Tail Characteristics Research Articles

Spitz-like tail carriage in two domestic cats

Domestic cats use visual cues to communicate with conspecifics and humans. This includes the position and movement of the tail. The tail up signal (i.e. tail held vertically with the tip curled) has been identified as a friendly greeting signal between cats and from cats to humans. We present here a report of two cats unable to display the classic tail up signal due to Spitz-like (curled) tail characteristics, a morphology not previously reported in the scientific literature. The cats, a British Shorthair (Mn, 2 y) and Domestic Longhair (Fn, 8.5 y), belonged to different households and were participants in a study of intraspecific social interactions. Video recordings show the cats displaying normal tail movements (waving, swishing, twitching) and position when the tail was hanging down. However, once the tail was raised to a vertical position, it immediately curled over the back. This Spitz-like tail posture was displayed both when greeting people and other cats. It had reportedly been present since adoption. There was no underlying medical history of injury or pain to indicate this was an acquired state. We suggest this Spitz-like tail is a spontaneously occurring anomaly, that does not seem to noticeably impair the cats’ communicative abilities, as normal affiliative behaviours were observed during interactions with the cohoused cat. We discuss the significance of this finding in relation to the formation of social bonds and the signalling of affiliative intent.

Real time monitoring peripheral nerve function with ICG and BDA-ICG by NIR-II fluorescence imaging

Neuroanatomical tract tracers are important for studying axoplasmic transport and the complex interconnections of the nervous system. Though traditional fluorescent tracers are widely used, they have several prominent drawbacks when imaging, including low resolutions and low tissue penetrations and inability to be supervised dynamically within a long peripheral nerve during the long term. Here, we explored the potential of ICG as a neural tracer for axoplasmic transport and for the first time demonstrated that ICG could be used to detect transport function within peripheral nerve by near-infrared region II (NIR-II) imaging. On basis of this finding, a novel bi-directional neural tracer biotinylated dextran amine-indocyanine green (BDA-ICG) was prepared and characterized with better long-term stability and higher nerve-to-background ratio than ICG in vivo, and successfully imaged the injured peripheral nerve from the healthy one within 24 h. Our results show that BDA-ICG are promising neural tracers and clinically available dyes with NIR-II emission tail characteristics as ICG.

Interval evaluation of wind power prediction based on skew-slash t distribution

Wind resources have characteristics of stochastic volatility, resulting in wind power forecast errors greatly. The short and medium-term wind power forecast error distribution of the actual wind farm operating system is analyzed, and a sharp peak and thick tail, with heavy tail and skewness characteristics, are presented. A skew-slash t distribution (SST distribution) is proposed to describe the error distribution of short-term wind power prediction, and the parameter estimation is carried out by probability density curve fitting. The fitting results of the wind power forecast error historical data of the Wind power plant based on SST distribution, t distribution, and t Location-slash distribution (TLS distribution) are obtained, and the fitting goodness and prediction accuracy are compared. The results show that the SST distribution can more effectively estimate the wind power prediction interval.

The Tail Characteristics of Several Asymmetric Distributions and Their Applications in the Field of Financial Data Modelling and Machine Learning

Asymmetric distributions play a pivotal role in various fields, including finance, machine learning, and artificial intelligence. In finance, asymmetric distributions, characterized by negative skewness, peakedness, heavy tails, and asymmetric fluctuations, provide a more accurate representation of financial data. These distributions are particularly useful in capturing the complex behavior of assets and risk management. In the field of machine learning, data disturbance, noise, outliers and other disturbances will have a negative impact on the performance and reliability of the model, asymmetric distributions offer a valuable tool for describing the tail behavior of noise and interference. This is crucial in designing more robust machine learning models that can handle outliers and noise effectively. Additionally, when dealing with unbalanced data classification, asymmetric distributions can be leveraged by adjusting the decision threshold of classifiers or employing specialized loss functions. In this paper, the asymptotic tail ratio behavior of probability density function(pdf) and cumulative distribution function (cdf) of three asymmetric distributions (asymmetric Laplace, generalized Logistic and asymmetric normal distribution) and Student-t distribution were considered respectively under some regular conditions. The detailed characteristics of these asymmetric tails will have a profound impact on the fields of financial data analysis, artificial intelligence, and unbalanced classification.

