Pieces Of Information Research Articles

Remember walking in their shoes? The relation of self-referential source memory and emotion recognition

ABSTRACT Deficits in the ability to read the emotions of others have been demonstrated in mental disorders, such as dissociation and schizophrenia, which involve a distorted sense of self. This study examined whether weakened self-referential source memory, being unable to remember whether a piece of information has been processed with reference to oneself, is linked to ineffective emotion recognition. In two samples from a college and community, we quantified the participants’ ability to remember the self-generated versus non-self-generated origins of sentences they had previously read or partially generated. We also measured their ability to read others’ emotions accurately when viewing photos of people in affect-charged situations. Multinomial processing tree modelling was applied to obtain a measure of self-referential source memory that was not biased by non-mnemonic factors. Our first experiment with college participants revealed a positive correlation between correctly remembering the origins of sentences and accurately recognising the emotions of others. This correlation was successfully replicated in the second experiment with community participants. The current study offers evidence of a link between self-referential source memory and emotion recognition.

Electron activated dissociation - a complementary fragmentation technique to collision-induced dissociation for metabolite identification of synthetic cathinone positional isomers

Over the last decade, a remarkable number of new psychoactive substances (NPS) have emerged onto the drug market, resulting in serious threats to both public health and society. Despite their abundance and potential toxicity, there is little information available on their metabolism, a crucial piece of information for clinical and forensic purposes. NPS metabolism can be studied using in vitro models, such as liver microsomes, cytosol, hepatocytes, etc. The tentative structural elucidation of metabolites of NPS formed using in vitro models is typically carried out using liquid chromatography combined with high-resolution tandem mass spectrometry (LC-HRMS2) with collision-induced dissociation (CID) as a fragmentation method. However, the thermally-excited ions produced with CID may not be sufficient for unambiguous identification of metabolites or their complete characterization. Electron-activated dissociation (EAD), a relatively new fragmentation approach that can be used to fragment singly-charged ions, may provide complementary structural information that can be used to further improve the confidence in metabolite identification. The aim of this study was to compare CID and EAD as fragmentation methods for the characterization and identification of synthetic cathinone positional isomers and their metabolites. The in vitro metabolism of 2-methylethcathinone (2-MEC), 3-methylethcathinone (3-MEC) and 4-methylethcathinone (4-MEC) was investigated with both CID and EAD methods using LC-HRMS2. Four, seven and six metabolites were tentatively identified for the metabolism of 2-MEC, 3-MEC and 4-MEC, respectively. Here, the metabolism of 3-MEC and 2-MEC is reported for the first time. The EAD product ion mass spectra showed different fragmentation patterns compared to CID, where unique and abundant product ions were observed in EAD but not in CID. More importantly, certain EAD exclusive product ions play a significant role in structural elucidation of some metabolites. These results highlight the important role that EAD fragmentation can play in metabolite identification workflows, by providing additional fragmentation data compared with CID and, thus, enhancing the confidence in structural elucidation of drug metabolites.

The Relationship Between Living Density and Mental Health: Moderating Role of Internet Use

As the living area per capita decreases these days rapidly, high living density has resulted in mental health problems. Existing studies have shown that high living density will cause low mental health status, and college students also suffer from a high living density of school dormitories. However, researches on whether perceptions of the living environment affect college students mental health and whether Internet use affects this relationship remains vacant. This study aims to explore the relationship between subjective and objective living density and college students mental health and the moderating role of Internet use through a questionnaire survey. The questionnaire mainly collected three main pieces of information from the participants: living density, Internet use and mental health. Using descriptive statistics, correlation analysis and regression analysis, this study found a negative correlation between participants perceptions of their living environment and stress levels. Different Internet usage times or feelings did not moderate such interaction. Therefore, attention should be paid to not only the subjective living density but also the objective perception of the living environment.

