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Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a new simple sensorless method for a Piezoelectric Actuator (PEA), which includes piezostack elements and a three-stage amplification mechanism. This research focuses on a piezoelectric actuator that incorporates a three-stage amplification system, where the outcome of one stage serves as the input for the subsequent one. The actuator receives two types of inputs: the voltage applied to the piezoelectric elements and the mechanical load it carries. Its output is defined by the rotation angle observed at the end of the third amplification stage. To indirectly measure the actuator's displacement, a basic external circuit is utilized. The precise movement of these actuators is essential. To circumvent the high costs and limitations associated with highly accurate displacement sensors, there has been a growing interest in sensorless control methods. Certain electrical signals, when measured, can provide an estimation of displacement. However, induced voltage measurements are not effective for piezoelectric stacks. Two more promising measures are the voltage and current of the piezoelectric material. Given that the electrical charge on these actuators closely reflects their displacement with minimal hysteresis across a broad frequency spectrum, it's proposed that displacement can be effectively gauged through current measurements that assess charge. The core contribution of this paper is the introduction and validation, both theoretically and experimentally, of a hybrid algorithm that leverages these two electrical signals to enhance the accuracy of displacement estimates. This was confirmed using a laboratory setup. The primary benefit of this research is the presentation of a straightforward sensorless control algorithm, poised for further exploration within the realm of piezoelectric actuators. The simplicity of both the theoretical model and the sensorless technique facilitates their application across a diverse range of piezoelectric actuators and amplification systems, thereby streamlining the design, modeling, and control strategy development for various actuators. The innovation of this study stems from the application of an uncomplicated sensorless estimation algorithm, coupled with a system-level perspective on piezoelectric actuators. This approach utilizes a simple, adaptable model suitable for a wide array of applications and operational techniques.

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Traditional survey methods can provide noisy data arising from recall, memory and other biases. Technological advances (particularly in neuroscience) are opening new ways of monitoring physiological processes through non-intrusive means. Such dense continuous data provide new and fruitful avenues for complementing self-reported data with a better understanding of human dynamics and human interactions. In this study, we use a survey to collect positive affect (feelings) data from more than 300 individuals over a period of 24 h, and at the same time, map their core activities (5000 recorded activities in total) with measurements of their heart rate variability (HRV). Our results indicate a robust correlation between the HRV measurements and self-reported affect. By drawing on the neuroscience and wellbeing literature we show that dynamic HRV results are what we expect for positive affect, particularly when performing activities like sleep, travel, work, exercise and eating. This research provides new insights into how to collect HRV data, model and interpret it.

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This study explores the potential use of low-cost natural fiber reinforced rope polymers (FRRP) to improve the compressive behavior of circular, square, and rectangular reinforced concrete (RC) specimens. A total of 42 specimens were tested under monotonic axial compression in three groups. Groups were formed to differentiate specimens with different cross-sectional shapes such as circular, square, and rectangular. The findings demonstrate that FRRP can effectively boost the compressive behavior of RC columns. Circular specimens with three-layer hemp FRRP exhibited a 200% increase in compressive strength and a 270% improvement in corresponding strain. Cotton FRRP provided a 117% boost in compressive strength and a 233% enhancement in strain. In square specimens, three-layer hemp FRRP resulted in a 110% rise in compressive strength and a 186% increase in strain, while cotton confinement yielded improvements of 95% and 144%, respectively. For the square and rectangular specimens, the improvement in the compressive behavior was reduced compared to the circular specimens because of stress concentrations near corners. Moreover, the study showed that the hemp FRRP confinement outperformed the cotton confinement. The investigation also revealed that the existing analytical models were inadequate in predicting the mechanical properties of RC confined with natural FRRP. Therefore, the study introduces novel equations to predict the compressive strength and corresponding strain for both hemp and cotton confined concrete in various cross-sectional types. These proposed equations exhibit a good level of accuracy in predicting the compressive strength and corresponding strain.

