High Kurtosis Values Research Articles

Navigating Market Turbulence: Exploring Banking Sector Volatility Amidst COVID-19

The objective of the study was to study the volatility in the banking industry during COVID-19 by employing the technical barometer Relative Strength Index. The secondary data was extracted from the official websites of the National Stock Exchange for the period 1st January, 2020 to 30th September, 2022. The top 5 banks of Bank Nifty Index were undertaken for the analysis purpose and it was found that the maximum average return was witnessed by SBI Bank during the period of study. The returns of HDFC Bank, AXIS Bank, SBI Bank and Kotak Bank were fairly symmetrical as their values lies between -0.5 and 0.5. The returns of ICICI bank were moderately skewed as the value is -0.78. The high values of the excess kurtosis indicates that returns are heavily peaked and possesses thick tails as the values among all banks is greater than 1. The median return is highest in case of ICICI Bank and lowest in case of HDFC Bank. It was further witnessed that in the year 2020, due to COVID-19, the stock prices declined measurably and entered the oversold zone with RSI value of 18.98 on 24th March, 2020. Thereafter, there were continuous ups and downs in stock prices due to panic and unrest during the pandemic phase. The chart of Bank Nifty index was normal and there were many occasions when it appeared bullish and bearish to investors. Due to the change in behaviour of the individual investors there was panic buying and selling in the market.

A Phase II Trial Evaluating Rapid Mid-Treatment Nodal Shrinkage to Select for Adaptive Deescalation in p16+ Oropharyngeal Cancer Patients Undergoing Definitive Chemoradiation

The purpose of this study is to determine if rapid mid-treatment nodal shrinkage (RMNS) can identify patients with p16+ oropharyngeal cancer (OPC) who can be safely deescalated with reduced dose chemoradiation therapy (CRT). The primary endpoint was 2-year progression free survival (PFS). Inclusion criteria were as follows: T1-3, N1, M0 (AJCC 8th edition) p16+ OPC with <10 pack-year smoking history. All patients were initially planned for standard dose CRT (70 Gy) and weekly cisplatin. Patients were evaluated with a CT scan at week 4 for RMNS, defined as >40% nodal volumetric reduction from baseline. If RMNS was achieved, they proceeded to deescalated CRT (60 Gy). If not, they received standard CRT. Biomarker correlates were collected at baseline and week 4 of CRT including plasma TTMV (tumor tissue modified viral) HPV DNA and MRI diffusion weighted imaging (DWI). Univariate logistic regression analyses (UVA) were performed to evaluate predictors of RMNS. Odds ratios with 95% CI are reported, using a p<0.05 for statistical significance with a two-sided test. Wilcoxon rank sum tests were used to evaluate differences between the two groups using p < 0.05, 2-sided) for statistical significance. All statistical procedures were performed using R () with no adjustments for multiple testing. Thirty-six patients were enrolled: median age: 60 years; 81% male; primary site: 36% base of tongue, 53% tonsil, 11% both; T-stage: 39% T1, 50% T2, 11% T3; N-stage: 100% N1; any smoking history: 58% yes, 42% no; 67% (n = 24) had RMNS and received deescalated CRT while the remaining proceeded to standard CRT. At a median follow-up of 32.4 months, 2-year PFS between the standard and deescalated groups were 91.7% vs 90.9%, respectively (p = 0.97). All patients with recurrence underwent successful salvage treatment with 2-year OS 100% for all patients. On UVA, rapid TTMV HPV DNA clearance (baseline to week 4) (OR 12.0 [1.65-250], p = 0.034), lower MRI diffusivity (ADC) at baseline (OR 0.79 [0.61-0.97], p = 0.042) and week 4 (OR 0.76 [0.60-0.91], p = 0.009), and higher MRI diffusional kurtosis at baseline (OR 1.09 [1.01-1.21], p = 0.051) and week 4 (OR 1.24 [1.09-1.52], p = 0.009) were significantly associated with RMNS. When comparing the deescalated and standard cohorts, the mean baseline and week 4 MRI ADC were significantly lower and week 4 MRI diffusional kurtosis was significantly higher in the deescalated group. In this phase II study, rapid mid-treatment nodal shrinkage appeared to select favorable risk p16+ oropharynx cancer patients for treatment de-escalation. Rapid clearance of TTMV HPV DNA at week 4 as well as MRI DWI biomarkers of low ADC and high diffusional kurtosis values were correlated with RMNS. A larger study is planned to incorporate RMNS and biomarkers for further treatment de-escalation. Additional trial information is available at ClinicalTrials.gov (Identifier: NCT03215719).

