| 1. |
- Ademuyiwa, Adesoji O., et al.
(författare)
-
Determinants of morbidity and mortality following emergency abdominal surgery in children in low-income and middle-income countries
- 2016
-
Ingår i: BMJ Global Health. - : BMJ Publishing Group Ltd. - 2059-7908. ; 1:4
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Child health is a key priority on the global health agenda, yet the provision of essential and emergency surgery in children is patchy in resource-poor regions. This study was aimed to determine the mortality risk for emergency abdominal paediatric surgery in low-income countries globally.Methods: Multicentre, international, prospective, cohort study. Self-selected surgical units performing emergency abdominal surgery submitted prespecified data for consecutive children aged <16 years during a 2-week period between July and December 2014. The United Nation's Human Development Index (HDI) was used to stratify countries. The main outcome measure was 30-day postoperative mortality, analysed by multilevel logistic regression.Results: This study included 1409 patients from 253 centres in 43 countries; 282 children were under 2 years of age. Among them, 265 (18.8%) were from low-HDI, 450 (31.9%) from middle-HDI and 694 (49.3%) from high-HDI countries. The most common operations performed were appendectomy, small bowel resection, pyloromyotomy and correction of intussusception. After adjustment for patient and hospital risk factors, child mortality at 30 days was significantly higher in low-HDI (adjusted OR 7.14 (95% CI 2.52 to 20.23), p<0.001) and middle-HDI (4.42 (1.44 to 13.56), p=0.009) countries compared with high-HDI countries, translating to 40 excess deaths per 1000 procedures performed.Conclusions: Adjusted mortality in children following emergency abdominal surgery may be as high as 7 times greater in low-HDI and middle-HDI countries compared with high-HDI countries. Effective provision of emergency essential surgery should be a key priority for global child health agendas.
|
|
| 2. |
- Thomas, HS, et al.
(författare)
-
- 2019
-
swepub:Mat__t
|
|
| 3. |
|
|
| 4. |
- Naghavi, Mohsen, et al.
(författare)
-
Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013
- 2015
-
Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 385:9963, s. 117-171
-
Tidskriftsartikel (refereegranskat)abstract
- Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65.3 years (UI 65.0-65.6) in 1990, to 71.5 years (UI 71.0-71.9) in 2013, while the number of deaths increased from 47.5 million (UI 46.8-48.2) to 54.9 million (UI 53.6-56.3) over the same interval. Global progress masked variation by age and sex: for children, average absolute diff erences between countries decreased but relative diff erences increased. For women aged 25-39 years and older than 75 years and for men aged 20-49 years and 65 years and older, both absolute and relative diff erences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10.7%, from 4.3 million deaths in 1990 to 4.8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.
|
|
| 5. |
- Ali-Eldin, Ahmed, 1985-, et al.
(författare)
-
How will your workload look like in 6 years? : Analyzing Wikimedia's workload
- 2014
-
Ingår i: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014). - : IEEE Computer Society. - 9781479937660 ; , s. 349-354
-
Konferensbidrag (refereegranskat)abstract
- Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.
|
|
| 6. |
- Rahmanian, Ali, 1989-, et al.
(författare)
-
CVF : Cross-Video Filtration on the edge
- 2024
-
Ingår i: MMSys '24. - : Association for Computing Machinery (ACM). - 9798400704123 ; , s. 231-242
-
Konferensbidrag (refereegranskat)abstract
- Many edge applications rely on expensive Deep-Neural-Network (DNN) inference-based video analytics. Typically, a single instance of an inference service analyzes multiple real-time camera streams concurrently. In many cases, only a fraction of these streams contain objects-of-interest at a given time. Hence, it is a waste of computational resources to process all frames from all cameras using the DNNs. On-camera filtration of frames has been suggested as a possible solution to improve the system efficiency and reduce resource wastage. However, many cameras do not have on-camera processing or filtering capabilities. In addition, filtration can be enhanced if frames across the different feeds are selected and prioritized for processing based on the system load and the available resource capacity. This paper introduces CVF, a Cross-video Filtration framework designed around video content and resource constraints. The CVF pipeline leverages compressed-domain data from encoded video formats, lightweight binary classification models, and an efficient prioritization algorithm. This enables the effective filtering of cross-camera frames from multiple sources, processing only a fraction of frames using resource-intensive DNN models. Our experiments show that CVF is capable of reducing the overall response time of video analytics pipelines by up to 50% compared to state-of-the-art solutions while increasing the throughput by up to 120%.
