Maximum Likelihood Estimation Method Research Articles

A pathway study of factors influencing quality of fertility life

BackgroudTo investigate the factors influencing fertility quality of life in infertile men, constructing a structural equation model of the factors influencing fertility quality of life in infertile men, and to provide suggested measures for improving fertility quality of life in infertile men.MethodsIt is a Observational study. Infertile men (n = 250) attending a fertility centre in a hospital in Xinjiang, matched 1:2 men with no obvious male factor in the control group (n = 500).The Quality of Fertility Life Scale, the Social Support Scale, the Fertility Stress Scale and the Positive Attention Awareness Scale were used to conduct the survey. The model was constructed by applying the maximum likelihood estimation method in Mplus 8.3 software, to explore the factors influencing the quality of reproductive life of infertile men through path analyses. Differences between the case and control groups were statistically significant (P < 0.05) in terms of total fertility quality of life scores, core entry dimensions, affective responses, physical and mental relationships, selective treatment dimensions, and treatment tolerance.ResultsPast medical history, history of exposure to hazardous environments, health insurance reimbursement, social support, fertility stress, and mindfulness are important factors affecting the quality of fertility life of infertile men.ConclusionThe quality of fertility life of infertile men is not optimistic. By improving the level of mindfulness, fertility stress, and social support, we propose appropriate measures to improve the quality of fertility life of infertile men. These measures can improve their confidence in clinical diagnosis and infertility treatment, enabling them to cope positively with these challenges.

Discovering Subgroups of Children With High Mortality in Urban Guinea-Bissau: Exploratory and Validation Cohort Study.

The decline in global child mortality is an important public health achievement, yet child mortality remains disproportionally high in many low-income countries like Guinea-Bissau. The persisting high mortality rates necessitate targeted research to identify vulnerable subgroups of children and formulate effective interventions. This study aimed to discover subgroups of children at an elevated risk of mortality in the urban setting of Bissau, Guinea-Bissau, West Africa. By identifying these groups, we intend to provide a foundation for developing targeted health interventions and inform public health policy. We used data from the health and demographic surveillance site, Bandim Health Project, covering 2003 to 2019. We identified baseline variables recorded before children reached the age of 6 weeks. The focus was on determining factors consistently linked with increased mortality up to the age of 3 years. Our multifaceted methodological approach incorporated spatial analysis for visualizing geographical variations in mortality risk, causally adjusted regression analysis to single out specific risk factors, and machine learning techniques for identifying clusters of multifactorial risk factors. To ensure robustness and validity, we divided the data set temporally, assessing the persistence of identified subgroups over different periods. The reassessment of mortality risk used the targeted maximum likelihood estimation (TMLE) method to achieve more robust causal modeling. We analyzed data from 21,005 children. The mortality risk (6 weeks to 3 years of age) was 5.2% (95% CI 4.8%-5.6%) for children born between 2003 and 2011, and 2.9% (95% CI 2.5%-3.3%) for children born between 2012 and 2016. Our findings revealed 3 distinct high-risk subgroups with notably higher mortality rates, children residing in a specific urban area (adjusted mortality risk difference of 3.4%, 95% CI 0.3%-6.5%), children born to mothers with no prenatal consultations (adjusted mortality risk difference of 5.8%, 95% CI 2.6%-8.9%), and children from polygamous families born during the dry season (adjusted mortality risk difference of 1.7%, 95% CI 0.4%-2.9%). These subgroups, though small, showed a consistent pattern of higher mortality risk over time. Common social and economic factors were linked to a larger share of the total child deaths. The study's results underscore the need for targeted interventions to address the specific risks faced by these identified high-risk subgroups. These interventions should be designed to work to complement broader public health strategies, creating a comprehensive approach to reducing child mortality. We suggest future research that focuses on developing, testing, and comparing targeted intervention strategies unraveling the proposed hypotheses found in this study. The ultimate aim is to optimize health outcomes for all children in high-mortality settings, leveraging a strategic mix of targeted and general health interventions to address the varied needs of different child subgroups.

