A review of programming code assessment approaches

Learning computer programming language in harmony with practical coding actively while ensuring proper content progression is critical in introductory programming cources. Novice programmers usually face difficulty in acquiring the foundation level programming concepts adequately that usually lead to disappointmenet and ultimately back off. Bloom's Taxonomy has been generally adopted by the educators as a standard for assessing learning progression of students. In past there have been lot of research work on adopting Bloom's Taxonomy and its variants for computer programming languages, however, none has specially looked at an automatic mechanism to evaluate the six levels of Bloom's taxonomy on code level directly. In this paper we reviewed different approaches for assessment of programming code and discuss the challenges involved to implement the Bloom's taxonomy in programming languages directly on code level

Epidemiology of Diabetic Foot Infection in the Metro-Detroit Area with a Focus on Independent Predictors for Pathogens Resistant to Recommended Empiric Antimicrobial Therapy

Background. The polymicrobial nature of diabetic foot infection (DFI) and the emergence of antimicrobial resistance have complicated DFI treatment. Current treatment guidelines for deep DFI recommend coverage of methicillin-resistant Staphylococcus aureus (MRSA) and susceptible Enterobacteriaceae. This study aimed to describe the epidemiology of DFI and to identify predictors for DFI associated with multidrug-resistant organisms (MDROs) and pathogens resistant to recommended treatment (PRRT). Methods. Adult patients admitted to Detroit Medical Center from January 2012 to December 2015 with DFI and positive cultures were included. Demographics, comorbidities, microbiological history, sepsis severity, and antimicrobial use within 3 months before DFI were obtained retrospectively. DFI-PRRT was defined as a DFI associated with a pathogen resistant to both vancomycin and ceftriaxone. DFI-MDRO pathogens included MRSA in addition to PRRT. Results. Six-hundred forty-eight unique patients were included, with a mean age of 58.4 ± 13.7 years. DFI-MDRO accounted for 364 (56%) of the cohort, and 194 (30%) patients had DFI-PRRT. Independent predictors for DFI-PRRT included history of PRRT in a diabetic foot ulcer, antimicrobial exposure in the prior 90 days, peripheral vascular disease, and chronic kidney disease. Long-term care facility residence was independently associated with DFI due to ceftriaxone-resistant Enterobacteriaceae, and recent hospitalization was an independent predictor of DFI due to vancomycin-resistant Enterococcus. Conclusions. An unexpectedly high prevalence of DFI-PRRT pathogens was identified. History of the same pathogen in a prior diabetic foot ulcer and recent antimicrobial exposure were independent predictors of DFI-PRRT and should be considered when selecting empiric DFI therapy

A review of graphene reinforced Cu matrix composites for thermal management of smart electronics

Heat dissipation remains a key challenge to be addressed, determining the performance and durability of smart electronic devices. Graphene reinforced metal matrix composites have been extensively studied as a thermal management material due to their high thermal conductivity and low coefficient of thermal expansion. The emphasis of this review is pivoted on the thermal conductivity enhancement of graphene reinforced Cu matrix composites developed in the recent literature. An overview of factors affecting thermal conductivity of composite namely defect processing route, density, graphene derivative, lateral size, concentration, alignment, graphene/matrix interfacial bonding and graphene modification are discussed. An extensive weightage is given to the processing route as it is the most influential factor in determining the enhancement efficiency. Furthermore, graphene based functional products such as heat spreader and heat sink developed for heat dissipation of electronic devices are also reviewed. Finally, the development and outlook for graphene based Cu composites are presented

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use

The value of open-source clinical science in pandemic response: lessons from ISARIC

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Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world’s largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ~30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death