Window Analysis Research Articles

A NEW METHOD FOR IMPROVING THE ECOLOGICAL ENVIRONMENT INDEX BASED ON DOWNSCALING OF LAND SURFACE TEMPERATURE

Abstract. In the process of urban construction and development, the ecological environment has always been an important consideration factor. How to quickly monitor the long-term ecological environment quality changes in large urban areas is currently one of the main research hotspots. With the continuous development of remote sensing intelligent cloud computing technology, the application of remote sensing methods to monitor urban ecological environment quality changes is becoming more efficient and convenient. Based on the remote sensing ecological index (RSEI) on the remote sensing intelligent cloud platform Google Earth Engine (GEE), this article innovatively proposes an improved remote sensing ecological index (DS-RSEI, Downscale-RSEI). Using the normalized difference vegetation index (NDVI) and land cover data (LULC), terrain data as auxiliary data, the method of moving window and principal component analysis is used to implement spatial downscaling of the heat components in the index, which increases the resolution of the original MODIS land surface temperature (LST) 1000-meter resolution product to 500 meters. When fusing with other images, it can supplement missing image details and improve the ability to evaluate the ecological spatial details of complex urban blocks. Through a comparative analysis of the RSEI index and the DS-RSEI index proposed in this article, it can be seen that DS-RSEI can express the details of ecological environment changes better in complex blocks.

Analysis of Genetic Diversity in Adzuki Beans (Vigna angularis): Insights into Environmental Adaptation and Early Breeding Strategies for Yield Improvement.

Adzuki beans are widely cultivated in East Asia and are one of the earliest domesticated crops. In order to gain a deeper understanding of the genetic diversity and domestication history of adzuki beans, we conducted Genotyping by Sequencing (GBS) analysis on 366 landraces originating from Korea, China, and Japan, resulting in 6586 single-nucleotide polymorphisms (SNPs). Population structure analysis divided these 366 landraces into three subpopulations. These three subpopulations exhibited distinctive distributions, suggesting that they underwent extended domestication processes in their respective regions of origin. Phenotypic variance analysis of the three subpopulations indicated that the Korean-domesticated subpopulation exhibited significantly higher 100-seed weights, the Japanese-domesticated subpopulation showed significantly higher numbers of grains per pod, and the Chinese-domesticated subpopulation displayed significantly higher numbers of pods per plant. We speculate that these differences in yield-related traits may be attributed to varying emphases placed by early breeders in these regions on the selection of traits related to yield. A large number of genes related to biotic/abiotic stress resistance and defense were found in most quantitative trait locus (QTL) for yield-related traits using genome-wide association studies (GWAS). Genomic sliding window analysis of Tajima's D and a genetic differentiation coefficient (Fst) revealed distinct domestication selection signatures and genotype variations on these QTLs within each subpopulation. These findings indicate that each subpopulation would have been subjected to varied biotic/abiotic stress events in different origins, of which these stress events have caused balancing selection differences in the QTL of each subpopulation. In these balancing selections, plants tend to select genotypes with strong resistance under biotic/abiotic stress, but reduce the frequency of high-yield genotypes to varying degrees. These biotic/abiotic stressors impact crop yield and may even lead to selection purging, resulting in the loss of several high-yielding genotypes among landraces. However, this also fuels the flow of crop germplasms. Overall, balancing selection appears to have a more significant impact on the three yield-related traits compared to breeder-driven domestication selection. These findings are crucial for understanding the impact of domestication selection history on landraces and yield-related traits, aiding in the improvement of adzuki bean varieties.

Energy and system size dependence of strongly intensive fluctuation measures in heavy-ion collisions at FAIR energies

