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This article discusses the effect of polarization relaxation on dielectric breakdown. We establish analytical statistical Weibull distributions, taking into account the changing local electric field instead of the usual static field in acceleration models. The time dependence of the local field is expressed using the universal Curie–Von Schweidler law. The derived distribution fits well with various observations on time-to-breakdown statistical distributions. In the case of voltage square pulse alternative stress, the calculated time to breakdown follows a power law with stress frequency, regardless of the field dependence of the acceleration model, consistent with observations. The dielectric lifetime is longer compared to continuous voltage stress, as observed in transistors and integrated capacitors. The analytical expression of the power law exponent depends on the field acceleration model and polarization current. It matches well with the measured values for metal–oxide–semiconductor and metal insulator metal capacitors with different dielectrics. The power law exponent increases with the static dielectric constant. The Weibull shape factor is shown to be lower in the AC mode than in the DC mode, as observed. The AC signal duty factor effect decreases the lifetime in the AC mode. Finally, we demonstrate that the consequence of polarization relaxation also affects the lifetime in the DC mode. The acceleration factor and lifetime projections are close to power laws with the field, regardless of the static field dependence of the acceleration model. [ABSTRACT FROM AUTHOR]

While Iran has notable renewable energy potentials, the renewable energy sector of this country has remained underdeveloped, mostly because of easy access to cheap fossil fuels. This paper discusses and analyzed the possibility for hydrogen generation from wind resource in urban city which is suffered from air pollution and is located in northwestern Iran. Wind energy and power density estimations were performed using the 2-parameter Weibull probability distribution function. These estimates were used to predict the power output, hydrogen output, economic viability, and environmental benefit of building a wind power-hydrogen production unit in one of several candidate sites in the studied area. Ultimately, the most suitable site for such a project was determined using the SWARA/WASPAS method. After investigating 16 different conventional wind turbines, the best one in terms of achievable power and hydrogen output and economic feasibility was determined. The results showed that Gamesa G80 with a capacity of 2 GW was the best wind turbine for the urban area. Gardaneh-Almas with a wind power density of 1252W/m2 was identified as the best site for power generation in the region. Potential wind-powered hydrogen output at this site was estimated to 97 ton/yr. LCOE and LCOH for this site were estimated to 0.047 $/kWh and 4.6 $/kg-H 2 , respectively. • Hydrogen production integrated urban area toward sustainable cities are discussed. • 3E analysis of wind energy for hydrogen production (WHP) is investigated. • A multi-aspect analysis of WHP is analyzed by WASPAS. • A sensitive analysis on wind energy integrated hydrogen system is performed. • The results showed the wind-hydrogen system is best solution in Iranian cities. [ABSTRACT FROM AUTHOR]

Hierarchical Bayes, or E‐Bayes, is frequently used to estimate the failure probability when solving a zero‐failure reliability evaluation model; however, the accuracy of the reliability estimation using these methods is not very good in practice. Due to this, a novel double‐modified hierarchical Bayes (DMH‐Bayes) is proposed for Weibull characteristic data in this study to enhance failure probability estimation and improve reliability point estimation accuracy. Meanwhile, in order to guarantee the preservation of the assessment findings' consistency and confidence level, the parametric Bootstrap method (P‐Bootstrap) and the L‐moment estimation method based on point estimation are introduced to obtain reliability confidence interval estimates. Based on Monte–Carlo simulation testing and analysis of a gyroscope bearing, the new model is confirmed to have better applicability and robustness while improving the accuracy of reliability assessment. [ABSTRACT FROM AUTHOR]

The lifetime data collected from the field are usually heavily censored, in which case, getting an accurate reliability evaluation based on heavily censored data is challenging. For heavily Type‐II censored data, the parameters estimation bias of traditional methods (i.e., maximum likelihood estimation (MLE) and least squares estimation (LSE)) are still large, and Bayesian methods are hard to specify the priors in practice. Therefore, considering the existing range of shape parameter for Weibull distribution, this study proposes two novel parameter estimation methods, the three‐step MLE method and the hybrid estimation method. For the three‐step MLE method, the initial estimates of shape and scale parameters are first respectively derived using MLE, then are updated by the single parameter MLE method with the range constraint of shape parameter. For the hybrid estimation method, the shape parameter is estimated by the LSE method with the existing range constraint of shape parameter, then the scale parameter estimate can be obtained by MLE. On this basis, two numerical examples are performed to demonstrate the consistency and effectiveness of the proposed methods. Finally, a case study on turbine engines is given to verify the effectiveness and applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

The Weibull distribution is an extensively used statistical model for analyzing the reliability of mechanical and electrical components. Due to the complexity of the nonlinear equations and the scarcity of failure data, the common method may not provide satisfactory results of the reliability. In this case, a new approach for Weibull parameter estimation and reliability analysis, based on successive approximation schemes, is presented. The shape and scale parameters are estimated by maximizing the likelihood functions, and the location parameter is obtained by constructing an approximate correction model between it and the failure data. In order to show the performance of the proposed method, an extensive Monte‐Carlo simulation study is conducted. Simulation results show that the proposed method provides better estimates and efficient confidence intervals for Weibull parameters. In addition, the proposed method works well in presence of small sample sizes. Finally, two real examples are analyzed to illustrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

This scientific attempt is dedicated to model and solve the production inventory system (PIS) in supply chain (SC). The original problem is modelled by an Optimal Control Problem (OCP) with disturbance term and stochastic deterioration in two levels SC that it is possible to return the goods. As we know, this research is the first attempt for modelling and resolving such a PIS in supply chain management (SCM) with stochastic deterioration. The SC model contains two-level; Each level consists of a firm and each firm has a manufacturing factory and a recycling. Also, items are broken down according to Weibull distribution and returned to the firm for recycling. The SC is modelled as a dynamic system and then is reformulated as an OCP. The OCP is changed to a system of equations so that an artificial neural network (ANN) is provided to solve it. For demonstrating the proposed scheme's effectiveness, performance, and efficiency of the model a real-world problem and an example are given. The results of validation and sensitivity analysis show the convergence of the variables' response to the target values and their logical behavior against the change of parameters. [ABSTRACT FROM AUTHOR]

Due to the increased cost and reliability, only few life failure data can be gathered. Therefore, it is significant to improve the parameter estimation accuracy of Weibull distribution with small samples. In this study, a Weibull parameter estimation method based on hybrid gray genetic algorithm with modified maximum likelihood method (HGLM) is proposed. In the HGLM, the solution model for the location parameter is established by using gray genetic algorithm. On the basis of the location parameter, the shape and scale parameters are acquired with improved maximum likelihood method. Then, a full Monte-Carlo sampling simulation study is conducted to compare the parameter estimation accuracy and fitting of the HGLM with three existing methods. Results show the HGLM can accurately and stably estimate the Weibull parameters with small samples. Finally, two cases are analyzed to demonstrate the application of the HGLM method and the feasibility of sample expansion is also discussed. [ABSTRACT FROM AUTHOR]

