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This document provides a commentary on a paper titled "Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks." The paper summarizes the current state of the field and proposes extensions to marked linear network point processes. It focuses on reviewing summary statistics for point processes and discusses other important topics in the field, such as marked temporal point processes and the Growth-Interaction process. The document explains different mark spaces, summary statistics, and product density/intensity functions for marked point processes. It also discusses concepts like mark filtering, marked intensity reweighted stationarity, kth-order intensity reweighted stationarity, and mark-weighted kth-order summary statistics. The text explores the use of test functions and weighted local summary statistics, as well as linear network summary statistics and marked inhomogeneous summary statistics. The authors propose proper estimators for these summary statistics and discuss their applications. [Extracted from the article]

Glutathione and 2-aminopyridine are used as carbon sources to prepare carbon dots (CDs) by a one-step hydrothermal reaction. The results show that the average particle diameter of CDs is 8.64 nm with uniform size distribution and the fluorescence quantum yield is 13.62%. We further demonstrate that novel CDs possess highly selective sensing of Fe3+ from 0.2 to 200 μM with a low detection limit (0.194 μM). Meanwhile, the fluorescence of CDs can be repeated many times by the addition of S2−. Moreover, the CDs are used for biological imaging of living cells with well cell penetrability and low toxicity. Furthermore, it is successfully applied for anti-counterfeiting and information encryption. More interestingly, it can be doped with hydrogel and filter paper to prepare solid-phase sensors exhibiting high sensitivity and fast response, demonstrating their tremendous potential for the simple, rapid, and low-cost monitoring of Fe3+ and S2−. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices should be designed taking security requirements into consideration, so that they can be used securely in open environments. Designing secure IoT devices requires the ability to design fast and secure cryptography modules. A component in these modules is the pseudorandom number generator (PRNG), which can be built using different strategies. Some of these strategies use chaotic maps, and in such cases, the chaotic map that is selected must be simple and feasible to implement in a digital device by using the IEEE-754 floating-point standard. The chaotic map must also generate number sequences whose statistical distribution looks uniform. In this way, this paper shows the digital implementation of a PRNG by using a non-scaled non-discretized skew tent map (STM). The proposed PRNG can produce uniformly distributed number sequences when the annulling chaos conditions are identified and avoided on the chaotic map. Furthermore, the pseudorandom sequences are generated in few milliseconds. Compared to similar PRNGs recently reported, the proposed PRNG has been successful, based on tests, such as the correlation coefficient, key sensitivity, statistical analysis, entropy analysis, key space and randomness. [ABSTRACT FROM AUTHOR]

During the optimization process, a large number of data are generated through the search. Machine learning techniques and algorithms can be used to handle the generated data to contribute to the optimization process. The use of machine learning enhancing metaheuristics applied to optimization problems has been drawing attention due to their capacity to add domain knowledge during the search process. This knowledge can accelerate metaheuristics and lead to better and promising solutions. This work provides a systematic literature review of machine learning enhancing metaheuristics and summarizes the current state of the classification of the research field, main techniques and machine learning models, validations strategies, and real-world optimization problems that the approach was applied. Our keyword search found 1.960 papers, published in the last 10 years. After considering the inclusion and exclusion criteria and performing backward snowballing procedure, we have analyzed 111 primary studies. The results show the predominance of the use of surrogate-assisted evolutionary algorithms (SAEAs) for improving the efficiency of the optimization, and the use of estimation of distribution algorithms (EDAs) to increase the effectiveness of the optimization. The objective function value is the mostly applied evaluating criteria to validate the algorithm with other methods. The developed techniques of the studies found are applied in diverse real-world applications such as developing machine learning models, physics simulations with expensive function evaluation, and the variants of the classical job shop scheduling problem. We also discuss trends and opportunities of the research field. [ABSTRACT FROM AUTHOR]

This paper considers theoretical foundations and mathematical methods of data analysis under the conditions of the Rice statistical distribution. The problem involves joint estimation of the signal and noise parameters. It is shown that this estimation requires the solution of a complex system of essentially nonlinear equations with two unknown variables, which implies significant computational costs. This study is aimed at mathematical optimization of computer algebra methods for numerical solution of the problem of Rician data analysis. As a result of the optimization, the solution of the system of two nonlinear equations is reduced to the solution of one equation with one unknown variable, which significantly simplifies algorithms for the numerical solution of the problem, reduces the amount of necessary computational resources, and enables the use of advanced methods for parameter estimation in information systems with priority of real-time operation. Results of numerical experiments carried out using Wolfram Mathematica confirm the effectiveness of the developed methods for two-parameter analysis of Rician data. The data analysis methods considered in this paper are useful for solving many scientific and applied problems that involve analysis of data described by the Rice statistical model. [ABSTRACT FROM AUTHOR]

The electroslag remelting (ESR) process is widely used to produce high-quality ingots and billets for high-alloyed steels and alloys. Both the single-phase and three-phase alternating current diagram with bifilar and monofilar connection are in use for heavy ingot manufacturing. The numerical simulation of the three-phase bifilar circuit for the 120 t three-phase bifilar six-electrode ESR furnace at different variants of electric connection was presented and discussed. At the bifilar diagram of power supply, the geometrical location of electrodes in a mould holds critical importance for performances: the close location of bifilar pair electrodes provides the highest heat productivity, but the equidistant location of electrodes gives a much more uniform heat distribution. The monofilar mulit-electrode diagram of three-phase connection without phase shift shows the most uniform distribution of potential and heat generation as well as a favorable magnetic field that makes this kind the most promising for providing a high quality of heavy ingots. [ABSTRACT FROM AUTHOR]

