Effect of weak randomness flaws on security evaluation of practical quantum key distribution with distinguishable decoy states

Quantum key distribution provides an unconditional secure key sharing method in theory,but the imperfect factors of practical devices will bring security vulnerabilities.In this paper,we characterize the imperfections of the sender and analyze the possible attack strategies of Eve.Firstly,we present a quantized model for distinguishability of decoy states caused by intensity modulation.Besides,considering that Eve may control the preparation of states through hidden variables,we evaluate the security of preparation in practical quantum key distribution(QKD)scheme based on the weak-randomness model.Finally,we analyze the influence of the distinguishability of decoy state to secure key rate,for Eve may conduct the beam splitting attack and control the channel attenuation of different parts.Through the simulation,it can be seen that the secure key rate is sensitive to the distinguishability of decoy state and weak randomness,especially when Eve can control the channel attenuation.

A High-Randomness and High-Stability Electronic Quantum Random Number Generator without Post Processing

Random numbers are one of the key foundations of cryptography.This work implements a discrete quantum random number generator(QRNG)based on the tunneling effect of electrons in an avalanche photo diode.Without any post-processing and conditioning,this QRNG can output raw sequences at a rate of 100 Mbps.Remarkably,the statistical min-entropy of the 8,000,000 bits sequence reaches 0.9944 bits/bit,and the min-entropy validated by NIST SP 800-90B reaches 0.9872 bits/bit.This metric is currently the highest value we have investigated for QRNG raw sequences.Moreover,this QRNG can continuously and stably output raw sequences with high randomness over extended periods.The system produced a continuous output of 1,174 Gbits raw sequence for a duration of 11,744 s,with every 8 Mbits forming a unit to obtain a statistical min-entropy distribution with an average value of 0.9892 bits/bit.The statistical min-entropy of all data(1,174 Gbits)achieves the value of0.9951 bits/bit.This QRNG can produce high-quality raw sequences with good randomness and stability.It has the potential to meet the high demand in cryptography for random numbers with high quality.

Integrated evaluation system under randomness and fuzziness for groundwater contamination risk assessment in a little town, Central China

An integrated evaluation system under randomness and fuzziness was developed in this work to systematically assess the risk of groundwater contamination in a little town, Central China. In this system, randomness of the parameters and the fuzziness of the risk were considered simultaneously, and the exceeding standard probability of contamination and human health risk due to the contamination were integrated. The contamination risk was defined as a combination of "vulnerability" and "hazard". To calculate the value of "vulnerability", pollutant concentration was simulated by MODFLOW with random input variables and a new modified health risk assessment(MRA) model was established to analyze the level of "hazard". The limit concentration based on environmental-guideline and health risk due to manganese were systematically examined to obtain the general risk levels through a fuzzy rule base. The "vulnerability" and "hazard" were divided into five categories of "high", "medium-high", "medium", "low-medium" and "low", respectively. Then, "vulnerability" and "hazard" were firstly combined by integrated evaluation. Compared with the other two scenarios under deterministic methods, the risk obtained in the proposed system is higher. This research illustrated that ignoring of uncertainties in evaluation process might underestimate the risk level.

DEALING WITH SPATIAL RELATIONS FOR SPATIAL OBJECTS WITH RANDOMNESS IN GIS

To design retrieval algorithm of spatial relations for spatial objects with randomness in GIS,this paper builds up the membership functions based on set theory idea,used for determination of topological spatial relations between random objects,such as between point and point,point and line or polygon,which provides theoretical basis for retrieving spatial relations between certain and random objects.Finally,this paper interprets detailed methods and steps of realizing them by means of some simple examples under the GIS’s environment.

