Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the impact of simulation fidelity on VR-enabled hazard detection on construction sites – A case study on crane lift operation

Crane operator training is an essential part of construction safety and is attracting extensive attention from researchers worldwide. Virtual reality (VR) is considered an effective tool to improve training outcomes by providing users with an immersive, risk-free experience in various environments. However, previous VR-based training platforms mainly focused on the scenario and task design; few studies attempted to investigate the impact of simulation fidelity on training efficiency. This research aims to explore the effect of simulation fidelity on training outcomes by comparing user performance in two scenarios. A typical construction site was modelled in a game engine using two rendering approaches; an eye-tracking system was adopted for data collection. The results from a subject experiment indicated the high efficiency of VR in operator safety training and demonstrated the usefulness of eye-tracking in measuring hazard detection performance. Findings showed that a higher level of simulation fidelity might not significantly improve the training efficiency, especially in hazard detection aspects.
An Ultra-deep Multiband Very Large Array (VLA) Survey of the Faint Radio Sky (COSMOS-XS): New Constraints on the Cosmic Star Formation History

We make use of ultra-deep 3 GHz Karl G. Jansky Very Large Array (VLA) observations of the COSMOS field from the multiband COSMOS-XS survey to infer radio luminosity functions (LFs) of star-forming galaxies (SFGs). Using ∼1300 SFGs with redshifts out to z ∼ 4.6, and fixing the faint and bright end shape of the radio LF to the local values, we find a strong redshift trend that can be fitted by pure luminosity evolution with the luminosity parameter given by α L ∝ (3.40 ± 0.11) − (0.48 ± 0.06)z. We then combine the ultra-deep COSMOS-XS data set with the shallower VLA-COSMOS 3 GHz large project data set over the wider COSMOS field in order to fit for joint density+luminosity evolution, finding evidence for significant density evolution. By comparing the radio LFs to the observed far-infrared and ultraviolet (UV) LFs, we find evidence of a significant underestimation of the UV LF by 22% ± 14% at high redshift (3.3 < z < 4.6, integrated down to 0.03Lz=3⋆ ). We derive the cosmic star formation rate density (SFRD) by integrating the fitted radio LFs and find that the SFRD rises up to z ∼ 1.8 and then declines more rapidly than previous radio-based estimates. A direct comparison between the radio SFRD and a recent UV-based SFRD, where we integrate both LFs down to a consistent limit ( 0.038Lz=3⋆ ), reveals that the discrepancy between the radio and UV LFs translates to a significant (∼1 dex) discrepancy in the derived SFRD at z > 3, even assuming the latest dust corrections and without accounting for optically dark sources.
Visual Management (VM) supporting collaborative practices in infrastructure engineering design

Managing the design of complex engineering systems requires an organisational structure and an information system to support collaboration among all stakeholders. Technological developments in information management have the potential to facilitate interactions across physical boundaries, even more during the Covid-19 pandemic. Visual Management (VM) is an information management strategy, as well as a means for communication between individuals, supporting collaborative work. However, there is a lack of effective understanding of how digital VM can support infrastructure engineering design. The adoption of digital collaborative VM in the context addressed is new, under rapid evolution, and there is limited understanding of how the users embrace VM while interacting with it. The aim of the paper is to explore the adoption of VM, focusing on digital whiteboards, to support collaborative practices in design processes. The ongoing investigation is carried out in collaboration with an infrastructure design and consultancy company, and follows the action research approach. The VM effectiveness was investigated by analysing the whiteboards applicability to diverse functions and comparing digital and manual implementations. Initial findings include understanding digital whiteboards as a means for collaboration among individuals with different perceptions to establish a common point of view, as it allows the information to be transferred across time and space, identifies abnormalities, and supports problem-solving. By creating a common ground, it has the potential to support complex and emergent interactions in the collaborative space.
Fast Beam Condition Monitor of the CMS experiment at HL-LHC

To achieve the challenging target of 1% precision for luminosity measurement at the high-luminosity LHC (HL-LHC) with instantaneous luminosity up to 7.5×1034 cm−2 s−1, the CMS experiment will employ multiple luminometers with orthogonal systematics. A key component of the proposed system is a standalone luminometer, the Fast Beam Condition Monitor (FBCM), which is fully independent of the central trigger and data acquisition and hence is able to operate outside of CMS data taking periods at 40 MHz providing bunch-by-bunch luminosity measurement with 1 s time granularity. FBCM will be placed inside the cold volume of the tracker as it utilizes silicon-pad sensors with an asynchronous read-out. A zero-hit-counting algorithm will be used to measure luminosity. FBCM will also provide precise timing information with a few ns precision enabling the measurement of beam-induced background. We report on the optimization of the design and the expected performance of FBCM.
Anomaly Detection of Distribution Network Based on Adversarial Dual Autoencoder

