Adjacent Patterns Research Articles

Spatial–Temporal Evolution and Driving Force Analysis of Blue–Green Space in the Chengdu–Chongqing Economic Circle, China

In the rapid process of urbanization, revealing the patterns and driving forces behind the evolution of blue–green spaces holds significant value for optimizing urban blue–green environments. This study systematically investigates the spatial–temporal evolution characteristics and driving forces of blue–green space in the Chengdu–Chongqing Economic Circle from 1990 to 2020, utilizing GIS technology, landscape pattern analysis, and geographic detectors. The research findings indicate the following: (1) The area of blue–green space in the study area exhibits a general trend of initial growth followed by decline, with significant changes occurring between 2010 and 2020. (2) The fragmentation degree of blue–green space is gradually increasing, while connectivity among landscapes is decreasing; however, there has been an increase in landscape distribution uniformity. More than 90% of blue–green spaces expanded mainly through adjacency patterns. (3) In examining driving forces, it was found that temperature, topographic relief, elevation, population density, and construction intensity are the primary driving factors. Notably, the influence of natural factors has diminished over time while human social factors have significantly intensified. This study offers solutions for optimizing the configuration of blue–green spaces within the Chengdu–Chongqing Economic Circle. It also serves as a reference case for promoting high-quality urbanization in developing countries undergoing rapid urbanization.

Development and Validation of a Portable EIT System for Real-Time Respiratory Monitoring.

This work contributes to the improvement of novel medical technologies for the prevention and treatment of diseases. Electrical impedance tomography (EIT) has gained attention as a valuable tool for non-invasive monitoring providing real-time insights. The purpose of this work is to develop and validate a novel portable EIT system with a small form factor for respiratory monitoring. The device uses a 16-electrode architecture with adjacent stimulation and measurement patterns, an integrated circuit current source and a single high-speed ADC operating with multiplexers to stimulate and measure across all electrodes. Tests were conducted on 25 healthy subjects who performed a pulmonary function test with a flowmeter while using the EIT device. The results showed a good performance of the device, which was able to recognize all respirations correctly, and from the EIT signals and images, correlations of 96.7% were obtained for instantaneous respiratory rate and 96.1% for tidal volume prediction. These results validate the preliminary technical feasibility of the EIT system and demonstrates its potential as a reliable tool for non-invasive respiratory assessment. The significance of this work lies in its potential to democratize advanced respiratory monitoring technologies, making them accessible to a wider population, including those in remote or underserved areas.

Real-time motion-induced error reduction for phase-shifting profilometry with projection points tracking method

Fringe projection profilometry is a significant method for three-dimensional measurement due to its non-contact and high accuracy. However, the motion-induced error will lead to the loss of measurement accuracy in dynamic scenes due to the disruption of the phase measurement process. In this paper, we introduce a novel motion error model that considers object motion causes the misalignment of the projection points on the camera, and propose the projection points tracking method to reduce the motion-induced error. First, the speckle pattern is added to projection sequences, after which the projection point displacements are determined using the digital image correlation (DIC) method between the adjacent speckle patterns. Finally, the fringe patterns are corrected using the image remapping method to calculate the 3D shape. Quantitative analysis and dynamic measurement experiments verify the feasibility of the proposed method. Different from 3D-DIC, we use one camera-projector system to realize high-accuracy measurement in dynamic scenes.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Fabrication of Large-Area Nanostructures Using Cross-Nanoimprint Strategy.

Nanostructures with sufficiently large areas are necessary for the development of practical devices. Current efforts to fabricate large-area nanostructures using step-and-repeat nanoimprint lithography, however, result in either wide seams or low efficiency due to ultraviolet light leakage and the overflow of imprint resin. In this study, we propose an efficient method for large-area nanostructure fabrication using step-and-repeat nanoimprint lithography with a composite mold. The composite mold consists of a quartz support layer, a soft polydimethylsiloxane buffer layer, and multiple intermediate polymer stamps arranged in a cross pattern. The distance between the adjacent stamp pattern areas is equal to the width of the pattern area. This design combines the high imprinting precision of hard molds with the uniform large-area imprinting offered by soft molds. In this experiment, we utilized a composite mold consisting of three sub-molds combined with a cross-nanoimprint strategy to create large-area nanostructures measuring 5 mm × 30 mm on a silicon substrate, with the minimum linewidth of the structure being 100 nm. Compared with traditional step-and-flash nanoimprint lithography, the present method enhances manufacturing efficiency and generates large-area patterns with seam errors only at the micron level. This research could help advance micro-nano optics, flexible electronics, optical communication, and biomedicine studies.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

