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Monitoring of indoor air quality at a large sailing cruise ship to assess ventilation performance and disease transmission risk

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science of the Total Environment 2025年 962卷 178286页

作者： Cheung, Ho Yin Wickson Kumar, Prashant Hama, Sarkawt Emygdio, Ana Paula Mendes Wei, Yingyue Anagnostopoulos, Lemonia Ewer, John Ferracci, Valerio Galea, Edwin R. Grandison, Angus Hadjichristodoulou, Christos Jia, Fuchen Lepore, Pierfrancesco Morawska, Lidia Mouchtouri, Varvara A. Siilin, Niko Wang, Zhaozhi Department of Civil and Environmental Engineering Faculty of Engineering and Physical Sciences University of Surrey GuildfordGU2 7XH United Kingdom Institute for Sustainability University of Surrey GuildfordGU2 7XH United Kingdom Hampton Road Teddington MiddlesexTW11 0LW United Kingdom Laboratory of Hygiene and Epidemiology Faculty of Medicine University of Thessaly Larissa41222 Greece Centre for Safety Resilience and Protective Security Fire Safety Engineering Group School of Computing and Mathematical Sciences Faculty of Engineering and Science University of Greenwich GreenwichSE10 9LS United Kingdom Public Health and Medical Public Affairs MSC Cruise Management UxbridgeUB11 1AF United Kingdom School of Earth and Atmospheric Sciences Faculty of Science Queensland University of Technology QLD4000 Australia VTT Technical Research Centre of Finland Ltd Espoo02150 Finland

Large passenger ships are characterised as enclosed and crowded indoor spaces with frequent interactions between travellers, providing conditions that facilitate disease transmission. This study aims to provide an indoor ship CO2 dataset for inferring thermal comfort, ventilation and infectious disease transmission risk evaluation. Indoor air quality (IAQ) monitoring was conducted in nine environments (three cabins, buffet, gym, bar, restaurant, pub and theatre), on board a cruise ship voyaging across the UK and EU, with the study conducted in the framework of the EU HEALTHY SAILING project. CO2 concentrations, temperature and relative humidity (RH) were simultaneously monitored to investigate thermal characteristics and effectiveness of ventilation performance. Results show a slightly higher RH of 68.2 ± 5.3 % aboard compared to ASHRAE and ISO recommended targets, with temperature recorded at 22.3 ± 1.4 °C. Generally, good IAQ (2 mainly varying between 400 and 1200 ppm. The estimated air change rates (ACH) and ventilation rates (VR) implied sufficient ventilation was provided in most locations, and the theatre (VR: 86 L s−1 person−1) and cabins (VR: >20 L s−1 person−1) were highly over-ventilated. Dining areas including the pub and restaurant recorded high CO2 concentrations (>2000 ppm) potentially due to higher footfall (0.6 person m−2 and 0.4 person m−2) and limited ACH (2.3 h−1 and 0.8 h−1), indicating a potential risk of infection;these areas should be prioritised for improvement. The IAQ and probability of infection indicate there is an opportunity for energy saving by lowering hotel load for the theatre and cabins and achieving the minimum acceptable VR (10 L s−1 person−1) for occupants' comfort and disease control. Our study produced a first-time dataset from a sailing cruise ship's ventilated areas and provided evidence that can inform guidelines about the optimisation of ventilation operations in large passenger ships, contributing to respiratory health, i

关键词： Indoor air pollution

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A Scalable Test Problem Generator for Sequential Transfer Optimization

arXiv

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arXiv 2023年

作者： Xue, Xiaoming Yang, Cuie Feng, Liang Zhang, Kai Song, Linqi Tan, Kay Chen The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The College of Computer Science Chongqing University Chongqing400044 China The Civil Engineering School Qingdao University of Technology Qingdao266520 China The Department of Computing Hong Kong Polytechnic University Hong Kong

Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorithm design, the development of a systematic benchmark suite for comprehensive comparisons of STO algorithms received far less attention. Existing test problems are either simply generated by assembling other benchmark functions or extended from specific practical problems with limited scalability. The relationships between the optimal solutions of the source and target tasks in these problems are also often manually configured, limiting their ability to model different similarity relationships presented in real-world problems. Consequently, the good performance achieved by an algorithm on these problems might be biased and hard to be generalized to other problems. In light of the above, in this study, we first introduce four concepts for characterizing STO problems and present an important problem feature, namely similarity distribution, which quantitatively delineates the relationship between the optima of the source and target tasks. Then, we present the general design guidelines of STO problems and a particular STO problem generator with good scalability. Specifically, the similarity distribution of a problem can be easily customized, enabling a continuous spectrum of representation of the diverse similarity relationships of real-world problems. Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator, which would serve as an arena for STO algorithms and provide more comprehensive evaluation results. The source code of the problem generator is available at htt s://ithub com/Xmin Hsueh/STOP-G. Copyright © 2023, The Authors. All rights reserved.

