Irregular Spacing Research Articles

Ridges and riblets: Shark skin surfaces versus biomimetic models

Shark skin has been an inspiration for biomimetic materials and structures due to its role in reducing drag and enhancing thrust, properties believed to be due to the textured surface composed of ridges on the surface of individual tooth-like scales (denticles). Attempts to replicate the hydrodynamic performance of shark skin have involved manufacturing both engineered riblets and fabrics with textured surfaces. However, there are no studies that compare the surface ornamentation of shark denticles to bioinspired materials. Using three-dimensional surface profilometry we analyzed the cross-sectional profile of the surface of shark denticles at two locations on 17 species and compared these data to values obtained from engineered structures (e.g., riblets) and competition swimsuits that are often proposed as having a comparable surface texture to shark skin. Of the variables measured, crown aspect ratio (p = 0.007), ridge height, ridge spacing, ridge aspect ratio, and ridge bumpiness (all p < 0.001) differed among the three materials. Overall, engineered riblet surfaces were very different than biological shark skin. Some of the competition swimsuit materials were more shark-like, with the fabric texture having similar height variation, but with irregular ridge spacing. Cross-sectional profile, which includes pathlength and aspect ratio in addition to ridge spacing and height, is an important feature of the skin’s surface, affecting water flow over the individual denticles, and future research will address these parameters. Quantitative 3D analysis of the surface of real shark denticle ridges enables the design of more biomimetic engineered shark skin surfaces.

Data-driven construction of Three-dimensional subsurface geological models from limited Site-specific boreholes and prior geological knowledge for underground digital twin

A digital twin is a digital replica or virtual representations of 3D physical entities in the real world. In practice, it is challenging for 3D modelling of subsurface stratigraphy in an underground digital twin due to insufficient site-specific measurements and a lack of efficient 3D spatial prediction tools. In this study, a data-driven and deep learning method, called IC-XGBoost3D, is proposed to build a 3D geological model from limited site-specific boreholes and 2D training images reflecting prior geological knowledge. Anisotropic stratigraphic relationships are learned from two perpendicular 2D training images, and the extracted stratigraphic statistics serve as the input for pre-training a 2D simulation slice. A sequence of 2D simulation slices is then simulated with constraints by site-specific boreholes and subsequently reassembled into a 3D geological model. Note that the parameters of the deep learning method are calibrated with site-specific data and prior training images before being applied to develop the geological model. The model performance is demonstrated and validated using an illustrative example. The proposed method can efficiently generate an anisotropic 3D geological model as a point cloud from two perpendicular training images and sparse boreholes with a high prediction accuracy. More importantly, the proposed method not only infers the most probable 3D geological domain, but also provides a quantitative evaluation of associated 3D stratigraphic uncertainty. Effects of irregular borehole spacing and single training image on the simulation performance are also investigated.

Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?

Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinical registries we are facing the question of what can be learned from the data in an early phase of the study, when only a baseline characterization and one follow-up measurement are available. Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual. Irregular spacing in time could then be used to gain more information on individual dynamics by leveraging similarity of individuals. We provide a brief overview of how variational autoencoders (VAEs), as a deep learning approach, can be linked to ordinary differential equations(ODEs) for dynamic modeling, and then specifically investigate the feasibility of such an approach that infers individual-specific latent trajectories by including regularity assumptions and individuals' similarity. We also provide a description of this deep learning approach as a filtering task to give a statistical perspective. Using simulated data, we show to what extent the approach can recover individual trajectories from ODE systems with two and four unknown parameters and infer groups of individuals with similar trajectories, and where it breaks down. The results show that such dynamic deep learning approaches can be useful even in extreme small data settings, but need to be carefullyadapted.

Vibration from Electric Hand-Held Harvesters for Olives

Olive harvesting is the most expensive cultivation operation in areas where full mechanization is not possible due to structural conditions such as low tree density, old trees, irregular spacing, and terraced fields, which are very frequent in many small Italian farms. Under these conditions the use of hand-held vibrating harvesters is quite wide spread, because they are capable of approximately three times the productivity of workers using manual harvesting methods. Unfortunately, the use of these machines exposes the operators to hand-arm vibration risk and acceleration values are affected by several factors, including harvester kinematics, rod material and geometry, and load conditions. In this study several models of electrical portable harvesters, obtained by combining six harvester heads and four rods (one telescopic), were tested under idling and load conditions, measuring acceleration values on the rod, near the hand positions. Assuming the use of the machinery for 4 h per day, the result is a level of daily vibration exposure A(8) for the most exposed hand ranging from about 11 to 40 m/s2, much higher than the daily exposure limit value of 5 m/s2 stated by the European Directive 2002/44/EC. With the same harvester head, reduction in vibration may be achieved by using carbon fiber rods rather than aluminum ones or by increasing the rod diameter. The most significant reduction is achievable by designing harvester heads whose kinematics inherently incorporate oscillation compensation.

SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces

Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces. Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs, we can obtain a well-trained SegNet model. When any unseen gather including the one with irregular trace spacing is inputted, the SegNet can output the probability distribution of different categories for waveform classification. Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background. Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer SegNet can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones, even when the proportion of randomly missing traces reaches 50%, 21 traces are missing consecutively, or traces are missing regularly.

Study on reamer tools to suppress spiral marks

Reaming is a finishing process that enlarges the drilled pilot holes to improve the accuracy of roundness. In machining using a reamer with regular tooth spacing, a polygonal deformation phenomenon called spiral mark occurs in the machined hole, due to self-excited vibration caused by time delay, which causes a problem of deterioration in product accuracy. In the previous studies, the authors clarified the generation mechanism of spiral marks and verified the suppressing effect of the spiral marks with the 6-blade irregular tooth spacing theoretically and experimentally. In this paper, the authors discuss the optimal design of 4-blade irregular tooth spacing reamers theoretically. The authors conducted the experiments to confirm the suppressive effect of the optimum design experimentally.

Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.

Ground-Roll Attenuation Using a Dual-Filter-Bank Convolutional Neural Network

Ground-roll attenuation is very challenging because of its high amplitudes and overlapping frequency content with desired signals. A particular challenge is to recover weak reflections underneath strong masking ground-roll. We propose a dual-filter bank setup combined with two convolutional neural networks (CNNs) to realize ground-roll attenuation. The rationale for using a dual-filter bank strategy is that it permits using two CNNs with different input kernel sizes and different complexities to recognize and extract broad-scale (low-wavelength) and narrow-scale (high-wavelength) features separately. We also apply a frequency filter to create a preliminary separation between the signal and the noise. In addition, we use a radial trace transform that focuses desired signal to a smaller area, facilitating separation of the reflections and ground-roll and accelerating training. The network training strategy combines synthetic and field data examples, in addition to noise injection to augment the number of available training samples. Tests on synthetic and field datasets show that the proposed strategy achieves superior ground-roll attenuation compared with standard methods, even in the case of data with irregular spatial spacing or ground-roll characteristics not contained in the training data.

Fluorescent Staining of Silicone Micro- and Nanopatterns for Their Optical Imaging

Performance of engineered surfaces can be enhanced by making them hydrophobic or superhydrophobic via coating them with low-surface-energy micro- and nanopatterns. However, the wetting phenomena of particularly irregular shape and spacing (super)hydrophobic patterns such as polysiloxane coatings are not yet fully understood from a microscopic perspective. Here, we show a new method to collect 3D confocal images from irregular polysiloxane micro- and nanorods from a single rod resolution to discuss their wetting response over long liquid/solid interaction times and quantify the length and diameter of these rods. To collect such 3D confocal images, fluorescent dye containing water droplets were left on our superhydrophobic and hydrophobic polysiloxane coated surfaces. Then their liquid/solid interfaces were imaged at different staining scenarios: (i) using different fluorescent dyes, (ii) when the droplets were in contact with surfaces, or (iii) after the droplets were taken away from the surface at the end of staining. Using such staining strategies, we could resolve the micro- and nanorods from root to top and determine their length and diameter, which were then found to be in good agreement with those obtained from their electron microscopy images. 3D confocal images in this paper, for the first time, present the long-time existence of more than one wetting state under the same droplet in contact with surfaces, as well as external and internal three-phase contact lines shifting and pinning. In the end, these findings were used to explain the time-dependent wetting kinetics of our surfaces. We believe that the proposed imaging strategy here will, in the future, be used to study many other irregular patterned (super)antiwetting surfaces to describe their wetting theory, which is today impossible due to the complicated surface geometry of these irregular patterns.

Angle-dependent sound absorption estimation using a compact microphone array.