Relationship between Randomness and Coefficient Alpha: A Monte Carlo Simulation Study

Cronbach’s Alpha coefficient is the most popular method of examining reliability. It is typically used when the researcher has several Likert-type items that are summed or averaged to make a composite score. Distribution of alpha coefficient has been subjected of many studies. In this study relationship between randomness and Cronbach alpha coefficient were investigated and in this context, present study was examined the question“What is the distribution of the coefficient alpha when a Likert-type scale is answered randomly?” Data were generated in the form of five point Likert-type items and Monte Carlosimulation was run for 5000 times for different item numbers.

A Mathematical Model Reveals That Both Randomness and Periodicity Are Essential for Sustainable Fluctuations in Stock Prices

Is it true that there is an implicit understanding that Brownian motion or fractional Brownian motion is the driving force behind stock price fluctuations? An analysis of daily prices and volumes of a particular stock revealed the following findings: 1) the logarithms of the moving averages of stock prices and volumes have a strong positive correlation, even though price and volume appear to be fluctuating independently of each other, 2) price and volume fluctuations are messy, but these time series are not necessarily Brownian motion by replacing each daily value by 1 or –1 when it rises or falls compared to the previous day’s value, and 3) the difference between the volume on the previous day and that on the current day is periodic by the frequency analysis. Using these findings, we constructed differential equations for stock prices, the number of buy orders, and the number of sell orders. These equations include terms for both randomness and periodicity. It is apparent that both randomness and periodicity are essential for stock price fluctuations to be sustainable, and that stock prices show large hill-like or valley-like fluctuations stochastically without any increasing or decreasing trend, and repeat themselves over a certain range.

INFLUENCE OF POLYMER CHARACTERISTICS AND MELT-SPINNING CONDITIONS ON THE PRODUCTION OF HIGH RANDOMNESS 60PHB/PET THERMOTROPIC LIQUID CRYSTAL COPOLYESTER FIBER

Six samples of linear high randomness 60PHB/ PET thermotropic liquid crystal copolyesters are made by melt copolymerization at 290℃ , whose randomness about 0.955 is measured by the discernible ’H-NMR spectrometer. High tenacity, high module fiber is prepared by melt spinning in liquid crystal phase. The effect of molecular weight, shear rate, temperature as well as spinning drawn ratio on the mechanical behavior of 60PHB / PET copolyester fiber are shown that, lower shear rate (2 10 s-1), higher temperature melting （300℃ ）, lower temperature spinning （280℃ ） and higher molecular weight are favourable to the increase of the fiber mechanical properties. With the variance of drawn ratio, fiber mechanical property has a transition point due to traversion from shear-orientation to drawn-orientation. The copolyester fiber has high crystallinity, high orientation at the crystalline region, high chain orientation and high regular fibrillar structure.

Improved quantum randomness amplification with finite number of untrusted devices based on a novel extractor

Quantum randomness amplification protocols have increasingly attracted attention tbr their tantastic ability to ampllI~, weak randomness to almost ideal randomness by utilizing quantum systems. Recently, a realistic noise-tolerant randomness amplification protocol using a finite number of untrusted devices was proposed. The protocol has the composable security against non-signalling eavesdroppers and could produce a single bit of randomness from weak randomness sources, which is certified by the violation of certain Bell inequalities. However, the protocol has a non-ignorable limitation on the min- entropy of independent sources. In this paper, we further develop the randomness amplification method and present a novel quantum randomness amplification protocol based on an explicit non-malleable two independent-source randomness extractor, which could remarkably reduce the above-mentioned specific limitation. Moreover, the composable security of our improved protocol is also proposed. Our results could significantly expand the application range for practical quantum randomness amplification, and provide a new insight on the practical design method for randomness extraction.

Collective Transport of Coupled Brownian Motors with Low Randomness

The transport properties of coupled Brownian motors in rocking ratchet are investigated via solving Langevin equation. By means of velocity, diffusion coefficient, and their ratio （Peclet number）, different features from a single particle have been found. In the regime of low-to-moderate D, the average velocity of elastically coupled Brownian motors is larger than that of a single Brownian particles; the Peclet number of elastically coupled Brownian motors is peaked functions of intensity of noise D but the Peclet number of a single Brownian motor decreases monotonously with the increase of a single Brownian motor. The results exhibit an interesting cooperative behavior between coupled particles subjected to a rocking force, which can generate directed transport with low randomness or high transport coherence in symmetrical periodic potential.

