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Woody and agricultural wastes are important fuels in many countries, and have the potential of being even more important in the future. The main problems of plant biomass combustion are low ash melting temperatures and increased emissions. The most widely used treatment to solve the problem of low ash melting point is blending a fuel with an additive. In this work, pellets were produced from wheat straw containing wood sawdust and paper sludge in the following proportions 40:40:20 and 45:45:10 (straw/sawdust/paper sludge). The purpose of this work was to study the influence of sludge and dendromass on the straw pellet parameters and combustion process. The highest calorific value of 15.71 MJ kg&minus, 1 was registered for a sample with a 10% paper sludge concentration. The effectiveness of paper sludge was proved, and the ash melting temperature was increased from 1025 to 1328 &deg, C for the same sample.

Automated Driving Systems (ADSs) commend a substantial reduction of human-caused road accidents while simultaneously lowering emissions, mitigating congestion, decreasing energy consumption and increasing overall productivity. However, achieving higher SAE levels of driving automation and complying with ISO26262 C and D Automotive Safety Integrity Levels (ASILs) is a multi-disciplinary challenge that requires insights into safety-critical architectures, multi-modal perception and real-time control. This paper presents an assorted effort carried out in the European H2020 ECSEL project—PRYSTINE. In this paper, we (1) investigate Simplex, 1oo2d and hybrid fail-operational computing architectures, (2) devise a multi-modal perception system with fail-safety mechanisms, (3) present a passenger vehicle-based demonstrator for low-speed autonomy and (4) suggest a trust-based fusion approach validated on a heavy-duty truck.

This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.

International audience; The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper

The bonding ability between basalt fiber bundle and asphalt mortar has a great influence on the properties of basalt fiber reinforced asphalt mixtures, yet the studies about the bonding between them is very limited. In this paper, a new test instrument called fiber-asphalt pulling machine and a new index called the nominal bonding stress (NBS) were designed and adopted to test and reveal the bonding between basalt fiber bundle and asphalt mortar. The procedures and rationality analysis of this new experiment was specified in this paper. In order to verify the reliability of the NBS index, the NBS values derived from this new test method and the rheological property indexes values of asphalt mortar under different powder&ndash, cement ratios were adopted to analyze the correlation between them. Two kinds of basalt fibers produced by two different manufacturers were adopted to conduct the parallel experiment. The results show that the nominal bonding stress between basalt fiber and asphalt mortar derived from this new test has a great correlation with the rheological properties of asphalt mortar, which also proves the credibility of this new test method and the NBS index.

In the paper, the numerical analysis of heat and mass transfer proceeding in an axially symmetrical articular cartilage sample subjected to the cryopreservation process is presented. In particular, a two-dimensional (axially symmetrical) model with imprecisely defined parameters is considered. The base of the heat transfer model is given by the interval Fourier equation and supplemented by initial boundary conditions. The phenomenon of cryoprotectant transport (Me2SO) through the extracellular matrix is described by the interval mass transfer equation. The liquidus-tracking (LT) method is used to control the temperature, which avoids the formation of ice regardless of the cooling and warming rates. In the LT process, the temperature decreases/increases gradually during addition/removal of the cryoprotectant, and the articular cartilage remains on or above the liquidus line so that no ice forms, independent of the cooling/warming rate. The discussed problem is solved using the interval finite difference method with the rules of directed interval arithmetic. Examples of numerical computations are presented in the final part of the paper. The obtained results of the numerical simulation are compared with the experimental results, realized for deterministically defined parameters.

Featured Application The paper assesses the ventilation system performance of a modern Finnish building with kitchen boosting and proposes a new design concept which addresses changes to the overall design approach for the future apartment residential building with high airtightness level in Finland. In general, new Finnish apartment buildings are equipped with mechanical balanced demand-based ventilation. The airflow rate in the kitchen hood is boosted on demand to improve pollutant extraction during cooking. However, in practice, it has been found that the system does not work as desired. The focus of the paper was to present the simulation results from a case building equipped with a ventilation system that is commonly used in Finland. In the analysis, the airflow rates are calculated for the room, apartment, and air handling unit (AHU) levels for various ventilation mode scenarios. A significant imbalance of over 10% between the supply and exhaust airflows at the room and apartment levels was observed in the boosting mode. This imbalance creates a pressure difference over the building envelope, particularly in small studio apartments. The calculated pressure difference for future buildings with high airtightness were at the warning level of 40 Pa below atmospheric level. The kitchen hood exhaust system showed a 28% lower airflow rate in certain scenarios. A new solution to guarantee the designed airflow rates was proposed and assessed. The new solution consists of replacing the apartment level flow control damper and a new balancing method for the kitchen hood exhaust branch. The proposed design was able to stay within 10% of the designed airflow rates in all operation modes.

In this paper, we provide a holistic survey of multimedia content protection applications in which blockchain technology is being used. A taxonomy is developed to classify these applications with reference to the technical aspects of blockchain technology, content protection techniques, namely, encryption, digital rights management, digital watermarking and fingerprinting (or transaction tracking), and performance criteria. The study of the literature reveals that there is currently no complete and systematic taxonomy dedicated to blockchain-based copyright protection applications. Moreover, the number of successfully developed blockchain-based content protection systems is very low. This points towards a research gap. To fill this gap, we propose a taxonomy that integrates technical aspects and application knowledge and can guide the researchers towards the development of blockchain-based multimedia copyright protection systems. Furthermore, the paper discusses some technical challenges and outlines future research directions.

