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A new concept of bio-based prestress technology with experimental Proof-of-Concept on Bamboo-Timber composite beams

Construction and Building Materials 2023年第1期402卷

作者： Zhang, Hexin Shen, Minhe Deng, Yu Andras, Peter Sukontasukkul, Piti Yuen, Terry Y.P. Tang, Yunchao Wong, Simon H.F. Limkatanyu, Suchart Singleton, Ian Hansapinyo, Chayanon School of Computing Engineering and the Built Environment and School of Applied Sciences Edinburgh Napier University 10 Colinton Road EdinburghEH10 5DT United Kingdom School of Civil Engineering and Architecture Guangxi University of Science and Technology Liuzhou545006 China Construction and Building Materials Research Center Department of Civil Engineering King Mongkut's University of Technology North Bangkok Thailand Department of Civil Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College of Urban Construction Zhongkai University of Agriculture and Engineering Guangzhou510225 China Faculty of Science and Technology Technological Higher Education Institute of Hong Kong 20A Tsing Yi Road Tsing Yi Island New Territories Hong Kong Department of Civil and Environmental Engineering Faculty of Engineering Prince of Songkla University Songkhla90112 Thailand Center of Excellence for Natural Disaster Management Department of Civil Engineering Faculty of Engineering Chiang Mai University Thailand

This paper presents a pioneering experimental proof-of-concept study to validate a novel concept of prestress technology that used only pure bio-based composite materials while achieved consistent prestressed stress distribution within the structure member, and provided in-situ flexibility, improved structural performance, and maximised the rate of utilisation of each material. Industrial level of facilities were used during this development. The prestress is achieved by pressurised/forced lamination of multiple components with different materials and geometrical properties. The prestressing process is activated during the pressure release stage during which the components are interacting with each other, creating different stress statuses that would favour the weaker and adverse the stronger components to maximise the strength exploitation of different materials. Using laminated bamboo and timber as an example pair, twenty-two glulam, non-prestressed and prestressed laminated bamboo-timber composite beams were manufactured, tested, and analysed to provide an in-depth understanding of the structural behaviours of these novel structural members. Failure modes, yielding, ultimate and serviceability limit loads, and corresponding deflections, as well as the histories of strain development at key positions of the specimens were examined. The experimental study confirmed the feasibility, effectiveness and industrial scalability of the proposed technology. The novel concept provides a new approach for developing the prestress technology for bio-based materials, and this experimental study laid the foundation for its future analytical development and numerical studies. © 2023 Elsevier Ltd

关键词： Timber

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Ultra-chaos: an insurmountable objective obstacle of reproducibility and replicability

Research Square

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Research Square 2021年

作者： Liao, Shijun Qin, Shijie Center of Advanced Computing School of Naval Architecture Ocean and Civil Engineering Shanghai Jiaotong University Shanghai200240 China School of Physics and Astronomy Shanghai Jiaotong University Shanghai200240 China

It is crucial for scientific progress to be able to replicate scientific findings1–4, because scientific claims should gain credence due to the reproducibility and replicability of their main supporting evidences. However, according to Nature’s survey of 1,576 researchers, more than 70% of the surveyed failed to reproduce another scientist’s experiments, and more than half agreed that there exists a significant "crisis of reproducibility"2. Here we reveal a new classification of chaos: normal-chaos and ultra-chaos. Unlike a normal-chaos, statistics of an ultra-chaos are sensitive to small disturbances. Some illustrative examples of ultra-chaos are given here. It is found that statistical non-reproducibility is indeed an inherent property of an ultra-chaos that is at a higher-level of disorder than a normal-chaos. So, it is impossible in practice to replicate experimental/numerical results of an ultra-chaos even in statistical meanings, since random environmental noises always exist and are out of control. Thus, ultra-chaos is indeed an insurmountable objective obstacle of reproducibility and replicability. Similar to Gödel’s incompleteness theorem, such kind of "incompleteness of reproducibility" reveals a limitation of scientific researches. It opens a new door and possibility to study reproducibility crisis, statistical significance, computational fluid dynamics (CFD), chaos theory, turbulence theory, and so on. © 2021, CC BY.

