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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]

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]

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]

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]

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.

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.

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]

One of the most common interests among cognitive psychologists is establishing ways to enhance human learning. An additional layer of complexity has been brought on by the rapid evolution of technology. Specifically, examining if the mechanisms involved in typing differ from those involved in handwriting. The literature concerning the implications of encoding modality on memory have been inconclusive. This present research examined whether encoding modality resulted in performance differences for word recall. Wammes et al.'s (2016) drawing versus handwriting methodology was utilized with the addition of a typing condition. The results replicated the drawing effect, whereby drawn words were better recalled than handwritten ones. Overall, the evidence did not suggest that the mechanisms involved in handwriting led to better free recall than those involved in typing. However, if the pen is indeed mightier than the keyboard (Mueller & Oppenheimer, 2014), then the effect is not explained by visual attention or sensorimotor action differences between modalities. Implications for education are discussed. [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.

As we navigate our day-to-day lives, we regularly adapt our behaviour according to what we predict may happen next in a given context. When an unexpected event occurs, our predictions about the world are disrupted and must be updated. Unexpected, isolated events, particularly with high emotionality, are also better recalled. In the present work, we investigated how oddballs affect recall dynamics. Seventy young, healthy participants encoded word lists containing either emotional or perceptual oddballs at varying stimulus onset asynchronies followed by free recall. It is well established that after recalling an item, we have a higher probability of recalling items encoded nearby, particularly those that were encoded after the item was recalled, a phenomenon known as forward contiguity of recall. We tested how novelty (oddballs versus control words) modulated forward contiguity as a function of salience type (emotional versus perceptual). The present results provide empirical evidence of forward contiguity modulation selectively by emotional salience and suggest that recall patterns after presenting emotional and perceptual oddballs are mediated by different mechanisms.

We do not memorize items in our surroundings with equal priority. Previous literature has widely shown that emotional stimuli are better remembered than neutral stimuli. However, given emotional stimuli and neutral stimuli often differ in both valence and arousal dimensions, it remains unclear whether the enhancement effects can be attributed to valence, or just to arousal. Importantly, most prior studies relied on a relatively small number of stimuli and non-emotional factors such as word length, imageability and other confounds were hard to control. To address these challenges, we analyzed multiple large databases of recognition memory and free recall tasks from previous research by items with many lexical and semantic covariates included, examining the effects of valence or arousal when controlling for each other. Our results showed a U-shaped relationship between valence and memory performance for both recognition and free recall, and a linear relationship between arousal and memory performance for both tasks. These findings showed that the memory enhancement effects can be attributed to both valence and arousal. We demonstrated these effects with generalizability across many stimuli and controlled for non-emotional factors. Together, these findings disentangle the contribution of valence and arousal in emotional memory enhancement effects and provide insights for current major theories of emotional memory.

Abstract Objective To compare forgetting patterns between patients with temporal lobe (TLE) and generalized (GGE) epilepsies and to assess whether recall is associated with epileptic activity. Methods Thirty‐three patients with TLE (13 left, 17 right, and 3 nonlateralized TLE), 42 patients with GGE, and 57 healthy controls (HCs) were asked to recall words, verbal story material, and the Rey‐Osterrieth complex figure at two delays. Accelerated long‐term forgetting (ALF) was defined by group performance comparable to HCs at 30 min and worse recall than HCs after 4 weeks. ALF was assessed by comparing raw test scores in a two‐way repeated measures analysis of variance (ANOVA) adjusted for the learning capacity. Results Compared to HCs, patients with R‐TLE remembered fewer items of the word list after 30 min as well as after 4 weeks. Patients with L‐TLE and GGE had comparable learning‐adjusted performance to HCs at the 30 min delay but scored less after 4 weeks (group by delay interaction F(3, 124) = 3.2, P = 0.026, ηp2 = 0.07). The epilepsy group (patients with TLE and GGE combined) performed as well as HCs at 30 min but worse after 4 weeks irrespective of experienced seizures during the 4‐week delay or interictal bilateral (TLE) or generalized (GGE) activity before the study. We noted no statistically significant differences between patient and HC verbal story (group by delay interaction F(3, 124) = 0.7, P = 0.570, ηp2 = 0.02) or complex figure (F(3, 124) = 0.8, P = 0.488, ηp2 = 0.02) recall. Significance Our data support verbal and visual memory impairment in both TLE and GGE with different performances between these groups in the task of word recall. We suggest the presence of ALF in patients with GGE and left TLE after adjusting for learning capacity. We could not confirm the influence of epileptic activity on long‐term forgetting patterns. Future studies are required to better define domain‐specific differences in memory impairment in TLE and GGE.

