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The importance of infrared or thermal imaging can be seen in various applications based on Machine Vision. The real-time based application in surveillance systems like detection and tracking of moving objects has shown strong potential. According to available research, there exist some significant issues in colored video frames due to sudden environmental change or other external effects like varying backgrounds. In contrast, the thermal video frames are less affected by sudden light intensity and varying backgrounds. The suggested work utilizes a combined approach of Pearson's correlation and Background Subtraction (BGS) technique over thermal video frame sequences for detection of moving objects. The automatically generated threshold value boosts the efficacy of the algorithm in differentiating between background and foreground frames. The quantitative analysis shows the higher value of different performance parameters like Accuracy, F-measure, Recall, etc. Moreover, the qualitative analysis of obtained results strongly indicates that the proposed method outperforms when compared with the existing methods. [ABSTRACT FROM AUTHOR]

Citizen science data are increasingly used for biodiversity monitoring. However, concerns are often raised over the accuracy of species identifications in citizen science databases, as data are collected mostly by non‐professionals. Misidentifications can simultaneously generate two error types: false positives (erroneous reports of a species) and false negatives (lack of reports of the misidentified species). Large‐scale assessments of identification errors should provide insights into the strengths and weaknesses of citizen science data. Here we show that citizen science photographic data for birds are trustworthy overall, although problems arise in hard‐to‐identify bird groups. We reviewed over 104 000 images of 377 passerine species from the southern Neotropics (Argentina) stored in eBird – a large citizen science platform – and quantified erroneous reports to calculate precision and recall metrics as measures for data accuracy. Precision increases with fewer false positives and recall increases with fewer false negatives; hence, high values of precision and recall will mirror a higher data accuracy. We found that 97% of the photos of all species were correctly identified. Most species (77%; n = 291) showed high accuracy in their identifications (precision and recall > 95%), with 122 species showing no errors. A few hard‐to‐identify species (10%; n = 40) showed low levels of data quality (63–90% precision or recall). Similarly, few species (12%; n = 46) exhibited intermediate precision or recall scores (90–95%). Further, we uncovered the existence of a complex network of cross‐identifications composed of 272 species, with a predominance of tyrant flycatchers and ovenbirds, reflecting the strong traffic of errors that occurs within these families. To our knowledge, our study provides the first large‐scale quantification of identification errors in photos submitted by citizen science contributors. We underscore the relevance of performing such assessments to understand how identification errors are distributed across a database before analysing data, and provide tools for citizen science stakeholders to direct more specific efforts toward species that need an improvement in data quality. [ABSTRACT FROM AUTHOR]

In today's world different data sets are available on which regression or classification algorithms of machine learning are applied. One of the classification algorithms is k-nearest neighbor (kNN) which computes distance amongst various rows in a dataset. The performance of kNN is evaluated based on K -value and distance metric used, where K is the total count of neighboring elements. Many different distance metrics have been used by researchers in literature, one of them is Canberra distance metric. In this paper the performance of kNN based on Canberra distance metric is measured on different datasets, further the proposed Canberra distance metric, namely, Modified Euclidean-Canberra Blend Distance (MECBD) metric has been applied to the kNN algorithm which led to improvement of class prediction efficiency on the same datasets measured in terms of accuracy, precision, recall, F1-score for different values of k. Further, this study depicts that MECBD metric use led to improvement in accuracy value 80.4% to 90.3%, 80.6% to 85.4% and 70.0% to 77.0% for various data sets used. Also, implementation of ROC curves and auc for k = 5 is done to show the improvement is kNN model prediction which showed increase in auc values for different data sets, for instance increase in auc values from 0.873 to 0.958 for Spine (2 Classes) dataset, 0.857 to 0.940, 0.983 to 0.983 (no change), 0.910 to 0.957 for DH, SL and NO class for Spine (3 Classes) data set and 0.651 to 0.742 for Haberman's data set. [ABSTRACT FROM AUTHOR]

Distractions and multitasking are generally detrimental to learning and memory.Nevertheless, people often study while listening to music, sitting in noisy coffee shops, orintermittently checking their e-mail. The experiment we replicated examined how distractionsand divided attention influence one’s ability to selectively remember valuable information.Participants studied lists of words that ranged in value from 1 to 10 points while completing adigit-detection task, while listening to music, or without distractions. Most of the figures weresuccessfully replicated using the given dataset and tools like Microsoft Excel and Google Sheets.Since we were able to arrive at the same conclusion as the original author, we believe thisexperiment is valid and reliable for application to further extension studies. Our extension studyexamined correlations between gender and recall ability and between age and recall ability. Weconcluded that there was no significant correlation between these variables, suggesting thesefactors did not affect the outcome. This extension further supports the author’s results, as age andgender were seemingly not confounding variables.

