Cumulative Distribution Function Research Articles

Agricultural Contribution of Chloride to a Saturated Riparian Buffer System: A Case Study in Central Illinois

ABSTRACT Increases in chloride concentrations in surface water and groundwater from anthropogenic sources is a growing concern throughout the world. In the midwestern United States, the agriculture contribution of chloride may elevate concentrations in surface and groundwater systems. A saturated riparian buffer (SRB), installed to reduce nutrient input into an adjacent stream, and the groundwater water upgradient were investigated to identify sources of chloride and to quantify the agricultural contribution. During a 7-year period, over 2,200 water samples collected from 35 wells, a diversion box, and a stream were analyzed for major anions. Additionally, the waters from four quarterly (seasonal) samplings (n = 155) during the seventh year were analyzed for cations. A cumulative probability curve identified three populations of chloride: (1) upgradient shallow groundwater; (2) diversion box (waters drained from an agricultural field), downgradient shallow groundwater (representing the SRB), diamicton groundwater (waters collected deeper from within the unweathered parent material); and (3) stream water. A background chloride concentration was established at 3.9 mg/L. Elevated concentrations above the background level coupled with principal component analysis suggest agricultural contribution from the diverted tile-drainage waters was a source of chloride. In winter and spring, chloride concentrations were highest in the diversion box, while up-gradient wells were the lowest. Waters downgradient of the diversion tiles had chloride concentrations lower than the tile but higher than the upgradient waters due to mixing of the diverted tile water and the upgradient groundwater. For the specific site, agriculture contributes chloride, but at concentrations below the chronic threshold of 230 mg/L.

Efficient prediction uncertainty quantification in dam behavior monitoring with attention-based sequence-to-sequence learning

Displacement is an intuitive monitoring indicator of dam structural behavior. Conventional deterministic modeling methods frequently disregard the inherent uncertainty associated with monitoring data. This study aims to quantify such uncertainties within dam displacement predictions by augmenting a sequence-to-sequence structure with the novel recurrent unit, termed as tiny gated unit (TGU), attention mechanism, as well as quantile regression, resulting in the Att-S2STQ model. Specifically, TGU adopts only one gate to identify nonlinearity in data sequences, while Bahdanau attention mechanism dynamically assigns weights to different parts of the input sequence. The inclusion of quantile regression allows Att-S2STQ to produce prediction intervals (PIs), from which the probability density functions (PDFs) and cumulative distribution functions (CDFs) are further derived via kernel density estimation. PIs, PDFs, and CDFs jointly serve to reveal the prediction uncertainty. The effectiveness of the model is illustrated through comparative experiments using real-world dam monitoring datasets. Results indicate that the proposed model outperforms traditional methods in point, interval, and probability predictions, while also having a simpler structure and faster training. The superiority of both accuracy and efficiency makes it a valuable tool for dam management, aiding in data-driven decision-making and enhancing operational safety.

Analysis of the Gaussian assumption of single sound source measured and processed through sound level meter

Machine learning (ML) techniques are constantly growing in acoustics. Such methods exploit large databases to train algorithms and earn complex feature correlations. The primary goal is to obtain inferences and predictions. Thus, ML often exploits statistics, and understanding the data distribution becomes fundamental when analyzing the available data. However, it is well-known that only some common methods can be used with large databases, e.g., normality tests are discouraged. The present work aims to explore the Gaussian assumption underlying a validated clustering technique used in long-term monitoring of sound level meters. The Gaussianity of single sound sources is analyzed through normal tests, normal probability plots, cumulative distribution functions, and high-order moments. The analysis involves a real-world measurement carried out through a sound level meter and speech recordings obtained from a common database used in machine learning. Results show that a single sound source can be deemed Gaussian in sound level meter long-term measurements, leading to important insights concerning the choice of algorithms.

