Common 1D Research Articles

Real-time 3D ultrasound imaging with a clip-on device attached to common 1D array transducers

Performing 3D ultrasound imaging at a real-time volume rate (e.g., >20 Hz) is a challenging task. While 2D array transducers remain the most practical approach for real-time 3D imaging, the large number of transducer elements (e.g., several thousand) that are necessary to cover an effective 3D field-of-view impose a fundamental constraint on imaging speed. Although solutions such as multiplexing and specialized transducers, including sparse arrays and row-column-addressing arrays, have been developed to address this limitation, they inevitably compromise imaging quality (e.g., SNR, resolution) in favor of speed. Coupled with the high equipment cost of 2D arrays, these compromises hinder the widespread adoption of 3D ultrasound imaging technologies in clinical settings. In this presentation, we introduce an innovative transducer clip-on device comprising a water-immersible, fast-tilting electromechanical acoustic reflector and a redirecting reflector to enable real-time 3D ultrasound imaging using common 1D array transducers. We will first introduce the principles underlying our novel technique, followed by validation studies incorporating simulation and experimental data. We will also demonstrate the feasibility of using the clip-on device to achieve a high 3D imaging volume rate that is suitable for advanced imaging modes such as shear wave elastography, blood flow imaging, and super-resolution ultrasound localization microscopy.

The Topological Origin of Boundary Charges at Edges of One-Dimensional Crystals without Inversion Symmetry

We report the edge states and non-zero boundary charges in one-dimensional photonic crystals (1D PhCs) without inversion symmetry. In contrast to common 1D systems, we show that edge states corresponding to non-zero boundary charges do exist in these asymmetric 1D PhCs even if we cannot obtain non-integral topological invariants. Moreover, an edge state could be observed in the interface between the PhC without inversion symmetry and the well-defined trivial PhC. Finally, the origin of the non-quantized boundary charges is unveiled by the non-central Wannier center. Not only exact solutions of photonic systems, but the above topological phenomena can also be found in the tight-binding models. This work proposes a way to study the 1D symmetries-broken systems and provides models to show the topological origin of boundary charges, which is suitable for both classic systems and quantum systems.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses.

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) 1H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T2 filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D 1H NMR methods such as 1D 1H presat and 1D 1H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D 1H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Recent Advances in Integrating 1D Nanomaterials into Chemiresistive Gas Sensor Devices

AbstractThere is rapid growth in the global gas sensor market driven by new regulations for industrial emission control and residential environments and increasing demand for portable devices. Nanosensors promise to be the next generation gas sensors for the direct detection of chemical species due to their ultrahigh sensitivity, fast response, ultralow weight, and low power consumption. However, transitioning nanosensors from basic research or prototype projects to commercial products encounters two major technical challenges: difficulty in scale up and inability to demonstrate real‐world use in changing humidity and temperature conditions. The advances in addressing these two technical challenges in recent literature are surveyed. The most common 1D nanomaterials in R&D for gas sensing, metal oxides, and carbon nanotubes, as well as less common metal nanowires and psudo‐1D crystals made from charge‐transfer complexes (CTCs) are included. This review delves into the specifics of each of these 1D nanomaterials for gas sensing applications, their synthesis and deposition, sensing performance, and commercial development in the last 5 years. It highlights the scalability of one‐step electrodeposition of CTC nanowires on prefabricated electrodes, at room temperature, and from solution in making gas nanosensors.

Nonlinear dynamics of 1D meta-structure with inertia amplification

Considering the low-frequency vibration isolation properties of the inertial amplification mechanism (IAM) and the band gap of the periodic structure, this paper is concerned with the dynamic characteristics of the nonlinear inertial amplification mechanism (NIAM), theoretically. This study focused on the influence of nonlinearity on vibration isolation characteristics. Both analytical and numerical methods are adopted. Through the analysis of a basic NIAM element, nonlinear modes and complex phase motions are observed. The bifurcation property shows that complex motions (periodic, quasi-periodic, and chaotic) can be obtained by altering the exciting frequency for some intended goals, especially for vibration reduction. Analysis of the dispersion relation and the vibration transmissibility of the 1D periodic meta-structure composed of multiple NIAMS (NIAMMS) demonstrates its superiority with a decrease of 18.4% in the lower bound of the band gap, compared with a common 1D nonlinear periodic meta-structures. Furthermore, it is possible to obtain an ultra-low and ultra-wide nonlinear band gap by properly adjusting the parameters such as nonlinear stiffness, mass ratio, assembly angle, and damping.

