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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Ezekiel Olaoluwa Omole Ola Olayemi Olanegan Adefunke Bosede Familua Peter Onu Femi Emmanuel Amoyedo Homan Emadifar Laith Abualigah Amrevuawho O. Fortune Department of Physical Sciences Mathematics Programme Landmark University Omu-Aran Nigeria SDG4 (Quality Education Research Group) Landmark University Omu-Aran Nigeria Department of Mathematics Federal University Oye-Ekiti Nigeria Department of Mathematics First Technical University Ibadan Oyo State Nigeria Department of Mechanical Engineering (Mechatronics) Landmark University Omu-Aran Nigeria Department of Physical Science Mathematics Programme Landmark University Omu-Aran Nigeria Department of Mathematics Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu India MEU Research Unit Middle East University Amman Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Alhiyya Amman University Amman Jordan Department of Computer Science Landmark University Omu-Aran Kwara State

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

This paper focuses on using zero-stable multistep formulae with evenly spaced off-grid points to numerically simulate differential equations originating from applied mathematics and engineering. The new multistep formulae used the differential system's collocation and power series interpolation as an approximated solution. In order to concurrently provide numerical solutions for the problems under consideration, the primary integrator and its auxiliary integrators are combined and applied. The integrators' theoretical analysis was examined and determined to be consistent with the current linear multistep method theorem. The techniques’s theoretical analysis were determined such as the order and error constants, consistency, zero-stability, convergency and the stability were also determined. The numerical outcomes of the techniques’s analysis contrasted with those of a few previously used techniques. The findings showed that the approach would provides greater accuracy than several other approaches already in use in the literature, making it a significant improvement over those approaches.

关键词： Interpolation Analytical models Boundary value problems Ordinary differential equations Stability analysis Mathematical models Usability

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Climate Change Resilience of Freshwater Supply on Small Islands: Research Gaps and Strategies for a Case Study in Grand Bahama

SSRN

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SSRN 2024年

作者： Imig, Anne Welsh, Kristen Klausner, Sophia Hotta, Carolina Iwane McKenzie, Zeko Perosa, Francesca Stephens, Mark Turner, Amber Thomas, John Chaves, Henrique Leite Bowen-O'Connor, Clare Moxey, Andrew Dokou, Zoi Rein, Arno Hydrogeology School of Engineering and Design Technical University of Munich Germany Small Island Sustainability Programme University of The Bahamas Nassau Bahamas Geosciences Department Oberlin College OberlinOH United States Hydrology and River Basin Management School of Engineering and Design Technical University of Munich Germany Department of Earth and Biological Sciences Loma Linda University Loma LindaCA United States School of Chemistry Environmental and Life Sciences University of The Bahamas Nassau Bahamas University of the West Indies Barbados Strata Environmental Nassau Bahamas Watershed Management Lab EFL – School of Technology University of Brasilia DF Brasilia Brazil University of Trinidad and Tobago Trinidad and Tobago School of Chemistry Environmental and Life Sciences University of The Bahamas Freeport Bahamas Department of Civil Engineering College of Engineering and Computer Science California State University SacramentoCA United States

Study Region: Grand Bahama Island, the Commonwealth of The Bahamas. Study Focus: In Grand Bahama, due to its low elevation, sea level rise and flooding during hurricane events has led to the salinization of groundwater, which is the only source of potable water for the island. In this study, we assessed available literature of groundwater resources in Grand Bahamas to identify key challenges and knowledge gaps for the protection of the potable water supply. A literature review guided our assessment of various sustainable groundwater management approaches for the island. Understanding the hydrogeology and identifying strategies to maintain the freshwater lenses in Grand Bahama are key to ensure the island remains *** Hydrological Insights for the Region: Existing hydrogeological data highlight the urgent need to improve the island’s water supply system and to identify safe groundwater yields to sustain its drinking water supply. Based on analysis of these data, we propose the implementation of nature-based solutions, such as managed aquifer recharge and reforestation measures, to restore the groundwater ecosystem service of freshwater supply. Furthermore, to ensure effective water supply management, legislation and regulations should be adapted to actively involve both institutions and the public in the decision-making process. The results of this review are applicable to other small island nations around the world that are facing the effects of climate change and seawater intrusion. © 2024, The Authors. All rights reserved.

