Features Of Prototype Research Articles

A Review of the Commercially Available ECG Detection and Transmission Systems-The Fuzzy Logic Approach in the Prevention of Sudden Cardiac Arrest.

Sudden cardiac death (SCD) constitutes a major clinical and public health problem, whose death burden is comparable to the current worldwide pandemic. This comprehensive review encompasses the following topics: available rescue systems, wearable electrocardiograms (ECG), detection and transmission technology, and a newly developed fuzzy logic algorithm (FA) for heart rhythm classification which is state-of-the art in the field of SCD prevention. Project “PROTECTOR”, the Polish Rapid Transtelephonic ECG to Obtain Resuscitation for development of a rapid rescue system for patients at risk of sudden cardiac arrest (SCA), is presented. If a lethal arrhythmia is detected on the basis of FA, the system produces an alarm signal audible for bystanders and transmits the alarm message along with location to the emergency medical center. Phone guided resuscitation can be started immediately because an automated external defibrillator (AED) localization map is available. An automatic, very fast diagnosis is a unique feature of the PROTECTOR prototype. The rapid detection of SCA is based on a processor characterized by 100% sensitivity and 97.8% specificity (as measured in the pilot studies). An integrated circuit which implements FA has already been designed and a diagnosis is made within few seconds, which is extremely important in ischemic brain damage prophylaxis. This circuit could be implemented in smart implants (Sis).

Patients' Use of Mobile Health for Self-management of Knee Osteoarthritis: Results of a 6-Week Pilot Study.

BackgroundIn a previous study, a prototype mobile health (mHealth) app was co-designed with patients, family physicians, and researchers to enhance self-management and optimize conservative management for patients with mild to moderate knee osteoarthritis (OA).ObjectiveThis study aims to evaluate the overall usability, quality, and effectiveness of the mHealth app prototype for aiding knee OA self-management from the perspectives of patients with OA and health care providers (HCPs).MethodsUsing methods triangulation of qualitative and quantitative data, we conducted a pilot evaluation of an mHealth app prototype that was codeveloped with patients and HCPs. We recruited adult patients aged ≥20 years with early knee OA (n=18) who experienced knee pain on most days of the month at any time in the past and HCPs (n=7) to participate. In the qualitative assessment, patient and HCP perspectives were elicited on the likeability and usefulness of app features and functionalities and the perceived impact of the app on patient-HCP communication. The quantitative assessment involved evaluating the app using usability, quality, and effectiveness metrics. Patient baseline assessments included a semistructured interview and survey to gather demographics and assess the quality of life (European Quality-of-Life 5-Dimension 5-Level Questionnaire [EQ-5D-5L]) and patient activation (patient activation measure [PAM]). Following the 6-week usability trial period, a follow-up survey assessed patients’ perceptions of app usability and quality and longitudinal changes in quality of life and patient activation. Semistructured interviews and surveys were also conducted with HCPs (n=7) at baseline to evaluate the usability and quality of the app prototype.ResultsInterviews with patients and HCPs revealed overall positive impressions of the app prototype features and functionalities related to likeability and usefulness. Between the baseline and follow-up patient assessments, the mean EQ-5D-5L scores improved from 0.77 to 0.67 (P=.04), and PAM scores increased from 80.4 to 87.9 (P=.01). Following the 6-week evaluation, patients reported a mean System Usability Scale (SUS) score of 57.8, indicating marginal acceptability according to SUS cutoffs. The mean number of goals set during the usability period was 2.47 (SD 3.08), and the mean number of activities completed for knee OA self-management during the study period was 22.2 (SD 17.8). Spearman rank correlation (rs) calculations revealed that the follow-up PAM scores were weakly correlated (rs=−0.32) with the number of goals achieved and the number (rs=0.19) of activities performed during the 6-week usability period. HCPs reported a mean SUS score of 39.1, indicating unacceptable usability.ConclusionsThis evidence-based and patient-centered app prototype represents a potential use of mHealth for improving outcomes and enhancing conservative care by promoting patient activation and patient-HCP communication regarding OA management. However, future iterations of the app prototype are required to address the limitations related to usability and quality.

