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Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of data security in these structures remains a gap in current research. Our study aims to bridge this gap by developing a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. The research progresses from the derivation of an exact formula for falsification probability to an approximation suitable for cases with significantly large hash lengths. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths. A numerical analysis quantifies the discrepancy between exact and approximate probabilities, underscoring the conditions for the effective application of the approximation. This work offers crucial insights into optimizing Merkle Tree structures for bolstering security in blockchain and IoT systems, achieving a balance between computational efficiency and data integrity. [ABSTRACT FROM AUTHOR]

Timing determinism in embedded real-time systems requires meeting timing constraints not only for individual tasks but also for chains of tasks that involve multiple messages. End-to-end analysis is a commonly used approach for solving such problems. However, the temporal properties of tasks often have uncertainty, which makes end-to-end analysis challenging and prone to errors. In this paper, we focus on enhancing the precision and safety of end-to-end timing analysis by introducing a novel probabilistic method. Our approach involves establishing a probabilistic model for end-to-end timing analysis and implementing two algorithms: the maximum data age detection algorithm and the end-to-end timing deadline miss probability detection algorithm. The experimental results indicate that our approach surpasses traditional analytical methods in terms of safety and significantly enhances the capability to detect the probability of missing end-to-end deadlines. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower 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.)

Slope deformation, a key factor affecting slope stability, has complexity and uncertainty. It is crucial for early warning of slope instability disasters to master the future development law of slope deformation. In this paper, a model for point prediction and probability analysis of slope deformation based on DeepAR deep learning algorithm is proposed. In addition, considering the noise problem of slope measurement data, a Gaussian-filter (GF) algorithm is used to reduce the noise of the data, and the final prediction model is the hybrid GF-DeepAR model. Firstly, the noise reduction effect of the GF algorithm is analyzed relying on two actual slope engineering cases, and the DeepAR point prediction based on the original data is also compared with the GF-DeepAR prediction based on the noise reduction data. Secondly, to verify the point prediction performance of the proposed model, it is compared with three typical point prediction models, namely, GF-LSTM, GF-XGBoost, and GF-SVR. Finally, a probability analysis framework for slope deformation is proposed based on the DeepAR algorithm characteristics, and the probability prediction performance of the GF-DeepAR model is compared with that of the GF-GPR and GF-LSTMQR models to further validate the superiority of the GF-DeepAR model. The results of the study show that: 1) The best noise reduction is achieved at the C1 and D2 sites with a standard deviation σ of 0.5. The corresponding SNR and MSE values are 34.91 (0.030) and 35.62 (0.674), respectively. 2) A comparison before and after noise reduction reveals that the R² values for the C1 and D2 measurement points increased by 0.081 and 0.070, respectively. Additionally, the MAE decreased from 0.079 to 0.639, and the MAPE decreased from 0.737% to 0.912%. 3) The prediction intervals constructed by the GF-DeepAR model can effectively envelop the actual slope deformation curves, and the PICP in both C1 and D1 is 100%. 4) Whether it is point prediction or probability prediction, the GF-DeepAR model excels at extracting feature information from slope deformation sequences characterized by randomness and complexity. It conducts predictions with high accuracy and reliability, indicating superior performance compared to other models. The results of the study can provide a reference for the theory of slope deformation prediction, and can also provide a reference for similar projects. [ABSTRACT FROM AUTHOR]

Background Pioglitazone significantly reduces the risk of stroke in people with diabetes, and in those with prediabetes, it markedly reduces the risk of stroke/myocardial infarction and new‐onset diabetes. Low‐dose pioglitazone provides most of the clinical benefits of high‐dose pioglitazone, with fewer adverse effects. We report an economic evaluation of the cost‐effectiveness of low‐dose pioglitazone versus placebo from a Canadian public payer perspective in 2023 Canadian dollars. Methods and Results A Markov model was developed at a lifetime horizon with an annual cycle length and 5 health states (event‐free, myocardial infarction, stroke, new‐onset diabetes, and death). Transition probabilities were extracted from the IRIS (Insulin Resistance Intervention in Stroke) trial. Health state costs and utilities were based on public sources. Annual discount rates of 1.5% were applied in the reference‐case analysis. Probabilistic analyses were conducted to deal with parameter uncertainty through 5000 simulations. The costs were estimated as $24 887 (interquartile range [IQR], $14 632–$41507) for low‐dose pioglitazone and $57 301 (IQR, $48 730–$67368) for placebo, resulting in a cost saving of −$30 287 (IQR, −$43 374 to −$14 587) in favor of low‐dose pioglitazone. Quality‐adjusted life years were estimated as 25.99 (IQR, 24.56–26.81) for the low‐dose pioglitazone and 19.44 (IQR, 18.68–20.13) for placebo, resulting in a difference of 6.37 (IQR, 5.07−7.36) in favor of low‐dose pioglitazone. Consistent findings were observed from scenario analyses and 1‐way probability sensitivity analyses. Conclusions Holding across a wide range of values in modeling parameters, low‐dose pioglitazone is found as the dominant strategy versus a placebo.

