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This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors' keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors' keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users' consensus on specific terms can identify the new patterns of users' tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved. [ABSTRACT FROM AUTHOR]

Overuse of empirical intravenous antibiotics in neonates in high-income countries (HICs) is well documented. The Kaiser Permanente neonatal early-onset sepsis (EOS) calculator is an evidence-based sepsis risk assessment tool that has demonstrated potential to reduce antibiotic usage in this population. The incidence of early-onset sepsis in most HICs is 0.4–0.8 per 1000 live births. The objective was to evaluate the calculator's impact on antibiotic rates and length of stay in a regional level II Special Care Nursery. A single-centre retrospective cohort study compared antibiotic administration rates in the first 72 h in neonates ≥35 weeks gestation born during two 6-month periods in 2019 (pre-EOS calculator) and 2021 (post-EOS calculator). Electronic and paper case records were accessed to capture data. Continuous data were summarised using mean and standard deviation, and categorical data were summarized using frequency distributions. There were 951 (2019) and 1129 (2021) infants born during the study periods. Following implementation of the calculator, antibiotic exposure decreased from 13.7% to 4.7% of all neonates without reported negative outcomes. Mean length of stay for neonates born across the two periods decreased from 2.38 to 2.13 days. Indications for antibiotic use shifted more towards clinical condition and away from obstetric risk factors. There were no culture-proven cases of sepsis or readmissions with EOS in either period. Implementation of the EOS calculator significantly reduced exposure to antibiotics, without adverse outcomes. [ABSTRACT FROM AUTHOR]

Abstract: The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not. [Copyright &y& Elsevier]

Statistical analysis of the time-to-event scenarios commonly involves the use of the exponential and Rayleigh distributions. However, the exponential distribution is constrained by its constant failure rate, while the Rayleigh distribution exhibits a growing (or increasing) failure rate. The linear failure rate distribution provides a broader perspective by encompassing both of these distributions, including the possibility of a non-increasing hazard function. In this paper, a new distribution is introduced, which could be considered as one of the major modifications to the linear failure rate distribution, known as the weighted linear failure rate distribution. The model proposed illustrates both monotonic and a known non-monotonic failure rate. This paper extensively explores the characteristics of the proposed distribution. It presents the maximum likelihood estimates for the unknown parameters and includes a simulation study. The analysis focuses on three real data sets obtained from the fields of musical education and engineering. By employing three decisive tools, it is observed that the current distribution provides a superior fit in comparison to several other widely recognized distributions. [ABSTRACT FROM AUTHOR]

It is an established and already accomplished fact that probability-based methodologies (or probability/statistical distributions) play the roles of imperative tools in illustrating and describing applied phenomena. Paying attention to these previous facts, this paper considers the evolution of a new probability distribution. Thus, the main achievement of this paper leads to a new probability distribution as a result of combining the exponentiated exponential model with a tangent-based stratagem. The new model is called the tangent exponentiated exponential (TEE) distribution. For the TEE distribution, certain distributional properties and estimators of the model parameters are derived mathematically. The appraisal of the estimators of the TEE distribution is accomplished through a simulation study. Moreover, the applicability and virtuoso of the TEE distribution are exemplified via two data sets, which are taken from the music engineering (named Data 1) and reliability sector (named Data 2). By considering the p -value and three other important statistical tests/methods as diagnostic tools, we find that the TEE distribution is the most useful and optimal model for Data 1 and Data 2 compared to certain candidate probability distributions. [ABSTRACT FROM AUTHOR]

As an effective tool to show the fuzziness of qualitative information, the probabilistic hesitant fuzzy set (PHFS) can utilize a group of membership degrees with a clear probability distribution to show the opinions of decision-maker (DM). Given this merit, many probabilistic hesitant fuzzy multi-criteria group decision-making (PHF-MCGDM) methods have been designed. However, most of the existing PHF-MCGDM methods have some limitations, including the difficulty of reflecting DMs' ambiguous and hesitant preferences for criteria weights and the inability to comprehensively show the impacts of DMs' irrational behaviors. To address these limitations, this paper develops a novel PHF-MCGDM method that integrates the defining interrelationships between ranked criteria (DIBR) approach and tri-reference point (TRP) theory. First, the PHF-DIBR approach is constructed to determine criteria weights by fully expressing DMs' ambiguous and hesitant preferences for the importance of criteria. Second, the novel probabilistic hesitant fuzzy correlation coefficient (NPHFCC) is developed for deriving the weights of DMs, which remedies the flaws of the existing correlation coefficients (CC). Moreover, TRP theory is used to describe the psychological behavior effects of DMs and derive the order of alternatives. Finally, the applicability of the proposed method is validated by the case about office flooring material selection, while the sensitivity and comparison analyses are also conducted to further prove its advantages and effectiveness. [ABSTRACT FROM AUTHOR]

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets. [ABSTRACT FROM AUTHOR]

