Performance and Early Results from the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectroscopy Mission

The Earth Surface Mineral Dust Source Investigation (EMIT) acquires new observations of the Earth from a state-of-the-art, optically fast F/1.8 visible to short wavelength infrared imaging spectrometer with high signal-to-noise ratio and excellent spectroscopic uniformity. EMIT was launched to the International Space Station from Cape Canaveral, Florida, on July 14, 2022 local time. The EMIT instrument is the latest in a series of more than 30 imaging spectrometers and testbeds developed at the Jet Propulsion Laboratory, beginning with the Airborne Imaging Spectrometer that first flew in 1982. EMIT's science objectives use the spectral signatures of minerals observed across the Earth's arid and semi-arid lands containing dust sources to update the soil composition of advanced Earth System Models (ESMs) to better understand and reduce uncertainties in mineral dust aerosol radiative forcing at the local, regional, and global scale, now and in the future. EMIT has begun to collect and deliver high-quality mineral composition determinations for the arid land regions of our planet. Over 1 billion high-quality mineral determinations are expected over the course of the one-year nominal science mission. Currently, detailed knowledge of the composition of the Earth's mineral dust source regions is uncertain and traced to less than 5,000 surface sample mineralogical analyses. The development of the EMIT imaging spectrometer instrumentation was completed successfully, despite the severe impacts of the COVID-19 pandemic. The EMIT Science Data System is complete and running with the full set of algorithms required. These tested algorithms are open source and will be made available to the broader community. These include calibration to measured radiance, atmospheric correction to surface reflectance, mineral composition determination, aggregation to ESM resolution, and ESM runs to address the science objectives. In this paper, the instrument characteristics, ground calibration, in-orbit performance, and early science results are reported. © 2023 IEEE.

iAssist: An online wellness platform to elevate the physical and mental health of the elderly

The physical and mental health of older adults is a critical issue that is often overlooked. With the recent increase in the number of people infected with the new variants of coronavirus, we are facing several problems, including a dearth of high-quality medical care. iAssist aims to be a platform that primarily focuses on the social benefit of promptly delivering medical aid to the elderly in our nation. It enables a variety of functions, such as doctor appointments, medicine orders, and lab appointments under one roof, with the goal of assisting caregivers, such as family members and healthcare professionals. Additionally, it offers a chatbot component that uses a social media messaging service, to inform users of new developments and assist in swiftly answering user questions. The technology stack used in iAssist makes the platform efficient and user-friendly for everyone involved. © 2022 IEEE.

A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing.

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of ∞ dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

Keep humanistic care help the development of high-quality nursing

In the context of normalized prevention of COVID- 19 epidemic and the in- depth promotion of building a healthy China, the consensus on integrating humanistic care into nursing work is taking root in the hearts of the people. As the guardian of people′s life and health and the main force in promoting the construction of a healthy China, the implementation of humanistic care by nurses in clinical work is crucial to improving the quality of nursing, improving patient outcomes, and promoting a harmonious relationship between nurses and patients. The acceleration of the current medical system reform process and the rapid release of the people′s demand for health services have further highlighted the urgency of improving the humanistic care capacity of nursing. This article focuses on improving nurses′ awareness, professional knowledge and skills of humanistic care, strengthening exploration and practice of nursing humanistic care management, focusing on creating a campus environment and culture of humanistic care for students, intensifying theoretical research on nursing humanistic care, integrating humanistic care into smart nursing, caring for nurses′ own development and physical and mental health, etc., will provide ideas for improving the humanistic care ability, helping the high-quality development of nursing, and promoting the construction of a healthy China. © Osmani F., 2023.

