Numerical Representation Research Articles

Abstract 13447: Political Orientation of Online Media Sources and Reporting of COVID-19 Vaccine Myocarditis

Background: Political orientation has played a role in patients' perceptions of risk associated with COVID-19 vaccination, including COVID-19 vaccine myocarditis (CVM). We examined the relationship between political orientation of popular online media outlets and reporting of CVM. Methods: Media sites were classified as “left” or “right" biased using the Allsides media bias rating report. For each site “COVID vaccine myocarditis” was searched in articles posted 5/21-12/22. Each search return was reviewed for the following: 1) Did it contain numerical data or reference the source of numerical data regarding CVM risk? 2) Did it report benefits of covid vaccination? 3) Did it mention covid infection -related myocarditis? Monthly reports of vaccine-related adverse events were obtained directly from the Vaccine Adverse Events Reporting System (VAERS). Correlations of reports of left- vs. right-leaning media were performed using Pearson Product Moment Correlation. Results: A total of 520 unique online reports regarding CVM were reviewed. Comparison of report volumes from left vs. right biased media sources by month during the assessment period demonstrated modest correlation (r=0.546, p=0.013), suggesting that monthly reporting volumes were driven by availability of information regarding CVM rather than media political alignment. Peak reporting across both left and right biased media was temporally proximate to peak volume of VAERS reporting. Left biased media sources were significantly more likely to include numerical data and/or links to source data vs. right biased sources (76.6% vs. 24.3%, p<0.001) and likewise were more likely to include data supporting benefits of covid vaccination (85.1% vs. 21.7%. p<0.001). In contrast, there was no difference regarding mention of COVID-19 infection-related myocarditis (24.5% vs. 24.3%, p=0.957). Conclusion: Political orientation of online news sites was not associated with frequency of CVM reports but was significantly related to report content, most notably whether reports included numerical vs verbal representations of CVM risk. These bias-related reporting characteristics may contribute to the relationship between political orientation and patient understanding of risk of CVM.

Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients

With the widespread use of social media platforms, sentiment analysis of user-generated content has become a crucial task in understanding public opinion and trends. In this paper, we compare the performance of three popular machine learning models, namely Random Forest, Support Vector Machine (SVM), and Logistic Regression, in predicting sentiments of post-COVID patients on social media tweets. The study utilizes a dataset of labeled tweets representing positive, negative, and neutral sentiments. The preprocessing of textual data involves tokenization, stop-word removal, and conversion to lowercase to create a suitable input for the models. We utilize Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to transform the text data into numerical features. The sentiment labels are converted to numeric representations for model training and evaluation. The three machine learning models are trained and evaluated on the dataset using metrics such as accuracy, precision, recall and F1-score. The evaluation results are presented and analyzed for each model, providing insights into their strengths and weaknesses in predicting sentiments. The experimental results demonstrate that Random Forest achieves the highest accuracy and F1-score, closely followed by SVM, while Logistic Regression performs slightly lower in comparison. However, all three models exhibit strong predictive capabilities, and their performances vary depending on the specific sentiment class. The findings provide valuable information for researchers and practitioners seeking to employ sentiment analysis in social media monitoring and other related applications. Overall, this study contributes to the understanding of the capabilities of Random Forest, SVM, and Logistic Regression models in sentiment analysis of social media tweets, and offers valuable insights for selecting the most suitable model for specific sentiment prediction tasks.

Toxic Comment Classification based on Personality Traits Using NLP

Concerns about the frequency of harmful remarks have been raised by the growth of online communication platforms, which makes it difficult to create inclusive and safe digital spaces. This study explores the creation of a strong framework that uses machine learning algorithms and natural language processing (NLP) methods to categorise harmful comments. In order to improve the accuracy and comprehensiveness of categorization, the study investigates the integration of personality trait analysis in addition to identifying hazardous language. A wide range of online comments comprised the dataset that was gathered and put through extensive preparation methods such as text cleaning, lemmatization, and feature extraction. To facilitate the training and assessment of machine learning models, textual data was converted into numerical representations by utilising TF-IDF vectorization and word embeddings. Furthermore, personality traits were extracted from comments using sentiment analysis and language clues, which linked linguistic patterns with behavioural inclinations. The study resulted in the development and assessment of complex categorization models that combined features from textual content and inferred personality traits. The findings show encouraging associations between specific personality qualities and the use of toxic language, providing opportunities to identify subtle differences in toxic comment contexts. In order to provide insights into developing more sophisticated and successful methods of reducing toxicity in online discourse, this study outlines the methodology, major findings, and consequences of incorporating personality traits analysis into the classification of toxic comments.