Event-by-event fluctuations of multiplicity and transverse momentum of charged hadrons produced in heavy-ion collisions at FAIR energies, 10A, 20A, 30A, and 40A GeV are studied in the framework of relativistic transport model, URQMD. Dependence of two families of strongly intensive measures of multiplicity(N) and transverse momentum (p T) fluctuations, Δ[p T, N] and Σ[p T, N], on collision centrality, centrality bin-widths and pseudorapidity windows are examined. Attempts are also made to study NN, N p T, and p T p T fluctuations using two window analysis method. The findings suggest that the measure, Δ[p T, N] be dealt with proper selection of centrality intervals. This measure also exhibits a strong dependence on the widths of η windows. The variable Σ[p T, N], however, is observed to be insensitive to the centrality bin-widths and shows a variation of <5% with the widths of η windows. The analysis of data after event mixing gives Δ[p T, N] and Σ[p T, N] values as ∼1 irrespective of the widths of η windows and collision centrality, as predicted by a model of independent particle emission, IPM. The study of joint fluctuations of the two quantities on two η windows separated in η space, reveals that Σ[N F, N B] values are ∼1 irrespective of the position of η windows whereas, the values of and firstly increase with η sep and later acquire saturations. The observed trend of centrality dependence of and agrees fairly well with those observed in MC simulated studies carried out for AA collisions at LHC energies in the framework model of string fusion.

Analysis of Topographic Feature Parameters of Dinosaur Valley Ring Tectonic Geomorphology Based on the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM)

Geomorphological surveys and terrain analysis are essential for geomorphology, hydrology, and geographic information systems (GIS). Terrain characterization parameters are fundamental for comprehending geomorphological processes, delineating landforms, and evaluating geohazards. Hence, this study relies on data from the Gaofen-2 satellite (GF-2) and the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM). Focusing on the ring tectonics landform of Dinosaur Valley, the research utilizes visual interpretation, GIS terrain parameter extraction techniques, and visual mapping to identify, measure, and analyze terrain features. The results indicate that ALOS DEM can offer accurate geomorphic feature information. The optimal threshold for extracting the surface water system network is 150, and the optimal analysis window for extracting terrain feature parameters is 16 × 16 (0.04 km2), determined through statistical methods. This study reveals distinct double-ring linear features and prominent ring-shaped terrain characteristics by extracting terrain feature parameters and utilizing mathematical and statistical analysis. The topography gradually ascends from the center to the ridgeline and then descends to the valley line, resembling a bowl shape. This study highlights the potential application of ALOS DEM for accurately identifying topographic features in the intricate mountains of the Yunnan Plateau. It establishes a practical research framework for high-precision topographic datasets and geomorphological characterization, facilitating further analysis of the evolution of topographic development and the assessment of tectonic activity.

Measuring an efficiency aggregation of medical diagnostic laboratories: A window NDEA approach

The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.

Determining the orientation of circular areas in video images

One of the ways to describe objects in images is to identify some of their characteristic points, or points of attention. The neighborhoods of attention points are described by descriptors (sets of features) in such way that they can be identified and compared. Based on these features, the search for identical points on other images is carried out. The feature selection process should take into account not only the position of the focus point, but also the size and possible shape of the local neighborhood. Different methods of finding identical characteristic points are known, which use rectangular or circular neighborhood to describe them. The rectangular windows used for neighborhood analysis do not allow selecting the same areas on the reference image and image which is rotated and analyzed. Round windows allow this. The article proposes and studies algorithms for determining the orientation of circular areas in video images, which makes it possible to calculate, in addition to invariant characteristics, characteristics that take into account the location of individual sections of such areas. These are the algorithms: • determination of the orientation of the circular area by moments of the first and second order; • calculation of the mutual orientation of the investigated circular area and the standard according to the moments of the first and second order. As a result of research and experiments, it was established that depending on the algorithm and the content of the circular areas, there are possible deviations in determining their orientation directions. Factors affecting this: accuracy of finding characteristic points; application of certain orientation determination algorithms; differences in the angle of video recording of the reference image and the image under investigation; the location of characteristic points, inside the object or on its border, especially when the background changes; size of neighborhood (analysis window) of characteristic points. It seems appropriate to continue the research orientation determination accuracy of circular areas by various algorithms. To reduce the influence of various side factors, artificial rather than real images should be studied