Key Success Factors of 3D OBN Survey in a Congested Oil Field in the Caspian Sea

In this paper, we present and demonstrate that the implementation of an efficient Project Management Strategy has effectively contributed in a safe and successful completion of a very complex 3D OBN Seismic Survey in congested Oil fields. Thus, delivering high quality data on schedule and within the predetermined budget at the full satisfaction of all involved parties and stakeholders. Strong commitment to HSSE Standards and working as an integrated One-Team with full collaboration and continuous communication between all the Team members are among the main Success Factors of the 3D seismic survey which was carried out during the critical period of COVID-19. Moreover, the deployment of experienced personnel, advanced and reliable Technologies with adequate equipment have also extended the efficiency of this OBN 3D seismic survey. Preliminary results of 3D seismic data processing, interpretation and reservoir characterization are also briefly presented and discussed as a clear enhancement of data quality was already observed compared to the legacy 3D OBC data set. A fast track small 3D cube was successfully processed as an utmost and urgent priority for appraisal well selection, design and drilling. Copyright © 2023, Society of Petroleum Engineers.

Assessing the Benefits of a Teleassessment Solution Using a FVM Perspective

The recent COVID-19 pandemic has served to highlight the benefits of digital health in general and telehealth in particular. One area of telehealth that is particularly important is that of teleassessment. Currently, we are witnessing an exponential growth in total knee and total hip replacements (TKR) (THR) due to an aging population coupled with longer life expectancy which is leading to a high likelihood of an unsustainable burden for healthcare delivery in Australia. To address this imminent challenge, the following proffers a tele-assessment solution, ARIADNE (Assist foR hIp AnD kNEe), that can provide high quality care, with access for all and support for high value outcomes. A fit viability assessment is provided to demonstrate benefits of the proffered solution. © 2022 IEEE Computer Society. All rights reserved.

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

Computer-Aided System for COVID-19 Using Semi-supervised-based Ensemble Learning and Reinforcement Learning

Coronavirus Disease 2019(COVID-19) has shocked the world with its rapid spread and enormous threat to life and has continued up to the present. In this paper, a computer-aided system is proposed to detect infections and predict the disease progression of COVID-19. A high-quality CT scan database labeled with time-stamps and clinicopathologic variables is constructed to provide data support. To our knowledge, it is the only database with time relevance in the community. An object detection model is then trained to annotate infected regions. Using those regions, we detect the infections using a model with semi-supervised-based ensemble learning and predict the disease progression depending on reinforcement learning. We achieve an mAP of 0.92 for object detection. The accuracy for detecting infections is 98.46%, with a sensitivity of 97.68%, a specificity of 99.24%, and an AUC of 0.987. Significantly, the accuracy of predicting disease progression is 90.32% according to the timeline. It is a state-of-the-art result and can be used for clinical usage. © 2022 IEEE.

Analysis of nutritional components and essential amino acids of Korean traditional porridge

Adequate intake of foods composed of proteins may be necessary for the elderly. This study aimed to analyze the nutritional components of traditional porridge based on the recipe of traditional Korean literature, Jeungbo sallim gyeongj, focusing on chicken porridge, Uyang (beef stomach) porridge, and carp porridge. We analyzed their general nutritional and essential amino acid components, showing that chicken porridge, Uyang porridge, and carp porridge were all nutritionally excellent, but essential amino acids and branched-chain amino acids were the highest in chicken porridge among the three types of porridges. As a result, chicken porridge can be the most suitable for the health of the elderly in the time of COVID-19. In conclusion, this study revealed that traditional porridge based on the recipe of Jeungbo sallim gyeongj could be a nutritionally high-quality source of essential amino acids for the elderly, which can help maintain immunity and muscle strength in the elderly. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

Research on Measurement and Evaluation of High-Quality Entrepreneurship: A Review and Synthesis Prospectus

How to measure and evaluate the quality of entrepreneurial activities is not only an important academic issue in the field of entrepreneurship research but also an important practical problem faced by economic policymakers, especially in the context of the global Covid-19 epidemic and the shift of China's economy from the entrepreneurship high-rate growth stage to the high-quality grow stage. In this paper, we explore the development process of defining and measuring the high-quality entrepreneurial activities, discuss and synthesize the various measurement index for identifying the high-quality entrepreneurship in a complex and uncertain context, concluding that measurement and evaluation of high-quality measurement index experiencing the process of single index to composite index with the consideration of impact of general entrepreneurship policy and specific environment, and also the measurement and evaluation more and more focused on antecedent of entrepreneurial activities which can effectively predict the high quality of entrepreneurial activities from the onset of new firms instead of consequence of entrepreneurial activities. At the end of the article, we propose three viewpoints: First, entrepreneurial quality can be measured using quantitative methods;second, there are limitations for the evaluation of high-quality entrepreneurial quality in practice;third, entrepreneurship indicators should be continuously updated with the accumulation of practice. © 2023 The authors and IOS Press.

