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[Introduction] The widespread adoption of electric vehicles (EVs) enhances environmental sustainability during travel, yet it simultaneously elevates strain on the mains supply. To assess the impact of EV charging on the power grid, a specialized charging load calculation method is developed for large EV cluster on the basis of the Monte Carlo method. [Method] In this approach, EVs were categorized into six groups based on their usage: private EVs, electric buses, electric taxis, online ride-hailing EVs, official EVs, and logistics EVs. Typical battery performance parameters for each group were identified, and probability models were established to characterize the variability in their travel and charging patterns. By integrating group scale forecasts, daily charging schedules for each group were simulated through random sampling. Subsequently, the daily charging load for each group was calculated, culminating in the total charging load for the EV cluster through an aggregation method. [Result] The EV cluster in a southern province in 2030 was taken as the simulation case. [Conclusion] The case analysis reveals that the proposed approach can provide the daily charging load of various EV groups and the entire EV cluster. Furthermore, among all types of EVs, electric buses have the highest peak charging load at 4 639.5 MW, followed by that of private EVs (not higher than 70% of the electric buses' peak load), and electric taxis have the lowest peak charging load. The charging peak of the entire EV cluster occurs between 19:00 and 23:00 at night, and the peak load can reach 10.0927 GW.

Multi-energy micro-grid has received widespread attention in the wave of continuous promotion and development of renewable energy. However, in the face of wind and solar uncertainty, its scheduling model needs to be further optimized. Therefore, a multi-energy micro-grid day-ahead optimal scheduling model was proposed to construct wind and solar uncertainty scenarios, and the application of energy storage station was considered. Multiple algorithms were introduced to propose the multi-energy micro-grid day-ahead optimal scheduling model. Finally, the research content was validated. The results confirmed that the wind and solar power output probability model could describe the characteristics of wind and solar power output at different periods. The generated scenes had a large number of wind speeds in the range of 1.5 m/s to 5 m/s, and the light intensity reached its peak at 14:00, which was consistent with the historical data of the research object. In addition, the total pre-scheduling cost of this optimized scheduling model within a day was 45.16×105 yuan, while the actual scheduling cost within a day was only 21.46×105 yuan. It saved costs by 41.65% and 44.95%, respectively, compared to the comparison algorithms. The research has driven innovation and optimization of the multi-energy micro-grid scheduling model. This provides a useful theoretical and practical basis for addressing the uncertainty of wind and solar energy and improving the economic efficiency of energy systems, which is crucial for the sustainable development of new energy.

Effective risk assessment of potential landslide disasters is crucial for informed decision-making in engineering projects. In practical engineering, the risk assessment of individual landslide disasters still relies mainly on qualitative evaluation methods. The qualitative risk level is obtained through the risk matrix based on danger and loss, which poses problems of multiple and discontinuous risk standards. This makes it difficult to compare and classify risks in actual engineering, and may also lead to errors in risk assessment results, misleading risk management. To address these issues, a probability model is employed to quantitatively describe the stability of landslide disasters, considering the size of losses to the element at risk. A quantified risk index for individual landslide disasters is obtained, introducing the concept of landslide disaster risk surface and risk contour. Corresponding evaluation and calculation software are developed to form a quantified risk assessment method for landslide disasters, eliminating the multiplicity and discontinuity of risk standards brought about by traditional landslide risk matrices. The accuracy of the evaluation is improved. By conducting quantitative risk assessment on six individual landslides in Fengjie County, Chongqing, the correctness and reliability of the quantitative risk assessment method were verified, providing a new approach for landslide disaster risk assessment.

Abstract In malaria-endemic areas of Sub-Saharan Africa, overlap of clinical symptoms between malarial and non-malarial febrile illnesses can lead to empiric use of antibiotics among children. Our study aimed to illustrate the potential impact of decreasing malaria prevalence from malaria control efforts on antibiotic use. We constructed a probabilistic decision tree model representing antibiotic prescription in febrile children

In malaria-endemic areas of Sub-Saharan Africa, overlap of clinical symptoms between malarial and non-malarial febrile illnesses can lead to empiric use of antibiotics among children. Our study aimed to illustrate the potential impact of decreasing malaria prevalence from malaria control efforts on antibiotic use. We constructed a probabilistic decision tree model representing antibiotic prescription in febrile children < 5 years. This model was used to predict change in absolute antibiotic use compared to baseline under levels of decreasing malaria prevalence. Model parameters were based on data from a hospital study in Ghana and validated via literature review. The baseline prevalence of malaria diagnoses was 52% among all hospitalized children. For our main results, we reported outcomes for a scenario representing a 50% decrease in malaria prevalence. Compared to baseline, absolute antibiotic prescription decreased from a baseline of 639 doses (95% CI 574–694) to 575 (95% CI 502–638). This reflected a 10% (95% CI 7%–13%) decrease in absolute antibiotic use. Our findings demonstrate that effective malaria control can reduce pediatric antibiotic use. However, until substantial progress is made in developing accurate diagnostics for non-malarial febrile illnesses, further reductions in antibiotic use will remain a challenge. [ABSTRACT FROM AUTHOR]

