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Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL 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.)

This study presents a methodological framework for optimal placement of pressure sensors in Water Distribution Networks (WDNs) for leakage monitoring under uncertainty. Monte Carlo simulation is utilized to simulate leakages of different magnitudes at various nodes in the WDN taking into consideration background noise and minimum resolution of pressure sensors. A novel sensor preselection algorithm based on community detection and maximum entropy computation to reduce the search space of the pressure Sensor Placement Problem (SPP) is presented. The pressure SPP is formulated as a multi-objective optimization problem that seeks to maximize Joint Entropy, Coverage, and minimize Total Correlation. NSGA-II is used to solve the SPP and the solutions in the optimal Pareto front are ranked using a hybrid Entropy TOPSIS to eliminate potential bias and subjective human judgement in optimal sensor configuration implementation. The sensor preselection algorithm achieved a 67% reduction in the search space (possible sensor positions) of the case study, C-TOWN WDN, with only 2.78% reduction in coverage. The result of the pressure SPP indicates only 21 pressure sensors are needed to cover 95.45% of the WDN under study. Finally, the overall performance of the proposed methodological framework is presented and compared with other related works. • A multiobjective optimal pressure sensor placement problem is formulated. • Sensor preselection algorithm based on Community Detection & Entropy is introduced. • Total correlation as a measure of redundancy ensures sparsity between sensors. • Entropy-TOPSIS eliminates bias in implemetation of optimal sensor configuration. • Limited number of pressure sensors can be placed optimally in distribution systems. [ABSTRACT FROM AUTHOR]

Library and Information Science (LIS) plays a vital role in promoting Sustainable Development Goals (SDGs). This paper examines the research gap on the challenges of LIS concerning the need to promote SDGs. We examine challenges, such as inadequate research on the specific impact of libraries on sustainable development, particularly in varying socioeconomic settings, and limited research on the obstacles that impede libraries from effectively carrying out sustainable initiatives, such as inadequate funding and institutional obstacles. The methodology adopted in the study is the Systematic Literature Review (SLR), which entails the inclusion and exclusion research design. The Information Behaviour Theory was adopted to seek an understanding of how people seek, use and share information. The role of libraries is critical for sustainable development. The article also examines the barriers facing LIS in promoting sustainable development. The findings reveal that libraries are unique in providing access to information and resources, offering educational programmes and events, implementing sustainable practices, and collaborating with other organisations to promote sustainability and contribute to the SDGs. The article recommends that libraries, as public and private sector entities, must collaborate to deliver greater dividends of the SDGs. The study also advocates for further studies regarding the long-term impact of library programmes on SDGs' achievement. [ABSTRACT FROM AUTHOR]

Portfolio theory is one of the theories in the financial field presented by Harry Markowitz. This theory states that investors should diversify their stock portfolio to reduce investment risk. This research has been conducted with the aim of investigating the applications of portfolio theory in information and knowledge retrieval. The current research is applied in terms of purpose and qualitative research. In the current research, the main concepts of modern portfolio theory were extracted using the method of content analysis and text analysis. In the next step, based on the method of evaluation and comparison, the functions of modern portfolio theory in information retrieval and the field of information science and epistemology were identified and determined. Finally, 12 experts in the fields of knowledge management, information and knowledge retrieval, indexing, cataloging, information systems and librarians were selected as an expert panel. Then, during 7 sessions and by conducting interviews, using questionnaires and holding individual and group meetings, the functions of modern portfolio theory were extracted. The results of the research showed that the modern theory of portfolio is effective in the process of information retrieval and can have an impact on 5 groups of creators, indexers, information system users, information system and librarians and have a positive effect on improving the retrieval process. The results of the research showed that the portfolio theory has an impact on the influential groups in the recovery process and can play a role in improving the performance of information recovery system. One of the most important applications of portfolio theory is its use in ranking retrieved documents, increasing the connection between retrieved documents, reducing false fallout, increasing the percentage of information recovery, creating the possibility of semantic search, increasing comprehensiveness and categorizing information based on subject areas. The current research can be the beginning of investigating other applications of the portfolio theory in information retrieval and makes it possible to carry out more detailed investigations in this field. [ABSTRACT FROM AUTHOR]

