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International audience; In this study, the effect of the chemical nature of different calcium (Ca)‐based minerals as flame retardant additives in combination with ammonium polyphosphate (APP), in 1:1 proportions, on the flame retardancy behavior and performance of ethylene vinyl acetate copolymer was discussed. Combining APP with partly and completely hydrated calcium oxide led to superior flame‐retardant function detected in mass loss calorimeter measurements with respect to the corresponding system containing carbonated calcium. This privileged character was attributed to the higher reactivity of hydrated Ca‐based fillers toward APP in comparison with Ca carbonate, which induced the formation of an intumescent residue. The difference between reactivity potential of hydrated and dry Ca was demonstrated by the newly formed thermally stable species, and further evidenced by thermogravimetric analysis performed on APP/fillers blends. Moreover, the presence of more crystalline domains in the Ca/phosphorus‐based compounds was evidenced by XRD analysis of the mass loss calorimeter test residues. The results of this work highlight the role of blend additive systems on the performance of flame retardancy of polymer materials.

International audience; Sustainable polymers are emerging fast and have received much more attention in recent years compared to petro-sourced polymers. However, they inherently have low-quality properties, such as poor mechanical properties, and inadequate performance, such as high flammability. In general, two methods have been considered to tackle such drawbacks: (i) reinforcement of sustainable polymers with additives; and (ii) modification of chemical structure by architectural manipulation so as to modify polymers for advanced applications. Development and management of bio-based polyurethanes with flame-retardant properties have been at the core of attention in recent years. Bio-based polyurethanes are currently prepared from renewable, bio-based sources such as vegetable oils. They are used in a wide range of applications including coatings and foams. However, they are highly flammable, and their further development is dependent on their flame retardancy. The aim of the present review is to investigate recent advances in the development of flame-retardant bio-based polyurethanes. Chemical structures of bio-based flame-retardant polyurethanes have been studied and explained from the point of view of flame retardancy. Moreover, various strategies for improving the flame retardancy of bio-based polyurethanes as well as reactive and additive flame-retardant solutions are discussed.

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Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.

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The symmetrized tropical semiring is an extension of the tropical semifield, initially introduced to solve tropical linear systems using Cramer's rule. It is equivalent to the real tropical hyperfield, which has been used in the study of tropicalizations of semialgebraic sets. Polynomials over the symmetrized tropical semiring, and their factorizations, were considered by Quadrat. Recently, Baker and Lorscheid introduced a notion of multiplicity for the roots of univariate polynomials over hyperfields. In the special case of the hyperfield of signs, they related multiplicities with Descarte's rule of sign for real polynomials. We investigate here the factorizations of univariate polynomial functions over symmetrized tropical semirings, and relate them with the multiplicities of roots over these semirings. We deduce a Descartes' rule for ``signs and valuations'', which applies to polynomials over a real closed field with a convex valuation and an arbitrary (divisible) value group. We show in particular that the inequality of the Descartes' rule is tight when the value group is non-trivial. This extends to arbitrary value groups a characterization of Gunn in the rank one case, answering also to the tightness question. Our results are obtained using the framework of semiring systems introduced by Rowen, together with model theory of valued fields.

Publisher Copyright: IEEE Despite the advances related to car-following and lane-changing behaviors, the influence of lane-changing on the car-following models, which results in a complex transient merging behavior, has not comprehensively been investigated. This paper presents a novel fuzzy controller based on a human factor to optimize the Follower Vehicle (FV) behavior subject to safety, comfort, and convenient traveled time in the complex behavior where the Lane Changer (LC) vehicle exits the temporary lane. The factor enables the controller to mimic the current driver behavior in terms of maximum pleasantness of drive. Accordingly, the data of real-life experiments were used to design the human-like fuzzy controller, to build a predictive model to suggest the appropriate acceleration, velocity, and travel distance. At best, the correlation coefficient of 0.93 and the Root Mean Square Error (RMSE) of 0.71 were achieved for modeling using the adaptive Neuro-Fuzzy Inference System (ANFIS) utilizing Gaussian function as a membership function. Furthermore, to evaluate the robustness of the controller to uncertainties and unknown disturbances for real-time driving experiments, a test-bed was fabricated to mount the feedback sensors, including vision, accelerometer, and distance measurement sensors. The results of running the controller in various driving scenarios showed 70% and 38% improvements in safety and ride comfort, respectively. The proposed intelligent controller is intended to be used for vehicle route guidance and on urban highways.

