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Case Studies in Chemical and Environmental engineering 2025年 11.0卷

作者： Hakim, Ardi Lukman Sari, Inzus Etika Nur Sabrina, Bunga Jasmine Nurkhalizah, Zanna Allia Ajeng Zahabiya, Ecce Kamila Hidayat, Aditya Nur Sulistiono, Dety Oktavia Sopiah, R. Nida Kusumawati, Yuly Ediati, Ratna Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Kampus ITS Keputih-Sukolilo Surabaya 60111 Indonesia Automotive Engineering Engineering Department Politeknik Negeri Jember Jember 68101 Indonesia Research Center for Environmental and Clean Technology National Research and Innovation Agency Technology Energy Cluster PUSPIPTEK 625 Building South Tangerang Indonesia

MCM-41 was synthesized from coal fly ash and used as a support for TiO₂/ZnO nanoparticles in methylene blue removal. The composite was prepared using alkali fusion and hydrothermal methods, yielding MCM-41 with a surface area of 1013.73 m²/g, which decreased to 457.81 m²/g upon TiO₂/ZnO impregnation. Characterization confirmed the hexagonal mesoporous structure, uniform dispersion of TiO₂/ZnO, and enhanced light absorption. Ultraviolet–Visible Diffuse Reflectance Spectroscopy (UV–Vis DRS) results revealed a band gap reduction from 3.22 eV (TZ) to 2.77 eV (TZM), improving photocatalytic activity under UV and visible light irradiation. The 35TiO₂/65ZnO@MCM-41 composite achieved 99.21 % MB degradation in 140 min. © 2025 The Authors

关键词： Coal fly ash MB degradation MCM-41 Photocatalysis TiO2/ZnO

Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification

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arXiv 2024年

作者： Yuan, Han Hong, Chuan Jiang, Pengtao Zhao, Gangming Tran, Nguyen Tuan Anh Xu, Xinxing Yan, Yet Yen Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States College of Computer Science Nankai University China Faculty of Engineering The University of Hong Kong Hong Kong Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Department of Radiology Changi General Hospital Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background: Pneumothorax is an acute thoracic disease caused by abnormal air collection between the lungs and chest wall. Recently, artificial intelligence (AI), especially deep learning (DL), has been increasingly employed for automating the diagnostic process of pneumothorax. To address the opaqueness often associated with DL models, explainable artificial intelligence (XAI) methods have been introduced to outline regions related to pneumothorax diagnoses made by DL models. However, these explanations sometimes diverge from actual lesion areas, highlighting the need for further improvement. Method: We propose a template-guided approach to incorporate the clinical knowledge of pneumothorax into model explanations generated by XAI methods, thereby enhancing the quality of these explanations. Utilizing one lesion delineation created by radiologists, our approach first generates a template that represents potential areas of pneumothorax occurrence. This template is then superimposed on model explanations to filter out extraneous explanations that fall outside the template’s boundaries. To validate its efficacy, we carried out a comparative analysis of three XAI methods (Saliency Map, Grad-CAM, and Integrated Gradients) with and without our template guidance when explaining two DL models (VGG-19 and ResNet-50) in two real-world datasets (SIIM-ACR and ChestX-Det). Results: The proposed approach consistently improved baseline XAI methods across twelve benchmark scenarios built on three XAI methods, two DL models, and two datasets. The average incremental percentages, calculated by the performance improvements over the baseline performance, were 97.8% in Intersection over Union (IoU) and 94.1% in Dice Similarity Coefficient (DSC) when comparing model explanations and ground-truth lesion areas. We further visualized baseline and template-guided model explanations on radiographs to showcase the performance of our approach. Conclusions: In the context of pneumothorax diagnos

关键词： Diagnosis

Enhancement of galena-potassium ethyl xanthate flotation system by low power ultrasound

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Transactions of Nonferrous Metals Society of China 2020年第4期30卷 1102-1110页

