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RESUMO A predominância do ingles como lingua franca nas ciencias e a pressao para elevar o ‘ranking’ internacional das universidades brasileiras intensificaram a demanda por apoio ao desenvolvimento de uma serie de letramentos academicos em ingles, por exemplo, em areas como a apresentacao de pesquisas em conferencias internacionais. Os desafios para fornecer apoio direcionado ao desenvolvimento de tais letramentos incluem espaco limitado no curriculo, negociacao de horarios regulares para reunioes entre estudantes e instrutores ocupados e o fato de que grandes instituicoes geralmente tem campus geograficamente dispersos. O ‘blended learning’ oferece uma possibilidade de enfrentar esses desafios atraves da flexibilidade oferecida pelo modo de ministrar o curso, que e parcialmente presencial e parcialmente ‘on-line’ e assincrono. Este artigo relata o desenho, o ensino e a avaliacao de duas edicoes de um curso em habilidades de apresentacao em conferencias, oferecido a estudantes de pos-graduacao na Faculdade de Medicina da Universidade de Sao Paulo em 2018-19. O artigo discute as possibilidades, limitacoes e desafios continuos do ‘blended learning’ no apoio a letramentos academicos em um curso projetado para aumentar as habilidades de apresentacao em ingles dos estudantes de medicina. ABSTRACT The predominance of English as a scientific lingua franca and the pressure to raise the international ranking of Brazilian universities have increased the demand for support in developing a range of academic literacies in English, for example in areas such as presenting one’s research at international conferences. The challenges to delivering targeted support for the development of such literacies include limited space in the curriculum, negotiating regular meeting times for busy students and instructors, and the fact that large institutions often have geographically dispersed campuses. Blended learning offers a possible means of addressing these challenges in the flexibility afforded by course delivery that is partly face-to-face and partly online and asynchronous. This paper reports on the design, delivery and evaluation of two editions of a course in conference presentation skills, delivered to postgraduate students in the Faculty of Medicine at the University of Sao Paulo in 2018-19. The paper discusses the affordances, limitations and continuing challenges of blended learning in support of academic literacies designed to enhance medical students’ presentational skills in English.

Artificial Intelligence Based Internet of Things (AI-IoT enables autonomous communication among social networks and IoT to leverage the promising solution in the modern paradigms. It can provide an interactive platform across the globe to enrich the quality of networking services to the end-users. Of late, the expansion of Information-centric networking has brought an incredible technique known as public auditing scheme for IoT-enabled sensors technologies. It uses a cloud-based medical cyber-physical system (M-CPS to rely on cloud-computing that ensures fast computing and reliable data storage. To exploit proxy authorizer and trusted auditor, an identity-based proxy authorized outsourcing with public auditing (ID-PAOPA is proposed. It uses proxy authorization and verification to upload medical data over cloud-based M-CPS. To substantiate the finding, this paper provides security proof based on the EC-DLP assumption. Lastly, the performance analysis proves that the proposed ID-PAOPA achieves less computation and auditing timing to fulfill the objectives of cloud-based M-CPS.

In this article, the problem of tracking control is considered for a class of uncertain strict-feedback nonlinear systems with deferred asymmetric time-varying full-state constraints. A novel adaptive robust full-state-constrained control scheme is developed. First, by introducing a novel shifting function, the original constrained system with any initial values is modified to a new constrained system, and the initial values of the modified constrained system remain 0. Then, to remove the feasibility condition caused by the barrier Lyapunov functions, the modified constrained system is further transformed into a new unconstrained system by a brand new nonlinear transformation. Furthermore, the tracking error system of the unconstrained system is constructed by using a new coordinate transformation, and a novel adaptive full-state-constrained control scheme is designed based on this error system through the backstepping recursion method and first-order filters. Finally, the resulting closed-loop system proves to be stable and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

In this study, we propose an adaptive fuzzy eventtriggered control scheme for an autonomous aerial refueling hose system involving uncertainty, an event-triggered mechanism, and actuator failures. The unknown nonlinear function is approximated using the designed fuzzy logic systems. Through introduction of the adaptive compensation scheme, the problem of an infinite number of actuator failures, including partial and complete failures, is solved. In addition, the event-triggered control strategy is designed to achieve vibration suppression while decreasing the communication burden between the controllers and actuators. The stability of the closed-loop system is demonstrated via the Lyapunov direct method. Finally, simulation examples are presented to confirm the validity of the proposed control scheme.