Time-Varying Fractal Analysis of Exchange Rates

The foreign exchange (forex) market is a dynamic and complex financial arena where the exchange rates of various currency pairs fluctuate continuously. Among these currency pairs, EUR/TRY and USD/TRY hold significant economic relevance due to their roles in international trade and finance. In this study, we analyze the multifractality of hourly EUR/TRY and USD/TRY exchange rate data for the whole period, as well as its time-varying individual and cross correlations, spanning from May 31, 2018, to March 21, 2022. We employ multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended fluctuation analysis (MF-DFA) methodologies. The aim of studying multifractality in exchange rates is to comprehend and model the complex and intricate nature of price movements and dynamics of the EUR/TRY and USD/TRY exchange rates. In the analysis of the whole period, multifractality is detected in individual exchange rates and cross correlations. In the rolling window analysis, we demonstrated how multifractality and cross correlation multifractality change over time. Additionally, contributions of the sources of the multifractality are investigated in a time-varying framework. Multifractal nature of these exchange rates indicate that they exhibit complex and scale-dependent behaviors, which go beyond the traditional linear models. The existence of multifractality in EUR/TRY and USD/TRY exchange rates has significant implications for financial modeling, risk management, and trading strategies. It implies that standard linear models may not capture the full complexity of these markets, necessitating the development of more sophisticated models that account for multifractal properties.

Thermal Performance Analysis of Windows, Based on Argon Gas Percentages between Window Glasses

Windows in new buildings in Korea are equipped with double or triple glazing, a low-E coating, and gas injection between the panes of glass, in accordance with the regulations for the reinforced insulation of windows. However, these windows have certain issues, such as thermal transmittance variations and injected gas leakage. In this study, we investigated the current status of double-glazed windows filled with argon gas and analyzed the actual deterioration degree in terms of insulation performance via field experiments and simulations. Accordingly, we manufactured a 26-millimeter low-E double-glazed window test specimen and conducted tests in accordance with the KS F 2278 standard. The test results indicated that the thermal performance decreased by 10.9% when the argon gas filling rate was reduced from 95% to 0%. The simulation results showed that the thermal performance of windows that were insulated using only glazing decreased by 22.6% with the decrease in the argon gas filling rate; the thermal performance of the double-glazed windows also decreased by 13.6%. A comprehensive analysis of the field surveys and simulations predicted a 92% probability that the argon gas filling rate of double-glazed windows would be below 65% by two years after completion, in addition to a ~4.3% decrease in thermal performance.

Pushing the boundaries of phosphorylase cascade reaction for cellobiose production II: Model-based multiobjective optimization.

The inherent complexity of coupled biocatalytic reactions presents a major challenge for process development with one-pot multienzyme cascade transformations. Kinetic models are powerful engineering tools to guide the optimization of cascade reactions towards a performance suitable for scale up to an actual production. Here, we report kinetic model-based window of operation analysis for cellobiose production (≥100 g/L) from sucrose and glucose by indirect transglycosylation via glucose 1-phosphate as intermediate. The two-step cascade transformation is catalyzed by sucrose and cellobiose phosphorylase in the presence of substoichiometric amounts of phosphate (≤27 mol% of substrate). Kinetic modeling was instrumental to uncover the hidden effect of bulk microviscosity due to high sugar concentrations on decreasing the rate of cellobiose phosphorylase specifically. The mechanistic-empirical hybrid model thus developed gives a comprehensive description of the cascade reaction at industrially relevant substrate conditions. Model simulations serve to unravel opposed relationships between efficient utilization of the enzymes and maximized concentration (or yield) of the product within a given process time, in dependence of the initial concentrations of substrate and phosphate used. Optimum balance of these competing key metrics of process performance is suggested from the model-calculated window of operation and is verified experimentally. The evidence shown highlights the important use of kinetic modeling for the characterization and optimization of cascade reactions in ways that appear to be inaccessible to purely data-driven approaches.

IoT Traffic Fractal Dimension Statistical Characteristics on the Kitsune Dataset Example

The paper considers a method for estimating the fractal properties of traffic, and also evaluates the statistical parameters of the fractal dimension of IoT traffic. An analysis of real traffic with attacks from the Kitsune dump and an analysis of the fractal properties of traffic in normal mode and under the influence of attacks such as SSDP Flood, Mirai, OS Scan showed that jumps in the fractal dimension of traffic when attacks occur can be used to create algorithms for detecting computer attacks in IoT networks. Studies have shown that in the case of online analysis of network traffic, when assessing the RF, preference should be given to the modified algorithm for estimating the Hurst exponent in a sliding analysis window.

