Recall Accuracy Research Articles

The Accuracy of Early Reproductive History and Physical Activity Participation Recall Across Multiple Age Ranges.

Background: The ability to accurately recall specific reproductive health events is an integral aspect of medical decision making and evaluating a female's overall health and wellness across their lifespan. The Health and Reproductive Survey (HeRS) was developed to recall reproductive events and environmental influences on reproductive characteristics throughout the lifespan of a female. This study aimed to determine how reliably women recall certain events during menarche and early reproductive years. It was hypothesized that age at menarche, hormonal contraceptive use, and physical activity would be recalled reliably among all age ranges, while the recall reliability for cycle regularity and length would be more inconsistent with advancing age. Materials and Methods: A total of 144 participants (age: 32.73 ± 11.92), completed the HeRS on two occasions spaced 4 months apart to investigate recall reliability. Cohen's kappa coefficient was used to assess the consistency of categorical responses and 95% limits of agreement were used for continuous data. Results: Although physical activity changes had greater variability than anticipated (0.79), the recall reliability among the youngest (1) and oldest (0.89) age groups was high, and females were able to consistently recall the age of menarche (0.83), physical activity level (0.9), cessation of period during early reproductive years (0.91), and birth control use following menarche (0.85) and during the early reproductive years (0.9). Conclusions: The HeRS is a useful tool for reliably recalling reproductive history and physical activity participation across multiple age ranges and can be utilized to gather crucial information throughout the reproductive lifespan.

Path Configuration Complexity Affects Spatial Memory Span on the eCorsi Task but Does Not Influence Performance of a Concurrent Auditory Discrimination Task.

Visuospatial working memory is often assessed using the Corsi block-tapping task where set size is used to estimate capacity. It is well established that characteristics of the Corsi task path configuration such as length, crossings, and angles influence recall accuracy suggesting that more complex path configurations increase the load on working memory. However, the interaction between set size and path configuration is not well understood. Here we used a secondary auditory task to probe if set size and path configuration impose a similar type of load on the system. Nineteen participants (age = 25.3 ± 3.9 years) performed a computerized version of the Corsi test either alone (single) or simultaneously with an auditory tone discrimination task (dual). The eCorsi task involved a set of simple (no crosses, shorter lengths, larger angles) or complex (>2 crosses, longer lengths, smaller angles) paths at set sizes of five to eight blocks. Results showed significantly lower recall accuracy for the complex compared to the simple paths (63.32% vs. 86.38%, p < 0.001) at all set sizes, regardless of task condition (single, dual). Auditory performance (accuracy and response time) was significantly lower in the dual compared to single task (85.34% vs. 99.67%, p < 0.001), but performance was not affected by the complexity of the eCorsi path configuration. These findings suggest that set size and path complexity impose a different type of load on the working memory system and may rely on different resources.

Sentiment Analysis Tweet KTT G-20 di Media Sosial Twitter Menggunakan Metode Naïve Bayes

The G-20 or The Group of Twenty is a group consisting of 19 countries with major economies plus 1 European Union. This group was formed in 1999 as a systematic forum with the aim of discussing important issues or problems related to the world economy. The countries included in the G-20 include Australia, Canada, Saudi Arabia, United States, India, Russia, South Africa, Turkey, Argentina, Brazil, Mexico, France, Germany, Italy, United Kingdom, China, India, Japan, and South Korea. From these data it can be concluded that the G-20 Summit is a forum capable of improving the standard of living of many people because of its ability to produce international policies, laws and regulations. Indonesia was once in the world's spotlight because in November 2022, Indonesia will host the G-20 Summit in Nusa Dua, Bali, to be precise. Ordinary people use Twitter to express emotions related to something, both negative and positive emotions. With the implementation of sentiment analysis data from tweets from 500 data tweets using the Naive Bayes algorithm, the result is an accuracy of 69%. The accuracy value with class precision for positive predictions is 78%, while the class precision accuracy value for negative predictions is 36%. The positive class recall accuracy value is 81%, while the negative class recall accuracy value is 32%.

An IoT and Fuzzy aware e-Healthcare system using feature optimization tuned T-CNN with high dimensional data

Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy.