This study constructs a discrete-time Markov Chain (DTMC) model for a baseball plate appearance (PA) employing Major League Baseball's pitch-by-pitch dataset. Based on the DTMC model, we propose a novel measure for a baseball PA, termed the Importance of Moment (IOM). The IOM quantifies the criticality of each ball-strike count situation, by assessing the probabilistic difference between the pitcher's and hitter's favourable outcomes (out vs reaching base). If the favours significantly vary right after a particular ball-strike count, then the count is deemed critical and is assigned a high IOM value. We empirically verify that IOM explains pitchers' behaviour of fastball speed. We then further investigate whether the behaviour of ace pitchers differs significantly from the majority. Several interesting properties are found from the analysis. Firstly, the path independence assumption generally holds, with the exception of the ball-strike count of 2B1S. Second, pitchers tend to throw the faster fastball at counts with higher IOM values. Lastly, ace pitchers are capable of pitching even faster fastball in two-strike situations in which IOM is high. The DTMC effectively models the probabilistic structure of a baseball PA, and the proposed IOM measure serves as a useful tool for explaining player behaviour. [ABSTRACT FROM AUTHOR]

Bird strikes pose one of the most significant threats to aviation safety, often leading to substantial loss of life and economic damage. Many bird strike incidents involve multiple birds. However, in previous bird strike studies, the problem of multiple bird strikes has often been neglected. In this paper, the bird slicing process of a rotating engine fan is examined, and a probability model is introduced to assess the risk of multiple impacts on the fan blades. In addition, this paper utilized an implicit–explicit calculation method. The parameters of blade root stress, tip displacement, plastic deformation, and energy were selected to investigate the effects of the time interval and strike position of a bird strike on the dynamic response of and damage to the blades. The results indicated that the position of bird strikes has a more pronounced effect on blade damage compared to the time interval between impacts. Damage to a blade is most severe when the blade root is struck multiple times. Multiple bird strikes may not always lead to a significant increase in maximum blade tip displacement, and may even have a dampening effect. [ABSTRACT FROM AUTHOR]

In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability problem. Further, to solve the double nested optimization problem in hybrid reliability calculation, an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point (MPP). The limit state function is linearized at these points, and an innovative random variable is defined to solve the equivalent static reliability analysis model. The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem. [ABSTRACT FROM AUTHOR]

The representability of the Burr distribution as a mixture of Weibull distribution is studied in order to justify its utility for modelling the statistical regularities in extreme values registered in non-stationary flows of informative events. A result of [24] is improved by extending the domain of admissible values of the parameters which provide the representability of the (generalized) Burr distribution as a scale mixture of the Weibull distribution. This result gives an argument in favour of application of the Burr distribution as a model of statistical regularities of extreme values registered within moderate regular time intervals, say, daily (short-term) extremes. In turn, if we are interested in the statistical regularities of the behaviour of the absolute extreme observation over a long period, say, a decade (the long-term extreme), then it can be noted that the daily extreme values form a sample of the Burr-distributed random variables. As is known, the Burr distribution belongs to the domain of max-attraction of the Fréchet distribution. The problem of improving the accuracy of the approximation of the distribution of the absolute extreme by the Fréchet distribution by the construction of an asymptotic expansion for the distribution of the extreme order statistics in the sample of independent identically Burr-distributed random variables is also considered. These results are illustrated by an example of fitting the Burr distribution to the data representing the extreme values of characteristics of the interplanetary magnetic field. [ABSTRACT FROM AUTHOR]

In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. We present a unique methodology that is based on the maximum likelihood approach and neutrosophic approach for estimating parameter of the proposed neutrosophic geometric distribution (NGD). The proposed methodology is supported by key likelihood inference results. The proposed distribution is specifically designed to handle variables with imprecise observation, hence effectively addressing a wide range of situations often encountered in the analysis of uncertain data. To evaluate the efficacy of the proposed neutrosophic model, we have carried out a comprehensive simulation experiment that rigorously examined the performance of the proposed model. The practical utility of NGD in the analysis of incomplete data is further exemplified through real-world applications.

Forward-looking imaging is crucial in many civil and military fields, such as precision guidance, autonomous landing, and autonomous driving. The forward-looking imaging performance of airborne radar may deteriorate significantly due to the constraint of the Doppler history. The deconvolution method can be used to improve the quality of forward-looking imaging; however, it will not work well for complex imaging scenes. To solve the problem of scene sparsity measurement and characterization in complex forward-looking imaging configurations, an efficient probability model-driven airborne Bayesian forward-looking super-resolution imaging algorithm is proposed for multitarget scenarios to improve the azimuth resolution. First, the data dimension of the forward-looking imaging scene was expanded from single-frame to multiframe spaces to enhance the sparsity of the imaging scene. Then, the sparse characteristics of the imaging scene were statistically modeled using the generalized Gaussian probability model. Finally, the super-resolution imaging problem was solved using the Bayesian framework. Because the sparsity characterization parameters are embedded in the entire process of imaging, the forward-looking imaging parameters will be updated during each iteration. The effectiveness of the proposed algorithm was verified using simulation and real data.