Multifrequency Electrical Impedance Tomography Images for Swine Pneumonia Detection Using Conductivity Differences

This article introduces a new MfEIT UDESC Mark I system, which consists of a 32-electrode setup featuring a modified Howland current source, low cost, portability, and non-radiation. The system is capable of reconstructing electrical conductivity tomographic images at a rate of 30.624 frames per second, taking about 5 min for imaging. The current source employs a 0.5 mA adjacent current application pattern with frequencies ranging from 10 kHz to 1 MHz. This article outlines the hardware, firmware, and software design specifications, which include the design of the current source, calibration procedures, and image reconstruction process. Tomographic images of conductivity were reconstructed in ex vivo healthy pig lungs and those with pneumonia, as a proof of concept for future applications in live pigs. The high spectral power density, combined with real-time system calibration provides clinical advantages in veterinary medicine. The goal is to identify lung areas affected by Mycoplasma hyopneumoniae in pigs through the analysis of electrical conductivity difference, offering a valuable tool to assist veterinarians to obtain images of respiratory diseases. The modified reconstruction method GREIT (EIDORS) was evaluated with experimental data and was compared with the Gauss–Newton and Total Variation methods, where GREIT 2D proved to be superior.

Abstract 1535: Differential immune microenvironments in triple-negative breast cancer and hormone receptor-positive HER2-negative breast cancer: A spatial analysis via multiplex immunofluorescence

Abstract Objective: To elucidate differences in immune cell (IC) composition and their spatial distribution between triple-negative breast cancer (TNBC) and hormone receptor-positive HER2-negative breast cancer (HR+HER2-BC), and to evaluate the effect of PD-L1 status on these parameters. Methods: We selected 32 patients with ≥60% tumor-infiltrating lymphocytes (TIL) (TNBC, n=18; HR+HER2- BC, n=14). Multiplex immunofluorescence was employed on resected tumor tissues. Using markers such as CK, CD20, CD8, CD4, and FOXP3, IC types and tumor cells (TC) were identified in both stromal and tumor areas. We analyzed and compared cellular density and proportional distribution of IC, and spatial interactions (both IC-TC and IC-IC) based on tumor subtype and PD-L1 status. Results: TNBC exhibited a unique IC composition with a higher proportion of CD8+IC (stroma: 27% vs 17%, p<0.001; tumor: 54% vs 31%, p<0.001) and CD4+FOXP3+IC (stroma: 3.9% vs 3.0%, p=0.036) than HR+HER2-BC. Among PD-L1 positive cases, TNBC had a denser stromal population of CD4+FOXP3+IC (146.4±67.1/mm2 vs 114.3±146.9/mm2, p=0.036). Both tumor subtypes displayed varied IC compositions based on PD-L1 status. Density plots revealed a pronounced clustering of IC near TC in TNBC, especially in PD-L1 positive cases. Analysis of cell-cell interactions showed diverse adjacency patterns between TC and various IC combinations with frequencies differing by tumor subtype and PD-L1 status. Conclusion: The immune landscape, typified by IC composition and spatial distribution, varies between TNBC and HR+HER2- BC, with PD-L1 status further modulating these patterns. Citation Format: Yoon Jin Cha, Inho Park, Su-Jin Shin. Differential immune microenvironments in triple-negative breast cancer and hormone receptor-positive HER2-negative breast cancer: A spatial analysis via multiplex immunofluorescence [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1535.