Hot Exoplanetary Atmospheres in 3D

Hot giant exoplanets are very exotic objects with no equivalent in the Solar System that allow us to study the behavior of atmospheres under extreme conditions. Their thermal and chemical day–night dichotomies associated with extreme wind dynamics make them intrinsically 3D objects. Thus, the common 1D assumption, relevant to study colder atmospheres, reaches its limits in order to be able to explain hot and ultra-hot atmospheres and their evolution in a consistent way. In this review, we highlight the importance of these 3D considerations and how they impact transit, eclipse and phase curve observations. We also analyze how the models must adapt in order to remain self-consistent, consistent with the observations and sufficiently accurate to avoid bias or errors. We particularly insist on the synergy between models and observations in order to be able to carry out atmospheric characterizations with data from the new generation of instruments that are currently in operation or will be in the near future.

Generative models for molecular discovery: Recent advances and challenges

AbstractDevelopment of new products often relies on the discovery of novel molecules. While conventional molecular design involves using human expertise to propose, synthesize, and test new molecules, this process can be cost and time intensive, limiting the number of molecules that can be reasonably tested. Generative modeling provides an alternative approach to molecular discovery by reformulating molecular design as an inverse design problem. Here, we review the recent advances in the state‐of‐the‐art of generative molecular design and discusses the considerations for integrating these models into real molecular discovery campaigns. We first review the model design choices required to develop and train a generative model including common 1D, 2D, and 3D representations of molecules and typical generative modeling neural network architectures. We then describe different problem statements for molecular discovery applications and explore the benchmarks used to evaluate models based on those problem statements. Finally, we discuss the important factors that play a role in integrating generative models into experimental workflows. Our aim is that this review will equip the reader with the information and context necessary to utilize generative modeling within their domain.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

Importance of Quercus spp. for diversity and biomass of vascular epiphytes in a managed pine-oak forest in Southern Mexico

Forestry management modifies the diversity, structure, and functioning of intervened forests. Timber extraction reduces tree density and basal area, leading to changes in the communities of vascular epiphytes. The objective of this study was to evaluate the diversity and biomass of vascular epiphytes in Quercus trees remaining in two pine-oak forest stands that have been subjected to two stages of the Silvicultural Development Method (release cutting, and thinning) in comparison with an unharvested old-secondary forest in southern Mexico. For each treatment, richness of epiphytes present on 60 oak trees was recorded and their dry biomass estimated. We calculated the true diversity (Hill numbers) and beta diversity using the Jaccard coefficient of similarity, and generated rank abundance curves per taxonomic epiphyte group (bromeliads, orchids, ferns and others). For each treatment, the relationships between overall diversity and epiphyte biomass to the host trees basal area were analyzed using log linear models. We recorded a total of 67 species of epiphytes species belonging to 10 families hosted by five species of oaks. The greatest species richness (0D) was recorded in the old-secondary forest. Fewer common (1D) and dominant (2D) species were recorded in the release cutting than in the other treatments. Epiphyte diversity and biomass were both slightly related to host tree basal area. Composition of epiphytes was similar (60%) among treatments, although orchids, bromeliads, and other families were more diverse in the old-secondary forest. Most bromeliad species were shared across all treatments, although orchids presented the most exclusive species in the unharvested forest. The bromeliad Tillandsia seleriana provided the greatest contribution to biomass in all treatments, followed by the orchid Camaridium densum. Generalized linear models indicated that epiphyte diversity was significantly related to treatment, and epiphyte biomass to basal area of host trees. Although forest management affects diversity, composition, and abundance of vascular epiphytes, most of their diversity and biomass can be maintained despite timber harvesting. This requires sparing some mature oaks during logging, as they contribute to conservation, establishment, and development of epiphytic communities, and maintaining untreated areas as a source of propagules for these communities.

