Amount Of Information Research Articles

라포형성 수사기법을 통한 수사 면담과 메타기억이 증언의 정보량과 정확도에 미치는 영향: 대인존재감의 조절효과를 중심으로

This study investigated the moderating effect of mattering on the relationship between rapport formation and the quantity of information in their testimonies during investigative interviews, and whether testimony accuracy correlates with meta-memory. 95 participants, whose levels of mattering and meta-memory were pre-measured, watched a mock crime video and then provided testimony following a 10-minute rapport formation process A rapport questionnaire was used to measure participants' perceived degree of rapport, and the recorded testimonies were evaluated for information quantity and accuracy. Results showed that the moderating effect of mattering on the degree of rapport formation and the quantity of information in the testimonies was not significant (p=.060), but the Johnson-Neyman technique identified a reinforcing effect of mattering, with better rapport formation leading to sharply higher utterance volumes in the interval below the mean (67.347; SD=9.608). Furthermore, the total meta-memory score was not significantly related to testimony accuracy, the sub-factor 'memory strategies' was positively related to accuracy. This suggests that witnesses with low levels of mattering benefit most from a good rapport with the investigator, and that assessing how effectively witnesses utilise strategic memory methods can provide important clues for assessing the reliability of their testimony.

Holographic entropy inequalities and multipartite entanglement

We study holographic entropy inequalities and their structural properties by making use of a judicious grouping of terms into certain multipartite information quantities. This allows us to recast cumbersome entropic expressions into much simpler ones which share interestingly rigid structures. By performing a systematic search over some of these structures, we are able to discover more than 1800 novel entropy inequalities for six parties, thereby demonstrating that these recastings provide a fruitful generating technique for uncovering new holographic entropy inequalities. In attempting to interpret the corresponding sign-definite quantities as correlation measures, we also obtain a no-go result: the superbalance property of holographic entropy inequalities turns out to preclude them from being monotonic under partial tracing. In the process, we also comment on the geometrical significance of multipartite information quantities and present various structural relations amongst them.

The Impact of Information Quantity on Cognitive Load and User Perceptions in Medical Website Design

The Impact of Information Quantity on Cognitive Load and User Perceptions in Medical Website Design

The Influence of Electronic Word of Mouth Through Tiktok Social Media Platform on Consumer Purchase Intention on Beauty Products Scarlett Whitening In Bandung City

This study examines the impact of Electronic Word of Mouth (e-WOM) on TikTok regarding Scarlett Whitening products and their influence on Purchase Intention. The objectives are to evaluate the effects of Information Quality, Information Quantity, Information Credibility, Information Usefulness, and Review Valence on Purchase Intention, mediated by Information Usefulness and Information Adoption. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and SmartPLS 3.0, data from 210 Generation Z and millennial respondents in Bandung were analyzed. The findings reveal that Information Quality, Quantity, Credibility, and Review Valence significantly enhance Information Usefulness, which in turn positively affects Information Adoption and Purchase Intention. High-quality, credible, and well-quantified information along with constructive reviews significantly improve user perception of usefulness, leading to higher information adoption and ultimately boosting purchase intention. These insights are crucial for marketers to design effective information strategies on TikTok to enhance consumer decision-making and increase product sales. Future research should incorporate additional variables such as trust and brand image to further understand their influence on purchase intention.

Digital: accelerating the pathway.

The Spherical Tokamak for Energy Production (STEP) programme is an ambitious but challenging endeavour to design and deliver a prototype fusion power plant. It is a rapid, fast-moving programme, designing a first of a kind device in a Volatile, Uncertain, Complex and Ambiguous (VUCA) environment, and digital tools play a pivotal role in managing and navigating this space. Digital helps manage the complexity and sheer volume of information. Advanced modelling and simulation techniques provide a platform for designers to explore various scenarios and iteratively refine designs, providing insights into the intricate interplay of requirements, constraints and design factors across physics, technology and engineering domains and aiding informed decision-making amidst uncertainties. It also provides a means of building confidence in the new scientific, technological and engineering solutions, given that a full-scale-integrated precursor test is not feasible, almost by definition. The digital strategy for STEP is built around a vision of a digital twin of the whole plant. This will evolve from the current digital shadow formed by system architecting codes and complex workflows and will be underpinned by developing capabilities in plasma, materials and engineering simulation, data management, advanced control, industrial cybersecurity, regulation, digital technologies and related digital disciplines. These capabilities will help address the key challenges of managing the complexity and quantity of information, improving the reliability and robustness of the current digital shadow and developing an understanding of its validity and performance.This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Somc methodological aspects of researches on informatization

