Compressive strength of concrete-filled stainless steel tube stub columns

Concrete-filled stainless steel tube (CFSST) members combine the advantages of the outstanding corrosion resistance of stainless steel and the composite action in concrete-filled steel tube (CFST) system. However, accurate calculation methods for this type of structures are currently limited and research into CFSST members with hot-rolled stainless steel tubes are not available. In this paper, the compressive behavior of CFSST stub columns has been investigated through a comprehensive experimental and numerical program. A total of 18 specimens, including 9 concrete-filled austenitic stainless steel tube (austenitic CFSST) and 9 concrete-filled duplex stainless steel tube (duplex CFSST) stub columns, were tested under compression. The varying parameters in the experimental study included the thickness of the stainless steel tube and the strength of the concrete. Finite element (FE) models duplicating the tests were developed, which were subsequently used in parametric study to generate a wider range of data and to investigate the influence of the tube thickness and concrete strength on the ultimate capacities of CFSST stub columns. Based on the generated data, it was found that the current European and Chinese standards for concrete-filled carbon steel tubes underestimate the resistances of CFSST members significantly. To this end, new calculation methods developed based on these European and Chinese design rules have been proposed, which were shown to provide improved strength predictions for both the austenitic and duplex CFSST members.</p

Students designing for students: a peer mentorship toolkit for a cross-campus, EDI, engineering transition scheme

The smooth transition of students from secondary education to university study is seen as a factor of student retention and achievement. This is especially important in the case of students from non-traditional backgrounds who may lack the social capital that could help ease their transition. Peer transition mentoring is one of the tools universities use to enhance the experience of new students. This study examines how the transition mentoring scheme of a highly selective institution (UCL) could be modified to cater for the students of a new EQF level 3 engineering preparatory programme (Foundation Engineering) which is aimed exclusively at students from under-represented groups. The transition mentoring scheme needs to address two practical obstacles: the lack of peer mentors with knowledge of the needs of the non-traditional student demographic and the physical distance between the main campus, where the peer mentors are located, and the off-campus location of the preparatory programme. A Students as Partners approach is implemented to examine the transition mentors’ perceptions of their role. Semi- structured interviews with 16 current and former transition mentors were conducted to investigate the experiences of peer mentors and to establish their training needs. The paper concludes with practical guidance on best practice for organising and managing training for students mentoring peers from non-traditional backgrounds

AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition

We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention modules are employed to select the most important local features that best describe an indoor place. Our AEGIS-Net is made of a semantic encoder, a semantic decoder and an attention-guided feature embedding. The model is trained in a 2-stage process with the first stage focusing on an auxiliary semantic segmentation task and the second one on the place recognition task. We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods. Our AEGIS-Net achieves exceptional performance and outperforms all six methods.Comment: Accepted by 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024

Robust Bioinspired MXene–Hemicellulose Composite Films with Excellent Electrical Conductivity for Multifunctional Electrode Applications

MXene-based structural materials with high mechanical robustness and excellent electrical conductivity are highly desirable for multifunctional applications. The incorporation of macromolecular polymers has been verified to be beneficial to alleviate the mechanical brittleness of pristine MXene films. However, the intercalation of a large amount of insulating macromolecules inevitably compromises their electrical conductivity. Inspired by wood, short-chained hemicellulose (xylo-oligosaccharide) acts as a molecular binder to bind adjacent MXene nanosheets together; this work shows that this can significantly enhance the mechanical properties without introducing a large number of insulating phases. As a result, MXene–hemicellulose films can integrate a high electrical conductivity (64,300 S m–1) and a high mechanical strength (125 MPa) simultaneously, making them capable of being high-performance electrode materials for supercapacitors and humidity sensors. This work proposes an alternative method to manufacture robust MXene-based structural materials for multifunctional applications

Exercise therapy in the application of sleep disorders

Sleep disorders often accompany neurological injuries, significantly impacting patient recovery and quality of life.The efficacy and adherence of traditional treatment methods have certain limitations. Exercise has been found to be a highly beneficial treatment method, capable of preventing and alleviating neurological injuries and sleep disorders. This article reviews relevant research findings from both domestic and international sources over the past few decades, systematically summarizing and analyzing the application of exercise therapy in sleep disorders,strategy of exercise intervention program and the potential molecular mechanisms by which exercise therapy improves sleep disorders. Shortcomings in current research and suggestions are presented, providing a reference for future in-depth studies on exercise interventions for sleep disorders

Enhanced carbon uptake and reduced methane emissions in a newly restored wetland

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research: Biogeosciences 125(1), (2020): e2019JG005222, doi:10.1029/2019JG005222.Wetlands play an important role in reducing global warming potential in response to global climate change. Unfortunately, due to the effects of human disturbance and natural erosion, wetlands are facing global extinction. It is essential to implement engineering measures to restore damaged wetlands. However, the carbon sink capacity of restored wetlands is unclear. We examined the seasonal change of greenhouse gas emissions in both restored wetland and natural wetland and then evaluated the carbon sequestration capacity of the restored wetland. We found that (1) the carbon sink capacity of the restored wetland showed clear daily and seasonal change, which was affected by light intensity, air temperature, and vegetation growth, and (2) the annual daytime (8–18 hr) sustained‐flux global warming potential was −11.23 ± 4.34 kg CO2 m−2 y−1, representing a much larger carbon sink than natural wetland (−5.04 ± 3.73 kg CO2 m−2 y−1) from April to December. In addition, the results showed that appropriate tidal flow management may help to reduce CH4 emission in wetland restoration. Thus, we proposed that the restored coastal wetland, via effective engineering measures, reliably acted as a large net carbon sink and has the potential to help mitigate climate change.We would like to thank Yangtze Delta Estuarine Wetland Ecosystem Ministry of Education & Shanghai Observation and Research Station for providing sites during our research. This research was supported by the National Key Research and Development Program of China (Grant 2017YFC0506002), the National Natural Science Foundation of China Overseas and Hong Kong‐Macao Scholars Collaborative Research Fund (Grant 31728003), the China Postdoctoral Science Foundation (Grant 2018M640362), the Shanghai University Distinguished Professor (Oriental Scholars) Program (Grant JZ2016006), the Open Fund of Shanghai Key Lab for Urban Ecological Processes and Eco‐Restoration (Grant SHUES2018B06), and the Scientific Projects of Shanghai Municipal Oceanic Bureau (Grant 2018‐03). The complete data set is available at https://data.4tu.nl/repository/uuid:536b2614‐c4ca‐43d2‐84dd‐6180fd859544

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced AoA solution. We propose a Deep Learning approach for deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2◦

From bibliometric analysis: 3D printing design strategies and battery applications with a focus on zinc-ion batteries

Three-dimensional (3D) printing has the potential to revolutionize the way energy storage devices are designed and manufactured. In this paper, we explore the use of 3D printing in the design and production of energy storage devices, especially zinc-ion batteries (ZIBs) and examine its potential advantages over traditional manufacturing methods. 3D printing could significantly improve the customization of ZIBs, making it a promising strategy for the future of energy storage. In particular, 3D printing allows for the creation of complex, customized geometries, and designs that can optimize the energy density, power density, and overall performance of batteries. Simultaneously, we discuss and compare the impact of 3D printing design strategies based on different configurations of film, interdigitation, and framework on energy storage devices with a focus on ZIBs. Additionally, 3D printing enables the rapid prototyping and production of batteries, reducing leading times and costs compared with traditional manufacturing methods. However, there are also challenges and limitations to consider, such as the need for further development of suitable 3D printing materials and processes for energy storage applications

DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced AoA solution. We propose a Deep Learning approach for deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2◦