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OBJECTIVE: To compare the agreement and cost of two recall methods for estimating childrens minimum dietary diversity (MDD). DESIGN: We assessed childs dietary intake on two consecutive days: an observation on day one, followed by two recall methods (list-based recall and multiple-pass recall) administered in random order by different enumerators at two different times on day two. We compared the estimated MDD prevalence using survey-weighted linear probability models following a two one-sided test equivalence testing approach. We also estimated the cost-effectiveness of the two methods. SETTING: Cambodia (Kampong Thom, Siem Reap, Battambang, and Pursat provinces) and Zambia (Chipata, Katete, Lundazi, Nyimba, and Petauke districts). PARTICIPANTS: Children aged 6-23 months: 636 in Cambodia and 608 in Zambia. RESULTS: MDD estimations from both recall methods were equivalent to the observation in Cambodia but not in Zambia. Both methods were equivalent to the observation in capturing most food groups. Both methods were highly sensitive although the multiple-pass method accurately classified a higher proportion of children meeting MDD than the list-based method in both countries. Both methods were highly specific in Cambodia but moderately so in Zambia. Cost-effectiveness was better for the list-based recall method in both countries. CONCLUSION: The two recall methods estimated MDD and most other infant and young child feeding indicators equivalently in Cambodia but not in Zambia, compared to the observation. The list-based method produced slightly more accurate estimates of MDD at the population level, took less time to administer and was less costly to implement.

Despite bilateral hippocampal damage dating to the perinatal or early childhood period and severely impaired episodic memory, patients with developmental amnesia continue to exhibit well‐developed semantic memory across the developmental trajectory. Detailed information on the extent and focality of brain damage in these patients is needed to hypothesize about the neural substrate that supports their remarkable capacity for encoding and retrieval of semantic memory. In particular, we need to assess whether the residual hippocampal tissue is involved in this preservation, or whether the surrounding cortical areas reorganize to rescue aspects of these critical cognitive memory processes after early injury. We used voxel‐based morphometry (VBM) analysis, automatic (FreeSurfer) and manual segmentation to characterize structural changes in the brain of an exceptionally large cohort of 23 patients with developmental amnesia in comparison with 32 control subjects. Both the VBM and the FreeSurfer analyses revealed severe structural alterations in the hippocampus and thalamus of patients with developmental amnesia. Milder damage was found in the amygdala, caudate, and parahippocampal gyrus. Manual segmentation demonstrated differences in the degree of atrophy of the hippocampal subregions in patients. The level of atrophy in CA‐DG subregions and subicular complex was more than 40%, while the atrophy of the uncus was moderate (−24%). Anatomo‐functional correlations were observed between the volumes of residual hippocampal subregions in patients and selective aspects of their cognitive performance, viz, intelligence, working memory, and verbal and visuospatial recall. Our findings suggest that in patients with developmental amnesia, cognitive processing is compromised as a function of the extent of atrophy in hippocampal subregions. More severe hippocampal damage may be more likely to promote structural and/or functional reorganization in areas connected to the hippocampus. In this hypothesis, different levels of hippocampal function may be rescued following this variable reorganization. Our findings document not only the extent, but also the limits of circuit reorganization occurring in the young brain after early bilateral hippocampal damage. [ABSTRACT FROM AUTHOR]

Objective. This study's primary objective was to examine the risk of recall for novel spine devices over time. Secondarily, we sought to analyze interbody fusion and vertebral body replacement (VBR) devices (corpectomy cages) as a risk factor for recall. Background. The recall risk of a novel spine device over time has not been reported. In addition, FDA regulations were lowered for interbody fusion devices to enter the market in 2007. As well, VBR implants were recently approved by the FDA for use in the cervical spine in 2015. Materials and Methods. Spine devices cleared between January 1, 2008 and December 31, 2018 were identified from the FDA's 510(k) database. All recall data were collected from the database in January 2021 to provide a 2-year minimum follow-up for a recall to occur. Product labels were used to classify interbody fusion and VBR devices. Cumulative incidence function was conducted to compare the overall risk of recall for FDA-cleared spine devices, and the hazard ratio determined for VBR and all other devices versus interbody implants during the study period. Results. A total of 2384 spine devices were cleared through 510 (k) in the study period. The hazard of recall at 5 years was 5.3% (95% CI: 4.4%-6.2%) and 6.5% (95% CI: 5.4%-7.7%) at 10 years. No significant difference in recall risk was identified for interbody fusion and VBR devices. Conclusion. The risk of recall at 5 and 10 years of a novel spine device is about half the 12% rate reported for orthopedic devices in general. Despite lowered FDA regulations for interbody fusion devices and recent approval for VBR device use in the cervical spine, no increased risk of recall was detected. Further research is necessary to explain the reason for the lower risk of recall with spine devices. [ABSTRACT FROM AUTHOR]

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms' effectiveness in broadband power system measurement. [ABSTRACT FROM AUTHOR]

