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The production effect refers to the finding that words read aloud are better remembered than those read silently. This pattern has most often been explained as arising from the incorporation of sensorimotor elements into the item representation at study, which could then be used to guide performance at later test. This theoretical framework views aloud items as being distinctive in relation to silent items, and thus the effect was thought to emerge only when production was manipulated within-subjects. This claim was later challenged, and a reliable (albeit smaller) between-subject production effect has since been shown in recognition memory. Across a series of meta-analyses, we extend this earlier work, replicating the between-subject production effect for recognition, and demonstrating no such effect for overall target recall. However, supporting recent theoretical claims, we further observed an interaction between the production effect and serial position within recall, such that a production effect was observed for late time points but not early time points (a similar, albeit smaller and noncredible trend was observed for recognition). Finally, we provide evidence that production reduces off-list intrusions. In summary, production has a reliable impact on recognition memory when manipulated between-subjects, but a more complex relationship with recall performance. [ABSTRACT FROM AUTHOR]

Familiar music facilitates memory retrieval in adults with dementia. However, mechanisms behind this effect, and its generality, are unclear because of a lack of parallel work in healthy aging. Exposure to familiar music enhances spontaneous recall of memories directly cued by the music, but it is unknown whether such effects extend to deliberate recall more generally – e.g., to memories not directly linked to the music being played. It is also unclear whether familiar music boosts recall of specific episodes versus more generalised semantic memories, or whether effects are driven by domain-general mechanisms (e.g., improved mood). In a registered report study, we examined effects of familiar music on deliberate recall in healthy adults ages 65–80 years (N = 75) by presenting familiar music from earlier in life, unfamiliar music, and non-musical audio clips across three sessions. After each clip, we assessed free recall of remote memories for pre-selected events. Contrary to our hypotheses, we found no effects of music exposure on recall of prompted events, though familiar music evoked spontaneous memories most often. These results suggest that effects of familiar music on recall may be limited to memories specifically evoked in response to the music (Preprint and registered report protocol at ). [ABSTRACT FROM AUTHOR]

• Soft variants of precision and recall introduced. • Set notation with soft cardinality is applied. • Application 1: evaluation of keyword extraction. • Application 2: evaluation of segmentation result. Precision and recall are classical measures used in machine learning. However, they are based on exact matching. This results in binary classification where the predicted item is either a true or false positive despite inexact matching is often preferred in pattern recognition. To address this problem, we introduce soft variants of precision and recall based on application-specific similarity measure. 2022 Elsevier Ltd. All rights reserved. [Display omitted] [ABSTRACT FROM AUTHOR]

This interdisciplinary research aims to examine the effects of photos used in Arabic news websites on both comprehension and recall among readers. The dual-coding theory was used as a theoretical framework. The research used an experimental design of two groups to test the effects of photos on comprehension and recall of the news content included in two news websites created specifically for the experiment. Two groups of 80 university students have participated voluntarily in the experiment. The results indicated that the use of photos in the news website has increased significantly the levels of both comprehension and recall among respondents. [ABSTRACT FROM AUTHOR]

To perform reliably and confidently from memory, musicians must able to recover from mistakes and memory failures. We describe how an experienced singer (the second author) recovered from mistakes and gaps in recall as she periodically recalled the score of a piece of vocal music that she had memorized for public performance, writing out the music six times over a five-year period following the performance. Five years after the performance, the singer was still able to recall two-thirds of the piece. When she made mistakes, she recovered and went on, leaving gaps in her written recall that lengthened over time. We determined where in the piece gaps started (losses) and ended (gains), and compared them with the locations of structural beats (starts of sections and phrases) and performance cues (PCs) that the singer reported using as mental landmarks to keep track of her progress through the piece during the sung, public performance. Gains occurred on structural beats where there was a PC; losses occurred on structural beats without a PC. As the singer's memory faded over time, she increasingly forgot phrases that did not start with a PC and recovered at the starts of phrases that did. Our study shows how PCs enable musicians to recover from memory failures. [ABSTRACT FROM AUTHOR]

This reflexive article examines how identity is informed by the personal, the political and the practice of filmmaking. As a South African filmmaker of mixed heritage (classified as 'coloured' under the Apartheid-era code), I recall a childhood of cinema, of cinematic moments, of screen heroes and of sociocultural traditions (imported, imposed and Indigenous) that shaped our ideas and expressions of masculinity as boys and men. Constant in this reflection is how spatial Apartheid (as effected through the Group Areas Act of 1950) impacted Black life in all its manifestations, but particularly the ways it determined how we lived and engaged with cinema in these designated and tightly controlled spaces. The fact that we could find resonance with films narratives, by interpreting them into our own lingo and bribing the screen icons into our realities gave us a window to reimagine our reality. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine the relationship between two types of cognitive abilities (numeracy and recall) and life insurance holdings in European countries. Households with better numeracy and recall are more likely to own life insurance. Interaction effects indicate a higher level of education decreases the positive effect of numeracy on life insurance holdings and increases the positive effect of recall on life insurance holdings. Multinomial regressions indicate that recall has a positive impact on the decisions to hold term life, whole life, and both term and whole life insurance policies and a negative impact on the decision not to hold any type of life insurance policy. We also find that recall has a greater impact on the decision to own term life policies than on the decision to own whole life (both term and whole life) policies. One possible reason is whole life policies consist of many options that are difficult to comprehend even with higher cognitive abilities. One implication of this study is marketing by life insurers should take into account household cognitive abilities. [ABSTRACT FROM AUTHOR]

