Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of parameters for modeling of chloride ion ingress into concrete

Within the management of structures, nonformal assessments where the condition of a structure is evaluated on the basis of visual inspections are the most common type of evaluation. This is because not all of the structures in huge infrastructure networks can be assessed in great depth due to cost constraints. The objective of a structural condition assessment is to determine the current state and estimate the future performance of a structure with a maximum degree of accuracy and a minimum of effort. There is therefore a need for advanced methodologies and predictive deterioration models for the assessment of structures/structural elements over time. Data collected during visual inspections should mainly provide information about the most serious problems and suggest the most suitable scheme for the extension of the performed inspection via monitoring, additional on‐site measurements and/or laboratory tests. The objective of this contribution is to sketch out the principles of structural evaluation associated with chloride diffusion processes and to feature several levels of structural assessment, starting with simple but conservative methods and progressing to more sophisticated ones. The article proposes a “guideline” which is aimed in particular at existing concrete/reinforced concrete structures, such as bridges, located in the mild European climate in places where deicing salts are applied.
Modeling Power Generation and Energy Efficiencies in Air-Cathode Microbial Fuel Cells Based on Freter Equations

The model proposed in this study was based on the assumption that the biomass attached to the anode served as biocatalysts for microbial fuel cell (MFC) exoelectrogenesis, and this catalytic effect was quantified by the exchange current density of anode. By modifying the Freter model and combining it with the Butler–Volmer equation, this model could adequately describe the processes of electricity generation, substrate utilization, and the suspended and attached biomass concentrations, at both batch and continuous operating modes. MFC performance is affected by the operating variables such as initial substrate concentration, external resistor, influent substrate concentration, and dilution rate, and these variables were revealed to have complex interactions by data simulation. The external power generation and energy efficiency were considered as indices for MFC performance. The simulated results explained that an intermediate initial substrate concentration (about 100 mg/L under this reactor configuration) needed to be chosen to achieve maximum overall energy efficiency from substrate in the batch mode. An external resistor with the value approximately that of the internal resistance, boosted the power generation, and a resistor with several times of that of the internal resistance achieved better overall energy efficiency. At continuous mode, dilution rate significantly impacted the steady-state substrate concentration level (thus substrate removal efficiency and rate), and attached biomass could be fully developed when the influent substrate concentration was equal to or higher than 100 mg/L at any dilution rate of the tested range. Overall, this relatively simple model provided a convenient way for evaluating and optimizing the performance of MFC reactors by regulating operating parameters.
Impact of metal grain work function variability on ferroelectric insulation based GAA MOSFET

In this work, a ferroelectric dielectric based TiN-GAA MOSFET with metal work-function variations (WFVs) has been proposed. The proposed model exhibits higher I on/I off ratio and lower subthreshold swing (58 mV/decade) as compared to conventional GAA MOSFET as a result of an amalgam of both gate all around (GAA) geometry and ferroelectric effect. The investigation is further extended to different technology nodes with WFV. The WFV induced threshold voltage is considered to be a function of the ratio of average grain size to grain area concept. The model exhibits a higher probability of threshold matching as compared to previously published results and the superiority is reflected in the Pelgrom plot. Thus the model can provide insight to counter the challenges created by WFV.
Adequacy Assessment of Generating Systems Incorporating Storage Integrated Virtual Power Plants

This paper investigates the impacts of incorporating energy storage devices (ESD) into a virtual power plant (VPP) model and subsequently using it for performing generating system adequacy studies. A two-step linearized optimization formulation is proposed for developing the overall VPP model featuring the aggregated characteristics and capacities of several distributed generators (DG), ESDs and loads together with distribution network information like topology and constraints. While the first step determines the (charging or discharging) operating mode of the batteries, the second step outputs the optimal dispatch schedules for the DGs and ESDs in accordance with a fair energy allocation based operating strategy adopted in this paper. The VPP is modeled using an augmented IEEE 69-bus distribution network, and the overall generation adequacy studies incorporating the VPP are carried out on the IEEE Reliability Test System using a sequential simulation algorithm. Several indices are presented for quantifying the impacts of the ESDs’ incorporation on the VPP model’s performance. Finally, sensitivity studies conducted for investigating the effects of variations in the ESD rated capacities and charging/discharging periods on the VPP performance and system adequacy indices are also described.
Frequency Control Method using Automated Demand Response for Isolated Power System with Renewable Energy Sources

