bluuebunny/crossref_metadata_2025 huggingface dataset:
IQ-MPM
Authors: Fang Yu , Qu Ziyin , Li Minchen , Zhang Xinxin , Zhu Yixin , Aanjaneya Mridul , Jiang Chenfanfu
Published: August 2020

We propose a novel scheme for simulating two-way coupled interactions between nonlinear elastic solids and incompressible fluids. The key ingredient of this approach is a
ghost matrix
operator-splitting scheme for strongly coupled nonlinear elastica and incompressible fluids through the weak form of their governing equations. This leads to a stable and efficient method handling
large time steps
under the CFL limit while using
a single monolithic solve
for the coupled pressure fields, even in the case with highly nonlinear elastic solids. The use of the Material Point Method (MPM) is essential in the designing of the scheme, it not only preserves discretization consistency with the hybrid Lagrangian-Eulerian fluid solver, but also works naturally with our novel
interface quadrature
(IQ) discretization for free-slip boundary conditions. While traditional MPM suffers from sticky numerical artifacts, our framework naturally supports discontinuous tangential velocities at the solid-fluid interface. Our IQ discretization results in an easy-to-implement, fully particle-based treatment of the interfacial boundary, avoiding the additional complexities associated with intermediate level set or explicit mesh representations. The efficacy of the proposed scheme is verified by various challenging simulations with fluid-elastica interactions.
Simple and scalable frictional contacts for thin nodal objects
Authors: Daviet Gilles
Published: August 2020

Frictional contacts are the primary way by which physical bodies interact, yet they pose many numerical challenges. Previous works have devised robust methods for handling collisions in elastic bodies, cloth, or fiber assemblies such as hair, but the performance of many of those algorithms degrades when applied to objects with different topologies or constitutive models, or simply cannot scale to high-enough numbers of contacting points.

In this work we propose a unified approach, able to handle a large class of dynamical objects, that can solve for millions of contacts with unbiased Coulomb friction while keeping computation time and memory usage reasonable. Our method allows seamless coupling between the various simulated components that comprise virtual characters and their environment. Furthermore, our proposed approach is simple to implement and can be easily integrated in popular time integrators such as Projected Newton or ADMM.
Learned motion matching
Authors: Holden Daniel , Kanoun Oussama , Perepichka Maksym , Popa Tiberiu
Published: August 2020

In this paper we present a learned alternative to the Motion Matching algorithm which retains the positive properties of Motion Matching but additionally achieves the scalability of neural-network-based generative models. Although neural-network-based generative models for character animation are capable of learning expressive, compact controllers from vast amounts of animation data, methods such as Motion Matching still remain a popular choice in the games industry due to their flexibility, predictability, low preprocessing time, and visual quality - all properties which can sometimes be difficult to achieve with neural-network-based methods. Yet, unlike neural networks, the memory usage of such methods generally scales linearly with the amount of data used, resulting in a constant trade-off between the diversity of animation which can be produced and real world production budgets. In this work we combine the benefits of both approaches and, by breaking down the Motion Matching algorithm into its individual steps, show how learned, scalable alternatives can be used to replace each operation in turn. Our final model has no need to store animation data or additional matching meta-data in memory, meaning it scales as well as existing generative models. At the same time, we preserve the behavior of Motion Matching, retaining the quality, control, and quick iteration time which are so important in the industry.
Variance-aware path guiding
Authors: Rath Alexander , Grittmann Pascal , Herholz Sebastian , Vévoda Petr , Slusallek Philipp , Křivánek Jaroslav
Published: August 2020

Path guiding is a promising tool to improve the performance of path tracing algorithms. However, not much research has investigated what target densities a guiding method should strive to learn for optimal performance. Instead, most previous work pursues the zero-variance goal: The local decisions are guided under the assumption that all other decisions along the random walk will be sampled perfectly. In practice, however, many decisions are poorly guided, or not guided at all. Furthermore, learned distributions are often marginalized, e.g., by neglecting the BSDF. We present a generic procedure to derive theoretically optimal target densities for local path guiding. These densities account for variance in nested estimators, and marginalize provably well over, e.g., the BSDF. We apply our theory in two state-of-the-art rendering applications: a path guiding solution for unidirectional path tracing [Müller et al. 2017] and a guiding method for light source selection for the many lights problem [Vévoda et al. 2018]. In both cases, we observe significant improvements, especially on glossy surfaces. The implementations for both applications consist of trivial modifications to the original code base, without introducing any additional overhead.
MGCN
Authors: Wang Yiqun , Ren Jing , Yan Dong-Ming , Guo Jianwei , Zhang Xiaopeng , Wonka Peter
Published: August 2020

