Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the inclusion features on mechanical properties of steel

ABSTRACT Polar steel requires good strength and excellent toughness for breaking icy surfaces in low-temperature sea water environments. In this study, the effect of inclusions and metallurgical structure on the mechanical property of polar steel was examined. The composition and morphology of the inclusions in steel were formed using different deoxidation methods during steel refining, and steel plates were rolled by the same rolling process. The results show that there are lower inclusion densities and more globular inclusions in steel deoxidized using Zr–Ti than deoxidized using Al. The mean diameters of grains in Zr–Ti-killed steel from different thicknesses are smaller than that in Al-killed steel, the impact toughness and yield strength of Zr–Ti-killed steel are substantially higher than those of Al-killed steel. It is determined that the use of the Zr–Ti deoxidizer has a positive effect on the ductility of the steel.
Use of NDT systems to investigate pavement reconstruction needs and improve maintenance treatment decision-making

ABSTRACT The objective is to improve the accuracy of the test results and establish a maintenance treatment decision-making system using the non-destructive testing (NDT) technology. Firstly, the evaluation unit length was theoretically determined and statistically verified. Secondly, the test frequency of falling weight deflector (FWD) test was statistically analysed and the deflection acceptance criterionwas obtained based on the probability density distribution curve. Meanwhile, the relationship between the FWD deflection and the pavement internal damage obtained by drilling was analysed. Subsequently, the test parameters of the three-dimensional ground-penetrating radar (3D GPR) were optimised by using the non-reference image quality assessing technology. Followed, pavement internal damage index of GPR was proposed for locating the defect structural layer. Lastly, the decision-making systemwas established. Results showed that the suitable evaluation unit length is 20 m, the deflection test frequency is no more than 10 m/point, the deflection accept criterion is 29.5 (0.01 mm) with 95% reliability and the optimal detection parameters of GPR is 1 cm, 10 µs and 25 ns respectively. The established decision-making system fully considering the surface and the interior conditions as well as the functional and structural conditions can effectively guide the maintenance plan formulation for similar reconstruction and expansion projects.
Design and testing of the phase mask for transfer of information by vortex beams

In the paper, design of the phase-only mask enabling generation of variable superpositions of vortices is described and analyzed. A variability of the generated mixed vortex field is achieved by intensity modulation of an array of point sources illuminating the mask. A usability of the vortex superpositions for information encoding, transfer and decoding is also demonstrated. The optical setup prospective for application in free-space communications is proposed and experimentally tested.
Phenomenological hysteresis model for vapor-liquid phase transitions

In this paper a new, phenomenological, temperature dependent hysteresis model of the vapor-liquid first-order phase transitions is presented in order to improve the existing equation-of-state type diffuse interface methods implicated in homogeneous two phase flow models. The proposed hysteresis approach describes not only equilibrium but also non-equilibrium phase transitions. A saturation temperature dependent upper limit of the allowable supersaturation is proposed, this limit ensures that unstable conditions could be always avoided. To overcome the flowing system caused memory handling problems of classical Preisach-type hysteresis models, a partial differential equation based hysteresis operator is proposed to describe the statistics of a model fluid consists of bistable, constant-mass clusters of molecules.
The Numerical Solution of Choked and Supercritical Ideal Gas Flow through Orifices and Convergent Conical Nozzles

The paper describes the numerical solution of the equations of compressible flow through axisymmetric convergent nozzles. The class of supercritical flows is considered, in which the gas velocities in the jet downstream from the throat are supersonic. The subsonic region of the flowfield is solved in the hodograph plane by a finite-difference method. The supersonic region is solved in the physical plane by the method of characteristics. The stream function distribution on the sonic line is adjusted iteratively to match the boundary conditions at the lip and free streamline. Discharge coefficients are evaluated and truncation errors in the results are considered.
Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite launched on December 23, 2017, observes various geophysical parameters with the aim of a better understanding of the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, a very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth’s surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties—cloud optical depth (COD) and cloud-particle effective radius (CER)—of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with the SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases on the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as the better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.
Planning a Minimum Sequence of Positions for Picking Parts from Multiple Trays Using a Mobile Manipulator

