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5th Collaborative European Research Conference, CERC 2019

作者： Bond, Raymond Engel, Felix Fuchs, Michael Hemmje, Matthias Kevitt, Paul Mc McTear, Mike Mulvenna, Maurice Walsh, Paul Zheng, Huiru Jane Faculty of Computing Engineering and Built Environment Ulster University Jordanstown Ireland Dortmund Germany Software Engineering and Computer Science Wilhelm Büchner University of Applied Sciences Darmstadt Germany Computer Science Fern University Hagen Germany Ireland SIGMA Research Group Cork Institute of Technology Cork Ireland

People’s worries about robot and AI software and how it can go wrong have led them to think of it and its associated algorithms and programs as being like Mary Shelley’s Frankenstein monster. The term Franken-algorithms has been used. Furthermore, there are concerns about driverless cars, automated General Practitioner Doctors (GPs) and robotic surgeons, legal expert systems, and particularly autonomous military drones. Digital Empathy grows when people and computers place themselves in each other’s shoes. Some would argue that for too long people have discriminated against computers and robots by saying that they are only as good as what we put into them. However, in recent times computers have outperformed people, beating world champions at the Asian game of Go (2017), Jeopardy (2011) and chess (1997), mastering precision in medical surgical operations (STAR) and diagnosis (Watson), and in specific speech and image recognition tasks. Computers have also composed music (AIVA), generated art (Aaron), stories (Quill) and poetry (Google AI). In terms of calling for more Digital Empathy between machines and people, we refer here to theories, computational models, algorithms and systems for detecting, representing and responding to people’s emotions and sentiment in speech and images but also for people’s goals, plans, beliefs and intentions. In reciprocation, people should have more empathy with machines allowing for their mistakes and also accepting that they will be better than people at performing particular tasks involving large data sets where fast decisions may need to be made, keeping in mind that they are not as prone as people to becoming tired. We conclude that if digital souls are programmed with Digital Empathy, and people have more empathy with them, by doing unto them as we would have them do unto us, this will help to secure Shelley’s monster. © 2019 CEUR-WS. All rights reserved.

关键词： Diagnosis

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Observation of a giant Goos-Hänchen shift for matter waves

arXiv

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arXiv 2024年

作者： McKay, S. de Haan, V.O. Leiner, J. Parnell, S.R. Dalgliesh, R.M. Boeni, P. Bannenberg, L.J. Le Thien, Q. Baxter, D.V. Ortiz, G. Pynn, R. Department of Physics Indiana University BloomingtonIN47405 United States Center for Exploration of Energy and Matter Indiana University Bloomington47408 United States BonPhysics Research and Investigations BV Laan van Heemstede 38 Puttershoek 3297AJ Netherlands Neutron Sciences Directorate Oak Ridge National Laboratory Oak RidgeTN37830 United States Faculty of Applied Sciences Delft University of Technology Mekelweg 15 Delft2629 JB Netherlands ISIS Rutherford Appleton Laboratory Oxfordshire ChiltonOX11 0QX United Kingdom Physics Department E21 Technical University of Munich GarchingD-85748 Germany SwissNeutronics AG Brühlstrasse 28 KlingnauCH-5313 Switzerland Quantum Science and Engineering Center Indiana University BloomingtonIN47408 United States Institute for Advanced Study PrincetonNJ08540 United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada

The Goos-Hänchen (GH) shift describes a phenomenon in which a specularly-reflected beam is laterally translated along the reflecting surface such that the incident and reflected rays no longer intersect at the surface. Using a neutron spin-echo technique and a specially-designed magnetic multilayer mirror, we have measured the relative phase between the reflected up and down neutron spin states in total reflection. The relative GH shift calculated from this phase shows a strong resonant enhancement at a particular incident neutron wavevector, which is due to a waveguiding effect in one of the magnetic layers. Calculations based on the observed phase difference between the neutron states indicate a propagation distance along the waveguide layer of 0.65 mm for the spin-down state, which we identify with the magnitude of the giant GH shift. The existence of a physical GH shift is confirmed by the observation of neutron absorption in the waveguide layer. We propose ways in which our experimental method may be exploited for neutron quantum-enhanced sensing of thin magnetic layers. Copyright © 2024, The Authors. All rights reserved.

