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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.

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]

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]

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 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]

Psychopathic traits are associated with impaired emotional processing. The present study examines the potential association between psychopathic traits and memory for emotional stimuli. Although a significant body of research suggests that memory is heightened for emotional stimuli, it is unclear how psychopathic traits may disrupt this process. Eighty-two male jail inmates completed an emotional memory task as well as portions of a standardised memory assessment. Psychopathic traits were not associated with the ability to freely recall images of positive, negative or neutral valence that participants had seen more than 15 min prior; psychopathic traits were also not associated with the ability to recognise these previously viewed images when shown them again. Exploratory analyses indicated trends toward reduced accuracy in recognising both positive and negative, but not neutral, emotional stimuli in individuals with higher levels of interpersonal and affective traits of psychopathy. As expected, psychopathy was unrelated to non-emotion-related memory functioning in auditory and visual domains as measured by the Wechsler Memory Scales 4th Edition. Overall, these results do not support the hypothesis that psychopathic traits significantly interfere with memory for emotional stimuli. [ABSTRACT FROM AUTHOR]

Background: Existing patient-reported outcome measures (PROMs) for chronic rhinosinusitis (CRS) use a variety of recall periods and response scales to assess CRS symptom burden. Global perspectives of CRS patients regarding optimal recall periods and response scales for CRS PROMs are unknown. Methods: This was a multi-center, cross-sectional study recruiting 461 CRS patients from sites across the United States, Saudi Arabia, New Zealand, and Austria. Participants chose which CRS symptom recall period (1 day, 2 weeks, 1 month, >1 month) was most reflective of their current disease state and upon which to best base treatment recommendations (including surgery). Participants also chose which of six response scales (one visual analogue scale and five Likert scales ranging from four to eight items) was easiest to use, understand, and preferred. Results: A plurality of participants (40.0%) felt their CRS symptoms' current state was best reflected by a 1-month recall period. However, most patients (56.9%) preferred treatment recommendations to be determined by symptoms experienced over a >1 month period. The four- and five-item Likert scales were the easiest to understand (26.0% and 25.4%, respectively) and use (23.4% and 26.7%, respectively). The five-item (26.4% rating it most preferred and 70.9% rating it preferred) and four-item Likert (22.3% rating it most preferred and 56.4% rating it preferred) response scales were most preferred. Conclusion: Future PROMs for CRS should consider assessment of symptoms over a 1-month period and use a four- or five-itemLikert response scale to reflect global patient preferences. These findings also inform interpretation of current CRS PROMs. [ABSTRACT FROM AUTHOR]

Models such as finite state automata are widely used to abstract the behavior of software systems by capturing the sequences of events observable during their execution. Nevertheless, models rarely exist in practice and, when they do, get easily outdated; moreover, manually building and maintaining models is costly and error-prone. As a result, a variety of model inference methods that automatically construct models from execution traces have been proposed to address these issues. However, performing a systematic and reliable accuracy assessment of inferred models remains an open problem. Even when a reference model is given, most existing model accuracy assessment methods may return misleading and biased results. This is mainly due to their reliance on statistical estimators over a finite number of randomly generated traces, introducing avoidable uncertainty about the estimation and being sensitive to the parameters of the random trace generative process. This article addresses this problem by developing a systematic approach based on analytic combinatorics that minimizes bias and uncertainty in model accuracy assessment by replacing statistical estimation with deterministic accuracy measures. We experimentally demonstrate the consistency and applicability of our approach by assessing the accuracy of models inferred by state-of-the-art inference tools against reference models from established specification mining benchmarks. [ABSTRACT FROM AUTHOR]

This article reports two accidents caused by defective Takata airbags ruptured, which led to the deaths of the drivers. This is the first public report on the deaths caused by Takata airbags in China. Determine the relationship between the driver death and airbag rupture through autopsy indings and vehicle inspection. Due to defects in the design of Takata's inflator, moist air was permitted to slowly enter the inflator, resulting the PSAN slowly degraded physically. The damaged propellant burned more rapidly than intended and overpressurized the inflator's steel housing, causing fragmentation and flying debris at high speed, killing or injuring vehicle occupants. To date, there are still tens of millions of defective Takata airbags that have not been recalled for repair, posing safety risks. This article suggests taking preventive measures to avoid the occurrence of similar accidents. [ABSTRACT FROM AUTHOR]

