicals Directory, SEMANTIC SCHOLAR, OpenAlex.
Research and Analysis of Fine-tuning Techniques for Cell Image Segmentation Model Based on SAM2
This study proposes a specialized framework for finetuning the Segment Anything Model 2 (SAM2) to address the challenging task of biomedical blood cell image and video segmentation. To overcome issues like poor contrast, blurred boundaries, and cell adhesion, the paper curated a hybrid dataset of 400 annotated images from the public LISC database and an in-house collection. A standardized preprocessing pipeline involving CLAHE and Z-score normalization was applied. The parameterefficient fine-tuning strategy selectively unfroze the final layers of the image encoder and the entire memory encoder, incorporating Low-Rank Adaptation (LoRA) and lightweight adapters. Training was guided by a composite loss function combining segmentation (Focal and Dice losses), temporal consistency, and morphological regularization terms. Experimental results show that fine-tuned model, ‘BloodCellSAM2’, achieves a mean Intersection over Union (mIoU) of 85.7% and a Dice coefficient of 91.3% on the test set, representing a significant improvement over the original SAM2 (mIoU: 65.2%) and a U-Net baseline (mIoU: 78.4%). This approach delivers high accuracy while training only 0.8% of the parameters, offering an efficient and robust solution for automated cell analysis in clinical and research settings.
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Application of Artificial Intelligence in Soccer Tactical Analysis and Player Performance Evaluation
Tactical analysis and player performance evaluation in soccer are undergoing a shift from traditional experiencebased judgment to a combination of data and theoretical support. This paper systematically reviews and categorizes two mainstream technical pathways and one novel technical pathway in the current field of football analysis: video and computer vision analysis methods, big data analytical methods, and the ecological dynamics method. Exploring the underlying principles, structural mechanisms, and respective weaknesses and strengths of the various technical methods, this paper points out that diverse methods have their own differences in research objectives as well as application contexts and show a high degree of complementation. Moreover, this paper suggests that these ways can be implemented in various areas, such as the assessment of player performance, tactics, and the work with the team in the long-term. Finally, the paper presents the problems of football analytics in terms of data quality, model interpretability, integration of methods, and the development of talent based on interdisciplinary talent. It also describes the future trends of the convergence of various technologies.
An Empirical Comparison of BERT and Lightweight Variants for IMDb Sentiment Classification
Pre-trained transformer models are now the most common choices for sentiment analysis and other text classification tasks. However, their large number of parameters and high inference cost make it hard to deploy them in limited resource settings. To solve this problem, several lightweight variants have been introduced, this paper presents a controlled empirical study of four transformerbased models - BERT-base, DistilBERT, TinyBERT, and ALBERT-base - on the IMDb movie review sentiment classification task. All models are fine-tuned under the same conditions. Besides standard metrics, total and persample inference time were measured as well. To better understand stability, the test set is divided into short and long review subsets based on word count and compare model performance across subsets. Results show an accuracy-efficiency trade-off. BERT-base achieves the highest test accuracy (86.3%), followed by ALBERT (85.7%), while DistilBERT is lower (85.2%) but offers about 2 times faster inference. TinyBERT is the fastest but has lower accuracy (81.3%). Across these models, performance on short reviews is higher than on long reviews. The performance drop from short to long texts is more severe for the smallest model, TinyBERT, finding that highly compressed models struggle more with long and complex texts. This paper discusses the implications of findings for selecting sentiment analysis models under different application requirements.
Shared Bicycle Riding Path Recognition and Dynamic Speed Setting for Electric Vehicles Based on 3D Stacking Models
Amid the rapid development of the “Internet Plus” sharing economy, shared mobility grapples with challenges like imprecise ride path detection and unreasonable speed settings. Using over 280,000 September 2025 Citi Bike ride records in New York City, this study establishes a three-dimensional stacked recognition model (integrating hotspot identification, vectorization, terrain modeling and network analysis) via MiniBatchKMeans clustering and other terrain feature techniques to identify major cycling paths and determine scientific e-bike speeds. The model locates key terrain areas, constructs a main cycling network covering over 60% of core traffic, and reveals NYC cycling’s multi-centered, corridor-based pattern linked to transport and commercial zones; it further proposes a zoned dynamic speed limit scheme (15 km/h in rush hours/ central areas, 20 km/h on main routes, 25 km/h in valleys/other regions), providing data and methodological support for optimizing urban slow traffic planning, regulating shared mobility and promoting coordinated integration with urban transport systems.
