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SSRN 2023年

作者： Lin, Yunhong Guo, Xiaodong Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China

How to solve the current engineering risks and problems faced in the shield receiving process has become an important research hotspot in the industry. To this end, we have carried out research into newly receiving hood system for tunnel shield machines under unreinforced ground conditions. Based on the concept of active pressurization, a new type of sealing device is used, using an active pressurized sealing system for the whole process from the shield breaking gate to the construction of the ring beam of the gate. The results of the study show that the newly receiving hood system is suitable for all kinds of unreinforced soils and all sizes of shield machines, and is set up in three models of 20m, 40m and super 40m water pressure, using the technique of building pressure in the earth bin before turning the knife plate to grind the enclosure to achieve a good sealing effect. In addition, the station end settlement data shows that with the new initiation technique the works are carried out without negative impact on the surrounding environment. The study proposes a new receiving hood system that provides a good solution to the risks faced by shield operations. © 2023, The Authors. All rights reserved.

关键词： Tunneling machines

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The Newly Launching Hood System for Tunnel Shield Machines: design and On-Site Operation

SSRN

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SSRN 2023年

作者： Lin, Yunhong Chang, Ruicheng Yao, Zhengyu Chen, Zhong He, Hongming Guangzhou Metro Design and Research Institute Co. Ltd Guangdong Guangzhou China Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming630093 China School of Geographic Sciences East China Normal University Shanghai200241 China Key Laboratory of Geographic Information Science Ministry of Education East China Normal University Shanghai200241 China Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China

The resolution of engineering risks and problems during the shield machine launching process has become a crucial research topic in the industry. To address this issue, we conducted a study on a newly launching hood system for unconsolidated soil conditions. Based on the concept of active pressurization and using a new sealing device, we applied an active pressurized sealing system throughout the construction process from the shield breakthrough door to the ring beam of the door. The research results demonstrated that the newly launching hood system is suitable for various unconsolidated soil layers and shield machine sizes, with three models of 20m, 40m, and above 40m water pressure. By using the technology of pressurizing the soil before turning the cutterhead to grind the surrounding structure, a good sealing effect was achieved. Moreover, station end settlement data showed that the use of the newly launching hood system does not have a negative impact on the surrounding environment during construction. The launching hood system for tunnel shield provides an excellent solution for the risks faced in shield machine operations. © 2023, The Authors. All rights reserved.

关键词： Grinding (machining)

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Arabic translation and validation of the clinician administered Staden schizophrenia anxiety rating scale (S-SARS)

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Heliyon 2024年第21期10卷 e39792页

作者： Fekih-Romdhane, Feten Al Mouzakzak, Fadila Abilmona, Ghinwa Moustafa, Ahmed Dahdouh, Oussama Hallit, Souheil Tunis El Manar University Faculty of Medicine of Tunis Tunis Tunisia The Tunisian Center of Early Intervention in Psychosis Department of psychiatry “Ibn Omrane” Razi hospital 2010 Manouba Tunisia Faculty of Science Lebanese University Fanar Lebanon School of Psychology Faculty of Society and Design Bond University Gold Coast QLD Australia Department of Human Anatomy and Physiology the Faculty of Health Sciences University of Johannesburg South Africa Centre for Data Analytics Bond University Gold Coast QLD Australia Research Department Psychiatric Hospital of the Cross P.O. Box 60096 Jal Eddib Lebanon School of Medicine and Medical Sciences Holy Spirit University of Kaslik P.O. Box 446 Jounieh Lebanon Psychology Department College of Humanities Effat University Jeddah 21478 Saudi Arabia Applied Science Research Center Applied Science Private University Amman Jordan

