Mixed Distribution Research Articles

Ventilation effectiveness in classroom infection risk control

The benefits of a good ventilation in classrooms are a well-studied topic regarding health and learning outcomes. However, many studies still show poor results regarding air quality, air change rate and air velocities. In this paper, typical Estonian classroom air distribution solutions were studied in an air distribution laboratory at Tallinn University of Technology. The air change efficiency was measured with CO2 tracer gas concentration decay method. For determining the contaminant removal effectiveness, continuous dose method was used to create a constant contaminant source. In addition, by using air velocity probes, we conducted draught measurements in the mock-up classroom. Tests were conducted using dedicated room-based air handling unit and thermal mannequins for imitating heat sources from students. We found that all solutions studied ensured the air change efficiency roughly corresponding to fully mixing air distribution, but local ventilation effectiveness values of contaminant removal showed large variation from 0.6 to 1.7 stressing the impact of source location. Grouped ceiling supply circular diffusers and single vertical supply grille air distribution commonly used in renovated educational buildings resulted in higher draught risk on the border of the occupied zone. High air velocities recorded in some areas of the classroom perimeter, well explain why draught is considered as one of the main reasons why the airflow rates are reduced, or supply air temperatures are lifted compared to designed values. Perforated duct diffusers resulted in acceptable air velocities. In conclusion, local ventilation effectiveness of contaminant removal showed that fully mixing is not a case with a point source, although air change efficiency determined with equally distributed source showed fully mixing conditions. Therefore, in those cases, the air change rate should be increased to achieve the same ventilation effectiveness. Based on the experiments conducted, a point source ventilation effectiveness measurement method for the breathing zone is proposed. This value determined at least with two source locations can be used in infection risk-based ventilation design.

Clustering of Polish Citizens on the Bases of Their Support for Leaving and Remaining the European Union

The article presents the clustering of Polish citizens based on the empirical dimension of support for European integration. The structure of the work is based on three key elements constituting the basis of the presented text. The first refers to the development of a scale to measure the extent of support for the integration of Poland with the EU. The second element covers an area of support scale modelling. After the substantial and statistical analysis of the adequacy of the probability distribution for the support scale, it was decided that a model in which the scale underwent mixing non-standard Beta distributions would be adopted. Applying the Maximal Likelihood Method (ML), the components for its fitted probability densities and estimators of prior (or mixing) probabilities were indicated. The procedure allowed us to define the clusters of which the population of voters was composed. The paper’s final section presents many practical and theoretical conclusions for political parties and scientists interested in the discussed area. The novelty of applying the ML method goes hand in hand with the findings that previously appeared in political science literature, although under different economic and geopolitical conditions.

Mix & Latch: An Optimization Flow for High-Performance Designs With Single-Clock Mixed-Polarity Latches and Flip-Flops

Flip-flops are the most used sequential elements in synchronous circuits, but designs based on latches can operate at higher frequencies and occupy less area. Techniques to increase the maximum operating frequency of flip-flop based designs, such as time-borrowing, rely on tight hold constraints that are difficult to satisfy using traditional back-end optimization techniques. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Mix &amp; Latch</i> , a methodology to increase the operating frequency of synchronous digital circuits using a single clock tree and a mixed distribution of positive- and negative-edge-triggered flops, and positive- and negative-level-sensitive latches. An efficient mathematical model is proposed to optimize the type and location of the sequential elements of the circuit. We ensure that the initial registers are not moved from their initial location, although they may change type, thus allowing the use of equivalence checking and static timing analysis to verify formally the correctness of the transformation. The technique is validated using a 28nm CMOS FDSOI technology, obtaining 1.33X post-layout average operating frequency improvement on a broad set of benchmarks over a standard commercial design flow. Additionally, the circuit area was also reduced by more than 1.19X on average for the same benchmarks, although the overall area reduction is not a goal of the optimization algorithm. To the best of our knowledge, this is the first work that proposes combining mixed-polarity flip-flops and latches to improve the circuit performance.

A discrete mixed distribution: Statistical and reliability properties with applications to model COVID-19 data in various countries.

