Shape Distance Research Articles

Adaptive 360° video timeline exploration in VR environment

Timeline control is a crucial interaction during video viewing, aiding users in quickly locating or jumping to specific points in the video playback, especially when dealing with lengthy content. 360°videos, with their ability to offer an all-encompassing view, have gradually gained popularity, providing a more immersive experience compared to videos with a single perspective. While most 360°videos are currently displayed on two-dimensional screens, the timeline design has largely remained similar to that of conventional videos. However, virtual reality (VR) headsets provide a more immersive viewing experience for 360°videos and offer additional dimensions for timeline design. In this paper, we initially explored 6 timeline design styles by varying the shape and interaction distance of the timeline, aiming to discover designs more suitable for the VR environment of 360°videos. Subsequently, we introduced an adaptive timeline display mechanism based on eye gaze sequences to optimize the timeline, addressing issues like obstructing the view and causing distractions when the timeline is consistently visible. Through two studies, we first demonstrated that in the 360°space, the three-dimensional timeline performs better in terms of usability than the two-dimensional one, and the reachable timeline has advantages in performance and experience over the distant one. Secondly, we verified that, without compromising interaction efficiency and system usability, the adaptive display timeline gained more user preference due to its accurate prediction of user timeline needs.

Gaussianity Evaluation of HD-sEMG Signals with Aging and Sex During Low and Moderate Isometric Contractions of the Biceps Brachii

IntroductionAging is associated with muscle decline, which alters both functional and anatomical properties of the neuromuscular system. These modifications can be reflected in high-density surface electromyography (HD-sEMG) signals. This study examines how age and sex impact the shape of the amplitude Probability Density Function (PDF) of HD-sEMG signals. Materials and MethodsMonopolar HD-sEMG signals were collected from the Biceps Brachii in a cohort of 17 individuals: 10 women (mean age: 22.9 ± 3.6 years) and 7 men (mean age: 24.4 ± 2.5 years) in the younger group, and 10 women (mean age: 69.8 ± 4.8 years) and 7 men (mean age: 72.8 ± 2.7 years) in the elderly group. The recordings were conducted during an elbow flexion at both 20% and 40% maximum voluntary contraction. The signal amplitude was evaluated using root means square amplitude (RMSA) and the PDF shape of each HD-sEMG signal was assessed through skewness, excess Kurtosis, and robust functional statistics. These shape distance metrics evaluate the departure from Gaussianity related to muscle aging. a) We conducted a comparison study of the HD-sEMG PDF shapes between younger and elderly individuals. b) Evaluating differences between men and women. c) Considering monopolar and Laplacian electrode configurations that are sensitive to different muscle regions. ResultsA) The HD-sEMG PDFs of elderly subjects demonstrated a lower departure from Gaussianity than their younger counterparts. B) Women exhibited lower RMSA values than men, and, on average, a lower departure from Gaussianity whatever the age and contraction level C) Trends of departure from Gaussianity with contraction level, seems to be influenced by the electrode configuration. In fact, a decrease in Gaussianity departure is observed with monopolar recordings where an increase is observed with Laplacian one, clearly indicating different muscle region assessment. DiscussionThe findings highlight the influence of factors such aging, sex, contraction level and electrode montage on the shape of the HD-sEMG PDF, emphasizing the significance of using this descriptor for monitoring and better assessment of muscle aging.

The influence of existing tunnel shape and pillar distance on cross tunnel interaction

ABSTRACT Underground transportation systems often involve multiple tunnels constructed closely together. Previous studies mainly focus on interaction between circular tunnels; by contrast, interaction mechanisms involving non-circular tunnels are not well understood. In this study, four physical three-dimensional centrifuge tests were performed in dry sand, simulating the response of existing circular and horseshoe-shaped tunnels to a newly excavated tunnel. Two different ratios between pillar depth and tunnel diameter (P/D) of 0.5 and 2.0 were considered. Furthermore, three-dimensional numerical back-analyses considering small-strain stiffness were undertaken. Results reveal that the ground settlement above an existing horseshoe-shaped tunnel is less sensitive to pillar depth than for circular ones. Furthermore, for P/D = 0.5, the existing horseshoe-shaped tunnel experiences both vertical and horizontal compression; more stress reduction occurs vertically than horizontally. A circular tunnel for the same pillar depth becomes compressed vertically but elongated horizontally; stress reduction around the existing circular tunnel is less vertically than horizontally. However, for P/D = 2.0, both types of tunnel become elongated vertically and compressed horizontally because of a larger reduction in vertical stresses than horizontal ones. These results demonstrate that both pillar depth and shape profoundly affect tunnel deformation mechanisms.