We propose a novel framework for computing descriptors for characterizing points on three-dimensional surfaces. First, we present a new non-learned feature that uses graph wavelets to decompose the Dirichlet energy on a surface. We call this new feature
Wavelet Energy Decomposition Signature
(WEDS). Second, we propose a new
Multiscale Graph Convolutional Network
(MGCN) to transform a non-learned feature to a more discriminative descriptor. Our results show that the new descriptor WEDS is more discriminative than the current state-of-the-art non-learned descriptors and that the combination of WEDS and MGCN is better than the state-of-the-art learned descriptors. An important design criterion for our descriptor is the robustness to different surface discretizations including triangulations with varying numbers of vertices. Our results demonstrate that previous graph convolutional networks significantly overfit to a particular resolution or even a particular triangulation, but MGCN generalizes well to different surface discretizations. In addition, MGCN is compatible with previous descriptors and it can also be used to improve the performance of other descriptors, such as the heat kernel signature, the wave kernel signature, or the local point signature.
Principal symmetric meshes
Authors: Pellis Davide , Wang Hui , Kilian Martin , Rist Florian , Pottmann Helmut , Müller Christian
Published: August 2020

The isolines of principal symmetric surface parametrizations run symmetrically to the principal directions. We describe two discrete versions of these special nets/quad meshes which are dual to each other and show their usefulness for various applications in the context of fabrication and architectural design. Our discretization of a principal symmetric mesh comes naturally with a family of spheres, the so-called Meusnier and Mannheim spheres. In our representation of principal symmetric meshes, we have direct control over the radii of theses spheres and the intersection angles of the parameter lines. This facilitates tasks such as generating Weingarten surfaces including constant mean curvature surfaces and minimal surfaces. We illustrate the potential of Weingarten surfaces for paneling doubly curved freeform facades by significantly reducing the number of necessary molds. Moreover, we have direct access to curvature adaptive tool paths for cylindrical CNC milling with circular edges as well as flank milling with rotational cones. Furthermore, the construction of curved support structures from congruent circular strips is easily managed by constant sphere radii. The underlying families of spheres are in a natural way discrete curvature spheres in analogy to smooth Möbius and Laguerre geometry which further leads to a novel discrete curvature theory for principal symmetric meshes.
ENIGMA
Authors: Edelstein Michal , Ezuz Danielle , Ben-Chen Mirela
Published: August 2020

In this paper we propose a
fully automatic
method for shape correspondence that is widely applicable, and especially effective for non isometric shapes and shapes of different topology. We observe that fully-automatic shape correspondence can be decomposed as a hybrid discrete/continuous optimization problem, and we find the best
sparse
landmark correspondence, whose sparse-to-dense extension minimizes a
local metric distortion.
To tackle the
combinatorial
task of landmark correspondence we use an evolutionary
genetic algorithm
, where the local distortion of the sparse-to-dense extension is used as the objective function. We design novel
geometrically
guided genetic operators, which, when combined with our objective, are highly effective for non isometric shape matching. Our method outperforms state of the art methods for automatic shape correspondence both quantitatively and qualitatively on challenging datasets.
Variable-width contouring for additive manufacturing
Authors: Hornus Samuel , Kuipers Tim , Devillers Olivier , Teillaud Monique , Martínez Jonàs , Glisse Marc , Lazard Sylvain , Lefebvre Sylvain
Published: August 2020

In most layered additive manufacturing processes, a tool solidifies or deposits material while following pre-planned trajectories to form solid
beads.
Many interesting problems arise in this context, among which one concerns the planning of trajectories for filling a planar shape as densely as possible. This is the problem we tackle in the present paper. Recent works have shown that allowing the bead width to vary along the trajectories helps increase the filling density. We present a novel technique that, given a deposition width range, constructs a set of closed beads whose width varies within the prescribed range and fill the input shape. The technique outperforms the state of the art in important metrics: filling density (while still guaranteeing the absence of bead overlap) and trajectories smoothness. We give a detailed geometric description of our algorithm, explore its behavior on example inputs and provide a statistical comparison with the state of the art. We show that it is possible to obtain high quality fabricated layers on commodity FDM printers.
A low-parametric rhombic microstructure family for irregular lattices
Authors: Tozoni Davi Colli , Dumas Jérémie , Jiang Zhongshi , Panetta Julian , Panozzo Daniele , Zorin Denis
Published: August 2020

New fabrication technologies have significantly decreased the cost of fabrication of shapes with highly complex geometric structure. One important application of complex fine-scale geometric structures is to create variable effective elastic material properties in shapes manufactured from a single material. Modification of material properties has a variety of uses, from aerospace applications to soft robotics and prosthetic devices. Due to its scalability and effectiveness, an increasingly common approach to creating spatially varying materials is to partition a shape into cells and use a parametric family of small-scale geometric structures with known effective properties to fill the cells.