This paper proposes a method for estimating the angle-dependent sound absorption coefficient of a large material sample using a compact microphone array. The method relies on the description of the pressure field as a pair of in-going and out-going waves or using an image source model and stands as a generalization of the classical two-microphone method. The array includes an irregular spacing normal to the surface to avoid spatial aliasing. Furthermore, the benefit of additional microphones parallel to the sample is investigated, while keeping the array compact. The approach is validated against the transfer matrix method as well as against locally and non-locally reactive surface models and compared to the two-microphone method. The sensitivity of the estimation to uncertainties in the microphone positions is evaluated by means of a Monte Carlo approach. Measurements above melamine foam and gravel samples are presented and illustrate the reduced uncertainty in the sound absorption estimation. In particular, the proposed method exhibits improved robustness compared to the two-microphone method, especially at low frequencies.

Effective bending modulus of thin-ply composites with non-uniform fibre spacing

Composite materials are gaining increasing interest for design purposes in many industries such as aeronautical and automotive, not only because of their reduced weight, but also due to their enhanced mechanical properties. The heterogeneity inherent in the nature of composites arises not only because of the mismatch in fibre/matrix properties but also due to the fibre arrangement within the matrix. Previous studies have shown that classical homogeneous elasticity models can either underestimate or overestimate stiffness properties for small ply thicknesses. As such, both positive (stiffer) and negative (more compliant) size effects become increasingly prevalent with decreasing ply thickness, when considering regular distributions of fibres within a matrix material. Current work addresses the effects of irregular fibre spacing on size effects and identifies a characteristic length that can be used to represent the heterogeneity in an equivalent homogeneous micropolar model. Results show that, based on the type of irregular fibre spacing, both kinds of positive and negative size effect arise in a composite ply. As a result, the bending characteristic length embedded in micropolar theory can be defined even for the difficult case whereby fibres are stiffer than matrix depending on the nature of the irregular fibre spacing.

Eye movement patterns and reading ability in children.

Eye movements are integral to the reading process. This study characterised the eye movement patterns of children differentiated by their reading ability, while completing a saccadic eye movement test with irregular target spacing (Development Eye Movement (DEM) test) using a novel eye movement classification algorithm. Participants included 196 Grade 2 Australian schoolchildren (mean age: 7.9±0.3years) who completed a computerised version of the DEM test, while their eye movements were recorded (Tobii TX300 eye-tracker). Children also completed a standardised reading comprehension test, which categorised them into below average and average or above reading ability groups. A novel eye movement classification algorithm was developed that considered the vertical and horizontal eye movements of each child. Compared to children with average or above reading ability, the below average reading group displayed poor vertical eye movement control, demonstrated by a significantly greater proportion of interline eye movements (vertical eye movements away from the current line) (p<0.001). Differences in horizontal eye movements were also observed, with below average readers demonstrating a smaller proportion of expected forward saccades (p<0.001) (within-line forward saccades with horizontal amplitude between the minimum and maximum horizontal spacing between digits), while this group also displayed longer fixation durations (p=0.001). The below average reading group demonstrated significantly poorer results on all standard DEM metrics than the average or above reading ability group: horizontal subtest time (p<0.001), vertical subtest time (p=0.004) and ratio (p=0.004). Children exhibiting below average reading ability were poorer at maintaining control of vertical (interline), as well as horizontal, eye movements compared to children with average or above reading ability. Future studies should explore the mechanisms underlying these differences, particularly in vertical eye movements, given that reading paragraphs (involving multiple lines of text) requires accurate eye movements in both the vertical and horizontal direction.

Modelling car ownership dynamics based on irregularly spaced panel data

This paper investigates car ownership dynamics based on irregularly spaced panel data of Denmark. The data originates from a national travel survey, where a fraction of the respondents are recurrent due to the random sampling scheme of the survey. While this creates a rich sample collected over a long time period and with desired variation between the panel observations, it introduces an estimation challenge due to the irregular spacing of the panel observations. This challenge is addressed by estimating changes in car ownership based on a generalised ordered logit model in which the irregular nature of the spacing between panel observations is controlled by including panel-specific weights in the log-likelihood function. The estimated model, which is formulated as a first-difference approach, includes several variables explaining the change of car ownership. Specifically, it is found that accessibility improvements, measured as the number of people that can be reached by public transport within a certain time interval, significantly reduce the likelihood of acquiring additional cars in the observation period. In line with the literature, it is also confirmed that changes in income, number of adults and driver’s licenses within the household have a significant impact on household car ownership changes.