Mobile manipulators are able to operate in a large workspace, and have the potential to replace human workers to perform a sequence of pick-and-place tasks at separate locations. Many existing works optimize the base position or manipulator configuration for a single manipulation task, however, very few of them consider a sequence of tasks. In this paper, we present a planner that plans a minimum sequence of base positions for a mobile manipulator to robustly collect objects stored in multiple trays. We use inverse kinematics to determine the base region where a mobile manipulator can grasp the target objects stored in a tray, and move the mobile manipulator to the intersections of base regions to reduce the operation time for moving the base. We ensure robustness by only considering the intersection whose radius of the inscribed circle is larger than the base positioning error. Then the minimization of the number of base positions is formulated as a 0-1 knapsack problem. Besides, considering different object placements in the tray, we analyze feasible policies for dynamically updating the base sequence based on either the remaining objects or the target objects to be picked. In the experiment, we examine our planner on various scenarios, including different object placements: (1) Regularly placed toy objects; (2) Randomly placed industrial parts; and different implementation policies: (1) Apply globally static base positions; (2) Dynamically update the base positions. The experiment results show that the time for moving the base decreases by 11.22 seconds (29.37%) to 17.26 seconds (36.77%) by reducing one base movement, and demonstrate the feasibility and potential of the proposed method.
Joint Discriminative Latent Subspace Learning for Image Classification

Latent subspace learning aims to produce a latent representation for better reconstruction and classification from high-dimensional data through exploiting the optimal subspace. Current latent subspace learning methods commonly have three problems: 1) The discriminative property is ignored when learning the latent subspace, 2) The redundancy exists between the latent subspace and the prediction space, 3) There is no unified latent subspace that exploits knowledge jointly from the raw space, latent subspace, and label space. In this paper, we formulate the Joint Discriminative Latent Subspace Learning (JDLSL) problem to address these issues, and provide its optimization solution. JDLSL learns image representation from two aspects: a) the joint learning of latent subspaces for data reconstruction and prediction, b) the joint learning of label space and latent subspace for data reconstruction. To integrate knowledge from the joint learning, we organize the sparsity-induced latent subspace, where row-sparsity and column sparsity are simultaneously imposed. We provide the theoretical proof for the discriminativity learning ability of the sparsity-induced latent subspace. Extensive experiments and comparisons with the state-of-the-art showed that the proposed method has better performance. JDLSL shows a competitive performance with deep features compared to deep learning architectures, reflecting it potential integrating with deep learning.
Closing the Gap between Big Data and the Design and Construction Industries with Building Information Modeling (BIM) Schema

By simply making online dinner reservations, determining our global position, and mapping the restaurant directions while in transit, we instantly observe the amount of exponentially increasing data in our daily lives compared to years past. This is an example of big data that is facilitated by a global internet of interconnected communication channels which synchronously share information and resources through common network programming. The problem is that big data is noticeably absent in our industry.
Socio-demographic Factors and Eating Behaviors Among Nurses in A Teaching Hospital

Nurses are known as shift workers. Shift work can disrupt regular meal habits resulting in abnormal eating behaviour. Hence, this study aims to determine the relationship between socio-demographic factors and eating behaviour. A cross-sectional study was conducted among 135 nurses using a self-administered questionnaire. This study found that the shift duties factor was significantly associated with emotional and external eating behaviour. This might be caused by how nurses cope with the burden of shift works. Health promotion strategies from both individuals and organisations should be intensified for these nurses to enhance healthy eating habits and promote healthy lifestyles. Keywords: eating behaviour; shift duty; nurses eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i18. 3071
Slip detection and control using tactile and force sensors