Anomaly detection on attributed graphs aims to identify individuals or groups whose patterns deviate significantly from most samples, which is very important for internet security, power system, and other applications. Especially in the power system, the energy flow pattern of the distribution network is very similar to the graph topological constraints. Taking each equipment and bus as nodes and the relevant electrical measurement data as attributes, the distribution network fault location problem is mapped to the anomaly detection task on the attributed graph. The traditional GNN-based anomaly detection algorithm for distribution networks has some problems, such as over-smoothing and inefficient embedding learning. To solve the problem of anomaly detection in the distribution network, an anomaly detection algorithm based on adversarial dual autoencoder (DGAE_GAN) is proposed, which is composed of a structural encoder and attribute encoder based on residual connection GAT. Through joint learning, the attributed graph is mapped to the low-dimensional space, and the decoders reconstruct the attributed graph. Then, a discriminator is invited to learn the sample similarity, and the game strategy can effectively alleviate the over-smoothing problem. Finally, the anomaly node detection task is realized by calculating the reconstruction errors from both the structure and attribute levels. We conducted multiple experiments on three real-world datasets, and the IEEE standard example shows that our proposed model is superior to the existing methods.
RPCs for the SND Muon System of the SHiP experiment

SHiP (Search for Hidden Particles) is a proposed beam dump experiment at CERN SPS, with the aim of exploring the so-called Hidden Sector. Since a large neutrino flux is expected to be produced at the beam dump, the experiment could also allow for the study of neutrino physics with unprecedented statistics. A dedicated Scattering and Neutrino Detector (SND), equipped with a downstream Muon Identification System, is thus being designed. The SND muon detector consists of iron filters interleaved with tracking planes, based on RPC technology. Each muon plane has an active area of about (2 x 4) m2 and consists of three large gaps read out by two panels of perpendicular strips. The SHiP RPCs have to provide a uniform spatial response as well as high efficiency. The design of the SND muon system is presented and the performance of a SHiP RPC prototype is discussed.
Exploring the thermal conditions in construction sites through IoT based methodologies

Despite improvements in reducing the rate of injuries to construction workers, the construction industry remains a high-risk industry. Working conditions in construction sites frequently pose heat hazards and presents uncontrolled heat risks. Technological solutions that generate early warnings have the potential to improve health and safety. However, little research has been done in the construction industry. The Smart Safety Vest system is proposed as an ‘internet of things’ (IoT) solution in construction sites. The system development has followed a series of technology readiness levels and construction site testing. This paper reports on the early preliminary data analysis on the validity and usability of the system in actual construction sites. Real-time monitoring of constructions sites via portable weather stations were conducted. Specifically, this paper presents the evaluation of the thermal conditions of four residential construction project sites. The environmental data collected in four residential construction project sites are quantitatively analysed which set the context for the further reporting of analysis of the smart vest data from construction workers. The initial results obtained during extremely hot summer conditions are used to derive recommendations. It presents the viability of an integral part of the real-time monitored future pervasive construction sites.
Traditional African vegetables in modular living walls: a novel approach towards smart cities

In terms of the 2030 Agenda for Sustainable Development and the United Nations (UN) Sustainable Development Goals (SDGs), sustainable cities and communities (SDG 11), combined with good health and wellbeing (SDG 3), are vital. The world is not making adequate progress in meeting the UN’s targets to address food security (SDG 2). In South Africa, the growing population, rapid urbanization, poverty, and unemployment exacerbate the issue of food security; even more so considering climate change (SDG 13). The built environment needs to respond to these demands and incorporate green initiatives that can provide ecosystem services. Novel approaches are required to optimize land use and promote sustainable built environments through food production. This paper consolidates literature on local vertical urban food production with vegetable crops to enhance ecosystem services and lower the carbon footprint of buildings in the Global South. The literature review points to the potential of small-scale edible living walls that utilize local materials and traditional African vegetable (TAV) species to enhance system sustainability and resilience. TAV species offer advantages as opposed to exotic food crops as they are tolerant to extreme heat and drought, have a high nutritional value, and have low irrigation and agrochemical maintenance requirements.
Business sustenance strategies in the competitive construction industry: Emerging contractor’s perspective