In this paper, twenty reinforced concrete specimens were designed for accelerated corrosion test, and a systematic study was conducted to investigate the effects of water-cement ratio, thickness of concrete cover, and spacing and quantity of steel bars on the corrosion characteristics of steel bars. The results showed that the mass loss of steel bars approximately increased with the water-cement ratio and decreased with an increase in the thickness of concrete cover. As the spacing between the steel bars in lower layer of the specimens increased, the mass loss of the steel bars in lower layer approximately showed an increasing trend. When there were more than two steel bars arranged in lower layer of the specimens, the mass loss of the middle steel bars was smaller than that of the steel bars located in the corner areas on both sides. The probability distribution type of the corrosion depth was mainly normal distribution, and the influence of water-cement ratio, thickness of concrete cover, and spacing and quantity of steel bars in lower layer of the specimens on the probability distribution was not obvious. The probability distribution type of the width of corrosion-induced cracks was mainly exponential distribution, and the influence of the spacing and quantity of steel bars in lower layer on the probability distribution was not obvious. When the water-cement ratio was large, the width of corrosion-induced cracks was less discrete from its mean value. With the increase of the quantity of steel bars in lower layer, the shape of the probability distribution curve of the width of corrosion-induced cracks tended to be flat. There was a good positive linear relationship between the mass loss of steel bars and the average width of corrosion-induced cracks, and the influence of water-cement ratio and the spacing between the steel bars in lower layer on this linear relationship was irregular. A good positive linear relationship between the maximum width of corrosion-induced cracks and the average corrosion depth of steel bars was established, which was irregularly affected by the water-cement ratio and the spacing between the steel bars in lower layer.

Approximate computing is an emerging and effective method for reducing energy consumption in digital circuits, which is critical for energy-efficient performance improvement of edge-computing devices. In this paper, we propose a low-power DNN accelerator with novel signed approximate multiplier based on probability-optimized compressor and error compensation. The probability-optimized compressor is customized for partial product matrix (PPM) of signed operands, which gets the optimal logic circuit after probabilistic analysis and optimization. At the same time, we explored the PPM truncation method, found out the impact of different partial product (PP) truncation numbers on circuit benefit and error, and achieved a more ideal performance-error tradeoff through a reasonable error compensation method. In the optimal case of 8 bits, the proposed approximate multiplier saves 49.84% power, 46.41% area and 24.65% delay compared to the exact multiplier. We employed the proposed approximate multiplier in the vector systolic array as the processing element (PE). Under the VGG-16 evaluation, the proposed accelerator achieves performance improvement of energy efficiency $1.96\times $ , while the error loss was only 0.95%.

In this paper, the almost surely globally asymptotical stability and the almost surely exponential stability for dual switching continuous-time nonlinear system are investigated by using the probability analysis method and stochastic Multi-Lyapunov function, respectively. Different from the previous research results, it is the first time that dual switching continuous-time nonlinear system is used as a study object to investigate its switching stability. Then, the probability analysis method is used to overcome the deficiency that the ergodicity no longer holds due to the variable transition rate of Markov process. Some sufficient conditions for the globally asymptomatic stability almost surely and the almost surely exponential stability of dual switching continuous-time nonlinear system are given under the pre-designed deterministic switching strategy. Finally, two numerical examples are provided to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Addressing the critical challenge of ensuring data integrity in decentralized systems, this paper delves into the underexplored area of data falsification probabilities within Merkle Trees, which are pivotal in blockchain and Internet of Things (IoT) technologies. Despite their widespread use, a comprehensive understanding of the probabilistic aspects of data security in these structures remains a gap in current research. Our study aims to bridge this gap by developing a theoretical framework to calculate the probability of data falsification, taking into account various scenarios based on the length of the Merkle path and hash length. The research progresses from the derivation of an exact formula for falsification probability to an approximation suitable for cases with significantly large hash lengths. Empirical experiments validate the theoretical models, exploring simulations with diverse hash lengths and Merkle path lengths. The findings reveal a decrease in falsification probability with increasing hash length and an inverse relationship with longer Merkle paths. A numerical analysis quantifies the discrepancy between exact and approximate probabilities, underscoring the conditions for the effective application of the approximation. This work offers crucial insights into optimizing Merkle Tree structures for bolstering security in blockchain and IoT systems, achieving a balance between computational efficiency and data integrity.

