Hasil untuk "Information theory"

T. Richardson, R. Urbanke

B. Hommel, J. Müsseler, G. Aschersleben et al.

W. R. Garner

J. Pierce

Abrar Ghaith

Background: The potential health advantages of functional food products have increased their appeal. Their reception and implementation are greatly influenced by consumer attitudes and purchasing intentions. The Theory of Planned Behavior (TPB) is used in this study to examine how attitudes toward and intentions to buy functional food products in Hungary are influenced by health consciousness (HC) and nutritional knowledge (KN). Methods: From March to May 2023, 396 adult Hungarian volunteers took part in an online survey. The poll measured respondents' intentions to buy functional food products as well as their health consciousness, nutritional knowledge, attitudes, and subjective norms (SN). Through pilot research, the questionnaire's reliability and validity were determined. Multiple regression analyses, one-sample t-tests, and Cronbach's alpha were all included in the statistical analysis. Results: The majority of participants were female, working students. In support of hypotheses H1 and H2, health consciousness significantly influenced attitudes and purchase intentions. H3 and H4 are supported by the findings that nutritional knowledge strongly influenced attitudes and purchase intentions. Both perceived behavioral control and subjective standards had a beneficial impact on purchase intentions supporting H5 and H6. Conclusion: This study provides important insights into Hungarian consumers' preferences for functional food items. It highlights the beneficial effects of health awareness, information, attitudes, arbitrary standards, and perceived behavioral control on buying intentions. It was discovered that attitudes play an important role in mediating the link between nutritional knowledge, health consciousness, and purchase intentions. It was discovered that attitudes play an important role in mediating the link between nutritional knowledge, health consciousness, and purchasing intentions. The dynamic functional food market's marketers and legislators should take these findings seriously. The subtleties of these attitudes and their implications for promotional tactics can be explored in greater detail in future research. Promoting healthier food options and informing policy and marketing decisions require an understanding of consumer perceptions and behaviors.

Iryna Voronenko, Alla Bohush, Oleksandr Voronenko et al.

In this work, the main trends in research and publication activity in digital transformation in the world and Ukraine are analyzed using meta- and bibliometric analysis. For this purpose, bibliometric data on scientific publications on the topic of digital transformations in the Google Scholar and Scopus databases were selected, which were additionally analyzed using the VOSviewer software package. Based on filtering the results obtained, an array was formed that included 366 scientific publications for 2019–2023 in Ukrainian in the Google Scholar database and 3,703 scientific publications in English for 2020–2023 in the Scopus database. Dynamic time analysis revealed a significant surge of scientific interest in the topic of digital transformations in recent years, while structural analysis revealed the multi-industry structure of existing research. The creation of bibliographic maps of keywords and publication maps allowed us to form an idea of the main thematic areas of research in the context of digital transformations and their opinion leaders. The data obtained became the basis for formulating recommendations for further areas of research in digital transformation, in particular, on the development of a unified roadmap for the digital transformation of education at different educational qualification levels and for various specialties. This will contribute to the formation of a single systemic approach to the digital transformation of Ukraine as a guarantee for the state’s sustainable development, well-being, strengthening of national security, speeding up the pace of European integration processes, as well as promoting national interests at the international level.

Mohammad Farahani, Abouzar Samimi, Hossein Shateri

In this paper, the problem of participation of a Battery Energy Storage (BES) in the Day-Ahead Market (DAM) and Real-Time Market (RTM) in three cases including Joint Energy and Reactive Power Market (JERPM), Energy Market (EM) and Energy and Reserve Market (ERM) is modeled based on a hybrid Stochastic Robust Optimization (SRO) model and it tries to maximize the profit of the BES owner in the face of uncertainty pertaining to the market prices. In the proposed model, in the first step, the decision maker or BES owner predicts energy, reserve and reactive power prices in each market according to historical network information. In the second step, by determining the uncertainty interval of prices for DAM and RTM, robust optimization is implemented aiming at maximizing the profit of the BES owner using a model of forming a robust counterpart of the objective function and dual theory. In the next step, by defining some different scenarios for the robust budget, Stochastic Programming (SP) assigns a probability to each scenario. Then, the final profit of each market is calculated through probabilistic weighting, and finally the market with more profit is opted as considered participation market. The proposed formulation based on the Mixed Integer Nonlinear Programming (MINLP) model is implemented in the GAMS software environment and the results demonstrate the maximum profitability of BES in EM (21% more) and also show participation in providing ancillary services, such as reserve to provide security in incidents and reactive power to maintain stability and reduce cost and losses, despite the decrease in profit, leads to neutralizing the negative impact of uncertainties in the BES profit..

Lev Tauz, Debarnab Mitra, Jayanth Shreekumar et al.

