Series Distribution Research Articles

Chemical characterization of sub-micron aerosols over the East Sea (Sea of Japan)

Studies of the land-sea-air interactions of aerosol are scarce considering their significant role in global environmental changes. Here, we investigated potential sources of sub-micron aerosols over the East Sea (Sea of Japan), which is strongly influenced by continental and marine aerosols. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was used to measure the size-resolved chemical compositions of sub-micron aerosols during the period March 10–15, 2020. Concentrations of all AMS species, black carbon (BC), PM10 (particulate matter <10 μm) and PM2.5 (particulate matter <2.5 μm) were higher when cruising in industrialized coastal areas compared to the offshore region. A positive matrix factorization (PMF) model identified five distinct sources, i.e., hydrocarbon-like organic aerosol, semi-volatile and low-volatile oxygenated aerosols, methanesulfonic acid (MSA), and dimethyl sulfide (DMS; C2H6S) oxidation, which accounted for 5.98 %, 21.6 %, 28.3 %, 34.5 %, and 9.64 % of the total organic mass, respectively. The spatiotemporal variation of MSA, as well as the MSA to sulfate ratio (MSA:SO42−) over the East Sea, was determined for the first time. The mass concentrations of MSA displayed a similar time series distribution pattern to those of DMS. The time series distributions of the MSA:SO42− ratio displayed distinct differences, with higher ratios downwind of the ocean (0.216 ± 0.083 μg·m−3) than land (0.089 ± 0.030 μg·m−3). The growth of ultrafine particles (10–35 nm) was observed during two of the elevated MSA:SO42− ratio events, suggesting a potential role of MSA in new particle formation.

Biodegradation levels of oils from the Chepaizi Uplift, Junggar Basin (NW China) evaluated by a full-range biodegradation index as constrained by adamantane, diamantane homologs and carboxylic acids

Biodegradation usually masks or even radically alters the original characteristics of molecular biomarkers, restricting the oil sources identification and the exploration of ultra-heavy oils (also known as oil sands, tar sands, asphalt or bitumen). The oils from the Lower Neogene Shawan Formation (N1s), Lower Cretaceous Tugulu Group (K1tg), and Upper Carboniferous Xibeikulasi Formation (C2x) in the Chepaizi Uplift, Junggar Basin (NW China) were biodegraded to different extent, consequently, many commonly used biomarker parameters became less powerful for the evaluation of the extent of biodegradation. To address this problem, the resistance of many adamantane and diamantane homologs and carboxylic acids to biodegradation was analyzed, and those with high bioresistance were selected to yield a more precise and extensive characterization of biodegradation from light to extreme. In contrast, oils from the eastern Chepaizi Uplift were generated from Permian sources, and have been subjected to heavy to extreme levels of biodegradation. In this study, fifteen oil samples obtained from the Chepaizi Uplift were analyzed by a set of mass spectrometric techniques. The results were applied to investigate the variations of commonly used biomarkers in the saturates and aromatics, to qualitatively rank oil biodegradation levels based on the PM scale and to provide a detailed assessment of the variation in carboxylic acids and adamantane and diamantane homologs in heavy oils from varying degrees of biodegradation. The results demonstrate that adamantane and diamantane homologs with various methyl groups, and carboxylic acids with various double bond equivalents (DBEs), exhibit an intact homolog series distribution and certain hypermethylation trend as biodegradation levels progress from light to extreme, which confirmed their reliability in refining the evaluation scale and characterizing the extent of biodegradation. In addition, the full-range biodegradation index (FRBI) presented here consists of the ratios of adamantane/diamantane homologs with different numbers of methyl substituents and the ratios of carboxylic acids with different DBE values, instead of the five Manco scores of 0–4 used previously, which can effectively improve the ranking precision of biodegradation of oils between subtle alteration changes, and universally unraveling to the full range of biodegradation, beginning from light to extreme.

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study.

BackgroundSince the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.ObjectiveWe aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.MethodsWe analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.ResultsOur prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).ConclusionsBased on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

Multi-scale regression based on detrending moving average and its application to seismic data

We investigate the statistical properties of multi-scale regression model based on detrending moving average (DMA). The performance of the multi-scale regression estimator based on DMA is evaluated by varying the length, distribution and structure for different position parameters. Using different position parameters for the detrending windows in simulation, we find that the variance of the estimated regression coefficients for position parameter [Formula: see text] is the smallest. By changing series length, distribution and structure, the estimated regression coefficients are stably near the theoretical values. The method is applied to analyze the dependence of inter-earthquakes time (IET) on inter-earthquakes distances (IED) between consecutive earthquakes in the California region. Results suggest that the cross-correlation between the IET and IED series is statistically significant. Scale-dependent statistic of estimated DMA multi-scale regression coefficient demonstrates significant dependence between IET and IED series.

GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task

GoodsForecast’s predictions obtained a second place in the M5 Uncertainty Competition. This result was achieved using an empirical method that estimates the probability distribution of time series by combining the forecasts of three methods. The first method considers past observations, estimating their histogram, the second method is used to estimate trends through a singular spectrum analysis, and the last one captures external factors with the help of a machine learning model. Forecasts made by these methods at different levels of aggregation were judgmentally combined to account for the particularities of the M5 data set. Quantiles, estimated by the combination of algorithms, were modified with rounding heuristics that resulted in the improvement of the competition target loss metric. This paper is devoted to a detailed description of the developed algorithmic scheme for evaluating the probability distribution, the experiments, and some insights gained by the authors during the problem- solving process.

MODELING OF TEMPORAL AND VERTICAL CHANGES IN 137Cs CONCENTRATION IN LAKE ONUMA.

According to the observed data of the activity concentration of $^{137}$Cs in Lake Onuma on Mount. Akagi, located in Gunma Prefecture, the decrease of the activity concentration slowed down since 2 years after the accident of Fukushima Daiichi Nuclear Power Plant. In the present study, we applied the time fractional diffusion equation to reproduce long-term changes in the time series of the activity concentration. In addition, we also reproduced the vertical distribution of the activity concentration with the parameter values, estimated by the time series data. We found that the model fit the observed data well for both time series and vertical distribution.

On a generalization of class of negative binomial distributions

Abstract A class of one-dimensional discrete power series distributions containing negative binomial distributions is considered. Properties of distributions of this class are investigated. Limit theorems generalizing similar theorems for the negative binomial distributions are proved. The proofs are based both on elementary asymptotic methods and on a modification of saddle-point method.

The Type II Topp-Leone-G Power Series Distribution with Applications on Bladder Cancer

Statistical distributions are important in modeling the real life of an item and therefore proper distributions that provide useful information for sound conclusions and decisions are needed. For that reason, the demand for developing new generalized distributions have become appropriate for data that have both monotonic and non-monotonic hazard rate functions. In this paper, we develop a new distribution called the Type II Topp-Leone-G Power Series (TIITLGPS) distribution by compounding the Type II Topp-Leone-G (TIITLG) distribution with the power series distribution. Statistical properties of the TIITLGPS distribution are obtained. A variety of shapes for the densities and hazard rate are presented of the considered special case. A simulation study to examine the efficiency of the maximum likelihood estimates is also conducted. Finally, the bladder cancer data example is analyzed for illustrative purposes, it is displayed that the introduced distribution provides better fit when compared to other non-nested distributions considered in this work.

Low Sidelobe Series-Fed Patch Planar Array with AMC Structure to Suppress Parasitic Radiation

For automobile radar systems, the antenna array requires a low sidelobe level (SLL) to reduce interference. A low-SLL and low-cost planar antenna array are proposed in this article for millimeter-wave automotive radar applications. The proposed array consists of six linear series-fed patch arrays, a series distribution network using a grounded co-planar waveguide (GCPW), and a bed of nails. First, a hybrid HFSS-MATLAB optimization platform is set up to easily obtain good impedance matching and low SLL of the linear series-fed patch array. Then, a six-way GCPW power divider is designed to combine the optimized linear sub-array to achieve a planar array. However, since CCPW is a semi-open structure, like a microstrip line, the parasitic radiation generated by the GCPW feeding network will lead to the deterioration of the SLL. To solve this problem, a bed of nails—as an artificial magnetic conductor (AMC)—is designed and placed above the feeding networking to create an electromagnetic stopband in the working band. Its working mechanism has been explained in detail. The feeding network cannot effectively radiate electromagnetic waves into free space. Thus, the parasitic radiation can be suppressed. A low-SLL planar array prototype working at 79 GHz is designed, manufactured, and measured. The measured results confirm that the proposed low-SLL planar array has a −10 dB impedance bandwidth of 3 GHz from 77 to 80 GHz and a maximum peak gain of 21 dBi. The measured SLL is −24 dB and −23 dB in the E-plane and H-plane at 79 GHz, respectively. The proposed low SLL array can be used for adaptive cruise control (ACC) system applications.

The exponentiated odd exponential half logistic-G power series class of distributions with applications

We propose a new generalized class of distributions called the Exponentiated odd Exponential Half Logistic-G Power Series (EOEHL-GPS) class of distributions. This model is obtained by compounding the Exponentiated odd Exponential Half Logistic-G distribution with the power series distribution. Statistical properties of the EOEHL-GPS class of distributions are discussed. Maximum likelihood estimates for the proposed model were obtained. A simulation study was carried out to assess the performance of the maximum likelihood estimates. Finally, real data examples are used to illustrate the usefulness of the new model compared to other models.

Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

An accurate wind speed prediction system is of great importance prerequisite for realizing wind power grid integration and ensuring the safety of the power system. Quantifying wind speed fluctuations can better provide valuable information for power dispatching. Therefore, this paper proposes a deterministic wind speed prediction system and an interval prediction method based on the Lorentzian disturbance sequence. For deterministic forecasting, a variational modal decomposition algorithm is first used to reduce noise. The preprocessed data are then predicted by a long and short-term neural network, but there is a significant one-step lag in the results. In response to such limitation, a wind speed slope is introduced to revise the preliminary prediction results, and the final deterministic wind speed prediction model is obtained. For interval prediction, on the basis of deterministic prediction, the Lorenz disturbance theory is introduced to describe the dynamic atmospheric system. B-spline interpolation is used to fit the distribution of Lorenz disturbance theory series to obtain interval prediction results. The experimental results show that the model proposed in this paper can achieve higher forecasting accuracy than the benchmark model, and the interval prediction based on the Lorentzian disturbance sequence can achieve a higher ground truth coverage rate when the average diameter is small through B-spline interpolation fitting.

Efficient and robust time series prediction model based on REMD-MMLP with temporal-window

Time-series prediction plays a critical role in realizing industrial automation. However, due to the non-linear, non-stationary and skew distribution of time series, many existing time series forecasting models have poor generalization performance and forecast accuracy. Although the comprehensive performance of deep learning is better, it takes too much time. To this end, we proposes a hybrid model based on REMD-MMLP (Recursive Empirical Mode Decomposition-Storage Multilayer Perceptron) and Temporal-Window. In REMD-MMLP, we first define a new time window mechanism based on DTW (Dynamic time warping) to search for the best training set of the model. Then, we propose a new REMD algorithm, which decomposes the input data into multiple IMF sub-sequences, and uses these IMF sequence time series as the hidden features of the data. Finally, we design a new storage mechanism in MLP to fully mine the historical information in the time series data to realize the storage and inheritance of the historical information to improve the prediction accuracy of the model. Our comprehensive experiments are conducted using seven different real-world datasets and contrasting models. The final results show that the REMD-MMLP model can be widely used in different practical application scenarios, with strong generality and higher prediction accuracy than state-of-the-art.

The Beta Exponential Power Series Distribution

In this paper, we investigate to propose a new statistical distribution based on power series. We introduce a new family of distributions which are constructed based on a latent complementary risk problem and are obtained by compounding Beta Exponential (BE) and Power Series distributions. The new distribution contains, as special sub-models, several important distributions which are discussed in the literature, such as Beta Exponential Poisson (BEP) distribution, Beta Exponential Geometric (BEG) distribution, Beta Exponential Logarithmic (BEL) distribution, Beta Exponential Binomial (BEB) distribution as special cases. The hazard function of the BEPS distributions can be increasing, decreasing or bathtub shaped among others. The comprehensive mathematical properties of the new distribution is provided such as closed-form expressions for the density, cumulative distribution, survival function, failure rate function, the r-th raw moment, maximum likelihood estimation and also the moments of order statistics. The proposed type of distributions is used to modeling simulated and real datasets.

On some limit properties for the power series distribution

Abstract In the present paper, we consider random variables with the power series distribution which is often used in the study of the generalized allocation scheme. We establish some asymptotic properties which include law of large numbers, moderate deviation principle, almost sure central limit theorem and the rate of convergence in the local limit theorem. These results supplements results obtained by A. V. Kolchin.

Revealing fundamentals of charge extraction in photovoltaic devices through potentiostatic photoluminescence imaging

Revealing fundamentals of charge extraction in photovoltaic devices through potentiostatic photoluminescence imaging

Approximations related to the sums of m-dependent random variables

In this paper, we mainly focus on the sums of non-negative integer-valued 1-dependent random variables and its approximation to the power series distribution. We first discuss some relevant results for power series distribution such as the Stein operator, uniform and non-uniform bounds on the solution of the Stein equation. Using Stein’s method, we obtain error bounds for the approximation problem considered. The obtained results can also be applied to the sums of m-dependent random variables via appropriate rearrangements of random variables. As special cases, we discuss two applications, namely, 2-runs and (k1,k2)-runs, and compare our bounds with existing bounds.