In order to identify the surge, multifractal spectrums are investigated in the analysis of dynamic pressure at the outlet of centrifugal compressor with 800 kW. At first, the time series of dynamic pressure, including stable and unstable states, are obtained. Then, the relationships between characteristics of multifractal spectrums and dynamic pressure series are studied and explained in detail. The results show that the multifractal spectrum parameter Δf(α) is narrow as the centrifugal compressor is working in stable state. However, Δf(α) becomes wide, as it is working in unstable states. Finally, probability analysis of multifractal variation tendency is used to describe the predictability of the variation. The studies indicated that the associated probability between the data of adjacent periods is highest and the predictability is the strongest in transition period. Thus, the method can be used to predict and avoid surge for centrifugal compressor in early time.

The preload greatly affects the operating state and service life of bearings. To ensure the reasonable assembly of the spindle bearings, the position of zero clearance for the bearings needs to be determined. Firstly, the variation of clearance is calculated by using the thick-walled cylinder theory to determine the zero clearance position of the bearings. Then, the finite element displacement model of bearing zero clearance is established, and a more accurate calculation method of zero clearance position is proposed. Considering the influence of random factors, the Kriging surrogate model is proposed to analyze the probability distribution characteristics of the inner and outer ring positions under the zero clearance of wind turbine spindle bearings. Finally, numerical examples show that the maximum error predicted by the Kriging model is within 0.1%, indicating that the proposed method has higher accuracy and applicability. [ABSTRACT FROM AUTHOR]

The synergistic effect of explosion and fire (SEEF) is one of the challenges of domino effect risk assessment for petrochemical storage tank farms. However, the domino effect probabilities at different orders and accident propagation patterns in storage tank farms under the SEEF are still unclear. An approach based on Bayesian networks is developed to model the propagation of domino effects in storage tank farms under the SEEF. The method accounts for the temporal and spatial synergistic effects of blast wave and fire thermal radiation in multiple accidents during domino accident escalation and the damage enhancement effect caused by the SEEF on the target units. Application of the method to a real tank farm highlights that the SEEF has a significant impact on the domino effect probabilities. Compared with only considering a single accident type, the SEEF leads to an increase in the domino effect probabilities of the tank farm. Installing water deluge systems and blast walls for all tanks at the same time is an effective safety measure to mitigate domino effects due to the SEEF. When safety resources are limited, the priority to equip critical tanks with water deluge systems and blast walls can also mitigate the domino effects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering 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.)

According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow values in the rivers in a basin coincide. Therefore, estimating the joint flood risks in a region is vital, especially when frequent occurrences of extreme events are experienced. This study focuses on estimating the joint flood risks due to river flow extremes in the Grand River watershed in Canada. For this purpose, the study uses copula analysis to investigate the joint occurrence of extreme river flow events in the Speed and Grand Rivers in the Grand River Watershed in Ontario, Canada. By estimating the joint return period for extreme flows in both rivers, we demonstrate the interdependence of the two river flows and how this interdependence influences the behavior of river flow extreme patterns. Our findings suggest that the interdependence between the two river flows leads to changes in the river flow extreme pattern. Determining the interdependence of floods at multiple locations using state-of-the-art tools will benefit various stakeholders, such as the insurance industry, the disaster management sector, and most importantly, the public. [ABSTRACT FROM AUTHOR]

In order to identify the surge, multifractal spectrums are investigated in the analysis of dynamic pressure at the outlet of centrifugal compressor with 800 kW. At first, the time series of dynamic pressure, including stable and unstable states, are obtained. Then, the relationships between characteristics of multifractal spectrums and dynamic pressure series are studied and explained in detail. The results show that the multifractal spectrum parameter Δ f (α) is narrow as the centrifugal compressor is working in stable state. However, Δ f (α) becomes wide, as it is working in unstable states. Finally, probability analysis of multifractal variation tendency is used to describe the predictability of the variation. The studies indicated that the associated probability between the data of adjacent periods is highest and the predictability is the strongest in transition period. Thus, the method can be used to predict and avoid surge for centrifugal compressor in early time. [ABSTRACT FROM AUTHOR]

