Multiple Sources Of Information Research Articles

Bodily cues of sex and emotion can interact symmetrically: Evidence from simple categorization and the garner paradigm.

Although much research has investigated how multiple sources of social information derived from faces are processed and integrated, few studies have extended this investigation to bodies. The current study addressed this gap by investigating the nature of the interaction between bodily cues of sex and emotion. Using the Garner paradigm, participants recruited from a university student participant pool categorized the sex or the emotional expression (happy and angry in Experiment 1 [n = 194], angry and sad in Experiment 2 [n = 129]) of bodies across two block types. In orthogonal blocks, participants categorized bodies along one dimension while the second dimension was varied (e.g., categorizing the sex of happy and angry bodies), whereas in control blocks the second dimension was held constant (e.g., categorizing the sex of only happy bodies). Responses were analyzed in two ways. Comparing response times across blocks revealed Garner interference (overall faster categorization in the control than in the orthogonal block) of sex on emotion perception and emotion on sex perception in Experiment 1 but not Experiment 2. Comparing condition level responses in orthogonal blocks indicated that sex cues moderated emotion categorization and emotion cues moderated sex categorization in both experiments. A symmetrical interaction between bodily sex and emotion cues can be observed in simple categorization as well as in the Garner paradigm, though the presence of Garner interference depended on the valence of the expressions used. Given similar results observed in face processing, finding interactivity in body perception is likely to generalize beyond the study sample. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

C2Store: C2 Server Profiles at Your Fingertips

How can we build a definitive capability for tracking C2 servers? Having a large-scale continuously updating capability would be essential for understanding the spatiotemporal behaviors of C2 servers and, ultimately, for helping contain botnet activities. Unfortunately, existing information from threat intelligence feeds and previous works is often limited to a specific set of botnet families or short-term data collections. Responding to this need, we present C2Store, an initiative to provide the most comprehensive information on C2 servers. Our work makes the following contributions: (a) we develop techniques to collect, verify, and combine C2 server addresses from five types of sources, including uncommon platforms, such as GitHub and Twitter; (b) we create an open-access annotated database of 335,967 C2 servers across 133 malware families, which supports semantically-rich and smart queries; (c) we identify surprising behaviors of C2 servers with respect to their spatiotemporal patterns and behaviors. First, we successfully mine Twitter and GitHub and identify C2 servers with a precision of 97% and 94%, respectively. Furthermore, we find that the threat feeds identify only 24% of the servers in our database, with Twitter and GitHub providing 32%. A surprising observation is the identification of 250 IP addresses, each of which hosts more than 5 C2 servers for different botnet families at the same time. Overall, we envision C2Store as an ongoing effort that will facilitate research by providing timely, historical, and comprehensive C2 server information by critically combining multiple sources of information.

Back analysis of a building collapse under snow and rain loads in a Mediterranean area

Abstract. At the end of February 2018 the Mediterranean area of Montpellier in France was struck by a significant snowfall that turned into an intense rain event caused by an exceptional atmospheric situation. This rain-on-snow event produced pronounced damage to many buildings of different types. In this study, we report a detailed back analysis of the roof collapse of a large building, namely Irstea Cévennes. Attention is paid to the dynamics of the climatic event, on the one hand, and the mechanical response of the metal roof structure to different snow and rain loads, on the other hand. The former aspect relies on multiple sources of information that provide reliable estimates of snow heights in the area before the rain came into play and substantially modified the load on the roof. The latter aspect relies on detailed finite-element simulations of the mechanical behavior of the roof structure in order to assess the pressure due to snow and rain loading, which could theoretically lead to failure. By combining the two approaches, it is possible to reconstruct the most probable scenario for the roof failure before its full collapse. As an example of building behavior and vulnerability to an atypical rain-on-snow event in the Mediterranean area of France, this detailed case study provides useful key points to be considered in the future for a better mitigation of such events in non-mountainous areas.

Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

ObjectivesIn 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal. Study designEcological retrospective study. MethodsWe applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables. ResultsWe showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality. ConclusionsThe present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

Penalized regression with multiple sources of prior effects.

In many high-dimensional prediction or classification tasks, complementary data on the features are available, e.g. prior biological knowledge on (epi)genetic markers. Here we consider tasks with numerical prior information that provide an insight into the importance (weight) and the direction (sign) of the feature effects, e.g. regression coefficients from previous studies. We propose an approach for integrating multiple sources of such prior information into penalized regression. If suitable co-data are available, this improves the predictive performance, as shown by simulation and application. The proposed method is implemented in the R package transreg (https://github.com/lcsb-bds/transreg, https://cran.r-project.org/package=transreg).