Artificial Intelligence Approach for the Discovery of Autoantigen Recognition By B-Cell Lymphomas

Artificial Intelligence approach for the discovery of autoantigen recognition by B-cell lymphomas Introduction With a common origin from mature B-cells, B-cell lymphomas express a unique clonal surface immunoglobulin (Ig) that may transmit survival signals autonomously or following binding to cognate antigens. An increasing number of potential autoantigen targets is described for several mature B-cell lymphomas, however, the discovery of novel targets represents a very complex and expensive task. Thanks to the development of artificial intelligence (AI) tools such as AlphaFold and advances in natural language processing methods such as Large Language Models, the study of proteins has benefited, making it possible to accelerate studies of interactions between proteins. In this context, we explored the training of AI models to predict autoantigen recognition of lymphoma-derived Ig from linear protein information. Methods First, 45 lymphoma-derived Ig were sequenced, synthesized as recombinant proteins, and probed onto human proteome arrays generating 370,000 antigens-antibodies interactions. Next, statistical methods were designed and implemented to reduce noise and filter the autoantigen-antibody interactions to be processed. Subsequently, sequence-based methods were explored to implement and validate predictive models of autoantigen-antibody interaction intensity. Lastly, sequence similarity network strategies were analyzed to identify preference relationships between antibodies and autoantigens. Results and Discussion From the 370,000 interactions, designed filters allowed to reduce the noise, generating a total of 270,000 valid interactions, which were used to train predictive models. Large language model methods, amino acid coding strategies via physicochemical properties, and spatial transformation techniques through Fourier transforms were explored as methods for the numerical representation of autoantigen and antibody sequences. Concatenation strategies and linear and non-linear combinations were explored to represent the autoantigen-antibody interaction complexes. More than 1000 predictive models were explored. The best performances were obtained by applying the pre-trained bepler and esm1b models, concatenation strategies, and using Random Forest algorithms as a training strategy. In addition, cross-validation methods with k-fold (k=10) were applied to prevent overfitting. The best results achieve a performance of 0.9 of Pearsons' coefficient and an MSE of 0.08. Alternatively, training strategies based on deep learning architectures such as CNN or GCN were used, although they presented similar results to those achieved by the selected method. Finally, the model was validated using molecular dynamics techniques, studying the affinity of interactions in a selected random sample, with a correlation between the predicted affinity results and the affinity of the interaction complexes measured by molecular dynamics. Conclusions The large language model methods explored for the training of predictive models have been combined with sequence similarity network methods for constructing autoantigen and antibody interaction networks. Interaction networks helped to identify patterns of antibody recognition preferences. In future work, the incorporation of unsupervised learning algorithms, enrichment analysis, and simulation of interactions via the generated predictive model is proposed to build an efficient pattern detection strategy to facilitate the discovery of autoantigen interaction of lymphoma derived Ig.

Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier

Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) as a classifier for proper recognition. Although CNN-LSTM efficiently learns the spatial and temporal data, but it highly ignores the pixels and their adjacent information which results in lower classification rate. So, in this paper, Bidirectional Encoder Representations from Transformers (BERT) is introduced along with CNN-LSTM in MS-AUNet-MSDATL model to resolve the above mentioned issues for efficient COVID-19 risk level classification. Initially, the segmented CT and CXR images from MS-AUNet is given as input to the BERT model. BERT structure consist of stack of transformer encoder layers which extracts fixed features from a pre-trained models to obtain the numerical representation of the given image. Then, the numerical expressions from the BERT are transformed to pre-learned CNN model for selecting the important features from the given representations. The LSTM model receives the CNN output and generates a new representation based on the data order. In addition, Fully Connected Layer (FCL) maps the CNN-LSTM results into categorization classes to learn pixels and their adjacent information for COVID-19 risk level detection and diagnosis. The complete work is termed as MS-AUNet-EMSDATL. Finally, the test findings shows that the MS-AUNet-MS-B-DATL achieves accuracy of 98.67% and 98.55% on CT and CXR images compared to the other existing frameworks.

