Inversion of three-stage stress-strain relation for stainless steel in tension and compression

Presented in this paper is a new stress-strain relation for stainless steel alloys that provides the stress as an explicit function of the strain. The relation is an approximate inversion of a recently proposed three-stage stress-strain relation based on a modified Ramberg-Osgood equation. The three-stage relation is a much more accurate formulation than the previous two-stage formulations and is applicable to both tensile as well as compressive stresses. The new relation is derived by making a rational function assumption on the fractional deviation of the actual stress-strain curve from an idealized linear elastic behaviour. The new expression is valid over the full range of the stress well beyond the elastic region. The validity of the inverted expression is tested over a wide range of material parameters. These tests demonstrate that, the new expression results in stress-strain curves which are both qualitatively and quantitatively in excellent agreement with the fully iterated numerical solution of the full-range stress-strain relation with a maximum error below 4%.     

Evaluating of air flow movements and thermal comfort in a model room with Euler equation: Two dimensional study

This paper presents the results of a study that consists of estimating the temperature distribution and air flow movement in a model room with a numerical model based on the Euler equations. Numerical results obtained for two scenarios of ventilation and heating are compared with the predictions of a Navier–Stokes model, as well as with experimental results. A comparison of the local thermal comfort indices PMV and PPD obtained experimentally and numerically is also presented. Results show that the Euler model is capable of properly estimating the temperature distribution, the air movement and the comfort indices in the room. Furthermore, the use of Euler equations allows a reduction of computational time in the order of 30% compared to the Navier–Stokes modeling.     

Dental Composites with Magnesium Doped Zinc Oxide Nanoparticles Prevent Secondary Caries in the Alloxan-Induced Diabetic Model

Antibacterial restorative materials against caries-causing bacteria are highly preferred among high-risk patients, such as the elderly, and patients with metabolic diseases such as diabetes. This study aimed to enhance the antibacterial potential of resin composite with Magnesium-doped Zinc oxide (Mg-doped ZnO) nanoparticles (NPs) and to look for their effectiveness in the alloxan-induced diabetic model. Hexagonal Mg-doped ZnO NPs (22.3 nm diameter) were synthesized by co-precipitation method and characterized through ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analysis. The Mg-doped ZnO NPs (1, 2.5 and 5% w/w) were then evaluated for antibacterial activity using a closed system in vitro biofilm model. Significant enhancement in the antibacterial properties was observed in composites with 1% Mg-doped ZnO compared to composites with bare ZnO reinforced NPs (Streptococcus mutans, p = 0.0005; Enterococcus faecalis, p = 0.0074, Saliva microcosm, p < 0.0001; Diabetic Saliva microcosm, p < 0.0001). At 1–2.5% Mg-doped ZnO NPs concentration, compressive strength and biocompatibility of composites were not affected. The pH buffering effect was also achieved at these concentrations, hence not allowing optimal conditions for the anaerobic bacteria to grow. Furthermore, composites with Mg-doped ZnO prevented secondary caries formation in the secondary caries model of alloxan-induced diabetes. Therefore, Mg-doped ZnO NPs are highly recommended as an antibacterial agent for resin composites to avoid biofilm and subsequent secondary caries formation in high-risk patients.     

Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.     

Physico-chemical analysis of tannery solid waste and structural characterization of its isolated humic acids after composting

In Marrakech, solid by-products from tanneries are highly polluting, generating large amounts of nitrogenous and organic matter. In the present study composting is tested as a cost-effective method for waste management to overcome many of the environmental hazards and produce a stable, rich material for soil fertilization. Two composting trials were conducted after neutralization by ammonia or lime. The aim of the neutralization was to avoid the antimicrobial effects of the acidity in the tannery waste, thus ensuring correct composting. Different techniques such as elemental analysis and ¹³C NMR spectroscopy were applied to analyse humic acids isolated from raw and composted materials, and to monitor the process of tannery waste composting, and the stability and maturity of the final product according to the means of neutralization. Comparison of data showed similar behaviour in both trials, but the composting process appeared to be more complete following neutralization with lime. The C, H and N content decreased, while the O increased. The FTIR and ¹³C NMR spectra show the decrease of aliphatic compounds demonstrated by the reduction of absorbance around 2922 cm⁻¹ and of the resonance in the C-alkyl area around 0–55 ppm. The humic acids newly formed during composting were richer in the O–N alkyl and oxidized aromatic structures that increased almost twofold on composting after neutralization with lime. The first principal component axis PC1 (54%) separated C-aliphatic, C-carboxylic and other less stable and less polycondensed compounds such as polyphenols from the more polycondensed O–N alkyl and oxidized C-aromatic compounds.     