关键词： Neutrons

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Data-driven surrogate modeling of seismic uplift capacity for strip anchors in frictional–cohesive soils with surcharge using isogeometric analysis

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Ocean Engineering 2025年 336卷

作者： Toan Nguyen-Minh Tram Bui-Ngoc Jim Shiau Tan Nguyen Trung Nguyen-Thoi Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam School of Engineering University of Southern Queensland Toowoomba 4350 QLD Australia Smart Computing in Civil Engineering Research Group Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam

The behavior of strip anchors under uplift loads in complex conditions, such as frictional–cohesive soils with surcharge and seismic loading, poses significant engineering challenges. This study presents a data-driven surrogate modeling framework to predict seismic uplift capacity, combining high-fidelity simulations with machine learning. A large dataset is generated using Upper Bound Limit Analysis (UBLA) implemented via Isogeometric Analysis (IGA) and Second-Order Cone Programming (SOCP). Key input variables i.e., cohesion factor ( c '/ γB ), internal friction angle ( φ ′), embedment ratio ( H / B ), seismic coefficients ( K h , K v ), and surcharge factor ( q / γB ), are evaluated to explore their impact on uplift capacity ( P u / γB ). Sensitivity analysis highlights embedment ratio and internal friction angle as major factors enhancing uplift, while seismic coefficients reduce it, especially in low-cohesion soils. The Levenberg-Marquardt Neural Network (LMNN) outperforms other machine learning models, achieving a mean squared error (MSE) of 0.006 ± 0.000 on training and 0.005 ± 0.001 on testing data, with an R 2 of 1.000. This integration of IGA-UB with LMNN provides reliable upper-bound estimates for uplift capacity, particularly in high-cohesion soils. The development of a closed-form solution ensures practical relevance, making this hybrid approach a powerful tool for engineering design under varied loading scenarios.

关键词： Closed-form solution Frictional-cohesive soils Isogeometric analysis Machine learning Sensitivity analysis Strip anchors Uplift capacity

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Optimizing a parameterized controlled gate with Free Quaternion Selection

arXiv

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arXiv 2024年

作者： Kurogi, Hiroyoshi Endo, Katsuhiro Sato, Yuki Sugawara, Michihiko Wada, Kaito Sugisaki, Kenji Kanno, Shu Watanabe, Hiroshi C. Nakano, Haruyuki Department of Chemistry Graduate School of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan Quantum Computing Center Keio University 3-14-1 Hiyoshi Kohoku-ku Kanagawa Yokohama223-8522 Japan 1-1-1 Umezono Ibaraki Tsukuba305-8568 Japan Toyota Central R & D Labs. Inc. Koraku Mori Building 10F 1-4-14 Koraku Bunkyo-ku Tokyo112-0004 Japan Department of Applied Physics and Physico-Informatics Keio University Hiyoshi 3-14-1 Kohoku-ku Yokohama223-8522 Japan Graduate School of Science and Technology Keio University 7-1 Shinkawasaki Saiwai-ku Kawasaki212-0032 Japan Keio University 2-15-45 Mita Minato-ku Tokyo108-8345 Japan Centre for Quantum Engineering Research and Education TCG Centres for Research and Education in Science and Technology Salt Lake Sector V Kolkata700091 India Mitsubishi Chemical Corporation Science & Innovation Center Yokohama227-8502 Japan Department of Chemistry Faculty of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

In variational algorithms, quantum circuits are conventionally parametrized with respect to single-qubit gates. In this study, we parameterize a generalized controlled gate and propose an algorithm to estimate the optimal parameters for locally minimizing the cost value, where we extend the free quaternion selection method, an optimization method for a single-qubit gate. To benchmark the performance, we apply the proposed method to various optimization problems, including the Variational Quantum Eigensolver (VQE) for Ising and molecular Hamiltonians, Variational Quantum Algorithms (VQA) for fidelity maximization, and unitary compilation of time evolution operators. In these applications, the proposed method shows efficient optimization and greater expressibility with shallower circuits than other methods. Furthermore, this method is also capable of generalizing and fully optimizing particle-number-conserving gates, which are in demand in chemical systems applications. Taking advantage of this property, we have actually approximated time evolution operators of molecular Hamiltonian and simulated the dynamics with shallower circuits in comparison to the standard implementation by Trotter decomposition. © 2024, CC BY-NC-SA.