By empowering customers to make fitting purchases, user reviews play an important role in reducing inefficiencies in the provisioning of product information. Because of the abundance of reviews and the signals they provide, this information may become confusing and risks overloading customers. Consequently, review hosting platforms have adjusted their designs to feature a signal "distilled" from a selective set of "top reviews" and their valences. The expected ease with which customers process this signal is intended to increase their satisfaction, thus reducing dispersion in their subsequent review ratings. In this study, we analyze the influential role that top reviews and their valence play under various scenarios: when customers are overloaded by a large number of reviews, when top reviews themselves are not parsimonious in number, and when the signals from top reviews are not in concordance with that from all the other reviews. We find that the valence of top reviews plays a central role in mitigating information overload. However, the influence of those top reviews diminishes when they too pose an overload risk but is strengthened when their signal is reaffirmed by signals from all other reviews. Finally, the impact of top reviews is weaker for less popular products. [ABSTRACT FROM AUTHOR]

This short think-piece describes my journey of discovery through the information landscape (from 2004 to present) and the development of a theory of information discernment which unpacks aspects of information literacy (IL)--the cognitive, metacognitive, affective and physiological states that shape how people make judgements about the information they encounter. Tracing my own path through developments in the field in recent decades sheds light both on wider changes and on shifts in my personal understanding of IL, from an initial perception of it as a relatively simple and obvious phenomenon to my current understanding of IL as something much more complex and contested. [ABSTRACT FROM AUTHOR]

This study analyzes search stopping points and stopping rules used in the online information search process based on the concepts of Information Foraging Theory. The research method is a descriptive)analytical study. Sources are collected from various databases with no time restriction (before August 2022). The results show that from the perspective of Information Foraging Theory, in the online information search process, the searcher enters an information patch by issuing a query. Then searcher decides whether to stay or leave the information patch by evaluating the relevance and irrelevance of information (scent-based stopping rules) and the time spent within and between patches (patch-based stopping rules). Each time the searcher leaves the information patch, he stops at a point and enters another information patch by issuing a query. The points of leaving one patch to entering another patch are temporary stopping points, because the search process continues and is not yet completed. Finally, by evaluating the information sufficiency (diet-based stopping rules), if searcher obtains enough information, he terminates the search session and stops at the permanent point. This study helps researchers to understand the different stopping rules at temporary and permanent stopping points. The implications of research are used in the design of information search behavior models. [ABSTRACT FROM AUTHOR]

Introduction: Volumetric Modulated Arc Therapy (VMAT) is a state-of-the-art prostate cancer treatment, defined by high dose gradients around targets. Its unique dose shaping incurs hidden complexity, impacting treatment deliverability, carcinogenesis, and machine strain. This study compares various aperture-based VMAT complexity indices in prostate cases using principal component and mutual information analyses. It suggests essential properties for an ideal complexity index from an information-theoretic viewpoint. Material and methods: The following ten complexity indices were calculated in 217 VMAT prostate plans: circumference over area (CoA), edge metric (EM), equivalent square field (ESF), leaf travel (LT), leaf travel modulation complexity score for VMAT (LTMCSV), mean-field area (MFA), modulation complexity score (standard MCS and VMAT variant MCSV), plan irregularity (PI), and small aperture score (SAS5mm). Principal component analysis (PCA) was applied to explore the correlations between the metrics. The differential entropy of all metrics was also calculated, along with the mutual information for all 45 metric pairs. Results: Whole-pelvis plans had greater complexity across all indices. The first three principal components explained 96.2% of the total variance. The complexity metrics formed three groups with similar conceptual characteristics, particularly ESF, LT, MFA, PI, and EM, SAS5mm. The differential entropy varied across the complexity metrics (PI having the smallest vs. EM the largest). Mutual information analysis (MIA) confirmed some metrics' interdependence, although other pairs, such as LTMCSV/SAS5mm, LT/MCSV, and EM/SAS5mm, were found to share minimal MI. Conclusions: There are many complexity indices for VMAT described in the literature. PCA and MIA analyses can uncover significant overlap among them. However, this is not entirely reducible through dimensionality reduction techniques, suggesting that there also exists some reciprocity. When designing predictive models of quality assurance metrics, PCA and MIA may prove useful for feature engineering. [ABSTRACT FROM AUTHOR]

Copyright of Arquivos de Neuro-Psiquiatria is the property of Thieme Medical Publishing Inc. 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.)