We propose a new approach to solving this problem for extruded, planar microstructures. Differently from existing methods for two-scale optimization based on regular grids with square periodic cells, which cannot conform to an arbitrary boundary, we introduce cell decompositions consisting of (nearly) rhombic cells. These meshes have far greater flexibility than those with square cells in terms of approximating arbitrary shapes, and, at the same time, have a number of properties simplifying small-scale structure construction. Our main contributions include a new family of 2D cell geometry structures, explicitly parameterized by their effective Young's moduli
E
, Poisson's ratios
v
, and rhombic angle
α
with the geometry parameters expressed directly as smooth spline functions of
E, v
, and
α.
This family leads to smooth transitions between the tiles and can handle a broad range of rhombic cell shapes. We introduce a complete material design pipeline based on this microstructure family, composed of an algorithm to generate rhombic tessellation from quadrilateral meshes and an algorithm to synthesize the microstructure geometry. We fabricated a number of models and experimentally demonstrated how our method, in combination with material optimization, can be used to achieve the desired deformation behavior.
Unsupervised
K
-modal styled content generation
Authors: Sendik Omry , Lischinski Dani , Cohen-Or Daniel
Published: August 2020

The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D. Generative Adversarial Networks (GANs) and style control mechanisms, such as Adaptive Instance Normalization (AdaIN), have proved particularly effective in this context, culminating in the state-of-the-art StyleGAN architecture. While such models are able to learn diverse distributions, provided a sufficiently large training set, they are not well-suited for scenarios where the distribution of the training data exhibits a multi-modal behavior. In such cases, reshaping a uniform or normal distribution over the latent space into a complex multi-modal distribution in the data domain is challenging, and the generator might fail to sample the target distribution well. Furthermore, existing unsupervised generative models are not able to control the mode of the generated samples independently of the other visual attributes, despite the fact that they are typically disentangled in the training data.

In this paper, we introduce uMM-GAN, a novel architecture designed to better model multi-modal distributions, in an unsupervised fashion. Building upon the StyleGAN architecture, our network learns multiple modes, in a
completely unsupervised manner
, and combines them using a set of learned weights. We demonstrate that this approach is capable of effectively approximating a complex distribution as a superposition of multiple simple ones. We further show that uMM-GAN effectively disentangles between modes and style, thereby providing an independent degree of control over the generated content.
Nonlinear color triads for approximation, learning and direct manipulation of color distributions
Authors: Shugrina Maria , Kar Amlan , Fidler Sanja , Singh Karan
Published: August 2020

We present nonlinear color triads, an extension of color gradients able to approximate a variety of natural color distributions that have no standard interactive representation. We derive a method to fit this compact parametric representation to existing images and show its power for tasks such as image editing and compression. Our color triad formulation can also be included in standard deep learning architectures, facilitating further research.
Lagrangian neural style transfer for fluids
Authors: Kim Byungsoo , Azevedo Vinicius C. , Gross Markus , Solenthaler Barbara
Published: August 2020

Artistically controlling the shape, motion and appearance of fluid simulations pose major challenges in visual effects production. In this paper, we present a neural style transfer approach from images to 3D fluids formulated in a Lagrangian viewpoint. Using particles for style transfer has unique benefits compared to grid-based techniques. Attributes are stored on the particles and hence are trivially transported by the particle motion. This intrinsically ensures temporal consistency of the optimized stylized structure and notably improves the resulting quality. Simultaneously, the expensive, recursive alignment of stylization velocity fields of grid approaches is unnecessary, reducing the computation time to less than an hour and rendering neural flow stylization practical in production settings. Moreover, the Lagrangian representation improves artistic control as it allows for multi-fluid stylization and consistent color transfer from images, and the generality of the method enables stylization of smoke and liquids likewise.
Vid2Curve
Authors: Wang Peng , Liu Lingjie , Chen Nenglun , Chu Hung-Kuo , Theobalt Christian , Wang Wenping
Published: August 2020

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods, because thin structures often lack distinct point features and have severe self-occlusion. We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera. Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on. Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures. Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods.
Computational design of skintight clothing
Authors: Montes Juan , Thomaszewski Bernhard , Mudur Sudhir , Popa Tiberiu
Published: August 2020

We propose an optimization-driven approach for automated, physics-based pattern design for tight-fitting clothing. Designing such clothing poses particular challenges since large nonlinear deformations, tight contact between cloth and body, and body deformations have to be accounted for. To address these challenges, we develop a computational model based on an embedding of the two-dimensional cloth mesh in the surface of the three-dimensional body mesh. Our
Lagrangian-on-Lagrangian
approach eliminates contact handling while coupling cloth and body. Building on this model, we develop a physics-driven optimization method based on sensitivity analysis that automatically computes optimal patterns according to design objectives encoding body shape, pressure distribution, seam traction, and other criteria. We demonstrate our approach by generating personalized patterns for various body shapes and a diverse set of garments with complex pattern layouts.
Robust motion in-betweening
Authors: Harvey Félix G. , Yurick Mike , Nowrouzezahrai Derek , Pal Christopher
Published: August 2020

