Distributional Characteristics Research Articles

Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market

Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator compared to existing approaches for independence-based identification. Moreover, the proposed method enables partial identification and dimension reduction even in the presence of dependent shocks. We illustrate the benefits of our approach by applying it to the global oil market model of Kilian, highlighting its ability to capture unmodeled higher-order dependence between oil supply and speculative oil demand shocks.

Prevalence Characteristics of Osteoporosis Fractures in the Elderly in Two Regions of China and Analysis of the Lag Effect of Air Pollutants on them.

Air pollution is increasing and threatening human health. The objective of this study is to investigate the population distribution characteristics of elderly osteoporosis fractures in Hebei Province and Xinjiang Uygur Autonomous Region and to analyze the effects of air pollutants on the number of elderly osteoporosis fracture inpatients in the two regions. A retrospective collection of elderly osteoporosis fracture cases was conducted in selected hospitals in Hebei Province and Xinjiang Uygur Autonomous Region from January 1, 2018 to December 31, 2022. The chi-square test was used to compare the distributional characteristics of the population in the two regions. Additionally, we used a distributed lag nonlinear model (DLNM) in order to assess the effect of air pollutants on the number of daily hospital admissions of elderly osteoporosis fracture patients in different regions. A total of 19,203 elderly osteoporosis fracture patients were included in the study. The average age of these patients was 76.66 ± 7.55 years, and the majority of them were female (13,514 instances, 70.37%). The disparities in age distribution (χ2 = 133.9 p < 0.001), fracture site (χ2 = 62.0 p < 0.001), and hospitalization cost (Z = -15.635 p < 0.001) between the two regions were statistically significant. The lag effect curves of PM2.5, PM10, and NO2 on the number of elderly osteoporosis fracture hospitalizations in Xinjiang Uygur Autonomous Region exhibited a similar pattern resembling a "W"-shaped curve. All three pollutants reached their highest values after a lag time of 14 days (PM2.5: RR = 1.053, 95% CI: 1.031, 1.074; PM10: RR = 1.031, 95% CI: 1.018, 1.043; NO2: RR = 1.125, 95% CI: 1.070, 1.182). In Hebei Province, the largest impacts of PM2.5 and PM10 were observed after a lag of 14 days (PM2.5: RR = 1.022, 95% CI: 1.013, 1.028; PM10: RR = 1.013, 95% CI: 1.008, 1.018). Similarly, the maximum effect of NO2 was observed after a lag of 11 days (RR = 1.020, 95% CI: 1.010, 1.028). There were differences in the epidemiological characteristics of hospitalized patients with osteoporosis fractures between the two regions, PM2.5, PM10, and NO2 increased the number of hospitalizations for osteoporosis fractures. Exposure to air pollutants such as PM2.5 increases the risk of osteoporosis fractures in the elderly population.

Shifting alpine plant distributions with global change: Testing the environmental matching hypothesis

ABSTRACT Species facing novel temperature, precipitation, and nitrogen (N) deposition regimes must move or adapt to persist. For long-lived plants, a primary form of climate acclimation is through shifting geographic range limits or establishing in favorable microclimates. One commonly assumed but rarely tested hypothesis is that these shifts can be predicted by environmental matching: that the environmental characteristics that define a current distribution should predict how a population will shift with environmental changes. To test this hypothesis, we transplanted four alpine and two subalpine plant species into environments with experimentally increased temperature, snow, and N. We predicted that species would perform best when environmental change matched their geographic distributional characteristics: increased temperature, snow, and N (two subalpine species), increased temperature (two dry meadow specialists), and increased snow (two snowbed specialists). Our results provided limited support for the environmental matching hypothesis. Snowbed specialists did not benefit from increased snow, dry meadow specialists' performance did not consistently differ among the treatments, and subalpine plants' survival was not affected by treatments while their growth response was variable among species. Our results suggest that global change effects will vary among species and distributional shifts are not easily predicted by species environmental preference.