In the article, an attempt is made to attract attention to the variety of methodological principles in the analysis of some factors of informatization of society. Most of the questions of informatization—such as the information crisis, information industry, information resources, etc.—are bound to technological deterministic explanations, and the aspects of the new electronic technology are amplified, with all problems being reduced to computerization. Using economic methodologies, the question of information value is considered. Economic research on informatization is correlated with attempts to analyze the quantity of information. The conclusion is drawn that most of the principles of analyzing informatization problems are conditioned by already known scientific paradigms.

Research on the Purchase Intention of Social Commerce Consumers in Video Streams: Dual Pathways of Affection and Rationality.

Social commerce blurs the boundary between online social interaction and online shopping. The emergence of video streams introduces novel marketing modalities to social commerce. However, there is a paucity of comprehensive studies investigating the impact of emerging marketing techniques such as short videos and live streaming on consumer purchase intention. This study employs Bourdieu's conceptual framework to construct a Field Theory-based model, investigating the impact of atmospheric and capital characteristics of social commerce platforms on consumer purchase intention through affective and rational pathways, respectively. A survey involving 515 Chinese social commerce consumers demonstrates that atmospheric characteristics (emotion and social presence) and capital characteristics (information quality and quantity) in video streams enhance similarity and power. Both similarity and power are associated with an increase in consumer purchase intention. This study validates the dual-path influence of social commerce characteristics and discusses theoretical and managerial implications.

Decoding Tiktok E-Wom: Unraveling Its Impact On Consumer Purchase Intentions For Saff & Co Products

This study examines how eWOM on TikTok affects consumer purchase intentions for Saff & Co products. This study examines various eWOM factors using the information adoption model (IAM) as a framework. The study used a quantitative approach with 275 Indonesian TikTok users interested in Saff & Co; the study found that respondents rated the eWOM variables positively. Specifically, Information Quality, Information Quantity, Information Credibility, and Information Adoption significantly influence purchase intentions. The insights provide a foundation for Saff & Co. and the perfumery industry, ensuring that marketing strategies are aligned with consumer preferences and improving overall satisfaction.

IGU-Aug: Information-guided unsupervised augmentation and pixel-wise contrastive learning for medical image analysis.

Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel-wise contrastive learning mainly helps with pixel-wise dense prediction tasks. The counter-part to an instance in instance-level CL is a pixel, along with its neighboring context, in pixel-wise CL. Aiming to build better feature representation, there is a vast literature about designing instance augmentation strategies for instance-level CL; but there is little similar work on pixel augmentation for pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge this gap. We first classify a pixel into three categories, namely low-, medium-, and high-informative, based on the information quantity the pixel contains. We then adaptively design separate augmentation strategies for each category in terms of augmentation intensity and sampling ratio. Extensive experiments validate that our information-guided pixel augmentation strategy succeeds in encoding more discriminative representations and surpassing other competitive approaches in unsupervised local feature matching. Furthermore, our pretrained model improves the performance of both one-shot and fully supervised models. To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning. Code is available at https: //github.com/Curli-quan/IGU-Aug.

Energetic Information from Information-Theoretic Approach in Density Functional Theory as Quantitative Measures of Physicochemical Properties.

The Hohenberg-Kohn theorem of density functional theory (DFT) stipulates that energy is a universal functional of electron density in the ground state, so energy can be thought of having encoded essential information for the density. Based on this, we recently proposed to quantify energetic information within the framework of information-theoretic approach (ITA) of DFT (J. Chem. Phys. 2022, 157, 101103). In this study, we systematically apply energetic information to a variety of chemical phenomena to validate the use of energetic information as quantitative measures of physicochemical properties. To that end, we employed six ITA quantities such as Shannon entropy and Fisher information for five energetic densities, yielding twenty-six viable energetic information quantities. Then, they are applied to correlate with physicochemical properties of molecular systems, including chemical bonding, conformational stability, intermolecular interactions, acidity, aromaticity, cooperativity, electrophilicity, nucleophilicity, and reactivity. Our results show that different quantities of energetic information often behave differently for different properties but a few of them, such as Shannon entropy of the total kinetic energy density and information gain of the Pauli energy density, stand out and strongly correlate with several properties across different categories of molecular systems. These results suggest that they can be employed as quantitative measures of physicochemical properties. This work not only enriches the body of our knowledge about the relationship between energy and information, but also provides scores of newly introduced explicit density functionals to quantify physicochemical properties, which can serve as robust features for building machine learning models in future studies.