Cattails (Typha latifolia L.) are naturally occurring aquatic macrophytes with significant industrial potential because of their abundance, high-quality fibers, and high fiber yields. This study is the first attempt to investigate how phenological development and plant maturity impact the quality of cattail fibers as they relate to composite applications. It was observed that fibers from all five growth stages exhibited a Weibull shape parameter greater than 1.0, with a goodness-of-fit exceeding 0.8. These calculations were performed using both the Least Square Regression (LSR) and Maximum Likelihood Estimation (MLE) methods. Among the estimators, the MLE method provided the most conservative estimation of Weibull parameters. Based on the Weibull parameters obtained with all estimators, cattail fibers from all five growth stages appear suitable for composite applications. The consistency of shape parameters across all five growth stages can be attributed to the morphological and molecular developments of cattail fiber during the vegetative period. These developments were confirmed through the presence of calcium oxalate (CaOx) plates, elemental composition, and specific infrared peaks at 2360 cm−1 contributing to the strength, cellulose peaks at 1635 cm−1, 2920 cm−1, and 3430 cm−1. In conclusion, it was found that the mechanical properties of cattail fiber remain similar when harvested multiple times in a single growing season. [ABSTRACT FROM AUTHOR]

This study examines the mechanical properties, specifically strength and stiffness, of technical hemp and flax fibers grown in Romania. Tensile testing was employed to determine stress–strain curves and the Young's modulus and to assess the failure strength of both fiber types. Although samples of various lengths were tested, no significant length-dependent variations were observed. However, a strong dependence on fiber diameter was noted, with the smallest diameters approaching the documented strength of elementary fibers. Due to the considerable variability in the experimental results pertaining to the characteristics of the reinforced fibers, a statistical analysis using a two-parameter Weibull distribution was employed. The analysis revealed three distinct stress–strain curve profiles, i.e., linear, bi-linear, and tri-linear patterns, with the average ultimate stress ranging from 412 to 566 MPa for hemp and 502 to 598 MPa for flax. [ABSTRACT FROM AUTHOR]

The production of high safety automotive structural parts with cast aluminum alloy still faces significant challenges, particularly in terms of comprehensive mechanical properties and performance consistency. To illustrate this point, the automotive battery enclosure made of AlSi10MgMn alloy material was used as an example to examine mechanical properties and bifilm defects. The study analyzed the room temperature mechanical properties of the test bars by using the ultimate tensile strength (UTS) and %Elongation values obtained through the two-parameter Weibull distribution analysis method. Additionally, the fracture surface of the tensile samples was analyzed by scanning electron microscopy (SEM), and the energy-dispersive X-ray spectrometry (EDS) technique was used to analyze the composition of the bifilm defects. The results show an average tensile strength of 273.4 MPa and %Elongation of 6.0% in the tensile sample, while the Weibull moduli of the average tensile strength and %Elongation are small. The fracture surface of the tensile sample shows typical bifilm defects, with the microscopic metallographic images showing curved and tangled cracks. The bifilm defects were combined with Al6(MnFe)Si, β-AlFeMnSi, and Si phase at the grain boundary, resulting in the formation of a compound defect. Furthermore, the complex inner surfaces of the bifilm were made up of typical fold, wrinkle, and cavity features. Due to the mechanical folding of the bifilm, gaps are observed at the boundary between the oxide film and the substrate. [ABSTRACT FROM AUTHOR]

The hybridization technique has recently been used to produce a new generation of composites called pseudo-ductile composites, which have shown higher failure strain compared to conventional composites, minimizing the risks of the occurrence of a catastrophic failure. The pseudo-ductility behavior in these composites is obtained by hybridization of fibers with high and low failure strains. In this study, a multi-scale finite element (FE) model incorporating micro and macro-scales is proposed to predict the failure behavior of pseudo-ductile composites. A micro-scale representative volume element (RVE), consisting of randomly distributed fibers, was generated using a Python code. Periodic boundary conditions (PBCs) were applied to the RVE generated with a periodic geometry. To account for fiber failure and ply fragmentation, the tensile strength of fibers was distributed based on the Weibull distribution function and a user-defined UMAT subroutine was developed. Tensile loading was then applied to the RVE to simulate the composite's mechanical behavior. For validation, an RVE was developed based on experimental data from recent research on thinply and conventional thickness composites. Numerical results were compared to experimental data, demonstrating acceptable agreement. In the final step, following a sequential multi-scale modeling approach, a macro-scale model was constructed based on the outputs of the micro-scale model subjected to tensile and shear loads. The results were compared with experimental data, revealing good agreement. The proposed model allows for the optimization of pseudo-ductile composite structures to achieve a desired set of mechanical properties without the need for conducting extensive experimental material tests. [ABSTRACT FROM AUTHOR]

Many industries are required to perform population viability analysis (PVA) during the consenting process for new developments to establish potential impacts on protected populations. However, these assessments rarely account for density‐dependent regulation of demographic rates. Excluding density‐dependent regulation from PVA‐based impact assessments is often assumed to provide a maximum estimate of impact and therefore offer a precautionary approach to assessment. However, there is also concern that this practice may unnecessarily impede the development of important industries, such as offshore renewable energy.In this study, we assess density‐dependent regulation of breeding success in 31 populations of seabird. We then quantify the strength and form of this regulation using eight different formulations. Finally, we use PVA to examine how each formulation influences the recreation of observed dynamics (i.e. model validation), as well as the predicted absolute and relative population response to an extrinsic threat (i.e. model projection).We found evidence of both negative (n = 3) and positive (n = 5) regulation of seabird breeding success. In populations exhibiting negative regulation, excluding density‐dependent regulation from PVA‐based impact assessment allowed uncontrolled population growth, such that model outcomes became biologically implausible. By contrast, in populations exhibiting positive regulation, excluding density‐dependent regulation provided an appropriate reconstruction of observed dynamics, but population decline was underestimated in some populations. We find that multiple formulations of density dependence perform comparably at the detection, validation and projection stages of analysis. However, we tentatively recommend using a log‐linear or Weibull distribution to describe density‐dependent regulation of seabird breeding success in impact assessments to balance accuracy with caution. Finally, we show that relative PVA metrics of impact assessment cannot necessarily be used to overcome PVA misspecification by assuming density independence in positively regulated populations.Synthesis and applications: We suggest that a density‐dependent approach when performing PVA‐based assessments for seabird populations will prevent biologically unrealistic, unconstrained population growth and therefore ensure meaningful PVA metrics in populations experiencing negative regulation. It will also maintain a precautionary approach for populations experiencing positive regulation, crucial when estimating impacts for these more vulnerable populations. These conclusions have immediate international application within the consenting processes for marine industries. [ABSTRACT FROM AUTHOR]