关键词： Scalability

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Improving Naturalness and Controllability of Sequence-to-Sequence Speech Synthesis by Learning Local Prosody Representations

Improving Naturalness and Controllability of Sequence-to-Seq...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Cheng Gong Longbiao Wang Zhenhua Ling Shaotong Guo Ju Zhang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin P.R.China National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China Hefei P.R.China Huiyan Technology (Tianjin) Co. Ltd Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9781728176055;9781728176062

State-of-the-art neural text-to-speech (TTS) networks are trained with a large amount of speech data, which significantly improves the quality of synthetic speech compared with traditional approaches. However, the prosody and controllability of the generated speech is still insufficient, especially in tonal languages. Moreover, the generated prosody is solely defined by the input text, which does not allow for different styles for the same sentence or words. In this study, we extended Tacotron2 with a pitch prediction task to capture discrete pitch-related representations. Specifically, the learned pitch-related suprasegmental information is fed simultaneously with traditional character features into the decoder to generate final Mel spectrogram. Experiments show that the proposed method can improve the quality of the generated speech (mean opinion score of 4.37 vs. 4.22). Moreover, we demonstrated that we can easily achieve word-level pitch control during generation by changing local pitch-related representations before passing them to the decoder network.

关键词： Conferences Neural networks Controllability Acoustics Decoding Speech synthesis Task analysis

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Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver

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Physical Review Letters 2022年第11期128卷 110501-110501页

作者： Xu-Fei Yin Yuxuan Du Yue-Yang Fei Rui Zhang Li-Zheng Liu Yingqiu Mao Tongliang Liu Min-Hsiu Hsieh Li Li Nai-Le Liu Dacheng Tao Yu-Ao Chen Jian-Wei Pan Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics University of Science and Technology of China Hefei 230026 China Shanghai Branch CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China JD Explore Academy Beijing 101111 China School of Computer Science Faculty of Engineering University of Sydney Sydney 2006 Australia Hon Hai Quantum Computing Research Center Taipei 114 Taiwan

The recognition of entanglement states is a notoriously difficult problem when no prior information is available. Here, we propose an efficient quantum adversarial bipartite entanglement detection scheme to address this issue. Our proposal reformulates the bipartite entanglement detection as a two-player zero-sum game completed by parameterized quantum circuits, where a two-outcome measurement can be used to query a classical binary result about whether the input state is bipartite entangled or not. In principle, for an N-qubit quantum state, the runtime complexity of our proposal is O(poly(N)T) with T being the number of iterations. We experimentally implement our protocol on a linear optical network and exhibit its effectiveness to accomplish the bipartite entanglement detection for 5-qubit quantum pure states and 2-qubit quantum mixed states. Our work paves the way for using near-term quantum machines to tackle entanglement detection on multipartite entangled quantum systems.

关键词： Entanglement detection Machine learning Quantum entanglement Quantum optics

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Ultra-High Dynamic Extinction Ratio Electro-Optical Modulator with Coupled Microring on SOI Platform

Ultra-High Dynamic Extinction Ratio Electro-Optical Modulato...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Zhuo Cheng Bigeng Chen Chunlei Sun Maoliang Wei Shaoliang Yu Lan Li Hongtao Lin Yunjiang Rao Research Center for Optical Fiber Sensing Zhejiang Lab Hangzhou China Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Hangzhou China State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Research Center for Intelligent Optoelectronic Computing Zhejiang Lab Hangzhou China Key Lab of Optical Fiber Sensing & Communications University of Electronic Science &Technology of China Chengdu China

A 5 th -order coupled microring electro-optical modulator with PIN structures is proposed and experimentally demonstrated on SOI integrated photonics platform. Ultra-high dynamic extinction ratio (ER) up to 60 dB is achieved benefiting from the high-order optical band-pass filter design. The device size on chip is only 400×220 μm 2 including electrodes and the driving voltage is 2.1 V corresponding to 0.23 W power consumption. Such integrated modulators would be promising in building compact and low-power distributed optical fiber sensing systems using low-background-noise optical pulses.

关键词： Optical filters Optical fibers Power demand Voltage Electro-optic modulators Extinction ratio Pins

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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A Novel RFID Authentication Protocol Based on A Block-Order-Modulus Variable Matrix Encryption Algorithm

arXiv

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arXiv 2023年

作者： Wang, Yan Liu, Ruiqi Gao, Tong Shu, Feng Lei, Xuemei Wu, Yongpeng Gui, Guan Wang, Jiangzhou School of Information and Communication Engineering Hainan University Haikou570228 China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China College of Electronic Science and Engineering Jilin University Changchun130012 China School of Information and Communication Engineering and Collaborative Innovation Center of Information Technology Hainan University Haikou570228 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China College of Electronic Information Engineering Inner Mongolia University Hohhot010021 China Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University Minhang Shanghai200240 China College of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is studied. Firstly, an adaptive modulus (AM) encryption algorithm is proposed. Subsequently, in order to enhance the security without additional storage of new key matrices, a self-updating encryption order (SUEO) algorithm is designed. Furthermore, a diagonal block local transpose key matrix (DBLTKM) encryption algorithm is presented, which effectively expands the feasible domain of the key space. Based on the above three algorithms, a novel joint AM-SUEO-DBLTKM encryption algorithm is constructed. Making full use of the advantages of the proposed joint algorithm, a two-way RFID authentication protocol, named AM-SUEO-DBLTKM-RFID, is proposed for mobile RFID systems. In addition, the Burrows-Abadi-Needham (BAN) logic and security analysis indicate that the proposed AM-SUEO-DBLTKM-RFID protocol can effectively combat various typical attacks. Numerical results demonstrate that the proposed AM-SUEO-DBLTKM algorithm can save 99.59% of tag storage over traditional algorithms. Finally, the low computational complexity as well as the low storage cost of the proposed AM-SUEO-DBLTKM-RFID protocol facilitates deployment within low-cost RFID tags. Copyright © 2023, The Authors. All rights reserved.

