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Explainable AI unlocks temperature-driven oscillatory viscoelastic transitions in sesame protein isolate during integrated heating–cooling cycles

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Food Hydrocolloids 2025年 169卷

作者： Mustafa Tahsin Yilmaz Abdulaziz S. Alkabaa Furkan Turker Saricaoglu Ahmad H. Milyani Osman Gul Mahmut Ekrem Parlak Wael S. Hassanein Department of Industrial Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science (AIADS) King Abdulaziz University Jeddah Saudi Arabia Department of Food Engineering Faculty of Engineering and Natural Science Bursa Technical University Bursa Türkiye Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Intelligent Engineering Systems (CEIES) King Abdulaziz University Jeddah Saudi Arabia Department of Food Engineering Faculty of Engineering and Architecture Kastamonu University Kastamonu Türkiye

The temperature-dependent viscoelastic behavior of sesame protein isolate (SePI) gels was investigated across integrated heating–cooling cycles (25–95 °C) under oscillatory rheometry (10 % strain, 0.1 Hz). Experiments were performed across a range of treatment conditions, including pressure levels of 0, 50, and 100 MPa and ionic concentrations (IC) of 0–200 mM. Empirical results showed that storage modulus ( G ′) consistently exceeded loss modulus ( G ″), particularly during cooling, indicating elastic-dominant gelation. Application of pressure and ionic concentration (IC) treatments enhanced viscoelastic recovery, yet condition-specific nonlinear trends in G ′ and G ″ responses—particularly across temperature cycles—and associated hysteresis effects remained difficult to isolate from aggregated empirical trends alone. To address these limitations, stacking ensemble mimicry models were developed and explainable AI (XAI) methods, including SHAP values, partial dependence plots (PDPs), and variance-based sensitivity indices (VBSIs), were employed. The XGBMeta-Stacker and LGBMMeta-Stacker models predicted G ′ and G ″ with high accuracy, achieving R 2 values above 0.94 for both training and testing sets. Despite variability and outliers in the temperature sweep dataset, both ensemble models showed strong predictive alignment with actual values, highlighting the robustness of the stacking strategy in complex rheological modeling. XAI analyses uncovered temperature-driven oscillatory viscoelastic transitions—repeated patterns unlikely to be captured when heating and cooling cycles are examined separately, particularly between 25 and 75°C—highlighting the necessity of integrated cycle analysis to reveal such behavior and enabling quantitative ranking of temperature, pressure, and IC influences across the domain. Temperature emerged as the dominant driver of G ′ and G ″ transitions, while pressure exerted stronger effects on viscous behavior under high-intensity conditions.

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Adaptive Locally Linear Embedding

arXiv

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arXiv 2025年

作者： Goli, Ali Alizadeh, Mahdieh Yazdi, Hadi Sadoghi Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Center of Excellence in Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

Manifold learning techniques, such as Locally linear embedding (LLE), are designed to preserve the local neighborhood structures of high-dimensional data during dimensionality reduction. Traditional LLE employs Euclidean distance to define neighborhoods, which can struggle to capture the intrinsic geometric relationships within complex data. A novel approach, Adaptive locally linear embedding(ALLE), is introduced to address this limitation by incorporating a dynamic, data-driven metric that enhances topological preservation. This method redefines the concept of proximity by focusing on topological neighborhood inclusion rather than fixed distances. By adapting the metric based on the local structure of the data, it achieves superior neighborhood preservation, particularly for datasets with complex geometries and high-dimensional structures. Experimental results demonstrate that ALLE significantly improves the alignment between neighborhoods in the input and feature spaces, resulting in more accurate and topologically faithful embeddings. This approach advances manifold learning by tailoring distance metrics to the underlying data, providing a robust solution for capturing intricate relationships in high-dimensional datasets. © 2025, CC BY-SA.

关键词： Topology

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Estimation of continuous environments by robot swarms: Correlated networks and decision-making*

arXiv

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arXiv 2023年

作者： Raoufi, Mohsen Romanczuk, Pawel Hamann, Heiko Mohsen Raoufi Pawel Romanczuk and Heiko Hamann are with Science of Intelligence Research Cluster of Excellence Marchstr. 23 Berlin10587 Germany Mohsen Raoufi and Pawel Romanczuk are with Institute for Theoretical Biology Department of Biology Humboldt Universität zu Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Bernstein Center for Computational Neuroscience Berlin Germany Department of Computer and Information Science University of Konstanz Konstanz Germany

Collective decision-making is an essential capability of large-scale multi-robot systems to establish autonomy on the swarm level. A large portion of literature on collective decision-making in swarm robotics focuses on discrete decisions selecting from a limited number of options. Here we assign a decentralized robot system with the task of exploring an unbounded environment, finding consensus on the mean of a measurable environmental feature, and aggregating at areas where that value is measured (e.g., a contour line). A unique quality of this task is a causal loop between the robots’ dynamic network topology and their decision-making. For example, the network’s mean node degree influences time to convergence while the currently agreed-on mean value influences the swarm’s aggregation location, hence, also the network structure as well as the precision error. We propose a control algorithm and study it in real-world robot swarm experiments in different environments. We show that our approach is effective and achieves higher precision than a control experiment. We anticipate applications, for example, in containing pollution with surface vehicles. © 2023, CC BY.