关键词： Chaos theory

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Measuring the concept of PID literacy: user perceptions and understanding of persistent identifiers in support of open scholarly infrastructure

arXiv

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arXiv 2022年

作者： Macgregor, George Lancho-Barrantes, Barbara S. Pennington, Diane Rasmussen Scholarly Publications & Research Data Scholarly Research Communications University of Strathclyde Glasgow United Kingdom Department of Computer & Information Sciences University of Strathclyde Glasgow United Kingdom School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom School of Computing Edinburgh Napier University Edinburgh United Kingdom

The increasing centrality of persistent identifiers (PIDs) to scholarly ecosystems and the contribution they can make to the burgeoning 'PID graph' has the potential to transform scholarship. Despite their importance as originators of PID data, little is known about researchers' awareness and understanding of PIDs, or their efficacy in using them. In this article we report on the results of an online interactive test designed to elicit exploratory data about researcher awareness and understanding of PIDs. This instrument was designed to explore recognition of PIDs (e.g. DOIs, ORCIDs, etc.) and the extent to which researchers correctly apply PIDs within digital scholarly ecosystems, as well as measure researchers' perceptions of PIDs. Our results reveal irregular patterns of PID understanding and certainty across all participants, though statistically significant disciplinary and academic job role differences were observed in some instances. Uncertainty and confusion were found to exist in relation to dominant schemes such as ORCID and DOIs, even when contextualized within real-world examples. We also show researchers' perceptions of PIDs to be generally positive but that disciplinary differences can be noted, as well as higher levels of aversion to PIDs in specific use cases and negative perceptions where PIDs are measured on an 'activity' semantic dimension. This work therefore contributes to our understanding of scholars' 'PID literacy' and should inform those designing PID-centric scholarly infrastructures, that a significant need for training and outreach to active researchers remains necessary. © 2022, CC BY.

关键词： Ecosystems

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Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Identifying users with wearable sensors based on activity patterns 11

Identifying users with wearable sensors based on activity pa...

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11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, EUSPN 2020

作者： Ehatisham-Ul-Haq, Muhammad Malik, Maryam Naseer Azam, Muhammad Awais Naeem, Usman Khalid, Asra Ghazanfar, Mustansar Ali Department of Computer Engineering University of Engineering and Technology Taxila Punjab47050 Pakistan Whitecliffe Technologies Wellington New Zealand School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering and Computer Science Victoria University of Wellington New Zealand School of Architecture Computing and Engineering University of East London United Kingdom

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user's identity based on his/her daily living physical activity patterns. © 2020 The Authors. Published by Elsevier B.V.

关键词： Wearable sensors

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Automated masks generation for coffee and apple leaf infected with single or multiple diseases-based color analysis approaches

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Informatics in Medicine Unlocked 2022年 28卷

作者： Hasan, Reem Ibrahim Yusuf, Suhaila Mohd Mohd Rahim, Mohd Shafry Alzubaidi, Laith School of Computing Faculty of Engineering Universiti Teknologi Malaysia Skudai 81310 Johor Malaysia Al-Nidhal Campus University of Information Technology & Communications Baghdad 00964 Iraq Faculty of Science & Engineering Queensland University of Technology Brisbane 4000 QLD Australia School of Engineering Architecture and Information Technology The University of Queensland Brisbane 4000 QLD Australia

Identification of plant disease is affected by many factors. The scarcity of rare or mild symptoms, the sensitivity of segmentation is influenced by light and shadow of images capturing conditions, and symptoms characteristics are represented by multiple lesions of varied colours on the same leaf at different stages of infection. Traditional approaches face several problems: contrast handling leads to mild symptoms being undetected and deals with edges results in curved surfaces and veins being considered new regions of interest. Thresholding of segmentation restricts it to a specific range of values, which prevents it from dealing with an entire area (healthy, injured, or noise). Deep learning approaches also face problems of dealing with imbalanced datasets. The existence of overlapped symptoms on the same leaf sample is rare. Most deep models detect a single type of lesion at a single time. Masks with a single type of infection are used for training these models that lead to misclassification. Manual annotation of symptoms is considered time-consuming. Therefore, the proposed framework in this study is an attempt to overcome certain drawbacks of traditional segmentation approaches to generate masks for deep disease classification models. The main objective is to label datasets based on a semi-automated segmentation of leaves and disordered regions. There is no need to manage contrast or apply filters that keep lesion characteristics unchanged. As a result, every pixel in the predetermined lesions is selected accurately. The approach is applied to three different datasets with single and multiple infections. The obtained overall precision is 90%. The average intersection over the union of the injured regions is 0.83. The brown and the dark brown lesions are more accurately segmented than the yellow lesions. © 2021 The Authors

关键词： Color processes analysis Overlapped plant diseases Semantic segmentation

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Implementation of an Intelligent Animal Monitoring System Using Wireless Sensor Network and IoT Platform

Implementation of an Intelligent Animal Monitoring System Us...

