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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]

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]

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]

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]

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]

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 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.