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General variable-order fractional scale derivatives are introduced and studied. Both the stretching and the shrinking cases are considered for definitions of the derivatives of the GL type and of the Hadamard type. Their properties are deduced and discussed. Fractional variable-order systems of autoregressive–moving-average type are introduced and exemplified. The corresponding transfer functions are obtained and used to find the corresponding impulse responses. [ABSTRACT FROM AUTHOR]

Reset control is a simple non-linear control technique that can help overcome the structural limitations of linear control. Fractional control uses the concept of fractional derivatives to expand the range of possibilities when modeling a controller, making it more robust. Fractional reset control merges the advantages of both areas and is the object of this paper. Fractional-order versions of different reset controllers were implemented, namely a fractional Clegg integrator, a fractional generalized first-order reset element, a fractional generalized second-order reset element, and fractional "constant in gain lead in phase" controllers with first- and second-order reset elements. These were computed directly from a numerical implementation of the Grünwald–Letnikov definition of fractional derivatives, and their performances were analyzed. [ABSTRACT FROM AUTHOR]

Wave energy is one of the most promising technologies for the future of renewable energy. Its steady, even if not dashing, development, both in theoretical and applied solutions, will bring the field, in the mid-term, to the point of being a viable, economical and sustainable technology, competitive when compared to more mature ones. This paper deals with the ISWEC (Inertial Sea Wave Energy Converter) power take off (PTO) technology and control strategies. In particular it focuses on the control strategy used for harnessing energy during its first deployment, and presents the development of a new controller designed using optimal control theory. The approach to the problem of this work is not to seek for the theoretical optimal control strategy. In fact, during the test period of the ISWEC into the sea, limitations of a model-optimized control strategy came out, due to well known shortcomings of hydrodynamics modeling theory for wave energy devices, and the idea of an easy-to-tune in the field controller arose. This work points out in the direction of developing a practical and effective strategy for industrial application, with the main objective of increasing the power production while simplifying the control law by which the device harnesses energy. [ABSTRACT FROM AUTHOR]

Railway networks are among the many physical systems that reveal a fractal structure. This paper studies the Portuguese railway system, and analyzes how it evolved over time, namely what concerns the structure of its different levels, and its distribution over the territory. Different mathematical tools are adopted, such as fractal dimension, entropy and state space portrait. The results are consistent with the historical evolution of the network. [ABSTRACT FROM AUTHOR]

The energy that can be captured from the sea waves and converted into electricity should be seen as a contribution to decrease the excessive dependency and growing demand of fossil fuels. Devices suitable to harness this kind of renewable energy source and convert it into electricity--wave energy converters (WECs)--are not yet commercially competitive. There are several types of WECs, with different designs and working principles. One possible classification is their distance to the shoreline and thus their depth. Near-shore devices are one of them since they are typically deployed at intermediate water depth (IWD). The selection of the WEC deployment site should be a balance between several parameters; water depth is one of them. Another way of classifying WECs is grouping them by their geometry, size and orientation. Considering a near-shore WEC belonging to the floating point category, this paper is focused on the numerical study about the differences arising in the power captured from the sea waves when the typical deep water (DW) assumption is compared with the more realistic IWD consideration. Actually, the production of electricity will depend, among other issues, on the depth of the deployment site. The development of a dynamic model including specific equations for the usual DW assumption as well as for IWD is also described. Derived equations were used to build a time domain simulator (TDS). Numerical results were obtained by means of simulations performed using the TDS. The objective is to simulate the dynamic behavior of the WEC due to the action of sea waves and to characterize the wave power variations according with the depth of the deployment site. [ABSTRACT FROM AUTHOR]

This paper extends Levy׳s identification method to non-commensurable fractional models. This identification method allows finding a transfer function that models a frequency response. Explicit expressions for the calculations, using five different variations of the method, are given. A sensitivity analysis supports an empirical way of finding the orders involved. Two application examples, concerning the identification of a model for a viscoelastic material and a model the human arm, are given. [ABSTRACT FROM AUTHOR]