Integrating wing morphometrics and mitochondrial DNA analysis to assess the filaria vector Mansonia uniformis (Diptera: Culicidae) populations in Thailand.

Mansonia uniformis (Diptera: Culicidae) is recognized as a vector of Brugia malayi and has been reported to transmit Wuchereria bancrofti, both causing lymphatic filariasis in humans. This study employed geometric morphometrics (GM) to investigate wing shape variation and analyzed genetic diversity through cytochrome c oxidase subunit 1 (COI) gene analyses in Ma. uniformis populations across Thailand. Wing GM analyses indicated significant differences in wing shape based on Mahalanobis distances among nearly all population pairs (p < 0.05), with no significant correlation between wing shape and geographic distance (r = 0.210, p > 0.05). Genetic analyses identified 63 haplotypes and 49 polymorphic sites, with the overall population exhibiting a nucleotide diversity of 0.006 (± 0.001) and a haplotype diversity of 0.912 (± 0.017). Deviations from neutrality, as indicated by Tajima's D and Fu's FS tests for the overall Ma. uniformis populations in Thailand, were statistically significant and negative, suggesting population expansion (both p < 0.05). Analysis of molecular variance revealed no significant genetic structure when all populations were categorized based on collection sites and geographic regions. However, significant differences in FST values were observed between some populations. These findings enhance our understanding of the geographical and genetic factors influencing Ma. uniformis populations, which are crucial for developing effective control strategies in Thailand.

Expert knowledge data-driven based actor–critic reinforcement learning framework to solve computationally expensive unit commitment problems with uncertain wind energy

With the expansion of power grid, unaffordable computational cost and time will pose serious challenges of time-efficient scheduling in unit commitment problem (UCP). However, existing optimization methods, i.e., mathematical programming methods and meta-heuristic algorithms, are powerless and time-consuming to handle computationally expensive UCP (CEUCP). Thus, reinforcement learning methods with strong inference and time-saving performances are motivated to solve the computationally expensive challenges in tackling CEUCPs. In this paper, a novel expert knowledge data-driven based actor–critic (AC) reinforcement learning methodology is proposed for solving CEUCPs. Specifically, in the proposed AC reinforcement learning methodology, expert knowledge, data-driven surrogate model, and improved meta-heuristic algorithm are integrated for further performance enhancement. Firstly, a novel action selection mechanism (based on the expert knowledge of thermal units characteristic) is integrated into AC to improve the efficiency of network training. Secondly, an improved extreme learning machine (ELM) data-driven surrogate model is proposed to build reward function in AC. In detail, original cost function in reward is replaced by a lightweight ELM model. Shape distance is integrated into ELM for enhancing accuracy. Finally, original marine predators algorithm (MPA) is improved for obtaining optimal dispatching decisions and rewards of AC method quickly and correctly. Original search pattern is replaced by quantum based representation for boosting convergence. The excellent performances of the proposed AC framework are verified by simulations of 10-units, 100-units, and 100-units with wind energy test systems.

Optimal Design for New Rotary Engine with Geometric Shape Functions on Combustion Chamber and Ports

This study presents an optimal design for a new rotary engine. Instantaneous shape functions were applied to the combustion chamber, and intake and exhaust ports were derived based on the rotor and housing design functions; then optimized design parameters were derived for a rotary engine. The three main parameters were shape distance, eccentric distance and rotor thickness. The design process for the optimized internal shape was subsequently defined considering target specifications for the rotary engine, and a prototype engine was designed and fabricated with a 336 cm3 intake volume. The compression pressure for the prototype engine was compared with the motoring test and calculated. These outcomes confirmed the new engine’s feasibility, and the derived geometric shape functions will be helpful to ensure optimal design for rotary engines using one- or three-dimensional computational fluid dynamics.