In the dynamic analysis of railway lines, both lateral and vertical track irregularities should be considered. In addition, uncertainties in the bridge structure and train load will have different effects on the train-bridge coupled system (TBCS). This paper presents a three-dimensional model of the TBCS to study the influence of various sources of randomness on the response of the TBCS. The Karhunen–Loéve expansion is combined with the point estimate method (KLE-PEM) to calculate the statistical moment of the system response with high accuracy and efficiency. Furthermore, applicability of KLE-PEM method is discussed and the influence of different combinations of sources of randomness on the train and bridge responses under different speeds is analyzed according to the maximum range of probability values. Random track irregularities are found to have a much greater influence on the train response than uncertain parameters, whereas uncertain parameters have the greatest influence on the displacement of the bridge. [ABSTRACT FROM AUTHOR]

Especially in recent years, the study of landslide phenomena is considered as very important because of the effects of climate change. The aim of this paper is to examine the stability of the slope located in Miacher Nagar village along the Hunza River (HR), using the Limit Equilibrium Method (LEM). The Miacher slope rises to a height of 900 m from the foot of the Hunza River and has a base angle of 50 degrees. Meta-sediments and quaternary recent glaciated deposits make up the majority of the slope's composition. The slope movement of Miacher was first triggered in 1995, and was further triggered in 2010 and 2013. The slope was geologically, geomorphologically, geotechnically and geochemically investigated as well as modeled by Slope/w to determine the safety factor. Soil samples were analyzed for their geotechnical, geological and geomorphological properties. The Limit Equilibrium Method (LEM) was employed in this study to analyze the Factor of Safety (FOS) of the slope, based on assumptions of the Morgenstern and Price, Ordinary, Janbu and Bishop Methods, using the Slope/w software. Various factors, including pore water pressure, unit weight, cohesion, angle of internal friction and overburden, were examined by analyzing different scenarios. The findings showed that an increase in cohesion and angle of internal friction resulted in an increase in FOS, whereas an increase in unit weight and overburden caused a decrease in FOS. The influence of pore water pressure was positive to a certain extent, but a further increase led to a significant reduction in FOS. The results showed that the Miacher slope is currently stable, as all FOS values were greater than one, based on the existing strength parameters and simulated results obtained using Slope/w. [ABSTRACT FROM AUTHOR]

Mine water disaster is one of the geological disasters which seriously affect the safety production of coal mine. It is of great practical significance to analyze the occurrence regularity of mine water disaster for preventing water inrush accident and safe production. In this paper, the statistical analysis method is used to select the water disaster accidents occurred in the mining area of Tangshan Mine over the years, and combined with the mine hydrogeology and water filling conditions, the development law of water disaster is analyzed, and the time series, section and block, water source category, water channel and water inrush are deeply analyzed. The results show that the frequency of water inrush is decreasing. Water inrush points are mainly distributed in the middle of the well field. The main source of water is fissure water of sandstone in coal measure formation. The main water-conducting channel is water-conducting fissure; the water inrush is generally not large and the amount of water inrush soon attenuates Finally, combined with the actual mine production, a set of water prevention and control suggestions for Tangshan mine water disaster are put forward.

Excess sludge from wastewater treatment holds promise as a carbon source for denitrification. While there have been attempts at technological breakthroughs for carbon recovery from sludge through pretreatment and hydrolysis, a low-carbon footprint solution remains elusive. In this study, we evaluated seven pretreatment technologies for carbon recovery from sludge using life cycle assessment (LCA) modeling. The carbon footprints of technological configurations involving alkaline, ultrasound, thermal, oxidation, mechanical, microwave, and alkali/thermal treatments were quantified based on 123 consistent datasets, and compared by incorporating probability and uncertainty analyses with Monte Carlo and global sensitivity methods. Alkaline treatment achieved carbon footprint savings with a median of −128.71 kg CO 2 -eq/t TS, and the other technological configurations all show loads in median values. Significant variations in carbon footprints were observed both within and across different technological configurations. Probability distributions reveal overlaps among these configurations. Alkaline treatment has a high probability of obtaining carbon footprints ranging from −300 to 300 kg CO 2 -eq/t TS, while those for the other configurations showed various distribution with modes ranging from 0 to 3000 kg CO 2 -eq/t TS. Energy and chemical consumptions during pretreatment emerging were identified as key factors influencing the carbon footprint, with contributing 95–99% of the result uncertainty. Recommendations based on the top 15% probability indicate that technological configurations involving alkali/thermal and alkaline treatments offer a good balance of carbon source quality (180.12 kg/t TS and 142.39 kg/t TS, respectively) and relatively low carbon footprints (517.29 ± 80.06 kg CO 2 -eq/t TS and 136.99 ± 648.64 kg CO 2 -eq/t TS, respectively). These findings help identify the most sustainable technologies for carbon recovery from sludge and their potential improvements from a carbon footprint perspective. This study thus underscores the need for further research and technological advancements in sludge treatment to facilitate carbon recovery through denitrification. [Display omitted] • Carbon footprints of 7 technologies recovering carbon source from sludge are assessed. • 123 datasets show parameter uncertainty and variations within and across technologies. • Carbon footprints of −230 kgCO 2 -eq/t TS have a high probability in alkaline treatment. • Energy and chemical consumptions contribute 95–99% of uncertainty to carbon footprints. • Best-practice of alkali/thermal treatment gets 517 kgCO 2 -eq/t TS at high carbon yield. [ABSTRACT FROM AUTHOR]

