2-[(2-Amino­phen­yl)sulfan­yl]-N-(4-meth­oxy­phen­yl)acetamide

In the title compound, C15H16N2O2S, the dihedral angle between the 4-meth­oxy­aniline and 2-amino­benzene­thiole fragments is 35.60 (9)°. A short intra­molecular N—H⋯S contact leads to an S(5) ring. In the crystal, mol­ecules are consolidated in the form of polymeric chains along [010] as a result of N—H⋯O hydrogen bonds, which generate R 3 2(18) and R 4 3(22) loops. The polymeric chains are interlinked through C—H⋯O inter­action and complete R 2 2(8) ring motifs

Ethyl (3E)-3-[2-(4-bromo­phenyl­sulfon­yl)hydrazin-1-yl­idene]butano­ate

The asymmetric unit of title compound, C12H15BrN2O4S, contains two mol­ecules (A and B), with slightly different conformations: the bromo­phenyl rings and the SO2 planes of the sulfonyl groups are oriented at dihedral angles of 50.2 (2) (mol­ecule A) and 58.24 (7)° (mol­ecule B), and the ethyl acetate groups make dihedral angles of 63.99 (19)° (A) and 65.35 (16)° (B) with their bromo­phenyl groups. In the crystal, both mol­ecules exist as inversion dimers linked by pairs of N—H⋯O hydrogen bonds, which generate R 2 2(14) loops. The dimers are linked by C—H⋯O inter­actions

Time to endoscopy for acute upper gastrointestinal bleeding::Results from a prospective multicentre trainee-led audit

Background: Endoscopy within 24?h of admission (early endoscopy) is a quality standard in acute upper gastrointestinal bleeding (AUGIB). We aimed to audit time to endoscopy outcomes and identify factors affecting delayed endoscopy (>24h of admission).Methods: This prospective multicentre audit enrolled patients admitted with AUGIB who underwent inpatient endoscopy between November and December 2017. Analyses were performed to identify factors associated with delayed endoscopy, and to compare patient outcomes, including length of stay and mortality rates, between early and delayed endoscopy groups.Results: Across 348 patients from 20 centres, the median time to endoscopy was 21.2h (IQR 12.0-35.7), comprising median admission to referral and referral to endoscopy times of 8.1?h (IQR 3.7-18.1) and 6.7?h (IQR 3.0-23.1), respectively. Early endoscopy was achieved in 58.9%, although this varied by centre (range: 31.0% - 87.5%, p=0.002). On multivariable analysis, lower Glasgow-Blatchford score, delayed referral, admissions between 7:00 and 19:00 hours or via the emergency department were independent predictors of delayed endoscopy. Early endoscopy was associated with reduced length of stay (median difference 1 d; p=0.004), but not 30-d mortality (p=0.344).Conclusions: The majority of centres did not meet national standards for time to endoscopy. Strategic initiatives involving acute care services may be necessary to improve this outcome

Urea Finishing Process: Prilling versus Granulation

Solid urea is the largest nitrogen fertilizer product which is produced in two forms of granules and prills. Although the chemical properties of both prills and granules remain similar, their different physical and mechanical properties are distinguishable and make them suitable for different application either as fertilizer or raw materials for chemical industry. The objective of this work is to analyses physical and mechanical properties of urea granules produced in two different plants in Malaysia using fluidized bed process and compare them with the imported urea prills to the country; hence make a process-product relationship for urea finishing processes. Results of size distribution of the samples show that the most of the granules fall in the size range between 2.40 and 3.50 mm, whereas the prills size is around 1.60 mm. Strength measurement using side crushing test also shows that the prills with the average failure load of 3.80 N remain significantly weaker than the granules with failure load of 10-17 N. Strength distribution of the particles also shows that a more uniform strength distribution is observed for the prills than the granules. It is concluded that the urea prilling process is the finishing process which produces the weaker and the more uniform size and strength of the particles than the fluidized-bed granulation process

Which investment (private or public) does contribute to economic growth more? a case study of South Africa

Economic growth is an important driver for the well-being of the citizens of a country. Despite a common view that investment is a key driver of economic growth, there are conflicting views on whether it is Public investment which drives Private Investment, or whether it is the other way around. Both theoretical views as well as empirical studies tend to have divergent views on this matter, and it is therefore important to try to understand which causes which, in order to help the policymakers. Using the standard time-series techniques, this study uses annual data and tests the relationship between Investment and economic growth, and also the direction of any causal link between Public and Private investment. This study contributes to the existing studies on the effects of Public and Private investment, with particular reference to South Africa, which is classified as a developing economy. The contribution of this study to the general body of empirical studies is important because, to date, there is no clear answer with regard to the causal link between public and private investment in developing countries. This paper attempts to provide further clarity on the issue. The findings of this study are that both Public and private Investment play a significant role in enhancing economic growth. As to which of these two plays a greater role, this study tends to indicate that Private Investment plays relatively a greater role in explaining economic growth than Public Investment

Meta-heuristic-based offloading task optimization in mobile edge computing

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks. </jats:p

Meta-heuristic-based offloading task optimization in mobile edge computing

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks