Many unexpected situations can occur while driving that may lead to dangerous accidents. Some of them may be caused by sudden health problems (e.g. heart attack, stroke, total collapse) or by driver inattention (e.g. microsleep, visual distraction). This has motivated the need for developing the methods that are able to monitor the driver’s state in the first step and to prevent the accidents in the second step (e.g. by activating an acoustic signal, or even by taking over driving). In this paper, we propose a method that can be used for detecting the abnormal driving situations. Our approach is based on two main steps. In the first step, the MNIST-like skeleton images are created with the use of human pose detector. In the second step, an appropriate neural network is used for the final classification. Since we also include the anomalies consisting in an unusual trajectory of a certain body part (not only an unusual shape of body, which can be detected from the isolated images), short sequences of images are examined. The LSTM (long short-term memory) autoencoder is used as a main network architecture. The experiments that are presented show that the proposed method achieves better results than other compared methods.
This study aims at investigating the mechanical and electromagnetic interference (EMI) shielding properties of cementitious composites with combined utilization of recycled carbon fiber (rCF) and steel slag. Different dosage of raw steel slag (SS) and wet-grinding steel slag (WSS) as replacement of cement were introduced to the cementitious composites with rCF. Wet-grinding can enhance hydration activity of SS, and also can improve the dispersibility of rCF in cement matrix thus optimizing the mechanical properties. The electrical resistivity of the composites decreases with increased WSS dosage, the conductive phase in WSS not only form new conductive pathways, but also act as bridges to connect rCFs, the WSS is more effective in lowering the electrical resistivity. The shielding effectiveness (SE) of the rCF-WSS cementitious composites increases with increased WSS dosage, which is higher than that of SS due to the higher conductivity and scattering effect of reduced particle fineness. The absorption loss SEA dominates the SET, which is mainly attributed to increasing electrical conductivity, the dielectric loss and magnetic loss also contribute to absorption loss. The synergistic effect of rCF and steel slag on electrically conductive and EMI shielding properties was demonstrated, wet-grinding process can promote the synergistic effect.
Catalytic hydrogenations are important and widely applied processes for the reduction of organic compounds both in academic laboratories and in industry. To perform these reactions in sustainable and practical manner, the development and applicability of non-noble metal–based heterogeneous catalysts is crucial. Here, we report highly active and air-stable nickel nanoparticles supported on mesoporous silica (MCM-41) as a general and selective hydrogenation catalyst. This catalytic system allows for the hydrogenation of carbonyl compounds, nitroarenes, N-heterocycles, and unsaturated carbon─carbon bonds in good to excellent selectivity under very mild conditions (room temperature to 80°C, 2 to 10 bar H 2 ). Furthermore, the optimal nickel/meso–silicon dioxide catalyst is reusable (4 cycles) without loss of its catalytic activity.
Inorganic nanoparticles (INPs) and nanomaterials (NMs) have a range of applications in various industries, including agriculture. Their small size, high surface-to-volume ratio, and unique physical and chemical properties make them suitable for use in different agricultural sectors. These materials have been recognized for their potential as nanofertilizers, enhancers of plant growth, and tools against plant pathogens. However, their long-term environmental and ecological impacts are still not well understood, particularly with regard to plant reproduction. This chapter focuses on (i) the classification of nanofertilizers, their commercial potential, and behavior in colloidal systems, specifically in the context of foliar application; (ii) the use of metal NPs, such as Au, Ag, ZnO, TiO2, and Fe2O3, in agriculture; (iii) the evaluation of INP uptake, distribution, and fate within the plant environment and their effects on field crop production, including yield, fruit quality, and physiological parameters; and (iv) the interaction of INPs and NMs with reproductive organs, flowers, and flowering and the impact on pollen quality and pollinators, as evaluated through agroecological assessments based on epigeic insect communities.
The van der Waals heterostructures have evolved as novel materials for complementing the Si-based semiconductor technologies. Group-10 noble metal dichalcogenides (e.g., PtS 2 , PtSe 2 , PdS 2 , and PdSe 2 ) have been listed into two-dimensional (2D) materials toolkit to assemble van der Waals heterostructures. Among them, PdSe 2 demonstrates advantages of high stability in air, high mobility, and wide tunable bandgap. However, the regulation of p-type doping of PdSe 2 remains unsolved problem prior to fabricating p–n junction as a fundamental platform of semiconductor physics. Besides, a quantitative method for the controllable doping of PdSe 2 is yet to be reported. In this study, the doping level of PdSe 2 was correlated with the concentration of Lewis acids, for example, SnCl 4 , used for soaking. Considering the transfer characteristics, the threshold voltage (the gate voltage corresponding to the minimum drain current) increased after SnCl 4 soaking treatment. PdSe 2 transistors were soaked in SnCl 4 solutions with five different concentrations. The threshold voltages from the as-obtained transfer curves were extracted for linear fitting to the threshold voltage versus doping concentration correlation equation. This study provides in-depth insights into the controllable p-type doping of PdSe 2 . It may also push forward the research of the regulation of conductivity behaviors of 2D materials.
This article describes the Split Hopkinson Pressure Bar test of aluminium alloy EN AW 7075-T6. This test is used for material testing at a high strain rate. Two approaches in measurement. In the first case was used one size of specimen and different initial pressure. In the second case was set one initial pressure and three different lengths of specimen. Based on measured data, constants of Johnson-Cook material model for FEM analysis were found.
Knowledge of soft tissue fiber structure is necessary for accurate characterization and modeling of their mechanical response. Fiber configuration and structure informs both our understanding of healthy tissue physiology and of pathological processes resulting from diseased states. This study develops an automatic algorithm to simultaneously estimate fiber global orientation, abundance, and waviness in an investigated image. To our best knowledge, this is the first validated algorithm which can reliably separate fiber waviness from its global orientation for considerably wavy fibers. This is much needed feature for biological tissue characterization. The algorithm is based on incremental movement of local regions of interest (ROI) and analyzes two-dimensional images. Pixels belonging to the fiber are identified in the ROI, and ROI movement is determined according to local orientation of fiber within the ROI. The algorithm is validated with artificial images and ten images of porcine trachea containing wavy fibers. In each image, 80–120 fibers were tracked manually to serve as verification. The coefficient of determination R2 between curve lengths and histograms documenting the fiber waviness and global orientation were used as metrics for analysis. Verification-confirmed results were independent of image rotation and degree of fiber waviness, with curve length accuracy demonstrated to be below 1% of fiber curved length. Validation-confirmed median and interquartile range of R2, respectively, were 0.90 and 0.05 for curved length, 0.92 and 0.07 for waviness, and 0.96 and 0.04 for global orientation histograms. Software constructed from the proposed algorithm was able to track one fiber in about 1.1 s using a typical office computer. The proposed algorithm can reliably and accurately estimate fiber waviness, curve length, and global orientation simultaneously, moving beyond the limitations of prior methods.
Background and Aims Bioremediation of soils contaminated with metal(loid)s is an attractive research area due to its sustainability and economic benefits. In the Slovak Republic, there are several abandoned mines containing high concentrations of arsenic (As) and antimony (Sb). This calls for new options for removing these hazardous metalloids from contaminated substrates. Studies on bioleaching of soils co-contaminated with both metalloids are very rare. This study aimed to test the effectiveness of bioleaching of soils heavily co-contaminated with As and Sb (up to 1463 mg.kg–1 and 5825 mg.kg–1, respectively) at a former stibnite mining site (Poproč, eastern Slovakia) through biostimulation and bioaugmentation. Methods Bioleaching of As and Sb from four soils was induced by biostimulation of autochthonous microflora with Sabouraud medium (SAB) and SAB+glucose, and bioaugmentation of the soil with bacterial strains Cupriavidus oxalaticus and Cupriavidus metallidurans. Soil samples were subjected to determination of physico-chemical properties, microbiological parameters, and additional mineralogical analysis. Results An inverse relationship between the total metalloid concentration and the microbial diversity was confirmed. In experiments with Cupriavidus metallidurans and Cupriavidus oxalaticus, mean bioleached As fractions were 37.6% and 41.3%, while Sb bioleaching was significantly lower, ranging between 17.0–26.2%. The mean bioleached fraction of As and Sb using SAB was 40.7% and 14.4%, respectively. The addition of glucose to SAB increased As bioleaching (50.7%) but not that of Sb. Conclusion Collectively, the results highlighted a role of microorganisms in the mobility of metalloids in soils with their prospective applications in remediation of contaminated sites.
To improve the pozzolanic reactivity, waste glass (WG) needs to be micronized to fine particles so as to expedite the leaching of active constituent. The key feature of this work is to examine the effect of wet-grinded WG on the mechanical and structural properties of cement based materials. The experimental results show that wet-grinding can improve the ions leaching behavior of WGP and decrease the stability of silicon oxide bond. The pozzolanic reactivity of WGP was dramatically enhanced after wet-grinding, as high as 144.1% at 1 d and 110.9% at 28 d when the mean grain size of WGP reached 0.90 µm. The ground WGP can promote the transformation of capillary pores to gel pores to improve the compactness of microstructure regardless of the reaction time.
This paper presents a parallel implementation of a non-local transform-domain filter (BM4D). The effectiveness of the parallel implementation is demonstrated by denoising image series from computed tomography (CT) and magnetic resonance imaging (MRI). The basic idea of the filter is based on grouping and filtering similar data within the image. Due to the high level of similarity and data redundancy, the filter can provide even better denoising quality than current extensively used approaches based on deep learning (DL). In BM4D, cubes of voxels named patches are the essential image elements for filtering. Using voxels instead of pixels means that the area for searching similar patches is large. Because of this and the application of multi-dimensional transformations, the computation time of the filter is exceptionally long. The original implementation of BM4D is only single-threaded. We provide a parallel version of the filter that supports multi-core and many-core processors and scales on such versatile hardware resources, typical for high-performance computing clusters, even if they are concurrently used for the task. Our algorithm uses hybrid parallelisation that combines open multi-processing (OpenMP) and message passing interface (MPI) technologies and provides up to 283× speedup, which is a 99.65% reduction in processing time compared to the sequential version of the algorithm. In denoising quality, the method performs considerably better than recent DL methods on the data type that these methods have yet to be trained on.
This article considers an N-firm oligopoly with abating and non-abating firms and analyses a dynamic setting in which the environmental regulator sets the tax rate to incentivise firms to undertake emission-reduction actions according to different hypotheses (fixed rule and optimal rule). The behaviour of the public authority sharply affects the firm’s (individual) incentive to move towards the abatement activity over time. This changes the number of (non)abating firms on the market and the corresponding social welfare outcomes. The article eventually shows that the environmental policy may cause oscillations resulting in a coexistence of the two types of firms in the long term and pinpoints the welfare outcomes emerging in the model.
In this paper we investigate the dynamics of a duopoly game with ambiguity aversion regarding uncertainty in demand and constant expectations concerning competitor production. The focus is on an asymmetric Cournot game where players engage in robust optimization and have different beliefs about the possible realizations of the random parameters of the price function. The players’ ambiguity aversion introduces multiple equilibria and instability that otherwise would not be present. The investigation of the global dynamics of the game reveals the emergence, through border-collision bifurcations, of periodic and chaotic dynamics.
A $$(k,g)$$ ( k , g ) -graph is a k -regular graph of girth $$g$$ g . Given $$k\ge 2$$ k ≥ 2 and $$g\ge 3$$ g ≥ 3 , $$(k,g)$$ ( k , g ) -graphs of infinitely many orders are known to exist and the problem of finding a ( k , g )-graph of the smallest possible order is known as the Cage Problem . The aim of our paper is to develop systematic (programmable) ways for lowering the orders of existing $$(k,g)$$ ( k , g ) -graphs, while preserving their regularity and girth. Such methods, in analogy with the previously used excision, may have the potential for constructing smaller ( k , g )-graphs from current smallest examples—record holders—some of which have not been improved in years. In addition, we consider constructions that preserve the regularity, the girth and the order of the considered graphs, but alter the graphs enough to possibly make them suitable for the application of our order decreasing methods. We include a detailed discussion of several specific parameter cases for which several non-isomorphic smallest examples are known to exist, and address the question of the distance between these non-isomorphic examples based on the number of changes required to move from one example to another.
Dry sliding wear behaviour of friction stir processed (FSP) AZ31 and AZ31/ZrC particles (5, 10, and 15 vol%) reinforced surface composite was investigated at different sliding speeds and loads. The samples were tested using a pin-on-disc apparatus with EN31 steel as the counter body to determine the role of FSP and ZrC reinforcement on the microstructure, hardness, and wear behaviour of AZ31. Base metal AZ31 alloy exhibits a hardness of 60 HV, whereas the 15 vol% ZrC-reinforced composites had the highest hardness of 108 HV. It was also identified that 15 vol% ZrC-reinforced composites exhibited lowest wear rate and friction coefficient under all testing conditions. Abrasion, delamination, oxidation, material softening, and plastic deformation are the primary wear mechanisms viewed from the wear tracks of the samples. Higher volume fraction of ZrC particles exhibited better wear resistance at all speeds and loads than AZ31 alloy. A wear map has been generated for different material compositions and wear conditions to identify the main wear mechanisms easily.
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