Knowledge, attitudes and practices towards COVID-19 among Pakistani residents: Information access and low literacy vulnerabilities

Background: Coronavirus disease (COVID-19) has accentuated the need for speedy access to information. Digital divide and socio-demographic disparity create an information hiatus and therefore unhealthy practices with regard to dealing with COVID-19, particularly in low- and middle-income countries.Aims: We assessed knowledge, attitudes, practices and their determinants regarding COVID-19 in Pakistan during March-April 2020.Methods: 905 adults ≥18 years (males and females) participated: 403 from a web-based survey; 365 from an urban survey; and 137 from a rural survey. Frequency of adequate knowledge, attitudes and practices for the three populations was determined based on available global guidelines. Multivariable logistic regression analysis determined factors of adequacy of knowledge, attitudes, practices, and association of knowledge with attitudes and practices.Results: Mean age of the participants was 33.5 (+ SD 11.1) years, 51% were females. More females and young adults (18-30 years) participated in the web-based survey. The urban survey and web-based survey participants had significantly higher adequate knowledge (2-7 times) and practices (4-5 times) towards COVID-19. Adequate knowledge had a significant influence on healthy attitudes and practices for COVID-19, after adjustment for covariates. Overall, two-thirds of the population had high levels of fear about COVID-19, which was highest among the rural survey population.Conclusion: Substantial gaps exist in adequate knowledge, attitudes and practices, particularly among rural populations, and underscores the variation in access to information according to level of education and access to the internet. Thus, a comprehensive, contextually congruent awareness raising strategy is urgently needed to confront COVID-19 among these populations

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively

Inclusive, supportive and dignified maternity care (SDMC)-Development and feasibility assessment of an intervention package for public health systems: A study protocol.

INTRODUCTION: Mistreatment, discrimination, and poor psycho-social support during childbirth at health facilities are common in lower- and middle-income countries. Despite a policy directive from the World Health Organisation (WHO), no operational model exists that effectively demonstrates incorporation of these guidelines in routine facility-based maternity services. This early-phase implementation research aims to develop, implement, and test the feasibility of a service-delivery strategy to promote the culture of supportive and dignified maternity care (SDMC) at public health facilities. METHODS: Guided by human-centred design approach, the implementation of this study will be divided into two phases: development of intervention, and implementing and testing feasibility. The service-delivery intervention will be co-created along with relevant stakeholders and informed by contextual evidence that is generated through formative research. It will include capacity-building of maternity teams, and the improvement of governance and accountability mechanisms within public health facilities. The technical content will be primarily based on WHO's intrapartum care guidelines and mental health Gap Action Programme (mhGAP) materials. A mixed-method, pre-post design will be used for feasibility assessment. The intervention will be implemented at six secondary-level healthcare facilities in two districts of southern Sindh, Pakistan. Data from multiple sources will be collected before, during and after the implementation of the intervention. We will assess the coverage of the intervention, challenges faced, and changes in maternity teams' understanding and attitude towards SDMC. Additionally, women's maternity experiences and psycho-social well-being-will inform the success of the intervention. EXPECTED OUTCOMES: Evidence from this implementation research will enhance understanding of health systems challenges and opportunities around SDMC. A key output from this research will be the SDMC service-delivery package, comprising a comprehensive training package (on inclusive, supportive and dignified maternity care) and a field tested strategy to ensure implementation of recommended practices in routine, facility-based maternity care. Adaptation, Implementation and evaluation of SDMC package in diverse setting will be way forward. The study has been registered with clinicaltrials.gov (Registration number: NCT05146518)

Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press

This research investigates the performance of medium density fiberboard (MDF) with respect to hot press parameters. The performance of the board, type of glue, and production efficiency determine the optimum temperature and pressure for hot pressing. The actual temperature of the hot press inside the MDF board determines the properties of the final product. Hence, the optimal hot press parameters for the desired product are experimentally obtained. Moreover, MDF is experimentally investigated in terms of its vertical density profile, bending, and internal bonding under the various input parameters of temperature, pressure, cycle time, and moisture content during the manufacturing process. The experimental study is carried out by varying the temperature, pressure, cycle time, and moisture content in the ranges of 200–220 °C, 145–155 bar, 260–275 s, and 8–10%, respectively. Consequently, the optimum input parameters of a hot-pressing temperature of 220 °C, pressure of 155 bar, cycle time of 256 s, and moisture content of 8% are identified for the required internal bonding (0.64 N/mm2), bending (32 N/mm2), and increase in both the core and peak density of the vertical density profile as per the ASTM standard

第975回千葉医学会例会・第19回歯科口腔外科例会

<p>It can be seen that the system clearly classified the expressions classes.</p

An improved gaussian mixture hidden conditional random fields model for audio-based emotions classification