Phenotypic differentiation despite gene flow: Beak morphology, bite performance, and population genetics of Loggerhead Shrikes (Lanius ludovicianus).

Previous studies of Loggerhead Shrikes (Laniidae: Lanius ludovicianus) in North America have indicated considerable intraspecific genetic and phenotypic differentiation, but the congruence between genetic and phenotypic differentiation remains obscure. We examined phenotypic differences in beak shape and bite force among geographic groupings across a 950 km range, from the lower Imperial Valley to the upper Central Valley of California, USA. We integrated these analyses with a population genetic analysis of six microsatellite markers to test for correspondence between phenotypic and genetic differences among geographic groups. We found significant phenotypic differentiation despite a lack of significant genetic differentiation among groups. Pairwise beak shape and bite force distances nevertheless were correlated with genetic (F ST) distances among geographic groups. Furthermore, the phenotypic and genetic distance matrices were correlated with pairwise geographic distances. Takentogether, these results suggest that phenotypic differences might be influenced by neutral processes, inbreeding (as indicated by high heterozygosity deficiencies we observed), local adaptation, and/or phenotypic plasticity.

Contactless weighing method based on deep learning and acoustic levitation

Abstract Acoustic weighing is a promising contactless method for screening the mass of micro-nano objects as it avoids contact contamination and losses. Existing acoustic weighing methods determine the mass of an object by detecting its oscillation trajectory with a laser sensor. However, this method suffers from several limitations, such as short measurement distance, poor accuracy in measuring transparent objects, and inducing damage to photosensitive samples. To solve these issues, this work proposes a contactless weighing method based on location-aware neural network (LANet) and acoustic levitation. The proposed LANet is a deep learning-based image processing method that detects object bit oscillation trajectories completely contactless, regardless of the color, shape, and oscillation distance of the levitated object. We employ a cross-stage aggregation module and cross-mixed feature pyramid strategy to build LANet network depth for enhanced feature extraction. In addition, to create a contactless environment, we built an acoustic levitation system, which drives the oscillation of objects. Finally, we verified the accuracy and effectiveness of the method. The results show that the proposed network can accurately detect the oscillation trajectories of various objects with high detection performance, even for small objects in low-contrast backgrounds. Meanwhile, the proposed method can accurately measure the mass of objects with a percentage error of no more than 7.83%.

Online coupling of miniaturized HPLC and high performance thin layer chromatography by a fractionation unit for effect directed analysis

The rising discharge of anthropogenic chemicals into aquatic environments poses a significant threat, necessitating effective monitoring strategies. This study introduces an innovative approach to effect-directed analysis (EDA) by coupling liquid chromatography (LC) with high-performance thin-layer chromatography (HPTLC), utilizing a modified MALDI spotter. The objective is to optimize fractionation parameters for sample application and assess the method's viability in identifying acetylcholinesterase (AChE) inhibitors, specifically malathion, parathion, and chlorfenvinphos.The optimization process involves controlling sample volume, spot shape, and spot distances on HPTLC plates. Successful application is demonstrated by a miniaturized LC system coupled to the HPTLC plate via spotter, allowing effective separation and identification of AChE inhibitors. The study further explores the method's application to water samples from a river with predominantly agricultural drainage area.Spiked samples reveal distinct active spots, identified through extraction and subsequent high-resolution mass spectrometry (HRMS) measurements. However, results indicate the absence of AChE inhibitors in non-spiked water samples, affirming the efficacy of the EU ban on most organophosphate pesticides. The usefulness of HPTLC in separation of coeluting substances from HPLC is emphasized, demonstrating its suitability for the effect-directed analysis of complex samples.This work shows the integration of HPTLC with liquid chromatography for EDA, offering a powerful tool for identifying and monitoring AChE inhibitors in water samples. The approach addresses limitations in current monitoring strategies and provides insights into the presence and impact of chemicals in aquatic ecosystems. The study contributes to ongoing efforts to enhance water quality monitoring, aligning with the principles of the EU Water Framework Directive.