Publisher Copyright: © 2022 The Authors The additive manufacturing (AM) process generates new material challenges with associated features like internal defects and inherent surface roughness, reducing fatigue performance. This paper introduces a new approach to characterizing internal defects and surface irregularities of additively manufactured stainless steel 316L samples using X-ray computed tomography (XCT). This method overcomes the limitations of previous methods and can effectively provide holistic information on the surface topologies of AM components. An equivalent defect size, root area(eff), based on defects' features and the interaction of internal and surface imperfections, is proposed for fatigue life and failure origins prediction.

International audience; This article presents a new two-stage stochastic model for the simultaneous optimization of component test plan and redundancy allocation. The optimal number of tests is determined for each component of the system in the first stage, and the system redundancy configuration is optimized in the second stage, after the realization of the system working condition. Scenario analysis is used for modeling variations in working conditions. A multiobjective reliability problem is formulated for series-parallel systems, and the allocation of redundancy is optimized by a genetic algorithm. The results of the feedback control system demonstrate the performance of the proposed model.

Cyclic voltammetry and potentiodynamic polarization analyses were utilized to investigate the mechanism and kinetics of glycine leaching reactions for chalcopyrite. The effects of pH (9–12), temperature (30–90 °C) and glycine concentration (0–2 mol/L) on corrosion current density, corrosion potential and cyclic voltammograms were investigated using chalcopyrite concentrate–carbon paste electrodes. Results showed that an increase in the glycine concentration from 0 to 2 mol/L led to an increased oxidation peak current density. Under the same conditions, corrosion current density was found to change from approximately 28 to 89 μA/cm whereas corrosion potential was decreased from –80 to –130 mV. Elevated temperatures enhanced the measured current densities up to 60 °C; however, above this level, current density was observed to decrease. A similar current density behavior was determined with pH. A pH change from 9 to 10.5 resulted in an increase in current density and pH higher than 10.5 gave rise to a reduced current density. In addition, the thermodynamic stability of copper and iron oxides was found to increase at higher temperatures.

International audience; We study the interdiction of smuggling network that arranging the activities of the police in order to successfully interdict criminals in smuggling goods. This work contributes to the literature of maximum flow network interdiction problems by addressing asymmetric information, uncertain conditions, multi commodity, and with multiple sources (origins) and sinks (destinations). Information Asymmetry realistically occurs due to incomplete information of interdictor (police) and operator (smuggler) about each other's performance, which is adapted from the real-world condition. We propose two mixed-integer programming models by reformulating a Min–Max bi-level mathematical model. In the first model, the type of interdiction is discrete (zero and one), while in the second model, the interdiction is assumed continuous, meaning that the partial interdiction is possible. The asymmetry type of the smuggler's information towards the police have formulated through a linear function while the asymmetry of the police information to the smuggler is formulated using an uncertain parameter through a two-stage stochastic programming framework. To solve the first model, an innovative exact hybrid method is proposed combining of a Decomposition Method and Progressive Hedging Algorithm (DM-PHA). An augmented Karush-Kuhn-Tucker (KKT) method is also used to solve the second model. Several sensitivity analyses are then conducted, and the results demonstrate the applicability and effectiveness of the proposed models as well as the solving approach. It is also shown that the proposed models can be used as a suitable approach in uncertain environment and under asymmetric information to determine the optimal interdiction decisions of police to prevent further smuggling.

The mortar component plays an essential role in forming different asphalt mixture distresses. The fine aggregate size affects the viscoelastic behavior of mortar. In this study, three cutting sizes of 3.35, 2.36, and 1.7 mm, on the aggregate grading curve were considered to construct the mortar scales, namely mortar (I), (II), and (III), respectively. With a unique loading mode, a frequency sweep test was performed on four different length scales, including bitumen, asphalt mastic, mortar, and asphalt mixture, to make a direct comparison through a multi-scale approach. Also, a stress sweep test was conducted to find the mortar scale's linear viscoelastic domain. The results indicate that different mortar types have variable linear viscoelastic domains. The master curves of dynamic modulus (E*) and phase angle (δ) show that adding filler to bitumen has more effect on the E* than the δ. Although scaling up from the mastic to mortar (II) causes a significant difference between the viscoelastic parameters, the results remained approximately the same. The main gap between the mixture and mentioned sub-scales is filled with mortar (I). While the Cole-Cole diagrams demonstrate that the bitumen to mortar (II) scales have the same behavior, the closest scale to the mixture is the mortar (I). Therefore, using a cutting size of 3.35 mm is recommended to fabricate mortar (for asphalt mixture with a nominal maximum aggregate size of 19 mm) instead of 2.36 mm, which was suggested in previous studies.

