Two-dimensional Representation Research Articles

An Integrated Three‐dimensional (3D) Model of the Larynx, Pharynx, and Skull for Anatomy Education

Gross anatomy curriculum for health profession students covers the larynx and pharynx and the important roles they play in the digestive and respiratory systems. However, the precise anatomical locations and boundaries between the pharynx, larynx and upper airway are complex topics for beginning anatomy students to grasp. This is a critically important topic not only for beginning health profession students, but also for more advanced students who are participating in clinical rotations and/or training in head and neck specialties. Traditional teaching methods to demonstrate the anatomy of this region have included didactic teaching sessions, cadaveric dissection, plastinated models, and two-dimensional representations in textbooks and anatomy atlases. In contrast, more recent teaching innovations include anatomical models created by 3D printing which are proving to be cost effective tools that can enhance learning. Current commercially available half-head models that show the upper airways often display distorted or exaggerated features of the anatomy. The rigidity of material and lack of moveable structures limit their utility as effective teaching tools. In addition, their high cost prevents institutions from being able to adequately supply larger classes. We sought to develop an integrated 3D printed model of the larynx, pharynx, and skull with details and features surpassing typical currently available commercial models. We hypothesize that this model will significantly improve students’ satisfaction and understanding of the boundaries and visuospatial relationships of these structures as compared to traditional teaching modalities. Digital files for bone and soft tissue structures from http://lifesciencedb.jp/bp3d/ have been extensively modeled to produce a life-size skull that demonstrates the upper airways. The model can be opened like a book; this allows the highly detailed anatomy of the nasal cavity, pharynx and its subdivisions, oral cavity and larynx to be visualized both in the intact skull and in sagittal section. This cost-effective 3D model provides anatomically accurate features that are not typically seen together in commercially produced half-head models, for example, the pharyngeal recess, epiglottic valleculae, piriform recess, pharyngeal muscles, and muscles of the larynx. An educational package is also being developed to accompany the model; It includes a learner guide, a video tutorial demonstrating proper handling and demonstration, a PowerPoint presentation over the anatomy of the region, and self-assessment questions to gauge individual's understanding of learning objectives. The efficacy of the model and its accompanying teaching package will be tested with student volunteers in an IRB approved study. The benefits of this model include both the enhancement of understanding through active learning as well as increased availability of anatomy education tools to institutions teaching gross anatomy.

The second and third waves in India: when will the pandemic be culminated?

An unprecedented upsurge of COVID-19-positive cases and deaths is currently being witnessed across India. According to WHO, India reported an average of 3.9 lakhs of new cases during the first week of May 2021 which equals 47% of new cases reported globally and 276 daily cases per million population. In this letter, the concept of SIR and fractal interpolation models is applied to predict the number of positive cases in India by approximating the epidemic curve, where the epidemic curve denotes the two-dimensional graphical representation of COVID-19-positive cases in which the abscissa denotes the time, while the ordinate provides the number of positive cases. In order to estimate the epidemic curve, the fractal interpolation method is implemented on the prescribed data set. In particular, the vertical scaling factors of the fractal function are selected from the SIR model. The proposed fractal and SIR model can also be explored for the assessment and modeling of other epidemics to predict the transmission rate. This letter investigates the duration of the second and third waves in India, since the positive cases and death cases of COVID-19 in India have been highly increasing for the past few weeks, and India is in a midst of a catastrophizing second wave. The nation is recording more than 120 million cases of COVID-19, but pandemics are still concentrated in most states. In order to predict the forthcoming trend of the outbreaks, this study implements the SIR and fractal models on daily positive cases of COVID-19 in India and its provinces, namely Delhi, Karnataka, Tamil Nadu, Kerala and Maharashtra.

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity scheduling behavior of pedestrians. The majority of the studies conducted on outdoor facilities utilize spatial representations by links and are unable to sufficiently represent the highly flexible behavior of pedestrians. This study proposes a new method to discretize data from the global positioning system (GPS) into a two-dimensional grid-based spatial representation with a high spatial resolution. The information regarding the stay at the point of interests (POIs) is extracted from the discretized data, and the activity scheduling model is estimated. The estimation results indicate that the visitors’ attributes, such as the age of the representative and number of children, affect the probability of the activity choice and the time spent at the POI. The probability of choosing the main gate increases in the latter half of the stay, confirming the existence of time pressure. The information on the decision-making behavior of the visitors to a facility, obtained from the GPS data, can be applied to the data-oriented spatial design process to create attractive and lively spaces.