Kurtosis Control of Amplitude-Modulated non-Gaussian Signals for Fatigue Test

The amplitude modulation method was used to generate non-Gaussian signals that acted as excitation for fatigue tests. The fatigue life of structures under non-Gaussian excitation has been proven to be closely related to the features of the amplitude modulation signal (AMS) and kurtosis of the structural response. In this study, the modelling of the AMS by Beta and Weibull distributions and the resulting kurtosis range problem is first reviewed. To solve this problem, a new method for creating an AMS based on a linear combination of Beta and Weibull distributions is proposed. To ensure that the high kurtosis of the amplitude-modulated non-Gaussian signal is correctly transferred to the structural response, the method is further developed to fulfil the specifications for the fatigue damage spectrum (FDS) by controlling the spectral content of the AMS. Herein, a Gaussian AMS with a low-pass cutoff frequency is first generated and then converted to a Weibull or Beta AMS based on the cumulative distribution function (CDF) transformation. The proposed method is verified using simulated and field-measured data. The results show that the full range of specified kurtosis is achieved with the new AMS modelling method. The high kurtosis of the non-Gaussian input signal can be transferred to the linear system response if the mean value of AMS during the period of the system impulse response is the same as AMS.

Multi-Sensor fusion and semantic map-based particle filtering for robust indoor localization

To enhance positioning accuracy and ensure long-term stability in large-scale indoor environments, this paper presents a robust particle filtering method that integrates IMU, magnetic field, Bluetooth, and semantic map data, implemented through a handheld portable device. In the particle prediction phase, based on peak candidates extracted in the time domain, the unintentional interference movement of the hand is removed through short-time Fourier transform in the frequency domain to reduce the noise of pedestrian dead reckoning. During the observation phase, we introduce a magnetic interference coefficient to model and quantify the degree of electromagnetic interference in different environments, thereby improving the accuracy of indoor magnetic headings and enhancing the alignment of particle swarms with real-world directional constraints. Furthermore, based on passable semantic information, the indoor map is segmented into distinct probability regions, allowing the particle filter to adaptively adjust the weight of the particle swarm when moving between regions, thus improving the robustness of the overall fusion system by preventing particles from entering infeasible areas. Experiments and validations were carried out in a large shopping mall and parking lot with multiple devices. Results demonstrate that the proposed method improves the average positioning accuracy by approximately 25% compared to the Bluetooth-only positioning system, with a 15% increase in cumulative distribution probability within 3 m.

Non‐Stationary Flash Drought Analysis and Its Teleconnection With Low‐Frequency Climatic Oscillations

ABSTRACTFlash droughts, characterised by their rapid onset and significant impacts on local communities and agriculture, pose challenges for monitoring and mitigation efforts due to their unpredictable nature. Therefore, this study aims to investigate the occurrence, characteristics and influencing factors of flash droughts in the Ganga River Basin (GRB) for the period 1981–2020. Flash droughts are identified using the pentad averaged root zone soil moisture (PRZSM). The Mann‐Kendall trend test is used to determine the spatial and temporal pattern of flash drought characteristics. Furthermore, a multivariate flash drought index (MFDI) is developed to account for the combined effects of flash drought characteristics. Finally, wavelet coherence analysis evaluates the relationship between climatic oscillations and MFDI at the sub‐basin scale. Utilising a revised flash drought identification approach incorporating non‐stationary cumulative distribution functions (CDFs), the study identifies flash droughts in the GRB, particularly emphasising higher occurrences in the Chambal and Upper Yamuna Sub‐basins. Analysis of flash drought characteristics under stationary and non‐stationary conditions reveals increased frequency, severity and decline rates, highlighting the impact of evaporation and latent heat flux. Furthermore, the Upper Ganga Sub‐basin demonstrates coherence with the DMI at shorter time scales (1 to 4‐year time scales), while the Lower Ganga Sub‐basin displays a pronounced association with the NINO3.4 index (5.65‐year time scale), indicating the impact of climate oscillations on flash drought dynamics in these regions. These findings provide valuable insights for drought monitoring, prediction and management strategies in a changing climate, emphasising the need for integrated approaches to address the complex interplay between climate variability and flash drought occurrences in the GRB.

Wildfire exposures and in-hospital length of stay following lung cancer surgery.