Chemistry Students' Mathematics Ability and Their Understanding of Buffer Solution

Mathematics's role in solving chemical phenomena has been well known. Basic mathematical operations such as integral, logarithm and differentiations are the tools for communicating chemistry concepts. This paper describes the effect of chemistry students' mathematical ability on understanding buffer solutions. 56 First-year university chemistry students at a public University in Malang, East Java, taking basic chemistry modules involved in this study. The respondents participated on a voluntary basis after getting a piece of comprehensive information about the study. An equivalent basic mathematical skill test (BMST) and Buffer Solution Test (BST) was implemented for data collection. This study found a positive correlation between students' mathematical ability and success in answering relevant buffer solution questions. The contribution of mathematical knowledge in predicting chemistry students' success in answering relevant buffer solution questions was also essentially high.

Are False Memory and Creative Thinking Mediated by Common Neural Substrates? An fMRI Meta-Analysis

ABSTRACT Episodic retrieval plays a functional-adaptive role in supporting divergent creative thinking, the ability to creatively combine different pieces of information. However, the same constructive memory process that provides this benefit can also lead to memory errors. Prior behavioral work has shown that there is a positive correlation between the false recognition of lure items in the Deese-Roediger-McDermott paradigm, and divergent creative thinking as assessed on the alternate uses task. Here, we conducted a meta-analysis of functional magnetic resonance imaging studies to test for convergence in the neural substrates associated with these cognitive linkages. Our individual meta-analyses of false recognition-related activity as well as divergent thinking-related neural activity replicated prior meta-analyses. However, there was no significant statistical overlap across the neural regions associated with false recognition and divergent creative thinking. These null findings may reflect the operation of distinct generative retrieval processes engaged during divergent thinking relative to false recognition.

Classification of mathematical test questions using machine learning on datasets of learning management system questions.

Every student has a varied level of mathematical proficiency. Therefore, it is important to provide them with questions accordingly. Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate questions for each student based on an analysis of their previous responses to questions. Particularly, the LMS manages learners and provides an online platform that can evaluate their skills. Questions need to be classified according to their difficulty level so that the LMS can recommend them to learners appropriately and thereby increase their learning efficiency. In this study, we classified large-scale mathematical test items provided by ABLE Tech, which supports LMS-based online mathematical education platforms, using various machine learning techniques according to the difficulty levels of the questions. First, through t-test analysis, we identified the significant correlation variables according to the difficulty level. The t-test results showed that answer rate, type of question, and solution time were positively correlated with the difficulty of the question. Second, items were classified according to their difficulty level using various machine learning models, such as logistic regression (LR), random forest (RF), and extreme gradient boosting (xgboost). Accuracy, precision, recall, F1 score, the area under the curve of the receiver operating curve (AUC-ROC), Cohen's Kappa and Matthew's correlation coefficient (MCC) scores were used as the evaluation metrics. The correct answer rate, question type, and time for solving a question correlated significantly with the difficulty level. The machine learning-based xgboost model outperformed the statistical machine learning models, with a 85.7% accuracy, and 85.8% F1 score. These results can be used as an auxiliary tool in recommending suitable mathematical questions to various learners based on their difficulty level.

The effects of required security on software development effort

The software industry has been under pressure to adopt security practices and reduce software vulnerabilities. But despite recognizing the importance of secure software development, such practices have not been broadly embraced. Costs are frequently pointed out as a barrier, although studies show that there is a lack of knowledge about the amount of resources needed to achieve a determined level of security assurance. This study quantifies the effort required to develop secure software in increasing levels of rigor and scope. We first developed an ordinal scale to quantify the degree of application of security practices. Next, we built a statistical cost model based on a data set with 1140 maintenance projects from two large companies. The model calibration revealed that the application of software security practices can impact the cost estimations ranging from 19% additional effort, on the first level of the scale, to 102% additional effort, on the highest level of the scale. These results suggest that the effort required to develop secure software is lower than it was estimated in previous studies, especially when considering the domain of Information Systems. This research builds on previous works on cost models for secure software development and goes one step further by providing empirical validation. The resulting model can be used by practitioners to estimate proper resources for adopting security practices. Additionally, the validated cost multipliers are an important piece of information for the research on secure software development investment models.