Emerging construction companies often liquidate due to the stiff competition among their rank in the construction market. This study investigated the skills and strategies adopted by emerging contractors to make their businesses sustainable within the highly competitive construction markets. A qualitative research approach was used to solicit information from Grade one to four emerging contractors in the Gauteng Province of South Africa. The data were analysed using qualitative content analysis to identify the main themes. The study’s findings indicate that emerging contractors face stiff competition among their ranks due to lack of work, significantly impacting their ability to remain in the construction business. Thus for an emerging contractor to stay competitive and sustainable in the construction industry, various skills and strategies need to be applied, including; improving the quality of work, delivering projects on time, willingness to adapt, keeping updated on the happenings in the construction industry, building good relationships with more prominent companies, reducing waste in the value chain to minimise cost, improving business cash flow, exploring joint ventures strategies, better investment decisions, among others. This study gives insight to emerging contractors on the strategies to adopt for business sustenance in the highly competitive construction market.
Climate-adaptive housing for the elderly: A preliminary study in New Zealand

A changing climate requires our built environment to be adaptable to better serve the communities for which it was intended. However, little is known about how climate change, especially rising temperatures, will impact housing and thus affect at-risk populations. This study was conducted as a preliminary study to develop and validate a research instrument (questionnaire survey) to be used in investigating the potential effects of rising temperatures on the elderly in Auckland, the largest city of New Zealand, and the role they perceive housing plays in reducing these effects. Prior to conducting more comprehensive full-scale research, the development and validation of the questionnaire survey was deemed vital to ensure the quality of the research instrument to be used. Assessment from experts and participants were used to revise the questionnaire. From this phase, the authors were able to execute the following: 1) made improvements to the research instrument such as modification of questions for better understanding of the participants, addition of relevant items, removal of unnecessary items, revision of terms to accommodate common understanding, and enhancement of question presentation (e.g., from tick box or open-ended to Likert scale); 2) checked the viability of the data collection process; and (3) planned for data coding scheme, processing, and analysis techniques. This study demonstrated that the proposed research instrument to be used in conducting the comprehensive research is feasible.
A recursive algorithm for the generation of symmetry-adapted functions : Principles and applications to the icosahedral group

Symmetry-adapted functions (SAF's) of order L can be generated recursively from powers of a seed function or from products of SAF's having lower order l. The algorithm uses a symmetry-adapted version of the decomposition of products of spherical harmonic functions via Wigner 3-j symbols. The totally symmetric SAF's are calculated for the point group 2/m{\bar 3}{\bar 5} up to L = 30. For L = 30, the icosahedral group has two independent SAF's; a simple method is suggested for making a unique choice for these two SAF's.
Paludiculture: peatland utilization to support climate change adaptation

Tropical peatlands are one of the largest carbon-storing ecosystems in the world. Draining tropical peatland causes environmental damage including greenhouse gas emissions. Drained peat is highly susceptible to fires that increase carbon emissions into the air. Rewetting is one way to solve the problem because, to achieve a zero or negative carbon balance, the water level should be close to or above the surface to maintain the water saturation of peat. The government of Indonesia has thoughtfully restored the peatland by implementing the 3R approach (rewetting, revegetation, and revitalizing local livelihood). After rewetting, paludiculture (wetland cultivation) is a promising land-use option for peatland. Through paludiculture, the peatland ecosystem will be improved which has already undergone drainage and will re-encourage carbon accumulative in peatland. This paper aims to determine the potential of paludiculture to support climate change adaptation including presenting challenges and opportunities in its implementation. Paludiculture has been shown to reduce greenhouse gas emissions by keeping peat moist. Besides having an ecological function, paludiculture also functions economically because it can be an alternative source of livelihood for people living around peat.
Imaging of voids in dielectric materials by x-ray induced partial discharge

A nondestructive testing technique for imaging of voids and inclusions in dielectric materials for high-voltage application is described. Void partial discharge occurrence is induced by a narrow pulsed x-ray beam scanning the bulk of epoxy sample. Phase resolved partial discharge analysis is recorded as a function of the beam position. The data matrix is used to construct digital gray scale images, which gives information about location, dimensions, and partial discharge activity in each defect in dielectric materials. This nondestructive technique can be an important tool to evaluate the degradation state of the material.
Erratum: Supernovae in the nuclear regions of starburst galaxies