Background: Research on anxiety in patients with schizophrenia of Arab origin is surprisingly scarce, particularly given that expressions of both psychotic disorders and anxiety disorders can be largely shaped by cultural factors. This study proposes to complement previous research by exploring the psychometric characteristics of an Arabic translation of the Staden Schizophrenia Anxiety Rating Scale (S-SARS) in chronic, remitted patients diagnosed with schizophrenia. As the Arabic version of the Generalized Anxiety Disorder 7-Item Scale (GAD-7) has not been previously validated in Arabic in patients with schizophrenia, this study had as a secondary aim to investigate the psychometric properties of this scale before its use. Method: This cross-sectional study was performed over a period of three months (August–October 2023). A total of 177 chronic inpatients diagnosed with schizophrenia (63.3 % males) who were remitted and clinically stable participated in the study. Results: Confirmatory factor analyses showed that all 10 items loaded onto a single factor and had high factor loading values between .53 and .81. The reliability of the S-SARS in its Arabic version was excellent as attested by a McDonald's omega and a Cronbach's alpha coefficients of .90 and .89, respectively. The score of Arabic S-SARS correlated positively with the GAD-7 scores (r = .55;p < .001), thus supporting good convergent validity. As for discriminant validity, findings showed positive correlations between S-SARS and depression scores as assessed using the Calgary Depressive Symptoms Scale. In addition, the Arabic S-SARS correlated negatively with general functioning, further supporting the good validity and clinical relevance of the scale. Finally, measurement invariance was established in the sex subsamples (males vs. females) at the scalar, metric and configural levels, with females showing more anxiety than males. Conclusion: Findings suggest that the Arabic S-SARS holds good psychometric p

关键词： Anxiety Arabic Psychometric properties S-SARS Schizophrenia Validation

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Effects of sodium alginate-poly(acrylic acid) cross-linked hydrogel beads on soil conditioner in the absence and presence of phosphate and carbonate ions

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Case Studies in Chemical and Environmental Engineering 2024年 9卷

作者： Hidayat, Endar Sarbani, Nur Maisarah Mohamad Lahiri, Sudip Kumar Samitsu, Sadaki Yonemura, Seiichiro Mitoma, Yoshiharu Harada, Hiroyuki Graduate School of Comprehensive Scientific Research Program in Biological System Sciences Prefectural University of Hiroshima Shobara 727-0023 Japan Department of Life System Science Faculty of Bioresources Science Prefectural University of Hiroshima Shobara 727-0023 Japan Data-driven Polymer Design Group Research Center for Macromolecules and Biomaterials National Institute for Materials Science 1−2−1 Sengen Tsukuba 305-0047 Japan Department of Mechanical & Industrial Engineering University of Toronto Toronto M5S 3G8 ON Canada Faculty of Allied Health Sciences Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam 603103 India

Hydrogel beads, composed of polymers, possess the ability to absorb substantial quantities of water simultaneously and gradually release it in dry conditions. In this work, hydrogel beads were synthesized using sodium alginate (S-Alg) and poly (acrylic acid) (P-Acc), either in the absence or presence of phosphate (PO₄ ³⁻) (S-Alg/P-Acc@PO₄ ³⁻) and carbonate (CO₃ ²⁻) anionic ions (S-Alg/P-Acc@CO₃ ²⁻). Subsequently, these beads were crosslinked with Ca²⁺ ions. The scanning electron microscopy (SEM) images demonstrated that the presence of anionic ions has an impact on the structure of the hydrogel beads and enhances porosity expansion while concurrently reducing the concentration of carboxyl (COOH) groups. The hydrogel beads exhibited a great swelling behavior, with S-Alg/P-Acc@CO₃ ²⁻ capable of absorbing up to 73.5 % of water in acidic conditions, but decreased under neutral and basic conditions. Soil water loss (SWL) experiments confirmed the ability of hydrogel beads to retain water in the soil. Additionally, observations of soil characteristics after a 14-day treatment revealed no significant difference in pH and C/N ratio (P-value <0.05). However, soil's available Fe and N–NO₃ ^- significantly reduced (P-value <0.05) while substantially promoting soil N–NH₄ ⁺, available phosphorus (P), cation exchange capacity (CEC), soil organic carbon (SOC), and exchangeable cation (Ca²⁺, K⁺ and Mg²⁺). The presence of hydroxyl (-OH) and –COOH groups was found to be crucial for enhancing soil properties. © 2024 The Authors

关键词： Anionic ions Hydrogel beads Poly (acrylic acid) Sodium alginate Soil characteristics Soil water retention Swelling

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A Systematic Review of Multi-Sensor Information Fusion for Equipment Fault Diagnosis

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Lin, Tantao Ren, Zhijun Zhu, Linbo Zhu, Yongsheng Feng, Ke Ding, Weiping Yan, Ke Beer, Michael Xi'an Jiaotong University Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an710049 China Xi'an Jiaotong University Frontier Institute of Science and Technology Xi'an710049 China Xi'an Jiaotong University School of School of Chemical Engineering and Technology Xi'an710049 China Xi'an Jiaotong University School of Mechanical Engineering Xi'an710049 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science Macau999078 China Leibniz University Institute for Risk and Reliability Hannover30167 Germany University of Liverpool Department of Civil and Environmental Engineering LiverpoolL69 3BX United Kingdom Tongji University International Joint Research Center for Resilient Infrastructure International Joint Research Center for Engineering Reliability and Stochastic Mechanics Shanghai200092 China