A novel information fusion method using improved entropy measure in multi-source incomplete interval-valued datasets

Multi-source data is a comprehensive data type that combines multiple sources of information or datasets. Compared to point-valued data, interval-valued data provides a more accurate representation of the uncertainty and variability associated with objects. In practical situations, data obtained from multiple sources may contain missing values for various reasons. Therefore, it is essential to develop multi-source information fusion technology in order to achieve information fusion or information extraction from multi-source incomplete data. This paper aims to explore the information fusion problem of multi-source incomplete interval-valued datasets. The primary contributions of this study involve utilizing the principle of statistical distribution and KL divergence to establish a metric for measuring the similarity between intervals. Firstly, this approach helps to reduce the problem of disregarding internal information within interval values, which can result in the loss of valuable information. Secondly, we establish an interval fuzzy similarity relation based on the mentioned concept of similarity among interval values. Moreover, we investigate the uncertainty measurement of incomplete interval-valued decision datasets and design an emerging information entropy fusion method. Finally, we comprehensively evaluate the effectiveness of the proposed method. Experimental results indicate that the proposed approach has advantage over the maximum, minimum, mean, and information entropy fusion method based on tolerance relationship. In addition, the distance metric used in this article can improve the fusion classification effect compared to several common interval-valued distance measures.

Towards a practical use of text mining approaches in electrodiagnostic data

Healthcare professionals produce abounding textual data in their daily clinical practice. Text mining can yield valuable insights from unstructured data. Extracting insights from multiple information sources is a major challenge in computational medicine. In this study, our objective was to illustrate how combining text mining techniques with statistical methodologies can yield new insights and contribute to the development of neurological and neuromuscular-related health information. We demonstrate how to utilize and derive knowledge from medical text, identify patient groups with similar diagnostic attributes, and examine differences between groups using demographical data and past medical history (PMH). We conducted a retrospective study for all patients who underwent electrodiagnostic (EDX) evaluation in Israel's Sheba Medical Center between May 2016 and February 2022. The data extracted for each patient included demographic data, test results, and unstructured summary reports. We conducted several analyses, including topic modeling that targeted clinical impressions and topic analysis to reveal age- and sex-related differences. The use of suspected clinical condition text enriched the data and generated additional attributes used to find associations between patients' PMH and the emerging diagnosis topics. We identified 6096 abnormal EMG results, of which 58% (n = 3512) were males. Based on the latent Dirichlet allocation algorithm we identified 25 topics that represent different diagnoses. Sex-related differences emerged in 7 topics, 3 male-associated and 4 female-associated. Brachial plexopathy, myasthenia gravis, and NMJ Disorders showed statistically significant age and sex differences. We extracted keywords related to past medical history (n = 37) and tested them for association with the different topics. Several topics revealed a close association with past medical history, for example, length-dependent symmetric axonal polyneuropathy with diabetes mellitus (DM), length-dependent sensory polyneuropathy with chemotherapy treatments and DM, brachial plexopathy with motor vehicle accidents, myasthenia gravis and NMJ disorders with botulin treatments, and amyotrophic lateral sclerosis with swallowing difficulty. Summarizing visualizations were created to easily grasp the results and facilitate focusing on the main insights. In this study, we demonstrate the efficacy of utilizing advanced computational methods in a corpus of textual data to accelerate clinical research. Additionally, using these methods allows for generating clinical insights, which may aid in the development of a decision-making process in real-life clinical practice.

Decisions, decisions, decisions: an exploration of factors affecting the decision-making of ‘Uni Connect’ students, when choosing higher education study

ABSTRACT This paper investigates influences on the decision-making process, during the consideration of applying to university, for UK further education college students from a widening participation background (Uni Connect). Much previous research has explored the international student recruitment market, however, there is relatively little literature concerning UK home student recruitment, particularly related to widening participation groups. Three semi-structured interviews were conducted with eight, second-year, level 3 vocational students, during their final year of college study. Findings suggest that Uni Connect students seek multiple sources of information and engage with a multitude of touch points, with the context of these interactions playing a significant role in influencing university choice. The exhibition of predominately rational rather than emotive behaviours and the use of multiple touch points, suggests that Uni Connect students take an iterative approach to their decision-making. The decision-making process appears to be individualised, with no equivocal or homogenic pattern emerging.

Expert system based fault diagnosis for railway point machines

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the “fault symptoms”). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets.

