Subsequently, maintaining a consistent media presence results in a more marked reduction of epidemic growth within the model, particularly evident in multiplex networks characterized by an adverse correlation in degree between layers, when contrasted with networks presenting a positive or non-existent correlation in the degree between layers.

Influence evaluation algorithms, prevalent now, often overlook the network structure's attributes, user interests, and the dynamic characteristics of influence propagation over time. find more This research, in response to these issues, explores user influence, weighted indicators, user interaction, and the similarity of user interests with topics; this exploration leads to the development of the dynamic user influence ranking algorithm, UWUSRank. Their activity, authentication records, and blog responses are used to establish a preliminary determination of the user's primary level of influence. Calculating user influence via PageRank is improved by addressing the problem of subjective initial values affecting objectivity. Subsequently, this paper extracts the impact of user interactions by introducing the propagation characteristics of information on Weibo (a Chinese Twitter-like platform) and precisely measures the contribution of followers' influence on the users they follow, based on varying interaction intensities, thereby overcoming the limitation of equally valuing follower influence. In parallel, we evaluate the influence of individualized user preferences, subject areas, and a real-time assessment of their impact on public opinion during its spread, tracking their effect during different time periods. Finally, to ascertain the effectiveness of integrating each user attribute—influence, interaction promptness, and similar interests—we conducted experiments utilizing real-world Weibo topic data. Exercise oncology When contrasted against TwitterRank, PageRank, and FansRank, the UWUSRank algorithm exhibits a 93%, 142%, and 167% increase in user ranking rationality, thereby demonstrating its practical merit. genetic conditions Utilizing this approach, research into user identification, information dissemination strategies, and public perception analysis within social networks is facilitated.

The exploration of the correlation among belief functions constitutes a significant aspect of Dempster-Shafer theory. An analysis of correlation, when viewed through the lens of uncertainty, furnishes a more comprehensive guide for managing uncertain information. Prior investigations of correlation have omitted a key aspect: accounting for uncertainty. For addressing the problem, this paper proposes a new correlation measure, the belief correlation measure, which is constructed using belief entropy and relative entropy. Considering the uncertainty inherent in information, this measure evaluates their relevance, leading to a more complete measure of the correlation between belief functions. The mathematical properties of the belief correlation measure, encompassing probabilistic consistency, non-negativity, non-degeneracy, boundedness, orthogonality, and symmetry, are present. Moreover, a method for information fusion is presented, predicated on the belief correlation measure. Using objective and subjective weights, the credibility and usefulness of belief functions are assessed more comprehensively, leading to a more detailed evaluation of each piece of evidence. The effectiveness of the proposed method is evident through numerical examples and application cases in multi-source data fusion.

Despite considerable progress in recent years, deep learning (DNN) and transformers face significant obstacles in supporting human-machine collaborations because of their lack of explainability, the mystery surrounding generalized knowledge, the need for integration with various reasoning techniques, and the inherent vulnerability to adversarial attacks initiated by the opposing team. Owing to these inherent weaknesses, stand-alone DNNs display restricted capacity for facilitating human-machine partnerships. This paper details a meta-learning/DNN kNN architecture, which overcomes these limitations by unifying deep learning with explainable nearest neighbor (kNN) learning to form the object level, using a deductive reasoning-based meta-level control system for validation and correction. The architecture yields predictions which are more interpretable to peer team members. Our proposal is considered through a framework that integrates structural and maximum entropy production analyses.