Cancer is a complex disease, responsible for a significant portion of global deaths. The increasing prioritisation of know-why over know-how approaches in biological research has favoured the rising use of both white- and black-box mathematical techniques for cancer modelling, seeking to better grasp the multi-scale mechanistic workings of its complex phenomena (such as tumour-immune interactions, drug resistance, tumour growth and diffusion, etc.). In light of this wide-ranging use of mathematics in cancer modelling, the unique memory and non-local properties of Fractional Calculus (FC) have been sought after in the last decade to replace ordinary differentiation in the hypothesising of FC's superior modelling of complex oncological phenomena, which has been shown to possess an accumulated knowledge of its past states. As such, this review aims to present a thorough and structured survey about the main guiding trends and modelling categories in cancer research, emphasising in the field of oncology FC's increasing employment in mathematical modelling as a whole. The most pivotal research questions, challenges and future perspectives are also outlined. [ABSTRACT FROM AUTHOR]

This paper addresses the fractional sliding mode control of MIMO nonlinear noncommensurable plants, which does not seem to have been covered until this moment in the literature on fractional sliding mode control. It includes simulation results to confirm the feasibility of the solution presented. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a unified fractional derivative, defined by two parameters (order and asymmetry). From this, all the interesting derivatives can be obtained. We study the one-sided derivatives and show that most known derivatives are particular cases. We consider also some myths of Fractional Calculus and false fractional derivatives. The results are expected to contribute to limit the appearance of derivatives that differ from existing ones just because they are defined on distinct domains, and to prevent the ambiguous use of the concept of fractional derivative. [ABSTRACT FROM AUTHOR]

Highlights: [•] The human arm can be modelled using fractional derivatives. [•] Fractional models have fewer parameters than integer models from the literature. [•] They are better adjusted to experimental data and parameter uncertainty is lower. [•] This agrees with literature on the fractional order of muscle viscoelastic behaviour. [Copyright &y& Elsevier]

Oscillating water columns (OWCs) are widely regarded as the simplest and most reliable type of wave energy converter. An economically viable OWC wave power plant requires maximizing the performance of the power take-off system, composed of an air turbine coupled to an electrical generator. Besides, the OWC power plant's inherent nonlinearities and modeling uncertainties must be dealt with. One solution is the use of fuzzy controllers since they capture both nonlinearities and uncertainties of the system, which can largely contribute to improving the overall efficiency. This paper shows how an inverse fuzzy model optimized by a genetic algorithm (GA) can improve the control of an OWC and, consequently, its performance. As a case study, the Mutriku power plant with a biradial turbine was addressed, considering 11 different sea states of the characteristic wave climate. Two optimization objectives were considered: maximizing turbine and generator output average powers. Compared with a well-known baseline control strategy, the GA-fuzzy control did not significantly contribute to increasing average turbine power. However, when maximizing generator yearly-accumulated power, there were improvements of over 9%. Ultimately, these research findings may play an essential role in control strategies for wave power plants. • An inverse model controller was proposed for wave energy control. • The inverse model was obtained with fuzzy modeling and optimized by genetic algorithms. • Focus on a real case study: An oscillating water column at the Mutriku power plant. • Comparison of the proposed algorithm with a well-known baseline strategy. • The proposed strategy showed yearly improvements of over 9% on average generator power. [ABSTRACT FROM AUTHOR]

Abstract: This work addresses an off-shore oscillating water column for producing electricity from sea waves. It describes the modelling of this device and the study of control techniques that could improve energy extraction. Optimisation techniques applied improved the device performance for a wide number of sea states. A control strategy was developed with the objective of improving the quality of the energy absorbed by the device. This proved to be effective. In a later stage of this work, some experiments considering a variable pitch Wells turbine were performed with the objective of applying phase and amplitude control: it was proved that it is possible to obtain a resonant response to a sinusoidal wave with a frequency different from the device’s natural frequency. [Copyright &y& Elsevier]

Abstract: This paper addresses the different possible definitions of variable-order derivatives and their numerical approximations; both approximations based upon the definitions and approximations consisting of non-linear transfer functions (in particular combining existing approximations of constant-order fractional derivatives, such as the Crone approximation, with fuzzy logic) are considered. There are different possible configurations, implementing variable-order fractional derivatives both with and without memory of past values of the time-dependent differentiation order. [ABSTRACT FROM AUTHOR]