In observational/field studies, competing risks and left-truncation may co-exist, yielding 'left-truncated competing risks' settings. Under the assumption of independent competing risks, parametric estimation methods were developed for left-truncated competing risks data. However, competing risks may be dependent in real applications. In this paper, we propose a Bayesian estimator for both independent competing risks and copula-based dependent competing risks models under left-truncation. The simulations show that the Bayesian estimator for the copula-based dependent risks model yields the desired performance when competing risks are dependent. We also comprehensively explore the choice of the prior distributions (Gamma, Inverse-Gamma, Uniform, half Normal and half Cauchy) and hyperparameters via simulations. Finally, two real datasets are analyzed to demonstrate the proposed estimators. [ABSTRACT FROM AUTHOR]

Since the classical element model cannot describe the nonlinear characteristics of rock during the entire compressive creep process, nonlinear elements and creep damage are generally introduced in the model to resolve this issue. However, several previous studies have reckoned that creep damage in rock only occurs in the accelerated creep stage and is only described by the Weibull distribution. Nevertheless, the creep damage mechanism of rocks is still not clearly understood. In this study, a reasonable representation of the damage variables of solid materials is presented. Specifically, based on the Gurson damage model, the damage state functions reflecting the constant creep stage and accelerated creep stage of rock are established. Further, the one-dimensional and three-dimensional creep damage constitutive equations of rock are derived by modifying the Nishihara model. Finally, the creep-acoustic emission tests of phyllite under different confining pressures are conducted to examine the creep damage characteristics of phyllite. And the proposed constitutive model is verified by analyzing the results of creep tests performed on saturated phyllite. Overall, this study reveals the relationship between the creep characteristics of rocks and the corresponding damage evolution pattern, which bridges the gap between the traditional theory and the quantitative analysis of rock creep and its damage pattern. [ABSTRACT FROM AUTHOR]

The transportation and disposal of tunnel muck have detrimental environmental impacts. For this reason, the practice of reusing such a resource in engineering materials has substantial environmental and economic benefits. Seasonal frozen regions are extensively distributed worldwide, and freeze–thaw (F-T) cycles can largely degrade the performance of engineering materials, underscoring the importance of knowledge on the F-T resistance of tunnel muck. This study investigates the impact of F-T cycles on the mechanical and microscopic behavior of slurry shield tunnel muck stabilized with basalt fiber (BF) and inorganic curing agent (ICA). The qualities of muck discharged from slurry shield tunneling were improved in the composite formation of fine sand and muddy silty clay. Then tests were conducted to obtain the stress-strain characteristics, uniaxial compressive strength (UCS), and microstructure of the improved tunnel muck with different numbers of F-T cycles (N) and BF contents. Based on the Weibull probability distribution function, the damage evolution model of ICA-BF stabilized tunnel muck was proposed. The results show that the UCS decreases parabolically when the F-T cycle increases, and the F-T damage degree is 24.5%–40.6% after 12 cycles. Increasing BF content can effectively improve tunnel muck's F-T resistance (an optimal ratio of 0.5%). The failure strain of the reinforced tunnel muck is linearly correlated to the BF content and has a weak correlation with the number of F-T cycles. The F-T damage to material strength can be divided into rapid (N=0–3), slow (N=3–9), and stable (N>9) stages. The established F-T damage model can accurately predict the evolution of the F-T damage degree. [ABSTRACT FROM AUTHOR]

Rock damage model is of great significance to the forecast of rock failure in engineering design such as tunnel, drilling, stability of slope, and mine. However, the applicability of statistical damage constitutive models and efficient model selection criteria of rock have not been fully investigated, which leads to difficulties in resolving issues practice via employing models. In the paper, we developed a rock damage constitutive model based on power law distribution and investigated the corresponding applicability in describing rock damage behavior. The uniaxial compression tests for different rocks (e.g., sandstone, granite, mudstone, shale, and marble) are carried out to verify the applicability of the model, and the criterion for model selection is proposed based on the rock porosity strain ratio. The results show that the proposed model exhibits better performance in predicting variation for the brittle rock damage compared with the common damage model based on Weibull distribution. Besides, the applicability of the proposed model can be extended to elastic–plastic rock by adopting the sigmoid function. The research in this study provides an effective model to describe rock damage behavior for brittle and elastic–plastic and presents a criterion for model selection. [ABSTRACT FROM AUTHOR]

In engineering applications, the notch effect and size effect significantly influence the evaluation of fatigue performance in components, necessitating special attention in life prediction. This study proposes a new probabilistic model, based on the theory of critical distance (TCD), to predict fatigue life, with the aim of quantitatively assessing the impact of notch effect and size effect. The stress distribution on the critical plane is first characterized using a sixth-order multinomial function, and the relative stress gradient function is utilized to calculate the value of the critical distance. Furthermore, the effect of the ratio of shear strain to normal strain on fatigue life under multiaxial loading is considered. Additionally, the integration of the Weibull distribution into the TCD is employed for conducting probabilistic modeling of fatigue life. Finally, fatigue experiments are conducted on notched specimens of Q355D steel, demonstrating that the life prediction results under 50% survival probability are superior to the traditional TCD method. [ABSTRACT FROM AUTHOR]

The presence of probability-driven models is highly influential in setting the stage for vital decision-making in domains including reliability, engineering, music engineering, and other closely interconnected scenarios. With a deep understanding of the consequential roles played by probability-arisen models, we have developed and implemented a new probabilistic model. This model is constructed by utilizing the sine-based function and the exponentiated Weibull distribution, and it is known as the exponent power sine exponentiated Weibull (EPSE-Weibull) distribution. Point estimators are derived for the EPSE-Weibull distribution. These estimators are then evaluated through a simulation study. The significance of the EPSE-Weibull distribution is demonstrated through the analysis of reliability and music engineering data sets. In addition to the above, we also utilize two deep learning algorithms, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR), to forecast the same data sets. The findings indicate that the ANN model consistently exhibits higher levels of accuracy, as evidenced by its lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values compared to the SVR model for both data sets. These findings indicate that ANN is better at capturing the fundamental patterns in the underlying data sets. In addition, visual representations, such as bar charts and line charts, further emphasize the superior performance of the ANN across both data sets. [ABSTRACT FROM AUTHOR]