This paper examines the paradoxical transparency involved in training machine-learning models. Existing literature typically critiques the opacity of machine-learning models such as neural networks or collaborative filtering, a type of critique that parallels the black-box critique in technology studies. Accordingly, people in power may leverage the models' opacity to justify a biased result without subjecting the technical operations to public scrutiny, in what Dan McQuillan metaphorically depicts as an "algorithmic state of exception". This paper attempts to differentiate the black-box abstraction that wraps around complex computational systems from the opacity of machine-learning models. It contends that the degree of asymmetry in knowledge is greater in the former than the latter. In the case of software systems, software codes are difficult to understand as only software experts with sufficient domain knowledge are equipped to formulate a sound critique. In contrast, the meanings of trained parameters in a machine-learning model are obscure even to the data scientists who configure and train the model. Hence, the asymmetry of knowledge lies only in how data examples are collected, the choice and configuration of machine-learning models, and the specification of features in model design. Under the trend of algorithmic decision-making proliferating with machine-learning heuristics, the paper contends that the more symmetric distribution of knowledge in machine learning could lead to a more transparent production process if proper policies are in place. [ABSTRACT FROM AUTHOR]

Numerous experimental data obtained by different methods confirm the theoretical ideas about the electric nature of the interaction between the solid and liquid soil phases, which were first suggested by the Maxwell-Boltzmann-Helmholtz-Gouy and Deryagin-Landau-Verwey-Overbeek theories about the double electric layer in two-phase disperse systems. The interaction between the solid phase and water in the diffuse part of this layer may be described by the theoretically derived equation ln P = A − BW, where P is the soil moisture pressure, W is the ratio between the masses of the water and the solid phase in the soil, and A and B are physically grounded parameters. The exponential form of this equation first predicted by the out-standing researcher Gouy 100 years ago results from the exponential distribution of ions in the diffuse layer near the electrically charged surface of the solid soil phase. [ABSTRACT FROM AUTHOR]

This scientific paper explores the operation of a multiprocessor task servicing system. Tasks are received into the system at random intervals and are characterized by several stochastic parameters, including the number of processors required for their execution, the maximum allowable busy time for these processors, and the permissible waiting time in the task queue. The organization of task servicing in this system follows a first-in, first-out (FIFO) approach, ensuring uninterrupted processing. The key servicing process involves periodically selecting the first task in the queue and assessing its feasibility for immediate execution. If the task meets the necessary criteria, it is dispatched for processing. This process continues iteratively until a task is found, the parameters of which prevent immediate servicing. It is important to note that tasks in the queue have a limited window of time within which they can be serviced; otherwise, they may exit the system without service. This paper focuses on systems characterized by exponential distributions for random variables related to task arrivals, servicing times, and waiting restrictions. A system of equations is derived that describes the system's steady-state behavior. These equations enable the calculation of probabilities associated with the system's various states. Additionally, the paper provides insights into the probability distributions of virtual waiting times for tasks that arrive in the system at any given moment. [ABSTRACT FROM AUTHOR]

The present study introduces a quadtree decomposition approach for assessing surface structures. The calculated quadtree variables like the number of blocks (nQT), the mean block size (mQT) and the standard deviation of the block sizes (sQT) seem to be suitable surface descriptors for quantifying the structural changes of the paper surface due to, e.g. production or printing conditions. The graphical representation of the quadtree decomposition is also intuitive and easy to interpret, thus giving a unique opportunity to observe and track structural changes locally. [ABSTRACT FROM AUTHOR]

Nowadays time series clustering is of great importance in manufacturing industries. Meanwhile, it is considerably challenging to achieve explainable solution as well as significant performance due to computation complexity and variable diversity. To efficaciously handle the difficulty, this paper presents a novel metaheuristic-based time series clustering method which can improve the effectiveness and logicality of existing clustering approaches. The proposed method collects candidate cluster references from hierarchical and partitional clustering through shape-based distance measure as well as dynamic time warping (DTW) on manufacturing time series data. By applying metaheuristics highlighting estimation of distribution algorithms (EDA), such as extended compact genetic algorithm (ECGA), on the collected candidate clusters, advanced cluster centroid combinations with minimal distances can be achieved. ECGA employs the least complicated and the most closely related probabilistic model structure regarding population space during generation cycle. This feature strengthens the comprehension of clustering results in how such optimal solutions were achieved. The proposed method was tested on real-world time series data, open to the public, from manufacturing industry, and showed noticeable performances compared to well-established methods. Accordingly, this paper demonstrates that obtaining both comprehensible result as well as prominent performance is feasible by employing metaheuristic techniques to time series data clustering methods. [ABSTRACT FROM AUTHOR]

Soil–water characteristic curve (SWCC) contains the key information related to the hydromechanical properties of unsaturated soil. It is noted that the shapes of the SWCC for the fine-grained soils could be different if the specimens were prepared with different initial void ratios. The pore structure of soil is altered when the specimen experiences mechanical or suction loading. Determination of a group of SWCCs for different specimens through the experimental measurement is time-consuming and costly. In this paper, a new method is proposed to predict the SWCCs for the fine-grained soil with different initial void ratios. The measured SWCC in the form of gravimetric water content (SWCC-w) for the specimen with a high void ratio and the volumetric shrinkage curve is adopted as the input information. The SWCC-w for the specimen with another void ratio is subsequently estimated using the proposed method in this paper. The estimated results from the proposed method in this paper show a good agreement with the experimental data from the published literature. The proposed method provides an alternative method for the prediction of the SWCC for the fine-grained soil with different initial void ratios. [ABSTRACT FROM AUTHOR]

