Product traceability is one of the major issues in supply chains management (e.g., Food, cosmetics, pharmaceutical, etc.). Several studies has shown that traceability allows targeted product recalls representing a health risk (e.g.: counterfeit products), thus enhancing the communication and risks management. It can be defined as the ability to track and trace individual items throughout their whole lifecycle from manufacturing to recycling. This includes real-time data analytics about actual product behavior (ability to track) and product historical data (ability to trace). This paper presents a comparative study between several works on product traceability and proposes a standardized traceability system architecture. In order to implement a counterfeit/nonconforming product detection algorithm, we implement a cosmetic supply chain as a multi-agent system implemented in Anylogic©. Data generated by this simulator are then used in order to identify genuine trajectories across the whole SC. The genuine product trajectories (behavior) are inferred using a frequent pattern mining algorithm (i.e., Apriori). This identified trajectories are used as a reference in order to identify counterfeit products and detect false alarms of product behavior
Deriving an accurate behavior model from historical data of a black box for verification and feature forecasting is seen by industry as a challenging issue especially for a large featured dataset. This paper focuses on an alternative approach where stochastic automata can be learned from time-series observations captured from a set of deployed sensors. The main advantage offered by such techniques is that they enable analysis and forecasting from a formal model instead of traditional learning methods. We perform statistical model checking to analyze the learned automata by expressing temporal properties. For this purpose, we consider a critical water infrastructure that provides a scenario based on a set of input and output values of heterogeneous sensors to regulate the dam spill gates. The method derives a consistent approximate model with traces collected over thirty years. The experiments show that the model provides not only an approximation of the desired output of a feature value but, also, forecasts the ebb and flow of the sensed data.
Thanks to the digital revolution, the construction industry has seen a recognizable evolution, where the world has been heading towards modern constructions based on the use of Building Information Modeling (BIM). This evolution was marked by the integration of this paradigm with immersive technologies like Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). During the last few years, the development of BIM started to emerge. This paper proposes a Systematic Literature Review (SLR) of recent studies about the integration of BIM with immersive environments using VR/AR/MR technologies. Four electronic databases were exploited to search for eligible studies, namely: Google Scholar, ACM Digital Library, IEEE Xplore, and Science direct. From an initial cohort of 239 studies, 28 were retained for analysis. The main findings of this review have been focused on stages of the projects’ life cycle in which the immersive technologies are being implemented, approaches/techniques used to ensure the integration of BIM with the three immersive technologies, along with the current limitations and perspectives.
The distributed devices in a smart city are characterized by different degrees of sensitivity. Some of them can be accessed by everyone whereas others are limited to a specific class of users (subjects). Therefore, we created an access control system named SOT-S (Subject-Object-Task System) supported by blockchains that sort processes applied by subjects on smart devices. SOT-S depends on three entities: subjects, objects and tasks. It determines if subjects have the access’s rights to objects or not, and also it defines the priorities among the subjects. SOT-S principles are applied through an equation that takes the values of the three entities. To increase the level of trust and maintain the information integrity on the system, the values associated to the entities and the access control rules are managed through a blockchain mechanism. To ensure the applicability of the proposed solution, we developed a test environment to integrate the proposed concepts. In addition, we created a network integrating the developed components where the architecture was built through smooth operations. Compared to the existing solutions, the evaluated parameters of our system components are protected from damages by blockchain technology. Also, SOT-S paradigm is easy to understand, implement, and deploy. Further, it assigns a value of trust to a given task/action executed by a subject on an object.
One of the major security issues in the Internet of Things (IoT) is maintaining the network availability against attacks and traffic congestion. In practice, the greedy behavioral attack is considered as an intelligent Denial of Service (DoS), which aims to compromise the availability of the network by consuming as much as possible the bandwidth of the deployed network. This attack is achieved by tuning the CSMA-CA network communication parameters that plays at the physical layer. In this paper, we propose an efficient modeling technique of attacks proper to the behavior of the greedy node in IoT networks while respecting unslotted IEEE 802.15.4. In fact, our developed greedy nodes algorithm relies on CSMA-CA protocol. This fashioned way of attack representation helped us to easily detect greedy nodes on large-scale IoT networks through simulations. Indeed, the obtained numerical results of different scenarios allow us to validate our approach and showed that the greedy nodes can monopolize the transmission channel during a significant period of time. Various relevant parameters (the number of sent/lost packets, the collision rate, and energy consumption) are considered to analyze and evaluate the impact of selfish nodes on the IoT networks.