In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesises high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of
in-betweening
in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a
time-to-arrival embedding
that allows variations of the transition length with a single model. The other is a
scheduled target noise
vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.
HeadBlaster
Authors: Liu Shi-Hong , Yen Pai-Chien , Mao Yi-Hsuan , Lin Yu-Hsin , Chandra Erick , Chen Mike Y.
Published: August 2020

We present HeadBlaster, a novel wearable technology that creates motion perception by applying ungrounded force to the head to stimulate the vestibular and proprioception sensory systems. Compared to motion platforms that tilt the body, HeadBlaster more closely approximates how lateral inertial and centrifugal forces are felt during real motion to provide more persistent motion perception. In addition, because HeadBlaster only actuates the head rather than the entire body, it eliminates the mechanical motion platforms that users must be constrained to, which improves user mobility and enables room-scale VR experiences. We designed a wearable HeadBlaster system with 6 air nozzles integrated into a VR headset, using compressed air jets to provide persistent, lateral propulsion forces. By controlling multiple air jets, it is able to create the perception of lateral acceleration in 360 degrees. We conducted a series of perception and human-factor studies to quantify the head movement, the persistence of perceived acceleration, and the minimal level of detectable forces. We then explored the user experience of HeadBlaster through two VR applications: a custom surfing game, and a commercial driving simulator together with a commercial motion platform. Study results showed that HeadBlaster provided significantly longer perceived duration of acceleration than motion platforms. It also significantly improved realism and immersion, and was preferred by users compared to using VR alone. In addition, it can be used in conjunction with motion platforms to further augment the user experience.
Using Bayesian induction methods in risk assessment and communication
Authors: Applen J. D.
Published: August 2020

Bayes's theorem allows us to use subjective thinking to find numerical values to formulate assessments of risk. It is more than a mathematical formula; it can be thought of as an iterative process that challenges us to imagine the potential for "unknown, unknowns." The heuristics involved in this process can be enhanced if they take into consideration some of the established risk assessment and communication models used today in technical communication that are concerned with the social construction of meaning and the kairos involved in rhetorical situations. Understanding the connection between Bayesian analysis and risk communication will allow us to better convey the potential for risk that is based on probabilistic assumptions.
Prototyping and public art
Authors: Oppegaard Brett
Published: August 2020

This experience report shares lessons learned from a multi-staged prototyping process, over a five-year period, that involved the creation and iterative development of a mobile platform and dozens of prototype examples of interactive locative-media artifacts, including locative journalism. Thematically linked to a public art collection, the mobile app was designed as a research instrument aimed at an external audience of passersby, actively using smartphones. This paper documents and outlines key decisions made about the platform and content in response to observed experiences. It also identifies emergent areas of research potential intertwined in the undertaking of such a prototyping process.
SmarterROUTES—A Data-driven Context-aware Solution for Personalized Dynamic Routing and Navigation
Authors: Bock Jelle De , Verstockt Steven
Published: August 2020

SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route by calculating the lowest-cost path from A to B using an underlying network of weighted connections. Our implementation goes a step further and ranks a set of such routes. The ranking is the result of additional weighing based on governmental data sets, extracts of the OpenStreetMap (OSM) database, or periodically adapted extracts of web services (e.g., Representational state transfer APIs). The selected data sets and their relative contributions to the overall ranking mechanism can be dynamically adapted by the end-user. Another major contribution toward a fully personalised navigation experience is the implementation of a road scene complexity scoring mechanism. Road complexity is estimated based on the combined input from geospatial data sources, traffic data, sensor analysis, and Street View–based complexity analysis. The latter input source uses Street View images as input for a Densenet Convolutional Neural Network (CNN), pre-trained on buildings using Bag-of-Words and Structure-from-motion features, outputting an image descriptor. The current version uses an adapted version of this network to predict road complexity scores. The predicted values correspond with the subjective complexity judgements of the end-users.
A hypertension patient-derived iPSC model demonstrates a role for G protein-coupled estrogen receptor in hypertension risk and development
Authors: Fredette Natalie C. , Malik Eliyah , Mukhtar Marah L. , Prossnitz Eric R. , Terada Naohiro
Published: August 2020