Inclusion of ageing error and growth variability using a bootstrap estimation of age composition and conditional age-at-length input sample size for fisheries stock assessment models

Statistical catch-at-age assessment models used for fisheries management integrate multiple sources of information that are statistically weighted in a joint likelihood framework; the relative statistical weighting between these sources of information is an important, yet often subjective aspect of stock assessment. Input sample size (ISS) is a quantity that is used to statistically weight composition data in these types of models. Both design-based bootstrap and model-based estimators have been proposed, however, these methods to determine ISS do not explicitly account for uncertainty from ageing error and growth variability that are inherent to expanded age composition and conditional age-at-length data. In this study, we evaluate the impact of including ageing error and growth variability within bootstrap methods that estimate age composition and conditional age-at-length ISS. We find that for all the stocks evaluated the ISS determined from bootstrap methods decreased as these addition sources of uncertainty were included. The decrease in ISS was species type specific, but generally decreased up to 40% when ageing error was introduced, up to 50% when growth variability was included, and up to 60% when both sources of uncertainty were included. These results indicate that there is more variability within age composition or conditional age-at-length data than would be accounted for with ISS estimates that do not include these sources of uncertainty. The method and results provided here allow for assessment scientists to statistically weight age composition and conditional age-at-length with ISS that takes into account ageing error and growth variability from either fishery-independent or fishery-dependent sources. Including these sources of uncertainty improves bootstrap estimates of ISS to capture all the sources of variability in age composition and conditional age-at-length and will subsequently improve stock assessment model quality.

Informing extinction risk: Summarizing population viability through a meta-analysis of multiple long-term monitoring programs for a declining estuarine fish species

Decisions about whether to designate a species for conservation and protection depend on the ability to summarize their population trajectories and their risk of extinction. Such decisions may rely on quantitative population viability analyses based on a time series of abundance index values that are derived from a monitoring program. In the case of the Longfin Smelt (Spirinchus thaleichthys), a decision to protect a distinct population segment of the species under the U.S. Endangered Species Act was informed by several indices of population abundance. In this paper, we combined individual population viability analyses into a single metric for extinction risk using a meta-analysis framework. Individual monitoring surveys for this species generally agreed that the trajectory of abundance was downward, but variation introduced uncertainty. Combining data from several surveys produced a better summary of the population growth rate. We also used the population growth rates in a simulation to estimate the probability that the abundance of Longfin Smelt dropped too low to recover. We found that this probability of quasi-extinction was substantial, exceeding 20 % over two decades. This study demonstrates a practical way that having multiple sources of information creates better information about the trajectory of a population. Individually, the surveys contribute information about specific life stages or ages to our understanding of the population. Combined into one metric and an associated graphical summary, this analysis succinctly communicates risk and creates a benchmark for evaluating future management decisions.

Research on multi-granularity sequential three-way decisions based on the fuzzy T-equivalence relation

The fuzzy decision-theoretic rough set (FDTRS) is a data processing method based on the fuzzy theory designed to handle decision problems with uncertainty and fuzziness. It simplifies the complexity of decision-making and provides decision strategies by integrating multiple sources of information. This approach makes the decision process more scientific and rational. The mainstream FDTRS model breaks the over restrictive limits of classical decision-theoretic rough sets, computes the fuzzy similarity relation between two objects utilizing the Gaussian kernel function, which is another major breakthrough and innovation in the field of decision-theoretic rough sets. However, this model suffers from the problems of insufficient decision information, single perspective and high decision-making cost when dealing with dynamic, complex and uncertain problems. Emerging in recent years, sequential three-way decisions not only provides a flexible mechanism to implement the idea of progressive computation, but also deals with dynamic, complex and uncertain problems. To this end, this paper proposes a sequential three-way decision model based on the fuzzy T-equivalence relation (FTE-based sequential three-way decision model) from the perspective of sequential three-way decisions, and gives relevant definitions and examples to effectively solve the problem of insufficient information. Then in order to solve the single-view problem, we extend the sequential three-way decisions to the case of multi-granularity, and propose FTE-based multi-granularity sequential three-way decisions in four aggregation strategies with the corresponding properties and algorithms. In addition, the rationality of models are tentatively analyzed through specific examples. Finally, six sets of UCI standard datasets are used to validate the proposed models, which demonstrate their good performance. These models contribute to the field of fuzzy decision-theoretic rough sets and sequential three-way decisions.