Hate speech specially racism, gender and religion discrimination, defaming comments are becoming one of the biggest problems in Twitter these days, that are making people to switch to other social media. Its effect is long-standing and unpreventable. To stop hateful activities from happening, Machine Learning approaches are needed to be applied. This research article focuses on the performance analysis and effectiveness of Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbor, Decision Tree, Random Forest and Support Vector Machine on detection of hate speech from Twitter. SVM, Decision Tree and Random Forest outperformed all the other models, achieving state-of-art 95.5%, 96.2% and 98.2% accuracy respectively on comments gather over a stretch. [ABSTRACT FROM AUTHOR]

The current study investigated mixed-emotional memories in groups of young, young-old, and old-old participants. We used a "word-suffix approach" to simulate the co-occurrence of positive and negative emotions. The participants engaged in a free-recall task for valenced words and mixed-emotional words (valenced words coupled with pejorative or endearment suffixes). Our results showed that the groups of older adults recalled higher numbers of suffixed words compared to their younger counterparts. Our findings highlighted older adults' tendency to perceive and remember emotionally ambivalent words to a greater extent than younger adults and showed that the young-old participants were particularly good at solving ambivalence by focusing on positive-dominant ambivalent words. [ABSTRACT FROM AUTHOR]

Background: A reliable and valid tool to measure short-term recall does not exist. Objective: The purpose of this study was o assess the reliability of a novel tool designed to evaluate the immediate recall and performance of prescribed exercises. This reliability study was secondary from a parent randomized controlled trial. Methods: Patients ages 18 to 65 with low back or unilateral knee pain longer than 6 weeks, reporting to a primary care or physical therapy clinic at one large military hospital. Participants were randomized into one of four groups based on number of exercises and type of instruction. The Home Exercise Assessment Tool (HEAT) is scored along an ordinal scale, (zero = low; three = high performance), with 2+ equal to meeting the intent of the exercise. Reliability of testing was assessed using a pool of four raters. Results: The interrater reliability of the HEAT performed on 30 participants (9 females, and 21males, mean age 36.7 (SD = 11.7)) and eight participants during pilot testing demonstrated moderate to good reliability (ICC = 0.66–0.77). Intrarater reliability during pilot testing was excellent (ICC = 0.93–0.96). Conclusion: The HEAT demonstrated moderate to good interrater and excellent intrarater reliability. This tool is unique in that it assesses both recall and adherence, and merits further testing in other settings. [ABSTRACT FROM AUTHOR]

Purpose: With increasing rates of migration worldwide, police are more likely than ever to interview witnesses who do not have the same first language as they do. We examined how to best approach this situation by comparing three different ways of conducting such interviews. Methods: Native Arabic speakers (N = 128) living in Sweden witnessed a video of a mock crime and were allocated to one of three interview conditions: a face‐to‐face interview in Swedish (i.e. their second language), a face‐to‐face interview with an interpreter translating from Swedish to Arabic or an Arabic language Self‐Administered Interview© (SAI). Results: For total number of details reported, the no interpreter condition resulted in moderately fewer details being reported than the interpreter and SAI conditions. A similar trend was seen for correct details; however, these differences were not statistically significant. Participants in the SAI condition were somewhat less accurate in their reports compared with both the interpreter and no interpreter conditions. Conclusions: If interviewing without an interpreter, there is minimal loss of reported detail when the witness speaks the interviewer's language at an intermediate level and the questions posed are few and simple. Moreover, provided that the witness has a sufficient level of literacy, administrating the SAI in the witness's native language can be an alternative for witnesses with no or limited verbal ability in the interviewer's language. [ABSTRACT FROM AUTHOR]

Ranitidine products contain unacceptable levels of N-nitrosodimethylamine. This study aimed to investigate changes in the treatment regimen and their influencing factors after the ranitidine recall. This retrospective study used data from nationwide Korean claims from 2019. Patients with gastrointestinal disorders treated with ranitidine for at least a month on 25 September 2019, were selected for this study. Other histamine-2 receptor antagonists (H2RAs), proton pump inhibitors (PPIs), potassium-competitive acid blockers (PCABs), and prostaglandin E1 analogs were administered as alternatives to ranitidine. Kaplan–Meier survival and Cox proportional hazards regression analyses were performed to gauge the time until switching to alternative drugs and assess the influencing factors. In total, 7502 patients were included in this study, among which 5164 (68.8%) switched from ranitidine to an alternative drug. The most prescribed alternative drugs were H2RAs, followed by PPIs, PCABs, and prostaglandin E1 analogs. Increasing age; Medical Aid insurance (MedAid); and a history of hypertension, diabetes mellitus, asthma, and osteoarthritis were associated with a higher probability of switching treatments. Patients with concomitant gastroesophageal reflux disease and peptic ulcers were more likely to switch to alternative drugs than patients with gastritis. Approximately two-thirds of patients with gastrointestinal disorders switched from ranitidine to alternative drugs within 3 months after ranitidine withdrawal. The Cox regression analysis showed that age (>55 years); insurance type (MedAid); comorbidities, such as hypertension, diabetes mellitus, asthma, and osteoarthritis, and gastrointestinal disorder severity influenced the switch from ranitidine to alternative drugs. [ABSTRACT FROM AUTHOR]