Human Cyber-physical Systems (HCPSs) provide accurate and high-quality services for industry 5.0. In HCPSs, machine learning (ML) prediction provides reliable prediction results for users based on matured models, while security and privacy protection are considerable issues. Based on the modified Okamoto-Uchiyama homomorphic encryption, we propose a verifiable privacy-preserving machine learning prediction scheme for the edge-enhanced HCPSs, which outputs the verifiable prediction results for users without privacy leakage. A batch of prediction results can be verified at one time, which improves the efficiency of verification. Security analysis shows that our scheme protects the privacy of inputs, ML models and prediction results. The experiment results demonstrate that the edge computing architecture greatly alleviates the computational burden of the cloud server. Furthermore, compared with other related schemes, our scheme shows the best execution efficiency. The batch verification can optimize the performance by about 15% compared with single verification on the same scale.

In this work, a reverse saliency (RS) spoke-type permanent magnet (PM) synchronous motor (PMSM) besides its hybrid adaptive notch filter (HANF) based self-sensing for driving a solar photovoltaic water pump system (WPS) is presented. The conventional spoke-PMSM driven WPS experiences two types of problems. Firstly, the motor is operated with a flux-weakening current to produce positive reluctance torque, thereby increasing the PM demagnetization risk. Secondly, an encoder is used to sense the rotor angle, which in turn affects the cost and reliability. To solve the first problem, the RS-PMSM is presented, which operates with a flux-intensifying current to produce positive reluctance torque. While second issue is resolved by using the HANF based rotor angle estimation. Although conventional methods give an estimate of rotor angle, they do not eliminate both the DC-offset and dominant harmonics. The superiority of both the RS-PMSM and HANF over existing solutions, is shown respectively by using finite-element methods and MATLAB/ Simulink based performances. Besides, a prototype motor is also manufactured. The performance of RS-PMSM prototype along with its HANF based self-sensing is obtained in the laboratory and various test results are reported. Meanwhile, a comparison of the HANF with available self-sensing techniques, is also performed.

This paper investigates the second-order consensus issue for multi-agent systems subject to both limited communication resources and replay attacks. First, an asynchronous dynamic edge event-triggered (DEET) communication scheme is developed to reduce the utilization of network resources in the absence of attacks. Then, we further consider the case of replay attacks launched by multiple adversaries, under which the transmitted information is maliciously replaced by a previous unnecessary message. To overcome the impact caused by replay attacks, a modified DEET scheme and an effective attack-resilient consensus protocol are well constructed, both of which successfully guarantee second-order consensus in the presence of replay attacks. In addition, internal dynamic variables are utilized in the proposed DEET schemes such that the triggering time sequence does not exhibit Zeno behavior. Finally, one numerical example is provided to illustrate our theoretical analysis.

We propose a dynamic investment model to study investment behavior for power grid-stabilizing, flexibility-providing energy projects. Our model adds to the literature in several novel ways. First, we use time series-based optimization approaches (i.e., a single-unit commitment model) to determine, validate and parametrize exponential Ornstein–Uhlenbeck (OU) revenues as a state variable for the real options model in a biogas plant flexibilization case study. Second, from a theoretical perspective, our findings suggest that for specific revenue processes (i.e., exponential OU processes) the well-known option-driven incentive to wait acts alongside a new uncertainty-driven intrinsic investment incentive. Hence, higher uncertainty may lead to earlier investment. Third, we show how the results influence managerial decision making in practice and can be used to shape the design of an optimal renewable support scheme.

Traditional cryptographic approaches may not always be suitable for cyber-physical systems (CPSs). In this correspondence, we present a control-theoretic approach allowing a networked controller and a smart actuator of a stochastic CPS to agree on a common secret key without resorting to classical cryptographic approaches. More precisely, by utilizing the asymmetry in the system model knowledge available to the control system defender and to the eavesdropper, we propose a key-agreement scheme utilizing a simultaneous input and state estimation algorithm. The validity of the proposed solution is shown through a numerical example.

To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that are used to encrypt the entire EMRs can hide the sensitive information, but results in the shared EMRs unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis even endanger patients' life. In order to solve the above problems, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted while other contents in EMR can be shared among users in this scheme. The doctors and researchers can quickly perform search operations based on keyword to extract the EMRs they requires. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud.

