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Design of closed-loop manufacturing-remanufacturing system network considering uncertain defect and waste rate: Scenario-based chance-constrained programming

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 259卷

作者： Wang, Gaolong Wang, Yue Li, Xinchao Lu, Shan Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Shenzhen Polytech Univ Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China

To facilitate sustainability by extending the life-cycle and residual value of production materials, a collaborate scheduling scheme for closed-loop manufacturing system with disturbance uncertainty is studied. Based on the features of the closed-loop manufacturing system under multi-product and multi-period scenarios, a mixedinteger linear optimization model is developed, where manufacturing and remanufacturing units are coordinated by recycling the production materials from work-in-process and final products. Based on this, a novel closed-loop manufacturing-remanufacturing scheduling framework is proposed. Additionally, in response to variations in product quality and processing capabilities, a scenario-based chance-constrained programming is introduced to address potential sources of uncertainty. Subsequently, a sequential optimization method is devised to reduce computational complexity. Finally, an industrial case from the electronic factory is investigated to demonstrate its applicability by implementing the proposed approach with comparison to several other strategies.

关键词： Closed-loop manufacturing-remanufacturing system Mixed-integer linear optimization Scenario-based chance-constrained programming

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Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level programming for Microgrid Scheduling

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1488-1500页

作者： Li, Yang Gao, Jiankai Li, Yuanzheng Chen, Chen Li, Sen Shahidehpour, Mohammad Chen, Zhe Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China State Grid Datong Elect Power Supply Co Datong 037008 Peoples R China Huazhong Univ Sci & Technol Sch Artifcial Intelligence & Automat Wuhan 430074 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China IIT Dept Elect & Comp Engn Chicago IL 60616 USA Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads and demand response by leveraging physical informed-inspired deep reinforcement learning (DRL) based bi-level programming. To overcome the non-convex limitations of Karush-Kuhn-Tucker (KKT)-based methods, a novel optimization solution method based on DRL theory is proposed to handle the bi-level programming through alternate iterations between levels. Specifically, by combining a DRL algorithm named asynchronous advantage actor-critic (A3C) and automated machine learning-prioritized experience replay (AutoML-PER) strategy to improve the generalization performance of A3C to address the above problems, an improved A3C algorithm, called AutoML-PER-A3C, is designed to solve the upper-level problem;while the DOCPLEX optimizer is adopted to address the lower-level problem. In this solution process, AutoML is used to automatically optimize hyperparameters and PER improves learning efficiency and quality by extracting the most valuable samples. The test results demonstrate that the presented approach manages to reconcile the interests between multiple stakeholders in MG by fully exploiting various flexibility resources. Furthermore, in terms of economic viability and computational efficiency, the proposal vastly exceeds other advanced reinforcement learning methods.

关键词： Microgrids Stakeholders Optimal scheduling Load modeling Job shop scheduling programming Renewable energy sources Deep reinforcement learning Switches Iterative methods Bi-level scheduling deep reinforcement learning demand response microgrid thermostatically controlled load

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Temperature-Aware Differential programming for Performance and Energy Optimization on 3D NAND High-Density Flash Memory

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Song, Yunpeng Yu, Dingcui Yan, Zhonghuan Wang, Yanyun Shi, Liang East China Normal Univ MoE Engn Res Ctr Software Hardware Codesign Techno Shanghai 200062 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China

3D NAND high-density flash memory is widely used in edge computing, IoT, and automotive applications due to its high performance, low latency, and low storage cost characteristics. These scenarios require operation in extreme temperature environments, with cross-temperature read/write occurring frequently. However, cross-temperature affects programming reliability, leading to high raw bit error rates (RBER), which degrades read performance and increases energy consumption. In this paper, we propose a novel temperature-aware differential programming (TADP) scheme to optimize read performance and energy consumption under cross-temperature read/write. Specifically, first, a temperature-aware compensatory programming scheme is proposed to reduce the cross-temperature-induced degradation of RBER. Second, a layer variation-aware compensatory programming scheme is proposed to reduce the compensatory programming latency. Finally, a degraded programming scheme is proposed to enhance the temperature toughness of poorly temperature-tough word-lines by using them as MLC. Evaluated on 233-layer 3D triple-level-cell (TLC) NAND flash, TADP achieved encouraging optimizations in programming reliability, energy consumption, and read performance with minimal capacity loss.