Retelling an event in a social setting often means talking about it less factually than we might if trying to recall it as accurately as possible. These distortions that arise from socially oriented retellings could affect the ability to later recall the same event accurately. Does retelling a story in a social situation impair memory compared to not retelling it at all? Or could retrieving the memory, even with a socially oriented mindset, still improve memory? We explored social retelling's effect on memory in a two-session study. Participants heard two stories twice and, after a distractor task, retold the stories according to one of three randomly assigned conditions: social retelling (retell the stories as if talking to friends), accuracy retelling (retell the stories as accurately as possible), or no retelling. A day later, everyone retold the stories as accurately as possible. Participants in the accuracy retelling group included more specific details in their session two retellings than did the social retelling group, which included more specific details than the no retelling group. Elaborations in session two did not differ across groups. Findings suggest retelling a story in a social situation benefits memory, though not as much as retelling a story accurately does. [ABSTRACT FROM AUTHOR]

The world is progressing at a rapid rate, and with continuous advancements in technology, the need for cooperative spectrum sensing has evolved. Cooperative spectrum sensing is an approach used to enhance detection performance, wherein secondary users collaborate with each other to sense the spectrum and identify spectrum holes. As technology improves over time, the spectrum becomes increasingly allocated to primary users. The role of cooperative spectrum sensing in cognitive radio fulfills the essential requirement of protecting primary users from harmful interference. The progressive evolution of technology has led to a reduction in available spectrum, prompting the emergence of the concepts of cognitive radio and cooperative spectrum sensing. To begin with, this paper introduces the fundamental application of cooperative spectrum sensing algorithms, encompassing primary and secondary users' models and an energy model. Subsequently, three machine learning algorithms, namely K-means, support vector machine, and logistic regression, are explained, and their schematics are presented in detail. The proposed model uses supervised and unsupervised learning techniques to develop a cooperative spectrum sensing framework. It compares the performance of three machine learning algorithms, K-means clustering, logistics regression and support vector machine. The comparison is based on accuracy, recall and precision metrics, and the results show that the K-means clustering algorithm has better performance than the other two algorithms. The findings highlight the superiority of the K-means clustering algorithm over logistic regression and support vector machine. [ABSTRACT FROM AUTHOR]

In traditional digital filing systems, people mostly use text as a key to categorise images, and retrieve them in the future. The use of other media as keys for image retrieval is rarely used, notwithstanding that multisensory digital media – mulsemedia – can be harnessed to improve users' performance and help them to retrieve their images. In this respect, olfactory media (engaging the sense of smell) is an example, as people can categorise their images by using congruent olfactory media. Accordingly, we investigated the impact of employing olfactory media as a key for retrieving a set of images. Moreover, we also studied the impact of the usage of olfactory media in this context on a user's performance and Quality of Experience (QoE). To this end, we developed an olfactory-enhanced application (SCENT2IMAGE) in which olfactory media was emitted alongside a 5X5 matrix of images, of which users had to recognize 4 images congruent with the emitted scents. Furthermore, we developed a word-only version of the application (WORD2IMAGE) in which words alone were used as an equivalent key instead of olfactory media. Forty-four participants were invited and took part in our experiment, evenly split into a control and experimental group. Results highlight that using olfactory media does have a significant impact on user performance by helping them find related images. Moreover, using olfactory effects in this context was also found to enhance user QoE. Lastly, our findings underscore that users were willing to use olfactory-enhanced applications for categorizing/retrieving their albums and images. [ABSTRACT FROM AUTHOR]

How does our environment impact what we will later remember? Early work in real-world environments suggested that having matching encoding/retrieval contexts improves memory. However, some laboratory-based studies have not replicated this advantageous context-dependent memory effect. Using virtual reality methods, we find support for context-dependent memory effects and examine an influence of memory schema and dynamic environments. Participants (N = 240) remembered more objects when in the same virtual environment (context) as during encoding. This traded-off with falsely "recognizing" more similar lures. Experimentally manipulating the virtual objects and environments revealed that a congruent object/environment schema aids recall (but not recognition), though a dynamic background does not. These findings further our understanding of when and how context affects our memory through a naturalistic approach to studying such effects. [ABSTRACT FROM AUTHOR]