RECOGNITION MEMORY IS BETTER FOR VOCAL melodies than instrumental melodies. Here we examine whether this vocal advantage extends to recall. Thirtyone violinists learned four melodies (28 notes, 16 s), two produced by voice and two by violin. Their task was to listen to each melody and then immediately sing (for vocal stimuli) or play back on violin (for violin stimuli) the melody. Recall of the melody was tested in ten consecutive trials. After a brief delay (*10 min), participants were asked to perform the four melodies from memory. Each performance was scored based on the accuracy of two measures: (1) intervals and (2) contour. The results revealed an advantage for vocal over violin melodies in immediate recall of the melodic contour and, after the delay, a reverse pattern with an advantage for violin over vocal melodies. The findings are consistent with the hypothesis that the voice facilitates learning of melodies and further show that the vocal advantage in recall is short-lived and based on contour. [ABSTRACT FROM AUTHOR]

A neurological condition affecting both the brain and behavior is identified as autism spectrum disorder (ASD). Due to the absence of a reliable medical test for detecting autism, diagnoses rely on historical evidence. Essential in assessing the degree of autism are models like Convolutional Neural Networks (CNNs) and Random Forest (RF). In order to reduce the number of diagnostic tests required for autism diagnosis, this research work presents a new hybrid model that combines the strengths of RF and CNNs, providing healthcare solutions. It is noteworthy that this model properly predicted the severity of autism with an astounding accuracy rate of 99.15% when applied to historical data gathered from the Kaggle Repository. [ABSTRACT FROM AUTHOR]

Background: The causality assessment tool can be utilized to assist patients in identifying adverse drug reactions (ADRs). Aim: To evaluate the accuracy of the causality assessment tool for patients identifying ADRs compared to assessments made by pharmacists, and to explore how patients recall and recognize symptoms as ADRs. Method: Mixed methods study consisting of self-administered questionnaires (phase 1) and semi-structured, face-to-face interviews (phase 2) with patients who had experienced ADRs in the past year at a tertiary care hospital in Thailand. Results: Out of 769 questionnaires, 716 were returned and 622 of these were both valid and had at least one ADR (86.8%). Classification of patient-reported symptoms using the causality assessment tool found 12 (1.9%) highly-probable ADRs, 399 (64.1%) probable ADRs, 207 (33.3%) possible ADRs, and 4 (0.6%) that were not classified as ADRs. There was fair agreement between patient-assessed and pharmacist-assessed causality classifications using the Naranjo algorithm (K = 0.268) and the World Health Organization Uppsala Monitoring Centre (WHO-UMC) criteria (K = 0.373). The timing relationship between the occurrence of symptoms and administration of a suspected drug was the most frequently mentioned reason that patients gave for recalling and recognizing suspected ADRs. Conclusion: Promoting the causality assessment tool for use by patients in collaboration with healthcare professionals is likely to enhance patients' ability to correctly identify ADRs and ultimately contribute to increased medication safety. [ABSTRACT FROM AUTHOR]

Objective: Using young adults' retrospective reports, the current study examines the moderating effects of parental differential treatment (PDT) on the association between family cohesion and sibling relationship quality during adolescence. Background: Sibling relationships in adolescence carry great implications for individual development. However, little is known about the potential interactions between family cohesion and PDT on sibling affection and hostility. Method: Retrospective data were collected from 325 young adults (M = 19.50 years, SD = 1.25) who recalled family of origin experiences with parents and a target sibling closest to their age. Results: Family cohesion was positively associated with sibling affection and negatively associated with sibling hostility. Additionally, fathers' differential control attenuated the relationship between cohesion and sibling affection and both mothers' and fathers' differential affection attenuated the relationship between cohesion and sibling hostility. Conclusion: Findings on the main effects suggest that perceived family cohesion is a crucial factor that promotes better sibling relationships (i.e., higher affection and lower hostility). However, offspring's perceptions of PDT can affect the relationship between family cohesion and sibling relationship quality, such that extreme levels of parents' (especially fathers') differential affection and control weaken the association. The significant moderation emerged with differences in parental sex (i.e., mother and father) and PDT dimensions (i.e., affection and control). Implications: To help siblings engage in more affectionate and fewer hostile interactions, parents (especially fathers) should consciously work to provide equal treatment toward their offspring and work on providing more contextual information when they have to show differentiation. [ABSTRACT FROM AUTHOR]

