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In exploring the intersection of scholarly research with technological advancement and societal impact, our analysis delves into nearly 40 million research papers spanning from 1950 to 2020 across all fields of study in science. Our scrutiny reveals an intriguing phenomenon: papers characterized by a higher CD index, often considered transformative, paradoxically exhibit a diminished propensity to influence technological and societal domains. This observation suggests a latent bias against the CD index, prompting a deeper inquiry into its implications. To unravel this trend, we introduce the concept of “disruptive citation,” a nuanced metric gauging the absolute disruptive impact of papers. Notably, papers drawing higher disruptive citations exhibit a significantly higher probability to influence both technological and societal spheres. Upon examining the heterogeneity across years and fields, we identify a bias against the CD index predominantly in the last two decades and within STEM fields. However, the positive effects of disruptive impact remain consistent across all years and fields. Our findings remain robust even when employing alternative measures of disruptive impact and controlling for total citations. By shedding light on these dynamics, our study seeks to enrich discussions regarding the recognition and role of disruptive scientific endeavors in shaping our world. [ABSTRACT FROM AUTHOR]

Background: In drug development, few molecules from a large pool of early candidates become successful medicines after demonstrating a favourable benefit-risk ratio. Many decisions are made along the way to continue or stop the development of a molecule. The probability of pharmacological success, or PoPS, is a tool for informing early-stage decisions based on benefit and risk data available at the time. Results: The PoPS is the probability that most patients can achieve adequate pharmacology for the intended indication while minimising the number of subjects exposed to safety risk. This probability is usually a function of dose; hence its computation typically requires exposure–response models for pharmacology and safety. The levels of adequate pharmacology and acceptable risk must be specified. The uncertainties in these levels, in the exposure–response relationships, and in relevant translation all need to be identified. Several examples of different indications are used to illustrate how this approach can facilitate molecule progression decisions for preclinical and early clinical development. The examples show that PoPS assessment is an effective mechanism for integrating multi-source data, identifying knowledge gaps, and forcing transparency of assumptions. With its application, translational modelling becomes more meaningful and dose prediction more rigorous. Its successful implementation calls for early planning, sound understanding of the disease-drug system, and cross-discipline collaboration. Furthermore, the PoPS evolves as relevant knowledge grows. Conclusion: The PoPS is a powerful evidence-based framework to formally capture multiple uncertainties into a single probability term for assessing benefit-risk ratio. In GSK, it is now expected for governance review at all early-phase decision gates. [ABSTRACT FROM AUTHOR]

BACKGROUND: Since the inception of the Youth Risk Behavior Surveillance System in 1991, all surveys have been conducted in schools, using paper and pencil instruments (PAPI). For the 2019 YRBSS, sites were offered the opportunity to conduct their surveys using electronic data collection. This study aimed to determine whether differences in select metrics existed between students who completed the survey electronically versus using PAPI. METHODS: Thirty risk behaviors were examined in this study. Data completeness, response rates and bivariate comparisons of risk behavior prevalence between administration modes were examined. RESULTS: Twenty‐nine of 30 questions examined had more complete responses among students using electronic surveys. Small differences were found for student and school response rates between modes. Twenty‐five of 30 adolescent risk behaviors showed no mode effect. CONCLUSIONS: Seven of 44 states and DC participated electronically. Because survey data were more complete; school and student response rates were consistent; and minor differences existed in risk behaviors between modes, the acceptability of collecting data electronically was demonstrated. [ABSTRACT FROM AUTHOR]

R.A. Fisher's 1922 paper On the dominance ratio has a strong claim to be the foundation paper for modern population genetics. It greatly influenced subsequent work by Haldane and Wright, and contributed 3 major innovations to the study of evolution at the genetic level. First, the introduction of a general model of selection at a single locus, which showed how variability could be maintained by heterozygote advantage. Second, the use of the branching process approach to show that a beneficial mutation has a substantial chance of loss from the population, even when the population size is extremely large. Third, the invention of the concept of a probability distribution of allele frequency, caused by random sampling of allele frequencies due to finite population size, and the first use of a diffusion equation to investigate the properties of such a distribution. Although Fisher was motivated by an inference that later turned out to lack strong empirical support (a substantial contribution of dominance to quantitative trait variability), and his use of a diffusion equation was marred by a technical mistake, the paper introduced concepts and methods that pervade much subsequent work in population genetics. [ABSTRACT FROM AUTHOR]