The aim of this paper is to introduce a discrete mixture model from the point of view of reliability and ordered statistics theoretically and practically for modeling extreme and outliers' observations. The base distribution can be expressed as a mixture of gamma and Lindley models. A wide range of the reported model structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, reversed hazard rate function, min-max models, mean residual life, mean past life, moments, order statistics and L-moment statistics. These properties can be formulated as closed forms. It is found that the proposed model can be used effectively to evaluate over- and under-dispersed phenomena. Moreover, it can be applied to analyze asymmetric data under extreme and outliers' notes. To get the competent estimators for modeling observations, the maximum likelihood approach is utilized under conditions of the Newton-Raphson numerical technique. A simulation study is carried out to examine the bias and mean squared error of the estimators. Finally, the flexibility of the discrete mixture model is explained by discussing three COVID-19 data sets.

Brunt of technology in stock trading - empirical evaluation of movement in trading modes

Technical indicators use volume patterns to confirm trends, momentum, return, and volatility of the market. Assessing the conviction behind market advances and declines based on exchange-defined differences in trading mode is a fascinating way to ascertain market insights. In this context, the study is novel and structured to add literature in two aspects - the study evaluates: 1) the relationships between trade movements in seven different stock trading platforms; 2) attempts to gather additional evidence for the mixed distribution hypothesis identifying the relationship between trade movements and index movement, return and volatility. The study recommends the model as a part of surveillance to assess the impact of trade movement generated in specific trading mode. It can also be used by technical traders to ascertain trading signals, and behavioural insights from the other trading modes mutually.

Sediment Properties and Provenance Study of Heavy Minerals Along Chinnavilai and Erayumanthurai Beach, South West Coast of India

The Chinnavilai and Erayumanthurai beach sediments of the Kanyakumari District consist of medium-grained, moderately well-sorted, unimodal, occasionally bimodal, mesokurtic, leptokurtic and platykurtic. The fine skewed and symmetrical nature of sediments implies the prevalence of high and low energy, entailing a mixed distribution of coarse and fine sediments. The washing and backwashing of waves cause the coarser sediments to retainment and entrapped amidst finer sediments. The heavy minerals distribution reflects that the less content in Erayumanthurai samples, whereas the Manavalakurichi, Enayam, Kurumpanai sediments comprise &gt;60%. The oval shape zircon is devoid of inclusions, whereas, the euhedral zircons comprise subhedral inclusions. Distinct fractures in zircons are caused due to transportation by waves and currents. The garnet and zircon grains are well-rounded, implying the source for these sediments are an admixture of medium to high-grade metamorphic rocks, reworked sediments, charnockite, and granite gneisses. Keywords : Sediment Texture, Provenance, Heavy Minerals, Kanyakumari,South West Coast of India

한우는 언제 비쌀까?

The objective of the study is to examine the structural characteristics of Hanwoo price volatility. GJR-GARCH is employed to analyze the asymmetric price volatility in response to good and bad news. And two competing hypotheses, mixed distribution hypotheses (MDH) and sequential information arrival hypothesis (SIAH), are utilized to test how information dissemination affects price volatility through trading volume. The results reveal that calendar effects exist in both the volatility and the rate of change in Hanwoo price. In particular, after the implementation of a five-day week, a decreasing rate of price is higher on Monday than on any other days of the week and Tuesday shows the highest price increasing rate. And also unlike the financial market, Hanwoo price exhibits a reverse leverage phenomenon in which price volatility decreases when bad news occurs. This asymmetry rapidly reduces the effect of short-term shock, but the shock persists for a long time.

A Simulation Study on Robustness of One Sample Inferential Statistics in Mixture Distribution

Mixture distribution refers to the combination of more than one probability distribution. Meanwhile, non-normality of data set may be inevitable and the cause may be as a result of mixed distributions thereby renders parametric tests ineffective. Montecarlo experiment was performed 5000 times under twelve sample sizes where data were generated from Gaussian and Cauchy distributions using R-statistical packages. At three commonly used alpha levels (0.1, 0.05 and 0.01), the robustness of the test statistics (Rank transformation t-test, Wilcoxon sign test (Distribution and Asymptotic), Signed rank test (Distribution and Asymptotic) and Trimmed t-test) were examined. When the type I error rate of a statistic approximately equal to the true error rate then the statistic is considered robust. At 0.1 and 0.05, Rank transformation t-test, Wilcoxon sign test (distribution) and Trimmed t-test in this order are robust. Meanwhile, at 0.01 Rank transformation andWilcoxon sign test (distribution) were identified to be robust. Also, further counts at all levels of significance revealed that the Rank transformation test is robust and thereby recommended when data comes from a mixed distribution. Hence, this study has been able to identify test statistics that are robust when data comes from a mixed distribution in one sample problem.