关键词： Authentication

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Towards Efficient Large-Scale Network Slicing: An LP Dynamic Rounding-and-Refinement Approach

arXiv

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arXiv 2021年

作者： Chen, Wei-Kun Liu, Ya-Feng Liu, Fan Dai, Yu-Hong Luo, Zhi-Quan School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we propose an efficient algorithm for the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements. The problem has been formulated as a mixed integer linear programming (MILP) formulation in the literature. We first propose a novel linear programming (LP) relaxation of the MILP formulation. We show that compared with a natural LP relaxation of the MILP formulation, the novel LP relaxation is much more compact in terms of smaller numbers of variables and constraints, and much stronger in terms of providing a better LP bound, which makes it particularly suitable to be embedded in an LP relaxation based algorithm. Then we design an efficient two-stage LP dynamic rounding-and-refinement algorithm based on this novel LP relaxation. In the first stage, the proposed algorithm uses an LP dynamic rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration;in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between the solution quality and the computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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Curvature Effect on Graphene-Based Co/Ni Single-Atom Catalysts

SSRN

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SSRN 2022年

作者： Tang, Shuaihao Xu, Liang Dong, Kejun Wang, Qichen Zeng, Jian Huang, Xin Li, Haotian Xia, Libin Wang, Lingling Energy Materials Computing Center School of Energy and Mechanical Engineering Jiangxi University of Science and Technology Nanchang330013 China Centre for Infrastructure Engineering School of Engineering Design and Built Environment Western Sydney University PenrithNSW2751 Australia Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Rare Earth Luminescence Materials and Devices of Jiangxi Province Ganzhou341000 China School of Physics and Electronics Hunan University Changsha410082 China

Graphene has been mostly applied as the substrate of metal-based catalysts, due to its unique structure and properties. Various strategies for designing graphene-based catalysts have been proposed, but the curvature has been less considered. In this work, new curved graphene models have been constructed by compressing lattice constant and curving C-C bonds. Curved graphene-based single-atom catalysts (SACs) have been obtained by doping Co, Ni, or CoNi atoms with pyridine nitrogen coordination on the curved graphene. Curvature effect on graphene and graphene-based SACs were investigated based on first-principles calculations. Through investigating their structure and catalytic performance for the hydrogen evolution reaction, the oxygen evolution reaction, and the oxygen reduction reaction, our calculations show that tuning the curvature of graphene is an effective strategy to modulate the catalytic performance and stability of graphene-based SACs. This strategy may provide novel design guidance for graphene-based SACs. © 2022, The Authors. All rights reserved.

关键词： Graphene

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Temporal Graph Representation Learning for Autism spectrum disorder Brain Networks

Temporal Graph Representation Learning for Autism spectrum d...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Peng Cao Guangqi Wen Lanting Li Xiaoli Liu Jinzhu Yang Osmar Zaiane College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Northeastern University Shenyang China College of Computer Science and Engineering Northeastern University Shenyang China A.I. Lab Alibaba Hangzhou China Amii University of Alberta Edmonton Canada

ISBN: (纸本)9781665429825

Modeling spatio-temporal dynamics in functional brain networks is critical for underlying the functional mechanism of autism spectrum disorder (ASD). In our study, we propose an end-to-end framework called temporal graph representation learning for brain networks, which thoroughly captures spatio-temporal features in resting-state functional magnetic resonance imaging (rs-fMRI) data. Specifically, we first transform rs-fMRI time-series into temporal multi-graph using a sliding window technique. A temporal multi-graph clustering is then designed to eliminate the inconsistency of the temporal multi-graph series. Then, a graph structure aware LSTM (GSA-LSTM) is proposed to capture the spatio-temporal embedding for temporal graphs. The proposed GSA-LSTM can not only capture discriminative features for prediction but also impute the incomplete graphs for the temporal multi-graph series. Extensive experiments on autism brain imaging data exchange (ABIDE) dataset shows the effectiveness of our proposed framework. The results demonstrate that the proposed dynamic brain network embedding learning outperforms the state of-the-art brain network classification models. Furthermore, the obtained clustering results are consistent with the previous neuroimaging-derived evidence of biomarkers for autism spectrum disorder (ASD).

关键词： Representation learning Neurological diseases Neuroimaging Autism Biological system modeling Magnetic resonance Transforms

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