A novel probability model with bounded support is introduced. The formulation of this new probability model is based on inverting the Slashed Pareto distribution. This new distribution has the merit of being very simple and not involving any complex mathematical function in its construction. Some interesting properties like moments, skewness and kurtosis, unimodality, L-Moments, L-skewness and L-kurtosis would be explored in detail. Various Survival properties including survival function, hazard rate function and mean residual life(MRL) like have been given. For estimating the parameters contained in the new model, methods like Method of Moments (MOM) and Maximum Likelihood Estimation (MLE) have been used.

In the public-private partnership (PPP) project, social factors are an important reason that leads to the escalation of commitment (EOC) of investors. Firstly, the study identified ten social factors that influence investor's EOC in PPP projects by literature review. Then, 126 valid data were collected by situational questionnaire, and regression models of social factors that influence investors' EOC were built at different stages of project completion. Finally, the study constructed the probability model of the investors' EOC in different stages. It is found that with the progress of the project, investors are more and more inclined to EOC. In different stages, the social factors that play a major role in the investors' EOC are different. Probability models based on the influence of social factors have good prediction ability. The findings can help engineering managers predict the risk of investor's EOC in advance and formulate preventive measures in different stages to promote the success of the project. [ABSTRACT FROM AUTHOR]

In practical scenarios, it is common to encounter fuzzy data that contains numerous imprecise observations. The uncertainty associated with this type of data often leads to the use of interval statistical measures and the proposal of neutrosophic versions of probability distributions to better handle such data. We present a unique methodology that is based on the maximum likelihood approach and neutrosophic approach for estimating parameter of the proposed neutrosophic geometric distribution (NGD). The proposed methodology is supported by key likelihood inference results. The proposed distribution is specifically designed to handle variables with imprecise observation, hence effectively addressing a wide range of situations often encountered in the analysis of uncertain data. To evaluate the efficacy of the proposed neutrosophic model, we have carried out a comprehensive simulation experiment that rigorously examined the performance of the proposed model. The practical utility of NGD in the analysis of incomplete data is further exemplified through real-world applications. [ABSTRACT FROM AUTHOR]

Disc-cutters play a crucial role in the penetration process during construction using a tunnel boring machine (TBM). Their wear status has a significant impact on working efficiency and costs that accounts for a large part of the budget. Discovering the wear pattern and characteristics of disc-cutters could provide a valuable guide for field maintenance and cutter replacement work. It also provides insight into the wear mechanism of cutters at different positions for optimizing the disc-cutter arrangement design. In this study, the cutter wears data of 34 disc-cutters over 643 days in the Chaoer River to Xiliao River water diversion tunnel was collected. The dataset contains 21862 manual readings measured from 1079 disc-cutters replaced in this project. The raw data from the hard copy version was transformed into a digital twin database by eliminating abnormal data, filling in empty values, and performing linear interpolations. It has been found that the cutters can pass statistical testing for an exponential probability distribution function with respect to the wear rate (w). The regression ratios of R2 are essentially greater than 0.8. These findings would help estimate the future service life of a currently working cutter, which means significant savings for the costly disccutters. The application of exponential distribution has the advantage of only one shape parameter, λ, whose reciprocal represents both the statistical mean and standard deviation of the wear rates. It is simple and practically amenable. A preliminary study was carried out to simulate the wear process between two neighboring cutters for drafting a replacement plan for disc-cutters by the Monte Carlo method. The prediction results agreed reasonably well with the measured information. [ABSTRACT FROM AUTHOR]

Tax havens may play a key role in the Profit-Shifting (P-S) activity of Multinational Companies (MNCs), since, among other characteristics, they are the territories with the most beneficial taxes for foreign investors. This article shows that Spanish MNCs facing higher tax rates in non-tax havens, and therefore those that stand to gain the most from P-S, are more likely to invest in tax havens. This outcome is robust to at least two different tax haven lists and various definitions of the non-haven tax rate. The size of the MNCs' activity, their use of intangible assets, and belonging to the Ibex 35 stock index also positively affect the probability of investing in tax havens. By economic sectors, once the endogeneity problem is controlled for, the incentive is greater for manufacturing than for service firms, but it is especially high for financial firms. Additionally, while non-haven tax rates positively influence the number of different tax havens used by firms, they have no effect on the number of affiliates located within them. Finally, the article estimates that Spanish MNCs have been able to save about 4 billion euros per year in Corporate Income Tax in the period 2013–2018 as a result of P-S. [ABSTRACT FROM AUTHOR]