Influences of the wall distance and initial shape on the dynamic behaviors of near-wall bubbles

The phenomenon of buoyancy-driven bubbles near a fluid-solid interface is common in both natural settings and industrial processes, such as oil and gas exploration and production. The intricate dynamics of bubbles are profoundly shaped by their interactions with adjacent solid walls. In this study, a direct numerical simulation of bubbles rising near a wall is conducted using the volume of fluid method. The motion behavior and wake vortex structure of bubbles with three types of migration trajectories (linear, zigzag, and spiral) are analyzed. The influences of the initial shape and wall distance of the bubbles on their motion trajectory, rising velocity, and wake structure is investigated. The results show that the presence of a nearby wall obstructs the diffusion of eddies across a bubble surface, and the repulsive force induced by the wall increases as the distance between the bubble and the wall decreases. Remarkably, at a dimensionless wall distance of 0.6, a change in the lateral lift direction triggers a collision between a zigzagging bubble and the wall, consequently setting the bubble into a bouncing motion mode. In the case of spiral-moving bubbles, proximity to the wall enhances the likelihood of oscillations within the bubble's migration trajectory along the x-y plane while maintaining stability along the z-y plane. While variations in the initial aspect ratio of bubbles have marginal impacts on the migration paths of linear and zigzagging bubbles, the initial shape affects the migration trajectory of spiral bubbles to some extent. The bubble migration trajectory first experiences oscillations when the initial deformation reaches its maximum.

Granular architecture of lotus seed starch and its impact on physicochemical properties

Lotus seed starch has high apparent amylose content (AAM). A representative definition of its granular architecture (e.g., lamellar structure) remained absent. This study defined the granular shape, crystalline and lamellar structures, and digestibility of twenty-two samples of lotus seed starch (LS) by comparing with those of potato and maize starches. LS granules had more elongated shape and longer repeat distance of lamellae than potato and maize starch granules. The enzymatic susceptibility of LS granules was more affected by AAM than granular architecture. Using these LSs as a model system, the relationships between lamellar structure of starch granules and properties of their gelatinized counterparts were investigated. In LSs, thinner amorphous lamella and thicker crystalline lamella were associated with higher swelling power and yield stress. The relationships were found to be connected via certain structural characteristics of amylopectin.

An experimental and statistical study on the characteristics of non-metallic inclusions that serve as hydrogen-induced crack nucleation sites in pipeline steel

This study consists of a statistical study to identify spatial distribution parameters of non-metallic inclusions (NMIs) at hydrogen-induced cracking (HIC) nucleation sites in both low-strength and high-strength steel pipes. The electrochemical cathodic charging method was used to induce HIC in pipeline steel plates, and the nucleation of the HIC was monitored using straight beam ultrasonic testing. Optical microscopy (OM) and scanning electron microscopy (SEM) were used to evaluate the shape, size, area fraction, and separation distance of NMIs. The hydrogen microprint technique (HMT), electron backscattered diffraction (EBSD) analysis, and finite element (FE) stress analysis were performed to characterize HIC nucleation sites. The findings showed that cubical and spinal NMIs, characterized by strong hydrogen trapping capacity due to high misfit strain and von Mises stress, are favored sites for HIC nucleation. The main finding of this study is that the shape and sharpness of NMIs are the factors that determine when NMIs will be a HIC nucleation site, rather than size, as generally accepted.

What if designing superhydrophobic polymer surfaces turned out to be very simple?

Various natural or artificial surface topographies or textures lead to slippery superhydrophobicity, but have little in common. Thus, simple questions need to be asked: what are the key parameters required for reaching slippery superhydrophobicity, i.e. forming a Cassie-Baxter's interface instead of a Wenzel's one? Is the double texturing scale, i.e. nano and micro, imperative to reach this superhydrophobicity regime as usually agreed upon the literature? In this paper, a simple model of the wetting of a textured surface based on the balance between the liquid pressure and the surface tension for a Cassie-Baxter's interface is described. The textured surface is supposed to be slippery superhydrophobic as long as a pure Cassie-Baxter's interface with a contact angle larger than 150° is established. The stability limits of such an interface upon pressure are studied depending on simple parameters such as asperities shape, size, or spacing distance. Then, this model is applied on simple theoretical surfaces and its conclusions are challenged with surface wetting situations described in the literature, showing great accordance. The proposed model offers a simple understanding of the relationship between surface texture and superhydrophobicity. Such an understanding guides the design of stable superhydrophobic surfaces using simple parameters. An ideal surface texture for superhydrophobicity can be described: An intrinsically hydrophobic surface with as small as possible re-entrant asperities that are spaced by more than their own width, and at least as high as their spacing distance.

