Miracle-dz/UniFAR-FTUs huggingface dataset:
Obstacle detection based on depth fusion of lidar and radar in challenging conditions

Purpose To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection. Design/methodology/approach Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist. Findings The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions. Originality/value A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.
Going Beyond Test Scores: The Gender Gap in Italian Children’s Mathematical Capability

This paper investigates the relationship between gender, attitudes, and test scores in mathematics. The study argues that measures of children’s capability in mathematics must include some indicators of attitudes toward the subject. These are particularly important when analyzing gender gaps because attitudes toward mathematics differ by gender. To this end, the study first analyzes the gender gap in attitudes and test scores separately using school fixed effects models. Second, it estimates a structural equation model, which takes into account that mathematical capability is a latent construct for which some indicators (test scores and attitudes) are observed. Using data from the Italian National Institute for the Evaluation of Education Systems (INVALSI) for school years 5 and 10 in 2014 and 2015, results confirm that when mathematics capability, including both attitudes and test scores, is measured, the gap between boys and girls changes, and it is therefore relevant to consider both concepts. HIGHLIGHTS Italy has one of the highest gender gaps in mathematics in the OECD. Gender gaps are substantial both in children's attitudes and their test scores. Tackling gender stereotypes may improve women's self-confidence in mathematics and the gender gap in scores. This may also help close the gender gap in STEM occupations.
New Trends in Nanoclay-Modified Sensors

Nanoclays are widespread materials characterized by a layered structure in the nano-scale range. They have multiple applications in diverse scientific and industrial areas, mainly due to their swelling capacity, cation exchange capacity, and plasticity. Due to the cation exchange capacity, nanoclays can serve as host matrices for the stabilization of several molecules and, thus, they can be used as sensors by incorporating electroactive ions, biomolecules as enzymes, or fluorescence probes. In this review, the most recent applications as bioanalyte sensors are addressed, focusing on two main detection systems: electrochemical and optical methods. Particularly, the application of electrochemical sensors with clay-modified electrodes (CLME) for pesticide detection is described. Moreover, recent advances of both electrochemical and optical sensors based on nanoclays for diverse bioanalytes’ detection such as glucose, H2O2, organic acids, proteins, or bacteria are also discussed. As it can be seen from this review, nanoclays can become a key factor in sensors’ development, creating an emerging technology for the detection of bioanalytes, with application in both environmental and biomedical fields.
Public Attitudes towards Digital Water Meters for Households

In response to droughts, various media campaigns and water-saving instructions are released. However, these often only have temporary water conservation effects. A promising development in this regard is Digital Water Meters (DWM), which can provide near real-time water-use feedback. Despite extensive DWM experience in some water-stressed regions, a profound understanding of the initial attitude towards DWM and message-tailoring opportunities are rarely empirically explored. This study aims to obtain insights into the attitude towards the introduction of DWM and explore opportunities for message tailoring, a topic of extra relevance as we may be on the threshold of a large-scale DWM implementation in many world regions. Messages tailored to (i) normative beliefs and attitudes on drinking water, (ii) water-use activity and (iii) phase of decision-making, seem particularly compatible with DWM. Through a survey (n = 1037) in the Netherlands, we observe that 93% of respondents have no objections if their utility invests in DWM and that 78% would accept a (free) DWM because of improved leakage detection, lower costs and environmental considerations. Finally, instead of sociodemographic factors, we observe that an attitude-based customer segmentation approach is an especially useful predictor of respondent’s motivation to endorse DWM and forms a promising basis for water conservation message-tailoring strategies.
Nutritional Analyses of Domestic and Poultry Chickens and Evaluation of the Subsequent Effects on Lowering Blood Glucose Levels in Alloxan-Induced Wister Rats

In the recent decades, peoples concentrate more on reduced-consumption of diets containing saturated fatty acids and replace them with essential polyunsaturated ones, including omega-3 and omega-6 fatty acids, due to their promising nutritional benefits. Therefore, the production of chicken meat having lower fat compositions, and riches in pro-healthy fatty acid and other macro-and micronutrients deserves great attention in the food industry. The research aimed to characterize Original Research Article Khatun et al.; JPRI, 33(31A): 98-109, 2021; Article no.JPRI. 68762 99 meat oils of two commercially available, domestic and poultry, chickens in the Northern part of Bangladesh (Rajshahi), followed by the investigation of their nutritional compositions and hypoglycemic effect in vivo. In this work, the oil was isolated from the dried meat sources by Soxhlet extractor and purified using a rotary evaporator. We subsequently characterized meat oil in terms of various parameters, such as iodine value, saponification number, unsaponifiable matter, free fatty acid (FFA) contents, peroxide value, and acid value. Besides, the nutritional parameters of chicken oil were investigated as percentages of essential fatty acids and several other micro-and macronutrients using gas-liquid chromatography and other conventional methods. Finally, Alloxaninduced Wister rats were fed with oil and chicken flesh to investigate the hypoglycemic effects. The experimental analyses show that chicken oil possesses a significant proportion of omega-3 and omega-6 fatty acid, macro-and micronutrients. Besides, oil and flesh were found to lower blood glucose levels in diabetic rats despite flesh could not significantly show hypoglycemic properties.
Simple, Yet Complex: Pre-Service Teachers’ Conceptions of Plagiarism at a Norwegian University