Climate change may be defined as a change in the timing of weather conditions over a period of time. In this paper, relying on IPCC scenarios to investigate the effects of climate change on water resources.

International audience; The paper deals with the characterization of piezoelectric ceramic type sensors integrated in a compact high-speed 2-DOF nano-positioner to measure large signal stage displacement. Such transducers are very attractive because they provide measurements in a wide frequency bandwidth with low noise, they do not require bulky electronic modules and are cost effective compared to traditional sensors. The transducers are installed in series with piezoelectric actuators and thick ceramic insulators are incorporated between them to reduce the cross-talk. The sensor provides accurate position measurements as long as the nano-positioner displacement is within the sensing bandwidth. This is verified by large amplitude sinusoidal excitations of the nano-positioner along both x and y axes within the sensor bandwidth. The sensor can also measure the displacement in response to a single tone sinusoid at frequencies well above the dominant imaginary zeros of the interaction force, which is beyond the sensor bandwidth. Experimental measurements are compared with those obtained using a LASER interferometer sensor. The results allow obtaining precise calibration factors of the piezoelectric ceramic transducers with respect to the input frequencies and accurate displacement/voltage transfer characteristics for closed loop control purposes.

International audience; In this paper, the problem of fault diagnosis for a satellite formation flight is considered. A scheme is investigated for fault detection, isolation, and estimation of a class of nonlinear systems. In this scheme, we consider the model uncertainty, input and environmental disturbances. For fault detection, a nonlinear observer is designed to minimize the uncertainty within $H_\infty$ framework. The Linear Matrix Inequality (LMI) formulation is used to obtain the observer gain matrices. In the next step, a bank of nonlinear robust unknown input observers is designed to isolate the faulty actuator. Fault isolation is achieved based on the generalized observer strategy. The proposed observer can simultaneously estimate faults and states; it can also decouple the unknown input disturbances and attenuate the effect of model uncertainty and external disturbances. In order to fulfill these goals, a Lipschitz formulation and Linear Parameter Varying (LPV) method are used in all of the proposed observers, which lead to less conservative LMI condition. Then, the mentioned approach is applied to fault diagnosis of a formation of satellites. Moreover, a distributed fault detection, isolation, and estimation scheme based on above observers is proposed. In this scheme, each satellite not only can diagnose its own faults but also it is able to diagnose its neighbors faults.

The mission of an advanced epoxy-based nanocomposite coating is to provide a given substrate with protection against an unwelcome guest; e.g. corrosive molecules/media, environmental stress, flame, thermal degradation or microorganisms. In such systems, the degree to which superior properties can be guaranteed depends on the state of network formation in the epoxy in the presence of nanoparticles. For low-filled epoxy nanocomposite coatings, barrierity was taken as the main mechanism controlling over the efficiency of corrosion inhibition in the coating against oxygen or other corrosive moieties, whilst in highly-loaded nanocomposites one should take a closer look at both physical and chemical interaction between the resin and nanoparticles. In this sense, epoxy/Fe3O4 systems were studied here as model nanocomposite coatings and their anti-corrosion and flame retardancy potentials were patterned in terms of qualitative cure analysis made in terms of Cure Index. Anti-corrosion and flame retardancy properties of the aforementioned nanocomposite coatings were mechanistically described at either low or high loading levels in view of Cure Index for postulates of structure-properties association in advanced nano-Fe3O4 incorporated epoxy nanocomposite coatings. We hope that speculations and visulizations provided here about structure-properties relationships are useful to be used in open discussions between experts in the hope of generalization to complex systems containing different types of nanoparticles whatever surface functionality.