Multiobjective Bayesian Optimization for Aeroengine Using Multiple Information Sources

Aero-engine performance optimization remains significant for both efficiency and safety during specific operating conditions. Previous works usually solve this optimization problem under a single-objective optimization framework, while multiple objectives need to be optimized simultaneously. Besides, the underlying optimization process requires a variety of function evaluations, and the evaluation cost for an aero-engine is expensive. In reality, the aero-engine model has multiple information sources with different costs and accuracy. The different costs and accuracy of the multiple information sources should be traded off to guide the search for the optimal in a cost-efficient way. Therefore, we propose a multi-information source framework for enabling efficient multi-objective Bayesian optimization. We construct the surrogate model with a multi-fidelity Gaussian process and choose the location-source pair with a modified acquisition function. Finally, we apply the proposed method to improve the performance indexes of the aero-engine, which confirms the efficiency of the proposed algorithm.

Impact of audio-visual tool on patient comfort level at a comprehensive cancer center infusion suite: Preliminary results.

348 Background: Patients receiving chemotherapy often experience anxiety which may result in dissatisfaction with care and decreased adherence to treatment, subsequently leading to unfavorable outcomes. Patient education is shown to ameliorate the anxiety and improve patient comfort. However, retention of information by patients may be sub-optimal in this setting and utilization of multiple information sources may enhance patient recall. We hypothesized that a short “welcome video” on what to expect at an infusion visit, in addition to nursing- patient education would improve patient comfort level. Methods: We conducted a single center prospective pre-post study at the University of New Mexico Comprehensive Cancer Center (UNMCCC) to evaluate the impact of an educational audio-visual tool on the comfort level of patients. An introductory welcome video was developed and made available on: (i) the UNMCCC website (ii) on iPads All patients receiving infusions are asked to complete a survey prior to their infusion. In the phase 1 patients completed surveys prior to the “welcome video” being released. In the phase 2, the video has been introduced. We plan to compare the comfort level of the two populations before and after the video release. Study was reviewed and approved by University of New Mexico Health Sciences IRB. Results: In the first phase, 43 patients completed the survey. Overall, patients felt to have a good understanding of what to expect on their infusion day (average score - 8.9/10), felt somewhat nervous (average score: 3/10), and were interested to learn more about treatment side effects (average score: 5/10), on the 10-point Likert scale (1- strongly disagree, 10- strongly agree (table 1). In the phase 2, preliminary data collection on 40 surveyed patients showed that only 15% (6/40) watched the welcome video. Strategies planned to enhance access including a text message link to the video at patient registration are being implemented as we continue to collect phase 2 survey results. Conclusions: At our center, patients already demonstrate a strong sense of comprehension regarding what to anticipate on their infusion day, primarily through nursing-patient education. Our goal is to further enhance their understanding by improving the delivery of our welcome video and refining the survey questions to yield more informative insights.[Table: see text]

185 Incidence, causes and warning symptoms of sudden cardiac death in the Mediterranean area. Results from a registry with multiple sources of information

185 Incidence, causes and warning symptoms of sudden cardiac death in the Mediterranean area. Results from a registry with multiple sources of information

State of charge estimation techniques of Li-ion battery of electric vehicles

The Lithium-ion batteries are widely utilized in the electric car, bus, and two-wheeler industries because of their high energy density, low cost, extended lifespan, high power density, and stable voltage. One of the essential systems that must be present in any electric vehicle (EV) is the battery management system (BMS). One major input to BMS is state of charge to ensure the battery's durability, safety, and reliable operation. The state-of-charge (SoC) estimation of EV batteries plays a crucial role in optimizing their performance and extending their lifespan. As batteries are nonlinear and time-variant devices, estimating the state of charge or instantaneous remaining charge within a battery is a particularly challenging task. This paper covers a deep understanding of SoC estimation techniques for BMS. The two main approaches to explaining estimation of instantaneous remaining charge are model-based which relies on various battery models and their mathematical equations to explain the battery characteristics. The second approach is data-driven which studies large measured battery data sets to understand the behavior of running algorithms. Model-based approaches are based on series-parallel combinations of resistance and capacitance electrical circuits, while data-driven approaches are based on neural networks and machine learning algorithms. The review highlights the strengths and limitations of each technique, suggesting that hybrid approaches could yield more robust results. It emphasizes the importance of future research in integrating multiple information sources and developing standard evaluation procedures to enhance SoC estimation accuracy and its practical application in EVs.

Sharing Reliable COVID-19 Information and Countering Misinformation: In-Depth Interviews With Information Advocates.