The Effect of Steel Electropolishing on the Tribological Behavior of a Steel–Bronze Pair in the Mixed and Boundary Lubrication Regimes

Electropolishing at high current densities without agitation of the electrolyte results in a pitting phenomenon that produces dimple-like surface features. Although pitting is unfavorable in the electropolishing process, its effect on surface modification, such as surface texturing, has not been thoroughly investigated. Surface topography and chemical composition analyses of electropolished steel revealed surface pits and an oxide surface layer, indicating the presence of surface texture and coating. The resulting surface is characterized by negative skewness and high kurtosis values. The tribological behavior of the electropolished steel-bronze pair is investigated by evaluating coefficients of friction and bronze wear using sliding tests conducted in mixed and boundary lubrication regimes. The results are compared to those of the ground steel-bronze pair. In the mixed and upper range of the boundary lubrication regime, coefficients of friction reduction up to 30% and shorter running-in phases are observed for electropolished steel (electropolished steel μavg = 0.019 vs. ground steel μavg = 0.028). In contrast, the coefficient of friction increased in the lower range of boundary lubrication regime by 50% (electropolished steel μavg = 0.098 vs. ground steel μavg = 0.065). Electropolishing, as a cost- and time-effective method applicable to complex geometries, presents an alternative method for achieving surface modifications aimed at friction reduction and improved tribological behavior for non-conformal contacts in the boundary and mixed lubrication regimes.

Effectiveness of Kurtosis-Adjusted Cumulative Noise Exposure in Assessing Occupational Hearing Loss Associated With Complex Noise.

Occupational noise-induced hearing loss (NIHL) is one of the most prevalent occupational diseases worldwide. Few studies have been reported on applying kurtosis-adjusted noise energy (e.g., kurtosis-adjusted cumulative noise exposure, CNE-K) as a joint indicator for assessing NIHL. This study aimed to analyze the effectiveness of CNE-K in assessing occupational hearing loss associated with complex noise in typical manufacturing industries. A cross-sectional survey of 1404 Chinese manufacturing workers from typical manufacturing industries was conducted. General demographic characteristics, noise exposure data, and noise-induced permanent threshold shifts (NIPTS) at 3, 4, and 6 kHz (NIPTS 346 ) were collected and analyzed. The role of kurtosis in high-frequency noise-induced hearing loss (HFNIHL) was also analyzed. The degree of overlap of the two logistic curves (i.e., between complex noise CNE-K and HFNIHL%, and between Gaussian noise CNE and HFNIHL%) was used to evaluate the effectiveness of CNE-K, using a stratified analysis based on age, sex, industry, or job type. The binary logistic regression analysis showed that in addition to age, sex, exposure duration, and Eight-hour Continuous Equivalent A-weighted Sound Pressure Level (L Aeq,8h ), kurtosis was a key factor influencing HFNIHL% in workers (odds ratio = 1.18, p < 0.05), and its odds ratio increased with an increase in kurtosis value. Multiple linear regression analysis demonstrated that the contribution of kurtosis to NIPTS 346 was second to L Aeq,8h . Complex noise led to a higher risk of NIHL than Gaussian noise at frequencies of 3, 4, 6, and 8 kHz after adjusting for age, sex, and CNE ( p < 0.05). As kurtosis increased, the notch in the audiogram became deeper, and the frequency at which the notch began to deepen shifted from 3 to 1 kHz. The logistic curve between complex noise CNE-K and HFNIHL% nearly overlapped with that between Gaussian noise CNE and HFNIHL%, and the average difference in HFNIHL% between the two curves decreased from 8.1 to 0.4%. Moreover, the decrease of average difference in HFNIHL% between the two logistic curves was evident in several subgroups, such as male workers, aged <30 and 30 to 50 years, furniture and woodworking industries and gunning and nailing job types with relatively high kurtosis values. Kurtosis, as an indirect metric of noise temporal structure, was an important risk factor for occupational NIHL. Kurtosis-adjusted CNE metric could be more effective than CNE alone in assessing occupational hearing loss risk associated with complex noise.

Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. In order to obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.

Kurtosis loss as a metric for hearing protection evaluation in impulsive noise environments.

Hearing loss standards depend on noise power and duration but are incomplete when the noise is primarily impulsive in nature rather than maintaining a continuous power level. Calculating the kurtosis of a noise exposure captures information about its impulsivity, and high kurtosis values cause additional hearing damage. In this paper, a method for measuring the reduction of noise kurtosis through hearing protection is outlined, and measurements demonstrate that spectral insertion loss is independent of the noise kurtosis and that kurtosis loss is not related to either the mean or standard deviation of spectral attenuation.

Tracing Uncertainty Contributors in the Multi‐Hazard Risk Analysis for Compound Extremes

AbstractIn this study, an iterative factorial multimodel Bayesian copula (IFMBC) framework was developed for revealing uncertainties in risk inferences of compound extremes under the consideration of diverse model structures and parameter sets. Particularly, an iterative factorial analysis (IFA) method would be advanced in IFMBC to track the dominant contributors to the imprecise predictions of multi‐hazard risks. The developed IFMBC framework was applied for the risk assessment of compound floods at two estuarine systems (i.e., Washington and Philadelphia) in US. The results indicate that the most likely compound events, under predefined return periods, exhibit noticeable uncertainties. Those uncertainties also present multiple hotspots which may be attributed to different impacts from different factors. By applying the IFA method, the results suggest the copula structure would likely be ranked as one of the top 2 impact factors for predictions of failure probabilities (FPs) in the scenarios of AND, and Kendall, with its contributions higher than 30% for FP in Kendall (more than 40% at Washington) and more than 25% for FP in Kendall (larger than 40% at Philadelphia). In comparison, the copula structure may not pose a visible effect on the predictive uncertainty for FP in OR, with its contribution possibly less than 5% under long‐term service time periods. However, the marginal distributions would have higher effects on FP in OR than the effects on the other two FPs. Particularly, the marginal distribution for the extreme variable with high skewness and kurtosis values tends to be ranked as one of the most significant impact factors for FP in OR. Also, the overall impacts from parametric uncertainties in both marginal and dependence models cannot be neglected for the predictions of all three FPs with their contributions probably larger than 20% under a short service time period. Compared with the traditional multilevel factorial analysis, the IFA method can provide more reliable characterization for uncertainty contributors in multi‐hazard risk analyses, since the traditional method seems to significantly overestimate the contributions from parameter uncertainties.