The Legal Division of PT. ABC has implemented EDMS using the Integrated Legal Document Management Application, Centralized Legal Administration System (CLAS), to make the management of documents more accessible. The urgency is the system's accuracy in retrieving data or document input. The study employed a quantitative method with an experimental approach. Data collection techniques were observation, application search tests, documentation, and literature. Data analysis regarding the recall, precision, and F-measure calculation formulas will be compared with the standard information retrieval system effectiveness. The calculation results show that the average recall value for the search equals 1 or 100%. For the precision value that uses quotation marks, it is equal to 0.9713, while for searching without quotation marks, it is equal to 0.3920. From these results, the precision value is lower than the recall value. The precision value of 0.9713 is in the range of 0.68–1.00, which is placed as high precision, meaning that information retrieval is effective. The F-measure value is 0.98, close to 1, so the tracing system is sound. CLAS is quite effective in retrieving information according to user needs.

This paper describes a rich dataset from a registered report investigating sleep’s effect on false memory in the Deese-Roediger-McDermott (DRM) paradigm. 534 young adults completed free recall either shortly or 12 hours after studying lists of semantic associates (e.g., hospital, nurse). Collected online, our recall data showcase high data quality, replicating classic behavioural effects (e.g., serial position curve). The dataset contains raw recall data with original spelling and recall order, accompanied by demographic information (e.g., gender, time-of-day preference). Its versatility supports reuse in modelling memory decay and search processes, understanding lexical effects and individual differences, and benchmarking online memory studies.

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.

In recent years, the widespread adaptation of Unmanned Aerial Vehicles (UAVs), commonly known as drones, among the public has led to significant security concerns, prompting intense research into drones’ classification methodologies. The swift and accurate classification of drones poses a considerable challenge due to their diminutive size and rapid movements. To address this challenge, this paper introduces (i) a novel drone classification approach utilizing deep convolution and deep transfer learning techniques. The model incorporates bypass connections and Leaky ReLU activation functions to mitigate the ‘vanishing gradient problem’ and the ‘dying ReLU problem’, respectively, associated with deep networks and is trained on a diverse dataset. This study employs (ii) a custom dataset comprising both audio and visual data of drones as well as analogous objects like an airplane, birds, a helicopter, etc., to enhance classification accuracy. The integration of audio–visual information facilitates more precise drone classification. Furthermore, (iii) a new Finite Impulse Response (FIR) low-pass filter is proposed to convert audio signals into spectrogram images, reducing susceptibility to noise and interference. The proposed model signifies a transformative advancement in convolutional neural networks’ design, illustrating the compatibility of efficacy and efficiency without compromising on complexity and learnable properties. A notable performance was demonstrated by the proposed model, with an accuracy of 100% achieved on the test images using only four million learnable parameters. In contrast, the Resnet50 and Inception-V3 models exhibit 90% accuracy each on the same test set, despite the employment of 23.50 million and 21.80 million learnable parameters, respectively.