In this paper, the performance of a lightning protection system (LPS) on a grid-connected photovoltaic (PV) park is studied by simulating different scenarios with the use of an appropriate software tool. The aim of this paper is to highlight the importance of an LPS and optimize its design for the protection of equipment and personnel in case of a direct lightning strike. In particular, developed potential due to lightning strikes is examined considering isolated and non-isolated external LPS. Moreover, the effect of the separation distance on the lightning performance of the PV installation is investigated for different soil structures and grounding systems. The extracted results are expected to support the design and implementation of a secure PV park and endorse its uninterrupted operation considering techno-economically balanced protection measures.

Recent advancements in sensor, mobile computing, and wireless communication technologies is creating new opportunities to effectively exploit real-time traffic data. Onboard vehicles collect, process, simulate traffic states in a distributed fashion and a local transportation management center coordinates the overall simulation with an optimistic execution technique. Such a distributed approach can provide more up-to-date and robust estimates with decreased communication bandwidth requirements and increased computing capacity. This paper proposes an online ad hoc distributed simulation. The physical operating platform for the model including operating system, communicational middleware, and traffic simulation model are described. Also, this paper investigates the analytical background of rollback process of the proposed ad hoc distributed simulation model. Flow rate diagram and cumulative number of vehicle diagram show that the overall system simulation speed and estimate accuracy may differ significantly as a function of the selected threshold.

The study focuses on the application of a nonparametric methodology for evaluating the sustainability of retrofitting interventions to be applied on different typologies of buildings and different climate zones of the Mediterranean area. The paper starts from the analysis of data collected through the HAPPEN project, that is a H2020 European project which proposes a holistic approach for a deep and sustainable renovation of the Mediterranean residential Building stock. Even if the European Commission allocated considerable funds for retrofitting interventions, the choice of the optimal solution is not always that easy because several variables have to be considered. The present manuscript proposes a methodology to compare different retrofitting solutions combining Life-Cycle Cost (i.e., LCC) estimations with the nonparametric Directional Distance Function approach (i.e., DDF). In detail, the literature suggests that the DDF can be effectively used for comparing different observations through efficiency scores. The main result of the paper is the definition of a hybrid methodology that, starting from estimates of LCC and applying a DDF technique, represents a simple method for evaluating the best retrofitting intervention. Results are represented by two scores where the former represents a holistic efficiency measure, while the latter shows an environmental efficiency score.

International audience; The tremendous demand for low-cost, low-consumption and high-capacity optical transmitters in data centers challenges the current InP-photonics platform. The use of silicon (Si) photonics platform to fabricate photonic integrated circuits (PICs) is a promising approach for low-cost large-scale fabrication considering the CMOS-technology maturity and scalability. However, Si itself cannot provide an efficient emitting light source due to its indirect bandgap. Therefore, the integration of III-V semiconductors on Si wafers allows us to benefit from the III-V emitting properties combined with benefits offered by the Si photonics platform. Direct epitaxy of InP-based materials on 300 mm Si wafers is the most promising approach to reduce the costs. However, the differences between InP and Si in terms of lattice mismatch, thermal coefficients and polarity inducing defects are challenging issues to overcome. III-V/Si hetero-integration platform by wafer-bonding is the most mature integration scheme. However, no additional epitaxial regrowth steps are implemented after the bonding step. Considering the much larger epitaxial toolkit available in the conventional monolithic InP platform, where several epitaxial steps are often implemented, this represents a significant limitation. In this paper, we review an advanced integration scheme of AlGaInAs-based laser sources on Si wafers by bonding a thin InP seed on which further regrowth steps are implemented. A 3 µm-thick AlGaInAs-based MutiQuantum Wells (MQW) laser structure was grown onto on InP-SiO2/Si (InPoSi) wafer and compared to the same structure grown on InP wafer as a reference. The 400 ppm thermal strain on the structure grown on InPoSi, induced by the difference of coefficient of thermal expansion between InP and Si, was assessed at growth temperature. We also showed that this structure demonstrates laser performance similar to the ones obtained for the same structure grown on InP. Therefore, no material degradation was observed in spite of the thermal strain. Then, we developed the Selective Area Growth (SAG) technique to grow multi-wavelength laser sources from a single growth step on InPoSi. A 155 nm-wide spectral range from 1515 nm to 1670 nm was achieved. Furthermore, an AlGaInAs MQW-based laser source was successfully grown on InP-SOI wafers and efficiently coupled to Si-photonic DBR cavities. Altogether, the regrowth on InP-SOI wafers holds great promises to combine the best from the III-V monolithic platform combined with the possibilities offered by the Si photonics circuitry via efficient light-coupling.

Among non‐destructive testing (NDT) techniques applied to structural health monitoring in existing timber structures, ranging from visual inspection to more sophisticated analysis, acoustic emission (AE) is currently seldomly used to detect mechanical stresses in wooden building assets. This paper presents the results from a systematic literature review on AE NDT applied to monitor micro and macro fracture events in softwood, specifically Scots pine. This survey particularly investigates its application with respect to the tree rings density and grain angle inspection, as influencing factors well correlated with physical and mechanical characteristics of wood. The literature review was performed in a three‐step process defined by the PRISMA (Preferred Reporting Items for Systematic reviews and Meta‐Analyses) flow diagram, leading to the selection of 31 documents from different abstract and citation databases (Scopus, Web of Science and Google Scholar). The outcomes have highlighted how laboratory experiments, including several types of tests (tensile, cutting, compressive, etc.), were conducted in most cases, while a very limited number of studies investigated on in situ monitoring. In addition, theoretical approaches were often explored in parallel with the experimental one. It emerges that—for tree ring density studies—a multi‐technique approach, which may include microscopic observations, could be more informative. Indeed, although not widely investigated, high/low tree ring density and grain angle were found as influencing factors on the AE parameters detected by the sensors, during condition and structural health monitoring experiments.

