Empirical Threshold Values Research Articles

A Novel Adaptive Frame Determination Methodology with Fixed and Adaptive Thresholds Using Affine Motion Parameter for Video Quality Enhancement

ABSTRACT There is shortage of novel methodologies in obtaining a better quality compressed video. Diverse exploration resulted in Group of pictures aiming at Intra, Bi-directional & Predicted frame identification for performing compression. The conventional GoP practices fixed frame pattern irrespective of the movement by the camera. This paper deals with the choice of adaptive frame identification by incorporating fixed and empirical values. The fixed values are chosen based on the various motion types. NSEW affine translation is incorporated to substitute B frames obtained from fixed and empirical threshold values with I or P frame. Motion Estimation & warping using affine are preferred for compression and decompression to obtain the compressed data and the warped. Four metrics explicitly have been analyzed to demonstrate the superiority of the suggested procedure.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.

Identifying the Genetic Distance Threshold for Entiminae (Coleoptera: Curculionidae) Species Delimitation via COI Barcodes.

Simple SummaryOver 12,000 species of the subfamily Entiminae (Coleoptera: Curculionidae) have been described worldwide, but there has yet to be realization of the potential for DNA barcodes to assist in species-level identification. Here, we analyzed the variation in intra- and interspecific genetic distance of 621 public- and 27 self-determined sequences, to determine parameters for species identification of Entiminae. Our study found no universal barcoding gap at the subfamily level, although a genetic distance threshold of 9.18% can delimit more than 88% of Entiminae species. We also inferred additional empirical threshold values for 14 genera (species > 2) that can delimit congeneric species.The subfamily Entiminae is the largest group in the family Curculionidae, and it has long represented a challenge in traditional and molecular classification. Here, we analyzed intra- and interspecific genetic distances of 621 public COI barcode sequences (658bp) from 39 genera and 110 species of Entiminae, to determine parameters most congruent in retaining established species. We found that the mean intraspecific genetic distance (3.07%) was much smaller than the mean interspecific one (21.96%), but there is a wide range of overlap between intra- and interspecific genetic distances (0.77–18.01%), indicating that there is no consistent, universal barcoding gap. Specifically, DNA barcoding gap analysis for morphospecies revealed that 102 of 110 morphospecies had barcoding gaps, and 9.18% was the optimum threshold of genetic distances for 97 species delimitation. We further confirmed this threshold with barcodes from 27 morphologically identified specimens (including 21 newly reported barcodes) sequenced from five genera and seven species. We also identified thresholds to delimit congeneric species within 14 selected genera (species > 2), which varied from 7.42% (Trichalophus) to 13.48% (Barypeithes). We herein present optimal parameters for species identification in the Entiminae. Our study suggests that despite no universal genetic distance threshold value in subfamily Entiminae, 9.18% is optimal for most species. We recommend a wider sampling of geographic populations to better account for intraspecific distance variation, and that genetic distance thresholds for species delimitation should be refined at the genus level.

Keystroke Dynamics-Based Authentication System Using Empirical Thresholding Algorithm

In a password-based authentication technique, whenever the typed password and username matches the system database, the secure login page allows the client to access it. Despite the password matching, the proposed method checks the similarity between the typing rhythm of entered password and the rhythm of password samples in client's database. In this paper, a novel algorithmic procedure is presented to authenticate the legal client based on empirical threshold values obtained from the timing information of the client's keystroke dynamics. The exploratory outcomes demonstrate an impressive diminish in both false rejection rate and false acceptance rate. Equal error rate and authentication accuracy are also assessed to show the superiority and robustness of the method. Therefore, the proposed keystroke dynamics-based authentication method can be valuable in securing the system protection as a correlative or substitute form of client validation and as a useful resource for identifying the illegal invasion.

Borrowing the Pentatomomorpha tome from the DNA barcode library: Scanning the overall performance ofcox1as a tool