An Approach to Reasoning Topological Relations Between Areal Objects Under Randomness

This paper describes the geometric and statistical properties of areal object under randomness. In order to describe formally such a uncertain topological relation, a new formal model (i.e. 4ID model) is proposed. On the basis of this, the effects of positional uncertainty on topological relations between areal objects are investigated in detail. Some possibility functions for the determination of relations are constructed based on the assumption that randomness of point location complies with a normal distribution, and the concept of uncertain sets of topological relations under randomness is introduced.

Effects of Topological Randomness on Cooperation in a Deterministic Prisoner's Dilemma Game

In this work, we consider an evolutionary prisoner＇s dilemma game on a homogeneous random network with the richest-following strategy adoption rule. By constructing homogeneous random networks from a regular ring graph, we investigate the effects of topologicaJ randomness on cooperation. In contrast to the ordinary view that the presence of smaJ1 amount of shortcuts in ring graphs favors cooperation, we find the cooperation inhibition by weak topological randomness. The explanations on the observations are presented.

Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.

Carbonic Gas Randomness Effect on Reinforced Concrete Carbonation

This paper deals the randomness effect of the pressure of carbonic gas on the carbonation phenomenon of the reinforced concrete. This analysis concentrates on the evaluation of carbonation depth (Xc) and the carbonation time (T1) which is the time necessary so that the face of carbonation arrives until the reinforcement from a probabilistic analysis. Monte Carlo simulations are realized under the assumption that the carbonic gas on the surface of the concrete is random variable with a log-normal probability distribution.

Pseudorandomness of Camellia-Like Scheme

Luby and Rackoff idealized DES by replacing each round function with one large random function. In this paper, the author idealizes Camellia by replacing each S-box with one small random function, which is named Camellialike scheme. It is then proved that five-round Camellia-like scheme is pseudorandom and eight-round Camellia-like scheme is super-pseudorandom for adaptive adversaries. Further the paper considers more efficient construction of Camellia-like scheme, and discusses how to construct pseudorandom Camellia-like scheme from less random functions.

Calculation of Two-Tailed Exact Probability in the Wald-Wolfowitz One-Sample Runs Test

The objectives of this paper are to demonstrate the algorithms employed by three statistical software programs (R, Real Statistics using Excel, and SPSS) for calculating the exact two-tailed probability of the Wald-Wolfowitz one-sample runs test for randomness, to present a novel approach for computing this probability, and to compare the four procedures by generating samples of 10 and 11 data points, varying the parameters n0 (number of zeros) and n1 (number of ones), as well as the number of runs. Fifty-nine samples are created to replicate the behavior of the distribution of the number of runs with 10 and 11 data points. The exact two-tailed probabilities for the four procedures were compared using Friedman’s test. Given the significant difference in central tendency, post-hoc comparisons were conducted using Conover’s test with Benjamini-Yekutielli correction. It is concluded that the procedures of Real Statistics using Excel and R exhibit some inadequacies in the calculation of the exact two-tailed probability, whereas the new proposal and the SPSS procedure are deemed more suitable. The proposed robust algorithm has a more transparent rationale than the SPSS one, albeit being somewhat more conservative. We recommend its implementation for this test and its application to others, such as the binomial and sign test.