In contrast to fault diagnosis relying solely on a single sensor, the method of multi-sensor information fusion for fault diagnosis (MSIFFD) broadens the spectrum of available information sources. It is renowned for its high accuracy and reliability, traits that have attracted growing attention within the research community and led to the generation of a substantial body of publications. However, there is currently a lack of a comprehensive and systematic review in this domain. Thereby, this review aims to thoroughly explore all research achievements in the field of MSIFFD. At the outset, an analysis is undertaken to delineate the fusion level of multi-sensor information, with a specific focus on its location and types of inputs and outputs within the information flow. Subsequently, an examination of the six primary fundamental operations for amalgamating multi-sensor information is undertaken to clarify the motivations and processes involved in information fusion across various operations. Following this, factors influencing fusion diagnostics and strategies aimed at improving their performance are examined to explore approaches for enhancing fusion accuracy. The subsequent sections delve into the analysis of sensor types and application scenarios, providing a reference guide for practical applications. Finally, this review outlines potential future challenges in the field of MSIFFD and provides a range of recommendations and possible solutions for consideration. © 1963-2012 IEEE.

关键词： Information fusion

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The reality of the loudness war in Japan -The case study on Japanese popular music- 149

The reality of the loudness war in Japan -The case study on ...

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149th Audio Engineering Society Convention 2020, AES 2020

作者： Watanabe, Kazuma Kazuhiko, Kawahara Nishida, Hiriko Okusa, Kosuke Graduate school of design Kyushu University 4-9-1 Shiobaru Minami-ku Fukuoka815-8540 Japan Faculty of Design Kyushu University 4-9-1 Shiobaru Minami-ku Fukuoka815-8540 Japan School of Data Science Yokohama City University 22-2 Seto Kanazawa-ku Yokohama236-0027 Japan

This paper focused on the loudness statistics of the songs, especially commercially succeeded albums of popular music in Japan during the year of 1989 to 2018. The purpose of this paper was to verify the existence of the "loudness war" in Japan. The statistical results showed there was a leap of loudness in the middle 1990s, which implies the existence of the loudness war was on in Japan. Moreover, the authors employed the time series analysis and estimated the average loudness from 2019 to 2028. The authors proposed the loudness estimation by using the ARIMA model. The model estimated the future average loudness would rise to -4 LUFS, in case of without any regulations. © 2021 149th Audio Engineering Society Convention 2020, AES 2020. All Rights Reserved.

关键词： Time series analysis

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Fatigue prediction for metallic additively manufactured lattice components using FCM based on average strain energy density approach

Fatigue prediction for metallic additively manufactured latt...

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International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS)

作者： R. De Biasi O. Oztoprak F. Zanini S. Carmignato F. Berto S. Kollmannsberger M. Benedetti Department of Industrial Engineering Via Sommarive 9 University of Trento Trento Italy School of Engineering and Design Chair of Computational Modeling and Simulation Arcisstraße 21 Technical University of Munich Munich Germany Department of Management and Engineering Stradella San Nicola 3 University of Padova Vicenza Italy Department of Chemical Engineering Materials Environment Via Eudossiana 18 University of Rome Sapienza Roma Italy Faculty of Civil and Environmental Sciences Chair of Data Science in Engineering Coudraystraße 13b Bauhaus-Universität Weimar Weimar Germany

ISBN: (数字)9798350373493

ISBN: (纸本)9798350373509

An accurate prediction of the fatigue life of metallic additively manufactured lattice structures is still a challenging task. The proposed work addresses this challenge of industrial relevance through the computation of an as-built geometry of a lattice structure directly from its X-ray computed tomography reconstruction. The computation is carried out by taking advantage of the Finite Cell Method and applies the average strain energy density method to predict fatigue failure. The proof of concept provided in this work shows an excellent agreement in the fatigue prediction, paving the way for a computationally accurate fatigue life prediction of other types of metamaterials.