With a pair of oppositely valenced stimuli, rating the first one sometimes leads to a more extreme evaluation for the second (e.g., if the second is negatively valenced, rating the first stimulus would lead to a more negative rating for the second). We considered an evaluation bias in the case of clinical diagnosis relating to eating disorders. A population sample which included experienced clinical psychologists and psychiatrists showed partial evidence of an evaluation bias, when judging descriptions of individuals designed to be consistent with eating disorders or not. Quantum probability theory, the probability rules from quantum mechanics without any of the physics, is particularly well-suited to modeling the evaluation bias (and constructive influences generally), because a measurement (or judgment) can change the state of the system. We applied a previous quantum model to the present result, an extension of the model embodying noisy processes, and belief adjustment model. We discuss how model fits inform an examination of rationality in the observed behavior., (© 2021 Cognitive Science Society LLC.)

A necessary criterion of Duncan Pritchard's Anti-luck Virtue Epistemology is his safety condition. A believer cannot know p unless her belief is safe. Her belief is safe only if p could not have easily been false. But "easily" is not to be understood probabilistically. The chance that p is false might be extremely low and yet p remains unsafe. This is what happens, Pritchard argues, in lottery examples and explains why knowledge is not a function of the probabilistic strength of one's evidence. This paper argues that, contra Pritchard, modality holds no epistemic advantage over this type of "probabilistic evidentialism" that he criticizes. I begin with a review of Pritchard's argument supporting modality over probability; second, I explain the problems with this argument, and third, I offer an alternative explanation of the lottery example (which purportedly shows modality is superior to probability). At the completion of the paper, modality and probability are on equal epistemic footing. [ABSTRACT FROM AUTHOR]

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions. [ABSTRACT FROM AUTHOR]

When scholars investigate the legacy of John Maynard Keynes's Treatise on Probability (1921) for the development of Keynes's thinking, the attention usually focuses on the connections among Keynes's probability theory, his conception of decision-making under uncertainty, and the theory of the functioning of the macroeconomic system that derives from it—through the marginal efficiency of capital, the preference for liquidity, and the self-referential functioning of financial markets. By contrast, this paper aims to investigate the connections between Keynes's probability theory, on the one hand, and his economic policy recommendations, on the other. It concentrates on the policy recommendations defended by Keynes during the Great Depression but also after the General Theory. Keynes's economic policy can be understood as a framework for decision-making in situations of uncertainty: fiscal policy aims to induce private agents to change their "rational" probability statements, while monetary policy aims to allow more weight to these statements. [ABSTRACT FROM AUTHOR]

Experiments are frequently used in the travel and hospitality literature to provide stronger evidence of causal relationships between various constructs. Recently, despite the use of online platforms for experimental studies, such studies have often failed to find significant results, as expected. To increase the probability of obtaining significant results for experiments using online panels, this paper suggests six practical recommendations across three categories: (i) handling less homogenous online participants; (ii) understanding and managing different motivations and abilities in online participants; and (iii) using effective and transparent experimental designs and procedures. This paper provides the results of three empirical investigations to support these recommendations. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the paper is to examine the status and determinants of financial inclusion in India by using the recent micro-level survey data. Design/methodology/approach: The authors construct a multidimensional financial inclusion index to measure the status of financial inclusion in the selected 17 states of India. Subsequently, the authors use the probit model estimation to examine the determinants of all financial inclusion indicators. Findings: The authors find that southern and north-eastern states perform better in the overall financial inclusion index. In contrast, states like Odisha, Madhya Pradesh, Chhattisgarh, Rajasthan and Uttar Pradesh lag behind. The estimated result shows that the probability of being financially included is higher among urban, richer, educated and salaried individuals. Further, the findings indicate the lower penetration of bank branches and ATMs in the rural parts of the country. Originality/value: While numerous studies have explored financial inclusion from a macro-level perspective, there exists a notable gap in the literature at the micro-level. This paper aims to address this gap and contributes to the existing literature in two ways. Firstly, it uses the recent micro-level survey data to construct a multidimensional financial inclusion index for the selected Indian states. Secondly, it examines individual-level attributes as the determining factors of financial inclusion, which has been overlooked in India. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0162 [ABSTRACT FROM AUTHOR]