The aim of this study was to investigate the image retrieval from selected search engines according to the written and semantic features of Persian language and determine their relevance using recall and precision formulas and to identify the most efficient search engine in retrieving images in Persian and by survey-analytical method. It was done using direct observation technique. After reviewing related researches, search keywords list was formed in the form of a checklist based on the written and semantic features of Persian language. Each of these keywords in the studied search engines, including two general search engines Google and Bing and Duckduckgo semantic search engine, which are among the most used search engines and have also provided the ability to search for images in Persian, search and the number of relevant and unrelated retrieved results were recorded. Then, the recall and precision of search results in each search engine were calculated and the relevance of images based on these features in each of the studied search engines was investigated. A variety of descriptive statistical techniques were applied to analyze the data along with Kolmogorov-Smirnov, Shapiro-Wilk, Kruskal-Wallis and Friedman tests. Findings demonstrated that Google, Bing and Duckduckgo search engines do not pay enough attention to the written and semantic features of Persian language and many of these features are ignored while searching and retrieving images. In the present study, Google search engine had a higher recall and precision than the other two search engines, and despite the claim of semantic search engines to provide better and more relevant information than other search engines, Duckduckgo search engine did not show good performance in retrieving images related to the written and semantic part of Persian language. There is also a significant difference between the recall and the precision of the three studied search engines.

Introduction: This research aimed to investigate the effect of physical training and selective and compulsory observational pattern on the intrinsic motivation and cognitive learning of basketball jump shot skill with the approach of basic psychological demands.Methods: Eighty male students (18-22 years old) were selected voluntarily and based on their pre-test scores, were randomly divided into four Self-Control, Paired, Experimenter-Control, and Control groups (20 people in each group). To examine the motivation level, the Intrinsic Motivation Inventory (IMI), and to examine the cognitive learning level, Recall Questionnaire (adapted from Knudson, 1993) was used. An educational video of the expert model that showed the movement components was presented to the participants. A retention test was conducted 24 hours later. One-way analysis of variance (ANOVA) was used to examine the data (p≥0.05).Results: The results showed that in the acquisition phase, the level of intrinsic motivation of the participants to learn the basketball jump shot skill in the Self-Control observation condition was significantly higher than the participants in the Paired and Control conditions. Also, in the retention phase, the level of intrinsic motivation in the Self-Control observation condition was significantly higher than in other experimental conditions. In addition, in the acquisition and retention phases of cognitive learning (recall success), the participants in the Self-Control observation condition scored significantly higher than the participants in the Paired and Control conditions.Conclusion: In general, the results of this study show that motivation affects learning. Also, task demonstration as a whole-part-whole is an effective and efficient method in the cognitive phase.

Abstract 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.

Background: Text recognition of cardboard pharmaceutical packages with machine vision is a challenging task due to the different curvatures of packaging surfaces and different printing methods. Methods: In this research, a novel deep learning method based on regions with convolutional neural networks (R-CNN) for recognizing binarized expiration dates and batch codes printed using different printing methods is proposed. The novel method recognizes the characters in the images without the need to extract handcrafted features. In detail, this approach performs text recognition considering the whole image as an input extracting and learning salient character features straight from packaging surface images. Results: The expiration date and manufacturing batch codes of a real-life pharmaceutical packaging image set are recognized with 91.1% precision with a novel deep learning-based model, while Tesseract OCR text recognition performance with the same image set is 38.3%. The novel model outperformed Tesseract OCR also in tests evaluating recall, accuracy, and F-Measure performance. Furthermore, the novel model was evaluated in terms of multi-object recognition accuracy and the number of unrecognized characters, in order to achieve performance values comparable to existing multi-object recognition methods. Conclusions: The results of this study reveal that the novel deep learning method outperforms the well-established optical character recognition method in recognizing texts printed using different printing methods. The novel method presented in the study recognizes texts printed with different printing methods with high precision. The novel deep learning method is suitable for recognizing texts printed on curved surfaces with proper preprocessing. The problem investigated in the study differs from previous research in the field, focusing on the recognition of texts printed with different printing methods. The research thus fills a gap in text recognition that existed in the research of the field. Furthermore, the study presents new ideas that will be utilized in our future research.