The creation of frameworks for lowering natural hazards is a sustainable development goal specified by the United Nations. This study aims to predict drought occurrence in Tamil Nadu, India, using 26 years of data, with only 3 drought years. Since the drought-occurrence years are minimal, it is an imbalanced dataset, which gives a suboptimal classification performance. The accuracy metric has a tendency to produce misleadingly high results by focusing on the accuracy of forecasting the majority class while ignoring the minority class; hence, this work considers the metrics' precision and recall. A novel strategy uses attribute (or instance) weighting, which allots weights to attributes (or instances) based on their importance, to improve precision and recall. These weights are found using a bio-inspired optimization algorithm, by designing its fitness function to improve precision and recall of the minority (drought) class. Since increasing precision and recall is a tug-of-war, multi-objective optimization helps to identify optimal attribute (or instance) weight balancing precision and recall while maximizing both. The newly introduced Synergistic Optimization Algorithm (SOA) is utilized for multi-objective optimization in order to ascertain weights for attributes (or instances). In SOA, to solve multi-objective optimization, each objective's population was generated using three distinct algorithms, namely, the Genetic, Firefly, and Particle Swarm Optimization (PSO) algorithms. The experimental results demonstrated that the prediction performance for the minority drought class was superior when utilizing instance (or attribute) weighting compared to the approach not employing attribute/instance weighting. The Gradient Boosting classifier with an attribute-weighted dataset achieved precision and recall values of 0.92 and 0.79, whereas, with instance weighting, the values were 0.9 and 0.76 for the drought class. The attribute weighting shows that in addition to the default drought indices SPI and SPEI, pollution factors and mean sea level rise are valuable indicators in drought prediction. From instance weighting, it is inferred that the instances of the months of March, April, July, and August contribute most to drought prediction. [ABSTRACT FROM AUTHOR]

The global dissemination of SARS-CoV-2 resulted in the emergence of several variants, including Alpha, Alpha + E484K, Beta, and Omicron. Our research integrated the study of eukaryotic translation factors and fundamental components in general protein synthesis with the analysis of SARS-CoV-2 variants and vaccination status. Utilizing statistical methods, we successfully differentiated between variants in infected individuals and, to a lesser extent, between vaccinated and non-vaccinated infected individuals, relying on the expression profiles of translation factors. Additionally, our investigation identified common causal relationships among the translation factors, shedding light on the interplay between SARS-CoV-2 variants and the host's translation machinery. [ABSTRACT FROM AUTHOR]

Acknowledging the critical role of working memory in language acquisition, this study examines the effects of multimodal input enhancement on working memory capacity (WMC) and collocation learning in adolescent and adult EFL learners. A cohort of 117 participants was randomly assigned to either experimental groups, receiving enhanced textual and auditory inputs, or control groups, experiencing standard inputs. Assessments included the Preliminary English Test, n-back test, and immediate and delayed collocation posttests. The results indicated that multimodal input significantly improved WMC and the recall and retention of collocations for all learners. Adolescents, in particular, excelled in both immediate and delayed tests and adapted their WMC more effectively in a multimodal context than adults. Additionally, an interaction between age and WMC was noted, affecting collocation recall and retention. These findings affirm the benefits of multimodal materials in enhancing cognitive functions and memory resources, thus improving language learning. The study offers practical insights for educational practices, advocating for the use of varied modalities in teaching materials to cater to different learning styles and cognitive needs. It also highlights the significance of designing age-appropriate materials and managing cognitive load in curriculum development, providing a tailored approach to language education for diverse learner populations. [ABSTRACT FROM AUTHOR]

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.