Impact of the Federal Reserve’s Interest Rate Announcements on the Earnings of US Bank Stocks
This paper systematically reviews domestic and foreign theoretical and empirical studies on the transmission mechanisms linking monetary policy and interest rate decisions to stock market outcomes, and further investigates the impact of the Federal Reserve’s interest rate announcements on U.S. bank stock returns. This paper establishes the theoretical foundation of banks’ stock’s reaction to the central bank’s monetary policy from the aspects of studying the existence of the existing theoretical mechanism and the common market view by including profit structure channels, liability-cost channel, riskpremium channel and expectations management channel. Moreover, it uses the typical policy cycle case of 2024 interest rates downward cut and analyses the differentiated market performance under the different size of the banks, the industrial chain’s spillover effect, the short and longterm abnormal returns and value logics of the banks’ stocks. This paper intend to use interest rate transmission to offer some reference to investors, and offer some clues for the future research about interest rate transmission and financial risk linkage. By systemically reviewing related literature, the paper distills the conclusions and methods in diverse research perspectives and the status of research outlook as well as research challenges.
Precipitation Trend Change in Lanzhou and the Yellow River Basin Under Global Warming
Global warming is reshaping precipitation patterns worldwide, with arid regions showing particularly striking changes. Lanzhou, a dry and rain-scarce area in northwest China, has experienced increasing precipitation and atmospheric moisture levels amid global warming. Research indicates this “warm and humid” phenomenon represents an improvement in arid regions ‘moisture conditions without altering their fundamental arid characteristics. The precipitation patterns and properties in Lanzhou differ fundamentally from those in eastern China, demonstrating that agricultural ecological challenges in arid areas cannot be resolved merely by increasing water availability. “However, despite the increase in precipitation, arid regions cannot rely solely on the increase of water resources to address agroecological challenges; the uneven temporal and spatial distribution of rainfall must also be taken into account.”
Three-Layer Glass Thickness Optimization and Solar Heat Gain Minimization Based on Quantum Genetic Algorithm
This study focuses on improving the thermal performance of external windows. Building operations account for 21% of total societal energy consumption, and heating through external windows can account for 40-50% of the air conditioning cooling load. Although three-layer glass windows have great potential, their performance hinges on the micro configuration of layer thicknesses, and an empirical equal-thickness design is challenging to balance the trade-off between lighting and insulation. Traditional genetic algorithms (GAs) are prone to premature convergence when solving high-dimensional, nonlinear optimization problems. This study focuses on the 300-2500 nm solar spectrum and, for the first time, applies an improved quantum genetic algorithm (QGA) to the collaborative optimization of three-layer glass window thicknesses ( l1 , l2 ,l3 ). The core innovation lies in: Based on the Fabry-Perot interference principle, an analytical formula for the single-layer equivalent model was adopted as the optical transmission model. This method has the advantages of clear physical meaning and high computational efficiency compared to the general transmission matrix method (TMM). Simulation experiments show that the optimized thickness combination ( l1 mm = 6.08 , l2 mm = 6.08 , l3 mm = 8.37 ) successfully achieves spectral-selective control. The average transmittance in the visible light region remains at 0.7957, while the transmittance in the near-infrared region is suppressed to 0.7860. Compared with traditional design, the energy-saving potential is significant. This study provides new algorithmic tools and theoretical support for the design of high-performance energy-saving windows.