Joseph Elliot Alchin,1,2 Christina Signorelli,1,2 Jordana Kathleen McLoone,1,2 Claire Elizabeth Wakefield,1,2 Joanna Elizabeth Fardell,1,2 Karen Johnston,1 Richard J Cohn1,2 1Kids Cancer Centre, Sydney Children’s Hospital, Randwick, NSW, Australia; 2School of Women’s and Children’s Health,UNSW Medicine, University of New South Wales, Sydney, NSW, AustraliaCorrespondence: Christina Signorelli, Kids Cancer Centre, Level 1 South, Sydney Children’s Hospital, High St, Randwick, NSW, 2031, Australia, Tel +61 431 547 350, Fax +61-2-9382-1789, Email c.signorelli@unsw.edu.auPurpose: Ongoing survivorship care allows childhood cancer survivors the opportunity to address treatment-related health problems and improve their quality of life. However, many survivors do not adhere to their healthcare professionals’ recommendations and the factors supporting their adherence remain unclear.Patients and Methods: Long-term childhood cancer survivors completed the “Re-engage” program, which assessed survivors’ heath needs and provided individualised recommendations for health interventions and surveillance developed by an expert multi-disciplinary team (MDT). We measured survivors’ recall of, and adherence to, their individualised healthcare recommendations at one and six months post-intervention. We conducted a series of univariate negative binomial regressions to investigate factors associated with the total number of recommendations that were correctly recalled and adhered to.Results: We analysed the data of 25 childhood cancer survivors who participated in Re-engage (mean age = 31.9 years). On average, survivors were provided with 6.6 recommendations (range = 1– 11). Survivors accurately recalled receiving 3.0 recommendations at one month post-intervention and 1.9 at six months. Survivors had adhered to an average of 1.3 recommendations by six-month follow-up. In total, 56% of participants reported that they did not adhere to any recommendations. By six-month follow-up, greater adherence to MDT recommendations was associated with having a history of a second cancer (B = 1.391; 95% confidence interval [CI], 0.686 to 2.097; p

Background: This study aims to investigate growing Internet use in relation to memory and cognition. Though literature reveals human capability to utilize the Internet as a transactive memory source, the formational mechanisms of such transactive memory systems are not extensively explored. The Internet's comparative effects on transactive memory and semantic memory are also relatively unknown. Methods: This study comprises two experimental memory task survey phases utilizing null hypothesis and standard error tests to assess significance of results. Results: When information is expected to be saved and accessible, recall rates are lower, regardless of explicit instructions to remember (Phase 1, N = 20). Phase 2 suggests the importance of order of attempted recall: depending on whether users first attempt to recall (1) desired information or (2) the information's location, subsequent successful cognitive retrieval is more likely to occur for (1) only desired information or both desired information and location thereof or (2) only desired information's location, respectively (N = 22). Conclusions: This study yields several theoretical advances in memory research. The notion of information being saved online and accessible in the future negatively affects semantic memory. Phase 2 reveals an adaptive dynamic—(1) as Internet users often have a vague idea of desired information before searching for it on the Internet, first accessing semantic memory serves as an aid for subsequent transactive memory use and (2) if transactive memory access is successful, the need to retrieve desired information from semantic memory is inherently eliminated. By repeatedly defaulting to first accessing semantic memory and then transactive memory or to accessing transactive memory only, Internet users may form and reinforce transactive memory systems with the Internet, or may refrain from enhancing and decrease reliance on transactive memory systems by repeatedly defaulting to access only semantic memory; the formation and permanence of transactive memory systems are subject to users' will. Future research spans the domains of psychology and philosophy. [ABSTRACT FROM AUTHOR]