Relay protection rejection and misoperation exist in the existing distribution network, which will affect the fault diagnosis results. To diagnose faults in distribution networks, this paper presents a fault diagnosis method for the distribution network based on the D-S evidence theory Bayesian network. First, the collected relay protection information is divided into two categories, protection information and circuit breaker information; the corresponding Bayesian network model is established based on their respective action logic, and the corresponding component failure probability is obtained by Bayesian backward inference. Second, the fault probabilities obtained from the two Bayesian networks are fused by the D-S evidence theory, and the obtained fault probabilities are used to diagnose the faulty component. Then, using the Bayesian network corresponding to the faulty component to perform Bayesian forward inference, the protection devices and circuit breakers are identified for misoperation or rejection to achieve the fault diagnosis of the distribution network. Finally, the correctness and reliability of the proposed diagnosis method are verified through the analysis of arithmetic cases. [ABSTRACT FROM AUTHOR]

This review paper is an homage to Arvid Palmgren's pioneering paper on rolling bearing service life to highlight its relevance a century later. It follows the evolution of bearing service life theory from Palmgren's fundamental research to the contemporary international standard ISO 281. Palmgren's theory, based on the previously published papers of Stribeck and Hertz, laid the basis for the later development of bearing service life assessment methodology. Based on the Weibull theory of probability of damage, Lundberg and Palmgren introduced stochastic elements to explain the effect of reliability on bearing service life prediction. Harris and Ioannides, who made a significant contribution to the revision of the international standard on rolling bearing load rating and rating life are mentioned as well. Zaretsky's critical analysis also was not neglected in this review, due to a different approach respecting the original influence of material properties and bearing performances. Despite standardization, ongoing research by leading advanced bearing industries and academic institutions continues to refine methodologies for service life assessment. Through a comprehensive review and analysis, this paper offers insight into the current state of bearing service life theory, highlighting the collaborative efforts bringing progress in this field. [ABSTRACT FROM AUTHOR]

Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model's classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification, combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss (COVID-TCL). Triplet loss can increase inter-class discreteness, and center loss can improve intra-class compactness. Therefore, COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period. Meanwhile, we use the extreme gradient boosting (XGBoost) as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture, to further improve the CNN-based model's classification effect and operation efficiency. The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%, and the sensitivity is 97.61%, which is higher than the other 7 reference models. The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model, the CNN-XGBoost architecture can further improve the CNN-based model's classification effect. [ABSTRACT FROM AUTHOR]

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment. [ABSTRACT FROM AUTHOR]

Crashes involving large trucks have a significant association with increasing fatalities or serious injuries. Identifying the factors affecting large trucks crashes can be helpful in reducing the number of crashes and corresponded consequences. The purpose of this review was to appraise the published papers on factors affecting large truckinvolved crashes. Data were compiled through searching Google Scholar, Scopus, Science Direct, and Web of Science using keywords included "large truck and accident severity", "large truck and accident", "large truck and crash severity", "large truck and crash", "large truck and fatal accident", "large truck and fatal crash", "large truck and injury", and "large truck and collide". The accomplishment process had two steps: excluding unrelated studies and reviewing related ones to select papers meeting the inclusion criteria. According to data collected, 31 papers achieved the inclusion criteria. The results indicated that speed, driver age, location type, collision type, seat belt wearing, light condition, vehicle technical condition, time, road surface condition, and accident type were common factors in the included papers. From the perspective of our study, speed, location type, and light condition seem to be the most frequent factors in crashes involving large trucks. The present study helps to access the studies on factors affecting the large truck-involved crashes. Thereby, the experts can perform measures to decrease the probability and severity of traffic accidents. [ABSTRACT FROM AUTHOR]