Connected consciousness, assessed by response to command, occurs in at least 5% of general anaesthetic procedures and perhaps more often in young people. Our primary objective was to establish the incidence of connected consciousness after tracheal intubation in young people aged 18–40 yr. The secondary objectives were to assess the nature of these responses, identify relevant risk factors, and determine their relationship to postoperative outcomes. This was an international, multicentre prospective cohort study using the isolated forearm technique to assess connected consciousness shortly after tracheal intubation. Of 344 enrolled subjects, 338 completed the study (mean age, 30 [standard deviation, 6.3] yr; 232 [69%] female). Responses after intubation occurred in 37/338 subjects (11%). Females (13%, 31/232) responded more often than males (6%, 6/106). In logistic regression, the risk of responsiveness was increased with female sex (odds ratio [OR adjusted ]=2.7; 95% confidence interval [CI], 1.1–7.6; P =0.022) and was decreased with continuous anaesthesia before laryngoscopy (OR adjusted =0.43; 95% CI, 0.20–0.96; P =0.041). Responses were more likely to occur after a command to respond (and not to nonsense, 13 subjects) than after a nonsense statement (and not to command, four subjects, P =0.049). Connected consciousness occured after intubation in 11% of young adults, with females at increased risk. Continuous exposure to anaesthesia between induction of anaesthesia and tracheal intubation should be considered to reduce the incidence of connected consciousness. Further research is required to understand sex-related differences in the risk of connected consciousness. [ABSTRACT FROM AUTHOR]

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers' interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles' contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns. [ABSTRACT FROM AUTHOR]

The search strategy of a literature review is of utmost importance as it impacts the validity of its findings. In order to build the best query to guide the literature search on clinical decision support systems applied to nursing clinical practice, we developed an iterative process capitalizing on previous systematic reviews published on similar topics. Three reviews were analyzed relatively to their detection performance. Errors in the choice of keywords and terms used in title and abstract (missing MeSH terms, failure to use common terms), may make relevant articles invisible. [ABSTRACT FROM AUTHOR]

Chronic Kidney Disease (CKD) is a major threat in medical analysis and is one of the major contributors of death as a noncommunicable disease, affecting 10 to 15 percent of the worldwide population. Accurate detection of CKD in its initial stages is thought to be critical for minimizing the effects of health complications of the patients such as hypertension, iron deficiency, bone disorders occurring due to imbalance of minerals, malnourishment, pH fluctuations and abnormalities, and neurological complications through timely intervention with appropriate treatments. This study offers the steps for predicting status of CKD using medical records which comprise the data preprocessing, managing missing values, and feature extraction. Several studies on the detection of CKD at an early stage have been conducted using machine learning techniques. The two classification models such as Decision Tree (DT) and K-Nearest Neighbor (KNN) is used in this study. The performance of each classification methods is compared with each other and identified that KNN is the best classifier. Also using the relevant data extracted from the clinical dataset after feature extraction, the patient is said to have CKD or not CKD based on KNN approach with accuracy of 97 percent. [ABSTRACT FROM AUTHOR]

This paper discusses the methods the course teacher used to teach the Information Processing and Retrieval course [Course Code: 19LISC302]. A few aspects, including Chain Indexing, KWIC, Boolean operators, queries, logical relations, and recall & precision, have been highlighted here for illustration. The course teacher was able to improve the students' knowledge of the mentioned areas by using teaching methods based on the Revised Bloom's Taxonomy. The methods also made it possible to follow the students' progress. Boolean operators, queries, logical relations, and recall & precision exercises are taken into consideration for the discussions here, but chain indexing and KWIC related classroom practice formats are only supplied at Annexure-i. The course teacher created the application that is connected to logical relations (Truth Table), and the students have used it in class to practice. Various multifaceted key phrases, a few queries, the Boolean operators AND, OR, and NOT, and the Truth Table are all included in the aforementioned programme. For students to successfully obtain pertinent data from any database, they must have a solid understanding of logical ideas. Meaningful results won't be produced by executing the queries and operators automatically or blindly. Additionally, it could need extra time from information seekers. Boolean operators are used in information retrieval systems to evaluate queries. The Boolean operators are typically linked to the classes in the queries. The evaluation for each class is either True or False. To effectively use the operators in search queries and extract the necessary data, learners need be familiar with the Truth Table-related concepts. In addition, for the aforementioned subjects other than Chain Indexing and KWIC, the Revised Bloom's Taxonomy based teaching and the students' evaluation techniques have also been presented here. Wherever needed, enough screenshots of the aforementioned programme are produced. Students used a learning approach based on Revised Bloom's Taxonomy to study the concepts linked to the aforementioned themes, and they have been duly documented below. With the use of the methodologies utilised in this study, the teacher was able to glean the essential information necessary for determining the pupils' degrees of learning ability and applying that information to the necessary charts. The charts show the students' competence levels according to themes, making it simple to see the students' skill status. Realizing their ability levels is aided by each level wise clearance and their overall clearance status. Chain indexing, KWIC, logical relations, queries with Boolean operators, database sample records format, RBT levels-based evaluation format, recall, and precision are the examples of exercise forms shown in Annexure-i. Further, overall progress charts, each level wise progress charts and Student performance evaluation report are given at Annexure-ii, Annexure-iii and Annexure-iv respectively. [ABSTRACT FROM AUTHOR]