作者： Can GUNGOREN Yasin BAKTARHAN Ismail DEMIR Safak Gokhan OZKAN Department of Mining Engineering Engineering FacultyIstanbul University-CerrahpasaBuyukcekmeceIstanbul 34500Turkey Department of Robotics and Intelligent Systems The Institute of the Graduate Studies in Science and EngineeringTurkish-German UniversityBeykozIstanbul 34820Turkey

The objective of this study is to investigate the improvement possibilities of the floatability of galena with ultrasonic application in the presence of potassium ethyl xanthate(KEX). For this purpose, micro-flotation experiments were carried out in addition to surface chemistry studies including zeta potential, contact angle, and bubble-particle attachment time measurements at various ultrasonic power levels and conditioning time. The results showed that, the maximum micro-flotation recovery of 77.5% was obtained with 30 W ultrasound power and 2 min conditioning time. In addition, more negative zeta potential values were obtained with ultrasound as well as higher contact angle and lower bubble-particle attachment time, which indicated the increased hydrophobicity of galena with ultrasound.

关键词： galena potassium ethyl xanthate flotation ultrasound bubble-particle attachment time

Quantum sensor network metrology with bright solitons

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Physical Review A 2023年第6期108卷 062612-062612页

作者： Dmitriy Tsarev Stepan Osipov Ray-Kuang Lee Sergey Kulik Alexander Alodjants Institute of Advansed Data Transfer Systems ITMO University Saint Petersburg 197101 Russia Institute of Photonics Technologies National Tsing Hua University Hsinchu 30013 Taiwan Center for Quantum Science and Technology Hsinchu 30013 Taiwan Quantum Technology Centre Faculty of Physics M. V. Lomonosov Moscow State University Moscow 119991 Russia Quantum Light Engineering Laboratory Institute of Natural and Exact Sciences South Ural State University Chelyabinsk 454080 Russia

We consider a multiparameter quantum metrology problem with bright soliton networks in the presence of weak losses. We introduce the general Heisenberg limit (GHL) σχ=1/Nk that characterizes fundamental limitations for unknown parameter measurement and estimation accuracy σχ within linear (k=1) and nonlinear (k=3) quantum metrology approaches to solitons. We examine multipartite NOON states specially prepared for the improvement of multiparameter estimation protocols. As a particular example of producing such states, we propose a three-mode soliton Josephson-junction (TMSJJ) model as a three-mode extension for the soliton Josephson-junction bosonic model, which we previously proposed. The energy spectrum of the TMSJJ exhibits sharp phase transition peculiarities for the TMSJJ ground state. The transition occurs from a Gaussian-like (coherent) state to the superposition of entangled Fock states, which rapidly approach the three-mode NOON state. We show that in the presence of weak losses the TMSJJ enables saturation scaling relevant to the optimal state limit close to the GHL. Our findings open prospects for quantum network sensorics with atomtronic circuits.

关键词： Quantum Fisher information Quantum metrology Quantum parameter estimation Quantum sensing Solitons

Enhanced Visual Feedback with Decoupled Viewpoint Control in Immersive Humanoid Robot Teleoperation using SLAM

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arXiv 2022年

作者： Chen, Yang Sun, Leyuan Benallegue, Mehdi Cisneros-Limón, Rafael Singh, Rohan P. Kaneko, Kenji Tanguy, Arnaud Caron, Guillaume Suzuki, Kenji Kheddar, Abderrahmane Kanehiro, Fumio University of Tsukuba Japan Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Japan Universite de Picardie Jules Verne MIS lab Amiens France France The Faculty of Engineering Center for Cybernetics Research University of Tsukuba Japan