The Changing Trajectory of Participation and Social Interaction at the Grassroots: Perception of Elected Dalit Representatives

For formerly excluded and marginalised people at the grassroots level of politics, the 73rd Constitutional Amendment Act has opened up previously unimaginable doors of opportunity. Because of the 73rd Amendment, scheduled castes have gained representation in panchayats; nonetheless, they were unable to participate in decision-making, which is reserved for upper and middle castes. The numerical or percentage representation of dalits at the grassroots level is the most frequently used criterion for assessing their political participation in India. This does not foster proper comprehension since representation, in and of itself, does not provide evidence of participation in a meaningful way. It is pointless for them to participate at the panchayat level unless the institutions in which they participate accept them openly and give them the authority to exercise influence over the outcome of the decision, thereby promoting their integration into mainstream society. Against this backdrop, the primary objective of this research is to identify the impact of political participation on the processes of changing the trajectories of social interaction among dalits at the grassroots level.

Impact of Selected Schemes of SERB: Empowering Indian Women in R&D Spectrum.

The role of women in science and technology has become crucial for the overall development of a nation. In India, the numeric representation of women up to graduate and postgraduate levels is the same as male candidates but declines from the doctorate level. A continued women's lower employment rate in universities and higher educational institutes must be analyzed for necessary action. The Government of India has initiated various schemes, programs, fellowships, and financial grants to the research community to carry out basic and advanced scientific and technological research. In this regard, the present study maps the participation of women through research proposal submission and approval in selected schemes of the Science and Engineering Research Board (SERB). The study also analyzed subject-wise trends and the role of institutes in developing the capacities of women scientists to perform research in cutting-edge technologies. The paper also describes the impact of SERB's women-centric POWER Scheme to uplift women scientists from different institutes. The review recommends potential areas where women researchers should be further encouraged to participate and generate solutions for societal problems using the tools of science and technology.

The Relationship Between Arachnoid Cysts and the Subarachnoid Space by Hounsfield Unit Value in Computed Tomography Scans: Identification of Isolated and Communicating Arachnoid Cysts in a Cohort Study

Background: Arachnoid cysts are formations containing cerebrospinal fluid (CSF). They may be associated with the subarachnoid space. This feature is a factor that can affect the change in the size of arachnoid cysts over time. Hounsfield Unit (HU) value measurements provide rational numeric representation of imaging of tissues in computed tomography (CT) examinations. In this study, we aimed to define isolated or communicated arachnoid cysts, and whether arachnoid cysts communicate with the subarachnoid distance, with the HU values obtained in CT examinations. Methods: Patients with radiological diagnosis of arachnoid cyst were identified retrospectively by examining the CT database. HU value measurements were recorded over the Picture Archiving and Communication System (PACS) in the area where the arachnoid cyst was detected. HU value measurements are a method that rationally shows the amount of radiograph absorption of tissues in CT examinations. HU value measurements were performed in cerebrospinal fluid (CSF) and arachnoid cysts, and whether arachnoid cysts had equal HU values with CSF. They were defined as homogeneous or heterogeneous by measuring HU values in different regions of arachnoid cysts. Results: Data on 478 cases were obtained. An average of 2.82 CT scans were detected per case. A change in arachnoid cyst size was detected in 47 (9.8%) cases. In cases of arachnoid cysts of varying size, the mean HU value of arachnoid cysts was 9.32±1.93 (6.43±1.67 in the lateral ventricle and 7.04±1.71 in the fourth ventricle). HU value of arachnoid cysts with a change in size significantly differed from CSF. Conclusion: Arachnoid cysts with HU values equal to CSF are not real arachnoid cysts, and significant volume changes are not expected. Arachnoid cysts, which have a different HU value than CSF, may secrete a different fluid from CSF. This type of arachnoid cyst is a real cyst that can be detected in various sizes on CT examinations at different times since they are not in communication with the subarachnoid space.