In the present study, we tested whether processing information in the context of an ancestral survival scenario enhances episodic memory performance in older adults and in stroke patients. In an online study (Experiment 1), healthy young and older adults rated words according to their relevance to an ancestral survival scenario, and subsequent free recall performance was compared to a pleasantness judgment task and a moving scenario task in a within-subject design. The typical survival processing effect was replicated: Recall rates were highest in the survival task, followed by the moving and the pleasantness judgment task. Although older adults showed overall lower recall rates, there was no evidence for differences between the age groups in the condition effects. Experiment 2 was conducted in a neurological rehabilitation clinic with a sample of patients who had suffered from a stroke within the past 5 months. On the group level, Experiment 2 revealed no significant difference in recall rates between the three conditions. However, when accounting for overall memory abilities and executive function, independently measured in standardized neuropsychological tests, patients showed a significant survival processing effect. Furthermore, only patients with high executive function scores benefitted from the scenario tasks, suggesting that intact executive function may be necessary for a mnemonic benefit. Taken together, our results support the idea that the survival processing task – a well-studied task in the field of experimental psychology – may be incorporated into a strategy to compensate for memory dysfunction.

Metode BERT dapat digunakan untuk menghasilkan hasil yang akurat dalam klasifikasi berita palsu dan berita benar. Hasil evaluasi menunjukkan bahwa model klasifikasi BERT memiliki akurasi sebesar 76% pada data validasi dalam mengklasifikasikan berita hoaks, yang menunjukkan performa atau kinerja model Machine Learning dalam melakukan klasifikasi berita hoaks. Sedangkan pada model klasifikasi BERT Multilingual memiliki akurasi lebih rendah, yakni 63%. Potensi metode ini dapat membantu dalam memerangi penyebaran berita palsu. Penelitian ini berpotensi memberikan kontribusi penting dalam memerangi penyebaran berita palsu di dunia digital yang semakin kompleks. Dengan menggunakan BERT sebagai pendekatan, model ini memungkinkan pengidentifikasian berita palsu yang lebih akurat, serta membantu masyarakat dalam menghindari konsumsi informasi yang salah. Dengan hasil yang positif ini, penelitian ini menunjukkan bagaimana teknologi machine learning dapat digunakan untuk melawan disinformasi dan menjadikan dunia maya menjadi tempat yang lebih terpercaya.

Detection of heart diseases (HD) at an early stage diminishes the mortality rate. However, handling huge data is cumbersome for physicians. Hence, there is a need for a tool regarding the automation and processing of large data to help in making precise decisions. The proposed methodology aims to predict cases of HD or cardiovascular diseases (CD) well in advance based on the features of the patient. In the proposed methodology, the Cleveland Dataset of HD collected from University of California, Irvine (UCI) repository is evaluated. The various phases in the proposed methodology include, insight about the data, analyzing the data, feature engineering and finally building the model. The K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), classifier and convolutional neural network (CNN) are used to predict the HD and are evaluated on the dataset. The metrics considered are classification accuracy (CA), recall (r) and precision (p). The resulted CA, ‘r’ and ‘p’ for KNN-based approach are 66.7%, 91.7% and 88.8%, respectively. The SVM-based approach with ‘linear’ kernel achieved CA, ‘r’ and ‘p’ are 74.2%, 85.0% and 74.0%, respectively. The ANN-based approach resulted in 70.08%, 77.0% and 84.2% of CA, ‘p’ and ‘r’, respectively. Finally, the CNN-based prediction achieved CA, ‘p’ and ‘r’ are 83.61%, 76.0% and 97.0%, respectively. The experimental study concludes that the CNN-based prediction model outperformed the KNN, SVM and ANN-based prediction approaches regarding the prediction accuracy. An optimization algorithm can be incorporated into the model in future.

