Sufficient Information Research Articles

Cardiac arrhythmias classification using photoplethysmography database

Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs.

A building imagery database for the calibration of machine learning algorithms

In the last decades, most efforts to catalog and characterize the built environment for multi-hazard risk assessment have focused on the exploration of census data, cadastral data sets, and local surveys. Typically, these sources of information are not updated regularly and lack sufficient information to characterize the seismic vulnerability of the building stock. Some recent efforts have demonstrated how machine learning algorithms can be used to automatically recognize specific architectural and structural features of buildings. However, such methods require large sets of labeled images to train, verify, and test the algorithms. This article presents a database of 5276 building images from a parish in Lisbon (Alvalade), whose buildings have been classified according to a uniform taxonomy. This database can be used for the testing and calibration of machine learning algorithms, as well as for the direct assessment of earthquake risk in Alvalade. The data are accessible through an open Github repository (DOI: 10.5281/zenodo.7625940).

Life Story Research with People Aging with Intellectual Disabilities: An Adaptation of the Lifeline Interview Method

A key feature of inclusive research is the accessibility of research procedures to meaningfully engage people with intellectual disabilities in research processes. Creating accessible research procedures requires innovations in methods traditionally used in research. This paper describes how the Lifeline Interview Method by Assink and Schroots was adapted and implemented in a study using life story research to better understand the experiences of older adults with intellectual disabilities. Twelve adults with intellectual disabilities over the age of 50 participated between two and seven times in interviews about their life histories. The interviewer assisted in the construction of timelines of key events in the participants’ individual life stories, and the participants decorated their lifelines throughout the course of the interviews. The lifeline process was an effective tool to engage the participants in the research process, support participation, and provide access for people with intellectual disabilities to retrieve their life experiences. Challenges in the lifeline process included barriers to gathering sufficient information to construct timelines and gatekeepers withholding access to information.

A Review of Screeners to Identify Risk of Developmental Language Disorder.

This review aims to comprehensively summarize, compare, and evaluate screeners used to identify risk for developmental language disorder (DLD), a common learning disability that is underidentified. Screening for DLD is a cost-effective way to identify children in need of further assessment and, in turn, provides much needed supports. We identified 15 commercially available English language DLD screeners in North America. We then characterized each screener on 27 aspects in three domains, including (a) accessibility information (acronym, subtest, website, cost, materials included, publish year, examiner qualification, age range, administration time, and administration format), (b) usability features (dialect compatibility, progress monitoring function, actionable follow-up instruction, group assessment capability, and online administration availability), and (c) technical standards (the availability of a technical manual, conceptual definition, the sample size used in classification accuracy calculation, sample distribution, year of sample collection, outcome measure, sample base rate, cutoff score, sensitivity, specificity, positive predictive value, and negative predictive value). We obtained sufficient accessibility information from 14 out of 15 (93%) screeners. In contrast, none of the screeners (0%) included comprehensive usability features. Ten screeners (67%) included a range of classification accuracy (70%-100% sensitivity and 68%-90% specificity). We provided areas of strength and weakness for each screener as a quick reference for users and generated screener recommendations for five practical scenarios. Our findings presented some DLD screeners that meet most standards and highlight numerous areas for improvement, including improving classification accuracy and clarifying follow-up instructions for children who are identified with DLD risk. Screening for DLD is critical to provide timely early identification, intervention, and classroom support, which in turn facilitates student outcomes.

The effects of flux on the radiation-induced embrittlement of reactor pressure vessel steels: review of current understanding and application to high fluences

It is necessary to quantify the effects of flux on reactor pressure vessel steel embrittlement under neutron irradiation, if surveillance or high-flux test reactor data is used to predict vessel embrittlement occurring at lower fluxes. This is particularly important when considering embrittlement occurring during extended (60–80 years) operation for which there is no direct experience. Dedicated investigations are time-consuming and expensive even when only small flux-fluence ranges are investigated, so collating data from multiple campaigns is necessary to provide sufficient information to cover the wide range of fluxes required for vessel assessment in the long term. This paper collates and reviews such data. The review finds that flux dependences probably differ in sign and strength in different regimes (low flux and fluence, intermediate flux at low and high fluence, high flux at low and high fluence) with the regime limits affected by composition and temperature. The current understanding of diffusion processes and microstructural development are invaluable in interpreting the trends and limits. Many contradictory data sets were found, however, and not all contradictions could be dismissed as resulting from poor quality data. Suggestions are made for investigations to clarify the uncertainties. One wide-ranging model of flux effects, based on an extensive data set, is used to compare high-fluence data from different sources, to assess whether embrittlement rates accelerate after a high, threshold fluence. The model helps to identify experiments which investigated comparable flux-fluence-temperature regimes. The comparable data are split evenly between data sets supporting acceleration after a particular fluence and data sets contradicting it. The model identifies regimes in which further campaigns would clarify the causes of these contrasting observations.

