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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]

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]

Paper-type gas diffusion layer (GDL) plays the role of structural support, mass transport, electron and heat transfer in the fuel cell. The deformation and fracture behavior of paper-type GDL directly determines its physical characteristics. In this paper, we obtained the distribution function of fiber length among intersections using statistical analysis. Based on the bending theory and fracture analysis, the deformation model and the maximum allowable pressure criterion of paper-type GDL are established. The deformation modulus is related to the modulus and the initial volume fraction of carbon fiber, with an ideal value of 1.4 MPa. The tensile fracture is the main form of fiber fracture. The maximum allowable pressure is related to the expected fracture probability, the strength and the initial volume fraction of carbon fiber. The residual strain increases and the deformation modulus decreases with load range, which means that the strength of the paper-type GDL is weakened when residual strain occurs. [Display omitted] • The fiber length between intersections obeys exponential distribution function. • The compression deformation model of paper-type GDL is established. • The maximum allowable pressure criterion for the paper-type GDL is obtained. [ABSTRACT FROM AUTHOR]

This document is a letter to the editor addressing a typographical error in the formulas for the double Pareto-lognormal distribution and the normal-Laplace distribution. The authors clarify that the formulas themselves are correct, but there was an error in the expression of the Mills ratio in the correction by Amini and Rabbani. The authors provide the correct formulas for both distributions and offer further explanation upon request. The authors are affiliated with Juraj Dobrila University of Pula in Croatia. [Extracted from the article]

In this review we describe the reversible photoalignment effect imposed on the director in nematic liquid crystals that provides an approach for fabrication of advanced optically addressed devices. Several new concepts have been developed to render photosensitive materials during the past decade. Functional soft azo dye compounds exhibiting distinct functionalities in response to polarized light are highly desirable for fabrication of optically rewritable electronic paper. An optically rewritable element base using simple and inexpensive materials can potentially enable the development of novel environmentally friendly, paper-like gadgets with improved functionality over regular electronic paper. We argue that an optically rewritable technique is relevant for some applications, where conventional paper might be irrelevant. In particular, we have tested and discussed several techniques of color and 3D image formation. This strategy for fabrication of novel devices offers versatile methods for visualization. We also show that the intensity modulation of the irradiation light has a potential to generate improved grayscale visualization. This principle is based on the statistical distribution control of photosensitive azo dye molecules, driven by the incident polarized light. Additionally, we discuss the functional characteristics of the developed prototypes. [ABSTRACT FROM AUTHOR]

We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Bubbles, as typical defects of oil–paper insulation systems, pose a great threat to the insulation state. In this article, the characteristics and mechanisms of the partial discharge (PD) inception, evolution, and induced breakdown of bubbles embedded between oil-impregnated paper (OIP) layers are investigated. The results show that the PD inception has a significant statistical time delay due to the lack of free electrons. The PD inception time delay (PDTD) follows an exponential distribution and leads to the large dispersion of the PD inception voltage (PDIV), which follows a Weibull distribution. The changes in PD behavior [charge, repetition rate, pulse current waveform, and phase-resolved PD (PRPD)] over time all show two separable stages (stages 1 and 2). The continuous recording of bubble area and gas composition under PD reveals that the evolution of bubble PD is attributed to the change in gas composition rather than the change in surface conditions as believed by researchers. The OIP breakdown induced by bubble PD indicates two breakdown modes, fast and slow, which correspond to direct-induced breakdown in stage 1 and cumulative-damage breakdown in stage 2. The former occurs because of the severe distortion of the electric field by the space charge at the head of the discharge channel inside the bubble, whereas the latter occurs because of the gradual deterioration of the OIP due to the continuous PD action. This study increases our understanding of the mechanisms of bubble discharge and of oil–paper insulation deterioration, and it provides guidance for fault diagnoses. [ABSTRACT FROM AUTHOR]