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Three-phase particulate composites offer greater design flexibility in the selection of phase materials and have more design variables than their two-phase counterparts, thus providing larger space for tailoring effective properties to meet intricate engineering requirements. Predicting effective elastic properties is essential for composite design. However, experimental methods are both expensive and time intensive, whereas the scope of analytical micromechanics models is limited by their inherent assumptions. The newly developed microstructure-free finite element modeling (MF-FEM) approach has been demonstrated to be accurate and reliable for two-phase particulate composites. In this study, we investigate whether the MF-FEM approach can be applied to three-phase particulate composites and, if applicable, under which conditions. The study commences with a convergence analysis to establish the threshold ratio between the element size and the RVE (representative volume element) dimension. We then validate the MF-FEM approach using experimental data on three-phase composites from the existing literature. Subsequently, the MF-FEM method serves as a benchmark to assess the accuracy of both traditional and novel analytical micromechanics models, in predicting the effective elasticity of two distinct types of three-phase particulate composites, characterized by their small and large phase contrasts, respectively. We found that the threshold element-to-RVE ratio (1/150) for three-phase composites is considerably smaller than the ratio (1/50) for two-phase composites. The validation underscores that MF-FEM predictions align closely with experimental data. The analytical micromechanics models demonstrate varying degrees of accuracy depending on the phase volume fractions and the contrast in phase properties. The study indicates that the analytical micromechanics models may not be dependable for predicting effective properties of three-phase particulate composites, particularly those with a large contrast in phase properties. Even though more time-intensive, the MF-FEM proves to be a more reliable approach than the analytical models.

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The growing awareness of the environment and sustainable development has prompted the search for solutions involving the development of bio-based composite materials for insulating applications, offering an alternative to traditional synthetic materials such as glass- and carbon-reinforced composites. In this study, we investigate the thermal and microstructural properties of new biocomposite insulating materials derived from flaxseed-gum-filled epoxy, with and without the inclusion of reinforced flax fibers. A theoretical approach is proposed to estimate the thermal conductivity, while the composite's microstructure is characterized using X-ray Computed Tomography and image analysis. The local thermal conductivity of the flax fibers and the flaxseed gum matrix is identified by using effective thermal conductivity measurements and analytical models. This study provides valuable insight into the thermal behavior of these biocomposites with varying compositions of flaxseed gum and epoxy resin. The results obtained could not only contribute to a better understanding the thermal properties of these materials but are also of significant interest for advanced numerical modeling applications.

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Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.

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A growing body of scientific evidence indicates that exposure to low dose ionizing radiation (< 2 Gy) is associated with a higher risk of developing radio-induced cancer. Additionally, it has been shown to have significant impacts on both innate and adaptive immune responses. As a result, the evaluation of the low doses inevitably delivered outside the treatment fields (out-of-field dose) in photon radiotherapy is a topic that is regaining interest at a pivotal moment in radiotherapy. In this work, we proposed a scoping review in order to identify evidence of strengths and limitations of available analytical models for out-of-field dose calculation in external photon beam radiotherapy for the purpose of implementation in clinical routine. Papers published between 1988 and 2022 proposing a novel analytical model that estimated at least one component of the out-of-field dose for photon external radiotherapy were included. Models focusing on electrons, protons and Monte-Carlo methods were excluded. The methodological quality and potential limitations of each model were analyzed to assess their generalizability. Twenty-one published papers were selected for analysis, of which 14 proposed multi-compartment models, demonstrating that research efforts are directed towards an increasingly detailed description of the underlying physical phenomena. Our synthesis revealed great inhomogeneities in practices, in particular in the acquisition of experimental data and the standardization of measurements, in the choice of metrics used for the evaluation of model performance and even in the definition of regions considered out-of-the-field, which makes quantitative comparisons impossible. We therefore propose to clarify some key concepts. The analytical methods do not seem to be easily suitable for massive use in clinical routine, due to the inevitable cumbersome nature of their implementation. Currently, there is no consensus on a mathematical formalism that comprehensively describes the out-of-field dose in external photon radiotherapy, partly due to the complex interactions between a large number of influencing factors. Out-of-field dose calculation models based on neural networks could be promising tools to overcome these limitations and thus favor a transfer to the clinic, but the lack of sufficiently large and heterogeneous data sets is the main obstacle.

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Significance: In handheld laser speckle contrast imaging (LSCI), motion artifacts (MA) are inevitable. Suppression of MA leads to a valid and objective assessment of tissue perfusion in a wide range of medical applications including dermatology and burns. Our study shines light on the sources of these artifacts, which have not yet been explored. We propose a model based on optical Doppler effect to predict speckle contrast drop as an indication of MA. Aim: We aim to theoretically model MA when an LSCI system measuring on static scattering media is subject to translational displacements. We validate the model using both simulation and experiments. This is the crucial first step toward creating robustness against MA. Approach: Our model calculates optical Doppler shifts in order to predict intensity correlation function and contrast of the time-integrated intensity as functions of applied speed based on illumination and detection wavevectors. To validate the theoretical predictions, computer simulation of the dynamic speckles has been carried out. Then experiments are performed by both high-speed and low-framerate imaging. The employed samples for the experiments are a highly scattering matte surface and a Delrin plate of finite scattering level in which volume scattering occurs. Results: An agreement has been found between theoretical prediction, simulation, and experimental results of both intensity correlation functions and speckle contrast. Coefficients in the proposed model have been linked to the physical parameters according to the experimental setups. Conclusions: The proposed model provides a quantitative description of the influence of the types of illumination and media in the creation of MA. The accurate prediction of MA caused by translation based on Doppler shifts makes our model suitable to study the influence of rotation. Also the model can be extended for the case of dynamic media, such as live tissue.