Provision of information by midwives for pregnant women in England on guidance on foods/drinks to avoid or limit

BackgroundThe National Health Service (NHS) website gives guidance for pregnant women in England on foods/drinks to avoid or limit because of microbiological, toxicological or teratogenic hazards. These include, for example, some types of soft cheeses, fish/seafood and meat products. This website and midwives are trusted sources of information for pregnant women, but the ways in which midwives can be supported to provide clear and accurate information are unknown.AimsThe aims were to: (1) determine midwives’ accuracy of recall of information and confidence in delivering the guidance to women; (2) identify barriers to provision; (3) identify the ways in which midwives provide this information to women.MethodsRegistered Midwives practicing in England completed an online questionnaire. Questions included those on what information they provided and their confidence in delivering it, the ways they provided information on foods to avoid/limit, their recall of some of the guidance, and what resources they used. Ethics approval was given by the University of Bristol.ResultsMore than 10% of midwives (n = 122) were ‘Not at all confident/Don't know’ in providing advice about ten items, including game meat/gamebirds (42% and 43%, respectively), herbal teas (14%) and cured meats (12%). Only 32% correctly recalled overall advice on eating fish, and only 38% the advice on tinned tuna. The main barriers to provision were lack of time in appointments and lack of training. The most usual methods of disseminating information were verbal (79%) and signposting to websites (55%).ConclusionMidwives were often unconfident about their ability to provide accurate guidance, and recall on items tested was frequently mistaken. Delivery of guidance by midwives on foods to avoid or limit needs to be supported by appropriate training and access to resources, and sufficient time in appointments. Further research on barriers to the delivery and implementation of the NHS guidance is needed.

Effect of Presenting Survival Information as Text or Pictograph During Periviable Birth Counseling: A Randomized, Controlled Trial

To examine whether presenting a 30% or a 60% chance of survival in different survival information formats would influence hypothetical periviable birth treatment choice and whether treatment choice would be associated with participants' recall or their intuitive beliefs about the chances of survival. An internet sample of women (N = 1,052) were randomized to view a vignette with either a 30% or 60% chance of survival with intensive care during the periviable period. Participants were randomized to survival information presented as text-only, in a static pictograph, or in an iterative pictograph. Participants chose intensive care or palliative care and reported their recall of the chance of survival and their intuitive beliefs about their infant's chance of survival. There was no difference in treatment choice by presentation with a 30% vs. 60% chance of survival (p=0.48), by survival information format (p=0.80), or their interaction (p=0.18). However, participants' intuitive beliefs about chance of survival significantly predicted treatment choice (p<0.001) and had the most explanatory power of any participant characteristic. Intuitive beliefs were optimistic and did not differ by presentation of a 30% or 60% chance of survival (p=0.65), even among those with accurate recall of the chance of survival (p=0.09). Physicians should recognize that parents may use more than outcome data to make treatment choices and in forming their own, often optimistic, intuitive beliefs about their infant's chance of survival.

Sleep Spindles Favor Emotion Regulation Over Memory Consolidation of Stressors in Posttraumatic Stress Disorder

BackgroundPosttraumatic stress disorder (PTSD) is a trauma-induced condition, characterized by intrusive memories and trauma-associated anxiety. Non–rapid eye movement (NREM) sleep spindles might play a crucial role in learning and consolidating declarative stressor information. However, sleep and possibly sleep spindles are also known to regulate anxiety, suggestive of a dual role for sleep spindles in the processing of stressors. Specifically, in individuals with high PTSD symptom burden, spindles might fail to regulate anxiety levels after exposure and instead might maladaptively consolidate stressor information. MethodsTo disentangle the role of spindles in declarative memory versus anxiety regulation after stressor exposure and to examine the role of PTSD in these processes, we measured nap sleep after a cohort of 45 trauma-exposed participants were exposed to laboratory stress. Participants (high vs. low PTSD symptoms) completed 2 visits: a stress visit involving exposure to negatively valent images before nap and a control visit. In both visits, sleep was monitored via electroencephalography. A stressor recall session occurred after the nap in the stress visit. ResultsStage 2 NREM (NREM2) spindle rates were higher in stress versus control sleep, indicative of stress-induced changes in spindles. In participants with high PTSD symptoms, NREM2 spindle rates in stress sleep predicted poorer recall accuracy of stressor images relative to participants with low PTSD symptoms, while correlating with greater reduction in stressor-induced anxiety levels after sleep. ConclusionsContrary to our expectations, although spindles are known to play a role in declarative memory processes, our findings highlight an important role for spindles in sleep-dependent anxiety regulation in PTSD.