The spontaneous condensation of wet steam often occurs in the steam ejector nozzle, this deteriorates the performance of the steam ejector. In this paper, we take changing the geometric parameters of the nozzle as the focus of our research and construct an internal connection between steam’s condensation behavior and the nozzle’s throat radius, the nozzle’s divergent section expansion angle, and the nozzle’s divergent section length. Our numerical simulation results indicate that an increase in the throat diameter and reduction of the divergent section’s expansion angle can inhibit steam condensation behavior, to a certain extent. In particular, the steam condensation behavior will disappear at a 0° expansion angle, but it is not affected by the change in the divergent section’s length. In addition, the irreversibility that is seen under different changes to the nozzle’s structure parameters was investigated and the results show that the entropy generation that is caused by a phase change accounts for a much higher proportion of the total entropy generation than heat transport and viscous dissipation do. This indicates that steam’s condensation behavior makes a large amount of irreversible energy, resulting in energy waste and reducing the performance of the nozzle. Therefore, this study can provide a theoretical reference for suppressing the spontaneous condensation behavior of steam by changing the nozzle’s geometry.

Accountability refers to the need of individuals or organizations to account for their activities, accept responsibility, and disclose results in a transparent manner. Nowadays, the pervasivity of digital systems is making increasingly critical security, reliability, and trustworthiness of such services. When a service is delivered by involving different (eventually conflicting) parties, accountability could be achieved by including in digital transactions a trusted third party (TTP). Blockchain decentralizes trust, thus avoiding to rely on a single TTP. However, to deal with accountability in concrete solutions, the issue of securely integrating digital identity and Blockchain should be solved. The paper describes the results of a three-year research project merging academic and industrial expertise, to design and implement a Blockchain-based platform for service accountability integrating eIDAS-compliant Public Digital Identity. The platform has been used in several real-life contexts made available by industrial project partners, which demonstrated the effectiveness and novelty of the solution.

open access article While audio data play an increasingly central role in computer-based music production, interaction with large sound collections in most available music creation and production environments is very often still limited to scrolling long lists of file names. This paper describes a general framework for devising interactive applications based on the content-based visualization of sound collections. The proposed framework allows for a modular combination of different techniques for sound segmentation, analysis, and dimensionality reduction, using the reduced feature space for interactive applications. We analyze several prototypes presented in the literature and describe their limitations. We propose a more general framework that can be used flexibly to devise music creation interfaces. The proposed approach includes several novel contributions with respect to previously used pipelines, such as using unsupervised feature learning, content-based sound icons, and control of the output space layout. We present an implementation of the framework using the SuperCollider computer music language, and three example prototypes demonstrating its use for data-driven music interfaces. Our results demonstrate the potential of unsupervised machine learning and visualization for creative applications in computer music.

Recently, environment-friendly microbial biopolymer has been widely applied as a new construction material in geotechnical engineering practices including soil stabilization, slope protection, and ground injection. Biopolymer is known to exhibit substantial improvements in geotechnical properties, such as shear strength enhancement and hydraulic conductivity reduction, through the formation of direct ionic bonds with soil particles, especially clay particles. Moreover, the rheological characteristics (e.g., pseudoplasticity, shear-rate dependent thixotropy) of biopolymers render distinctive behaviors such as shear thinning and lubrication effect under a high strain condition, while recovering their viscosities and shear stiffnesses when they are at rest. To ensure the practical applicability of biopolymer-based soil treatment, it is important to understand the interfacial interaction (i.e., friction) between biopolymer-treated soil and adjoining structural members which can be constructed in a biopolymer-treated ground. Thus, in this paper, interfacial shearing behavior of biopolymer-treated soil along solid surfaces as well as internal shearing on biopolymer-soil matrix were explored via direct and interface shear test. Experimental results show a predominant effect of the soil moisture content on the interfacial shear behavior of biopolymer-treated soil which attributes to the rheology transition of biopolymer hydrogels. At low moisture content, condensed biopolymer biofilm mobilizes strong intergranular bonding, where the interfacial shear mainly depends on the physical condition along the surface including the asperity angle. In contrast, the biopolymer induced intergranular bonding weakens as moisture content increases, where most interfacial failures occur in biopolymer-treated soil itself, regardless of the interface condition. In short, this study provides an overall trend of the interfacial friction angle and adhesion variations of xanthan gum biopolymer-treated sand which could be referred when considering a subsequent structural member construction after a biopolymer-based ground improvement practice in field.