Pattern-dependent resistivity variations in inkjet-printed conductors due to non-uniform ink drying

When fabricating inkjet-printed electronic devices and circuits, inkjet-printed conductive materials require drying and sintering to improve electrical conductivity. Electrical conductivity should be the same irrespective of pattern design, size, location, or density of adjacent patterns. However, we demonstrate that spatial variations in the drying process for inkjet-printed patterns with proximity to others cause resistivity variations. These resistivity variations are studied here experimentally for different circuit patterns and in arrays of inkjet-printed square electrodes. This variation depends not only on the location of each electrode in an array but also on the number of electrodes. This means that for the same drying temperature and duration, the array with a larger number of electrodes exhibits a larger resistivity variation. The sooner an electrode dries, the lower resistivity it achieves. The resistivity variation between an individual electrode and the center electrode in a 7 × 7 electrode array can be a factor of seven. This variation decreases for lower numbers of electrodes to a factor of three for a 3 × 3 array. Furthermore, x-ray photoelectron spectroscopy analyses provide evidence for the residual presence of carbon-based materials within electrodes after the drying process. These results confirm that the location of electrodes within an array significantly influences the amount of residual carbon-based materials, thereby contributing to resistivity variations. Although intense pulsed light sintering can decrease this variation, its optimal parameters depend on the printed designs, and our simulation results show a non-uniform temperature profile over the electrode arrays. Temperature increases more at the center of patterns than the corners, which can be useful in this case to improve resistivity uniformity. In this study, for the first time, we show how different printed shapes and designs can result in non-uniform resistivity after drying and sintering.

Повышение выхода пиломатериалов при распиловке с брусовкой

The log cutting theory accepts that the volumetric yield of edged sawn timber from the maximum volume cant will be maximal. According to current standards, edged sawn timber must have specified thickness and width. Some cants are not used for the production of centre yield because the widths of their faces are aliquant of the centre yield thickness. The volume of such cant in centre yield production is not taken into account in the log cutting theory and the conclusion that their volumetric yield from the maximum volume cant will be maximal is not obvious. The 1st stage of cant sawing is obtaining a two-edged cant from a log. At that, due to the deviation of the roundwood axis from the centre line of the sawing pattern, a narrow and a wide face are obtained. We consider the dimensioning of the narrow face of a two-edged cant, as its size determines the volumetric yield of centre yield. Within the narrow face of a two-edged cant 2 zones are allocated: unconditional and probabilistic. In the unconditional zone, an integer number of edged boards is obtained. In the range of roundwood diameters from 17 to 29 cm, only the roundwood with the diameters of 21 and 25 cm have provided the maximum volume two-edged cants, but the volumetric yield of the centre yield from the roundwood of these diameters has not been maximal. It follows from this that the maximum volume cant does not guarantee the maximal volumetric centre yield. The probability zone includes a non-integer number of edged boards. It is impossible to determine their number in an analytical way, so the methods of probability theory have been used. The distribution function of the narrow face of a two-edged cant has been derived. In order to use the distribution function to obtain a non-integer number of edged boards, the width of the probability zone has been calculated, as well as the size of the part of the probability zone decisive in obtaining a non-integer number of edged boards and the confidence interval. Further, the “Distribution function of the width of the narrow face of a two-edged cant” table was used to determine the non-integer number of edged boards. Obtaining the noninteger number of the edged boards from a two-edged cant can be implemented in practice using changeable or adjacent sawing patterns. The presented results can be applied when determining the number of sorting groups of roundwood before its feeding to the sawmill and when changing the technology of centre yield production.

Evaluation of adjacent and opposite current injection patterns for a wearable chest electrical impedance tomography system

Objective. Wearable electrical impedance tomography (EIT) can be used to monitor regional lung ventilation and perfusion at the bedside. Due to its special system architecture, the amplitude of the injected current is usually limited compared to stationary EIT system. This study aims to evaluate the performance of current injection patterns with various low-amplitude currents in healthy volunteers. Approach. A total of 96 test sets of EIT measurement was recorded in 12 healthy subjects by employing adjacent and opposite current injection patterns with four amplitudes of small current (i.e. 1 mA, 500 uA, 250 uA and 125 uA). The performance of the two injection patterns with various currents was evaluated in terms of signal-to-noise ratio (SNR) of thorax impedance, EIT image metrics and EIT-based clinical parameters. Main results. Compared with adjacent injection, opposite injection had higher SNR (p < 0.01), less inverse artifacts (p < 0.01), and less boundary artifacts (p < 0.01) with the same current amplitude. In addition, opposite injection exhibited more stable EIT-based clinical parameters (p < 0.01) across the current range. For adjacent injection, significant differences were found for three EIT image metrics (p < 0.05) and four EIT-based clinical parameters (p < 0.01) between the group of 125 uA and the other groups. Significance. For better performance of wearable pulmonary EIT, currents greater than 250 uA should be used in opposite injection, 500 uA in adjacent one, to ensure a high level of SNR, a high quality of reconstructed image as well as a high reliability of clinical parameters.