The ambiguity-resolved detector: a detector for the mixed-integer GNSS model

In this contribution, we introduce the ambiguity-resolved (AR) detector and study its distributional characteristics. The AR-detector is a new detector that lies in between the commonly used ambiguity-float (AF) and ambiguity-known (AK) detectors. As the ambiguity vector can seldomly be known completely, usage of the AK-detector is questionable as reliance on its distributional properties will then generally be incorrect. The AR-detector resolves the shortcomings of the AK-detector by treating the ambiguities as unknown integers. We show how the detector improves upon the AF-detector, and we demonstrate that the, for ambiguity-resolved parameter estimation, commonly required extreme success rates can be relaxed for detection, thus showing that improved model validation is also possible with smaller success rates. As such, the AR-detector is designed to work for mixed-integer GNSS models.

Spatial distributed characteristics of carbon dioxide emissions based on fossil energy consumption and their driving factors at provincial scale in China

The spatial distribution characteristics of carbon dioxide (CO2) emissions of coal, crude oil and natural gas in China during 2019 were revealed through Exploratory Spatial Data Analysis (ESDA) method. Then spatial differences of the impacts of socio-economic factors on CO2 emissions were discussed by using geographical weighted regression (GWR) model. Furthermore, the relationship between urbanization and CO2 emissions was explored by combing the center of gravity model with coupling coordination development degree model. The findings demonstrate that the high spatial agglomeration effect and risk areas of coal-based CO2 emissions are concentrated in the northern region (the maximum emissions up to 1005.09 Mt), and the same type of natural gas-based CO2 emissions are distributed in the central region, while the low spatial agglomeration effect and risk areas of crude oil-based CO2 emissions are mainly in the western region. Under the positive impact from energy intensity (average influence coefficient is 1.59), CO2 emissions show a good coupling trend with economic urbanization, but present a spatial differentiation of unbalanced western and coordinated eastern regions with population urbanization. This study enriches the content of energy-based carbon emissions from geographic and management disciplines, also gives scientific foundation for differential formulation of carbon emission reduction policies.

Distribution of Carbon-Sequestering Microbes in Different Habitats and the Interaction with Habitat Factors in a Natural Karst Cave

The distributional characteristics of microorganisms in karst cave ecosystems have been widely studied. However, in such a dark, humid, and oligotrophic habitat, studies on the differences in carbon-sequestering bacteria in multiple habitats are limited. Therefore, to learn the distribution characteristics of carbon-sequestering colonies in cave habitats and their correlation with habitat factors (e.g., pH, Ca2+, Mg2+, etc.), samples from five cave habitats (weathered rock walls, underground river water, drips, sediments, and air) were collected from the twilight and dark zones of Shiziyan Cave (CO2 concentration 5385 ppm). The results of high-throughput sequencing and statistical analyses showed that there were significant differences in the distribution of communities in different habitats, with higher abundance in sediments habitat and underground river water habitat, and the dominant phyla of Pseudomonadota (30.53%) and Cyanobacteria (75.11%) in these two habitats. The microbial diversity of the carbon-sequestering microbial community was higher in sediments than in underground river water. The pH, and Ca2+, SO42−, and NO3− concentrations can alter the diversity of carbon-sequestering microbes, thereby affecting carbon cycling in caves. Carbon metabolism analyses suggest that microbes in the habitat can cooperate and coexist by participating in different carbon metabolic pathways. These results expanded our understanding of carbon-sequestering microbial communities in cave systems and their responses to the environment.