Ethical challenges from a problem-solving intervention with workplace involvement: a qualitative study among employees with common mental disorders, first-line managers, and rehabilitation coordinators

ABSTRACT Purpose This study aims to explore ethical challenges potentially arising from a problem-solving intervention with workplace involvement (PSI-WPI) in primary health care (with first-line manager involvement) for employees on sickness absence due to common mental disorders. Methods A qualitative design guided by the theoretical framework for systematic identification of ethical aspects of healthcare technologies. Semi-structured interviews were performed with coordinators (n = 6), employees (n = 13), and first-line managers (n = 8). Reflexive thematic analysis was used to analyse and interpret themes. Results A main theme was identified “the workplace and healthcare hold different organizational value logics” and four sub-themes: “the PSI-WPI challenged the organizational goals and values of the workplace and healthcare”, “the PSI-WPI challenged organizational values on fairness”, “the PSI-WPI challenged the professional roles of first-line managers and rehabilitation coordinators” and “the PSI-WPI introduced a need for the employee to juggle the employee and patient roles”. Conclusion Different organizational value logics, values, and goals can introduce ethical challenges. We advise clarifying stakeholders’ roles and preparing employees and managers for the return to work process by providing sufficient information. The ethical challenges and suggested measures to minimize them, should be considered when planning return to work interventions that involve several stakeholders.

INSTANTANEOUS LOAD-DEFLECTION BEHAVIOUR OF REINFORCED CONCRETE BEAMS

Beam deflection under load and concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. This research work represents a comparative analysis of experimental and theoretical deflections of simply supported beams with different proportions of mild steel as reinforcement and also different curing conditions i.e. Painting, Air and Water curing. Theoretical deflections were measured using AS 3600-2018, ACI 318-14 and Eurocode 2:2004. To differentiate among these codes Shrinkage was kept variable by different curing conditions. Crack width was measured by image processing using ImageJ software and compared with theoretical results using Eurocode 2:2004 and ACI 224.1R-07. Processing of RGB images with sufficient information will provide the parameters of cracks. RGB images were converted into Gray scale proceed by a filtering and thresholding. Crack parameters were automatically determined by Ridge Detection plugin. Deflections of simply supported beams from experimental and calculation results using AS 3600-2018, Eurocode 2:2004 and ACI 318-14 are very close. Image processing is a very effective technique in the study of crack in concrete and for automatic detection “Ridge Detection” plugin is very helpful.

Effectiveness of Physical Activity Interventions in Sedentary People during COVID-19 Lockdown: A Systematic Review of Randomized Controlled Trials

Background: The social restriction measures during the COVID-19 pandemic resulted in decreasing physical activity levels. We aimed to evaluate whether the interventions reported in randomized controlled trials (RCTs) assessing the effects of physical activity during the COVID-19 lockdown on people with sedentary behavior were effective. Methods: We searched several databases from their inception until 1 April 2023, including RCTs evaluating the effects of any physical activity intervention in increasing the physical activity level in people with sedentary behavior during COVID-19 confinement, based on evidence derived from intervention studies with a control group. Studies were excluded if they (i) did not include humans; (ii) did not include sufficient information regarding the interventions or regarding the outcomes of interest; (iii) did not have a control group. We used the Rayyan systematic review platform for the selection of the studies to include based on the title/abstract information. Results: Starting with 2461 records from the original literature search, and after reviewing them according to the latest PRISMA recommendations, 11 RCTs including a total of 1770 participants were available and were included in the systematic review. We found that most of the studies examined (73%) reported the beneficial effects of the proposed interventions on improving the physical activity, reducing the sedentary time, and positively contributing to the psychological well-being of the participants. Conclusions: The results of the present systematic review on RCTs of interventions to increase physical activity in sedentary people during the COVID-19 lockdown show the beneficial effects of diverse online-delivered strategies, which can be applied even after the pandemic in conditions in which access to in-person activities is not possible.