Exposure of Mice to Thirdhand Smoke Modulates In Vitro and In Vivo Platelet Responses

Smoking is a risk factor for a variety of deleterious conditions, such as cancer, respiratory disease and cardiovascular disease. Thrombosis is an important and common aspect of several cardiovascular disease states, whose risk is known to be increased by both first- and secondhand smoke. More recently, the residual cigarette smoke that persists after someone has smoked (referred to as thirdhand smoke or THS) has been gaining more attention, since it has been shown that it also negatively affects health. Indeed, we have previously shown that 6-month exposure to THS increases the risk of thrombogenesis. However, neither the time-dependence of THS-induced thrombus formation, nor its sex dependence have been investigated. Thus, in the present study, we investigated these issues in the context of a shorter exposure to THS, specifically 3 months, in male and female mice. We show that the platelets from 3-month THS-exposed mice exhibited enhanced activation by agonists. Moreover, we also show that mice of both sexes exposed to THS have decreased tail bleeding as well as decreased thrombus occlusion time. In terms of the role of sex, intersex disparities in thrombus development and hemostasis as well as in platelet aggregation were, interestingly, observed. Together, our findings show that exposing mice to THS for 3 months is sufficient to predispose them to thrombosis; which seems to be driven, at least in part, by an increased activity in platelets, and that it does not manifest equally in both sexes.     

Majority bargaining for resource division

We address the problem of how a set of agents can decide to share a resource, represented as a unit-sized pie. The pie can be generated by the entire set but also by some of its subsets. We investigate a finite horizon non-cooperative bargaining game, in which the players take it in turns to make proposals on how the resource should for this purpose be allocated, and the other players vote on whether or not to accept the allocation. Voting is modelled as a Bayesian weighted voting game with uncertainty about the players’ weights. The agenda, (i.e., the order in which the players are called to make offers), is defined exogenously. We focus on impatient players with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by the deadline), no player receives anything. We provide a Bayesian subgame perfect equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting outcome. We show that the equilibrium is unique, computable in polynomial time, results in an instant Pareto optimal outcome, and, under certain conditions provides a foundation for the core and also the nucleolus of the Bayesian voting game. In addition, our analysis leads to insights on how an individual’s bargained share is influenced by his position on the agenda. Finally, we show that, if the conflict point of the bargaining game changes, then the problem of determining the non-cooperative equilibrium becomes NP-hard even under the perfect information assumption. Our research also reveals how this change in conflict point impacts on the above mentioned results.     

Nano finish grinding of brittle materials using electrolytic in-process dressing (ELID) technique

Recent developments in grinding have opened up new avenues for finishing of hard and brittle materials with nano-surface finish, high tolerance and accuracy. Grinding with superabrasive wheels is an excellent way to produce ultraprecision surface finish. However, superabrasive diamond grits need higher bonding strength while grinding, which metal-bonded grinding wheels can offer. Truing and dressing of the wheels are major problems and they tend to glaze because of wheel loading. When grinding with superabrasive wheels, wheel loading can be avoided by dressing periodically to obtain continuous grinding. Electrolytic inprocess dressing (ELID) is the most suitable process for dressing metal-bonded grinding wheels during the grinding process. Nano-surface finish can be achieved only when chip removal is done at the atomic level. Recent developments of ductile mode machining of hard and brittle materials show that plastically deformed chip removal minimizes the subsurface damage of the workpiece. When chip deformation takes place in the ductile regime, a defect-free nano-surface is possible and it completely eliminates the polishing process. ELID is one of the processes used for atomic level metal removal and nano-surface finish. However, no proper and detailed studies have been carried out to clarify the fundamental characteristics for making this process a robust one. Consequently, an attempt has been made in this study to understand the fundamental characteristics of ELID grinding and their influence on surface finish.     

Hybridation des méthodes de recherche taboue et du recuit simulé pour l’optimisation d’une antenne en réseau adaptative

The increasing pollution of the electromagnetic environment has prompted the study of array pattern nulling techniques. These techniques are very important in radar, sonar and communication systems for minimising degradation in signal-to-noise ratio performance due to undesired interferences. Adaptive array antennas backed by strong signal processing algorithms are able to automatically change the beam pattern in accordance with the changing signal environment. It not only directs maximum radiation in the direction of the desired mobile user but also introduces nulls for interfering directions while tracking the desired mobile user at the same time. The adaptation is achieved by multiplying the incoming signals with complex weights and then summing them together to obtain the desired radiation pattern. Adaptive array optimization is an NP-hardproblem. In this paper, a technique based on the coupling between tabu search and simulated annealing methods is presented to solve this problem. Several illustrative examples of patterns with imposed single and multiple null directions are given to show the versatility of the present method.