Despite the number of sailings canceled in the past few months, as demand has increased, the utilization of ships has become very high, resulting in sudden peaks of activity at the import container terminals. Ship-to-ship operations and yard activity at the container terminals are at their peak and starting to affect land operations on truck arrivals and departures. In response, a Truck Appointment System (TAS) has been developed to mitigate truck congestion that occurs between the gate and the yard of the container terminal. The vehicle booking system is developed and operated in-house at large-scale container terminals, but efficiency is low due to frequent truck schedule changes by the transport companies (forwarders). In this paper, we propose a new form of TAS in which the transport companies and the terminal operator cooperate. Numerical experiments show that the efficiency of the cooperation model is better by comparing the case where the transport company (forwarder) and the terminal operator make their own decision and the case where they cooperate. The cooperation model shows higher efficiency as there are more competing transport companies (forwarders) and more segmented tasks a truck can reserve.

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners&rsquo, grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.

[EN] Software programming is one of the key abilities for the development of Computational Thinking (CT) skills in Science, Technology, Engineering and Mathematics (STEM). However, specific software tools to emulate realistic scenarios are required for effective teaching. Unfortunately, these tools have some limitations in educational environments due to the need of an adequate configuration and orchestration, which usually assumes an unaffordable work overload for teachers and is inaccessible for students outside the laboratories. To mitigate the aforementioned limitations, we rely on cloud solutions that automate the process of orchestration and configuration of software tools on top of cloud computing infrastructures. This way, the paper presents ACTaaS as a cloud-based educational resource that deploys and orchestrates a whole realistic software programming environment. ACTaaS provides a simple, fast and automatic way to set up a professional integrated environment without involving an overload to the teacher, and it provides an ubiquitous access to the environment. The solution has been tested in a pilot group of 28 students. Currently, there is no tool like ACTaaS that allows such a high grade of automation for the deployment of software production environments focused on educational activities supporting a wide range of cloud providers. Preliminary results through a pilot group predict its effectiveness due to the efficiency to set up a class environment in minutes without overloading the teachers, and providing ubiquitous access to students. In addition, the first student opinions about the experience were greatly positive., This research was funded by Conselleria d'Innovacio, Universitat, Ciencia i Societat Digital for the project "CloudSTEM" grant number AICO/2019/313, and the Vicerrectorado de Estudios, Calidad y Acreditacion of the Universitat Politecnica de Valencia grant number PIME/19-20/166.

Clinical studies have demonstrated that robot-involved therapy can effectively improve the rehabilitation training effect of motor ability and daily behavior ability of subjects with an upper limb motor dysfunction. This paper presents an impedance-based assist-as-needed controller that can be used in robot-aided rehabilitation training for subjects with an upper extremity dysfunction. Then, the controller is implemented on an end-effector upper extremity rehabilitation robot which could assist subjects in performing training with a spatial trajectory. The proposed controller enables subjects&rsquo, arms to have motion freedom by building a fault-tolerant region around the rehabilitation trajectory. Subjects could move their upper limb without any assistance within the fault-tolerant region while the robot would provide assistance according to the subjects&rsquo, functional ability when deviating from the fault-tolerant region. Besides, we also put forward the stiffness field around the fault-tolerant region to increase the robot&rsquo, s assistance when subjects&rsquo, hand is moving outside the fault-tolerant region. A series of columnar rigid walls would be constructed in the controller according to the subjects&rsquo, functional ability, and the stiffness of the wall increases as the motion performance deteriorates. Furthermore, the controller contains five adjustable parameters. The controller would show different performances by adjusting these parameters and satisfy the requirement of robot-aided rehabilitation training at different rehabilitation stages such as passive, assistant, active, and resistant training. Finally, the controller was tested with an elderly female participant with different controller parameters, and experimental results verified the correctness of the controller and its potential ability to satisfy the training requirements at different rehabilitation stages. In the close future, the proposed controller in this work is planned to be applied on more subjects and also patients who have upper limb motor dysfunctions to demonstrate performance of the controller with different parameters.

Retinal artery&ndash, vein (AV) classification is a prerequisite for quantitative analysis of retinal vessels, which provides a biomarker for neurologic, cardiac, and systemic diseases, as well as ocular diseases. Although convolutional neural networks have presented remarkable performance on AV classification, it often comes with a topological error, like an abrupt class flipping on the same vessel segment or a weakness for thin vessels due to their indistinct appearances. In this paper, we present a new method for AV classification where the underlying vessel topology is estimated to give consistent prediction along the actual vessel structure. We cast the vessel topology estimation as iterative vascular connectivity prediction, which is implemented as deep-learning-based pairwise classification. In consequence, a whole vessel graph is separated into sub-trees, and each of them is classified as an artery or vein in whole via a voting scheme. The effectiveness and efficiency of the proposed method is validated by conducting experiments on two retinal image datasets acquired using different imaging techniques called DRIVE and IOSTAR.

Automatically fixing bugs in software programs can significantly reduce the cost and improve the productivity of the software. Toward this goal, a critical and challenging problem is automatic fix localization, which identifies program locations where a bug fix can be synthesized. In this paper, we present AgxFaults, a technique that automatically identifies minimal subsets of program statements at which a suitable modification can remove the error. AgxFaults works based on dynamically encoding semantic of program parts that are relevant to an observed error into an unsatisfiable logical formula and then manipulating this formula in an increasingly on-demand manner. We perform various experiments on faulty versions of the traffic collision avoidance system (TCAS) program in the Siemens Suite, programs in Bekkouche&rsquo, s benchmark, and server real bugs in the Defects4J benchmark. The experimental results show that AgxFaults outperforms single-path-formula approaches in terms of effectiveness in finding fix localization and fault localization. AgxFaults is better than program-formula-based approaches in terms of efficiency and scalability, while providing similar effectiveness. Specifically, the solving time of AgxFaults is 28% faster, and the running time is 45% faster, than the program-formula-based approach, while providing similar fault localization results.

