Design Justice in Practice: Community-led Design of an Online Maker Space for Refugee and Migrant Women

While varying degrees of participatory methods are often explored by the HCI community to enable design with different user groups, this paper seeks to add weight to the burgeoning demand for community-led design when engaging with diverse groups at the intersections of marginalisation. This paper presents a 24-month-long qualitative study, where the authors observed a community-based organisation that empowers refugee and migrant women in Australia through making. We report how the organisation led its own process to pivot from face-to-face to online delivery during the COVID-19 pandemic, analyzing the design and delivery of an app and the intersectional challenges faced by the women as they learnt to navigate online making. This paper expands feminist intersectional praxis in HCI to new contexts and critiques the positionality of researchers in this work. It contributes to the literature on design justice, providing an exemplar of how community-led design more effectively dismantles the compounding constraints experienced by intersectional communities. This paper also argues that the ethos of care and safe spaces, which are central to black feminist thought, are vital to community-led design and underpin the 10 design justice principles when executed in practice. © 2023 ACM.

A telehealth-based training model for an ambulatory care pharmacy skills course

Description: Increasingly, pharmacy services are provided using telehealth-based modalities. This paper describes a pharmacy skills course that utilised telehealth principles to train students on the technical and communications skills necessary for the ambulatory care setting. Zoom breakout rooms, electronic health records, YouTube video vignettes, and teaching assistants portraying patients/physicians simulated a telehealth-based ambulatory care setting. Evaluation: Five quizzes and six written assignments were utilised to measure student's knowledge and skills. At the end of the course, students were evaluated through a three-station objective structured clinical exam (OSCE). Students also completed a pre/post attitudes survey. Result(s): Overall, students performed well on various assessments including quizzes and written assignments. The majority of the students performed well on the OSCE. Significant improvement was noted on all items in the attitudes survey. Conclusion(s): This study suggests that a telehealth training model can be effective in teaching pharmacy students both the technical and communication skills necessary for practice in the ambulatory care setting. Copyright © 2021 FIP.

A Conceptual Model for Integrating Sustainable Supply Chain, Electric Vehicles, and Renewable Energy Sources

The effects of climate change can be seen immediately in ecosystems. Recent events have resulted in a commitment to the Paris Agreement for the reduction of carbon emissions by a significant amount by the year 2030. Rapid urbanisation is taking place to provide room for an increasing number of people's residences. Increasing the size of a city and the number of people living there creates a daily need for consumable resources. In the areas of transportation, supply chains, and the utilisation of renewable energy sources, deliver on pledges that promote the accomplishment of the Sustainable Development Goals established by the United Nations. As a result, the supply chain needs to be handled effectively to meet the requirements of growing cities. Management of the supply chain should be in harmony with the environment;nevertheless, the question of how to manage a sustainable supply chain without having an impact on the environment is still mostly understood. The purpose of this study is to present a conceptual model that may be used to maintain a sustainable supply chain with electric vehicles in such a way that caters to both environmental concerns and human requirements. As part of the continual process of achieving sustainability, interrelationships between the various aspects that are being investigated, comprehended, and applied are provided by the model that was developed. It is self-evident that governmental and international organisations that are concerned with supply-demand side information will benefit from such a model, and these organisations will locate viable solutions in accordance with the model's recommendations. Beneficiaries consist of individuals who are active in the supply chain and are concerned with supply-demand side information. These individuals also need to understand how to effectively manage this information.

Potential Benefit of Masking and Other COVID-19 Infection Prevention Measures on Late-onset Infectionsin theNICU

Background: The incidence of nosocomial blood stream infections (BSI) among NICU admissions remains high, with significant mortality and morbidity. Due to COVID-19, there are increased infection prevention (IP) measures in NICUs including universal masking for all healthcare workers and families, social distancing, visitation restrictions, and increased attention to hand hygiene. These measures may also affect late-onset infection rates and offer understanding of novel interventions for prevention. Methods: We examined infection rates from three neonatal centers during the 24 months prior to implementation of COVID-19 IP measures (PRE-period) compared to the months after implementation from April 2020 (POST-period). Late-onset infections were defined as cultureconfirmed infection of the blood, urine, and other sterile fluids or identification of respiratory viral pathogens. An interrupted time series analysis of infection per 1000 patient days was performed based on a change-point Poisson regression with a lagged dependent variable and the number of patient days used as offsets. Each month was treated as independent with additional analysis using an observation-driven model to account for serial dependence. Results: Multicenter analysis to date included all infants cared for at three centers (Level 3 and 4) from 2018-2020. Monthly BSI rates decreased in the POSTperiod at the three centers (Figure 1). At all centers actual BSI rate was lower than the expected rate in the POST-period (Figure 2). The combined BSI rate per 1000 patient days was 41% lower compared to the rate prior to implementation (95% CI, 0.42 to 0.84, P = 0.004). In subgroup analysis of BSI by birthweight, during the POST-period there was a 39% reduction in infants < 1000g (P = 0.023), a 44% decrease for 1000-1500g patients (P = 0.292) and a 53% decrease in those > 1500g (0.083). Examining single center data from the University of Virginia through March 2021, there was a 36% decrease in all late-onset infections (BSI, UTI, Viral, and peritonitis) (95% CI, 0.46 to 0.90, P=0.011). Conclusion: In this preliminary analysis, we found a reduction of BSI after the implementation of COVID-19 infection prevention measures. Additionally, there were fewer viral infections, though there were a limited number of episodes. Further analyses of multicenter data and a larger number of patients from all 12 centers of our study network will elucidate the significance of these findings and the role some of these IP measures, such as universal masking, may have in infection prevention in the NICU (Supported in part by Grant Funding from the Gerber Foundation).