In this paper a theoretical analysis for determination of elastic and flexural modulus of natural fibre reinforced hybrid composite is proposed. The proposed analytical model is based on the classical lamination approach to predict the theoretical modulus. Experimental tensile and 3-point bending tests were carried out using standard specimens of inter and intra-ply hybrid composite materials. The elastic and flexural modulus predicted by the new approach showed a better agreement with the experimental results, with a maximum deviation of 11.21% and 11.65 % respectively. From the results it was observed that the theoretical value of elastic and flexural modulus was not accurately predicted by rule of hybrid mixture method. The proposed approach can be adopted as a primary tool to predict the modulus of the composite and it can also be used to optimise the composite parameters such as volume fraction of fibre, ply and fibre orientation, etc. for the maximum performance.
After two years of COVID-19 first infection and its speedy propagation, death and infection cases are till exponentially increasing. Unfortunately, during this a non-fully controlled situation, we noticed that the existing solutions for COVID-19 detection based on chest X-ray were not reliable enough in relation to the number of infected patients and the severity of the outbreak. To handle this issue by increasing the reliability and the efficiency of COVID-19 detection, we therefore deploy and compare the results of a set of reconfigurable classification approaches and deep learning techniques. Indeed, we have achieved a score of up to 99% accuracy with a dataset of 15,000 X-ray images, which makes the selected detection technique, deep learning, more reliable and effective.
Coverage area maximization is a crucial issue that must be considered in Wireless sensor network (WSN) deployment as long as it impacts the sensor network efficiency. In this paper, a novel approach based on particle swarm optimization (PSO) and voronoi diagram is developed to solve WSN deployment problem. The objective of the proposed solution is to reduce both of the coverage hole and coverage overlapping in the region of interest (RoI). In order to achieve it, the PSO fitness function is designed using voronoi diagram for the purpose of efficiently assessing the coverage hole of a particle solution and therefore, compute the improved deployment of the sensor nodes within the target area. The simulation results demonstrate that the proposed algorithm provides a noteworthy initial coverage enhancement.
Gamification can be seen as the intentional use of game design elements in non-game tasks, in order to produce psychological outcomes likely to influence behaviour and/or performance. In this respect, we hypothesize that gamification would produce measurable effects on user performance, that this positive impact would be mediated by specific motivational and attentional processes such as flow and that gamification would moderate the social comparison process. In three experimental studies, we examine the effects of gamified electronic brainstorming interfaces on fluency, uniqueness and flow. The first study mainly focuses on time pressure, the second on performance standard and the third one introduces social comparison. The results highlight some effects of the gamified conditions on brainstorming performance, but no or negative effects on flow. All three studies are congruent in that gamification did not occur as a psychological process, which questions popular design trends observed in a number of sectors.
Technical constraints imposed by low-power and lossy networks (LLNs) require to defer complexity to routing protocols in order to efficiently and reliably transmit packets. However, despite these constraints, the deployment of this type of network has increased considerably over the last years, particularly in smart cities area with focus on sensing applications. In order to effectively address this challenge, we propose a new mechanism for IPV6 Routing Protocol for LLNs (RPL) based on the Operator Calculus (OC) approach. In this multi-constrained path optimization problem, OC is applied to extract the feasible end-to-end paths while assigning a rank to each network node. Unlike the standard RPL and its variants which adopt a full distributed strategy, the aim is to provide RPL with a tuple containing the most efficient paths from a source node to the sink by considering multiple routing metrics. The definitive choice of the route is then delegated to RPL in order to take dynamic topology changes into account. The solution thus combines a multi-objective and semi-distributed routing algorithm with the RPL. Furthermore, to benchmark our proposal, we perform a comprehensive evaluation and compare it with other state of the art works. Performance evaluation results show that RPL-OC allows a great improvement compared to OFFL and the standard RPL, mainly in terms of end-to-end delay and energy consumption.Syarif, AbdusyBrahmia, Mohamed-el-AmineDollinger, Jean-FrançoisAbouaissa, AbdelhafidIdoumghar, Lhassane
The phenomenon of summer mortality in Pacifi c oysters cultures also occurs in Brazilian crops, with predominance in the adult phase, generating signifi cant losses for local producers. In the search for a technological solution to mitigate its effects, the mechanical vapour compression and hydraulic refrigeration concepts are evaluated as two proposed cooling technologies. The comparative analysis carried out with numeric simulations indicated that the hydraulic cooling system presents disadvantages regarding both the size of the compression column and the energy effi ciency, compared to the mechanical vapour compression cycle. By computing only the compression power, a COP value of 6.9 results for the MVCS at TCOND = 29.5 oC and TEVAP = 7.2 oC, while for HRS the COP value is around 3.1 for identical conditions. Results from the analysis contradict former publications, but are consistent with recent fi ndings reported in literature.