311 Background: Wildfires pose substantial health and safety threats to patients recovering from lung cancer surgery. Without specific disaster preparedness guidelines, oncologists might resort to improvisational strategies, such as increasing the length of stay (LOS), a federal care quality measure with financial implication for hospitals, to better protect the health and safety of this medically vulnerable population. Methods: Individuals aged ≥18 years who received curative-intent lobectomy or pneumonectomy for stage I-III NSCLC between 2004 and 2021 were selected from the National Cancer Database. Exposure was defined as a Federal Emergency Management Agency (FEMA) Presidential Disaster Declaration in the county of the treatment facility between date of surgery and date of discharge from the hospital. Differences in the cumulative distribution function of LOS, defined as days between date of surgery and date of discharge from the hospital, were evaluated between exposed and propensity score matched unexposed patients, who were treated at the same facility but at a time when no disasters occurred, using the product-limit method and log-rank test. In sensitivity analysis, patients with discharge date equal to date of death (in-hospital mortality) were excluded, and the association was evaluated stratified by stage. Results: Patients exposed to a wildfire disaster declaration in the county of the treating facility had longer LOS (p<0.001) than unexposed patients (9.4days compared to 7.5 days, respectively) overall and for each of the stages (I-III) for which surgery is the recommended treatment modality. In sensitivity analysis, there were no in-hospital mortality difference between exposed and unexposed patients (10.8% and 10.5%, respectively, p=0.76). Excluding patients who died in the hospital did not change the main results. Conclusions: Patients whose facility is impacted by a wildfire disaster during recovery from lung cancer surgery have longer length of stay than similar patients treated at the same facility but at times when no disaster occurred. Future studies should evaluate whether extended hospital stay improves safety and quality of surgical care. Moreover, these findings should be considered for disaster preparedness guidelines tailored to vulnerable patient population.

Bivariate cubic normal distribution for non-Gaussian problems

Probabilistic models play critical role in various engineering fields. Numerous critical issues exist in probabilistic modeling, especially for non-Gaussian correlated random variables. However, conventional parameter-based bivariate distribution models are generally developed for specific types of random variables, limiting their flexibility and applicability. A flexible bivariate distribution model is proposed, in which the joint cumulative distribution function is derived with the probability equivalently expressed as the summation of three basic probabilities corresponding to simple functions. These three basic probabilities are calculated with the aid of univariate cubic normal distribution, and thus the proposed model is named as bivariate cubic normal (BCN) distribution. The proposed BCN distribution has been applied in modeling several common bivariate distributions and actual engineering datasets. Results show that the BCN model accurately models both typical theoretical distributions and the JCDF of practical datasets, offering a significant improvement over existing models. Furthermore, the proposed BCN distribution has been utilized in seismic reliability assessment and the calculation of the mean recurrence interval and hazard curve of hurricane wind speed and storm size. Results demonstrate that the BCN distribution excels in modeling and matching capabilities, proving its accuracy and effectiveness in civil engineering applications.

Edge Computing Method for False Data Injection Attack Detection in Electromechanical Transient Simulation Grid

Because edge computing is close to the terminal, it has significant advantages of low latency and high-security realtime control and has huge application prospects in the current popular smart grid. Because its edge side is close to the terminal, it can also effectively avoid the risk of information leakage when control instructions or communication data are transmitted to remote cloud center servers over a long distance. However, edge devices have a greater probability of encountering false data injection attacks (FDIAs) from illegal terminals because of their proximity to terminals. The transient electromechanical situation of the smart grid due to FDIAs is analyzed under edge computing. Due to the characteristics that the status values of grid nodes have temporal correlation before and after and spatial correlation between nodes, the Long Short-Term Memory (LSTM) for training time series data is selected to predict the status values of grid nodes in advance. The FDIA detection method is also proposed based on the LSTM network. By calculating the predicted value at each historical moment and the root means square error (RMSE) of the system state estimation at that moment, the detection threshold of the scheme is calculated through the cumulative distribution function of RMSE. Simulation experiments are conducted on the Institute of Electrical and Electronics Engineers (IEEE)-14 node standard system. The detection rate is as high as 99.71%, which verifies the effectiveness of the proposed FDIA detection scheme. This paper studies the security threat of FDIA to the stable power system operation and provides theoretical analysis and practical reference for other power grid security protection strategies.