Prediction and Controlling of Auditory Perception in Augmented Environments. A Loudness-Based Dynamic Mixing Technique

At the core of augmented reality audio (ARA) technology lies the ARA mix, a process responsible for the assignment of a virtual environment to a real one. Legacy ARA mix models have focused on the natural reproduction of the real environment, whereas the virtual environment is simply mixed through fixed gain methods. This study presents a novel approach of a dynamic ARA mix that facilitates a smooth adaptation of the virtual environment to the real one, as well as dynamic control of the virtual audio engine, by taking into account the inherent characteristics of both ARA technology and binaural auditory perception. A prototype feature extraction technique of auditory perception characteristics through a real-time binaural loudness prediction method was used to upgrade the legacy ARA mix model into a dynamic model, which was evaluated through benchmarks and subjective tests and showed encouraging results in terms of functionality and acceptance.

Cluster headache epidemiology including pediatric onset, sex, and ICHD criteria: Results from the International Cluster Headache Questionnaire.

To validate the diagnoses and to investigate epidemiological data from an international, non-clinic-based, and large (n=1604) survey of participants with cluster headache. There are several limitations in current epidemiological data in cluster headache including a lack of large non-clinic-based studies. There is also limited information on several aspects of cluster headache, such as pediatric incidence. The International Cluster Headache Questionnaire was an internet-based survey that included questions on cluster headache demographics, criteria from the International Classification of Headache Disorders (ICHD), and medications. A total of 3251subjects participated in the survey, and 1604 respondents met ICHD criteria for cluster headache. For validation, we interviewed a random sample of 5% (81/1604) of participants and confirmed the diagnosis of cluster headache in 97.5% (79/81). Pediatric onset was found in 27.5% (341/1583) of participants, and only 15.2% (52/341) of participants with pediatric onset were diagnosed before the age of 18. Men were more likely to have episodic cluster headache between ages 10 and 50, but the sex ratio was approximately equal for other ages. An overwhelming majority of respondents had at least one autonomic feature (99.0%, 1588/1604) and had restlessness (96.6%, 1550/1604), but many also had prototypical migrainous features including photophobia or phonophobia (50.1%, 804/1604), pain aggravated by physical activity (31.4%, 503/1604), or nausea and vomiting (27.5%, 441/1604). Interestingly, the first-line medications for acute treatment (oxygen) and preventive treatment (calcium channel blockers) were perceived as significantly less effective in chronic cluster headache (3.2±1.1 and 2.1±1.0 respectively on a 5-point ordinal scale) compared with episodic cluster headache (3.5±1.0 and 2.4±1.1, respectively, p<0.001 for both comparisons). Cluster headache often occurs in the pediatric population, although they are typically not diagnosed until adulthood. The onset of cluster headache is the inverse of that in migraine; in migraine women are more likely to have migraine between ages 10 and 50 but the sex ratio is approximately equal otherwise. Prototypical migrainous features are not useful in differentiating cluster headache from migraine. Participant data from a large international study also suggest that chronic cluster headache is not only less responsive to newer treatments (like noninvasive vagus nerve stimulation and galcanezumab), but to traditional first-line treatments as well.

Reprezentacje obiektów przyrodniczych z dziecięcej perspektywy

In presented article we have described examples of children’s strategies for meaning fulfilment in the verbal and visual code. In conducted research, we attempted to reconstruct children’s abilities of conceptualizing objects with (1) prototypical, (2) prototypical and individual features, and (3) nonprototypical features. We were interested also in children’s methods of categorization (recognizing an object at various levels of abstraction) and constructing knowledge in terms of selected domains (appearance, environment, behavior and the relationship between animals and humans). 170 children of two age groups (3/4 and 4/5 years old) participated in the study. They were first asked to recognize an object on the basis of selected features shown in pictures. Then children were interviewed and asked to make a drawing. Research has shown that exposing children to a fragment of an image allows them to recognize the whole object and express their personal knowledge. The presented research showed also that children make conceptualization mainly through personal experiences and that when describing animals, children focus on observable animal activity and behavior, as well as animal appearance, size, the environment of living and the relation with humans.