Understanding and simulating the evolution of fracture in cellulose-based paper materials is vital for many eco-sustainable applications of packaging solutions. In this work, we conduct an integrated experimental-statistical mechanics analysis to elucidate how the microstructural mechanisms govern the fracture behavior of paper. We employ in-situ tensile tests combined with confocal microscopy to observe and analyze key microstructural phenomena, such as fibers' activation and recruitment, and progressive tensile failure of unnotched paper samples under uniaxal loading. The developed stochastic mechanics critical cross-section model to treat the cellulose fiber network is able to decouple the stochastic morphological parameters from the constitutive model of the fibers. Notably, it robustly predicts the anisotropic properties of paper materials in the machine direction (MD) and cross-machine direction (CD) as a direct consequence of the different statistical distribution of fibers' orientation, for the same fiber constitutive model. The proposed approach is finally employed to get an insight into the role of the main morphological parameters, local/non-local fibers' strain redistribution, and finite elasticity of fibers on the whole mechanical behavior of paper. [Display omitted] • Stochastic network model of a cellulose fiber network. • Mechanical simulation of paper material till fibers' failure. • In-situ micro-mechanical testing of paper. • The model decouples the effect of fibers' morphology from their constitutive law. [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]

We report a simple paper filter-based cross flow filtration method for air purification. We have examined a monolith filter array with a cross flow ratio of 1.0 and parallel and staggered filter arrays with a cross flow ratio of about 0.7 in terms of pressure drop and collection efficiency. The monolith filter array shows a monotonic increase in pressure drop from approximately 0.9 to 2.1 kPa. The parallel and staggered filter arrays reduce the increase in pressure drop, which can be maintained as low as 0.8 kPa over the entire range of dust loadings examined. The monolith filter array shows the highest collection efficiency, which is more than 95% over the entire range of dust loadings examined. The collection efficiency of the parallel and staggered filter arrays is initially as high as 70% and decreases to 30% when the dust loading increases to 40 and 100 g m−2, respectively. In particular, collected particles are more uniformly distributed on the staggered filter array than on the parallel filter array, resulting in the initial collection efficiency of 70% being sustained for a longer time. This uniform distribution in the staggered filter array may prevent a rapid increase in local pressure drop. This study provides insight into the development of filtration systems using simple paper filters without increasing the pressure drop and decreasing the collection efficiency. Copyright © 2022 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Cellulose fibers come in a wide range of shapes and sizes. The heterogeneity of the fiber length, width, wall thickness, curl and external fibrillation is detrimental to the mechanical performance of products such as paper and paperboard. Although micro-mechanical models of these materials sometimes incorporate features of this heterogeneity, so far there is no standardized method of fully incorporating this. We examine a large number of industrial mechanical fiber pulps to determine what information such a standardized method would have to have. We find that the method must allow for both non-Gaussian distributions and dependence between the variables. We present a method of characterizing mechanical pulp under these conditions that views the individual fiber as outcome of a sampling process from a multivariate distribution function. The method is generally applicable to any dataset, even a non-Gaussian one with dependencies. Using a micro-mechanical model of a paper sheet the proposed method is compared with previously presented methods to study whether incorporating both a varying fiber size and dependencies is necessary to match the response of a sheet modeled with measured characterization data. The results demonstrate that micro-mechanical models of paper and paperboard should not neglect the influence of the dependence between the characteristic shape features of the fibers if the model is meant to match physical experiments. [ABSTRACT FROM AUTHOR]

The article is a summary of the September 2024 issue of the journal "Statistics in Transition. New Series." The issue contains 12 articles covering a wide range of topics, written by authors from various countries including Czechia, Poland, India, Cameroon, Brazil, Jordan, Saudi Arabia, Nigeria, Iran, and Botswana. The articles discuss subjects such as circular data analysis, the impact of social cash transfers on household wellbeing in Poland, autoregressive processes with Lindley-distributed innovations, logistic regression and neural networks for credit score prediction, measuring the gross domestic product affected by the shadow economy, estimation of extropy and entropy measures, improved estimation of the mean through regressed exponential estimators, forecasts of COVID-19 mortality risk in Nigeria, nonparametric Bayesian optimal designs, and the use of Bennet indicators for scanner data analysis. The issue also includes conference papers and research communicates and letters on various topics. [Extracted from the article]

Copyright of Brazilian Journal of Education, Technology & Society (BRAJETS) / Cadernos de Educação Tecnologia e Sociedade (CETS) is the property of Brazilian Journal of Education, Technology & Society - BRAJETS 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.)