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

Active Keyword Selection to Track Evolving Topics on Twitter

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers. © 2022 IEEE.

Inclusive Study Group Formation at Scale

Underrepresented students face many significant challenges in their education. In particular, they often have a harder time than their peers from majority groups in building long-term high-quality study groups. This challenge is exacerbated in remote-learning scenarios, where students are unable to meet face-to-face and must rely on pre-existing networks for social support. We present a scalable system that removes structural obstacles faced by underrepresented students and supports all students in building inclusive and flexible study groups. One of our main goals is to make the traditionally informal and unstructured process of finding study groups for homework more equitable by providing a uniform but lightweight structure. We aim to provide students from underrepresented groups an experience that is similar in quality to that of students from majority groups. Our process is unique in that it allows students the opportunity to request group reassignments during the semester if they wish. Unlike other collaboration tools our system is not mandatory and does not use peer-evaluation. We trialed our approach in a 1000+ student introductory Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We find that students from underrepresented backgrounds were more likely to ask for group-matching support compared to students from majority groups. At the same time, underrepresented students that we matched into study groups had group experiences that were comparable to students we matched from majority groups. B-range students in high-comfort and high-quality groups had improved learning outcomes. © 2023 Owner/Author.

Seeking ABET Accreditation for Computing Programmes in Africa: A Case Study of UNAM's BSc in Computer Science

ABET is the worldwide leader in accrediting pro-grams in computing, engineering, applied and natural sciences, and engineering technology. Only four of 54 countries in Africa have programmes which achieved ABET accreditation. During COVID-19 and in times of intense competition, it has become especially important for institutions to distinguish their programs as having high quality. Using the University of Namibia's Bachelor of Science in Computer Science as a case study, we use a systematic methodology to evaluate and analyse the programme in a detailed, step-by-step, easy-to-understand manner. We evaluate this program with respect to ABET's General Criteria and then examine how well it does against ABET's Program Criteria in computer science. Our aim is to pinpoint shortcomings and solutions. The results for the selected program, despite ticking positives for a number of criterion, show the need for considerable work to meet ABET accreditation. Our research stands to inspire university programs in Africa to strive for ABET accreditation as a way of distinguishing themselves. © 2022 IEEE.

Toward High-quality Face-Mask Occluded Restoration

Face-mask occluded restoration aims at restoring the masked region of a human face, which has attracted increasing attention in the context of the COVID-19 pandemic. One major challenge of this task is the large visual variance of masks in the real world. To solve it we first construct a large-scale Face-mask Occluded Restoration (FMOR) dataset, which contains 5,500 unmasked images and 5,500 face-mask occluded images with various illuminations, and involves 1,100 subjects of different races, face orientations, and mask types. Moreover, we propose a Face-Mask Occluded Detection and Restoration (FMODR) framework, which can detect face-mask regions with large visual variations and restore them to realistic human faces. In particular, our FMODR contains a self-adaptive contextual attention module specifically designed for this task, which is able to exploit the contextual information and correlations of adjacent pixels for achieving high realism of the restored faces, which are however often neglected in existing contextual attention models. Our framework achieves state-of-the-art results of face restoration on three datasets, including CelebA, AR, and our FMOR datasets. Moreover, experimental results on AR and FMOR datasets demonstrate that our framework can significantly improve masked face recognition and verification performance. © 2023 Association for Computing Machinery.