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Cyber Resilience (ICCR), International Conference on

作者： Ateeq Ur Rehman Jehangir Arshad Muhammad Tariq Sadiq Abdul Rehman Munir Ahmad Mohammad Kamrul Hasan Hussam Al Hamadi Tauqeer Faiz Department of Electrical Engineering GC University Lahore Pakistan Department of Electrical & Computer Engineering Lahore Campus COMSATS University Islamabad Lahore Pakistan School of Architecture Technology and Engineering University of Brighton Brighton United Kingdome NCBAE School of Computer Science Lahore Pakistan Faculty of Information Science and Technology Center for Cyber Security Universiti Kebansaan Malaysia (UKM) Bangi Selangor Malaysia College of Engineering and IT University of Dubai School of IT Skyline University College Sharjah UAE Faculty of Engineering Computing and Science (FECS) - Swinburne University of Technology Australia

ISBN: (数字)9781665461221

ISBN: (纸本)9781665461238

Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Animals Databases Cows

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A Machine Vision-Based Person Detection Under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System

A Machine Vision-Based Person Detection Under Low-Illuminanc...

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Smart Technologies in computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： Julie Ann B. Susa Sammy V. Militante Aimee G. Acoba Ryan Soriente Evangelista Luisito Lolong Lacatan Charlemagne G. Laviňa Bartolome T. Tanguilig Dept. of Computer Engineering Southern Luzon State University Lucban Quezon Philippines College of Engineering and Architecture University of Antique Antique Philippines Electronics Eng'g Technology Department Technological University of the Philippines Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan ng Cabuyao Laguna Philippines

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance.

关键词： Deep learning Training Visualization Computer vision Heuristic algorithms Computational modeling Lighting

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Bridging gender and human-centered design: a design verification study

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Procedia CIRP 2020年 91卷 824-831页

作者： Bahar Khayamian Esfahani School of Architecture Computing and Engineering University of East London London E16 2RD UK

This article highlights the importance of adding gender awareness in human-centered design. Aspects of both paradigms are presented, and a lack of understanding of gender in the human-centered design approach was identified. Human-centered design sees gender as static and stable regarding male or female such that the implication of principles in products, systems or services appeals to one gender or another linking gender differences, and stereotypes. Evidence is provided through a design verification study, conducted in the context of fostering sun protection behavior in young men age 18 to 24. The results verify gender-aware sun protection interventions for young men. It also shows the inclusion of gender has great potential to bridge the gap between the world of designers and users. It also presents a discussion of the analytical procedures and the findings which emerged in this investigation led to the verification of design recommendations for sun protection interventions. Evidence is further presented for increasing emphasis on the influence of the designer’s own gender and their gendered perceptions in their designs. Finally, the implication of gender-aware human-centered design would address the low levels of sun protection in young men.

关键词： Human-Centered Design Gender Design Verification Study Sun Protection Behavior Young Men

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Three-body problem - from Newton to supercomputer plus machine learning

Research Square

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Research Square 2021年

作者： Liao, Shijun Li, Xiaoming Yang, Yu School of Physics and Astronomy Shanghai Jiaotong University Shanghai200240 China Center of Advanced Computing School of Naval Architecture Ocean and Civil Engineering Shanghai Jiaotong University Shanghai200240 China School of Mechanics and Construction Engineering Jinan University Guangzhou510632 China

The famous three-body problem can be traced back to Newton in 1687, but quite few families of periodic orbits were found in 300 years thereafter. In this paper, we propose an effective approach and roadmap to numerically gain planar periodic orbits of three-body systems with arbitrary masses by means of machine learning based on an artificial neural network (ANN) model. Given any a known periodic orbit as a starting point, this approach can provide more and more periodic orbits (of the same family name) with variable masses, while the mass domain having periodic orbits becomes larger and larger, and the ANN model becomes wiser and wiser. Finally we have an ANN model trained by means of all obtained periodic orbits of the same family, which provides a convenient way to give accurate enough predictions of periodic orbits with arbitrary masses for physicists and astronomers. It suggests that the high-performance computer and artificial intelligence (including machine learning) should be the key to gain periodic orbits of the famous three-body problem. © 2021, CC BY.

关键词： Neural networks

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