Pentatomomorpha (Hemiptera: Heteroptera) occurs worldwide with almost 19,000 species within six superfamilies. Their wide morphological, behavioral, and ecological diversity is remarkable, making them subject of basic and applied studies. The mitochondrial cytochrome c oxidase subunit I (cox1) proposed as a DNA barcode for Metazoa is used in species discovery and identification, relying on threshold values to split intra- and interspecific sequences and a comprehensive library to accurate identification. Here, we scanned all the pentatomomorphan cox1 sequences on Barcode of Life Data System library aiming to provide an overview of available data; verify barcoding gaps at generic level; infer individual empirical threshold values to distinguish congeneric species; and test efficiency of cox1 based on the Probability of Correct Identification (PCI) analysis. Our final dataset comprised 12,189 sequences, covering five superfamilies, 32 families, 460 genera, and 1068 species. The dataset abundance and composition were biased to families with economic importance, that is, Pentatomidae, Lygaeidae, Scutelleridae, Coreidae, and Rhyparochromidae. Barcode gaps were detected for most of the analyzed genera, reaffirming the efficiency of cox1 for Pentatomomorpha. We inferred threshold values for 131 genera and found a global PCI of 74.33%, suggesting that one out of four analyzed species suffer from operational biases or hide cryptic species. We brought examples to illustrate how cox1 can be used to flag inconsistencies, refine, and shed light onto future studies on Pentatomomorpha. We emphasize the efficiency of cox1 as DNA barcode for these true bugs, advocating for its combined use with, for example, morphology in integrative approaches.

Potential short‐term earthquake forecasting by farm animal monitoring

AbstractWhether changes in animal behavior allow for short‐term earthquake predictions has been debated for a long time. Before, during and after the 2016/2017 earthquake sequence in Italy, we deployed bio‐logging tags to continuously observe the activity of farm animals (cows, dogs, and sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct–Nov 2016) and over a subsequent longer observation period (Jan–Apr 2017). Relating 5,304 (in 2016) and 12,948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a building (stable), but not during their time on a pasture. We detected these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1–20 hr) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous bio‐logging of animal collectives has the potential to provide statistically reliable patterns of pre‐seismic activity that could yield valuable insights for short‐term earthquake forecasting. Based on a priori model parameters, we provide empirical threshold values for pre‐seismic animal activities to be used in real‐time observation stations.

Frequency distribution of foliar nickel is bimodal in the ultramafic flora of Kinabalu Park (Sabah, Malaysia).

The aim of this study was to test the frequency distributions of foliar elements from a large dataset from Kinabalu Park (Sabah, Malaysia) for departure from unimodality, indicative of a distinct ecophysiological response associated with hyperaccumulation. We collected foliar samples (n = 1533) comprising 90 families, 198 genera and 495 plant species from ultramafic soils, further foliar samples (n = 177) comprising 45 families, 80 genera and 120 species from non-ultramafic soils and corresponding soil samples (n = 393 from ultramafic soils and n = 66 from non-ultramafic soils) from Kinabalu Park (Sabah, Malaysia). The data were geographically (Kinabalu Park) and edaphically (ultramafic soils) constrained. The inclusion of a relatively high proportion (approx. 14 %) of samples from hyperaccumulator species [with foliar concentrations of aluminium and nickel (Ni) >1000 μg g-1, cobalt, copper, chromium and zinc >300 μg g-1 or manganese (Mn) >10 mg g-1] allowed for hypothesis testing. Frequency distribution graphs for most elements [calcium (Ca), magnesium (Mg) and phosphorus (P)] were unimodal, although some were skewed left (Mg and Mn). The Ni frequency distribution was bimodal and the separation point for the two modes was between 250 and 850 μg g-1. Accounting for statistical probability, the established empirical threshold value (>1000 μg g-1) remains appropriate. The two discrete modes for Ni indicate ecophysiologically distinct behaviour in plants growing in similar soils. This response is in contrast to Mn, which forms the tail of a continuous (approximately log-normal) distribution, suggestive of an extension of normal physiological processes.

Three segmentation techniques to predict the dysplasia in cervical cells in the presence of debris

Cervical Cancer is one of the most pandemic causes of cancer related death in females. Pap smear test is one of the most commonly used screening test for the cervical cancer. Existing algorithms focus on the segmentation of nucleus and cytoplasm either using single-cell images or multiple cells images. Images captured from the Pap smear slides are called smear images. Smear image contains cervical cells along with debris, debris are inflammatory cells, red blood cells, dye. Debris significantly influence the outcome of image segmentation. An accurate nuclei segmentation method can improve the success rate of cervical cancer screening. Therefore, this paper reveals about three segmentation techniques which are used for automated segmentation of cervical cell nuclei in the presence of debris. Three segmentation techniques namely, Automated Seed Region Growing, Extended Edge Based Detection and Modified Moving k-means techniques are proposed to extract the cervical cell nuclei. These techniques are extracting the area of nuclei from smear images using the morphological property of nucleus. Some debris have area that corresponds with the area of nucleus of normal cells, it may interfere with outcome and may give false positive results. The empirical area threshold value demonstrate the superior performance of all proposed methods. The qualitative and quantitative analysis also done on proposed techniques. Experimental analysis shows that Modified Moving k-means give favorable result in dysplasia detection in the presence of debris. A new dataset PapsmearJP is collected during the study with the help of a pathologist for the validation of the work.