Re-construction of original face images from plastic surgery images using harmony search algorithm and IRBFN model

Face Reconstruction is the only biometric that allows you to perform passive identification in one to many environments. By changing the geometry and texture of the facial region, there can be an increased intraclass variability between images of an individual before and after surgery. Even so, the nonlinear changes caused by plastic surgery have a hard time being modelled by facial reconstruction systems. An algorithm for plastic surgery face reconstruction is presented here, that uses Harmony Search to confront the challenges involved. A non-disjoint algorithm generates the face granules, each representing different information with varying resolutions and sizes. A Scale-Invariant Feature Transform (SIFT) and Co-occurrence of Adjacent Local Binary Patterns were applied to extract discriminating information from granules of face images. As the concluding step, we used Harmony Search Method (HSM) to combine the various responses to attain the optimal solution. Finally Improved Radial Basis Function Network (IRBFN) algorithm is used for feature matching. The feature matching predict, where the face is recognized or not. The modified method, proposed in this work, has the better performance in classifying more images with higher accuracy. An obtained sensitivity of 100 % and specificity of 97.5 % was achieved by the proposed classifier. The results proved that it is possible to use the developed algorithms understand different pre and post surgical image conditions, evaluate their strength and future development trends, and provide all-facial features basis for detection of pre and post surgical images.

PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm.

In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.

A noise-controlling method by hybrid current-stimulation and voltage-measurement for electrical impedance tomography (HCSVM-EIT)

Image reconstruction in electrical impedance tomography (EIT) is a typical ill-posed inverse problem, from which the stability of conductivity reconstruction affects the reliability of physiological parameters evaluation. In order to improve the stability, the effect of boundary voltage noise on conductivity reconstruction should be controlled. A noise-controlling method based on hybrid current-stimulation and voltage-measurement for EIT (HCSVM-EIT) is proposed for stable conductivity reconstruction. In HCSVM-EIT, the boundary voltage is measured by one current-stimulation and voltage-measurement pattern (high-SNR pattern) with a higher signal-to-noise ratio (SNR); the sensitivity matrix is calculated by another current-stimulation and voltage-measurement pattern (low-cond pattern) with a lower condition number; the boundary voltage is then transformed from the high-SNR pattern into the low-cond pattern by multiplying by an optimized transformation matrix for image reconstruction. The stability of conductivity reconstruction is improved by combining the advantages of the high-SNR pattern for boundary voltage measurement and the low-cond pattern for sensitivity matrix calculation. The simulation results show that the HCSVM-EIT increases the correlation coefficient (CC) of conductivity reconstruction. The experiment results show that the CC of conductivity reconstruction of the human lower limb is increased from 0.3424 to 0.5580 by 62.97% compared to the quasi-adjacent pattern, and from 0.4942 to 0.5580 by 12.91% compared to the adjacent pattern. In conclusion, the stable conductivity reconstruction with higher CC in HCSVM-EIT improves the reliability of physiological parameters evaluation for disease detection.

Influence of Implant Adjacent Teeth on the Accuracy of Digital Impression.