Probability distributions are very important in describing practical phenomena. Among the available probability distributions, the three-parameter generalized inverse Weibull (for short "GI-Weibull") model has very useful applications in reliability, management, and healthcare studies, among others. This paper considers a weighted adaptation of the proposed new model. We proposed a weighted version of the GI-Weibull distribution, which may be called the weighted generalized inverse Weibull (for short "WGI-Weibull") distribution. The appeal of the WGI-Weibull distribution lies in its altered form of the GI-Weibull distribution, which does not necessitate the incorporation of extra parameters. Various properties of the WGI-Weibull distribution are obtained through mathematical derivation. Finally, a medical application (i.e., a medical data set) is studied and analyzed to illustrate the WGI-Weibull model. A comparison is conducted between the WGI-Weibull distribution and the GI-Weibull and inverse flexible Weibull models utilizing the medical data set. Employing three evaluation metrics, it is shown that the WGI-Weibull model describes the medical phenomena much better than the GI-Weibull and some Weibull models. [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]

This paper studies the quantized iterative learning control with encoding–decoding mechanism of a class of impulsive differential inclusion systems with random data dropouts. First, the set-valued mappings in the differential inclusion systems are transformed into single-valued mappings by using the Steiner-type selector. Then, a learning algorithm based on the intermittent update principle is designed to address the data asynchronism problem caused by two-sided data dropouts. If the data are successfully transmitted at the actuator and measurement sides, then the control input is effectively updated. Furthermore, a suitable scaling sequence is introduced to ensure the system output to achieve zero-error tracking performance for a desired trajectory. An upper bound of the quantization level is determined such that the quantization error is always bounded. The results show that the quantization method reduces the burden of network communication at the cost of increasing the amount of computation, and the learning algorithm does not require the data dropouts to satisfy a certain probability distribution. Finally, the effectiveness of the learning algorithm is verified by numerical simulations of the switched reluctance motor system. • This paper enriches the iterative learning control (ILC) results for uncertain systems. • We establish an ILC research framework for continuous-time systems with unreliable networks. • We construct a novel learning algorithm to deal with the data asynchronous problem. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-attribute decision-making (MADM) method for interval rough numbers (IRNs) considering different distribution types, namely uniform, exponential, and normal distributions. Upper and lower approximate interval dominance degrees are defined and aggregated using dynamic weights to obtain pairwise comparisons of IRNs. The properties of dominance are verified, and an attribute weight determination method based on the dominance balance degree is introduced. The proposed MADM method is data-driven and does not rely on the subjective preferences of decision-makers. Case analysis demonstrates the effectiveness and rationality of the proposed method, revealing that the distribution type of IRNs significantly impacts decision results, potentially leading to reversed ranking outcomes. The proposed method offers a comprehensive framework for handling MADM problems with IRNs under different distributions. [ABSTRACT FROM AUTHOR]

Parallel-connected digital valve arrays are commonly utilized in the pilot stage of the proportional directional valve to enhance dynamic performance and reliability. However, when the digital valve array is driven by a digital signal, it is difficult to optimally assign the signal pulses to each valve. If the assignment is not well executed, it can significantly reduce the switching uniformity of the digital valves or lead to performance degradation of the system. In this paper, a model-based sliding mode control strategy based on the intelligent distribution of control law is proposed and successfully applied to a proportional valve driven by digital valve arrays. The intelligent distribution strategy encompasses a logic distribution algorithm and a circular sliding distribution algorithm that automatically assigns control laws to different valves based on the rolling of the PWM signal cycle. Experimental results confirm that the proposed strategy not only simultaneously reduces the total number of valve switches and enhances the switching uniformity among the valves, but also adapts to the variation in the number of valves. The proposed strategy is not limited to the application of digital valve arrays, it is also applicable in other fields of multi-actuators driven by digital signals, and can simultaneously improve the control accuracy, lifetime, and maintenance friendliness. • A digital proportional valve is proposed, where the pilot stage consists of fixed orifices and digital valve arrays. • The intelligent distribution strategy is designed based on a logic distribution and a circular sliding distribution. • The proposed MSMC with intelligent distribution can be adapted to DVAs with any number of valves. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new two-parameter lifetime distribution based on the type II Topp-Leone-G family and the inverse length biased exponential distribution which is called the type II Topp-Leone inverse length biased exponential (TIITL-ILBE) distribution. Various statistical properties of the proposed distribution, including the ordinary moments, moment generating function, incomplete moments, conditional moments, vitality and mean residual life functions, moments of the residual and reversed residual lifetime functions and Rényi entropy are derived. The method of maximum likelihood for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under complete samples is investigated. Also, The methods of maximum likelihood and Bayesian for estimation and some simulation results for the model parameters of the TIITL-ILBE distribution under joint censored samples is investigated. The real life data set results utilizing that the proposed distribution exhibits greater flexibility and evaluated empirically under complete and joint censored data. [ABSTRACT FROM AUTHOR]