The research is concerned with the effect of Quranic meanings in directing society's behaviors through poetry, which in turn is an effective tool in purifying and refining the human soul. Whether in a direct way to adopt community issues or in a suggestive manner, and in an unconscious activity that attempts to mislead the reader about the direct intent of the poetic significance. Because of the value of poetry and its impact on man and his taste, ancient and modern, the Quranic meanings were embodied in the poem "You taught me life'' by the poet "Mohammed Mustafa Hamam Al-Masry.'' He is one of the first generations of poets in the modern era. The poem is well-known for its representation and recitation. However, this is due to the agility of its language and the follow-up of the Qur'anic meanings, which came spontaneously and smoothly, evoked by the ideas mixed with the sincerity of passion of the committed Arab poet. Moreover, this led to a phenomenon that contributed to the production of a coherent text, but we did not find any interest in analyzing this poem and identifying the secrets of creativity, and its deep values. Therefore, we will explore and analyze these aesthetic secrets and show how they were employed to build society because of the connotations they carried inspired by the Holy Qur'an, which is a method for building society. We will first clarify the terms used in the research, identify the writer of the poem and his literary product, and move on to the analytical techniques of recalling meanings. The Qur'anic implications, the aesthetics of quotation, and its value in achieving creativity and then revealing the empowerment of these meanings in the mind of the reader. [ABSTRACT FROM AUTHOR]

Background: Even with all the care taken during the production process, the pharmaceutical industries are still subject to manufacturing medicines with quality deviations, generating commercialized products without the required quality and necessitating their subsequent recall from the market. The objective of this study was to evaluate the reasons that led to the recall of medicines in Brazil in the period evaluated. Methods: This is a descriptive study (using document analysis), on the recall of substandard medicines registered on the website of the National Health Surveillance Agency (ANVISA), from 2010 to 2018. The variables studied were the type of medicine (reference, generic, similar, specific, biological, herbal, simplified notification, new and radiopharmaceutical), type of pharmaceutical dosage form (solid, liquid, semi-solid and parenteral preparation), and reason for recall (Good manufacturing practices, quality and quality/good manufacturing practices). Results: A total of n = 3,056 recalls of substandard medicine were recorded. Similar medicines had a higher recall index (30.1%), followed by generics (21.3%), simplified notification (20.7%) and reference (12.2%). Different dosage forms had similar recalls: solids (35.2%), liquids (31.2%) and parenteral preparations (30.0%), with the exception of semi-solids (3.4%). The reasons for the highest occurrences were related to good manufacturing practices (58.4%) and quality (40.4%). Conclusion: The probable cause for this high number of recalls is the fact that, even with all the quality controls and processes in accordance with good manufacturing practices, errors can occur, both human and in automated processes, thus causing the release of batches that should not have been approved. In summary, it is necessary for manufacturers to implement a robust and well structured quality system in order to avoid such deviations, and it is up to ANVISA to apply greater oversight in the post marketing of these products. [ABSTRACT FROM AUTHOR]

The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes, and it is estimated that 643 million people will develop the condition (11.3% of the total population) by 2030. If this trend continues, the number will jump to 783 million (12.2%) by 2045. At present, the number of people with diabetes in Taiwan has reached 2.18 million, with an average of one in ten people suffering from the disease. In addition, according to the Bureau of National Health Insurance in Taiwan, the prevalence rate of diabetes among adults in Taiwan has reached 5% and is increasing each year. Diabetes can cause acute and chronic complications that can be fatal. Meanwhile, chronic complications can result in a variety of disabilities or organ decline. If holistic treatments and preventions are not provided to diabetic patients, it will lead to the consumption of more medical resources and a rapid decline in the quality of life of society as a whole. In this study, based on the outpatient examination data of a Taipei Municipal medical center, 15,000 women aged between 20 and 80 were selected as the subjects. These women were patients who had gone to the medical center during 2018–2020 and 2021–2022 with or without the diagnosis of diabetes. This study investigated eight different characteristics of the subjects, including the number of pregnancies, plasma glucose level, diastolic blood pressure, sebum thickness, insulin level, body mass index, diabetes pedigree function, and age. After sorting out the complete data of the patients, this study used Microsoft Machine Learning Studio to train the models of various kinds of neural networks, and the prediction results were used to compare the predictive ability of the various parameters for diabetes. Finally, this study found that after comparing the models using two-class logistic regression as well as the two-class neural network, two-class decision jungle, or two-class boosted decision tree for prediction, the best model was the two-class boosted decision tree, as its area under the curve could reach a score of 0.991, which was better than other models. [ABSTRACT FROM AUTHOR]

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]

• 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]