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For a 24/7 database system, backups should be implemented right after a large volume of data has been updated, putting their backup windows in non-busy states with user's convenience.From this viewpoint, this paper studies periodic and random incremental backup policies, in which, incremental backup is implemented right after data update and full backup is performed at periodic times KT , or at a number N of data updates, respectively. We firstly describe the stochastic processes of data update and database failure, and then model the expected cost rates for data backup and data restoration.Respective K ∗ , N ∗ , K f ∗ , and N f ∗ are obtained to minimize their expected cost rates in analytical ways, respectively. Finally, numerical examples are given to illustrate the optimum policies. [ABSTRACT FROM AUTHOR]

The reliability theory has been an important element of the classical geodetic adjustment theory and methods in the linear Gauss-Markov model. Although errors-in-variables (EIV) models have been intensively investigated, little has been done about reliability theory for EIV models. This paper first investigates the effect of a random coefficient matrix A on the conventional geodetic reliability measures as if the coefficient matrix were deterministic. The effects of such geodetic internal and external reliability measures due to the randomness of the coefficient matrix are worked out, which are shown to depend not only on the noise level of the random elements of A but also on the values of parameters. An alternative, linear approximate reliability theory is accordingly developed for use in EIV models. Both the EIV-affected reliability measures and the corresponding linear approximate measures fully account for the random errors of both the coefficient matrix and the observations, though formulated in a slightly different way. Numerical experiments have been carried to demonstrate the effects of errors-in-variables on reliability measures and compared with the conventional Baarda's reliability measures. The simulations have confirmed our theoretical results that the EIV-reliability measures depend on both the noise level of A and the parameter values. The larger the noise level of A, the larger the EIV-affected internal and external reliability measures; the larger the parameters, the larger the EIV-affected internal and external reliability measures.

Abstract This research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods in balancing power usage optimization with user comfort. By integrating reinforcement learning and a unique column‐and‐constraint generation strategy, the study aims to fill this gap and offer a comprehensive solution. Furthermore, the increasing adoption of renewable energy sources like solar panels underscores the importance of developing advanced energy management techniques, driving the exploration of innovative approaches such as the one proposed herein. The constraint coordination game (CCG) method is designed to efficiently manage the power usage of each appliance, including the charging and discharging of the energy storage system. Additionally, a deep learning model, specifically a deep neural network, is employed to forecast indoor temperatures, which significantly influence the energy demands of the air conditioning system. The synergistic combination of the CCG method with deep learning‐based indoor temperature forecasting promises significant reductions in homeowner energy expenses while maintaining optimal appliance performance and user satisfaction. Testing conducted in simulated environments demonstrates promising results, showcasing a 12% reduction in energy costs compared to conventional energy management strategies.

Life categories and probability distributions are part of a new field in reliability that has emerged as a result of the daily generation of data that has become more complex across practical fields. This study demonstrated how well the U-statistics technique can be applied to real-world testing problems, producing more efficient processes that are on par with or even more successful than conventional approaches. Furthermore, there was room for improvement in the performance of these methods. An approach tending toward normalcy was supported by comparing a unique U-statistic test with the used better than age in moment generating ordering (UBAmgf) test statistic, In this manuscript, a novel nonparametric technique has been developed to test the belonging of a dataset to a distribution of a new statistical class survival function, the moment generating function for used better than aged (UBAmgf). This type of test was crucial in practical life, such as implementing a specific strategy of proposed therapy for a particular disease, deeming it futile if the survival data was exponential (accepting $ {\mathrm{H}}_{0} $) (the suggested therapeutic approach does not exhibit positive or negative effects on the patients). Once the survival data was UBAmgf, the treatment or device or system employed yields an expected overall current value better or higher than the older device governed by the asymptotic survival function (discussed in the Applications section). The appropriateness of the proposed statistical test's application range was properly determined by calculating its test efficiency and critical values and comparing them with other tests, whether in complete or censored data. Finally, we applied this proposed test technique in the manuscript to a different set of real data in both cases.