Sales Trend Analysis With Machine Learning Linear Regression Algorithm Method

The development of online business in Indonesia is now very rapid, with the process being done by ordering goods through resellers or distributors using one of the social media. Item purchases are made based on product information, prices, discounts and inventory quantities using a decision model. In the sales process, Toko Serbu Aek Batu usually releases several different items to be offered to the market at different prices, but not all items are in high demand. Multiple linear regression is an analysis that describes the relationship between dependent variables and factors that affect more than one independent variable. The purpose of this study is to analyze sales trends using a linear regression method using rapidminer. The results of this study are prediction calculations using manual calculations with rapidminer the same results, predicting the price desired by buyers using a linear regression algorithm with the original price is not much different and rapidminer is very accurate to be used in predicting sales trends at the price desired by customers, so that sellers can pay more attention to things that are very influential in the sales process.

Evaluation of Landslide Susceptibility in Tekes County, Yili Prefecture Based on the Information Quantity Method

Evaluation of Landslide Susceptibility in Tekes County, Yili Prefecture Based on the Information Quantity Method

Marketing of higher educational services on social media platforms: Analysing its impact on faculty–student relationship quality and institution's brand performance

AbstractHigher education institutions (HEIs) have focused on branding to facilitate meaningful and productive conversations about their brand values with potential students, given the competitive global expansion environment in which HEIs operate. This study empirically explores the association between brand performance and relationship quality of HEM. The quantitative findings were evaluated by students from Indian HEIs (N = 365). For the evaluation of the data, structural equation modelling was implemented. A Regression Analysis was used to examine the Social Exchange Theory. The results of this research expose that information quantity, content sharing, and surveillance had a significant influence on relationship quality. Also, social communication, content sharing, information quantity, collaborative learning, and surveillance had a significant impact on brand performance. Future investigation should consider additional mediating aspects regarding social media users' opinions.

Analysis of the Influence of Information Quality, Information Quantity, Information Credibility and Satisfaction mediated by Information Usefulness and Information Adoption and its effect on Purchase Intention on TikTok social media on fashion products in Jakarta

The development of technology affects people's life patterns in the digital and modern direction which has an impact on the development of the internet. The number of internet users in Indonesia continues to increase from year to year, which affects the development of the number of social media users in Indonesia. Traditional WOM promotions have been replaced by promotions through eWOM through social media. TikTok has become one of the trending social media platforms with fashion product content in its application as a promotional medium. This study aims to find out how the influence of eWOM consisting of information quality, information quantity, information credibility, information usefulness, information adoption, and satisfaction variables on TikTok social media on the purchase intention of consumers of fashion products in Jakarta. This research was conducted using a quantitative method. The number of samples in this study was 178 respondents. The sampling technique in this study is nonprobability with the judgemental sampling method. The data collection technique used a questionnaire with a likert scale of 1-5. Data analysis using the Structural Equation Model (SEM) method using the SmartPLS 4.0 data processing application.

Evaluation and mitigation of the limitations of large language models in clinical decision-making

Clinical decision-making is one of the most impactful parts of a physician’s responsibilities and stands to benefit greatly from artificial intelligence solutions and large language models (LLMs) in particular. However, while LLMs have achieved excellent performance on medical licensing exams, these tests fail to assess many skills necessary for deployment in a realistic clinical decision-making environment, including gathering information, adhering to guidelines, and integrating into clinical workflows. Here we have created a curated dataset based on the Medical Information Mart for Intensive Care database spanning 2,400 real patient cases and four common abdominal pathologies as well as a framework to simulate a realistic clinical setting. We show that current state-of-the-art LLMs do not accurately diagnose patients across all pathologies (performing significantly worse than physicians), follow neither diagnostic nor treatment guidelines, and cannot interpret laboratory results, thus posing a serious risk to the health of patients. Furthermore, we move beyond diagnostic accuracy and demonstrate that they cannot be easily integrated into existing workflows because they often fail to follow instructions and are sensitive to both the quantity and order of information. Overall, our analysis reveals that LLMs are currently not ready for autonomous clinical decision-making while providing a dataset and framework to guide future studies.