Research on an Enhanced Multimodal Network for Specific Emitter Identification

Specific emitter identification (SEI) refers to the task of distinguishing similar emitters, especially those of the same type and transmission parameters, which is one of the most critical tasks of electronic warfare. However, SEI is still a challenging task when a feature has low physical representation. Feature representation largely determines the recognition results. Therefore, this article expects to move toward robust feature representation for SEI. Efficient multimodal strategies have great potential for applications using multimodal data and can further improve the performance of SEI. In this research, we introduce a multimodal emitter identification method that explores the application of multimodal data, time-series radar signals, and feature vector data to an enhanced transformer, which employs a conformer block to embed the raw data and integrates an efficient multimodal feature representation module. Moreover, we employ self-knowledge distillation to mitigate overconfident predictions and reduce intra-class variations. Our study reveals that multimodal data provide sufficient information for specific emitter identification. Simultaneously, we propose the CV-CutMixOut method to augment the time-domain signal. Extensive experiments on real radar datasets indicate that the proposed method achieves more accurate identification results and higher feature discriminability.

Expanding Karst Groundwater Tracing Techniques: Incorporating Population Genetic and Isotopic Data to Enhance Flow-Path Characterization

Karst aquifers are unique among groundwater systems because of variable permeability and flow-path organization changes resulting from dissolution processes. Over time, changes in flow-path connectivity complicate interpretations of conduit network evolution in karst hydrogeology. Natural and artificial tracer techniques have long provided critical information for protecting karst aquifers and understanding the potential impacts on ecosystems and human populations. Conventional tracer methods are useful in karst hydrogeologic studies for delineating flow paths and defining recharge, storage, and discharge properties. However, these methods only provide snapshots of the current conditions and do not provide sufficient information to understand the changes in interconnection or larger-scale evolution of flow paths in the aquifer over time. With advances in population genetics, it is possible to assess population connectivity, which may provide greater insights into complex groundwater flow paths. To assess this potential, we combined the more traditional approaches collected in this and associated studies, including artificial (dye) and natural (geochemistry, isotopes, and discharge) tracers, with the population genetic data of a groundwater crustacean to determine whether these data can provide insights into seasonal or longer changes in connections between conduits. The data collected included dye trace, hydrographs, geochemistry, and asellid isopod (Caecidotea bicrenenta) population genetics in Fern Cave, AL, USA, a 25 km-long cave system. Combined, these data show the connections between two separate flow paths during flood events as the downstream populations of isopods belonging to the same subpopulation were measured in both systems. Additionally, the sub-populations found in higher elevations of the cave suggest a highly interconnected unsaturated zone that allows for genetic movement in the vadose zone. Although upstream populations show some similarities in genetics, hydrologic barriers, in the form of large waterfalls, likely separate populations within the same stream.

Assessment of Knowledge and Attitudes Related to HIV/AIDS Among the Population With Increasing Incidence Rate.

Introduction Lack of awareness and negative attitudes towardpeople living with HIV/AIDS (PLWHA) are key barriers to minimizing the transmission of HIV. Therefore, the present survey-based study aimed to assess the knowledge regarding HIV/AIDS and attitudes toward PLWHA. Methods In the present study, we collected data from 612 Kyrgyz national participants using a self-administered questionnaire. Results Among the participants, 59% (361) were females, and 41% (251) were males. The mean age of the participants was 26.23(SD = 7.7) years. All participants were aware of HIV/AIDS, and 59.1% (362) agreed to have sufficient information about HIV/AIDS. Overall, the participants displayed a high level of knowledge about HIV/AIDS transmission, and 89.2% (546) of them were aware of sexual transmission of HIV/AIDS. Among the participants, 54% (330) believed that using condoms during sexual intercourse could prevent the transmission of HIV/AIDS. Concerning social attitudes, 17% (104) of the participants agreed that HIV-infected individuals should be isolated from society. Moreover, 39% (238) of them disagreed to work with PLWHA.The results of the study suggest that female participants were more aware of the modes of HIV/AIDS transmission than males. However, misconceptions regarding transmission routes were present in both genders. Conclusion The present study revealed that study participants had correct knowledge about HIV/AIDS transmission modes such as unsafe blood transfusionand injectable drug abuse. However, knowledge about unsafe tattooing and mother-to-baby mode of HIV/AIDS transmission was observed to be lower. Female participants were found to be more aware of HIV/AIDS transmission. There is a need to address the knowledge and awareness gap in the general population of Kyrgyzstan, especially among the male population.