Ride and Comfort Measurements - A Challenge of Subjective and Objective Correlation

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Traditionally, vehicle ride and comfort is evaluated, subjectively as well as objectively. Based on the outcome of subjective and objective tests, it is refined by optimizing primary suspension system, secondary suspension system, seating system, rubber bushings, frame and BIW for mass, stiffness, damping, geometry etc. Many a time subjective assessment results stands in contradiction to the objective assessment results; emphasizing need for having good correlation between subjective and objective test results. In such cases, it is ambiguous to decide suitable design refinement action and can lead to no improvement situation. Hence, it is essential to have concurring test procedures for subjective and objective ride evaluation. This paper describes a novel methodology to address the above said challenge. There are defined set of test events and measurement data points to be used in subjective and objective testing. The subjective assessment is performed using clearly defined goals for what to look for and how to look for different ride and comfort attributes and sub attributes. Similarly for objective test, accelerations, displacements and loads are measured at defined locations on vehicle application specific, various types of service roads for long enough duration, to have no or minimum impact of road inputs or events having high kurtosis value, on the objective ride measurement results. Measured data is post processed separately to arrive at objective ride index value for each of the subjective attribute i.e. bounce, pitch, roll, head toss, bobbing, choppy ride etc… For this, set of measured data is processed and analyzed for interested and relevant frequency ranges instead of limiting it to ISO 2631. This approach provides direct comparison of subjective rating number and objective ride index value for each of the attribute. This new approach has proven very useful in refining the vehicle ride and comfort by achieving 85~90% correlation between subjective and objective test results.&lt;/div&gt;&lt;/div&gt;

Measuring parametric and semiparametric downside risks of selected agricultural commodities

In this paper, we evaluate the downside risk of six major agricultural commodities – corn, wheat, soybeans, soybean meal, soybean oil and oats. For research purposes, we first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which we later use for measuring downside risks via parametric and semiparametric approaches. Modified value-at-risk (mVaR) and modified conditional value-at-risk (mCVaR) provide more accurate downside risk results than do ordinary value-at-risk (VaR) and conditional value-at-risk (CVaR). We report that soybean oil has the lowest mVaR and mCVaR because it has two very favourable features – skewness around zero and low kurtosis. The second-best commodity is soybeans. The worst-performing downside risk results are in wheat and oats, primarily because of their very high kurtosis values. On the basis of the results, we propose to investors and various agents involved with these agricultural assets that they reduce the risk of loss by combining these assets with other financial or commodity assets that have low risk.

Applying Kurtosis as an Indirect Metric of Noise Temporal Structure in the Assessment of Hearing Loss Associated With Occupational Complex Noise Exposure.

Objective:The association of occupational noise-induced hearing loss (NIHL) with noise energy was well documented, but the relationship between occupational noise and noise temporal structure is rarely reported. The objective of this study was to investigate the principal characteristics of the relationship between occupational NIHL and the temporal structure of noise.Methods:Audiometric and shift-long noise exposure data were collected from 3102 Chinese manufacturing workers from six typical industries through a cross-sectional survey. In data analysis, A-weighted 8-h equivalent SPL (LAeq.8h), peak SPL, and cumulative noise exposure (CNE) were used as noise energy indicators, while kurtosis (β) was used as the indicator of noise temporal structure. Two NIHL were defined: (1) high-frequency noise-induced hearing loss (HFNIHL) and (2) noise-induced permanent threshold shift at test frequencies of 3, 4, and 6 kHz (noise-induced permanent threshold shift [NIPTS346]). The noise characteristics of different types of work and the relationship between these characteristics and the prevalence of NIHL were analyzed.Results:The noise waveform shape, with a specific noise kurtosis, was unique to each type of work. Approximately 27.92% of manufacturing workers suffered from HFNIHL, with a mean NIPTS346 of 24.16 ± 14.13 dB HL. The Spearman correlation analysis showed that the kurtosis value was significantly correlated with the difference of peak SPL minus its LAeq.8h across different types of work (p < 0.01). For a kurtosis-adjusted CNE, the linear regression equation between HFNIHL% and CNE for complex noise almost overlapped with Gaussian noise. Binary logistic regression analysis showed that LAeq.8h, kurtosis, and exposure duration were the key factors influencing HFNIHL% (p < 0.01). The notching extent in NIPTS at 4 kHz became deeper with the increase in LAeq.8h and kurtosis. HFNIHL% increased most rapidly during the first 10 years of exposure. HFNIHL% with β ≥ 10 was significantly higher than that with β < 10 (p < 0.05), and it increased with increasing kurtosis across different CNE or LAeq.8h levels. When LAeq.8h was 80 to 85 dB(A), the HFNIHL% at β ≥ 100 was significantly higher than that at 10 ≤ β < 100 or β < 10 (p < 0.05 and p < 0.01, respectively).Conclusions:In the evaluation of hearing loss caused by complex noise, not only noise energy but also the temporal structure of noise must be considered. Kurtosis of noise is an indirect metric that is sensitive to the presence of impulsive components in complex noise exposure, and thus, it could be useful for quantifying the risk for NIHL. It is necessary to re-evaluate the safety of permissible exposure limit of 85 dB(A) as noise with a high kurtosis value can aggravate or accelerate early NIHL.