Bird strikes pose one of the most significant threats to aviation safety, often leading to substantial loss of life and economic damage. Many bird strike incidents involve multiple birds. However, in previous bird strike studies, the problem of multiple bird strikes has often been neglected. In this paper, the bird slicing process of a rotating engine fan is examined, and a probability model is introduced to assess the risk of multiple impacts on the fan blades. In addition, this paper utilized an implicit–explicit calculation method. The parameters of blade root stress, tip displacement, plastic deformation, and energy were selected to investigate the effects of the time interval and strike position of a bird strike on the dynamic response of and damage to the blades. The results indicated that the position of bird strikes has a more pronounced effect on blade damage compared to the time interval between impacts. Damage to a blade is most severe when the blade root is struck multiple times. Multiple bird strikes may not always lead to a significant increase in maximum blade tip displacement, and may even have a dampening effect.

As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver's condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) array, as the key component of large-scale PV power stations, is prone to frequent failure that directly affects the efficiency of PV power stations. Therefore, accurate classification of the operating state of PV arrays is the basis for fault location. Thus, a novel classification method for PV array operating state was designed based on nonparametric estimation and a 3σ method. The actual data analysis proves the hypothesis that performance ratio (PR) distribution characteristics of PV arrays can characterize the operating state of PV arrays. The modeling curve of the PV array with an excellent performance has only one peak and the peak value is large, while the distribution curve of the PV array with a poor performance has a small peak. In this paper, the distribution characteristics of PV arrays are modeled, the peak value is used to classify the operating state of PV arrays, and finally the effectiveness of the proposed method is compared. Overall, this paper makes a valuable contribution by proposing a novel method for accurately classifying the operating state of PV arrays. The proposed method can help improve the efficiency and fault diagnosis of PV power stations. [ABSTRACT FROM AUTHOR]

Field segmentation is the basis of protocol format inference.The subsequent steps of protocol format inference,such as message structure identification,field semantic inference and field value constraint inference,highly depend on the quality of field segmentation.Field segmentation of binary protocol is a big challenge because of the lack of character coding and delimitation,the flexibility of field length and the expansiveness of field range.To improve feature construction and decision rules,this paper proposes a novel binary protocol field segmentation method based on probability model.First,it constructs the field boundary constraint relationship of binary protocol messages from the internal structure of message and the value change between messages.Then,it combines various constraints in the way of probability,calculating the probability of each position becoming the boundary by factor graph model.Finally,the most likely field boundaries are obtained from probability.Experiments show that the proposed method can achieve more accurate and robust results than the traditional methods in binary protocol field segmentation.

Typhoon rainstorm is the main inducing factor of landslides in southeast hills and mountains in our country. Revealing the correlation between typhoon path and landslides has important theoretical and practical significance for monitoring and early warning of typhoon-rainstorm soil landslides in southeast hills and mountains. In this paper, based on the typhoon data directly passing through Fujian Province or indirectly affecting Fujian Province from 2015 to 2019, as well as the rainfall data and typhoon-rainstorm landslide data during these typhoon events, the Kriging interpolation method in ArcGIS software is used to couple the typhoon path, rainfall data and typhoon-rainstorm landslide data. Through the logistic regression method in SPSS software, the landslide probability model based on typhoon track in Quanzhou City is obtained. Using Kriging interpolation method and logistic regression method, we take the landslide occurred during typhoon Dujuan as an example to verify the model. The correct probability of landslide caused by rainfall data measured by actual rainfall station is 77%, and the correct probability of actual landslide is 100% with a satisfactory prediction effect. Based on the actual rainfall calculation data, the false alarm rate is 21%, but the actual landslide false alarm rate is 0, which is in line with the safety. Therefore, the model can be used to predict the occurrence probability of typhoon-rainstorm landslides under the influence of typhoon passing through Quanzhou City.

After an earthquake, a track-bridge system may suffer damage and residual deformation, posing a threat for trains passing over a bridge. However, since railway transportation is an important channel for disaster relief, the timely passage of trains is of great significance for the transportation of materials and personnel. In the past, subjective and experience-based judgments were made on whether a train can pass over an earthquake-damaged bridge, and no relevant numerical calculations were carried out. Therefore, a strategy for the evaluation of train performance when passing over existing bridges after earthquakes was proposed. The strategy considers the randomness of track irregularity, and evaluates the running safety performance (RSP) and running comfort performance (RCP) of trains passing over existing bridges from the perspective of probability. The probability law of a lateral residual bridge deformation influence on the RSP and RCP after an earthquake was revealed. Finally, the maximum train passing speed which ensures the RSP was recommended. [ABSTRACT FROM AUTHOR]