Rapid typing of Klebsiella pneumoniae and Pseudomonas aeruginosa by Fourier-transform Infrared spectroscopy informs infection control in veterinary settings.

The emergence of multi-drug resistant (MDR) pathogens linked to healthcare-associated infections (HCAIs) is an increasing concern in modern veterinary practice. Thus, rapid bacterial typing for real-time tracking of MDR hospital dissemination is still much needed to inform best infection control practices in a clinically relevant timeframe. To this end, the IR Biotyper using Fourier-Transform InfraRed (FTIR) spectroscopy has the potential to provide fast cluster analysis of potentially related organisms with substantial cost and turnaround time benefits. A collection of MDR bacterial isolates (n = 199, comprising 92 Klebsiella pneumoniae and 107 Pseudomonas aeruginosa) obtained from companion animal (i.e., dogs, cats and horses) clinical investigations, faecal and environmental screening from four veterinary facilities between 2012 and 2019 was analysed retrospectively by FTIR spectroscopy. Its performance was compared against MLST extracted from whole genomes of a subset of clustering isolates (proportionally to cluster size) for investigation of potential nosocomial transmission between patients and the surrounding hospital environments. Concordance between the FTIR and MLST types was overall high for K. pneumoniae (Adjusted Rand Index [ARI] of 0.958) and poor for P. aeruginosa (ARI of 0.313). FTIR K. pneumoniae clusters (n = 7) accurately segregated into their respective veterinary facility with evidence of intra-hospital spread of K. pneumoniae between patients and environmental surfaces. Notably, K. pneumoniae ST147 intensely circulated at one Small Animal Hospital ICU. Conversely, Pseudomonas aeruginosa FTIR clusters (n = 18) commonly contained isolates of diversified hospital source and heterogeneous genetic background (as also genetically related isolates spread across different clusters); nonetheless, dissemination of some clones, such as P. aeruginosa ST2644 in the equine hospital, was apparent. Importantly, FTIR clustering of clinical, colonisation and/or environmental isolates sharing genomically similar backgrounds was seen for both MDR organisms, highlighting likely cross-contamination events that led to clonal dissemination within settings. FTIR spectroscopy has high discriminatory power for hospital epidemiological surveillance of veterinary K. pneumoniae and could provide sufficient information to support early detection of clonal dissemination, facilitating implementation of appropriate infection control measures. Further work and careful optimisation need to be carried out to improve its performance for typing of P. aeruginosa veterinary isolates.

ЭЛЕКТРОННОЕ ПРАВОСУДИЕ В РОССИИ: ЗНАЧЕНИЕ, ПЕРСПЕКТИВЫ РАЗВИТИЯ И СОВЕРШЕНСТВОВАНИЯ

In the modern world, information and communication technologies are widely used in almost all areas of human activity. In legal proceedings, the introduction of information technology began in the 80s, and today electronic justice has already been created. In the Russian Federation, this opportunity appeared on January 1, 2017; over five years, quite a lot of work by domestic and foreign scientists has been devoted to it. However, despite a significant amount of research on the topic of e-justice, not all aspects of this problem are sufficiently developed. It is necessary to pay serious attention to the analysis of modern problems in the development of electronic justice and the prospects for its improvement in the Russian Federation.
 Thus, at present, electronic justice is just beginning its development. New services are appearing in Russia and abroad, the purpose of which is to simplify the legal process and solve emerging problems that affect the development of electronic justice. The presence of problems in the use of electronic justice and the lack of sufficient information in this area determine the relevance of the topic considered in the work.