关键词： Decision making

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Worldwide Research Trends and Hotspot on IOMT Based on Bibliometric Analysis

Worldwide Research Trends and Hotspot on IOMT Based on Bibli...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Hafiz Muhammad Zeeshan Md Belal Bin Heyat Mohd Ammar Bin Hayat Saba Parveen Faijan Akhtar Eram Sayeed Soha Akhtar Priya Singh Mohd Mohsin Saima Parveen Bandana Singh Abid Iqbal Jian Ping Li Asmaa Sayed Abdelgeliel Department of Computer Science National College of Business Administration and Economics Lahore Pakistan CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Hangzhou China M.A.H. Inter College Laxmipur Deoria India College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Government Co-ed Sarvodaya Vidyalaya New Delhi India Hamdard Public School New Delhi India Dreamvessels Technology UP India Department of Electronics and Communication Engineering Meerut Institute of Engineering and Technology Meerut India Department of Mathematics Shah Abdul Latif University Khairpur Pakistan School of Media and Communication Shenzhen University Shenzhen China Central Library Prince Sultan University Riyadh Kingdom of Saudi Arabia Botany & Microbiology Department South Valley University Qena Egypt

COVID-19's control hinges on precise virus identification in healthcare. The Internet of Medical Things (IoMT) denotes the online network of medical devices and apps facilitating data exchange. IoMT's recent rapid expansion indicates a rising use of internet-linked medical tools in healthcare. However, concerns about security and timely healthcare delivery impede IoMT-based application adoption. Safeguarding patient information and averting unauthorized access to medical systems remain paramount. Digital Clinical Trials (DCTs) are gaining significance, offering participation avenues regardless of time or location constraints. For their widespread adoption, IoMT and IoHT-based medical device development is crucial. The Internet of Health Things (IoHT), interlinking medical devices and apps, simplifies health data sharing. Scopus, a comprehensive academic database, was searched for IoMT-related papers between January 2017 and December 2022. Utilizing statistical tools like R, Biblioshiny, Bibliometrix, VOSviewer, and Microsoft Excel 365, 578 pertinent publications were analyzed, offering valuable insights. Additionally, 500 relevant papers were found in the Web of Science (WoS) database, contributing to this field's understanding and advancement.

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Ergodic Capacity Analysis of NOMA-Based Two-Way Relaying Systems

TechRxiv

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TechRxiv 2022年

作者： Khan, Wali Ullah Elhalawany, Basem M. da Costa, Daniel B. Ruby, Rukhsana Waqar, Omer Wu, Kaishun Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Egypt AI & Telecom Research Center Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan SnT University of Luxembourg Luxembourg School of Computer Science and Software Engineering Shenzhen University Shenzhen China BC Canada

In this work, a non-orthogonal multiple access (NOMA) relying on a two-way relaying (TWR) approach is investigated to promote more efficient use of the available spectrum. Specifically, a NOMA-enabled half-duplex decode-and-forward relay node is used to simultaneously exchange data between two cellular users and a base station (BS), while assuming that the direct links between the two users and the BS are unavailable or negligible. In our analysis, the ergodic capacity is evaluated, in which high signal-to-noise ratio approximations, as well as upper bounds are derived owing to the intractability related to the exact analysis. The results reveal the superiority of the proposed TWR-NOMA scheme compared with the traditional one-way relaying (OWR-NOMA) scheme. Additionally, simulation results show that selecting a relay station (RS) closer to the BS can achieve a higher ergodic sum capacity for the system than selecting a far RS. © 2022, CC BY.

关键词： Spectrum efficiency

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Wireless Large AI Model: Shaping the AI-Native Future of 6G and Beyond

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Li, Xinyi Zhang, Maojun Chen, Yixuan Huang, Chongwen Yang, Zhaohui Chen, Xiaoming Zhang, Zhaoyang Jin, Richeng Huang, Yongming Feng, Wei Yang, Tingting Bai, Baoming Gao, Feifei Yang, Kun Liu, Yuanwen Muhaidat, Sami Yuen, Chau Huang, Kaibin Wong, Kai-Kit Niyato, Dusit Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China School of Automation Southeast University Nanjing210096 China Tsinghua University Beijing100084 China Pengcheng Laboratory Shenzhen518066 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China Suzhou215163 China The University of Hong Kong Hong Kong KU 6G Research Center Computer and Communication Engineering Khalifa University Abu Dhabi127788 United Arab Emirates School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