This paper explores the concept of autonomous decision rules of Industry 4.0. The goal is to model and simulate a production system in which products autonomously choose the resource in which they will be processed based on shortest completion time (shortest queue length). For validation and benchmark, a production system previously presented in the literature is modeled. The performance of the autonomous model is evaluated in comparison to a model with dedicated lines for each product. In the previous work considered, a specific interarrival function and only deterministic parameters for the processing times are used, with no consideration of setup times. After validation, the proposed models are simulated with other parameters (different probability distributions for interarrival and processing times) and are extended to include machine setups, according to different rules. Reductions in total throughput times and their variability were observed for the systems with autonomous products, when compared to those with dedicated lines, which indicates greater responsiveness and stability of the shop floor. An optimization model for scheduling was also proposed and applied to the same system and input data of the simulation of autonomous products, for comparison. The results showed that the proposed decision rule based on the queues' length, used in the simulation, can yield solutions of good quality, close to the optimal solution. This paper contributes to extend and generalize the results of the literature and the discussion about variability seeks to motivate organizations to implement systems with autonomous products. [ABSTRACT FROM AUTHOR]

Preferences can vary both across respondents (i.e. inter-respondent preference heterogeneity) and across choice tasks within respondents (i.e. intra-respondent preference heterogeneity). Ignoring the existence of intra-respondent preference heterogeneity could bias preference elicitation and demand forecast. Thus far, most studies covering inter- and intra-respondent preference heterogeneity have applied the mixed multinomial logit model. Meanwhile, the behavioural explanations for such preference variations remain under-explored. This paper accommodates inter- and intra-respondent preference heterogeneity through a two-layer latent class modelling structure, where the continuous random distributions are replaced with discrete mixtures in both layers. A latent variable representing variety-seeking is included to explain class membership probabilities, offering additional behavioural insights concerning the source of preference heterogeneity both across and within respondents. Two aspects associated with variety-seeking are examined: novelty-seeking (i.e. the inclination to adopt new modes) and alternation (i.e. the tendency to vary one's behaviour regularly by selecting different modes continuously). In the context of new shared mobility, this paper finds the role of both aspects in preference heterogeneity. Specifically, novelty seekers are found to be more likely to fall into the class with higher probabilities of switching from existing modes to the new air taxi service than novelty avoiders, and alternation seekers are more likely to belong to the class with higher probabilities to exhibit intra-respondent preference heterogeneity than alternation avoiders. This paper, therefore, provides empirical evidence to identify the target customers of the new air taxi service. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, numerous large-scale workflow are executed in the cloud environment. Therefore, the workflow scheduling in cloud environment has become an emerging topic. This paper focuses on a reliability constrained multi-objective workflow scheduling problem (RCMOWSP) with the objectives of minimum execution cost and time. To solve the RCMOWSP, this paper proposes a knowledge-based multi-objective estimation of distribution algorithm (KMOEDA) with several problem-specific operators. First, an idle time-based decoding scheme is applied to sort the permutation of tasks greedily. In the global search strategy, a probability model is constructed to improve the diversity of population. Based on the problem-specific knowledge, a reliability-aware local search strategy is designed to performs local search around the solutions that violate reliability constraint. An elite enhancement strategy with a task perturbation operator and a resource perturbation operator is introduced to further improve the elite non-dominated solutions in the external archive. A comprehensive experiment is conducted to verify the performance of KMOEDA. The comparative results show that the KMOEDA significantly outperforms several relative multi-objective workflow scheduling approaches in solving the RCMOWSP. [ABSTRACT FROM AUTHOR]

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent. [ABSTRACT FROM AUTHOR]

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs. [ABSTRACT FROM AUTHOR]

Distant supervised relation extraction has been increasingly popular in recent years, which generates datasets automatically without human intervention. However, the distant supervised assumption has the limitation that the generated datasets have inevitable labeling errors. This paper proposes the method of Hierarchical Symmetric Cross Entropy for Distant Supervised Relation Extraction (HSCERE) to alleviate the impact of the noisy labels. Specifically, HSCERE simultaneously utilizes two extractors with the same network structure for collaborative learning. This collaborative learning process guides the optimization of the extractor through a joint loss function, namely Hierarchical Symmetric Cross Entropy (HSCE). Within the HSCE loss, the predicted probability distribution of the extractors serves as the supervisory signal, guiding the optimization of the extractors on two levels to reduce the impact of noisy labels. The two levels include the internal optimization within each extractor and the collaborative optimization between extractors. Experiments on generally used datasets show that HSCERE can effectively handle noisy labels and can be incorporated into various methods to enhance their performance. [ABSTRACT FROM AUTHOR]

Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architecture is unknown until its parameters are tuned using classical routines. Moreover, the task becomes even more complicated since well-known trainability issues, e.g., barren plateaus (BPs), can occur. In this paper, we aim to achieve two improvements in QAS: (1) to reduce the number of measurements by an online surrogate model of the evaluation process that aggressively discards architectures of poor performance; (2) to avoid training the circuits when BPs are present. To detect the presence of the BPs, we employed a recently developed metric, information content, which only requires measuring the energy values of a small set of parameters to estimate the magnitude of cost function's gradient. The main idea of this proposal is to leverage a recently developed metric which can be used to detect the onset of vanishing gradients to ensure the overall search avoids such unfavorable regions. We experimentally validate our proposal for the variational quantum eigensolver and showcase that our algorithm is able to find solutions that have been previously proposed in the literature for the Hamiltonians; but also to outperform the state of the art when initializing the method from the set of architectures proposed in the literature. The results suggest that the proposed methodology could be used in environments where it is desired to improve the trainability of known architectures while maintaining good performance. [ABSTRACT FROM AUTHOR]

In survey statistics, estimating and reducing population variation is crucial. These variations can occur in any sampling design, including stratified random sampling, where stratum weights may increase the variance of estimators. Calibration techniques, which use additional auxiliary information, can help mitigate this issue. This paper examines three calibration-based estimators—calibration variance, calibration ratio, and calibration exponential ratio estimators—within the framework of stratified random sampling. The study generates data from normal, gamma, and exponential distributions to test these estimators. Results demonstrate that the proposed calibration estimators offer more accurate estimates of population variance and outperform existing methods in estimating population variance under stratified random sampling, providing more accurate and reliable estimates. [ABSTRACT FROM AUTHOR]