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]

[Display omitted] • The PVDF fluorescent test paper loaded with molecularly imprinted ratiometric fluorescence sensors (CdTe@SiO 2 @Si QDs-MIPs) was successfully prepared to selectively and visually detect catechol (CT). • The test paper made the CT detection out of equipment and realized the on-site analysis of CT. • PVDF membrane effectively improved the uneven distribution of the polymer on ordinary paper, enhanced the visual effect of the test paper. • Visualization of the PVDF fluorescent test paper can pre-judge CT concentration roughly in river water. Various methods have been proposed for the determination of catechol (CT), however, the actual applications of these methods were restricted by instrument dependence, complex operations and difficulty of the on-site monitoring. In this work, a molecularly imprinted ratiometric fluorescent test paper based on hydrophobic polyvinylidene difluoride (PVDF) membrane was successfully prepared for facile and on-site monitoring of CT. Firstly, a uniform core–shell molecularly imprinted ratiometric fluorescence sensor (CdTe@SiO 2 @Si QDs-MIPs) was successfully synthesized. Then the test paper was fabricated by facilely coating CdTe@SiO 2 @Si QDs-MIPs on a PVDF membrane by the vacuum filtration method. Ultimately, under optimal conditions, CdTe@SiO 2 @Si QDs-MIPs exhibited excellent stability and a relatively lower limit of detection (LOD) of 0.11 μM, satisfied recovery rates of 97.73%-102.30% and relative standard deviations of 2.01%-2.34%. Moreover, the selective study and ions inference experiment synergistically proved that the polymer has good anti-interference ability. Afterwards, the PVDF membrane was eventually selected to be the optimal substrate due to the uniform distribution of fluorescent substances on its surface, thus, greatly improved the accuracy of detection. The resultant test paper hugely improved the practicability and provided a reference for water quality testing. The facile and efficient strategy reported here opens a window for the rapid, visual and on-site detection of CT in river water samples. [ABSTRACT FROM AUTHOR]

The distribution grid experiences node voltage fluctuations due to the growing uncertainty of large-scale renewable energy sources A practical solution is establishing a chance-constrained optimal model to deal with the uncertainties. However, using this method needs to know the accurate probability distribution of node power injections, which has limitations in application. Therefore, this paper proposes a distributionally robust chanceconstrained optimization method for power grid operation based on the ambiguity set of probability distributions. Firstly, considering voltage security constraints, this paper establishes a chance-constrained model to minimize the cost of active power regulation. Besides, based on the Wasserstein ambiguity set, a linearized method is proposed to convexify the objective function. Moreover, the conditional risk value (CVaR) is applied to convert the uncertain model into a deterministic model. The effectiveness of the proposed method is validated through optimization results obtained for the modified PG&E69-bus distribution grid. [ABSTRACT FROM AUTHOR]

In order to solve the problem of inaccurate image feature extraction caused by traditional extraction methods, this paper proposes a remote sensing image size extraction method based on nonlinear multi feature fusion for topographic maps. In this paper, SVM and DS evidence theory are combined to extract image features and classify pre processed remote sensing images. Based on the classification results, basic probability distributions are constructed, and a DS fusion algorithm using matrix analysis is introduced to simplify the complexity of decision level fusion algorithms; We use a multi feature fusion algorithm based on feature proximity, using the proximity vector formed by the attraction between the feature vector and the original graphics pattern as the fusion feature to complete the extraction of remote sensing image features. The simulation results show that after using this method, its soft threshold classifier outputs 0.9865, 0.9965, 0.7852, 0.9921, 0.9847, 0.6879, -0.5898, -0.5678, -0.6897, -0.4785. The algorithm in this paper can distinguish the shape features of terrain images well, and can extract the features of terrain images more accurately, which has strong feasibility. [ABSTRACT FROM AUTHOR]

This paper is concerned with the modeling of complex living systems composed of active particles able to interact with each other and with the surrounding environment. Specifically the discrete thermostatted kinetic theory for active particles is generalized by extending the role of nonconservative interactions and by introducing the role of mutations. In particular the classical proliferation/destruction operator is generalized in order to take into consideration the role of nonlinear interactions, namely the dependence on the distribution functions and their moments. The natural birth and death events are recovered included the logistic growth rate. Finally the role of the outer environment is taken into consideration by introducing microscopic external agents. New thermostat operators are presented whose structures augment the degree of nonlinearity of the evolution equations. The different Cauchy problems are established and future research perspectives are discussed in the whole paper. [ABSTRACT FROM AUTHOR]