The use of liquefied natural gas (LNG) in the transportation sector is increasing, and boil-off gas (BOG) management systems are considered viable options to increase economic efficiency and reduce greenhouse gas emissions at LNG refueling stations. The present study proposed an economically optimized method by investigating four refueling station scenarios, including different BOG management systems. Among the four scenarios, the scenario in which compressed natural gas was produced from BOG had the lowest minimum selling price (MSP) and was the most economical. Sensitivity and uncertainty analyses were conducted for the economically optimal scenario, which identified the factors with the most influential impact and their uncertainties on the MSP. Finally, to determine the feasibility of the business through profitability analysis, the net present value, discounted payback period, and present value ratio due to changes in the discount rate were presented, and the discounted cash flow rate of return was found to be 13.22%. As a result of this study, a BOG management system can contribute to improving the economic feasibility for LNG refueling stations by reliquefying BOG and re-selling it (the most efficient way is scenario 4) and will provide an economical guide for countries with much demand for LNG in the transport sector. [ABSTRACT FROM AUTHOR]

This paper develops an efficient evolutionary hybrid optimization technique based on the adaptive salp swarm algorithm (ASSA) and pattern search (PS) for the reliability evaluation of earth slopes considering spatial variability of soils under the framework of the limit equilibrium method. In the ASSA, to improve the salp swarm approach's exploration ability while also avoiding premature convergence, two new equations for the leaders' and followers' position updating procedure are introduced. The proposed hybrid algorithm (ASSPS) benefits from the effective global search ability of the adaptive salp swarm algorithm as well as the powerful local search capability of the pattern search method. The suggested ASSPS algorithm's efficiency is confirmed using mathematical test functions, and its findings are compared with the standard salp swarm algorithm as well as some efficient optimization techniques. Then, the ASSPS is applied for calculation of the lowest safety factor and reliability index of earth slopes. The safety factor is formulated using the Morgenstern and Price approach and the advanced first-order second-moment (AFOSM) method is implemented for the reliability calculation model. The ASSPS's efficacy for the evaluation of the minimum reliability index of slopes is investigated by considering two literature-based case studies. The numerical experiments demonstrate that the new algorithm could generate better optimal solutions and significantly outperform other methods in the literature. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Minerals & Processing / Huagong Kuangwu yu Jiagong is the property of Industrial Minerals & Processing 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.)

Background: Pioglitazone significantly reduces the risk of stroke in people with diabetes, and in those with prediabetes, it markedly reduces the risk of stroke/myocardial infarction and new-onset diabetes. Low-dose pioglitazone provides most of the clinical benefits of high-dose pioglitazone, with fewer adverse effects. We report an economic evaluation of the cost-effectiveness of low-dose pioglitazone versus placebo from a Canadian public payer perspective in 2023 Canadian dollars., Methods and Results: A Markov model was developed at a lifetime horizon with an annual cycle length and 5 health states (event-free, myocardial infarction, stroke, new-onset diabetes, and death). Transition probabilities were extracted from the IRIS (Insulin Resistance Intervention in Stroke) trial. Health state costs and utilities were based on public sources. Annual discount rates of 1.5% were applied in the reference-case analysis. Probabilistic analyses were conducted to deal with parameter uncertainty through 5000 simulations. The costs were estimated as $24 887 (interquartile range [IQR], $14 632-$41507) for low-dose pioglitazone and $57 301 (IQR, $48 730-$67368) for placebo, resulting in a cost saving of -$30 287 (IQR, -$43 374 to -$14 587) in favor of low-dose pioglitazone. Quality-adjusted life years were estimated as 25.99 (IQR, 24.56-26.81) for the low-dose pioglitazone and 19.44 (IQR, 18.68-20.13) for placebo, resulting in a difference of 6.37 (IQR, 5.07-7.36) in favor of low-dose pioglitazone. Consistent findings were observed from scenario analyses and 1-way probability sensitivity analyses., Conclusions: Holding across a wide range of values in modeling parameters, low-dose pioglitazone is found as the dominant strategy versus a placebo.