Spatiotemporal datasets, which consist of spatially-referenced time series, are ubiquitous in diverse applications, such as air pollution monitoring, disease tracking, and cloud-demand forecasting. As the scale of modern datasets increases, there is a growing need for statistical methods that are flexible enough to capture complex spatiotemporal dynamics and scalable enough to handle many observations. This article introduces the Bayesian Neural Field (BayesNF), a domain-general statistical model that infers rich spatiotemporal probability distributions for data-analysis tasks including forecasting, interpolation, and variography. BayesNF integrates a deep neural network architecture for high-capacity function estimation with hierarchical Bayesian inference for robust predictive uncertainty quantification. Evaluations against prominent baselines show that BayesNF delivers improvements on prediction problems from climate and public health data containing tens to hundreds of thousands of measurements. Accompanying the paper is an open-source software package (https://github.com/google/bayesnf) that runs on GPU and TPU accelerators through the Jax machine learning platform. Spatiotemporal data consisting of measurements gathered at different times and locations is challenging to analyse due to variability and noise impact across different scales. The authors propose a statistical approach that delivers models of large-scale spatiotemporal datasets applicable to data-analysis tasks of forecasting and interpolation. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to consider the multiple birth properties for multi-type Markov branching processes. We first construct a new multi-dimensional Markov process based on the multi-type Markov branching process, which can reveal the multiple birth characteristics. Then the joint probability distribution of multiple birth of multi-type Markov branching process until any time t is obtained by using the new process. Furthermore, the probability distribution of multiple birth until the extinction of the process is also given. [ABSTRACT FROM AUTHOR]

The trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship. Several ship maneuverability mathematical models and each one with a particular hydrodynamic coefficient approximation using towing tank tests are existed. However, it is presented difficult to directly inverse estimate the hydrodynamic maneuvering coefficients of a ship maneuverability mathematical model from operational data consisting of ship trajectory and maneuvering operation records. This paper proposed a method for estimating the hydrodynamic maneuvering coefficients of the MMG 3DOF model using three types of time-series ship motions (surge, sway, and yaw velocity) as observed data. In the assumption of this paper, there is uncertainty in observations and the hydrodynamic maneuvering coefficients of the MMG 3DOF model. The proposed method outputs samples of the simultaneous posterior probability distribution of the hydrodynamic maneuvering coefficients by the MCMC method using the observed data and stochastic model. A robust trajectory with a wide range can be presented by conducting ship maneuvering simulations using these samples. To verify the feasibility of the proposed method, this paper conducted observation system simulation experiments (OSSE) using the KVLCC2 L7 model and applied the proposed method to several free-running model ship tests. Results showed that on the assumption that MMG 3DOF model can explain the ship's state and trajectory in real world, the proposed method can estimate the ship hydrodynamic maneuvering coefficients of the MMG 3DOF model corresponding to the observed ship trajectory and control data including the error of observed data. [ABSTRACT FROM AUTHOR]

The estimation of a certain threshold beyond which an extreme value distribution can be fitted to the tail of a data distribution remains one of the main issues in the theory of statistics of extremes. While standard Peak over Threshold (PoT) approaches determine this threshold graphically, we introduce in this paper a general framework which makes it possible for one to determine this threshold algorithmically by estimating it as a free parameter within a composite distribution. To see how this threshold point arises, we propose a general framework for generating three-component hybrid distributions which meets the need of data sets with right heavy-tail. The approach involves the combination of a distribution which can efficiently model the bulk of the data around the mean, with an heavy-tailed distribution meant to model the data observations in the tail while using another distribution as a link to connect the two. Some special examples of distributions resulting from the general framework are generated and studied. An estimation algorithm based on the maximum likelihood method is proposed for the estimation of the free parameters of the hybrid distributions. Application of the hybrid distributions to the S &P 500 index financial data set is also carried out. [ABSTRACT FROM AUTHOR]

Our research paper introduces a newly developed probability distribution called the transformed MG-extended exponential (TMGEE) distribution. This distribution is derived from the exponential distribution using the modified Frechet approach, but it has a more adaptable hazard function and unique features that we have explained in detail. We conducted simulation studies using two methods: rejection sampling and inverse transform sampling, to produce summaries and show distributional properties. Moreover, we applied the TMGEE distribution to three real datasets from the health area to demonstrate its applicability. We used the maximum likelihood estimation technique to estimate the distribution's parameters. Our results indicate that the TMGEE distribution provides a better fit for the three sets of data as compared to nine other commonly used probability distributions, including Weibull, exponential, and lognormal distributions. [ABSTRACT FROM AUTHOR]

Groundwater is a vital factor affecting the stability of water-rich slope of open-pit mine, and the distribution of groundwater inside water-rich slope is always uncertain. To investigate the uncertainty of groundwater distribution inside the water-rich slope, based on the engineering background of DEZIWA open-pit mine, this paper adopts the research method of field investigation and test (single and multiple borehole pumping tests), numerical simulation (Visual MODFOW), and theoretical analysis (nonlinear fitting theory, distribution fitting theory, and one-sample Kolmogorov–Smirnov test) to study the uncertainty of the distribution of groundwater table line within the water-rich slope of open-pit mine. Results obtained from the above research indicate that, Visual MODFLOW is an effective tool for obtaining groundwater distribution information inside the water-rich slope of open-pit mine. When the open-pit mine excavated to the final boundary, it is found that within multiple cross-sections of the southern and western regions of water-rich slope of the open-pit mine, groundwater table line can all be delineated by a series of 3-term Fourier equations, which can be characterized by identical equation forms but varying fitting coefficients. Furthermore, based on the findings of distribution fitting and one-sample Kolmogorov–Smirnov test, it becomes evident that the probability distribution of the fitting coefficients of the aforementioned 3-term Fourier equations can all be described by the normal distribution models. Use the established normal distribution models in this paper, uncertainty of the groundwater distribution within the water-rich slope of DEZIWA open-pit mine can be described indirectly and quantitatively, and the research methods of this paper can provide a meaningful reference to the slope engineering with similar conditions. [ABSTRACT FROM AUTHOR]