Modular multilevel converters (MMCs) are characterized by the multi-harmonic coupling interactions among internal dynamics. In this work, multiple d-q references and multi-harmonic frames, which are developed through dynamic phasor and harmonic state-space, are used to derive the generalized impedance models of MMC in the d-q and modified sequence domain, respectively. Although some previous researches have been devoted to revealing the equivalence of different impedance models, this paper attempts to elaborate the relation of different multi-frequency modeling approaches, and to provide a more comprehensive review of MMC impedance models. A rigorous comparison of modeling procedure, impedance response and stability analysis result between different MMC models is conducted. It is demonstrated that the MMC impedance models in the two domains can be related by a generic linear transformation matrix. Furthermore, the stability analysis results given by generalized Nyquist criterion are identical in these two domains. Application recommendations for different impedance models are given as well. The developed impedance models and their effectiveness for stability and resonance analysis are validated by a 501-level aggregated model in MATLAB/SIMULINK.

In recent years, Python has experienced explosive growth in adoption, particularly among open source projects. While Python's dynamically-typed nature provides developers with powerful programming abstractions, that same dynamic type system allows for type-related defects to accumulate in code bases. To aid in the early detection of type-related defects, type annotations were introduced into the Python ecosystem (i.e., PEP-484) and static type checkers like mypy have appeared on the market. While applying a type checker like mypy can in theory help to catch type-related defects before they impact users, little is known about the real impact of adopting a type checker to reveal defects in Python projects. In this paper, we study the extent to which Python projects benefit from such type checking features. For this purpose, we mine the issue tracking and version control repositories of 210 Python projects on GitHub. Inspired by the work of Gao et al. on type-related defects in JavaScript, we add type annotations to test whether detects an error that would have helped developers to avoid real defects. We observe that 15% of the defects could have been prevented by mypy. Moreover, we find that there is no significant difference between the experience level of developers committing type-related defects and the experience of developers committing defects that are not type-related. In addition, a manual analysis of the anti-patterns that most commonly lead to type-checking faults reveals that the redefinition of Python references, dynamic attribute initialization and incorrectly handled Null objects are the most common causes of type-related faults. Since our study is conducted on fixed public defects that have gone through code reviews and multiple test cycles, these results represent a lower bound on the benefits of adopting a type checker. Therefore, we recommend incorporating a static type checker like mypy into the development workflow, as not only will it prevent type-related defects but also mitigate certain anti-patterns during development.

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources over the communicating users. In this scheme, classification learning is used for identifying replicas and access granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.

This paper proposes a novel optimized Proportional-Integral-Derivative-Acceleration (PIDA) controlled static synchronous compensator (STATCOM) to improve the frequency response of power systems through reactive power modulation. The proposed controller can maintain the system frequency within permissible limits upon load change or generation loss. The gains of the PIDA controller are fine-tuned using the Marine Predator Algorithm (MPA), which is a newly metaheuristic optimization technique. Moreover, the performance of the MPA-PIDA controlled STATCOM is tested in the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. The results show that the MPA-PIDA controller can provide better frequency response compared with the multi-band controller presented in the previous literature. In addition, the robustness of the proposed controller is verified when case studies are integrated with wind generation. The results show that the primary frequency response is greatly improved by using the MPA-PIDA controlled STATCOM in all studied cases. Moreover, the results are given in the form of time domain simulations using MATLAB/SIMULINK.

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme.

This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.

For data-driven dynamic stability assessment (DSA) in power systems, learning cases collected from actual historical records appear to be more reliable than those obtained from numerical simulations with an inevitable reality gap. However, due to the scarceness of transient events in practical systems, historical case sets generally encounter the small sample size and class imbalance problems. To tackle these challenging issues, this paper proposes a novel data/model jointly driven framework to generate high-quality cases for power system DSA applications. Model-driven numerical simulations are first utilized for rough case generation, based upon which case refinement is then intelligently carried out via cycle generative adversarial network (CycleGAN) learning. In this data-driven manner, the CycleGAN is able to produce refined cases highly resembling actual historical ones. A long short-term memory based semi-supervised learning scheme is further designed to reliably label all the refined cases. Numerical tests are comprehensively carried out on the realistic Guangdong Power Grid in South China. With only a small and skewed historical case set initially provided, the proposed framework is able to generate highly realistic cases to augment the set and mitigate the class imbalance issue. These synthetic cases further help derive a more discerning DSA model, which contributes to enhanced reliability and adaptability of online DSA in practical power grids.