The tail risk of crude oil Price_Based on EPU and geopolitical risk perspective

This study investigates the conditional prediction of crude oil price growth rates as a function of geopolitical risk (GPR) and economic policy uncertainty (EPU), characterizing the extreme tail risk characteristics. Utilizing monthly data on crude oil futures price growth rates from 1997 Jan to 2022 Dec, we employ Quantile Regression and Growth at Risk models to capture the heterogeneous and asymmetric effects of GPR and EPU across different quantiles of the oil price distribution. The sample period covers major geopolitical events, economic recessions, and the emergence of new energy sources, providing a comprehensive view of the factors influencing crude oil market dynamics.The empirical findings reveal significant asymmetries in the forecast density function of crude oil price growth rates, with periodic right-skewed deviations during financial crises and epidemic situations, and prevalent left-skewed deviations during the remaining sample periods. The impact of EPU on crude oil prices is found to be more pronounced in the short to medium term, while the influence of GPR persists over a longer duration, exacerbating the upside risk of price growth. The study highlights the effectiveness of EPU and GPR in capturing the tail characteristics of crude oil price growth, offering valuable risk identification indicators for policymakers and market participants.Furthermore, the analysis uncovers the heterogeneous effects of EPU and GPR across different quantiles of the oil price distribution. The results indicate that the relationship between these uncertainty measures and crude oil prices varies depending on the market conditions and the specific quantile examined. This finding underscores the importance of considering the entire distribution of crude oil prices rather than focusing solely on the conditional mean.The study contributes to the existing literature by providing a comprehensive examination of the role of geopolitical risk and economic policy uncertainty in driving crude oil price dynamics, with a particular focus on tail risks. The findings have important implications for risk management strategies in the energy sector.By employing advanced econometric techniques and considering a wide range of geopolitical and economic factors, this study offers new insights into the complex and evolving nature of crude oil price dynamics. The results highlight the need for policymakers and market participants to consider the asymmetric and heterogeneous effects of uncertainty measures when assessing the risks and opportunities in the crude oil market.

Numerical analysis of functionally graded piezoelectric bionic fishtail based on Hermite element-free method

Piezoelectric bionic fishtails have good flexibility, response speed, anti-interference ability, and have great application prospects in ocean exploration. However, the inherent drawbacks of the mechanical properties of traditional homogeneous piezoelectric materials significantly affect the propulsion performance and reliability of the piezoelectric bionic fishtails. To fill this gap, this paper develops a functionally graded piezoelectric bionic fishtail (FGPBF) by imitating the tail characteristics of groupers. The geometric structure and working principle of the FGPBF are introduced in detail. Based on the first-order shear deformation theory and Hermite element-free method, an element-free model for the FGPBF is established. The effects of gradient factor, substrate material, substrate thickness and electrical load on the propulsion performance of the FGPBF are addressed. The results show that the current results are in good agreement with the finite element results. The deformation of the FGPBF is negatively correlated with the thickness and stiffness of the substrate and linearly positively correlated with the electrical load. As the gradient factor increases, the deflection of the FGPBF first increases and then decreases. When the gradient factor is 2, the potential is 200 V, the dimensionless aluminum substrate thickness is 1, the propulsion performance of the FGPBF is improved by 28% compared to the homogeneous piezoelectric bionic fishtail.