This research addresses the pressing need for enhanced energy management in smart homes, motivated by the inefficiencies of current methods in balancing power usage optimization with user comfort. By integrating reinforcement learning and a unique column‐and‐constraint generation strategy, the study aims to fill this gap and offer a comprehensive solution. Furthermore, the increasing adoption of renewable energy sources like solar panels underscores the importance of developing advanced energy management techniques, driving the exploration of innovative approaches such as the one proposed herein. The constraint coordination game (CCG) method is designed to efficiently manage the power usage of each appliance, including the charging and discharging of the energy storage system. Additionally, a deep learning model, specifically a deep neural network, is employed to forecast indoor temperatures, which significantly influence the energy demands of the air conditioning system. The synergistic combination of the CCG method with deep learning‐based indoor temperature forecasting promises significant reductions in homeowner energy expenses while maintaining optimal appliance performance and user satisfaction. Testing conducted in simulated environments demonstrates promising results, showcasing a 12% reduction in energy costs compared to conventional energy management strategies. [ABSTRACT FROM AUTHOR]

Life categories and probability distributions are part of a new field in reliability that has emerged as a result of the daily generation of data that has become more complex across practical fields. This study demonstrated how well the U-statistics technique can be applied to real-world testing problems, producing more efficient processes that are on par with or even more successful than conventional approaches. Furthermore, there was room for improvement in the performance of these methods. An approach tending toward normalcy was supported by comparing a unique U-statistic test with the used better than age in moment generating ordering (UBAmgf) test statistic, In this manuscript, a novel nonparametric technique has been developed to test the belonging of a dataset to a distribution of a new statistical class survival function, the moment generating function for used better than aged (UBAmgf). This type of test was crucial in practical life, such as implementing a specific strategy of proposed therapy for a particular disease, deeming it futile if the survival data was exponential (accepting H0) (the suggested therapeutic approach does not exhibit positive or negative effects on the patients). Once the survival data was UBAmgf, the treatment or device or system employed yields an expected overall current value better or higher than the older device governed by the asymptotic survival function (discussed in the Applications section). The appropriateness of the proposed statistical test's application range was properly determined by calculating its test efficiency and critical values and comparing them with other tests, whether in complete or censored data. Finally, we applied this proposed test technique in the manuscript to a different set of real data in both cases. [ABSTRACT FROM AUTHOR]

Integer variables are among the neo-logistic programming issues that most frequently pertain to system reliability improvement. If 'k' out of 'n' elements are present in each technology, then the configuration can be employed with 'k' out of 'n' systems. Only particular objective function structures and restrictions may be used in a heuristic technique to precisely address a dependability optimization problem. As more constraints are placed on it, the more useful it becomes; it is worthless for boosting dependability in a large system. This paper analyzes existing research on integrated reliability models with redundancy and explores system reliability optimization before proposing new recommendations. [ABSTRACT FROM AUTHOR]

A new proportional stochastic order is defined based on the concept of quantile residual life. In addition, a new aging class of life distributions, namely, decreasing proportional failure rate distributions, was defined. Moreover, two other aging classes based on the proposed stochastic ordering are defined and investigated: decreasing proportional α-quantile residual life and its dual increasing proportional α-quantile residual life. The relationship between the proportional failure rate and proportional α-quantile residual life has been discussed. Some well-known and useful models are classified in terms of the proportional α-quantile residual life function. Finally, a new modified Pareto distribution with a decreasing proportional failure rate and an increasing proportional α-quantile residual life function was presented and studied.

This study focuses on an innovative life distribution category known as the 'New Better than Renewal used in Moment Generating Function' (NBRUmgf) class. It explores the relationships between this particular aging model and established aging categories, and its applicability within a shock model. Moreover, it investigates the consistency of this aging concept through specific reliability operations, which are pivotal tools in reliability engineering. The research involves computing Pitman's asymptotic efficiencies for this testing method and compares them with alternative approaches. Additionally, the study presents an extensive table of percentiles for the test statistic associated with this proposed technique. To underscore the significance of the study's findings, various real-world datasets are employed, demonstrating the efficacy of our test methodology across diverse types of actual data. [ABSTRACT FROM AUTHOR]