Deploying and scaling distributed parallel deep neural networks on the Tianhe-3 prototype system

Due to the increase in computing power, it is possible to improve the feature extraction and data fitting capabilities of DNN networks by increasing their depth and model complexity. However, the big data and complex models greatly increase the training overhead of DNN, so accelerating their training process becomes a key task. The Tianhe-3 peak speed is designed to target E-class, and the huge computing power provides a potential opportunity for DNN training. We implement and extend LeNet, AlexNet, VGG, and ResNet model training for a single MT-2000+ and FT-2000+ compute nodes, as well as extended multi-node clusters, and propose an improved gradient synchronization process for Dynamic Allreduce communication optimization strategy for the gradient synchronization process base on the ARM architecture features of the Tianhe-3 prototype, providing experimental data and theoretical basis for further enhancing and improving the performance of the Tianhe-3 prototype in large-scale distributed training of neural networks.

Design and prototype of TOMO: an app for improving drug resistant TB treatment adherence.

Background: Drug resistance and multi drugs tuberculosis (DR/MDR-TB) are associated with patients' low adherence to undergoing complex treatment. Driven by the increasing use and penetration of a smartphone and the End of TB Strategy that seeks for digital health solution, Center for Tropical Medicine Universitas Gadjah Mada has developed TOMO, an Android-based app for improving medication adherence in MDR-TB. Objective: This paper aims to present the sequential steps to develop the app, its general architecture, and its functionalities. Methods: It is a design thinking process involving two MDR-TB referral centers, district health offices, primary health centers, and MDR-TB patients in Central Java and Yogyakarta, Indonesia. We adopted the Principles for Digital Development to develop and design the app. MDR-TB treatment guideline from the Indonesian Ministry of Health was used to develop functionalities of the app for improving adherence. Results: TOMO app could be used by patients, primary health centers, clinical teams, and case managers. The app prototype features include adverse event records and reports, medication-taking reminders, and communication between the patient and the TB-MDR case manager. We have successfully tested the functionalities based on four use cases: patients with high adherence, patients with low adherence, patients with adverse events, and patients following treatment in the primary health center without any visit to the MDR-TB center. Conclusion: TOMO app has contributed to the limited body of literature on improving TB-MDR adherence with digital health intervention, especially using a health app. The app has been tested using four scenarios. We will follow up with usability testing before implementing the app in a real setting.

Designing an Indoor Air Quality Monitoring App for Asthma Management in Children: User-Centered Design Approach.

BackgroundIndoor air pollution is a well-known risk factor that triggers and exacerbates asthma, the most common pediatric chronic disease. Using a mobile app to monitor indoor air quality could be promising in engaging children in keeping their indoor air quality clean and healthy as secondary environmental prevention for asthma management. However, no app is available to allow children to monitor, assess, and improve their indoor air quality.ObjectiveThis study aims to design a mobile app that encourages children to monitor indoor air quality and track their asthma conditions through a user-centered, iterative design approach.MethodsWe reviewed existing apps for indoor air quality monitoring or asthma management for children and conducted two sets of semistructured interviews with 12 children with asthma. We then iteratively created prototypes and evaluated and revised them.ResultsParticipants raised a series of outstanding questions on the prototype features and content that described their needs and perspectives, which informed the final designs. Following the identified requirements and recommendations, we developed two versions of the app: AirBuddy for presenting concrete information for indoor air quality and AirPet for gamifying the practice of monitoring indoor air quality.ConclusionsBy following an iterative, user-centered design process, we developed two versions of an app to encourage children with asthma to monitor indoor air quality and track their asthma condition. The user-centered design approach revealed two crucial aspects that require deeper consideration when creating a child-friendly app, including balancing brevity and expressivity and considering the longitudinal effects of gamification. As a next step, we plan to conduct a longitudinal deployment study to evaluate the real-world effects of our apps.