Hypertension (HTN) is a polyfactorial disease that can manifest severe cardiovascular pathologies such as heart failure or stroke. Genome-wide association studies (GWAS) of HTN indicate that single-nucleotide polymorphisms (SNPs) contribute to increased risk for HTN and resistance to some HTN drug regimens (Hiltunen TP et al., J Am Heart Assoc 4: e001521, 2015; Le MT et al., PLoS One 8: e52062, 2013; McDonough CW et al., J Hypertens 31: 698–704, 2013; Vandell AG et al., Hypertension 60: 957–964, 2012). However, cellular mechanistic insights of such SNPs remain largely unknown. Using a bank of induced pluripotent stem cells (iPSCs) derived from patients with HTN and CRISPR/Cas9-mediated gene-editing approach, we investigated the effects of a female HTN risk-associated SNP (rs1154431) of the G protein-coupled estrogen receptor (GPER) (Bassuk SS, Manson JE., Clin Chem 60: 68–77, 2014) in vascular endothelial cells. Although GPER1 deletion reduced endothelial nitric oxide synthase (eNOS) activation in iPSC-derived endothelial cells (iECs), the polymorphism itself did not significantly affect eNOS and NO production in a comparison of isogenic hemizygous iECs expressing either normal (P16) or HTN-associated (L16) GPER. Interestingly, we demonstrate for the first time that GPER plays a role in regulation of adhesion molecule expression and monocyte adhesion to iECs. Moreover, the L16 iECs had higher expression of inflammation genes than P16 iECs, implying that the risk variant may affect carrier individuals through increased inflammatory activity. This study further indicates that iPSCs are a useful platform for exploring mechanistic insights underlying hypertension GWAS endeavors.
Torpor expression is associated with differential spermatogenesis in hibernating eastern chipmunks
Authors: Gagnon Marianne F. , Lafleur Christine , Landry-Cuerrier Manuelle , Humphries Murray M. , Kimmins Sarah
Published: August 2020

Hibernators suppress physiological processes when expressing torpor, yet little is known about the effects of torpor on male reproductive physiology. Studies of hibernating mammals suggest that deep torpor negatively impacts spermatogenesis and that transitions between torpor and euthermic arousals increase cellular oxidative stress, with potentially damaging effects on sperm. Here, we hypothesize that variation in torpor expression affects the reproductive readiness of hibernators by impacting their sperm production. To test this, we examined the relationship between torpor expression and spermatogenesis in captive eastern chipmunks ( Tamias striatus). We determined torpor depth with temperature data loggers and assessed its relationship with spermatogenesis by examining spermatogenic progression, cell division, sperm counts, sperm maturity, and DNA damage. We show that deep hibernators (high levels of torpor) largely halted spermatogenesis in late hibernation in comparison with shallow hibernators (low levels of torpor), where ongoing spermatogenesis was observed. Despite these differences in spermatogenic state during hibernation, spermatogenic progression, sperm numbers, and maturity did not differ in spring, potentially reflecting similar degrees of reproductive readiness. Interestingly, shallow hibernators exhibited higher rates of DNA damage in spermatogenic cells during hibernation, with this trend reversing in spring. Our results thus indicate that once heterothermy is terminated, deep hibernators resume spermatogenesis but are characterized by higher rates of DNA damage in spermatogenic cells at the seasonal stage when spring mating commences. Therefore, our study confirmed posthibernation recovery of sperm production but also a potential impact of deep torpor expression during winter on DNA damage in spring.
Polarity-dependent adaptations of motoneuron electrophysiological properties after 5-wk transcutaneous spinal direct current stimulation in rats
Authors: Bączyk Marcin , Drzymała-Celichowska Hanna , Mrówczyński Włodzimierz , Krutki Piotr
Published: August 2020

Transcutaneous spinal direct current stimulation applied systematically for 5 wk evoked polarity-dependent adaptations in the electrophysiological properties of rat spinal motoneurons. After anodal polarization sessions, motoneurons became more excitable and could evoke higher maximum discharge frequencies during repetitive firing than motoneurons in the sham polarization group. However, no significant adaptive changes of motoneuron properties were observed after repeated cathodal polarization in comparison with the sham control group.
Variations in loop gain and arousal threshold during NREM sleep are affected by time of day over a 24-hour period in participants with obstructive sleep apnea
Authors: Puri Shipra , El-Chami Mohamad , Shaheen David , Ivers Blake , Panza Gino S. , Badr M. Safwan , Lin Ho-Sheng , Mateika Jason H.
Published: August 2020

Loop gain and the arousal threshold during non-rapid eye movement (NREM) sleep are greater in the morning compared with the afternoon and evening. Loop gain measures are correlated to chemoreflex sensitivity and the critical closing pressure measured during NREM sleep in the evening, morning, and afternoon. Breathing (in)stability and efficaciousness of treatments for obstructive sleep apnea may be modulated by a circadian rhythmicity in loop gain and the arousal threshold.
Is the healthy respiratory system built just right, overbuilt, or underbuilt to meet the demands imposed by exercise?
Authors: Dempsey Jerome A. , La Gerche Andre , Hull James H.
Published: August 2020