Statistical methods have widespread applications in real-life scenarios. Researchers have expressed keen interest in implementing different functions to raise new probability distributions. However, very limited efforts have been made to raise new methodologies by applying trigonometric functions. In this paper, we propose a new method based on the trigonometric function. The suggested trigonometric-based method is called a new trigonometric cosine- G (NTC- G) family to generate flexible models. The new method is developed using the cosine function. A subcase of the NTC- G method termed as a new trigonometric cosine-Weibull (NTC-Weibull) is studied. Certain mathematical properties of the NTC-Weibull model are presented. For the NTC-Weibull model, the point estimators are acquired. Finally, the suitability of the NTC-Weibull distribution is demonstrated in light of a healthcare data set. Using the numerical and graphical approaches, the evaluation results of the NTC-Weibull distribution are compared with the Weibull and its four well-known existing modifications. [ABSTRACT FROM AUTHOR]

Takagi–Sugeno (T–S) fuzzy model is an effective technology for describing complex nonlinear industrial processes and dynamic systems with unmeasurable parameters. However, to stabilize this type of system, feedback linearization techniques and complex adaptive schemes usually need to be adopted. This paper focuses on the stabilization of uncertain fractional-order (FO) systems with unmeasurable states, external disturbances, and time delays, where certain "IF-THEN" rules based on an FO T–S model are proposed to describe FO systems. A new FO H ∞ performance model is established to provide stabilization sufficient conditions. A fuzzy observer is implemented to reconstruct system states, and a feedback controller is established to stabilize the integrated system. Benefiting from a continuous frequency distribution model, the stabilization can be discussed through integer-order stabilization theories, and several stabilization conditions are obtained. Moreover, the proposed control approach avoids the usage of feedback linearization techniques, which effectively reduces the computational complexity. Apart from theoretical analysis, through a numerical experiment, the feasibility of derived results is verified. [ABSTRACT FROM AUTHOR]

The article talks about the points raised by Professor A. H. Welsh on an article regarding the use of Bayesian analysis Air France Flight 447 search. Topics discussed include the use of subjective probabilities as a distinct characteristic of Bayesian analysis and judgment progation and uncertainty distribution into probability distribution on the wreck's location. Also mentioned is the U.S. Coast Guard's Search and Rescue Optimal Planning System which uses Bayesian program.

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T- X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced version of the Exponential Distribution Optimizer (EDO) called mEDO is introduced to tackle global optimization and multi-level image segmentation problems. EDO is a math-inspired optimizer that has many limitations in handling complex multi-modal problems. mEDO tries to solve these drawbacks using 2 operators: phasor operator for diversity enhancement and an adaptive p-best mutation strategy for preventing it converging to local optima. To validate the effectiveness of the suggested optimizer, a comprehensive set of comparative experiments using the CEC'2020 test suite was conducted. The experimental results consistently prove that the suggested technique outperforms its counterparts in terms of both convergence speed and accuracy. Moreover, the suggested mEDO algorithm was applied for image segmentation using the multi-threshold image segmentation method with Otsu's entropy, providing further evidence of its enhanced performance. The algorithm was evaluated by comparing its results with those of existing well-known algorithms at various threshold levels. The experimental results validate that the proposed mEDO algorithm attains exceptional segmentation results for various threshold levels. [ABSTRACT FROM AUTHOR]

Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we considered a new probability distribution with new applications in the field of engineering, in particular, in music engineering. The new probability distribution is mainly based on the Weibull distribution and cosine function. We call this the weighted cosine flexible Weibull distribution. The point estimators for the new distribution are obtained. We evaluate these point estimators for three sets of parameter values. The illustration of the weighted cosine flexible Weibull distribution is provided for two practical data set which are drawn from the reliability and music engineering. In addition, to dual robust machine learning approaches, we implement the Lasso (Least Absolute Shrinkage and Selection Operator) and Elastic Net (ENet) to improve the predictive performance of the data. This was done in response to the existence of outliers in the same datasets. We provide comparative analysis to see how well these approaches performed in comparison to the Step Indicator Saturation (SIS) method. [ABSTRACT FROM AUTHOR]

The remarkable advancement of quantum steganography offers enhanced security for quantum communications. However, there is a significant concern regarding the potential misuse of this technology. Moreover, the current research on identifying malicious quantum steganography is insufficient. To address this gap in steganalysis research, this paper proposes a specialized quantum steganalysis algorithm. This algorithm utilizes quantum machine learning techniques to detect steganography in general quantum secure communication schemes that are based on pure states. The algorithm presented in this paper consists of two main steps: data preprocessing and automatic discrimination. The data preprocessing step involves extracting and amplifying abnormal signals, followed by the automatic detection of suspicious quantum carriers through training on steganographic and non-steganographic data. The numerical results demonstrate that a larger disparity between the probability distributions of steganographic and non-steganographic data leads to a higher steganographic detection indicator, making the presence of steganography easier to detect. By selecting an appropriate threshold value, the steganography detection rate can exceed 90%. [ABSTRACT FROM AUTHOR]