Nonlinear seismic site response classification using K-means clustering algorithm: Case study of the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake, Japan

Spectral ratio methods have been widely used in evaluation of nonlinear seismic site response. Nevertheless, it remains inefficient and subjective to identify stations with nonlinear site response according to empirical threshold values of spectral ratio nonlinear degree indicators. This study, which was the first to apply the machine learning clustering algorithm to address this problem, used the September 6, 2018 Mw6.6 Hokkaido Iburi-Tobu earthquake (Japan) as an example. First, we calculated the surface/borehole and horizontal/vertical spectral ratios using strong ground motion data recorded by KiK-net vertical array and K-NET stations, respectively. The degree of nonlinear site response (DNL) and percentage of nonlinear site response (PNL) were computed using the difference between the strong motion of the mainshock and weak aftershocks as the reference for linear site response. Then, the K-means clustering algorithm was incorporated in the identification of nonlinear site response using the DNL, PNL, strength of ground motion (PGA) and site condition (VS20 or VS30) as explanatory variables. After careful multicollinearity diagnosis and confirmation of the optimum clustering number, we successfully classified the stations into two clusters with nonlinear and linear site responses. Overall, the clustering results were found in good agreement with the classification results based on empirical thresholds of several nonlinear indicators. For the stations identified with nonlinear site response, the reduction of amplification and frequency shift could be observed from the spectral ratio curves regarding the ground motions in the mainshock and the reference weak aftershocks, demonstrating typical nonlinearity response characteristics. Furthermore, a comprehensive indicator of nonlinear site response occurrence probability (NLscore) was obtained from a linear weighted combination of the normalized variables (PGA, VS30/VS20, DNL and PNL). The NLscore ranking of the top several stations was found consistent with the clustering identification results, irrespective of the choice of combination scheme. It was demonstrated that the performance of clustering algorithm in this application was satisfactory and that the identification results were convincing and robust. This work provides an enlightening example of using state-of-art machine learning technique to solve the traditional earthquake engineering problems.

APPLYING EMPIRICAL THRESHOLDING ALGORITHM FOR A KEYSTROKE DYNAMICS BASED AUTHENTICATION SYSTEM

Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system. Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network. In current circumstances, impostors can hack the system to obtain a user’s password, while it has also been easy to find out a person’s private password. Thus, the existing structure is exceptionally flawed. One way to strengthen the password-based authentication technique, is by keystroke dynamics. In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified. The timing features of the user’s keystroke dynamics are collected to calculate the threshold values. In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values. The first step involves the extraction of timing features from the typed password samples. The password training database for each user is constructed using the extracted features. Moreover, the empirical threshold limits are calculated from the timing features in the database. The second step involves user authentication by applying these threshold values. The experimental analyses are carried out in MATLAB simulation, and the results indicate a significant reduction in false rejection rate and false acceptance rate. The proposed methodology yields very low equal error rate of 0.5% and the authentication accuracy of 99.5%, which are considered suitable and efficient for real-time implementation. The proposed method can be a useful resource for identifying illegal invasion and is valuable in securing the system as a correlative or substitute form of client validation.

A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter (QMD), Gini coefficient (GC), basal area larger than mean (BALM) and density of stems (N) –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, GC and BALM were the most important variables in the identification of FSTs. Lower, medium and high values of GC and BALM characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using QMD and N. Then we used similar structural predictors derived from ALS – maximum height (Max), L-coefficient of variation (Lcv), L-skewness (Lskew), and percentage of penetration (cover), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.