In the healthy, untrained young adult, a case is made for a respiratory system (airways, pulmonary vasculature, lung parenchyma, respiratory muscles, and neural ventilatory control system) that is near ideally designed to ensure a highly efficient, homeostatic response to exercise of varying intensities and durations. Our aim was then to consider circumstances in which the intra/extrathoracic airways, pulmonary vasculature, respiratory muscles, and/or blood-gas distribution are underbuilt or inadequately regulated relative to the demands imposed by the cardiovascular system. In these instances, the respiratory system presents a significant limitation to O2transport and contributes to the occurrence of locomotor muscle fatigue, inhibition of central locomotor output, and exercise performance. Most prominent in these examples of an “underbuilt” respiratory system are highly trained endurance athletes, with additional influences of sex, aging, hypoxic environments, and the highly inbred equine. We summarize by evaluating the relative influences of these respiratory system limitations on exercise performance and their impact on pathophysiology and provide recommendations for future investigation.
Reaching movements are automatically redirected to nearby options during target split
Authors: Kurtzer Isaac L. , Muraoka Tetsuro , Singh Tarkeshwar , Prasad Mark , Chauhan Riddhi , Adhami Elan
Published: August 2020

This work demonstrates that target selection during an ongoing reach is automatically linked to the option nearest a voided target. Online reaction times for two options are longer than for redirecting to a single option. Attempts to override the near-target tendency result in a high number of errors at the normal delay and further delays when the attempt is successful.
Approximate Analytical Solution for a Coupled System of Fractional Nonlinear Integrodifferential Equations by the RPS Method
Authors: Al e’damat Ayed
Published: August 2020

In this work, a modified residual power series method is implemented for providing efficient analytical and approximate solutions for a class of coupled system of nonlinear fractional integrodifferential equations. The proposed algorithm is based on the concept of residual error functions and generalized power series formula. The fractional derivative is described under the Caputo concept. To illustrate the potential, accuracy, and efficiency of the proposed method, two numerical applications of the coupled system of nonlinear fractional integrodifferential equations are tested. The numerical results confirm the theoretical predictions and depict that the suggested scheme is highly convenient, is quite effective, and practically simplifies computational time. Consequently, the proposed method is simple, accurate, and convenient in handling different types of fractional models arising in the engineering and physical systems.
First-Order and High-Order Repetitive Control for Single-Phase Grid-Connected Inverter
Authors: Guo Cheng , Zhong Linzhen , Zhao Jun , Gao Guanbin , Huang Yingbo
Published: August 2020

With the increasing demand of users for power sources and quality, how to provide high-quality renewable clean energy has become a key issue of power electronics. The main idea of this paper is to develop a composite control including a PI control and repetitive control for a single-phase grid-connected inverter to eliminate the effects of harmonics, which can obtain better steady-state and dynamic responses of the single-phase inverter system and reduce the net current harmonics. The modelling of a single-phase inverter is first introduced; then a first-order repetitive control is developed for the proposed grid-connected inverter. Moreover, a high-order repetitive controller is adopted to further improve the robustness against the uncertainties in the period of signals. The stability and performance analysis are given for the first-order repetitive control and high-order repetitive control. Finally, comparative simulations are conducted in a circuit-level inverter model, which show the effectiveness of the proposed method.
Oxidation Mechanism of Biomedical Titanium Alloy Surface and Experiment
Authors: Ma Kai , Zhang Rui , Sun Junlong , Liu Changxia
Published: August 2020

The biological activity, biocompatibility, and corrosion resistance of implants depend primarily on titanium dioxide (TiO2) film on biomedical titanium alloy (Ti6Al4V). This research is aimed at getting an ideal temperature range for forming a dense titanium dioxide (TiO2) film during titanium alloy cutting. This article is based on Gibbs free energy, entropy changes, and oxygen partial pressure equations to perform thermodynamic calculations on the oxidation reaction of titanium alloys, studies the oxidation reaction history of titanium alloys, and analyzes the formation conditions of titanium dioxide. The heat oxidation experiment was carried out. The chemical composition was analyzed with an energy dispersive spectrometer (EDS). The results revealed that titanium dioxide (TiO2) is the main reaction product on the surface below 900°C. Excellent porous oxidation films can be obtained between 670°C and 750°C, which is helpful to improve the bioactivity and osseointegration of implants.
Detrimental Effects of UVB on Retinal Pigment Epithelial Cells and Its Role in Age-Related Macular Degeneration
Authors: Mahendra Camille Keisha , Tan Loh Teng Hern , Pusparajah Priyia , Htar Thet Thet , Chuah Lay-Hong , Lee Vannajan Sanghiran , Low Liang Ee , Tang Siah Ying , Chan Kok-Gan , Goh Bey Hing
Published: August 2020