关键词： Geometry Accuracy Lattices Fatigue Metamaterials Topology X-ray imaging Stress Strain Testing

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Forecasting battery capacity for second-life applications using physics-informed recurrent neural networks

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eTransportation 2025年 25卷

作者： Sina Navidi Kristupas Bajarunas Manuel Arias Chao Chao Hu School of Mechanical Aerospace and Manufacturing Engineering University of Connecticut Storrs 06269 CT USA Faculty of Aerospace Engineering Delft University of Technology Delft HS 2926 Netherlands Institute of Data Analysis and Process Design Zurich University of Applied Science Winterthur 8401 Switzerland

Accurately forecasting lithium-ion battery capacity degradation is crucial for optimizing the second-life utilization of these batteries, enabling reliable operation, reduced maintenance costs, and extended life cycle performance. However, achieving consistent forecasting accuracy across cells and over time remains challenging due to significant cell-to-cell variability and substantial changes in real-world usage conditions during the transition from first to second life. In this study, we propose a new physics-informed machine learning method that integrates an aging-aware electrochemical model with a recurrent neural network, creating a physics-informed recurrent neural network (PI-RNN). This hybrid model leverages both physics-based insights and data-driven learning to predict capacity fade under diverse usage conditions, including transitions from first- to second-life applications. We evaluate PI-RNN using two datasets: an open-source NASA dataset comprising 28 lithium cobalt oxide/graphite cells, and a newly collected dataset of 39 commercial lithium iron phosphate/graphite cells, where cells were initially cycled to 80% capacity in their first life before undergoing milder cycling in their second life. While PI-RNN performs comparably to data-driven models in the first-life phase, it demonstrates a clear advantage in second-life forecasting, reducing root mean squared error by approximately 40%–70% compared to baseline models when forecasting periods span the transition from first to second life, even when trained on as few as two cells. Parametric studies highlight the advantages of incorporating physics-based modeling, and uncertainty quantification ensures the reliability of long-term capacity forecasting. In addition, we conducted benchmarking studies to systematically assess the advantages and limitations of the proposed model, thus identifying the scenarios where this approach excels.

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Robust AI-Synthesized Image Detection via Multi-feature Frequency-aware Learning

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arXiv 2025年

作者： Cai, Hongfei Liu, Chi Shen, Sheng Qu, Youyang Gui, Peng Faculty of Data Science City University of Macau China Design and Creative Technology Vertical Torrens University Australia NSW Australia Shandong Provincial Key Laboratory of Computer Networks Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

The rapid progression of generative AI (GenAI) technologies has heightened concerns regarding the misuse of AI-generated imagery. To address this issue, robust detection methods have emerged as particularly compelling, especially in challenging conditions where the targeted GenAI models are out-of-distribution or the generated images have been subjected to perturbations during transmission. This paper introduces a multi-feature fusion framework designed to enhance spatial forensic feature representations with incorporating three complementary components, namely noise correlation analysis, image gradient information, and pretrained vision encoder knowledge, using a cross-source attention mechanism. Furthermore, to identify spectral abnormality in synthetic images, we propose a frequency-aware architecture that employs the Frequency-Adaptive Dilated Convolution, enabling the joint modeling of spatial and spectral features while maintaining low computational complexity. Our framework exhibits exceptional generalization performance across fourteen diverse GenAI systems, including text-to-image diffusion models, autoregressive approaches, and post-processed deepfake methods. Notably, it achieves significantly higher mean accuracy in cross-model detection tasks when compared to existing state-of-the-art techniques. Additionally, the proposed method demonstrates resilience against various types of real-world image noise perturbations such as compression and blurring. Extensive ablation studies further corroborate the synergistic benefits of fusing multi-model forensic features with frequency-aware learning, underscoring the efficacy of our approach. © 2025, CC BY-NC-SA.

关键词： Image coding

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An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for Nuclear Reactor Physics

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SSRN 2022年

作者： Gong, Helin Cheng, Sibo Chen, Zhang Li, Qing Quilodrán-Casas, César Xiao, Dunhui Arcucci, Rossella SJTU-ParisTech Elite Institute of Technology Shanghai 200240 China Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Faculty of Sciences and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom

This paper proposes an approach that combines reduced-order models with machine learning in order to create an digital twin to predict the power distribution over the core during the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is realised using SVD autoencoder reduced order model and machine learning methods. The inverse model is based on a generalised latent assimilation method. The effectiveness in the sense of accuracy and real-time solver of the digital twin is illustrated through a real engineering problem in nuclear reactor physics - reactor core simulation in the life cycle of HPR1000 affected by control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose. © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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