Higher moment of portfolio optimization with Polynomial Goal Programming approach

The mean-variance (MV) model has been introduced in portfolio optimization to minimize the risk and achieve the target rate of return in the investment. However, the higher moment skewness and kurtosis are not considered in this model. The investors prefer portfolio with high skewness value and low kurtosis value so that the probability of getting extreme negative rates of return will be reduced. Therefore, the MV model has been extended to the mean-variance-skewness-kurtosis (MVSK) model by incorporating the skewness and kurtosis factor. The objective of this study is to construct the optimal portfolio of the MVSK model by using the polynomial goal programming (GP) approach. The data of this study comprises technology companies that listed in Malaysian stock market. In the fourth industrial revolution, technology companies play an important role in the development of a country. The results of this study show that the optimal portfolio of MVSK model outperforms the MV model by giving higher portfolio skewness value and lower portfolio kurtosis value. This study is significant because the investors can maximize the portfolio skewness value and minimize the portfolio kurtosis value with the MVSK model.

Influence of Molecular Weight Distribution on the Thermoresponsive Transition of Poly(N-isopropylacrylamide).

A series of poly(N-isopropylacrylamide) (PNIPAm) homopolymers with narrow molecular weight distributions (MWDs) is prepared via photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization. The thermal transition temperature of these polymer samples is analyzed via turbidity measurements in water/N,N'-dimethylformamide mixtures, which show that the cloud point temperatures are inversely proportional to the weight average molecular weight (Mw ). Binary mixtures of the narrowly distributed PNIPAm samples are also prepared and the statistical parameters for the MWDs of these blends are determined. Very interestingly, for binary blends of the PNIPAm samples, the thermoresponsive transition is not only dependent on the Mw , which has been shown previously, but also on higher order statistical parameters of the MWDs. Specifically, at very high values of skewness and kurtosis, the polymer blends deviate from a single sharp thermoresponsive transition toward a broader thermal response, and eventually to a regime of two more distinct transitions. This work highlights the importance of in-depth characterization of polymer MWDs for thermoresponsive polymers.

A Method for Human Burn Diagnosis Using Machine Learning and SLIC Superpixels Based Segmentation

The burn is a global health issue. The treatment of burn is performed by a burn surgeon. Burn region color is used to categorize burn types by a burn surgeon. Due to the lack of resources in many places burn patient dies. In such a situation a computer-based diagnosis system is very helpful. The severity of the burn is based on the depth and size of the burn region. The burn severity analysis can be accurately done by using the CIELab image as it contains all spectrums of colors. In the proposed work, the CIELab image is segmented using SLIC (Simple Linear Iterative Clustering) superpixel segmentation algorithm to form the same color pixels clusters. These clusters are used to diagnosis the severity of a burn, The severity of a burn is measured by the value of kurtosis. A high kurtosis value implies a larger depth and a small value implies a smaller depth. Based on the depth of burn, grafting or non-grafting is suggested by a burn surgeon. We observed depth of the burn is high in full-thickness burn compared to the deep dermal and the superficial dermal burn. The proposed method can correctly identify full-thickness burn and most of the deep dermal burn image. The open-access standard data set BURNS BIP-US DATABASE is used for training and testing of the proposed model on SVM (Support Vector Machine). The proposed model can classify with an accuracy of 89.16% and F1- score 90%. The achieved accuracy of the model is better than 79% of multidimensional scaling (MDS) in previous work.