Online on the frontline: A longitudinal social media analysis of UK healthcare workers’ attitudes to COVID-19 vaccines using the 5C framework

This paper explores vaccine hesitancy among healthcare workers (HCWs) in the UK, where different COVID-19 vaccines were being rolled out through a national vaccination campaign from 2020 to 2022, consisting of a first and second dose programme. Through a mixed-method approach using qualitative discourse analysis and network analysis of Twitter data, we assessed HCW perceptions and views about the administration and delivery of COVID-19 vaccines in the United Kingdom (UK). We were also interested in exploring HCWs' personal experiences and attitudes towards taking COVID-19 vaccines themselves. We drew upon sociology, ethics, communication studies and used research methods concentrating on social media and media analysis. By employing the ‘5C framework’ of ‘confidence, complacency, constraints, calculation, and collective responsibility’ we evaluated a longitudinal selection of tweets to capture relevant factors driving vaccination views and behaviours among HCWs. We found differing positions expressed about COVID-19 vaccines and policy during the first dose compared with the second, through a drop in confidence compounded by supply and access issues, as well the news of a vaccine mandate for HCWs by the UK government in 2021. HCWs asked calculation questions to the community or brought forward competing pieces of information about vaccine policy and guidelines. Constraint levels in access issues were noted, especially for those with work and caregiving responsibilities, and student nurses found they did not have equal vaccination access. HCWs also displayed collective responsibility on social platforms to both encourage vaccination and express concerns through the organisation of social action against vaccine mandates.

How to identify and interpret weak signals of change in the forest bioeconomy

Weak signals, the first signs of a potentially emerging issue that could disrupt current trends and megatrends, are hidden among disconnected pieces of information. Analyzing weak signals can help decision makers generate preparedness and a rapid-response capacity for possible systemic changes in the forest bioeconomy, where weak signals have not been studied extensively. This exploratory study sought to understand how to identify and interpret weak signals of change that may have an impact on the forest bioeconomy. An international panel of experts in the forest bioeconomy and in foresight provided their views through a two-stage Real-Time Delphi method which utilized the multiple rounds of traditional Delphi and the instantaneous feedback of the Real-Time Delphi method. This study introduces four conceptual frameworks to use when searching for weak signals: Internal/external operating environment, the Multi-level perspective, the Three Horizons Framework, and Complexity theory. These conceptual frameworks were applied in the analysis of collected data with the aim of developing the theory and practice on how to identify and interpret weak signals. According to the results, observations beyond the traditional dominating operating environment and a combination of observations from various sources could help to identify weak signals that may affect the forest bioeconomy. The Real-Time Delphi insights suggest that weak signal analysis becomes stronger via collaboration and interaction between futures specialists and forest bioeconomy experts.

Instagram Sebagai Media Informasi untuk Memperkenlakan Keberadaan dan Kegiatan dalam Komunitas @Pf_Ent Palembang

PF Entertainment is a community of lovers of Korean-style modern dance or Korean Dance Cover which uses the mass media Instagram as an information medium for the public to introduce their existence and activities. However, as time goes by, this media competes with other media such as TikTok, which is currently very popular. The aim of this research is to utilize knowledge of Instagram media in introducing the existence and activities of PF Entertainment. This study uses a qualitative method. The theory used is Effective Communication Techniques. The result of this research is that by using Instagram, of course the admin always updates the activities carried out with 2 to 7 pieces of information in one month so that followers can follow and find out about developments in the PF Entertainment community.

Dissociable online integration processes in visual working memory.

Visual working memory has severe capacity limits, creating a bottleneck for active processing. A key way of mitigating this limitation is by chunking, i.e. compressing several pieces of information into one visual working memory representation. However, despite decades of research, chunking efficiency remains debated because of mixed evidence. We propose that there are actually 2 integration mechanisms: Grouping combines several objects to one representation, and object-unification merges the parts of a single object. Critically, we argue that the fundamental distinction between the 2 processes is their differential use of the pointer system, the indexing process connecting visual working memory representations with perception. In grouping, the objects that are represented together still maintain independent pointers, making integration costly but highly flexible. Conversely, object-unification fuses the pointers as well as the representations, with the single pointer producing highly efficient integration but blocking direct access to individual parts. We manipulated integration cues via task-irrelevant movement, and monitored visual working memory's online electrophysiological marker. Uniquely colored objects were flexibly grouped and ungrouped via independent pointers (experiment 1). If objects turned uniformly black, object-integration could not be undone (experiment 2), requiring visual working memory to reset before re-individuation. This demonstrates 2 integration levels (representational-merging versus pointer-compression) and establishes the dissociation between visual working memory representations and their underlying pointers.