The paper presents a procedure for numerically modelling the surface of corroded specimens. A 3D laser scan is utilised to derive surface morphology and corrosion pit depth distribution. This corrosion depth is analyzed with a probability model to assess the morphology difference and the characteristic of the corrosion pit depth at a particular volumetric change. Furthermore, the goodness fit test statistic is carried out to observe the propensity of corrosion depth to a specific distribution, i.e. the Gaussian and non-Gaussian. The corroded specimens that conform to Gaussian distribution are numerically modelled with ANSYS APDL to generate a Gaussian surface. Furthermore, the non-Gaussian surface model is simplified as a single corrosion pit with various geometrical shapes. The mesh convergence is carried out to provide accurate stress distribution. The procedure is adjustable and applicable to the other surface morphology. [ABSTRACT FROM AUTHOR]

The stability classification of loess deposits around tunnels is a vital prerequisite for safe construction in underground environment. Due to the fuzziness and randomness of loess physical and mechanical parameters, the stability prediction of loess deposits shows uncertainty. Existing loess deposit stability classification models rarely consider the uncertainty of influencing factors. A novel classification probability model of loess deposits is proposed for the above problems based on Monte Carlo simulation and multi-dimensional normal cloud (MCS-Cloud). Specifically, five loess parameters, including water content, cohesion, internal friction angle, elastic modulus, and Poisson ratio, were selected as predictors for the stability level of loess deposits. The weights of the predictors were obtained through 50 test samples. After acquiring the numerical characteristics of the normal cloud, the stability level can be comprehensively evaluated with the weighted multi-dimensional normal cloud model. The classification model was applied to the loess tunnel in Yan'an, China. The prediction results are in good agreement with practical engineering, denoting the rationality of the weighted multi-dimensional normal cloud. Finally, the stability classification of loess deposits was discussed from the perspective of uncertainty analysis with the application of MCS. Results proved that the MCS-Cloud model is feasible for classifying the stability of loess deposits surrounding tunnels. The obtained classification probability can be used for quantitative risk assessment of loess tunnels. [ABSTRACT FROM AUTHOR]

Shale oil, as an unconventional oil and gas resource with huge reserves, has become an important replacement resource and great significance to develop. Aiming at the problems of rapid decline oil production in the depletion development of shale oil reservoirs by "depleted horizontal well+volume fracturing", and the lower recovery factor predicted by the existing productivity evaluation methods in shale reservoir, we construct a productivity evaluation method for fractured horizontal wells based on naive Bayes algorithm. Taking the Jimsar shale reservoir in Xinjiang oilfield as the target reservoir, we establish the multi-classification Naive Bayes prior probability model and the conditional probability model containing five attributes of geological parameters and engineering parameters by taking the three-year cumulative production as the classification evaluation index. Then we obtain the posteriorprobability model of the four types of production capacity based on Bayesian theory to achieve the capacity assessment of fractured horizontal wells in shale oil reservoirs. The results show that the proposed shale reservoir capacity classification method based on the Naive Bayes model is applicable. The accuracy of productivity prediction is 94% for Class I wells, 71% for Class II wells, 87% for Class III wells, and 92% for Class IV wells, respectively. The productivity classification trend distribution map of fracturing horizontal wells in Jimsar shale reservoir is drawn, and the high-production potential area is mainly distributed in the southeast of the reservoir. The analysis of the constructed posterior probability model shows that the optimization of fracturing construction parameters can improve the probability of high well production rate. The study provides a guiding basis for the subsequent large-scale fracturing reconstruction of horizontal wells for shale oil development. [ABSTRACT FROM AUTHOR]

Soft sensor technology is an important solution for timely prediction of some difficult-to-measure variables in the chemical process. For the data-driven soft sensor models, the modeling process needs a certain amount of labeled samples. In some actual processes, the number of unlabeled samples is very large, but the labeling process of unlabeled samples is very time-consuming and laborious. When soft sensor modeling is performed in the case of sparse labeled samples, the generalization ability of the model is bound to be poor. To address this problem, a semi-supervised soft sensor method based on vine copula regression and tri-training algorithm (tri-training VCR) is proposed in this paper. Semi-supervised learning is to enhance the performance of supervised learning models by using unlabeled samples. The tri-training algorithm is used to expand the labeled samples through the cooperation of three vine copula regression models with different structures and a new method to judge the confidence degree of pseudo-labeled samples, so as to improve the prediction accuracy of the soft sensor model. A numerical example and an industrial example are used to demonstrate the effectiveness of the proposed method. • A semi-supervised style algorithm based on vine copula regression and tri-training algorithm has been developed. • A new confidence evaluation method is proposed to more accurately judge the reliability of pseudo-label samples. • Multiple vine copula regression models are reasonably integrated through variance information. • Two examples are used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The worldwide occurrence of numerous ship–bridge collisions has prompted a substantial concern regarding the safety of sea-crossing bridges. The majority of conventional risk models for ship–bridge collisions assume that collisions occur in inland waterways instead of sea waterways. Therefore, a new probability model should be established to assess the risks of ship–bridge collisions in sea waterways, considering the harsh navigation conditions such as strong wind and turbulent current in sea waterways. This study proposed a new risk assessment method to evaluate the collision probability of a ship with a sea-crossing bridge. Additionally, the motion state of a ship under the coupling effect of wind and current was analyzed to optimize the new probability model, and the drift trajectory of an aberrant ship was obtained. Moreover, the proposed model was applied to analyze the annual frequency of ship collisions at the Zhoushan-Daishan Sea-crossing Bridge in the East China Sea. For large tonnage ships, the annual frequency of ship collision was higher when calculated using the probability model based on the AASHTO Guide than that evaluated using the current model, whereas a significantly larger collision frequency was computed by the proposed model for small tonnage ships. Furthermore, we analyzed the relationship between navigation circumstances and the annual frequency of ship collision. The findings of this study will assist in appropriately assessing the risks of ship–bridge collisions for ships navigating through sea-crossing bridges under the influence of wind and current and provide a feasible method for improving the design and management of sea-crossing bridges. [ABSTRACT FROM AUTHOR]