Non- Prototypical Reduplication in Odia

The paper discusses in detail echo formations and expressives found in the Odia language and the strategies for forming them. The complex semantic structure as well as the wide semantic and conceptual space they occupy, make them complex categories that have been ignored both by Western and Asian linguists because they are a prototypical grammatical feature not fitting the traditional grammar writing (Abbi, 2018). Odia has morphological fixed segmentism with the variants /ph/ and /m/. Morphological fixed segmentism is a kind of affixation, and so it resembles affixing morphology generally. [ɡaɽɪ &gt; ɡaɽɪ phaɽɪ ‘car and other transport’ ; ʧɪra &gt; ʧɪra mɪra ‘ torn and such’ ] This paper endeavours to find the reason behind this particular choice of phonemes by Odia speakers. Furthermore, the paper investigates if Odia base-reduplicant structures follow the Syllable Contact Law (Vennemann, 1988). According to the Syllable Contact Law, sonority should fall across syllable boundaries. This paper proves that Odia base-reduplicant structures follow the Syllable Contact Law as rise in sonority across syllable boundaries is a marked feature in Odia.’

Can We "Brain-Train" Emotional Intelligence? A Narrative Review on the Features and Approaches Used in Ability EI Training Studies.

Recent studies had demonstrated that specific emotional intelligence (EI) abilities (as measured using the MSCEIT) were related to better performance on cognitive tasks that involved emotional information but not on their non-emotional counterparts. These findings suggest that cognitive control and other executive functioning processes (e.g., working memory) contribute to EI abilities. A well-functioning EI ability is crucial for a number of everyday activities and life outcomes. However, the evidence for training ability EI remains vague as to how these improvements occur. The purpose of this narrative review was to synthesize findings from past EI training research, specifically focusing on their methodology. This was to identify key aspects of the interventions used, to determine the prototypical features between them, as well as to propose a compelling research agenda for future EI training studies. Based on the features found in these studies, we identified two possible approaches in which EI improvements occurred. The first approach was through increasing emotional knowledge and related competencies through teaching and practice. These features were found in the majority of training interventions using a workshop-style training format, reflections, role-plays, and practice with other participants. The second approach used brain-training principles to improve basic cognitive processes, such as executive control or emotional inhibition. Using a cognitive training approach to EI training can provide several advantages, such as allowing researchers to examine EI improvements using the theories of (1) transfer; (2) plasticity; and (3) process-specific changes.

Particle hanging on a string near a Schwarzschild black hole

The literature features many instances of spacetimes containing two black holes held apart by a thin distribution of matter on the axis joining the holes. For all such spacetimes, the Einstein field equations are integrated with an energy-momentum tensor that does not include a contribution from the axial matter; the presence of this matter is inferred instead from the existence of a conical singularity in the spacetime. And for all such spacetimes, the axial matter is characterized by a pressure (or tension) equal to its linear energy density; the matter is therefore revealed to have a very specific equation of state. Our purpose with this paper is to show that the axial matter can be introduced at the very start of the exercise, through the specification of a distributional energy-momentum tensor, and that one can choose for it any equation of state. To evade no-go theorems regarding line sources in general relativity, we retreat to a perturbative expansion of the gravitational field, using the Schwarzschild metric as a description of the background spacetime. Instead of a second black hole, our prototypical system features a point particle at a fixed position outside the Schwarzschild black hole, attached to a string extending to infinity. This matter is described in terms of a distributional energy-momentum tensor, and we examine different equations of state for the string. To integrate the field equations we introduce a new "Weyl" gauge for the metric perturbation, which allows us to find closed-form expressions for the gravitational potentials. Our solutions are linearized versions of multi-hole spacetimes, and some of them feature strings with a varying tension, unequal to the energy density. We describe the properties of these spacetimes, and begin an exploration of their extended thermodynamics.