Quantum key distribution (QKD) is a popular protocol that provides information theoretically secure keys to multiple parties. Two important post-processing steps of QKD are 1) the information reconciliation (IR) step, where parties reconcile mismatches in generated keys through classical communication, and 2) the privacy amplification (PA) step, where parties distill their common key into a new secure key that the adversary has little to no information about. In general, these two steps have been abstracted as two distinct problems. In this work, we consider a new technique of performing the IR and PA steps jointly through sampling that relaxes the requirement on the IR step, allowing for more success in key creation. We provide a novel LDPC code construction known as Block-MDS QC-LDPC codes that can utilize the relaxed requirement by creating LDPC codes with pre-defined sub-matrices of full-rank. We demonstrate through simulations that our technique of sampling can provide notable gains in successfully creating secret keys.

Juan Carlos Angulo, Sheila López-Rosa

Both entropy and complexity are central concepts for the understanding and development of Information Theory, playing an essential role in the increasingly numerous applications in a huge diversity of fields [...]

Christiane Caneva, Caroline Pulfrey

The introduction of digital technologies in educational institutions has gained significant momentum worldwide as societies recognize the central role of education in preparing individuals for a technology-driven world. This paper explores the concept of 'school digital capacity', which encompasses various factors critical to the effective integration of technology into teaching and learning practices. Educational leadership emerges as a cornerstone of this capacity, with leaders playing a critical role in shaping the digital landscape of their institutions. Using a mixed-methods approach, this research explores the perceptions of educational leaders in the context of a large-scale digital education reform project. Key findings highlight the importance of leadership and a coherent digital strategy in improving digital efficacy and teacher engagement with technology. However, challenges are evident, including a lack of clear strategies, inadequate human resource allocation, limited knowledge of digital education and insufficient professional development opportunities. The study highlights the need for improved training programmes for educational leaders to equip them with the necessary digital skills and strategic acumen. Collaborative networks between schools and increased support from ministries of education are recommended to facilitate effective digital integration and capacity development.

Chan Xu, Shuowen Zhang

In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cramér-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cramér-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.

Aleksei Cherkasov, Kirill M. Gerke, Aleksey Khlyupin

Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic reconstructions, stationarity and representativity analysis. The most important characteristic of any such descriptor is its information content - or its ability to describe the structure at hand. For example, from crystallography it is well known that experimentally measurable $S_2$ correlation function lacks necessary information content to describe majority of structures. The information content of this function can be assessed using Monte-Carlo methods only for very small 2D images due to computational expenses. Some indirect quantitative approaches for this and other correlation function were also proposed. Yet, to date no methodology to obtain information content for arbitrary 2D or 3D image is available. In this work, we make a step toward developing a general framework to perform such computations analytically. We show, that one can assess the entropy of a perturbed random field and that stochastic perturbation of fields correlation function decreases its information content. In addition to analytical expression, we demonstrate that different regions of correlation function are in different extent informative and sensitive for perturbation. Proposed model bridges the gap between descriptor-based heterogeneous media reconstruction and information theory and opens way for computationally effective way to compute information content of any descriptor as applied to arbitrary structure.

Sun Ju Chang, Kyoung-eun Lee, Eunjin Yang et al.

Abstract Background The Internet is considered an important channel for providing health information to older adults. We developed an intervention to improve eHealth literacy in older adults according to the information-motivation-behavioral skills (IMB) theory and Intervention Mapping. This study aimed to analyze the effect of a developed intervention on information, motivation, behavioral skills, and behaviors related to eHealth information in older adults. Methods Forty-six older adults over the age of 65 were recruited from two senior welfare centers in a city in South Korea. We divided the participants into four groups and conducted one intervention per group from March to December 2019. One intervention consisted of 5 sessions and was performed once a week (2 h/1 time) for 5 weeks, culminating in a total lecture time of 10 h. One lecture instructor and two assistant instructors supported the participants in the computer practices. Results Participants’ computer/web knowledge, perceived ease of use, perceived enjoyment, and attitude toward eHealth information showed statistically significant increases. The eHealth literacy efficacy score, searching performance score, and understanding score were also significantly increased. However, there was no significant difference in perceived usefulness. Conclusion The application of the current theory-based methodology can improve the quality of research in eHealth literacy. Additionally, various interventions should be developed and continuously applied to improve eHealth literacy among older adults.

Fatemeh Heidarnezhad, Fatima Fahimnia

Objective: The main purpose of this study is to identify the relationship between the level of participation in knowledge management and the critical thinking tendency among librarians of central libraries of public universities in Tehran. Methodology: This research is descriptive-correlational in terms of research method and is a survey branch. In addition, according to the purpose, it falls into the category of applied research. The statistical population of this study consisted of librarians of the central libraries of public universities in Tehran, whose number was 210 people. Using the Morgan table, a sample number of 136 people was obtained. Data were collected using two standard questionnaires: Employee Participation in Knowledge Management (Kulkarni et al. (2007)) and the California Critical Thinking Tendency Questionnaire (1992). The supervisor validated both questionnaires. The reliability of the questionnaires was obtained using Cronbach's alpha coefficient for the questionnaire on employee participation in knowledge management was 0.946 and for the questionnaire of critical thinking tendency was 0.933. The data analysis was done through descriptive statistics and inferential statistics using SPSS software. Results: The results of data analysis showed that there is a significant positive relationship between the variable of critical thinking tendency and participation in knowledge management. Conclusion: Researchers define and examine critical thinking in two dimensions: critical thinking skills and tendencies, in previous researches the relationship between knowledge management and the skills dimension was investigated, but in this research, the relationship between participation in knowledge management and the dimension of tendency to critical thinking was investigated.