Thickness loss due to corrosion wear generally occurs on ship structural members in corrosive environments. For corrosion wear, a probabilistic model can be successfully identified from the thickness measurements data to evaluate and estimate wear conditions. On the other hand, thickness loss due to mechanical wear may occur in some ship structural members in addition to thickness loss due to corrosion wear. In this study, a new probabilistic model for the case where mechanical wear is superimposed to corrosion wear is proposed. The condition observed in the actual structure is a mixture of corrosion wear condition and condition of corrosion wear with mechanical wear. When identifying a probabilistic model based on the plate thickness measurements data, it is impossible to distinguish whether the measurements data represent a corrosion condition due to corrosion wear or corrosion wear with mechanical wear. Therefore, a method is examined to obtain a model that would be the maximum likelihood by classifying the data using latent variables. By classifying the data and identifying both of a probability model of corrosion wear and a probability model of corrosion wear with mechanical wear, respectively, the posterior distribution of the latent variables is updated. These processes are repeated until the likelihood of the mixed probability models is maximized. For verification, a set of corrosion data consisting of mixed wear conditions were simulated and analyzed. It shows that both probability models could be properly identified with the proposed method, and in particular, the amount of diminution corresponding to high cumulative probability could also be properly estimated. Furthermore, this method was applied to thickness measurements data of ship structural members in the lower cargo holds of bulk carriers, where mechanical wear is also a concern, and it was confirmed that there was no difference in the corrosion wear condition of the members in the lower part of cargo holds of bulk carriers, however, significant differences were observed in the degree and extent of mechanical wear, indicating that the influence of mechanical damage is very significant in the wear condition of these members. • A model of thickness diminution when mechanical wear is superimposed on corrosion wear was proposed, and a probability model was constructed to represent the state of diminution of a mechanically damaged member. • A procedure to classify the thickness measurements data representing only thickness diminution into the data due to corrosion wear and data due to corrosion data with mechanical wear was studied. • By applying the proposed model to the thickness measurements data of inner bottom plate, bilge hopper plate and lower stool side plate in the cargo holds of bulk carriers, the degree of mechanical damage and the extent of damaged area of each member was clarified. [ABSTRACT FROM AUTHOR]

• New paradigm of buckling capacity probability model for single-layer gridshells. • Probability model evolution laws under various parameters. • Joint well-formedness is strongly tied to structural buckling modes. • Evolution mechanism of probability model of non-linear buckling capacity. • New guidelines for construction and acceptance of single-layer gridshells. This research offers a detailed probability analysis of the non-linear buckling capacity (NBC) of single-layer gridshells (SLGs), impacted significantly by stochastic initial geometric imperfections during construction. Employing the constrained stochastic imperfection modal method, which adeptly incorporates topology constraints of actual initial imperfection scenarios. The key factors, such as the imperfection simulation method, imperfection amplitude, number of rings, and the rise-to-span ratio, are evaluated through systematic numerical analysis. The results reveal that the traditional random imperfection method cannot obtain the actual bimodal distribution of the NBC, and it can overestimate the maximum and minimum NBCs by 5.16 % and 52.49 %. Furthermore, the joint well-formedness is introduced to quantify the joint local stiffness, and it is found to have a highly intertwined correlation with the structural non-linear buckling mode. The evolution mechanism of probability models can be elucidated through the joint initial deviations leading to changes in joint well-formedness (i.e., local stiffness), based on which it is concluded that an imperfection amplitude of 1/1500 of the structural span should be recommended to be the imperfection amplitude acceptance limit to fully utilize the structural global buckling capacity; significant deviations in primary rib joints and inner ring joints markedly affect the NBC more than those in non-primary rib joints and outer ring joints. The aforementioned conclusions are essential for revising guidelines for permissible joint deviations and assessment methodologies for critical joints throughout the construction. [ABSTRACT FROM AUTHOR]