Retinal pigment epithelial (RPE) cells are an essential part of the human eye because they not only mediate and control the transfer of fluids and solutes but also protect the retina against photooxidative damage and renew photoreceptor cells through phagocytosis. However, their function necessitates cumulative exposure to the sun resulting in UV damage, which may lead to the development of age-related macular degeneration (AMD). Several studies have shown that UVB induces direct DNA damage and oxidative stress in RPE cells by increasing ROS and dysregulating endogenous antioxidants. Activation of different signaling pathways connected to inflammation, cell cycle arrest, and intrinsic apoptosis was reported as well. Besides that, essential functions like phagocytosis, osmoregulation, and water permeability of RPE cells were also affected. Although the melanin within RPE cells can act as a photoprotectant, this photoprotection decreases with age. Nevertheless, the changes in lens epithelium-derived growth factor (LEDGF) and autophagic activity or application of bioactive compounds from natural products can reverse the detrimental effect of UVB. Additionally, in vivo studies on the whole retina demonstrated that UVB irradiation induces gene and protein level dysregulation, indicating cellular stress and aberrations in the chromosome level. Morphological changes like retinal depigmentation and drusen formation were noted as well which is similar to the etiology of AMD, suggesting the connection of UVB damage with AMD. Therefore, future studies, which include mechanism studies via in vitro or in vivo and other potential bioactive compounds, should be pursued for a better understanding of the involvement of UVB in AMD.
Macrocystic Adult Penile Lymphangioma: A Rare Presentation
Authors: Kalathia Jaisukh , Patel Kaushal , Agrawal Santosh
Published: August 2020

Adult penile lymphangioma (APL) is a rare multifactorial vascular malformation. Usually, it is asymptomatic and located on the coronal sulcus or shaft of the penis. It is benign in nature with rare chances of recurrence. APLs can be approached via various modalities from simple “watch and wait” to electrofulguration, laser ablation, and surgical excision. We report a rare variant of lymphangioma of the penis inculcating the shaft being excised surgically with favorable outcome along with brief review of literature.
Stochastic Stabilization of Malware Propagation in Wireless Sensor Network via Aperiodically Intermittent White Noise
Authors: Zhong Xiaojing , Peng Baihao , Deng Feiqi , Liu Guiyun
Published: August 2020

In this paper, we propose a novel heterogeneous model to describe the propagation dynamics of malware (viruses, worms, Trojan horses, etc.) in wireless sensor networks. Our model takes into consideration different battery-level sensor nodes contrary to existing models. In order to control the spread of malware, we design an aperiodically intermittent controller driven by white noise, which has striking advantages of lower cost and more flexible control strategy. We give a distinct condition on stability in probability one using graph-theoretical Lyapunov function and stochastic analysis method. Our results show that the nonlinear malware propagation system can be stabilized by intermittent stochastic perturbation under the intermittent time related to stochastic perturbation intensity. Our theoretical results can be applied to understand the observed mechanisms of malware and design interventions to control the spread of malware. Numerical simulations illustrate our analytical results clearly.
Iterative Analysis of Nonlinear BBM Equations under Nonsingular Fractional Order Derivative
Authors: Ali Gauhar , Ahmad Israr , Shah Kamal , Abdeljawad Thabet
Published: August 2020

The present research work is devoted to investigate fractional order Benjamin-Bona-Mahony (FBBM) as well as modified fractional order FBBM (FMBBM) equations under nonlocal and nonsingular derivative of Caputo-Fabrizio (CF). In this regards, some qualitative results including the existence of at least one solution are established via using some fixed point results of Krasnoselskii and Banach. Further on using an iterative method, some semianalytical results are also studied. The concerned tool is formed when the Adomian decomposition method is coupled with some integral transform like Laplace. Graphical presentations are given for various fractional orders. Also, the concerned method is also compared with some variational-type perturbation method to demonstrate the efficiency of the proposed method.
A Novel Speckle Noise Removal Algorithm Based on ADMM and Energy Minimization Method
Authors: Chen Bo , Lv Yan , Zou Jinbin , Chen Wensheng , Pan Binbin
Published: August 2020