Abstract In response to mounting concerns regarding environmental problem and depletion of Energy Resources, the introduction of Renewable Energy Sources (RESs), has been advancing in recent years. The system frequency deviation is a serious problem for a RESs-integrated power system. In this paper, we propose a system frequency control method using the automated demand response (ADR) for an isolated power system with RESs. The ADR can automatically adjust the consumption power of appliances after receiving the DR signal. It is assumed that consumption power of controllable loads is automatically varied based on the electricity price information from the real-time pricing (RTP). This method improves the supply-demand balancing, and hence the system frequency control is achieved. Furthermore, the stability of controller is demonstrated by indicating the poles of the control system.
Plane segmentation from point clouds

Recently, attempts have been made to automate data acquisition, which is also related to efforts to automate data processing. The paper deals with the automation of terrestrial laser scanning data processing. The approaches for point cloud segmentation are briefly described. An algorithm based on random sample consensus is proposed for automated plane identification and plane segmentation from point clouds. The proposed approach was tested by processing point clouds; the results of the testing are also described. Based on the proposed algorithm, a standalone application for automated plane segmentation from laser scanner data using Matlab® software was developed.
Application of Fuzzy Modelling to Predict Construction Projects Cash Flow

Construction project managers are always looking for methods for forecasting future projects and preventing of potential delays in the project. One of the most crucial requirements of construction project managers and financial planners is awareness of project cash flow and financial status. On the other hand, the unique properties of construction projects with uncertainties such as activity duration, the variability of resources, material costs and also ambiguity in the employer’s payments are factors that have an effect on the correct prediction of project cash flow. Hence, the project team should examine project cash flow under uncertainty environment. There are many approaches for considering uncertainty such as fuzzy sets, interval theory, rough and grey system. But the most well-known approach is fuzzy sets which has wide applications in engineering and management. Hence in this paper, we proposed a new method for forecasting project cash flow under fuzzy environment. Finally, the proposed method was applied on an “Engineering, Procurement and Construction” (EPC) project and it is demonstrated that the proposed model has a high performance in the prediction of project cash flow.
“I Just Like to Share My Life with My Partner:” Mobile Phone Integration in Romantic Partners’ Face-to-Face Interactions

ABSTRACT Participants (N = 312) in romantic relationships completed an online survey to determine common integrated phone activities, motivations for integrating phones into time together, and whether phone integration is associated with relationship satisfaction. The most frequent activities included showing social media posts, photos, and videos; showing texts; and involving the partner in video calls. Primary motivations for integrating phones into partners’ co-present time were enjoyment, psychological inclusion, phubbing avoidance, transparency, and habitual use. Findings indicate that participants were often intentional in integrating the phone into co-present time, and frequency of joint involvement with the phone was associated with relationship satisfaction.
Children's lives in an era of school closures: Exploring the implications of COVID‐19 for child labour in Ghana

Abstract On 11 March 2020, the World Health Organisation declared COVID‐19 a global pandemic. Subsequently, governments worldwide implemented strict regimes of lockdowns and school closures to contain the transmission of the virus. Ghana's government on 15 March 2020 also announced a lockdown and closure of schools, lasting up till January 2021. Against this backdrop, the paper examined the implications of school closures on child labour in Ghana. Qualitative data for the study were collected between October 2020 to February 2021 in a small rural community in northern Ghana. Findings from 16 semi‐structured interviews with schoolchildren aged 8–13 years show how school closures have meant that children from contexts of poverty: (a) are driven into child labour as they are either forced to accompany their parents to work on farms or sell foodstuff by the roadside; and thus, ultimately (b) engage in no learning during the lockdown period.
Online ECG Emotion Recognition for Unknown Subjects via Hypergraph-Based Transfer Learning

Electrocardiogram (ECG) signal based cross-subject emotion recognition methods reduce the influence of individual differences using domain adaptation (DA) techniques. These methods generally assume that the entire unlabeled data of unknown target subjects are available in training phase. However, this assumption does not hold in some practical scenarios where the data of target subjects arrive one by one in an online manner instead of being acquired at a time. Thus, existing DA methods cannot be directly applied in this case since the unknown target data is inaccessible in training phase. To tackle the problem, we propose a novel online cross-subject ECG emotion recognition method leveraging hypergraph-based online transfer learning (HOTL). Specifically, the proposed hypergraph structure is capable of learning the high-order correlation among data, such that the recognition model trained on source subjects can be more effectively generalized to target subjects. Meanwhile, the structure can be easily updated by adding a hyperedge which connects a newly coming sample with the current hypergraph, resulting in further reduce the individual differences in online manner without re-training the model. Consequently, HOTL can effectively deal with the online cross-subject scenario where unknown target ECG data arrive one by one and varying overtime. Extensive experiments conducted on the Amigos dataset validate the superiority of the proposed method.
Petit Suisse cheese added açai: characterization and effect of the use of thickeners