Movie Rating Prediction based on Data Mining and Machine Learning
In personalized recommendation, accurately predicting the user’s rating of movies can significantly improve the quality of recommendations, enhance user experience and platform stickiness. In the analysis of the film industry, it is helpful for film market performance evaluation, audience preference analysis and content creation guidance; In the field of academic research, it is also one of the important benchmarks for testing the performance of data mining and machine learning models. This paper systematically reviews the research progress of film rating prediction based on data mining and machine learning. This paper first reviews the traditional prediction methods relying on metadata and collaborative filtering, and analyzes their advantages and limitations. Then, the sentiment feature extraction and modeling method based on text comments is discussed, and the role of sentiment information in improving the interpretability and accuracy of prediction is expounded. Furthermore, the multimodal fusion strategy of integrating metadata, text, vision, audio and other multi-source information is discussed, and the technical characteristics and research status of different fusion levels are summarized. This paper also summarizes the common datasets and evaluation indicators in this field, and points out that there are still deficiencies in data integrity, modal fusion depth and interpretability. Finally, this paper looks forward to future research in the directions of deep multimodal interaction, cold start mitigation, and model interpretability enhancement, in order to provide a systematic reference for researchers in related fields.
Optimizing Energy Transmission of Multi- Layer Glass by Fish School Algorithm
In cold northern regions, winter heating energy consumption is high, with exterior window heat loss accounting for 50% of total building heat loss. Threelayer glass is the mainstream exterior window material here, yet most existing studies focus on double-layer glass or traditional algorithms, lacking exploration of three-layer glass thickness collaborative optimization and fish swarm algorithm application. This study aims to maximize the indoor solar incident energy in northern winters. A three-layer glass solar radiation transmittance calculation model is constructed for the 300-2000nm solar band to clarify the mapping relationship between incident energy and thickness parameters. The fish swarm algorithm is introduced, taking the thickness parameter combination of “three layers of glass + two layers of air” as the optimization object. The iterative optimization of foraging, clustering, and tail-following is achieved through the MATLAB platform. The results show that the algorithm has good convergence. The optimal parameter combination is L1 = 6.86mm, L2 = 6.52mm, L3 = 5.81mm, Lair1 = 11.58mm, and Lair2 = 6.63mm. At this time, the near-infrared band transmittance is 0.8 to 0.95, the visible light band transmittance is stable at 0.7 to 0.85, and the ultraviolet band transmittance is effectively controlled. This study provides quantitative parameters for the energysaving design of three-layer glass windows in northern winters and offers a new idea for the multi-parameter collaborative optimization of building windows.
Analysis of the Relationship between Zhengzhou Subway Passenger Flow and Weather
Nowadays, subways have become the primary mode of transportation due to their various advantages, such as large carrying capacity, convenience, speed, and low cost. Zhengzhou, a new first-tier city known as a transportation hub, is primarily populated by young working professionals. The young group has a fast-paced commute and high demand for transportation efficiency, so they tend to prefer the subway as their daily commuting mode. Different weather conditions also affect people’s choice of transportation mode. Weather, as an important factor of daily change, also influences people’s choice of transportation mode. Based on this social reality, this paper employs a combination of correlation analysis, multiple linear regression analysis, and stepwise regression analysis to study the impact of weather on subway ridership. Through data analysis from April to September 2025, it was found that there is a negative correlation between subway ridership on the same day and both minimum temperature and total precipitation. When the minimum temperature or total precipitation increases, subway ridership on the same day decreases accordingly. The study confirms that minimum temperature and total precipitation have a tangible impact on daily ridership. This research can provide reference suggestions for subway dispatch management, ridership prediction, and emergency management.
Research and Analysis of Action Recognition Based on Video
In recent years, along with the swift development of computer vision and deep learning technologies, videobased action recognition has turned into one of the core research directions within the field of artificial intelligence. Its accomplishments are extensively applied in such real scenarios as intelligent monitoring, human-computer interaction, and autonomous driving. This paper first presents the research background and practical significance of video-based action recognition in recent years then analyzes the current challenges such as those of complex background motion blur and similarity among categories. Next it expounds upon the principle’s structures and application effects of representative models such as 3D convolutional neural networks two - stream networks and models based on the Transformer and also analyzes the advantages and disadvantages of various models. Finally, it summarizes the application scenarios of video action recognition, explores the existing technical difficulties, and also looks forward to the development trends in the future like lightweight models and few - shot learning. This paper offers comprehensive references for researchers in relevant fields, making them able to grasp the research current situation and carry out in - depth research.