The aim of this study was to evaluate the effect of adjacent teeth patterns on the accuracy of digital scans of parallel and divergent implants for three-unit prostheses. A maxillary typodont model with implants in the locations of the first premolars and first molars was used to develop three clinical scenarios for three-unit prostheses: (S1) Partially edentulous arch with missing first premolars and first molars only; (S2) partially edentulous arch with missing first premolars, second premolars and first molars; and (S3) partially edentulous arch with missing canines, first premolars, second premolars, first molars, and second molars. On one side, the implants were parallel, and for the other side, the implants had a 15-degree buccolingual angle. With the aid of scan bodies, 10 digital impressions were taken for each scenario and for each side. To evaluate the accuracy, a reverse engineering software was used to measure trueness, precision, and interimplant distance. The best trueness for parallel implants was observed for S2 (30.0 µm), followed by S3 (67.3 µm) and S1 (74.8 µm) (p < 0.001). Likewise, S2 had the best precision for parallel implants (31.3 µm) followed by S3 (38.0 µm) and S1 (70.3 µm) (p < 0.001). For the divergent implants, S2 exhibited the best trueness (23.1 µm), followed by S3 (48.2 µm) and S1 (59.4 µm) (p = 0.007). Similarly, the S2 had the best precision (12.3 µm) followed by S3 (62.1 µm) and S1 (66.9 µm) (p < 0.001). The S2 had the least interimplant distance deviation followed by S1 and S3. The difference was significant for parallel implants (p = 0.03), but insignificant for divergent implants (p = 0.15). Regardless of the presenting scenario, digital implant impressions for three-unit prostheses appear to be clinically accurate. A clear interimplant area between scan bodies enhanced the accuracy of digital impressions. This observation can be attributed to more accessible axial surface scanning of the scan body.

In situ measurement of dynamic surface charge on dielectrics interacted with plasma jet arrays

As a flexible tool to generate large-scale non-thermal plasma in ambient air, plasma jet arrays have attracted academic attention from multiple fields of biomedicine and material surface sciences. Optimization of plasma processing that heavily relies on trial-and-error experiments requires quantitative in situ diagnostics of plasma–surface interaction. This study focuses on the fundamental surface charge evolution in quartz dielectric deposited by plasma jet arrays. The thin quartz plate is attached to an electro-optic BSO crystal that is conductive and grounded in the back. These jet arrays are driven by a positive microsecond pulsed voltage with kHz frequency. Special attention is paid to the properties and mechanisms of the intriguing convergence of multiple adjacent surface charge patterns. The measured 1D and 2D jet arrays verify the trend of homopolar charge fusion at the plasma–surface interface, which was initially thought to be repulsive under electrostatic forces. The fusion of surface charges is considerably enhanced when more charges are transported onto targeted dielectrics, as well as at smaller separations. The simulation results reveal the mechanism of charge fusion that occurs when surface ionization waves (IWs) are in close proximity, and the high electric field between their heads provides a cluster of high-energy electrons that forces two IWs to merge until they connect. In the case of extremely small separations, the close-in IWs directly merge in bulk before touching the target. Quantitative in situ measurements of surface charges and the corresponding simulations provide fresh insight into the electrical interaction in large-scale atmospheric-pressure plasma jets. The properties of charge fusion can help with manufacturing and optimizing large-area uniform plasma jet sources for various dielectric materials.

Detection of the most dominant drivers of atrial fibrillation based on repetitive activity in sequential electro-anatomical mapping

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work is part of Personalize AF. This project received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860974, This work was also supported by the Swiss National Supercomputing Centre (CSCS), project s1074. Introduction Detecting and targeting the most dominant drivers of atrial fibrillation (AF) might improve the outcome of AF ablation procedures. We hypothesize that dominant drivers entrain their vicinity in a temporally and spatially stable manner resulting in local as well as adjacent repetitive activation patterns. Purpose We present a novel approach to detect the most dominant drivers of AF based on driver repetitiveness and the degree to which they entrain their vicinity. Methods We retrospectively analyzed a dataset of high-density bi-atrial sequential mapping in ablation-naive persistent AF patients (n=13, 30s recording per site). Candidate drivers (focal activity or re-entry) were detected based on local activation time analysis of unipolar electrograms. Focal activity was defined as radial spread of activation, re-entry as a circular activation sequence on at least 4 splines of the mapping catheter that covered &amp;gt;50% of the AF cycle length. As a measure of driver stability, we determined the driver's local repetitiveness (in % of recording). As a measure of driver-associated entrainment, we determined the directionally coherent repetitiveness of adjacent recordings within 30 mm (Figure 1). Following our hypothesis, most dominant drivers can be recognized by their high local and directional coherent adjacent repetitiveness. Results A total of 459 recordings were analyzed (35±5 per patient). We detected 130 focal activities (10±3 per patient) and 42 re-entries (4±2 per patient) in total. The spatial distribution of all drivers is shown in Figure 2 (blue bars). Focal activities were more repetitive than re-entries (Figure 2, median [IQR] 3.2% [1.9%, 5.6%] vs. 1.8% [1.8%, 2.2%], p&amp;lt;0.001 Mann-Whitney U-test). When applying a 90th percentile threshold (8.2%) to both the local and directional coherent adjacent repetitiveness to detect the most dominant drivers, we were able to detect 9 dominant drivers (all focal activities, 7 patients). Most dominant drivers were located in the upper right atrium or left pulmonary vein region (Figure 2). Interestingly, only 9 of the 130 initially detected drivers (7%) showed an activation pattern in line with a repetitive entrainment of their vicinity. Furthermore, in 6 patients (46%), no dominant drivers were detected. This might indicate that not all patients have temporally and spatially stable drivers. Conclusion In this abstract we present a novel methodology to detect the most dominant drivers of AF. Using this method, we found that some (but not all) patients have temporally and spatially stable drivers that are able to entrain their vicinity.