The probability life distributions usually describe the time to event or survival time. Therefore, these life distributions play a crucial role in the analysis and projection of the maximum life expectancy using the Laplace transform technique for nonparametric hypothesis testing. A flexible framework for monitoring randomized clinical trials utilizing prior knowledge of certain phenomena under uncertainty is provided by the Laplace transform. By considering the previous data, medical professionals can also utilize these estimators to calculate the chance of tumor recurrence, cardiovascular death, and AIDS for HIV patients. However, censoring occurs in clinical trials and medical studies when the precise event's time of occurrence is unknown. The present study aims to propose a new test for exponential better than used (EBU). The suggested statistical test's efficiency and critical values are computed and compared with those of previous classes. Furthermore, the suggested test statistic is provided in bivariate form as well. Finally, the methodology is then tested using medical data in order to provide evidence that it is applied efficiently. [ABSTRACT FROM AUTHOR]

In order to assess the stable operating duration of an ultrasonic vibration system, a reliability-based analysis method for the stability of the ultrasonic vibration system is proposed. Firstly, the failure mechanisms of the ultrasonic vibration system are analyzed, and the resonant frequency and amplitude are selected as two degradation features of the system. Subsequently, accelerated degradation experiments under different force loads were conducted, and the degradation model of the ultrasonic vibration system was established by comparing experimental data with degradation, distribution, and acceleration models. Finally, Copula functions were introduced to connect the two degradation features, resonant frequency, and amplitude, and lifetime curves were plotted under the influence of univariate and bivariate degradation factors. Through the analysis of the lifetime curves, the conclusion is drawn that the decay of amplitude is the primary indicator of system lifetime, and it is predicted that the developed ultrasonic vibration system can operate continuously and stably for 26.69 h. This research is of great significance for enhancing the reliability and lifespan management of ultrasonic vibration systems. [ABSTRACT FROM AUTHOR]

The reliability aspects of the operation of radio navigation systems constitute a crucial element for the safety of maritime navigation.. Technological progress in ship traffic monitoring is achieved through the design of ship systems and shore infrastructure equipped with Automatic Identification System (AIS) devices. One of the issues with AIS operation is the limited availability of the service in the form of data streams with an extended data age recorded on the receiving side. Another problem is the emission and reception by ships of incomplete positional reports without navigational parameters. Such situations render the system operationally unfit in terms of processed information. Therefore, it is essential to investigate the operational characteristics of radio navigation systems and develop tools to monitor the AIS service status on the receiving side. This article presents the development of a model for the availability of an AIS for vessels based on the determined mean time of the occurrence of incomplete navigation parameter values in AIS messages and the results of research in the domain of time and frequency using a mathematical method of the Fast Fourier Transform (FFT). The study results refer to six basic navigation parameters and show a varying service availability factor for the navigation parameters under study, i.e. the latitude (LAT), longitude (LON), speed over ground (SOG), course over ground (COG), heading (HDT), and the rate of turn (ROT). The data recorded by three receiving AIS stations on the Polish coast, i.e. PLKOL, PLSZZ, and PLSWI, were used as a key source of practical knowledge on the limitations of the AIS service availability. The experiment observed interruptions in the regular transmission of data from navigation equipment in the AIS service operational zone. As a result, the functional relationship was described based on the spectral analysis of the frequency of occurrence of times between the service repair (Time To Repair, TTR), and the model was proposed to be applied to the study of other variables. The presented model is a tool that allows for improving the monitoring of vessel traffic in terms of reliability, which directly affects the improvement of maritime traffic safety.

Accelerated life tests are usually applied to obtain quick failures of components that may have high reliability. In this research, a comparative statistical study will be conducted between two hydroxylated fullerenes, C60(OH)29 and Sc3N@C80(OH)18, to determine which one is more preferable and has the potential to be more applicable in the field of industrial engineering. Some experimental studies showed that Sc3N@C80(OH)18 is better than C60(OH)29 in terms of physical and chemical properties. This statistical study confirms the experimental results through some statistical inference methods, such as Bayesian estimation.