Spatial characteristics and dynamic differences of power industry’s low carbon transition efficiency

The power industry’s low carbon transition is pivotal for achieving carbon reduction and sustainable development. This study uses the super epsilon-based measurement (Super-EBM) model and the Malmquist index to evaluate the power industry’s low carbon transition efficiency using data from 30 provinces in China from 2010 to 2020, and utilizes the Tobit model to comprehensively analyze the factors affecting the low carbon transition of power industry. In addition, this paper examines the spatial differences in the power industry's low carbon transition efficiency as well as its distributional characteristics and dynamic evolutionary patterns. Conclusion is drawn as follows this paper analyzes the regional differences, spatial distribution characteristics and dynamic evolutionary trends of the power industry’s low carbon transition. The main conclusions are as follows: (1) The power industry’s low carbon transition efficiency in China shows an uptrend, with the western China region having the highest overall level of efficiency, greater fluctuations in the central China region, and more stability in the eastern China region, technological progress is a central factor in increasing total factor productivity, the efficiency of the power industry’s low carbon transition is positively influenced by the electricity prices, and negatively influenced by the energy structure, environmental regulations and economic structure; (2) the Intraregional differences and hypervariable density are the main reasons sources of the overall differences in the efficiency of the power industry’s low carbon transition; Intraregional differences in the eastern, central, and western China regions are decreasing year by year, but the efficiency of the power industry’s low carbon transition in the western China region is still distributed in a multipolar way; (3) The dynamic evolutionary trends of the efficiency distribution of the low carbon transition in power industry is influenced by the type of spatial lag in the neighboring area. Where areas with low efficiency makes it difficult to achieve short-term leapfrog development, and areas with a cluster of high-efficiency provinces are prone to “Siphon Effect”. The findings provide a theoretical basis for promoting the efficiency of the power industry's low carbon transition and coordinating the strategic adjustment of economic and environmental green development.

Disentangled variational auto-encoder for multimodal fusion performance analysis in multimodal sentiment analysis

Multimodal Sentiment Analysis (MSA) holds extensive applicability owing to its capacity to analyze and interpret users' emotions, feelings, and perspectives by integrating complementary information from multiple modalities. However, inefficient and unbalanced cross-modal information fusion substantially undermines the accuracy and reliability of MSA models. Consequently, a critical challenge in the field now lies in effectively assessing the information integration capabilities of these models to ensure balanced and equitable processing of multimodal data. In this paper, a Disentanglement-based Variable Auto-Encoder (DVAE) is proposed for systematically assessing fusion performance and investigating the factors that facilitate multimodal fusion. Specifically, a distribution constraint module is presented to decouple the fusion matrices and generate multiple low-dimensional and trustworthy disentangled latent vectors that adhere to the authentic unimodal input distribution. In addition, a combined loss term is modified to effectively balance inductive bias, signal reconstruction, and distribution constraint items to facilitate the optimization of neural network weights and parameters. Utilizing the proposed evaluation method, we can evaluate the fusion performance of multimodal models by contrasting the classification degradation ratio derived from disentangled hidden representations and joint representations. Experiments conducted with eight state-of-the-art multimodal fusion methods on the CMU-MOSEI and CMU-MOSEI benchmark datasets demonstrate that DVAE is capable of effectively evaluating the effects of multimodal fusion. Moreover, the comparative experimental results indicate that the equalizing effect among various advanced mechanisms in multimodal sentiment analysis, as well as the single-peak characteristic of the ground label distribution, both contribute significantly to multimodal data fusion.

Evaluating iodine deficiency and goiter in hilly areas of District Poonch Azad Jammu Kashmir.

Iodine deficiency is considered as a global challenge, even after decades of efforts to solve the issue. Our objective was to assess the iodine deficiency status and associated prevalence of goiter in population groups (school-age children and women) from rural areas of District Poonch, and to assess the determinants of iodine deficiency in the area. Cross-sectional study was conducted in District Poonch Azad Jammu and Kashmir from 25 April 2022 to 30 June 2023. A total of 150 goiter patients from different villages of the District Poonch was included. Their urinary iodine concentration and goiter prevalence was assessed. Using palpation techniques, a trained and experienced public health officer assessed the presence of goiters based on WHO/ UNICEF/ICCIDD criteria. Descriptive statistics were computed for continuous variables and presented in frequency and percentage, based on the distributional characteristics of the data. chi-square was used to check association between socio-demographic factors and goiter. It was a HEC Project No.: 20-16988/NRPU/R&D/HEC/2021. Iodine status and associated goiter prevalence was high and 59.3% of them were severely iodine deficient. Within the district, the highest severe iodine deficiency was observed in 81.1% goiter patients of the Rawalakot subdivision, Hajira and Abbaspur subdivisions. Regarding goiter status 40% of the patients were classified with palpable-visible goiter and 56% were characterized with visible but nodular goiter. Study showed that there was a severe iodine deficiency and associated goiter prevalence in the area. Policymakers should take actions for future to overcome iodine deficiency in future.