Comparing End-to-End Machine Learning Methods for Spectra Classification

In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual status of the experiment (e.g., time-resolved status of the sample), which helps guide the experiment. The two major spectral changes what we aim to capture are either the change in intensity distribution (e.g., drop or appearance) of peaks at certain locations, or the shift of those on the spectrum. This study aims to develop deep learning (DL) classification frameworks for one-dimensional (1D) spectral time series. In this work, we deal with the spectra classification problem from two different perspectives, one is a general two-dimensional (2D) space segmentation problem, and the other is a common 1D time series classification problem. We focused on the two proposed classification models under these two settings, the namely the end-to-end binned Fully Connected Neural Network (FCNN) with the automatically capturing weighting factors model and the convolutional SCT attention model. Under the setting of 1D time series classification, several other end-to-end structures based on FCNN, Convolutional Neural Network (CNN), ResNets, Long Short-Term Memory (LSTM), and Transformer were explored. Finally, we evaluated and compared the performance of these classification models based on the High Energy Density (HED) spectra dataset from multiple perspectives, and further performed the feature importance analysis to explore their interpretability. The results show that all the applied models can achieve 100% classification confidence, but the models applied under the 1D time series classification setting are superior. Among them, Transformer-based methods consume the least training time (0.449 s). Our proposed convolutional Spatial-Channel-Temporal (SCT) attention model uses 1.269 s, but its self-attention mechanism performed across spatial, channel, and temporal dimensions can suppress indistinguishable features better than others, and selectively focus on obvious features with high separability.

Engineering phase and polarization singularity sheets

Optical phase singularities are zeros of a scalar light field. The most systematically studied class of singular fields is vortices: beams with helical wavefronts and a linear (1D) singularity along the optical axis. Beyond these common and stable 1D topologies, we show that a broader family of zero-dimensional (point) and two-dimensional (sheet) singularities can be engineered. We realize sheet singularities by maximizing the field phase gradient at the desired positions. These sheets, owning to their precise alignment requirements, would otherwise only be observed in rare scenarios with high symmetry. Furthermore, by applying an analogous procedure to the full vectorial electric field, we can engineer paraxial transverse polarization singularity sheets. As validation, we experimentally realize phase and polarization singularity sheets with heart-shaped cross-sections using metasurfaces. Singularity engineering of the dark enables new degrees of freedom for light-matter interaction and can inspire similar field topologies beyond optics, from electron beams to acoustics.

Forest cover and heterogeneous pastures shape the diversity of predatory rove beetles in tropical riparian habitats

In tropical landscapes, forest remnants have been reduced to narrow strips of vegetation along rivers and streams surrounded by agricultural land that affects biodiversity, depending on the habitat and landscape characteristics. To assess the effect of riparian forest loss on the diversity of Staphylininae predatory rove beetles, we considered two habitat conditions (river sites with riparian vegetation and sites with heterogeneous pastures) within two micro-basin types (with >70% and <40% forest cover) in a tropical montane cloud forest landscape, Mexico. Beetles were collected using baited pitfall traps during the rainy season of 2014. No differences were found between micro-basin types and, although species richness (0D) was similar between habitat conditions, when the diversity of common (1D) and dominant (2D) species was considered, sites with heterogeneous pastures were almost twice as diverse as those with riparian vegetation. All diversity measurements were greater in sites with heterogeneous pastures of either micro-basin type. Air temperature and canopy cover were the environmental variables that best explained the variation in beetle species composition. The greatest environmental differences related to species composition were detected between habitat conditions and were more evident in sites with heterogeneous pastures and low forest cover in the surroundings. The results suggest that replacing riparian vegetation with heterogeneous pastures, within micro-basins that lost between 30% and 60% of their forest cover, does not significantly reduce the diversity of predatory rove beetle but rather modifies the beetle composition. Effective formulation of management strategies to mitigate the impact of land use modification therefore requires an understanding of the interaction between vegetation remnants and landscape characteristics.

A selective comprehensive reversed-phase×reversed-phase 2D-liquid chromatography approach with multiple complementary detectors as advanced generic method for the quality control of synthetic and therapeutic peptides.

There is a huge, still increasing market for synthetic and therapeutic peptides. Their quality control is commonly based on a generic reversed-phase liquid chromatography (RPLC) method with C18 stationary phase and acetonitrile gradient with 0.1% trifluoroacetic acid in the mobile phase. It performs exceptionally well for a wide variety of impurities, yet structurally closely related impurities with similar sequences, not resolved in preparative RPLC, may easily coelute in the corresponding QC run as well. To address this problem an advanced generic 2D-LC impurity profiling method was developed in this work. It employs a selective comprehensive (high resolution sampling) RP×RP 2D-LC separation using a 100×2.1mm ID column with the common acidic generic gradient in the first dimension, while RPLC under basic pH on a short 30×3mm ID column is used in the second dimension. Recording data with a UV detector at 215nm after 1D separation provides the common generic 1D chromatogram. However, after the 2D separation a flow splitter enabled recording of the signals of complementary detectors comprising a diode array detector (DAD) in-line with a charged aerosol detector (CAD) and a quadrupole-time-of-flight (QTOF) mass spectrometer (MS) with an electrospray ionization (ESI) source. Generic conditions of this 2D-LC method have been established through optimization of 2D stationary and mobile phase considering different pH values and buffer concentrations. The orthogonal separation principle has been documented by a number of therapeutic peptides including Exenatide, Octreotide, Cyclosporine A and Oxytocin as well as some other proprietary synthetic peptides. The information density can be further enhanced by using the QTOF-MS detector by data independent acquisition with SWATH. Through this sequential window acquisition of all theoretical fragment ion mass spectra it became possible to collect MS/MS data comprehensively in the high-resolution sampling window, thus enabling the extraction of 2D-EICs from fragment ions and the generation of 2D-contour plots of all product ions. Using Oxytocin as an example for an important therapeutic peptide, the ability of this advanced generic sRP-UV×RP-DAD-CAD-ESI-QTOF-MS/MS method with SWATH for peptide quality control is discussed.