As reliance on wind energy intensifies globally, optimizing the efficiency and reliability of wind turbines is becoming vital. This paper explores sophisticated maintenance strategies, crucial for enhancing the operational sustainability of wind turbines. It introduces an innovative approach to maintenance scheduling that utilizes a mathematical model incorporating an alternating renewal process for accurately determining repair rate thresholds. These thresholds are important for identifying optimal maintenance timings, thereby averting failures and minimizing downtime. Central to this study are the obtained generalized analytical expressions that can be used to predict the total repair time for an observed entity. Four key lemmas are developed to establish formal proofs for the probability density function (PDF) and cumulative distribution function (CDF) of repair rates, both above and below critical repair rate thresholds. The core innovation of this study lies in the methodological application of PDFs and CDFs to set repair time thresholds that refine maintenance schedules. The model’s effectiveness is illustrated using simulated data based on typical wind turbine components such as gearboxes, generators, and converters, validating its potential for improving system availability and operational readiness. By establishing measurable repair rate thresholds, the model effectively prioritizes maintenance tasks, extending the life of crucial turbine components and ensuring consistent energy output. Beyond enhancing theoretical understanding, this research provides practical insights that could inform broader maintenance strategies across various renewable energy systems, marking a significant advancement in the field of maintenance engineering

Analyzing the reliability of the aging class of life distribution provides important information about how long a product lasts and sustainability measures that are essential for determining the environmental impact and formulating resource-saving plans. The study emphasizes the goodness-of-fit technique of the nonparametric test for the NBRUmgf class because age data are crucial for applications. Evaluations were conducted using the test’s asymptotic properties and Pitman efficiency methodology for some selected asymmetric probability models, and the outcomes were compared with those of alternative methods. We assessed the test’s power against widely used reliability distributions for some well-known alternative asymmetric distributions, including the Weibull, Gamma, and linear failure rate (LFR) distributions, and provided percentiles for both censored and uncensored data. This study shows the efficacy of the test in various sectors using real-world datasets and comprehensive tables of test statistics.

In this paper, we study properties of a series–parallel system. The component lifetimes may be dependent and non-identically distributed (DNID) discrete random variables. We consider the number of failed components upon system failure. We derive the probability mass function and the expected value of this quantity. In addition, we find the conditional probabilities corresponding to this variate given some partial information about the system failure. We also provide a numerical example to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

M. I. Yadrenko discovered that the expectation of the minimum number N1 of independent and identically distributed uniform random variables on (0, 1) that have to be added to exceed 1 is e. For any threshold a > 0, K. G. Russell found the distribution, mean, and variance of the minimum number Na of independent and identically distributed uniform random summands required to exceed a. Here we calculate the distribution and moments of Na when the summands obey the negative exponential and Lévy distributions. The Lévy distribution has infinite mean. We compare these results with the results of Yadrenko and Russell for uniform random summands to see how the expected first-passage time E (N a) , a > 0 , and other moments of Na depend on the distribution of the summand. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents a survey and analysis of the selected basic theses of some operational sciences and praxiology. This project is related to the ongoing research and development works on the description of the reliability of action. These works refer to the concept of such a description presented by Józef Konieczny within the framework of systems of action engineering (and formal praxiology). The original concept is universal and can be useful in reliability engineering, but its analysis, development and application require prior research on its foundations. For this reason, the aim of this work is to formulate observations and conclusions regarding the description of the state of availability of the individual elements of a chain of action - which is one of the many stages of the mentioned research and development works. [ABSTRACT FROM AUTHOR]

The reliability theory has been an important element of the classical geodetic adjustment theory and methods in a linear Gauss-Markov model since Baarda invented reliability in 1968. Although geodetic reliability has been widely investigated and applied to a variety of geodetic, photogrammetric, and remote sensing problems, there is no report of theoretical progress to improve further Baarda’s reliability measures among linear models. We propose the power of the effect of the minimum detectable outlier on parameters as an alternative external reliability measure, present a regularized external reliability, and demonstrate for the first time that the external reliability measure of Baarda’s type is not the best and can be significantly improved through regularization for inverse ill-posed problems. An ill-posed regression example is used to illustrate the regularized external reliability measure, which is shown to perform much better than the external reliability measure of Baarda’s type.

Abstract The increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method.

This study focuses on investigating the deformation and stability of high slopes under various working conditions to address the potential risks posed by slope collapse to both personal safety and infrastructure. By conducting a case study on a specific high slope project, the behavior of the slope is examined. Reliability theory is employed to analyze and calculate the probability of slope failure, in order to evaluate the slope's reliability and stability under three conditions: natural state, anchor and frame beam support, and anchor support. Analysis of field measured data reveals that the slope exhibits relatively stable behavior when supported by the anchor and frame beam. Reliability analysis of geotechnical parameters indicates that the cohesion, angle of internal friction, modulus of elasticity, and density of the surface geotechnical body have a greater influence on the results compared to other parameters. Using the stochastic finite element method to analyze and calculate the slope, the failure probability of the slope in its natural state is determined to be 35.9%. In contrast, the failure probability reduces to 1.3% for the anchor and frame beam support scheme and 6.1% for the anchor support scheme. These findings suggest that the anchor and frame beam support scheme demonstrates the most effective performance in terms of slope stability. The numerical simulation results validate the actual protective effect of the slope and offer valuable insights for similar slopes reinforced by anchor and frame beam systems.