Optimization of electronic nose drift correction applied to tomato volatile profiling.

E-noses can be routinely used to evaluate the volatile profile of tomato samples once the sensor drift and standardization issues are adequately solved. Short-term drift can be corrected using a strategy based on a multiplicative drift correction procedure coupled with a PLS adaptation of the component correction. It must be performed specifically for each sequence, using all sequence signals data. With this procedure, a drastic reduction of sensor signal %RSD can be obtained, ranging between 91.5 and 99.7% for long sequences and between 75.7 and 98.8% for short sequences. On the other hand, long-term drift can be fixed up using a synthetic reference standard mix (with a representation of main aroma volatiles of the species) to be included in each sequence that would enable sequence standardization. With this integral strategy, a high number of samples can be analyzed in different sequences, with a 94.4% success in the aggrupation of the same materials in PLS-DA two-dimensional graphical representations. Using this graphical interface, e-noses can be used to developed expandable maps of volatile profile similitudes, which will be useful to select the materials that most resemble breeding objectives or to analyze which preharvest and postharvest procedures have a lower impact on the volatile profile, avoiding the costs and sample limitations of gas chromatography.

Auto-Ignition and Numerical Analysis on High-Pressure Combustion of Premixed Methane-Air mixtures in Highly Preheated and Diluted Environment

ABSTRACT This work investigates both autoignition and combustion characteristics in highly preheated and diluted combustion of a laminar premixed stoichiometric CH4/O2/N2 mixture in a cylindrical combustor operating at elevated pressures. The analysis was carried out for a range of operating parameters, including reactant preheat temperatures of 1100–1500 K, combustor pressures of 1-10 atm, and in a highly diluted mixture, achieved by decreasing the oxygen content in the oxidizer from 21% to 3% on volume basis. Simulations were conducted using the laminar premixed adiabatic PFR (plug flow reactor) model of Ansys Chemkin Pro. Two-dimensional pictorial representation was performed using the finite volume-based CFD code Ansys Fluent 19.2. Finite-rate chemistry with the detailed chemical mechanism GRI Mech 3.0 was used for combustion analysis. Results showed that OH and HCO mole fractions decreased with increasing combustor pressure and N2 dilution (or decreased O2 content), while the mole fractions increased with reactant temperature. It was also found that, by reducing the oxygen content in the mixture, the flame stabilized far away from the combustor inlet. In contrast, an increase in combustor pressure and reactant temperature stabilized the flame toward the combustor inlet. These flame stabilization characteristics at different locations of the combustor are explained in terms of ignition delay time, which were calculated using the closed homogenous reactor (CHR) model available in the Ansys Chemkin Pro package. The flame peak temperature decreased with increased N2 dilution and increased by increasing the reactant temperature. Moreover, the peak temperature varied marginally when the combustor pressure was increased. Finally, a regime diagram was prepared to show the various combustion modes, such as HiTAC, MILD combustion, and the no ignition region as a function of O2 content and reactant temperature for different operating pressures. The CO and NO emission were reduced with an increase in pressure in the MILD combustion region.

Efficient unsupervised drift detector for fast and high-dimensional data streams

Stream mining considers the online arrival of examples at high speed and the possibility of changes in its descriptive features or class definitions compared with past knowledge (i.e., concept drifts). The fast detection of drifts is essential to keep the predictive model updated and stable in changing environments. For many applications, such as those related to smart sensors, the high number of features is an additional challenge in terms of memory and time for stream processing. This paper presents an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams. We propose a straightforward two-dimensional data representation that allows the faster processing of datasets with a large number of examples and dimensions. We developed an adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests considering each feature individually. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts. The experimental evaluation considering synthetic and real data demonstrates the method’s versatility in several domains, including entomology, medicine, and transportation systems.