The analysis of human emotions plays a significant role in providing sufficient information about patients in monitoring their feelings for better management of their diseases. Audio-based emotions recognition has become a fascinating research interest for such domains during the last decade. Mostly, audio-based emotions systems depend on the recognition stage. The existing model has a common issue called objectivity suppositions problem, which might decrease the recognition rate. Therefore, this study investigates the improved version of a classifier that is based on hidden conditional random fields (HCRFs) model to classify emotional speech. In this model, we introduced a novel methodology that will incorporate multifaceted dissemination with the help of employing a combination of complete covariance Gaussian concreteness function. Due to this incorporation, the proposed model tackle most of the limitations of existing classifiers. Some of the well-known features like Mel-frequency cepstral coefficients (MFCC) are extracted in our experiments. The proposed model has been validated and evaluated on two publicly available datasets likes Berlin Database of Emotional Speech (Emo-DB) and the eNTER FACE’05 Audio-Visual Emotion dataset. For validation and comparison against the existing techniques, we utilized 10-fold cross validation scheme. The proposed method achieved significant improvement under the p-value <0.03 for classification. Moreover, we also prove that computational wise, our computation technique is less expensive against state of the art works

FAIR Health Informatics: A Health Informatics Framework for Verifiable and Explainable Data Analysis

The recent COVID-19 pandemic has hit humanity very hard in ways rarely observed before. In this digitally connected world, the health informatics and investigation domains (both public and private) lack a robust framework to enable rapid investigation and cures. Since the data in the healthcare domain are highly confidential, any framework in the healthcare domain must work on real data, be verifiable, and support reproducibility for evidence purposes. In this paper, we propose a health informatics framework that supports data acquisition from various sources in real-time, correlates these data from various sources among each other and to the domain-specific terminologies, and supports querying and analyses. Various sources include sensory data from wearable sensors, clinical investigation (for trials and devices) data from private/public agencies, personnel health records, academic publications in the healthcare domain, and semantic information such as clinical ontologies and the Medical Subject Heading ontology. The linking and correlation of various sources include mapping personnel wearable data to health records, clinical oncology terms to clinical trials, and so on. The framework is designed such that the data are Findable, Accessible, Interoperable, and Reusable with proper Identity and Access Mechanisms. This practically means to tracing and linking each step in the data management lifecycle through discovery, ease of access and exchange, and data reuse. We present a practical use case to correlate a variety of aspects of data relating to a certain medical subject heading from the Medical Subject Headings ontology and academic publications with clinical investigation data. The proposed architecture supports streaming data acquisition and servicing and processing changes throughout the lifecycle of the data management. This is necessary in certain events, such as when the status of a certain clinical or other health-related investigation needs to be updated. In such cases, it is required to track and view the outline of those events for the analysis and traceability of the clinical investigation and to define interventions if necessary

A robust clustering algorithm using spatial fuzzy C-means for brain MR images

Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come with several problems such as noise and other imaging artifacts added during acquisition process. The researchers have actual challenges for segmentation under the consideration of these effects. In medical images, a well-known clustering approach like Fuzzy C-Means widely used for segmentation. The performance of FCM algorithm is fast in noise-free images; however, this method did not consider the spatial context of the image due to which its performance suffers when images corrupted with noise and other imaging relics. In this paper, a weighted spatial Fuzzy C-Means (wsFCM) segmentation method is proposed that considered the spatial information of image. Moreover, a spatial function is also developed that integrate a membership function. In order assess this function, a neighborhood window is established around a pixel and more weights have been assigned to those pixels which have greater correlation with central pixel in local neighborhood. By integration of this spatial function in membership function, the modified membership function strengthens the original membership function in handling the noise and intensity inhomogeneity, which has the ability to preserves and maintains structural information like edges. A comprehensive set of experimentation is performed on publicly accessible simulated and real standard brain MRI datasets. The performance of the proposed method has been compared with existing state-of-the-art methods. The results show that the performance of the proposed method is better and robust in handling noise and intensity inhomogeneity than of the existing works. Keywords: Clustering algorithm, MRI, Fuzzy C-mean

Adaptively Directed Image Restoration Using Resilient Backpropagation Neural Network

Abstract In this modern era, visual data transmission, processing, and analysis play a vital role in daily life. Image denoising is the process of approximately estimating the original version of a degraded image. The presence of unexpected noise (e.g., fixed, random, and Gaussian) is the root cause of degradation, which has been reduced to some extent by many linear and non-linear filters based on a median value. The real issue is developing a strategy that should be generalized enough to effectively restore an image corrupted with multi-nature noise. Many researchers have developed novel concepts, but their tactics must acquire the highest performance in this area. This article proposes a constrained strategy for this problem, i.e., an adaptively directed denoising filter (ADD filter) based on a neural network. It consists of three major stages: training, filtering, and enhancing. First, we train a feed-forward back-propagation neural network on noisy and noise-free pixels for effective differentiation. Second, we apply a one-pass selective filter to the noisy image. The objective of this one-pass filter is to minimize noise using an adaptive median or directional filter based on density. Finally, the iterative directional filter is applied to the pre-processed image to enhance its visual quality. The extensive experiments depict that the proposed system has achieved better subjective results and improved local (structural similarity) and global (peak signal-to-noise ratio or mean square error) statistical measures