QoS based optimal resource allocation and workload balancing for fog enabled IoT

This paper is aimed at efficiently distributing workload between the Fog Layer and the Cloud Network and then optimizing resource allocation in cloud networks to ensure better utilization and quick response time of the resources available to the end user. We have employed a Dead-line aware scheme to migrate the data between cloud and Fog networks based on data profiling and then used K-Means clustering and Service-request prediction model to allocate the resources efficiently to all requests. To substantiate our model, we have used iFogSim, which is an extension of the CloudSim simulator. The results clearly show that when an optimized network is used the Quality of Service parameters exhibit better efficiency and output

3D multi-agent models for protein release from PLGA spherical particles with complex inner morphologies

In order to better understand and predict the release of proteins from bioerodible micro- or nanospheres, it is important to know the influences of different initial factors on the release mechanisms. Often though it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte-Carlo (MC) and Cellular Automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins. Results have confirmed that this modelling approach has increased the resolution over the complexity involved, opening promising perspectives for future developments, especially complementing in vitro experimentation

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

A journey into Almere’s diverse diet

There is a strong relation between food and identity. Especially when people move to another country, traditional food (or simply food from their country of origin), symbolizes a link with culture, communityand ethnic identity. As people move around the globe they introduce new foods in the places they land. Almere is becoming one of the largest majority minority cities of the Netherlands. Walking around thecity, the diversity of food ingredients and eating cultures as shown in shops and restaurants is immediately clear. The aim of this project was to get an insight into the diets of the residents of Almere so as to learn about eating patterns in a multicultural city and how multiculturality affects the diets of both newcomers and people who have been living here for generations

COVID-19 highlights the model dilemma in biomedical research

Scientists worldwide struggle to identify suitable animal models to study SARS-CoV-2 infections. Interspecies-related differences, such as host specificity, divergent immune responses, or the unavailability of species-specific reagents hamper the research. Human-based models, such as micro-engineered multi-organs-on-chip, may hold the solution

Bivariate Extension of the Quadrature Method of Moments for Batch Crystallization Models

This Article presents a bivariate extension of the quadrature method of moments for solving two-dimensional batch crystallization models involving crystals nucleation, size-dependent growths, aggregation, and dissolution of small nuclei below certain critical size in a dissolution unit. In this technique, orthogonal polynomials of lower order moments are used to find the quadrature abscissas (points) and weights. Several benchmark problems with different combinations of processes are considered in this Article. The accuracy and efficiency of the proposed method are validated against the analytical solutions and the high-resolution finite volume scheme. Excellent agreements were observed in all test problems. It was found that the current method is very efficient and accurate as compared to the high-resolution finite volume scheme. copyright 2010 American Chemical Society [accessed November 18th, 2010

Corporate governance, political connections, and bank performance

This study investigates the impact of corporate governance characteristics and political connections of directors on the profitability of banks in Pakistan. The study uses the data of 26 domestic banks over the latest and large period of 2007–2016. Our findings firstly affirm that bank profitability is negatively affected by the presence of politically connected directors on the board, reporting significantly lower return on assets, return on equity, net interest margin, and profit margin. Secondly, our findings also affirm the negative political influence on the sustainability of the banking industry, reporting significantly lower return on assets, return on equity, net interest margin, and profit margin during the government transition of banks having politically connected directors sitting on their board. Our findings further report an inverted U-shaped relationship between board size and bank profitability, suggesting that a board size beyond 8–9 members decreases the profitability. The study further finds a positive impact of board composition, board independence, and director compensation on bank profitability, while also finding a negative impact of frequent board meetings, presence of foreign directors, and audit committee independence

A Performance Study on the Signal-On-Fail Approach to Imposing Total Order in the Streets of Byzantium

Any asynchronous total-order protocol must somehow circumvent the well-known FLP impossibility result. This paper exposes the performance gains obtained when this impossibility is dealt with through the use of abstract processes built to have some special failure semantics. Specifically, we build processes with signal-on-fail semantics by (i) having a subset of Byzantine-prone processes paired to check each other’s computational outputs, and (ii) assuming that paired processes do not fail simultaneously. By dynamically invoking the construction of signal-on-fail processes, coordinator-based total-order protocols which allow less than one-third of processes to fail in a Byzantine manner are developed. Using a LAN-based implementation, failure-free order latencies and fail-over latencies are measured; the former are shown to be smaller compared to the protocol of Castro and Liskov which is generally regarded to perform exceedingly well in the best-case scenarios

A multi-tree genetic programming representation for melanoma detection using local and global features

© Springer Nature Switzerland AG 2018. Melanoma is the deadliest type of skin cancer that accounts for nearly 75% of deaths associated with it. However, survival rate is high, if diagnosed at an early stage. This study develops a novel classification approach to melanoma detection using a multi-tree genetic programming (GP) method. Existing approaches have employed various feature extraction methods to extract features from skin cancer images, where these different types of features are used individually for skin cancer image classification. However they remain unable to use all these features together in a meaningful way to achieve performance gains. In this work, Local Binary Pattern is used to extract local information from gray and color images. Moreover, to capture the global information, color variation among the lesion and skin regions, and geometrical border shape features are extracted. Genetic operators such as crossover and mutation are designed accordingly to fit the objectives of our proposed method. The performance of the proposed method is assessed using two skin image datasets and compared with six commonly used classification algorithms as well as the single tree GP method. The results show that the proposed method significantly outperformed all these classification methods. Being interpretable, this method may help dermatologist identify prominent skin image features, specific to a type of skin cancer