Spectrum Analysis of Signal in Wolfram Mathematica System

The purpose of this paper is a spectrum analysis of signals of various nature, construction of the signal scalogram using Morlet wavelet, modification of the scalogram to obtain a more informative graphic representation of the signal. Spectral analysis of the signal is constructed by means of the Fourier transform. A modification of the graphical representation of the result of the wavelet transform has been developed with the help of the Mathematica system. For this, a wavelet scalogram has been used as a two-dimensional representation of the original signal. A scale has been introduced on it for the value of the signal amplitude depending on the time and period of its constituent components. This graphical representation allows us to obtain additional information about the dynamic properties of the original signal. A modification of the representation of the original signal scalogram has been developed for a more complete spectrum analysis (determination of the period of the constituent components). The paper contains an example using a modified scalogram for the analysis of a signal containing two pulses, an audio signal and white noise. The basic wavelet in this case is the Morlet wavelet. A comparison of the scalogram, obtained using the built-in function, and the modified scalogram has been made in the paper. The disadvantage of the first scalogram is the impossibility of assessing the frequency of the signal; its advantage is the ability to assess the localization of the pulse. For a modified scalogram, the advantage is the estimation of the signal periodicity, and the disadvantage is the inaccuracy in determining the range of pulse localization. For spectrum analysis in Mathematica, it is recommended to use a combination of two approaches (using a standard built-in function to determine the localization of the pulse) and a modified scalogram (to determine the periods of the constituent components).

Double covering of the modular A5 group and lepton flavor mixing in the minimal seesaw model

In this paper, we investigate the double covering of modular $\Gamma^{}_5 \simeq A^{}_5$ group and derive all the modular forms of weight one for the first time. The modular forms of higher weights are also explicitly given by decomposing the direct products of weight-one forms. For the double covering group $\Gamma^\prime_5 \simeq A^\prime_5$, there exist two inequivalent two-dimensional irreducible representations, into which we can assign two right-handed neutrino singlets in the minimal seesaw model. Two concrete models with such a salient feature have been constructed to successfully explain lepton mass spectra and flavor mixing pattern. The allowed parameter space for these two minimal scenarios has been numerically explored, and analytically studied with some reasonable assumptions.

A first encounter with the Hartree-Fock self-consistent-field method

This paper is intended to serve as a bridge between introductory textbooks on quantum mechanics, which typically do not cover the Hartree-Fock self-consistent-field method, and more advanced ones which treat this important computational method for fermionic many-body systems in an abstract and formal way. We derive the Hartree-Fock equation for the 1s orbital of a realistic two-electron atom. By employing a two-dimensional basis-set representation, we avoid the use of variational calculus and are able to visualize key aspects of the method. We explain the basic self-consistent-field algorithm and provide a python script to illustrate how the algorithm works in practice. Utilizing perturbation theory, we perform an analysis of the convergence behavior of the self-consistent-field algorithm, thereby facilitating a deeper understanding of the numerical examples presented. We expect that this work will be useful for teaching computational techniques to physics students.

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

Las técnicas de Captura Masiva de Datos (MDC) y la sección principal en edificios con grandes deformaciones

El levantamiento de edificios es esencial para el desarrollo de la representación gráfica de la arquitectura. Sus principios fueron acotados en la Carta del rilievo architettonico de Nápoles (1999), donde se establece dicho levantamiento como un elemento de investigación iconográfica, bibliográfica y archivística. La representación de la sección de un edificio no aparece hasta la edición vitruviana de Cesarino (1521), dado que el secretismo medieval impedía revelar el despliegue de un edificio. La representación bidimensional del espacio métrico, proyectivo o topológico arroja nuevas cuestiones programáticas a través de las metodologías generadas por la Massive Data Capture (MDC) y, por ello, la determinación de la sección principal es una cuestión esencial. La propuesta metodológica la define como la producida por un plano de referencia (τ) perpendicular al horizonte astronómico de una latitud (ϕ), el cual contiene un vector de regresión del azimut (Ab ) y los puntos singulares (ci ) de las secciones transversales.

Laterality and visuospatial strategies among young children: A novel 3D-2D transcription task

ABSTRACT Recent findings showed that children, like adults, exhibit directional biases leading to asymmetrical drawings. This appears to be the result of a complex interaction between several biological, motoric, and cultural factors. We created a drawing task designed to investigate the influence of laterality (i.e., hemispherical functional specialization and handedness) and sex on children’s graphical asymmetries. This task consists of transcribing a symmetrical three-dimensional landscape model to a two-dimensional representation. Sixty-six French pre-school children, aged between 5 and 6 years, were asked to undertake the 3D-2D transcription task, as well as the classical Alter’s directionality task. The novel task exhibited higher sensitivity than the Alter’s directionality test when examining the spatial biases resulting from handedness, and sex. Specific drawing patterns related to these variables were identified. These results suggest that, in addition to the influence of biomechanical factors and handedness, sex plays a role in children’s early graphomotor development. They also support the influence of laterality as a key factor underlying early directional biases.