This research delves into the realm of Maritime Cybersecurity, focusing on the application of Artificial Intelligence (AI) mechanisms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). The maritime industry faces evolving cyber threats, necessitating innovative approaches for robust defense. The maritime sector is increasingly reliant on digital technologies, making it susceptible to cyber threats. Traditional security measures are insufficient against sophisticated attacks, necessitating the integration of AI mechanisms. This research aims to evaluate the effectiveness of KNN, RF, and ANN in fortifying maritime cybersecurity, providing a proactive defense against emerging threats. Investigate the application of KNN, RF, and ANN in the maritime cybersecurity landscape. Assess the performance of these AI mechanisms in detecting and mitigating cyber threats. Explore the adaptability of KNN, RF, and ANN to the dynamic maritime environment. Provide insights into the strengths and limitations of each algorithm for maritime cybersecurity. The study employs these AI algorithms to analyze historical maritime cybersecurity data, evaluating their accuracy, precision, and recall in threat detection. Results demonstrate the effectiveness of KNN in identifying localized anomalies, RF in handling complex threat landscapes, and ANN in learning intricate patterns within maritime cybersecurity data. Comparative analyses reveal the strengths and weaknesses of each algorithm, offering valuable insights for implementation. In conclusion, the integration of KNN, RF, and ANN mechanisms presents a promising avenue for enhancing maritime cybersecurity. The study underscores the importance of adopting AI solutions to the maritime domain's unique challenges. While each algorithm demonstrates efficacy in specific scenarios, a hybrid approach may offer a comprehensive defense strategy. As the maritime industry continues to evolve, leveraging AI mechanisms becomes imperative for staying ahead of cyber threats and safeguarding critical assets. This research contributes to the ongoing discourse on maritime cybersecurity, providing a foundation for future developments in the field. [ABSTRACT FROM AUTHOR]

Artificial Intelligence-based system for Tuberculosis (TB) detection has been proposed in this work. Manual diagnostic through radiologists can be misdiagnosis due to human error. Designing an artificial intelligence-based decision support system can help in the accurate prediction of TB through lung Chest X-ray (CXR) image. In the proposed work, data centric and model centric approaches are applied. In data centric approach, images play an important role in feature extraction. In this context, experimentation on six varied kinds of image enhancement applied to the publicly available TB image dataset along with data pre-processing and data augmentation. In model centric approach, three pre-trained Convolutional Neural Network (CNN) with modification with specialized layers are proposed. Comparative Study of six image enhancement techniques along with original dataset in behavior response with network architectures is evaluated for best performance. Evaluation of the networks is based on accuracy, precision, recall and AUC. Sharpening of images applied on Modified ResNet50 is the best performer with accuracy 99.05%, precision 98.87%, recall 100% and AUC of 99.29%. In comparability with original dataset, sharpened images performed 0.8% better in metric evaluation, which gives better classification approach and predictability. [ABSTRACT FROM AUTHOR]

Purpose: Despite stringent regulations enforced by the United States Food and Drug Administration (USFDA), numerous drug products still enter the market without adequate assurance of safety and efficacy, resulting in frequent recalls. To explore the real picture of such recalls, this analysis focuses on the USFDA drug recalls with a glance at medical devices, food products, biological products, cosmetics, tobacco products, and veterinary products from 2019 to 2023. The research aims to quantify and categorize these drug recalls, examining various contributing factors to recommend strategies for minimizing future recalls. Additionally, it investigates the influence of global and national crises, such as the COVID-19 pandemic, on the USFDA’s drug recall process. Methods: Data from USFDA Enforcement reports were collected and sorted, and descriptive statistics were used to analyse drug recalls. Results: Between 2019 and 2023, the USFDA documented 31125 recalls, of which 6217 involved drug products from 593 different companies. These recalls are frequently initiated by the companies themselves, with primary causes including sterility assurance failures, contamination, and improper storage conditions. Conclusions: Drug recalls are a double-edged sword: while they are essential for protecting public health, they can also disrupt supply chains and lead to drug shortages, especially when there are no alternatives available. To prevent such disruptions, strict adherence to current Good Manufacturing Practices, along with comprehensive internal audits and rigorous USFDA inspections are vital for manufacturers to maintain quality standards and minimize the risk of recalls. [ABSTRACT FROM AUTHOR]

The world of pathology encompasses a broad spectrum of diseases beyond the boundaries of specialties, with each disease having specific and interesting gross and microscopic features. Some of these features are pattern-based, while others are eponyms that compare them to objects, food, animals, etc., due to their striking resemblance and quick recall. Medical nomenclature is of vital importance, and it has significantly evolved historically, with etymological roots from Latin, Greek, and Roman languages of ancient times to the current internationally uniform codes of English and other modern languages. Eponyms are valuable medical literary epithets that have been used across specialties and include animal names, food names, discoverer names, geographic references, and more. The subject of pathology, in particular, has immense use of eponyms as they are valuable tools for assisting in adult learning, or andragogy. The specialty of pathology is unique in its complex patterns and diagnostic algorithm, always in need of alternate systems to arrive at quick and accurate diagnosis. Also known as intuitive thinking or reflex thinking, pattern viewing and eponyms trigger a reflex recognition system, reducing recall time and aiding in precise diagnosis. The present article aimed to review the terminological phenomenon of animal eponym usage in the context of pathological diagnosis. [ABSTRACT FROM AUTHOR]