The texture analysis as a predictive method in the assessment of the cytological specimen of CT-guided FNAC of the lung cancer

The lung cancer is the principle cause of the worldwide deaths and its prognosis is poor with a 5-year overall survival rate. Computed tomography (CT) gives many information about the prognosis, but the problem is the subject interpretation of the findings. Thanks to the computer-aided diagnosis/detection (CAD), it is possible to reduce the second opinion. "Radiomics" is an extension of CAD and overlaps the quantitative imaging data of the CT texture analysis (CTTA) with the clinical information, increasing the power and precision of the decision going through the personalized medicine. The aim of this study is to describe the role of the radiomics in the characterization of the pulmonary nodule. For this study, we retrospectively analyzed the images of the 87 NSCLC patients with a waiver of informed consent from the Institutional Review Board (IRB) at the Campania University "Luigi Vanvitelli" of Naples. All tumors were semiautomatically segmented by a radiologist with 10years of experience using three diameters (AW Server 3.2). The examinations were acquired using 128 MDCT (GSI CT, GE) with a peak tube voltage of 120 kVp, tube current of 100 or 200mA, and rotation times of 0.5 or 0.8s. To confirm the imaging results, the FNAC was performed and for every nodule the following parameters were extracted: the presence of the solid component (named = 1), papillary component (named = 2), and mixed component (named = 3). Feature calculation was performed using the HealthMyne software and Integrated Platform That Enables Better Patient Management Decisions For Oncology. The radiologist uses the Rapid Precise Metrics (RPM)™ functionality to identify a lesion with the algorithm and these methods are put to work. The correlation between each feature and the tumor volume was calculated using a two-step cluster statistical analysis. In this retrospective study, in one year from 2018 to 2019 20 patients with lung adenocarcinoma confirmed with FNAC were enrolled. The pathologic results were subdivided into three categories: the solid architecture (group 1), papillary architecture (group 2), and mixed architecture (group 3). Nine lesions resulted with component 1, seven patients with component 2, and 3 patients with component 3. Eight females and 12 males with a median age 61 and 15years (mean ± SD = 67.4 ± 9.7years, range 39-73years) were enrolled. The two results suggest, with p < 0.05, that the GGO variable is a good discriminating estimator of the kurtosis variable: GGO = "no" implies a high kurtosis value, while GGO = "yes" implies a low value. The numerous data obtained from the automatic analysis allow to have a fertile ground on which to develop a new concept of medicine which is precision medicine. The limit of this study is the poor sample. In the future, in order to have a more mature and consolidated discipline, it is necessary to increase the large scale of observations with further studies to establish the rigorous evaluation criteria. In order for radiomics to mature as a discipline in the future, it will be necessary to develop studies that consolidate its role to standardize the collected data.

Estimation of Length of Node-to-Node Paths Distribution in the Global Network

The experiment aimed at finding a distribution of path lengths between nodes in the global network and an estimation of parameters of that distribution is described.In particular, the method of measurement of path length with traceroute utility of the GNU/Linux system and limitations on the selection of nodes imposed by traceroute are described. ‘e measurement results are provided and high values of skewness and kurtosis for all resulting distributions are noted. Simulation model of this experiment was developed to test the experiment validity in the determination of distribution parameters in the global network. This model is also described. It is shown that high values of skewness and kurtosis of the measured distributions are not the result of the measurement technique, therefore the global network could not be described by the Barabasi–Albert ´ model. Several most viable hypotheses explaining diffierences in skewness and kurtosis of experimentally obtained pathlength distribution estimations and values derived from the Barabasi–Albert model are listed. Results of diffierent hypotheses ´ simulations are provided. It is shown that the most fitting hypothesis is that definitive influence on skewness and kurtosis of path-length distribution estimations is caused by the quasi pre-fractal structure of the global network.