Mental context reinstatement (MCR) is a key part of the cognitive interview. However, police face challenges delivering MCR in real‐life situations. Over the years, modifications have been made to make MCR more user‐friendly for officers and ensure witness engagement. The current study evaluates the impact of vocalizing MCR generations aloud on mock‐witness's immediate and delayed recollections. Participants watched a staged multiple‐car collision and were interviewed about it the next day. Half verbalized mental images aloud (aMCR), while the other half kept them silent in their minds (cMCR). After a week, participants took part in a delayed recall attempt. No significant differences in immediate recall performance were found. During the delayed recall, participants who engaged in aMCR previously recalled significantly more and more correct details than those who received cMCR. aMCR might lead to more coherent representations in working memory, resulting in improved consolidation and better future recall. [ABSTRACT FROM AUTHOR]

Thunderstorms are natural disasters that impact people, animals, and the economy. Thunderstorms' detrimental repercussions can be avoided by identifying their occurrence in advance. The current work, in this respect, uses soft computing techniques such as K-Nearest Neighbour (KNN), Decision Tree (DT), Logistic Regression (LR), and Support Vector Machine (SVM) with various kernel functions to categorize the occurrence of thunderstorms over Ranchi, India. These techniques were trained and tested using two data sets: daily average and hourly meteorological datasets. The primary purpose of this study is to find which dataset-classifier combination is optimal for categorizing thunderstorm occurrence in Ranchi. No classifier was found to adequately classify either the Day Average Dataset or the Modified Day Average Dataset. On the other hand, the Hourly Dataset was found to be more balanced in terms of the number of thunderstorms that occurred than the Day Average and Modified Average datasets. The F-Score value of the incidence of thunderstorm incidents after using different classifiers was used to compare the outcomes of these datasets. The results reveal that using SVM with radial basis function. The Hourly Dataset is the best for thunderstorm day classification. For the overall and only incidence of thunderstorms classes, SVM-RBF gets 0.81 and 0.74 F-Scores, respectively. Other approaches, like grid search and Bagging, have been used to increase SVM-RBF performance. Grid search and Bagging are used on SVM-RBF to produce a hybrid Grid-Bag-SVM-RBF classifier with 82.04% accuracy and F-scores of 0.83 and 0.78 for overall and just thunderstorm occurrence, respectively. [ABSTRACT FROM AUTHOR]

Children are interviewed to provide information about past events in various contexts (e.g., police interviews, court proceedings, therapeutic interviews). During an interview, various factors may influence the accuracy of children's responses to questions about recent events. However, behavioral research in this area is limited. Sparling et al. (2011) showed that children frequently provided inaccurate responses to questions about video clips they just watched depending on the antecedents (i.e., the way a question was asked) and consequences (i.e., the response of the interviewer to their answers). In the current study, we replicated and extended the procedures reported by Sparling et al. and found that two of five children were sensitive to the various antecedents and consequences that we manipulated. Our findings indicate a need for more research in this area to determine the relevant environmental variables that affect children's response accuracy. [ABSTRACT FROM AUTHOR]

Educational materials for preschool children are often embedded with fantastical elements. Nevertheless, there is still little empirical evidence on their effectiveness, especially as concerns long-term retention. Virtual reality offers new ecological possibilities for investigating this type of learning, especially through the impact of immersion. In a between-design study, 168 children aged four to six years followed a virtual reality presentation on China presented by either a realistic young girl or an anthropomorphic animal. The level of immersion was manipulated, with half of the children following the presentation in immersive virtual reality (IVR) and the other half in desktop virtual reality (D-VR). Participants were asked to complete a new/old recognition task and a quiz task immediately after the presentation and once again one week later, with an additional transfer task being added for the second series. One week later, children performed significantly better on the new/old recognition task in the realistic condition when compared to the anthropomorphic condition. However, there were no differences observed in the quiz task and in the transfer task. It therefore seems that, under certain conditions, children remember a cultural presentation better when it is presented by a realistic avatar. The results further showed that the children performed significantly worse in the IVR conditions on all tasks. A possible explanation for this result is that IVR demands excessive cognitive resources from preschool children. Further studies should explore this unexpected result, as well as what could be done to make IVR effective for learning in preschool children. [ABSTRACT FROM AUTHOR]