Change-point analysis of geophysical time-series: application to landslide displacement rate (Séchilienne rock avalanche, France)

The rank-sum multiple change-point method is a robust statistical procedure designed to search for the optimal number and the location of change points in an arbitrary continue or discrete sequence of values. As such, this procedure can be used to analyse time-series data. Twelve years of robust data sets for the Sechilienne (French Alps) rockslide show a continuous increase in average displacement rate from 50 to 280 mm per month, in the 2004–2014 period, followed by a strong decrease back to 50 mm per month in the 2014–2015 period. When possible kinematic phases are tentatively suggested in previous studies, its solely rely on the basis of empirical threshold values. In this paper, we analyse how the use of a statistical algorithm for change-point detection helps to better understand time phases in landslide kinematics. First, we test the efficiency of the statistical algorithm on geophysical benchmark data, these data sets (stream flows and Northern Hemisphere temperatures) being already analysed by independent statistical tools. Second, we apply the method to 12-yr daily time-series of the Sechilienne landslide, for rainfall and displacement data, from 2003 December to 2015 December, in order to quantitatively extract changes in landslide kinematics. We find two strong significant discontinuities in the weekly cumulated rainfall values: an average rainfall rate increase is resolved in 2012 April and a decrease in 2014 August. Four robust changes are highlighted in the displacement time-series (2008 May, 2009 November–December–2010 January, 2012 September and 2014 March), the 2010 one being preceded by a significant but weak rainfall rate increase (in 2009 November). Accordingly, we are able to quantitatively define five kinematic stages for the Sechilienne rock avalanche during this period. The synchronization between the rainfall and displacement rate, only resolved at the end of 2009 and beginning of 2010, corresponds to a remarkable change (fourfold increase in mean displacement rate) in the landslide kinematic. This suggests that an increase of the rainfall is able to drive an increase of the landslide displacement rate, but that most of the kinematics of the landslide is not directly attributable to rainfall amount. The detailed exploration of the characteristics of the five kinematic stages suggests that the weekly averaged displacement rates are more tied to the frequency or rainy days than to the rainfall rate values. These results suggest the pattern of Sechilienne rock avalanche is consistent with the previous findings that landslide kinematics is dependent upon not only rainfall but also soil moisture conditions (as known as being more strongly related to precipitation frequency than to precipitation amount). Finally, our analysis of the displacement rate time-series pinpoints a susceptibility change of slope response to rainfall, as being slower before the end of 2009 than after, respectively. The kinematic history as depicted by statistical tools opens new routes to understand the apparent complexity of Sechilienne landslide kinematic.

Optimal threshold selection in the POT method for extreme value prediction of the dynamic responses of a Spar-type floating wind turbine

This paper concerns the calculation of the short term extreme structural responses of a Spar-type floating wind turbine by applying the peak-over-threshold (POT) method. A new approach is proposed for finding an optimum threshold value by incorporating a declustering algorithm into the POT method. To select the clusters and check the Poisson character of the number of clusters we use the concept of a dispersion index. Then, the method of maximum product of spacing is utilized to estimate the parameters in the Generalized Pareto distribution of the largest values in all the selected clusters over the optimum threshold value. As examples of calculations, optimum threshold values of the out-of-plane and in-plane blade root bending moments and the tower – Spar interface bending moments of the NREL 5-MW OC3-Hywind floating wind turbine have been obtained by utilizing the new approach. The extreme response probability plot based on an optimum threshold value has been compared with the probability plot based on an empirical threshold value, and the accuracy and efficiency of the new approach have been convincingly validated. Goodness-of-fits plots have also been included in this article for testing the accuracy of the proposed new approach. Finally, in order to make a fair comparison, we have also extrapolated the short-term extreme values using the different POT methods and compared them with the reference values obtained directly from a large number of time-domain simulations.

The ratio between corner frequencies of source spectra of P- and S-waves—a new discriminant between earthquakes and quarry blasts

This study analyzes and compares the P- and S-wave displacement spectra from local earthquakes and explosions of similar magnitudes. We propose a new approach to discrimination between low-magnitude shallow earthquakes and explosions by using ratios of P- to S-wave corner frequencies as a criterion. We have explored 2430 digital records of the Israeli Seismic Network (ISN) from 456 local events (226 earthquakes, 230 quarry blasts, and a few underwater explosions) of magnitudes Md = 1.4–3.4, which occurred at distances up to 250 km during 2001–2013 years. P-wave and S-wave displacement spectra were computed for all events following Brune’s source model of earthquakes (1970, 1971) and applying the distance correction coefficients (Shapira and Hofstetter, Teconophysics 217:217–226, 1993; Ataeva G, Shapira A, Hofstetter A, J Seismol 19:389-401, 2015), The corner frequencies and moment magnitudes were determined using multiple stations for each event, and then the comparative analysis was performed. The analysis showed that both P-wave and especially S-wave displacement spectra of quarry blasts demonstrate the corner frequencies lower than those obtained from earthquakes of similar magnitudes. A clear separation between earthquake and explosion populations was obtained for ratios of P- to S-wave corner frequency f 0(P)/f 0(S). The ratios were computed for each event with corner frequencies f 0 of P- and S-wave, which were obtained from the measured f 0 I at individual stations, then corrected for distance and finally averaged. We obtained empirically the average estimation of f 0(P)/f 0(S) = 1.23 for all used earthquakes, and 1.86 for all explosions. We found that the difference in the ratios can be an effective discrimination parameter which does not depend on estimated moment magnitude M w . The new multi-station Corner Frequency Discriminant (CFD) for earthquakes and explosions in Israel was developed based on ratios P- to S-wave corner frequencies f 0(P)/f 0(S), with the empirical threshold value of the ratio for Israel as 1.48.