This research article explores the dynamic interplay between social self-perception and behavioral influence in digital simulations, shedding light on the complexities that define our online interactions. In an era where virtual spaces play an increasingly significant role in shaping human behavior, understanding the mechanisms through which individuals perceive themselves in simulated social environments is paramount. Drawing on interdisciplinary insights from psychology, computer science, and sociology, this study employs a variety of methodologies, including virtual reality experiments, agent-based modeling, and data analytics, to comprehensively examine the intricate relationship between self-perception and behavior in digital contexts. Aiming for this goal requires the decision whether to communicate information truthfully or if deceptive lies might improve the reputation even more. The basis of this decision involves not only an individual's belief about others, but also their understanding of others' beliefs, described by the concept of Theory of Mind, and the mental processes from which these beliefs emerge. In the present work, we used the Reputation Game Simulation as an approach for modeling the evolution of reputation in agent-based social communication networks, in which agents treat information approximately according to Bayesian logic. We implemented a second-order Theory of Mind based message decision strategy that allows the agents to mentally simulate the impact of different communication options on the knowledge of their counterparts' minds in order to identify the message that is expected to maximize their reputation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate the use of Rough Set Theory to identify information that can be used in project management, namely the knowledge about the appropriate prioritization of activities and the use of resources directed towards the adopted project objectives. Rough Set Theory operates on large data sets and permits the reduction of irrelevant data and the induction of decision rules that discover recursion and relationships in the data. The discovered rules can prove valuable for decision-making project managers. All the rules found in course of the proposed rule-finding process have been considered interesting and are discussed in the paper. The paper also provides an example of the application of Rough Set Theory in the field mentioned above. [ABSTRACT FROM AUTHOR]

Magnetic resonance-guided focused ultrasound (MRgFUS) lesioning of the ventralis intermedius nucleus (VIM) has shown promise in treating drug-refractory essential tremor (ET). It remains unknown whether focal VIM lesions by MRgFUS have broader restorative effects on information flow within the whole-brain network of ET patients. We applied an information-theoretical approach based on intrinsic ignition and the concept of transfer entropy (TE) to assess the spatiotemporal dynamics after VIM-MRgFUS. Eighteen ET patients (mean age 71.44 years) underwent repeated 3T resting-state functional magnetic resonance imaging combined with Clinical Rating Scale for Tremor (CRST) assessments one day before (T0) and one month (T1) and six months (T2) post-MRgFUS, respectively. We observed increased whole brain ignition-driven mean integration (IDMI) at T1 (p < 0.05), along with trend increases at T2. Further, constraining to motor network nodes, we identified significant increases in information-broadcasting (bilateral supplementary motor area (SMA) and left cerebellar lobule III) and information-receiving (right precentral gyrus) at T1. Remarkably, increased information-broadcasting in bilateral SMA was correlated with relative improvement of the CRST in the treated hand. In addition, causal TE-based effective connectivity (EC) at T1 showed an increase from right SMA to left cerebellar lobule crus II and from left cerebellar lobule III to right thalamus. In conclusion, results suggest a change in information transmission capacity in ET after MRgFUS and a shift towards a more integrated functional state with increased levels of global and directional information flow. • For the first time, functional consequences of a focal VIM-lesion, are described by advanced information theory-based measures. • Information broadcasting between hubs of the tremor network is improved post-MRgFUS, correlating with tremor improvement. • VIM-MRgFUS improves global information transfer, as evidenced by increased mean integration, suggesting restorative effects. [ABSTRACT FROM AUTHOR]

The goal of this essay is to explain why communication has not been reduced to a science in the 70 years since Shannon published his seminal Mathematical Theory of Communication, and, more pointedly, why data science is powerless to stop the proliferation of conspiracy theories. We depend on axiomatic choice for the selection of initial conditions, models, and images because loss functions have no foundation in reality. Axiomatic choice brings with it the essential irony of information, which cannot be eliminated through scientific distillation. Pedagogically, this means that the present educational focus on measurable results to the exclusion of imagination and aesthetic judgment is a dead end for communication from which there is no escape, even in theory. [ABSTRACT FROM AUTHOR]