Evolution, Evaluation, and Challenges of Automatic Music Style Clustering Techniques: A Review from Handcrafted Features to Self-Supervised Learning
Automatic discovery and clustering of music styles are fundamental to music information retrieval, recommendation systems, and computational musicology. Recent advances in deep learning and selfsupervised representation learning have substantially improved audio embeddings, enabling more effective unsupervised clustering based on musical style and acoustic characteristics. This paper reviews representative approaches to music style clustering, including traditional feature-based methods, two-stage frameworks combining deep embeddings with clustering, and emerging selfsupervised online clustering models that jointly optimize representation learning and clustering structure. Furthermore, this paper summarizes commonly used datasets and evaluation metrics in this field, reviews experimental findings from representative studies, and analyzes the advantages and limitations of various methods. Finally, this paper points out the current open challenges, including inconsistent evaluation standards, difficulties in cross-cultural and multi-label style recognition, insufficient interpretability of clustering results, and scalability issues in large-scale streaming scenarios. This paper aims to provide researchers with a clear and structured technical roadmap to facilitate the development and evaluation of future music style clustering systems.
Endangered Species Recognition from Camera Trap Images with Vision Transformers
Automatic identification of endangered species from camera trap images grows more important for wildlife conservation. This task still poses challenges. Lighting conditions vary. Partial occlusion occurs. There are subtle inter-species differences. These differences require fine visual discrimination. This study proposes a dualbranch deep learning ensemble model. It integrates Swin Transformer and ConvNeXt architectures. The model captures complementary global and local features. It’s for the identification of 10 rare species. The team used 2,000 research-grade images. These images come from iNaturalist. Our model reached a Top-1 accuracy of 90.83%. That’s 3.5% higher than EfficientNet-B0, ViT-B16, and Swin-T. The training process used progressive unfreezing. It also used layer-wise learning rate decay. These methods achieve stable multi-scale feature adaptation on limited data. They also suppress overfitting. GradCAM visualizations confirm that the model consistently attends to anatomically discriminative regions—such as rosette patterns and stripe configurations—thereby reducing interspecies confusion. Test-time augmentation further enhances robustness against occlusion and illumination variability. The final system supports practical edge deployment, running at 15 FPS on an NVIDIA Jetson Nano with INT8 quantization. This work demonstrates that hybrid Transformer–CNN architectures are effective and deployable for real-world conservation monitoring.
The Methods and Mechanisms of Preserving Content and Structure in Image Style Transfer
As artificial intelligence (AI) technology continues to advance, image style transfer has gradually moved from research to practical applications, becoming widely integrated into fields such as art creation, advertising design, and virtual reality. However, existing research often struggles to maintain the fidelity of the original image’s content and structure while pursuing stylistic effects. This contradiction has become a key bottleneck restricting the further development of image style transfer technology. With advances in image processing, a key challenge is preserving the original image’s structure and content while achieving a stylized effect. This paper reviews and analyzes the design principles and underlying mechanisms of various AI models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Transformer architectures, in addressing the challenge of content preservation. Through the integration of existing literature, the results indicate that there is currently no universal solution, and trade-offs and choices need to be made for specific scenarios. Based on the limitations of existing methods, this paper explores potential future advancements in the technology, aiming to offer valuable insights for further development.
Fundamentals, Challenges, and Improved Algorithms of PID Control for Quadrotor UAVs
The quadrotor unmanned aerial vehicles (UAVs) are widely employed in power line inspections, agricultural monitoring, cargo delivery, and surveying operations because of their straightforward design and remarkable agility. However, traditional proportional-integralderivative (PID) controllers perform poorly under wind, load changes, and sensor noise, which limits the UAV’s precision and flight reliability. Thus, this paper reviews the current development of PID control for quadcopter UAVs, examines the limitations of traditional PID controllers, and compares the performance and applications of various improved algorithms, offering guidance for controller selection and parameter tuning. By analyzing relevant literature on improvement strategies, such as fuzzy adaptive PID, evolutionary learning-optimized PID, data-driven adaptive PID, deep reinforcement learning PID (DRL-PID), and hybrid model PID, and integrating existing simulation and experimental data, key performance indicators such as stability, speed, disturbance rejection, and computational load are summarized. The results show that data-driven PID like DRL-PID achieves the highest accuracy, while fuzzy adaptive PID remains widely used in engineering for its low computational load and real-time performance. Each algorithm has its strengths in different applications: fuzzy adaptive PID is suitable for real-time tasks, RL-GA and hybrid models are suitable for high-precision mapping, and WRLS adaptive PID is suitable for logistics with frequently changing loads. Meanwhile, lightweight DRLPID and multi-UAV cooperative control are promising future directions.