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Given the excessive demolition of structures each year, the issues related to the generated structural waste are striking. Bricks being a major constituent in the construction industry, also hold a significant proportion of the construction waste generated annually. The reuse of this brick waste in new constructions is an optimal solution considering cost-effectiveness and sustainability. However, the problems related to the substandard peak stress and ultimate strain of concrete constructed with recycled brick aggregates (CRAs) limit its use in non-structural applications. The present study intends to improve the unsatisfactory mechanical characteristics of CRAs by utilizing low-cost glass fiber chopped strand mat (FCSM) sheets. The efficacy of FCSM sheets was assessed by wrapping them around CRA specimens constructed with different concrete strengths. A remarkable increase in the peak compressive stress and the ultimate strain of the CRA specimens were observed. For low, medium, and high strength CRAs, the ultimate strain improved by up to 320%, 308%, and 294%, respectively, as compared to the respective control specimens. Several existing analytical models were utilized to predict the peak compressive stress and ultimate strain of the CRAs strengthened using FCSM sheets. None of the considered models reproduced experimental results accurately. Therefore, equations were formulated using regression predicting the peak stress and ultimate strain of the CRAs confined with FCSM sheets. The predicted values were found to correlate well with the experimental values.

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Microfluidic artificial lungs (µALs) are being investigated for their ability to closely mimic the size scale and cellular environment of natural lungs. Researchers have developed µALs with small artificial capillary diameters (10-50 µm; to increase gas exchange efficiency) and with large capillary diameters (~100 µm; to simplify design and construction). However, no study has directly investigated the impact of capillary height on µAL properties. Here, we use Murray's law and the Hagen-Poiseuille equation to design single-layer, small-scale µALs with capillary heights between 10 and 100 µm. Each µAL contained two blood channel types: capillaries for gas exchange; and distribution channels for delivering blood to/from capillaries. Three designs with capillary heights of 30, 60, and 100 µm were chosen for further modeling, implementation and testing with blood. Flow simulations were used to validate and ensure equal pressures. Designs were fabricated using soft lithography. Gas exchange and pressure drop were tested using whole bovine blood. All three designs exhibited similar pressure drops and gas exchange; however, the µAL with 60 µm tall capillaries had a significantly higher wall shear rate (although physiologic), smaller priming volume and smaller total blood contacting surface area than the 30 and 100 µm designs. Future µAL designs may need to consider the impact of capillary height when optimizing performance.

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Objective. Patients who receive proton beam therapy are exposed to unwanted stray neutrons. Stray radiations increase the risk of late effects in normal tissues, such as second cancers and cataracts, and may cause implanted devices such as pacemakers to malfunction. Compared to therapeutic beams, little attention has been paid to modeling stray neutron exposures. In the past decade, substantial progress was made to develop semiempirical models of stray neutron dose equivalent, but models to routinely calculate neutron absorbed dose and kerma are still lacking. The objective of this work was to develop a new physics based analytical model to calculate neutron spectral fluence, kerma, and absorbed dose in a water phantom.Approach. We developed the model using dosimetric data from Monte Carlo simulations and neutron kerma coefficients from the literature. The model explicitly considers the production, divergence, scattering, and attenuation of neutrons. Neutron production was modeled for 120-250 MeV proton beams impinging on a variety of materials. Fluence, kerma and dose calculations were performed in a 30 × 180 × 44 cm3phantom at points up to 43 cm in depth and 80 cm laterally.Main Results. Predictions of the analytical model agreed reasonably with corresponding values from Monte Carlo simulations, with a mean difference in average energy deposited of 20%, average kerma coefficient of 21%, and absorbed dose to water of 49%.Significance. The analytical model is simple to implement and use, requires less configuration data that previously reported models, and is computationally fast. This model appears potentially suitable for integration in treatment planning system, which would enable risk calculations in prospective and retrospective cases, providing a powerful tool for epidemiological studies and clinical trials.