Abstract: The Archimedes Wave Swing (AWS) is a a fully-submerged Wave Energy Converter (WEC), that is to say, a device that converts the energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. In this paper, neural network (NN) models for this AWS prototype are developed. NNs are then used together with proven control strategies (phase and amplitude control, internal model control and switching control) to maximise energy production. Simulations show an yearly average electricity production increase of 160% over the performance of the original AWS controller. [Copyright &y& Elsevier]

Abstract: This paper addresses the Archimedes Wave Swing (an offshore wave energy converter, which produces electricity from sea waves). It compares the performances of latching control (a discrete, highly non-linear, intrinsically sub-optimum control strategy), of reactive control, of phase and amplitude control (two optimum control strategies that involve non-causal transfer functions, which have to be implemented with approximations, thus rending the control sub-optimum), and of feedback linearisation control (a non-linear control strategy). From extensive simulations it is concluded that the latter performs clearly better irrespective of the sea state, and leads to a significant increase of absorbed wave power. [Copyright &y& Elsevier]

Abstract: In this paper two sets of tuning rules for fractional PIDs are presented. These rules are quadratic and require the same plant time–response data used by the first Ziegler–Nichols tuning rule for (usual, integer) PIDs. Hence no model for the plant to control is needed—only an S-shaped step response is. Even if a model is known rules quickly provide a starting point for fine tuning. Results compare well with those obtained with rule-tuned integer PIDs. [Copyright &y& Elsevier]

This paper aims to contribute to the improvement of the energy extracted from Wave Energy Converters, arrayed in a wave farm, through the implementation of several actions, namely wave extrapolation using a dynamic neural network, wave type identification resorting to a machine learning system, and coordination of the interoperability, communication, and control of all the devices belonging to the wave farm via a Multi-Agent System. The Multi-Agent System was initially developed and subsequently tested through simulations. Simulations used the Inertial Sea Wave Energy Converter, which extracts power from sea waves, using gyroscopic motion, to generate electricity. This device can be originally controlled by proportional-derivative controllers with parameters that change with the sea state. With this Multi-Agent System, ocean waves and corresponding sea states are identified using a machine learning wave classifier method. Information is passed on so that all devices can use optimal control parameters. Therefore, energy extraction by the wave farm is enhanced due to the combination of the Multi-Agent System with automatic learning methods, while its architecture is resilient to communication problems between devices. Simulation results for a case study showed an increase in the average power absorbed, proving the effectiveness of combining a Multi-Agent System with automatic learning methods. Conclusions demonstrated that the proposed system architecture is a viable alternative to the original wave forecasting model. Additionally, this Multi-Agent System can be adapted to wave farms with other Wave Energy Converters and other types of control. [ABSTRACT FROM AUTHOR]

In this paper, we address the general fractional integrals and derivatives with the Sonine kernels on the spaces of functions with an integrable singularity at the point zero. First, the Sonine kernels and their important special classes and particular cases are discussed. In particular, we introduce a class of the Sonine kernels that possess an integrable singularity of power function type at the point zero. For the general fractional integrals and derivatives with the Sonine kernels from this class, two fundamental theorems of fractional calculus are proved. Then, we construct the n-fold general fractional integrals and derivatives and study their properties. [ABSTRACT FROM AUTHOR]

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative. [ABSTRACT FROM AUTHOR]

Ocean waves hold a promising potential as a sustainable source of clean energy. However, in order to make wave energy converters financially viable, particularly in the case of the oscillating water column (OWC), it is crucial to optimize the power take-off system to maximize performance. Most control strategies focus solely on turbine performance, often at the expense of overlooking the overall wave-to-wire system performance. Therefore, a model predictive control (MPC) strategy using short-term air pressure prediction was proposed to enhance electrical energy delivered to the grid. The Mutriku wave power plant (Spain) was case-studied for evaluating the control approaches, which focused on maximizing average power (turbine or generator) from an OWC equipped with the innovative biradial turbine. In comparison to the reference baseline strategy, the implementation of MPC (with a 16-second forecast) resulted in a minor 2%-increase in the average turbine power. However, when it comes to the generator power accumulated over a year, the MPC strategy produced an improvement of more than 8%. Since generator power is the ultimate product, this improvement is significant. Nonetheless, the MPC strategy exhibited a non-smooth control response compared to other strategies. Improving control action quality should be a primary focus of future research. • A model predictive control was proposed for an oscillating-water-column power plant. • Short-term air pressure forecasting was performed using an autoregressive model. • The proposed algorithm was applied to the Mutriku wave power plant. • Improvements in the annual average generator power output of over 8% were observed. [ABSTRACT FROM AUTHOR]