The tool's life statistics module in CNC machining centers typically associates tool's usage time with the program's running duration, leading to the tool idle time being logged as a loss in tool life. This often triggers premature tool replacements. To enhance scheduling accuracy across multiple machining centers, we leverage spindle current variations to discern between tool loads and idle periods. Initially, real-time data from the machining center was gathered, and we employed the three-parameter Weibull Distribution method, using 1.351 (A) as the threshold to distinguish between idle and loaded tool states. Subsequently, we proposed a refined method to calculate the tool's available time, enabling a more precise estimation of its remaining operational lifespan. We further devised a scheduling approach for multiple CNC machining centers based on the tool's availability time. Ultimately, empirical trials exhibited a 10% increase in average cutting tool utilization efficiency and a 12.5% enhancement in machining center productivity. [ABSTRACT FROM AUTHOR]

The problem of estimating multicomponent stress-strength reliability R k , n for two-parameter inverse Weibull distributions under progressive type-II censoring is considered. We derive maximum likelihood estimator, Bayes estimator and generalised confidence interval of R k , n when all parameters are unknown. We study the reliability of stress-strength system with multiple types of components using signature-based approach. When different types of random stresses are acting on a compound system, we derive MLE, maximum spacing estimator of multi-state reliability. Using generalized pivotal quantity, the generalized confidence interval and percentile bootstrap intervals of the reliability are derived. Under a common stress subjected to the system, we also derive the estimators of the reliability parameter. Different point estimators and generalized, bootstrap confidence intervals of the reliability are developed. Risk comparison of the classical and Bayes estimators is carried out using Monte-Carlo simulation. Application of the proposed estimators is shown using real-life data sets. [ABSTRACT FROM AUTHOR]

Accelerated life tests (ALTs) play a pivotal role in life testing experiments as they significantly reduce costs and testing time. Hence, this paper investigates the statistical inference issue for the Weibull inverted exponential distribution (WIED) under the progressive first-failure censoring (PFFC) data with the constant-stress partially ALT (CSPALT) under progressive first-failure censoring (PFFC) data for Weibull inverted exponential distribution (WIED). For classical inference, maximum likelihood (ML) estimates for both the parameters and the acceleration factor are derived. Making use of the Fisher information matrix (FIM), asymptotic confidence intervals (ACIs) are constructed for all parameters. Besides, two parametric bootstrap techniques are implemented. For Bayesian inference based on a proposed technique for eliciting the hyperparameters, the Markov chain Monte Carlo (MCMC) technique is provided to acquire Bayesian estimates. In this context, the Bayesian estimates are obtained under symmetric and asymmetric loss functions, and the corresponding credible intervals (CRIs) are constructed. A simulation study is carried out to assay the performance of the ML, bootstrap, and Bayesian estimates, as well as to compare the performance of the corresponding confidence intervals (CIs). Finally, real-life engineering data is analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

In this paper, an optimal age replacement policy has been studied for a parallel system with a random number of components. The objective functions have been constructed based on the expected cost rate and the availability functions. It is supposed that the lifetime of the system components has a Weibull distribution. Three famous members of the power series class of distributions, namely geometric, zero-truncated Poisson, and logarithmic distributions have been considered for the number of components. The optimal model and the conditions for finding the optimal solution have been studied in terms of the hazard rate function of the system. Numerical and graphical computations have been given. Finally, a real data set has been used to illustrate the results. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Overusing renewable resources for various purposes is making it necessary to use fewer non-renewable ones to generate energy. Finding alternative renewable energy sources is essential for energy production. This study concentrated on using wind direction and speed to produce wind energy among renewable energy sources. Data on wind direction and speed were statistically analyzed to determine the current distribution pattern, which is then used to project the amount of wind energy that will be available in the future. METHODS: This study concentrated on choosing wind direction and speed to minimize the potential for current electricity generation from wind turbines, using data collected between 1981 and 2023. The wind speed and direction distribution pattern was assessed through the Weibull distribution, beta distribution, and three-parameter Weibull distribution. The Anderson-Darling test and the Kolmogorov-Smirnov test were employed in this study to determine the goodness-of-fit of a specific distribution. The forecasting analysis was expanded from 2024 to 2050 based on the three-parameter Weibull distribution and Anderson-Darling test results for future sustainable wind energy production. FINDINGS: The average wind speed was found to be 6.51 meters per second, with a standard deviation of 0.280 meters per second between 1981 and 2023. The wind direction varied between a minimum of 3.56 and a maximum of 356.44 degrees for the same duration. The study discovered that the three-parameter Weibull distribution caused less error in the wind speed data distribution pattern than both the Weibull distribution and beta distribution, based on the results of the Anderson-Darling and Kolmogorov-Smirnov tests. From both the tests on Weibull distribution, beta distribution, and three-parameter Weibull distribution, this study found that the Anderson-Darling test was the most appropriate for forecasting the wind speed corresponding to the wind direction for the periods between 2024 and 2050 to produce sustainable wind energy from the wind turbine. CONCLUSION: The outcomes of this study demonstrate that there is a good likelihood that the parameter Weibull distribution and Anderson-Darling test will be used in other nations to aid in the complementary integration of wind energy. This research has the potential to significantly reduce the amount of environmentally hazardous energy sources used to meet societal requirements. This study offers a trustworthy technique for assessing wind direction and speed, which helps design sustainable wind power plants, construct engineering curricula, and estimate clean, environmentally friendly energy sources. [ABSTRACT FROM AUTHOR]

Life prediction of the parachute canopy is an important consideration before multiple parachute jumps which can prevent unnecessary catastrophic scenarios for skydivers. The present research emphasized the prediction of the life as well as the reliability of the C-9 parachute canopy using a scale-down approach and mathematical modeling. In the research, a rectangular form of small stitched specimens was prepared from ripstop and plain-woven parachute canopy fabric where the seam angle was kept at 45°. Initially, the specimens were subjected to 5, 10, 15, 20, and 25 cycles of tensile impact force considering the critical situation of the C-9 parachute, and subsequently strength loss data was noted. In the results, it was found that, unlike plain-woven, the ripstop fabric specimen increased in strength and work of rupture up to 10 impact load cycles before dropping. Meanwhile, the breaking elongation decreases for both fabric specimens with the impact load cycles. Furthermore, the strength-loss data was extrapolated to assess the life of specimen and thereafter Weibull distribution was used determine the reliability of the specimen. Reliability results indicate that the plain-woven fabric is less reliable, has a faster rate of degradation, and will have a shorter service life than the ripstop fabric. The research revealed that the life of the parachute canopy is expected to be two times more in ripstop than in plain-woven fabric and hence the ripstop canopies may be preferred over plain-woven fabric for long-run applications. [ABSTRACT FROM AUTHOR]