关键词： Groundwater resources

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USEV: Universal Speaker Extraction with Visual Cue

arXiv

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arXiv 2021年

作者： Pan, Zexu Ge, Meng Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300072 China The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The University of Bremen 28359 Germany Kriston AI Xiamen China

A speaker extraction algorithm seeks to extract the target speaker's speech from a multi-talker speech mixture. The prior studies focus mostly on speaker extraction from a highly overlapped multi-talker speech mixture. However, the target-interference speaker overlapping ratios could vary over a wide range from 0% to 100% in natural speech communication, furthermore, the target speaker could be absent in the speech mixture, the speech mixtures in such universal multi-talker scenarios are described as general speech mixtures. The speaker extraction algorithm requires an auxiliary reference, such as a video recording or a pre-recorded speech, to form top-down auditory attention on the target speaker. We advocate that a visual cue, i.e., lip movement, is more informative than an audio cue, i.e., pre-recorded speech, to serve as the auxiliary reference for speaker extraction in disentangling the target speaker from a general speech mixture. In this paper, we propose a universal speaker extraction network with a visual cue, that works for all multi-talker scenarios. In addition, we propose a scenario-aware differentiated loss function for network training, to balance the network performance over different target-interference speaker pairing scenarios. The experimental results show that our proposed method outperforms various competitive baselines for general speech mixtures in terms of signal fidelity. Copyright © 2021, The Authors. All rights reserved.

关键词： Mixtures

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Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition

arXiv

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arXiv 2022年

作者： Hu, Mengjiao Jiang, Xudong Sim, Kang Zhou, Juan Helen Guan, Cuntai Interdisciplinary Graduate Programme NTU Institute for Health Technologies Nanyang Technological University Singapore Center for Sleep and Cognition Department of Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore School of Electrical & Electronic Engineering Nanyang Technological University Singapore Singapore Singapore Translational Magnetic Resonance Research Yong Loo Lin School of Medicine National University of Singapore Singapore School of Computer Science and Engineering School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizophrenia and depression that have no visible alteration in specific slices. In this study, we propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices according to neighboring voxel positions and inputted to 2D CNN models pre-trained on the ImageNet to extract feature maps from three views (axial, coronal, and sagittal). Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third view dimension unprocessed by the 2D CNN model. Multi-metric and multi-view information are fused for final prediction. Our approach outperforms handcrafted feature-based machine learning, deep feature approach with a support vector machine (SVM) classifier and 3D CNN models trained from scratch with better cross-validation results on publicly available Northwestern University Schizophrenia Dataset and the results are replicated on another independent dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Selective Listening by Synchronizing Speech with Lips

arXiv

引用

arXiv 2021年

作者： Pan, Zexu Tao, Ruijie Xu, Chenglin Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Kuaishou Technology Shenzhen518063 China The Chinese University of Hong Kong Shenzhen China The University of Bremen Bremen28359 Germany

A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available. In this work, we don’t rely on the target speaker’s pre-enrolled speech, but rather use the target speaker’s face track as the speaker cue, that is referred to as the auxiliary reference, to form an attractor towards the target speaker. We advocate that the temporal synchronization between the speech and its accompanying lip movements is a direct and dominant audio-visual cue. Therefore, we propose a self-supervised pre-training strategy, to exploit the speech-lip synchronization cue for target speaker extraction, which allows us to leverage abundant unlabeled in-domain data. We transfer the knowledge from the pre-trained model to the attractor encoder of the speaker extraction network. We show that the proposed speaker extraction network outperforms various competitive baselines in terms of signal quality, perceptual quality, and intelligibility, achieving state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Multi-Leader Multi-Follower Game-based Incentive Scheme for Socially-Aware Mobile Crowdsensing

Multi-Leader Multi-Follower Game-based Incentive Scheme for ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Jiangtian Nie Jun Luo Zehui Xiong Dusit Niyato Ping Wang Yang Zhang ERI@N Interdisciplinary Graduate Programme Nanyang Technological University (NTU) Singapore School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore York University Canada Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology Wuhan China

ISBN: (纸本)9781728195056;9781728195063

As the paradigm of crowdsensing involves the data collection from users, the issue of designing reward to incentivize the users is fundamentally important to be addressed, thereby effectively enhancing the participation. In this paper, we revisit this issue in the context of socially-aware crowdsensing which integrates crowdsensing into social networks. For example, in crowdsensing-based healthcare services, the accuracy of diet recommendation for a certain user can be promoted by exploiting the nutritional information contributed and shared by the socially-connected friends of him/her taking similar types of food. To be more general and practical, we study the incentive schemes in presence of multiple crowdsensing service providers and multiple users. Understanding the behaviors of users and service providers in socially-aware crowdsensing is of paramount importance for incentive schemes. Considering this, we propose a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among service providers and users, where the social influence of users and the strategic interconnections of service providers are jointly and formally integrated into the game modeling. Through backward induction methods, we theoretically validate the existence and uniqueness of the Stackelberg equilibrium. Simulations are conducted to evaluate game equilibrium properties, and the results are presented to assess and demonstrate the performance effectiveness of the proposed game model.