Distance transform based text-line extraction from unconstrained handwritten document images

Text-line extraction (TLE) is the process of segmenting a document page into lines of text for processing by modules such as language and writer identification or Optical Character Recognition (OCR). Designing of an appropriate TLE method is always a challenging research problem especially in the domain of unconstrained handwritten documents. This is because of the vast number of potential interactions between the text lines. For example, these lines are not always straight, lines written close to each other can overlap with ascenders and descenders, and lines can interact with other content on the page. In this paper, we present a novel language-independent text-line extraction method for unconstrained handwritten documents which handles complexities such as touching and multi-skewed text lines, overlapping characters and irregular inter-line spacing. Our method preprocesses the document pages using a distance transform based method and uses a novel path detection algorithm to separate individual text-line. The proposed method has been tested on six standard datasets which are publicly available and the experimental results show that our method achieves a promising accuracy over state-of-the-art TLE methods.

Self-organizing Strategies in Game of Agent Movement

In this paper, a stochastic game model of self-organization of strategies of stochastic game of mobile agents in the form of cyclic behavioral patterns, which consist of coordinated strategies for moving agents in a limited discrete space, is developed. The behavioral pattern of a multi-agent system is a visualized form of orderly movement of agents that arises from their initial chaotic movement during the learning of a stochastic game. The mobility of multi-step stochastic game agents is ensured by the fact that in discrete moments of time they randomly, simultaneously and independently choose their own pure strategy of moving in one of the possible directions. Current player payments are defined as loss functions that depend on the strategies of neighboring players. These functions are formed from the penalty for irregular spacing of agents in a limited discrete space and the penalty for collisions when moving agents. Random selection of players’ strategies aims to minimize their average loss functions. The generation of sequences of pure strategies is performed by a discrete distribution based on the vectors of mixed strategies. The elements of the vectors of mixed strategies are the conditional probabilities of choosing the appropriate pure displacement strategies. Mixed strategies change over time, adaptively taking into account the value of current losses. This provides an increase in the probability of choosing those strategies that lead to a decrease in the functions of average losses. The dynamics of the vectors of mixed strategies is determined by the Markov recurrent method, for the construction of which a stochastic approximation of the modified condition of complementary non- rigidity, which is valid at Nash equilibrium points, is performed, and a projection operator for expandable unit epsilon simplex is applied. The convergence of the recurrent game method is ensured by compliance with the fundamental conditions and limitations of stochastic approximation. The stochastic game begins with untrained mixed strategies that set a chaotic picture of moving agents. During the learning of the stochastic game, the vectors of mixed strategies are purposefully changed so as to ensure an orderly, conflict-free movement of agents. As a result of computer simulation of stochastic game, cyclic patterns of self-organization of mobile agents on the surface of a discrete torus and within a rectangular region on a plane are obtained. The reliability of the experimental studies was confirmed by the similarity of the obtained patterns of moving agents for different sequences of random variables. The results of the study are proposed to be used in practice for the construction of distributed systems with elements of self-organization, solving various flow and transport problems and collective decision-making in conditions of uncertainty.

A New Solution Based on UTD-PO Method for Multiple-Diffraction by a Series of Buildings with Irregular Height and Spacing

A new solution based on the uniform theory of diffraction-physical optics (UTD-PO) method is presented for the prediction of multiple diffractions caused by a series of buildings with irregular height and interspacing. The prediction results, obtained using the proposed solution, are compared with the available measurement data and results based on UTD slope-diffraction method to validate the accuracy. Further, the computational complexity of the proposed algorithm is compared with that of the available UTD slope-diffraction method to demonstrate its computational efficiency.

Volatility Estimation and Forecasts Based on Price Durations

Abstract We investigate price duration variance estimators that have long been neglected in the literature. In particular, we consider simple-to-construct non-parametric duration estimators, and parametric price duration estimators using autoregressive conditional duration specifications. This paper shows (i) how price duration estimators can be used for the estimation and forecasting of the integrated variance of an underlying semi-martingale price process and (ii) how they are affected by discrete and irregular spacing of observations, market microstructure noise, and finite price jumps. Specifically, we contribute to the literature by constructing the asymptotic theory for the non-parametric estimator with and without the presence of bid/ask spread and time discreteness. Further, we provide guidance about how our estimators can best be implemented in practice by appropriately selecting a threshold parameter that defines a price duration event, or by averaging over a range of non-parametric duration estimators. We also provide simulation and forecasting evidence that price duration estimators can extract relevant information from high-frequency data better and produce more accurate forecasts than competing realized volatility and option-implied variance estimators, when considered in isolation or as part of a forecasting combination setting.