In the 21st century, various socio-economic crises have revealed that traditional economic science and (neo)classical thinking are unable to explain all the complexity of current economic problems, therefore the application of more complex and non-trivial economic concepts is gaining relevance. In addition to behavioral and evolutionary economic thinking, models of quantum economics have been developed in recent years, which allow solving economic problems, using mainly quantum thinking and the principles of quantum physics, in particular particle-wave dualism, the principle of uncertainty, the absence of a subject-object distinction, superposition and confusion. The article addresses 3 research questions (RQ). According to RQ1, the paper finds that quantum economics research is dominated by the following topics: quantum economics, quantum finance, quantum decision making, and quantum game theory. According to these four thematic descriptors, the article carries out a systematic modern review of scientific works in the period from 1978 to 2022 (if only 50 works were published in 1978-1999, then in 2000-2022 already 3430), with an emphasis on the most cited (Google Scholar) English-language journal articles. The analysis showed that articles on this topic are published mainly in journals of a non-economic profile, that the peaks of publication activity occur at times of powerful socio-economic upheavals (for example, the dot.com bubble, the substandard/financial crisis, the European debt crisis, the coronavirus pandemic, etc.). In accordance with RQ2, the article defines the key characteristics of the quantum economic model: in contrast to neoclassical economics (based on mechanistic classical physics, is rational and deterministic, with the help of the invisible hand of the market it leads to a stable equilibrium), the quantum approach, on the contrary, considers the economy as more complex, empirically oriented, uncertain, probabilistic, superpositional, as an archetypal example of a quantum social system that has its own versions of duality, measurement, and entanglement. According to RQ 3, the article determines to what extent quantum economics can update (neo)classical economics (integration of new ontological premises into economic thinking, more experimental and practical approach, connection between the concept of entanglement and sustainable development, management of financial risks based on the concept of quantum probability, rethinking the concept of randomness by quantum probability, introduction of quantum money, equilibrium using quantum games, etc.). [ABSTRACT FROM AUTHOR]

Starting from the dynamics of a bouncing ball in classical and quantum regime, we have suggested in a previous paper to add an arbitrary function of time to the standard expression of the probability current in quantum mechanics. In this paper, we suggest a way to determine this function: imposing a suitable normalization condition. The application of our proposal to the case of the harmonic oscillator is discussed. [ABSTRACT FROM AUTHOR]

Since its nascence, computational organization theory has predominantly relied on classical probability theory to model and simulate organizational properties. However, key assumptions of classical probability theory conflict with empirical observations of organizational behaviors and processes, thereby raising the question if an alternate theoretical basis for probabilistic modeling of organizations might improve the relevancy of computational organization research. In the context of the garbage can model of organizational decision-making, this paper provides two examples—order effects and system measurement—to illustrate the inadequacy of classical probability theory and to stimulate discussion on the merits of incorporating quantum probability theory in computational models. This paper recommends that future work explore the sensitivity of computational organization theory models to probability theories, the impacts associated theoretical assumptions might have on modeling and simulating dynamic organizational interdependencies, and the implications to community practices. [ABSTRACT FROM AUTHOR]

The object of research is the reliability of information in information-control systems. The work considers a solution to an important scientific and practical problem – increasing the reliability of information in real-time control systems, in particular in positioning systems on road transport. To solve this task, the use of information redundancy, in particular, parallel information redundancy, is proposed. The paper studies the influence of the probability of occurrence of a controlled event, the majority coefficient, as well as the number and quality of information sensors on the reliability of information in an information control system with parallel information redundancy. For these dependences, the corresponding plots were constructed. The paper shows that a change in the quality of information sensors by 20 % can lead to an increase in the reliability of information by more than 0.5. Also, the paper shows that an increase in the majority index by 1 can lead to a change in the reliability of information by more than 0.3. The paper investigates parallel information redundancy systems with different structures and defines formulas for calculating the probabilistic characteristics of their operation. The mathematical modeling was carried out taking into account the probability of occurrence of the controlled event, which made it possible to determine the tendencies to build information-control systems with parallel information redundancy to control events with variable probability of occurrence. The results obtained are based on the theory of probability. The results reported in this work can be used in the construction of an information and control system with a programmatically variable structure to control the occurrence of an event with a variable probability of occurrence, in particular in positioning systems on road transport. [ABSTRACT FROM AUTHOR]