Cognitive ability is increasingly recognized as a significant factor influencing household portfolio decisions. However, different cognitive abilities, such as numeracy, fluency and recall, may yield different investment results. The aim of this paper is to empirically examine the associations of three cognitive abilities (numeracy, fluency and recall) with household portfolio composition using Survey of Health, Aging and Retirement (SHARE) data across 16 European countries and the multinomial logit model. Our empirical analyses focus on the impacts of differences in country characteristics, specifically the level of economic development and the existence of a national health system (NHS). The results indicate that numeracy and fluency have positive impacts on the decision to hold safe and relatively risky assets, as well as fully diversified portfolios in developed countries, but have no significant effects in emerging countries. Additionally, all three cognitive abilities positively influence the decision to hold fully diversified portfolios in the countries with NHS, while no significant effects are observed in the countries without NHS. Our findings reveal a decreased impact of cognitive abilities on portfolio types in the emerging countries and the non-NHS countries. Notably, a significant and positive correlation is found between the holding of no financial assets in both non-NHS countries and advanced countries. One important implication of this study is that marketing strategies of financial advisors should take into account household cognitive abilities, as well as differences in economic development among countries and the presence or absence of NHS. [ABSTRACT FROM AUTHOR]

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance. [ABSTRACT FROM AUTHOR]

We extend the prophet inequality problem to a competitive setting. At every period, a new realization of a random variable with a known distribution arrives, which is publicly observed. Then, two players simultaneously decide whether to pick an available value or to pass and wait until the next period (ties are broken uniformly at random). As soon as a player gets a value, he leaves the market and his payoff is the value of this realization. In the first variant, namely the "no recall" case, the agents can only bid at each period for the current value. In a second variant, the "full recall" case, the agents can also bid for any of the previous realizations which has not been already selected. For each variant, we study the subgame-perfect Nash equilibrium payoffs of the corresponding game. More specifically, we give a full characterization in the full recall case and show in particular that the expected payoffs of the players at any equilibrium are always equal, whereas in the no recall case the set of equilibrium payoffs typically has full dimension. Regarding the welfare at equilibrium, surprisingly the best equilibrium payoff a player can have may be strictly higher in the no recall case. However, the sum of equilibrium payoffs is weakly larger when the players have full recall. Finally, we show that in the case of 2 arrivals and arbitrary distributions, the prices of Anarchy and Stability in the no recall case are at most 4/3, and this bound is tight. [ABSTRACT FROM AUTHOR]

The world of pathology encompasses a broad spectrum of diseases beyond the boundaries of specialties, with each disease having specific and interesting gross and microscopic features. Some of these features are pattern-based, while others are eponyms that compare them to objects, food, animals, etc., due to their striking resemblance and quick recall. Medical nomenclature is of vital importance, and it has significantly evolved historically, with etymological roots from Latin, Greek, and Roman languages of ancient times to the current internationally uniform codes of English and other modern languages. Eponyms are valuable medical literary epithets that have been used across specialties and include animal names, food names, discoverer names, geographic references, and more. The subject of pathology, in particular, has immense use of eponyms as they are valuable tools for assisting in adult learning, or andragogy. The specialty of pathology is unique in its complex patterns and diagnostic algorithm, always in need of alternate systems to arrive at quick and accurate diagnosis. Also known as intuitive thinking or reflex thinking, pattern viewing and eponyms trigger a reflex recognition system, reducing recall time and aiding in precise diagnosis. The present article aimed to review the terminological phenomenon of animal eponym usage in the context of pathological diagnosis.

Introduction: Reminder/recall interventions have been shown to improve immunization coverage. The perception of mothers/caregivers may influence the outcome of such interventions. The attitude of Nigerian mothers to reminders/ recalls using cell phones was evaluated. Methods: This was a crosssectional observational study carried out (August to October 2012) on mothers attending the child welfare clinic of the Institute of Child Health, University of Benin, Benin City. The instrument was an interviewer administered questionnaire which sought information on respondents’ access to phones, their ability to read, perception and preference with regard to reminders/ recalls. Results: All 203 mothers had access to a phone although 188 (92.6%) currently owned a phone. Majority of the mothers 163 (80.3%) could read. Of the 203 mothers 127(62.6%) agreed that mothers should be reminded about immunization appointments of their children. Of those who disagreed, most agreed that mothers who forget/did not keep appointments could be reminded. More mothers 126(70.8%) favoured reminders compared to recalls 52 (29.2%) There was no significant difference in the proportion of mothers who preferred telephone calls and those who preferred text messages. Those with post secondary education were more likely to prefer text messages. Conclusion: The mothers studied are favourably disposed to receipt of reminder/ recalls for their children’s immunization appointments. There is good access to telephones among the study population enough to support the use of this technology for a reminder / recall intervention but the use of text messages may be limited by literacy.