This paper investigates the decarbonization of Tabriz-City focusing on its wind power for electrification. Statistical, quantitative, comparative, and simulation-research-methods used to analyze the existing and future total energy consumption, demand, and cost in the city according to carbon-based and wind-based electricity. The Monte Carlo Simulation Method has been used to estimate the probability of the Levelized Cost of Electricity. Tabriz thermal power plant generates 1kWh electricity that costs 0.15 US$/kW without subsidies and produces 575 g.CO2/kW.h overall while it will plunge to 0.05US$/kWh producing 7 g.CO2/kWh using wind. The NPV and IRR (32%) analysis show that that investment in wind-based electricity is three times cheaper than thermal power electricity in Tabriz. It is shown that the electrification of an oil-based economy with wind-based power plants is an economical investment for the city. Besides the hub-height and rotor sweep area, the capacity factor is the most decisive in the productivity of the alternative turbines. [ABSTRACT FROM AUTHOR]

This document delves into evaluating wind power potential within Morocco's Oriental region, encompassing an extensive study of 23 locations over 43 years. The analysis was conducted using the advanced MERRA2 data reanalysis system coupled with MATLAB software. Our comprehensive study aims to map the wind energy capabilities across these sites. We employed eight distinct algorithms to adapt the Weibull distribution for the wind speed data. Additionally, the research includes an analysis of the wind rose and assesses the Capacity Factor (CF) to determine the most efficient periods for wind energy production. Our findings highlight that sites S4, S7, and S11 create an ideal geographic formation for wind farm placement. Within this formation, site S8, boasting a (CF) of 36.97%, emerges as a critical location, especially when paired with the EWT DW54 500 wind turbine model. This investigation opens new avenues for advancing wind energy in the region. [ABSTRACT FROM AUTHOR]

A study was conducted on the tensile stress of an epoxy resin (Resoltech® 1050/1056). This was done by gathering a sample of 39 tensile strength data under consistent levels of stress. The tensile stress resistance is often characterized using a three-parameter Weibull distribution and the reliability of this characterization, given by confidence intervals (CIs). This approach commonly utilizes data-resampling techniques to estimate the CI of its parameters. CIs are constructed from six existing point-estimation methods. Herein, the jackknife was carried out to calculate the CIs using 39 subsamples and bootstrap methods using 100 or 200 subsamples. To date, there have been no studies exploring the effectiveness of subsampling methods for constructing CIs related to tensile strength. In this study, jackknifed and bootstrapped samples are used to implement the percentile method and three variations of the bias correction methods. We then performed simulations to evaluate the reliability of these methods using a Weibull random number generator. Our results showed that while the bias-corrected approach generated the most stable CIs from replicate samples, its accuracy was contingent on the point-estimation method employed. We also found that the different methods for calculating CIs resulted in significantly varying widths of the CIs. [ABSTRACT FROM AUTHOR]

Recurring bottlenecks significantly contribute to urban freeway congestion, making their analysis essential. This study examines six bottlenecks on Beijing’s Ring Road using multi-day data, identifying them via Dynamic Time Warping and Fuzzy C-Means Clustering (DTW+FCM). Key parameters—free-flow speed, critical speed, critical density, and jam density—are calibrated using fundamental diagram models. The Weibull distribution analyzes flow and speed patterns during congestion phases. The DTW+FCM method effectively identified bottlenecks and congestion levels. Severe congestion lasting over 10 hours on the West Second and Third Ring Roads averaged speeds of 15 km/h. The S3 model best fits data for the West Ring Roads, while the Van Aerde model suits the North Ring Roads. Different methods yield varying traffic capacity estimates, highlighting the need for nuanced approaches in urban expressway planning to maintain traffic quality and comfort. These findings offer valuable guidance for research and practical traffic management solutions. [ABSTRACT FROM AUTHOR]

This study predicted the fatigue life (N) of UHPC incorporated with different volume fractions (Vf = 0.0%, 0.5%, 1.0%, 1.5% and 2.0%) of steel fiber under flexural cyclic loading at various stress levels (S). The Weibull distribution, a twoparameter model, was utilized to estimate the distribution of fatigue life in UHPC. Subsequently, three methods were employed to calculate the parameters: the graphical method, the method of moments, and the method of maximum likelihood. The averaged values of these parameters were then obtained to enhance the accuracy of the estimation. The results are presented in the form of S-N diagrams, which depict the quantitative relationship between stress (S) and fatigue life (N). This relationship was determined using the Wohler equation, the modified Wohler equation, and the power equation. By employing these equations, the flexural fatigue strength of UHPC can be accurately predicted. Subsequently, the fatigue failure probability (Pf) was incorporated to enhance the reliability of the S-N quantitative relation. The fatigue testing results were presented in the form of S-N-Pf curves, which comprehensively reflect the relationship between stress, fatigue life, and failure probability. Furthermore, the mathematical relation of the S-N-Pf curves was derived to predict the fatigue life of UHPC with a given failure probability, providing a more comprehensive and accurate assessment of its fatigue behavior. [ABSTRACT FROM AUTHOR]

AbstractFoliar tissue sampling has been used in the greenhouse industry to diagnose nutrient disorders for many crops. Leaf tissue nutrient standards for pentas (Pentas lanceolata Forssk.) were non-existent before a recent rate study was published. This study expands prior work and presents a novel method to create data-driven nutrient interpretation ranges by fitting models based on the Sufficiency Range Approach (SRA) to provide more refined ranges of deficient, low, sufficient, high, and excessive values for 11 essential elements based on n = 407 data points. Data distributions were analyzed by fitting Normal, Gamma, and Weibull distributions. This study establishes using SRA to create refined interpretation ranges for pentas foliar nutrient analysis results with a higher degree of precision than what is currently available to the greenhouse industry for diagnosing fertility status. [ABSTRACT FROM AUTHOR]

During the operation of solid oxide fuel cells (SOFCs), the Ni-8YSZ anodes are subjected to thermal mismatch and reoxidation, accompanied by the risk of damage and failure. These damages and failures are generally induced by small defects at the microscopic level, leading to the degradation of the structural bearing capacity. Therefore, the distribution and quantification of the stresses in the real microstructure of Ni-8YSZ electrodes is essential. In this study, the real Ni-8YSZ microstructure was reconstructed based on nano-computed tomography, and the stress distribution of the real microstructure was analyzed based on the finite element method under reoxidation and different operating temperatures. The failure probability of 8YSZ at different degrees of reoxidation was evaluated according to the Weibull method, and the amount of damaged 8YSZ elements was statistically counted. The study results indicate a high level of stress in the thin necks and relatively sharp areas of the microstructure. The 8YSZ has a high failure probability at a reoxidation extent of 5–10%. [ABSTRACT FROM AUTHOR]