Producción Científica, Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach., Consejo Nacional de Desarrollo Científico y Tecnológico - (processes 474290/2008-5, 473576/2011-2, 552269/2011-5, 201902/2015-0 and 405228/2016-3)

In recent years, many researchers have focused on developing a feasible solution for storing and exchanging medical images in the field of health care. Current practices are deployed on cloud-based centralized data centers, which increase maintenance costs, require massive storage space, and raise privacy concerns about sharing information over a network. Therefore, it is important to design a framework to enable sharing and storing of big medical data efficiently within a trustless environment. In the present paper, we propose a novel proof-of-concept design for a distributed patient-centric image management (PCIM) system that is aimed to ensure safety and control of patient private data without using a centralized infrastructure. In this system, we employed an emerging Ethereum blockchain and a distributed file system technology called InterPlanetary File System (IPFS). Then, we implemented an Ethereum smart contract called the patientcentric access control protocol to enable a distributed and trustworthy access control policy. IPFS provides the means for decentralized storage of medical images with global accessibility. We describe how the PCIM system architecture facilitates the distributed and secured patient-centric data access across multiple entities such as hospitals, patients, and image requestors. Finally, we deployed a smart contract prototype on an Ethereum testnet blockchain and evaluated the proposed framework within the Windows environment. The evaluation results demonstrated that the proposed scheme is efficient and feasible., 18 Pages, 12 Figures, 2 Tables. Included detailed analysis of the proposed framework. Submitted to MDPI-Applied Science

Contacts between robots and environment are often assumed to be rigid for control purposes. This assumption can lead to poor performance when contacts are soft and/or underdamped. However, the problem of balancing on soft contacts has not received much attention in the literature. This paper presents two novel approaches to control a legged robot balancing on visco-elastic contacts, and compares them to other two state-of-the-art methods. Our simulation results show that performance heavily depends on the contact stiffness and the noises/uncertainties introduced in the simulation. Briefly, the two novel controllers performed best for soft/medium contacts, whereas &ldquo, inverse-dynamics control under rigid-contact assumptions&rdquo, was the best one for stiff contacts. Admittance control was instead the most robust, but suffered in terms of performance. These results shed light on this challenging problem, while pointing out interesting directions for future investigation.

This paper investigates the potential of a novel vibration-based thermal health monitoring method for continuous and on-board damage detection in fiber reinforced polymer sandwich structures, as typically used in aerospace applications. This novel structural health monitoring method uses the same principles, which are used for vibration-based thermography in combination with the concept of the local defect resonance, as a well known non-destructive testing method (NDT). The use of heavy shakers for applying strong excitation and infrared cameras for observing thermal responses are key hindrances for the application of vibration-based thermography in real-life structures. However, the present study circumvents these limitations by using piezoelectric wafer active sensors as excitation source, which can be permanently bonded on mechanical structures. Additionally, infrared cameras are replaced by surface temperature sensors for observing the thermal responses due to vibrations and damage. This makes continuous and on-board thermal health monitoring possible. The new method is experimentally validated in laboratory experiments by a sandwich structure with face layer debonding as damage scenario. The debonding is realized by introduction of an insert during the manufacturing process of the specimen. The surface temperature sensor results successfully show the temperature increase in the area of the debonding caused by a sinusoidal excitation of the sandwich structure with the PWAS at the first resonance frequency of the damage. This is validated by conventional infrared thermography. These findings demonstrate the potential of the proposed novel thermal health monitoring method for detecting, localizing and estimating sizes of face layer debonding in sandwich structures.

Exoskeletons are an essential part of humankind’s future. The first records regarding the subject were published several decades ago, and the field has been expanding ever since. Their developments will be imperative for humans in the coming decades due to our constant pursuit of physical enhancement, and the physical constraints the human body has. The principal purpose of this article is to formalize research in the field of exoskeletons and introduce the field to more researchers in hopes of expanding research in the area. Exoskeletons can assist and/or aid in the rehabilitation of a person. Recovery exoskeletons are mostly used in medical and research areas; performance exoskeletons can be used in any area. This systematic review explains the precedents of the exoskeletons and gives a general perspective on their general present-day use, and provides a general categorization model with a brief description of each category. Finally, this paper provides a discussion of the state-of-the-art, and the current control techniques used in exoskeletons.

Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications.

Horizontal loads such as earthquake and wind are considered dominant loads for the design of tall buildings. One of the most efficient structural systems in this regard is the tube structural system. Even though such systems have a high resistance when it comes to horizontal loads, the shear lag effect that is characterized by an incomplete and uneven activation of vertical elements may cause a series of problems such as the deformation of internal panels and secondary structural elements, which cumulatively grow with the height of the building. In this paper, the shear lag effect in a typical tube structure will be observed and analyzed on a series of different numerical models. A parametric analysis will be conducted with a great number of variations in the structural elements and building layout, for the purpose of giving recommendations for an optimal design of a tube structural system.