Microresonators enhancing long-distance dynamical entanglement generation in chiral quantum networks

Chiral quantum networks provide a promising route for realising quantum information processing and quantum communication. Here, we describe how two distant quantum nodes of chiral quantum network become dynamically entangled by a photon transfer through a common 1D chiral waveguide. We harness the directional asymmetry in chirally-coupled single-mode ring resonators to generate entangled state between two atoms. We report a concurrence of up to 0.969, a huge improvement over the 0.736 which was suggested and analyzed in great detail in Ref. [1]. This significant enhancement is achieved by introducing microtoroidal resonators which serve as efficient photonic interface between light and matter. Robustness of our protocol to experimental imperfections such as fluctuations in inter-nodal distance, imperfect chirality, various detunings and atomic spontaneous decay is demonstrated. Our proposal can be utilised for long-distance entanglement generation in quantum networks which is a key ingredient for many applications in quantum computing and quantum information processing.

Molecular level nucleation mechanisms of hierarchical MFI and MOR zeolite structures via non-stochastic pathways.

Understanding the chemical mechanism of crystal nucleation at the molecular level is crucial for the design of architectural structures of valuable materials in the future. In this study, it has been revealed that amorphous silicate precursors, which play a role in the nucleation processes of zeolitic frameworks, can be regularly fragmented in mass spectroscopy due to the hydroxyl functional groups in their molecular structures. In this way, by using the mass spectra acquired from LDI-TOF MS, the systematic evolution stages of a common 1D precursor converting to the 3D unit cells of MFI and MOR zeolite structures observed in the same reaction medium were constructed through a nucleation mechanism at the molecular level for the first time. Here we show a novel nucleation pathway that does not occur via stochastic assembly of atoms or distinct building blocks by molecular recognition. Each of the proposed nucleation mechanisms of these different frameworks carrying structural similarities is from different combinations of sequential self-attaching intramolecular covalent couplings of identical origin precursors. The dynamic molecular structure capable of forming finite building units of target frameworks during the nucleation process of this precursor, which is the polymerized form of simple 6-membered siloxane chains, has been arranged around structure directing agents before a hydrothermal reaction.

A Comparative Study of Five Networks for Reservoir Classification Based on Geophysical Logging Signals

Unconventional reservoir classification suffers low accuracy because of the complex geophysical properties. With good performance and moderate cost, geophysical logging is considered to be of great potential as the compromise solution between seismic and core. Since the “sweet-spot” depending on the sands' properties are implied not in the logging “point” but “segment”, 1D-CNN is supposed to be a better fit for reservoir classification: It doesn't pay too much attention to the logging signal sequence itself like LSTM, but focuses on feature extraction from the whole signals combination. Moreover, samples would have various sizes because of different layer thickness. While 1D-CNN requires all samples must be converted to a uniform size before input. 1D fully convolutional network (1D-FCN) can receive any size input of layer thickness without manually aligning it to the same size. For the first time in this paper, structures of the five networks including two fully connected networks (global mapping artificial neural network, GM-ANN and point-to-point mapping artificial neural network, PPM-ANN), two fully convolutional networks (1D fully convolutional network with decision-level fusion, 1D-FCN-DEF and 1D fully convolutional network with data-level fusion, 1D-FCN-DAF) and the common 1D convolutional neural network (1D-CNN) are compared and evaluated the suitability for processing logging data in detail. Results on tight gas in Ordos basin of China illustrated by receiver operating characteristics (ROC) curves show that 1D-FCN-DEF and 1D-FCN-DAF have achieved the higher area under the curve (AUC) with the values of 0.8889 and 0.9107 respectively in average comparing to 0.7515, 0.8006 and 0.8364 of GM-ANN, PPM-ANN and 1D-CNN. Case study proves that 1D-FCNs are more accurate than other networks. This paper provides a suitable new idea for logging interpretation and expands the application scope of DL in geophysical logging signals processing.