关键词： programming Reliability Flash memories Three-dimensional displays Energy consumption Error correction codes Optimization Threshold voltage Thermal stability Temperature Cross-temperature programming high-density 3D NAND flash memory read performance energy consumption

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A Compelling Image: The Tower of Babel and the Proliferation of programming Languages During the 1960s

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IEEE ANNALS OF THE HISTORY OF COMPUTING 2025年第1期47卷 22-35页

作者： Nofre, David Independent Scholar Oegstgeest Leiden The Netherlands

In 1961 and in 1969, two towers of Babel of programming languages were published. The first one appeared on the cover of Communications of the ACM. The second one appeared on Jean E. Sammet's programming Languages: History and Fundamentals. These two towers have come to symbolize the fragmentation that plagued the development of programming as a result of the multiplicity of notations. I will argue that contrary to this common view, the tower on the cover of Communications should be understood as a proud display of the research that profoundly transformed computer programming during the late 1950s. The tower of Sammet's book reflected instead the book's goal of bringing order into the field of programming languages. Symbolically, Sammet's book marked the end of a decade that witnessed the emergence of a logico-linguistic approach to the study of programming notations, a decade that also spurred Sammet's fascination with Babel.

关键词： Poles and towers Computer languages programming Painting Visualization History Symbols

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Applying the Block-Based programming Language ALICE for Developing programming Competencies in University Students

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IEEE ACCESS 2025年 13卷 21471-21485页

作者： Vinueza-Morales, Mariuxi Cardenas-Cobo, Jesennia Cabezas-Quinto, Jessica Vidal-Silva, Cristian Univ Estatal Milagro Fac Ciencias Ingn Milagro 091050 Ecuador Univ Talca Escuela Ingn Desarrollo Videojuegos & Real Virtual Fac Ingn Talca 3460000 Chile

The information society is part of modern life, and algorithmic thinking and programming are relevant to everybody, regardless of educational background. Today's world needs professionals with computing competencies. Higher education programming classes usually use text-based programming languages to develop programming competencies, usually without success for all students. Traditional programming languages consider syntax barriers that complicate their adoption and usefulness for students. Block-based programming languages such as SCRATCH and ALICE permit the development of programming competencies without text coding and syntax restrictions to solve that issue. There are different research works on SCRATCH experiences, but not many experiences on how to use ALICE overall in higher education. This article aims to evaluate the effectiveness of the ALICE programming language in enhancing programming learning outcomes for university students and addressing the limited number of empirical studies of ALICE in higher education contexts. The research involved a literature review to provide a theoretical foundation, followed by an experimental phase that involved two groups: the control group, which followed the traditional programming instruction method with a text-based programming language, and the experimental group, which used the block-based ALICE environment. The experimental group achieved better results with an average score of 80, while the control group scored 65 points on a 100-point scale. The results obtained highlight the effectiveness of the ALICE environment in supporting the programming learning process for first-level Software Engineering university students.

关键词： programming programming profession Education Computer languages Java Syntactics Problem-solving programming environments Object oriented programming Surveys programming competencies programming teaching programming learning block-based programming language ALICE

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A Feasible Study of a Deep Learning Model Supporting Human-Machine Collaborative Learning of Object-Oriented programming

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2024年 17卷 413-427页

作者： Wang, Feng Hsu Ming Chuan Univ Dept Comp Sci & Informat Engn Taoyuan City 333 Taiwan

Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in education. In addition, there needs to be more research on applying deep learning technology in education. In this article, we develop an intelligent agent using a performer-based encoder-decoder neural model to classify object-oriented programming (OOP) errors in student code and generate hint feedback in natural language to help students correct the code. This study investigates the feasibility of deploying this agent in an educational setting to support the learning of OOP. This study first examines the low-speed inference problem of the deep learning model. A fast inference algorithm is proposed for the model, which achieves a speedup of eighty times. This study further explores integrating a human-machine collaborative learning process with the deep learning agent. Students were surveyed about their perceptions of the agent in supporting learning. Student responses are interpreted within the learning partnerships model (LPM) framework to show how the agent's technical automation and autonomy features support student-agent learning partnerships. Finally, implications and suggestions for educational application and research of deep learning technology are presented.