Tuning Intrinsic Spin Hall Effect in Platinum/Ferrimagnetic Insulator Heterostructure in Moderately Dirty Regime.

Studying the mechanisms of the spin Hall effect (SHE) is essential for the fundamental understanding of spintronic physics. By now, despite the intensive studies of SHE on heavy metal (HM)/metallic magnet heterostructures, the SHE on HM/ferrimagnetic insulator (FMI) heterostructures still remains elusive. Here, we study the mechanism of SHE in the Pt/Tm3Fe5O12 (TmIG) heterostructure. We first tune the crystallinity and resistivity of Pt by an annealing method, and then study the spin-orbit torque (SOT) in the tuned-Pt/TmIG devices. The SOT generation efficiency per unit electric field and spin Hall angle were obtained, which are insensitive to the annealing temperature. We further demonstrate that the intrinsic contribution in the moderately dirty regime is responsible for the SHE in our Pt/TmIG bilayer. Our study provides an important piece of information for the SHE in FMI-based spintronic physics.

Cost-sensitive probabilistic predictions for support vector machines

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature. On the other hand, the tuning of the regularization parameters in SVM is known to imply a high computational effort and generates pieces of information that are not fully exploited, not being used to build a probabilistic classification rule.In this paper we propose a novel approach to generate probabilistic outputs for the SVM. The new method has the following three properties. First, it is designed to be cost-sensitive, and thus the different importance of sensitivity (or true positive rate, TPR) and specificity (true negative rate, TNR) is readily accommodated in the model. As a result, the model can deal with imbalanced datasets which are common in operational business problems as churn prediction or credit scoring. Second, the SVM is embedded in an ensemble method to improve its performance, making use of the valuable information generated in the parameters tuning process. Finally, the probabilities estimation is done via bootstrap estimates, avoiding the use of parametric models as competing approaches. Numerical tests on a wide range of datasets show the advantages of our approach over benchmark procedures.

Раскрытие отдельной информации при представлении статей в отчетности коммерческой организации

The article discusses the problems of information disclosure, shows what and to what extent information in reporting should be disclosed in order to meet the requirements of regulations, taking into account the needs of the main users of reporting. When preparing financial statements, companies need to consider whether the primary users of the financial statements will understand that portion of the information that is not disclosed in detail. Similarly, judgments regarding materiality must take into account the quantitative and qualitative characteristics of a piece of information, its relevance and necessity to the primary users of the financial statements. It is important to determine the necessary amount and structure of information to present it in financial statements in a manner that is understandable and sufficient for users.

Evaluation of the Performance of Standard Tests in the Differential Diagnosis of Malaria and Typhoid Fever

Malaria and typhoid fever are microbial conditions that share almost similar symptoms including hyperthermia. These diseases are endemic in sub-Saharan Africa where resources for proper control are scarce and biological diagnosis fundamentally based on direct and indirect screening tests. The present investigation aims to evaluate the performance of the usual tests for the differential diagnosis of malaria and typhoid fever, in connection with a few conducive environmental determinants of hygiene and sanitation. We conducted a descriptive cross-sectional study over a period of three months at the Bangangté District Hospital and the Cliniques Universitaires des Montagnes. The study population consisted of patients presenting with hyperthermia. Participant’s blood and stool specimens were subjected for screening according to standard protocols. The size of the study population was 90, with age average, 36 years. Men were relatively more represented than women (sex ratio1.04). The alleged most commonly water source was the tap (36.7%). Assessment of various tests and temperature displayed positive correlation (R=0.212; P=0.044) between the Plasmodium load and the increased temperature. The Widal-et-Felix test yielded 75% sensitivity, 51% specificity, 90% positive-predictive value and 97% negative-predictive value compared to stool culture for Salmonella Typhi. Concerning the SD Bioline RDT, 79% sensitivity, 70% specificity, 65% positive-predictive value and 82% negative-predictive value compared to the thick blood smear for Plasmodium were recorded. Biological crossed pieces of information (Blood Smear/stool culture) for malaria/typhoid fever co-infection were observed in 7.4% of cases. The rapid diagnostic tests assessment resulted in 34.78% false positives and 17.74% of false-negatives, while the Widal-et-Felix showed 90.74% of false-positives and 3.03% of false-negatives. The Youden index for the Widal-et-Felix (degree of inaccuracy, 0.26, far from 1) implied that this test is not appropriate for the diagnosis of typhoid fever alone.