Improving the accuracy of wind speed statistical analysis and wind energy utilization in the Ningxia Autonomous Region, China

Accurate wind speed characterization is important for wind farms to efficiently utilize wind energy which plays a significant role in reducing the dependence on fossil fuels and promoting global carbon peak and carbon neutrality. Since hourly wind speed density exhibit a variety of shapes, their characteristic features change substantially within a day, influenced by meteorological factors, therefore, the hourly wind speed analysis is considered to improve the accuracy of wind speed characterization. In this paper, 4 commonly used wind speed probability distributions (Weibull, Rayleigh, Gamma, Lognormal distribution) are applied for hourly wind speed analysis, and the best fitting distribution of each hourly wind speed series is identified by the goodness-of-fit test. Then the hourly wind power distribution of wind farms is determined by the corresponding optimal hourly wind speed distribution and the relation between wind power and wind speed. Finally, the hourly expected wind power calculated by the hourly wind power distribution is used to guide the hourly operation of the wind farm. The measured wind speed and wind power data of Dashuikeng wind farm in the Ningxia Autonomous Region of China is taken as an example to illustrate the results, 1) the hourly wind speed analysis performs a more accurate characterization of wind speed than annual wind speed series analysis. 2) The range of the average value of hourly wind speed series is 5.7 m/s–6.9 m/s, and the average wind speed, overall, during the daytime is smaller than that during the nighttime. 3) Gamma distribution shows a better performance than the other three distributions in characterizing the hourly wind speed series. 4) Wind energy resources are not fully consumed during the daytime, especially the period of 11:00–18:00. The proposed method improves the characterization of wind speed and guidance of the hourly operation for the wind farm, which has an important significance in raising the efficiency of wind energy utilization.

On bivariate alpha logarithmic series distribution and its properties

Here we propose a bivariate version of the alpha logarithmic series distribution (ALSD) of Kumar and Riyaz (South African Statist. J., 2014) and shown that its marginals are also ALSD. We study some of its important properties by deriving expressions for its probability mass function, factorial moments, conditional probability generating function, covariance etc. The parameters of the model are estimated by the method of maximum likelihood and the distribution has been fitted to certain real life data sets.

A new species abundance distribution model including the hydrological niche differentiation in water-limited ecosystems

The formation and maintenance mechanism of biodiversity is a key research topic in community ecology. Establishing a model that can be used to explain and predict the changes of community biodiversity is of great significance in facilitating the analysis and protection of biodiversity. How hydrological niche differentiation determines species abundance distribution requires further exploration. In this paper, by analyzing the characteristics of evolutionary stability of plant communities, the evolutionary stability theory of community water resource allocation is studied in water-limited environments. The water consumption of an individual biomass growth is used to quantify the hydrological niche of the species. The arithmetic mean and harmonic mean ratio of the hydrological niche of a species in the community as a new diversity index is used to measure community hydrological niche differentiation. A new hydrological niche plant diversity model is established, and the species abundance curve and species area curve, including the community hydrological niche differentiation factor, are obtained. It was discovered that the distribution model of species abundance was close to the Fisher log series distribution, which unifies and expands the classical diversity theories. The quantitative relationship between the hydrological niche and plant diversity is given. It is proposed that the plant diversity of a community is determined by regionally available water resources and changes in the species abundance that are filtered by the local biological and abiotic environment and driven by the full utilization of water resources.

Springs regarded as hydraulic features and interpreted in the context of basin-scale groundwater flow

Springs are sources of freshwater supply. Furthermore, they can also deliver valuable insight into the hydrogeologic processes of a mountainous region, a natural conservation area or a remote study site with no wells. In order to assess the appearance, peculiarities, quality, stability, longevity and resilience of springs and related ecosystems, they need to be regarded in the context of basin-scale groundwater flow systems. The application of spring data evaluation on a basin scale was demonstrated via the carbonate system of Transdanubian Mts., Hungary. The readily measurable physical parameters of springs, the elevation of spring orifice, temperature and volumetric discharge rate provided reasonable classification and characterisation of springs and the related groundwater flow systems. Applying these parameters seemed prospective in a basin-scale understanding of flow systems in data-scarce regions, as monitoring discharge rate and water temperature are cost-effective, requiring no specific tools and analysing procedures. The combined cluster and discriminant analysis (CCDA) can handle uneven data distribution, unequal length and spacing of time series, data gaps, and consider the time-dependent variability of parameters. The optimal number of groups can be determined based on frequently sampled springs (or other entities). The less monitored springs (or other entities) can be classified using a similarity-based approach and linear discriminant analysis (LDA). Diagnosing the relation of springs to groundwater flow systems can advance sustainable water resources management, considering the ecological water needs maintaining various ecosystem services, therefore enhancing the resilience of springs and groundwater-dependent ecosystems.