Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Abstract Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. These results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.
STUDIES OF MOLECULAR DOCKING BETWEEN LUTEOLIN 3’-(4”- ACETYLGLUCURONIDE) AND 3CL PROTEASE OF SARS-CoV-2

The pandemic caused by SARS-CoV-2 has been the center of attention worldwide since the end of 2019. No therapeutic alternative is effective against the virus. Among the studied molecules is luteolin, a flavonoid extracted from Lonicera japonica Thunb. with binding affinity to the SARS-CoV-2 protease 3CL, important for viral replication. Thus, this evaluated, by molecular docking techniques, the interaction between luteolin derivatives and SARS-CoV-2 3CL. luteolin-4’-Glucoside, luteolin 6-C-glucoside 8-C-arabinoside and luteolin 3’-(4”-acetylglucuronide) derivatives were tested as 3CL ligands, all of which showed a more favorable binding energy value than that found for the standard 3N ligand (-7.8 to -8.0 kcal/mol vs -4.5 kcal/mol). The 3’- (4”-acetylglyucuronide) luteolin derivative, demonstrated for the first had the best binding value, -8.0 kcal/mol. The types of bond found between the target and the luteolin derivatives were mostly alkyl-type bonds, which may explain the more favorable values compared to the standard ligand. The results presented, combined with pharmacological activities and the low toxicity already demonstrated for luteolin, make it, together with its derivatives, a promising alternative for the treatment of COVID-19 and encourage more detailed studies. molecular, a interação entre derivados de luteolina e 3CL do SARS-CoV-2. Derivados de luteolina-4’-glucosídeo, luteolina 6-C-glucosídeo 8-C-arabinosídeo e luteolina 3’-(4”-acetilglucuronato) foram testados como ligantes para 3CL, todos os quais mostraram um valor de energia de ligação mais do que o encontrado para o demonstrado pela primeira vez, apresentou o melhor valor de ligação, -8,0 kcal/mol. Os tipos de ligação encontrados entre o alvo e os derivados da luteolina eram principalmente ligações do tipo alquil, o que pode explicar os valores mais favoráveis em comparação com o ligante padrão. Os resultados apresentados, aliados às atividades farmacológicas e à baixa toxicidade já demonstrada para a luteolina, tornam-na, juntamente com seus derivados, alternativas promissoras para o tratamento da COVID-19 e incentivam estudos mais detalhados.
Comparing science communication theory with participatory practice: case study of the Australian Climate Champion Program

While short-term participatory science communication activities have been well researched, long-term programs have received scant attention. Analysing survey data and participant discussions, I investigated interactions between Australian farmers and scientists engaged in the Climate Champion Program (2009–2016). I compared their interactions to three theorised science communication models: deficit, dialogue and participatory. I found their interactions illustrated a mix of the characteristics of all three models. While farmers and scientists appeared to be motivated to interact by deficit and dialogue objectives, respectful and trusting relationships emerged from long-term participation, which was key to making deficit- and dialogue-style communication more effective.
An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media

Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this work
Metrology Enabled Tooling for the Assembly of Aero-Structures

Metrology and Tooling are considered as discrete disciplines within Manufacturing Engineering, however, assembly tooling often acts as a checking mechanism. Assembly tooling has the primary function of controlling part location during assembly; with a secondary requirement as a quality gate. In-tool checks are manual mechanical checks of the assembly, these gauging checks assume the tooling has the correct, nominal geometry. Tooling conformance is certified periodically; however these intervals can be up to three years. Further examination of the metrology requirements within the aerospace industry with respect to large scale assembly tooling identify a requirement to: reduce manual metrology checks, reduce tooling recertification time, and enable greater automation. Currently, there is a lack of integration between metrology and Wing-box assembly tooling. This research investigates how to increase manufacturing confidence with respect to tooling conformance; and, ultimately improve the manufacturing process for aero-structures, through the increased and enhanced use of metrology in the assembly tooling environment. The Metrology Enhanced Tooling for Aerospace (META) framework has been created to provide a robust framework for deploying metrology in the tooling environment. The major elements of the framework are subsequently detailed and demonstrated in three chapters: i) large volume metrology networks, for the measurement of tooling structures; testing instrument performance, quantifying and improving the uncertainty estimation, and ultimately, establishing a rapid measurement process for assembly tooling; ii) embedded metrology systems demonstrates how local measurement systems can be utilised to replace and improve on, traditional in-tool checks; and iii) metrology feedback presents an example of an automated tooling pick-up that manipulates the assembly to achieve the design intent. The contributions can be summarised as: firstly, the creation of the META framework for the deployment of metrology in assembly tooling environment, accommodating and facilitating a number of the future tooling and assembly requirements. Secondly, the establishment of a generic commissioning methodology and measurement strategy for the rapid measurement of assembly tooling to increase tooling confidence. The research output was demonstrated in a case study, through a combination of physical measurement and digital automation simulation to prove the process time was greatly decreased from current methods.
Co-Occurrence of Overweight/Obesity, Anemia and Micronutrient Deficiencies among Non-Pregnant Women of Reproductive Age in Ghana: Results from a Nationally Representative Survey