Predicting short-term major postoperative complications in intestinal resection for Crohn’s disease:A machine learning-based study

BACKGROUND Due to the complexity and numerous comorbidities associated with Crohn’s disease(CD),the incidence of postoperative complications is high,significantly impacting the recovery and prognosis of patients.Consequently,additional stu-dies are required to precisely predict short-term major complications following intestinal resection(IR),aiding surgical decision-making and optimizing patient care.AIM To construct novel models based on machine learning(ML)to predict short-term major postoperative complications in patients with CD following IR.METHODS A retrospective analysis was performed on clinical data derived from a patient cohort that underwent IR for CD from January 2017 to December 2022.The study participants were randomly allocated to either a training cohort or a validation cohort.The logistic regression and random forest(RF)were applied to construct models in the training cohort,with model discrimination evaluated using the area under the curves(AUC).The validation cohort assessed the performance of the constructed models.RESULTS Out of the 259 patients encompassed in the study,5.0%encountered major postoperative complications(Clavien-Dindo≥III)within 30 d following IR for CD.The AUC for the logistic model was 0.916,significantly lower than the AUC of 0.965 for the RF model.The logistic model incorporated a preoperative CD activity index(CDAI)of≥220,a diminished preoperative serum albumin level,conversion to laparotomy surgery,and an extended operation time.A nomogram for the logistic model was plotted.Except for the surgical approach,the other three variables ranked among the top four important variables in the novel ML model.CONCLUSION Both the nomogram and RF exhibited good performance in predicting short-term major postoperative complic-ations in patients with CD,with the RF model showing more superiority.A preoperative CDAI of≥220,a di-minished preoperative serum albumin level,and an extended operation time might be the most crucial variables.The findings of this study can assist clinicians in identifying patients at a higher risk for complications and offering personalized perioperative management to enhance patient outcomes.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Frequency Domain Fatigue Evaluation on SCR Girth-Weld Based on Structural Stress

The Steel Catenary Riser(SCR)is a vital component for transporting oil and gas from the seabed to the floating platform.The harsh environmental conditions and complex platform motion make the SCR’s girth-weld prone to fatigue failure.The structural stress fatigue theory and Master S-N curve method provide accurate predictions for the fatigue damage on the welded joints,which demonstrate significant potential and compatibility in multi-axial and random fatigue evaluation.Here,we propose a new frequency fatigue model subjected to welded joints of SCR under multiaxial stress,which fully integrates the mesh-insensitive structural stress and frequency domain random process and transforms the conventional welding fatigue technique of SCR into a spectrum analysis technique utilizing structural stress.Besides,a full-scale FE model of SCR with welds is established to obtain the modal structural stress of the girth weld and the frequency response function(FRF)of modal coordinate,and a biaxial fatigue evaluation about the girth weld of the SCR can be achieved by taking the effects of multi-load correlation and pipe-soil interaction into account.The research results indicate that the frequency-domain fatigue results are aligned with the time-domain results,meeting the fatigue evaluation requirements of the SCR.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

Theoretical and experimental study of a bi-stable piezoelectric energy harvester under hybrid galloping and band-limited random excitations

In the practical environment,it is very common for the simultaneous occurrence of base excitation and crosswind.Scavenging the combined energy of vibration and wind with a single energy harvesting structure is fascinating.For this purpose,the effects of the wind speed and random excitation level are investigated with the stochastic averaging method(SAM)based on the energy envelope.The results of the analytical prediction are verified with the Monte-Carlo method(MCM).The numerical simulation shows that the introduction of wind can reduce the critical excitation level for triggering an inter-well jump and make a bi-stable energy harvester(BEH)realize the performance enhancement for a weak base excitation.However,as the strength of the wind increases to a particular level,the influence of the random base excitation on the dynamic responses is weakened,and the system exhibits a periodic galloping response.A comparison between a BEH and a linear energy harvester(LEH)indicates that the BEH demonstrates inferior performance for high-speed wind.Relevant experiments are conducted to investigate the validity of the theoretical prediction and numerical simulation.The experimental findings also show that strong random excitation is favorable for the BEH in the range of low wind speeds.However,as the speed of the incoming wind is up to a particular level,the disadvantage of the BEH becomes clear and evident.