In immersive humanoid robot teleoperation, there are three main shortcomings that can alter the transparency of the visual feedback: (i) the lag between the motion of the operator’s and robot’s head due to network communication delays or slow robot joint motion. This latency could cause a noticeable delay in the visual feedback, which jeopardizes the embodiment quality, can cause dizziness, and affects the interactivity resulting in operator frequent motion pauses for the visual feedback to settle;(ii) the mismatch between the camera’s and the headset’s field-of-views (FOV), the former having generally a lower FOV;and (iii) a mismatch between human’s and robot’s range of motions of the neck, the latter being also generally lower. In order to leverage these drawbacks, we developed a decoupled viewpoint control solution for a humanoid platform which allows visual feedback with low-latency and artificially increases the camera’s FOV range to match that of the operator’s headset. Our novel solution uses SLAM technology to enhance the visual feedback from a reconstructed mesh, complementing the areas that are not covered by the visual feedback from the robot. The visual feedback is presented as a point cloud in real-time to the operator. As a result, the operator is fed with real-time vision from the robot’s head orientation by observing the pose of the point cloud. Balancing this kind of awareness and immersion is important in virtual reality based teleoperation, considering the safety and robustness of the control system. An experiment shows that the effectiveness of our solution. © 2022, CC BY.

关键词： Remote control

Optimal kernel choice for score function-based causal discovery 24

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Optimal kernel choice for score function-based causal discov...

Proceedings of the 41st International Conference on Machine Learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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Grouping fast-moving and slow-moving inventory using K-medoids clustering

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AIP Conference Proceedings 2023年第1期2536卷

作者： Purbandini Indah Werdiningsih Endah Purwanti Araeyya Yenofa Putri Information Systems Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia Robotics and Artificial Intelligent Engineering Faculty of Technology Advance and Multidicipline Engineering Universitas Airlangga Surabaya Indonesia

Inventories are materials or products that are owned and stored for future use by the company. Inventory control is a prime factor in the operation of a company because it affects various things, such as profits, costs, and corporate planning and strategy. However, several problems are often experienced, for example, excess or lack of stock in storage. These problems arise because of the mismatch in the number of pre-orders (PO). This research proposes to overcome the problem with a data mining approach for grouping fast-moving and slow-moving products by applying k-medoids clustering with the Python programming language. The research location is PT. Lenko Surya Perkasa Branch Office Sidoarjo. K-Medoids method is applied to the company's data from January 2018 to July 2020. Then, it will group the data based on several parameters, namely the number of products sold each year or period, the number of sales transactions per year or period, products that are damaged or returned every year or period, inventory turnover ratio (TOR) partial or annually, and days inventory (DIS) or inventory saving time (WSP). The result obtains the grouping works with 5 clusters, according to the request of the company in 2018, 2019, and 2020 datasets. In 2018, C1 is slow-moving, C2 is fast-moving, C3 is super slow-moving or non-moving, C4 is fairly slow-moving, and C5 is fairly fast-moving consisting 61, 25, 2, 2, and 1 member with a silhouette score of 0.547. In 2019, C1 and C2 are super slow-moving or non-moving, C3 is slow-moving, C4 is fast-moving, and C5 is fairly fast-moving containing 65, 1, 1, 22, and 2 members respectively with a silhouette score of 0.654. In 2020, C1, C2, and C5 is super slow-moving or non-moving, C3 is fairly fast-moving, C4 is slow-moving consisting 84, 3, 1, 2, and 1 member for C1, C2, C3, C4, and C5 respectively with a silhouette score of 0.75. From the results obtained, k-medoids clustering can classify health products at PT. Lenko Surya Perkasa Branch Off

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Medical product sales forecasting at pharmaceutical distribution company (Case study: PT. Lenko Surya Perkasa Branch Office Sidoarjo)

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AIP Conference Proceedings 2023年第1期2536卷

作者： Purbandini Indah Werdiningsih Endah Purwanti Annisa Anjani Information Systems Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia Robotics and Artificial Intelligent Engineering Faculty of Technology Advance and Multidicipline Engineering Universitas Airlangga Surabaya Indonesia

