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Changes in lake surface temperature on the Tibetan Plateau from 2000 to 2022 and their driving factors

Science China Earth Sciences 2025年第05期68卷 1641-1655页

作者： Yuanlin QIU Jie CHEN Deliang CHEN State Key Laboratory of Water Resources Engineering and Management Wuhan University Department of Earth System Sciences Tsinghua University Regional Climate Group Department of Earth Sciences University of Gothenburg

Lake surface temperature（LST） is a key parameter in regulating regional water-carbon cycles and biological processes, playing a critical role in the energy and mass balance of lakes. The Tibetan Plateau（TP） is home to thousands of lakes and is highly sensitive to climate change. Therefore, the response of these lakes to a warming climate is crucial for the water security and ecological stability of the “Asian Water Tower”. However, the long-term trend of LST and its driving factors on the TP over the past two decades remain unclear. Here, we employ an all-weather land surface temperature dataset and a representative lake method to investigate the interannual trend of LST on the TP from 2000 to 2022. The analysis uses temperature data from 519 to 581 lakes with interannual dynamic changes in surface area. The results show that lakes on the TP exhibit an overall warming trend, with an average rate of 0.10±0.27°C（10 a）-1. Among the representative lakes, 61% show a warming trend. The most significant warming occurs in autumn, with 91% of the representative lakes showing an increase in LST(0.47±0.30°C（10 a）-1). The warming rate in spring is about half of that in autumn. In winter, the LST trend exhibits a polarized pattern: although some lakes experience significant warming, more than half show intense cooling. In summer, the trend of LST change is more moderate than that in other seasons. The positive feedback from the reduction in lake ice duration drives the LST trend on the TP. The lake ice duration reduces every 10-day, the annual LST increases 0.4°***, altitude and lake expansion can also influence LST changes. Lakes at lower altitudes generally experience higher warming trends. Lake expansion can exacerbate lake warming in autumn by enhancing thermal inertia and delaying lake freeze-up.

关键词： Tibetan Plateau Lake surface temperature Climate change Random forest Attribution analysis

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An Uncertainty Quantization-Based Method for Anti-UAV Detection in Infrared Images

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Computers, Materials & Continua 2025年第4期83卷 1439-1458页

作者： Can Wu Wenyi Tang Yunbo Rao Yinjie Chen Hui Ding Shuzhen Zhu Yuanyuan Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Air Traffic Management System Nanjing210000China National Airspace Management Center Beijing100094China

Infrared unmanned aerial vehicle(UAV)target detection presents significant challenges due to the inter-play between small targets and complex *** methods,while effective in controlled environments,often fail in scenarios involving long-range targets,high noise levels,or intricate backgrounds,highlighting the need for more robust *** address these challenges,we propose a novel three-stage UAV segmentation framework that leverages uncertainty quantification to enhance target *** framework incorporates a Bayesian convolutional neural network capable of generating both segmentation maps and probabilistic uncertainty *** utilizing uncer-tainty predictions,our method refines segmentation outcomes,achieving superior detection ***,this marks the first application of uncertainty modeling within the context of infrared UAV target *** evaluations on three publicly available infrared UAV datasets demonstrate the effectiveness of the proposed *** results reveal significant improvements in both detection precision and robustness when compared to state-of-the-art deep learning *** approach also extends the capabilities of encoder-decoder convolutional neural networks by introducing uncertainty modeling,enabling the network to better handle the challenges posed by small targets and complex environmental *** bridging the gap between theoretical uncertainty modeling and practical detection tasks,our work offers a new perspective on enhancing model interpretability and *** codes of this work are available openly at https://***/general-learner/UQ_Anti_UAV(acceessed on 11 November 2024).

关键词： Object segmentation uncertainty quantification bayesian convolutional neural network

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Joint sequential decision of maintenance and spare parts inventory for multi-unit repairable systems

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International Journal of Reliability and Safety 2025年第1期19卷 22-59页

作者： Zhang, Xiaohong Li, Yuxin Zhang, Jianfei Gan, Jie Zhang, Yongfei Shen, Juan Division of Industrial and System Engineering School of Economics & Management Taiyuan University of Science & Technology Shanxi Province Taiyuan China School of Economics & Management Taiyuan University of Science & Technology Shanxi Province Taiyuan China

Joint decision-making for preventive maintenance and spare parts inventory in multi-component systems is crucial for industrial applications, especially as many expensive, complex equipments can be repaired and reused. This study investigates this joint decision-making using a discrete multi-state degradation model, focusing on the unique characteristics of repairable systems, including their structure and maintenance strategies. First, the operational interactions among production, maintenance, and inventory are analysed to derive state transition probability models for degradation and ordering processes. Subsequently, a sequential decision model is developed to minimise the total system cost, identifying preventive maintenance thresholds, inspection periods, and order batch sequences. To address the problem, a combination of global dynamic programming and genetic algorithms is employed. Numerical experiments with wind turbine spindles validate the decision model, demonstrating its effectiveness in addressing maintenance and inventory optimisation in repairable multi-unit systems while ensuring an optimal dynamic combination of decision variables. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Wind turbines