The reinforcement efficiency on a composite depends on the effective transfer of the stress between matrix and fiber. This work presents an experimental and comparative study of fiber-matrix bond strength for fiber-matrix interface between glass fibers and carbon fibers added to the slag-based geopolymer matrix. This analysis was performed by pull-out test. A total of 18 tests have been conducted, three for each type of fiber at each embedded length of 10 mm, 20 mm and 30 mm. The critical embedded length and the maximum interfacial shear (bond) strength were analyzed, and SEM observations were carried out for the cross-section of each fiber to measure diameter and observe the interface. It was found that the greatest efficiency was obtained by reinforcing with the glass fibers, incorporated at 20 mm in the slag-based matrix.
Industrial cyber-physical systems (ICPS) are heterogeneous inter-operating parts that can be physical, technical, networking, and even social like agent operators. Incrementally, they perform a central role in critical and industrial infrastructures, governmental, and personal daily life. Especially with the Industry 4.0 revolution, they became more dependent on the connectivity by supporting novel communication and distance control functionalities, which expand their attack surfaces that result in a high risk for cyber-attacks. Furthermore, regarding physical and social constraints, they may push up new classes of security breaches that might result n serious economic damages. Thus, designing a secure ICPS is a complex task since this needs to guarantee security and harmonize the functionalities between the various parts that interact with different technologies. This paper highlights the significance of cyber-security infrastructure and shows how to evaluate, prevent, and mitigate ICPS-based cyber-attacks. We carried out this objective by establishing an adequate semantics for ICPS’s entities and their composition, which includes social actors that act differently than mobile robots and automated processes. This paper also provides the feasible attacks generated by a reinforcement learning mechanism based on multiple criteria that selects both appropriate actions for each ICPS component and the possible countermeasures for mitigation. To efficiently analyze ICPS’s security, we proposed a model checking based framework that relies on a set of predefined attacks from where the security requirements are used to assess how well the model is secure. Finally, to show the effectiveness of the proposed solution, we model, analyze, and evaluate the ICPS security on two real use cases.
The challenge of improving the efficiency of the different phases of a building or infrastructure life demands considerations of innovative technologies such as Augmented Reality (AR) and Virtual Reality (VR). During this last decade, AR/VR systems for construction started to be emerged. These applications aim to virtualize or augment in real time the content of the Building Information Modeling (BIM) in order to support continuous improvement. To ensure the maturity of these applications, implementing a maturity model is needed. Based on literature, several maturity models for BIM have been proposed. However, it stays generic and needs to be adapted to the AR and VR technologies in the BIM context. To that end, we started in this paper by proposing an adapted AR/VR maturity model for BIM that aims to evaluate the maturity of these technologies according to the BIM lifecycle. This model has been conceived based on a mapping between three existing maturity models corresponding to AR, VR and BIM technologies from the most adapted existing works that deal with our goal. As a result of this mapping, three maturity levels have been identified and a detailed description of each level has been established. This model will be proposed to construction companies in order to evaluate their maturities on the use of AR/VR technologies.
Cybersickness (CS) is an affliction that limits the use of virtual reality (VR) applications. For decades, the measurement of cybersickness has presented one of the most challenges that have aroused the interest of VR research community. Having strong effects on users’ health, cybersickness causes several symptoms relating to different factors. In most cases, the literature studies for VR cybersickness evaluation adopt the questionnaire-based approaches. Some studies have focused on physiological and postural instability-based approaches, while others support the VR content. Despite the attention paid to define measurements for assessing cybersickness, there is still a need for a more complete evaluation model that allows measuring cybersickness in real time. This paper defines a conceptual model that integrates subjective and objective evaluation of CS in real time. The proposed model considers three CS factors (i.e. individual, software and hardware). The aim is to consider the heterogeneous findings (subjective and objective measures) related to the selected CS factors that define integrated indicators. The theoretical part of the model was initially validated by researchers who have comprehensive knowledge and skills in VR domain. As a research perspective, we intend to evaluate the proposed model through a practical case study.