This paper applies Bayesian inference with normal-normal conjugate to forecast renewable energy generation. The generation forecasts a probability distribution rather than a quantitative value. An assumed normal distribution is initialized for renewable energy generation. This assumed normal distribution's parameters, the mean μ, and the standard deviation σ, are inferred by Bayesian inference afterward. However, applying Bayesian inference barely shall encounter an intractable integral. To circumvent the intractable integral, this paper considers the normal-normal conjugate method. This method fixes the assumed normal distribution's σ and characterizes μ as another normal distribution and then infers the latter normal distribution parameters. A case study of waste-to-energy generation forecast in Taiwan is investigated in this paper. It has been found from the investigation that the Bayesian inferred probability distribution outperforms the assumed one. [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]

Thermal energy storage and utilization has been widely concerned due to the intermittency, renewability, and economy of renewable energy. In this paper, the potential energy function of binary Na2CO3–K2CO3 salt was first constructed using the Deep Potential GENerator (DPGEN) enhanced sampling method. Deep potential molecular dynamics simulations were performed to calculate the thermal properties and structural evolution of binary carbonates. The results show that as the temperature increases from 1073 to 1273 K, the viscosity and thermal conductivity decrease from 5.011 mPa s and 0.502 W/(m K) to 2.526 mPa s and 0.481 W/(m K), respectively. The decrease in viscosity is related to the distance and interaction between the molten salt ions. In addition, the diffusion coefficients, energy barriers, ionic radius, angular distribution function, and coordination number of molten salt were calculated and analyzed. The CO32− exhibits a stable planar triangular structure. The ionic radius of Na+ is smaller than that of K+, which makes Na+ suffer less spatial hindrance during motion and has a higher diffusion coefficient. The energy barriers that Na+ needs to overcome to escape the Coulomb force is greater than that of K+ ions, so molten salt containing Na+ may possess greater heat storage potential. We believe that the potential function constructed with DPGEN enhanced sampling strategy can provide more convincing results for predicting the thermal properties of molten salts. This paper aims to provide a technical route to develop the novel complex molten salt phase change material for thermal energy storage. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal power flow model that takes into account the uncertainty in the probability distribution of wind power. The model can schedule controllable generators under any possible distribution of wind power to ensure the safe and economic operation of the system. Firstly, considering the incompleteness of historical wind power data, the paper models the uncertainty of wind power using second-order moments of probability distribution and their fluctuation intervals. Subsequently, a robust optimal power flow model based on probability distribution model and joint chance constraints is established. The Lagrangian duality theorem is then employed to eliminate random variables from the optimization model, transforming the uncertainty model into a deterministic linear matrix inequality problem. Finally, a convex optimization algorithm is used to solve the deterministic problem, and the results are compared with traditional chance-constrained optimal power flow model. The feasibility and effectiveness of the proposed method are validated through case study simulations. [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]