Self-administered questionnaires often use examples or lists of examples to aid respondent recall. We report on the results of a field experiment examining how such examples in survey questions affect the episodic recall of events. Building on part-set cuing theory, the authors propose that examples increase recall when they cue low-accessibility subcategories of events, but may decrease recall when they cue high-accessibility subcategories. Further, cuing with examples rather than subcategory names may in some situations clarify questions and reduce non-useable open-ended responses. Findings from a survey of 2137 adult Medicaid recipients are generally consistent with these predictions. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. [ABSTRACT FROM AUTHOR]

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

Objective: To compare forgetting patterns between patients with temporal lobe (TLE) and generalized (GGE) epilepsies and to assess whether recall is associated with epileptic activity. Methods: Thirty‐three patients with TLE (13 left, 17 right, and 3 nonlateralized TLE), 42 patients with GGE, and 57 healthy controls (HCs) were asked to recall words, verbal story material, and the Rey‐Osterrieth complex figure at two delays. Accelerated long‐term forgetting (ALF) was defined by group performance comparable to HCs at 30 min and worse recall than HCs after 4 weeks. ALF was assessed by comparing raw test scores in a two‐way repeated measures analysis of variance (ANOVA) adjusted for the learning capacity. Results: Compared to HCs, patients with R‐TLE remembered fewer items of the word list after 30 min as well as after 4 weeks. Patients with L‐TLE and GGE had comparable learning‐adjusted performance to HCs at the 30 min delay but scored less after 4 weeks (group by delay interaction F(3, 124) = 3.2, P = 0.026, ηp2 = 0.07). The epilepsy group (patients with TLE and GGE combined) performed as well as HCs at 30 min but worse after 4 weeks irrespective of experienced seizures during the 4‐week delay or interictal bilateral (TLE) or generalized (GGE) activity before the study. We noted no statistically significant differences between patient and HC verbal story (group by delay interaction F(3, 124) = 0.7, P = 0.570, ηp2 = 0.02) or complex figure (F(3, 124) = 0.8, P = 0.488, ηp2 = 0.02) recall. Significance: Our data support verbal and visual memory impairment in both TLE and GGE with different performances between these groups in the task of word recall. We suggest the presence of ALF in patients with GGE and left TLE after adjusting for learning capacity. We could not confirm the influence of epileptic activity on long‐term forgetting patterns. Future studies are required to better define domain‐specific differences in memory impairment in TLE and GGE. [ABSTRACT FROM AUTHOR]

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies. [ABSTRACT FROM AUTHOR]

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

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

Purpose: Free recall tasks target cognitive and linguistic processes such as episodic memory, lexical access, and retrieval. Consequently, the free recall paradigm is suitable for assessing memory deterioration caused by aging, which can be influenced by stimulus presentation modality. Hence, the present study aimed to determine if aging has an effect on visual and auditory recall abilities. Materials and Methods: Twenty young adults (mean age: 25.4 ± 0.99) and older adults (mean age: 63.3 ± 3.51) participated in the study. Participants performed a free recall task under two conditions - related and unrelated and two modalities - visual and auditory where they were instructed to recall as many items as possible with no specific order and time limit. Results: Free recall performance was calculated as the mean number of correctly recalled items. Although younger participants recalled a higher number of items, the performance across conditions and modality was variable. Conclusions: The findings of the present study revealed an age-related decline in the efficiency of episodic memory, which is crucial to remember recent events. [ABSTRACT FROM AUTHOR]

Credit card Fraud detection is a critical task in various industries, including finance and e-commerce, where identifying fraudulent activities can help prevent financial losses and protect users. It begins by combining two datasets containing fraudulent and non-fraudulent transactions to create a comprehensive dataset for analysis. Data is preprocessed by removing unnecessary features, calculating distance metrics, and generating new variables to capture temporal patterns and transaction history. Multicollinearity issues are addressed through feature selection. Improved Random Forest (RF) algorithm is used to improve fraud detection. The experimental results indicate that the improved Random Forest algorithm achieves commendable accuracy in fraud detection. The proposed model achieves 99.87% training accuracy and 99.41% testing accuracy. The Model's performance is evaluated by measuring precision, recall, F1-score and support. Our research emphasizes the importance of considering improved algorithms to achieve better results. The findings provide valuable insights for organizations aiming to enhance their fraud detection capabilities and make informed decisions to protect their systems and users. [ABSTRACT FROM AUTHOR]