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Piezoelectric elements (PEMs) are used in a variety of applications. In this paper, we developed a full analytical model and a simple system identification (SI) method of a piezoelectric actuator, which includes piezostack elements and a three-stage amplification mechanism. The model was derived separately for each unit of the system. Next, the units were combined, while taking into account their coupling. The hysteresis phenomenon, which is significant in piezoelectric materials, is described extensively. The theoretical model was verified in a laboratory setup. This setup includes a piezoelectric actuator, measuring devices and an acquisition system. The measured results were compared to the theoretical results. Some of the most well-known forms of system identification are shown briefly, while a new and simple algorithm is described systematically and verified by the model. The main advantage of this work is to provide a solid background and domain knowledge of modelling and system identification methods for further investigations in the field of piezoelectric actuators. Due to their simplicity, both the model and the system identification method can be easily modified in order to be applied to other PEMs or other amplification mechanism methods. The main novelty of this work lies in applying a simple system identification algorithm while using the system-level approach for piezoelectric actuators. Lastly, this review work is concluded and some recommendations for researchers working in this area are presented.

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Multitrait models can increase the accuracy of breeding value prediction and reduce bias due to selection by using traits measured before and after it has occurred. However, as the number of traits grows, a similar trend is expected for the number of parameters to be estimated, which directly affects the computing power and the amount of data required. The aim of the present study was to apply reduced rank (principal components model-PCM) and factor analytical models (FAM), to estimate (co)variance components for nineteen traits, jointly evaluated in a single analysis in Campolina horses. A total of 18 morphometric traits (MT) and one gait visual score (GtS), along with genealogical records of 48,806 horses, were analysed under a restricted maximum likelihood framework. Nine PCM, nine FAM and one standard multitrait model (MTM) were fitted to the data and compared to find the best suitable model. Based on Bayesian information criterion, the best model was the FAM option, considering five common factors (FAM5). After performing an intraclass analysis, none of MT were genetically negatively correlated, whereas GtS was negatively related to all MT, except for the genetic correlations among GtS and BLL, and between GtS and BLLBL (0.01 and 0.10 respectively). From all MT, two traits were derived computing ratios involving other traits, those had negative correlations with others MT, but all favourable for selection. Similar patterns were observed between the genetic parameters obtained from MTM and FAM5 respectively. The heritability estimates ranged from 0.09 (head width) to 0.47 (height at withers). Our results indicated that FAM was efficient to reduce the multitrait analysis dimensionality, and therefore, traits can be combined based on the first three eigenvectors from the additive genetic (co)variance matrix. In addition, there was sufficient genetic variation for selection, benefiting its potential implementation in a breeding program.

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The properties of polylactic acid (PLA) filaments have not yet been analysed in detail, and they are strongly affected by the extrusion process used in some additive manufacturing systems. Here we present the mechanical, thermal, physical, and fractographical properties of an extruded filament (not the bulk material or scaffolds), the basic building block of any PLA structure printed via material extrusion. This research aims to create a reference point for the modelisation of additively manufactured structures via extrusion processes, as the main building block is characterised in detail for a deep understanding. Furthermore, we investigated the natural ageing (up to one year), the effect of the printing (extruding) temperature (180 and 190 °C), and the effect of the crosshead speed during the tensile tests (10-1 to 102 mm/min) to provide a deeper analysis of the material. The results showed that the material extruded at 190 °C performed better than the material extruded at 180 °C. However, after one hundred days of natural ageing, both materials behaved similarly. This was related to the flow-induced molecular orientation during the extrusion. The crosshead rate produced a logarithmic increase of the mechanical properties, consistent with the Eyring model. Additionally, the ageing produced significant changes in both the elastic modulus and the yield strength: from 2.4 GPa and 40 MPa, in one-day-aged samples, up to 4 GPa and 62 MPa once entirely aged. Finally, it was observed that the glass transition and the enthalpic relaxation increased with ageing, agreeing with the Kohlraushch-William-Watts model.

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In this work, an analytical procedure for the preliminary design of shape memory alloy spring-based actuators is investigated. Two static analytical models are considered and interconnected in the frame of the proposed procedure. The first model, based on the works from An, is able to determine the material properties of the SMA components by means of experimental test data and is able to size the SMA component based on the requirements of the system. The second model, based on a work from Spaggiari, helps to design and size an antagonist spring system that allows one to obtain the geometric characteristics of springs (SMA and bias) and the mechanical characteristics of the entire actuator. The combined use of these models allows one to define and size a complex SMA actuator based on the actuation load requirements. To validate the design procedure, static experimental tests have been performed with the entire SMA actuator.