To address the problem of the small failure probability of highly reliable structures such as rockfill dams, marine platforms and super high-rise buildings, in this paper, a method to estimate the extreme value distribution of the earth and rock dam response under nonstationary random seismic excitation is presented, and the small failure probability based on the SGLD model is analysed. First, a preliminary estimation of the extreme value distribution based on the kernel density estimation is made and then utilized as the raw data. Second, the main body of the extreme value distribution is obtained by combining the least squares method and a fitting process based on the SGLD model. Finally, the tail of the extreme value distribution is acquired by fitting to a quadratic equation. By using an example of earth-rock dam engineering, a comparison between the direct kernel density estimation method and the method proposed in this paper is performed. The results showed that the proposed method is closer to the extreme value distribution and probability of exceedance curves calculated by Monte Carlo simulation, which verified its effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

Dempster-Shafer (D-S) evidence theory has extensive applications in the field of data fusion. It uses the mass function to replace the probability distribution in Bayesian Probability theory, which has the advantages of weak constraints and representing uncertainty. However, many existing uncertainty measures can not be well applied to the mass function, leading to many defects in some scenarios. For example, many methods can not fully reflect the relationship between focal elements. Therefore, how to improve the uncertainty measurement, and make full use of the advantages of the mass function, so as to further accurately express the system state, is still a hot topic worth studying. To solve this problem, we propose a Belief Interval Euclidean Distance (BIED) entropy of the mass function. This method combines nonspecificity and discord measurement of the mass function and is suitable for various situations. In this paper, we introduce a large number of numerical examples to demonstrate the practicality of the BIED entropy. Compared with existing advanced methods, the BIED entropy can better identify the quantity relationship and similarity relationship between focal elements, and exhibit a linear trend with the changing cardinality of focal elements. Finally, we implement a BIED entropy-based multi-sensor data fusion on various kinds of datasets. The experimental results indicate that BIED entropy has relatively high robustness, with the target recognition accuracy close to 79.33% and the F1-Score close to 77.29%. [ABSTRACT FROM AUTHOR]

This paper considers the beta exponential distribution as a distribution function of inefficacy score in a stochastic frontier model. The beta exponential distribution is a three-parameter distribution, and it is more flexible than commonly used probability density functions in a stochastic frontier model (SFM). This new model, a "Normal-Beta Exponential SFM", nests another five SFMs. This paper presents a simulated log-likelihood function and simulated inefficiency estimator of a normal-beta exponential SFM, a closed form log-likelihood function and closed form inefficiency estimator of a normal-weighted exponential SFM, and an empirical study using a normal-beta exponential SFM. In our empirical study, we have used a likelihood ratio test to compare the performance of SFMs and a normal-beta exponential SFM fits the data better than other nested special case SFMs. Furthermore, the empirical result shows that parameters of a normal-beta exponential SFM can be estimated with less standard error or high certainty than a normal-gamma SFM. [ABSTRACT FROM AUTHOR]

The new continuous class of probability distribution named the Power Burr X-T (PBX-T) family is proposed in this paper. The essential characteristics of the PBX-T family of distributions, i.e. conventional moments and associated procedures, stress-strength reliability, and Rényi entropy, are derived and studied. Some special models belonging to the new family have been comprehensively explored concerning their shapes. Extensive simulations have been conducted to compare the maximum likelihood (ML) estimates with other classical estimators. It is found that the mean squared errors and the bias of the ML estimates are relatively least for large samples. Moreover, real-life applications from medical and engineering fields have been used to demonstrate the potentiality and adequacy of the suggested sub-models from the PBX-T family. The values of observed goodness-of-fit measures show that the sub-models of the suggested family of distributions are superior in adequacy parallel to the other generalized models. [ABSTRACT FROM AUTHOR]

In this paper a retrial queuing system is considered with the help of two-way communication where the server is subject to random breakdowns. This is a M/M/1//N type of system so the population of the source is finite. The server becoming idle enables calls the customers in the orbit (outgoing call or secondary customers). The service time of the primary and secondary customers follows exponential distribution with different rates μ1 and μ2 respectively. All the random variables included in the model construction are assumed to be totally independent of each other. The novelty of this paper is to show the effect of the different distributions of failure time on the main performance measures such as the mean waiting time of an arbitrary customer or the utilization of the service unit. In order to achieve a valid comparison a fitting process is done; thus, in case of every distribution the mean value and dispersion is the same. Graphical illustrations are given with the help of the self-developed simulation program. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) algorithms with deterministic actors (policy) commonly apply noise to the action space for exploration. These exploration methods are either undirected or require extra knowledge of the environment. In the aim of addressing these fundamental limitations, this paper introduces a parameterized stochastic action-noise policy (as a probability distribution) that correlates with the objectivity of the RL algorithm. This policy is optimized based on state-action values of predicted future states. Consequently, the optimization does not rely on the explicit definition of the reward function which improves the adaptability of this exploration strategy for different environments and algorithms. Moreover, this paper presents a predictive model of system dynamics (transitional probability) with the capacity to capture the uncertainty of the environments with optimal design and fewer parameters. It significantly reduces the model complexity while maintaining the same level of accuracy as current methods. This research evaluates and analyzes the proposed method and models while demonstrating significant increase in performance and reliability across various locomotion and control tasks in comparison with current methods. [ABSTRACT FROM AUTHOR]