Characterization of structural conditions for pavement rehabilitations

For concrete pavements it is critical to identify weak spots and locations with very poor load transfer efficiency and then to apply localized repairs before placing a structural overlay. Collecting a continuous deflection profile is the most efficient way to identify those locations. The Texas Department of Transportation’s Total Pavement Acceptance Device (TPAD) has been used successfully on two field projects with main goal of identifying these problematic areas. TPAD is an integrated nondestructive testing tool and it collects continuous deflections at approximately 50mm intervals with three rolling geophone sensors while traveling over the pavement at 2mph. Empirical threshold values have been established to identify and prioritize the problematic areas. Refinements to the threshold values can be made with additional field performance monitoring data. In addition to continuous deflections, the TPAD unit also collects continuous Ground Penetrating Radar (GPR) data. In a project on IH 20, the GPR data presented clear evidence that there are wet areas beneath the existing concrete slabs due to clogged edge drains. Trapped moisture is one of the main causes for the recurring failures observed on IH20.On the jointed concrete pavement on Loop 12 in Dallas, five different short sections with high deflections were successfully identified. The responsible district has implemented recommendation for pre-overlay treatment on those five short sections through full depth repairs. The TPAD data provides quantifiable and defensible data for selecting the required repair areas before the overlay to minimize premature failures. With its continuing utilization and additional field performance monitoring, it is anticipated that the refinements to the threshold values for TPAD analyses can be made.

Unsupervised classification of airborne laser scanning data to locate potential wildlife habitats for forest management planning

To account for ecological objectives in forest management planning, potential habitats need to be mapped, characterized and evaluated for utility in alternative management practices. Airborne laser scanning (ALS) is increasingly used to derive predictive maps of habitat quality. Unlike ecologically driven approaches that require spatially and temporally co-located training data of the specific species, we tested whether indicative information on the habitat potential could be obtained by means of an unsupervised classification of ALS data. Based on a literature review, altogether five ALS features quantifying vegetation height, cover and diversity were expected to capture the essential variation in the habitat requirements of western capercaillie (Tetrao urogallus L.) and hazel grouse (Tetrastes bonasia L.), which are the most important game birds occurring in the studied area. The features were extracted from sparse density, leaf-off ALS data at a resolution of 256 m2 and partitioned using an unsupervised k-means algorithm. By analysing the persistence of the cluster ensemble formed by the partitioning, altogether 158 plots in 16 structural classes were assigned for field measurements to determine which real-world forest phenomena affected the clustering. The clustering was found to stratify the area mainly in terms of size-related attributes such as timber volume and basal area. The understorey, shrub and herb layers had less correspondence with the clustering, indicating that an unsupervised classification is not directly suitable for habitat mapping. The result was improved using empirical threshold values for the ALS features determined according to the plots labelled as the most potential habitats in the field measurements. This semi-supervised classification of the data indicated 4 per cent of the total forest area as suitable for the specific species, which appears a reasonable estimate of the core area of the habitats considered. Overall, the partitioning formed aggregated, stand-like spatial patterns, even though the neighbourhoods of the individual 256 m2 cells were not considered at all. The result could be further refined by spatial optimization to produce indicative maps for forest management planning with ALS as the sole data source.