Distribution and source analysis of soil toxic organic compounds of coal-electricity production base in arid and semi-arid region of China

The presence and distribution of toxic organic compounds in soil pose significant challenges. Whether their distributional characteristics are more complex, especially in arid and semi-arid regions with harsh climatic conditions? This study analyzed the composition, classification, spatial distribution, and sources of 123 toxic organic compounds in 56 soil samples of coal-electricity production base. Those compounds were classified into 11 categories, mainly pesticides (41 compounds), organic synthesis intermediates (31 compounds), and drugs (23 compounds). Seventeen of those compounds were detected over the rate of 30 %, with 13 of them being under the Toxic Substances Control Act (TSCA) inventory. The primary sources of toxic organic compounds were determined using Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF), including the degradation of pesticide residues (22.03 %), emissions of plastic pellets (16.64 %), industrial waste emissions (12.80 %), emissions from livestock (12.74 %), plastic films (11.22 %) and coal-to-liquid projects (10.78 %). This research underscores the widespread presence of toxic organic compounds in soil, highlighting their origins and distribution patterns, which are essential for developing targeted environmental management strategies in arid and semi-arid regions.

A Comparative Analysis of Chinese and American News Discourse Based on Appraisal Theory

This paper takes news reports about Fukushima nuclear wastewater discharge from China Daily and Washington Post as the research corpus, conducts a comparative analysis on the corpus from three subsystems of appraisal theory including attitude, engagement and graduation, then statistically analyzes the use of appraisal resources in the discourses and their distributional characteristics to explore attitudes and stances of the Chinese and American media towards Japanese wastewater nuclear discharge. The results show that the overall distribution of appraisal resources in China Daily and Washington Post is comparable and the difference in the frequency of using attitude resource is not significant, but the uses of engagement resources and graduation resources in China Daily are higher than those in Washington Post.

Osteological age-at-death estimation in an archaeological sample avoiding age-mimicry: a six-step approach

In human osteoarchaeology, individual age-at-death is traditionally assigned by scoring characteristic expressions of specific traits and applying formulas or algorithms. However, it is well known that the resulting age estimates in target samples suffer from bias due to the effect of age-mimicry, both at the individual and at the population level. A general statistical methodology to obtain unbiased estimates of the age-at-death distribution in skeletal samples has been available for more than two decades. Even so, the procedure is rarely used. This may be related to the fact that this methodology requires external input which has selection of distributional characteristics to be reported. In this paper, we translate the general methodology into a clearly stated six-step procedure to be followed. We illustrate the six steps using an archaeological sample of 675 adult individuals and 15 scoring methods from traditional age-estimation procedures. By clearly identifying the actions that are necessary for its application we intend to make the approach more accessible for osteoarchaeologists while at the same time highlighting some challenges that need to be addressed in the future. Our study demonstrates that the approach is feasible and illustrates the absence of age-mimicry. A combined analysis of five informative traits allowed to obtain estimates of several characteristics of the target sample age distribution. However, its routine use will benefit from improved input from relevant reference samples and improved statistical software.