Aim/Purpose The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology In our work, we analyze various machine learning algorithms and implement KNN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution To the researchers' best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendations for Researchers With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Impact on Society NER helps recognize important textual components including names of individuals, places, and monetary value among others. When dealing with enormous datasets, it is critical to sort unstructured data and to find vital information by identifying the major entities in a text. Future Research Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages. [ABSTRACT FROM AUTHOR]

We reviewed the child and the adult literature on the impact of witnesses drawing what happened on the number of details recalled and the accuracy of the reported details. Most experiments reported a beneficial effect of drawing what happened (or drawing the scene) on the number of details reported primarily in free recall and sometimes also in cued recall. The consensus across studies was that drawing protocols did not negatively impact the accuracy of reported details (when accuracy was attainable) unless suggestive details were drawn. These results were largely consistent regardless of interviewer expertise or protocol used. Draw‐and‐tell instructions should be considered by forensic investigators for the following reasons: (1) the beneficial effect on number of details recalled with no detriment to accuracy, (2) the added benefit for children who need additional interviewer support, and (3) the ease at which the instruction can be implemented with minimal expertise or training. However, more ecologically valid research is needed to establish the efficacy of drawing (1) in forensic interviews, (2) in the presence of misinformation, (3) across instances of repeated event memory, and (4) across sequential interviews. [ABSTRACT FROM AUTHOR]

The forward testing effect (FTE) refers to the finding that retrieval practice of previously studied material can facilitate recall of newly studied (critical) material. Such interim retrieval practice can also lead to a differential FTE, i.e., a more pronounced FTE for items at early than later serial positions in the critical material. The present study examined whether this differential FTE also holds with interim semantic generation of extra-list items, and whether it is influenced by study material. Consistent with prior work, the results of two experiments showed that both interim retrieval practice and interim semantic generation induce the general (list-level) FTE when unrelated study lists are applied, whereas retrieval practice only creates the effect with categorised study lists. Critically, however, the differential FTE was present in response to retrieval practice but absent in response to semantic generation. This pattern held regardless of which material was studied, thus experimentally dissociating the general (list-level) from the differential (item-level) FTE. The findings may suggest that retrieval practice, but not semantic generation, induces a reset of the encoding process which promotes attentional encoding such that a more pronounced FTE arises for early than middle and late serial positions in the critical list. [ABSTRACT FROM AUTHOR]

The accuracy of the recall of the severity of nausea and vomiting of pregnancy (NVP) with Pregnancy-Unique Quantification of Emesis (PUQE) questionnaire has been questioned. We aimed to compare PUQE scores of women recalling the worst episode of NVP of their current pregnancy in different gestational weeks (gwks). Total of 2343 pregnant women (gwks 7–40) were recruited. Four groups were formed according to the gwks at reply: ≤16 gwks (n = 554), ≤20 gwks (n = 1209), >20 gwks (n = 1134) and ≥24 gwks (n = 495). PUQE scores were similar between the groups. Consequently, consistency of PUQE scores across the groups endorses the useability of the PUQE questionnaire in retrospective assessment of the overall severity of NVP in different gwks, regardless of passing of the peak NVP symptoms. What is already known on this subject? Retrospective evaluation of the severity of nausea and vomiting of pregnancy (NVP) has been argued to be disposed to recall bias. Structured Pregnancy-Unique Quantification of Emesis (PUQE) questionnaire is a validated tool for assessing the severity of NVP. What do the results of this study add? When the women recalled the most severe NVP symptoms of their current pregnancy, no differences in the PUQE scores were found despite different gestational weeks at reply. Of distinct PUQE questions, women answering in early pregnancy reported longer duration of nausea than women answering in late pregnancy, but other questions were rated similarly. What the implications are of these findings for clinical practice and/or further research? Our aim was to compare the PUQE scores between the women who filled in the PUQE questionnaire in early or in late pregnancy, instructed to recall their worst symptoms in their current pregnancy. As there were no differences between the groups in total PUQE scores, our results support the application of PUQE questionnaire to assess the severity of NVP during pregnancy not only concurrent to the peak symptoms but also retrospectively. [ABSTRACT FROM AUTHOR]

Background and Purpose: Older adults with chronic obstructive pulmonary disease (COPD) are at risk for physical and cognitive impairment. Cognitive function is associated with falls in older adults. However, it is unknown whether a relationship exists between cognitive function and falls in patients with COPD. The aim of this study was to examine the relationships between cognitive function, balance, and gait speed in older adults with COPD. Patients and Methods: A secondary analysis was performed using data from the 2010 wave of the Health and Retirement Study (HRS). Cognitive (immediate and delayed recall, executive function) and physical (gait speed, tandem balance time) measure data were extracted for older adults with COPD (n = 382) and an age-matched control group without COPD (n = 382) who met inclusion/exclusion criteria. Multivariate linear regression modeling was performed to examine associations between cognitive function and mobility or balance while controlling for age, gender, body mass index, grip strength, and education. Results: In older adults with COPD, delayed recall was significantly associated with tandem balance performance (β= 1.42, P <.05). Other cognitive measures were not associated with gait speed or balance. Conclusion: In older adults with COPD, one of four cognitive functions was associated with a static standing balance task. Screening of cognitive function, specifically delayed recall, should be a part of the management of falls in this population. [ABSTRACT FROM AUTHOR]