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Over the years, induction machines (IMs) have become key components in industry applications as mechanical power sources (working as motors) as well as electrical power sources (working as generators). Unexpected breakdowns in these components can lead to unscheduled down time and consequently to large economic losses. As breakdown of IMs for failure study is not economically feasible, several IM computer models under faulty conditions have been developed to investigate the characteristics of faulty machines and have allowed reducing the number of destructive tests. This paper provides a review of the available techniques for faulty IMs modelling. These models can be categorised as models based on electrical circuits, on magnetic circuits, models based on numerical methods and the recently proposed in the technical literature hybrid models or models based on finite element method (FEM) analytical techniques. A general description of each type of model is given with its main benefits and drawbacks in terms of accuracy, running times and ability to reproduce a given fault.

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Having rigorous mathematical models is essential for the design and scaling of adsorption columns. In this study, the dynamic behavior of the sulfamethoxazole adsorption on sugarcane bagasse was studied and compared using analytical models and a theoretical mechanistic model. Initially, fixed-bed column tests were carried out at different flow rates and bed heights. Then, the experimental data were fitted with the most widely used analytical kinetic models, and their fit and fixed-bed parameters were compared with the mechanistic model. Of all analytical models analyzed, the Log-Gompertz model was the one that had the best agreed with experimental data. Although some analytical models fitted the experimental data accurately, their usefulness was questionable. Their parameters did not show a clear relationship with the change in operating conditions, and in certain cases had different behavior from that observed in experimentation. Conversely, the mechanistic model not only predicted the breakthrough curves with great accuracy in the initial and transition stage (R2 > 0.92; SSE < 0.06), but it also estimated relevant parameters. Additionally, the effects of the global mass transfer coefficient (Ki) and the axial dispersion coefficient (Dz) on breakthrough curves were studied using the mechanistic model. Increasing Ki increased the slope of the breakthrough curves with a faster adsorption rate. Similarly, high values of Dz produced lower adsorption capacities of the adsorbent; and it was established that the axial dispersion is relevant in SMX adsorption on SB. The theoretical model presented can be used for the design, scaling, and optimization of adsorption columns.

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Structural evaluation of masonry against dynamic seismic actions invariably requires appropriate cyclic compression constitutive models. However, not many research studies have been dedicated to date to investigate the cyclic compression behaviour of masonry. Therefore, series of experimental investigation followed by analytical model verification were employed in this research to better understand the cyclic compression characteristics of masonry. Twelve masonry wallettes were experimentally tested under cyclic compression loading with different unit-to-mortar assemblies, which are commonly found in masonry structures. The experimental results indicated that the cyclic compression behaviour is greatly influenced by the masonry compressive strength and deformation properties. Thereafter, the ability of five literature analytical models to predict the masonry structural response under cyclic compression loading was investigated. The advantages and limitations of these models are presented and discussed, and the most appropriate analytical model to define the cyclic compression characteristics of masonry has been evaluated and reported. The suggested analytical model is shown to predict the cyclic compression characteristics of different masonry assemblies such as the envelop response, the stiffness degradation, the plastic strain history of the unloading and reloading stages.

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Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant's life history and environmental variability that ultimately influences a population's ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.

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The extended introduction in this paper reviews the theoretical modelling of leading- and trailing-edge noise, various bioinspired aerofoil adaptations to both the leading and trailing edges of blades, and how these adaptations aid in the reduction of aerofoil-turbulence interaction noise. Attention is given to the agreement between current theoretical predictions and experimental measurements, in particular, for turbulent interactions at the trailing edge of an aerofoil. Where there is a poor agreement between theoretical models and experimental data the features neglected from the theoretical models are discussed. Notably, it is known that theoretical predictions for porous trailing-edge adaptations do not agree well with experimental measurements. Previous works propose the reason for this: theoretical models do not account for surface roughness due to the porous material and thus omit a key noise source. The remainder of this paper, therefore, presents an analytical model, based upon the acoustic analogy, to predict the far-field noise due to a rough surface at the trailing edge of an aerofoil. Unlike previous roughness noise models which focus on roughness over an infinite wall, the model presented here includes diffraction by a sharp edge. The new results are seen to be in better agreement with experimental data than previous models which neglect diffraction by an edge. This new model could then be used to improve theoretical predictions for far-field noise generated by turbulent interactions with a (rough) porous trailing edge. This article is part of the theme issue 'Frontiers of aeroacoustics research: theory, computation and experiment'.

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