Manipulation of mitochondrial poly(A) polymerase family proteins in Trypanosoma brucei impacts mRNA termini processing

RNA-specific nucleotidyltransferases (rNTrs) add nontemplated nucleotides to the 3′ end of RNA. Two noncanonical rNTRs that are thought to be poly(A) polymerases (PAPs) have been identified in the mitochondria of trypanosomes – KPAP1 and KPAP2. KPAP1 is the primary polymerase that adds adenines (As) to trypanosome mitochondrial mRNA 3′ tails, while KPAP2 is a non-essential putative polymerase whose role in the mitochondria is ambiguous. Here, we elucidate the effects of manipulations of KPAP1 and KPAP2 on the 5′ and 3′ termini of transcripts and their 3′ tails. Using glycerol gradients followed by immunoblotting, we present evidence that KPAP2 is found in protein complexes of up to about 1600 kDa. High-throughput sequencing of mRNA termini showed that KPAP2 overexpression subtly changes an edited transcript’s 3′ tails, though not in a way consistent with general PAP activity. Next, to identify possible roles of posttranslational modifications on KPAP1 regulation, we mutated two KPAP1 arginine methylation sites to either mimic methylation or hypomethylation. We assessed their effect on 3′ mRNA tail characteristics and found that the two mutants generally had opposing effects, though some of these were transcript-specific. We present results suggesting that while methylation increases KPAP1 substrate binding and/or initial nucleotide additions, unmethylated KPAP1is more processive. We also present a comprehensive review of UTR termini, and evidence that tail addition activity may change as mRNA editing is initiated. Together, this work furthers our understanding of the role of KPAP1 and KPAP2 on trypanosome mitochondrial mRNA 3′ tail addition, as well as provides more information on mRNA termini processing in general.

Insight into the current genomic diversity, conservation status and population structure of Tunisian Barbarine sheep breed.

Local livestock breeds play a crucial role in global biodiversity, connecting natural and human-influenced environments and contributing significantly to ecosystem services. While commercial breeds dominate industrial systems, local livestock breeds in developing countries, like Barbarine sheep in Tunisia, are vital for food security and community maintenance. The Tunisian Barbarine sheep, known for its adaptability and distinctive fat-tailed morphology, faces challenges due to historical crossbreeding. In this study, the Illumina Ovine SNP50K BeadChip array was used to perform a genome-wide characterization of Tunisian Barbarine sheep to investigate its genetic diversity, the genome structure, and the relationship within the context of Mediterranean breeds. The results show moderate genetic diversity and low inbreeding. Runs of Homozygosity analysis find genomic regions linked to important traits, including fat tail characteristics. Genomic relationship analysis shows proximity to Algerian thin-tailed breeds, suggesting crossbreeding impacts. Admixture analysis reveals unique genetic patterns, emphasizing the Tunisian Barbarine's identity within the Mediterranean context and its closeness to African breeds. Current results represent a starting point for the creation of monitoring and conservation plans. In summary, despite genetic dilution due to crossbreeding, the identification of genomic regions offers crucial insights for conservation. The study confirms the importance of preserving unique genetic characteristics of local breeds, particularly in the face of ongoing crossbreeding practices and environmental challenges. These findings contribute valuable insights for the sustainable management of this unique genetic reservoir, supporting local economies and preserving sheep species biodiversity.

Effect of sorbitan ester structure on the separation between tetrahydrofuran and water