Investigation of Seventh Grade Students’ Transition Skills among Representations

This descriptive case study was aimed at revealing the level of students’ transition skills among representations (verbal, numeric, algebraic, graphic) at the completion of 7th grade. The participants included 133 students attending 7th grade at a state school located in Ankara in Turkey. Students’ skills in transitioning between representations were tested using the “Test of Skills in Transitioning between Representations (TSTBR)”, which included 12 open-ended questions and analyzed student answers through a graded rubric developed by the researcher. Descriptive statistics such as frequency and percentage were used to examine students’ skills in making transitions between representations. Thus, it was determined in the findings that a majority of students had moderate transition skills. According to the mean scores, the number of students with low representation transition skills was greater than the number of students with high representation transition skills. Also, the students were most successful at transitioning from other representations to algebraic, verbal, numeric, and graphic representations, respectively. The mean scores for each representation transition were examined and possible reasons for the difficulties that students faced were discussed.

Numerical Representation for Action in Crows Obeys the Weber-Fechner Law

The psychophysical laws governing the judgment of perceived numbers of objects or events, called the number sense, have been studied in detail. However, the behavioral principles of equally important numerical representations for action are largely unexplored in both humans and animals. We trained two male carrion crows (Corvus corone) to judge numerical values of instruction stimuli from one to five and to flexibly perform a matching number of pecks. Our quantitative analysis of the crows’ number production performance shows the same behavioral regularities that have previously been demonstrated for the judgment of sensory numerosity, such as the numerical distance effect, the numerical magnitude effect, and the logarithmical compression of the number line. The presence of these psychophysical phenomena in crows producing number of pecks suggests a unified sensorimotor number representation system underlying the judgment of the number of external stimuli and internally generated actions.

Abdominal organ segmentation via deep diffeomorphic mesh deformations

Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability in the shape, size, and position of abdominal organs. Three-dimensional numeric representations of abdominal shapes with point-wise correspondence to a template are further important for quantitative and statistical analyses thereof. Recently, template-based surface extraction methods have shown promising advances for direct mesh reconstruction from volumetric scans. However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed. We close this gap and employ template-based mesh reconstruction methods for joint liver, kidney, pancreas, and spleen segmentation. Our experiments on manually annotated CT and MRI data reveal limited generalization capabilities of previous methods to organs of different geometry and weak performance on small datasets. We alleviate these issues with a novel deep diffeomorphic mesh-deformation architecture and an improved training scheme. The resulting method, UNetFlow, generalizes well to all four organs and can be easily fine-tuned on new data. Moreover, we propose a simple registration-based post-processing that aligns voxel and mesh outputs to boost segmentation accuracy.

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction.

In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltage-gated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations─molecular fingerprints, descriptors, and graph-based numerical representations─are rigorously benchmarked. Additionally, a novel training and evaluation data set framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated data set covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.

Economical Situation of the M/M/1/κ Markovian Queueing system with Encouraged Incoming and maintained of Reneged Customers

Today’s businesses all strive to operate smoothly and efficiently in order to meet customer demandand provide the best possible service because of the intense competition. The queuing theory is crucial in this situation. Queueing models can assist businesses in understanding their performance in advance, enabling themto plan effectively for providing seamless and effective customer service as well as long-term sustainability. Businesses entice customers to sign up for the system by offering promotions and discounts. Customers wait even longer in queue to receive services as a result of incentives like discounts.An analysis is conducted on a finite Markovian single-server queuing model with encouraged arrivals, reneging, and retention of reneged customers. Iterative derivation is used to reach the model's steady state solution. Additionally, the queueing model's performance metrics are gathered. As unique example of this model,several significant queuing models are derived. In order to develop a cost model, a model's economic analysis is presented, and a discussion of numerical representation is also included.Customers are added when they are welcomed, which changes the way theybehave in a queueing system.

Basic Principles of Rotational Thromboelastometry (ROTEM®) and the Role of ROTEM-Guided Fibrinogen Replacement Therapy in the Management of Coagulopathies.