China's railway network is wide, and some of them cross the seismic zone, and the ratio of high-speed railway (HSR) bridges is high. Therefore, the safety of trains on the bridge may be endangered in the event of an earthquake. Because the response of track–bridge system is sensitive to the randomness of bridge structural parameters during the earthquake, while the train wheelset is directly in contact with the track system, the running safety of train (RST) may be also sensitive to the randomness of structural parameters. In this paper, the model of train–bridge coupled system (TBCS) under earthquake was established, and the accuracy of the model was verified by test results. To efficiently calculate the safety performance of trains considering the randomness of structural parameters, the point estimation method (PEM) was used in this paper, and the applicability of PEM was proved by comparing with the calculation results of Monte Carlo simulation (MCS). Then, PEM was used to discuss the running safety performance of trains under different ground motion (GM) intensities, different train speeds, and different pier heights. Finally, based on the maximum probability, the GM intensity threshold of a bridge based on running safety is determined. [ABSTRACT FROM AUTHOR]

In this article, a novel training set optimization method in an artificial neural network (ANN) constructed for high bandwidth interconnects design is proposed based on rigorous probability analysis. In general, the accuracy of an ANN is enhanced by increasing training set size. However, generating large training sets is inevitably time-consuming and resource-demanding, and sometimes even impossible due to limited prototypes or measurement scenarios. Especially, when the number of channels in required design are huge such as graphics double data rate (GDDR) memory and high bandwidth memory (HBM). Therefore, optimizing the training set selection process is crucial to minimizing the training datasets for developing an efficient ANN. According to rigorous mathematical analysis of the uniformity of the training data by probability distribution function, optimization flow of the range selection is proposed to improve accuracy and efficiency. The optimal number of training data samples is further determined by studying the prediction error rates. The performance of the proposed method in terms of accuracy is validated by comparing the scattering parameters of arbitrarily chosen strip and microstrip type GDDR interconnects obtained from EM simulations with those predicted by ANNs using default and the proposed training-set selection methods. [ABSTRACT FROM AUTHOR]

Especially in recent years, the study of landslide phenomena is considered as very important because of the effects of climate change. The aim of this paper is to examine the stability of the slope located in Miacher Nagar village along the Hunza River (HR), using the Limit Equilibrium Method (LEM). The Miacher slope rises to a height of 900 m from the foot of the Hunza River and has a base angle of 50 degrees. Meta-sediments and quaternary recent glaciated deposits make up the majority of the slope’s composition. The slope movement of Miacher was first triggered in 1995, and was further triggered in 2010 and 2013. The slope was geologically, geomorphologically, geotechnically and geochemically investigated as well as modeled by Slope/w to determine the safety factor. Soil samples were analyzed for their geotechnical, geological and geomorphological properties. The Limit Equilibrium Method (LEM) was employed in this study to analyze the Factor of Safety (FOS) of the slope, based on assumptions of the Morgenstern and Price, Ordinary, Janbu and Bishop Methods, using the Slope/w software. Various factors, including pore water pressure, unit weight, cohesion, angle of internal friction and overburden, were examined by analyzing different scenarios. The findings showed that an increase in cohesion and angle of internal friction resulted in an increase in FOS, whereas an increase in unit weight and overburden caused a decrease in FOS. The influence of pore water pressure was positive to a certain extent, but a further increase led to a significant reduction in FOS. The results showed that the Miacher slope is currently stable, as all FOS values were greater than one, based on the existing strength parameters and simulated results obtained using Slope/w.