This article presents a brief history of the term "information" and its different meanings, which are both important and difficult because the different meanings of the term imply whole theories of knowledge. The article further considers the relation between "information" and the concepts "matter and energy", "data", "sign and meaning", "knowledge" and "communication". It presents and analyses the influence of information in information studies and knowledge organization and contains a presentation and critical analysis of some compound terms such as "information need", "information overload" and "information retrieval", which illuminate the use of the term information in information studies. An appendix provides a chronological list of definitions of information. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning. [ABSTRACT FROM AUTHOR]

In this article, a methodology for utilizing eight Thinking Maps that are linked to a cognitive taxonomy will be explored. Firstly, the various taxonomies focusing on cognitive processes will be elaborated upon, namely Bloom's Taxonomy as well as Barret's Taxonomy that is used in the education system. Following this, the article will explore the questioning strategy that links questions to either taxonomy and how this strategy is currently employed in classrooms worldwide. A section then elucidates the eight Thinking Maps and what types of thought processes are associated with each map. A methodology will be discussed that links the eight Thinking Maps to verbs that are all associated with the previously mentioned taxonomies. Finally, an adaptive systematic methodology will be elaborated upon, linking to the information processing theory. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) based lung screening followed by computer-vision-based automated segmentation has great potential in early diagnosis of COVID-19, but low sample size and high patient-to-patient variability of infection characteristics are two of the major challenges. We propose a novel Mutual Information Balanced Net (MIB-Net) with three distinctive features. First, it captures mutual information across feature maps, CT scans, and predicted segmentation labels to optimize the contribution of different feature maps for better segmentation accuracy. Second, it incorporates a three-dimensional structural prior of CT scans based on predicted segmentation of neighboring CT slices to improve accuracy. Third, a novel Weight Optimization Filter (WOF) is used to adjust the proportion of 3D structural prior to be incorporated, which further improves the segmentation accuracy. Experimental results show that the proposed approach outperforms state-of-the-art biomedical image segmentation models by 7.87%, 3.85%, and 5.01% in Dice Score, respectively for three popular COVID-19 CT datasets. The better performance of the proposed approach suggests that it may serve as an attractive alternative to Lung CT-based screening for COVID-19. • Incorporation of Mutual Information to control the parallel information flow. • Novel MI Balancer architecture for COVID-19 segmentation. • Incorporation of the 3D structural prior to learn inter-slice spatial dependence. • Novel WOF to balance between the current slice and neighboring prediction. [ABSTRACT FROM AUTHOR]

Guiding tourists in displaying civilized behavior is pivotal in destination management. This research tries to understand what forms of interpretation can effectively promote tourists' desirable behaviors. Grounded in social information processing theory, it introduces the tool of anthropomorphic interpretation cues and investigates its' efficacy in shaping tourists' civilized behavioral intentions through meaningful experience. Hypotheses are developed by integrating the social information processing theory, the three-factor theory of anthropomorphism and the narrative transportation theory, in three experiments. First, we confirm how anthropomorphic interpretation cues substantially enhance civilized behavioral intentions. Second, we show that the perception of meaningful experience mediates the effect of anthropomorphic interpretation cues in interpretive communication. Third, we show that the presence (vs. absence) of narrative within anthropomorphic interpretation cues amplifies the intervention effect. These findings offer valuable insights for destination managers, suggesting the strategic implementation of anthropomorphic cues and narrative approaches in interpretation to encourage tourists' civilized behavior. [Display omitted] • This study focuses on the impact of anthropomorphic interpretation cues on tourists civilized behavioral intentions. • Anthropomorphic interpretation cues can enhance tourists civilized behavioral intentions. • Meaningful experience mediates the effect of anthropomorphic interpretation cues on tourists civilized behavioral intentions. • Narrative strengthens the anthropomorphic interpretation cues on tourists civilized behavioral intentions. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie 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.)

Copyright of Filosofia UNISINOS is the property of Universidade do Vale do Rio dos Sinos 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.)