Probability life distributions usually describe the time to an event or survival time. Therefore, these life distributions play a crucial role in the analysis and projection of maximum life expectancy using the goodness of fit approach for nonparametric hypothesis testing. This study suggests a nonparametric technique to determine whether the data follow an exponential distribution or belong to the mathematical class of the moment generating function for used better than aged (UBAmgf). These tests can be applied to both censored and non-censored data. The upper percentile points of the test statistics are generated, and the suggested test's asymptotic normality is established. Some well-known alternative asymmetric probability models are used to compute the Pitman asymptotic relative efficiency (PARE) and powers of the proposed test. To demonstrate the paper's conclusions, some asymmetric real-world datasets are examined. [ABSTRACT FROM AUTHOR]

It is not uncommon that the living situation of a unit (e.g. a component in a reliability system) plays a critical role in affecting the performance of another unit or even the whole system. The study on stochastic behaviors of conditional residual lifetimes appears to be particularly important for analyzing the performances of interested units. This paper studies stochastic comparisons on the conditional residual lifetimes of units under the dependent information when another unit has survived up to a given time. Sufficient (and necessary) conditions are established for comparing conditional residual lifetimes of different units under various dependence structures as well as different events according to some traditional stochastic orders. We provide numerical examples based on the well-known FGM copula and Archimedean copulas to verify the conditions. Three real scenarios in reliability theory and risk theory are also considered to show the applicability of our results. [ABSTRACT FROM AUTHOR]

The deceleration lane is an important part of the freeway, and the rationality of its design parameters affects the exit accident rate. The traditional calculation method is based on the design of speed and vehicle parameters using deterministic methods, ignoring the randomness of the driver's deceleration behavior. It is necessary to calculate the length and slope of the deceleration lane in detail according to the deceleration characteristics of the driver in the deceleration section by using the uncertainty method. This paper describes a study on the maximum longitudinal slope of the downhill section of the deceleration lane, where the safety of diverging vehicles is unfavorable. By collecting deceleration lane data from interchanges around Xi'an (Shaanxi Province, China, Coordinates: 108.95, 34.27) and analyzing the deceleration characteristics of vehicles, we propose a new deceleration model. In addition, the limit-state functions of the length and slope of the deceleration lane have been established based on the reliability theory. Finally, according to the deceleration characteristics, we determined the probability distribution of key parameters in the vehicle deceleration process. We used the Monte Carlo Simulation (MCS) and the Improved First-Order Second Moments (IFOSM) calculation model to calculate the length and slope of the deceleration lane, respectively. Finally, we propose the recommended values for the length and slope of the deceleration lane. The results of the study showed that: (1) The movement process of the vehicle on the deceleration section adopts a uniform deceleration, and the truck and the car start to decelerate from the starting of the taper section and diverging point, respectively. (2) The control vehicle in the deceleration lane calculation model is the compact car. (3) The reliability theory has good applicability in calculating freeway alignment indexes. It fully considers the probability of driver deceleration behavior in the calculation model, which provides a more suitable method for the calculation of deceleration lane indexes. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Mechanics is the property of Chinese Journal of Applied Mechanics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increase in wind power penetration leads to the risk of energy supply interruption in integrated electricity‐heat energy systems (IHES). This paper presents an analytical‐based approach for the efficient operational reliability evaluation of the IHES. The wind power distribution characteristic model considering correlation is constructed based on Hidden Markov Model (HMM). To reduce computational complexity, a critical system state identification (CSSI) method is proposed that characterises both random device failures and wind power uncertainty. Under each critical system state, the analytical function relationship between the reliability index and the uncertainty factors is established based on the virtual stochastic response surface (VSRS) method. The analytical function can directly calculate the reliability index when the wind power output varies, avoiding a large number of repeated calculations of the optimal load‐shedding model of the system and reducing the time required for reliability assessment. This allows the operator to evaluate the operational reliability of the integrated energy system in real‐time. The numerical simulation of the test system combining the IEEE 33‐node distribution system and the existing 28‐node heating system proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Self-driving cars and other vehicles are being increasingly demonstrated, released, and deployed on roads. Yet only a handful of special-case vehicles have obtained a permit to go fully driverless, and our passenger cars still require drivers at the steering wheel to correct the car if something goes wrong. Safety drivers, which operate autonomous vehicles in testing phases, upon occasions were unable to correct the faulty behavior of their vehicles, inducing accidents and, unfortunately, casualties. This article proposes a method to analyze the reliability of autonomous vehicles applying the classical reliability theory and software reliability growth models (SRGMs). This method is then applied to real-world data to get some predictions on what is really needed to safely remove the driver and go fully driverless. [ABSTRACT FROM AUTHOR]