Two inverse eigenvalue problems for matrices whose graphs are trees

Inverse eigenvalue problem refers to the problem of reconstructing a matrix of a desired structure from a prescribed eigendata. In this paper, we discuss two additive inverse eigenvalue problems for matrices whose graph is tree. In order to analyze the problems, the vertices of the given tree are labeled in a suitable way so that concrete recurrence relations between the characteristic polynomials of the leading principal submatrices of the matrix can be obtained in a simple form. The method of obtaining the entries of the required matrix is constructive and provides an algorithm for computing the same. We provide some numerical examples to illustrate the results. The computations are done using SCILAB by feeding the eigendata and the adjacency pattern of the tree as inputs.

A deep-learning framework considering multiple motifs for traffic travel time prediction

How to accurately predict short-term traffic travel time (TTT) is an important problem in intelligent transportation systems. Traffic data usually exhibit high non-linearities and complex patterns, and predicting TTT is a challenge. Most previous studies have used the topological adjacency of road networks to explore spatial correlations. However, a real road network contains higher-order connectivity patterns that have different statistical significance. The topology adjacency cannot reflect these higher-order connectivity patterns. To obtain topological adjacency and higher-order connection pattern information, a novel deep-learning framework (multi-motif graph convolutional recurrent neural networks) for TTT prediction is proposed. The accuracy of TTT prediction was improved with this model. There are two blocks in each unit of the model: (a) spatial blocks, which capture spatial pattern information by the multi-motif graph convolution network and motif graph embedding, and (b) temporal blocks, which capture temporal pattern information by the combination of a long short-term memory network and a fully connected layer. To prove the effectiveness and accuracy of the prediction model, experiments were conducted on real-world TTT data sets.

Optical Insight Into Riverine Influences on Dissolved and Particulate Organic Carbon in a Coastal Arctic Lagoon System

AbstractArctic coastal margins receive organic material input from rivers, melted sea ice, and coastal erosion, phenomena that are all undergoing changes related to global climate. The optical properties of coastal Arctic waters contain information on this organic material, and we examined three optical properties of seawater (absorption, backscatter, and fluorescence) for their relationships to variability in dissolved and particulate organic carbon (DOC and POC) in Stefansson Sound, Alaska, a coastal Arctic embayment. During open water periods in 2018 and 2019, DOC was inversely correlated with salinity (r2 = 0.97) and positively correlated with dissolved organic matter fluorescence (fDOM; r2 = 0.67). DOC showed strong correlation with the nonparticulate absorption coefficient at 440 nm (ag(440)) only in 2018 (r2 = 0.95). The vertical structure of fDOM in Stefansson Sound aligned with density profiles more strongly in 2018 than in 2019, and higher levels of fDOM, ag(440), and backscatter seen near the bottom in 2019 suggest wind‐driven mixing and/or bottom resuspension events. In both years, DOC correlated strongly with the spectral slope of the absorption coefficient between 412 and 550 nm (r2 = 0.70), and POC was well correlated with spectral backscattering at 470, 532, and 660 nm (r2 = 0.90, 0.71, and 0.59). These interannual differences in the spatial and vertical distributions of DOC and POC, and their respective correlations with optical proxies, likely reflect regional climatological factors such as precipitation over the adjacent watersheds, wind patterns, and residual sea ice in late summer.