This paper presents models of economic growth for all the countries of the Group of Seven (G7) in the 1973–2016 period. The models consist of differential equations, of both integer and fractional order, where the gross domestic product (GDP) is a function of the country's land area, arable land, population, school attendance, gross capital formation (GCF), exports of goods and services, general government final consumption expenditure (GGFCE), and broad money (M3). Results show that fractional models have a better performance, measured by several summary statistics, without increasing the number of parameters, or sacrificing the ability to predict GDP evolution in the short term. A standard validation procedure for economic growth models is presented for the assessment of future models. [ABSTRACT FROM AUTHOR]

Bone is a living tissue that is constantly being renewed, where different cell types can induce a remodeling action to its structure. These mechanisms are typically represented through differential equations, accounting for the biochemical coupling between osteoclastic and osteoblastic cells. Remodeling models have also been extended to include the effects of tumorous disruptive pathologies in the bone dynamics. This article provides a novel approach to existing biochemical models, acting on two different stages. First, the models are said to physiologically better explain an osteolytic metastatic disease to the bone than the multiple myeloma previously considered. Second, and most importantly, variable order derivatives were introduced, for the first time in biochemical bone remodeling models. This resulted in a set of equations with less parameters that describe tumorous remodeling, and provide similar results to those of the original formulation. A more compact model, that promptly highlights tumorous bone interactions, is then achieved. Comparison of simulations and parameters is provided. Such results are a one-step-closer insight to, in a near future, easily provide clinical decision systems ensuring tailored personalized therapy schemes, for more efficient and targeted therapies. [ABSTRACT FROM AUTHOR]

When developing control architectures for physical human–robot interaction, it is often necessary to use a model of the human operator. This paper presents a comparative study of both direct and inverse models of the human arm at the elbow joint, relating the force at the hand with the arm angle and vice versa. Specifically, models of integer and fractional (commensurable and non-commensurable) nature are identified from the experiments. Likewise, for comparison purposes, neural networks models are also obtained. Taking into account their parameter variability, it is shown that fractional models are more adequate to describe human arm behaviour; they are simpler, more exact and with less parameter uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes an implementation, based on a multi-agent system, of a management system for automated negotiation of electricity allocation for charging electric vehicles (EVs) and simulates its performance. The widespread existence of charging infrastructures capable of autonomous operation is recognised as a major driver towards the mass adoption of EVs by mobility consumers. Eventually, conflicting requirements from both power grid and EV owners require automated middleman aggregator agents to intermediate all operations, for example, bidding and negotiation, between these parts. Multi-agent systems are designed to provide distributed, modular, coordinated and collaborative management systems; therefore, they seem suitable to address the management of such complex charging infrastructures. Our solution consists in the implementation of virtual agents to be integrated into the management software of a charging infrastructure. We start by modelling the multi-agent architecture using a federated, hierarchical layers setup and as well as the agents' behaviours and interactions. Each of these layers comprises several components, for example, data bases, decision-making and auction mechanisms. The implementation of multi-agent platform and auctions rules, and of models for battery dynamics, is also addressed. Four scenarios were predefined to assess the management system performance under real usage conditions, considering different types of profiles for EVs owners', different infrastructure configurations and usage and different loads on the utility grid (where real data from the concession holder of the Portuguese electricity transmission grid is used). Simulations carried with the four scenarios validate the performance of the modelled system while complying with all the requirements. Although all of these have been performed for one charging station alone, a multi-agent design may in the future be used for the higher level problem of distributing energy among charging stations. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Bone is constantly being renewed: in the adult skeleton, bone resorption and formation are in a tightly coupled balance, allowing for a constant bone density to be maintained. Yet this micro-environment provides the necessary conditions for the growth and proliferation of tumor cells, and thus bone is a common site for the development of metastases, mainly from primary breast and prostate cancer. Mathematical and computational models with differential equations can replicate this bone remodeling process. These models have been extended to include the effects of disruptive tumor pathologies in the bone dynamics, as metastases contribute to the decoupling between bone resorption and formation and to the self-perpetuating tumor growth cycle. Such models may also contemplate the counteraction effects of currently used therapies, and, in the case of treatments with drugs, their pharmocokinetics and pharmacodynamics. We present a thorough overview of biochemical models for bone remodeling, in the presence of a tumour together with anti-cancer and anti-resorptive therapy, formulated as systems of first-order differential equations, or simplified using variable order derivatives. The latter models, of which some are new to this paper, result in equations with fewer parameters, and allow accounting for anomalous diffusion processes. In this way, more compact and parsimonious models, that promptly highlight tumorous bone interactions, are achieved, providing an effective framework to counteract the loss of bone integrity on the affected areas. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the approximate controllability for a class of retarded semilinear neutral control systems of fractional order by investigating the relations between the reachable set of the semilinear retarded neutral system of fractional order and that of its corresponding linear system. The research direction used here is to find the conditions for nonlinear terms so that controllability is maintained even in perturbations. Finally, we will show a simple example to which the main result can be applied. [ABSTRACT FROM AUTHOR]