Do we remember when to better recall what? Repetition benefits are probably not due to explicit temporal context memory

A number of temporal context-based theories of the spacing effect assume that temporal context is retrieved during repetitions, allowing better memory on a later test. Since associations in memory are often bidirectional, if we can use temporal context to recall an item, perhaps we can also use the item to explicitly recall its temporal context. If so, explicit memory-for-when during study would be a good predictor of later recall for repeated items. Experiment 1 appeared to be consistent with our predictions; items with memory-for-when during List 2 showed superior recall accuracy on a final free recall test. However, the effect was primarily driven by better memory-for-when in the primacy region. Experiment 2 used an incidental learning procedure during List 1 to reduce this confound since the primacy region is greatly attenuated in incidental learning. While the benefits of having memory-for-when persisted in Experiment 2, they were again attributable solely to the first few items on the list. The results suggest that explicit temporal context information is unlikely to underlie the benefits of spaced repetition. Either temporal context information is strengthened implicitly, our measures of context failed to capture the type of context used in spacing, or other mechanisms entirely produce the benefits of repetition.

Multimodal biomedical image retrieval and indexing system using handcrafted with deep convolution neural network features

The advances in biomedical imaging equipment have produced a massive amount of medical images that are generated by the different modalities. Consequently, a huge volume of data has been produced and caused a complex and time-consuming retrieving process of the relevant cases. To resolve this issue, the Content-Based Biomedical Image Retrieval (CBMIR) system is applied to retrieve the related images from the earlier patients’ databases. However, the previous handcrafted features methods that applied the CBMIR model have shown poor performance in many multimodal databases. In this paper, we focus on designing CBMIR technique using Deep Learning (DL) models. We present a new Multimodal Biomedical Image Retrieval and Classification (M-BMIRC) technique for retrieving and classifying the biomedical images from huge databases. The proposed M-BMIRC model involves three dissimilar processes as following: feature extraction, similarity measurement, and classification. It uses an ensemble of handcrafted features from Zernike Moments (ZM) and deep features from Deep Convolutional Neural Networks (DCNN) for feature extraction process. Additionally, the Hausdorff Distance based similarity measure is employed to identify the resemblance between the queried image and the images that exist in the database. Moreover, the classification process gets executed on the retrieval images using the Probabilistic Neural Network (PNN) model, which allocates the class labels of the tested images. Finally, the experimental studies are conducted using two benchmark medical datasets and the results ensure the superior performance of the proposed model in terms of different measures include Average Precision Rate (APR), Average Recall Rate (ARR), F-score, accuracy, and Computation Time (CT).

Abstract OT2-04-01: A Video Intervention to Improve Patient Understanding of Tumor Genomic Testing in Patients With Metastatic Cancer