The yeast ISW1b ATP-dependent chromatin remodeler is critical for nucleosome spacing and dinucleosome resolution

Isw1 and Chd1 are ATP-dependent nucleosome-spacing enzymes required to establish regular arrays of phased nucleosomes near transcription start sites of yeast genes. Cells lacking both Isw1 and Chd1 have extremely disrupted chromatin, with weak phasing, irregular spacing and a propensity to form close-packed dinucleosomes. The Isw1 ATPase subunit occurs in two different remodeling complexes: ISW1a (composed of Isw1 and Ioc3) and ISW1b (composed of Isw1, Ioc2 and Ioc4). The Ioc4 subunit of ISW1b binds preferentially to the H3-K36me3 mark. Here we show that ISW1b is primarily responsible for setting nucleosome spacing and resolving close-packed dinucleosomes, whereas ISW1a plays only a minor role. ISW1b and Chd1 make additive contributions to dinucleosome resolution, such that neither enzyme is capable of resolving all dinucleosomes on its own. Loss of the Set2 H3-K36 methyltransferase partly phenocopies loss of Ioc4, resulting in increased dinucleosome levels with only a weak effect on nucleosome spacing, suggesting that Set2-mediated H3-K36 trimethylation contributes to ISW1b-mediated dinucleosome separation. The H4 tail domain is required for normal nucleosome spacing but not for dinucleosome resolution. We conclude that the nucleosome spacing and dinucleosome resolving activities of ISW1b and Chd1 are critical for normal global chromatin organisation.

Luminescence feature of new 3,6-di(thiazolidin-5-one-2-yl)-carbazole derivative: synthesis, photophysical properties, density functional theory studies, and crystal shape effect.

A new carbazole chromophore conjugated with substituted thiazolidine-4-one (CzPT) was synthesized by applying the Knoevenagel reaction between 3,6-diformyl-N-hexylcarbazole and ethyl 2-aceto-2-(5-oxo-3-phenylthiazolidin-2-ylidene)acetate. The chemical structure of the new derivative (CzPT) was elucidated by spectral studies. The CzPT absorption spectra in different solvents exhibited a red shift for λmax by increasing solvent polarity. Bands at 430-474 nm appeared and were attributed to intramolecular charge transfer with high π-π* characteristics. CzPT fluorescence spectra exhibited a red shift after increasing the solvent polarity. To understand the Stokes' shift ( behaviour of the CzPT derivative referring to the polarity of solvents, Lippert-Mataga and linear solvation-energy relationship (LSER) models were employed in which the LSER exhibited respectable results compared with Lippert-Mataga (r2 = 0.9707). Moreover, time-dependent density functional theory absorption spectra in hexane and dimethylformamide showed that λmax had a major contribution in the highest occupied molecular orbital to lowest unoccupied molecular orbital transition in both solvents. In addition, the reduced uniformity of crystal features may lead to dislocation or anomalous arrangement of crystals with irregular spacing, which automatically enhances the optical properties of such crystals.

‘Haste makes waste’: The tradeoff between walking speed and target-stepping accuracy

BackgroundWhen environmental conditions require accurate foot placement during walking (e.g., on a rough path), we typically walk slower to avoid tripping, slipping or stumbling. Likewise, hurrying too much is a common situational circumstance of walking-related falls. This suggests a tradeoff between walking speed and stepping accuracy in situations that demand precise foot placement. Research questionHow can this expected tradeoff between walking speed and stepping accuracy best be parameterized? MethodsIn Experiment 1, participants (n = 20) walked at five different speeds over an irregularly spaced sequence of projected stepping targets. Participants were instructed to place their feet accurately onto the targets, while following a constant-speed cue running alongside the walkway. Stepping accuracy was parameterized as overall (RMSE, root mean square error), variable (VE) and constant (CE) stepping errors, quantified over targets as well as per target. In Experiment 2, we determined preferred walking speed and stepping accuracy for regularly and irregularly spaced stepping targets. ResultsRepeated-measures ANOVAs revealed that RMSE and VE grew linearly with increasing speeds, both over targets as well as per target. Per target CE varied in magnitude and sign with variations in inter-target spacing: for shorter inter-target spacing targets were overshot (CE > 0), while for longer inter-target spacing targets were undershot (CE < 0). This effect was stronger for faster speeds and for targets preceded by the shortest and longest inter-target spacing. Preferred walking speed and per-target VE did not differ between regularly and irregularly spaced targets. SignificanceParticipants stepped less precisely when walking faster. The linear increase in VE with faster speeds was consistent with Schmidt’s law regarding the speed-accuracy tradeoff. The systematic comparison of stepping errors over regularly and irregularly spaced stepping-target conditions further provided important clues on how to best parameterize stepping accuracy: per stepping target using VE (i.e., stepping inconsistency), complemented with CE (i.e., stepping bias) in case of irregular inter-target spacing.