Preferred knowledge formats in mobile learning in Namibian public universities: a students’ perspective

Purpose The aim of this study is to identify the various knowledge formats suitable for mobile learning in Namibian public universities. Design/methodology/approach Taking a qualitative design, data were collected through focus group interviews of students in two public universities in Namibia. Findings Results suggest that students consume knowledge in various formats, with videos the most preferred format. Furthermore, the results demonstrate that students’ preferences are shaped by various factors such as costs and quantity of information. Research limitations/implications The study was limited to two public Namibian universities. This limited scope, as well as its qualitative nature, means that findings cannot be arbitrarily generalized to other contexts but cardinal as a source of insight in contextually similar environments. Practical implications The findings reveal that teachers should be cognizant of the preferred knowledge formats for mobile learning to achieve its full potential in terms of effective teaching and learning. Therefore, teachers should package learning information for mobile learning in line with learners’ characteristics and preferences. Originality/value There is scant information on knowledge formats suitable for mobile learning. Consequently, this study contributes to the nascent body of knowledge on this important issue. Furthermore, although mobile learning uptake is on the ascendancy in Africa, to the best of our knowledge, no study of this nature has been undertaken in this context thereby leaving knowledge gaps for designers, researchers and practitioners in mobile learning.

Toward a common categorization for valued components: using a review of valued components and indicators in the lower James Bay Region of Ontario and Quebec, Canada, to support cumulative impact science in Canada

ABSTRACT Cumulative impacts and regional assessments (RA) require the integration and analysis of large quantities of interdisciplinary scientific information. Yet, the information available is not often readily accessible nor standardized across impact assessments. We proposed here a categorization of valued components that have been derived from projects in the James Bay Lowlands and that is aligned with the IAAC’s Tailored Impact Statement Guidelines (TISG) template, a key document to support project and regional impact assessment deliverables. We compiled valued components and indicators from previous and ongoing environmental impact assessments in the James Bay Lowlands. We identified trends associated with valued components and indicators and combined these trends with the TISG. We then derived a categorization of valued components nested under seven systems. From the reviewed work, we identified 197 valued components (23% valued components were common for two or more projects) and 313 indicators. Our categorization is composed of seven systems and 34 potential valued components allowing for easy connections between future project-level impact assessments and RAs. Our categorization is a valuable communication tool across all stakeholders and rightsholders involved in impact assessments, including communities, Indigenous leaders, IA practitioners, industry, and government.

Industry perspective on regulatory authority (RA) quality reviews of biosimilar applications – an evaluation of RA guidance and expectations for chemical, manufacturing, and controls information through in-depth query analysis

ABSTRACT Purpose Evaluate the type and quantity of quality information (i.e. Chemistry, Manufacturing, and Control) requested by the US FDA and EMA in queries pertaining to biosimilar applications. Methods Numbers/types of queries received following regulatory submissions (FDA/EMA, n = 7/n = 5) for seven biosimilars (PF-filgrastim [Nivestym], PF-rituximab [Ruxience®], PF-trastuzumab [Trazimera®], PF-bevacizumab [Zirabev®], PF-pegfilgrastim [Nyvepria®], PF-adalimumab [Abrilada™/Amsparity®], PF-infliximab [Ixifi]) from a single product portfolio were analyzed considering published regulatory authority (RA) guidance and in relation to sections/subsections of Module 3: Quality from the Common Technical Document regulatory dossier and topics based on keyword assignment. Results Queries were most frequently assigned (FDA/EMA %, range) to Drug Substance Manufacture (subsection 3.2.S.2; 21–35%/13–50%), Control of Drug Substance (3.2.S.4; 3–11%/5–17%), Drug Product Pharmaceutical Development (3.2.P.2; 1–12%/1–15%) and Manufacture (3.2.P.3; 17–41%/2–13%), and Analytical Similarity (3.2.R; 4–21%/4–20%). The proportion of Drug Substance and Drug Product queries was significantly different between RAs (n1 = 952, n2 = 468, p-value <0.001; two-sample proportion z-test). Topic assignments included: Control (12–27%/12–28%), Manufacturing (56–72%/34–66%), Stability (1–12%/2–24%), Biosimilarity (5–16%/5–25%), and Container Closure (0–3%/0–9%). Conclusion The focus of both RAs on topics related to manufacturing and controls is valuable in understanding expectations for scientific and technical content related to gaining biosimilar approval.