Image segmentation by thresholding has been widely used in industrial practice. Due to the complexity of the real environment, the universality of image segmentation algorithm is greatly challenged, so different segmentation methods need to be designed for different application scenarios. In information theory, the relative arithmetic-geometric divergence is an efficient information distance measure used to measure similarity (or dissimilarity) between different information systems. It overcomes the deficiency of traditional divergence measure and can better reflect the similarity or dissimilarity between different systems. In this paper, an image threshold segmentation method is designed and implemented based on relative arithmetic-geometric divergence. The presented method is applied to the segmentation of nondestructive testing images, degraded document images, medical images and security inspection surveillance images. In comparison with some classical image threshold segmentation algorithms, the effectiveness of the proposed method is verified successfully. For the proposed method, it provides the advantages of briefer algorithm, easy to implement, and less computing time. The proposed method can meets with the real time requirement, and it has a good prospect of application and far-reaching research value. [ABSTRACT FROM AUTHOR]

In the environment of rapidly changing technological background that facilitates the effects of globalization and the ever-decreasing distance between cultural heritage of different ethnic and national origins, it is an essential task to find and implement innovative strategies that would ensure inclusive dialogic spaces of fast reaction, spaces sensitive to local cultural developments as well as environments free from the domineering tendencies of identification in its traditional and practically dated definition. The present article attempts to form a foundation for an alternative approach to cultural communication. By means of shifting the focus from textual and semantic interpretations of cultural interactions as well as cultural environments, this article sets the direction for communication based on information instead of meaning. The suggested approach, therefore, stems from Claude Shannon's information theory and its development associated with Fred I. Dretske (theoretical-informational approach). The spaces of cultural interaction are understood as dialogic spaces that in accordance with the mentioned approach will be dislocated in order to gain advantage in the sphere of cultural communication in such a way that would allow synergetic functioning alongside the ideas of posthumanist, (post)digital era, as well as conscious utilisation of the specificities of technogenesis. [ABSTRACT FROM AUTHOR]

Recommender systems provide the ability to personalize and adapt environments for student learning. To customize learning experiences, applications of recommender systems research in education have resulted in evidence of various recommender system approaches such as content-based filtering, collaborative filtering, and knowledge-based. This research focuses on those applications in higher education when given a non-MOOC classroom setting and examines the theoretical basis for the approaches. Learning and information systems theories are considered in this systematic review of the literature published from 2017 to early 2022. Findings indicate varying adaptive learning design recommender approaches and the potential to build the theoretical base of both learning and information systems theories. [ABSTRACT FROM AUTHOR]

Copyright of Földrajzi Közlemények is the property of Magyar Foldrajzi Tarsasag 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.)

Measurement of cardiac electrical activity also known as electrocardiogram (ECG) is prone to a variety of noise which deteriorates the accuracy of extracted clinical parameters. Raw ECG signals are non-liner and non-stationary in nature which limits the use of traditional filters for denoising. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an advanced version of Ensemble Empirical Mode Decomposition (EEMD), has shown usefulness in denoising ECG. However, it suffers from initial noisy intrinsic mode functions (IMFs) which are difficult to identify and denoise. Studies have shown various methods from information theory perspective to identify such noise corrupted IMFs. However, none of the study could provide a consummate solution. This article proposes the use of sample entropy to identify the noisy IMFs and utilize the non-local mean smoothening technique to reduce the effect of noise. Proper identification of noisy IMFs followed by adaptive smoothing using the NLM algorithm improves denoising performance of the CEEMDAN algorithm. The proposed method is verified considering real ECG data from MIT-BIH. The comparative analysis from the results suggests the use of the proposed method for improved performance of denoising of ECG signals under the influence of higher noise. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

In this paper, we apply the minimum information theory to the uncapacitated p-median facility location problem to determine the most probable allocation solution. Indeed, we investigate the bi-level p-median model introduced by [TRANSPORT RES B-METH. 123 (2019) 1–20] in the case that facilities have unlimited capacities. At the upper level, minimizing the cost of establishing facilities and allocating demands is considered, while the most probable allocation solution in the customers' point of view is determined through the lower level. By adding the Karush-Kuhn-Tucker optimality conditions of the lower-level problem to the upper-level constraints, the bi-level model is reduced to a one-level linear mixed integer problem. Some numerical examples are provided to verify the added value of the proposed model and its solution method. [ABSTRACT FROM AUTHOR]