Visual Social Distance Alert System Using Computer Vision Deep Learning

One of the principles and best measures to contain the ongoing viral episode is the support of the alleged social distancing (SD). To agree to this limitation, governments are receiving limitations over the base between close to home separation between individuals. Given this real situation, it is critical to enormously gauge the consistence to such physical requirement in our life, so as to make sense of the purposes behind the potential breaks of such separation impediments and comprehend if this suggests a likely danger. To this end, the proposed research work presents the Video Social Distancing issue, characterized as the programmed assessment of the between close to home good ways from a picture, and the portrayal of related individuals' conglomerations. Video Social Distancing is significant for a non-obtrusive investigation of whether individuals follow the Social Distancing limitation, and to give insights about the degree of security of explicit territories at whatever point this imperative is abused. It has been first viewed that, estimating Video Social Distancing isn't just a mathematical issue, however it additionally infers a more profound comprehension of the social conduct in the scene. The point is to genuinely identify possibly risky circumstances while keeping away from bogus alerts (e.g., a family with youngsters or family members, a senior with their guardians), the entirety of this by following current security strategies. At that point, the proposed research work will discuss about how video social distancing is related with past writing in social signal processing and show a way to investigate new computer vision techniques that can give an answer for such issue. This paper is concluded with future moves that are identified with the viability of video social distancing frameworks, moral ramifications and future application situations. © 2020 IEEE.

Classification of COVID-19 cases using Fine-Tune Convolution Neural Network (FT-CNN)

The new human Corona affliction (COVID-19) is a lungs ailment accomplished by incredible outrageous respiratory issue crown 2 (SARS-CoV-2). Given the impacts of COVID-19 in pneumonic sensitive tissue, chest radiography imaging acknowledges an immense part in the screening, early region, and checking of the conjectured people. It affected the general economy besides cruelly. In the event that positive cases can be perceived early, this pandemic infection spread can be condensed. Guess of COVID-19 infection is incredible to perceive patients in danger for sicknesses. This paper proposes an exchange learning model utilizing Convolution Neural Network (CNN) for COVID-19 solicitation from chest X-shaft pictures. For picture approach, utilized proposed Fine-tuned CNN plan (FT-CNN). The strongly assembled pictures by our model show the presence of COVID-19. The outcomes got in COVID measure utilizing FT-CNN with an arranging exactness of 90.70% and testing precision of 90.54% feature the use of Transfer Learning models in disease assumption. © 2021 IEEE.

Yolo-v4 Deep Learning Model for Medical Face Mask Detection

The flora and fauna is facing a social disaster owing to the fast transfer of (Corona Virus). The disease with COVID-19 is mainly transmitted by respiratory droplets that are inhaled when people smell, talk, hack or sting. Wearing a veil is an incredible, powerful and easy way to prevent 82% of all respiratory infections. In this way, a number of cover protection structures and recognition structures have been put in place to provide compulsory provision for emergency clinics, air terminals, courier transport, sports offices, and retail outlets. Upper and lower tests have shown unusual strengths in specific real-world projects. Some of this may have been highlighted in the article. Ongoing revelations of articles relying on higher and lower reading models have yielded promising results by finding something found in the pictures. This paper focuses on a response to help to support a larger social request and to wear public covers using the revelation of the YOLO c4 object continuously engraved with images. The proposed Yolo v4 learning model has been pre-programmed with good program limitations. The organization ensures fast access that can deliver consistent results without resolving accuracy, or in complex arrangements. The proposed strategy will be divided into three categories: No dress, cover, and no veil. The model has outdone some of the proposed strategies in the past by gaining 99.98% accuracy during the preparation/testing. © 2021 IEEE.

A Review on Indian State/City Covid-19 Cases Outbreak Forecast utilizing Machine Learning Models

Several scene supposition models for COVID-19 are being utilized by experts around the globe to settle on trained choices and keep up fitting control measures. Man-made awareness Machine Learning (ML) based choosing fragments have shown their significance to foresee perioperative results to improve the dynamic of things to come course of activities. The ML models have been utilized in different application spaces which required the obvious check and prioritization of undesirable parts for a danger. A few supposition techniques are in fact unavoidably used to oversee imagining issues. This evaluation shows the limitation of Machine Learning models to ascertain the amount of moving toward patients influenced by COVID-19 which is considered as a typical risk to humanity. Specifically, four standard choosing models, for example, Linear apostatize, keep up vector machine, MLP, Decision Tree, Boosted Random Forest, Regression Tree, and Extra Tree have been utilized in this evaluation to figure the compromising elements of COVID-19. Three kinds of guesses are made by the aggregate of the models, for example, the number of starting late polluted cases after and before starter vexing, the number of passing's after and before groundwork vexing, and the number of recuperation after and before groundwork vexing. The outcomes made by the evaluation display a promising structure to utilize these systems for the current situation of the COVID-19 pandemic. © 2021 IEEE.