This study separates tetrahydrofuran (THF)-water mixtures containing varying THF percentages, using sorbitan esters (Spans) with different tail characteristics. We probe the separation mechanisms using attenuated total reflectance-Fourier transform infrared spectroscopy and small angle X ray scattering (SAXS). THF and water are miscible and interact through hydrogen bonds. Water splits the COC absorbance band of THF into a peak at ≈1,070 cm−1 (crystalline THF) and a dominant peak at ≈1,050 cm−1 (glassy THF), indicating disorder. Depending on the Span, separation occurs for mixtures containing up to 70% THF (v/v, relative to water). Spans with unsaturated tails separate the lowest THF percentages. Tail length and number of Span tails enhances ordering of THF, and the crystalline THF peak at ≈1,070 cm−1 dominates. Spans interact with THF through hydrogen bonds, as reflected in the splitting of the COC band of THF. Furthermore, C-H…O hydrogen bonds cause a blueshift in the νas(CH2) band of Spans with increasing THF. This effect is most significant in Span 40 and 60, indicating that they interact with THF more strongly than Span 20, Span 80 and Span 85. In contrast, they interact with water less strongly than Span 20, Span 80 and Span 85, as indicated by their flocculation at low THF percentages. Therefore, we propose that separation between THF and water occurs primarily through two mechanisms: 1) Span 20, Span 80 and Span 85 compete against THF for interactions with water through their hydrophilic head, and 2) Span 40 and Span 60 preferentially interact with THF through their tails. Nonetheless, water also interacts with the heads of Span 40 and Span 60, as indicated by SAXS. SAXS shows that in THF Spans self-assemble into micelles, which aggregate into either surface fractals or mass fractals. There are two persistence lengths because of the limited order in THF. Water orders self-assembled structures, likely by favoring the formation of micelles which host water in their interior. Therefore, we identify a single persistence length (≈25 Å), representative of the distance between the micelle centers.

Hydrophobic Matching Dictates over the Linear Rule of Mixtures in Binary Lipid Membranes.

Biological membranes feature heterogeneous mixtures of lipids with different head and tail characteristics. Their biophysical properties are dictated by the intimate interaction among different constituent lipids. Previous studies suggest that the membrane area-per-lipid (APL) deviates from the linear rule of mixtures (LRM) for binary lipid membranes, but the underlying mechanism remains elusive. Our molecular dynamics (MD) simulations of binary lipid membranes consisting of lipids with different tail characteristics reveal a competitive mechanism whereby lipids tend to deform each other to minimize the hydrophobic mismatch between their tails. Depending on the relative tail lengths and saturation levels, this may result in an either positive or negative deviation of APL from the LRM. As lipid packing plays an essential role in membrane fusion and peptide-membrane binding, our findings may help guide the selection of lipids for the effective rational design of nanoliposomes and membrane-targeting peptides.

Global energy market connectedness and inflation at risk

After the outbreak of COVID-19 and the Russia–Ukraine war, historically high inflation has become a problem that plagues many countries, leading researchers to model and explore inflation dynamics from a conditional mean-based perspective to a conditional quantile-based perspective and examine the inflation distribution’s tail characteristics, also known as inflation-at-risk. Given the rising uncertainty in the global energy market during COVID-19 and large-scale energy supply shocks brought about by the Russia–Ukraine war, this paper first extracts the global energy connectedness index (GECI) based on the MSCI energy index of 29 countries, then incorporates GECI into the quantile regression to characterize the dynamic distribution of inflation. The results show that the predictive effect of GECI on short-, medium-, and long-term inflation dynamics is mainly reflected in upper quantiles of inflation, with heterogeneity across countries. We find that the dynamic distributions of inflation under the impact of major global events are skewed, and there are significant changes in inflation risks. The out-of-sample forecast verifies the validity and forward-looking property of GECI. The results provide practical regulatory tools and decision-making suggestions for policymakers to prevent and respond to high and low inflation risks.

Tail risk transmission in technology-driven markets

The soaring popularity of blockchain investing and cryptocurrencies has captured the attention of policymakers and investors alike. However, as cryptocurrencies remain the most volatile and high-risk investment options, they have displayed extreme asymmetric patterns over time. Considering these concerns, we conducted a comprehensive analysis of the tail risk transmission of these technology-driven markets using the Conditional autoregressive Value at risk (CAViaR) model, which sheds valuable light on the market's tail characteristics. We combined the CAViaR approach with the time-frequency methods proposed by Diebold and Yilmaz (2012) and Barunik and Krehlik (2018) to further enhance our analysis. Our results revealed a range of asymmetric economic and financial patterns across markets and highlighted the varying exposure of these markets to different circumstances over time. Finally, we investigated the impact of global factors on the tail risk transmission of technology-driven markets both in the long and short term. This study offers a wealth of insights for policymakers, investors, financial market participants, and scholars of digital finance to help navigate these rapidly evolving markets.