The fixed-time control problem for a class of interconnected systems is studied in this article via an asynchronous event-triggered control strategy, in which all subsystems are interconnected by discontinuous interactions. Especially, due to the discontinuity of interactions, the existence of solutions for the concerned systems is solved via the framework of differential inclusion. Unlike the common ones, the asynchronous event-triggered mechanism in this article generates samplings, triggering events, and control updates asynchronously among multiple subsystems. More significantly, due to the discontinuity of the interactions, a fixed-time discontinuous control law and a specific event-triggered scheme are designed to stabilize this class of interconnected systems in a fixed time. In addition, the interexecution time is lower bounded by a positive constant, and it is assured that the Zeno behavior will not happen. Finally, the effectiveness of the obtained results is illustrated by an example.

This paper investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First of all, an uncertain Takagi-Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization and dropouts, are taken into account to improve the robustness of the filtering method. Based on Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov-Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results.

This article studies the H∞ exponential synchronization problem for complex networks with quantized control input. An aperiodic sampled-data-based event-triggered scheme is introduced to reduce the network workload. Based on the discrete-time Lyapunov theorem, a new method is adopted to solve the sampled-data problem. In view of the aforementioned method, several sufficient conditions to ensure the H∞ exponential synchronization are acquired. Numerical simulations show that the proposed control schemes can significantly reduce the amount of transmitted signals while preserving the desired system performance.

Predictive Current Control (PCC) is an emerging technique offering advantages like easy inclusion of multi-variable objectives, non-linear constraints and excellent dynamic performance. In the conventional PCC (CPCC), the computational complexity is increased with the increase in the number of voltage vectors (VV). As the control action gets updated in every control interval, the deployment of fast-processors with large data-handling capability is inevitable, which limits the real-time application of PCC scheme. Further, applying a single VV for the complete sample period causes an increase in the ripples in torque and flux under steady-state operation. This paper introduces a duty-modulated PCC scheme with low complexity for the PMSM drive, to address the above limitations. The proposed scheme utilizes only three VVs in every sample interval, unlike the CPCC, where seven VVs are employed. This yields a reduction in the number of computations without forfeiting the drive performance. Subsequently, an effective duty modulation scheme is employed to improve the performance of PMSM drive under steady-state, where the application of active and zero VV aid to minimize the flux and torque ripples. In order to affirm the effectiveness of the proposed scheme, comprehensive comparisons are performed with the CPCC and recent literature.

Abnormal violent behavior by people with mental disorders is common. When patients with mental disorders make some abnormal behaviors in public places, they may cause physical and mental harm to others and themselves. Thus, it is necessary to monitor the behavior of human with mental disorders under surveillance video. However, it is a comprehensive challenge to detect abnormal behavior of human (especially patients with mental disorders) based on abnormal detection and motion recognition technology. To address these issues, in this paper, we propose an end-to-end abnormal detection framework from a new perspective in conjunction with the Graph Convolutional Network (GCN) and a 3D Convolutional Neural Network (CNN). Specifically, we first train a one-class classifier to extract features and forecast abnormal scores in the framework. To improve the performance in abnormal behavior detecting, GCN is used to start modeling toward the similarity between video clips for the modification of noise labels. Then, based on this framework, GCN will recognize the normal behavior clips in the abnormal video and delete them, while the clips identified as abnormal behavior are retained. Finally, we use 3D CNN to extract video features and classify abnormal behaviors. In order to better detect the violent behavior of patients with mental disorders, the paper focuses on the UCF-Crime dataset of violent behavior. By experimenting with the dataset, the classification accuracy reaches 37.9%, which is 9.50% higher than that of the current state-of-the-art approaches. This proves the feasibility of classifying abnormal behaviors with this framework.

Dual-active-bridge (DAB) based DC-DC converter is usually chosen for energy conversion. In this paper, there exists many coupled relationships such as the controversy between local minimum current stress and transmission power, which influence efficiency promotion greatly. To analyze complicated coupled relationship and obtain the global minimum values, after unifying backflow power and transmission power into one integral function, a novel extended-phase-shift (EPS) control based on complete differential algorithm is proposed, which can not only eliminate the backflow power completely, but can realize a type of multi-objective optimizations including improvement of transmission power and decrease of current stress. Through the proposed method in this paper, the efficiency can be improved gradually. Moreover, the coupled relationships among variables are clarified clearly, which can avoid power loss increase during optimization. A DAB-based prototype is established and the experimental results verify the correctness and effectiveness of the proposed method.