For a long time, many methods are developed to make temporal signal analyses based on time series. However, for geographical systems, spatial signal analyses are as important as temporal signal analyses. Nonstationary spatial and temporal processes are associated with nonlinearity, and cannot be effectively analyzed by conventional analytical approaches. Fractal theory provides a powerful tool for exploring complexity and is helpful for spatio-temporal signal analysis. This paper is devoted to researching spatial signals of geographical systems by means of wave-spectrum scaling. The traffic networks of 10 Chinese cities are taken as cases for positive studies. Fast Fourier transform and least squares regression analysis are employed to calculate spectral exponents. The results show that the wave-spectral density distribution of all these urban traffic networks follows scaling law, and the spectral scaling exponents can be converted to fractal dimension values. Using the fractal parameters, we can make spatial analyses for the geographical signals. The analytical process can be generalized to temporal signal analyses. The wave-spectrum scaling methods can be applied to both self-similar fractal signals and self-affine fractal signals in the geographical world., 22 pages, 7 figures, 4 tables

We describe the Wearable Sensory Apparatus (WSA) System, which has been implemented and verified in accordance with the relevant standards. It comprises the Inertial Measurement Units (IMUs), real-time wireless data transmission over Ultrawideband (UWB), a Master Unit and several IMU dongles forming the Wireless Body Area Network (WBAN). The WSA is designed for, but is not restricted to, wearable robots. The paper focuses on the topology of the communication network, the WSA hardware, and the organization of the WSA firmware. The experimental evaluation of the WSA incorporates the confirmation of the timing using the supply current WSA profile, measurements related to determining the less error prone position of the master device on the backpack, measurements of the quality of the data transfer in a real environment scenario, measurements in the presence of other microwave signals, and an example of raw IMU signals during human walking. Placement of the master device on the top of the backpack was found to be less error prone, with less than 0.02% packet loss for all the IMU devices placed on different body segments. The packet loss did not change significantly in public buildings or on the street. There was no impact of Wi-Fi bands on the WSA data transfer. The WSA hardware and firmware passed conformance testing in a certified lab. Most importantly, the WSA performed reliably in the laboratory and in clinical tests with exoskeletons and prostheses.

Sleeve valve pipe grouting, an effective method for reinforcing soil layers, is often employed to correct the deformation of subway tunnels. In order to study the effect of grouting on rectifying the displacement of existing tunnels, this paper proposes a mechanical model of the volume expansion of sleeve valve pipe grouting taking into consideration the volume expansion of the grouted soil mass. A formula for the additional stress on the soil layer caused by grouting was derived based on the principle of the mirror method. In addition, a formula for the horizontal displacement of a tunnel caused by grouting was developed through a calculation model of shearing dislocation and rigid body rotation. The results of the calculation method proposed herein were in good agreement with actual engineering data. In summary, enlarging the grouting volume within a reasonable range can effectively enhance the grouting corrective effect. Further, with an increase in the grouting distance, the influence of grouting gradually lessens. At a constant grouting length, setting the bottom of the grouting section at the same depth as the lower end of the tunnel can maximize the grouting corrective effect.

In this paper, we propose a new multiple-input multiple-output (MIMO) transmission scheme, called parallel complex quadrature spatial modulation (PCQSM). The proposed technique is based on the complex quadrature spatial modulation (CQSM) to further increase the spectral efficiency of the communication system. CQSM transmits two different complex symbols at each channel use. In contrast with CQSM, the new transmission scheme splits the transmit antennas into groups, and modulates the two signal symbols using the conventional CQSM before transmission. Based on the selected modulation order and the number of possible groups that can be realized, the incoming bits modulate the two signal symbols and the indices of the transmit antennas in each group. We demonstrated that while the complexity and performance of the proposed scheme is the same as that of CQSM, the number of required transmit antennas is significantly reduced. The proposed PCQSM achieves such a benefit without requiring any additional radio frequency (RF) chains. The results obtained from Monte Carlo simulation showed that at a Bit Error Rate (BER) of 10&minus, 4, the performance of the PCQSM with two antenna groups closely matches that of CQSM, and outperformed quadrature spatial modulation (QSM) and parallel quadrature spatial modulation (PQSM) by over 0.7 dB. As the number of antenna groups increased to 4, the BER performance of PCQSM with reduced number of transmit antenna and modulation order matches that of QSM. The BER of the proposed scheme using maximum likelihood (ML) receiver is also analyzed theoretically and compared with the BER obtained via simulations.

The demand for electric machines has increased in the last decade, mainly due to applications that try to make a full transition from fuel to electricity. These applications encounter the need for tailor-made electric machines that must meet demanding requirements. Therefore, it is necessary for small-medium companies to adopt new technologies offering customized products fulfilling the customers&rsquo, requirements according to their investment capacity, simplify their development process, and reduce computational time to achieve a feasible design in shorter periods. Furthermore, they must find ways to retain know-how that is typically kept within each designer to retrieve it or transfer it to new designers. This paper presents a framework with an implementation example of a knowledge-based engineering (KBE) system to design industrial electric machines to support this issue. The devised KBE system groups the main functionalities that provide the best outcome for an electric machine designer as development-process traceability, knowledge accessibility, automation of tasks, and intelligent support. The results show that if the company effectively applies these functionalities, they can leverage the attributes of KBE systems to shorten time-to-market. They can also ensure not losing all knowledge, information, and data through the whole development process.

The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies.

The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm&rsquo, s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar&rsquo, s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm&mdash, a new element-based topology optimization method capable of forming smooth boundaries&mdash, to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem&mdash, the force inverter design&mdash, is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar&rsquo, s AM filter.