ABSTRACT In recent decades, research on plagiarism has helped to raise awareness of the complex and multifaceted nature of plagiarism. Yet despite these strides, several influential academic contexts have yet to be examined. The purpose of this qualitative study was to examine pre-service teachers’ conceptions of plagiarism at a university in Norway, a country largely excluded from these studies, in part because of its non-EU membership status. Results showed that while students understood several basic elements of plagiarism, their own definitions were narrow in scope, highlighting traditional viewpoints such as plagiarism as transgression. Results also revealed that students’ concerns regarding plagiarism aligned with establishing a moral identity as a writer rather than situating plagiarism within the greater context of proper academic writing. Pedagogical implications are discussed.
A Software Security Optimization Architecture (SoSOA) and its Adaptation for Mobile Applications

Security attacks become daily news due to an exposure of a security threat in a widely used software. Taking software security into consideration during the analysis, design, and implementation phases is a must. A software application should be protected against any security threat such as unauthorized distribution or code retrieval. Due to the lack of applying a software security standard architecture, developers may create software that may be vulnerable to many types of security threats. This paper begins by reviewing different types of known software security threats and their countermeasure mechanisms. Then, it proposes a new security optimization architecture for software applications. This architecture is a step towards establishing a standard to guarantee the software's security. Furthermore, it proposes an adapted software security optimization architecture for mobile applications. Besides, it presents an algorithmic implementation of the newly proposed architecture, then it proves its security. Moreover, it builds a secure mobile application based on the newly proposed architecture.
An anti-lock braking system algorithm using real-time wheel reference slip estimation and control

Knowledge of the tyre-road interface traction limit during braking of a road vehicle can drastically improve safety and ensure stable braking on varied road conditions. This study proposes an optimal reference slip algorithm that determines the road surface while the vehicle is braking, by implicitly tracking the traction limit. It presents wheel slip variance regulation as a potential approach towards reference wheel slip estimation for wheel slip regulation (WSR). The variance regulation approach computes reference wheel slip using past wheel slip estimates and regulates wheel slip variation at a set point. This variance regulation problem was solved using least-squares estimation, yielding reference slip dynamics. A 3-staged nested control architecture was developed with reference slip dynamics to yield an anti-lock braking system (ABS) algorithm consisting of a brake controller, WSR algorithm and reference slip estimation. The algorithm was experimentally corroborated in a Hardware-in-Loop setup consisting of the pneumatic brake system of a heavy commercial road vehicle, and IPG TruckMaker®, a vehicle dynamics simulation software. The proposed ABS algorithm was tested on straight roads with homogeneous surfaces, split friction surfaces, and transition friction surfaces. It ensured stable braking in all road cases, with a 7%–18% reduction in braking distance on homogeneous road surfaces compared to the same vehicle without ABS. The vehicle directional stability was retained on a split-friction surface, and the ABS algorithm was observed to adapt to sudden transitions in the road surface.
Development and Pilot Analysis of the Bruise Visibility Scale

Introduction The accuracy of assessing and documenting injuries is crucial to facilitate ongoing clinical care and forensic referrals for victims of violence. The purpose of this cross-sectional, pilot study was to evaluate the inter-rater reliability and criterion validity of a newly developed Bruise Visibility Scale (BVS). Methods: The instrument was administered to a diverse sample (n = 30) with existing bruises. Bruises were assessed under fluorescent lighting typical of an examination room by three raters who were randomly selected from a pool of eight experienced clinical nurses. Colorimetry values of the bruise and surrounding tissue were obtained using a spectrophotometer. Results: The BVS demonstrated good single (ICC = 0.71, 95% CI = 0.54 – 0.84) and average agreement (ICC = 0.88, 95% CI = 0.78 – 0.94) between raters. A significant, positive moderate correlation was found between mean BVS scores and overall color difference between the bruise and surrounding skin (Pearson’s r = 0.614, p < 0.001). Conclusion: With further research, the BVS has the potential to be a reliable and valid tool for documenting the degree of clarity in bruise appearance.
Concrete Cracks Monitoring using Deep Learning-based Multiresolution Analysis

In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.
Evolution of Water-in-Oil Droplets in T-Junction Microchannel by Micro-PIV

