Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bilevel Multi-Objective Model for Sustainable Road Pricing Design on A Bimodal Transport Network

This study proposes a bilevel multi-objective model to find road tolls for sustainable urban development on a bimodal transportation network. Three objectives, including minimization of total travel time budget, total exhaust emissions and negative impacts for health, are considered in the upper level model, which correspond to economic, environmental and social sustainability, respectively. Meanwhile, the lower level is a joint model of mode split and route choice for each individual traveler in which mode choice equilibrium follows the binary logit discrete choice model and path choice equilibrium is determined by a three-objective user equilibrium that achieves the travel time budget surplus (TBS) maximization. For solving the proposed bilevel optimization model, the paper develops an algorithm that combines the multi-objective genetic algorithm (NSGA-II) and a path-based traffic assignment algorithm. Then the feasibility and effectiveness of the algorithm are well demonstrated by a numerical example.
Fast Characterization of AC Windings

The paper gives contributions on the characterization of arbitrary AC winding structures used in rotating field electrical machines. The wide range of winding layouts imposes the maximum generality of the methodology used to compute the winding distributions and the winding factors for each spatial harmonic order. The authors found that the single-conductor distribution concept allows for maximum freedom in the definition of the winding functions. The proposed approach is easily implementable in design code for quick evaluation and comparison of different single- and multi-phase AC windings.
Online CP Decomposition for Sparse Tensors

Tensor decomposition techniques such as CANDECOMP/PARAFAC (CP) decomposition have achieved great success across a range of scientific fields. They have been traditionally applied to dense, static data. However, today's datasets are often highly sparse and dynamically changing over time. Traditional decomposition methods such as Alternating Least Squares (ALS) cannot be easily applied to sparse tensors, due to poor efficiency. Furthermore, existing online tensor decomposition methods mostly target dense tensors, and thus also encounter significant scalability issues for sparse data. To address this gap, we propose a new incremental algorithm for tracking the CP decompositions of online sparse tensors on-the-fly. Experiments on nine real-world datasets show that our algorithm is able to produce quality decompositions of comparable quality to the most accurate algorithm, ALS, whilst at the same time achieving speed improvements of up to 250 times and 100 times less memory.
Efficient Secure Access to IEEE 21451 Based Wireless IIoT Using Optimized TEDS and MIB

With integration between Internet-of-Things (IoT) and industrial systems, Industrial Internet of Things (IIoT) raises more efficiency and security access for monitoring and control. The IEEE 21451 standard, gradually accepted by manufacturers and users, aims to solve the problem of unified sensor communication standard for sensor networks, especially for wireless sensor networks. Although IEEE 21451-1-5 can realize the simple network management for wireless IIoT, efficient security access during the network management continues to be an unresolved problem. In this paper, we propose an efficient secure access scheme for IEEE 21451 based wireless IIoT using optimized Transducer Electronic Data Sheet (TEDS) and Management Information Base (MIB). First, MIB is adapted to index efficiently TEDS of IIoT, which is extended to record the parameters of network status. Second, optimized MIB is reconstructed to monitor and configure the attributes of IIoT management data. Third, MIB based Dynamic Role based Access Control (DRBAC) is proposed for flexible security access in wireless IIoT. Simulation results verify the advantages of the propose scheme. The proposed scheme is momentous to realize the simplifying, extensibility and security data access in wireless IIoT.
Reliability Analysis of a Novel Fault Tolerant Multilevel Inverter Topology

Proper functioning of the Multilevel Inverter (MLI) is dependent on the healthy performance of its power electronic components. Amongst all power electronic components, power electronic switches and capacitors are most prone to faults. Thus, a failure on these devices may lead to undesired outcomes or even complete shutdown of the application. Thus, the development of a highly reliable MLI topology which is able to deliver the desired output, during worst faulty conditions, has become a recent research trend. In this paper, a novel fault tolerant MLI topology is proposed which is able to sustain single switch failure on all of its main inverter switches. Not only that, the proposed topology is able to achieve inherent DC-link capacitor voltage balancing. The reliability of the proposed topology is analyzed mathematically. The validity of the proposed topology is verified by the obtained OPAL-RT results.
Maximum Power Point Tracking of PV System Under Partial Shading Through a Novel Flower Pollination