Bone remodelling consists of cycles of bone resorption and formation executed mainly by osteoclasts and osteoblasts. Healthy bone remodelling is disrupted by diseases such as Multiple Myeloma and bone metastatic diseases. In this paper, a simple mathematical model with differential equations, which takes into account the evolution of osteoclasts, osteoblasts, bone mass and bone metastasis growth, is improved with a pharmacokinetic and pharmacodynamic (PK/PD) scheme of the drugs denosumab, bisphosphonates, proteasome inhibitors and paclitaxel. The major novelty is the inclusion of drug resistance phenomena, which resulted in two variations of the model, corresponding to different paradigms of the origin and development of the tumourous cell resistance condition. These models are then used as basis for an optimization of the drug dose applied, paving the way for personalized medicine. A Nonlinear Model Predictive Control scheme is used, which takes advantage of the convenient properties of a suggested adaptive and democratic variant of Particle Swarm Optimization. Drug prescriptions obtained in this way provide useful insights into dose administration strategies. They also show how results may change depending on which of the two very different paradigms of drug resistance is used to model the behaviour of the tumour. [ABSTRACT FROM AUTHOR]

This paper studies the economic growth of the countries in the Group of Twenty (G20) in the period 1970–2018. It presents dynamic models for the world's most important national economies, including for the first time several economies which are not highly developed. Additional care has been devoted to the number of years needed for an accurate short-term prediction of future outputs. Integer order and fractional order differential equation models were obtained from the data. Their output is the gross domestic product (GDP) of a G20 country. Models are multi-input; GDP is found from all or some of the following variables: country's land area, arable land, population, school attendance, gross capital formation (GCF), exports of goods and services, general government final consumption expenditure (GGFCE), and broad money (M3). Results confirm the better performance of fractional models. This has been established employing several summary statistics. Fractional models do not require increasing the number of parameters, neither do they sacrifice the ability to predict GDP evolution in the short-term. It was found that data over 15 years allows building a model with a satisfactory prediction of the evolution of the GDP. [ABSTRACT FROM AUTHOR]

This paper presents the identification, development and simulation of a complex order, third generation CRONE (Commande Robuste d'Ordre Non Entier) controller for a new Darrieus type vertical-axis wind turbine design. A prototype of this wind turbine was developed and tested in a wind tunnel. Data acquired in the tests were used to develop and validate an accurate model. The CRONE control system design methodology was then used to develop a robust controller, which was tested in simulation. Simulations allow for parameter uncertainty in different wind conditions, with and without the presence of turbulence. This control system can be easily implemented using a micro-controller. • Third generation CRONE controller. • Darrieus type vertical-axis wind turbine. • Prototype tested in a wind tunnel. [ABSTRACT FROM AUTHOR]

• We proposed a decision support system in the surgical oncology domain. • The proposed web platform allowed postoperative surgical outcomes prediction. • It can be used to collect clinical data in a harmonized format at the oncology centers. • The platform provides health professionals a support tool to the design of postoperative care. Background : The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives : The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results : The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions : The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org. [ABSTRACT FROM AUTHOR]

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense. [ABSTRACT FROM AUTHOR]