The rampant spread of misinformation about COVID-19 has been linked to a lower uptake of preventive behaviors such as vaccination. Some individuals, however, have been able to resist believing in COVID-19 misinformation. Further, some have acted as information advocates, spreading accurate information and combating misinformation about the pandemic. This work explores highly knowledgeable information advocates' perspectives, behaviors, and information-related practices. To identify participants for this study, we used outcomes of survey research of a national sample of 1498 adults to find individuals who scored a perfect or near-perfect score on COVID-19 knowledge questions and who also self-reported actively sharing or responding to news information within the past week. Among this subsample, we selected a diverse sample of 25 individuals to participate in a 1-time, phone-based, semistructured interview. Interviews were recorded and transcribed, and the team conducted an inductive thematic analysis. Participants reported trusting in science, data-driven sources, public health, medical experts, and organizations. They had mixed levels of trust in various social media sites to find reliable health information, noting distrust in particular sites such as Facebook (Meta Platforms) and more trust in specific accounts on Twitter (X Corp) and Reddit (Advance Publications). They reported relying on multiple sources of information to find facts instead of depending on their intuition and emotions to inform their perspectives about COVID-19. Participants determined the credibility of information by cross-referencing it, identifying information sources and their potential biases, clarifying information they were unclear about with health care providers, and using fact-checking sites to verify information. Most participants reported ignoring misinformation. Others, however, responded to misinformation by flagging, reporting, and responding to it on social media sites. Some described feeling more comfortable responding to misinformation in person than online. Participants' responses to misinformation posted on the internet depended on various factors, including their relationship to the individual posting the misinformation, their level of outrage in response to it, and how dangerous they perceived it could be if others acted on such information. This research illustrates how well-informed US adults assess the credibility of COVID-19 information, how they share it, and how they respond to misinformation. It illustrates web-based and offline information practices and describes how the role of interpersonal relationships contributes to their preferences for acting on such information. Implications of our findings could help inform future training in health information literacy, interpersonal information advocacy, and organizational information advocacy. It is critical to continue working to share reliable health information and debunk misinformation, particularly since this information informs health behaviors.

Price of Anarchy with multiple information sources under competition

We characterize the Price of Anarchy (PoA) in a single channel under the presence of K competing sources. As performance metric we consider the Age of Information, which measures the freshness of information in a remote system. In our main results we show that when the service times of all sources are equal the PoA is 2−1K, and that otherwise the PoA is unbounded from above. Numerical computations show that the PoA increases with the disparity of the service rates.

Zu Besonderheiten des Fachbegriffes ublíženie na zdraví aus der Perspektive des Strafrechts in der Slowakei und in Deutschland

Summary The presented article points out the peculiarities of the Slovak criminal term bodily harm (sk. ublíženie na zdraví) from the perspective of its translation into German, based on the following premises: (1) diversity of sources of information, (2) dual nature of language and (3) theory of contextualism (see Preyer & Peter [2005]). Due to the differences between German and Slovak criminal law it is necessary to take into account the expressive and conceptual aspects of the basic term and its position in terminology in order to choose a suitable foreign language equivalent and then compare this information with possible equivalents in German. The selection of the most appropriate equivalent must be preceded by a thorough examination of the above aspects on the basis of multiple information sources (such as professional texts, legislative texts, lexicographic publications, corpora). This procedure ensures a reliable determination of the degree of equivalence of the basic term in relation to a foreign language term and is a prerequisite for the selection of the most suitable foreign language equivalent. With regard to a detailed examination of cotextual/contextual relations, it reveals the relationship of the initial term to the related terms and thus also helps in the appropriate selection of the relevant foreign language equivalents of related terms within the terminological field. This procedure can be applied also to other terms.

Recommendations to improve the interpretation of global flood forecasts to support international humanitarian operations for tropical cyclones

AbstractInternational humanitarian organisations increasingly turn to forecast teams to support the coordination of efforts to respond to disasters caused by hazards such as tropical cyclones and large‐scale fluvial floods. Such disasters often occur where there is limited local capacity or information available to support decision making and so global forecasting capacity is utilised to provide impact‐based flood forecast bulletins. A multidisciplinary team joined together to provide forecast bulletins and expertise for such events through the UK Foreign and Commonwealth Development Office (FCDO). This paper captures the successes and challenges from two cyclones: Hurricane Iota in Central America (November 2020) and Cyclone Eloise in Mozambique (January 2021). Recommendations to improve global forecasting systems are made which will benefit the international community of researchers and practitioners involved in disaster prediction, anticipatory action and response. These include the need for additional data and expertise to support the interpretation of global models, clear documentation to support decision makers faced with multiple sources of information, and the development of user relevant metrics to assess the skill of global models. We discuss the value of effective partnerships and improving synergies between global models and local contexts, highlighting how global forecasting can help build local forecasting capability.