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Patch-based Histopathological Images for Non- Hodgkin Lymphoma Detection using Voting CNN with Layer Freezing

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第4期52卷 1222-1234页

作者： Luthfiarta, Ardytha Dewi, Ika Novita Hidayat, Erwin Yudi Supriyanto, Catur Putra, Permana Langgeng Wicaksono Ellwid Yousif, Ahmed Sabeeh Department of Informatics Engineering Universitas Dian Nuswantoro Indonesia Department of Information System Universitas Dian Nuswantoro Indonesia Technical College of Management Mousl Northern Technical University Iraq

Non-Hodgkin Lymphoma (NHL) is characterized by its diverse subtypes of lymphoid malignancies, presenting challenges for accurate diagnosis due to the variability in tissue morphology and immunophenotypic profiles. This research proposes a novel automated approach for NHL subtype classification using histopathological images, integrating a combination of patch-based analysis with a voting ensemble of Convolutional Neural Networks (CNN). Pre-trained CNN models such as DenseNet169, MobileNetV2, and NASNetMobile were enhanced using a layer-freezing technique to preserve learned low-level features while fine-tuning higher-level layers for improved specificity in NHL detection. A majority voting mechanism aggregates predictions from individual image patches, enhancing classification robustness. The proposed model was evaluated on the IICBU 2008 Lymphoma image dataset, achieving a classification accuracy of 99.11% and an F1- score of 99.11%, surpassing previous methods. This approach demonstrates significant potential in improving the accuracy, efficiency, and clinical applicability of automated NHL subtype classification from histopathological images. © 2025 IAENG International Journal of Computer Science

关键词： Convolutional neural networks

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7.2.2 On Methodology and system Framework for Knowledge management in Allied system engineering

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INCOSE International Symposium 2004年第1期14卷 1437-1449页

作者： Chen, Yuh-Min Chen, Yuh-Jen Kuo, Jen-Yao Wang, Chin-Bin Ho, ChengTer Institute of Manufacturing Engineering National Cheng Kung University Tainan Taiwan System Engineering Center Industrial Technology Research Institute Hsin-Chu Taiwan Dept. of Information Management Nan-Hua University Chia-Yi Taiwan Dept. of Industrial Engineering and Management National Kaohsiung University of Applied Sciences Kaohsiung Taiwan

This paper presents a systematic approach to development of a methodology and a system framework for knowledge management to support allied system engineering. The approach of this research has laid-down four phases: allied system engineering characterization, knowledge identification, analysis and modeling, KM strategy and methodology development, and system development. The methodology consists of a rationale behind the methodology, a life cycle model for knowledge management, and a distributed knowledge management framework. Based on the proposed methodology, a knowledge management system framework is developed using component technology. Besides providing functions for knowledge management and sharing, the system possesses properties to meet the hierarchical, distributed, flexible and dynamic-configurable characteristics of allied system engineering. © 2004 The Authors.

关键词： Knowledge management

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A Non-Sequential Steganography Method Using Perfect Square Quotient Differencing (NSPSQD)

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SN Computer Science 2025年第5期6卷 1-26页

作者： Basak, Ratan Kumar Chatterjee, Ritam Dasgupta, Kousik Dutta, Paramartha Computer Science and Engineering Institute of Engineering and Management (IEM) University of Engineering and Management Kolkata Kolkata India Computer Science and Engineering Kalyani Government Engineering College Kalyani India Computer and System Science Visva-Bharati University Bolpur India

In today’s digital world, keeping data safe is a top priority. Two common methods used to protect data are steganography and cryptography. Steganography hides secret data within everyday files (like images, GIFs or videos), while cryptography scrambles the data into an unreadable format. This paper introduces a new way to hide data using a technique called Perfect Square Quotient Differencing. Instead of embedding data in a straight sequence, the method hides information in two steps within the components of an image pixel (called the quotient and remainder). In the first step, a perfect square quantization technique is applied to the quotient part. In the second step, the Two Least Significant Bit (2LSB) method is used on the remainder part. A new range-table is also introduced to help determine how much data can be hidden in the first step. This two-step approach allows a large amount of data to be hidden (about 3 bits per pixel on average). The method was tested on many animated color images, and its performance was measured using tools like Peak-Signal-to-Noise-Ratio (PSNR), Mean Square Error (MSE), Universal Image Quality Index (UIQI), and Payload Curve. The results show that this method works better than several modern steganography techniques. Additionally, tests were conducted to ensure the method is secure against potential attacks. This new algorithm could be particularly useful for protecting digital documents stored in cloud-based platforms, offering a robust and efficient way to keep data safe.