Speckle noise removal in medical ultrasound images is a challenging task. In this paper, a new model is proposed to removal speckle noise, alternating direction method of multipliers algorithm is employed to solve the new energy minimization model. The convexity, existence, and uniqueness of the new energy minimization model’s solution are proved. Series of experiments are designed in this paper. Numerical results show that the new algorithm can reduce the step effect effectively obtain good results in visual effect and quantitative measures by comparing with some traditional models.
Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets
Authors: Li Rui , Luo Ying , Zhang Qun , Chen Yijun , Liang Jia
Published: August 2020

Bistatic radar imaging can overcome limitations of monostatic radar imaging and obtain abundant target feature information; thus, it is followed with interest. Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.
Influence of Altitude on Caffeine, 5-Caffeoylquinic Acid, and Nicotinic Acid Contents of Arabica Coffee Varieties
Authors: Girma Bealu , Gure Abera , Wedajo Feyisa
Published: August 2020

The influence of altitude on caffeine, 5-caffeoylquinic acid (5-CQA), and nicotinic acid contents of Arabica coffee varieties grown in Southwest Ethiopia was investigated. High-performance liquid chromatography with diode array detector (HPLC-DAD) was used for the determination of the target analytes. Coffee samples were collected from four coffee varieties, named as 74112, 7454, 7440, and 74110, which are cultivated in high, mid, and low altitudes in the study area. The findings of the study showed that the contents of caffeine and 5-CQA in both raw and roasted coffee beans decrease as the growing altitude increases and, thus, for all varieties, their highest concentrations were recorded in lowland coffee beans. Nevertheless, the contents of nicotinic acid increase as the altitude rises and, thus, the highest nicotinic acid content was recorded in highland coffee samples. Besides, after roasting, the contents of caffeine were increased, whereas the contents of 5-CQA were lowered, indicating the possibility of its degradation during the roasting process. Both green and roasted coffee beans also contained relatively higher concentrations of nicotinic acids. Other than the growing altitudes, the contents of caffeine, 5-CQA, and nicotinic acid in coffee beans also vary with coffee varieties. Therefore, coffee varieties that are cultivated at various altitudes may have different biochemical compositions such as caffeine, CGAs, and nicotinic acid that could greatly influence the flavor, aroma, and stimulating attributes of coffee cup quality as well as dietary benefits.
Silencing of Long Noncoding RNA LINC00324 Interacts with MicroRNA-3200-5p to Attenuate the Tumorigenesis of Gastric Cancer via Regulating BCAT1
Authors: Wang Shuang , Cheng Yuanyuan , Yang Pingping , Qin Guang
Published: August 2020

Purpose. This study was aimed at exploring the effect of long noncoding RNA LINC00324 (LINC00324) on gastric cancer (GC) and the potential molecular mechanisms. Methods. The expression of LINC00324 and miR-3200-5p in GC tissues and cells was detected by qRT-PCR. LINC00324 was silenced in GC cells by transfection of si-LINC00324. Then, the proliferation, migration, and invasion of GC cells were analyzed by MTT, wound healing, and transwell assays, respectively. The interactions between LINC00324 and miR-3200-5p and between miR-3200-5p and BCAT1 were determined by a dual-luciferase reporter and/or RNA pull-down assay. Results. The expression of LINC00324 was upregulated in GC cells and tissues, but miR-3200-5p was downregulated. Silencing of LINC00324 inhibited the proliferation, migration, and invasion of GC cells. LINC00324 directly targeted miR-3200-5p, and miR-3200-5p directly targeted BCAT1. si-LINC00324 negatively regulated BCAT1 expression via binding to miR-3200-5p. Furthermore, silencing of LINC00324 reversed the promoting effects of BCAT1 on the proliferation, migration, and invasion of GC cells. Conclusion. Silencing of LINC00324 inhibited the proliferation, migration, and invasion of GC cells through regulating the miR-3200-5p/BCAT1 axis.
Stability and Multistability of a Bounded Rational Mixed Duopoly Model with Homogeneous Product
Authors: Zhou Wei , Zhao Na , Chu Tong , Chang Ying-Xiang
Published: August 2020

In this paper, a mixed duopoly dynamic model with bounded rationality is built, where a public-private joint venture and a private enterprise produce homogeneous products and compete in the same market. The purpose of this research is to study the stability and the multistability of the established model. The local stability of all the equilibrium points is discussed by using Jury condition, and the stability region of the Nash equilibrium point has been given. A special fractal structure called “hub of periodicity” has been found in the two-parameter space by numerical simulation. In addition, the phenomena of multistability (also called coexistence of multiple attractors) are also studied using basins of attraction and 1-D bifurcation diagrams with adiabatic initial conditions. We find that there are two different coexistences of multiple attractors. And, the fractal structure of the attracting basin is also analyzed, and the formation mechanisms of “holes” and “contact” bifurcation have been revealed. At last, the long-term profits of the enterprises are studied. We find that some enterprises can even make more profits under a chaotic circumstance.