Research and Analysis of Explainable Machine Learning Methods for Credit Scoring and Loan Approval
Financial institutions depend on credit scoring and loan approval systems to make essential decisions, as the accuracy of risk assessment directly affects asset performance, regulatory compliance, and fair treatment of consumers. Traditional statistical models remain widely used due to their simplicity and transparency, but their linear structure limits their ability to capture complex borrower behavior. Tree-based machine learning models, including Gradient Boosting Decision Trees, XGBoost, and Random Forest, offer stronger predictive performance; however, their explainability and transparency remain challenging in regulated credit settings. SHAP-based frameworks in Explainable Artificial Intelligence enable feature-level attribution that links model predictions to underlying financial characteristics. This survey reviews mainstream explainable tree-based credit scoring approaches, summarizes empirical findings, and discusses practical implementation challenges. It also outlines future directions related to regulatory requirements, fairness, drift adaptation, and scalable deployment. The results indicate that explainable tree-based scoring models can achieve strong performance while providing meaningful interpretability, although several unresolved challenges require further research.
Task-Aware In-Context Retrieval for Visual Question Answering
Multimodal In-Context Learning (ICL) has demonstrated remarkable potential in enabling Large Vision-Language Models to adapt to new tasks without parameter updates. However, existing training-free methods primarily rely on visual or semantic similarity for demonstration retrieval, often overlooking the latent task intent of the query. This limitation leads to “task-mismatch” problem and the examples presented by this retrieval method reveal visual similarities but different logical reasoning pattern, consequently misleading the model. This paper presents a novel Task-Aware Retrieval (TAR) framework aimed at enhancing Visual Question Answering (VQA). An unsupervised semantic clustering mechanism utilizing Sentence-BERT is proposed to categorize questions into distinct task clusters through a data-driven approach. A hybrid retrieval strategy is utilized during inference to select demonstrations that correspond with the latent task intent and visual context of the test instance. Experimental results on the OK-VQA dataset indicate that the proposed method attains an accuracy of 41.44%, surpassing robust training-free baselines. Qualitative analysis confirms that TAR effectively addresses reasoning errors resulting from task misalignment, consequently validating the significance of intent consistency in multimodal ICL.
Optimization Design of Three-Layer Glass Thickness Based on the Ant Colony Algorithm
In this paper, the three-layer glass system is taken as the research object, and its optical transmission performance is optimized to enhance the energy-saving effect. Based on the theory of thin-film interference, an optical transmission model that accurately describes the propagation of light in multi-layer media is established to evaluate the spectral transmittance under different thickness combinations. To solve the problem efficiently, an improved ant colony algorithm is introduced. This algorithm simulates the optimization behavior of ants foraging and is characterized by strong global search capability and good robustness, making it suitable for such multi-dimensional, nonlinear combinatorial optimization problems. The optimization objective is to maximize the total solar radiation transmission energy of the glass system within the main solar radiation band of 300-2000 nm, which covers the visible and near-infrared energy crucial for building lighting and passive heat gain. Through iterative optimization of the algorithm, an optimal set of glass thickness configurations is finally obtained: 4.2 mm, 5.7 mm, and 3.9 mm. This solution can make the most effective use of incident solar radiation while ensuring structural safety and process feasibility. The optimization process converges around the 60th generation, demonstrating the algorithm's good efficiency and stability. By integrating optical models and intelligent algorithms, this study provides a scientific and efficient methodology for the design of photothermal performance and energysaving potential of building envelopes.