Interactive mapping of Covid-19 disinformation in Ibero-America

Fake news, created with different intentions and disseminated through various social networks and messaging platforms, has become a significant means of disinformation in the current landscape. In the face of certain events or controversial situations, the number of fake new items increases, a situation that can become worrying. One such event was the arrival of the Covid-19 pandemic. To combat this phenomenon, various fact-checking bodies appeared with the objective of demonstrating and disseminating whether a certain piece of information was correct. At the international level, these organizations have joined together in the #CoronaVirusFacts/#DatosCoronaVirus Alliance. The organization Chequeado from Argentina has collected information coming from Latin American (Latam) countries plus Spain and Portugal in a specific database, which allows for their analysis on an independent basis. The objective of this work is to analyze and make graphically visible the thematic distribution, media typology, and digital formats of the fake news that circulated regarding the coronavirus pandemic in Ibero-America by means of the social network analysis technique (SNA) and others, using the information from the aforementioned database. Its final visualization is shown by means of interactive mapping, which can be manipulated by the user to analyze the results through the application of different integrated filtering strategies.

Molecular data storage using direct analysis in real time (DART) ionization mass spectrometry for decoding

Molecular data storage is becoming a viable alternative to traditional information storage systems. Here, we propose a method where the presence or absence of a given molecule in a mixture of compounds represents a bit of information. As a novel approach, direct analysis in real time (DART) ionization mass spectrometry is used to recover and decode the information stored at the molecular level. Nicotinic acid derivatives were synthesized and used as the ‘bit compounds’. Their volatility and ease of ionization make these molecules especially suitable for DART-MS detection. The application of DART-MS as a method with an ambient ionization technique, enables the re-reading of digital chemical codes embedded in the material of ordinary objects. Our method is designed to store and read back short pieces of digital information, up to several hundred bits. These codes can have the function of barcodes or QR codes, as shown in our proof-of-principle applications. First, modelling a QR code as a link to our university's website, three solutions were prepared, each representing 22 bits. Proceeding further, the bit compounds were incorporated into a polymer matrix that is suitable for 3D printing, and a toy ship was created with a hidden barcode. In addition, decoding software was developed to process the DART-MS spectra. The nicotinic acid components representing the bits dominated the DART-MS spectra and error-free decoding was achieved.

Agreement in All-in-One Dataset between Diagnosis and Prescribed Medication for Common Cardiometabolic Diseases in the NDB-K7Ps.

The Japanese National Database (NDB), a useful data source for epidemiological studies, contains information on health checkups, disease diagnoses, and medications, which can be used when investigating common cardiometabolic diseases. However, before the initiation of an integrated analysis, we need to combine several pieces of information prepared separately into an all-in-one dataset (AIOD) and confirm the validation of the dataset for the study. In this study, we aimed to confirm the degree of agreement in data entries between diagnoses and prescribed medications and self-reported pharmacotherapy for common cardiometabolic diseases in newly assembled AIODs. The present study included 10,183,619 people who underwent health checkups from April 2018 to March 2019. Over 95% of patients prescribed antihypertensive and antidiabetic medications were diagnosed with each disease. For dyslipidemia, over 95% of patients prescribed medications were diagnosed with at least one of the following: dyslipidemia, hypercholesterolemia, or hyperlipidemia. Similarly, over 95% of patients prescribed medications for hyperuricemia were diagnosed with either hyperuricemia or gout. Additionally, over 90% of patients with self-reported medications for hypertension, diabetes, and dyslipidemia were diagnosed with each disease, although the proportions differed among age groups. Our study demonstrated high levels of agreement between diagnoses and prescribed medications for common cardiometabolic diseases and self-reported pharmacotherapy in our AIOD.