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. [ABSTRACT FROM AUTHOR]

The advent of the big data era brings opportunities and challenges to the innovation of mental health education for college students in higher education institutions. In this paper, we monitor the mental health of college students in higher education institutions by collecting students’ consumption data, access control data, network data and historical achievement data, and use a convolutional neural network to extract features from behavioral data and then input the feature set into random forest model for training. The recognition effect of the RF algorithm is analyzed by comparing it with other algorithms, and the problems of college students’ mental health education are analyzed based on the correlation of features. The accuracy of identifying mental health problems of college students based on RF analysis of big data reached 0.72, the recall rate reached 0.58, and the F1-Measure was 0.67. For the correlation of different features, the most significant correlation effect was the time spent in the dormitory on rest days and the number of swiping access cards, with the correlation coefficients reaching 0.2719 and -0.2191, respectively. The analysis based on big data can accurately grasp the current situation and problems of mental health education of college students in higher education institutions and promote the comprehensive development of mental health education of college students.

Abstract Objective: To compare the agreement and cost of two recall methods for estimating children’s minimum dietary diversity (MDD). Design: We assessed child’s 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.

In this paper, we first use CNNS to collect rich samples of folk dance music, establish a model framework and a functional system framework, and conduct a comprehensive process analysis of the music. Then, the instrumental features, frequency features, and timbre features are extracted to obtain the spectral information. In the stage of chord analysis and encoding, a multivariate chord encoding model is established based on the acquired spectral information, including two parts: chord representation preprocessing and chord encoding. By utilizing this model, the chord structure of music was successfully and accurately encoded, allowing for analysis with up to 98% accuracy. Furthermore, significant recall results were achieved, reaching over 0.9, which suggests that the extracted chord features are highly reliable and accurate in recognizing musical chord information.

Abstract Background Many medicine quality problems are detected after they arrive at health facilities. Thus, critically defective medicines that may pose health risks to patients need to be withheld or recalled. Aims To investigate the withheld and recalled medicines in relation to the types of defects, their total numbers, therapeutic categories, pharmaceutical dosage forms, and country of manufacturer during the study period. Methods A retrospective review was performed on withheld and recalled medicines published on the publicly available National Medicines Regulatory Authority (NMRA) official website in Sri Lanka between June 2018 and August 2021. Details on substandard medicines (SM) were extracted and documented. Each record of SM was individually reviewed to determine the type of defect, subsequent action taken by NMRA, therapeutic category, pharmaceutical dosage form, and country of manufacturer. Results A total of 163 defects were identified in 143 defective medicines, among which the most common types of defects were contamination (n = 59, 36.2%), stability defects (n = 41, 25.2%), packaging and labelling defects (n = 27, 16.6%) and active pharmaceutical ingredient defects (n = 26, 15.9%). Out of 143 total defective medicines identified, anti-infectives accounted for 41.9%, while parenteral preparations (44.0%) were found to be frequently defective. Nearly 70% of the recalled and withheld medicines were of Indian origin, and some manufacturers were identified to be repeatedly involved. Conclusions This study revealed that contamination was the most frequent cause of defective medicines, while parenteral preparations and anti-infectives were the most susceptible pharmaceutical dosage form and therapeutic category found to be substandard, respectively. In addition, the findings show that some manufacturers were accountable for repetitive withholdings and recalls, which reflects the ignorance of quality control measures and weak regulatory inspections as a violation of Good Manufacturing Practice (GMP).