Mathematical Models in Understanding and Managing Coral Bleaching
Because of the drastic changes in the climate in recent years, coral bleaching is a major threat worldwide. Mathematical models have the benefit of providing people with more potent tools to experiment with more complex environmental concerns. Dynamic model and mechanistic model are two important models of mathematical frameworks. On one side, dynamic models provide a macro-scale view of the reef ecosystems: mimic the holistic evolution of coral systems through capturing feedback loops and identifying ecological thresholds. On the other side, mechanistic models focus on elucidating the finescale driving of the whole process: dissect the underlying micro-physiological processes, like photosynthetic dysfunction and oxidative stress. These mathematical systems do not just amass data and offer a straightforward calculation tool, but they also allow predicting the future of uncertain situations reasonably in various climatic conditions. Moreover, the combination of multidisciplinary knowledge and quantifiable measurements indicates that such mathematical models provide potent support to conservation practices of corals and can streamline the process of evidence-based management practices. This article not only reviews how math models played roles in elucidating the causes of bleaching but emphasize how they enhance warning systems and inform the formulation of targeted conservation strategies through case studies.
Understanding Feature Contributions for Traffic Accident Severity Prediction
Concern around global traffic accidents have increased due to the large toll they take on people and the economy. To investigate factors affecting the result of vehicle accidents in the UK, this study attempts to predict the severity of accidents using a Random Forest model. It investigates difference feature sets including time, context and interaction. Following data preprocessing and class balancing using the Synthetic Minority Oversampling Technique (SMOTE), five feature sets (baseline, temporal, context, interaction and extended) were trained and assessed over 10 iterations. The results show that overall accuracy remains around 0.78 and AUC around 0.60 across all models, indicating stable performance. However, the inclusion of contextual and interaction features slightly improves recall for fatal and serious crashes and reduces the dominance of a few strong predictors, leading to a more balanced feature contribution. These results suggest that richer feature design enhances Random Forest’s ability to capture complex crash patterns beyond the majority of slight accidents.
Short-term Road Traffic Flow Prediction based on LSTM and Transformer Models
With the acceleration of urbanization, traffic congestion has become a prominent problem that hinders the operational efficiency of cities and affects the travel experience of residents. Accurate short-term traffic flow prediction is the support for intelligent transportation systems to achieve early prediction and congestion warning. Traditional prediction models have difficulty capturing the nonlinear and time-series dependent characteristics of traffic flow, resulting in limited prediction accuracy. This paper takes the 2-minute traffic flow data of the Creteil Ring Road in France from 7:00 to 9:00 as the research object. It adopts a Long Short-Term Memory (LSTM) and a Transformer model to preprocess traffic flow data, construct a standardized data set, and conduct comparative experiments on LSTM, Transformer models. The experimental results are presented intuitively through flow time series comparison diagrams, training loss curve diagrams, and model evaluation index comparison diagrams. The mean absolute error (MAE) of the LSTM model is 0.65, the root mean square error (RMSE) is 0.82, and the coefficient of determination(R²) reaches 0.9061. The predicted values are highly consistent with the actual traffic flow fluctuations, and the error distribution is concentrated. Research shows that compared with the Transformer model, the LSTM model has more advantages in short-term traffic prediction tasks. These models can provide precise data support for practical applications such as traffic signal timing optimization and route planning, and provide technical references for the efficient operation of intelligent transportation systems.
Optimization of Ultraviolet Transmittance of Three-Layer Glass Based on Quantum Genetic Algorithm
In response to the current issue of high ultraviolet incident intensity in residential buildings and considering the objective factor that ultraviolet rays can cause damage to the human body, this paper studies which glass thickness combination can minimize the indoor ultraviolet transmittance. In the design process, this paper introduces the quantum genetic algorithm to optimize the ultraviolet transmittance. By deriving the objective function of the ultraviolet transmittance and combining the simulation of the random population with the equal probability superposition state of qubits, multiple runs are conducted to obtain the optimal one. The optimal glass thickness combinations of L1 = 2.7 mm, L2 = 2.3 mm, L3 = 2.2 mm were obtained. The experimental results show that the algorithm logic optimized through multiple iterations has improved the stability and accuracy of the algorithm’s optimization. The three-layer glass thickness design optimized by the quantum genetic algorithm significantly weakens the ultraviolet transmittance, thereby reducing the harm of ultraviolet rays to the human body to a certain extent, ensuring the physical health of residents and improving their quality of life. This research also provides new ideas for the optimization of intelligent construction design.