In this paper, a motor gearbox fault-detection system based on a hierarchical flow-based model is proposed. The proposed system is used for the anomaly detection of a motion sound-based actuator module. The proposed flow-based model, which is a generative model, learns by directly modeling a data distribution function. As the objective function is the maximum likelihood value of the input data, the training is stable and simple to use for anomaly detection. The operation sound of a car's side-view mirror motor is converted into a Mel-spectrogram image, consisting of a folding signal and an unfolding signal, and used as training data in this experiment. The proposed system is composed of an encoder and a decoder. The data extracted from the layer of the pretrained feature extractor are used as the decoder input data in the encoder. This information is used in the decoder by performing an interlayer cross-scale convolution operation. The experimental results indicate that the context information of various dimensions extracted from the interlayer hierarchical data improves the defect detection accuracy. This paper is notable because it uses acoustic data and a normalizing flow model to detect outliers based on the features of experimental data. [ABSTRACT FROM AUTHOR]

This paper presents the probability distribution of an azimuth produced by inversing between a pair of points whose positions are given as planimetric Cartesian coordinates, eastings and northings. Planimetric azimuths (a.k.a. grid azimuths) are computed as the arctangent of the ratio of the differences of the eastings and the northings. We organize the paper as the solution to the following problem. The problem’s inputs are two survey points, A and B. The points are pairs of planimetric coordinates that are understood to follow a bivariate normal distribution, and the coordinates are correlated not only within each point but also between different points. The problem is to determine the most probable value and the standard deviation for the azimuth a from A to B. The exact arctangent distribution is derived, and it is shown to be bimodal under the circumstances common to land surveying; that is, the standard deviations of the coordinates are very small compared to the distance between the points. However, one of the modes is so small and located so remotely as to be negligible, leaving the distribution nearly normal. The distribution appears to have no algebraically closed-form solution, so its moments are computed using numeric integration and compared with those determined from the standard propagation-of-variance approximation. The results are presented graphically and they generally resemble total-station pointing errors, which is a similar problem. [ABSTRACT FROM AUTHOR]

This paper aims to investigate whether increasing non-linear opportunities in a neural network-based scheduling model improves its predictive performance. More specifically, this paper experiments on a trip distribution model that is part of an activity-based scheduling model called Skyline-seqNN from the ongoing thesis A neural network scheduling model. The motivation behind that model's proposed structure is to lay the groundwork for a neural network discrete choice model (DCM) that achieves to model travel demand on a detailed level while also being suitable for experimental analysis. Similar to a four-step model framework in the sequential aspect, the model system from the referenced paper utilizes the three sub-models; trip generation, trip distribution, and mode choice using a utility-maximizing micro-simulation approach. The trip generation model first decides whether, at every 10-minute interval between 05:00 am and 11:00 pm, an individual in the next time step should stay and continue the current activity or take an activity-defined trip. The distribution and mode choice models are used whenever a trip is selected. The trip distribution model decides the trip's destination by evaluating travel times and land use descriptions of each zone. The mode choice model learns the probability distribution of modes given each mode's travel time to the selected destination zone. Tests performed in this paper show how successive non-linear opportunities between input features in the trip distribution model increase its predictive performance. The data used for training and evaluation comes from a travel questionnaire from 2015 performed in Stockholm containing 10819 individuals and days. [ABSTRACT FROM AUTHOR]

Reliability assessment in distribution systems done using network methods in which failure probability distributions and negative components repair or the severity of failure and repair is clear. However, in real networks and Industry, these amounts are usually not precise and it conforms a probability distribution. Fuzzy logic is a typical method to consider indecision in engineering parameters, responsible consumption and production. As an innovation, in this paper using fuzzy logic, uncertainties are considered in assessment reliability indicator. Affecting factors on the rate of line failure and distribution transformers and factors affecting load consumption is considered as membership functions and by choosing the right rules failure rate and restoration time, are gained by considering the effect of these uncertainties. [ABSTRACT FROM AUTHOR]

Probability distributions are frequently used for statistical modeling of real-life phenomena in every field of life. Most often, real-life data sets are skewed in nature, and therefore, asymmetrical probability distributions are very competent for such scenarios. In the recent gains in distribution theory, researchers are focusing on developing new symmetrical and asymmetrical trigonometric-based probability distributions for data modeling in various fields. In the literature, however, there is no published work on statistical modeling of the transaction confirmation times on blockchain technology using trigonometric-originated probability distributions. For the first time covering the aforesaid research gap, this paper introduces a new trigonometric-based probabilistic approach for modeling the transaction confirmation times on blockchain technology. The new approach is called a new sine- G family of distributions. Using the proposed method, a new probability distribution called a new sine-Weibull distribution is studied. The proposed model is very flexible and obeys the symmetrical and asymmetrical shapes of its density function. The estimators of the new sine-Weibull distribution are derived, and their evaluation is tested through a simulation study. The applicability of the new sine-Weibull distribution is demonstrated by analyzing the average waiting time until the Bitcoin transaction is confirmed on blockchain technology. [ABSTRACT FROM AUTHOR]