The study of juncture in the perspective of the English as a Foreign Language (EFL) context is limited, particularly to the one associated with the recalling process. This research aims to describe a glimpse of the juncture phenomenon by indicating the phonetic recalling process. An experimental method was applied to conduct the research. An experiment involving stimuli to phonetic knowledge was given to participants in the EFL context. There were twenty students involved in this experimental study. Analysed acoustically, the result of the preliminary data shows that junctures may occur during speech production of the stimuli with the indication of the phonetic recalling process at the syllabic level. This indication might refer to the participants' attempt to re-access the phonetic knowledge stored in the brain. The pause duration might mark the occurrence of junctures, indicating phonetic recalling. It was also revealed that the participants' attitudes towards the English might influence the occurrence of junctures. The study showed that junctures happened before pronouncing the words in monosyllabic words. In contrast, juncture might occur in the first, middle, and last syllable in multisyllabic words. The locations of junctures might relate to the most unfamiliar syllable of the words from the participants' perspective. From this perspective, juncture may also occur in other speakers, not only Sundanese but also other foreign languages, not only English. This preliminary research may serve as a foundation to conduct a relevant study on the other local or regional languages in Indonesia. [ABSTRACT FROM AUTHOR]

Deep Learning is becoming an emerging field in the Internet of Things (IoT) due to its ability to provide a comprehensive approach to automatic feature extraction and predictive modeling for analysis and decision-making. This paper introduces an IoT-based Dance Movement Recognition Model based on a Deep Learning Framework. The framework consists of a convolutional neural network (CNN) with a data-centric architecture to identify dance movements from the acquired data gathered by an IoT device. The IoT device collects 3D motion data captured by three accelerometers. Feature extraction is then done with the CNN architecture, resulting in a flattened matrix representing the movement. Subsequently, a Multi-Layer Perception (MLP) is used to classify the movements. The proposed system is experimentally evaluated on a standardized dataset of 16 dance steps with three-speed levels. The results show that our model outperforms state-of-the-art approaches in accuracy, evaluation time, and classification accuracy. The proposed model reached 90.74% accuracy, 87.12% precision, 83.78% recall and 84.39% F1-Score. The proposed model can serve as a basis for a reliable and intuitive system that can be used to monitor patient's dance movements with accuracy. [ABSTRACT FROM AUTHOR]

Previous research established that readers acquire accurate and inaccurate information from fiction. The current study explored factors that might moderate these effects. Participants read fictional stories that each contained three assertions. The first two assertions in each story were either correct information or implausible misinformation, allowing a manipulation of the (implicit) credibility of the narrator. The last assertion in each story was the critical one, and was correct information, implausible misinformation, or plausible misinformation. After reading, participants answered general knowledge questions that were related to the critical assertions they encountered during reading. Encountering misinformation led to lower accuracy than being presented with correct information, and being presented with plausible misinformation led to higher production of that misinformation. The narrator credibility manipulation interacted with the type of critical assertion: When the critical assertion was presented accurately in a story, credible narrators presenting true critical assertions led to greater accuracy on the general knowledge test than when noncredible narrators presented this same information. These findings are discussed with respect to theories of validation during language comprehension. [ABSTRACT FROM AUTHOR]

Introduction: Fecal incontinence (FI) is characterized by both irregular and unpredictable bowel symptoms. An accurate history of symptoms is important for diagnosis and guiding management. Whether a patient's history of bowel symptoms is reliable or if there is recall bias is unknown. Aim: To evaluate the accuracy of FI symptoms based on patient's recall compared with a prospective stool diary. Methods: FI (Rome IV) patients completed a bowel questionnaire that included leakage episodes and stool consistency. Subsequently they completed a one‐week FI stool diary. Agreement and correlation between historical recall and stool diary were compared. Results: One hundred patients participated. On average they reported 12 bowel movements (BMs) and five FI episodes per week. Fifty‐two percent had completed under‐graduation, 33% high school and 15% postgraduation. Using recall, 23% of patients accurately reported the number of FI episodes, whereas 41% underestimated and 36% overestimated its prevalence compared to the FI diary. Similarly, the concordance for the number of BMs was 30%, urgency was 54%, amount of stool leakage was 16%, and stool consistency was 12.5%. The concordance for nocturnal FI events, use of pads and lack of stool awareness were 63%, 75%, and 66.6% respectively. Conclusion: There is poor concordance for key bowel symptoms including the number of FI episodes as reported by FI patients, suggesting significant recall bias. Thus, historical recall of chronic FI symptoms may be less accurate. A prospective stool diary could provide more accurate information for the evaluation of FI patients. [ABSTRACT FROM AUTHOR]