A vine-copulas based multi-sensor fusion structural damage monitoring method and its application in dam engineering

As a civil engineer that is susceptible to the mechanism of slow and continuous deterioration, the dam must detect damage according to the static data of the arranged sensors, which is an important issue in structural health monitoring. Due to the characteristics of huge solid structures, damage with a slight degree and small range usually does not cause significant changes in the monitoring data of a single sensor. Therefore, it is difficult to effectively detect local damage by traditional monitoring methods that establish operating warning values for a single sensor. Currently, there have been some approaches to fuse sensor information that consider correlations between multiple sensors as well as the overall operational behavior of the dam. However, their objectives are focused on improving the accuracy of response variable prediction models without addressing the local damage identification aspect. In this study, we apply the Vine-Copula model for the first time in a multi-sensor information fusion dam damage detection method based on static monitoring data. In this way, it improves the sensitivity of local damage identification for huge solid structures. A prediction model is fitted with the measuring data of each sensor to obtain the residual series. Then the joint probability distribution of the multidimensional residuals is acquired by constructing the Vine-Copula model. Finally, the joint cumulative distribution function value is used as an alarm limit to determine whether the structure has an abnormal damage condition. The effectiveness and accuracy of the proposed method were demonstrated in both a numerical arch dam simulating local damage and an actual rockfill dam engineering with cracks at the crest.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Source distribution, ecological risks, and controlling factors of heavy metals in river sediments: Receptor model-based study in a transboundary river basin

In the context of transboundary rivers, which constitute intricate fluvial ecosystems, the persistent threat of heavy metals (HMs) contamination poses significant risks to ecosystem health. In this study, ecotoxicological hazards, governing factors, and the distribution of nine HMs (uranium (U), lead (Pb), cadmium (Cd), nickel (Ni), chromium (Cr), manganese (Mn), iron (Fe), zinc (Zn), and copper (Cu)), as well as sediment characteristics (sand, silt, clay, organic matter, and pH) are assessed within the sediment. The current investigation encompasses the analysis of twenty-seven sediment samples, utilizing inductively coupled plasma mass spectrometry, in the transboundary river basin of Bangladesh, specifically the Teesta River. Notably, the findings underscore the predominance of Cd as a contaminant, responsible for 51.85%, 81.84%, and 100% of the geo-accumulation index, contamination factor, and enrichment factor, respectively. The Teesta River emerges as moderately to highly polluted, with cumulative probabilities of 7.4%, 85.2%, and 7.4% denoting "medium", "high", and "priority" pollution levels, respectively. Regions in the upstream and downstream middle sections of the study area exhibit relatively higher pollution levels, particularly in proximity to Kaunia Upazila in the Rangpur district. Ecologically, the potential risk index indicates a low likelihood of ecological impacts at 77.8%, alongside a moderate risk observation of 22.2%. The current results attribute the distribution of these HMs to the pH and organic matter content within the sediment, serving as pivotal factors. To unravel the origins of the HMs, the positive matrix factorization (PMF) model successfully identifies four contributing factors, primarily from geogenic sources. Validation of the PMF model through Spearmen correlation and principal component analysis (PCA) reveals a consistent pattern, affirming its efficacy in this analysis. Within the region, HM sources are identified as originating from anthropogenic activities such as irrigation, industrial discharges, and domestic effluent, in addition to substantial inputs from geogenic sources. Recognizing the transboundary nature of metal pollution, the current study underscores the imperative for continuous and vigilant monitoring, coupled with the implementation of robust management practices. The interplay of both anthropogenic and geogenic factors necessitates a comprehensive approach to effectively and sustainably combat HM contamination.

The incidence of postoperative periprosthetic femoral fracture following total hip replacement: An analysis of UK National Joint Registry and Hospital Episodes statistics data.