We consider the problem of generating balanced training samples from an unlabeled data set with an unknown class distribution. While random sampling works well when the data are balanced, it is very ineffective for unbalanced data. Other approaches, such as active learning and cost-sensitive learning, are also suboptimal as they are classifier-dependent and require misclassification costs and labeled samples, respectively. We propose a new strategy for generating training samples, which is independent of the underlying class distribution of the data and the classifier that will be trained using the labeled data. Our methods are iterative and can be seen as variants of active learning, where we use semi-supervised clustering at each iteration to perform biased sampling from the clusters. We provide several strategies to estimate the underlying class distributions in the clusters and to increase the balancedness in the training samples. Experiments with both highly skewed and balanced data from the UCI repository and a private data set show that our algorithm produces much more balanced samples than random sampling or uncertainty sampling. Further, our sampling strategy is substantially more efficient than active learning methods. The experiments also validate that, with more balanced training data, classifiers trained with our samples outperform classifiers trained with random sampling or active learning. [ABSTRACT FROM AUTHOR]

Over the last few decades, stochastic thermodynamics has emerged as a framework to study the thermodynamics of small-scaled systems. The relation between entropy production and precision is one of the most prominent research topics in this field. In this paper, I answer the question how much dissipation is needed to follow a pre-determined trajectory. This will be done by deriving a trade-off relation between how precisely a mesoscopic system can follow a pre-defined trajectory and how much the system dissipates. In the high-precision limit, the minimal amount of dissipation is inversely proportional to the expected deviation from the pre-defined trajectory. Furthermore, I will derive the protocol that maximizes the precision for a given amount of dissipation. The optimal time-dependent force field is a conservative energy landscape which combines a shifted version of the initial energy landscape and a quadratic energy landscape. The associated time-dependent probability distribution conserves its shape throughout the optimal protocol. Potential applications are discussed in the context of bit erasure and electronic circuits. Controlling non-equilibrium systems generally incurs a thermodynamic cost. This paper introduces a trade-off relation between the precision at which one can control a system and the entropy production. [ABSTRACT FROM AUTHOR]

While modern rendering systems are efficient for modeling complex light paths in complex environments, the rendering of refractive caustics still takes a long time. Caustics are light patterns that occur when light is refracted and reflected from a surface. Due to an irregular density distribution of these specular events, rendering algorithms mainly rely on direct sampling of the bidirectional dispersion distribution function on these surfaces to plot trajectories. This requires a lot of calculations. Photonic maps are also used. However, there are difficulties that limit the applicability of caustic maps. Since each photon in the photon buffer must be processed, one has to choose between a strongly underestimated caustic sampling and a large decrease in speed in order to use a sufficient number of photons for caustics in order to obtain high-quality images. Complex specular interactions cause oversampling in bright focal areas, while other areas of the caustic map remain undersampled and noisy. At the same time, speed takes precedence over realism in most interactive applications. However, the desire to improve the quality of graphics prompted the development of various fast approximations for realistic lighting. This paper presents a combined method for rendering refraction of light and caustics using backward integration for illumination and direct integration for viewing rays. An approach for simultaneous propagation of light and for tracking rays in a volume is used; therefore, it does not require storing data of an intermediate volume of illumination. In the implementation of this method, the distance between the light planes is set to one voxel, which provides at least one sample per voxel for all orientations. The method does not use preliminary calculations, and all rendering parameters can be changed interactively. As a result, using the proposed method, it is possible to create plausible approximations of complex phenomena, such as refractions and caustics. The effect of refraction on the shadow is shown. Complex light patterns occurring due to the curved geometry of objects are demonstrated. The visualization results show the importance of refraction for the appearance of transparent objects, e.g., the effect distortions caused by refraction in the medium and refraction on the interfaces between media. The difference in refractive indices between different media causes a complex interaction between light and shadow areas. It is shown how refraction and caustics improve rendering of functionally defined objects by providing additional information about their shape and location. [ABSTRACT FROM AUTHOR]

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is widely utilized in smart grid for its reliable features, flexibility, and cost benefits that it offers to customers on reduction of the energy bill. In the smart grid, demand response aggregator, power customers, and utility operator all strive to increase their individual profits. However, it is extremely challenging to guarantee profits for all of the candidates simultaneously. In this paper, these criteria are employed to execute a problem with multiple objectives by combining the concept of DSM and dynamic economic emission dispatch considering uncertainties of renewable energy sources (RESs). The uncertainties of RESs can bring adverse effects on the grid operator. Thus, Weibull probability distribution function and lognormal probability distribution function are used to model the uncertainties of wind speed and solar irradiation, respectively, in this paper. Further, the multi-objective improved DSM scheme is optimized by Class Topper optimization algorithm. The objective here is to optimally schedule load demand and generation pattern simultaneously to improve load factor, minimize electricity bill and maximize the profits of all candidates of the day-ahead electricity market simultaneously. Error in the load forecasting model may lead to growing operational cost of energy generation. In this paper, a machine learning model using linear regression is used for short-term load forecasting (STLF) to forecast day-ahead load demand. The simulation results show the importance of the improved DSM scheme and the advantage of STLF, which further help to improve the economy and efficiency of the smart grid. [ABSTRACT FROM AUTHOR]

The paper examines methods for assessing the distribution of elements in a stochastic matrix assuming the exponential distribution of elements in the corresponding adjacency matrix of a graph. Two cases are considered: the first assumes the homogeneity of all the graph vertices, while the second assumes the heterogeneity in the distribution of vertices with the corresponding density calculations. Hypothesis tests are formulated for the respective distributions to determine the membership of two graph vertices in the same cluster. [ABSTRACT FROM AUTHOR]