Water-in-oil droplets have huge importance in chemical and biotechnology applications, despite their difficulty being produced in microfluidics. Moreover, existing studies focus more on the different shape of microchannels instead of their size, which is one of the critical factors that can influence flow characteristics of the droplets. Therefore, the present work aims to study the behaviours of water-in-oil droplets at the interfacial surface in an offset T-junction microchannel, having different radiuses, using micro-PIV software. Food-grade palm olein and distilled water seeded with polystyrene microspheres particles were used as working fluids, and their captured images showing their generated droplets’ behaviours focused on the junction of the respective microfluidic channel, i.e., radiuses of 400 µm, 500 µm, 750 µm and 1000 µm, were analysed via PIVlab. The increasing in the radius of the offset T-junction microchannel leads to the increase in the cross-sectional area and the decrease in the distilled water phase’s velocity. The experimental velocity of the water droplet is in agreement with theoretical values, having a minimal difference as low as 0.004 mm/s for the case of the microchannel with a radius of 750 µm. In summary, a small increase in the channel’s size yields a significant increase in the overall flow of a liquid.
Remote Sensing Image Scene Classification Based on Fusion Method

Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance.
AutoCoach: An Intelligent Driver Behavior Feedback Agent with Personality-Based Driver Models

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.
The breakdown process in an atmospheric pressure nanosecond parallel-plate helium/argon mixture discharge

The breakdown process in an atmospheric pressure nanosecond helium/argon mixture discharge with parallel-plate electrodes is investigated by temporally and spatially resolved optical emission spectroscopy (OES). The spatially resolved electric field is obtained from the Stark splitting of the He i 492.1 nm line. Using the emissions from the He ii 468.6 nm, He i 667.8 nm, and Ar i 750.4 nm lines and a collisional–radiative model, the spatially resolved Te, high and Te, low (representing the effective Te in the high energy and low energy part of the EEDF, respectively) are obtained. It is found that, compared with the average electric field provided by the external pulser, the electric field is greatly enhanced at certain location and is significantly weakened at other places. This observation shows the effect of the ionization wave propagation, as predicted in [, ]. The value of Te, high is much larger than that of Te, low, which indicates that an elevated high energy tail in the EEDF is built up under the influence of strong electric field during the breakdown process. Initially, the spatial distribution of the Te, low and the Te, high generally follows that of the electric field. However, at the end of the breakdown period, the location of the highest Te, low and Te, high is shifted away from the cathode sheath, where the electric field is strongest. This indicates the existence of a non-local effect and is supported by the result from a simple Monte-Carlo simulation.
A Neural Network System for Fault Prediction in Pipelines by Acoustic Emission Techniques

ABSTRACT The problem of evaluating the risk of failure associated with the propagation of a crack in a pipe under pressure has great practical relevance, and it may be tackled with acoustic-emission techniques. Artificial neural networks may be trained to classify the acoustic emissions generated by the crack according to the phase of propagation, and such a classification permits to evaluate the risk of mantaining a system in operation. In order to train the network, a human specialist has to estimate the transition times between any two consecutive phases by inspecting the results of a previous hydrostatic test, and such determination of the transition times has a high degree of subjectivity and uncertainty, affecting the classification performance of the network. In this paper, we propose a human-independent method for the estimation of the transition times, and we show successful applications to the data from two hydrostatic tests. For a test on a 2 m-long pipe, the method exhibited 98% of correct-classification rate, an improvement of 8% over results obtained with human-determined transition times. For a 40 m-long pipe, under experimental conditions comparable to those found in industrial applications, the method exhibited 91% of correct-classification rate. The proposed method provides a fully automated framework for the evaluation of the state of a crack.
Formulasi Krim Antioksidan Ekstrak Daun Kapas (Gossypium SP.)

Gossypium sp. leaf is one of the plants that contain flavonoids that can act as antioxidants. This study aims to look at the effect of combination of the stearic acid and triethanolamine which function as emulsifier in creams and determine antioxidant activity of cream Gossypium sp. leaf extract. Cream is formulated with a variety of stearic acid and triethanolamine emulsifier that F1 (8%: 2%), F2 (12%: 3%), F3 (16%: 4%). Tests carried out at the first day and the 28th days. Evaluation preparations include organoleptic test, homogeneity, viscosity, pH, and type of the cream. The results showed that the variation of concentration emulsifier that fill quality preparations with some parameters such as organoleptic, homogeneity, the type of cream and viscosity. pH of cream does not qualify are 4.5-6, but still in the neutral pH range (pH ± 7). Antioxidant activity of the cream has a large reduce power at the first day DPPH reduce percent value for F1, F2, and F3 are 80,73%, 83,05% and 83,51% respectively, while after the storage at the 28th days DPPH reduce percent value for F1, F2, and F3 are 80,43%, 82,49% and 79,02% respectively. Preparation creams that fill physical quality cream is F1 by using emulsifier triethanolamine and stearic acid with a concentration of 8%: 2%
Basics of systems approach to methodology of assessing waterworks facili-ties on natural environment

According to the regulatory and environmental requirements on operating waterworks facilities referred to as objects of activity, the environmental influence of these facilities must be assessed in terms of water resource utilization, which is exactly the goal of this study. The basics of the methodology of this assessment are developed by the system approach centred around the notion of System. The results of studying the interaction of the object of activity (OA) with the natural environments (NENV) of the basin geosystem are used in developing the basics of the methodology of environmental influence assessment (EIA). The study results have been used in designing and building hydropower engineering facilities in the North Caucasus in the South of Russia.