International audience; The Computer Aided Process Planning (CAPP) systems are recently developed in manufacturing engineering to provide links between Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) systems. The CAPP systems are developed by considering the different issues of computer applications in production engineering. Optimization techniques can be applied to the CAPP to increase efficiency in part production processes. The energy consumption of part production process can be analyzed and optimized using the CAPP systems in order to increase added value in the part manufacturing process. Also, artificial neural networks as well as cloud manufacturing systems can be applied to the CAPP systems to share advantages of the different CAPP systems in different industry applications. Flexible process planning systems are developed using dynamic CAPP in order to cope with product varieties in process of part production. To develop potential energy saving strategies during product design and process planning stages, the advanced CAPP systems can be used. In this paper, a review of Computer Process Planning systems (CAPP) is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers.

The authors are also grateful to Dr Zabihollah Hasanzadeh for providing cartilage tissue, Ghaffari Chemical Industries for the technical support, and Mojtaba Khozaee for isolating chondrocyte cells from the human articular cartilage specimens. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Sharif University of Technology (grant number G950103). In the present study, alginate/cartilage extracellular matrix (ECM)-based injectable hydrogel was developed incorporated with silk fibroin nanofibers (SFN) for cartilage tissue engineering. The in situ forming hydrogels were composed of different ionic crosslinked alginate concentrations with 1% w/v enzymatically crosslinked phenolized cartilage ECM, resulting in an interpenetrating polymer network (IPN). The response surface methodology (RSM) approach was applied to optimize IPN hydrogel's mechanical properties by varying alginate and SFN concentrations. The results demonstrated that upon increasing the alginate concentration, the compression modulus improved. The SFN concentration was optimized to reach a desired mechanical stiffness. Accordingly, the concentrations of alginate and SFN to have an optimum compression modulus in the hydrogel were found to be 1.685 and 1.724% w/v, respectively. The gelation time was found to be about 10 s for all the samples. Scanning electron microscope (SEM) images showed homogeneous dispersion of the SFN in the hydrogel, mimicking the natural cartilage environment. Furthermore, water uptake capacity, degradation rate, cell cytotoxicity, and glycosaminoglycan and collagen II secretions were determined for the optimum hydrogel to support its potential as an injectable scaffold for articular cartilage defects.

International audience; When studying the interbank money market (IMM), it is common to model banks as agents interacting through loans to tackle its complexity. However, the use of agent abstraction in the IMM is mostly limited to some specific cases. Besides, recent advancements show that it is promising to use blockchain technology to improve its security in a decentralized way. Based on this observation, this paper proposes an agent-oriented, blockchain-based design of the IMM trading systems, where the main objective is to decide on the times and methods of liquidity supply and demand by various market players based on what has been learned from the information available. The models in this paper are suitable for use by both academics and practitioners in this field.

Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts. By using RNN, we can encode input text to a lower space of semantic features while considering the sequential behavior of words. By using CNN, we can transfer the representation of input text to a flat structure to be used for classifying text. In this article, we proposed a novel recurrent CNN model to capture not only the temporal but also the spatial features of the input poem/verse to be used for poet identification. Considering the shortcomings of the normal RNNs, we try both long short-term memory and gated recurrent unit units in the proposed architecture and apply them to the poet identification task. There are a large number of poems in the history of literature whose poets are unknown. Considering the importance of the task in the information processing field, a great variety of methods from traditional learning models, such as support vector machine and logistic regression, to deep neural network models, such as CNN, have been proposed to address this problem. Our experiments show that the proposed model significantly outperforms the state-of-the-art models for poet identification by receiving either a poem or a single verse as input. In comparison to the state-of-the-art CNN model, we achieved 9% and 4% improvements in f-measure for poem- and verse-based tasks, respectively.

Here, we try to build the structure of a Theory of computation based on considering time as a fuzzy concept. Actually, there are some reasons to consider time as a fuzzy concept. In this article, we don’t go to this side but we remind that Brower and Husserl ideas about the concept of time were similar. Throughout this article, we present the Theory of Computation with Fuzzy Time. Considering the classical definition of Turing Machine we change and modify the concept of Time to Fuzzy time. We call this new Theory TC* and this type of computation “Fuzzy time Computation”. We have relatively large number of fundamental unsolved problems in Complexity Theory. In the new Theory some of the major obstacles and unsolved problems are solved. It should be mentioned that in this article, we consider fuzzy number a symmetric one. The point about the symmetry is in the proof of Lemma 3, although we are able to generalize it. More specifically, we define the new classes of complexity Theory, P*, NP*, BPP* in TC* analogues to the definitions P, NP, BPP as their natural substituted definition. We show P*≠ NP*, P*= BPP*. Finally, we have Theorem 4, P≠NP .