AdaNS: Adaptive negative sampling for unsupervised graph representation learning

Recently, unsupervised graph representation learning has attracted considerable attention through effectively encoding graph-structured data without semantic annotations. To accelerate its training, noise contrastive estimation (NCE) samples uniformly negative examples to fit an unnormalized graph model. However, this uniform sampling strategy may easily lead to slow convergence, even the vanishing gradient problem. In this paper, we theoretically show that sampling those hard negatives close to the current anchor can relieve the above difficulties. With this finding, we then propose an Adaptive Negative Sampling strategy, namely AdaNS, which efficiently samples the hard negatives from the mixing distribution regarding the dimensional elements of the current node representation. Experiments show that our AdaNS sampling strategy applied on top of representative unsupervised models, e.g., DeepWalk, GraphSAGE, can outperform the existing negative sampling strategies in the tasks of node classification and visualization. This also further demonstrates that sampling those hard negatives can bring performance improvements for learning the node representations.

Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19

Research in highway safety continues to struggle to address two potentially important issues; the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evidence that riskier drivers likely made up a larger proportion of vehicle miles traveled during the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pandemic (and focusing on crashes where risky behaviors were observed), the empirical analysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.

Compilation of cutting load spectrum for CNC lathe based on DBN-BP prediction model

Load spectrum can effectively represent variations in statistical load, and it is the foundation data for the overall product’s reliability test, reliability design and fatigue life analysis. However, compiling a load spectrum of small sample conditions is difficult because of the complicated and variable load conditions of machine tools and kaleidoscopic cutting process. Additionally, extreme load extrapolating is susceptible to subjective influences, thereby resulting in some uncertainties. Therefore, this study proposes a cutting load spectrum compilation method for computerised numerical control (CNC) lathe based on deep belief network-back propagation (DBN-BP) load prediction model, combining multi-source load condition information with deep learning. Firstly, single-layer BP and multi-layer restricted Boltzmann machine (RBM) were used to create a cutting load prediction model. Mixed distribution of mean and range of dynamic cutting load and parameters in copula function are predicted by using cutting process parameters. The number of hidden layers and single-layer nodes in the prediction model were determined using mean average (MA) precision and training time. Secondly, dynamic cutting load conditions were divided into the prediction model’s training, test and prediction sets; and the model’s accuracy was verified using comparative tests. Lastly, the two-dimensional mean-range matrix of load in each typical working condition was compiled using the prediction model, and the cutting load spectrum and two-dimensional CNC lathe programme loading spectrum were constructed. The proposed method saves time and personnel for numerous cutting tests when working with small samples and also assures load prediction accuracy.

Endogenous Driving Forces in Ecology-Production-Living Space Changes at Micro-Scale: A Mountain Town Example in Inland China

Studying land use transition and restructuring has value for promoting sustainable regional development, especially in China’s vast rural areas, which are undergoing rapid changes. Current research tends to focus on the macro level, and analyses of driving forces are mostly based on the correlation analysis of influencing factors. However, in the case of villages and towns, which are at the micro level, it is important to know who promotes land use transition and how to promote it under the influence of the macro environment. This study, therefore, focused on the endogenous driving force behind land use transition and its characteristics at the micro level of villages and towns. On the basis of our theoretical framework, an empirical study was carried out on the transformation and restructuring of ecology–production–living (EPL) spaces in the town of Zhulin in Central China over the past 30 years. We found the following: (1) The overall distribution of EPL spaces in Zhulin shifted from mixed distribution to relatively concentrated distribution, and the spatial transfer of EPL spaces showed fluctuations in the expansion and contraction of different types of spaces. (2) Land use transition was more active in spatial interface areas than in noninterface areas, where the interconversion of ecological space and agricultural production space was concentrated at the terrain interface. In addition, transformation processes related to living space and non-agricultural production space were concentrated at the urban–rural interface. (3) Macro-level social and economic changes were the root cause of land use transformation, and the autonomous spatial governance capability of villagers’ self-organization institutions was key to regulating land use transformation. The spatial interface was a sensitive area for land use transformation in a natural state. An endogenous driving mode of active response to land use transformation based on rural autonomous spatial governance capability and spatial interface sensitivity is proposed. How to improve the rural governance capacity of key local actors in different regions and at different levels is an aspect worthy of further consideration.