Damage of masonry structures relative to their properties: Development of ground movement fragility curves

The objective of this paper is to study the effect of the properties of masonry buildings on the threshold values of the deflection ratio, which is used to assess the damage category of buildings. Based on the results obtained from a sensitivity analysis, fragility curves are drawn and used to develop a probabilistic approach to help assess the damage for different structure typologies due to ground settlement.The threshold values of the building deflection ratio are primarily empirical. The analytical method of Burland et al. (1977) was used to define analytical threshold values to assess the building damage in relation to the building properties: length L, height H, equivalent Young’s modulus E and equivalent shear modulus G. This paper starts with a comparison of various empirical and analytical threshold values of the deflection ratio. Next, a sensitivity analysis is performed using the analytical method of Burland et al. (1977) and modified by Saeidi et al. (2012) to take into account the anisotropic behaviour associated to the masonry mechanical properties and the existence of openings. The investigated parameters are the E/G and L/H ratios and the position of the neutral axis of the beam simulating the building. The results show a significant influence of the E/G and L/H ratios and suggest defining a range of the threshold deflection ratio associated to different damage categories for different building typologies. Fragility curves are then developed based on the definition of the building typologies. These fragility curves are a useful tool to estimate the probability of reaching each category of damage once the deflection ratio and the properties of the relative structure are determined. To improve their accuracy, several intervals of the properties of the buildings were used to develop these fragility curves. The choice of these intervals to draw the fragility curves was based on various bibliographic studies and numerical studies in the case of E/G and statistic studies in the case of L/H.

Assignment of Exciton Domain in Light Harvesting Systems Based on the Variational Polaron Approach.

In large light harvesting systems, not all pigments are coupled strongly. This is evidenced by the formation of delocalized states in certain domains of strongly coupled pigments. The threshold value for assigning pigments to domains is usually defined, and the pigment pairs in which the electronic coupling is greater than this value are included in the same domain to describe the dynamic localization effect implicitly. However, domain assignment by a single threshold value may make it difficult to include the possible localization of exciton states by temperature and the difference in the electronic excitation energy between pigments. In this study, we use the variational polaron approach for domain assignment to include such possible localization. To demonstrate the validity of domain assignment by the variational approach, we applied it to pigments in photosystem II (PSII) and compared the domain model constructed by the single threshold value. We showed that domain assignment by the variational approach could be used to determine the valid domain model in PSII without using the empirical threshold value at least at 77 K.

Instantaneous phase threshold for reflector detection in ultrasonic images.

A method for reflector detection, based on the instantaneous phase of the aperture data for ultrasonic images, is proposed. The instantaneous phase (IP) image is obtained by replacing the amplitude information by the instantaneous phase in the delay-and-sum (DAS) beamforming. From the analysis of the IP image, a threshold level is defined in terms of the number of signals used for imaging. This threshold is applied to the IP image, resulting in a two-level image which gives a statistical indication of whether the pixels of a region in the image are related to a reflector or noise/artifacts. Because the proposed method uses only the instantaneous phase of the signals, it is less sensitive to attenuation than conventional DAS amplitude images. The point spread function of a 32-element array with half-wavelength pitch at 5 MHz in water is simulated and the reflector is detected for signal-to-noise ratio values larger than -29.6 dB. A phantom and an aluminum plate with artificial defects are tested with the proposed technique, using linear arrays of 64 and 16 elements, respectively. When compared with DAS amplitude images and with two-level images obtained by thresholding the amplitude images using empirical threshold values, the proposed technique reduced artifacts and dead zone, and detected all reflectors, increasing reflectors' detectability and decreasing the occurrence of false indication of reflectors. The proposed technique can be used as additional information for amplitude image analysis, with the advantages that it does not need time-gain compensation and that it considers an objective threshold value.

A new cloud and aerosol layer detection method based on micropulse lidar measurements

AbstractThis paper introduces a new algorithm to detect aerosols and clouds based on micropulse lidar measurements. A semidiscretization processing technique is first used to inhibit the impact of increasing noise with distance. The value distribution equalization method which reduces the magnitude of signal variations with distance is then introduced. Combined with empirical threshold values, we determine if the signal waves indicate clouds or aerosols. This method can separate clouds and aerosols with high accuracy, although differentiation between aerosols and clouds are subject to more uncertainties depending on the thresholds selected. Compared with the existing Atmospheric Radiation Measurement program lidar‐based cloud product, the new method appears more reliable and detects more clouds with high bases. The algorithm is applied to a year of observations at both the U.S. Southern Great Plains (SGP) and China Taihu sites. At the SGP site, the cloud frequency shows a clear seasonal variation with maximum values in winter and spring and shows bimodal vertical distributions with maximum occurrences at around 3–6 km and 8–12 km. The annual averaged cloud frequency is about 50%. The dominant clouds are stratiform in winter and convective in summer. By contrast, the cloud frequency at the Taihu site shows no clear seasonal variation and the maximum occurrence is at around 1 km. The annual averaged cloud frequency is about 15% higher than that at the SGP site. A seasonal analysis of cloud base occurrence frequency suggests that stratiform clouds dominate at the Taihu site.