The durability of concrete materials in harsh environmental conditions, particularly in cold regions, has garnered significant attention in civil engineering research in recent years. Concrete structures in these areas are often damaged by the combined effects of alkali–silica reaction (ASR) and freeze–thaw cycles, leading to structural cracks and significant safety hazards. Numerous studies have demonstrated that polypropylene fiber concrete exhibits excellent crack resistance and durability, making it promising for applications in cold regions. This study elucidates the impact of alkali content on concrete durability by comparing the mechanical properties and durability of different alkali–aggregate concretes. The principal experimental methodologies employed include freeze–thaw cycle experiments, which examine patterns of mass loss; fluctuations in the dynamic modulus of elasticity; and changes in mechanical properties before and after freeze cycles. The findings indicate that increased alkali content in concrete reduces its strength and durability. At 100% alkali–aggregate content, compressive strength decreases by 35.5%, flexural strength by 32.9%, mass loss increases by 35.85%, relative dynamic elastic modulus by 39.4%, and residual strength by 97.28%, indicating higher alkali content leads to diminished durability. Additionally, this paper introduces a constitutive damage model, validated by a strong correlation with experimental stress–strain curves, to effectively depict the stress–strain relationship of concrete under varying alkali contents. This research contributes to a broader understanding of concrete durability in cold climates and guides the selection of materials for sustainable construction in such environments. [ABSTRACT FROM AUTHOR]

It is a deep-rooted and already observed reality that probabilistic models play an important role in describing and explaining the events/situations under consideration. Focusing attention on the fundamental role and applicability of the probabilistic models, this article considers the development of a new probability model derived through the implementation of a trigonometric function. The suggested model is named as a new cosine flexible Weibull (NCF-Weibull) distribution. This model is accomplished by exploiting the cosine functional approach. Some statistical characteristics of the NCF-Weibull distribution are given. Alongside, the estimates of the NCF-Weibull model are also obtained. Besides them, a simulation study for different settings of the parameter values of the NCF-Weibull model is also provided. Through the simulation study it is evidenced that the estimators of the NCF-Weibull distribution consistently perform well in terms of stability, consistency, and efficiency. Finally, the optimality of the NCF-Weibull distribution is judged by using two applications from different sectors. Based on five evaluation criteria alongside the p -value, the NCF-Weibull distribution shows optimal performances for both data sets. [ABSTRACT FROM AUTHOR]

Background: Hypertension is a common complication in patients with osteoarthritis (OA). There is increasing interest in the relationship between hypertension and OA. However, hypertension has been reported to negatively affect symptoms and quality of life in patients with OA. Therefore, treating hypertension is crucial for patients with OA. However, there is a lack of realworld studies on the effects of medications for treating hypertension on OA. Methods: Data from the FAERS database from January 2004 to December 2023 were extracted for disproportionality analyses, and proportional reporting ratios (PRRs) were used to assess the association between medications for hypertension and all types of arthritis. Adverse event signals were identified and determined using reporting odds ratios (RORs) Adverse event signals were considered to have occurred if a drug-induced adverse event was recorded more than or equal to 3 and the lower limit of the ROR confidence interval was more than 1. We selected five classes of drugs including, calcium channel blockers (CCBs), angiotensin converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), thiazide diuretics and ß-blockers and representative drugs were analysed for osteoarthritis-related adverse reactions, and age and gender subgroups were analysed for drugs of significance. We also analysed the occurrence of AEs in relation to time using the Weibull distribution. Results: In terms of overall data, we found significant OA adverse reaction signals only for ARBs among the five drug classes.ARB AEs for spinal osteoarthritis (ROR 4.64, 95% CI 3.62-5.94), osteoarthritis (ROR 3.24 95% CI 2.82-3.72) and gouty arthritis (ROR 3.27 95% CI 1.22-8.75) were the three adverse reactions with the loudest signals. Next, we found that valsartan had strong osteoarthritis adverse reaction signals among the three ARBs, namely, irbesartan, cloxartan, and valsartan. We also analysed age and gender subgroups and found that osteoarthritis signals were strongest in the 18-65 and 65+ population, while females seem to be more prone to valsartan-related OA AEs. Conclusion: ARBs, especially valsartan, have significant positive signals for OA AEs. Therefore, ARB drugs, especially valsartan, should be used with caution when treating patients with OA combined with hypertension. [ABSTRACT FROM AUTHOR]

Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H2. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania's north coast has potential for wind energy, aiding green hydrogen production for energy goals. [ABSTRACT FROM AUTHOR]

Background: Overall Survival (OS) and Progression-Free Interval (PFI) as survival times have been collected in The Cancer Genome Atlas (TCGA). It is of biomedical interest to consider their dependence in pathway detection and survival prediction. We intend to develop novel methods for integrating PFI as condition based on parametric survival models for identifying pathways associated with OS and predicting OS. Results: Based on the framework of conditional probability, we developed a family of frailty-based parametric-models for this purpose, with exponential or Weibull distribution as baseline. We also considered two classes of existing methods with PFI as a covariate. We evaluated the performance of three approaches by analyzing RNA-seq expression data from TCGA for lung squamous cell carcinoma and lung adenocarcinoma (LUNG), brain lower grade glioma and glioblastoma multiforme (GBMLGG), as well as skin cutaneous melanoma (SKCM). Our focus was on fourteen general cancer-related pathways. The 10-fold cross-validation was employed for the evaluation of predictive accuracy. For LUNG, p53 signaling and cell cycle pathways were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. For GBMLGG, ten pathways (e.g., Wnt signaling, JAK-STAT signaling, ECM-receptor interaction, etc.) were detected by all approaches. Furthermore, three approaches with the consideration of PFI demonstrated better predictive performance compared to the approaches without the consideration of PFI. For SKCM, p53 signaling pathway was detected only by our Weibull-baseline-based model. And three approaches with the consideration of PFI demonstrated significantly better predictive performance compared to the approaches without the consideration of PFI. Conclusions: Based on our study, it is necessary to incorporate PFI into the survival analysis of OS. Furthermore, PFI is a survival-type time, and improved results can be achieved by our conditional-probability-based approach. [ABSTRACT FROM AUTHOR]

