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Color correction is an important methodology where a digital image's colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing. [ABSTRACT FROM AUTHOR]

Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education "e-learning." The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students' perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students' perspective. The study was analyzed based on the student's data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students' perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.

The trend of online food delivery is booming in developing and emerging countries and COVID-19 has made it further under the spot. Now, it is the time for marketers and owners of restaurants to develop effective marketing strategies. Therefore, this research aimed to investigate the effect of communication on e-service quality and food quality on customers’ loyalty. These relations were further explored by the mediating role of communication. MethodologyThe data were collected from customers ordering food online. The collected data were further analyzed by using AMOS. The results highlighted that both e-service quality and food quality can enhance the loyalty of customers. Moreover, the presence of communication also, explained that e-service quality and food quality can significantly and positively affect customer loyalty. This research has highlighted the broader domains but still there are some limitations that can be addressed by future studies. The study is based on one country and future studies can compare the results of developed and developing countries. In addition, the model of study can be enhanced by introducing subjective knowledge or information credibility as potential moderators because they can also generate loyalty. [ABSTRACT FROM AUTHOR]

Previous studies have shown the vulnerability of hematological patients with the Coronavirus disease of 2019 (COVID-19). We aimed to compare the outcomes and risk factors for poor survival in patients with hematological conditions hospitalized with COVID-19 infection. Single centre, retrospective, cohort study included all patients with a hematological condition admitted to Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India between 1 April 2020 and 31 May 2021. Of a total of 154 patients, 81 were in the pre-delta group and 73 were in the delta group out of which 21 (25.97%) in the pre-delta group and 24 (33.88%) patients in the delta group died. Haematological characteristics—age > 60 years, progressive hematological cancer, more than two lines of anti-cancer therapy, and active chemo-immunotherapy or targeted therapy were associated with higher mortality in the delta group. COVID-19 characteristics associated with higher mortality during the delta wave were severity of COVID infection, higher oxygen requirements, and COVID plasma therapy There were no deaths in individuals (n = 15) within the delta group who received COVID-19 vaccination. This study adds to the evidence that patients with hematological diseases are a particularly vulnerable group and the delta variant of the virus is associated with higher mortality. We could identify patient characteristics and features related to COVID-19 infection and underlying hematological conditions that were associated with poor outcomes in the delta sub-group. Vaccination was found to be an effective strategy for overcoming mortality and morbidity in these patients. [ABSTRACT FROM AUTHOR]

In this work ABAQUS/Explicit FE package is used to create a 2D model replicating Orthogonal turning process of Ti-6Al-4V workpiece with an Arbitrary Lagrangian-Eulerian remeshing algorithm. Response Surface methodology (RSM) and Taguchi method was used to formulate the objective function and obtain optimum machining parameters, in DoE software. The influence of each machining parameter on cutting force is studied with the help of ANOVA and the significance and contribution of each parameter was identified. Further, a comparison of three optimization methods namely RSM, Taguchi and Teaching and Learning based Optimization (TLBO) was made. From ANOVA, it is concluded that depth of cut (72.64%) is the most significant parameter influencing cutting force. Among the three optimization methods, Taguchi gives the least response value of followed by TLBO and RSM. [ABSTRACT FROM AUTHOR]