Experimental Observations of Bedload Tracer Movement: Effects of Mixed Particle Sizes and Bedforms

AbstractPredicting the transport of bedload tracer particles is a problem of significant theoretical and practical interest. Yet, little understanding exists for transport in rivers in the presence of bedforms, which may trap grains and thereby influence travel distance. In a series of flume experiments with a sandy gravel bed in a large experimental flume, bed elevation and tracer travel distances were measured at high resolution for a range of discharges. As discharge increased, bedform height increased and bedform length decreased, increasing bedform steepness. For all tracer sizes and flow conditions, bedforms act as primary controls on the tracer travel distances. Bedform trapping increases linearly with the ratio of bedform height to tracer grain size, with 50% trapping efficiency for a ratio of two and 90% trapping efficiency for a ratio of four. A theoretical model based on the extended active layer formulation for sediment transport is able to capture much of the distribution of measured travel distances for all tracer sizes and discharges, providing a first connection between tracer transport theory and bedform trapping and indicating normal diffusion of tracers at relatively small timescales. Variable bedform geometry can influence trap efficiency for individual bedforms and the theoretical model can help identify “preferential trapping” conditions. The distribution of tracer travel distances for a mixture of grain sizes and variable discharge, as expected in natural rivers, displays heavy tail characteristics.

A Progressive Bayesian Filtering Framework for Nonlinear Systems With Heavy-Tailed Noises

This article studies the Bayesian filtering problem for nonlinear systems with heavy-tailed noises. Because of the nonlinearity and heavy tail characteristics, the Gaussian distribution or particle sets may fail to express the posterior probability density distribution; thus, the progressive Bayesian filtering framework is proposed. With the filtering framework, the measurement update is divided into several steps, and the intermediate posterior distributions are chosen as the importance proposal distributions to improve the approximation of posterior probability density distributions. Moreover, termination conditions for the progressive measurement update are also proposed to improve the robustness of the progressive Bayesian filter against outliers. Finally, a simulation example is exploited to illustrate the effectiveness and superiority of the proposed filtering framework.

WHITE TAILED JUNGLE RAT MORPHOLOGICAL DIVERSITY STUDY (Maxomys sp.) OF NORTH SULAWESI ACCORDING FROM THE TAIL MORPHOLOGICAL CHARACTERISTICS

Tail characteristics of the White-Tailed Jungle Rat (Maxomys sp.) of North Sulawesi have been examined by us to reveal the population diversity. Usually, examining population diversity or species uses numerous morphological features as markers for variation. However, in this study, we are only using one morphological feature, which is the characteristic of the tail from the samples. Several experts have used tail characteristics as the sole identifier for species identifications. On another account, diversity studies using various white-tailed jungle rats are also being performed, and the result of the said study can be compared with the result of the study in which only a single feature is the tail characteristics of the white-tailed jungle rat. The characteristics of white-tailed jungle rats that were examined include total length, the total length of the white section and the present/lack of fine hairs on the tip. The number of samples is 35. The samples are obtained from the jungles of North Sulawesi, which are The District of Minahasa, North Minahasa, South Minahasa, and Southeast Minahasa. The results of morphological observation of the white-tailed jungle rat were analyzed using mutual cluster analysis, statistics program, SPSS IBM, 26. The analysis results from a dendrogram showed two main clusters: the first with the numbers 9, 19 and 12 and the second with the remaining sample numbers. On the second cluster, there are two more formed sub-cluster. The results of this study are nearly identical to the study results that use various morphological diversities. This study recommends that the tail characteristics of a white-tailed rat can represent the diversity of white-tailed rats.