Different from conventional imaging, ghost imaging (GI) is an indirect modality of imaging that needs multiple measurements of the second-order correlation of data collected from two detectors. In some particular cases, the exposure time of two detectors or the rotation speed of the ground glass may not meet the need of experimental condition, resulting in motion blur that reduces the quality of the reconstructed image. In this paper, we propose a method to solve this problem. By convolving the data from the reference arm with the Hessian matrix, the intensity of the light in the data is replaced by the gradient of intensity and the influence of the motion blur in the reconstructed image can be reduced.

Unmanned aerial vehicles (UAVs) play a key role in modern surveillance-related missions. A major task for performing these missions is to find the precise location of a moving target in real-time, for which the main challenge is to estimate the target position to high precision using the noisy measurements from the airborne sensors. In this paper, we present a closed-form on-line simultaneous target localization and UAV trajectory optimization method based on the visual platform, which can effectively minimize the localization uncertainty to the target. The proposed method can be elucidated explicitly using two phases, of which, in the target localization phase, the expended information filtering (EIF) is exploited, which can express the predicted Fisher information matrix (FIM) of the target explicitly and iteratively, and in the UAV trajectory optimization phase, the property of the predicted FIM is exploited to establish the UAV waypoint optimization objective by taking account of the UAV motion limit. Compared with existing methods of the same class, the proposed method not only estimates the next target position more correctly, but also takes the error of the target motion into consideration, thus improving the effectiveness of the optimized UAV trajectory. Both simulations and field experiments were conducted, which show that the proposed method outperformed the existing methods.

Short fiber reinforcement is added to concrete materials to improve a variety of performance measures related to structural safety, serviceability, and health diagnosis. Mesoscale models have been developed to understand the individual and collective actions of these fibers on various material properties. Those modeling efforts have predominately focussed on the mechanical (i.e., stiffness and strength) contributions of fibers. This paper introduces computationally efficient, semi-discrete representations of fibers within coupled mechanical and transport processes in cement-based matrices. Basic simulations are done to study: the use of conductive fibers for self-sensing, and the influences of fibers on early-age plastic settlement. It is found that the models can account for directional bias on fiber orientation, as may occur during material casting. With respect to plastic settlement, fibers may play competing roles: mechanical restraint offered by the fibers reduces settlement, whereas enhanced hydraulic conductivity along the fiber&ndash, matrix interface may increase settlement by facilitating the bleeding process.

In light of digitalisation, we are witnessing an increased volume of collected data and data generation and exchange acceleration. Therefore, the European Union (EU) has introduced the General Data Protection Regulation (GDPR) as a new framework for data protection on the European level. However, GDPR allows the member states to change some parts of the regulation, and the member states can always build on top of the GDPR. An example is the collection of biometric data with electronic signatures. This paper aims to compare the legislation on data protection topics in the various EU member states. The findings show that the member states included in the study generally do not have many additional/specific laws (only in 29.4% of the cases). However, almost all have other/additional legislation to the GDPR on at least one topic. The most additional legislation is on the topics of video surveillance, biometry, genetic data and health data. We also introduce a dynamic map that allows for quick navigating between different information categories and comparisons of the EU member states at a glance.

This paper presents a novel meta-functional auxetic unit (MFAU) cell designed to improve performance and weight ratio for structural bridge bearing applications. Numerical investigations were conducted using three-dimensional finite element models validated by experimental results. The validated models were exposed to compression and buckling actions to identify structural failure modes, with special attention placed on the global behaviours of the meta-functional auxetic (MFA) composite bridge bearing. This bearing uses an unprecedented auxetic sandwich core design consisting of multiple MFAU cells. Numerical predictions of the elastic local critical buckling loads of the MFAU cell were in excellent agreement with both the analytical and experimental results, with an observed discrepancy of less than 1%. These results demonstrate that local buckling failures of MFAU cells can potentially be incurred prior to yielding under compression due to their slenderness ratios. Surprisingly, the designed sandwich core used in the MFA composite bridge bearing model can mimic an auxetic structure with significant crashworthiness, implying that this novel core composite structure can be tailored for structural bridge bearing applications. Parametric studies were thus carried out in order to enrich our insight into the MFA composite elements. These insights, stemming from both experimental and numerical studies, enable a novel design paradigm for MFAU that can significantly enhance the structural performance of MFA composite bridge bearings in practice.

Discretization and feature selection are two relevant techniques for dimensionality reduction. The first one aims to transform a set of continuous attributes into discrete ones, and the second removes the irrelevant and redundant features, these two methods often lead to be more specific and concise data. In this paper, we propose to simultaneously deal with optimal feature subset selection, discretization, and classifier parameter tuning. As an illustration, the proposed problem formulation has been addressed using a constrained many-objective optimization algorithm based on dominance and decomposition (C-MOEA/DD) and a limited-memory implementation of the warping longest common subsequence algorithm (WarpingLCSS). In addition, the discretization sub-problem has been addressed using a variable-length representation, along with a variable-length crossover, to overcome the need of specifying the number of elements defining the discretization scheme in advance. We conduct experiments on a real-world benchmark dataset, compare two discretization criteria as discretization objective, namely Ameva and ur-CAIM, and analyze recognition performance and reduction capabilities. Our results show that our approach outperforms previous reported results by up to 11% and achieves an average feature reduction rate of 80%.