The modified control charts for monitoring the error detection of process control under different estimators&amp;nbsp;

The reflected power function distribution (RPFD) has increasing importance in practical life due to its application in diversified fields of life. Organisations often face difficulty monitoring operations to identify and remove errors during production. That is why there is a need to introduce control charts that effectively monitor the processes, mainly when the number of errors follows RPFD and the manufacturing process is in control. The current study suggested memory-based control charts as a solution to the problem. The control charts are based on the estimation methods and play a remarkable role in enhancing the machine process reliability. The parameters of RPFD are evaluated through the percentile estimator (PE) and modified maximum likelihood estimator methods (MMLM). Further, we create memory-based control charts, i.e., hybrid exponentially weighted moving average (HEWMA) and extended exponentially weighted moving average (EEWMA), using the PE and MMLM. The findings reflect that HEWMA control charts based on PE provide a better result in estimating the defects. The implications of the study will be helpful for practitioners and policy makers from reliability engineering, management sciences, and statisticians.

EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters.

The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.

An investigation of traffic speed distributions for uninterrupted flow at blackspot locations in a mixed traffic environment

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.

A Reliable Observation Point Selection Method for GB-SAR in Low-Coherence Areas

Ground-Based Synthetic Aperture Radar (GB-SAR), due to its high precision, high resolution, and real-time capabilities, is widely used in the detection of slope deformations. However, the weak scattering coefficient in low-coherence areas poses a great challenge to the observation point selection accuracy. This paper introduces a selection process for reliable observation points that integrates phase and spatial information. First, for various observation points with differentiated stability, we propose to utilize maximum likelihood estimation (MLE) methods to achieve stability assessment. Second, a phase correction approach is proposed to address unwrapped phase errors encountered at less stable points. Third, adaptive filtering for deformation information at observation points is achieved using estimated variance combined with wavelet filtering thresholds. Finally, in dealing with unknown deformation trends, we propose utilizing a clustering method to accurately identify these trends, thereby enhancing the precision in identifying reliable observation points (ROPs). The experimental results demonstrate that this method enhances the accuracy of observation point selection in low-coherence areas, providing a broader observational field for deformation detection.

The link between functional response and longevity of Trichogramma evanescens strains indigenous to Türkiye: A comparative assessment of parameters

AbstractA previous comprehensive survey in the Mediterranean and Southeastern regions of Türkiye investigating the natural egg parasitism of lepidopteran maize pests, Sesamia spp, (Lepidoptera: Noctuidae) Ostrinia nubilalis Hbn. and Chilo partellus (Swinhoe; Lepidoptera: Crambidae) by Trichogramma evanescens Westwood (Hymenoptera: Trichogrammatidae) yielded successful establishment of laboratory cultures of six strains molecularly clustering into two main groups. In this study, the functional response and adult longevity of the strains reared and tested on a factitious host, Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) were investigated to provide insights into their potential as candidate biocontrol agents under constant laboratory conditions. The functional response modelling process consisted of two main sequential steps: model selection using polynomial logistic regression and parameter estimation using an iterative maximum likelihood estimation method. The functional response of two strains showed negative linear parameters (type II): HAP068M and HAP268S. Although their attack rate did not differ, the handling time of HAP268S was longer than that of HAP068M. In contrast, four strains had positive linear and negative quadratic parameters (type III) in their functional response: HAP044S, HAP070S, HAP210S, and HAP258M. The strains exhibiting type III functional response did not differ in their handling time, while HAP044S had a higher attack coefficient than HAP070S, HAP210S, and HAP258M. The longevity of both female and male adults significantly differed between strains. More importantly, the females belonging to strains exhibiting type II functional response presented a shorter longevity, compared to those with type III functional response. Males also showed a similar trend in their longevity. This paper discusses the differences in functional response types and estimated parameters of the strains in relation to their relevance for biological control programs and reveals a link between their functional response and longevity as potential reciprocal predictors.

Cross-Cultural Adaptation and Validation of the Portuguese Version of the Multidimensional Scale of Dating Violence 2.0 in Young University Students.