AbstractThe mechanical properties and failure characteristics of soil–rock mixtures (SRMs) directly affect the stability of tunnels constructed in SRMs. A new SRM modelling method based on the combined finite–discrete element method (FDEM) was proposed. Using this new SRM modelling method based on the FDEM, the mechanical characteristics and failure behaviour of SRM samples under uniaxial compression, as well as the failure mechanism of SRMs around a tunnel, were further investigated. The study results support the following findings: (1) The modelling of SRM samples can be achieved using a heterogeneous rock modelling method based on the Weibull distribution. By adjusting the relevant parameters, such as the soil–rock boundaries, element sizes and modelling control points, SRM models with different rock contents and morphologies can be obtained. (2) The simulation results of uniaxial compression tests of SRM samples with different element sizes and morphologies validate the reliability and robustness of the new modelling method. In addition, with increasing rock content, VBP (volumetric block proportion), the uniaxial compressive strength and Young’s modulus increase exponentially, but the samples all undergo single shear failure within the soil or along the soil–rock interfaces, and the shear failure angles are all close to the theoretical values. (3) Tunnels in SRMs with different rock contents all exhibit X-shaped conjugate shear failure, but the fracture network propagation depth, the maximum displacement around the tunnel, and the failure degree of the tunnel in the SRM roughly decreases via a power function as the rock content increases. In addition, as the rock content increases, such as when VBP = 40%, large rocks have a significant blocking effect on fracture propagation, resulting in an asymmetric fracture network around the tunnel. (4) The comparisons of uniaxial compression and tunnel excavation simulation results with previous theoretical results, laboratory test results, and numerical simulation results verify the correctness of the new modelling method proposed in this paper. [ABSTRACT FROM AUTHOR]

Highlights To enhance the mechanical properties of the adhesive layer in joints, a thin–structural open–cell porous Ni–foam was embedded into the adhesive interface through ultrasonics (US) assisted. The porous metal foam is effective in interlocking itself with adhesive. After the ultrasonic vibration was applied for 0.17 s, the adhesive penetrated Ni–foam, generating enough residue. This technique effectively improves the energy absorption and shear strength of the CFRP/Al single lap joint by more than 250.33% and 118.4% with a 0.5 mm thick Ni–foam insert, respectively. The fatigue resistance increased by 154% with a 0.3 mm thick Ni–foam insert based on the fatigue testing results and the Weibull distribution method. The S–N curves were established at different reliabilities for engineering applications. With the Ni–Foam insert, the failure modes were changed through crack deflexion, Ni–Foam degumming, and crack blocking. Ultrasonic vibration assisting Ni‐foam inserts CFRP/Al adhesive joints. The shear strength of joints with Ni‐foam was maximally increased by 118.4%. The fracture forms of joints with Ni‐foam were changed. The fatigue life of the joints with Ni‐foam was maximally increased by 154%. S–N curve of joint with Ni–foam of different reliability levels was got. [ABSTRACT FROM AUTHOR]

AbstractWe introduce and study two new statistical models of ballot truncation – the process wherein voters neglect to rank every candidate during ranked choice voting (RCV). These models allow the incorporation of ballot truncation into simulations of ranked choice elections, resulting in more realistic predictions of likely outcomes. Our Independent Truncation model is based on a survival analysis approach, while our Plackett-Luce Truncation model extends the classical Plackett-Luce ranking model. Using ballot data from Cambridge MA elections, we motivate our new models and fit them to obtain parameter estimates. We then demonstrate our models in a simulation study to measure the effect of ballot truncation on minority representation in a hypothetical multi-winner election. [ABSTRACT FROM AUTHOR]

Aims Methods Results Conclusions This study was conducted to examine disproportionality, times to onset, incidence rates and outcomes of lenvatinib‐associated cardiac adverse events (AEs) using the Japanese Adverse Drug Event Report database.We analysed data for the period between April 2004 and May 2023. Data on cardiac AEs were extracted and the relative disproportionality of AEs was estimated using reporting odds ratios (RORs). Furthermore, Weibull distribution parameters were calculated.Of the 2 230 863 reports analysed, we identified 7684 reports of AEs associated with lenvatinib, including 317 cardiac AEs. Signals were detected for eight cardiac AEs: hypertension, cardiac failure, myocarditis, myocardial infarction, immune‐mediated myocarditis, cardiomyopathy, angina unstable and cardiotoxicity. Among these, fatal outcomes were observed for cardiac failure, myocarditis and myocardial infarction. Histograms of median times to onset for the eight detected cardiac AE signals showed that AEs occurred at a median of 3.5–134.5 days after lenvatinib administration. The Weibull distributions showed that cardiac failure occurred early after administration (early failure type), myocarditis occurred in a dose‐dependent manner (wearout failure type), and myocardial infarction occurred constantly throughout the exposure period (random failure type).We focused on cardiac AEs associated with lenvatinib as post‐marketing AEs. Serious outcomes can arise after lenvatinib administration. Patients should be monitored for signs of onset of these AEs not only at the start of administration, but also over an extended period. [ABSTRACT FROM AUTHOR]

In today's security landscape, the proliferation of unauthorized drones in restricted airspace has emerged as a significant threat. These drones pose various risks, from potential surveillance and espionage to more sinister possibilities such as physical attacks. Consequently, the development of effective anti-drone laser systems has become increasingly vital. Our study focuses on three main objectives: modeling internal reliability, identifying critical components, and studying the factors affecting the reliability of anti-drone systems. We aim to enhance the overall performance and effectiveness of anti-drone laser systems by analyzing the reliability of critical components and understanding how system parameters influence system reliability. To this end, reliability block diagram (RBD) methodology has been employed to compute the reliability of the laser subsystem in the anti-drone system. Additionally, we conduct a comprehensive review of component-wise reliability to identify vulnerable points within the system, thus enabling targeted improvements and optimizations. To capture the realistic scenario of system failure behavior, different distributions have been used to compute the reliability of the system, ensuring a thorough understanding of its operational reliability in diverse conditions. Finally, the energy values and probability of hitting are obtained for the anti-drone laser system to effectively mitigate environmental challenges. [ABSTRACT FROM AUTHOR]

To monitor time and magnitude, different types of control charts have been proposed in the literature. This article proposes a new maximum exponentially weighted moving average (Max-EWMA) control chart by assuming Weibull distribution. The test-statistic of the Max-EWMA chart consists of two EWMA statistics, where one statistic is used for monitoring magnitude while the other to monitor time interval between events. The performances of the proposed chart is assessed on the basis of run length characteristics like, average run length (ARL), standard deviation of the run length (SDRL), and quantiles of the run length distribution. Besides extensive simulation studies, real data sets are also considered to show the applications of the proposed chart. The results show that the proposed chart is efficient in detecting different size of small shifts. [ABSTRACT FROM AUTHOR]