The Bayesian Network is considered as one of the most efficient theoretical models in the uncertain reasoning and knowledge representation fields. The Bayesian Network construction consists of two main phases: Structure learning and Parameter Learning. Actually, determining the correct structure represents a major challenge that had been widely studied and still needs to be resolved. In this paper, we introduce a novel algorithm combining an improvement of score based algorithm with an effective nodes ordering method. Based on the parents searching principle of K2 algorithm, we proposed an extension of the used search-space. Besides, using graph acyclic propriety and information theory, we introduce node ordering method. Experiment simulations on the well-known networks prove that the proposed algorithm can efficiently and accurately extract the nearest topology of the original. The real application of our method exhibits its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in Gafsa area (southwestern Tunisia). [ABSTRACT FROM AUTHOR]

This paper revisits the empirical relationship between interest rates and money demand from a novel perspective, i.e., information theory. Particularly, we utilize the model-free transfer entropy to quantify the flow of information from interest rates to monetary aggregates and present three findings. First, we document a hump-shaped informational link between interest rate and M1 monetary aggregate, with a rounded high point in the late 1980s and early 1990s. Second, we identify three structural shifts in the information transmission from interest rate to M1. The first two breakpoints occurred in the early 1980s and mid-1990s, likely as a response to the removal of Regulation Q and the introduction of sweep technology, respectively. The third shift took place during the relatively less-explored period of the early 2000s. Finally, we unravel a previously unreported pivotal distinction between the first two changepoints despite the apparent similarity in inducing money demand instability: the 1980s financial deregulations facilitate the transmission of information, whereas the 1990s financial reforms acted as an impediment to the information flow. Our results are robust to alternative entropy measures. [ABSTRACT FROM AUTHOR]

Missing person cases typically require a genetic kinship test to determine the relationship between an unidentified individual and the relatives of the missing person. When not enough genetic evidence has been collected the lack of statistical power of these tests might lead to unreliable results. This is particularly true when just a few distant relatives are available for genotyping. In this contribution, we considered a Bayesian network approach for kinship testing and proposed several information theoretic metrics in order to quantitatively evaluate the information content of pedigrees. We show how these statistics are related to the widely used likelihood ratio values and could be employed to efficiently prioritize family members in order to optimize the statistical power in missing person problems. Our methodology seamlessly integrates with Bayesian modeling approaches, like the GENis platform that we have recently developed for high-throughput missing person identification tasks. Furthermore, our approach can also be easily incorporated into Elston-Stewart forensic frameworks. To facilitate the application of our methodology, we have developed the forensIT package, freely available on CRAN repository, which implements all the methodologies described in our manuscript. • We introduced IT metrics to quantify relationship informativeness in familial pedigrees. • IT metrics allow selection of key DNA contributors in missing person cases. • Our method uses advanced algorithms for fast, large-scale kinship analysis. • IT metrics aid to deal with null exclusion-power contributors, when usual methods fail. • Core functionality implemented in open source forensIT R package, available on CRAN. [ABSTRACT FROM AUTHOR]

In recent years, UNet and its latest extensions, such as UNeXt and TransUNet, have become the leading medical image segmentation methods. However, medical image usually contain a high amount of noise and a small number of samples, making it difficult for the models to learn features accurately, and potentially leading to overfitting. To address these challenges, we propose a DIM-UNet model based on Diffusion models , Information bottleneck theory , and MLP. DIM-UNet introduces two key modules: the Diffusion-MLP module and the IB-MLP module. The Diffusion-MLP module can de-noise the feature map while capturing global features by combining the ideas of diffusion models. The IB-MLP module is located at the bottom of the model, using information bottleneck theory to compress the learned features. This module can retain the most relevant features to the target task and discard irrelevant features to improve the model's generalization ability. We compare DIM-UNet with state-of-the-art models on three public datasets and achieve competitive segmentation results. • We propose a Diffusion-MLP module that can denoise feature maps while capturing global features. • We propose an IB-MLP module that compresses irrelevant and retains only relevant features. • We studied the impact of various parameters in Diffusion-MLP and IB-MLP on model performance. [ABSTRACT FROM AUTHOR]