The current state of the web, which is dominated by centralized cloud services, raises several concerns regarding different aspects such as governance, privacy, surveillance, and security. A way to address these issues is to decentralize the platforms by adopting new distributed technologies, such as IPFS and Blockchain, which follow a full peer-to-peer model. This work proposes a set of guidelines to design decentralized systems, taking the different trade-offs these technologies face with regard to their consistency requirements into consideration. These guidelines are then illustrated with the design of a decentralized questions and answers system. This system serves to illustrate a framework to create decentralized services and applications that uses IPFS and Blockchain technologies and incorporates the discussion and guidelines of the paper, providing solutions for data access, data provenance, and data discovery. Thus, this work proposes a framework to assist in the design of new decentralized systems, proposing a set of guidelines to choose the appropriate technologies depending on the relevant requirements, e.g., considering if Blockchain technology may be required or IPFS might be sufficient.

In the present paper, the numerical modeling of an innovative latent thermal energy storage unit, suitable for direct integration into the condenser or evaporator of a heat pump is presented. The Modelica language, in the Dymola environment, and TIL libraries were used for the development of a modular model, which is easily re-usable and adaptable to different configurations. Validation of the model was carried out using experimental data under different operating modes and it was subsequently used for the optimization of a design for charging and discharge. In particular, since the storage unit is made up of parallel channels for the heat transfer fluid, refrigerant, and phase change material, their number and distribution were changed to evaluate the effect on heat transfer performance.

In this paper, we consider two mechanisms capable of inducing strain localisation in particulate geomaterials in compression: the apparent negative stiffness and the incremental incompressibility caused by dilatancy. It is demonstrated that the apparent negative stiffness can be produced by the rotation of clusters of particles in the presence of compression. The clusters are formed by connecting the particles by the bonds that still remain intact in the process of bond breakage in compression. We developed a 2D isotropic model of incremental incompressibility showing that a single strain localisation zone is formed inclined at 45° to the direction of axial compressive loading. This mechanism of localisation was analysed through Particle Flow Code (PFC) 2D and 3D simulations. It is shown that, in the simulations, the peak stress (the onset of localisation) does correspond to the incremental Poisson’s ratio, reaching the critical values of 1 (in 2D) and 0.5 (in 3D).

Fire is a major decay agent of rocks and can generate immediate catastrophic effects as well as directional and anisotropic damage that affect long-term weathering processes. Temperature increase is the most relevant factor, among other components in a fire, generating mineral transformations and bulk mechanical damage. Mineralogical changes at high temperatures are key to understanding the overall mechanical behaviour. However, most studies to date were carried out after rock specimens were heated to a target temperature and cooled down to room temperature. Therefore, these studies are missing the observation of the actual mineral processes during heating. This paper aims to compare mineralogical changes in crystalline rocks during heating by means of XPS and different XRD techniques. Samples of four different granitoids were heated to several temperatures up to 1000 °C to evaluate their chemical and structural changes. Results show how standardised thermal expansion coefficients are not a suitable indicator of the bulk effect of high temperatures on rocks. Results also show how thermal expansion estimations from XRD lattice measurements may be an alternative to bulk dilatometric tests, as they can be performed with limited sampling, which may be necessary in some studies. Nevertheless, XRD and XPS results need to be interpreted carefully in relation to the bulk effects of temperature increase in the rocks, as the structural behaviour may seemingly contradict the macroscopic effect. This research was funded by “Top Heritage (P2018/NMT-4372) programme from the Regional Government of Madrid (Spain)”, “Grants PIC2020-116896RB-C21”, “PIC2020-116896RB-C22” funded by MCIN/AEI/ 10.13039/501100011033,” Grant AICO/2020/175” by the Regional government of Comunidad Valenciana (Spain).

International audience; Thermal energy storage (TES) using phase change materials (PCMs) is an innovative approach to meet the growth of energy demand. Microencapsulation techniques lead to overcoming some drawbacks of PCMs and enhancing their performances. This paper presents a comprehensive review of studies dealing with PCMs properties and their encapsulation techniques. Thus, it is essential to critically examine the existing techniques and their compatibility with different types of PCMs, coating materials, and the area of application. The main objective of this review is to describe each microencapsulation process and to determine different factors that influence the performance of resulting microcapsules. Microencapsulation efficiency, as well as the limitation of each technique, are investigated, and optimum operating conditions of each process are highlighted. Furthermore, up-to-date studies of multifunctional PCMs microcapsules development with enhanced performances and new application directions are also presented. This review aims to be a useful guide for future researches dealing with low thermal energy storage applications of PCMs microcapsules.

This paper investigates the aeroacoustic interactions of small hovering rotors, using both experiments and computations. The experiments were conducted in an anechoic chamber with arrays of microphones setup to evaluate the azimuthal and polar directivity. The computational methodology consists of high fidelity detached eddy simulations coupled to the Ffowcs-Williams and Hawkings equation, supplemented by a trailing edge broadband noise code. The aerodynamics and aeroacoustics of a single rotor are investigated first. The simulations capture a Reynolds number effect seen in the performance parameters that results in the coefficient of thrust changing with the RPM. The acoustic analysis enables the identification of self-induced noise sources. Next, dual side-by-side rotors are studied in both counter-rotating and co-rotating configurations to quantify the impact of their interactions. Higher harmonics appear due to the interactions and it is verified that the counter-rotating case leads to more noise and a less uniform azimuthal directivity. Difficulties that arise when trying to validate small rotor calculations against experiments are discussed. Comparisons of computational and experimental results yield further insight into the noise mechanisms that are captured by each methodology.