Assessment of electronic health literacy and its association with self-management among gout patients: A cross-sectional study.

This study aimed to assess electronic health (eHealth) literacy and self-management ability among gout patients in China and analyze their correlation. A cross-sectional study was conducted on 168 male gout patients (mean age: 44.5±13.8 years, range, 22 to 76 years) using a gout patient self-management assessment scale and the Chinese version of eHealth Literacy Scale between August 2020 and February 2021. The correlation between eHealth literacy and gout self-management was examined. Multivariate regression analysis was conducted to identify the predictors of gout patients' self-management ability. The scores of eHealth literacy and self-management were 28.56±1.72 and 3.59±0.78, respectively, which were both in the medium level, with the lowest score in the lifestyle management dimension. Pearson's correlation analysis showed that eHealth literacy was significantly and positively associated with the total score of gout self-management and its four dimensions. Literacy in eHealth (β=0.399, p<0.001, 95% confidence interval [CI]: 0.023-0.069), living with family members (β=0.336, p=0.001, 95% CI: 0.313-1.234), living in an urban area (β=0.312, p=0.005, 95% CI: 0.191- 1.022), and having college or a higher level of education (β=0.268, p=0.016, 95% CI: 0.032-0.302) were the significant predictors of gout patients' self-management ability, explained 30.6% of the total variance. Literacy in eHealth was the most significant predictor of gout self-management ability. Gout patients had the lowest score in the perceived ability of searching and applying online health-related information, indicating that both the quantity and quality of gout-specific information needs to be enlarged in electronic resources. Healthcare providers could design and implement eHealth literacy interventions to strengthen patients' gout self-management ability.

Using spatial aggregation of soil multifunctionality maps to support uncertainty‐aware planning decisions

AbstractTo ensure soil preservation, it is essential to incorporate the soil's ability to provide ecosystem services into the spatial planning process. For well‐informed planning decisions, stakeholders need spatially explicit information on the state of the soils and the functions they fulfil, with sufficient spatial resolution and quantified uncertainty. It has been shown that Digital Soil Mapping (DSM) products can provide such information. However, in some cases, fine spatial resolution coupled with high levels of uncertainty may lead stakeholders to overlook the inherent uncertainties in the information. Spatial aggregation of DSM products opens up a promising avenue for obtaining maps that are more tailored to the users' scales of decision making while facilitating uncertainty communication. In this perspective, we propose a new spatial aggregation approach relying on spatially constrained agglomerative clustering (AC). The spatial aggregation approach is applied to a 25‐m‐resolution soil potential multifunctionality index (SPMI) map developed for the coastal plain of the Occitanie Region. This DSM product was increasingly aggregated to obtain SPMI maps of different resolutions displaying two distinct areal metrics: proportions of area above a given threshold of SPMI, and mean SPMI. Each map was evaluated through a set of indicators selected for their potential impact on user decision making: mean spatial resolution, overall predicted uncertainty, quantity of information and mean within‐unit variability. The maps were compared with respect to these indicators to other maps obtained with alternative aggregation methods employed in DSM literature (maps aggregated according to some administrative units and QuadMaps). We show that all the tested aggregation methods produced a substantial decrease of the map uncertainty with moderate loss of spatial resolution. However, only AC preserved the fine spatial pattern of the initial DSM product while enabling fine tuning of the uncertainty displayed to end‐users. We show that AC can simplify the identification of extensive regions characterized by low uncertainty without losing information regarding soil multifunctionality, thereby facilitating and enhancing the efficiency of planning decisions.