Postoperative periprosthetic femoral fracture (POPFF) after total hip replacement (THR) requires complex surgery and is associated with a high morbidity, mortality, and cost. Although the United Kingdom based National Joint Registry (NJR) captures over 95% of THRs treated with revision, before June 2023 it did not capture POPFF treated with fixation. We aimed to estimate the incidence and epidemiology of POPFF treated with either surgery in England. We performed a retrospective analysis of a mandatory, prospective database (NJR) linked to Hospital Episode Statistics (HES). All linkable primary THRs between 01/01/2004 and 31/12/2020 were included. Revision or fixation of POPFF were identified using a combination of procedural and diagnosis codes. We identified 809,832 THRs representing 5,542,332 prosthesis years at risk. A total of 5,100 POPFF were identified that had been surgically treated by revision, fixation, or both, and 2,831 of these fractures were treated with fixation alone, meaning 56% were not represented with revision data alone. The incidence of POPFF needing surgery was 0.92 (95% CI 0.90, 0.95) per 1,000 prostheses years. This incidence was higher in patients over the age of 70 at the time of primary THR (1.31 [95% CI 1.26, 1.35] per 1,000 prostheses years) and for patients who underwent THR for hip fracture (2.19 [95% CI 1.97, 2.42] per 1,000 prostheses years). This incidence appears to be increasing year on year. The cumulative probability of sustaining a POPFF within 10 years of THR was 1% and over 15% of patients died within 1 year of surgery for a POPFF. To date, the incidence of POPFF may have been underestimated with over 50% of cases missed if the case identification in this study is correct. After including these cases, we observed that POPFF is the largest reason for major reoperation following THR and patients sustaining these injuries have a high risk of death. The prevention and treatment of POPFF and requires further resource allocation and research.

Effects of Socio-Economic and Demographic Factors on Meat Consumption Pattern in Iran: A Demand System Approach

Meat as one of the most important resources of protein has a special role in human nutrition. Understanding the meat consumption structure of households is essential for planning and policymaking in this regard. In this research, we studied consumption patterns of meat products including chicken, veal, lamb, and fish for households in Iran (Mashhad city) using demand system estimation. The hypothesis of this study is that chicken is a necessary goods and other types of meat are luxury goods. Given the cross-sectional nature of the data and presence of zero expenditure for some households, we used the censored demand model based on a consistent two-step approach. For this purpose, at first, four Probit models were estimated to determine the factors affecting the probability of purchasing each selected meat product. After that, the probability density function (PDF) and the cumulative distribution function (CDF) were calculated for each selected meat product, and the Almost Ideal Demand System (AIDS) considering PDF and CDF was estimated for all types of meat using a non-linear seemingly unrelated regression. Also, the effect of demographic variables on meat consumption pattern was considered in demand system. The results of expenditure elasticities confirmed the hypothesis. The highest own-price elasticity was related to veal. Based on compensated price elasticities, all types of meat were net substitutes for chicken and chicken was also a net complement for all types of meat. On the other hand, the only substitute for lamb and chicken was veal, but with compensating income effect fish also became a substitute for them. So, in the event of an increase of the price of lamb and chicken, we recommend subsidizing the consumers with low purchasing power in order to increase the diversity of consumption of protein products. This can increase the consumption of fish.

Annual Variations of PM10, PM2.5 and PM1.0 Fraction at an Urban Site in Ansan, Korea

This study examines particulate matter (PM) concentrations, specifically PM10, PM2.5, and PM1.0, at an urban site in Ansan, South Korea. Three BAM-1020 continuous monitors measured PM10, PM2.5, and PM1.0 hourly from January to December 2021, with data acquisition rates of 97.1%, 97.3%, and 93.4%, respectively. Average annual concentrations were 45.7±48.4 µg/m³ (PM10), 26.4±22.7 µg/m³ (PM2.5), and 18.5±14.5 µg/m³ (PM1.0). PM10 was 91% of the annual air quality standard, while PM2.5 exceeded this standard by 176%, indicating severe pollution. Statistical analyses using probability density function (PDF) and cumulative distribution function (CDF) showed 98.5% of data converged at 150 µg/m³ (PM10), 75 µg/m³ (PM2.5), and 50 µg/m³ (PM1.0), with the remaining 1.5% corresponding to approximately 131 hours, annually. In 2021, all six observed yellow dust events occurred in spring (March to May). PM10, PM2.5, and PM1.0 concentrations increased from January to March, peaked in March, decreased until September, and rose again. The coarse fraction ratio increased stepwise from February to May, likely due to yellow dust and episodes of high PM concentration in the spring. The PM1.0 concentration was higher in summer and fall compared with that in spring and winter. The PM1.0/PM2.5 ratio was highest in summer and fall even though the annual PM1.0concentration was lowest, suggesting a higher secondary formation rate of fine particles during these periods. This underscores the need for further scientific investigation on PM1.0. The study provides insights into the simultaneous observation and distribution characteristics of PM10, PM2.5, and PM1.0, highlighting the mass concentration, distribution, and ratio of PM1.0. These findings are crucial for understanding PM distribution in Ansan, an industrial complex, and can serve as essential data for chemical composition comparisons, data validation, public health strategies, and cost-benefit analyses for regional PM improvement.