Copyright of Iranian Journal of Information Processing & Management is the property of Iranian Information & Documentation Center (IRANDOC) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital picture forgery detection has recently become a popular and significant topic in image processing. Due to advancements in image processing and the availability of sophisticated software, picture fabrication may hide evidence and hinder the detection of such criminal cases. The practice of modifying original photographic images to generate a forged image is known as digital image forging. A section of an image is copied and pasted into another part of the same image to hide an item or duplicate particular image elements in copy-move forgery. In order to make the forgeries real and inconspicuous, geometric or postprocessing techniques are frequently performed on tampered regions during the tampering process. In Copy-Move forgery detection, the high similarity between the tampered regions and the source regions has become crucial evidence. The most frequent way for detecting copy-move forgeries is to partition the images into overlapping square blocks and utilize Discrete cosine transform (DCT) components as block representations. Due to the high dimensionality of the feature space, Gaussian Radial basis function (RBF) kernel based Principal component analysis (PCA) is used to minimize the dimensionality of the feature vector representation, which improves feature matching efficiency. In this paper, we propose to use a novel enhanced Scale-invariant feature transform (SIFT) detector method called as RootSIFT, combined with the similarity measures to mark the tampered areas in the image. The proposed method outperforms existing state-of-the-art methods in terms of matching time complexity, detection reliability, and forgery location accuracy, according to the experimental results. The F1 score of the proposed method is 92.3% while the literature methods are around 90% on an average. [ABSTRACT FROM AUTHOR]

The recall and visualization of people and places from memory is an everyday occurrence, yet the neural mechanisms underpinning this phenomenon are not well understood. In particular, the temporal characteristics of the internal representations generated by active recall are unclear. Here, we used magnetoencephalography (MEG) and multivariate pattern analysis to measure the evolving neural representation of familiar places and people across the whole brain when human participants engage in active recall. To isolate self-generated imagined representations, we used a retro-cue paradigm in which participants were first presented with two possible labels before being cued to recall either the first or second item. We collected personalized labels for specific locations and people familiar to each participant. Importantly, no visual stimuli were presented during the recall period, and the retro-cue paradigm allowed the dissociation of responses associated with the labels from those corresponding to the self-generated representations. First, we found that following the retro-cue it took on average ∼1000 ms for distinct neural representations of freely recalled people or places to develop. Second, we found distinct representations of personally familiar concepts throughout the 4 s recall period. Finally, we found that these representations were highly stable and generalizable across time. These results suggest that self-generated visualizations and recall of familiar places and people are subserved by a stable neural mechanism that operates relatively slowly when under conscious control. [ABSTRACT FROM AUTHOR]

Clay, earthen elements, powders, and water mixtures are typically manipulated into desired shapes to create ceramics. The ceramic is then dispersed in a blast furnace, which is a high-temperature oven, after it has been molded. Varnishes are paint-like materials that are used to decorate ceramics and are resistant to water. Manually identifying ceramic samples can lead to the discovery of flaws. The chemical composition of the finished ceramic tiles is not visible. Exact identification of ceramic samples is still a difficult task for the user, as it necessitates more hands-on experience. With this overview, this paper focus on designing an effective workflow for classifying the ceramic samples based on chemical composition. The ceramic chemical sample dataset from KAGGLE with the dataset size of 88 with 19 features having 5 classes of ceramic as 0-"FLQ Ceramic", 1- "DYBS Ceramic", 2-"DYNS Ceramic", 3- "DYY Ceramic", 4- "DYMQC Ceramic". The data is preprocessed and normalized for splitting the training and testing dataset. The normalized dataset is fitted to Anova test analysis to extract the significant features. The insignificant features from Anova test is deleted from the original database dataset and anova normalized dataset is fitted to Exhaustive feature selection using KNN, Naive Bayes, Logistic regression, Decision Tree and Random Forest classifier. The exhaustive feature selected dataset is applied to the above-mentioned classifier algorithms to examine the efficiency with the presence and absence of feature scaling. The exhaustive feature selected dataset is again applied to random oversampling in order to improve the classifier performance. The random oversampled exhaustive feature selected dataset is applied to the above mentioned classifier algorithms to compare the performance metrics like precision, accuracy, fscore, recall and run time. Experimental results shows that the decision tree exhibits 49% accuracy with exhaustive feature selected dataset. The same decision tree classifier shows 100% accuracy after applying random oversampling with exhaustive feature selected dataset. [ABSTRACT FROM AUTHOR]