Background: To test and validate novel CT techniques, such as texture analysis in radiomics, repeat measurements are required. Current anthropomorphic phantoms lack fine texture and true anatomic representation. 3D‐printing of iodinated ink on paper is a promising phantom manufacturing technique. Previously acquired or artificially created CT data can be used to generate realistic phantoms. Purpose: To present the design process of an anthropomorphic 3D‐printed iodine ink phantom, highlighting the different advantages and pitfalls in its use. To analyze the phantom's X‐ray attenuation properties, and the influences of the printing process on the imaging characteristics, by comparing it to the original input dataset. Methods: Two patient CT scans and artificially generated test patterns were combined in a single dataset for phantom printing and cropped to a size of 26 × 19 × 30 cm3. This DICOM dataset was printed on paper using iodinated ink. The phantom was CT‐scanned and compared to the original image dataset used for printing the phantom. The water‐equivalent diameter of the phantom was compared to that of a patient cohort (N = 104). Iodine concentrations in the phantom were measured using dual‐energy CT. 86 radiomics features were extracted from 10 repeat phantom scans and the input dataset. Features were compared using a histogram analysis and a PCA individually and overall, respectively. The frequency content was compared using the normalized spectrum modulus. Results: Low density structures are depicted incorrectly, while soft tissue structures show excellent visual accordance with the input dataset. Maximum deviations of around 30 HU between the original dataset and phantom HU values were observed. The phantom has X‐ray attenuation properties comparable to a lightweight adult patient (∼54 kg, BMI 19 kg/m2). Iodine concentrations in the phantom varied between 0 and 50 mg/ml. PCA of radiomics features shows different tissue types separate in similar areas of PCA representation in the phantom scans as in the input dataset. Individual feature analysis revealed systematic shift of first order radiomics features compared to the original dataset, while some higher order radiomics features did not. The normalized frequency modulus |f(ω)| of the phantom data agrees well with the original data. However, all frequencies systematically occur more frequently in the phantom compared to the maximum of the spectrum modulus than in the original data set, especially for mid‐frequencies (e.g., for ω = 0.3942 mm−1, |f(ω)|original = 0.09 * |fmax|original and |f(ω)|phantom = 0.12 * |fmax|phantom). Conclusions: 3D‐iodine‐ink‐printing technology can be used to print anthropomorphic phantoms with a water‐equivalent diameter of a lightweight adult patient. Challenges include small residual air enclosures and the fidelity of HU values. For soft tissue, there is a good agreement between the HU values of the phantom and input data set. Radiomics texture features of the phantom scans are similar to the input data set, but systematic shifts of radiomics features in first order features, due to differences in HU values, need to be considered. The paper substrate influences the spatial frequency distribution of the phantom scans. This phantom type is of very limited use for dual‐energy CT analyses. [ABSTRACT FROM AUTHOR]

Accurate aircraft turnaround time prediction is an important way to coordinate the operation time of airport ground service and improve the efficiency of airport operation. In this paper, by analyzing the aircraft turnaround operation process, a description model based on Time Transition Petri Net is proposed. The model describes the flight turnaround operation process and the logical relationship of the operation. According to the model, a dynamic prediction method of turnaround time based on Bayesian theorem is designed. According to the actual landing time of the flight, the aircraft turnaround time is predicted. The specific method is to obtain the prior probability distribution and joint distribution law of each operation link according to the flight history data, and use Shapiro-Wilke to test the prior probability distribution of each link. Based on the analysis and comparison between the actual turnaround data of a large airport in China and the forecast data proposed in this paper, the root-mean-square error 3.75 minutes and the mean absolute error 3.40 minutes can be calculated. This paper contributes to the improvement of flight punctuality rate and airport clearance level. [ABSTRACT FROM AUTHOR]

In this paper, based on high-resolution satellite images near an ice bridge in the Canadian Basin, we extracted floe size parameters and analyzed the temporal and spatial variations in the parameters through image processing techniques. The floe area shows a decreasing trend over time, while the perimeter and mean clamped diameter (MCD) exhibit no obvious pattern of change. In addition, the roundness of floes, reflected by shape parameters, generally decreases initially and then increases, and the average roundness of small floes is smaller than that of large floes. To correct the deviations from power law behaviour when assessing the floe size distribution (FSD) with the traditional power law function, the upper-truncated power law distribution function and the Weibull function are selected. The four parameters of the two functions are important parameters for describing the floe size distribution, and L r and L 0 are roughly equal to the maximum calliper diameter and the average calliper diameter of the floes in the region. D in the upper-truncated power law distribution function represents the fractal dimension of the floes, and r in the Weibull function represents the shape parameter of the floes, both of which increase and then decrease with time. In this paper, we investigate the response of the rate of change in the FSD parameter to the differences in the monthly average temperature and find that D , r and air temperature are positively correlated, which verifies the influence of air temperature on the floe size distribution. [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]

This paper investigates the thermodynamic properties of strongly interacting gravitational systems. By deriving the partition function for such systems, the authors obtain expressions for various thermodynamic quantities, including internal energy, specific heat, Helmholtz free energy, entropy, chemical potential, and pressure. The paper introduces an interaction parameter that quantifies the degree of non-ideality in the system, and explores its effects on the thermodynamic properties. The authors also calculate the moments generating function, which provides information about the distribution of particle positions. Additionally, the distribution function for the system is derived. The paper highlights the existence of an upper bound temperature beyond which the partition function becomes negative, indicating a limit on the validity of the model. [ABSTRACT FROM AUTHOR]

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. [ABSTRACT FROM AUTHOR]

In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey. [ABSTRACT FROM AUTHOR]