The focus of this paper is impulse noise reduction in digital color images. The most popular noise reduction schemes are the vector median filter and its many variants that operate by minimizing the aggregate distance from one pixel to every other pixel in a chosen window. This minimizing operation determines the most confirmative pixel based on its similarity to the chosen window and replaces the central pixel of the window with the determined one. The peer group filters, unlike the vector median filters, determine a set of pixels that are most confirmative to the window and then perform filtering over the determined set. Using a set of pixels in the filtering process rather than one pixel is more helpful as it takes into account the full information of all the pixels that seemingly contribute to the signal. Hence, the peer group filters are found to be more robust to noise. However, the peer group for each pixel is computed deterministically using thresholding schemes. A wrong choice of the threshold will easily impair the filtering performance. In this paper, we propose a peer group filtering approach using principles of Bayesian probability theory and clustering. Here, we present a method to compute the probability that a pixel value is clean (not corrupted by impulse noise) and then apply clustering on the probability measure to determine the peer group. The key benefit of this proposal is that the need for thresholding in peer group filtering is completely avoided. Simulation results show that the proposed method performs better than the conventional vector median and peer group filtering methods in terms of noise reduction and structural similarity, thus validating the proposed approach. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of "distant node modeling deficiency" and "failure of the homophily assumption". In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which integrates adaptive embedding mechanisms for both spatial and frequency domains to address the aforementioned issues. Specifically, for the first problem, we propose the "Distant Spatial Embedding Module", aiming to select and aggregate distant nodes through high-order random walk transition probabilities to enhance modeling capabilities. For the second issue, we design the "Proximal Frequency Domain Embedding Module", constructing adaptive filters to separate high and low-frequency signals of nodes, and introduce frequency-domain guided attention mechanisms to fuse the relevant information, thereby reducing the noise introduced by the failure of the homophily assumption. We deploy the SFA-HGNN on six publicly available heterophilic networks, achieving state-of-the-art results in four of them. Furthermore, we elaborate on the hyperparameter selection mechanism and validate the performance of each module through experimentation, demonstrating a positive correlation between "node structural similarity", "node attribute vector similarity", and "node homophily" in heterophilic networks. [ABSTRACT FROM AUTHOR]

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance. [ABSTRACT FROM AUTHOR]

The stochastic P-bifurcation behavior of bi-stability in a generalized van der Pol oscillator with the fractional damping under colored noise and thermal excitation is investigated. Firstly, using the principle of minimal mean square error and linearization method, the non-linear stiffness terms can be equivalent to a linear stiffness which is a function of the system amplitude, and the original system is simplified to an equivalent integer order van der Pol system. Secondly, the system amplitude stationary probability density function is obtained by the stochastic averaging, and then based on the singularity theory, the critical parametric conditions for the system amplitude stochastic P-bifurcation are found. Finally, the types of the stationary probability density function of the system amplitude are qualitatively analyzed in each area divided by the transition set curves. The consistency between the analytical results and the numerical results acquired from Monte-Carlo simulation also testifies the theoretical analysis in this paper and the method used in this paper can directly guide the design of the fractional order controller to adjust the response of the system. [ABSTRACT FROM AUTHOR]

Background: In the digital environment, users' academic reading behaviour has changed, working with many articles simultaneously to search, filter, scan, link, annotate and analyse content fragments. The semantic enhancement environment has been widely set with semantic technologies to offer additional and handy support for users and thus facilitate the reading process. Despite many efforts devoted to developing a semantic enhancement environment, less attention has been paid to its actual effects. Objectives: This study aims to explore the effects of a semantic enhancement environment. Methods: Elaborating on cognitive load theory and focusing on academic reading, this study compared users' cognitive load and reading effect under the semantic enhancement environment and plain text environment and verified the three hypotheses. Results and conclusions: Through experiments and statistical analysis, this study found that, under a semantic enhancement environment, users had their cognitive load reduced and their reading effect improved. Consequently, a semantic enhancement environment promotes academic reading. Takeaways: This study adds to previous literature on semantic publishing by empirically corroborating the conducive effects of semantic enhancement environment in academic reading. Additionally, it justifies semantic publishing and provides a reference for the future design of semantically enhanced reading environments. Lay Description: What is already known about this topic?: The effects of a semantic enhancement environment are open to discuss.Academic reading is fundamental to researches and hence needs promotion.Limited studies investigated the effects of a semantic enhancement environment on academic reading.Publishers require effective reading environment designs. What this paper adds?: This study constructed a semantic enhancement reading environment to conduct a reading experiment.The experiment tested cognitive load, by using WP scale, and reading effects, by answer time and answer score.Semantic enhancement environment reduces users' cognitive load, promotes users' reading effects, and bolsters users' understanding of academic reading materials, especially in microstructure content.The design of semantic enhancement reading environments needs to fully consider the features of reading materials and give users a controllable way of interaction. The implications of study findings for practitioners: Compared to plain text environment, semantic enhancement environment is preferred in academic reading.Publishers are encouraged to add semantic enhancements to their publications.This study discussed how to design an effective learning and reading environment from the perspectives of reading resources, media forms of resources, and interaction modes. [ABSTRACT FROM AUTHOR]