关键词： Crowdsensing Social networking (online) System performance Conferences Games Medical services Data collection

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Origin and enhancement of large spin Hall angle in Weyl semimetals LaAlX (X=Si, Ge)

arXiv

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arXiv 2020年

作者： Ng, Truman Luo, Yongzheng Yuan, Jiaren Wu, Yihong Yang, Hyunsoo Shen, Lei Engineering Science Programme National University of Singapore 117575 Singapore Department of Mechanical Engineering National University of Singapore 117575 Singapore Faculty of Science School of Material Science and Engineering Jiangsu University Zhenjiang212013 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore

We study the origin of the strong spin Hall effect (SHE) in a recently discovered family of Weyl semimetals, LaAlX (X=Si, Ge) via a first-principles approach with maximally localized Wannier functions. We show that the strong intrinsic SHE in LaAlX originates from the multiple slight anticrossings of nodal lines and points near EF due to their high mirror symmetry and large spin-orbit interaction. It is further found that both electrical and thermal means can enhance the spin Hall conductivity (σSH). However, the former also increases the electrical conductivity (σc), while the latter decreases it. As a result, the independent tuning of σSH and σc by thermal means can enhance the spin Hall angle (proportional to σSH/σc), a figure of merit of charge-to-spin current interconversion of spin-orbit torque devices. The underlying physics of such independent changes of the spin Hall and electrical conductivity by thermal means is revealed through the band-resolved and k-resolved spin Berry curvature. Our finding offers a new way in the search of high SHA materials for room-temperature spin-orbitronics applications. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric conductivity

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Giant enhancement of anomalous Hall effect in Cr modulation-doped non-collinear antiferromagnetic Mn3Sn thin films

arXiv

引用

arXiv 2021年

作者： Chen, Xin Xie, Hang Zhang, Qi Luo, Ziyan Shen, Lei Wu, Yihong Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Electronic Engineering Southern University of Science and Technology Xueyuan Rd 1088 Shenzhen518055 China School of Physics and Electronics Central South University Changsha410083 China Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Engineering Science Programme National University of Singapore Singapore117575 Singapore Research Institute Chongqing Liang Jiang New Area Chongqing401123 China

We report on Cr doping effect in Mn3Sn polycrystalline films with both uniform and modulation doping. It is found that Cr doping with low concentration does not cause notable changes to the structural and magnetic properties of Mn3Sn, but it significantly enhances the anomalous Hall conductivity, particularly for modulation-doped samples at low temperature. A Hall conductivity as high as 184.8 Ω-1 cm-1 is obtained for modulation-doped samples at 50 K, in a sharp contrast to vanishingly small values for undoped samples at the same temperature. We attribute the enhancement to the change of Fermi level induced by Cr doping. © 2021, CC BY.

关键词： Temperature

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Artificial intelligence for modelling infectious disease epidemics

引用

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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The Response of EEG Signal on Brain Relaxation Meditation Using Different Type of Zikr

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Journal of Physics: Conference Series 2021年第1期2071卷

作者： N N Abd Razak K A Sidek K Y D Alaa N F Mohd Nasir Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia. Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Kingdom of Saudi Arabia. Biomedical Electronic Engineering Programme Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Malaysia.

Depression and stress are increasingly prevalent in today's society, owing to people's hectic, competitive, and demanding lifestyles. These illnesses had become very common, particularly among young and middle-aged people, and suicidal ideation had been identified as one of the leading causes of death by the World Health Organization (WHO). Nature sound (sound of downpours or beach) has been linked to depression and anxiety in neurology research, and it has been shown to be an alternative to alleviate anxiety. The electroencephalogram (EEG) waveform has been discovered to possess the potential in identifying information from the brain signal as well as data from the past via Bluetooth communication. The waveform use in the study are the result of a few experiments. In this study, EEG data were collected from eight subjects, four males and four females, in between the age of 20 to 30 years old and in good health, using the BrainLink device. The participants were asked to listen to two playlists of zikr, Allah, Ya Allah, and SubhanAllah, during the experiments. To smooth the signal, the Butterworth filter was used. Later, the extracted features were Alpha, Beta, and Delta waves, which were segmented based on the filtered signal. To complete the decision-making stage, the average value of amplitude differences and the p-value test were performed in the final phase. Based on the results of the experiments, it is clear that zikr is dominant in Delta wave. In terms of data hypothesis analysis, the results of both experiment were differentiated to determine which brain signal was dominant, and p-value testing was performed. Furthermore, this research is an alternative to current methods because it suggest that zikr has the tendency to alter a person's brain state to be either in a relaxed or calm mode regardless of the type of zikr recitation. As an outcome, the study recommends the relationship of the reaction of EEG signal on brain relaxation with different types of zikr

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