Psychometric properties of MG-ADL items and MG-ADL score: An assessment of distributional characteristics, validity and factor structure in two large datasets

BackgroundThe Myasthenia Gravis–Activities of Daily Living scale (MG-ADL) is an 8-item outcome measure to assess symptoms and functional limitations in myasthenia gravis (MG) patients. The MG-ADL score is an equally weighted level sum score that is used as primary outcome measures in clinical trials, in clinical practice, and as an end-point in health economic evaluation. This data analysis aims to obtain detailed knowledge of measurement properties of MG-ADL items and the MG-ADL score. MethodsCross-sectional data from a real-world prospective study (MRW) were combined with longitudinal data from the ADAPT trial. Outcome measures included were MG-ADL, Quantitative Myasthenia Gravis score (QMG), MG 15-item Quality of Life (MG-QOL15r) and EQ-5D-5L. Patients were categorized by their Myasthenia Gravis Foundation of America (MGFA) clinical classification. The following measurement properties were assessed: distributional characteristics, inter-item correlation, convergent, known groups and construct validity and internal factor structure. ResultsCorrelations of items within MG-ADL dimensions were moderate, while MG-ADL correlations between comparable MG-QOL15r and QMG items were mixed. Known groups validity for the MG-ADL score was demonstrated for MGFA class. Mean MG-ADL item level scores by MGFA class demonstrated construct validity. PCA, including all four outcome measures, resulted in a nine factor solution. DiscussionPsychometric properties of individual MG-ADL items were moderate to good. This study showed that the MG-ADL adequately captures the multidimensional heterogeneous nature of MG. This is, however, accompanied by mixed psychometric performance of the MG-ADL score, which may complicate health economic modelling.Registration: MyRealWorld-MG was registered on November 25, 2019, with registration numberNCT04176211. The ADAPT randomized clinical trial is registered atClinicalTrials.gov(NCT03669588).

Comparative analysis of surface roughness level for bearing coating using VMD of vibration signal

Degradation in surface finish of the material is an important concern in engineering applications. However, most of the conventional techniques available to monitor the surface roughness requires dismantling of the machine element. Though in rotary components like bearing, it is practically not feasible. Therefore, in this work, an on-board technique is proposed to compare the level of surface roughness based on vibration signature. To demonstrate, five different industrial standard coatings (Nickel, Copper, Zinc phosphate (ZnP), Silver, and Black oxide) were carried out on raw five ball bearings (NBC6205). Two sets of coated bearing were prepared, where first set was utilised for experimentation and second for the purpose of measuring surface roughness of bearing surface. These coated bearings were tested at five different RPMs ranging from 300 RPM to 1500 RPM and their vibration signals were recorded. The recorded vibration signals must be having characteristics originated from ball rolling on different level of surface roughness and hence distributed in nature. Further, to target distributed characteristics present in the signal, commonly used statistical parameters for vibration signature analysis (RMS, Crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy) were calculated. Then, correlation of the parameters was checked in relation to the different levels of surface roughness but no relation was found. The signals were then decomposed into six intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD) method. Again, same statistical parameters were calculated for these decomposed levels, it has been noted that Shannon entropy have shown correlation to surface roughness in at least one decomposed level between 900 to 1500 RPM with minimum value of chain index as 176.65. Moreover, in this RPM range, responsive frequency bands found to be shifted towards higher side i.e. from 567 to 4590 Hz.

Unique Plant Resources and Distribution Patterns in the Valley Forest of the Irtysh River Basin.

The river valley forests of the Irtysh River Basin are a germplasm bank of Salicaceae species and rare plant resources in China, and the distribution varies with the river and is highly distinctive. However, there is a dearth of systematic research on the characteristics of plant resources. In this study, a comprehensive investigation was conducted in the trunk stream and six tributaries with valley forest distribution in the Irtysh River Basin, and 244 quadrats were set up. The analysis focused on the composition of the flora and resource characteristics. The results reveal the following: (1) The valley forests of the Irtysh River Basin contain 256 species of plants belonging to 57 families and 178 genera, among which 19 species of trees, 23 species of shrubs, and 214 species of herbs were investigated. (2) Among the identified species, 226 (88.67%) were recognized as resource plants, with medicinal plants being the most abundant (176 species, 68.75% of the total). (3) The distribution patterns of trees, shrubs, and herbs of each resource type vary across rivers. Elevation drop, river length, and river distance all significantly affect the number of specie. This study elucidated the current status and distributional characteristics of plant resources in the valley forests of the Irtysh River Basin, which is essential for both biodiversity conservation and sustainable resource utilization.