Vaccination acceptance and its associated factors in patients on hemodialysis in Austria: A nationwide survey.

Vaccines are essential in disease prevention among patients on chronic hemodialysis (HD). However, during the coronavirus disease 2019 pandemic, there has been an increased rate of vaccination hesitancy. A better understanding of patients' opinions may help identify a more targeted approach to increase vaccination rates. Questionnaires with 43 questions based on the recommendations of the Strategic Advisory Group of Experts (SAGE) on Immunization Working Group on Vaccine Hesitancy were administered to patients during routine HD sessions at different dialysis centers in Austria. In total, 347 patients participated in this study. Approximately 81% of the patients were aged > 54 years, and 65% were men. Further, 53% of patients were receiving HD from private units. In ~ 72% of patients, the dialysis physicians were the source of vaccination information. Meanwhile, the source of information in 28% of patients was the primary care physician (28%), and 18% of patients obtained vaccination details from the internet. The number of younger (aged < 55 years) patients who were more likely to use online content as the main source of information was significantly higher than that of older patients (32 vs. 15%, p = 0.001). Furthermore, the number of older patients who wanted to receive more information from the dialysis physician was significantly higher than that of younger patients (57 vs. 38%, p = 0.009). Only 65% of patients had a good understanding of the mechanisms of action of vaccines. The younger population (aged 18 - 54 years) had a higher number of individuals with a good understanding of vaccine mechanisms than the older population (78 vs. 62%, p = 0.016). Moreover, 86% of the whole population wanted to complete the recommended vaccinations. However, only 39% of respondents had sufficient information about the vaccination plan in Austrian. Numerous patients receiving HD wanted to obtain more information from their dialysis physicians. Increased awareness among providers and targeted communication might increase vaccination rates.

Spatial super-resolution of finite-time Lyapunov exponent fields with generative adversarial networks

In this article, two generative adversarial networks that enhance the spatial resolution of two- and three-dimensional finite-time Lyapunov fields by a factor of 4 in each dimension are presented. Using these models, high-resolution distributions can be approximated based on low-resolution input, the latter of which is associated with a computational cost and storage reduced by factors of about 42 and 43, respectively. Evaluating various test cases, the performance and generalizability of this approach are assessed. Shortcomings are only observed in the case of high-frequency spatial fluctuations where no sufficient statistical information is available in the low-resolution space. The major flow structures, however, are adequately rendered, giving rise to Lagrangian analyses of complex flow configurations that may otherwise remain elusive due to an excessive computational cost.

Data-Knowledge-Driven Self-Organizing Fuzzy Neural Network.

Fuzzy neural networks (FNNs) hold the advantages of knowledge leveraging and adaptive learning, which have been widely used in nonlinear system modeling. However, it is difficult for FNNs to obtain the appropriate structure in the situation of insufficient data, which limits its generalization performance. To solve this problem, a data-knowledge-driven self-organizing FNN (DK-SOFNN) with a structure compensation strategy and a parameter reinforcement mechanism is proposed in this article. First, a structure compensation strategy is proposed to mine structural information from empirical knowledge to learn the structure of DK-SOFNN. Then, a complete model structure can be acquired by sufficient structural information. Second, a parameter reinforcement mechanism is developed to determine the parameter evolution direction of DK-SOFNN that is most suitable for the current model structure. Then, a robust model can be obtained by the interaction between parameters and dynamic structure. Finally, the proposed DK-SOFNN is theoretically analyzed on the fixed structure case and dynamic structure case. Then, the convergence conditions can be obtained to guide practical applications. The merits of DK-SOFNN are demonstrated by some benchmark problems and industrial applications.