Rotational thromboelastometry (ROTEM) is a viscoelastic method, which provides a graphical and numerical representation of induced hemostasis in whole blood samples. Its ability to quickly assess the state of hemostasis is used in the management of bleeding from a variety of causes. The separate activation of particular parts of hemocoagulation in INTEM, EXTEM, and FIBTEM tests allows for a more comprehensive and faster evaluation of the missing component of hemostasis followed by targeted therapy. One of the most common cause of coagulopathy is trauma-induced coagulopathy. Fibrinogen replacement therapy by ROTEM allows for the use of a standard dosage of fibrinogen, which has been shown to be successful in preventing dilutional coagulopathy following colloid and crystalloid replacement and excessive amount of allogeneic blood transfusions. The best reflection of fibrinogen activity is observed in the FIBTEM assay, where fibrinogen replacement therapy is recommended at an MCF (maximum clot firmness) of FIBTEM < 10 mm and FIBTEM A10 < 7 mm. ROTEM also plays an important role in the diagnostic and management of inherited fibrinogen disorders. These can be manifested by bleeding complications, where changes in the MCF parameter are the most useful tool for assessing the effectiveness of fibrinogen replacement therapy. ROTEM-guided bleeding management algorithms effectively reduce the number of transfusions, healthcare costs, and complications, leading to the improvement of patient safety and overall health.

Chemical and Creep Models Applied to Concrete Damaged by Alkali–Silica Reactions

Concrete structures that experience internal swelling reactions are often affected by other deleterious mechanisms, such as creep and shrinkage. In Brazil and many other countries around the world, numerous cases of building foundations and concrete dams were investigated due to the damage associated with internal expansions. Macroscopic models for the numerical representation of these expansions must take into account the influence of key environmental parameters such as temperature, degree of saturation, and the rate of development of the chemical reaction. To be relevant in structural applications, concrete creep models must consider several important phenomena, such as non-linearity, multi-axiality, and thermal and drying effects. In order to prevent these pathologies, to plan rehabilitation work, and to develop new design procedures, numerical simulation using the finite element method (FEM) is a very useful tool. This work aimed to implement a chemical model to simulate the advancement of the internal expansion reactions and a mechanical model to simulate creep and shrinkage phenomena in COMSOL Multiphysics® to reassess concrete structures suffering from these mechanisms. Both models were implemented separately to evaluate their responses and compare them with the theoretical results and experimental benchmarks proposed by the developers of these models. The numerical results obtained presented an excellent agreement with the experimental results, with a deviation of less than 10%, which showed that the implementation of the developed numerical models was very efficient. Moreover, this research holds significant importance as the mathematical models used to simulate internal expansions in concrete are currently only available in limited-use FEM software’s. Therefore, demonstrating the successful implementation of these models in widely used finite element programs and their ability to produce reliable results would be a valuable contribution.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing

BackgroundDrug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.ResultsThis study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.ConclusionWe demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph.

The role of numerical and nonnumerical magnitudes in pigeons' conditional discrimination behavior.

Research on approximate numerical estimation suggests that numerical representations can be influenced by nonnumerical magnitudes. Current theories of numerical cognition differ on the nature of this interaction. The present project evaluated the effect of task requirements on the stimulus control exerted by numerical and nonnumerical magnitudes on pigeons' numerical discrimination behavior. In a series of experiments, we explored the effects of cumulative area and item size on pigeons' numerical discrimination. The effect of cumulative area was assessed by presenting visual displays in which cumulative area and item number were either positively correlated, uncorrelated, or negatively correlated. The effect of item size was evaluated by presenting displays in which the size of individual items was varied across trials. Results confirmed that pigeons' numerical discrimination behavior accorded with Weber's law, a prime indicator of nonsymbolic numerical representation. The results further indicated that pigeons did not use numerical information when nonnumerical magnitudes also provided reliable information to solve the discrimination task. However, task manipulations that rendered the information provided by nonnumerical magnitudes unreliable successfully shifted stimulus control toward numerical magnitudes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

How Do People Process Different Representations of Statistical Information? Insights into Cognitive Effort, Representational Inconsistencies, and Individual Differences.