ObjectiveTo investigate the situation and influencing factors of recall colposcopy attendance among women with positive cervical cancer screening results in Ordos city, in order to provide a reference for improving colposcopy recall compliance. MethodsFrom a cervical cancer screening program conducted at maternal and child health hospitals in different banners (districts) of Ordos city, Inner Mongolia Autonomous Region between January 2016 and December 2020, we extracted the data of 4 094 women (aged 35 – 64 years) who tested positive in the first detection. Data on the urbanization rate and gross domestic product (GDP) of the banners (districts) were also collected simultaneously. A multilevel logistic regression model was used to analyze the main factors influencing women′s attendance at for colposcopy recall. ResultsOf the 4 094 women who were recalled, 2 724 (67.0%) actually underwent the recall colposcopy. Of the 2 742 women who had a recall colposcopy, 2 681 (97.8%) had the recall examination at the original screening facilities and only 61 (2.2%) had the recall re-examination or colposcopy at other medical facilities. The multilevel logistic regression analysis showed that women aged 55 to 64 years, with an education of junior high school and above, with abnormal vaginal discharge, and with a history of cervical cancer screening were more likely to have the recall colposcopy; whereas, the women from banners with urbanization rate of more than 80% and GDP between 11 and 27 billion yuan RMB were less likely to have the recall colposcopy, in addition, those from regions with urbanization rate of 70% to 80% and GDP of 11 to 27 billion yuan and above were more likely to have the re-examination or colposcopy at medical facilities other than the original screening facilities. ConclusionAmong women with positive cervical cancer screening results in Ordos city, the recall colposcopy rate is low and is mainly influenced by individual factors such as age, education, abnormal vaginal discharge, and history of cervical cancer screening, as well as the urbanization rate and GDP of residential areas.

Drawing a referent of a to-be-remembered word often results in better recognition and recall of this word relative to a control task in which the word is written, a pattern dubbed the drawing effect. Although this effect is not always found in pure lists, we report three experiments in which the drawing effect emerged in both pure- and mixed-lists on recognition and recall tests, though the effect was larger in mixed lists. Our experiments then compared drawing effects on memory between pure- and mixed-list contexts to determine whether the larger mixed-list drawing effect reflected a benefit to draw items, a cost to write items, or a combination. In delayed recognition and free-recall tests, a mixed-list benefit emerged for draw items in which memory for mixed-list draw items was greater than pure-list draw items. This mixed-list drawing benefit was accompanied by a mixed-list writing cost compared to pure-list write items, indicating that the mixed-list drawing effect does not operate cost-free. Our findings of a pure-list drawing effect are consistent with a memory strength account, however, the larger drawing effect in mixed lists suggest that participants may also deploy a distinctiveness heuristic to aid retrieval of drawn items. [ABSTRACT FROM AUTHOR]

Intrusion Detection Model (IDM) is an essential device for network defence in current trend. Malicious users analyse the vulnerabilities of IDSs to capture unauthorized access. Furthermore, intrusion detection encompasses numerous numerical attributes and models, resulting in elevated detection errors and triggering false alarms. Hence, optimal computational intelligence shall be incorporated in IDM to achieve high detection rate and less number of false alarms. Considering the same, a new hybrid IDM framework is developed as the combination of Fuzzy Genetic Algorithm with Multi-Objective Particle Swarm Optimization that maximizes the detection accuracy, minimizes the false alarms and takes less computational complexity which will be explained first phase. The existing IDSs are constraint to the information trained incur into false positives based on user continuity for normal activity. The objective of this proposal is to extract optimal classification rules automatically from training data that helps to identify types of attacks correctly including the unknown attack types. For achieving this goal, Multi-Objective Particle Swarm Optimization (MOPSO) is used as classifier to enhance the identification of the rare attack classes within the IDM. The effectiveness of this method lies in its capacity to leverage information within an unfamiliar search space, guiding subsequent searches towards valuable subspaces. It provides better separability of various classes' i.e. normal behaviour and false alarms. In this FGA-MOPSO model, Principal Component Analysis (PCA) serves as the feature selection technique employed to identify pertinent features within the dataset, thereby enhancing the classifier's performance and Fuzzy Genetic Algorithm (FGA) is used to create new population for training the classifier with the help of three operations namely selection, crossover and mutation that helps to practice more patterns in training phase and to obtain better understanding of the proposed classifier. The simulation will illustrate that the system is competent to speed-up the training and testing process of intrusions detection is important for network applications.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Arun Kumar] Last name [Ramamoorthy]. Also, kindly confirm the details in the metadata are correct.Checked and Verified for Author 1. In Author 2 name, Given Name was [K.] and last name was[Karuppasamy], But its is just the opposite. Given Name is [Karuppasamy] and Last Name is [K.]. I have edited it. [ABSTRACT FROM AUTHOR]

The study aimed to classify two classes of vehicles, Tuktuk and Motorcycle, using a modified RCNN model. The MAjN_IRAQ Dataset, created from a camera system in Baghdad city, was used for training, detection, and classification of some vehicles to allow them to enter some crowded streets of Baghdad and to prevent others from entering the same streets. New layers were added and the number and size of filters were changed, which led to improve the process of training, detection and classification of vehicles with high accuracy, which leads to improving the proposed model's performance. The results showed that the modified RCNN model performed better when trained for 80 epochs. It improved performance measures such as precision, recall, and F1 score measure. The model was compared to other transfer learning methods (Alex Net, VGG16, and VGG19) and showed superior results for the Tuktuk class. The training and testing time for the proposed RCNNmodified model was also lower compared to the other models. At 80 epochs, the precision for the Tuktuk class was approximately 0.94, while for the Motorcycle class, it was approximately 0.89. The TPR was higher for the Tuktuk class at approximately 0.93, while the lower value was approximately 0.84 for VGG16. When the VGG16 model was used, the F1 score was better in the Motorcycle category (about 0.95) but worse in the Tuktuk category (0.86%). Both the suggested RCNN-modified model and the Alex Net model worked well in a reasonable amount of time. [ABSTRACT FROM AUTHOR]