In 2022 and 2023, Medtronic recalled implantable defibrillators because they may deliver less than full-energy shocks. The 2022 problem truncates the second phase of the waveform (SCP-T2), resulting in ∼32-J shocks, and is mitigated by downloadable software. The 2023 malfunction truncates the first phase of the waveform, resulting in 0- to 12-J shocks due to a glassed feedthrough problem (GFT-T1) that might be avoided by programming B>AX shock polarity. The purpose of this study was to assess the consequences of GFT-T1 and SCP-T2 shocks in the Food and Drug Administration's Manufacturers and User Facility Device Experience (MAUDE) database and to estimate the incidences of GFT-T1 and SCP-T2. We analyzed MAUDE reports supplemented by Medtronic data; lead failures were excluded. The incidences of SCP-T2 and GFT-T1 were estimated using USA volumes for devices with glassed feedthroughs. One hundred thirty-two devices delivered truncated shocks: 27 (20.5%) were GFT-T1; 103 (78.0%) were SCP-T2; and 2 (1.5%) truncated both phases (BOTH-T1&2). Of 54 ventricular fibrillation (VF) patients, 21 (38.9%) were not defibrillated by truncated shocks: 8 (38.1%) received GFT-T1 shocks, 12 (57.1%) received SCP-T2 shocks, and 1 received a BOTH-T1&2 shock; 2 patients suffered unrelated deaths; 1 was externally rescued; 1 outcome was unknown; the others were defibrillated by subsequent shocks or terminated spontaneously. The majority of patients (79.1%) shocked for ventricular tachycardia (VT) were converted, primarily (94.1%) by SCP-T2 shocks. Estimated incidences of GFT-T1 and SCP-T2 were 0.0078%–0.0088% and 0.1062%–0.1110%. GFT-T1 and SCP-T2 shocks can result in failure to terminate VF/VT, but they may be preventable. Although the incidences of these truncated shocks are very low, heightened surveillance is warranted. [ABSTRACT FROM AUTHOR]

Background: People with posttraumatic stress disorder (PTSD) may have cognitive decline, a risk which can be particularly threatening at old age. However, it is yet unclear whether initial cognitive decline renders one more susceptible to subsequent PTSD following exposure to traumatic events, whether initial PTSD precedes cognitive decline or whether the effects are reciprocal. Objective: This study examined the bidirectional longitudinal associations between cognitive function and PTSD symptoms and whether this association is mediated by depressive symptoms. Method: The study used data from two waves of the Israeli component of the Survey of Health, Ageing, and Retirement in Europe (SHARE), collected in 2013 and 2015. This study focused on adults aged 50 years and above (N = 567, mean age = 65.9 years). Each wave used three measures of cognition (recall, fluency, and numeracy) and PTSD symptoms following exposure to war-related events. Data were analyzed using mediation analysis with path analysis. Results: Initial PTSD symptoms predicted cognitive decline in recall and fluency two years later, while baseline cognitive function did not impact subsequent PTSD symptoms. Partial mediation showed that older adults with more PTSD symptoms had higher depressive symptoms, which in turn were linked to subsequent cognitive decline across all three measures. Conclusions: This study reveals that PTSD symptoms are linked with subsequent cognitive decline, supporting approaches addressing this direction. It further indicates that part of this effect can be explained by increased depressive symptoms. Thus, treatment for depressive symptoms may help reduce cognitive decline due to PTSD. [ABSTRACT FROM AUTHOR]