Reliability Challenges of LLM Agents: Systemic Failure Causes and Governance
As Large Language Model (LLM) agents advance toward industrial applications, their systemic reliability challenges become critical obstacles. This review comprehensively analyzes these issues to establish a framework for trustworthy agents, first categorizing key systemic failures into four types: Cognitive-level, Decision-level, Execution-level, and Security & Boundary failures. It then identifies five root causes: training data limitations, model architecture/algorithm defects, imperfect alignment and reinforcement learning, brittle tool/ environment interactions, and vulnerabilities in knowledge representation/update. To address these, the paper proposes a dual-track strategy with a multi-level governance system—emphasizing pre-deployment benchmarks, post-deployment monitoring, and a macro-framework covering multi-agent coordination, interpretability, traceability, responsibility attribution, and full lifecycle risk management—alongside intrinsic enhancement of model factuality, reasoning, alignment, and architecture. This paper aims to provide a systematic perspective and structured approach for building more reliable and responsible LLM agents. It promotes the integration of theoretical safety and practical deployment, driving the advancement of LLM agents from the experimental to the industrial level.
The Mechanism of Sea Ice Melting and Anti-Melting Due to Albedo Feedback and Sea Ice Age-Roughness-Melt Ponds-Albedo Feedback
This essay focuses on how the albedo feedback and sea ice age-roughness-melt ponds-albedo feedback cause the mechanism of sea ice melting and anti-melting. This essay uses lots of research results to make a carding logic about two albedo feedbacks and uses the relationship deduction and backwards deduction to prove that the two feedbacks are valid. Finally, three Figures prove two theories directly, because Fig. 1 and Fig. 2 have a similar tendency to prove that the sea ice albedo is proportional to the percentage of sea ice area in the total area of ocean, and in Fig. 3, two lines are in perfect accordance with the previous prediction in the Explanation of Sea Ice Age-roughnessmelt ponds-albedo Feedback Theory Part. Also, some drawbacks that cause some abnormal values are mentioned in the Discussion Part, for example, albedos of both old and first-year sea ice in four seasons are replaced by other albedos that have a similar occurring situation.The research significance is that searching the mechanism of sea ice melting and anti-melting can help the researches about Arctic Amplification.
Application of Tolerance Analysis in Wing Design
With the increasing demand of modern aircraft for high lift ratio, low fuel consumption and high reliability, the geometric accuracy control of wings in the design and manufacturing stages has become particularly important. Based on this background, this study conducts a review and qualitative analysis and explores how geometric deviation can affect the performance of the wing through the aerodynamic mechanism. By analyzing the velocity field, pressure field, streamlined structure and regional average performance indicators, the study further reveals the typical pneumatic problems that may lead to geometric deviation. The research results indicate that the front edge shape error and surface smoothness are the most sensitive tolerance types, which are enough to cause significant changes in the lift coefficient C_L and drag coefficient C_ D even within the range of conventional manufacturing standards. In general, simulation data demonstrate that accurate tolerance analysis and surface quality control can effectively solve the instability of pneumatic performance, improve the overall aerodynamic reliability of the wing, and provide direction for future manufacturing process optimization.
Development and Outlook of Extr eme Ultraviolet (EUV) Lithography Technology
Extreme ultraviolet (EUV) lithography has emerged as a core approach for expanding semiconductor fabrication beyond the boundaries of deep ultraviolet (DUV) patterning. Due to the ever-diminishing reduction of device size, even utilizing multiple patterning techniques does not address the basic physical issue of optical resolution of conventional 193 nm lithography. This study introduces the concepts of photolithography, the technological revolution from DUV to EUV, and the architectural characteristics of current EUV exposure systems. It addresses additional issues, which include but are not limited to limited source power, mask defects, resist stochastics, multilayer mirror contamination, and pellicle transmission loss. The current research was supplemented with industry roadmaps, and it is concluded in the paper that the future scalability of EUV lithography will be dependent on the use of highnumerical-aperture (High-NA) exposure devices, novel photoresist materials (including metal-oxide resists), and improvements to computational lithography to minimize stochasticity and improve the accuracy of the pattern. This study emphasizes the ongoing relevance of optical lithography for the scalability of semiconductor devices and sheds light on the technical directions needed in order to realise semiconductor units at sub-2 nm.