Sum of defects (SOD) chart for joint monitoring of the two parameters of a zero-inflated Poisson process

Rapid technological advancements and automation in today's world have led to many high quality manufacturing processes, where most of the products are free from defects. The defects data from high quality processes are commonly modeled by twoparameter zero- inflated Poisson (ZIP) distribution. Most of the existing control charting procedures for ZIP processes are developed assuming that all the manufactured units are inspected one by one, and most often separate control charts are proposed for monitoring the two parameters of a ZIP process. However, joint monitoring of the two parameters in a single chart can offer significant operational advantages because one needs to focus on a sole chart and a single charting statistic. Again, in many manufacturing set ups 100% inspection may not always be feasible. Recently, a few one-chart schemes with group inspection of small samples are reported in literature, and all these schemes require estimation of the two parameters based on accumulated samples till the current sampling stage for computation of the monitoring statistics. This accumulation of samples introduces several limitations in these schemes. In this paper, probability mass function and the cumulative distribution function (cdf) of the sum of defects (SOD) in a sample of size are defined, and a control chart for SOD is proposed. Both the parameters of a ZIP process can be jointly monitored using the SOD chart. Again, since no accumulation of the samples is required for application of the SOD chart, it is free from all limitations of the existing one-chart schemes with group inspection. Two case studies based on past data are presented. The performance of the SOD chart is found to be very encouraging.

Developmental Validation of the Microreader 23HS Plex ID System: A Novel Supplementary Non-CODIS STR Multiplex Assay for Forensic Application.

A novel supplementary non-CODIS STR multiplex assay designated as the "Microreader 23HS Plex ID System" was developed. The Microreader 23HS Plex ID System enables simultaneous profiling of 23 STR loci and the amelogenin locus. The majority of these loci are non-CODIS STRs (D4S2408, D9S2157, D20S161, D3S2459, D18S1364, D13S305, D1S2142, D19S400, D6S1017, D7S1517, D2S1776, D2S1360, D3S1744, D16S3391, D3S1545, D11S4463, D20S85, D1S549, D10S2325, D21S2055), with the exception of three CODIS STRs (D2S441, D12S391, and D22S1045). Followed the recommendations of Scientific Working Group on DNA Analysis Methods (SWGDAM) and the Chinese validation standards, a comprehensive set of validation studies were conducted, encompassing PCR conditions, stutter ratio and peak height balance, sensitivity, precision and accuracy, reproducibility, species specificity, inhibition, as well as mixture testing. The results demonstrated that the Microreader 23HS Plex ID System is a reliable and robust assay, with well-balanced peak heights, high precision and accuracy, species specificity, and resistance to common inhibitors. The sensitivity of the assay was determined to be 0.125ng of template DNA. In mixture study, all minor alleles were detected in two-sample mixtures across various ratios (1:19, 1:9, 1:4, 3:7, 2:3, 1:1, 3:2, 4:1, 9:1, and 19:1). In population study, a total of 500 unrelated individuals of Han ethnicity from East China were genotyped. The allele frequencies and forensic population genetic parameters were calculated, with a cumulative random match probability of 7.757×10-27, and a total power of discrimination exceeding 0.999,999,999,999,999,999,999,999,99. In conclusion, the Microreader 23HS Plex ID System shows promise as a valuable supplementary tool for forensic applications, particularly in addressing complex kinship testing and challenges posed by STR mutation.