Overweight/obesity (OWOB) often co-occurs with anemia or micronutrient deficiencies (MNDs) among women of reproductive age (WRA) in Ghana; identifying the risk factors of these conditions is essential for prevention. We aimed to examine the prevalence of OWOB, anemia, and MNDs and their co-occurrence and risk factors among non-pregnant women 15–49 years of age in Ghana. Data were from a 2017 two-stage national survey of 1063 women. We estimated the weighted prevalence of single and co-occurring malnutrition, and used logistic regression to explore risk factors. The prevalence of OWOB, anemia, and ≥1 MND was 39%, 22%, and 62%, respectively; that of OWOB co-occurring with anemia was 6.7%, and OWOB co-occurring with ≥1 MND was 23.6%. There was no significant difference between observed and expected prevalence of co-occurrence OWOB with anemia or MND. Risk factors were: living in southern (vs. northern) belt, high- (vs. low-) wealth household, being ≥ 25 years old, and being married (vs. single) for OWOB, and living in northern (vs. southern) belt and medium- (vs. low-) wealth household for anemia and ≥1 MND, respectively. Different interventions are required for addressing OWOB in WRA than those for anemia and MNDs.
Real-time guidewire tracking and segmentation in intraoperative x-ray

During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guidewires observed on the fluoroscopic images, haptic information and the patients' physiological signals. For this purpose, real-time and accurate guidewire segmentation and tracking can enhance the visualization of guidewires and provide visual feedback for physicians during the intervention as well as for robot-assisted interventions. Nevertheless, this task often comes with the challenge of elongated deformable structures that present themselves with low contrast in the noisy fluoroscopic image sequences. To address these issues, a two-stage deep learning framework for real-time guidewire segmentation and tracking is proposed. In the first stage, a Yolov5s detector is trained, using the original X-ray images as well as synthetic ones, which is employed to output the bounding boxes of possible target guidewires. More importantly, a refinement module based on spatiotemporal constraints is incorporated to robustly localize the guidewire and remove false detections. In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box. The network contains two major modules, namely a hessian-based enhancement embedding module and a dual self-attention module. Quantitative and qualitative evaluations on clinical intra-operative images demonstrate that the proposed approach significantly outperforms our baselines as well as the current state of the art and, in comparison, shows higher robustness to low quality images.
Use of digital techniques in the diagnosis of oral diseases in children

Innovative computer techniques including artificial intelligence technique have been widely used in many areas, including dentistry. Increasing requirements in the field of diagnostics and dental treatment are conducive to the digitization of many areas of dentistry. The mentioned diagnosis and treatment are based on the knowledge of a specialist. The introduction of artificial neural networks (ANN) makes it possible to support the doctor’s decision regarding the diagnosis and treatment plan. The article presents the possibilities offered by artificial intelligence in the field of paediatric dentistry, both in terms of diagnostics and in the treatment process. There are many publications and ongoing research on the use of machine learning methods for dental treatment. Various types of analysis are based on digital images such as intraoral photographs, panoramic images, computed tomography images, and cephalometric radiography.
Design of a modular, multi-arm concentric tube robot featuring roller gears

This paper describes the design of a modular concentric tube robot actuation system featuring roller gears. We extended prior work in a single-arm system within the literature to a two-arm system. We present mechanical design and initial performance verification experiments, including two-handed manipulation experiments for the proposed system. The overarching contribution is a modular, potentially low cost, approach to the actuation of multi-arm concentric tube robot systems.
Processing Agroindustry By-Products for Obtaining Value-Added Products and Reducing Environmental Impact

Over four billion tons of foods are produced annually on the planet, and about a third is wasted. A minimal part of this waste is incinerated or sent to landfills for treatment, avoiding contamination and diseases; the rest is disposed of elsewhere. The current review was aimed at broadening the panorama on the potential of agroindustrial by-products in applications such as biofuels, biomaterials, biocompounds, pharmaceuticals, and food ingredients. It also exposes the main chemical, physical, and biochemical treatments for converting by-products into raw materials with added value through low environmental impact processes. The value of agroindustrial waste is limited due to the scarce information available. There is a need for further research in unexplored areas to find ways of adding value to these by-products and minimizing their contamination. Instead of throwing away or burning by-products, they can be transformed into useful materials such as polymers, fuels, antioxidants, phenols, and lipids, which will effectively reduce food waste and environmental impact.
Stability of Graphene Oxide Composite Membranes in an Aqueous Environment from a Molecular Point of View

We used molecular dynamics to investigate the stability of graphene oxide (GO) layers supported on three polymeric materials, namely a polyvinylidene fluoride (PVDF), a pristine and a crosslinked polyamide–imide (PAI and PAI-cr). The membrane configurations consisted of a few layers of GO nanosheets stacked over the specified polymeric supports and submerged in water. We monitored the position, the tilt angle, and the radial distribution function of the individual GO nanosheets in respect to the plane of the supports. We showed that the outermost GO nanosheets were more distorted than those attached directly on the supports. The greatest distortion was observed for the GO nanosheets of the PVDF-supported system. Next, we recorded the density profiles of the water molecules across the distance from the layers to the polymer and discussed the hydrogen bonds between water hydrogens and the oxygen atoms of the GO functional groups.
The Practice of “Two-Wheelers” in Sfax City (Tunisia)