The search for the development of new dairy products has been growing in recent years. Petit Suisse is a cheese that has been standing out in Brazil, reaching expansion in the market. The cheese added by fruits, such as açai (Euterpe oleracea Mart.), enhances the energy and nutritional value of the product. Its texture is an important feature, influencing the acquisition by the public. Thus, the objective was to develop and analyze the texture and physicochemical parameters of Petit Suisse cheeses added with açai. Three formulations (F1, F2 and F3) were prepared with different thickeners (xanthan gum and gelatin) and analyzed for texture, moisture, protein and pH parameters. The results indicated similarity between the different formulations for the texture parameters: F1 and F3 showed no significant difference (p ≥ 0.05), and F2 differed from F3 only in elasticity and cohesiveness. It was concluded that the application of different thickeners in Petit Suisse influenced its quality, being considered the best formulation that contained the mixture of thickeners (F3).
Active Contrastive Set Mining for Robust Audio-Visual Instance Discrimination

The recent success of audio-visual representation learning can be largely attributed to their pervasive property of audio-visual synchronization, which can be used as self-annotated supervision. As a state-of-the-art solution, Audio-Visual Instance Discrimination (AVID) extends instance discrimination to the audio-visual realm. Existing AVID methods construct the contrastive set by random sampling based on the assumption that the audio and visual clips from all other videos are not semantically related. We argue that this assumption is rough, since the resulting contrastive sets have a large number of faulty negatives. In this paper, we overcome this limitation by proposing a novel Active Contrastive Set Mining (ACSM) that aims to mine the contrastive sets with informative and diverse negatives for robust AVID. Moreover, we also integrate a semantically-aware hard-sample mining strategy into our ACSM. The proposed ACSM is implemented into two most recent state-of-the-art AVID methods and significantly improves their performance. Extensive experiments conducted on both action and sound recognition on multiple datasets show the remarkably improved performance of our method.
Tilting Sheaves on Brauer-Severi Schemes and Arithmetic Toric Varieties

The derived category of coherent sheaves on a smooth projective variety is an important object of study in algebraic geometry. Over the past decades, a lot of techniques have been invented to study the structure of derived category of coherent sheaves. One important device relevant for this study is the notion of tilting sheaf, which was first introduced by Baer [Ba]. This thesis is concerned with the existence of tilting sheaves on some smooth projective varieties. The main techniques we use in this thesis are Galois descent theory and Proposition 4.1. 8: a bundle on a smooth projective variety is a tilting bundle if it is a tilting bundle after a finite Galois extension. First we construct a tilting bundle on a general Brauer-Severi variety. Our major result shows the existence of tilting bundles on some Brauer-Severi schemes. As an application, we prove that there are tilting bundles on an arithmetic toric variety whose split toric variety has a splitting fan. Our findings extend the works of Costa and Miró-Roig [CM1] and Blunk [Bl].
Comprehension of different types of novel metaphors in monolinguals and multilinguals – ERRATUM

It has been suggested that multilingualism can lead to increased cognitive flexibility and creativity. No studies to date, however, have investigated whether this advantage leads to a greater propensity to find meaning in different kinds of novel metaphors. This article reports a self-paced reading study that focuses on whether such an increased propensity is displayed by multilingual English speakers, as opposed to monolingual English speakers. The article explores the difference between two broad types of novelty in metaphorical expressions, which are distinguished by how readily they conform to existing metaphorical schemata. The results indicate that both monolinguals and multilinguals find novel metaphors that conform readily to an existing schema easier to comprehend those that do not. They also take longer to seek meaning in metaphors that conform readily to an existing schema. Multilinguals are more likely than monolinguals to find meaning in both types of novel metaphor. The theoretical distinction drawn between metaphors that conform readily to an existing schema and those that do not highlights the variability of meaning in novel metaphors. It also focuses attention on the different extents to which hearers seek rich meanings as opposed to less rich but more easily derived ones.
Extremely efficient PM2.5 estimator based on analysis of saliency and statistics

Air pollution is a crucial environmental problem, especially the fine particulate matter (PM2.5) which has become one of the focal points. PM2.5 is a complex pollutant which can intrude the lungs and threaten people's health during the whole lives. In order to enable people to know the PM2.5 index of their surroundings at any time, an image-based PM2.5 predictor with saliency detection (IPPS) is proposed. The proposed predictor first obtains the non-salient regions based on saliency detection technologies. Then, the authors extract two features of the entropy and intensity values of non-salient image saturation map. Finally, they multiply these two features into the approximation of PM2.5 concentration. Experiments show that the proposed IPPS is superior in accuracy and efficiency.
Human Body Posture Detection in Context: The Case of Teaching and Learning Environments