Abstract Background: Tumor genomic testing (TGT) has become increasingly adopted as part of standard cancer care for many cancers. Despite national guidelines around patient education prior to TGT, available evidence suggests that most patients’ understanding of genomics remains limited, particularly lower income and minority patients, and most patients are not informed regarding potential incidental germline findings. Purpose: The primary object of this ongoing clinical trial is to evaluate the effectiveness of concise, animated videos to provide patient pre-test education prior to TGT as a supplement to patient-provider discussion. Trial Design: This prospective observational cohort study will enroll a total of 150 cancer patients in three clinical cohorts: Cohort 1: breast cancer (n=50); Cohort 2: lung cancer (n=50); Cohort 3: cancer patients of any tumor type (n=50). The primary objective is to assess change in patient knowledge of TGT following exposure to the video to evaluate the hypothesis that exposure to a brief educational video will increase patient knowledge about tumor genomic testing. Secondary objectives include assessing changes following exposure to the video, including: 1) genomic knowledge 2) trust in Physician 3) comparison of results between the three patient cohorts. Methods: Based on published guidelines around pre-TGT provider-patient education and patient focus groups and interviews, content for a series of videos was developed to standardize patient pretest education of TGT. A base animated video was created to be applicable to any cancer type; additional tumor type content was added for the breast and lung cancer-specific videos. Participant recruitment is occurring at The Ohio State University’s Comprehensive Cancer Center. Eligibility criteria include: age 18 years or older, biopsy-confirmed cancer, and provider plan to undergo TGT. This study requires participants to complete surveys at three timepoints: before video viewing (T1), immediately after video viewing (T2), and after provider communicates TGT results to the patient (T3). Four survey instruments are completed at T1/T2/T3: video message-specific recall, objective genomic knowledge/understanding, the 11-item Trust in Physician, and attitudes around genetic/genomic testing. TGT intention surveys are captured at T1 and T2. Participant evaluation of the video is collected at T2. For the primary objective, we will use a two-sided Wilcoxon signed-rank test with alpha of 0.05, giving us 90% power to detect an effect size of 0.47 in change of recall accuracy from pre- to post- video intervention. All secondary outcomes will be summarized using descriptive statistics and compared pre-/post-video using Wilcoxon signed-rank test. For comparisons of MBC and MLC, endpoints will include 1) baseline and pre-/post-video intervention change in genomic knowledge/understanding in MBC versus MLC patients; 2) baseline and pre-/post-video intervention change in trust in provider. The baseline and the change from pre- to post-video intervention in the secondary outcomes will be compared between MBC and MLC patients using Wilcoxon rank sum test. Conclusions: The long-term goal of this project is to test this broadly applicable, modular video-based intervention to be administered prior to tumor NGS to ensure equitable access to informed care. The use of a short video in this setting is innovative and we are demonstrating capacity to complete such a project. Study enrollment began March 30, 2022 and to date, 33 participants have enrolled (n=18 breast cancer; n=9 lung cancer; n=6 other cancer types). ClinicalTrials.gov NCT05215769. Citation Format: Daniel Stover, Deloris Veney, Heather Hampel, Amanda E. Toland, Carolyn Presley, Tasleem Padamsee, Clara Lee, Shelly Hovick, Leigha Senter. A Video Intervention to Improve Patient Understanding of Tumor Genomic Testing in Patients With Metastatic Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT2-04-01.

Remote Sensing Image Change Detection Network Based on Twin High-Resolution Representation

With the increase of spatial resolution of remote sensing images, features of feature imaging become more and more complex, and the change detection methods based on techniques such as texture representation and local semantics are difficult to meet the demand. Most change detection methods usually focus on extracting semantic features and ignore the importance of high-resolution shallow information and fine-grained features, which often lead to uncertainty in edge detection and small target detection. For single-input networks when two temporal images are connected, the shallow layer of the network cannot provide the information of the individual original image to the deep layer features to help reconstruct the image, and therefore, the change detection results may be missing in detail and feature compactness. For this purpose, a twins context aggregation network (TCANet) is proposed to perform change detection on remote sensing images. In order to reduce the loss of spatial accuracy of remote sensing images and maintain high-resolution representation, we introduce HRNet as our backbone network to initially extract the features of interest. Our proposed context aggregation module (CAM) can amplify the convolutional neural network receptive field to obtain more detailed contextual information without significantly increasing the computational effort. The side output embedding module (SOEM) is proposed to improve the accuracy of small volume target change detection as well as to shorten the training process and speed up the detection while ensuring the performance. The method has experimented on the publicly available CDD dataset, the SYSU-CD dataset, and a challenging DSIFN dataset. With significant improvements in precision, recall, F1 score, and overall accuracy, the method outperforms the five methods mentioned in the literature.

Parental recall bias in observational studies: Child dental service use.

Parents are frequently used to recall events of exposure and disease as a proxy for their children in observational health research. To assess the validity of parental recall of children's utilisation of dental services. Parents in the Longitudinal Study of Australian Children (LSAC) were asked to recall their children's overall dental service use in the previous year and whether the study child had received fillings or extractions due to decay in the previous 2 years. True positives were identified through parents who correctly recalled child dental service utilisation with actual dental treatment received in a linked dataset. Of the 10 090 participants from the LSAC, 1290 study children had linked dental treatment data eligible for inclusion in this study. The absolute true-positive rate for parental recall of dental service use in the previous year was 82% (n=1263). Overall true-positive percentages were lower for recall of fillings (40%) and extractions (7%) in the previous 2 years. Increasing number of recall days was associated with the rate of true-positive recall adjusted for other factors in all three models. Accuracy of parental recall for tooth fillings and extractions was low. The use of parental recall for specific dental treatment over a time span of 2 years in further research is not recommended.

A Signal Processing Framework for the Detection of Abnormal Cardiac Episodes.

