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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]

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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.

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]

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.

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]

Collective air distribution is widely used for thermally comfortable indoor environments but is limited to unified thermal comfort requirements. Advanced collective air distribution has the potential to generate non-uniform thermal environments for individual thermal preferences. This study proposes a subzone division optimization method for nonuniform thermal environments to improve thermal preference satisfaction. The proposed method divided non-uniform thermal environments into subzones using probability analysis-based K-means clustering, minimizing and maximizing the thermal environment differences within and among the subzones, respectively. The number of subzones was determined to maximize thermal preference satisfaction with the coupled control between the nonuniform environments and individual thermal preferences while limiting the percentage of thermal discomfort. The results based on experiments of advanced collective air distribution, i.e. , stratum ventilation, showed that thermal preference satisfaction and the percentage of thermal discomfort increased with an increasing number of subzones. Compared with the conventional method (i.e. , uncoupled control without subzone division optimization) and recent method (i.e. , coupled control without subzone division optimization), the proposed method improved the thermal preference satisfaction by 34.7 % and 11.7 %, respectively, for four subzones. Moreover, with the proposed method, increasing the system control flexibility increased thermal preference satisfaction. Compared with the variable air volume system, the constant air volume system and the system with variable supply airflow rate and temperature improved thermal preference satisfaction by 5.8 % and 22.5 %, respectively. The proposed method contributes to extending the capability of collective air distribution for individual thermal preferences. • Occupied zone is non-uniformly divided into subzones. • Similarity in thermal environments of space units within subzone is maximized. • Difference in thermal environments of space units among subzones is maximized. • Thermal preference satisfaction is maximized while limiting discomfort percentage. • Thermal preference satisfaction increases with increased system control flexibility. [ABSTRACT FROM AUTHOR]

The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel occurs randomly, indicating that the vibration behavior of a vehicle cannot be comprehensively evaluated using a deterministic approach. Thus, a probability analysis framework is proposed to obtain vehicle vibration characteristics while considering the randomness of the OOR wheel. The probability model of the random OOR wheel is derived by reducing the high-dimensional variables into a few independent variables of the radius, amplitude, and phase. Then, the vertical vehicle-track coupled system with OOR wheels is modelled. A DPIM (direct probability integral method) is further developed to analyze the evolution of excitation to response probabilities. Finally, the statistics of the random vibration of the vehicle are calculated. The proposed framework is verified using a numerical case. Results show that the PDF (probability density function) shape of the vehicle random vibration, induced by the Gaussian-distributed OOR wheel, deviates from the Gaussian distribution due to the nonlinear wheel/rail contact force. Instead, it exhibits a right-skewed shape, significantly impacting the dynamic performance. As the mean or coefficient of variation of the OOR wheel amplitude increases linearly, the reliability of the vehicle Sperling index experiences a quadratic or double-sloping decrease. Consequently, a maintenance threshold for OOR wheel amplitudes is given based on reliability considerations. Compared to Monte Carlo simulation, the proposed framework offers a computational efficiency improvement of at least one order of magnitude. [ABSTRACT FROM AUTHOR]

The metaverse is an emerging technology with the potential to revolutionize our interactions with digital environments. Soft computing presents exciting opportunities in shaping this immersive virtual world. This paper provides a systematic review of the research on soft computing methods in the metaverse, highlighting the interdisciplinary nature of the field and the need for coordination to shape its future. The systematic literature review conducted in this article identifies the contributors and domains in soft computing, emphasizing the need for new developments and joint applications in soft computing and the metaverse. The study categorizes soft computing techniques into five classes - machine learning, fuzzy systems, evolutionary computing, probability analysis, and mixed/hybrid methods - contributing to the emerging metaverse-related research and development. We propose a decision framework for selecting the most suitable soft computing method to assist researchers and developers in methodically assessing the alternative methods. The findings provide a roadmap and opportunities for soft computing models and applications shaping the future of the metaverse. This article can serve as a useful reference for researchers, practitioners, and policymakers working in soft computing and the metaverse. • We present a systematic review of research on soft computing methods in the metaverse. • We show how soft computing revolutionizes the metaverse and our interaction with the digital world. • The study highlights the interdisciplinary nature of soft computing methods in the metaverse. • We categorize soft computing into five main classes. • We present a decision framework for selecting the most suitable soft computing method. [ABSTRACT FROM AUTHOR]