Dating violence has become a problem of social relevance with short- and long-term health consequences. Nurses are in a privileged position to detect and address this problem in health facilities and as school nurses in schools, providing health education and detecting this violence correctly. The aim of this study was to evaluate the cross-cultural validation of the Portuguese version of the Multidimensional Scale of Dating Violence-Short (MSDV 2.0). A validation investigation was carried out in two phases: (1) cross-cultural adaptation of the items and content validation of the Portuguese version of MSDV 2.0 and (2) psychometric validation. Phase (1): The items of the original version include a cross-cultural translation from Spanish to Portuguese and analysed by a group of experts in gender violence and by the authors of the original scale, then a back translation was made and again reviewed by the experts. Young university students also participated for face validity, and a pilot test was carried out. Phase (2): Confirmatory factor analysis was performed using the robust maximum-likelihood estimation method, which confirmed the five-dimensional structure, obtaining good fit rates (chi-square significance (χ2) = 187.860 (p < 0.0001); root mean square error of approximation (RMSEA) = 0.049; comparative fit index (CFI) = 0.937; Tucker-Lewis index (TLI) = 0.923). Reliability analysis indicated adequate internal consistency (Cronbach's alpha (α) = 0.88 to 0.70). Finally, scores of the Portuguese versions MSDV 2.0 were correlated, as expected, positively with the Depression, Anxiety, and Stress Scale (DASS-21) (r = 0.36 to 0.16) and negatively with the Medical Outcomes Study Questionnaire Short Form 36, Health Survey (SF-36) (r = -0.30 to -0.14). To date, it is the only instrument that measures dating violence in a multidimensional way validated in the Portuguese university context.

Predicting affective and cognitive learning outcomes: A quantitative analysis using climate change vectors

Recent changes in the atmospheric conditions have exerted a lot of pressure on the surface of the earth causing attitudinal change, fear of survival as well as pattern of living. Schools are not out of these influences from the changes in climatic conditions. These study, therefore. examines the nexus between climate change vectors such as classroom temperature variation, persistent drought, severe flood occurrence on class attendance, class participation and academic performance among secondary school students. The study adopted an ex post facto research design, and a total of 1,881 were used for data collection. Two research instruments, the climate change variation scale and class attendance, class participation, and academic performance scale were used for data collection. The instrument was validated using factor analysis to assess the dimensionality of the items as well as obtain factors using component analysis and varimax rotation. To assess principal the mode fitness and acceptability, the confirmatory factor analysis (CFA) was carried out using the maximum likelihood estimation method, and the factor loadings from exploratory factor analysis and CFA were not too different. The collected data were analyzed using the simple and multilinear regression techniques. The results showed that relatively, persistent, severe flooding and classroom temperature variation contributes significantly to class participation, class attendance, and academic performance among students. Similarly, the variables; severe flood, drought and classroom temperature when compositely examined contributes to the variation in class attendance, class participation and academic performance among students. Implications and recommendations of the study were stated.

Modeling rainfall extremes along the coastal and Northern parts of Ghana

The main objective of the study was to determine the appropriate distribution for extreme rainfall along the coastal and northern sectors of Ghana. For stakeholders and policymakers to make appropriate risk-mitigating measures to lessen the damage caused by flood and drought, it is necessary to make proper inferences about extreme rainfall. In this study, we used both the multivariate and univariate extreme value data analysis approaches. The Generalized Extreme Value (GEV) with the Block Maxima approach and Generalized Pareto Distribution (GPD) with the Peak over the threshold (that is all excesses and decluster peaks approaches) were used in this study. Historical gridded monthly maximum rainfall data from 1970 to 2020 were obtained from the Climatic Research Unit and were grouped as the coastal and northern stations. The Maximum Likelihood Estimation method was used to estimate the model parameters, and both the unit root test and the Mann-Kendall tests were used to test for trend in the data. With the multivariate extreme modelling approach, the logistic bivariate GEV model was chosen as the “best” model. However, the dependence value was 0.965, so the extreme rainfall should be modelled independently using the univariate extreme value approaches. Hence, based on the information criteria and analysis of deviance approaches, the GEV distribution was considered the “best” fit for the extreme rainfall dataset for the northern part of Ghana. In contrast, the GPD distribution was the “best” fit for the coastal station. Comparatively, for the volume of rainfall in the year 2020, the extreme rainfall is expected to be higher in the coastal station of Ghana in the next two years. Also, extreme rainfall in 2 years would not exceed the maximum occurrence of rainfall (279.267), which happened in September 2020 at the northern station of Ghana.