Gusev Evgeny, Bakulin Vladimir, Chernykh Vera

The paper investigates the issue of promising ways to predict the durability of polymer composites under extreme conditions. A study of the possibility of predicting the durability of the material, based on the information obtained in the first years of operation. Based on themodern provisions of molecular kinetic theory (MKT), the issue ofmatching the defining parameters ofmathematical models calculated because of solving forecasting problems within the framework of the formulated variational formulation, considering the results of experimental measurements at the macro level, with the corresponding defining parameters of physical models describing molecular interactions at the microlevel, was investigated. The results of computational experiments are presented.

Seong-Kwan Lim

Virtual Reality (VR) is one of the core components of the fourth industrial revolution as a technology that makes the virtual world feel as if it is real. VR is being used in various fields such as entertainment, advertisement, education, medical care, training, sports, and tourism, as well as providing contents for such things as games and videos. Libraries are already looking for ways to utilize VR from various angles, such as operating experiential programs. The purpose of this thesis is to develop and propose a VR-based library user education program. In order to achieve the purpose of the study, we analyzed previous studies from a theoretical perspective to find a way to construct a user education program, and also to derive possible implications based on examples of countries such as the United States and Korea that are already introducing and applying VR technology to library services. Therefore, the user education program proposed in this study can be used as a basic building block when many libraries want to develop VR-based programs in the future.

Guangyao Chen, Mingwu Wang, Jiahui Yan et al.

As a common geological hazard in underground engineering, rockburst has many uncertain factors and interactions, and is of a complicated mechanism. Based on the information fusion theory, a methodology using a novel connection cloud model, which can overcome the defect of the traditional cloud model requiring factors to be normal distribution when determining the basic probability assignment function of the evidence is proposed for predicting the rockburst intensity. And concepts of Lance-distance and Deng-entropy are also introduced here to improve the traditional evidence fusion algorithm. The practical application shows that the result obtained from the proposed model is consistent with that in the actual site testing, so this model is feasible and effective for the prediction of the rockburst intensity. Furthermore, compared with results from other six improved evidence fusion rules, the improved evidence fusion method proposed here has higher fusion convergence precision, faster convergence rate, and more accurate and reliable results, this may provide a new reference for other similar uncertain problems.

Anirudh Sridhar, H. Vincent Poor

We study the problem of estimating the source of a network cascade given a time series of noisy information about the spread. Initially, there is a single vertex affected by the cascade (the source) and the cascade spreads in discrete time steps across the network. The cascade evolution is hidden, but one can observe a time series of noisy signals from each vertex. The time series of a vertex is assumed to be a sequence of i.i.d. samples from a pre-change distribution $Q_0$ before the cascade affects the vertex, and the time series is a sequence of i.i.d. samples from a post-change distribution $Q_1$ once the cascade has affected the vertex. Given the time series of noisy signals, which can be viewed as a noisy measurement of the cascade evolution, we aim to devise a procedure to reliably estimate the cascade source as fast as possible. We investigate Bayesian and minimax formulations of the source estimation problem, and derive near-optimal estimators for simple cascade dynamics and network topologies. In the Bayesian setting, an estimator which observes samples until the error of the Bayes-optimal estimator falls below a threshold achieves optimal performance. In the minimax setting, optimal performance is achieved by designing a novel multi-hypothesis sequential probability ratio test (MSPRT). We find that these optimal estimators require $\log \log n / \log (k - 1)$ observations of the noisy time series when the network topology is a $k$-regular tree, and $(\log n)^{\frac{1}{\ell + 1}}$ observations are required for $\ell$-dimensional lattices. Finally, we discuss how our methods may be extended to cascades on arbitrary graphs.

S. Ihara

Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao et al.

Useful knowledge, embedded in a database, is likely to change over time. Identifying recent changes in temporal databases can provide valuable up-to-date information to decision-makers. Nevertheless, techniques for mining high-utility patterns (HUPs) seldom consider recency as a criterion to discover patterns. Thus, the traditional utility mining framework is inadequate for obtaining up-to-date insights about real world data. In this paper, we address this issue by introducing a novel framework, named utility-driven mining of Recent/trend high-Utility Patterns (RUP) in temporal databases for intelligent systems, based on user-specified minimum recency and minimum utility thresholds. The utility-driven RUP algorithm is based on novel global and conditional downward closure properties, and a recency-utility tree. Moreover, it adopts a vertical compact recency-utility list structure to store the information required by the mining process. The developed RUP algorithm recursively discovers recent HUPs. It is also fast and consumes a small amount of memory due to its pattern discovery approach that does not generate candidates. Two improved versions of the algorithm with additional pruning strategies are also designed to speed up the discovery of patterns by reducing the search space. Results of a substantial experimental evaluation show that the proposed algorithm can efficiently identify all recent high-utility patterns in large-scale databases, and that the improved algorithm performs best.