Stark points on elliptic curves via Perrin-Riou’s philosophy

In the early 90’s, Perrin-Riou (Ann Inst Fourier 43(4):945–995, 1993) introduced an important refinement of the Mazur–Swinnerton-Dyer p-adic L-function of an elliptic curve E over $$\mathbb {Q}$$ , taking values in its p-adic de Rham cohomology. She then formulated a p-adic analogue of the Birch and Swinnerton-Dyer conjecture for this p-adic L-function, in which the formal group logarithms of global points on E make an intriguing appearance. The present work extends Perrin-Riou’s construction to the setting of a Garret–Rankin triple product (f, g, h), where f is a cusp form of weight two attached to E and g and h are classical weight one cusp forms with inverse nebentype characters, corresponding to odd two-dimensional Artin representations $$\varrho _g$$ and $$\varrho _h$$ respectively. The resulting p-adic Birch and Swinnerton-Dyer conjecture involves the p-adic logarithms of global points on E defined over the field cut out by $$\varrho _g\otimes \varrho _h$$ , in the style of the regulators that arise in Darmon et al. (Forum Math 3(e8):95, 2015), and recovers Perrin-Riou’s original conjecture when g and h are Eisenstein series.

Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction

Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points (outliers) and requires high computational cost in real-world applications. In order to resolve the problems mentioned above, we introduce a new elastic feature extraction algorithm called the sparse fuzzy 2D discriminant local preserving projection (SF2DDLPP). First, the membership matrix is calculated using the fuzzy k-nearest neighbours (FKNN), which is applied to the intraclass weighted matrix and the interclass weighted matrix. Second, two theorems are developed to directly solve the generalized eigenfunctions. Finally, the optimal sparse fuzzy 2D discriminant projection matrix is regressed using the elastic net regression. The experiments show the effectiveness and stability of this algorithm on several face (ORL, Yale, AR and Yale B), USPS and palm print datasets.

Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem.

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.

On the reductions of certain two-dimensional crystabelline representations

Crystabelline representations are representations of the absolute Galois group \(\smash {G_{\mathbb {Q}_p}}\) over \(\smash {\smash {\overline{\mathbb {Q}}_p}}\) that become crystalline on \(\smash {G_{F}}\) for some abelian extension \(\smash {F/\mathbb {Q}_p}\). Their relation to modular forms is that the representation associated with a finite slope newform of level divisible by \(\smash {p^2}\) is crystabelline. In this article, we study the connection between the slopes of two-dimensional crystabelline representations and the reducibility of their modulo \(\smash {p}\) reductions. This question is inspired by a theorem by Buzzard and Kilford which implies that the slopes on the boundary of the \(\smash {2}\)-adic eigencurve of tame level \(\smash {1}\) are integers (and in arithmetic progression); an analogous theorem by Roe which says that the same is true for the \(\smash {3}\)-adic eigencurve; Coleman’s halo conjecture and the ghost conjecture which give predictions about the slopes on the \(\smash {p}\)-adic eigencurve of general tame level; and Hodge theoretic conjectures by Breuil, Buzzard, Emerton, and Gee which indicate that there is a connection between all of these and the slopes of two-dimensional crystabelline representations whose reductions modulo \(\smash {p}\) are reducible. We prove that the reductions of certain two-dimensional crystabelline representations with slopes in \(\smash {(0,\frac{p-1}{2})\backslash \mathbb {Z}}\) are usually irreducible, with the exception of a small region where the slopes are half-integers and reducible representations do occur.

Learning Laparoscopic Surgery Skills With a 4K Ultra-High Definition 2D vs a Three-Dimensional HD Laparoscopic System: Results From a Prospective Randomized Trial.

Background. In minimally invasive surgery (MIS), the loss of stereoscopic depth perception in a two-dimensional (2D) representation is most challenging. Recently introduced 4K ultrahigh definition (UHD) 2D optical systems could potentially facilitate the learning and use of compensation mechanisms for the loss of depth perception. However, the role of the new 4K technology against three dimensional (3D) in learning and implementation of MIS remains unknown. The aim of this trial was to determine the influence of 4K UHD 2D vs 3D HD representation on the acquisition of MIS skills. Methods. This was a prospective randomized study involving 62 MIS-inexperienced study participants. We compared a laparoscopic 4K UHD 2D (system A) vs a laparoscopic 3D HD system (system B) for differences in learning MIS skills using the Lübeck Toolbox (LTB) video box trainer. We evaluated participants' performance regarding the repetitions required to reach the goal of each LTB task. Results. Comparing systems A and B, participants using the laparoscopic 3D system required fewer repetitions to achieve goals of LTB tasks No. 1 (P = .0048) and No. 3 (P = .0014). In contrast, for LTB tasks No. 2 and No. 4, no significant difference could be determined between both groups. Conclusion. Our results indicated that MIS basic skills can be learned quicker using a 3D HD system vs a 4K UHD 2D system. However, for MIS tasks in confined spaces, the learning speed with 4K UHD 2D imaging seems to be comparable to a 3D HD system.