关键词： Hamiltonians

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Two dynamical models for male infertility and their stability and sensitivity analysis

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Scientific reports 2025年第1期15卷 18873页

作者： Somaye Ghiasi Hafezi Mahdi Ranjbar Atena Ghasemabadi Sohrab Effati Alvand Naserghandi Kosar Namakin Farzad Allameh Biostatistics School of Health Mashhad University of Medical Sciences Mashhad Iran. Department of Applied Mathematics Faculty of Mathematical Science Ferdowsi University of Mashhad Mashhad Iran. mahdi.ranjbar@um.ac.ir. Esfarayen University of Technology Esfarayen North Khorasan Iran. Department of Applied Mathematics Faculty of Mathematical Science Ferdowsi University of Mashhad Mashhad Iran. s-effati@um.ac.ir. Center of Excellence of Soft Computing and Intelligent Information Processing (SCIIP) Ferdowsi University of Mashhad Mashhad Iran. s-effati@um.ac.ir. Men's Health & Reproductive Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran. Student Research Committee Shahid Beheshti University of Medical Sciences Tehran Iran.

Mathematical models can estimate the success rate of assisted reproductive technology (ART) and determine which treatment suits the best. In this study, the authors introduce two new dynamic models that can predict the success rate of ART models for each individual based on the etiology of their infertility, clinical findings, and lab tests. The study shows that the equilibrium point free of disease is asymptotically stable and forward bifurcation occurs. Our dynamical models evaluate ART success rates in our subgroups considering couples under Intrauterine insemination (IUI), In Vitro Fertilization-Embryo Transfer (IVF), Intra-Cytoplasmic Sperm Injection (ICSI) treatment methods. Sensitivity analysis of parameters suggests that an increase in infertile couples who use IUI and ICSI treatment methods can result in better disease control and a more favorable outcome. It should be noted that the proposed models provide a systematic framework for simulating the dynamics of infertility and assessing the effectiveness of various ART. From a clinical perspective, these models have the potential to enhance decision-making by predicting the probability of ART success under diverse treatment scenarios. Such predictions may facilitate the personalization of treatment strategies based on individual patient profiles and contribute to more efficient resource allocation and planning within fertility centers.

关键词： Asymptotically stable Dynamical models Forward bifurcation Male infertility

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How do they like it? Higher education teachers’ professional development preferences for blended learning and technology acceptance profiles

Computers in Human Behavior Reports

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Computers in Human Behavior Reports 2025年 18卷

作者： Garone, Anja Howard, Sarah K. Yang, Jie Tondeur, Jo Pynoo, Bram Bruggeman, Bram Struyven, Katrien Faculty of Psychology and Education Vrije Universiteit Brussel Pleinlaan 2 Brussels 1050 Belgium School of Education University of Leeds Leeds LS2 9JT United Kingdom Faculty of Engineering and Information Sciences School of Computing and Information Technology University of Wollongong Wollongong 2522 NSW Australia Vrije Universiteit Brussel Multidisciplinair Instituut Lerarenopleiding Brussels Research Institute for Teacher Education Pleinlaan 2 Brussels 1050 Belgium Education Policy Department VIVES University of Applied Sciences Doorniksesteenweg 145 Kortrijk 8500 Belgium UHasselt Research on Innovative and Society-engaged Education (U-RISE) School for Educational Studies Hasselt University Martelarenlaan 42 Hasselt 3500 Belgium Teacher Education Department Secondary Education Artevelde University of Applied Sciences Kattenberg 9 Gent 9000 Belgium