Although oscillating water column (OWC) wave energy converters are arguably one of the most studied technologies, it is not clear which chamber geometry, among all of the available alternatives, would provide the best performance at a site of interest. In this work, a numerical model based on the Navier-Stokes equations for two compressible fluids, using a volume-of-fluid interface-capturing approach, is implemented to determine the best performing OWC geometry in a case study off the Port of Vigo (NW Spain). Four general shapes of OWC are analyzed: classic, stepped-bottom, U-shaped and L-shaped, and geometrical variants are investigated. In total, 18 chamber geometries are studied, considering the same turbine geometry in all of them. It was found that the U-shaped and L-shaped designs are the most easily tuned to resonate at a period of interest. Of these two, the L-shaped performs better. The best performance is achieved for an L-shaped OWC design with a shallow entrance, a high horizontal chamber duct and a wide vertical duct, for which a maximum capture-width ratio of 71.6% was achieved. [ABSTRACT FROM AUTHOR]

Music is a type of time-series data. As the size of the data increases, it is a challenge to build robust music genre classification systems from massive amounts of music data. Robust systems require large amounts of labeled music data, which necessitates time- and labor-intensive data-labeling efforts and expert knowledge. This paper proposes a musical instrument digital interface (MIDI) preprocessing method, Pitch to Vector (Pitch2vec), and a deep bidirectional transformers-based masked predictive encoder (MPE) method for music genre classification. The MIDI files are considered as input. MIDI files are converted to the vector sequence by Pitch2vec before being input into the MPE. By unsupervised learning, the MPE based on deep bidirectional transformers is designed to extract bidirectional representations automatically, which are musicological insight. In contrast to other deep-learning models, such as recurrent neural network (RNN)-based models, the MPE method enables parallelization over time-steps, leading to faster training. To evaluate the performance of the proposed method, experiments were conducted on the Lakh MIDI music dataset. During MPE training, approximately 400,000 MIDI segments were utilized for the MPE, for which the recovery accuracy rate reached 97%. In the music genre classification task, the accuracy rate and other indicators of the proposed method were more than 94%. The experimental results indicate that the proposed method improves classification performance compared with state-of-the-art models. [ABSTRACT FROM AUTHOR]

The infiltration phenomena has been studied by several authors for decades, and numerical and approximate results have been shown through the asymptotic solution in short and long times. In particular, it is worth highlighting the works of Philip and Parlange, who used time and volumetric content as independent variables and space as a dependent variable, and found the solution as a power series in t 1 / 2 that is valid for short times. However, several studies show that these models are not applicable to anomalous flows, in which case the application of fractional calculus is needed. In this work, a fractional time derivative of a Caputo type is applied to model anomalous infiltration phenomena. Fractional horizontal infiltration phenomena are studied, and the fractional Boltzmann transform is defined. To study fractional vertical infiltration phenomena, the asymptotic behavior is described for short and long times considering an arbitrary diffusivity and hydraulic conductivity. Finally, considering a constant flux-dependent relation and a relation between diffusivity and hydraulic conductivity, a fractional cumulative infiltration model applicable to various types of soil is built; its solution is expressed as a power series in t ν / 2 , where ν ∈ (0 , 2) is the order of the fractional derivative. The results show the effect of superdiffusive and subdiffusive flows in different types of soil. [ABSTRACT FROM AUTHOR]

The present work addresses the need for an efficient, versatile, accurate and open-source numerical tool to be used during the design stage of wave energy converters (WECs). The device considered here is the heaving point-absorber developed and tested by Sandia National Laboratories. The smoothed particle hydrodynamics (SPH) method, as implemented in DualSPHysics, is proposed since its meshless approach presents some important advantages when simulating floating devices. The dynamics of the power take-off system are also modelled by coupling DualSPHysics with the multi-physics library Project Chrono. A satisfactory matching between experimental and numerical results is obtained for: (i) the heave response of the device when forced via its actuator; (ii) the vertical forces acting on the fixed device under regular waves and; (iii) the heave response of the WEC under the action of both regular waves and the actuator force. This proves the ability of the numerical approach proposed to simulate accurately the fluid–structure interaction along with the WEC's closed-loop control system. In addition, radiation models built from the experimental and WAMIT results are compared with DualSPHysics by plotting the intrinsic impedance in the frequency domain, showing that the SPH method can be also employed for system identification. [ABSTRACT FROM AUTHOR]