• The research process to improve the accuracy and personalization of group recommendations. • The genetic algorithm is designed based on the initial population. • The proposed Genetic Algorithm-Probability Model-Group Recommendation (GAPMGR). • The GAPMGR method proposed has significantly improved team. This work aims to construct an innovative entrepreneurship project learning platform based on a group recommendation algorithm, which assists college students in better identifying and utilizing appropriate team resources during the entrepreneurial process. In order to achieve this goal, the genetic algorithm and probability models are introduced in the research process to improve the accuracy and personalization of group recommendations. First, the genetic algorithm is designed based on the initial population of individuals consisting of college students' diverse innovative entrepreneurship project information and personal characteristics. Individuals are iteratively optimized to find the optimal team combinations through crossover, mutation, and selection. Simultaneously, a probability model is employed to establish the group recommendation algorithm, and a large number of data samples and features are utilized for model training and prediction. Finally, the effectiveness of the proposed Genetic Algorithm-Probability Model-Group Recommendation (GAPMGR) is validated through experiments and comparative analysis. The research findings indicate that the GAPMGR method proposed has significantly improved team combination recommendations for college students' innovative entrepreneurship project learning platforms. Compared to traditional methods, the GAPMGR algorithm's group recommendation performance has improved by 4.92%, enabling more accurate matching of suitable team resources. By introducing the genetic algorithm and probability models, the innovative entrepreneurship project learning platform based on group recommendation can effectively enhance students' entrepreneurial abilities and team collaboration effects. This platform can provide personalized team resource recommendations for students, promote interdisciplinary cooperation, and facilitate the successful implementation of innovative entrepreneurship projects. In the fields of education and entrepreneurship, this work provides a valuable entrepreneurial learning tool for college students and serves as a reference for educational institutions and entrepreneurship mentors. In the field of technology, this work explores the application of the genetic algorithm and probability models in group recommendation, offering new ideas and methods for further research and application. [ABSTRACT FROM AUTHOR]

This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage lane changes of vehicles in the blocked lane(s) trying to move to other lanes, and distributes those maneuvers upstream of the incident point. A simulation case study is conducted in which one lane of a four-lane section of the I-66 interstate highway in the U.S. is temporarily blocked due to an incident, to understand how Sentinel impacts traffic flow and how different parameters - traffic flow, system penetration rate, and incident duration - affect Sentinel’s performance. The results show that Sentinel has a positive impact on traffic flow, reducing average delay by up to 37%, particularly when it has a considerable penetration rate. Working alongside Traffic Incident Management Systems (TIMS), Sentinel can be a valuable asset for reducing traffic delay and potentially saving billions of dollars annually in costs associated with congestion caused by freeway incidents. [ABSTRACT FROM AUTHOR]

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In the process of cradle construction for high-pier, long-span, continuous, rigid-frame (HLCR) bridges, the strength failure and instability damage of the main pier will directly affect the bridge construction safety. Therefore, it is necessary to study the reliability of the main pier during the construction of HLCR bridges. This paper starts from the factors that easily affect the stability of the main pier during HLCR bridge cradle construction, establishes the resistance and load probability model of the main pier during the maximum cantilever stage and the maximum unbalanced load of the continuous, rigid-frame bridge's hanging cradles, fully considers the influence of random factors on the reliability of the pier, and calculates and analyzes the reliability index β through calculation examples. The results show that the changes of various random factors during the construction process have different degrees of influence on the reliability of the bridge pier. Our work provides a basis for the safe control of hanging cradles in the construction of HLCR bridges. [ABSTRACT FROM AUTHOR]

Fair distribution of generated power has a significant impact on the performance of the power system. Many methods have been proposed for the safe and secure operation of power systems under the uncertainties of distributed generators and system load. In this paper, we present an optimal power distribution algorithm for distributed generators against uncertainties and load changes of direct-current and alternating-current transmission systems. In this optimal algorithm, considering the stable-state constraints for all uncertainties is performed. In order to establish these constraints at the lowest cost, the adaptive droop coefficients are employed to optimize the power sharing, reloading and modifying the power coefficient of each distributed generator in the power system. Simulation results show the efficiency of the proposed method to improve the performance of the system and reduce the total cost. The voltage/power deviation from reference value in the proposed method is about 1-1.5% where in the conventional droop control, it is more than 2-3%. In addition, in the same uncertainty of the load/distributed generator power in the test system, proposed method requires 20% less power redistribution compared to the conventional droop method. Also, total cost increasing (due to uncertainty increasing) in the conventional droop method is higher than the proposed method (about 10-15%) which shows the robustness of the suggested method against uncertainty changes. [ABSTRACT FROM AUTHOR]