Birnbaum Saunders distribution for imprecise data: statistical properties, estimation methods, and real life applications

A neutrosophic statistic is a random variable and it has a neutrosophic probability distribution. So, in this paper, we introduce the new neutrosophic Birnbaum–Saunders distribution. Some statistical properties are derived, using Mathematica 13.1.1 and R-Studio Software. Two different estimation methods for parameters estimation are introduced for new distribution: maximum likelihood estimation method and Bayesian estimation method. A Monte-Carlo simulation study is used to investigate the behavior of parameters estimates of new distribution, compare the performance of different estimates, and compare between our distribution and the classical version of Birnbaum–Saunders. Finally, study the validity of our new distribution in real life.

Issues related to typological fragility curves derivation starting from observed seismic damage

In this study typological fragility curves are proposed with a macro-seismic approach. To this scope, a stock of 56.338 residential masonry buildings struck by L’Aquila 2009 seismic sequence is analyzed, whose AeDES forms are archived within Da.D.O. platform, that is a web-gis database collecting the observed seismic damage data related to buildings surveyed after several Italian earthquakes.Moreover, issues significantly influencing the fragility curves derivation are in depth discussed. In particular, a criterion for the buildings stock completion adding undamaged and not surveyed buildings is proposed, based on the distributions known of the residential building typologies. Comparisons highlight that the database completion affects the resulting fragility curves, and in particular for low damage levels. Furthermore, it is shown how the fundamental parameters estimation, by using the Maximum Likelihood Estimation (MLE) method, is conspicuously influenced by the PGA intervals number (nint), that is an issue often ignored when fragility curves are derived. The numerical investigations show that, although a non-monotonic trend is observed, the fundamental parameters tend to converge to the asymptotic values as the nint of PGA increases, and that they are markedly dispersed when nint is low.

Lifetime Analysis of Dies Manufactured by Conventional Processes and Reconditioned by Deposition Welding Operation.

The refurbishment of dies by the deposition welding of wear areas is an efficient and economical process. The aim of this study was to conduct a comparative analysis of the lifetimes of different types of dies for the manufacturing of wagon wheels. The analyzed dies were manufactured by conventional processes (Type I) and reconditioned through a deposition welding procedure using a dedicated electrode (Type II). The Anderson-Darling test was conducted to analyze the goodness of fit of the lifetime data specific to the die types. The maximum likelihood estimation method (MLE) with a 95% confidence interval (CI) was applied in order to estimate the lifetime distribution parameters. It was found that the lifetimes of type II dies were longer than those of type I dies. The mean time to failure (MTTF) recorded for reconditioned dies was 426 min, while the mean time to failure of dies manufactured by conventional processes was approximatively 253 min. In addition, an accentuated hazard rate for type I dies compared to type II dies was observed. The results of this analysis emphasized the fact that dies can be restored to their initial operating capacity by successfully using deposition welding procedures that confer a high resistance to operational loads. At the same time, the use of these procedures allows for the sustainable development of resources and waste management.