Description of the Joint Probability of Significant Wave Height and Mean Wave Period

The bivariate probability distribution of significant wave heights and mean wave periods has an indispensable guiding role in the implementation of offshore engineering, which has attracted great attention. This work gives a new bivariate method to describe the bivariate distribution of significant wave height and mean wave period at the NanJi, BeiShuang, and XiaoMaiDao stations from 2018 to 2020. A mixed lognormal distribution is used for univariate probability analysis of wave data, and the method of connecting two mixed lognormal distributions with copula functions is applied to construct bivariate distribution. The results show that compared with Weibull and lognormal distributions, the mixed lognormal distribution shows good performance in fitting marginal distributions. In the bivariate probability analysis, the conditional model overestimates the probability of lower wave heights, and the bivariate function model has a poor fitting effect in the region with larger periods. In contrast, the copula model based on mixed lognormal distribution is more suited to describe the joint distribution of significant wave height and mean wave period.

Tectonic tremors immediately after the 2011 Tohoku-Oki earthquake detected by near-trench seafloor seismic observations

Temporal seismic observations from pop-up type ocean-bottom seismometers were used to detect tectonic tremors immediately following the 2011 Tohoku-Oki earthquake in the northern periphery of the aftershock area. Near-field observations clearly distinguished tremors from regular earthquakes based on their spectral shape in the frequency band of 1–4 Hz. In addition to tremors accompanied by very low-frequency earthquakes (VLFEs), we detected 130 tremors without known VLFE activity during April–October 2011. The newly detected tremors were in the vicinity of a sequence of small repeating earthquakes, indicating a mixed distribution of tremors and regular interplate earthquakes in the region. Tremor activity was high immediately after the deployment of seismometers and gradually decreased. In addition, the tremor activity fluctuated with two activations with an interval of approximately 90 days, similar to the intervals between tremor bursts after 2016. The results of the study suggest that the observed tremors occurred under the influence of aseismic slip caused by the decaying afterslip of the preceding Tohoku-Oki and Mw 7.4 interplate earthquakes and episodic accelerations with a quasi-periodicity unique to the area.

Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping

High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample “voting mechanism” based on multisource data and random forests to achieve fine mapping of urban land use. First, Zhengzhou City was selected as the study area. Based on full integration of multisource features, random forests were used to perform the preliminary classification of multiple samples. Finally, the preliminary classification results were filtered according to the “voting mechanism” to achieve high-precision urban land-use classification mapping. The results showed that the overall classification accuracy of Level I features increased by 5.66% and 14.32% and that the overall classification accuracy of Level II features increased by 9.02% and 12.46%, respectively, compared with the classification results of other strategies. Therefore, this method can significantly reduce the influence of mixed distribution of land types and improve the accuracy of urban land-use classification at a fine scale.

On the Salinity Distribution Induced Mixing at the Mouth of the Estuary

Salinity is an essential parameter in estuaries because it determines the mixing process and distribution of biota in the estuary ecosystem. In the paper, we investigate the dynamics of salinity distribution in the Dumai estuary. Salinity and tidal data observation were carried out during the transitional season around the mouth of the estuary. We get the salinity time series data in a tidal cycle. We observed that the Formzhal number is about 0.239, indicating that the tidal type in Dumai estuary waters is mixed directional, tending to double a day, with the highest and lowest tides in the Dumai estuary being 1.26 and 0.47 m, respectively. The salinity variability due to tides was studied by harmonic analysis, while the spatiotemporal distribution was studied using an analytical solution developed by previous researchers. The results show the presence of time lag between tidal elevation and salinity variation. Low tide and salinity, which indicate the effect of river discharge, are significant. This study further indicated that high salinity occurs during strong mixing conditions. It implied that the high salinity from the offshore entrance to shallower waters allowed for mixing processes. This paper ends with a comprehensive discussion of the vertical distribution of salinity at the mouth of the estuary.