In this paper, a new group contribution method for predicting the standard enthalpy of formation in the liquid phase(ΔfHliq°) of pure organic compounds is developed. This new method is based on experimental ΔfHliq° values of 1100 compounds containing C, H, N, O and halogens atoms. A set of 880 data points (80% of the data) are used to develop the method, and the remaining 220 data points (20% of the data) are applied to evaluate the predictive capability of the proposed method. An uncertainty analysis for the predicted values is performed to quantify prediction errors. This method is based only on the molecular structure of the compound. Accurate results were obtained, clarified by an average absolute deviation of 5.79 kJ/mol and an average standard deviation of 6.64 kJ/mol. The model details and application examples are illustrated.
The objective of this work is to evaluate the capability of different combinations of a turbulence model and a Lagrangian particle tracking (LPT) model integrating a particle-wall interaction (PWI) model to predict particle-laden flow in 90-deg bends, as well as the impact of the PWI model on the prediction of the referred flow. The experimental data from Kliafas and Holt (1987) (LDV measurements of a turbulent air-solid two-phase flow in a 90° bend. Experiments in Fluids, 5: 73-85) concerning a vertical to horizontal square-sectioned duct with a hydraulic diameter of 0.1 m that are connected by a 90-deg bend with a curvature ratio of 3.52, served as the benchmark for the aimed analysis. Air with glass spheres of 50 μm diameter flows in the experimental duct system with a Reynolds number of 3.47×105. The airflow was modelled by four different turbulence models: a low Reynolds number k-ε model, the SST k-ω model, the v2-f model, and the RSM SSG model. The particle-phase was modelled by a LPT formulation, and the particle-wall interaction was calculated using four different models: Brauer, Grant & Tabakoff, Matsumoto & Saito and Brach & Dunn PWI models. The 3D simulation results of mean streamwise velocities from the sixteen RANS-LPT/PWI combinations were compared qualitatively and quantitatively to experimental and numerical data available in the literature. The four turbulence models produced errors for the gas-phase in the order of 8%. Concerning the particle-phase, the errors produced by all RANS-LPT/PWI combinations were below 4% for bend angles up to 15° and up to 18% for bend angles higher than 30°. The best results for the particle-phase were obtained with the SST k-ω and v2-f model combined with the LPT/Brauer or LPT/Brach & Dunn PWI models, which produced errors inferior to 14%.
Drug traversal across the blood-brain barrier has come under increasing scrutiny recently, particularly concerning the treatment of sicknesses, such as brain cancer and Alzheimer’s disease. Most therapies and medicines are limited due to their inability to cross this barrier, reducing treatment options for maladies affecting the brain. Carbon dots show promise as drug carriers, but they experience the same limitations regarding crossing the blood-brain barrier as many small molecules do. If carbon dots can be prepared from a precursor that can cross the blood-brain barrier, there is a chance that the remaining original precursor molecule can attach to the carbon dot surface and lead the system into the brain. Herein, tryptophan carbon dots were synthesized with the strategy of using tryptophan as an amino acid for crossing the blood-brain barrier via LAT1 transporter-mediated endocytosis. Two types of carbon dots were synthesized using tryptophan and two different nitrogen dopants, urea and 1,2-ethylenediamine. Carbon dots made using these precursors show excitation wavelength-dependent emission, low toxicity, and have been observed inside the central nervous system of zebrafish (Danio rerio). The proposed mechanism for these carbon dots abilities to cross the blood-brain barrier concerns residual tryptophan molecules which have attached to the carbon dots surface, enabling them to be recognized by the LAT1 transporter. The role of carbon dots for transport open promising avenues for drug delivery and imaging in the brain.
In the context of the Industry 4.0 and of the digital factory, digital twin and virtual reality represent key technologies to design, simulate and optimize cyber-physical production system and interact with it remotely or in a collaborative way. Moreover, these technologies open up new possibilities which can be involved in the co-design and ergonomics studies of workstations, based on Industry 4.0 components like cobots. In order to satisfy these needs and to create dynamic and immersive virtual environment, it is therefore necessary to combine the capacities of the digital twin to perform simulation of the production system with the capacities of the immersive virtual environment in term of interactions. This paper proposes a co-simulation and communication architecture between digital twin and virtual reality software, then it presents a use case on a human-robot collaborative workplace design and assessment.
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