—This paper describes an approach to detect and classify human posture in an individual context, more precisely in a classroom ambience. The posture can be divided into two main groups: “Confident/Not Confident”, aiming for the teacher’s posture evaluation, and “Interested/Not Interested”, targeted for the students. We present some relevant concepts about these postures and how can they be effectively detected using the OpenPose library. The library returns the main key points of a human posture. Next, with TensorFlow, an open-source software library for machine learning, a deep learning algorithm has been developed and trained to classify a given posture. Lastly, the neural network is put to the test, classifying the human posture from a video input, labeling each frame. The experimental results presented in this paper confirm the effectiveness of the proposed approach.
Minimizing Noise in HyperLogLog-Based Spread Estimation of Multiple Flows

Cardinality estimation has become an essential building block of modern network monitoring systems due to the increasing concerns of cyberattacks (e.g., Denial-of-Service, worm, spammer, scanner, etc.). However, the ever-increasing attack scale and the diversity of patterns (i.e., flow size distribution) will produce a biased estimation of existing solutions if apply a monotonic hypothesis for network traffic. The most representative solution is virtual HyperLogLog (vHLL), which extended the proven HLL, a single element cardinality estimation solution, to a multi-tenant version using a memory random sharing and noise elimination approach. In this paper, we show that the assumption made by vHLL’s does not work for large-scale network traffic with diverse flow distributions. To resolve the issue, we propose a novel noise elimination method, called Rank Recovery-based Spread Estimator (RRSE), which is tolerant to both attack and normal traffic scenarios while using limited computation and storage. We show that our recovery function is more reliable than state-of-the-art approaches. Moreover, we implemented RRSE in a programmable switch to show the feasibility.
DATA FUSION UNSTEADY AERODYNAMIC MODELING BASED ON EXPERIMENTAL DATA

Dynamic stall prediction at high angles of attack is faced with the dual challenges of insufficient accuracy of calculation data and lack of experimental data. In order to make full use of the characteristics of different data sources to establish a dynamic stall aerodynamic time-domain prediction model, this paper proposed a data fusion modelling method, which combines a Computational Fluid Dynamics solver with a neural network model. By fusing experimental data and Computational Fluid Dynamics simulation data, combined with an integrated neural network model, an unsteady aerodynamic data fusion modelling framework for airfoil dynamic stall is established. Based on the NACA0012 airfoil dynamic stall test data, and the Computational Fluid Dynamics numerical simulation results, the proposed data fusion framework performs high precision in the prediction of wind tunnel test data, including lift and moment coefficients at different pitch angles, balanced angles of attack and reduced frequencies. Results show that the proposed data fusion framework not only has higher prediction accuracy, but also has strong abilities in both generalization and convergence.
How Emotional Attachment Effects Intention: The Case of Continuous Knowledge Sharing Intention in Virtual Community

: Most existing researches explore continuous knowledge - sharing from the perspective of the rational cognition. This paper argues that the continuous intention and behaviors should be influenced by long-term emotional factors, and it can be explored from the perspective of emotional attachment to the influential factors of continuous knowledge sharing willingness of virtual communities. Based on the Expectation Confirmation Theory, this paper introduced the attachment and constructed a model of virtual community users' continuous knowledge sharing intention. Structural equation modeling method is used to analyze the data and verify the model. The results show that attachment not only has a direct positive effect on the long-term knowledge sharing intention of virtual community users, but also mediated the relationship between satisfaction, perceived usefulness and the intention of continuous knowledge sharing.
Evaluating Virtual Reality 360 Video to Inform Interface Design for Driverless Taxis

Autonomous, self-driving taxis are a commonly cited solution for future mobility but inevitably raise myriad human-centred design and usability challenges. However, conducting usability and user experience studies in imagined, future vehicles is troublesome, given the absence of safe, road-worthy exemplars (and indeed, recent COVID-19 restrictions). Applying a novel virtual reality (VR) Wizard-of Oz methodology, fifty-two participants were presented with immersive 360° videos (VR360-videos), which captured interactions with a range of potential human-machine interface (HMI) solutions, aiming to address vehicle identification and on-boarding tasks. Interactions and experiences were acted out by members of the research team, with potential HMIs and constituent tasks informed by a participatory design study, literature review and user requirements elicitation exercise also conducted by the authors. To evaluate the methodological approach, the VR360-videos were presented using either a VR-headset or laptop web-browser, with respondents offered the opportunity to participate in the laboratory or from their home, thus affording a 2 × 2 between-subjects study design with fixed factors of Method and Location. An overview of the user experience results indicated that all HMIs were generally received positively, with subjective ratings unaffected by Method or Location. Qualitative data suggested potential for greater immersion/presence using a VR-headset, although there were indications of higher “simulator sickness” using this approach. The study provides a novel methodology to deliver immersive user experiences for evaluation. Results will be used to inform HMI design and future evaluations associated with remaining journey stages (in-transit, arrival, payment etc.).