The feasibility of using near-infrared observations to discover supernovae in the nuclear and circumnuclear regions of nearby starburst galaxies is investigated. We provide updated estimates of the intrinsic core-collapse supernova rates in these regions. We discuss the problem of extinction, and present new estimates of the extinction towards 33 supernova remnants in the starburst galaxy M 82. This is done using H I and H_2 column density measurements. We estimate the molecular to atomic hydrogen mass ratio to be 7.4 +- 1.0 in M 82. We have assembled near-infrared photometric data for a total of 13 core-collapse supernovae, some unpublished hitherto. This constitutes the largest database of IR light curves for such events. We show that the IR light curves fall into two classes, ``ordinary'' and ``slow-declining''. Template JHKL light curves are derived for both classes. For ordinary core-collapse supernovae, the average peak JHKL absolute magnitudes are -18.4, -18.6, -18.6, and -19.0 respectively. The slow-declining core-collapse SNe are found to be significantly more luminous than the ordinary events, even at early times, having average peak JHKL absolute magnitudes of -19.9, -20.0, -20.0, and -20.4 respectively. We investigate the efficiency of a computerised image subtraction method in supernova detection. We then carry out a Monte Carlo simulation of a supernova search using K-band images of NGC 5962. The effects of extinction and observing strategy are discussed. We conclude that a modest observational programme will be able to discover a number of nuclear supernovae.
Stabilization by competing instability mechanisms

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Second harmonic generation: Hamiltonian structures and particular solutions

For the equations describing second harmonic generation (SHG), we find a linear system different from one found by Kaup (but connected with it by a gauge transformation), which enables us to consider the SHG equations as related to the first negative flow in the coupled KdV hierarchy and to find therefore the Hamiltonian structures and the recursion operator. With the help of the recursion operator we write down the simplest commuting flows and consider their stationary points in order to find particular solutions of the SHG equations. We also investigate some particular invariant solutions of the SHG equations that can be of physical interest, and find their connections with invariant solutions of the Tzitzeica equation. The latter are either connected with Painleve III or solvable in elliptic functions.
A low-cost driving risk prediction system for hilly roads via surveillance cameras and onsite webcams: poster abstract

Smart transportation is a potential research area that aims to make the roads safer to drive, mainly in hilly areas where roads are curvy, narrow, and dangerous. It uses predictive power of artificial intelligence to identify road risks. These risks can be predicted by the use of surveillance cameras and onsite webcams to have a safer drive. However, generating road risk descriptions using traditional models need high computational costs. Therefore, we propose a low-cost, lightweight driving risk prediction system for hilly roads that uses the meta-learning concept to generate accurate and distinctive descriptions. Meta-learning utilizes both supervised and reinforcement concepts by learning simultaneously to obtain a global optimal solution on our RoadCaption dataset. The experiments show that our system generates captions that predict the road conditions of hilly areas with high accuracy.
Focus on what matters: improved feature selection techniques for personal thermal comfort modelling

Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods.
On efficient operation of a V2G-enabled virtual power plant: when solar power meets bidirectional electric vehicle charging

Virtual power plants (VPP) can increase reliability and efficiency of power systems with a high share of renewables. However, their adoption largely depends on their profitability, which is difficult to maximize due to the heterogeneity of their components, different sources of uncertainty and potential profit streams. This paper proposes two profit-maximizing operating strategies for a VPP that aggregates solar systems and electric vehicle (EV) chargers with vehicle-to-grid (V2G) support, and generates profit by trading energy in day-ahead and imbalance electricity markets. Both strategies solve a two-stage stochastic optimization problem. In the first stage, energy bids are placed by solving a sequence of linear programs, each formulated for a specific forecast scenario. In the second stage, given the day-ahead commitments and real-time measurements, the decisions with respect to charging or discharging EVs are made sequentially for every hour and adjustments to the day-ahead commitments are settled in the imbalance market. The two strategies differ in how they solve the sequential decision making problem in the second stage. But, they both foresee the effect of their current (dis)charge decisions on the feasibility of fulfilling the EV charging demands using a one-step lookahead technique. The first strategy employs a heuristic algorithm to find a feasible charging schedule for every EV that is connected to a charger. The second one utilizes a soft actor-critic reinforcement learning method with a differentiable projection layer that enforces constraint satisfaction. We empirically evaluate the proposed operating strategies using real market prices, solar traces, and EV charging sessions obtained from a network of chargers in the Netherlands, and analyze how the uptake of V2G could affect profitability of this VPP.