Mapping landscape ecological patterns using numeric and categorical maps.

The reciprocal relationships between ecological process and landscape pattern are fundamental to landscape ecology. Landscape ecologists traditionally use raster maps portraying classified features such as land use or land cover categories, and metrics suggested by the patch-corridor-matrix conceptual model of pattern. Less attention has been given to the landscape gradient conceptual model and raster maps portraying numeric features such as greenness or percent vegetation cover. We introduce the open-source tool GraySpatCon to calculate and map a variety of landscape pattern metrics from both conceptual models using either categorical or numeric maps. The 51 metrics, drawn mostly from the landscape ecology and image processing literatures, are calculated from the frequencies of input pixel values and/or the pixel value adjacencies in an analysis region. GraySpatCon conducts either a moving window analysis which produces a continuous map of a pattern metric, or a global analysis which produces a single metric value. We describe an implementation in the GuidosToolbox desktop application which allows novice users to interactively explore GraySpatCon functionality. In the R desktop environment, we demonstrate several metrics using an example map of percent tree cover and illustrate a multi-scale moving window analysis to identify scale domains. Comparisons of computational efficiency indicate a substantial GraySpatCon advantage over related software in the R environment.

A Multi-Featured Factor Analysis and Dynamic Window Rectification Method for Remaining Useful Life Prognosis of Rolling Bearings.

Currently, the research on the predictions of remaining useful life (RUL) of rotating machinery mainly focuses on the process of health indicator (HI) construction and the determination of the first prediction time (FPT). In complex industrial environments, the influence of environmental factors such as noise may affect the accuracy of RUL predictions. Accurately estimating the remaining useful life of bearings plays a vital role in reducing costly unscheduled maintenance and increasing machine reliability. To overcome these problems, a health indicator construction and prediction method based on multi-featured factor analysis are proposed. Compared with the existing methods, the advantages of this method are the use of factor analysis, to mine hidden common factors from multiple features, and the construction of health indicators based on the maximization of variance contribution after rotation. A dynamic window rectification method is designed to reduce and weaken the stochastic fluctuations in the health indicators. The first prediction time was determined by the cumulative gradient change in the trajectory of the HI. A regression-based adaptive prediction model is used to learn the evolutionary trend of the HI and estimate the RUL of the bearings. The experimental results of two publicly available bearing datasets show the advantages of the method.

Does window/door opening behaviour during summer affect the bedroom environment and sleep quality in a high-density sub-tropical city

To investigate the effects of window and/or door opening on sleep quality, a two-week field intervention study was performed in 50 bedrooms in the summer in a high-density city (Shanghai). Each participant slept as they did normally in their own bedroom during the first week and changed the state of windows or doors from open to closed or vice versa in the second week. Their bedroom environment and sleep quality were objectively measured. Available data from 256 person-nights in which the intervention was effective were used for analysis of window or door manipulation. The indoor carbon dioxide (CO2) concentrations were lower and the air temperatures and PM2.5 concentrations were higher when windows were open. The perceived air freshness and noise intensity were both higher and the duration of REM-sleep (Rapid Eye Movement sleep) was lower in this condition. No significant differences were found in other sleep quality indexes. These results suggest that in a high-density city, the increased indoor temperature, PM2.5 concentration as well as noise caused by opening windows may disturb sleep and offset the positive effects of improving the ventilation. Thus, window opening should not be recommended as a universal way of achieving bedroom ventilation to promote sleep. Affordable retrofit solutions for bedroom ventilation should take these aspects into account.

Crude oil volatility spillover and stock market returns across the COVID-19 pandemic and post-pandemic periods: An empirical study of China, US, and India

We utilized two empirical methods to examine how the COVID-19 pandemic, stock market and crude oil marketplace are connected in terms of tax return and volatility consequence. The techniques used in this study include the time-domain approach originally proposed by Diebold and Yilmaz (2012), and the frequency dynamics method developed by Barunik and Krehlik (2018). Our findings revealed that short-term return spillover was more prevalent, while long-term volatility spillover was more common. By conducting a moving window analysis, we have discovered that the COVID-19 pandemic has had a major impact on the global economy, resulting in an unprecedented level of risk. This impact is reflected in the drastic reduction in oil prices and the triggering of the US stock market circuit breaker on four separate occasions, causing significant losses for investors in a short period of time. The COVID-19 pandemic has resulted in greater volatility in the oil and stock markets than was experienced during the 2008 global financial crisis, and its effects continue to be felt. It is difficult to predict the full extent of the pandemic's impact on financial markets, both in the short and long term. Our research provides valuable insights to help investors and policymakers reduce risks and develop effective economic policies during the pandemic.