In real-world scenarios, the information quality provided by RGB and thermal (RGB-T) sensors often varies across samples. This variation will negatively impact the performance of semantic segmentation models in utilizing complementary information from RGB-T modalities, resulting in a decrease in accuracy and fusion credibility. Dynamically estimating the uncertainty of each modality for different samples could help the model perceive such information quality variation and then provide guidance for a reliable fusion. With this in mind, we propose a novel uncertainty-guided trustworthy fusion network (UTFNet) for RGB-T semantic segmentation. Specifically, we design an uncertainty estimation and evidential fusion (UEEF) module to quantify the uncertainty of each modality and then utilize the uncertainty to guide the information fusion. In the UEEF module, we introduce the Dirichlet distribution to model the distribution of the predicted probabilities, parameterized with evidence from each modality and then integrate them with the Dempster–Shafer theory (DST). Moreover, illumination evidence gathering (IEG) and multiscale evidence gathering (MEG) modules by considering illumination and target multiscale information, respectively, are designed to gather more reliable evidence. In the IEG module, we calculate the illumination probability and model it as the illumination evidence. The MEG module can collect evidence for each modality across multiple scales. Both qualitative and quantitative results demonstrate the effectiveness of our proposed model in accuracy, robustness, and trustworthiness. The code will be accessible at https://github.com/KustTeamWQW/UTFNet. [ABSTRACT FROM AUTHOR]

Maintaining road pavement is a challenging task, due to the vast size of the roads network and the limited maintenance budget available to road agencies. Therefore, most agencies aim at selecting the most deteriorated road segments for maintenance, within their available budget. However, the majority of the current developed asset management tools aim at improving the average performance of the roads network, regardless of the condition of the maintained segments. To overcome this limitation, this study first proposes a novel measure for roads network performance using reliability theory, then it proposes a hybrid optimization approach that is integrated with the proposed performance measure to enable road agencies to focus on the most deteriorated segments in the network while maximizing the network performance. The hybrid optimization approach combines dynamic programming with a traditional genetic algorithm. The proposed algorithm was applied to two major road systems in Jordan and validated by comparing its performance with that of a traditional genetic algorithm. The results show that the proposed algorithm outperforms the traditional genetic algorithm, because of its ability to select the most deteriorated segments in the network, while achieving better network condition at lower cost. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, there are many problems and challenges in the management of Class II vaccines, for example, regulatory regulations, the regulatory system and the quality supervision for second-class vaccines are not yet complete. Blockchain technology will play an increasingly important role in the safety supervision of Class II vaccines. Firstly, we have explored the vulnerabilities and causes of second-class vaccine safety supervision in this paper. Then we have studied the impacts of blockchain technology on second-class vaccine supervision and the potential drivers of adoption by reliability theory and fuzzy dynamic decision-making method. Finally, based on the study results, a modular and sustainable supply chain integration optimization design has be proposed for the second-class vaccine safety supervision system, the supervision system based on optimization design has deep traceability and disintermediation to track and monitor the quality of each stage of the entire supply chain, and has characteristics such as intelligence, scalability, and compatibility. This study provides theoretical basis and technical support for embedding blockchain ICT into the multi-agent collaborative supervision of class II vaccines.