In the agri-food supply chain, the perishable nature of the commodities opens a series of challenges at all decision levels: strategical, tactical and operational. This paper presents an age-based model for the inventory-routing problem of perishable commodities with stochastic demands. To deal with this source of uncertainty, the conditional value-at-risk (CVaR) has been adopted as a risk measure, and then the model has been applied to the agri-food supply chain under investigation. The model is the core of a dynamic approach for the optimal distribution and inventory planning over a short-medium horizon. To address a problem as realistic as possible, we assume that the demand for products of each age is unknown and we model this uncertainty by means of random variables with a probability distribution that can be estimated from historical data. Computational experiments on test cases based on a real-life agri-food company located in Italy show the effectiveness of the proposed approach for evaluating different risk attitudes. [ABSTRACT FROM AUTHOR]

So far in the literature, the two-parameter Weibull distribution and its other extensions are frequently implemented to analyze the wind speed energy data sets. However, based on our study of the literature, there is no published work about analyzing the wind speed energy data sets using new probability distributions that are developed via trigonometric functions. In this paper, I attempt to cover this amazing and interesting research gap. Therefore, I incorporate a trigonometric function, especially, the sine function to introduce a new statistical distributional method. The proposed method is called a new modified sine- G family of distributions. Some distributional properties of the new modified sine- G method are obtained. Using the new modified sine- G method, a new extension of the Weibull distribution called a new modified sine-Weibull distribution is studied. The new modified sine-Weibull distribution is applied to analyze four wind speed energy data sets. All three data sets are taken from the weather station at Sotavento Galicia in the Canary Islands, Spain, located at 43.3544 North and 7.8812 West. [ABSTRACT FROM AUTHOR]

Purpose: The goal of the paper was to analyze data from a short loop supply chain (supplier - manufacturer - customer), seek the reasons for the presence of the bullwhip effect (or a reverse bullwhip effect) and quantify its intensity within a practical case study from an automotive industry company based in Western Romania. Methodology: Data for this research were gathered over 27 weeks for the case of the most important 50 supplier-customer pairings of the manufacturing plant. The collected data were then analyzed using Holt-Winters exponential smoothing (level, trend and seasonality) and the methods of descriptive statistics: dispersion (range, variance, and standard deviation) and frequency distribution (count, percent, and frequency). Results: Our results confirm the existence of both the bullwhip effect (BE) and the reverse bullwhip effect (RBE) within the supply chain of the production site, as there are 54% of cases where order variation between the group of analyzed suppliers and customers is surprisingly greater for the latter, with only 12% of cases being the source of the classic bullwhip effect. Conclusion: According to our research, both effects are present, with a higher prevalence of the RBE, but the intensity of both effects can be significantly reduced by improving planning schedules, internal performance and logistics metrics, whilst also increasing the integration of suppliers and manufacturers in the upstream and downstream supply chain material and information flows. [ABSTRACT FROM AUTHOR]

Purpose: The fundamental driving force of entrepreneurship is the individual with their ambitions and entrepreneurial spirit, but also with their obstacles that prevent them from following an entrepreneurial path. These obstacles can be of a personal nature, but also factors of the immediate living environment, the prevailing cultural values, the general attitude of society towards entrepreneurship, etc. The purpose of this paper is to discuss the known barriers to early-stage youth entrepreneurship compared to the total population based on the Global Entrepreneurship Monitor (GEM) data for the 34 countries studied. Methodology: The methodology includes frequency distribution, descriptive statistics and linear regression analysis. Data from GEM 2016-2020 on early-stage entrepreneurial activity, perceived skills, and fear of failure were used. Results: We showed that there are differences in perceived entrepreneurial skills between youth and the total population, namely that youth perceive lower levels of entrepreneurial knowledge, experience, and skills. We confirmed that perceived entrepreneurial skills have a positive, statistically significant impact on total early-stage entrepreneurial activity, which is true for both the total population and youth. The test of the influence of fear of failure on total early-stage entrepreneurial activity for a sample of the total population and youth indicates a negative influence, but it is not statistically significant. Conclusion: From the study, recommendations emerge for policymakers regarding entrepreneurship education as a key intervention to help youth acquire and develop relevant entrepreneurial skills that are thus beneficial for the youth to better overcome obstacles in the business environment as well as in various life situations. [ABSTRACT FROM AUTHOR]

This paper explores two areas of risk assessment modelling in economics and business: the Stress-Strength model and Bayesian techniques. The model assumes that the probability of stress exceeding strength is a measure of risk. The interpretation of stress and strength largely depends on the particular event or phenomenon being modelled. The use of the Stress-Strength model is demonstrated through the Gaussian assumption of probability distributions for random model parameters, particularly in assessing the risk of not achieving a required margin value. The concept of the capability function, representing the difference between strength and stress, is introduced in the modelling process. The probability distribution for the capability function is initially determined based on the Gaussian distribution of the random variables used in the model, allowing for evaluating the risk metric. The Bayesian approach is then applied to generalise the problem statement when dealing with unknown parameters of probability distributions for the Stress and Strength models. The uncertainty of these parameters is modelled through uniform probability distributions, and equations for calculating prior and posterior estimates are consistently obtained. Since multidimensional integrals are involved in these calculations, and solutions cannot be obtained in closed analytical form, Monte Carlo simulation is used to solve this computation problem. [ABSTRACT FROM AUTHOR]