Blended learning is useful in higher education for catering to diverse student learning needs, however, higher education teaching staff need to be trained and supported so that it is applied attentively. Higher education teachers are diverse themselves with complex professional development needs. This study aims to examine the relationships between professional development preferences and 217 teaching staff grouped into technology acceptance profiles: high, moderate and low. Association rules analysis was run on an 18-item questionnaire dataset of the teaching staff's professional development preferences for blended learning. Results show that the high group is highly motivated to professionalise themselves collaboratively with added central support. The moderate group prefers centrally organised and guided professional development initiatives. The low group prefers centrally organised initiatives with guidance as well as incentive for professionalisation. These results highlight the differences between the groups, and how these preferences can be useful for designing targeted initiatives along with adapted communication strategies for groups with different technology acceptance levels. © 2025 The Authors

关键词： Association rules analysis Blended learning Data mining Higher education Professional development Teaching staff

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Correction to: Deep CNN-based autonomous system for safety measures in logistics transportation

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Soft computing 2021年第18期25卷 12371-12371页

作者： Rouari, Abdelkarim Moussaoui, Abdelouahab Chahir, Youssef Rauf, Hafiz Tayyab Kadry, Seifedine Computer Science Department Faculty of Sciences Ferhat Abbas University Setif Setif Algeria Computer Science Department University of Caen Caen France Department of Computer Science Faculty of Engineering & Informatics University of Bradford Bradford UK Faculty of applied computing and technology Noroff University College Kristiansand Norway

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Machine Learning-Driven Prediction of Optical and Physical Properties in Lanthanum and Gold-Doped Zinc Borotellurite Glasses for Optoelectronic Applications

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Ceramics International 2025年

作者： S.N. Nazrin Camellia Doroody Liyana Adilla Burhanuddin Neesha Jothi N. Ibrahim Halimah Badioze Zaman M.H.M. Tahir Manzoore Elahi M. Soudagar S. Ramesh Sagar Shelare Shubham Sharma Institute of Informatics and Computing in Energy (IICE) College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional (UNITEN @ The Energy University) Putrajaya Campus Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura-140401 Punjab India Zhejiang Xingyu Mechanical and Electrical Technology Company Ltd. Tongqin Wuyi County Jinhua Zhejiang 311400 China Department of Electronic Systems Engineering (ESE) Malaysia–Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia Dept. of Digital Economy Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman 31900 Kampar Perak Department of English Language and Literature Faculty of Languages and Communication Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Center of Advanced Manufacturing and Materials Processing (AMMP) Faculty of Engineering University of Malaya Kuala Lumpur Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Phagwara Punjab 144411 India Department of Applied Chemistry Priyadarshini College of Engineering Nagpur Maharashtra 440019 India University Centre for Research and Development Chandigarh University Mohali Punjab 140413 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Jadara University Research Center Jadara University Jordan

Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimization of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The melt-quenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass's amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO 3 , BO 4 , and BO 3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical performance for photonic and optoelectronic applications. The enhanced optical properties of the synthesized glass system make it a promising candidate for photonic and optoelectronic applications, including optical fibers, solid-state lasers, and planar waveguides , where its tunable refractive index and improved transparency can optimize signal transmission and device efficiency. Additionally, the presence of gold oxide nanoparticles suggests potential use in nonl

关键词： Artificial neural networks (ANN) Energy Gold oxide Gradient boosting (GB) Random forest (RF) XGBoost (XGB)

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Understanding the effects of COVID-19 pandemic on Brunei students learning activities: A data analytics approach

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AIP Conference Proceedings 2023年第1期2968卷

作者： Siti Salihah Binti Sahari D. S. Sankar S. H. Shah Newaz K. K. Viswanathan School of Applied Sciences and Mathematics Universiti Teknologi Brunei Jalan Tungku Link Gadong BE1410 Brunei Darussalam. School of Computing and Informatics Universiti Teknologi Brunei Jalan Tungku Link Gadong BE1410 Brunei Darussalam. KAIST Institute for Information Technology Convergence Daejeon 34141 South Korea. Department of Mathematical Modelling Faculty of Mathematics Samarkand State University University Blvd 15 Samarkand 140104 Uzbekistan. Department of Applied Mathematics and Informatics Termez State University Termez City Uzbekistan 190100.