Faktor-Faktor Yang Memengaruhi Risiko Produksi Bawang Putih Di Indonesia

Garlic productivity in Indonesia is still low and fluctuates every year. The productivity fluctuations indicate production risks. This research aims to analyze the factors that influence the productivity and risks of garlic production in Indonesia. The data used in this study is secondary data from BPS 2014 in the three largest production centers, namely Central Java, West Nusa Tenggara, and East Nusa Tenggara, with a total sample of 305 farmer respondents from 333 farmer respondents. The method used in this study is a multiple linear regression model with the Just and Pope model approach. Multiple linear regression analysis for the productivity function is estimated through the Ordinary Least Squares (OLS) method, whereas for the production risk function, estimation is conducted using the Maximum Likelihood Estimation (MLE) method. The results showed that the risk level of garlic production in Indonesia was high. Factors significantly increased productivity, including seeds, labor, the dry season, and climate change. Meanwhile, factors that significantly reduced productivity are organic fertilizers and SP36 fertilizers. The risk-inducing production factor is climate change, while the risk-reducing factors are pest attacks and liquid pesticides. To boost productivity, farmers can cultivate garlic during the dry season, increase the use of high-quality seedlings and labor, and reduce the application of organic and SP36 fertilizers. To mitigate production risks, farmers can enhance the use of liquid pesticides following recommended usage standards.

A New Modified-X family of distributions with applications in modeling biomedical data

In the present article, we derive a new Modified-X (NM-X) family of probability distributions by using a popular approach known as the T-X method. For the NM-X family, some of the main distributional (or statistical) properties such as quantile function, identifiability property, order statistics, residual life and reverse residual life, moments, and moments generating function are determined. A well-known method of estimation such as the Maximum Likelihood estimation method is used for estimating the model parameters of the introduced family of distributions. A Monte Carlo simulation method is incorporated to judge the performance of the estimators with different selected values of parameters. To check the tails behaviors of the proposed model, the risk measurements such as value-at-risk (VaR), tailed-value-at-risk (TVaR), tailed-variance (TV), and tailed-variance-premium (TVP) are investigated. For illustrative purposes, a special sub-model of the proposed class is derived and named a New Modified Weibull distribution (abbreviated as the NM-Weib). Furthermore, four applications from the field of medicine are analyzed to demonstrate the application, and effectiveness of the NM-Weib distribution. By adopting different diagnostic criteria, the NM-Weib distribution is compared with Weibull (Weib) distribution, Alpha Power Transformed-Weib (APTra-Weib) distribution, new Exponent-Weib (NExpo-Weib) distribution, Flexible Reduced Logarithmic-Weib (FRLog-Weib) distribution, Marshal Olkin-Weib (MO-Weib) distribution, and Kumaraswamy-Weib (K-Weib) distribution. Finally, based on the diagnostic criteria, it is observed that the NM-Weib distribution provides a suitable and best fit for each considered biomedical data set.

A Stochastic Frontier Approach to Study the Relationship between the Hygienic Quality of Bulk Tank Sheep Milk and Technical Efficiency of the Coagulation Process.

Sheep milk from local breeds is important for the production of high-quality cheeses throughout the Mediterranean region, such as Manchego cheese in Spain. To maintain sustainable and efficient production, it is necessary to reach a better understanding of how the composition and hygiene of the milk affect the coagulation process, with the aim of optimizing production yield. This study implemented a stochastic production frontier function to estimate the potential production of curd and efficiency using data from the four seasons of a study of 77 Manchega sheep farms. The Cobb-Douglas production frontier model was estimated using the maximum likelihood estimation method. The results showed that the content of protein, lactose, and fat exhibited increasing returns to scale, with protein content being the most significant factor for curd production. Approximately half of the inefficiency was due to factors related to the technological properties and the hygiene of the milk. The pH, curd firmness, and concentration of lactic acid bacteria improved the efficiency of coagulation, while the concentration of spores of lactate-fermenting Clostridium spp., Pseudomonas spp., staphylococci, and catalase-negative gram-positive cocci favored the inefficiency of the coagulation process. To date, this is the first study to evaluate the effect of different factors, such as microbial groups, milk composition, and technological properties, on the efficiency of the coagulation process in dairy sheep.