An attribute category clustering method based on hierarchical clustering is proposed in order to study the big data intelligent analysis and processing technology. The proposed model combines the attribute categories with similar fault type distribution, reduces the data dimension, and binarizes it. To address the problem of more missing values of continuous data, a data completion method based on attribute distribution function is adopted. Through the perspective of selection and estimation of project unit price in construction enterprises, this paper summarizes the data mining process facing the characteristics of project cost data, and puts forward the method of analyzing and processing project cost data based on clustering algorithm. Finally, the processed data sets are subjected to bottom-up hierarchical clustering analysis, and finally the ideal analysis results can be obtained. The experimental results show that the preprocessing method based on attribute clustering proposed in this paper can effectively merge attributes, reduce the dimension after binary transformation and effectively reduce the amount of data under the condition of ensuring data information. [ABSTRACT FROM AUTHOR]

Inventory management considering back-ordering policy is becoming a more effective strategy for balancing limited supply with unpredictable demand. Back-logging of inventory is widespread in numerous businesses, including manufacturing, airline, spare part service, and retail industries. This paper develops a control-theoretical method, Smith predictor, for continuous review inventory systems for perishable items with backordering and multi-supplier supply chain. The proposed model aims to respond quickly to market demand changes and generates different orders to smooth and reduce the bullwhip effect in a reasonable time. This modified control theory eases the flow of information in inventory systems. Finally, the block diagram is used to simulate a three-echelon supply chain for perishable products where backorder is allowed. The proposed model is examined and verified using Normal, Exponential, and Gamma distribution demands in MATLAB's Simulink. The results illustrate that the proposed model is consistent with the Exponential distribution. [ABSTRACT FROM AUTHOR]

The laser inertial measurement unit is a precision device widely used in rocket navigation system and other equipment, and its quality is directly related to navigation accuracy. In the quality evaluation of laser inertial measurement unit, there is inevitably uncertainty in the index input information. First, the input numerical information is in interval form. Second, the index input grade and the quality evaluation result grade are given according to different national standards. So, it is a key step to transform the interval information input by the index into the data form consistent with the evaluation result grade. In the case of uncertain input, this paper puts forward a method based on probability distribution to solve the problem of asymmetry between the reference grade given by the index and the evaluation result grade when evaluating the quality of laser inertial measurement unit. By mapping the numerical relationship between the designated reference level and the evaluation reference level of the index information under different distributions, the index evidence symmetrical with the evaluation reference level is given. After the uncertain input information is transformed into evidence of interval degree distribution by this method, the information fusion of interval degree distribution evidence is carried out by interval evidential reasoning algorithm, and the evaluation result is obtained by projection covariance matrix adaptive evolution strategy optimization. Taking a five-meter redundant laser inertial measurement unit as an example, the applicability and effectiveness of this method are verified. [ABSTRACT FROM AUTHOR]

In reliability engineering or medical studies, failure of units or individuals may occurring due to several failure modes which is known by competing risks model. In this paper, we have adopted a competing risks model under consideration that, the failure time has generalized inverted exponential distribution (GIED) and the failure modes are independent and partially observed. Under type-II progressive censoring scheme (type-II PCS), the experiment is designed as a quite popular scheme for collecting data specially, for a highly reliable product. The point estimates of the model parameters are formulated under classical approach (maximum likelihood and bootstrap) and Bayesian approach with respect to the symmetric squared error loss function. Also, the approximate confidence intervals under a large sample theory, bootstrap technique's and Markov chain Monte Carlo method (MCMC) are constructed. The estimators are assessed and compared with building the simulation study. Analysis a set of type-II PCS competing risks mortality data for illustration purpose. [ABSTRACT FROM AUTHOR]

The semi-Markov model is another name for the continuous-time Markov model. This model exponentially disperses the amount of time spent on each sojourn. This work aims to develop a model of infectious disease transmission using a continuous-time hybrid Markov/semi-Markov model. The model will combine assumptions from the Markov and semi-Markov models. The sojourn time in the semi-Markov model may have an exponential or a Weibull distribution, depending on the circumstances. A hybrid Markov/semi-Markov model can consist of both types of sojourn time distributions. In general, there are two parameterizations in the semi-Markov model: the transition intensity function and the sojourn time distribution, each of which has a different probabilistic and inferential perspective. This paper uses data on COVID-19 cases in DIY, Indonesia, from March 15, 2020, to July 31, 2020. This research uses four states: susceptible, infected, recovered, and deceased, where the sojourn time in the susceptible state is in the Weibull distribution while the sojourn time in other states has an Exponential distribution. This is because, at the beginning of the spread of COVID-19, few cases were found, so the distribution of sojourn time in each state tends to be constant except for the sojourn time in susceptible states. Under the semi-Markov model, the Weibull distribution leads to a dynamic probability with a higher degree of decline and a slight difference. In the final section, a comparison is made of the Markov, semi-Markov, and hybrid Markov/semi-Markov models. The hybrid Markov/semi-Markov model shows the best results with the smallest AIC value. Next, make a prediction equation for the SIRD model assuming a hybrid Markov/semi-Markov, which gives a MAPE < 20%. This means that the model's ability to predict COVID-19 cases is good. [ABSTRACT FROM AUTHOR]