Integral stochastic ordering of the multivariate normal mean-variance and the skew-normal scale-shape mixture models

‎In this paper‎, ‎we introduce integral stochastic ordering of two‎ most important classes of distributions that are commonly used to fit data possessing high values of skewness and (or)‎ ‎kurtosis‎. ‎The first one is based on the selection distributions started by the univariate skew-normal distribution‎. ‎A broad‎, ‎flexible and newest class in this area is the scale and shape mixture of multivariate skew-normal distributions‎. ‎The second one is the general class of Normal Mean-Variance Mixture distributions‎. ‎We then derive necessary and sufficient conditions for comparing the random vectors from these two classes of distributions‎. ‎The integral orders considered here are the usual‎, ‎concordance‎, ‎supermodular‎, ‎convex‎, ‎increasing convex and directionally convex stochastic orders‎. ‎Moreover‎, ‎for bivariate random vectors‎, ‎in the sense of stop-loss and bivariate concordance stochastic orders‎, ‎the dependence strength of random portfolios is characterized in terms of order of correlations‎.

Grain Size Analysis of Dune and Bar Sediments of the Shyok River between Khalsar and Hunder Village, Karakoram Range, Ladakh, India

Abstract The Nubra and Shyok valleys are located between the central Karakoram and Ladakh range of northwest India and have preserved excellent sand dune and bar sediments between Khalsar and Hunder village. The present study aims to understand the evolution of these landforms using grain size characteristics. The sand dunes are formed by the aeolian processes and the bars are produced by the fluvial processes. The actions of fluvial and aeolian processes are different and landforms formed by these processes have completely different sedimentological and grain size characteristics. The mean grain size of dune sediments is dominated by fine sand while in bar sediments, it varies from medium to fine sand. The sand-silt-clay ternary plot shows that all samples are concentrated in sand corner. The histograms of sand dune sediments are unimodal while the histograms of bar sediments vary from unimodal to bimodal. The grain size parameters reveal that the bar and sand dune sediments are different but deposited in the same moderate to low energy riverine environments. The dune sediments are well sorted and are transported and deposited in uniform energy environment. Due to energy variations in the hydrodynamic system, the bar sediments are moderate to well-sorted at Diskit and Hunder villages. Overall 45.5% of sediments samples are platykurtic in nature, indicating the maturity of the deposited sand. The high and low values of kurtosis imply that some fractions of sediments were sorted in a high energy environment. The present study of grain size parameters reflects different sediment character for bars and sand dunes that are deposited in modrate to low energy environments. The results of the study may be used as an analougue for understanding depositional environment in high altitude region.

On the Use of Kurtosis Control Methods in Shaker Testing for Fatigue Damage

Abstract Random vibration testing with increased kurtosis introduces high peaks into shaker drive signals to simulate land vehicle vibration more accurately and also to shorten the required test time. Two methods of controlling kurtosis by phase manipulation in the inverse fast Fourier transform were implemented, tested, and compared. The first method generates high-kurtosis excitations with a gradual, smooth pattern of peak heights from low to high, and the second method produces isolated high peaks with intervals of stable background vibration between them. When applying a kurtosis increase for accelerated fatigue damage testing, the kurtosis control method must be able to pass high-kurtosis values from the generated shaker table vibration into the stress response of the unit under test. However, this is not always the case, and only one of the considered methods was capable of doing so. In the article, the fatigue damage spectrum model was used for an evaluation of the time to failure. An experimental study was carried out using operational vibration measured in a car. Shaker testing of cantilever specimens was performed for Gaussian, non-Gaussian, and accelerated non-Gaussian excitations.