In recent times[1]–[49], multiple Unmanned Aerial Vehicles (UAVs) are being widely utilized in several areas of applications such as agriculture, surveillance, disaster management, search and rescue operations. Degree of robustness of applied control schemes determines how accurate a swarm of UAVs accomplish group tasks. Formation and trajectory tracking controllers are required for the swarm of multiple UAVs. Factors like external environmental effects, parametric uncertainties and wind gusts make the controller design process as a challenging task. This article proposes fractional order formation and trajectory tacking controllers for multiple quad-rotors using Super Twisting Sliding Mode Control (STSMC) technique. To compensate the effects of the disturbances due to parametric uncertainties and wind gusts, Lyapunov function based adaptive controllers are formulated. Moreover, Lyapunov theorem is used to guarantee the stability of the proposed controllers. Three types of controllers, namely fixed gain Super Twisting Sliding Mode Control (STSMC), integer order Adaptive Super Twisting Sliding Mode Control (ASTSMC) and fractional order ASTSMC methods are tested for the swarm of UAVs by performing the numerical simulations in MATLAB/Simulink environment. From the presented results, it is verified that in presence of wind disturbances and parametric uncertainties, the proposed fractional order ASTSMC technique showed improved robustness as compared to the fixed gain STSMC and integer order ASTSMC.

This paper investigates the network-based H ∞ control problem for Takagi-Sugeno fuzzy Markov jump systems with communication delays. Firstly, we propose a novel mode-dependent event-triggered communication scheme (ETCS) based on aperiodically sampled data, which largely reduce the data transmission. Secondly, to obtain a more applicable result, an asynchronous controller based on a hidden Markov model is designed for the first time to stabilize the fuzzy Markov jump systems with general transition rates. Thirdly, a less restrictive Lyapunov-Krasovskii functional, which is discontinuous and only required to be positive definite at endpoints of each subinterval of the holding intervals, is newly introduced for event-triggered asynchronous control issue. Based on the above methods, sufficient conditions with less conservatism are obtained to ensure the stochastic stability of the resulting closed-loop systems with a prescribed H ∞ performance. A co-design method of the desired controller and ETCS is then presented. Finally, truck-trailer model and mass-spring-damper model are provided to demonstrate the effectiveness and practicability of the developed results.

Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore it may take a large number of iterations to converge to the optimal solution. Successive relaxation is a popular technique that can be applied to solve a fixed point equation. It has been shown in the literature that, under a special structure of the MDP, successive over-relaxation technique computes the optimal value function faster than standard value iteration. In this work, we propose a second order value iteration procedure that is obtained by applying the Newton-Raphson method to the successive relaxation value iteration scheme. We prove the global convergence of our algorithm to the optimal solution asymptotically and show the second order convergence. Through experiments, we demonstrate the effectiveness of our proposed approach., Comment: Accepted for publication at IEEE Transactions on Automatic Control

Hutton et al. (2016) argued that computational hydrology can only be a proper science if the hydrological community makes sure that hydrological model studies are executed and presented in a reproducible manner. Hut, Drost and van de Giesen replied that to achieve this hydrologists should not “re-invent the water wheel” but rather use existing technology from other fields (such as containers and ESMValTool) and open interfaces (such as the Basic Model Interface, BMI) to do their computational science (Hut et al., 2017). With this paper and the associated release of the eWaterCycle platform and software package (available on Zenodo: https://doi.org/10.5281/zenodo.5119389, Verhoeven et al., 2022), we are putting our money where our mouth is and providing the hydrological community with a “FAIR by design” (FAIR meaning findable, accessible, interoperable, and reproducible) platform to do science. The eWaterCycle platform separates the experiments done on the model from the model code. In eWaterCycle, hydrological models are accessed through a common interface (BMI) in Python and run inside of software containers. In this way all models are accessed in a similar manner facilitating easy switching of models, model comparison and model coupling. Currently the following models and model suites are available through eWaterCycle: PCR-GLOBWB 2.0, wflow, Hype, LISFLOOD, MARRMoT, and WALRUS While these models are written in different programming languages they can all be run and interacted with from the Jupyter notebook environment within eWaterCycle. Furthermore, the pre-processing of input data for these models has been streamlined by making use of ESMValTool. Forcing for the models available in eWaterCycle from well-known datasets such as ERA5 can be generated with a single line of code. To illustrate the type of research that eWaterCycle facilitates, this paper includes five case studies: from a simple “hello world” where only a hydrograph is generated to a complex coupling of models in different languages. In this paper we stipulate the design choices made in building eWaterCycle and provide all the technical details to understand and work with the platform. For system administrators who want to install eWaterCycle on their infrastructure we offer a separate installation guide. For computational hydrologists that want to work with eWaterCycle we also provide a video explaining the platform from a user point of view (https://youtu.be/eE75dtIJ1lk, last access: 28 June 2022)​​​​​​​. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both Open Science and FAIR science.