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Tighter Analysis for Decentralized Stochastic Gradient Method: Impact of Data Homogeneity

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IEEE Transactions on Automatic Control 2025年

作者： Li, Qiang Wai, Hoi-To The Chinese University of Hong Kong Dept. of System Engineering & Engineering Management Faculty of Engineering Shatin District Hong Kong Hong Kong

This paper studies the effect of data homogeneity on multi-agent stochastic optimization. We consider the decentralized stochastic gradient (DSGD) algorithm and perform a refined convergence analysis. Our analysis is explicit on the similarity between Hessian matrices of local objective functions which captures the degree of data homogeneity. We illustrate the impact of our analysis through studying the transient time, defined as the minimum number of iterations required for a distributed algorithm to achieve comparable performance as its centralized counterpart. When the local objective functions have similar Hessian, the transient time of DSGD can be as small as for smooth (possibly non-convex) objective functions, for strongly convex objective functions, where is the number of agents and is the spectral gap of graph. These findings provide a theoretical justification for the empirical success of DSGD. Our analysis relies on a novel observation with higher-order Taylor approximation for gradient maps that can be of independent interest. Numerical simulations validate our findings. © 2025 IEEE.

关键词： Optimization algorithms

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A NONPARAMETRIC ROBUST OPTIMIZATION APPROACH FOR CHANCE-CONSTRAINED KNAPSACK PROBLEM

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SIAM Journal on Optimization 2025年第2期35卷 739-766页

作者： Xu, Liang Zhang, Chao Xu, Zhou Long, Daniel Zhuoyu School of Business Administration Southwestern University of Finance and Economics Chengdu China Big Data Laboratory on Financial Security and Behavior SWUEF Laboratory of Philosophy and Social Sciences Ministry of Education Chengdu China School of Medical Informatics Chongqing Medical University Chongqing China Department of Logistics Management and Maritime Studies Hong Kong Polytechnic University Hong Kong Department of System Engineering and Engineering Management Chinese University of Hong Kong Hong Kong

A chance-constrained knapsack problem (CCKP) is a knapsack problem restricted by a chance constraint, which ensures that the total capacity constraint under uncertain volume can be violated only up to a given probability threshold. CCKP is challenging to solve due to its combinatorial nature and the involvement of its chance constraint. Existing solution methods for CCKP with tractability guarantees mainly focus on two approaches: (1) a full-information approach (stochastic programming) that assumes the uncertain volume follows certain distributions, such as normal or empirical distribution;(2) a partial-information approach (robust optimization) that adopts specific statistics of the unknown distribution, such as the mean and variance. The existing full-information approach lacks robustness under limited samples due to its strong assumption;the existing partial-information approach can be further improved, as the uncertainty set or distributional ambiguity set can be ameliorated. With these concerns in mind, we propose a nonparametric robust approach for CCKP by involving a novel nonparametric statistic to form a new distributional ambiguity set. Furthermore, we develop an upper bound on the violation probability of the chance constraint under the distributional ambiguity set to approximate CCKP by a deterministic robust counterpart. In terms of solution methodology, we decompose the deterministic robust counterpart into cardinality-constrained knapsack problems, which can be efficiently solved by the proposed dynamic programming algorithm. Computational results show that our proposed solution methods produce better solutions to CCKP compared with existing approaches. © 2025 Society for Industrial and Applied Mathematics.

关键词： Stochastic programming

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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2025 International Conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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Model-based Risk Assessment of Power Converters: Case Study of On-board Battery system for Railway

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2025年 6卷 221-236页

作者： Saeed, Mariam Guerrero, Juan Manuel Lopez, Victor Ortega, David Larrazabal, Igor Valera, Juan Jose Falke, Ewald Briz, Fernando University of Oviedo Department of Electrical Computer & System Engineering Gijón Spain Ingeteam Power Technology S.A. Traction R&D Zamudio Spain Stadlerrail Management AG Bussnang Switzerland

On-board energy storage systems for railway traction are becoming a clear trend for many new rail projects, both for retrofit and new designs. This has raised safety and reliability concerns in railway industry given the novelty of the technology. Reliability analysis is mostly based on detailed modeling of the physics of failure. However, it is thought for system optimization and do not consider catastrophic and human factor failures. On the other hand, risk assessment studies are widely used for identifying, analyzing and prioritizing all possible modes of failure. However, they are not based on systems models and are not extendable to other converter topologies or battery configurations. To overcome the limitations of the two aforementioned approaches, this paper proposes a method for risk assessment based on systems models. The proposed methodology is applied to study the critical risks of using a multilevel converter topology integrating a configuration of two series low-voltage ( © 2020 IEEE.

关键词： Failure rate

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