Networks with higher-order interactions are examined from a metric perspective, and a new approach to defining distance for hypergraphs is introduced, building on previous methodologies documented in scholarly publications. This metric, a novel approach, combines two important considerations: (1) the node separation within each hyperedge, and (2) the distance that separates the hyperedges of the network. In this respect, determining distances is done on a weighted line graph of the hypergraph. A range of ad hoc synthetic hypergraphs are used to illustrate the approach, with the structural insights extracted by the novel metric being the focal point. The method's efficacy and performance are empirically verified through computations on large-scale real-world hypergraphs, unveiling novel insights into the structural attributes of networks, exceeding the scope of pairwise interactions. A novel distance measure allows for the generalization of efficiency, closeness, and betweenness centrality, specifically within the structure of hypergraphs. A comparison of these generalized metrics to their counterparts calculated for hypergraph clique projections reveals significantly differing assessments of node properties (and functions) regarding information transferability. The distinction is more pronounced in hypergraphs that frequently include hyperedges of considerable size, where nodes associated with these large hyperedges are rarely interconnected via smaller ones.

Within the contexts of epidemiology, finance, meteorology, and sports, the prevalence of count time series data has prompted a rising demand for studies that are methodologically sound and have practical implications. This paper surveys the progress in integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models during the past five years, emphasizing their application to data categories, including unbounded non-negative counts, bounded non-negative counts, Z-valued time series, and multivariate counts. For each dataset, our examination centers on three primary elements: advancements in model design, methodological evolution, and broadening practical applications. In order to integrate the INGARCH modeling field as a whole, we present a summary of recent methodological advancements in INGARCH models across different data types and highlight some potential future research areas.

The progression of database utilization, including platforms like IoT, has brought forth the crucial need to understand and implement data privacy protections. Yamamoto's pioneering 1983 research focused on the source (database), composed of both public and private information, to uncover theoretical constraints (first-order rate analysis) on coding rate, utility, and decoder privacy, examining these in two specific instances. Building upon the 2022 research of Shinohara and Yagi, this paper investigates a broader case. To ensure encoder privacy, we explore two key issues. Firstly, we analyze the first-order relationship between coding rate, utility, decoder privacy, and encoder privacy, where utility is gauged by expected distortion or excess distortion probability. The second task involves establishing the strong converse theorem for utility-privacy trade-offs, with utility assessed through the measure of excess-distortion probability. A more nuanced approach to analysis, including a second-order rate analysis, could be spurred by these findings.

We explore distributed inference and learning methodologies within networked systems, employing a directed graph model. Diverse features are observed by a subset of nodes, all imperative for the inference procedure that takes place at a distant fusion node. To combine insights from the observed distributed features, we formulate a learning algorithm and architecture, employing processing units across the networks. Through the application of information-theoretic tools, we investigate the flow and combination of inference across a network. The conclusions drawn from this investigation guide the design of a loss function capable of balancing the model's performance against the transmission volume across the network. The bandwidth requirements, coupled with the design criteria, are examined in our proposed architecture. Furthermore, we explore the practical application of neural networks in typical wireless radio access, alongside experiments showcasing improvements over existing state-of-the-art techniques.

By means of Luchko's general fractional calculus (GFC) and its expansion in the form of the multi-kernel general fractional calculus of arbitrary order (GFC of AO), a nonlocal probabilistic framework is introduced. Probability, probability density functions (PDFs), and cumulative distribution functions (CDFs) are extended through nonlocal and general fractional (CF) methods, with their characteristics thoroughly explained. A consideration of nonlocal probability distributions in the context of AO is undertaken. A broader examination of operator kernels and their non-local implications in probability theory is facilitated by the application of the multi-kernel GFC.

An exploration of diverse entropy measures hinges on a two-parameter non-extensive entropic expression involving the h-derivative, thereby extending the traditional Newton-Leibniz calculus. By demonstrating its ability to characterize non-extensive systems, the new entropy, Sh,h', replicates prominent non-extensive entropies, including Tsallis, Abe, Shafee, Kaniadakis, and the classical Boltzmann-Gibbs entropy. The analysis of generalized entropy includes the examination of its associated properties.

Maintaining and managing ever-more-intricate telecommunication systems is a task becoming increasingly difficult and often straining the capabilities of human experts. A consensus exists in both academia and industry regarding the crucial need for augmenting human decision-making with sophisticated algorithmic instruments, with the objective of moving towards more self-sufficient and autonomously optimizing networks.