关键词： Deep learning Codes programming programming profession Object oriented modeling Man-machine systems Intelligent agents Deep learning technology human-machine collaborative learning intelligent tutoring system learning partnerships model (LPM) object-oriented programming (OOP)

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Rethinking programming Education: A Lecture-Free Approach

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IEEE ACCESS 2025年 13卷 34758-34767页

作者： Shoufan, Abdulhadi Khalifa Univ Ctr Cyber Phys Syst Dept Comp & Informat Engn Abu Dhabi U Arab Emirates

Just-in-time hands-on experience is essential for learning programming. It not only helps reinforce learned concepts immediately, but also encourages the exploration of new concepts and language constructs. Integrating hands-on activities into traditional programming classrooms, however, presents a fundamental challenge. Coordinating instructor explanations with student coding activities and providing timely feedback to all students before moving forward is impractical for large or medium-sized classes. This paper demonstrates how cognitive and constructive learning principles can transform a classroom into an active learning environment that blends conceptual learning and hands-on experience for an object-oriented programming course. Instead of attending traditional lectures, our students use the class time to participate in Moodle-based activities that feature diverse question types and immediate feedback. They work at their own pace, individually or in small teams, while the instructor guides them and provides support as needed. In a section of 41 students, 88% preferred this approach over lectures. These students scored 14.2% higher on the same exam compared to 111 peers enrolled in other sections that used traditional teaching in the same semester.

关键词： programming programming profession Codes Education Cognitive load Encoding Active learning Transforms Surveys Java lecture-free teaching cognitive load theory Moodle immediate feedback java

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programming of subthalamic nucleus deep brain stimulation with hyperdirect pathway and corticospinal tract-guided parameter suggestions

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HUMAN BRAIN MAPPING 2023年第12期44卷 4439-4451页

作者： Segura-Amil, Alba Nowacki, Andreas Debove, Ines Petermann, Katrin Tinkhauser, Gerd Krack, Paul Pollo, Claudio Nguyen, T. A. Khoa Univ Hosp Bern Dept Neurosurg Bern Switzerland Univ Bern ARTORG Ctr Biomed Engn Res Bern Switzerland Univ Hosp Bern Dept Neurol Bern Switzerland ARTORG Murtenstr 50 CH-3008 Bern Switzerland

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for advanced Parkinson's disease. Stimulation of the hyperdirect pathway (HDP) may mediate the beneficial effects, whereas stimulation of the corticospinal tract (CST) mediates capsular side effects. The study's objective was to suggest stimulation parameters based on the activation of the HDP and CST. This retrospective study included 20 Parkinson's disease patients with bilateral STN DBS. Patient-specific whole-brain probabilistic tractography was performed to extract the HDP and CST. Stimulation parameters from monopolar reviews were used to estimate volumes of tissue activated and to determine the streamlines of the pathways inside these volumes. The activated streamlines were related to the clinical observations. Two models were computed, one for the HDP to estimate effect thresholds and one for the CST to estimate capsular side effect thresholds. In a leave-one-subject-out cross-validation, the models were used to suggest stimulation parameters. The models indicated an activation of 50% of the HDP at effect threshold, and 4% of the CST at capsular side effect threshold. The suggestions for best and worst levels were significantly better than random suggestions. Finally, we compared the suggested stimulation thresholds with those from the monopolar reviews. The median suggestion errors for the effect threshold and side effect threshold were 1 and 1.5 mA, respectively. Our stimulation models of the HDP and CST suggested STN DBS settings. Prospective clinical studies are warranted to optimize tract-guided DBS programming. Together with other modalities, these may allow for assisted STN DBS programming.