Shock Capturing in Large Eddy Simulations by Adaptive Filtering

A method for shock capturing by adaptive filtering for use with high-resolution, high-order schemes for Large Eddy Simulations (LES) is presented. The LES method used in all the examples here employs the Explicit Filtering approach and the spatial derivatives are obtained with sixth-order, compact, finite differences. The adaptation is to drop the order of the explicit filter to two at gridpoints where a shock is detected, and to then increase the order from 2 to 10 in steps at successive gridpoints away from the shock. The method is found to be effective in a series of tests of common inviscid 1D and 2D problems of shock propagation and propagation of waves through shocks. As a prelude to LES, the 3D Taylor–Green problem for the inviscid and a finite viscosity case were simulated. An assessment of the overall performance of the method for LES was carried out by simulating an underexpanded round jet at a Reynolds number of 6.09 million, based in centerline velocity and diameter at nozzle exit plane. Very close quantitative agreement was found for the development of centerline mean pressure when compared to experiment. Simulations on several increasingly finer grids showed a monotonic extension of the computed part of the inertial range, with little change to low frequency content. Amplitudes and locations of large changes in pressure through several cells were captured accurately. A similar performance was observed for LES of an impinging jet containing normal and curved shocks.

The Highest and Lowest Photonic Bandwidths, Absorption Coefficients and Field Localizations among Common 1D Quasiperiodic Structures Containing Graphene and Silicon Dioxide

By employing the transfer matrix method, we report the terahertz spectral properties of graphene induced photonic band gap in four types of common one-dimensional quasiperiodic structures based on double-period, Fibonacci, Thue-Morse and Cantor sequences with approximately the same number of layers. In this paper, a graphene monolayer is sandwiched between two adjacent dielectric slabs. It is found that due to the presence of graphene in each structure, an additional gap (GPBG) is emerged. The upper band edge of GPBG moves to the higher frequencies and gets enlarged, as the chemical potential and incident angle increase. The biggest and the smallest bandwidths belong to the Cantor and double-period sequences. Also, the height of the absorption peak at the GPBG edge goes up as the temperature, graphene thickness and damping factor increase. Moreover, the highest and lowest field localizations exist in the Cantor and Fibonacci structures, respectively. Some other features are presented in the results section.

StrokeAggregator

When creating line drawings, artists frequently depict intended curves using multiple, tightly clustered, or overdrawn, strokes. Given such sketches, human observers can readily envision these intended, aggregate , curves, and mentally assemble the artist's envisioned 2D imagery. Algorithmic stroke consolidation---replacement of overdrawn stroke clusters by corresponding aggregate curves---can benefit a range of sketch processing and sketch-based modeling applications which are designed to operate on consolidated, intended curves. We propose StrokeAggregator , a novel stroke consolidation method that significantly improves on the state of the art, and produces aggregate curve drawings validated to be consistent with viewer expectations. Our framework clusters strokes into groups that jointly define intended aggregate curves by leveraging principles derived from human perception research and observation of artistic practices. We employ these principles within a coarse-to-fine clustering method that starts with an initial clustering based on pairwise stroke compatibility analysis, and then refines it by analyzing interactions both within and in-between clusters of strokes. We facilitate this analysis by computing a common 1D parameterization for groups of strokes via common aggregate curve fitting. We demonstrate our method on a large range of line drawings, and validate its ability to generate consolidated drawings that are consistent with viewer perception via qualitative user evaluation, and comparisons to manually consolidated drawings and algorithmic alternatives.

Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis

In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

Large Time Step Roe scheme for a common 1D two-fluid model

We present the Large Time Step (LTS) extension of the Roe scheme and apply it to a standard two-fluid model. Herein, LTS denotes a class of explicit methods that are not limited by the CFL (Courant–Friedrichs–Lewy) condition, allowing us to use very large time steps compared to standard explicit methods. The LTS method was originally developed in the nineteen eighties (LeVeque, 1985), where the Godunov scheme was extended to the LTS Godunov scheme. In the present work, the relaxation of the CFL condition is achieved by increasing the domain of dependence. This might lead to difficulties when it comes to boundary and source terms treatment. We address and discuss these difficulties and propose different ways to treat them. For a shock tube test case, where there are neither source terms nor difficulties associated with the boundaries, the method increases both accuracy and efficiency. For a water faucet test case that includes a source term, the method increases the efficiency, while the accuracy strongly depends on the appropriate treatment of boundary conditions and source terms.