For the maximum utilization of solar energy, photovoltaic (PV) power systems are operated at the maximum power point (MPPT) under changing environmental conditions, and maximum power point is generally achieved using traditional methods. However, the occurrence of multi-peak due to partially shaded conditions increase complexity of the tracking prosses and conventional technique can't guarantee convergence to global maximum power point always. This paper proposed a MPPT method for solar energy system using a novel flower pollination (FP) algorithm. This paper reports the conception of FP algorithm by emphasizing the importance of the Levy flight in affecting the proposed method's convergence. In order to verify the effectiveness of the proposed method, the MPPT control model of the PV system is established in MATLAB and compared with two well established techniques. The simulation results indicate that the proposed MPPT technique can very quickly track the varies of external environment and effectively handle the partially shaded condition.
Finite Element Analysis of Municipal Drainages Dredging Robot

The paper was mainly research on municipal drainage dredging robot by finite elements method. It took a certainly type of municipal drainage dredging robot as an example. In most dangerous working condition, which is the max working resistance acts on the tank and just one nose wheel dangling, the two main structure parts of robot were analyzed in finite element analysis software ANSYS Workbench13.0 and got the equivalent stress and total deformation. The maximum equivalent of stress of the tank is much less than material yield strength, and which shows that structure is reasonable. There existed stress concentration in collector, and it should be chamfered to reduce the stress concentration. The analysis data indicated that the structure of the two parts was reasonable.
Artificial Intelligence and Mobile Phone Sensing Based User Activity Recognition

With the development of Micro Electro Mechanical Systems, a growing number of portable devices and wearable devices equipped with built-in sensors, which can detect the physical movements, such as identifying the action type and record the duration of exercise. Since the amount of data collected from sensors grows, automatic activity recognition becomes an important issue to living in a smart life. Therefore, this paper aims to use various kinds of machine learning techniques to build the automatic activity classification model, including Logistic regression, Decision tree, Random forest and Support vector machine algorism. Furthermore, we evaluated the prediction performance of four supervised machine learning classification models. Results of the experiments show that under specific acceptance of accuracy and minimum model training time, the decision tree algorithm creates the best model. However, if consider the accuracy as the only pursue, adopting the support vector machine algorithm will get the better model.
Global Maximum Power Point Tracking Scheme on a Partially Shaded Photovoltaic Array

This paper proposes an algorithm capable of estimate the global maximum power point of a large photovoltaic (PV) array system operating under partially shaded conditions. The power-voltage characteristic of a photovoltaic array is a nonlinear function that becomes more complex under partial shading conditions, in fact, the power-voltage curve may exhibit multiple local maximum power points. The proposed algorithm uses two PV reference cells, every 900 m2, one operating in open-circuit and the other one in short-circuit, to estimate the solar radiation and the ambient temperature conditions of the PV arrays. The results show an estimation error of the ambient conditions lower than 1% of the actual values. As a result, the arrays provide the maximum power possible for any distribution of irradiance and temperature conditions.
La prise de décision des joueurs de sports collectifs - Une revue de question en psychologie du sport

Players' decision-making in team sports. An overview in sport psychology. The purpose of this paper is to highlight elements that help to understand players' decision making in team sports. The analysis of the literature in sport psychology has enabled us to set out the cognitive and perceptive processes used by expert players. We therefore make a detailed presentation of the main theoretical models, of the investigation methods and of the main results that are useful to study the process. Recent papers attest that the intricacy of the context in sport teams is better studied, particularly thanks to naturalist approaches which - along with individual-centered and cognitive approaches - enable us to consider dynamical and situated aspects of decision as well the influence of collective activity. We bring out the underlying epistemologies of these two decision- making approaches.
The Structure-property of ZnO/Perovskite CH3NH3PbBr3 Coaxial Nanocables

Nanotechnology has received considerable attention because of its potential to revolutionize the fields in which it is exploited. Here, we demonstrated a novel nanostructure which the perovskite CH<inf>3</inf>NH<inf>3</inf>PbBr<inf>3</inf> were filled into the ZnO nanotube. Therefore, a novelty ZnO/ perovskite CH<inf>3</inf>NH<inf>3</inf>PbBr<inf>3</inf> coaxial nanocables were fabricated. The structural properties of the as-fabricated coaxial nanocable were investigated. The ZnO/ perovskite CH<inf>3</inf>NH<inf>3</inf>PbBr<inf>3</inf> coaxial nanocables have the potential application for advanced nano-devices.
Optical Auto-correlators Using Single GaSe Nanoribbons for Femto-Joule Ultrafast Pulses Characterization

High quality single-crystal GaSe nanoribbons (NRs) are fabricated and demonstrated as waveguides with good performance. By taking advantage of the strong confinement and long interaction length of the waveguiding approach, significantly enhanced nonlinear light–matter interaction is observed in GaSe NRs. Based on transverse SH generation, a single GaSe NR-configured optical auto-correlator is constructed for ultrashort pulse characterization. Because of the large second-order nonlinear susceptibility, high sensitivity with femto-joule-level pulse energy that has higher sensitivity than those reported before is demonstrated.
Utilising Fog Computing for Developing a Person-Centric Heart Monitoring System