Background Several measures of occupational exposure to pesticides have been used to study associations between exposure to pesticides and neurobehavioral outcomes. This study assessed the impact of different exposure measures for glyphosate and mancozeb on the association with neurobehavioral outcomes based on original and recalled self-reported data with 246 smallholder farmers in Uganda. Methods The association between the 6 exposure measures and 6 selected neurobehavioral test scores was investigated using linear multivariable regression models. Exposure measures included original exposure measures for the previous year in 2017: (i) application status (yes/no), (ii) number of application days, (iii) average exposure-intensity scores (EIS) of an application and (iv) number of EIS-weighted application days. Two additional measures were collected in 2019: (v) recalled application status and (vi) recalled EIS for the respective periods in 2017. Results Recalled applicator status and EIS were between 1.2 and 1.4 times more frequent and higher for both pesticides than the original application status and EIS. Adverse associations between the different original measures of exposure to glyphosate and 4 neurobehavioral tests were observed. Glyphosate exposure based on recalled information and all mancozeb exposure measures were not associated with the neurobehavioral outcomes. Conclusions The relation between the different original self-reported glyphosate exposure measures and neurobehavioral test scores appeared to be robust. When based on recalled exposure measures, associations observed with the original exposure measures were no longer present. Therefore, future epidemiological studies on self-reported exposure should critically evaluate the potential bias towards the null in observed exposure–response associations. [ABSTRACT FROM AUTHOR]

Introduction: Formal educational attainment, or years of schooling, has a well-established positive effect on cognitive health across the life course. We hypothesized that the content and difficulty of the curriculum influence this relationship, such that more challenging curricula in high school lead to higher levels of socioeconomic attainment in adulthood and, in turn, to better cognitive outcomes in older adulthood. Methods: We estimated multilevel structural equation models (MSEMs) in data from 2,405 individuals who attended one of 1,312 US high schools in 1960 and participated in the Project Talent Aging Study in 2018. Results: A college preparatory curriculum and a greater number of semesters of math and science in high school were positively related to word recall and verbal fluency at an average age of 75. Effects were robust to controlling for adolescent cognitive ability, academic performance, socioeconomic background, and school characteristics. Discussion: We discuss the implications of these findings for educational policy. [ABSTRACT FROM AUTHOR]

In contrast to sighted students who acquire mathematical expressions (MEs) from their visual sources, blind students must keep MEs in their memory using the Tactile or Auditory Modality. In this work, we rigorously investigate the ability to temporarily retain MEs by blind individuals when they use different input modalities: Auditory, Tactile, and Auditory–Tactile. In the experiments with 16 blind participants, we meticulously measured the users' capacity for memory retention utilizing ME recall. Based on a robust methodology, our results indicate that the distribution of the recall errors regarding their types (Deletions, Substitutions, Insertions) and math element categories (Structural, Numerical, Identifiers, Operators) are the same across the tested modalities. Deletions are the favored recall error, while operator elements are the hardest to forget. Our findings show a threshold to the cognitive overload of the short-term memory in terms of type and number of elements in an ME, where the recall rapidly decreases. The increase in the number of errors is affected by the increase in complexity; however, it is significantly higher in the Auditory modality than in the other two. Therefore, segmenting a math expression into smaller parts will benefit the ability of the blind reader to retain it in memory while studying. [ABSTRACT FROM AUTHOR]

Objectives: Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. Methods: We systematically searched publications related to EM research and NLP across databases including MEDLINE, Embase, Scopus, CENTRAL, and ProQuest Dissertations & Theses Global. Independent reviewers screened, reviewed, and evaluated article quality and bias. NLP usage was categorized into syndromic surveillance, radiologic interpretation, and identification of specific diseases/events/syndromes, with respective sensitivity analysis reported. Performance metrics for NLP usage were calculated and the overall area under the summary of receiver operating characteristic curve (SROC) was determined. Results: A total of 27 studies underwent meta‐analysis. Findings indicated an overall mean sensitivity (recall) of 82%–87%, specificity of 95%, with the area under the SROC at 0.96 (95% CI 0.94–0.98). Optimal performance using NLP was observed in radiologic interpretation, demonstrating an overall mean sensitivity of 93% and specificity of 96%. Conclusions: Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP‐based research to augment EM health care management. [ABSTRACT FROM AUTHOR]