Note, for instance, that the probabilities given by Castellares et al. ([1]) in page 4 are the same than those shown in expression (9.115) in the book by Johnson et al. ([3]), with the change of parameters Graph HT ht and Graph HT ht Neyman type A (NTA) distribution is frequently used in Biology, Biodosimetry, Environmental Sciences, Epidemiology, etc. A count random variable I X i is said to follow a stopped-sum Poisson, compound Poisson, multiple Poisson or clustered-Poisson distribution, if it can be represented as Graph HT ht where I N i is a Poisson random variable with parameter Graph HT ht and Graph HT ht are independent, identically distributed random variables that are also independent of I N i . [Extracted from the article]

This paper addresses an unrelated parallel machines scheduling problem with the need of additional resources during the processing of the jobs, as well as during the setups that machines need between the processing of any two jobs. This problem is highly complex, and therefore in this paper we propose several constructive heuristics to solve it. To improve the performance of these heuristics, we propose several variations, including randomisation with different probability distributions and a local search phase, having this way GRASP algorithms. The results of extensive experiments over randomly generated instances show several findings on the different parameters that characterise our constructive algorithms. In particular, we highlight the fact that non-uniform probability distributions might be advisable for choosing elements of a restricted candidate list in GRASP algorithms. [ABSTRACT FROM AUTHOR]

Due to the extensively accessible bandwidth of many tens of GHz, millimeter-wave (mmWave) and sub-terahertz (THz) frequencies are anticipated to play a significant role in 5G and 6G wireless networks and beyond. This paper presents investigations on mmWave bands within the indoor environment based on extensive simulations; the study considers the behavior of the omnidirectional and directional propagation characteristics, including path loss exponents (PLE) delay spread (DS), the number of clusters, and the number of rays per cluster at different frequencies (28 GHz, 39 GHz, 60 GHz and 73 GHz) in both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios. This study finds that the PLE and DS show dependency on frequency; it was also found that, in NLOS scenarios, the number of clusters follows a Poisson distribution, while, in LOS, it follows a decaying exponential distribution. This study enhances understanding of the indoor channel behavior at different frequency bands within the same environment, as many research papers focus on single or two bands; this paper considers four frequency bands. The simulation is important as it provides insights into omnidirectional channel behavior at different frequencies, essential for indoor channel planning. [ABSTRACT FROM AUTHOR]

The construction industry is characterized by its fragmented nature, which introduces numerous uncertainties and challenges for project management. Analyzing project impact factors is crucial for federal agencies to mitigate risks, make informed decisions, and ensure successful project outcomes. Although many previous studies explored critical project impact factors for various project types, this paper's primary objective is to identify and categorize the impact factors critical to projects' success in federal facilities. Additionally, the study ranked impact factors based on their significance and relevance to project management teams in federal facilities. The study surveyed federal agencies to identify the top-ranking factors within and across categories based on their impact. The research methodology encompassed a thorough literature review, data mapping, survey development, and analysis to identify, classify, and rank impact factors for federal projects. Spearman's rank-order correlations were computed for correlations between the impact factor categories. The impact factors were ranked based on their relevance and importance for the federal facilities based on frequency distributions of survey results—aligning with the data mapping and correlations exercises. The study's findings indicate that the top five factors with a maximum negative impact on the federal capital facility project belong to the early project life cycle phases, classified within the Governance and Stakeholders and Planning and Execution categories. The paper's findings aim to emphasize the optimization of stakeholder engagement and strategic planning to address critical risks for federal capital facilities projects early in the project life cycle—particularly the detailed engineering and procurement stages. The study's findings contribute to a better understanding of project management in the federal sector and offer beneficial guidance for future project planning and execution. [ABSTRACT FROM AUTHOR]