In this study, through experimental research and an investigation on large datasets of the durability parameters in ocean engineering, the values, ranges, and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed. Based on a modified theoretical model of chloride diffusion and the reliability theory, the service lives of concrete structures exposed to the splash, tidal, and underwater zones were calculated. Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed, and a cover thickness requirement was proposed. In addition, the effects of the different time-varying relationships of the boundary condition (Cs) and diffusion coefficient (Df) on the service life were compared; the results showed that the time-varying relationships used in this study (i.e., Cs continuously increased and then remained stable, and Df continuously decreased and then remained stable) were beneficial for the durability design of concrete structures in marine environment.

Collapse is one of the most significant geological hazards in mountain tunnel construction, and it is crucial to accurately predict the collapse probability. By introducing the reliability theory, this paper proposes a calculation method for the collapse probability in mountain tunnel construction based on numerical simulation, support vector regression (SVR), and the Monte Carlo (MC) method. Taking the Jinzhupa Tunnel Project in Fujian Province as a case study, three-dimensional models were constructed, and the safety factors of the surrounding rock were determined using the strength reduction method. By defining the shear strength parameters of the surrounding rock as random variables, the problem was formulated as a reliability model, and the safety factor was chosen as the reliability index. To increase computational efficiency, the SVR model was trained to replace numerical simulations, and the MC method was adopted to calculate the probability of collapse. The results showed that the cause of the collapse was the change in the excavation method and the very late installation of supports. The feasibility and reliability of the proposed method have been verified, indicating that the method can be used to predict the probability of collapse in a practical risk assessment of mountain tunnel construction. [ABSTRACT FROM AUTHOR]

Over the last few decades, the statisticians and reliability analysts have looked at putting exponentiality to the test using the Laplace transform technique. The non-parametric statistical test used in this study, which is based on this technique, evaluates various treatment modalities by looking at failure behavior in the survival data that were gathered. Following use of the suggested strategy, patient survival times are recorded. In this investigation, it was presupposed that the Laplace transform order of (UBAC2) attribute or the constant failure rate would determine how the observed data behave (exponential scenario). If the survival data are exponential, the recommended treatment approach is ineffective. If the survival data are UBAC2L, the technique in use produces a better or a higher expected total present value than an older one governed by an exponential survival function (discussed in the Applications section). The efficiency and critical values of the test are calculated and compared to those of other tests in order to ensure that the suggested statistical test is applied correctly. [ABSTRACT FROM AUTHOR]

Designers may introduce a system with multiple technologies in series to improve system efficiency. The configuration can be applied to k out of n systems if each technology contains k out of n factors. The k out of n configuration method is successful until every component of the system is successful. The efficiency of the entire system is more in amount than that of a single system factor in a k out of n shape. An Integrated Reliability Model (IRM) for the k out of n, here, an additional system is suggested to account for both the efficiencies of the factors and the number of factors in every phase and the different constraints to optimize the efficiency of the system. To enhance system efficiency, the authors employed the numerous methods of Lagrangean approach to determine the numbers and efficiency of the factors as well as the reliabilities of the phase under different parameters namely load, size, and cost. The dynamic programming approach and simulation method have been adapted to attain an integer result as well as to see the values real. [ABSTRACT FROM AUTHOR]

The article is about Khaim Borisovich Kordonsky (Tula, Russia, 1919 - Boston, USA, 1999) - Soviet mathematician, Doctor of Technical Sciences, Professor at the Riga Red Banner Institute of Civil Aviation Engineers; specialist in the field of probability and reliability theory, leader of the development of the first computer system for making aircraft schedules, Honoured Scientist of the Latvian SSR, Laureate of the State Prize of the Latvian SSR. Kordonsky's entire scientific and teaching career is connected with RCIIGA. He is one of the founders of reliability theory and the author of the first monograph on the application of probability theory to solving real-world problems. The book "Applications of Probability Theory in Engineering" was published in 1963 and was reviewed by Academician Yuri Vladimirovich Linnik, the greatest expert in probability theory and mathematical statistics in the USSR. Khaim Borisovich also worked on scientific problems in statistics, medicine and technical diagnostics. He conducted joint research with the Department of Mathematical Statistics of the Faculty of Informatics and Cybernetics of Moscow State University, headed by Academician Yuri Vasilievich Prokhorov, and collaborated with Academician A. N. Kolmogorov. [ABSTRACT FROM AUTHOR]