Rogue bioelectrical waves in the brain: the Hurst exponent as a potential measure for presurgical mapping in epilepsy

Objective. Brain electromagnetic activity in patients with epilepsy is characterized by abnormal high-amplitude transient events (spikes) and abnormal patterns of synchronization of brain rhythms that accompany epileptic seizures. With the aim of improving methods for identifying epileptogenic sources in magnetoencephalographic (MEG) recordings of brain data, we applied methods previously used in the study of oceanic ‘rogue waves’ and other freak events in complex systems. Approach. For data from three patients who were awaiting surgical treatment for epilepsy, we used a beamformer source model to produce volumetric maps showing areas with a high proportion of spikes that could be classified as ‘rogue waves’, and areas with high Hurst exponent (HE). The HE describes the extent to which a system is exhibiting persistent behavior, may predict the likelihood of freak events. These measures were compared with the more standard measure of kurtosis, which has been shown to be a reliable method for localizing interictal spikes. Main results. There was partial concordance between the three different volumetric maps indicating that each measure provides different information about the underlying brain data. The HE, when combined with a simple connectivity analysis based on phase slope index (PSI), was able to identify the probable epileptogenic zone in all three patients, despite very different patterns of abnormal activity. The differences between distributions of high HE and high kurtosis values indicates that while spikes are propagated through cortex from the epileptogenic zone, the persistent dynamical conditions under which the spikes are generated may not be propagated in a similar way. Finally, the patterns of persistent activity, indicating a departure from ‘healthy criticality’ in brain networks may explain the wide range of social and cognitive impairments that are seen in epilepsy patients. Significance. The HE is a potentially useful addition to the clinician’s battery of measures which may be used convergently to guide surgical intervention.

The causality relationship between remittances and the real effective exchange rate: the case of the Kyrgyz Republic

We assessed the causality relationship between the inflow of remittances and the real effective exchange rate (REER) of the Kyrgyz Republic, one of the most remittance-dependent economies in the world. We utilised the procedure suggested by Hong [15]. In the first step, we estimated univariate generalised autoregressive conditionally heteroskedasticity (GARCH) models for the logarithmic difference of the variables and saved the centred standardised residuals and their squared values. In the second step, we computed the sample cross-correlation function (CCF) between the standardised residuals and squared standardised residuals. Finally, we calculated Hong’s [15] Q-statistic and compared it to the upper tailed critical value of N(0;1) at an appropriate level. Descriptive statistics showed more volatile standard deviations for remittances compared with the REER. The skewness values indicated that increases are more likely to occur for the REER and decreases are more likely to occur for remittances. High kurtosis values suggested the existence of heavy tails in the return distribution. The skewness and kurtosis values showed that the returns are not normally distributed. At the 1% significance level, the Lagrange Multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH) rejected the null hypothesis of ‘no ARCH effects’ for both variables. At the 1–5% significance level, the augmented Dickey–Fuller (ADF) test rejected the null hypothesis that a unit root is present in the return series of both variables. The test for a structural break (a supremum Wald test) in the return series, without imposing a known break date, rejected the null hypothesis of no structural break at the 5% and 1% levels for the REER and remittances, respectively. The estimated break date for the REER was May 2015, and for the remittances, it was February 2008. The logarithmic differences of seasonally adjusted real monthly data for the period extending from January 2005 to December 2017 were used in the estimations. While the existing literature has focused mostly on the impact of remittances on the exchange rate (a unidirectional causality in the mean from remittances on the exchange rate), we examined how remittances cause the REER in the mean and variance as well as how the REER causes remittances in the mean and variance. The derived results did not reveal evidence of causality in the mean or variance from the remittances to the REER for the case of the Kyrgyz Republic. That means the changes and volatility of the remittances did not contain useful information for predicting the REER. On the contrary, the REER caused remittances in the mean and variance. That means past information and the volatility of the REER were useful for predicting remittances’ returns and volatility.