The rainy and windy weather conditions during the passage of tropical cyclone (TC) usually cause large-amplitude rain-wind induced vibration (RWIV) of stay cables in bridges, threatening the fatigue life of stay cables and even the safety of bridge structures. In this study, the probability analysis of RWIV of stay cables under TC-induced wind and rainfall environment is performed using both wind tunnel test and numerical simulation techniques. RWIV amplitudes associated with various combinations of wind speed and rainfall rate are obtained by wind tunnel test on a prototypic sectional cable model. Surrogate models including polynomial and Kriging methods are introduced to fit the experiment results, allowing for the construction of three-dimensional surface representing RWIV amplitude or the capacity of the stay cable. A case study is performed to the Jintang Cable-stayed Bridge located at the southeast coast of China. Ten-thousand-year synthetic TC tracks around the bridge site are then generated by Monte Carlo technique. Subsequently, wind speeds and rainfall rates at the bridge site for every storm are calculated using the three-dimensional TC wind field model and HuRRDE (Hurricane Rainfall Rate and Distribution Estimator) model, achieving the joint probability distribution of wind speed, wind direction and rainfall rate. The occurrence probabilities of various RWIV amplitudes during TC process are finally calculated to conduct the probability analysis. The framework and results in this study can be utilized as a guideline and reference for the design and early warning of stay cables against RWIV. • Fragility analysis for RWIV of stay cables under TC-induced wind and rainfall environment is performed. • Joint probability distribution of wind speed and rainfall rate is achieved using Monte Carlo technique. • Surrogate models of RWIV amplitudes are developed by fitting experiment results. • A case study is applied to a cable-stayed bridge located at the southeast coast of China. [ABSTRACT FROM AUTHOR]

We determine the probability, structure dependent, that the block Wiedemann algorithm correctly computes leading invariant factors. This leads to a tight lower bound for the probability, structure independent. We show, using block size slightly larger than r , that the leading r invariant factors are computed correctly with high probability over any field. Moreover, an algorithm is provided to compute the probability bound for a given matrix size and thus to select the block size needed to obtain the desired probability. The worst case probability bound is improved, post hoc, by incorporating the partial information about the invariant factors. [ABSTRACT FROM AUTHOR]

Insulator pollution flashover occurs frequently and is harmful to the power transmission and distribution network. But the randomness of the pollution flashover and the complex influencing factors make it difficult to be predicted. The objective of this paper is to study the mechanism of the discharging on insulator surfaces and design a the calculation method which derives flashover probability. By calculating the distorted electric field in the initial streamer discharge quantitatively and combining it with the equivalent circuit of the insulator surface, the initial discharge voltage is derived. By analyzing the change in power input and dissipation in the process of the arc discharge, we propose the criterion on arc development and extinction, and the critical flashover conductivity of the insulator is obtained. Based on the probability density function, the behavior of the factors on the critical flashover conductivity is described, and the probability of insulator pollution flashover is calculated under different environmental conditions. In the validation experiment, the measured initial discharge voltage and pollution flashover ratio are close to the calculated values. The methods proposed in this paper could be used in the quantitative calculation of the initial discharge voltage and the prevention of the pollution flashover on insulators. [ABSTRACT FROM AUTHOR]

During the construction of deep-buried diversion tunnels, many uncertainties exist in the variables governing the safety of the support structure, which will affect the safety of the tunnel. Therefore, the probability analysis of the deep tunnel based on Monte Carlo simulation is analyzed. For that a case study of diversion tunnels at Jinping II hydropower station, southwestern China is selected and analyzed. First, the dispersions of geostress and marble mechanical parameters are studied based on the field monitoring of the bolt stress and the laboratory testing of the Jinping marble. Subsequently, the statistical characteristics of the radial subgrade modulus are obtained considering the 100,000-fold sampling. Finally, the influence of the main uncertain factors on the internal force and deformation of the supporting structure is examined through the establishment of a numerical model. The maximum bending moment and axial force appear in the vault and invert of the tunnel, respectively. When the design requires the reliability of the tunnel support structure to achieve 90% in the case of Jinping II hydropower station, the value of the bending moment and the axial force cannot be higher than 0.57 MPa and 8.0 MN, respectively. The factors those having a greater impact on the safety of the tunnel support structure are the vertical load and the radial subgrade modulus. [ABSTRACT FROM AUTHOR]

An investigation of three heavy metals concentration in water distribution network was carried out. A total of 33 samples were taken from 11 villages and the concentration of heavy metals was measured by ICP-MS. The related health risks via ingestion and dermal pathways were assessed for residents of Robat Karim County, Iran, using hazard quotient (HQ), hazard index (HI), and lifetime cancer risk (CR). Uncertainty analysis for the most susceptible groups (infants and children) were carried out using Monte Carlo Simulation technique. The findings revealed that heavy metals concentrations were below their respective national and international guideline values. The mean As, Cd and Cu concentrations ranged from 0.1-2.6, 1-5 and 0.1-192 µg/L, respectively. Copper showed appreciable variable levels in sampling locations but below the safe limit. The results of the HI values of tested heavy metals through combined pathways were below the safety level (HI < 1) for all groups. Simulation of cancer risk probability distribution for Cd through exposure to drinking water for adults, children and infants were in the range of 4.56 e-4 to 1.25 e-3, 1.15 e-3 to 3.8 e-3 and 1.23 e-3 to 4.04 e-3, respectively. The cancer risks resulting from exposure to Cd were higher than those of arsenic. The carcinogenic risk for three studied groups were in the order of adults > children > infants. Overall, the calculated carcinogenic risk effect for Cd indicated that the ingestion of the drinking water would cause cancer risk due to lifetime consumption. [ABSTRACT FROM AUTHOR]