Effect of extracellular matrix fiber cross-linkage on cancer cell motility and surrounding matrix deformation

Cancer incidence is increasing annually, and the invasion of cancer into the stroma significantly affects cancer metastasis. The stroma primarily comprises an abundant extracellular matrix (ECM) that interacts closely with cancer cells. Cancer cells use the ECM as a scaffold to migrate from a tumor via mechanical actions such as pushing and pulling the fibers. The purpose of this study is to clarify the effects of elastic modulus differences on cell migration behavior based on the same ECM fiber structure. We observe temporal changes in the morphology of cancer cells and the surrounding ECM to elucidate the relationship between changes in the mechanical properties of the ECM and the invasive behavior of cancer cells. We analyze the shape and migration distance of cancer cells and the displacement field of the ECM by varying the fiber elastic modulus but fixing the ECM density. Increasing the elastic modulus results in a protruding cell shape, which indicates the maximum displacement of the ECM around the cell. Additionally, differences in cell migration speed and dispersion based on the elastic modulus are observed. The behavior of cells with increasing elasticity is classified via cluster analysis. Owing to the chemical cross-linking of the fibers, some cells cannot deform the surrounding tissue. This is attributable to the gel state of the ECM and microscopic fluctuations in the fiber density around the cells. We successfully assessed the effect of changes in the ECM modulus on cell mortality and morphology to reveal the mechanism of cancer invasion.

A Representation of Cloth States based on a Derivative of the Gauss Linking Integral

Robotic manipulation of cloth is a complex task because of the infinite-dimensional shape-state space of textiles, which makes their state estimation very difficult. In this paper we introduce the dGLI Cloth Coordinates, a finite low-dimensional representation of cloth states that allows us to efficiently distinguish a large variety of different folded states, opening the door to efficient learning methods for cloth manipulation planning and control. Our representation is based on a directional derivative of the Gauss Linking Integral and allows us to represent spatial as well as planar folded configurations in a consistent and unified way. The proposed dGLI Cloth Coordinates are shown to be more accurate in the representation of cloth states and significantly more sensitive to changes in grasping affordances than other classic shape distance methods. Finally, we apply our representation to real images of a cloth, showing that with it we can identify the different states using a distance-based classifier.

Determining the gas-phase structures of α-helical peptides from shape, microsolvation, and intramolecular distance data

Mass spectrometry is a powerful technique for the structural and functional characterization of biomolecules. However, it remains challenging to accurately gauge the gas-phase structure of biomolecular ions and assess to what extent native-like structures are maintained. Here we propose a synergistic approach which utilizes Förster resonance energy transfer and two types of ion mobility spectrometry (i.e., traveling wave and differential) to provide multiple constraints (i.e., shape and intramolecular distance) for structure-refinement of gas-phase ions. We add microsolvation calculations to assess the interaction sites and energies between the biomolecular ions and gaseous additives. This combined strategy is employed to distinguish conformers and understand the gas-phase structures of two isomeric α-helical peptides that might differ in helicity. Our work allows more stringent structural characterization of biologically relevant molecules (e.g., peptide drugs) and large biomolecular ions than using only a single structural methodology in the gas phase.

Robust Rules for Optimal Colorimetric Sensing Based on Gold Nanoparticle Aggregation.