Compression strength refers to a structure ability to endure loads that cause it to shrink in size. The ultimate load in Newtones scaled by the gross cross section area of the block in square millimeters yields the bearing capacity of a concrete masonry unit. The machine learning technology could extend the interdisciplinary field for the estimation of concrete block compressive strength. With this review, this project aims to estimate the concrete block compressive strength retrieved from the KAGGLE machine-learning repository. The compressive concrete blocks dataset contains 9 features and 1050 concrete block and it is processed with encoding and missing values. The exploratory data analysis is done to analyze the correlation of all the features. The raw dataset is applied to linear regression, Linear Kernel SVR, Polynomial Kernel SVR, Radial Basis Function SVR, Sigmoid Kernel SVR, Decision Tree to analyze the performance of compressive strength estimation of concrete blocks. The proposed model uses Fully Connected Deep Neural Network (FCDNN) where the compressive concrete blocks dataset is applied to estimate the compressive strength estimation of concrete blocks. The performance metrics utilized for analysis are Mean Squared Error, R-Squared error. Experimental results shows that the Fully Connected Deep Neural Network shows Mean Squared Error of 29.5196 and R-Squared error of 0.885 when analyzed with the all regression models. [ABSTRACT FROM AUTHOR]

Paper aims: This article investigates the worldwide trend of growth in the number of recalls, as well as in the number of products involved in each campaign. Originality: To investigate these facts, a study of the automotive recall was developed, comprising Brazil, the European Union, and the United States of America. Research method: Due to the different availabilities between the locations, search tools and software were developed to obtain and group hidden data from 2010 to 2019. Main findings: In this work, the impacts of the recall were analyzed using three categories of algorithms: clustering, classification, and regression. Analyzes were made about the results obtained and discussions were built about the importance of applying the machine learning technique. Implications for theory and practice: The use of search tools and software to obtain and group hidden data in databases and opens the opportunity for new research in various areas. [ABSTRACT FROM AUTHOR]

Movie sentiment analysis is a technique used to analysis sensations, emotions, sentiments, opinion related to the movies. It gained an increasing interest because of the continuous growth of the movies' market. The viewers express their sentiments using comments. The huge quantity of the generated comments requires using machine learning algorithms to ensure the automation of the sentiment analysis to help people to decide which movie worth his time. The collected movies feedbacks are classified into positive and negative In the literature, many solutions have been proposed to apply machine learning algorithms with movie sentiment analysis. Compared to those works, we propose in this article, comparing 35 different algorithms. They are evaluated using the following metrics: Kappa, Accuracy, F1-Score, Recall and Precision. We used two well-known datasets: Cornell Movie Review dataset and Large Movie Review Dataset that are applied in the most of the existing works as benchmarks. [ABSTRACT FROM AUTHOR]

PurposeThe purpose of this paper is to derive into practical recommendations from multisensory stimulation with virtual reality (VR) and scent to help educators develop effective teaching strategies geared toward aspects of the learning experience, recall, and creativity in a stereotypical learning context.Design/methodology/approachThe paper is based on a randomized experiment in which student participants were subdivided into three treatment groups and one control group. Each group was stimulated by a different combination of visual, auditory, and olfactory stimuli (2D SMELL, VR, and VR SMELL) and the outcomes were compared against those of the control group (2D). Consistent with the Cognitive Theory of Multimedia Learning, hypotheses were constructed to study the effect of different combinations of stimuli on the learning experience and learning outcomes related to recall and creativity in a stereotypical learning context.FindingsTraditional video content alone and bundled with a coherent olfactory stimulus prompted higher self-reported ratings of perceived quality of the sensory experience. Olfactory stimulus in combination with either VR or a traditional video prompted higher self-reported ratings on perceived immersion. In a stereotypical learning context, the highest recall scores were achieved with traditional video alone. Both VR alone and bundled with an olfactory stimulus resulted in enhanced creativity.Research limitations/implicationsThe findings of this study should be interpreted in the context of adopting multisensory stimulations combined with VR technology as part of stereotypical learning contexts. Most professional educators do not have robust knowledge or experience in using build-on-purpose multisensory stimuli but are increasingly engaged in using multisensory tools such as VR, as part of their teaching practice. In relation to recall, the results are consistent with the hypothesis that in a stereotypical learning context, a multisensory experience involving VR and olfactory stimuli can be related to an undesired cognitive load for learners. There exists a possibility that the low-technical version of the VR goggles used, as well as the contents of the instructional video may have influenced the learning outcomes in terms of recall. Hence, future research should consider such aspects and focus on richer learning contexts.Originality/valueThis work offers practical recommendations for instructional design strategies aiming to create multisensory stimulations with VR and olfactory components to foster a richer learning experience and enhanced learning outcomes, under the assumptions of a stereotypical learning context.