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.

Plant Robot for At-Home Behavioral Activation Therapy Reminders to Young Adults with Depression

Adolescents with depression who participate in behavioral activation therapy may find it hard to be motivated to perform tasks at home that their therapists recommend. We describe the initial design and usability evaluation of a home device (“PlantBot”) that could be used to remind young adults with depression at home of their behavioral activation therapy-related tasks. The prototype features electronics in a two-layer base, with a fake plant on top and supported using the Amazon Echo voice agent. We use an online panel study to evaluate the usability of our system with youth with past depression (N= 30). Initial findings highlight the device’s usability, potential benefit, and attractiveness of the plant component, as well as multiple improvements to be made.

Reexamining the Three-Dimensional Meanings of Persse’s Love Scripts in Small World

Small World, the masterpiece of contemporary English writer David Lodge, is famous for its pursuing theme. Persse’s love pursuit is one of the prominent events in the novel. Readers always laugh at Persse’s pursuit, but leaves the reasons of why Persse’s pursuit is so funny untouched. This paper establishes “A continuum of Script Development” model based on Schank & Abelson (1977)’s personal script category, Gioia & Poole (1984)’s prototypical feature of script and Gibbs (2003)’s dynamic construing of script to reveal the knowledge structure of Persse’s love script. At the meantime, the paper specifies three actors (narrator, characters and readers) who construct the meanings of Persse’s love script. It is found that there are three different trails of Persse’s love pursuit under the examination of the script theory. The clashes between the three love scripts are one of the main reasons to achieving the humorous and ironic effects of the novel. Moreover, it expands the meaning of the metaphor LIFE IS A JOURNEY, and it highlights the death-rebirth theme of the novel.

Dynamic landscape of protein occupancy across the Escherichia coli chromosome.

Free-living bacteria adapt to environmental change by reprogramming gene expression through precise interactions of hundreds of DNA-binding proteins. A predictive understanding of bacterial physiology requires us to globally monitor all such protein-DNA interactions across a range of environmental and genetic perturbations. Here, we show that such global observations are possible using an optimized version of in vivo protein occupancy display technology (in vivo protein occupancy display-high resolution, IPOD-HR) and present a pilot application to Escherichia coli. We observe that the E. coli protein-DNA interactome organizes into 2 distinct prototypic features: (1) highly dynamic condition-dependent transcription factor (TF) occupancy; and (2) robust kilobase scale occupancy by nucleoid factors, forming silencing domains analogous to eukaryotic heterochromatin. We show that occupancy dynamics across a range of conditions can rapidly reveal the global transcriptional regulatory organization of a bacterium. Beyond discovery of previously hidden regulatory logic, we show that these observations can be utilized to computationally determine sequence specificity models for the majority of active TFs. Our study demonstrates that global observations of protein occupancy combined with statistical inference can rapidly and systematically reveal the transcriptional regulatory and structural features of a bacterial genome. This capacity is particularly crucial for non-model bacteria that are not amenable to routine genetic manipulation.