A three-dimensional representation of the random distribution of fractures in rock masses, known as the discrete fracture network (DFN), is widely used to analyze the stability of jointed rock slopes. In this paper, a new framework for constructing three-dimensional DFN models in rock masses has been proposed to overcome the limitations of conventional circular or polygon-based models. The framework utilizes the universal elliptical disc (UED) model and integrates it with the discrete element method in 3DEC for the stability evaluation of jointed rock slopes. This paper starts by introducing the basic principles of the UED model. The procedures for constructing a three-dimensional DFN using the UED model is then outlined. In the present study, a case study of a rock slope in Zhejiang Province, China is used to demonstrate the implementation of the proposed framework. A comprehensive comparative study is conducted to investigate the impacts of several UED model parameters, including the ratio of major axis to minor axis and rotation angle, and discontinuity density, on the stability of rock slopes and compared to the conventional Baecher disc model. The results show that the framework can effectively integrate the UED model into 3DEC and provide a realistic representation of the three-dimensional DFN, leading to improved accuracy and efficiency in the stability evaluation of jointed rock slopes. The framework also shed light on the interactive effects of the UED model parameters and discontinuity density on the rock slope stability, providing a strong reference for using the UED model in constructing DFN models for rock slopes. Highlights: Three-dimensional discrete fracture networks (DFNs) of rock masses are successfully constructed using the universal elliptical disc (UED) model. Python subroutines are developed in 3DEC to construct 3D DFNs using the UED model for improved discrete element analyses of jointed rock slopes. The effects of several characteristic parameters of the UED model on the stability of jointed rock slopes are investigated. Based on a key block model, the mechanisms underlying the relationship between jointed rock slope stability and fracture discontinuity are rigorously explained. The advantages of the UED model over the conventional Baecher disc model are comprehensively discussed based on a rock slope case study. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNN's) present some of the leading applications of Artificial Intelligence (AI) which have proven suitability on various machine-learning use cases. Forecasting demand of intermittent on-line sales is a task which needs to be carried out automatically for a large number of Stock Keeping Units (SKU's). This paper discusses the intermittent online sales and proposes an AI-based model for forecasting demand. We provide empirical evidence by utilizing data from 17 different sellers with approximately 3000 orders in total. Our findings indicate that thanks to their multi-layered learning structure, the DNN's can provide up to 35% better accuracy than the classic models such as Moving Average, Exponential Smoothing, Croston's method and ARIMA. Also, it was revealed that the time between orders' arrivals follow Exponential distribution and the order sizes also generally follow Exponential distribution. Thus, most of the time, Poisson Exponential distribution can be used for modelling intermittent sales process through online platforms. The analyses show that Poisson Exponential distribution can generate values close to real sales with less than 7% error margin with real data. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely employed in the field of image compression, the most significant of which is the lossy image compression method on the basis of convolutional neural networks. And we study the application of convolutional neural networks to remote sensing image compression. Consequently, we implement an enhanced residual attention module (ERAM) on the primary and hyper coders. ERAM can apply a spatial attention mechanism to generate importance masks for potential features of the original image. And the importance mask can adaptively adjust the bit distribution and allocate more bits to substantial target regions and fewer bits to unimportant background regions. Additionally, ERAM has the capacity to lessen the channel redundancy of potential features. Furthermore, an accurate probability estimate of a potential feature can affect to a great degree the effectiveness of entropy coding and hence image compression. We propose to utilise the discrete mixed Laplace entropy model to parameterize the probability distribution of latent features, which can improve the compression efficiency of latent features in the entropy encoding process. In the field of remote sensing image compression, the experiments demonstrate that the algorithm described in this paper has good subjective effects as well as good objective effects. [ABSTRACT FROM AUTHOR]

The data sets provided by Information and Communication Technologies have been extensively used to study the human mobility in the framework of complex systems. The possibility of detecting the behavior of individuals performing the urban mobility may offer the possibility of understanding how to realize a transition to a sustainable mobility in future smart cities. The Statistical Physics approach considers the statistical distributions of human mobility to discover universal features. Under this point of view the power laws distributions has been extensively studied to propose model of human mobility. In this paper we show that using a GPS data set containing the displacements of mobile devices in an area around the city Rimini (Italy), it is possible to reconstruct a sample of mobility paths and to study the statistical properties of urban mobility. Applying a fuzzy c-means clustering algorithm, we succeed to detect different mobility types that highlight the multilayer structure of the road network. The disaggregation into homogeneous mobility classes explains the power law distributions for the path lengths and the travel times as an overlapping of exponential distributions, that are consistent with a maximum entropy Principle. Under this point of view it is not possible to infer other dynamical properties on the individual mobility, except for the average values of the different classes. We also study the role of the mobility types, when one restricts the analysis to the an origin-destination framework, by analyzing the daily evolution of the mobility flows. [ABSTRACT FROM AUTHOR]

The Poisson multinomial distribution (PMD) describes the distribution of the sum of n independent but non-identically distributed random vectors, in which each random vector is of length m with 0/1 valued elements and only one of its elements can take value 1 with a certain probability. Those probabilities are different for the m elements across the n random vectors, and form an n × m matrix with row sum equals to 1. We call this n × m matrix the success probability matrix (SPM). Each SPM uniquely defines a PMD . The PMD is useful in many areas such as, voting theory, ecological inference, and machine learning. The distribution functions of PMD , however, are usually difficult to compute and there is no efficient algorithm available for computing it. In this paper, we develop efficient methods to compute the probability mass function (pmf) for the PMD using multivariate Fourier transform, normal approximation, and simulations. We study the accuracy and efficiency of those methods and give recommendations for which methods to use under various scenarios. We also illustrate the use of the PMD via three applications, namely, in ecological inference, uncertainty quantification in classification, and voting probability calculation. We build an R package that implements the proposed methods, and illustrate the package with examples. This paper has online supplementary materials. [ABSTRACT FROM AUTHOR]