The information bottleneck principle was recently proposed as a theory meant to explain some of the training dynamics of deep neural architectures. Via information plane analysis, patterns start to emerge in this framework, where two phases can be distinguished: fitting and compression. We take a step further and study the behaviour of the spatial entropy characterizing the layers of convolutional neural networks (CNNs), in relation to the information bottleneck theory. We observe pattern formations which resemble the information bottleneck fitting and compression phases. From the perspective of semiotics, also known as the study of signs and sign-using behavior, the saliency maps of CNN's layers exhibit aggregations: signs are aggregated into supersigns and this process is called semiotic superization. Superization can be characterized by a decrease of entropy and interpreted as information concentration. We discuss the information bottleneck principle from the perspective of semiotic superization and discover very interesting analogies related to the informational adaptation of the model. In a practical application, we introduce a modification of the CNN training process: we progressively freeze the layers with small entropy variation of their saliency map representation. Such layers can be stopped earlier from training without a significant impact on the performance (the accuracy) of the network, connecting the entropy evolution through time with the training dynamics of a network. [ABSTRACT FROM AUTHOR]

This paper is a semantic review research as a preliminary study to analyze the use of Absorptive Capacity (AC) theory in Information Systems (IS) research. This study aims to contribute to filling the gaps in the AC literature by analyzing the evolution of AC theory and models. A systematic literature review was carried out using an analysis of articles from ten leading journals in the IS field based on Scientific Journal Rankings. The selection of SI journals in the 1st quartile status aims to cover research related to research topics with good quality. The results show that the implementation of AC in IS is different from the initial concept of AC that was formed. The study identified the main driving factor for AC that has not been fully revealed in AC research in IS, namely Organizational Behavior. This study opens up future research opportunities to explore each dimension of AC development. [ABSTRACT FROM AUTHOR]

Copyright of Caderno Brasileiro de Ensino de Física is the property of Caderno Brasileiro de Ensino de Fisica 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.)

Many methods of measuring creativity have been studied - mainly in psychology. In recent years, there have been attempts to incorporate such creativity into mathematical modelling, a topic handled in mathematics education. Accordingly, some studies have been trying to assess creativity in it. However, there have been no clear criteria or formulas that can be used for any problem, since a rubric has been created for each problem and evaluated individually. In the present study, to measure creativity in the Fermi problem, a type of mathematical modelling, a formula that applies information theory used in information science is proposed and examined using Structural Equation Modeling (SEM). A survey of Japanese junior high school students (n = 364) was conducted and analyzed, and the results show that the model using the formula proposed in the present study is a good fit. In addition, a moderate positive correlation (r = .41, p < .01) is found between creativity in the Fermi problem and creativity in psychology measured by the Test for Creative Thinking-Drawing Production (TCT-DP). [ABSTRACT FROM AUTHOR]

The article proposes a consideration of the dual-process theory of higher cognition as a theory of the classification of acts of information processing. One of the reasons why the dual-process approach has been criticized is the fact that the information processing process can sometimes have characteristics that undermine a clear-cut attribution to one of tire two traditionally defined opposite types. To avoid this criticism, it is proposed that die object of classification should not be the processes of information processing, but separate acts of combining two units of information. Unlike a process, a particular act of information processing at a particular moment in time cannot simultaneously have opposite characteristics, nor can it simultaneously have and not have some characteristic. In order to show die qualitative difference between various information processing acts as falling individually into either Type 1 or Type 2 processing, it is proposed to classify them by a feature that is present in one type and absent in another. It is suggested to take conscious control as such a feature. As a result, in the information processing acts corresponding to Type 2 category, units of information are combined in a consciously controlled way, whereas in the acts to be considered as Type 1, those units either al- ready are combined or combine autonomously due to the existence of indirect associative connections. [ABSTRACT FROM AUTHOR]