This Special Issue (SI), titled "Probability Theory and Stochastic Modeling with Applications", is concerned with the theory and applications of stochastic models. Footnotes 1 B Disclaimer/Publisher's Note: b The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). It consists of sixteen papers, and we would like to thank all the authors for their positive answers to our call for papers for the SI, as well as for their efforts to provide a high-quality contribution. Using fuzzy variables and robust optimization, the authors provide the electricity transaction policy under different expected costs so that the expectation of the risk-averse distribution system operator is fulfilled. [Extracted from the article]

The paper aims to assess the effect of the existing actions to assist a train driver in various operational situations, as well as to numerically evaluate the effect of such assistance on the resultant indicator of an error-free driver performance. The paper calculates and analyses the probability of at least one of the independent events or actions aimed at improving the quality of driver performance and reduction of the probability of error. The model of an environment was created, in which the probability of errorfree driver performance is affected by a number of factors. [ABSTRACT FROM AUTHOR]

The objective of this paper is to characterize the structure of the set \Theta for a continuous ergodic upper probability \mathbb {V}=\sup _{P\in \Theta }P \Theta contains a finite number of ergodic probabilities; Any invariant probability in \Theta is a convex combination of those ergodic ones in \Theta; Any probability in \Theta coincides with an invariant one in \Theta on the invariant \sigma-algebra. The last property has already been obtained in Cerreia-Vioglio, Maccheroni, and Marinacci [Proc. Amer. Math. Soc. 144 (2016), pp. 3381–3396], which first studied the ergodicity of such capacities. As an application of the characterization, we prove an ergodicity result, which improves the result of Cerreia-Vioglio, Maccheroni, and Marinacci [Proc. Amer. Math. Soc. 144 (2016), pp. 3381–3396] in the sense that the limit of the time means of \xi is bounded by the upper expectation \sup _{P\in \Theta }E_P[\xi ], instead of the Choquet integral. Generally, the former is strictly smaller. [ABSTRACT FROM AUTHOR]

Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making. [ABSTRACT FROM AUTHOR]

Aviation networks exhibit multi-layer characteristics and heterogeneity of nodes and edges. Identifying key nodes in a multi-layer heterogeneous aviation network (MHAN) and elucidating its cascading failure process are of great practical significance for enhancing the ability to resist failure and strengthening network resilience. Therefore, this paper first establishes the basic model of MHAN and then designs an intra-layer node importance evaluation method based on the improved TOPSIS-grey correlation analysis (ITG) method and an inter-layer influence weight quantification method based on the gravity model. By integrating the information transmission characteristics between network nodes, a key node identification method in MHAN through aggregating multi-source information is proposed. Finally, based on the actual overload operation of aviation networks, the initial load adjustment coefficient, capacity limit, and overload coefficient are introduced based on the traditional capacity–load model, a cascading failure model of MHAN considering overload condition and failure probability is constructed, and a node influence index based on this model is defined to verify the accuracy of the key node identification results. The instance analysis conducted on the aviation network in western China demonstrates that the intra-layer node importance evaluation method based on ITG yields results with better distinguishability and higher accuracy. The key nodes are predominantly hub en-route nodes and sector nodes. In the cascading failure model, increasing the overload coefficient and capacity limit within a specific range while reducing the initial load adjustment coefficient helps reduce the network failure scale. The key nodes identified by the node influence index are consistent with those identified by the method proposed in this paper, validating the accuracy and effectiveness of the key node identification method in MHAN through aggregating multi-source information herein. [ABSTRACT FROM AUTHOR]