Introduction: Drug recalls may impact treatment plans or access to suitable therapies. Thus, they inadvertently affect treatment outcomes. Objective: We aimed to examine the impact of recalls on patients’ safety using pantoprazole-containing products recall as a case study in terms of the occurrence of potential drug-drug interactions (pDDIs). Methods: This retrospective study used de-identified electronic health records of adult patients who had a prescription for oral proton pump inhibitors (PPIs) including pantoprazole, esomeprazole, lansoprazole, or omeprazole from April 2020 through September 2021 from a large tertiary care hospital. The study outcome definition was the prevalence of pDDIs in PPIs users before and after the recall date (March 2021). Changes in the prevalence of pDDIs were modeled using interrupted time-series. The rate ratio of pDDIs in the 12 months before and 6 months after the recall was modeled using negative binomial regression. Results: A total of 1,826 pDDIs were identified, and the median monthly prevalence of pDDI before the recall was 102.5 which increased to 115.5 after the recall. A change in the level of pDDIs occurred immediately after the recall date, followed by a gradual decrease over time. The rate of pDDIs was 69% higher after the recall compared to the baseline (rate ratio 1.69; 95% confidence interval, 0.75–1.91). Discussion: Recall of pantoprazole-containing products was associated with a higher rate of pDDIs. However, the prevalence of pDDIs gradually decreased over time. We highlight the importance of planning of recall process and coordinating all potential stakeholders to avoid potential harms.Word count: 1450.

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 Background The US Food and Drug Administration (FDA) regulates goods accounting for 20% of US consumers’ total expenditure. The agency’s potential susceptibility to corporate lobbying and political influence may adversely affect the its abilities to fulfill its duties as a vital federal agency. This study assesses whether the FDA’s product recall classifications in recall scenarios are influenced by firms’ lobbying activities. Methods The universe of all FDA recalls between 2012 and 2019 is obtained from the FDA’s website. Firm names are matched to federal-level lobbying data obtained from the Center for Responsive Politics – a non-profit and nonpartisan organization that tracks lobbying expenditures and campaign contributions. Analyses are conducted using ordinary-least-squares regressions, in which the dependent variable is recall classification and independent variables are three different measures of firms’ lobbying activities in the one year prior to the recall. Results Firms that engage in lobbying appear more likely to receive favourable classifications from the FDA. When examining the above results by product type, we find that classification of food recalls seems to be subject to lobbying influence, but the same does not appear to be true for drug and device recalls. Evidence is consistent with the conjecture that the distinction between medical and food firms may be a result of medical firms targeting lobbying efforts at FDA approvals, rather than recalls. Conclusions Between 2012 and 2019, the FDA’s product recall classifications seem to be significantly influenced by firms’ lobbying activities. Lobbying firms appear to have received more favorable (i.e., less severe) recall classifications compared to non-lobbying firms.

Human memory is known to be supported by sleep. However, less is known about the effect of sleep on false memory, where people incorrectly remember events that never occurred. In the laboratory, false memories are often induced via the Deese-Roediger-McDermott (DRM) paradigm where participants are presented with wordlists comprising semantically related words such as nurse, hospital and sick (studied words). Subsequently, participants are likely to falsely remember that a related lure word such as doctor was presented. Multiple studies have examined whether these false memories are influenced by sleep, with contradictory results. A recent meta-analysis suggests that sleep may increase DRM false memory when short lists are used. We tested this in a registered report (N = 488) with a 2 (Interval: Immediate versus 12 h delay) × 2 (Test Time: 9:00 versus 21:00) between-participant DRM experiment, using short DRM lists (N = 8 words/list) and free recall as the memory test. We found an unexpected time-of-day effect such that completing free recall in the evening led to more intrusions (neither studied nor lure words). Above and beyond this time-of-day effect, the Sleep participants produced fewer intrusions than their Wake counterparts. When this was statistically controlled for, the Sleep participants falsely produced more critical lures. They also correctly recalled more studied words (regardless of intrusions). Exploratory analysis showed that these findings cannot be attributed to differences in output bias, as indexed by the number of total responses. Our overall results cannot be fully captured by existing sleep-specific theories of false memory, but help to define the role of sleep in two more general theories (Fuzzy-Trace and Activation/Monitoring theories) and suggest that sleep may benefit gist abstraction/spreading activation on one hand and memory suppression/source monitoring on the other.

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