Allergens are one of the leading causes of food recalls in the US. The Food and Drug Administration (FDA) enforces requirements relating to major food allergens (MFAs) and gluten-free labeling to ensure food safety for allergic and celiac patients, respectively. Violative foods are subject to recalls. In this study, recall data for FDA-regulated foods were analyzed for fiscal years (FYs) 2013–2019 to identify trends and root causes associated with 1471 food allergen and gluten recalls. Of the 1471 recalls, 1415 recalls were due to MFAs, 34 recalls were due to gluten-free labeling violation and 23 recalls involved other allergens. Recalls due to MFAs overall increased during the study period with a peak incidence in FY 2017. MFA recall health hazard classifications were assessed as Class I (51.2%), Class II (45.5%), and Class III (3.3%). A majority of MFA recalls involved one allergen (78.8%). Milk was the most common MFA involved in MFA recalls (37.5%), followed by soy (22.5%) and tree nuts (21.6%). Almond, anchovy, and shrimp were the most common allergens recalled within the MFA groups of tree nuts, fish, and Crustacean shellfish, respectively. About 97% of MFA recalls involved one product category and among them, the category of ‘bakery products, dough, bakery mixes and icings’ ranked first (367 recalls), followed by the category of ‘chocolate and cocoa products’ (120 recalls). Labeling-associated errors accounted for 71.1% of MFA recalls with known root causes (914 out of 1286). It is important for the industry to develop and implement appropriate allergen controls to reduce the number of MFA recalls.

We propose a quality control method based on machine learning neural networks to enhance the quality of high-frequency (HF) radar data. Unlike traditional quality control methods that rely on radar signals as indicators and involve extensive data manipulation in specialized software, our approach employs a Bi-LSTM neural network model. This method aims to improve data quality and streamline the quality control process. Through a series of analyses, we demonstrate the feasibility of using machine learning techniques to enhance radar data quality.

The glm R package is commonly used for generalized linear modeling. In this paper, we evaluate the ability of the glm package to predict binomial outcomes using logistic regression. We use single-cell RNA-sequencing datasets, after a series of normalization, to fit data into glm models repeatedly using 10-fold cross-validation over 100 iterations. Our evaluation criteria are glm’s Precision, Recall, F1-Score, Area Under the Curve (AUC), and Runtime. Scores for each evaluation category are collected, and their medians are calculated. Our findings show that glm has fluctuating Precision and F1-Scores. In terms of Recall, glm has shown more stable performance, while in the AUC category, glm shows remarkable performance. Also, the Runtime of glm is consistent. Our findings also show that there are no correlations between the size of fitted data and glm’s Precision, Recall, F1-Score, and AUC, except for Runtime.

The mnemonic benefits of animate (e.g., Tiger) over inanimate(e.g., Table) stimuli have been demonstrated across severaldifferent memory paradigms. Given the ubiquity of inanimate,computer-generated voices we investigated if the animacy of apresentation source confers mnemonic benefits. We asked: isinformation delivered by a human voice better rememberedthan information presented by a computer-generated voice?Word-lists were presented auditorily by either a human or acomputer-generated voice and memory was measured using afree recall assessment. In Experiment 1, words presented in ahuman voice were better remembered than words presented ina computer voice. Experiment 2 demonstrated that beliefsabout the animacy of a computer-generated voice were notsufficient for any benefits to accrue, suggesting a possibleboundary condition for the effect. Both experiments replicatedthe mnemonic benefits of animate words and demonstratedfurther extensions of the effect to spoken word presentation.

Kulit merupakan organ luar terluas yang menutupi tubuh manusia. Akibat intensitas tinggi paparan lingkungan luar, kulit dapat mengalami berbagai masalah kesehatan, salah satunya adalah kanker kulit. Deteksi dini dibutuhkan agar penanganan lebih lanjut kepada pasien dapat segera dilakukan. Pemanfaatan artificial intelligence (AI)-based solution pada pengolahan citra kanker kulit dapat digunakan untuk mendeteksi adanya potensi kanker kulit. Pada makalah ini, dilakukan klasifikasi jenis kanker kulit jinak dan ganas dengan memanfaatkan metode convolutional neural network (CNN) arsitektur GoogLeNet. Arsitektur GoogLeNet memiliki keunggulan dengan adanya inception module yang memungkinkan proses konvolusi dan pooling berjalan secara paralel, yang dapat memperpendek waktu komputasi, sehingga mempercepat proses klasifikasi tanpa mengurangi akurasi sistem. Penelitian ini terdiri atas beberapa tahapan, dimulai dengan akusisi data sejumlah enam ratus data citra kanker kulit yang diperoleh dari situs Kaggle.com, kemudian dilakukan penyeragaman ukuran masukan serta pemanfaatan dull razor filtering untuk mengurangi derau citra masukan akibat rambut-rambut halus yang tumbuh di sepanjang epidermis kulit. Setelah proses preprocessing selesai dilakukan, arsitektur GoogLeNet memproses masukan citra, lalu mengklasifikasikan masukan ke dalam kategori kanker kulit jinak (benign) atau kanker kulit ganas (malignant). Kinerja sistem kemudian diuji dengan parameter kinerja, seperti presisi, recall, dan f-1 score, serta dibandingkan dengan metode serupa. Sistem berhasil memperoleh hasil yang memuaskan, di antaranya adalah akurasi 97,73% dan loss 1,7063. Sementara itu, untuk parameter presisi, recall, dan f-1 score, masing-masing diperoleh nilai rata-rata 0,98. Kinerja sistem yang diusulkan berhasil mendapatkan akurasi yang lebih baik dibandingkan dengan penelitian terdahulu dengan penggunaan dataset yang jauh lebih sedikit. Hasil pengujian ini menunjukkan bahwa metode CNN mampu melakukan deteksi dan klasifikasi pada kanker kulit secara akurat, sehingga diharapkan metode ini dapat membantu pekerja medis dalam melakukan diagnosis kepada masyarakat luas.