Diabetes, a non-communicable disease, is associated with a condition indicative of too much glucose in the bloodstream. In the year 2022, it was estimated that about 422 million were living with the disease globally. The impact of diabetes on the world economy was estimated at $ 1.31 trillion in the year 2015 and implicated in the death of 5 million adults between the ages of 20 and 79 years globally. If left untreated for an extended time, could result in a host of other health complications. The need for predictive models to supplement the diagnostic process and aid the early detection of diabetes is therefore important. The current study is an effort geared toward developing a machine learning framework for the prediction of diabetes, expected to aid medical practitioners in the early detection of the disease. The dataset used in this investigation was sourced from the Kaggle database. The dataset consists of 100,000 entries, with 8,500 diabetics and 91,500 non-diabetics, indicating an imbalanced dataset. The dataset was modified to achieve a more balanced dataset consisting of 8,500 entries each for the diabetic and non-diabetic classes. Gradient Boosting classifier (GBC), Adaptive Boosting classifier (ADA), and Light Gradient Boosting Machine (LGBM) were the best three performing classifiers after comparing fifteen classifiers. The proposed framework is a stack model consisting of GBC, ADA, and LGBM. The ADA classifier was utilized as the meta-model. This model achieved an average accuracy, area under the curve (AUC), recall, precision, and f1-score of 91.12 ± 0.75 %, 97.83 ± 0.29 %, 92.03 ± 1.55 %, 90.40 ± 1.01 %, and 91.12 ± 0.77 %, respectively. The selling point of the proposed framework is the high recall of 92.03 ± 1.55 %, indicating that the model is sensitive to both the diabetic and the non-diabetic classes. [ABSTRACT FROM AUTHOR]

The objective of the article was to investigate the possible inducing factors that contributed to determine the frequency of social conflicts at the subnational level in Peru between 2016 and 2021, including income inequality, social exclusion, fiscal centralism, corruption and revocation of authorities, for which four regression models were built. Disaggregated official data from the 24 departments and the provinces of Lima and Callao were analyzed. Economic inequality was associated with the Gini coefficient. To establish the association between social conflict and the inducers, it was estimated using Spearman’s Rho correlation coefficient. Statistical calculation was also employed to appreciate the collinearity between the inducers. The results showed that the revocation of subnational authorities determines 42.5% of social conflict. On the other hand, corruption and fiscal centralism determine 28.5% of the perception of suffering social exclusion. Inequality and social conflict determined 21.8% of the relevance of the execution and quality of public spending by the national government in the regions. Sixty percent of social conflicts in Peru are of an environmental nature. The population that has declared the greatest discrimination corresponds to Puno (28%). 55.6% of those surveyed consider corruption to be one of the country’s main problems. Corruption and social exclusion have a negative impact on the effectiveness of economic results and promote social conflicts. Inefficient use of fiscal resources translates into low quality of services and diminished credibility of the national and subnational governments. This situation highlights the need to design public policies that reduce conflicts and promote adequate governance. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) offers a promising solution to expedite various phases of the systematic review process such as screening. Objective: We aimed to assess the accuracy of an AI tool in identifying eligible references for a systematic review compared to identification by human assessors. Methods: For the case study (a systematic review of knowledge translation interventions), we used a diagnostic accuracy design and independently assessed for eligibility a set of articles (n = 300) using human raters and the AI system DistillerAI (Evidence Partners, Ottawa, Canada). We analysed a series of 64 possible confidence levels for the AI's decisions and calculated several standard parameters of diagnostic accuracy for each. Results: When set to a lower AI confidence threshold of 0.1 or greater and an upper threshold of 0.9 or lower, DistillerAI made article selection decisions very similarly to human assessors. Within this range, DistillerAI made a decision on the majority of articles (93–100%), with a sensitivity of 1.0 and specificity ranging from 0.9 to 1.0. Conclusion: DistillerAI appears to be accurate in its assessment of articles in a case study of 300 articles. Further experimentation with DistillerAI will establish its performance among other subject areas. [ABSTRACT FROM AUTHOR]

Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RTPCR) for the detection of COVID-19 illness. This article presents gray-level co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multilayer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection. [ABSTRACT FROM AUTHOR]

This research investigates the utilization of machine learning methodologies for the purpose of forecasting the fluctuations in stock values inside the financial market. The application of a Random Forest classifier is utilized on a dataset including historical stock prices (namely, the KSE-100 Index) to generate predictions regarding the future movement of stocks, specifically whether they would experience an increase or decrease. The model is trained via a sliding window methodology and is assessed through the utilization of precision, recall, and F1-score criteria. The study furthermore incorporates the utilization of back testing and hyper-parameter tweaking techniques in order to enhance the performance of the model. The findings indicate that the model demonstrates a precision score of 58%, representing an enhancement compared to the previous score of 48%. Nevertheless, the model's total accuracy stands at a mere 58%, underscoring the need for future enhancements. The report additionally proposes potential avenues for future research, such as exploring alternate data sources, employing sentiment analysis techniques, and developing more advanced algorithms. The findings of this study hold significant significance for investors and financial institutions, as they highlight the potential of machine learning in facilitating informed investment decisions and improving financial forecasts and analysis. [ABSTRACT FROM AUTHOR]