In recent decades, the use of two-wheelers (motorcycles and bicycles) for travel has become an alternative to cumbersome and polluting modes, in many cities around the world and especially in developed countries. It offers several advantages. It is less polluting, less expensive and less cumbersome, especially compared to the private car. In Sfax city, and compared to other Tunisian cities, the use of two-wheelers is a specific aspect of travel. But over the past two decades, this phenomenon has continued to decline. The main objective of this article is to assess the current situation of the use of both motorized and non-motorized wheelers in the Great Sfax. It aims first of all, to show the importance of the use of this mode in this city, compared to other means of transport. It presents the trend of this mode since the 1980s. He also explains the advantages and disadvantages of this mode and the various factors explaining the decline in its use. Next, it shows its spatial distribution by movement pattern. It also determines, in a third place, the differences in practices of this mode among the Sfaxiens according to their opportunities and their strategy of displacement. Finally, we propose some alternatives to encourage the use of travel mode. Based on this research, we found that the use of two-wheelers is a Sfaxian practice of some importance in terms of mobility. Two-wheelers flows are of great importance in all road transport flows. Admittedly, this importance has been declining over the past two decades. The decline in the development of the two-wheelers can be
3D printing of generative art using the assembly and deformation of direction-specified parts

Purpose A methodology for designing and printing three-dimensional (3D) objects with specified printing-direction using fused deposition modeling (FDM), which was proposed by a previous paper, enables the expression of natural directions, such as hair, fabric or other directed textures, in modeled objects. This paper aims to enhance this methodology for creating various shapes of generative visual objects with several specialized attributes. Design/methodology/approach The proposed enhancement consists of two new methods and a new technique. The first is a method for “deformation”. It enables deforming simple 3D models to create varieties of shapes much more easily in generative design processes. The second is the spiral/helical printing method. The print direction (filament direction) of each part of a printed object is made consistent by this method, and it also enables seamless printing results and enables low-angle overhang. The third, i.e. the light-reflection control technique, controls the properties of filament while printing with transparent polylactic acid. It enables the printed objects to reflect light brilliantly. Findings The proposed methods and technique were implemented in a Python library and evaluated by printing various shapes, and it is confirmed that they work well, and objects with attractive attributes, such as the brilliance, can be created. Research limitations/implications The methods and technique proposed in this paper are not well-suited to industrial prototyping or manufacturing that require strength or intensity. Practical implications The techniques proposed in this paper are suited for generatively producing various a small number of products with artistic or visual properties. Originality/value This paper proposes a completely different methodology for 3D printing than the conventional computer-aided design (CAD)-based methodology and enables products that cannot be created by conventional methods.
A Virtual Reality and Questionnaire Approach to Gathering Real-World Data for Agent-Based Crowd Simulation Models

Abstract Gathering real-world data is a crucial process in developing realistic, agent-based crowd simulation models. In order to gather real-world data, three types of data need to be considered: physical, mental, and visual. Existing data gathering methods do not collect all three data types, but they provide a limited amount of data for agent-based simulations. This article proposes using a combination of Virtual Reality and Questionnaires as a means to gathering real-world data. This hybrid method collects all three data types and is validated by comparing it to data collected from the real world. Two data gathering experiments (real world and our proposed method) were conducted to collect all three types of data for comparison. Experimental results show that the proposed method can collect similar data to the real-world experiment, in particular for mental and visual data. The Chi-Square Goodness-of-Fit Test proves that there is no significant difference between the real world and our proposed method for mental and visual data, whilst the test shows there is significant difference in physical data, in particular, completed time. We propose an adjustment factor for the completed time data that mitigates the gap between virtual space and real space, and allows the results collected to be input into agent-based simulations as real-world data. Overall, the proposed method is cost effective, time efficient, reproducible, ecologically valid, and able to collect three types of data for agent-based crowd simulation models.
Spatial coherency analysis of seismic ground motions from a rock site dense array implemented during the Kefalonia 2014 aftershock sequence

The objective of studies presented in this paper is to analyse the spatial incoherency of seismic ground motions using signals from a velocimeter dense array located on a rock site, recording the aftershock sequence of the two M6 Kefalonia earthquakes that occurred in January/February 2014 (Kefalonia island, Greece). The analyses are carried out with both horizontal and vertical components of velocigrams for small separation distances of stations (<100 m). The coherencies of seismic ground motions identified from strong motion windows are compared with those identified from coda parts of signals. It is realized that there is no significant difference between the coherencies estimated from those two parts of signals. The influence of earthquake event number on the result of coherencies and the dispersions of coherencies estimated from different earthquake events are presented. Finally, coherencies estimated from the dense array are compared with several coherency models proposed and widely used in the literature. The possibility of modifying some parameters of those existing coherency models to fit with in situ coherencies are discussed and presented. Copyright © 2017 John Wiley & Sons, Ltd.