Results from The Quiet Project- UK Acoustic Community's response to COVID19

The COVID-19 Lockdown created a new kind of environment both in the UK and globally, never experienced before or likely to occur again. A vital and time-critical working group was formed with the aim of gathering crowd-source high quality baseline noise levels and other supporting information. The acoustic community were mobilised through existing networks engaging private companies, public organisations, and academics to gather data in accessible places. A website was designed to advertise the project, provide instructions and to formalise the uploading of noise data, observations, and Soundscape feedback. The data was collected at 99 locations by 80 acousticians (64 male, 16 female) using professional grade calibrated instrumentation with 83% of measurements including spectral data. The locations covered 19 urban, 61 suburban, and 19 rural sites. The Lockdown 1 dataset consisted of a total of 1.6 GB of measurements and material (video, photos) covering 834 days between 1st April and 14th July 2020. This makes the award winning Quiet Project the largest ever noise and soundscape database ever recorded. The paper presents the quietest places in the UK and Ireland. As a government funded research project the databank will be made publicly available to assist future research. © 2022 Internoise 2022 - 51st International Congress and Exposition on Noise Control Engineering. All rights reserved.

Simulation Teaching During the Pandemic: Report of an Experience in a Higher Education Private Institution

The COVID-19 pandemic has impacted virtually every sector of our society, including the educational arena. This article reports the experience of discrete-event simulation teaching as part of the industrial engineering curriculum of a higher education private institution in three time instances: the pre-pandemic period (2019), where teaching was in person;the main pandemic period (2020 and the first half of 2021), where teaching was 100% remote, and the hybrid pandemic period (2nd half of 2021). We conducted comparisons of the teaching process along these instances regarding several points, by performing both qualitative and quantitative analyses. This article concludes that, despite some pedagogical difficulties, it was possible to maintain high quality in the teaching-learning process, compatible with the pre-pandemic period. The article also makes a forecast of how the teaching process of this type of discipline will be in the near future, after having been influenced by the pandemic period. © 2022 IEEE.

SELF-ORGANIZED TEACHERS LEARNING TO OVERCOME THE CHALLENGES OF REMOTE STUDIES

The time of Covid-19 required appropriate infrastructure, effective planning, digitally competent adult teachers, high quality training content, user-friendly tools, as well as digitally competent learners, to make education and training systems in the digital area. The readiness of the teacher to implement the remote studies, digital skills and ability to learn, as well as mutual cooperation were the determining factors in ensuring quality education during the Pandemic. This article provides a brief insight into a survey of university teachers about the challenges and disadvantages of distance learning, and describes how teachers developed their skills by collaborating and teaching each other. © 2022 Proceedings of the 19th International Conference on Cognition and Exploratory Learning in the Digital Age, CELDA 2022. All rights reserved.

The New Progress and Methods of Privacy Protection on Medical and Health Big Data*

In recent years, due to the emergence of COVID-19(Corona Virus Disease 2019), how to have a higher quality medical environment has become a troubling problem. The proposal of the Office of the State Council on promoting the development of 'Internet plus medical and health' has brought a lot of convenience to the public, but also brought about the problem of data leakage and other user privacy protection. In view of the problems of user's personal information storage and user's health data processing in the medical and health context, how to ensure that these data are not stolen, leaked or tampered with has become a major challenge faced by current researchers. Based on the privacy protection of users in the context of health care, this paper classifies the current privacy protection mechanisms, and introduces the latest progress of related technologies. Finally, according to the integrated information, the research direction of privacy protection technologies in the field of health care is prospected. © 2022 IEEE.

CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic. The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus. We make the very first attempt to release a high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites. We annotate each utterance of the conversation with seven different categories of medical entities, including diseases, symptoms, medical tests, medical history, remedies, medications and other aspects as additional labels. Finally, we propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems. We use pre-trained language models for dialogue generation, incorporating annotated medical entities, to generate a virtual doctor's response that addresses the patient's query. Experimental results show that the proposed dialog models perform comparably better when supplemented with entity information and hence can improve the response quality. © 2022 Association for Computational Linguistics.