Modelling the sustainable harvest of wild populations for the conservation of a threatened amphibian

AbstractHarvesting wild populations is increasingly used to support conservation initiatives, including translocations, captive assurance colonies, and breeding programs. Best‐practice guidelines recommend that population viability analyses should be developed to assess the potential impact of harvest on source populations, allowing quantification of the risk incurred by the conservation programme. We developed a stochastic population model to assess the relative impact of harvesting southern bell frogs (Litoria raniformis) from three well‐studied populations. We compared the probability of local extinction when harvesting from three life stages (spawn, tadpoles, and adults) and at 21 harvest proportions (from 0 to 1, in 0.05 increments). Source populations with an estimated average of 100 adult frogs and with three or fewer breeding opportunities every 5 years showed increased risk of local extinction and lower minimum adult populations from all harvest strategies. Sources with an estimated average population &gt;600 adult frogs and breeding opportunities in at least eight out of every 10 years were more resilient to harvest. For these populations, harvesting up to 25% of spawning masses or late‐stage tadpoles had a negligible impact on the probability of local extinction and caused a slight decrease in the minimum adult population. Harvesting adults impacted source populations more strongly than harvesting spawn or tadpoles. Our model highlights the importance of frequent breeding opportunities to offset the effects of harvesting L. raniformis. Populations sustained by environmental water delivery should be prioritized for the two consecutive breeding seasons following harvest in the absence of natural wetland inundation. We demonstrate a tractable approach to assess and compare the relative risk of wild harvest for threatened amphibians, with implications for the conservation of our focal species, L. raniformis. We encourage conservation practitioners to implement population viability analyses prior to wild harvest, whenever sufficient demographic information is available to build the requisite models.

A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges. First, huge feature distinction between support and query images causes knowledge transferring barrier, which harms the segmentation performance. Second, limited support prototypes cannot adequately represent features of support objects, hard to guide high-quality query segmentation. To deal with the above two issues, we propose self-distillation embedded supervised affinity attention model to improve the performance of few-shot segmentation task. Specifically, the self-distillation guided prototype module uses self-distillation to align the features of support and query. The supervised affinity attention module generates high-quality query attention map to provide sufficient object information. Extensive experiments prove that our model significantly improves the performance compared to existing methods. Comprehensive ablation experiments and visualization studies also show the significant effect of our method on few-shot segmentation task. On COCO-20i dataset, we achieve new state-of-the-art results. Training code and pretrained models are available at https://github.com/cv516Buaa/SD-AANet.

R2-trans: Fine-grained visual categorization with redundancy reduction

Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. Existing FGVC methods usually select discriminant regions found by a trained model, which is prone to neglect other potential discriminant information. On the other hand, the massive interactions between the sequence of image patches in ViT make the resulting class token contain lots of redundant information, which may also impact FGVC performance. In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target. Specifically, our model calculates the ratio of high-weight regions in a batch, adaptively adjusts the masking threshold, and achieves moderate extraction of background information in the input space. Moreover, we also use the Information Bottleneck (IB) approach to guide our network to learn a minimum sufficient representations in the feature space. Experimental results on three widely-used benchmark datasets verify that our approach can achieve better performance than other state-of-the-art approaches and baseline models. The code of our model is available at: https://github.com/SYe-hub/R-2-Trans.

Changes in oral home care and smoking habits during COVID-19 pandemic: A cross-sectional study.

The coronavirus disease-19 (COVID-19) pandemic has caused disruption in the health behavior in many aspects of life. While hand hygiene was promoted as one of the precautionary measures to mitigate and contain COVID-19, oral health and smoking might have received less attention in the media campaigns. The aim of this study was to examine health behavioral changes in terms of oral home care habits, smoking, and perception of dental care during the COVID-19 pandemic. An online survey was designed to assess oral home care, smoking habits, and attitude towarddental services of participants aged 18 years and older. The data were collected between September and November 2021. The strength of association between changes in oral home care habits, smoking, and attitude toward invasive/long dental procedures and each variable was measured by χ2 analysis. Estimates of relative risk were also calculated for all variables. Predictors of avoiding dental procedures were estimated by a binary logistic regression. A total of 532 participants, based in the United Arab Emirates, took part in this online survey with a response rate of 88.7%. The age of the participants ranged between 18 and 67 with mean age of 34.9 ± 9.0 years. The majority of the participants have adopted changes in their routine oral home care habits, with 82.1% of them changing the toothbrush more frequently. Participants who changed their oral home care habits were more likely to have received sufficient information on the importance of maintaining oral health. Likewise, the changes in smoking habits were significantly associated with receiving information on the relationship between smoking and the severity of the COVID-19 (p < 0.001). The findings showed that positive behavior towardoral home care and smoking was noticed during the pandemic particularly when public receives sufficient and up-to-date information.