This paper deals with precipitation events, particularly the problem of recognising events individually, of distinguishing one local meteorological/precipitation event from another. The final aim was to arrive at a more appropriate conceptualization of "distance" between individual annual events. A criterion for identifying annual precipitation events, inside a 5 min-timestep series of pluviometric data, was proposed. Using this numerical criterion, a series of annual distances between events were obtained and compared with those obtained by other two known and applied statistical methods. This comparison would be important for linking the so-called statistical methods to the real data of the series, analysed through a numerical process. This would make it possible to provide a kind of "measure" for the understanding of real data via a statistical model. Results showed that distances between events, obtained through numerical analyses, are almost always different from those obtained by statistical methods. The supposed Poisson-process, generally matched to, and widely applied in the field of rainfall data, showed some contradiction with the analysis conducted in this paper. The so-called Lag method shows a numerical method type distribution, but produces some important internal deviations from numerical results. It seems that the method proposed in this paper can reproduce the real distances between events more realistically compared to the statistical methods considered here. Exponential distribution does not seem highly performing, at least in the case of the annual analysis, with series of a 5-min time-step resolution, obtained from rain gauges installed in the Veneto Region of Italy. It is believed that the numerical criterion presented in this paper can be considered adequate for determining the distances between events, within an annual time-window. [ABSTRACT FROM AUTHOR]

The cutting force prediction of worn tool is very important for wear state identification, tool life optimization, and machining quality improvement. The wearing force is mainly produced by the tool-workpiece contact which influenced by several factors such as the flank wear and the spring back. In this paper, the prediction of cutting force for worn ball-end tool with spring back influence is investigated. Firstly, the tool is separated into differential cutting edges and the differential cutting force is divided into shearing force computed by shearing coefficients and contacting force computed by contact stress. Considering the influence of spring back, the real contact width is applied instead of the flank wear width and a variable declining coefficient is introduced into the stress distribution function. Secondly, the cutter-workpiece engagement of base cutting process and spring back cutting process is analyzed and the cutting force is computed with the accumulation of all the engaging differential cutting forces. Then, a spring back value computation method with the measured force data of single-tooth cutting is presented. Lastly, the machining experiments are designed and carried out for parameter identification and accuracy validation. As the experimental results shows, the deviation between the average force of predicting results and the measured data is small. Comparing to the prediction without spring back, the instantaneous cutting force computed in this paper also shows better fitness and accuracy. [ABSTRACT FROM AUTHOR]

Probability distributions of wind data in a short time scale, such as hourly, are critically important for the risk assessment in relation to wind energy, optimal route scheduling for commercial airlines and unmanned aerial vehicles. It is known that the probability distributions of wind direction, wind speed, and wind gust have a large variety—ranging from Weibull to Lognormal distributions. A systematic method is needed to determine which distribution best fits a given wind dataset in a very efficient manner. This paper attempts to provide such a method. It is the best group fitting (BGF) method that efficiently fits the hourly wind data to a large number of distributions simultaneously, in contrast to the traditional one-by-one data fitting. The BGF method utilizes a Python package named Fitter which uses Scipy package, tries 80 distributions simultaneously, and checks what is the most probable distribution. We then apply the BGF method for the hourly data of u-wind, v-wind, wind speed, and wind gust in four geologically different locations, including the Alta Wind Energy Center, the Rocky Mountain region, the Colorado Plateau, and the San Diego urban district. The performance of the commonly used distributions and a wider selection of distributions are ranked for these locations at different times of a day. Our datasets and results show that, in addition to the traditional distributions such as Weilbull distribution and Lognormal distribution, the Burr distribution class stands out among the wind distributions and even outperforms the most used distributions in the literature. Our fitting results to the operational wind data have demonstrated that it is feasible to use BGF to make an efficient group fitting, which may imply that BGF can be used to search for the best probability distribution from different hourly weather datasets. [ABSTRACT FROM AUTHOR]

This paper describes the formation of the natural repose angle of non-cohesive bulk materials. The repose angle of particulates α is the angle between the tangent plane to the surface of the slope and the solid base. The paper presents a thermodynamic theory for the formation of the most energy-efficient natural slope α ≈ 30 ∘ that can be found very often both in nature and industry. The theoretical foundation is based on Janssen theory of the statistical distribution of vertical and horizontal stress and adds its own consideration about dissipative work during the movement of material as a slope is being formed. The presented model is expanded to include an experimental part describing four methods of creation of a natural repose angle on three sand samples. The experiments performed demonstrated the validity of the submitted theory and the thermodynamic model with certain deviations derived from the essence of the experiments performed. These experiments explain a frequent occurrence of natural slopes with a repose angle of around 30 ∘ . [ABSTRACT FROM AUTHOR]

The tolerance of pedestrians to red light signals is a crucial factor in urban residents' travel experiences and road traffic safety. To address the current lack of research on methods for measuring pedestrian red light tolerance time at crossings, this paper proposes a stacking model for predicting the red light tolerance time of individual pedestrians. The model employs the XGBoost (XGB), random forest (RF), support vector machine regression (SVR), and multilayer perceptron (MLP) models as primary models, with a multiple linear regression model as the secondary-layer meta-model, as well as Bayesian hyperparameter tuning. Using random survival forest (RSF) and K-means clustering methods, the dataset was divided into three categories: the low, medium, and high tolerance groups. The feature variables influencing the red light tolerance time were grouped accordingly. Stacking models were established for each tolerance and feature group. The experimental results demonstrated that the feature group voted on by multiple machine learning models performed the best in all three tolerance groups. In the low tolerance group, the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 6.58, 1.91, and 19.78%, respectively. For the medium tolerant group, the MSE, MAE, and MAPE were 4.82, 1.53, and 7.63%, respectively. For the high tolerance group, the MSE, MAE, and MAPE were 33.32, 3.89, and 10.14%, respectively. Individual XGB, RF, SVR, MLP, and ungrouped stacking models were established for comparative analysis of the test set. The results indicated that the proposed grouped stacking model outperformed the other models overall. The method provides a means of obtaining the probability distribution function of the length of the waiting crowd's tolerance time for each red light range and thus determining the duration of the red light at the crossing signal for different personnel compositions. [ABSTRACT FROM AUTHOR]

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]