Currently, research on the dynamic damage characteristics of sintered NdFeB is still in its early stages. Previous dynamic mechanical experiments on sintered NdFeB have shown that it is a strain rate-sensitive material. So, it is necessary to establish an appropriate damage constitutive model to describe the dynamic mechanical behavior of sintered NdFeB, with the aim of expanding its practical application range. This paper first establishes a damage evolution model by combining the Weibull distribution with a wing crack propagation model that considers crack spacing. Then, the damage model is integrated with the Zhu-Wang-Tang (ZWT) constitutive model to create the ZWT damage constitutive model. The model is fitted against previous experimental data to determine the specific parameters. Finally, to verify the accuracy of the newly established damage constitutive model, it is compared with the damage constitutive model proposed by Li. The comparison results fully affirmed the accuracy of the newly established ZWT damage constitutive model. [ABSTRACT FROM AUTHOR]

In system reliability, practitioners may be interested in testing the homogeneity of the component lifetime distributions based on system lifetimes from multiple data sources for various reasons, such as identifying the component supplier that provides the most reliable components. In this paper, we develop distribution-free hypothesis testing procedures for the homogeneity of the component lifetime distributions based on system lifetime data when the system structures are known. Several nonparametric testing statistics based on the empirical likelihood method are proposed for testing the homogeneity of two or more component lifetime distributions. The computational approaches to obtain the critical values of the proposed test procedures are provided. The performances of the proposed empirical likelihood ratio test procedures are evaluated and compared to the nonparametric Mann–Whitney U test and some parametric test procedures. The simulation results show that the proposed test procedures provide comparable power performance under different sample sizes and underlying component lifetime distributions, and they are powerful in detecting changes in the shape of the distributions. [ABSTRACT FROM AUTHOR]

Traffic incidents adversely affect the safety and mobility of our transportation network. As such, accurate prediction of incident duration is critical in developing strategies and deploying resources to quickly clear incidents and restore traffic to pre-incident conditions. This study introduces a mixture density network (MDN) based on Gamma and Weibull distributions to estimate incident clearance duration. The MDN is known for being highly flexible and can recognize multiple components in the distribution of a target variable. A total of 58,167 incidents from highways in Jacksonville, Florida, gathered from 2014 to 2017, were used as a case study. The comparison between MDN, basic artificial neural network (ANN), and XGBoost revealed that the MDN outperformed the other models by having the lowest mean square error (MSE) and mean absolute error (MAE). The MSE of Gamma MDN was the lowest, estimated at 926 min, compared to 935, 945, and 975 min of the Weibull MDN, ANN, and XGBoost, respectively. Based on the Gamma MDN, the key variables influencing incident clearance duration estimated by the permutation feature importance and shapley additive explanations algorithms include the type of agencies that responded to incidents, the number of agencies involved, incident type, and incident severity. The practical contribution and the application of this study in diverse areas have been discussed. It is expected that the findings will help to improve incident clearance strategies. Specifically, the developed model could be utilized to develop incident management strategies that will proactively address the safety and mobility impacts of traffic incidents on roadways. Practical Applications: The findings from this study are useful to researchers, traffic operators, and incident responders to develop effective and proactive traffic incident management strategies. Researchers can develop an artificial intelligence algorithm with MDN to predict clearance duration based on real-time traffic and weather data. Traffic operators and incident management teams can use these results to strategically allocate appropriate incident response resources along freeway segments while understanding the impact of surrounding conditions when clearing incidents. Besides, the model could be used to proactively inform drivers upstream of the traffic incident, depending on its length of clearance duration. This information would enable drivers to take proactive measures, including detouring in case of incidents with overly long clearance duration. [ABSTRACT FROM AUTHOR]

Abstract–The Weibull distribution is the most applied model in reliability field for lifetime analysis. The Weibull location parameter, characterizing the minimum possible life, plays a significant role in engineering applications. In this paper, we consider the interval estimation on the location parameter when the product's lifetime follows the three-parameter Weibull distribution with a known shape parameter. A novel approach based on the relationship between the minimum order statistics, the location parameter, and the sample size is developed to construct confidence intervals for the Weibull location parameter. Thereafter, we compare it with other two interval estimation approaches by the performances of the coverage probability and the average length via simulations and a real application. The results show that the proposed method outperforms the pivot quantity (PQ) method and the bias-corrected and accelerated (Bca) bootstrap method in small and medium samples in terms of coverage probability. [ABSTRACT FROM AUTHOR]

Probability distributions are very important in describing practical phenomena. Among the available probability distributions, the three-parameter generalized inverse Weibull (for short "GI-Weibull") model has very useful applications in reliability, management, and healthcare studies, among others. This paper considers a weighted adaptation of the proposed new model. We proposed a weighted version of the GI-Weibull distribution, which may be called the weighted generalized inverse Weibull (for short "WGI-Weibull") distribution. The appeal of the WGI-Weibull distribution lies in its altered form of the GI-Weibull distribution, which does not necessitate the incorporation of extra parameters. Various properties of the WGI-Weibull distribution are obtained through mathematical derivation. Finally, a medical application (i.e., a medical data set) is studied and analyzed to illustrate the WGI-Weibull model. A comparison is conducted between the WGI-Weibull distribution and the GI-Weibull and inverse flexible Weibull models utilizing the medical data set. Employing three evaluation metrics, it is shown that the WGI-Weibull model describes the medical phenomena much better than the GI-Weibull and some Weibull models. [ABSTRACT FROM AUTHOR]

The main aim of this research is to analyze the characteristics of the wind speed and wind power density in Baghdad City within micro-scale meteorological conditions at Mustansiriyah University Meteorological Station (MUMS). Temperature, atmospheric pressure and wind speed data were taken for one year (2016) measured at a height of 18 m above the earth's surface. Hourly, monthly, and seasonal changes in wind speed at an altitude of 30 m were estimated using a power law. The mean diurnal and monthly air density was calculated. Different statistical distributions were used, including the Rayleigh, Gamma and Weibull distributions, and the best distribution function was selected to evaluate the wind power density in the study area. The results showed the highest monthly mean of wind speed recorded in June and July. Therefore, these months have the highest wind power density. The lowest monthly mean of wind speed and wind power density was observed in December. The maximum and minimum values of air density were recorded in December, January and July, August, respectively. The monthly variation of the shape parameter (k) ranges between 0.99 - 1.81, while the monthly variation of the scale parameter(c) ranges between 1.07 - 2.3 m s-1. It was also found that the Weibull distribution was more accurate than the Rayleigh and Gamma distributions. The prevailing wind direction is northeast (NE) and east-northeast (ENE) most of the time. The research results showed that the study area is not suitable for using wind energy to generate energy. [ABSTRACT FROM AUTHOR]