The rainfall, one of the most important natural input resource for dryland agricultural production system, is erratic and temporal in nature. An attempt has been made to analyze thirty years (1990-2019) of meteorological data for prediction of probable week of onset and withdrawal of monsoon and to end with crop planning in North Gujarat region (India). The highest and lowest amount of weekly rainfall was observed in 27th and 39th SMW, respectively. The probability distribution functions viz. generalized extreme value, Gumbel maximum, Gamma and Weibull were found best-fit for prediction of weekly rainfall. The analysis revealed 26th SMW (25 Jun - 01 Jul) and onwards as the most suited sowing time of kharif crops. There are also chances of occurrence of moisture stress during 34th and 35th SMW. The results would be useful for agricultural scientists, researchers, decision makers and policy planners in the field of agricultural crop planning and irrigation management for semi arid regions. [ABSTRACT FROM AUTHOR]

Network security situation prediction is difficult due to its strong uncertainty, but DS evidence theory performs well in solving the problem of uncertainty. Based on DS evidence theory, this study analyzed the prediction of the network security situation, designed a prediction model based on the improved DS evidence theory, and carried out a simulation experiment. The experimental results showed that the improved method could predict accurately in the case of a large conflict, and had strong anti‐jamming abilities as compared with the original method. The experimental results prove the effectiveness of the improved method in the prediction of the network security situation and provide some theoretical basis for the further application of DS evidence theory.

The rainfall, one of the most important natural input resource for dryland agricultural production system, is erratic and temporal in nature. An attempt has been made to analyze thirty years (1990-2019) of meteorological data for prediction of probable week of onset and withdrawal of monsoon and to end with crop planning in North Gujarat region (India).The highest and lowest amount of weekly rainfall was observed in 27th and 39th SMW, respectively. The probability distribution functions viz. generalized extreme value, Gumbel maximum, Gamma and Weibull were found best-fit for prediction of weekly rainfall. The analysis revealed 26th SMW (25 Jun – 01 Jul) and onwards as the most suited sowing time of kharif crops. There are also chances of occurrence of moisture stress during 34th and 35th SMW. The results would be useful for agricultural scientists, researchers, decision makers and policy planners in the field of agricultural crop planning and irrigation management for semi arid regions.

The use of liquefied natural gas (LNG) in the transportation sector is increasing, and boil-off gas (BOG) management systems are considered viable options to increase economic efficiency and reduce greenhouse gas emissions at LNG refueling stations. The present study proposed an economically optimized method by investigating four refueling station scenarios, including different BOG management systems. Among the four scenarios, the scenario in which compressed natural gas was produced from BOG had the lowest minimum selling price (MSP) and was the most economical. Sensitivity and uncertainty analyses were conducted for the economically optimal scenario, which identified the factors with the most influential impact and their uncertainties on the MSP. Finally, to determine the feasibility of the business through profitability analysis, the net present value, discounted payback period, and present value ratio due to changes in the discount rate were presented, and the discounted cash flow rate of return was found to be 13.22%. As a result of this study, a BOG management system can contribute to improving the economic feasibility for LNG refueling stations by reliquefying BOG and re-selling it (the most efficient way is scenario 4) and will provide an economical guide for countries with much demand for LNG in the transport sector.

In this paper, effect of uncertainty of fastening system properties on the wheel/rail dynamic force (WRDF) was investigated. For this purpose, two deterministic and epistemic models of vehicle/track interaction were developed, using the finite element method. Validity of the results obtained from the models was shown through comparison between the model results and those obtained from field tests. The effects of uncertainty of fastening system properties on the WRDF in different axle loads, vehicle speeds, rail irregularities and various track maintenance conditions were derived through probability and sensitivity analyses. Using the results obtained, some contours were developed to obtain the amplification of WRDF (due the uncertainty of fastening system properties) as a function of vehicle axle load, vehicle speed and track maintenance conditions. [ABSTRACT FROM AUTHOR]