Graphical representation formats (e.g., icon arrays) have been shown to lead to better understanding of the benefits and risks of treatments compared to numbers. We investigate the cognitive processes underlying the effects of format on understanding: how much cognitive effort is required to process numerical and graphical representations, how people process inconsistent representations, and how numeracy and graph literacy affect information processing. In a preregistered between-participants experiment, 665 participants answered questions about the relative frequencies of benefits and side effects of 6 medications. First, we manipulated whether the medical information was represented numerically, graphically (as icon arrays), or inconsistently (numerically for 3 medications and graphically for the other 3). Second, to examine cognitive effort, we manipulated whether there was time pressure or not. In an additional intervention condition, participants translated graphical information into numerical information before answering questions. We also assessed numeracy and graph literacy. Processing icon arrays was more strongly affected by time pressure than processing numbers, suggesting that graphical formats required more cognitive effort. Understanding was lower when information was represented inconsistently (v. consistently) but not if there was a preceding intervention. Decisions based on inconsistent representations were biased toward graphically represented options. People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did. Graph literacy was not related to processing efficiency. Our study was conducted with a nonpatient sample, and the medical information was hypothetical. Although graphical (v. numerical) formats have previously been found to lead to better understanding, they may require more cognitive effort. Therefore, the goal of risk communication may play an important role when choosing how to communicate medical information. This article investigates the cognitive processes underlying the effects of representation format on the understanding of statistical information and individual differences therein.Processing icon arrays required more cognitive effort than processing numbers did.When information was represented inconsistently (i.e., partly numerically and partly graphically), understanding was lower than with consistent representation, and decisions were biased toward the graphically represented options.People with higher numeracy processed quantitative information more efficiently than people with lower numeracy did.

Buckling behavior of multilayer cylindrical shells composed of functionally graded nanocomposite layers under lateral pressure in thermal environments

In this study, the stability behavior of multilayer cylindrical shells made of functionally graded nanocomposite layers (FG-NCLs) subjected to the lateral pressure in thermal environments is investigated. It is postulated that nanocomposite layers forming layered cylindrical shells are made of single-walled carbon nanotube (SWCNT)-reinforced polymers that have four types of profiles based on the uniform and linear distributions of mechanical properties. The material properties of SWCNTs are assumed to be dependent on location as well as temperature and are obtained from molecular dynamics simulations. The governing equations are derived as partial differential equations within shear deformation theory (SDT) and solved in a closed form, using the Galerkin procedure, to determine the lateral critical pressure (LCP) in thermal environments. The numerical representations relate to the buckling behavior of multilayer cylindrical shells made of functionally graded nanocomposite layers under the uniform lateral pressure for different CNT patterns and temperatures within SDT and Kirchhoff-Love theory (KLT).

The Lure of Beauty: People Select Representations of Statistical Information Largely Based on Attractiveness, Not Comprehensibility.

People differ in whether they understand graphical or numerical representations of statistical information better. However, assessing these skills is often not feasible when deciding which representation to select or use. This study investigates whether people choose the representation they understand better, whether this choice can improve risk comprehension, and whether results are influenced by participants' skills (graph literacy and numeracy). In an experiment, 160 participants received information about the benefits and side effects of painkillers using either a numerical or a graphical representation. In the "no choice" condition, the representation was randomly assigned to each participant. In the "choice" condition, participants could select the representation they would like to receive. The study assessed gist and verbatim knowledge (immediate comprehension and recall), accessibility of the information, attractiveness of the representation, as well as graph literacy and numeracy. In the "choice" condition, most (62.5%) chose the graphical format, yet there was no difference in graph literacy or numeracy (nor age or gender) between people who chose the graphical or the numerical format. Whereas choice slightly increased verbatim knowledge, it did not improve gist or overall knowledge compared with random assignment. However, participants who chose a representation rated the representation as more attractive, and those who chose graphs rated them as more accessible than those without a choice. The sample consisted of highly educated undergraduate students with higher graph literacy than the general population. The task was inconsequential for participants in terms of their health. When people can choose between representations, they fail to identify what they comprehend better but largely base that choice on how attractive the representation is for them. People differ systematically in whether they understand graphical or numerical representations of statistical information better. However, assessing these underlying skills to get the right representation to the right people is not feasible in practice. A simple and efficient method to achieve this could be to let people choose among representations themselves.However, our study showed that allowing participants to choose a representation (numerical v. graphical) did not improve overall or gist knowledge compared with determining the representation randomly, even though it did slightly improve verbatim knowledge.Rather, participants largely chose the representation they found more attractive. Most preferred the graphical representation, including those with low graph literacy.It would therefore be important to develop graphical representations that are not only attractive but also comprehensible even for people with low graph literacy.