DGT-based pulse shaping filter for Generalized Frequency Division Multiplexing system

The future wireless communication systems belonging to the next generation will encounter many diverse demands, like the need for data rates that surpass the capabilities of fifth-generation networks, ultra-low power consumption to cater to battery-operated communication sensors, and extremely short response times essential for control applications. A unified physical layer waveform is envisaged to address these flexible requirements, introduced as Generalized Frequency Division Multiplexing (GFDM) for beyond 5G (B5G) communication systems. One of the important features of GFDM is the utilization of flexible pulse shaping filtering in the time domain, resulting in improved performance. In this paper, a pulse-shaping filter is designed for the GFDM system using discrete Gabor representation, discrete biorthogonality condition and Wigner distribution. An analytical expression for the analysis window is derived using the biorthogonality condition. Further, the performance of the GFDM system is analyzed in terms of out-of-band (OOB) radiation and symbol error rate (SER). The results are presented for the Quadrature Amplitude Modulation (QAM) scheme over Additive White Gaussian Noise (AWGN), Rayleigh, Nakagami-m, two wave with diffuse power (TWDP), and Nakagami-q fading channels. The detailed analysis shows that the OOB radiation and SER performance exhibit strong dependence on the choice of the pulse shaping filter.

Impact of rainfall and vapor pressure deficit on latewood growth and water stress in Douglas-fir in a Mediterranean climate

Both elevated temperature and reduced precipitation have been related to growth declines in Douglas-fir (Pseudotsuga menziesii) in the northwest U.S. However, the impact of high vapor pressure deficit (VPD) on Douglas-fir growth and physiological stress is not fully understood. We investigated how inter- and intra-annual rainfall and VPD correlated to the radial growth and carbon isotope signature (δ13C) of latewood for ∼ 50-year-old Douglas-fir trees in the western Cascade Mountains in Oregon. Latewood δ13C reflects variation in stomatal restriction of photosynthetic gas exchange and, therefore, was used as a proxy for the relative degree of physiological water stress. We cored three trees at each of nine sampling sites (n = 27 trees) and used a moving window analysis to test the period of the year in which VPD and rainfall best predicted mean latewood radial growth and δ13C. Latewood growth, measured as the basal area increment, was more sensitive to daytime VPD than the timing and amounts of rainfall, especially in early summer. In contrast, δ13C was equally sensitive to the average daytime VPD and total rainfall during spring and summer. We used the results of the moving window analysis in a linear mixed effects model to test how the effect of VPD and rainfall on yearly latewood growth and δ13C differed among our nine sites. We found no evidence for statistical differences in the effects of VPD and rainfall on growth (p = 0.93 and p = 0.91) or δ13C (p = 0.31 and p = 0.81) among our nine sites. However, the marginal effects of VPD on latewood growth at each site were weakly related to soil moisture deficits at 100 cm suggesting that site-to-site differences in soil moisture availability may be important in buffering the negative effects of seasonal aridity on growth. In contrast, there was no evidence that soil moisture differences among sites influenced the marginal effects of VPD on latewood δ13C. We conclude that increases in VPD during summer are likely to reduce latewood growth and increase water stress in Douglas-fir in the Pacific Northwest region. However, more research is needed to better understand the magnitude of this effect across sites with variable subsurface water storage and microclimate.