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

Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency. [ABSTRACT FROM AUTHOR]

This paper focuses on the dual-thresholds event-triggered (ET) based H ∞ control problem for nonlinear networked control systems (NCSs) with imperfect communication channels under interval type-2 fuzzy modeling technique. During the procedure of devising fuzzy state-feedback controller, two imperfect communication channels of the NCSs are taken into account, and the corresponding mathematical models are built by using two independent Bernoulli random distribution procedures. To deal with the negative effects of time-varying packet loss issue caused by imperfect communication channels and the limited network bandwidth, an innovative dual-thresholds ET scheme is firstly proposed for the nonlinear NCSs. Different from the existing ET mechanisms equipped a fixed threshold and adaptive ET schemes, the proposed dual-thresholds ET scheme considers the state error information and the time-varying data loss information, which can both abate the utilization of limited communication resources and solve the negative impact of packet dropouts. Then, according to the Lyapunov stability theory, the sufficient criteria are established to devise the desired state-feedback controller subjected to mismatched membership functions. Finally, the advantage of the presented strategy can be testified via some simulation results. [ABSTRACT FROM AUTHOR]

With the increasing number of computer network users, the problem of network communication security has become more and more serious. Encryption technology, as one of the key technologies for securing communication, has important research and application values. However, the security of conventional encryption methods is gradually challenged, and at the same time, traditional encryption algorithms require a large amount of computing resources, which makes it difficult to realize real-time encryption and decryption operations. Therefore, how to improve the confidentiality and efficiency of computer network communication security encryption system has become a current urgent problem to be solved. This paper firstly discussed the analysis of its application in the computer network communication security encryption system based on the ant colony optimization (ACO) algorithm. Through the theoretical analysis of the ACO algorithm in terms of bioheuristic optimization algorithm and the analysis of the characteristics of the algorithm, the optimization scheme of key distribution based on the ACO algorithm was discussed. Then, the differences between the ACO algorithm and the traditional encryption algorithm in terms of encryption duration, key distribution time, encryption strength and random number generation speed were further analyzed and verified by experimental data. The data results showed that the key distribution of the encryption system using the ACO algorithm had higher security and better performance compared with the traditional key distribution algorithm. By counting and comparing the data, it was found that the ACO algorithm showed very obvious advantages in terms of encryption and decryption time, key distribution time, encryption strength and random number generation. This indicates that the ACO algorithm has very important application prospects in computer network communication security encryption systems. [ABSTRACT FROM AUTHOR]

This paper examined the sources of faculty's personal information and the reasons for creating information. We tested the hypothesis that there is no significant relationship between the demographic characteristics of the respondents and their choice of sources of personal information. This study's population comes from four public universities and two private universities in Ghana. A sample survey research design and a sample of 235 faculty were used, and data were collected using a questionnaire. Data analysis was conducted using Multinomial Logistic Regression in addition to frequency distributions. Demographic characteristics of faculty relate aptly to their personal information sources and be integrated into information literacy activities. Improving the PIM performance of faculty will require understanding their personal information sources to improve productivity and promote good health. [ABSTRACT FROM AUTHOR]

This paper introduces a new three-parameter distribution called the modified Kies Gompertz (MKGO) distribution to identify an effective statistical distribution in a real-life modeling data set. The distribution's probability density function may take numerous forms and graphs, and it could be negatively and positively skewed and uni-modal. We established several statistical properties of the MKGO distribution, such as moments, skewness, kurtosis, moment generating function, distribution of order statistic, quantile function, stress strength reliability and two cases of entropy measures. Further, we proposed six methods for estimating the unknown parameters of the suggested model. A brief Monter Carlo (MC) simulation study is illustrated in terms of average estimate (AE) and mean square error (MSE). to show the efficiency of these estimators. Finally, we analyzed four real data sets in the engineering sector to evaluate the potential of the proposed model with that of various existing distributions. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. [ABSTRACT FROM AUTHOR]

This paper studies the relations between outside properties and personalities of created characters in comics/anime. Fans of comics/animes can generally predict the personality of a character from how the character is expressed in the drawing. From this facţ it seems that people have some common unconscious senses for relationships between outside properties and the inside personality of characters. We propose a method to study the relationships between outside properties and personality by calculating the conditional probability for certain attributes. We analyze the dataset of Character Elements Kingdom, which is a fan site of characters appearing in comics and anime. Given the appearance or personality as the conditional attribute, the posterior probability distribution for a category of attributes is obtained. From the posterior probability distribution, we discuss the relationships between outside properties and personalities of created characters. Through the discussion, we confirmed that there were some tendencies for the relationships between appearance and personality, e.g., "black hair girls seemed to be relatively stable though blond hair girls seemed to be relatively active." These findings suggested typical patterns of designing created characters in Japanese comics/animes. We believe the knowledge obtained through our study can be referenced for creating new characters in some narrative content. [ABSTRACT FROM AUTHOR]