Probabilistic characterisation of rock mass deformation modulus from P-wave velocity based on information criteria

ABSTRACT Determination of rock mass deformation modulus (Erm ) is an important but very challenging task in rock mechanics and rock engineering. Direct measurements of Erm by in-situ tests are expensive, time-consuming, and sometimes even impossible. Since the nondestructive and easy geophysical method is commonly used to measure the P-wave velocity (Vp ) in the field, the study has developed an empirical correlation between Erm and Vp based on 834 collected datasets by considering both inherent and transformation uncertainties. The information criteria are employed to select the best model for deriving a general empirical correlation. The maximum likelihood estimation (MLE) method and computational intelligence techniques like particle swarm optimisation (PSO), artificial bee colony (ABC), and differential evolution (DE) algorithms are used to specify the incorporated parameters of each candidate model. Based on the derived general empirical equation, a Bayesian equivalent sample approach using Markov Chain Monte Carlo (MCMC) simulation is applied to conduct probabilistic characterisation of Erm by combining prior knowledge and project-specific observed Vp data. Comparative studies of the results from theory and in-situ measured Erm values suggest that the proposed method is effective and validate the applicability of the derived general empirical correlation.

Improved hourly estimate of aerosol optical thickness over Asian land by fusing geostationary satellites Fengyun-4B and Himawari-9

Asian over-land aerosols are complexities due to a mixture of anthropogenic air pollutants and natural dust. The accuracy of the aerosol optical thickness (AOT) retrieved from the satellite is crucial to their application in the aerosol data assimilation system. Fusion of AOTs with high spatiotemporal resolution from next-generation geostationary satellites such as Fengyun-4B (FY-4B) and Himawari-9, provides a new high-quality dataset capturing the aerosol spatiotemporal variability for data assimilation. This study develops a complete fusion algorithm to estimate the optimal AOT over-land in Asia from September 2022 to August 2023 at 10 km × 10 km resolution with high efficiency. The data fusion involves four steps: (1) investigating the spatiotemporal variability of FY-4B AOT within the past 1 h and 12 km radius calculation domain; (2) utilizing the aerosol spatiotemporal variability characteristics to estimate FY-4B pure and hourly merged AOTs; (3) performing bias corrections for FY-4B and Himwari-9 hourly merged AOT for different observation times and seasons considering pixel-level errors for each satellite; (4) fusing the bias-corrected FY-4B and Himawari-9 hourly merged AOT based on maximum-likelihood estimation (MLE) method. Compared to the original FY-4B AOT, validation with AERONET observation confirms that the root mean square error (RMSE) of hourly merged FY-4B AOT decreases by around 40.6 % and the correlation coefficient (CORR) increases by about 27.8 %. Compared to FY-4B and Himawari-9 merged AOT, the fused AOT significantly decreases (increases) RMSE (CORR) by around 24.7 % (7.3 %) and 20.2 % (5.6 %). In addition, fused AOT is double the number of single-sensor merged AOT. Fusion aerosol map accurately describes the spatial and temporal variations in Asian regions controlled by air pollution and dust storms. Further studies are required for other landscapes with different satellite combinations to promote the application in the data assimilation system.

A finite failure software reliability model using extended log-logistic distribution

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models.

A New Hybrid Algorithm Based on the Firefly Algorithm and Cuckoo Search to Estimate Survival Analysis of Truncated Inverse Gompertz Distribution

This paper introduces the Right Truncated Inverse Gompertz Distribution (RTIGD) with two parameters δ and ϑ with some of its properties as; (Survival-Function, Hazard-Function, cumulative distribution function, probability density function, ‎r-th moment, mean, variance, Moment Generating Function, Median, and Mode. In addition, we propose a new hybrid algorithm, namely the firefly Algorithm with Cuckoo Search Algorithm (FA_CSA) to estimate the parameters based on the survival functions of RTIGD. Simulation is utilized to compare the proposed algorithm with traditional methods, for example, the Maximum Likelihood Estimator and moment method and the standard algorithms Firefly Algorithm and Cuckoo Search. In most cases, the results demonstrate that the proposed algorithm (FA_CSA) provides an accurate estimation for the survival function since it has less mean squared error than the other estimation methods.