In this paper, some results concerning the detection and control of the relative motion (slippage) of two bodies in contact are presented. The main motivation of this research can be found in advanced manipulation with robotic systems, in which, depending on the particular task to be executed, it might be desirable either to avoid or to exploit the slippage of the manipulated object. The main contribution of this paper is that, besides the linear Coulomb friction effect, the rotational case is also addressed and, therefore, both translational and rotational motions are taken into account. This result is achieved by using an integrated sensor constituted by a force/torque and a tactile sensor. Experimental results are presented considering both the detection of the slippage and its subsequent control.
Environmental Impacts and Immobilization Mechanisms of Cadmium, Lead and Zinc in Geotechnical Composites Made from Contaminated Soil and Paper-Ash

Paper-ash is used for remediation of heavily contaminated soils with metals, but remediation efficiency after longer periods has not been reported. To gain insights into the mechanisms of immobilization of cadmium (Cd), lead (Pb), and znic (Zn), a study was performed in the laboratory experiment in uncontaminated, artificially contaminated, and remediated soils, and these soils treated with sulfate, to mimic conditions in contaminated soil from zinc smelter site. Remediation was performed by mixing contaminated soil with paper-ash to immobilize Cd, Pb, and Zn in the geotechnical composite. Partitioning of Cd, Pb, and Zn was studied over one year in seven-time intervals applying the sequential extraction procedure and complementary X-ray diffraction analyses. This methodological approach enabled us to follow the redistribution of Cd, Pb, and Zn over time, thus, to studying immobilization mechanisms and assessing the remediation efficiency and stability of newly formed mineral phases. Cd, Pb, and Zn were effectively immobilized by precipitation of insoluble hydroxides after the addition of paper-ash and by the carbonization process in insoluble carbonate minerals. After remediation, Cd, Pb, and Zn concentrations in the water-soluble fraction were well below the limiting values for inertness: Cd by 100 times, Pb by 125 times, and Zn by 10 times. Sulfate treatment did not influence the remediation efficiency. Experimental data confirmed the high remediation efficiency and stability of insoluble Cd, Pb, and Zn mineral phases in geotechnical composites.
Light Transport Simulation and Realistic Rendering: State of the Art Report

The field of light transport simulation quickly growths in last decades. Nowadays there are about hundreds of books and papers that are quite difficult to cover for applied researcher or developer. Unlike similar surveys, in this paper we make attempt to provide short roadmap to select the best method for some light transport problem based on scene and calculated phenomena constraints. In our paper we propose several classifications for light transport simulation algorithms based on their mathematical properties, robustness and required scene constraints. These classifications help to understand advantages, disadvantages and limitations of the methods. In this paper we use not only a survey of existing works but also our own experience with the methods that we have implemented over the last decade in different software products. Some results of our experiments are shown in the paper. Finally, we propose a short guide for method selection in form of block scheme.
Human Factors for Vehicle Platooning: A Review

Vehicle platooning (a group of two or more consecutive connected autonomous vehicles that travel simultaneously at the same velocity with a short inter-vehicular distance based on vehicle to vehicle communication) has significant potential to advance traffic, including enhancing travel safety, improving traffic efficacy and decreasing energy consumption. Much focus has been put on developing machine learning-based autonomous driving systems. However, the interactions between humans and the autonomous driving system have not been widely studied, although understanding the human factor is critical as that can cause human errors and potential accidents. Besides, vehicle platooning introduces a new cooperative driving paradigm for drivers. From such circumstances may emerge a new pattern for human interaction with the vehicle platoons. This study presents a semisystematic methodology to review existing studies of human factors in vehicle platoons. Among the human factors, user acceptance and trust significantly impact the sustained development of autonomous driving and concerned user satisfaction. Achieving higher user satisfaction can present business advantages for vehicle platooning service providers in the future. In this paper, the human-vehicle platoon interaction is classified into three groups: pedestrians, other drivers and in-platoon driver interaction. Then the research gaps are highlighted for the field.
Evaluation of fecal smear methods for research on Cryptosporidium spp. oocysts in the feces of dairy calves