This paper proposes a telecom big data based user offloading self-optimisation (TBDUOS) scheme. Its aim is to assist telecom operators to effectively balancing the load distribution with achieving good service performance and customer management in heterogeneous relay cellular systems. To achieve these objectives, in the cell-level offloaded traffic analysis stage, the optimal offloaded traffic is calculated to minimise the total blocking probability. In the user-level offloading stage, the user portrait is drawn and the K-MEANS algorithm is employed to manage the users clustering in the heavily loaded cell, and finally shifting users to assistant cells. Simulation results show the TBDUOS scheme can effectively reduce the handover failure and call dropping of specific users, especially voice/stream users, high consumption users, high level users. The TBDUOS scheme can also reduce the blocking probability.

This article suggests that much of the use of the Navajo language in contemporary Navajo written poetry, especially English dominant poetry, serves as an icon of proper Navajo usage. It is a purist view of the Navajo language. Navajo poetry is implicated, even if tacitly, in a discourse of linguistic purism that is tied to an oppositional linguistic ideology that sees Navajo and English as discrete and distinct “objects.” Navajo poetry erases the contemporary sociolinguistic diversity - including bilingual Navajo - on the Navajo Nation. And in so doing, it closes off parts of Navajo sociolinguistic realities and in its stead creates an imagined Navajo language community.

During the last few years, mobile phones and social networks have deeply changed relationships and, insidiously, the use and representations of languages in Vanuatu. In spite of being very recent, it seems that new ways of communication imply changes regarding the various ways of using and adapting languages, amongst which are code-switching and language-shifting. Bislama, the national local lingua franca, is becoming more and more used in phone conversations. Internet and especially social networks (such as Facebook) are revealing new language strategies in social intercourses. This article examines interactions of languages that are mediated through social networks and mobile phone exchanges. More specifically, this paper discusses different language ideologies that are manifest in and deployed over forms of telecommunication.

Most existing voltage optimization approaches of low voltage distribution networks (LVDNs) assume users follow the regulations unconditionally,which is not always true.To tackle this, we propose a real-time peer to peer flexibility trading scheme for LVDNs. Besides distributed optimization, our scheme offers a way to let users trade their flexibility, in case some users do not want to follow the system regulations at some point. The scheme consists of flexibility allocation and trading. Player compatibility equilibrium and Bernoulli trials are employed to approximate the Pareto optimum in flexibility allocation,while consensus and voltage reference are used to provide real-time offers for flexibility trading. The proposed scheme is computationally tractable and can work with limited communication in a decentralized manner. All the user data is used locally so that privacy is well protected. A network based on a real Belgian semiurban LVDN is employed to validate the proposed scheme with three different scenarios. Besides, the flexibility allocation is benchmarked with a centralized ACOPF algorithm. The case studies result clearly prove the effectiveness and robustness of the proposed scheme. ispartof: Ieee Transactions On Power Systems vol:37 issue:4 pages:2821-2832 status: published

In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of our proposed system and landing scheme.

One of the main advantages brought by the Internet of Things (IoT) is the possibility of having large amounts of data from several sources that allow us, once analyzed, to make decisions in various domains in real time. This implies the need to be able to process large volumes of data in more or less limited processing times depending on the application domain. In this sense, Complex Event Processing (CEP), used in conjunction with an Enterprise Service Bus (ESB), has proven to be very efficient in multiple domains. In search for greater efficiency, some CEP engines offer the option of using Flow-Based Programming (FBP) rather than their traditional programming using CEP together with an event bus. However, its use, while it may be more efficient, can lead to other limitations. In this paper, we analyze and describe the performance and limitations of using a CEP engine with an ESB versus a CEP engine with FBP. This will allow developers to decide which option is more convenient for their IoT system depending on the application domain and its specific needs.