A Note on Rounding Error in The Variables: Understanding Its Influence on Statistical Estimators

When estimating the variance, higher moments, and regression coefficients using rounded data, bias is commonly introduced into these estimates. Sheppard’s correction can be used to approximately adjust for this bias under certain conditions. This study investegated the conditions that allow for the valid application of Sheppard’s corrections. A significant observation from the research was that the rounding error-often displaying a uniform distribution-was not independent of the original, unrounded variable. The study provided a thorough investigation of instances where variables, distributed uniformly, undergo rounding. This investigation yielded crucial insights into the statistical behavior and potential ramifications of such variables within statistical analyses. An integral part of this study was the computation and discussion of the density function of the rounding error. Deriving this density function facilitated a nuanced analysis of the differences between the moments of rounded data and the original, unrounded data. Sheppard’s regularities, under which Sheppard’s correction is applied, were discussed. The letter also carefully considered the effects of rounding error on regression estimates. In this context, Sheppard’s correction was implemented to the biased regression parameters to evaluate its efficacy in providing more accurate estimations. Further, our research provides new insights into how rounding can affect statistical outcomes, depending on the underlying distribution of the data. We have discovered that the impact is minimal under uniform distribution assumptions but becomes significantly pronounced under non-uniform distributions. This highlights the importance of considering the distributional characteristics of data when applying rounding corrections to ensure the reliability of statistical inferences.

Scale Effects of Individual Tree Thinning in Chinese Fir Plantations

Thinning is a crucial approach to the near-natural conversion of plantations, contributing to the sustainability of forest management. However, over a rotation, the effects of thinning on tree growth, structure, and species diversity, as well as their scale-dependent outcomes, remain unclear. Ten years after conducting individual tree thinning in a Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) monoculture in southwest China, we analyzed the thinning effects within the influence zone (r = 0–2.5 m) centered around stumps and center trees. The results indicate that (1) individual tree thinning did not significantly promote the increment of diameter at breast height, volume, and biomass of Chinese fir. The four nearest Chinese fir around stumps and center trees were in a state of random distribution, high mixing, and dominance; (2) individual tree thinning significantly promoted the formation of species abundance and richness at r = 2–2.5 m. With increased scale, more species were found, leading to increased stability in diameter and species distribution. Linear and nonlinear relationships between diameter classes, mingling, and dominance became more stable; (3) later-coming populations within both modes exhibited a common characteristic of random distribution, high mixing, and disadvantage at each scale, but with significant differences in species composition (kj = 0.22–0.61). These results suggest that individual tree thinning has limited impacts on the growth and structure of retained trees in Chinese fir plantations but significantly enhances the formation of species diversity, demonstrating scale effects. This underscores the feasibility of individual tree thinning in the near-natural conversion of plantations.

Distribution and correlation analysis of typical features of electricity use profiles in non-residential buildings

Building electrical system will play a critical role in the demand response of future distributed energy systems. Understanding the distributive characteristics of the physical features of building electricity use profiles provides a useful reference for planning and designing building electrical systems. This study proposed a methodology and framework for analyzing the distribution and correlations of physical electricity use features. Five physical features were extracted from the electricity use profiles of the buildings. For value-based features, including the weekend ratio and variation factor, distribution fitting and feature regression were conducted to reveal correlations with attributes such as building types and sizes. For curve-based features, including the power-temperature curve, weekday and weekend schedules, clustering analysis was first used to identify several typical curves, and CART classification models were developed to establish correlations between typical curve types and building attributes. The proportions and distributions of all features were analyzed based on a dataset of 4168 non-residential buildings in Jiangsu Province, China. The K–S test p-values of the fitted distributions of weekend ratios and variation factors were 0.074 and 0.189, respectively. The overall accuracies of the CART classification model for power-temperature types, weekday schedule types and weekend schedule types were 62.7 %, 66.3 % and 56.7 % respectively. Potential application procedures and tools were provided for practical use. Variations in weekday and weekend schedules are discussed, and future research perspectives are outlined. The proposed methodology and results provide useful support for the planning and design of distributed building electrical systems.