Appropriate design and reporting of superiority, equivalence and non-inferiority clinical trials incorporating a benefit-risk assessment: the BRAINS study including expert workshop.

Randomised controlled trials are designed to assess the superiority, equivalence or non-inferiority of a new health technology, but which trial design should be used is not always obvious in practice. In particular, when using equivalence or non-inferiority designs, multiple outcomes of interest may be important for the success of a trial, despite the fact that usually only a single primary outcome is used to design the trial. Benefit-risk methods are used in the regulatory clinical trial setting to assess multiple outcomes and consider the trade-off of the benefits against the risks, but are not regularly implemented in publicly funded trials. The aim of the project is to aid the design of clinical trials with multiple outcomes of interest by defining when each trial design is appropriate to use and identifying when to use benefit-risk methods to assess outcome trade-offs (qualitatively or quantitatively) in a publicly funded trial setting. A range of methods was used to elicit expert opinion to answer the project objectives, including a web-based survey of relevant researchers, a rapid review of current literature and a 2-day consensus workshop of experts (in 2019). We created a list of 19 factors to aid researchers in selecting the most appropriate trial design, containing the following overarching sections: population, intervention, comparator, outcomes, feasibility and perspectives. Six key reasons that indicate a benefit-risk method should be considered within a trial were identified: (1) when the success of the trial depends on more than one outcome; (2) when important outcomes within the trial are in competing directions (i.e. a health technology is better for one outcome, but worse for another); (3) to allow patient preferences to be included and directly influence trial results; (4) to provide transparency on subjective recommendations from a trial; (5) to provide consistency in the approach to presenting results from a trial; and (6) to synthesise multiple outcomes into a single metric. Further information was provided to support the use of benefit-risk methods in appropriate circumstances, including the following: methods identified from the review were collated into different groupings and described to aid the selection of a method; potential implementation of methods throughout the trial process were provided and discussed (with examples); and general considerations were described for those using benefit-risk methods. Finally, a checklist of five pieces of information that should be present when reporting benefit-risk methods was defined, with two additional items specifically for reporting the results. These recommendations will assist research teams in selecting which trial design to use and deciding whether or not a benefit-risk method could be included to ensure research questions are answered appropriately. Additional information is provided to support consistent use and clear reporting of benefit-risk methods in the future. The recommendations can also be used by funding committees to confirm that appropriate considerations of the trial design have been made. This research was limited in scope and should be considered in conjunction with other trial design methodologies to assess appropriateness. In addition, further research is needed to provide concrete information about which benefit-risk methods are best to use in publicly funded trials, along with recommendations that are specific to each method. The rapid review is registered as PROSPERO CRD42019144882. Funded by the Medical Research Council UK and the National Institute for Health and Care Research as part of the Medical Research Council-National Institute for Health and Care Research Methodology Research programme.

Deconstructing Haptic Feedback Information in Robot-Assisted Needle Insertion in Soft Tissues.

This paper evaluates the role and effectiveness of different types of haptic feedback in presenting relevant feedback information during needle insertion in soft tissues through a remotely operated robot. We carried out three experiments with human subjects to analyze the effect of grounded kinesthetic feedback, cutaneous vibrotactile feedback, and cutaneous pressure feedback for rendering the elastic and the viscous force components of a simplified needle-tissue interaction model in a simulated environment. Results showed that providing the two pieces of feedback information through different channels, i.e., kinesthetic and cutaneous, led to the best performance, yielding an improvement in detecting a different tissue layer with respect to providing both information through the same commercial grounded kinesthetic interface. Moreover, results indicate that cutaneous pressure feedback is more suited for rendering the elastic component of the interaction with respect to vibrotactile cutaneous sensations. Finally, results suggest that rendering this elastic component where the user holds the input interface is not so important, confirming that delocalized cutaneous sensations can be an effective solution.