Accurately forecasting electricity demand is crucial for maintaining the balance between supply and demand of electric energy in real‐time, ensuring the reliability and cost‐efficiency of power system operations. The integration of numerous active loads and distributed renewable energy sources into the grid has led to increased load variability, rendering the traditional point forecasting approach inadequate for meeting the evolving needs of the power system. Probabilistic forecasting, which predicts the complete probability distribution of loads and provides more extensive information on load uncertainty, has emerged as a key solution to address these challenges. The long short‐term memory (LSTM) model, known for its strong performance in modeling long series, is commonly utilized in load forecasting. Therefore, this study focuses on short‐term electric load probability forecasting for users in a specific park in Yantai. We propose a short‐term load probability forecasting model based on integrated feature selection (IFS), genetic algorithm (GA) optimization of LSTM, and quantile regression (QR), referred to as the IFS‐GA‐QRLSTM model. Initially, the integrated feature selection method is employed to identify the most influential factors affecting electric load, optimizing the model's input features and reducing data redundancy. To address the subjective nature of parameter selection in the LSTM model, we use a GA to optimize model parameters. The combination of optimized LSTM with QR enables direct generation of quantile load predictions, which are further used in kernel density estimation to construct the probability density distribution. We compare the proposed method with five basic models, QRLSTM, IFS‐QRCNN, IFS‐QRRNN, IFS‐QRLSTM, and IFS‐QRGRU, for point prediction, interval prediction, and probability prediction. Experimental results demonstrate that the proposed method in this paper exhibits better prediction performance, smaller prediction errors, and greater effectiveness compared to the aforementioned models. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of estimation of parameters of the Kumaraswamy exponential distribution using progressive type-I interval censored data. The maximum likelihood estimators (MLEs) of the parameters are obtained. As it is observed that there is no closed-form solutions for the MLEs, we implement the Expectation-Maximization (EM) algorithm for the computation of MLEs. Bayes estimators are also obtained using different loss functions such as the squared error loss function and the LINEX loss function. For the Bayesian estimation, Lindley's approximation method has been applied. To evaluate the performance of the various estimators developed, we conduct an extensive simulation study. The different estimators and censoring schemes are compared based on average bias and mean squared error. A real data set is also taken into consideration for illustration. [ABSTRACT FROM AUTHOR]

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In this paper, a necessary stochastic maximum principle for stochastic model governed by mean-field nonlinear controlled Itô-stochastic differential equations is proved. The coefficients of our model are nonlinear and depend explicitly on the control variable, the state process as well as of its probability distribution. The control region is assumed to be bounded and convex. Our main result is derived by applying the Lions’s partial-derivatives with respect to random measures in Wasserstein space. The associated Itô-formula and convex-variation approach are applied to establish the optimal control. [ABSTRACT FROM AUTHOR]

The seepage field of a suspended waterproof curtain strip foundation pit in a deep phreatic aquifer is theoretically analyzed, and a calculation method for the foundation pit water inflow and water level outside the curtain is derived. In this paper, it is assumed that the horizontal hydraulic gradient of the seepage field above the aquiclude below the bottom of the waterproof curtain decreases linearly. The separation variable method is used to solve the seepage field in the regular area inside the foundation pit, and the hydraulic head distribution function inside the foundation pit is obtained. According to the hydraulic head distribution function inside the foundation pit, the calculation expression of the water inflow of the foundation pit is further deduced theoretically. On this basis, the improved resistance coefficient method is applied to link the water level outside the pit with the solved hydraulic head inside the pit and solved. In addition, the calculation results presented in this study are compared with the calculation results of the existing model and the measured data of the foundation pit project of Yangwan Station, which proves that the analytical method can effectively meet the requirements of engineering applications. [ABSTRACT FROM AUTHOR]

In order to fully explore the development degree and distribution law of the structural plane of a tunnel surrounding rock in three-dimensional space, this paper studies the geometric characteristic parameters of a structural plane in the study area through field investigation, data acquisition and statistical analysis. The structural plane is divided into three dominant groups by using DIPS. v5. 103 software. The probability distribution model of occurrence, trace length, diameter and spacing of the structural plane is established. This paper focuses on the error correction of structural plane occurrence and the estimation of average trace length based on the rectangular window method. The discrete fracture network model is generated by using MATLAB R2021b software, and the discrete fracture network model is verified from three aspects: structural plane occurrence, average trace length and area density. The verification results are compared with the measured data, and the simulation results are in line with the actual situation on site. Based on the discrete fracture network model, the volume joint number of rock mass is calculated. Based on the JSR index, BQ classification method and RQD classification, the development degree of fractures and surrounding rock classification in this area are evaluated. A method of surrounding rock classification based on three evaluation indexes is discussed to comprehensively and accurately classify the quality of rock mass in this area. [ABSTRACT FROM AUTHOR]