This article proposes a model to estimate the probability of a vehicle reaching a near-term goal state using one or multiple lane changes based on parameters corresponding to traffic conditions and driving behavior. The proposed model not only has broad application in path planning and autonomous vehicle navigation, it can also be incorporated in advance warning systems to reduce traffic delay during recurrent and non-recurrent congestion. The model is first formulated for a two-lane road segment through systemic reduction of the number of parameters and transforming the problem into an abstract statistical form, for which the probability can be calculated numerically. It is then extended to cases with a higher number of lanes using the law of total probability. VISSIM simulations are used to validate the predictions of the model and study the effect of different parameters on the probability. For most cases, simulation results are within 4% of model predictions, and the effect of different parameters such as driving behavior and traffic density on the probability match our expectation. The model can be implemented with near real-time performance, with computation time increasing linearly with the number of lanes. [ABSTRACT FROM AUTHOR]

Transmission error of harmonic reducer is coupled by eccentric vectors such as geometric eccentricity and motion eccentricity during parts machining and assembly， and has frequency domain characteristics. The probability of transmission error distribution caused by various single factors conforms to Rayleigh distribution. The multi-factor coupling model of transmission error based on Rayleigh distribution is established through spatial kinematics and harmonic engagement principle， and a prediction model of transmission error of harmonic reducer with confidence interval up to 99% is completed. The weight coefficient of each transmission error source can be calculated by the prediction model. An inverse analysis method based on weight coefficient of error source to re-optimize allocation of transmission error from control of machining and manufacturing accuracy of parts is proposed， which can reduce the difficulty and cost of machining and manufacturing to the greatest extent while meeting the design index of transmission error and has important engineering application value. Finally， the reverse theoretical analysis and experimental verification comparison of transmission accuracy of 40 type harmonic gear reducer are carried out. The measured results agree with the theoretical calculation values.

A probability-based analytical model for predicting the seismic residual deformation of bilinear single-degree-of-freedom (SDOF) systems with a kinematic/Takeda hysteretic model is proposed based on a statistical analysis of the nonlinear time history response, and the proposed model explicitly incorporates the influence of record-to-record variability. In addition, the influence of primary parameters such as the natural vibration period, relative yield force coefficient, stiffness ratio and peak ground acceleration (PGA) on the seismic residual/maximum deformation ratio (dR/dm) are investigated. The results show that significant dispersion of the dR/dm ratio is observed for SDOF systems under different seismic ground motion records, and the dispersion degree is influenced by the model parameters and record-to-record variability. The statistical distribution of the dR/dm results of SDOF systems can be described by a lognormal distribution. Finally, a case study for seismic residual deformation and reparability assessment of the bridge structure designed with a single pier is carried out to illustrate the detailed analytical procedure of the probability-based analytical model proposed in this study. [ABSTRACT FROM AUTHOR]

The measurement of customer lifetime value (CLV) has long been of primary interest to marketing research. To date, almost all CLV modeling studies have been conducted in either the contractual or the non-contractual transaction setting. The semi-contractual transaction setting, however, has been increasingly observed lately but rarely studied. In this article, we deal with the CLV measurement in one type of semi-contractual transaction setting, in which firms can unilaterally terminate their transaction relationship with customers under certain conditions, resulting customers' survival duration being artificially truncated. We propose an innovative model to capture CLV in the setting, which consists of two sub-models. The first sub-model is a novel customer base analysis (CBA) model, BG-t/BB. It adds an "artificially truncated" feature to the classic BG/BB model. This BG-t/BB model is then augmented by a monetary value (MV) sub-model, Gamma-Gamma, so that we can capture each customer's overall monetary value. We also develop and report a systematic plan for data collection and model testing, and then discuss how our model can be used to support managers to determine the optimal customer termination timing. [ABSTRACT FROM AUTHOR]

Distribution characteristics and strength of 3D discrete fractures have a decisive effect on the rock slope stability. How to use the fractures obtained on site to establish a 3D fractured rock slope for stability analysis is a difficult problem in the field of rock mass engineering. This study selected the rock slope on the right bank of Yigong Zangbu Bridge for a detailed field investigation. Exposed joints and fractures were measured and counted. Occurrence, size, and position distribution models of the fractures were used to obtain the parameters of the spatial distribution using probability and statistic theory. Then, a complex 3D discrete fracture network (DFN) was established and simplified. After that, a 3D rock slope model was built and the DFN was used to cut the slope model. Finally, the strength reduction method was used to analyze the slope stability. The results reveal that the overall slope stability is good (safety factor is 2.0), but the local block deformations are large and are mainly concentrated in the lower part of the slope. Due to the existence of structural planes dipping outside the slope on the right bank, local unstable blocks are easily formed. In this study, the whole process from field fracture acquisition to the DFN generation is applied to the 3D fracture rock slope stability analysis, which is of great importance for complex rock mass engineering assessment and disaster prevention. [ABSTRACT FROM AUTHOR]