The aim was to investigate how the congruence of odors and visual objects in virtual reality (VR) affects later memory recall of the objects. Participants (N = 30) interacted with 12 objects in VR. The interaction was varied by odor congruency (i.e., the odor matched the object's visual appearance, the odor did not match the object's visual appearance, or the object had no odor); odor quality (i.e., an authentic or a synthetic odor); and interaction type (i.e., participants could look and manipulate or could only look at objects). After interacting with the 12 objects, incidental memory performance was measured with a free recall task. In addition, the participants rated the pleasantness and arousal of the interaction with each object. The results showed that the participants remembered significantly more objects with congruent odors than objects with incongruent odors or odorless objects. Furthermore, interaction with congruent objects was rated significantly more pleasant and relaxed than interaction with incongruent objects. Odor quality and interaction type did not have significant effects on recall or emotional ratings. These results can be utilized in the development of multisensory VR applications. [ABSTRACT FROM AUTHOR]

This study advances Surface Inspection (SI) in display panel manufacturing using a data‐centric approach, addressing high gray zone rates and data discrepancies. We employed a data flywheel method with dual labeling to improve dataset quality. Results show expanded automated coverage and enhanced classification, reducing defect leakage, demonstrating AI's impact in smart manufacturing processes. [ABSTRACT FROM AUTHOR]

The purpose of the current study was to expand upon previous research on RECALL, a dialogic reading intervention modified for autistic children aimed at increasing engagement. Children ages 3–6 years (n = 6) with language delays with or without co-occurring autism were tested using a multiple baseline across participants design. During baseline, the interventionist used dialogic reading and asked questions after every page. During intervention, the interventionist used RECALL, including a least to most prompting hierarchy with visual prompt cards. Children were more responsive and produced more meaningful correct responses during the intervention. Response type (linguistic vs. non-linguistic) also changed from baseline to intervention, though the pattern varied across participants. Intervention was not associated with increased responsiveness to adult bids for attention or pauses designed to encourage the child to initiate an interaction, though a few children showed changes in these responses over time.

The IMPRoving Exposure aSSessment Methodologies for Epidemiological Studies on Pesticides (IMPRESS) project (http://www.impress-project.org/) aimed to further the understanding of the performance of pesticide exposure assessment methods (EAMs). To achieve this the IMPRESS project used two approaches to assess EAM performance, using existing and newly collected data from five studies from three different countries and use of published secondary data to undertake three meta-analyses for selected chronic health outcomes. Based on the findings of the IMPRESS project we provide in this paper insights on the overarching research question “How can exposure assessments for pesticides in epidemiological studies be improved”? Exposure assessment is a critical component of pesticide epidemiological studies. EAMs used and epidemiological practices employed need to reflect the changing nature and complexities of pesticide exposure in various occupational settings. To properly assess the association between exposure and selected health outcomes, the choice of EAM should provide a clear exposure contrast within the study population. Acquiring a practical understanding of the pesticide use practices is crucial to determine whether factors such as frequency or intensity of exposure have to be considered in planned analyses. Biomonitoring may be more beneficially applied intensively in a focussed exposure assessment analysis of a particular cohort, which can be used to determine the most relevant exposure factors within that cohort-specific context. Overall, improving pesticide exposure assessment in epidemiological studies requires a multi-disciplinary approach. A next step for the wider scientific community may be to consider the development of a decision tree to aid the selection of suitable EAMs. Such a decision tree would need to consider and be based on multiple parameters including, but not limited to, study type, health endpoint, socio-demographic context, farming system, pesticide used, and application methods.

Efficient privacy-preserving record linkage (PPRL) is essential for integrating data from different providers without exposing personally identifiable information (PII). This study investigates the effectiveness of a novel blocking technique, implemented on Bloom filters (a space-efficient probabilistic data structure), to enhance efficiency and maintain privacy in a real-world evaluation. This methodology involves the utilization of Anonlink, an open-source Python-based PPRL system which generates a type of Bloom filter, Cryptographic Longterm Keys (CLK), for secure record linkage. Initially, the relevant PII fields of the two datasets undergo anonymization into CLKs. Utilizing the CLK’s property of similarity preservation, we create manageable blocks of records based on bits in common within the Bloom filters. This allows us to reduce the number of comparisons of non-matching records to improve linkage efficiency. Finally, record linkage is performed to identify potential matches within the blocked datasets. This blocking technique for CLKs is evaluated in terms of efficiency and record matching precision, aiming to determine the optimal balance between the two factors. Preliminary results indicate a significant reduction in computational burden, with recall minimally affected. Moreover, the implemented blocking technique poses no additional risks of privacy breaches. Preliminary evaluation of the blocking technique shows a promising avenue for secure and efficient data integration, especially in datasets with PII, warranting further investigation for validation and wider application.