Environment sound classification using an attention-based residual neural network

Complexity of environmental sounds impose numerous challenges for their classification. The performance of Environmental Sound Classification (ESC) depends greatly on how good the feature extraction technique employed to extract generic and prototypical features from a sound is. The presence of silent and semantically irrelevant frames is ubiquitous during the classification of environmental sounds. To deal with such issues that persist in environmental sound classification, we introduce a novel attention-based deep model that supports focusing on semantically relevant frames. The proposed attention guided deep model efficiently learns spatio-temporal relationships that exist in the spectrogram of a signal. The efficacy of the proposed method is evaluated on two widely used Environmental Sound Classification datasets: ESC-10 and DCASE 2019 Task-1(A) datasets. The experiments performed and their results demonstrate that the proposed method yields comparable performance to state-of-the-art techniques. We obtained improvements of 11.50% and 19.50% in accuracy as compared to the accuracy of the baseline models of the ESC-10 and DCASE 2019 Task-1(A) datasets respectively. To support the attention outcomes that have focused on relevant regions, visual analysis of the attention feature map has also been presented. The resultant attention feature map conveys that the model focuses only on the spectrogram’s semantically relevant regions while skipping the irrelevant regions.

Self-supervised learning for Environmental Sound Classification

Environmental Sound Classification (ESC) is one of the most challenging tasks in signal processing, digital forensic and machine learning. Numerous methods have been proposed to perform ESC. The conventional models’ training depends on an enormous amount of annotated data, specifically while training the deep models. This paper presents a self-supervised learning (SSL)-based deep classifier for ESC, which is an under-explored method in the field of ESC. SSL mechanism directs the model to effectively learn prototypical features from the data itself by solving a pretext task. The model proposed in this paper takes spectrogram images as input. A pretext or an auxiliary task is defined as identification of the type of data augmentation applied to the signal. The model learned by solving the pretext task is further fine-tuned for developing the deep model for ESC. The model’s performance is evaluated on two benchmark sound classification datasets, i.e. ESC-10 and DCASE 2019 Task-1(A) datasets. The experiments and results show that the SSL model attains an improvement of 12.59% and 11.17% in accuracy compared to the baseline models of the DCASE 2019 Task-1(A) and ESC-10 datasets respectively. Moreover, the model also shows competitive performance to state-of-the-art methods.

Learnable Dynamic Temporal Pooling for Time Series Classification

With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time series classification show that convolutional neural networks (CNN) achieved the state-of-the-art performance as a single classifier. In this work, pointing out that the global pooling layer that is usually adopted by existing CNN classifiers discards the temporal information of high-level features, we present a dynamic temporal pooling (DTP) technique that reduces the temporal size of hidden representations by aggregating the features at the segment-level. For the partition of a whole series into multiple segments, we utilize dynamic time warping (DTW) to align each time point in a temporal order with the prototypical features of the segments, which can be optimized simultaneously with the network parameters of CNN classifiers. The DTP layer combined with a fully-connected layer helps to extract further discriminative features considering their temporal position within an input time series. Extensive experiments on both univariate and multivariate time series datasets show that our proposed pooling significantly improves the classification performance.

Single View Point Cloud Generation via Unified 3D Prototype

As 3D point clouds become the representation of choice for multiple vision and graphics applications, such as autonomous driving, robotics, etc., the generation of them by deep neural networks has attracted increasing attention in the research community. Despite the recent success of deep learning models in classification and segmentation, synthesizing point clouds remains challenging, especially from a single image. State-of-the-art (SOTA) approaches can generate a point cloud from a hidden vector, however, they treat 2D and 3D features equally and disregard the rich shape information within the 3D data. In this paper, we address this problem by integrating image features with 3D prototype features. Specifically, we propose to learn a set of 3D prototype features from a real point cloud dataset and dynamically adjust them through the training. These prototypes are then integrated with incoming image features to guide the point cloud generation process. Experimental results show that our proposed method outperforms SOTA methods on single image based 3D reconstruction tasks.

Interpreting Multivariate Shapley Interactions in DNNs

This paper aims to explain deep neural networks (DNNs) from the perspective of multivariate interactions. In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN. Input variables with strong interactions usually form a coalition and reflect prototype features, which are memorized and used by the DNN for inference. We define the significance of interactions based on the Shapley value, which is designed to assign the attribution value of each input variable to the inference. We have conducted experiments with various DNNs. Experimental results have demonstrated the effectiveness of the proposed method.