The use of mobile cellular communication is growing all over the world, with the increasing popularity of mobile devices. Fifth Generation (5G) has recently emerged to satisfy the increasing demand for high data bit rates, to maintain network connectivity every time and everywhere, opening the possibility of connecting all the devices in the network. A key piece of this shift is the deployment of Small Cells over the Macrocells layer which introduces a new type of network called Heterogeneous Networks (HetNets), which involve more Handovers. In addition, the user has to select instantly and at inferior energy cost, the suitable base station in the midst of thousands. In this paper, to decide of the best Handover with reduced complexity of computation, we apply a Handover technique based on Distribution Analysis Detector combined with Compressive Sampling Techniques, after that, we propose to estimate the performance of networks in terms of Channel Capacity and Outage Probability based on Rice and Nakagami distribution models. We obtain Channel Capacity and Outage Probability by using concepts from Information Theory. Through numerical evaluations, we show that the Nakagami distribution model is more efficient than the Rice distribution model. [ABSTRACT FROM AUTHOR]

The aim of the research is to identify the effectiveness of the educational pillars strategy based on Vygotsky's theory in mathematical achievement and information processing of first-grade intermediate students. In pursuit of the research objectives, the experimental method was used, and the quasi-experimental design was used for two equivalent groups, one control group taught traditionally and the other experimental taught according to the educational pillars strategy. The research sample consisted of (66) female students from the first intermediate grade, who were intentionally chosen after ensuring their equivalence, taking into account several factors, most notably chronological age and their level of mathematics, and they were distributed equally into two groups, one experimental and the other control. The research tools were represented in the teacher's handbook for the application of the educational pillars strategy, the achievement test in mathematics, and the test of information processing skills. The researchers applied the experiment in the first semester of the (2019/2020) academic year. One of the researchers taught the experimental group by applying the educational pillars strategy, while the control group studied according to the usual method. The mathematical achievement test and the data processing skill test were applied to the experimental and control research groups, and then the data necessary for statistical analysis and access to results were obtained. The results showed that there were statistically significant differences between the mean scores of the two groups and control groups in the mathematical achievement test. There were also statistically significant differences between the mean scores of the two groups on the test of the skill of information processing, and the size of the effect was calculated by describing a function of the effectiveness of the strategy on the two independent variables (mathematics achievement, information processing), as it became clear that the educational pillars strategy based on Vygotsky's theory has great effectiveness. On mathematics achievement and on developing students' information processing skills. [ABSTRACT FROM AUTHOR]

The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice. [ABSTRACT FROM AUTHOR]

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Word sense disambiguation (WSD), identifying the specific sense of the target word given its context, is a fundamental task in natural language processing. Recently, researchers have shown promising results using long short term memory (LSTM), which is able to better capture sequential and syntactic features of text. However, this method neglects the dependencies among instances, such as their context semantic similarities. To solve this problem, we proposed a novel WSD model by introducing a cache-like memory module to capture the semantic dependencies among instances for WSD. Extensive evaluations on standard datasets demonstrate the superiority of the proposed model over various baselines. [ABSTRACT FROM AUTHOR]

When studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) when every online review is assumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales. [ABSTRACT FROM AUTHOR]

• The new fusion of mutual information with OTSU threshold (MI_OTSU) method used for hyperspectral band selection. • The performance of the classification accuracy improved high. • 92.16% of average accuracy achieved with less number on 36 bands. Hyperspectral data are a curse with huge dimensionality, high redundancy of spectral information, and are noisy in nature. Hundreds of narrow adjacent bands are present in HS (Hyperspectral) data with high spectral information and it always leads to a computational complexity in space and time. The information theoretic methods are used for hyperspectral band selection to avoid computational complexity. In order to address this issue, the new fusion of Mutual Information (MI) with Otsu (MI_Otsu) threshold method is proposed for hyperspectral band selection by employing three different entropy measures such as joint, conditional, and relative. The proposed approach identifies the probabilities, entropy, and mutual information between two hyperspectral bands. The optimal threshold is obtained using Otsu multi-threshold technique and highly informative bands will be selected. In addition, the SVM (Support Vector Machine) classification technique is adapted for further classification of selected bands to analyze the performance of the proposed algorithm. The experimental analysis is carried out using the real-time dataset from the test site 'Indian Pines' in Northwestern Indiana recorded by AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor that demonstrates the effectiveness of this proposed approach. It is proved that the proposed work shows the competitive performance even with less selected bands and the relative MI_Otsu method shows a higher accuracy of 92.16% with the comparison of joint and conditional MI_Otsu. [ABSTRACT FROM AUTHOR]