Regression-based model predicting cost contingencies for road network projects

Cost contingencies represent a significant markup percentage for construction projects. Many construction projects experience cost overruns due to unexpected events before or during the construction phase. Determining the cost contingency for construction projects is one of the most frequent and widespread financial problems many countries face. Since the amount included in the estimates is always insufficient to absorb the actual cost overruns of the project. The main objective of this paper is to develop a simple realistic regression model for predicting the appropriate contingency amount for road network projects before the bidding stage. The regression model has been developed based on data from real case studies for 75 road network projects to adequately consider all potential risks may face the project. Good-of-fit tests were used to select the best-fitting statistical distribution for a given dataset. Based on the analysis of selected data, the log-logistic probability distribution function is the best-fit curve representing cost overrun behavior, and the cumulative distribution function has been used to determine the realistic probability of incurred cost overrun. Based on the analysis of real case studies, it is suggested that an average of 59.722% be used as a cost overrun contingency amount for road network projects. The study outcomes will help practitioners and researchers improve future accuracy/calculations of contingency amount for road construction projects and improve the decision-making process.

A novel cross-entropy-based importance sampling method for cumulative time-dependent failure probability function

Cumulative time-dependent failure probability function (C-T-FPF) can reflect the effects of distribution parameters of random inputs and the upper bound of service time interval, which vary in their design domains, on time-dependent failure probability. However, solving C-T-FPF involves a time-consuming triple-layer framework. Thus, we propose an efficient method by combining cross-entropy-based importance sampling (IS) with adaptive Kriging model (CE-IS-AK). The innovations of CE-IS-AK include two aspects. Firstly, we construct a space augmented by both distribution parameters and the upper bound of service time interval, on which the triple-layer framework is decoupled to a single-layer one. And in the augmented space, an optimal IS density is proposed to reduce the required candidate sample size for estimating C-T-FPF. Secondly, we employ Gaussian mixture model (GMM) to approximate the optimal IS density, and the parameters in GMM are determined by minimizing the cross entropy of GMM and the optimal IS density. Moreover, to reduce the model evaluations in the proposed method, Kriging model is adaptively embedded to replace the actual model, and a first failure instant based learning function is proposed to train Kriging model adaptively. Due to the proposed single-layer framework and IS strategy assisted by the Kriging model, the efficiency is greatly improved for estimating C-T-FPF, which is validated by several examples.

An optimal estimation approach in non-response under simple random sampling utilizing dual auxiliary variable for finite distribution function

Auxiliary data needs to be incorporated into survey sampling in order to create a precise population parameter estimator. This study investigates improving the efficiency of these estimators, the researchers use study variable [cumulative distribution function, (CDF)] and auxiliary variable (CDF, mean and ranks) to achieve this goal in the condition of non-response. The researchers suggest two ideal families of exponential estimators to obtain an enhanced population (CDF) estimators in simple random sampling under non-response, by utilizing the data from dual auxiliary variable. The first-order approximation is used for the bias, mean squared error (MSE), and minimal mean squared error of the new and existing estimators. Using dual auxiliary information, the new families of exponential estimators turn out to be precise and efficient while estimating the distribution function, whenever non-response is contained within three Situation such as (Situation-I when non-response occurs in both study and auxiliary variables, next to it Situation-II when non-response occurs only in study variable and Situation-III when non-response occurs only in auxiliary variable). We employ a single population to assess the effectiveness and suitability of the suggested family estimators; varying values of k = 2 and 3 produce varying mean square error and percentage relative efficiency values. The suggested families of estimators have lower (MSE) and higher (PRE) values for the population which showed in tables 2–13 The percentage relative efficiency (PRE) of proposed families of estimators when k = 2 for all the three situation of non-response is 183.52715, 189.78448, 184.82791, 185.49885, and 223.06144 and when k = 3 for all the three situation of non-response is 223.06144, 119.32556, 165.89993, 166.29598, 112.90767 and 113.25086.