Spurred by outstanding optical properties, chemical stability, and facile bioconjugation, plasmonic metals have become the first-choice materials for optical signal transducers in biosensing. While the design rules for surface-based plasmonic sensors are well-established and commercialized, there is limited knowledge of the design of sensors based on nanoparticle aggregation. The reason is the lack of control over the interparticle distances, number of nanoparticles per cluster, or multiple mutual orientations during aggregation events, blurring the threshold between positive and negative readout. Here we identify the geometrical parameters (size, shape, and interparticle distance) that allow for maximizing the color difference upon nanoparticle clustering. Finding the optimal structural parameters will provide a fast and reliable means of readout, including unaided eye inspection or computer vision.

Identification of heavily polluted areas based on a novel grey integrated incidence model: A case study of the Yangtze River Delta, China

Identifying the severity of regional pollution is an important test for the government to develop precise air pollution management policies. By investigating the temporal patterns of six air pollutants, it is found that air pollution in Yangtze River Delta (YRD) is caused by high pollutant concentrations in individual cities. Therefore, a novel grey projection tetrahedron integrated incidence model (GTIIM) is proposed to identify the administrative cities in need of priority control. First, GTIIM model is constructed and its mechanism and properties are discussed. The GTIIM addresses the problem of separating the shape similarity and distance similarity between two time series in existing grey comprehensive incidence models. Then, the GTIIM model allows cities to be ranked according to the severity of pollution, a process that focuses on the numerical and temporal trend characteristics of air pollution time series. Results suggest that (1) the spatial distribution of heavily polluted cities differs for each pollutant. (2) Cities located in the northern part of the YRD contribute more to air pollution, with pollution levels gradually increasing from south to north. (3) The air pollution of ChangZhou, ChuZhou and YangZhou are relatively serious and need to be prioritized for urban air pollution control. Our work provides new insights into recognizing spatial pattern of heavily polluted administrative cities based on time series correlation analysis.

Metric Variation on Neurocranium among Some Ancient and Contemporary Human Populations (Adult Males) of Indian SubContinent

Background: It is always difficult to assess the inter-group relationships by utilizing data based on sample univariate populations. One usually faces the problem of biological taxonomy in this case. To understand such problem multivariate statistical analysis is necessary. This study deals with neurocranium characteristics of four population groups from Indian Sub-Continent. Methods: Skulls of the four populations (Harappa, Kumhar Tekri, Contai, 24 parganas) were studied. Altogether 60 adult male skulls (Harappa=10, Kumhar Tekri=10, Contai=20, 24 Parganas=20) were measured. Four length and four breadth measurements were taken. Cranial index was calculated. From the data obtained, size and shape factors were calculated. D2 P values were calculated and dendrogram was computed on the basis on D2 P values. Results: Highest value of maximum cranial length, glabella inion length, nasion inion length, glabella lambda length and minimum frontal breadth were recorded in Harappa and its lowest values were recorded in Contai skull. While Harappa, Kumhar Tekri and 24 Parganas skull were mainly orthocranial, Contai skull was mainly hypsicranial. Mean values for size and shape distance between 6 pairs were 1.14 and 1.92 respectively. This implies that the four population groups show a tendency to differ more in shape distance than size distance. Dendrogram depicts that Contai and 24 Parganas populations form a cluster. But other two are distantly placed with this cluster. However, the Harappa maintains a far distance with other three population groups. Conclusion: Higher means of shape values than the size is recoded. This implies that the four population groups show a tendency to differ more in shape distance than size distance. Finding of this study is indicative of interesting corroboration between neurocranium characteristics and inter-group relationships.

Mechanical properties, durability performance and interlayer adhesion of 3DPC mixtures: A state‐of‐the‐art review

AbstractIn this study, the strength and durability performance of 3D printing concrete mixtures (3DPC) were examined in terms of the materials used in their production and printing process parameters (interlayer interval time, printing speed, and nozzle shape). Due to the anisotropic nature of 3DPC, it was understood that the compressive strength depends on the loading direction. It was emphasized that it is necessary to control the shrinkage behavior of 3DPC mixtures due to the lack of formwork and the high dosage of binders. It was reported that the interlayer adhesion of 3DPC is highly dependent on the interlayer interval time and printing speed parameters. It was understood that nozzle shape and standoff distance affect the mechanical properties and surface roughness of 3DPC specimens, and this effect is more pronounced in mixtures with high static yield stress values.