Cardiologists rely on the long duration Holter electrocardiogram (ECG) recordings in general for assessment of abnormal episodes and such process found to be tedious and time consuming. An automatic abnormal cardiac episode detection algorithm is the need of the hour that needs to be optimized to reduce the manual burden. The current study presents a signal processing framework with a cross-database to detect abnormal episodes in long-term ECG signals. The data was pre-processed to remove power line interference and baseline drift using basis pursuit sparsely decomposed tunable-Q wavelet transform (BPSD-TQWT). A total of 44 features of time domain, frequency domain, and time-frequency domain characteristics were extracted from the ECG signal. This proposed work tested classification performance with support vector machine (SVM), K-nearest neighbour (KNN), decision tree, naïve Bayes, the nearest mean classifier, and the nearest root mean square classifiers. The trained models with open-source data were used to predict the abnormal episodes from the proprietary database and vice versa. Finally, the performance was analysed via recall rate, specificity, precision, F1-score, and accuracy. Among six classification models, SVM performed best. With an open-source database, the SVM model achieved 95.01% accuracy, and detected the abnormal episodes from proprietary database with an accuracy of 99.31%. In addition, with the proprietary database SVM model classified the normal-abnormal cardiac episodes with an accuracy of 99.89% and detected the abnormal episodes from proprietary database with an accuracy of 92.51%. When the performance results were compared with the literature, it was observed that the proposed framework performed well. As a result, the proposed framework could be used in an autonomous diagnosis system.

Urban building extraction based on information fusion-oriented deep encoder-decoder network in remote sensing imagery

The building extraction technology in remote sensing imagery has been a research hotspot. Building extraction in remote sensing imagery plays an important role in land planning, disaster assessment, digital city construction, etc. Although many scholars have explored many methods, it is difficult to realize high-precision automatic extraction due to the problems in high-resolution remote sensing images, such as the same object with different spectrum, the same spectrum with different object, noise shadow and ground object occlusion. Therefore, this paper proposes an urban building extraction based on information fusion-oriented deep encoder-decoder network. First, the deep encoder-decoder network is adopted to extract the shallow semantic features of building objects. Second, a polynomial kernel is used to describe the middle feature map of deep network to improve the identification ability for fuzzy features. Third, the shallow features and high-order features are fused and sent to the end of the encoder-decoder network to obtain the building segmentation results. Finally, we conduct abundant experiments on public data sets, the recall rate, accuracy rate, and F1-Score are greatly improved. The overall F1-score increases by about 4%. Compared with other state-of-the-art building extraction network structures, the proposed network is better to segment the building target from the background.

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera.

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

Sentinel-2 Research on the Detection and Classification Methods of Maritime Ship Targets from Remote Sensing Images

There are problems such as low recognition accuracy and large classification error in the existing classification methods for ship identification based on optical remote sensing images. In this paper, we will analyze the characteristics of ships and determine the indicative factors for applying remote sensing to monitor ships in combination with optical remote sensing images. Using optical remote sensing image data, combined with U-Net and AttU-Net deep neural network models, we assist in extracting new remote sensing indices with strong generality and clear physical meaning, and establishing rules for monitoring ships, so as to establish a more general and clear physical meaning of the monitoring and identification method of remote sensing satellite images. The method is applied and evaluated with port optical remote sensing image data. The data show that compared with traditional machine learning methods, the accuracy of ship monitoring using U-Net and AttU-Net deep learning models in this paper reaches 89.04%, and the recall rate and accuracy rate are better than SVM. it shows that the model can detect ships effectively.

Is word learning capacity restored after a daytime nap?