Heart disease and stroke are becoming the leading causes of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that help physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECGs have limited accuracy and rely on external resources to analyse the signals and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyse the signal and identify abnormal behaviour. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud, (c) the overall power consumption and (d) the confidentiality of private data. Based on this concept, the HEART system presented in this work, combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate, and instant heart monitoring. The wearable device is remotely trained by a physician to learn to accurately identify critical events related to each particular patient. Following this training session, the wearable device becomes capable of interpreting a large number of heart abnormalities without relying on cloud services and edge resources, when the medical doctor is not present. The Fog computing approach extends the cloud computing paradigm by migrating data-processing closer to production site, thus accelerating the system's responsiveness to events. The HEART system's performance concerning the accuracy of detecting abnormal events and the power consumption of the wearable device is evaluated. Results indicate that a very high success rate can be achieved in terms of event detection ratio and the battery is able to sustain operation up to a full week without the need for recharge.
Data-driven and Event-driven Integration Architecture for Plant-wide Industrial Process Monitoring and Control

Efficiency of industrial processes and a high quality of the products can be achieved with advanced monitoring and control solutions. In addition, industrial processes often consume large amounts of energy and through their efficiency and use of resources also have a significant environmental impact. For industrial process optimisation it is necessary to integrate distributed data and functionality into plant-wide coordinating level solutions. From an implementation point of view this is challenging due to different communication protocols and messaging structures. In this paper an integration architecture is proposed that decouples control systems using a message bus mediator approach. The mediator acts as a unified point of access that through adapters facilitates integration of existing control systems to advanced plant-wide data-driven and event-driven control. It is demonstrated with a laboratory case presenting two examples applicable to real-life industrial problems.
Direct duty ratio control of connected converter in DC microgrid

A direct duty ratio control (DDRC) method is proposed in this paper. The method uses the change of DC bus voltage in each control period to predict the inductor current. To make the DC bus voltage work in a definite range, a linear control relationship between the DC bus voltage and the inductance current is established. In order to improve the control precision and dynamic response, the duty ratio of the converter is deduced and predicted by the change of inductor current. In addition, the dynamic performance of the system under impulse disturbance is improved by adding virtual capacitor to the voltage loop and the parameter design of virtual capacitor is provided. Finally, the simulated setup of single-phase inverter in DC microgrid is built and the effectiveness of this method is verified by simulation.
New hybrid mode current controller with fast response without sub-harmonic oscillation

An wide bandwidth current controller is required for fast charging / discharging of supercapacitor applications. To do this, peak current mode is generally used, which has fast response characteristics. On the other hand, the peak current mode control must have a slope compensation function to restrain sub-harmonics oscillation. However, if the controlled output voltage is varied, the slope must be changed accordingly. However, it is not easy to change the slope by every changed output voltage. As other solution, the slope is selected as the maximum value, a slow response problem occurs. Therefore, in this paper, we propose a hybrid mode controller that uses a peak current and a newly specified valley current. By the proposed hybrid mode control, the sub-harmonic oscillation does not occur when the duty is larger than 0.5 with fast response.
Using 3D Technologies in Materials Teaching Engaging students in early R &D activities

Teaching industry oriented courses to students with poor background and lack of the appropriate skills and knowledge in those areas is a strenuous task. The course concepts assimilation can sometimes be very difficult and designing and adapting courses can be a challenging task. When the industry paradigm starts to change and moves towards new fabrication processes like additive manufacturing (AM) the current curricula are sometimes lacking and the information transmission can be compromised. In that sense adapting lecture class and explaining AM superficially is relatively easy, but adapting laboratorial courses to fit all AM technologies also poses a challenge, not only by acquiring different AM technologies but also to arrange a time schedule for that. The result is that a group of students only experiences one technology a time and even then, they do not directly operate the machines. This also impacts on the assimilation process and perhaps more active learning strategies should be implemented. For that three bachelor students that had poor background on AM, material science, computer assisted design (CAD), were inserted in an R &D project and challenged to design customized porcelain tableware products to be produced by robocasting (R3D) technology. Surprisingly with a few hints and orientations on the early stages they were successful in producing a porcelain prototype by R3D. Moreover, the contact with R &D environment and real objectives motivated them and in the end, was extremely rewarding for them as the results were presented in national and international events. Even more the project participation was extremely useful in the development and enrichment of competencies related to materials, AM and CAD modeling, which enabled them to be later engaged in related projects involving R3D technologies and CAD modelling. This result not only shows the benefits of exposing early on students to R &D environment, it also hints that in a near future adapting a course for AM techniques might be helpful in the enrichment and development of competencies related with product development, CAD modeling and AM strategies.
Albe 1998 - La grande Motte 2009 : quelles avancées en 10 ans ?