In the textile and packing industries, the lifetime of the items is exhibited by some statistical distribution. This paper aims to develop a multiple dependent state repetitive (MDSR) sampling plan by assuming that the lifetime of an item comes from an exponentiated half logistic distribution (EHLD). The MDSR sampling plan will be more economical than the other sampling plans on hand such as the multiple dependent state sampling schemes, the repetitive sampling plan, the single sampling plan, and the resubmitted sampling plan concerning the size of the sample required to test the lot. The design parameters for the developed sampling plans are obtained by minimizing the average sample number when both the consumer's and producer's risks are satisfied at corresponding quality levels. Tables are given for practical use. The proposed MDSR sampling plan is compared with the single sampling plan and some examples are also provided. [ABSTRACT FROM AUTHOR]

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

Compared to traditional subtractive manufacturing processes, powder bed fusion (PBF) shows promise for making complex metal parts with design freedom, short development time, and environmental sustainability. However, there is a consensus within the additive manufacturing (AM) community that the random geometrical defects (e.g., porosity, lack-of-fusion) produced in PBF processes pose a great challenge to fabricating load-bearing parts, particularly under dynamic loading conditions. Therefore, it is imperative to quantify defect sizes and distributions to predict the critical, life-limiting defect size that significantly reduces fatigue life. This paper presents a comprehensive analysis of the defects induced by selective laser melting to quantify their sizes and statistical distributions. Then four cumulative distribution functions (i.e., Weibull, Gamma, Gumbel, and Lognormal CDFs) are leveraged and compared to predict the maximum defect size based on the principle of statistics of extremes. Both the peak over threshold (POT) approach and the block maxima (BM) approach are used for these predictions. The results show that the BM approach-based predictions for all CDFs to be much larger than the measured maxima while the POT approach-based predictions have less deviation. The Weibull and Gamma CDFs were best correlated to the data, measured by Pearson's R correlation coefficient, while the Gumbel and Lognormal CDFs were also well correlated. [Display omitted] • The Feret Caliper diameter method has physical significance unlike area measurements. • Weibull and Gamma CDFs closely agreed with the measured maxima of each POT data case. • Weibull and Gamma CDFs resulted in the highest correlation values for all POT cases. • Sample size did not significantly affect correlation. • Block Maxima approach predictions deviated from the measured maximum by large margins. [ABSTRACT FROM AUTHOR]

A standard assumption of the theory of machine learning is the data are generated from a fixed but unknown probability distribution. Although this assumption is based on the foundations of the theory of probability, however, for most learning problems we usually technically random shuffle the original datasets, such as random split into training and test datasets before the training model, to satisfy the assumption, and then we use the shuffled training dataset to train a machine learning model. However, honestly, for real-life learning applications, the data pairs are observed batch by batch under their own original order and it is not necessary to randomly shuffle the original order in advance. From a mathematical point of view, we test if the random shuffling will play a non-negligible influence on the generalization of learning machines. We reduce the problem of random shuffling into the problem of distribution-shift detection. This paper is devoted to testing the null hypothesis that random shuffling does not affect the generalization of learning machines and introduces a distributions-free martingales method against the null hypothesis. We report the five real-life benchmarks of experimental performance with the help of Support Vector Machines and a multi-layer perceptron model. The results show a bonafide fact that the distribution shift in itself of the data is an inescapable reality when we build machine learning algorithms as the original order. [ABSTRACT FROM AUTHOR]

Consensus reaching process mainly includes consensus measurement and consensus improvement. Ignoring the bounded rational behavior of group members will affect the credibility of decision results. This paper proposes a consensus model that considers bounded rational behaviors of group members under uncertain environment. Firstly, we propose a similarity measure of probabilistic linguistic term sets (PLTSs) based on the Jensen-Shannon divergence, which can measure the difference between different probability distributions corresponding to PLTSs that are used to represent uncertain information. Then, the DEMATEL method is used to determine the weights of criteria according to the relationship between criteria, and a weight penalty mechanism is introduced to improve the consensus level of a group. Next, we combine the TODIM and VIKOR methods to elicit the ranking of alternatives. Finally, a numerical example is given to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Thanks to their ability to simulate the travel behavior at the individual scale, agent-based models have gained popularity over the last years. These models are data-intensive, with regards to transport supply and demand. In particular, a detailed description of the population and its travel behavior is required. Bayesian Networks (BNs) are directed acyclic graphs representing joint probability distributions. They have recently been employed for population synthesis and daily activity patterns generation in studies showing that BNs effectively capture the causality links existing between variables and are easily interpretable. Moreover, given their flexible structure, BNs can be adapted for situations in which data from various sources is combined. In this paper, our goal is to estimate a BN for both population and activity pattern synthesis in Switzerland. We evaluate the performance of this approach compared to the statistical matching algorithm using aggregated and disaggregated metrics. In particular, we show that understanding the dependency structure linking the population characteristics and its mobility behavior is key to generate representative synthetic agents and daily activity patterns. This study is a contribution towards the development of interpretable, flexible and behaviorally rich travel demand generation models. [ABSTRACT FROM AUTHOR]