Multimodal Training Improves Spatial Reasoning Skills in Female College Students

Spatial reasoning is a valuable cognitive tool which enables navigation of the body in relation to other objects in space, permits deconstruction of dimensional forms, allows reconstruction of two-dimensional representations into three-dimensional objects, and facilitates diagramming of processes or concepts. A considerable body of work has suggested that these skills are particularly important for success in science, technology, engineering, and mathematics (STEM) fields (reviewed in Wai, Lubinski, & Benbow, Journal of Educational Psychology 101: 817-835, 2009); however, much of this data has been derived from studies with relatively narrow populations in terms of participant sex, race, academic ability, or socioeconomic condition and very few of these studies have attempted spatial reasoning training from a diversity of academic disciplines. The current study was designed to assess the influence of a week-long, academically interdisciplinary spatial reasoning Bootcamp on a small, diverse group of female participants’ spatial reasoning abilities and to determine if Participants’ subsequent performance in organic chemistry and physics coursework differed from that of a Match (non-Bootcamp participant) Control group. The data indicate that Bootcamp participants significantly improved their scores in some commonly accepted measures of spatial reasoning abilities and that Bootcamp participants’ final grades in organic chemistry, but not physics, were significantly higher than the Match Control group. The data suggest that intentional programming to improve student spatial reasoning abilities can influence short-term spatial skill performance that may translate into improved success in certain types of subsequent coursework.

Electromagnetic Metasurfaces and Reconfigurable Metasurfaces: A Review

Metamaterial is a kind of material/structure that is artificially designed and has exceptional electromagnetic properties and/or other physical properties, not found in nature. A class of electromagnetic metamaterial with only one or a few layers of periodic or aperiodic arranged cell structures in the direction of electromagnetic waves propagation can be referred to as a metasurface. Metasurface can be considered as a two-dimensional representation of metamaterial and can realize the controlling of the amplitude, phase, and polarization state/direction of the incident electromagnetic wave. According to the novel electromagnetic characteristics of metasurface and its big advantages, a series of new planar devices and systems based on metasurface can be developed. The goal of this review article is firstly to provide introductions for basic metasurface, its significance properties, and application principles. Meanwhile, the main research progresses of regular metasurfaces and the newly developed reconfigurable metasurfaces are analyzed, focusing on the types of amplitude modulation, phase modulation, polarization modulation, and multidimensional modulation. Finally, the research significances of metasurface development trend and important engineering practical applications are analyzed in the end.

Topological and nematic superconductivity mediated by ferro-SU(4) fluctuations in twisted bilayer graphene

We propose an SU(4) spin-valley-fermion model to investigate the superconducting instabilities of twisted bilayer graphene (TBG). In this approach, bosonic fluctuations associated with an emergent SU(4) symmetry, corresponding to combined rotations in valley and spin spaces, couple to the low-energy fermions that comprise the flat bands. These fluctuations are peaked at zero wave-vector, reflecting the "ferromagnetic-like" SU(4) ground state recently found in strong-coupling solutions of microscopic models for TBG. Focusing on electronic states related to symmetry-imposed points of the Fermi surface, dubbed here "valley hot-spots" and "van Hove hot-spots", we find that the coupling to the itinerant electrons partially lifts the huge degeneracy of the ferro-SU(4) ground state manifold, favoring inter-valley order, spin-valley coupled order, ferromagnetic order, spin-current order, and valley-polarized order, depending on details of the band structure. These fluctuations, in turn, promote attractive pairing interactions in a variety of closely competing channels, including a nodeless $f$-wave state, a nodal $i$-wave state, and topological $d+id$ and $p+ip$ states with unusual Chern numbers $2$ and $4$, respectively. Nematic superconductivity, although not realized as a primary instability of the system, still appears as a consequence of the near-degeneracy of superconducting order parameters that transform as one-dimensional and two-dimensional irreducible representations of the point group $D_{6}$.