关键词： deep brain stimulation hyperdirect pathway Parkinson's disease programming tractography

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Individual differences in computer programming: a systematic review

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BEHAVIOUR & INFORMATION TECHNOLOGY 2025年第2期44卷 357-375页

作者： Navarro-Cota, Christian Molina, Ana I. Redondo, Miguel A. Lacave, Carmen Autonomous Univ Baja Calif UABC Fac Engn Architecture & Design Ensenada Mexico Univ Castilla La Mancha UCLM Dept Informat Syst & Technol Ciudad Real Spain Autonomous Univ Baja Calif UABC Fac Engn Architecture & Design Ensenada Tijuana Transpeninsular Highway 3917 Ensenada 22860 Mexico

The demand for programmers has grown exponentially in recent years, making programming an indispensable skill. However, the complex nature of programming poses various challenges for novice programmers, leading to high dropout rates in programming courses. The recognition of individual differences, encompassing distinct neurocognitive profiles, is acknowledged to influence the accuracy of predicting programming skill outcomes. As a result, an increasing awareness exists regarding the substantial contribution of human factors, including personality or cognitive ability, to programming performance. This study conducts a Systematic Literature Review (SLR) to explore recent research on individual differentiation or programmer profiling in programming tasks. The primary goal is to examine pertinent research exploring personal characteristics' influence on programming in various contexts. As a result of this review, a taxonomy has been introduced to enhance the comprehension of programmers' multifaceted individual differences, categorising influential factors into nine distinct dimensions. Furthermore, it has been found that individual differences influence performance in programming tasks, as well as in the learning of this discipline. In conclusion, this SLR emphasises human factors' critical role in programming and proposes a taxonomy that is a valuable framework for researchers, educators, and practitioners to enhance their understanding of human factors' influence on programming performance.

关键词： programming human factors individual differences systematic literature review

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Simultaneous or independent programming and reconfiguration of structural and fluorescent information in multi-stimuli-responsive liquid crystalline polymer film

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CHEMICAL ENGINEERING JOURNAL 2025年 505卷

作者： Zhang, Jianying Zhang, Shuoning Liu, Jiale Ren, Yunxiao Chen, Jiajun Hu, Wei Yang, Huai Univ Sci & Technol Beijing Inst Adv Mat & Technol Beijing Adv Innovat Ctr Mat Genome Engn Beijing 100083 Peoples R China Peking Univ Dept Mat Sci & Engn Coll Engn Beijing 100871 Peoples R China

Stimuli-responsive material is a kind of intelligent material which is widely used in the field of information programming, due to its physical properties that can change with external stimuli. However, challenges remain in constructing multi-stimuli responsive material systems and their application in programming and reconfiguring complex information. Herein, a multi-stimuli-responsive system was constructed by introducing a cyanostyrene-derivative with aggregation-induced emission (AIE-CSD) into a cholesteric liquid crystal polymer (CLCP). The obtained AIE-CSD-based CLCP (A-CLCP) can achieve programming and reconfiguration of complex information combined with structural and fluorescent color, due to its reversible multi-stimuli-responsive properties to temperature, humidity, photo, and pH. Carboxylic acid derivatives hydrogen bonded liquid crystals in the A-CLCP enable its structural color to respond to temperature and humidity;Chemo- and photo-chromic properties of the AIE-CSD molecule endow the CLCP with programmable fluorescent color;In addition, by adjusting the acidifying condition, the structural and fluorescent information can be programmed either simultaneously or independently. The multi-stimuli-responsive liquid crystalline polymer films with programmable and reconfigurable structural and fluorescent information exhibit tremendous potential in advanced anticounterfeiting and information-encrypted storage.

关键词： Liquid crystalline polymer Stimuli-responsive programming Reconfiguration

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