The relative advantages and disadvantages of sum scores and estimated factor scores are issues of concern for substantive research in psychology. Recently, while championing estimated factor scores over sum scores, McNeish offered a trenchant rejoinder to an article by Widaman and Revelle, which had critiqued an earlier paper by McNeish and Wolf. In the recent contribution, McNeish misrepresented a number of claims by Widaman and Revelle, rendering moot his criticisms of Widaman and Revelle. Notably, McNeish chose to avoid confronting a key strength of sum scores stressed by Widaman and Revelle—the greater comparability of results across studies if sum scores are used. Instead, McNeish pivoted to present a host of simulation studies to identify relative strengths of estimated factor scores. Here, we review our prior claims and, in the process, deflect purported criticisms by McNeish. We discuss briefly issues related to simulated data and empirical data that provide evidence of strengths of each type of score. In doing so, we identified a second strength of sum scores: superior cross-validation of results across independent samples of empirical data, at least for samples of moderate size. We close with consideration of four general issues concerning sum scores and estimated factor scores that highlight the contrasts between positions offered by McNeish and by us, issues of importance when pursuing applied research in our field. [ABSTRACT FROM AUTHOR]

When information is incomplete or uncertain, Fermatean hesitant fuzzy sets (FHFSs) can provide more information to help decision-makers deal with more complex problems. Typically, determining attribute weights assumes that each attribute has a fixed influence. Introducing probability information can enable one to consider the stochastic nature of evaluation data and better quantify the importance of the attributes. To aggregate data by considering the location and importance degrees of each attribute, this paper develops a Fermatean hesitant fuzzy multi-attribute decision-making (MADM) method with probabilistic information and an ordered weighted averaging (OWA) method. The OWA method combines the concepts of weights and sorting to sort and weigh average property values based on those weights. Therefore, this novel approach assigns weights based on the decision-maker's preferences and introduces probabilities to assess attribute importance under specific circumstances, thereby broadening the scope of information expression. Then, this paper presents four probabilistic aggregation operators under the Fermatean hesitant fuzzy environment, including the Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (FHFPOWA/FHFPOWG) operators and the generalized Fermatean hesitant fuzzy probabilistic ordered weighted averaging/geometric (GFHFPOWA/GFHFPOWG) operators. These new operators are designed to quantify the importance of attributes and characterize the attitudes of decision-makers using a probabilistic and weighted vector. Then, a MADM method based on these proposed operators is developed. Finally, an illustrative example of selecting the best new retail enterprise demonstrates the effectiveness and practicality of the method. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of accurately analyzing the reliability of aviation power systems (APS) using traditional models by introducing the Aviation Power System Reliability Probability Network Model (APS-RPNM). The model directly transforms the system architecture into an equivalent probability network, aiming to develop a precise reliability model that captures system functions and fault logic. By classifying APS components into five distinct structural patterns and mapping them to corresponding nodes in the APS-RPNM, the model is successfully constructed. Specifically, None-Input-to-Multiple-Output components are transformed into two-state nodes, while Multiple-Input-to-None-Output, Single-Input-to-Multiple-Output, and Multiple-Input-to-Single-Output components are mapped to three-state nodes. For Multiple-Input-to-Multiple-Output components, a novel approach employing multiple two-state sub-nodes is adopted to capture their complex functional logic. A case study comparing the performance of the APS-RPNM with the traditional minimal path set method in reliability analysis was conducted. The results demonstrate that the APS-RPNM not only simplifies the model construction process and eliminates errors stemming from subjective engineering judgments but also enables the efficient computation of power supply reliability for all load points in a single inference by integrating all of the components. This significantly improves computational efficiency and system dependency analysis capabilities, highlighting the APS-RPNM's tremendous potential in optimizing the reliability design of APS. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low object detection accuracy due to complex background and insufficient semantic information of shallow features in the object detection SSD algorithm, this paper improves the existing SSD algorithm. First, the original vgg16 network is replaced by the ResNet50 network, and the residual network structure as well as the Batch Normalization layer are added, which are used to improve the accuracy of the feature extraction network; Second, a feature fusion module is designed to fuse adjacent feature maps to improve the detection effect by integrating contextual information; Third, the SE attention mechanism is introduced to give channel weights adaptively and enhance the useful feature channels; Finally, the object detection analysis experiments are conducted on the PASCAL VOC2012 dataset. The experimental results show that the improved SSD algorithm in this paper is able to achieve an mean average precision of 72.7% in the data set, which is 2.1% better than the original SSD-VGG16 and greatly improves the object detection effect. [ABSTRACT FROM AUTHOR]