The emergence of sixth-generation and beyond communication systems is expected to fundamentally transform digital experiences through introducing unparalleled levels of intelligence, efficiency, and connectivity. A promising technology poised to enable this revolutionary vision is the wireless large AI model (WLAM), characterized by its exceptional capabilities in data processing, inference, and decision-making. In light of these remarkable capabilities, this paper provides a comprehensive survey of WLAM, elucidating its fundamental principles, diverse applications, critical challenges, and future research opportunities. We begin by introducing the background of WLAM and analyzing the key synergies with wireless networks, emphasizing the mutual benefits. Subsequently, we explore the foundational characteristics of WLAM, delving into their unique relevance in wireless environments. Then, the role of WLAM in optimizing wireless communication systems across various use cases and the reciprocal benefits are systematically investigated. Furthermore, we discuss the integration of WLAM with emerging technologies, highlighting their potential to enable transformative capabilities and breakthroughs in wireless communication. Finally, we thoroughly examine the high-level challenges hindering the practical implementation of WLAM and discuss pivotal future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Sorting

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End-to-end Transformer for Compressed Video Quality Enhancement

arXiv

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arXiv 2022年

作者： Yu, Li Chang, Wenshuai Wu, Shiyu Gabbouj, Moncef School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Department of Computing Sciences Tampere University Tampere Finland

Convolutional neural networks have achieved excellent results in compressed video quality enhancement task in recent years. State-of-the-art methods explore the spatiotemporal information of adjacent frames mainly by deformable convolution. However, offset fields in deformable convolution are difficult to train, and its instability in training often leads to offset overflow, which reduce the efficiency of correlation modeling. In this work, we propose a transformer-based compressed video quality enhancement (TVQE) method, consisting of Swin-AutoEncoder based Spatio-Temporal feature Fusion (SSTF) module and Channel-wise Attention based Quality Enhancement (CAQE) module. The proposed SSTF module learns both local and global features with the help of Swin-AutoEncoder, which improves the ability of correlation modeling. Meanwhile, the window mechanism-based Swin Transformer and the encoder-decoder structure greatly improve the execution efficiency. On the other hand, the proposed CAQE module calculates the channel attention, which aggregates the temporal information between channels in the feature map, and finally achieves the efficient fusion of inter-frame information. Extensive experimental results on the JCT-VT test sequences show that the proposed method achieves better performance in average for both subjective and objective quality. Meanwhile, our proposed method outperforms existing ones in terms of both inference speed and GPU consumption. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

arXiv

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arXiv 2022年

作者： Malialis, Kleanthis Roveri, Manuel Alippi, Cesare Panayiotou, Christos G. Polycarpou, Marios M. KIOS Research Innovation Center of Excellence Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Università della Svizzera Italiana Lugano Switzerland

In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time, and inadequate to solve the task at hand. Learning in presence of concept drift aims at designing machine and deep learning models that are able to track and adapt to concept drift. Typically, techniques to handle concept drift are either active or passive, and traditionally, these have been considered to be mutually exclusive. Active techniques use an explicit drift detection mechanism, and re-train the learning algorithm when concept drift is detected. Passive techniques use an implicit method to deal with drift, and continually update the model using incremental learning. Differently from what present in the literature, we propose a hybrid alternative which merges the two approaches, hence, leveraging on their advantages. The proposed method called Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines and state-of-the-art methods in terms of learning quality and speed;we experiment how it is effective under severe class imbalance levels too. © 2022, CC BY-NC-SA.

关键词： Learning algorithms

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Exploring the Non - Hermitian Topology of Exceptional Points in Lasers and Fiber Loops

Exploring the Non - Hermitian Topology of Exceptional Points...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Alexander Schumer Hadiseh Nasari Yuzhou G. N. Liu Gisela Lopez-Galmiche Jason Leshin Helena E. Lopez-Aviles Lei Ding Yousef Alahmadi Absar U. Hassan Qi Zhong Stefan Rotter Patrick LiKamWa Demetrios N. Christodoulides Mercedeh Khajavlkhan Institutefor Theoretical Physics Vienna University of Technology (TU Wien) Vienna Austria Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles USA CREOL The College of Optics & Photonics University of Central Florida Orlando FL USA Center of Excellence for Telecomm Applications Joint Centers of Excellence Program King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia Department of Physics & Astronomy University of Southern California Los Angeles CA USA

ISBN: (纸本)9798350345995

When non-Hermitian eigenvalue surfaces form intertwined Riemann surfaces, the corresponding non-Hermitian singularities, also know as exceptional points (EPs), are located at the center of this specific topology. Various experimental platforms have recently been presented in which interesting new physics associated with these branch point singularities can be explored. In particular, it has been shown that dynamically encircling EPs may lead to a so-called chiral state transfer. The chirality in this context refers to the fact that in such an encircling protocol, the final state at the end of an EP -enclosing loop solely depends on the encircling direction (clockwise or counterclockwise), but not on the initial state. Such a chiral behavior is intrinsically linked to the state vector deviating from adiabatically following the system's eigenstates associated with the eigenvalue surfaces; instead, due to the non-Hermitian violation of the adiabatic theorem, non-adiabatic jumps let the system undergo transitions from lossy eigenstates to eigenstates with gain. Here, we present two experiments that implement concepts that go beyond this established paradigm.

关键词： Attenuation Chirality Fiber lasers Lasers Surfaces Waveguides

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