The problems that occur in PT. Lenko Surya Perkasa Branch Office Sidoarjo based on sales and warehouse data for the period October 2019 - August 2020 said that the number of damaged products continued to increase, around 23% of the total product, low sales quantity, and manual restock quantity estimates that often missed, made the warehouse storage level higher so that the product damaged due to being in the warehouse for too long. Because of that, we need a system that can predict how many sold of the product as a basis for consideration restocking products to minimize the level of warehouse storage and damaged products. The results of this study said that the least square method was not suitable because the data did not have a physical meaning of a mathematical model. However, it can describe the trend of sales which in this case tends to decrease. To explain the variation of a dependent variable y as a function of x most tempting approach to the problem is to use a simple polynomial through least square with MAPE values 42,40468 and MSE 3687209.365 with the optimum orde of the polynomial is 6. The comparison method used in this study is a simple moving average using Ma (2), Ma (3), Ma (4) stated that forecast results from SMA also produce curves that are similar to the actual data. It shows that SMA is also suitable to use when the data did not have a physical meaning of a mathematical model but did not fit when there is a big enough data spike, proven by the MAPE and MSE values which are still higher than simple polynomial regression.

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GenAI for Simulation Model in Model-Based systems engineering

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arXiv 2025年

作者： Zhang, Lin Zhang, Yuteng Niyato, Dusit Ren, Lei Gu, Pengfei Chen, Zhen Laili, Yuanjun Cai, Wentong Bruzzone, Agostino School of Automation Science and Electrical Engineering Beihang University Xueyuan Road No.37 Haidian Beijing China Hangzhou International Innovation Institute Beihang University 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou China State Key Laboratory of Intelligent Manufacturing Systems Technology Yongding Road No.51 Haidian Beijing China College of Computing and Data Science Nanyang Technological University Block N4-02a-32 Nanyang Avenue Singapore University of Genoa Via Balbi 5 Genoa16126 Italy

Generative AI (GenAI) has demonstrated remarkable capabilities in code generation, and its integration into complex product modeling and simulation code generation can significantly enhance the efficiency of the system design phase in Model-Based systems engineering (MBSE). In this study, we introduce a generative system design methodology framework for MBSE, offering a practical approach for the intelligent generation of simulation models for system physical properties. First, we employ inference techniques, generative models, and integrated modeling and simulation languages to construct simulation models for system physical properties based on product design documents. Subsequently, we fine-tune the language model used for simulation model generation on an existing library of simulation models and additional datasets generated through generative modeling. Finally, we introduce evaluation metrics for the generated simulation models for system physical properties. Our proposed approach to simulation model generation presents the innovative concept of scalable templates for simulation models. Using these templates, GenAI generates simulation models for system physical properties through code completion. The experimental results demonstrate that, for mainstream open-source Transformer-based models, the quality of the simulation model is significantly improved using the simulation model generation method proposed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Open source software

Nested Bayesian Optimization for Computer Experiments

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arXiv 2021年

作者： Wang, Yan Wang, Meng AlBahar, Areej Yue, Xiaowei School of Statistics and Data Science Faculty of Science Beijing University of Technology Beijing100124 China Grado Department of Industrial and Systems Engineering Virginia Tech BlacksburgVA24061 United States

Computer experiments can emulate the physical systems, help computational investigations, and yield analytic solutions. They have been widely employed with many engineering applications (e.g., aerospace, automotive, energy systems). Conventional Bayesian optimization did not incorporate the nested structures in computer experiments. This paper proposes a novel nested Bayesian optimization method for complex computer experiments with multi-step or hierarchical characteristics. We prove the theoretical properties of nested outputs given that the distribution of nested outputs is Gaussian or non-Gaussian. The closed forms of nested expected improvement are derived. We also propose the computational algorithms for nested Bayesian optimization. Three numerical studies show that the proposed nested Bayesian optimization method outperforms the five benchmark Bayesian optimization methods that ignore the intermediate outputs of the inner computer code. The case study shows that the nested Bayesian optimization can efficiently minimize the residual stress during composite structures assembly and avoid convergence to local optima. Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian distribution