Sleep is thought to be involved in the consolidation of new memories encoded during the day, as proposed by complementary learning systems accounts of memory. Other theories suggest that sleep's role in memory is not restricted to consolidation. The synaptic homeostasis hypothesis proposes that new learning is implemented in the brain through strengthening synaptic connections, a biologically costly process that gradually saturates encoding capacity during wake. During slow-wave sleep, synaptic strength is renormalized, thus restoring memory encoding ability. While the role of sleep in memory consolidation has been extensively documented, few human studies have explored the impact of sleep in restoring encoding ability, and none have looked at learning beyond episodic memory. In this registered report we test the predictions made by the complementary learning systems accounts and the synaptic homeostasis hypothesis regarding adult participants' ability to learn new words, and to integrate these words with existing knowledge. Participants took a polysomnographically-monitored daytime nap or remained awake prior to learning a set of new spoken words. Shortly after learning, and again on the following day, we measured participants' episodic memory for new words. We also assessed the degree to which newly learned words engage in competition with existing words. We predicted that sleep before encoding would result in better episodic memory for the words, and facilitate the overnight integration of new words with existing words. Based on existing literature and theory we further predicted that this restorative function is associated with slow-wave and sleep spindle activity. Our pre-registered analyses did not find a significant benefit of napping prior to encoding on word learning or integration. Exploratory analyses using a more sensitive measure of recall accuracy demonstrated significantly better performance in the nap condition compared to the no-nap condition in the immediate test. At the delayed test there was no longer a significant benefit of the nap. Of note, we found no significant effect of slow-wave activity prior to encoding on episodic memory or integration of newly learned words into the mental lexicon. However, we found that greater levels of Stage 2 sleep spindles were significantly associated with greater improvements in lexical competition from the immediate to the delayed test. Therefore, our results demonstrate some support for theories that implicate sleep spindles in restoring encoding capacity.

Detection of Missing Insulator Caps Based on Machine Learning and Morphological Detection.

Missing insulator caps are the key focus of transmission line inspection work. Insulators with a missing cap will experience decreased insulation and mechanical strength and cause transmission line safety accidents. As missing insulator caps often occur in glass and porcelain insulators, this paper proposes a detection method for missing insulator caps in these materials. First, according to the grayscale and color characteristics of these insulators, similar characteristic regions of the insulators are extracted from inspection images, and candidate boxes are generated based on these characteristic regions. Second, the images captured by these boxes are input into the classifier composed of SVM (Support Vector Machine) to identify and locate the insulators. The accuracy, recall and average accuracy of the classifier are all higher than 90%. Finally, this paper proposes a processing method based on the insulator morphology to determine whether an insulator cap is missing. The proposed method can also detect the number of remaining insulators, which can help power supply enterprises to evaluate the degree of insulator damage.

Benefits of collaborative remembering in older and younger couples: the role of conversation dynamics and gender

ABSTRACT We experimentally explored whether and how conversation dynamics would benefit collaborative remembering in intimate couples over time. To this end, we ran a study with a three-factor mixed design with relationship type (couples vs. strangers) and age (older adults vs. younger adults) as between-participants variables, and remembering condition (collaborative vs. individual) as a within-participants variable. Thirty pairs of intimate couples (fifteen long-term relationship older couples, fifteen short-term relationship younger couples) and thirty pairs of corresponding stranger-pairs (including older strangers and younger strangers) were compared with respect to recall accuracy and conversation dynamics, specifically considering the role of gender. Results revealed significant collaborative facilitation only in older couples. Also, females’ communication behaviours facilitated males’ collaborative remembering performance only in older (vs. younger) couples. In addition, a gender-specific pattern of shifts from the individual to collaborative context emerged only in older couple (vs. strangers). The findings are consistent with the notion that a longer experience of collaboration and more effective conversation dynamics allow older (vs. younger) couples to perform better at collaborative remembering. We discuss processes underlying the observed gender differences, and the social and motivational implications of collaborative remembering.

Feeling known and informed: Serial qualitative interviews evaluating a consultation support intervention for patients with high-grade glioma.

Interventions to support patients' engagement in shared decision making (SDM) are lacking within high-grade glioma (HGG) healthcare. Consultation Planning, Recording and Summarising (CPRS) has shown evidence of increasing patient decision self-efficacy, reducing uncertainty, and regret of decisions. This is the first study of CPRS within a HGG population and delivered over serial medical consultations. A one-arm prospective qualitative longitudinal design was used to evaluate the CPRS intervention and evaluated with participants at sequential clinic appointments depending on their care, in Edinburgh, Scotland. We report on serial semi structured interviews of 16 patients and their partners. Consultation planning before the consultation supported patients to feel known by strengthening the patient voice within the consultation. It prepared patients to actively participate in the consultation, despite the distressing nature of the content. Recording and summarising supported patients to understand their situation. The provision of a consultation record enabled accurate recall, a paced uptake of information and supported the family to feel fully informed. Ultimately, patients understood why decisions were being made rather than being part of making decisions. The CPRS intervention helped patients to understand and to feel known by increasing patient capacity for communication in the consultation, with support before, during, and after the consultation. The intervention focused on preparing patients for SDM but patients did not perceive that they had meaningful choices to make. Further research could look at the inclusion of patient decision aids to support this process.