|
|
| 7. |
- Rahmanian, Ali, 1989-
(författare)
-
Edge orchestration for latency-sensitive applications
- 2024
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The emerging edge computing infrastructure provides distributed and heterogeneous resources closer to where data is generated and where end-users are located, thereby significantly reducing latency. With the recent advances in telecommunication systems, software architecture, and machine learning, there is a noticeable increase in applications that require processing times within tight latency constraints, i.e. latency-sensitive applications. For instance, numerous video analytics applications, such as traffic control systems, necessitate real-time processing capabilities. Orchestrating such applications at the edge offers numerous advantages, including lower latency, optimized bandwidth utilization, and enhanced scalability. However, despite its potential, effectively managing such latency-sensitive applications at the edge poses several challenges such as constrained compute resources, which holds back the full promise of edge computing.This thesis proposes approaches to efficiently deploy latency-sensitive applications on the edge infrastructure. It partly addresses general applications with microservice architectures and party addresses the increasingly more important video analytics applications for the edge. To do so, this thesis proposes various application- and system-level solutions aiming to efficiently utilize constrained compute capacity on the edge while meeting prescribed latency constraints. These solutions primarily focus on effective resource management approaches and optimizing incoming workload inputs, considering the constrained compute capacity of edge resources. Additionally, the thesis explores the synergy effects of employing both application- and system-level resource optimization approaches together.The results demonstrate the effectiveness of the proposed solutions in enhancing the utilization of edge resources for latency-sensitive applications while adhering to application constraints. The proposed resource management solutions, alongside application-level optimization techniques, significantly improve resource efficiency while satisfying application requirements. Our results show that our solutions for microservice architectures significantly improve end-to-end latency by up to 800% while minimizing edge resource usage. Additionally, the results indicate that our application- and system-level optimizations for orchestrating edge resources for video analytics applications can increase the overall throughput by up to 60%.
|
|
| 8. |
- Rahmanian, Ali, et al.
(författare)
-
Microsplit : efficient splitting of microservices on edge clouds
- 2022
-
Ingår i: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC). - : IEEE. - 9781665486118 - 9781665486125 ; , s. 252-264
-
Konferensbidrag (refereegranskat)abstract
- Edge cloud systems reduce the latency between users and applications by offloading computations to a set of small-scale computing resources deployed at the edge of the network. However, since edge resources are constrained, they can become saturated and bottlenecked due to increased load, resulting in an exponential increase in response times or failures. In this paper, we argue that an application can be split between the edge and the cloud, allowing for better performance compared to full migration to the cloud, releasing precious resources at the edge. We model an application's internal call-Graph as a Directed-Acyclic-Graph. We use this model to develop MicroSplit, a tool for efficient splitting of microservices between constrained edge resources and large-scale distant backend clouds. MicroSplit analyzes the dependencies between the microservices of an application, and using the Louvain method for community detection---a popular algorithm from Network Science---decides how to split the microservices between the constrained edge and distant data centers. We test MicroSplit with four microservice based applications in various realistic cloud-edge settings. Our results show that Microsplit migrates up to 60% of the microservices of an application with a slight increase in the mean-response time compared to running on the edge, and a latency reduction of up to 800% compared to migrating the entire application to the cloud. Compared to other methods from the State-of-the-Art, MicroSplit reduces the total number of services on the edge by up to five times, with minimal reduction in response times.
|
|
| 9. |
- Rahmanian, Ali, et al.
(författare)
-
RAVAS: interference-aware model selection and resource allocation for live edge video analytics
- 2023
-
Ingår i: 2023 IEEE/ACM Symposium on Edge Computing (SEC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798400701238 ; , s. 27-39, s. 27-39
-
Konferensbidrag (refereegranskat)abstract
- Numerous edge applications that rely on video analytics demand precise, low-latency processing of multiple video streams from cameras. When these cameras are mobile, such as when mounted on a car or a robot, the processing load on the shared edge GPU can vary considerably. Provisioning the edge with GPUs for the worst-case load can be expensive and, for many applications, not feasible. In this paper, we introduce RAVAS, a Real-time Adaptive stream Video Analytics System that enables efficient edge GPU sharing for processing streams from various mobile cameras. RAVAS uses Q-Learning to choose between a set of Deep Neural Network (DNN) models with varying accuracy and processing requirements based on the current GPU utilization and workload. RAVAS employs an innovative resource allocation strategy to mitigate interference during concurrent GPU execution. Compared to state-of-the-art approaches, our results show that RAVAS incurs 57% less compute overhead, achieves 41% improvement in latency, and 43% savings in total GPU usage for a single video stream. Processing multiple concurrent video streams results in up to 99% and 40% reductions in latency and overall GPU usage, respectively, while meeting the accuracy constraints.
|
|
| 10. |
|
|