The importance of neutron scattering techniques for the characterization of samples in soft condensed matter has been demonstrated all along the present book. The fine understanding of the physical properties is closely linked to progress in the field of instrumentation. This chapter describes the advances over the last decade in technical domains, such as neutron detection, electronics and sample environment. The news software for data reduction and analysis are also discussed before to conclude with the ILL and LLB projects for new instruments. Resume. Tout au long de ce livre, nous avons vu l'importance des techniques de diffusion de neutrons pour la caracterisation des echantillons en matiere molle. La comprehension toujours plus detaillee de la physique des echantillons est intimement liee aux progres instrumentaux. Ce chapitre presente les avancees et les developpements realises pendant ces dix dernieres annees dans les domaines techniques, comme ceux de la detection, de l'electronique et des environnements echantillons. Les nouveaux moyens de traitement et d'analyse des donnees sont ensuite presentes. Nous terminons par les projets instrumentaux de l'ILL et du LLB.
Phosphorus Influence on the Photodarkening Effect of Yb-APS Fiber

We fabricated an ytterbium-doped phosphorus-low aluminophosphosilicate ternary laser fiber. Owing to photodark-ening and mode instability, the fiber-based kW-level laser setup showed obvious degradation of laser power and slope efficiency as compared to that with cladding-power-stripper-free.
Design and Optimization of a Radial High-Temperature Superconducting Magnetic Bearing

High-temperature superconducting magnetic bearings can be suspended without contact and self-stability. They are widely used in many industries. Before the actual production of magnetic bearings, the structural design and parameter optimization are very important. The design and optimization method of radial high-temperature magnetic bearing are studied in this paper. The force calculation expression was deduced. The bearing capacity of high-temperature magnetic bearings was optimized. The performance of the radial HTSMB was analyzed and verified by FEM.
Italian Futurism and “The Fourth Dimension”

During the first three decades of the twentieth century, artists in nearly every major modern movement were influenced by a highly popular concept known as “the fourth dimension. ” In this period, “the fourth dimension” signified a higher, unseen dimension of space which might hold a reality truer than that of visual perception. Linked closely to the philosophical idealism which dominated the era, belief in a fourth dimension encouraged bold, formal experimentation by liberating artists from the domination of three-dimensional visual reality. If some artistic advocates of a fourth dimension, such as the Cubists, did not reject visual perception completely, Kupka, Malevich, Mondrian, and Van Doesburg found support in the idea for their creation of a totally abstract art. The Futurists Boccioni and Severini thus joined a distinguished list of artists attracted to “the fourth dimension,” a group ranging from Analytical and Synthetic Cubists as well as Duchamp, Picabia, and Kupka, to Russian Futurists and Supr.
Robust controllable FD-FWM based Micro-combs

The FD-FWM is a proven approach to implement coherent Microcomb generation. Comb repetition rates can be finely controlled in a robust way, while maintaining the coherence during the whole process, whereas full-coherent type-I and type-II combs can be accessible. We present our recent advances towards effective control of optical combs exploiting integrated chip-based resonators.
BCI for Assessing the Emotional and Cognitive Skills of Children with Special Educational Needs

This research aims to improve the pedagogical rehabilitation of children with Special Educational Needs (SEN) based on play-like activities for orientation in space, shapes, colors and emotions. The designed EEG-based portable Brain-Computer Interface (BCI) measures and features in real time the brain signals in order to analyze the attentional or emotional states of a child. The output performance scores are used either as an input control in the play scenarios or to facilitate the assessment of the emotional states and cognitive performance of a child by special educators. The proposed integration of BCI with programmable robots assists in creating an engaging environment for learning skills by imitation and forming alternative BCI training session adapted to children with SEN. The concept of added BCI into the play-like activities evolves into technology for self-regulatory training of emotional or cognitive skills via neurofeedback exposed on the robot. A low resolution EMOTIV brain-listening headset is used to translate the current child brain activity into robot commands in real time. Then, they are wirelessly transmitted to robot sensors, modules and controllers and consequently used as an input neural control. Thus, the attention or positive emotional responses of children with SEN are reinforced.