Background: Cognitive impairments are fundamental in schizophrenia with verbal memory impairments commonly occurring not only in patients but also in unaffected genetically susceptible individuals. Deficits in verbal memory produce difficulty in problem-solving, emotional distress, and worsening of daily life skills resulting in a poor quality of life. Objectives: This study aims to evaluate the verbal memory in unaffected first-degree relatives of patients of schizophrenia in comparison to healthy controls as well as patients of schizophrenia in remission and to find correlation to symptom domains. Methods: It was a hospital-based, descriptive, cross-sectional case-control study. Three groups (n=40, each group) of patients, first-degree relatives, and controls were taken. Subjects were screened for mental retardation and remission was ascertained in the patient group by the Positive and Negative Syndrome Scale (PANSS). Rye’s Auditory Verbal Learning (RAVL) test was used to assess verbal leaning. Results: Verbal memory impairments were significant both in patients and unaffected first-degree relatives. Further, these impairments showed a strong correlation to negative symptoms. Conclusion: Significant (p

The purpose of this paper is to find how generation cohorts Y and Z process online content and to make a parallel between attention and recall in online consumer response framework. It also uncovers differences between two generations and offers valuable insights for managers. Eye-tracking was combined with in-depth qualitative interviews to uncover values behind the attention and indicators of recall. The eye-tracking study was conducted in a behavioural lab on a simulated website, followed by interviews recording. Despite similar attentional patterns, the two cohorts have different values that the content needs to satisfy to be preferred and remembered. The laddering provided a look into these values and deeper understanding of biometrical measures. The study is limited to laboratory settings and small sample size. The results can inform decision-makers on how to optimise the digital content and increase content recall for the target age groups within cohorts Y and Z. This is the first study that combines eye-tracking and laddering interviews in interpreting path from attention to the recall of online content. [ABSTRACT FROM AUTHOR]

Objectives: To estimate median survival time until the appearance of anomalies indicating a potential implant failure associated with fluid ingress in implanted cochlear implant (CI) devices of the initial version of Advanced Bionics HiRes Ultra and HiRes Ultra 3D series. Study Design: Retrospective review. Methods: Cochlear implantation was performed in a standard fashion. Implant integrity was tested at follow-up visits by measuring impedance and electrically evoked compound action potential (ECAP). Additional tests such as electrical field imaging (EFI) were conducted by the manufacturer. Based on these tests, the presence or absence of an anomaly was classified. Results: Of the 349 devices implanted at this institution, 181 showed anomalies in accordance with the special failure mode and for this reason, 120 implants were already explanted. The median survival time without anomalies was 1062 days. So far, the suspicion of device failures has been confirmed in all cases in which a post-implantation analysis was already available. Conclusions: Regular tests at the follow-up visits are necessary to monitor the integrity of CIs. [ABSTRACT FROM AUTHOR]

The last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances. [ABSTRACT FROM AUTHOR]

While items learned immediately before testing are generally remembered better than prior items in a study list, in delayed testing this relationship is reversed, yielding a negative recency effect. To adjudicate between the strategic rehearsal and spacing accounts of this phenomenon, we examined performance of 169 participants on a delayed recognition test following multiple sessions requiring the study and immediate free recall testing of 16 lists of 16 words. This revealed a strong effect of the amount of spacing between initial study position and initial free recall position on the degree of negative recency, supporting the spacing account. Furthermore, these spacing effects were nonmonotonic, suggesting that they are mediated by consolidation processes. Additional analyses indicate that strategies and rehearsal opportunities may also contribute to the effects of within-list encoding position on subsequent long-term memory, but for recall more than for recognition. [ABSTRACT FROM AUTHOR]

At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a preconfigured item planning that was done by him earlier. The approach is made in II-phases. Product detection, followed by product recognition. For product detection, we have used You Only Look Once Version 5 (YOLOV5), and for product recognition, we have used both the shape and size features along with the color feature to reduce the false product detection. Experimental results were carried out using the SKU-110 K data set. The analyses show that the proposed approach has improved accuracy, precision, and recall. For product recognition, the inclusion of color feature enables the reduction of error date. It is helpful to distinguish between identical logo which has different colors. We can achieve the accuracy percentage for feature level as 75 and score level as 81. [ABSTRACT FROM AUTHOR]

Camrelizumab (SHR-1210) is a humanized IgG4 monoclonal anti-programmed cell death protein 1 (PD-1) antibody that has been shown to inhibit the binding of PD-1 to PD-L1, thereby blocking the immune escape of various types of cancer, including lung squamous cell carcinoma (LSCC). Reactive cutaneous capillary endothelial proliferation (RCCEP) is the most common adverse event in camrelizumab-treated patients. Here, we introduce a case of LSCC with recall RCCEP induced by stereotactic body radiation therapy (SBRT). A 76-year-old LSCC patient developed RCCEP when he received camrelizumab and chemotherapy. After discontinuing camrelizumab treatment, the RCCEP lesions spontaneously regressed and fell off. However, when the patient received subsequent SBRT, the RCCEP occurred again at the same sites. This case may provide clues for additional study of the immune reactivation effect of SBRT or the underlying mechanism of RCCEP. [ABSTRACT FROM AUTHOR]