AI as a Technology Bridge for Families: Practical Pathways and Challenges
Traditional AI focused on embedding human thinking to achieve intelligence, while modern AI prioritizes acquiring knowledge from data and making independent judgments. It processes data from daily life and generates outputs to realize intelligent applications. Currently, with the national policy promoting the Internet of Everything and fostering an intelligent product ecosystem, household AI has already occupied a partial position in families and brought significant convenience. This paper explores the specific paths such as technology implementation, product ecosystem construction, and user adaptation, supplemented by specific cases. Additionally, the paper analyzes potential challenges including intergenerational cognitive differences, data privacy risks, economic inequality, and dependence issues. The research aims to provide reference for accelerating the practical implementation of household AI and balancing technological development with risk mitigation.
Control Method of DC Motor Based on PID Control
DC motor control has long been a fundamental topic in industrial automation, robotics, and embedded systems. Among numerous control strategies, PID control remains the most widely applied approach due to its simple structure, intuitive tuning process, and strong engineering practicality. However, with increasing system complexity and diverse application scenarios, traditional PID methods face challenges such as parameter sensitivity, limited robustness, and difficulties in dealing with disturbances and nonlinearities. This paper provides a comprehensive review of PID-based control methods for DC motors. The survey first introduces the characteristics of DC motor models and summarizes the basic principles of PID control. It then categorizes existing research into several groups, including classical tuning techniques, improved PID structures, adaptive and intelligent PID algorithms, observerassisted PID approaches, and PID implementations within embedded platforms. For each category, representative methods, performance characteristics, advantages, and limitations are systematically analyzed. Furthermore, this review discusses key engineering challenges and identifies research gaps that remain insufficiently addressed in current literature. Overall, the paper aims to present an organized understanding of PID-based DC motor control development and to highlight future research trends in practical and intelligent control strategies.
Plant Roots and Ecosystem Stability: A Structured Review
Plant roots are multifunctional organs that not only anchor plants but also regulate water and nutrient flow, construct soil structure, and influence biogeochemical cycles. Root characteristics include systemic structure (such as taproot, fibrous roots, and basal roots), tissue anatomy (like epidermis, cortex, endodermis, and xylem/phloem), and rhizosphere processes (such as root hairs, air cavities, exudates, and mycorrhizae). These characteristics affect ecosystem functions, including water redistribution, soil aggregate formation, carbon sequestration, and resilience to disturbances. However, the complexities of the underground and methodological differences have historically made generalization difficult. This paper explores the impacts of root morphology and function on ecosystem stability, including root classification, specialized structure, and the role of roots in processes such as water and nutrient uptake, plant-microbe interactions, soil structure, and carbon cycling. And it also discusses the contributions of roots to ecological stability and resilience, such as controlling erosion, mitigating drought and nutrient changes, and promoting recovery, while identifying current challenges and methodological needs. The results indicate that root morphological diversity and functional differentiation significantly influenced water redistribution, soil aggregation, and carbon sequestration. Rhizosphere microorganisms, through interactions with roots, promoted nutrient acquisition and enhanced ecosystem stability. Roots also played a crucial role in preventing soil erosion and mitigating drought.
A Literature Review of Agile Development Methods in the Past Decade: Evolutionary Paths, Core Controversies, and Future Trends
Nowadays, agile methodology has emerged as a core and indispensable framework for project execution across diverse industries. Beyond mere process optimization, it is a people-centric, adaptive approach that relies on continuous feedback loops and incremental delivery cycles to enable teams to respond swiftly and effectively to evolving market demands. It also consistently delivers high-quality software products that create tangible value for customers. Through systematic literature analysis and in-depth comparative research, this study systematically delineates the evolutionary trajectory of agile methodology over the past decade. It traces the methodology's expansion from initial cross-role collaboration within small teams to enterprise-wide adoption across entire organizational ecosystems. The study further identifies core controversies in its practical implementation, clarifies context-specific applicable scenarios, and offers forwardlooking projections of its future development trends. This study reveals that agile methodology has undergone a distinct evolution over the past decade—shifting from cross-role collaboration (2014–2018) to enterprise-wide extension (2019–2024)—with core controversies centered on cross-scenario adaptability, integration with emerging technologies, and ethical risks, while its future will move toward in-depth AI integration and cross-industry fulldomain adaptability, and enterprises need to balance agility and structure for effective agile transformation.