Modelling exchange rate volatility under jump process and application analysis

<abstract> <p>Exchange rate is an important part of financial markets. Our analysis finds that the fluctuations of exchange rates have several obvious features, such as spikes, thick tails, fluctuation aggregations and asymmetry. Based on this, we build novel GARCH class model by introducing a jumping process to describe the dynamics of their fluctuations. Our empirical results show that the models with jump factors can better characterize the agglomeration and thick tail characteristics of these return fluctuations than the models without jump factors. In particular, the model with double exponential jumps can fully handle and capture the fluctuation characteristics of the returns. Our findings will be useful for individuals and governments to predict exchange rate fluctuations, provide reference for the effective management of exchange rate risk in China, and further improve the financial risk management mechanism.</p> </abstract>

Modeling RMB Exchange Rate Volatility – Application of GARCH Family Models

The exchange rate risk caused by the two-way fluctuation of the RMB exchange rate will bring many effects. The volatility of the foreign exchange market is the most common feature of the financial market. Therefore, the research on the volatility of the RMB exchange rate is of great significance in economic and financial aspects. Through statistical analysis of the RMB exchange rate data, an ARMA model was established to eliminate the auto-correlation of the sequence, and the GARCH family model was combined to fit the data. Comparing different distribution hypotheses, the EGARCH model under the GED distribution determined by the information criterion can match well the financial time series peak and thick tail characteristics. That the RMB exchange rate has agglomeration volatility and leverage effect is also shown, and provides relevant suggestions for preventing RMB exchange rate risks.

Research on extreme risk measurement in the international carbon emission futures market, based on a two-component Beta-Skew-t-EGARCH-POT model

ABSTRACT The European Union Allowance (EUA)-driven carbon emissions trading market is selected as a research object. First, the one-component and two-component Beta-Skew-t-EGARCH models are used to depict the characteristics of the return series, considering return series features such as volatility clustering and asymmetry. Moreover, the extreme VaR risk value is directly estimated. Then, the POT method of extreme value theory is applied to describe the extreme tail characteristics of the return series, and the one-component and two-component Beta-Skew-t-EGARCH-POT models constructed, to estimate the extreme VaR risk value, once again. Finally, the performance of each model in extreme VaR risk measurement is tested using six indicators that include the relative error rate, independence test, and dynamic quantile test. The empirical results show that the two-component Beta-Skew-t-EGARCH-POT model performs better in extreme risk measurement, inferring that this model is better suited to describing risk in the carbon market.

A spatial copula interpolation in a random field with application in air pollution data.

Interpolating a skewed conditional spatial random field with missing data is cumbersome in the absence of Gaussianity assumptions. Copulas can capture different types of joint tail characteristics beyond the Gaussian paradigm. Maintaining spatial homogeneity and continuity around the observed random spatial point is also challenging. Especially when interpolating along a spatial surface, the boundary points also demand focus in forming a neighborhood. As a result, importing the concept of hierarchical clustering on the spatial random field is necessary for developing the copula model with the interface of the Expectation-Maximization algorithm and concurrently utilizing the idea of the Bayesian framework. This article introduces a spatial cluster-based C-vine copula and a modified Gaussian distance kernel to derive a novel spatial probability distribution. To make spatial copula interpolation compatible and efficient, we estimate the parameter by employing different techniques. We apply the proposed spatial interpolation approach to the air pollution of Delhi as a crucial circumstantial study to demonstrate this newly developed novel spatial estimation technique.

Study on Tail Characteristics of Systemic Risk in US and Chinese Stock Markets

Trade friction between the United States and China has increased financial risks. Since 2017, the stock markets of the United States and China have shown extremely high-risk dependence. By constructing Markov SJC Copula model, this paper makes an empirical analysis on the systemic risk of American and Chinese stock markets. The results show that SJC Copula can describe the systemic risk of American and Chinese stock markets well. In addition, studies show that the risk dependence of the U.S. and Chinese stock markets also has obvious tail dynamic characteristics. Under the high-risk zone system, the dependence of the lower tail risk is more significant.