In a traditional quasi-resonant (QR) control yback converter, the switching frequency of the system will increase with the decrease of the load, so the switching loss under light load becomes high, which greatly affects the average efficiency of the system in the whole load range. An improved QR control method called valley skip (VS) control can reduce the switching loss by decreasing the frequency of the system under the light load. However, the switching frequency of conventional VS controller may change suddenly at some load points, and this sudden frequency hopping causes serious audible noise in the transformer. This paper proposes a valley locking control scheme to overcome the shortcomings of the traditional VS control technology. It can eliminate the audio noise caused by the frequency hopping in VS control. In order to verify the feasibility of the proposed method, a controller IC is fabricated with the proposed method and tested by a prototype experimental board of 24 W/12 V.

Data protection is a severe constraint in the heterogeneous IoT era. This article presents a Hardware-Software Co-Simulation of AES-128 bit encryption and decryption for IoT Edge devices using the Xilinx System Generator (XSG). VHDL implementation of AES-128 bit algorithm is done with ECB and CTR mode using loop unrolled and FSM-based architecture. It is found that AES-CTR and FSM architecture performance is better than loop unrolled architecture with lesser power consumption and area. For performing the Hardware-Software Co-Simulation on Zedboard and Kintex-Ultra scale KCU105 Evaluation Platform, Xilinx Vivado 2016.2 and MATLAB 2015b is used. Hardware emulation is done for grey images successfully. To give a practical example of the usage of proposed framework, we have applied it for Biomedical Images (CTScan Image) as a case study. Security analysis in terms of the histogram, correlation, information entropy analysis, and keyspace analysis using exhaustive search and key sensitivity tests is also done to encrypt and decrypt images successfully., Comment: The manuscript is accepted in IEEE Consumer Electronics Magazine . 6 pages in double column format. A total of 4 figures

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

RDF graph has been increasingly used to store and represent information shared over the Web, including social graphs and knowledge bases. With the increasing scale of RDF graphs and the concurrency level of SPARQL queries, current RDF systems are confronted with inefficient concurrent query processing on massive data parallelism. The situation becomes more severe in the face of data-intensive queries (aka heavy query), which usually lead to suboptimal response time (latency) as well as throughput collapse. In this article, we present Wukong+G, the first graph-based distributed RDF query processing system that efficiently exploits the hybrid parallelism of CPU and GPU. Wukong+G is made fast and concurrent with four key designs. First, Wukong+G tames massive random memory accesses in graph exploration by efficiently mapping data between CPU and GPU for latency hiding, including a set of techniques like query-aware prefetching, pattern-aware pipelining and fine-grained swapping. Second, Wukong+G scales up by introducing a GPU-friendly RDF store to support RDF graphs exceeding GPU memory size, by using techniques like predicate-based grouping, pairwise caching and look-ahead replacing to narrow the gap between host and device memory scale. Third, Wukong+G scales out through a communication layer that decouples the transferring process for query metadata and intermediate results, and further leverages both native and GPUDirect RDMA to enable efficient communication on a CPU/GPU cluster. Finally, Wukong+G simultaneously runs multiple queries on a single GPU to improve overall throughput and fully exploits hardware heterogeneity (CPU/GPU) by scheduling a single query on CPU and GPU adaptively. We have implemented Wukong+G by extending a state-of-the-art distributed RDF store (i.e., Wukong) with distributed GPU support. Evaluation on a heterogeneous CPU/GPU cluster with RDMA-capable network shows that Wukong+G outperforms Wukong by up to 9.0× (from 2.3×) and scales well on 10 GPU cards for heavy queries. Wukong+G can also improve both latency and throughput by more than one order of magnitude when facing hybrid workloads.

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically.

This paper presents a suite of analytics that are proposed to be embedded in next-generation smart sensors in electric power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in signal processing and machine learning for real-time event detection and classification. Meanwhile, a robust synchrophasor estimation mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit (most accurate) synchrophasor estimation algorithms at all times. Embedding such analytics within the sensor and closer to where the waveforms are captured, the proposed distributed intelligence solution technology mitigates the potential risks to communication failures and latencies as well as malicious cyber threats. Our experiments demonstrate that the introduced scheme achieves improved quality of measurements with a promising event detection and classification accuracy, collectively resulting in enhanced online situational awareness in modern power grids.