Prediction and confidence intervals of willingness-to-pay for mixed logit models

Heterogeneity in agents’ preferences is generally analysed through mixed logit models, which assume taste parameters are distributed in the population according to a certain mixing distribution. As a result, if the utility function is linear in attributes, the willingness to pay is the ratio of two random parameters and is itself random. This paper proposes a technique built on the Delta method, partly analytical and partly based on simulations, to obtain the sampling distribution of the willingness to pay, accounting for both heterogeneity and sampling error. The paper contributes to the literature by: (i) redressing some imprecisions in Bliemer and Rose (2013) that produce biased results; (ii) proposing a faster estimation process, compared to the Krinsky and Robb (1986, 1990) method that, relying on simulation only, proves computationally more demanding; (iii) comparing the performance of different methods using both synthetic and real data sets. The paper shows, via a Monte Carlo study, that the method we develop and the Krinsky and Robb one produce similar results, while outperforming that proposed by Bliemer and Rose.

ROCOF of higher order for semi-Markov processes

In this paper, we study the rate of occurrence of failures (ROCOF) of higher order for continuous time semi-Markov processes (SMP). This indicator gives information on whether there are a lot of failures or only a few within a time interval. It also considers the relative positioning of tuples of failures in time. Here, we consider SMP with a mixed probability distribution for the initial law of the system taking into account the possible random starting from any state with any duration. Furthermore, under suitable assumptions on the transition rates, we determine an explicit formula for the ROCOF of higher order and we recover as particular cases previous results obtained in the literature. A numerical example demonstrates the possibility of using this index in real applications. A possible application in the field of mathematical finance is also discussed.

Abnormal Alpha-1 Antitrypsin Levels and Other Risk Factors Associated with Lung Function Impairment at 6 and 12 Months after Hospitalization Due to COVID-19: A Cohort Study.

Respiratory function deficits are common sequelae for COVID-19. In this study, we aimed to identify the medical conditions that may influence lung function impairment at 12 months after SARS-CoV2 infection and to analyze the role of alpha-1 antytripsin (AAT) deficiciency (AATD). A cohort study was conducted on hospitalized COVID-19 pneumonia patients in Granada (Spain) during the first infection wave who were referred to a post-COVID-19 hospital clinic. The patients were monitored with three follow-up visits from May 2020 to May 2021. Previous medical history, hospital admission data, baseline parameters and physical examination data were collected at the first visit. Pulmonary function tests were performed at 6 and 12 months together with the determination of AAT level and AATD genotype. After 12 months, 49 out of 157 patients (31.2%) continued to have lung function impairment. A multivariate analysis showed a statistically significant association of lung function impairment with: higher Charlson index; pneumonia with a central and/or mixed distribution; anemia on admission; time in intensive care; need for corticosteroid boluses; abnormal respiratory sounds at 6 months; elevated lactate dehydrogenase at 12 months; abnormal AAT; and MZ genotype. Our results suggest that these medical conditions predispose COVID-19 patients to develop long-term lung function sequelae.

Robust mixture regression modeling based on the normal mean-variance mixture distributions

Mixture regression models (MRMs) are widely used to capture the heterogeneity of relationships between the response variable and one or more predictors coming from several non-homogeneous groups. Since the conventional MRMs are quite sensitive to departures from normality caused by extra skewness and possible heavy tails, various extensions built on more flexible distributions have been put forward in the last decade. The class of normal mean-variance mixture (NMVM) distributions that arise from scaling both the mean and variance of a normal random variable with a common mixing distribution encompasses many prominent (symmetric or asymmetrical) distributions as special cases. A unified approach to robustifying MRMs is proposed by considering the class of NMVM distributions for component errors. An expectation conditional maximization either (ECME) algorithm, which incorporates membership indicators and the latent scaling variables as the missing data, is developed for carrying out maximum likelihood (ML) estimation of model parameters. Four simulation studies are conducted to examine the finite-sample property of ML estimators and the robustness of the proposed model against outliers for contaminated and noisy data. The usefulness and superiority of our methodology are demonstrated through applications to two real datasets.