This empirical study investigates the digital transformation imposed by COVID-19 pandemic and various restrictions following it on the mode of teaching and learning activities in the schools and higher learning institutions of Brunei Darussalam. A primary survey was conducted from the students and the public of Brunei Darussalam to study on how the COVID pandemic restrictions imposed the vital changes in the mode of learning from the transitional physical mode to online mode. Based on the data collected and the data analytics performed, we found that majority of students and parents preferred to have physical learning instead of online learning since only some students have become familiar with online education platforms such as Skype, Zoom, Google meets, Microsoft Teams. The empirical findings of this study through a primary survey enable the authors to accept the alternative hypothesis for e-learning benefits while rejecting the alternative hypothesis for the e-learning challenges. Besides that, a control variable, namely, home-schooling experience also has a significant effect on students’ e-learning process. These students might have experienced some challenges adapting to some of the popular online e-learning platforms.

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Cell membrane vaccine delivery system: A review of the recent advances against breast cancer

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International Journal of Pharmaceutics 2025年 681卷

作者： Ayah Rebhi Hilles Syed Mahmood Riyanto Teguh Widodo Ahmad Khusairi Azemi Abu Hassan Nordin Abdin Shakirin Mohamad Norpi Muhammad Fauzi Abd Jalil Zarif Mohamed Sofian Mazlan Mohamed Kamarul Ariffin Hambali Nani Izreen Mohd Sani Roslina Rashid Muhammad Luqman Nordin INHART International Islamic University Malaysia Jalan Gombak 53100 Kuala Lumpur Malaysia Department of Pharmaceutical Technology Faculty of Pharmacy Universiti Malaya 50603 Kuala Lumpur Malaysia Universiti Malaya Research Centre for Pharmaceuticals and Advanced Therapeutics (UBAT) Department of Pharmacology Faculty of Medicine Universiti Malaya 50603 Kuala Lumpur Malaysia Centre of Advanced Materials (CAM) Faculty of Engineering Universiti Malaya 50603 Kuala Lumpur Malaysia Faculty of Pharmaceutical Sciences Chulalongkorn University Pathum Wan Bangkok 10330 Thailand Institute of Climate Change Adaptation and Marine Biotechnology Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia Faculty of Applied Sciences Universiti Teknologi MARA (UiTM) Campus Arau 02600 Perlis Malaysia Faculty of Pharmacy and Health Sciences Universiti Kuala Lumpur Royal College of Medicine Perak Perak 30450 Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan City Campus Pengkalan Chepa Kota Bharu 16100 Kelantan Malaysia Faculty of Earth Science Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Animal and Wildlife Research Group Faculty of Earth Science Jeli Campus Universiti Malaysia Kelantan Jeli 17600 Kelantan Malaysia Faculty of Veterinary Medicine Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kelantan Malaysia

Natural cells exhibit unique properties, such as homotypic targeting and prolonged circulation in the bloodstream, making them attractive candidates for breast cancer therapy vaccine delivery systems (VDSs). To improve delivery efficiency, these cells can be integrated with nanocarriers, paving the way for next-generation VDSs in the form of biomimetic nanomedicines. This review highlights recent progress in cell membrane-based vaccine delivery systems (CVDSs) and other types of vaccine delivery systems as a potential for breast cancer vaccine development. Cell membrane sources, including red blood cells (RBCs), cancer cells, and platelets are explored. Additionally, the methods for extracting these membranes and the mechanisms driving the functionality of biomimetic CVDSs are detailed. This review also highlights recent advancements and cutting-edge strategies of cell membrane vaccine delivery systems, with a particular focus on the use of cell-derived biomimetic nanotechnology for breast cancer. It also highlights breast cancer vaccines under development encompass protein-based (e.g., peptide vaccines), nucleic acid-based (DNA and RNA vaccines), immune cell-based (including dendritic cell and autologous tumor cell vaccines), and bacterial/viral vector-based vaccines. Each platform aims to elicit targeted anti-tumor immune responses.

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