Abstract 17924: Machine Learning Analysis of the Atrial ECG in Patients With Persistent Atrial Fibrillation Predicts Long-Term Outcomes

Background: The surface Atrial ECG (A-ECG) time and frequency domains features are not currently used clinically in patients with persistent atrial fibrillation (PeAF). Machine Learning (ML) can provide novel insight into various data sources. Research question: Can ML analysis of the A-ECG predict the outcome of catheter ablation (CA) of PeAF? Aim: To use ML Tools to assess A-ECG prediction of CA outcomes in PeAF patients at 1 and 2 years. Methods: Thirty-two patients (age 63±9 years; 71% males) with PeAF underwent two 5-minute epochs of surface ECG; 4 were excluded for significant artifacts and 2 more for unrelated clinical issues. The Pre-CA A-ECG was derived by QRST subtraction and independent component analysis filtering and divided into 50% overlap 10-second windows for spectral analysis. A-ECG mean dominant frequency DF, mean organization index and median amplitude M-AMP were computed for each lead. Freedom from atrial arrhythmia AA (Responders) was assessed at one and two years. The two 5-minute segments per patient were included in the data set. Classification Learner (Mathworks Inc., Natick, MA, USA) was used to train classifiers, looking for the minimum number of features able to explain the outcomes. Once the best (2-3) features were identified, a MATLAB program was used to visualize the decision regions and correlate with ablation outcomes. Results: ML identified A-ECG features DF and M-AMP of V 1 to well explain outcome. Specifically, an Artificial Neural Network (ANN) with 1 fully connected hidden layer of size 7 can explain the data with 90.4% accuracy. The figure shows the datasets for year 1 and year 2 CA outcomes, superimposed with ANN decision regions, for Responders and non-Responders. High DF and high M-AMP are predictors of responders and Low DF low M-AMP for non-Responders. Conclusion: ML analysis of the A-ECG is a promising tool to predict Responders to PVI CA for treatment of PeAF. This predictive algorithm needs to be tested on a prospective cohort.

Abstract 19210: Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction From the Surface Ecg

Introduction: Cardiac wall motion abnormalities (WMA) are key prognostic indicators for major cardiovascular events and mortality, yet they are poorly detected by current ECG screening tools such as Q waves. Deep learning (DL) models have improved analysis of complex data and novel tools may provide interpretation of these models. Hypothesis: ECG features beyond the Q wave may be identified by analyzing an ECG-trained DL model to improve WMA detection over standard ECG measurements. Methods: We developed a DL neural network using 36,964 unique patients (62.9±16.9 years, 47% female, 58% white) labeled by natural language processing of echocardiography reports within 60 days. To understand how the CNN model identifies WMAs from the ECG input, we implemented Shapley Additive exPlanations (SHAP) and DeepExplainer on R-wave aligned beats over 300ms surrounding the QRS complexes to identify hot-spots across the ECG. We then retrained classifiers on 60mg windows of the ECG to identify which timepoints produced the greatest performance (ablation method). Results: Probing the CNN revealed that ECG regions throughout the QRS and T-waves identified WMA. Analysis output shows that such ECG regions arose throughout the QRS and T-waves, and not just at early regions corresponding to Q waves (Panel A) . An aggregate view of the ECG reports summarizes the most discriminatory ECG regions for all patients (Panel B) . Stepwise analysis of limited windows within aligned ECG beats also confirmed this analysis (Panel C) . We found that the highest performance window was 60-120ms (AUC 0.814), containing most of the QRS, with the lowest performance in the window of 0-60 ms (AUC 0.699) and 0.20-0.80ms (AUC 0.707). Conclusions: The DL model show promise in accurately detecting WMAs and provides key insights into critical ECG regions for WMA detection beyond the Q wave. Further analysis of WMA detection with these expanded regions is needed.

A survey of high utility sequential patterns mining methods

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given.

WEIGHTED SUMMATION OF AFM IMAGES USING LOCAL CORRELATION METRIC

The problem of weighted summation of component images of the surface of a material formed in two synchronous&#x0D; channels of an atomic force microscope (AFM) is considered. A computationally simple quality metric&#x0D; for combining component AFM images based on local correlation coefficients is proposed, which takes into account&#x0D; the contribution of each of the component AFM images to the resulting combined AFM image and the correlation&#x0D; between component AFM images. It is shown that local correlation provides a higher accuracy of AFM images&#x0D; combination quality estimation in comparison with global correlation. The dependences of the local correlation&#x0D; metric on the size of the correlation analysis window and the contribution of component AFM images to the resulting&#x0D; combined AFM images are obtained. A scheme for adaptive weighted summation of component AFM images&#x0D; is proposed.