Effect of Bi-Er-O addition on the electrical properties of ZnBiCoMnSb based varistor

In order to restrain the breakdown voltage gradient E1 mA at a low level and simultaneously improve nonlinear coefficient α, the rare earth element Er was doped into ZnBiCoMnSb based varistor ceramic in the form of Bi-Er-O phase. As a result, the excellent electrical performances including low E1 mA of 190 V/mm, high α of 55.6 as well as low leakage current IL of 2.5 μA/cm2 are obtained in 3 wt% Bi-Er-O phase modified ZnBiCoMnSb based varistor ceramic. It is found that the concentration of Er at grain boundaries is slightly higher than that in ZnO grains, while it is uniformly distributed throughout the bulk ceramic. This characteristic of element distribution mainly contributes to the enhancement of α by forming interface state densities and deep bulk traps at grain boundaries, rather than E1 mA. The reported herein strategy to restrain E1 mA and improve α at the same time on ZnBiCoMnSb based varistor ceramic can act as a reference for further fabrication of low-E1 mA varistor ceramics with excellent nonlinear performances by rare earth elements doping.

Consistency of the Signature of Phonotraumatic Vocal Hyperfunction Across Different Ambulatory Voice Measures.

Although different factors and voice measures have been associated with phonotraumatic vocal hyperfunction (PVH), it is unclear what percentage of individuals with PVH exhibit such differences during their daily lives. This study used a machine learning approach to quantify the consistency with which PVH manifests according to ambulatory voice measures. Analyses included acoustic parameters of phonation as well as temporal aspects of phonation and rest, with the goal of determining optimally consistent signatures of PVH. Ambulatory neck-surface acceleration signals were recorded over 1 week from 116 female participants diagnosed with PVH and age-, sex-, and occupation-matched vocally healthy controls. The consistency of the manifestation of PVH was defined as the percentage of participants in each group that exhibited an atypical signature based on a target voice measure. Evaluation of each machine learning model used nested 10-fold cross-validation to improve the generalizability of findings. In Experiment 1, we trained separate logistic regression models based on the distributional characteristics of 14 voice measures and durations of voicing and resting segments. In Experiments 2 and 3, features of voicing and resting duration augmented the existing distributional characteristics to examine whether more consistent signatures would result. Experiment 1 showed that the difference in the magnitude of the first two harmonics (H1-H2) exhibited the most consistent signature (69.4% of participants with PVH and 20.4% of controls had an atypical H1-H2 signature), followed by spectral tilt over eight harmonics (73.6% participants with PVH and 32.1% of controls had an atypical spectral tilt signature) and estimated sound pressure level (SPL; 66.9% participants with PVH and 27.6% of controls had an atypical SPL signature). Additionally, 77.6% of participants with PVH had atypical resting duration, with 68.9% exhibiting atypical voicing duration. Experiments 2 and 3 showed that augmenting the best-performing voice measures with univariate features of voicing or resting durations yielded only incremental improvement in the classifier's performance. Females with PVH were more likely to use more abrupt vocal fold closure (lower H1-H2), phonate louder (higher SPL), and take shorter vocal rests. They were also less likely to use higher fundamental frequency during their daily activities. The difference in the voicing duration signature between participants with PVH and controls had a large effect size, providing strong empirical evidence regarding the role of voice use in the development of PVH.