The objective of this study is to compare the direct fecal smear (DFS) and centrifugal sedimentation (CS) methods in the detection of Cryptosporidium spp. oocysts in fecal samples of dairy calves. One hundred and fourteen fecal samples were collected from calves aged up to six months from 10 dairy farms located in Palotina and Francisco Alves, Paraná, Brazil. The microscopic analysis revealed the presence of Cryptosporidium spp. oocysts in 51.75% (59/114) of the samples in both methods. In CS, 48.25% (55/114) of the samples were positive, while in DFS slides, only 6.14% (7/114) were positive. Only 4 samples were positive exclusively in DFS. To ensure that there were no false-negative results in the microscopic analysis, the 55 samples that were negative in both DFS and CS were selected for molecular analysis using the nested PCR (nPCR). Of these 55 samples, 24% (13/55) were positive and forwarded for sequencing part of the genome, which made it possible to identify C. parvum, C. bovis and C. ryanae. Besides the characterization of the Cryptosporidium species, it was possible to identify bacteria of the genus Acinetobacter interfering directly in the analyzed samples. The microscopic analysis also revealed higher sensitivity when CS was used to make the fecal smears. However, some samples that were negative in this technique had positive PCR results. Thus, molecular analysis is indicated to confirm cases of Cryptosporidium spp. Further studies are necessary to prove the specificities of the used primers since the results obtained in nPCR were positive for the protozoan but, when genetic sequencing was performed, Acinetobacter spp. was identified.
Novelty promotes recognition memory persistence by D1 dopamine receptor and protein kinase A signalling in rat hippocampus

Strategies for improving memory are increasingly studied, and exposure to a novel experience can be an efficient neuromodulator. Novelty effects on memory depend on D1‐family dopamine receptors (D1Rs) activation. Here, we evaluated the novelty effect on memory persistence of Wistar rats and investigated the contribution of D1Rs and their signalling pathways by protein kinase A (PKA) and C (PKC). Animals with infusion cannulae inserted into the CA1 hippocampus area were trained on the novel object recognition (NOR) task, which involved exploring two different objects. After training, some rats received intrahippocampal infusions of vehicle or D1Rs agonist; others explored a novel environment for 5 min and were infused with a variety of drugs targeting D1Rs and their signalling pathways. We demonstrated that pharmacological stimulation of D1Rs or novelty exposure promoted NOR memory persistence for 14 days and that the novelty effect depended on D1Rs activation. To determine if the D1 and D5 receptor subtypes were necessary for the impact of novelty exposure on memory, we blocked or stimulated PKA or PKC—protein kinases activated mainly by D1 and D5, respectively. Only PKA inhibition impaired the effect of novelty on memory persistence. After novelty and D1Rs blocking, PKA but not PKC stimulation maintained the memory persistence effect. Thus, we concluded that novelty promoted memory persistence by a mechanism‐dependent on activating hippocampal D1Rs and PKA pathway.
The History and Practice of AI in the Environmental Sciences

Artificial intelligence (AI) and machine learning (ML) have become important tools for environmental scientists and engineers, both in research and in applications. Although these methods have become quite popular in recent years, they are not new. The use of AI methods began in the 1950s and environmental scientists were adopting them by the 1980s. Although an “AI Winter” temporarily slowed the growth, a more recent resurgence has brought it back with gusto. This paper tells the story of the evolution of AI in the field through the lens of the AMS Committee on Artificial Intelligence Applications to Environmental Science. The environmental sciences possess a host of problems amenable to advancement by intelligent techniques. We review a few of the early applications along with the ML methods of the time and how their progression has impacted these sciences. While AI methods have changed from expert systems in the eighties to neural networks and other data-driven methods, and more recently deep learning, the environmental problems tackled have remained similar. We discuss the types of applications that have shown some of the biggest advances due to AI usage and how they have evolved over the past decades, including topics in weather forecasting, probabilistic prediction, climate estimation, optimization problems, image processing, and improving forecasting models. We finish with a look at where AI as employed in environmental science appears to be headed and some thoughts on how it might be best blended with physical / dynamical modeling approaches to further advance our science.