This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative migration model. The core advantage of IBBO lies in its adeptness at maintaining equilibrium between exploration and exploitation. This is accomplished by integrating tactics such as advancing towards a random habitat, adopting the crossover operator from genetic algorithms (GA), and utilizing the global best (Gbest) operator from particle swarm optimization (PSO) into the IBBO framework. The IBBO demonstrates its efficacy by enabling the RNN to optimize the system parameters, resulting in significant outage probability reduction. Through comprehensive simulations, we showcase the superiority of the IBBO-RNN over existing approaches, highlighting its capability to achieve remarkable gains in SOP minimization. This paper compares nine methods for predicting outage probability in wireless-powered communications. The IBBO-RNN achieved the highest accuracy rate of 98.92%, showing a significant performance improvement. In contrast, the standard RNN recorded lower accuracy rates of 91.27%. The IBBO-RNN maintains lower SOP values across the entire signal-tonoise ratio (SNR) spectrum tested, suggesting that the method is highly effective at optimizing system parameters for improved secrecy even at lower SNRs. [ABSTRACT FROM AUTHOR]

Aiming at the practical application of Unmanned Underwater Vehicle (UUV) in underwater combat, this paper proposes a battlefield ambush scene with UUV considering ocean current. Firstly, by establishing these mathematical models of ocean current environment, target movement, and sonar detection, the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively. Then, based on the Hybrid Quantum-behaved Particle Swarm Optimization (HQPSO) algorithm, the path with the highest target search probability is found. Finally, through simulation calculations, the influence of different UUV parameters and target parameters on the target search probability is analyzed, and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated, and the optimal search path scheme is obtained. The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat. [ABSTRACT FROM AUTHOR]

In this paper, we develop a personal synthesis of the most outstanding research on the teaching and learning of probability in the past years. We conducted a systematic search to examine publications on this topic in mathematics education, statistics education, education, and psychology journals. This exploration was complemented by additional studies published in conference proceedings or books. We classified these papers to highlight the main recent research tendencies in the field, according to the theme studied and considering the research objectives. Epistemological analyses suggest that informal inference based on simulation diminishes the topic abstraction but reduces probability to its frequentist view. Topics receiving particular attention include children's probabilistic knowledge, the effect of visualizations on solving conditional probability problems, teachers' education and probability modelling. In the final section, we recommend relevant points in which more investigation is needed to complete our knowledge about teaching and learning. In particular, we miss research on teachers' mathematical knowledge of many probability concepts and on their didactic knowledge. [ABSTRACT FROM AUTHOR]

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The large deviations rate function Λ(d) of d-dimensional symmetric simple random walk S(d)(n) describes the log-asymptotic behavior of the fundamental probability P(S(d)(n) = x) in the large deviations regime, that is, when n = O || |x|), but also in the so-called moderate deviations regime, that is, when n1/2 = o(|x|) and |x| = o(n). In Beneš (2019), we provided precise asymptotics for P(S(d)(n) = x) in dimensions 1 and 2 and, as an immediate consequence, we obtained (d) for d = 1 and d = 2. The techniques developed in that paper do not translate readily to higher dimensions. In the present paper, we show that for d Z+, (d)(a) = log d + 1 2 Xd i=1 (ai + ai) log(ai + ai) + (ai - ai) log(ai - ai), where a = (a1, . . ., ad) satisfies Pd i=1 |ai| = 1. The quantities ai = ai(a) are not explicit but are given in terms of the solution of a system of quadratic equations. This result represents partial progress towards the generalization of the results in Beneš (2019) to any dimension d Z+. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm's runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500. [ABSTRACT FROM AUTHOR]