A Queuing model for Dealing with Patients with Severe Disease

This paper suggests a proposed single server queueing model for severe diseases especially in Out-patient Department. The Outpatient Department of a hospital is visited by patients of all types ofdisease. Some of these diseases require immediate medical attention as severe complications may ariseif treatment is delayed. The goal of the study was to develop a queueing model considering patientswith severe disease and to study the improvement in the service time using the model. The singleserver queueing model was modied and analyzed. The eciency of the model was tested by usingoutpatient medical service, arrivals and departure of patients over a period of one year of a localhospital in Guwahati. The result indicated the average outpatient medical service response times forservice improve over the general model

Integrated approach to the assessment of CO2e-mitigation measures for the road passenger transport sector in Bahrain

The transport sector is one of the fastest-growing energy-consuming sectors in the world and it contributes greatly to emissions of carbon dioxide equivalent (CO2e). In Bahrain, CO2e emissions from the transport sector grew by an average of 8% annually between 1994 and 2006. The aim of this research was to develop an integrated approach to assess the measures adopted to reduce CO2e emissions by the transport sector within the context of climate change mitigation. This approach used the multi-criteria analysis methodology of the Analytic Hierarchy Process (AHP) to embed conventional assessment methods and a participatory approach. Three extensions to the original AHP methodology were developed: multi-AHP models, scenario packaging, and the examination of the plausibility of the results. The AHP results showed that certain fuel economy standards achieved the highest scores against five qualitative and quantitative criteria. Using socially and politically acceptable options, an integrated approach to CO2e mitigation could achieve a reduction in emissions of around 22% by 2030 (compared with 2010), at a cost of USD 112 per metric tonne of avoided CO2e emissions. Results from surveys of policymakers, experts, and the general public indicated that the outcomes of scenario packaging were plausible. The contributions of this research are two-fold. First, for the first time in Bahrain, the preferences of the general public have been considered and integrated with both the preferences of policymakers and experts and the results obtained from conventional assessment methods. Second, a structured approach for the integration of different assessment methods, transferable to other contexts, was developed and examined. Furthermore, multi-AHP models were introduced that can reflect the preferences of different concerned groups. Applications of this approach include assessment of the implementation of mitigation measures that could affect a number of concerned groups, decision making in energy-consuming sectors, and development of mitigation policy packages

Formulation of the simple Markovian model using fractional calculus approach and its application to analysis of queue behavior of severe patients

In this paper, we introduce a fractional order of a simple Markovian model where the arrival rate of the patient is Poisson, i.e. independent of the patient size. Fraction is obtained by replacing the first order time derivative in the difference differential equations which govern the probability law of the process with the Mittag-Leffler function. We derive the probability distribution of the number N(t) of patients suffering from severe disease at an arbitrary time t. We also obtain the mean size (number) of the patients suffering from severe disease waiting for service at any given time t, in the form of E ν 0.5,0.5 (t), for different fractional values of server activity status, ν = 1,0.95,0.90 and for arrival rates α = β = 0.5. A numerical example is also evaluated and analysed by using the simple Markovian model with the help of simulation techniques

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Not AvailableKnowledge about the yield gain over the years due to associated changes in the yield component traits is essential for a critical understanding of yield-limiting factors. To estimate genetic gain in grain yield (GY) and component agronomic traits of wheat varieties released between 1900 and 2016 for northwestern plain zone (NWPZ) of India and to identify agronomic and/or genetic basis of the realized gains, two sets of wheat varieties comprising mega varieties and two recently developed varieties were evaluated under timely sown, tilled, and early sown conservation agriculture (CA) conditions for four consecutive years under irrigated conditions. The average annual genetic gain in GY since 1,905 under timely sown irrigated conditions was found to be 0.544% per yr over the average of all varieties and 0.822% per year (24.27 kg per ha per yr) over the first released variety, NP4. The realized mean yield increased from 2,950 kg per ha of the variety NP4 released in 1,905–5,649 kg per ha of HD3086 released in 2014. Regression analysis revealed a linear reduction in height and peduncle length (PL) over the years with a simultaneous and linear increase in biomass at the rate of 43.9 kg per ha per yr or relatively at 0.368% per year mainly because of delayed heading and increased crop duration. Regression analysis showed no linear trend for tiller number and thousand-grain weight (TGW). Though harvest index (HI) was found to linearly increase relatively at the rate of 0.198% per annum, polynomial regression improved the fitness of data with the indication of no increase in HI since 1982. Interestingly, genetic gain evaluation under early sown CA conditions for 4 years showed similar relative gain (RG) [a relative improvement in varieties across breeding periods (BP)] (0.544% per year) but with a higher absolute value (29.28 kg per ha per year). Major mega varieties like Kalyan Sona, HD2009, PBW 343, HD2967, and HD3086, which occupied a comparatively larger area, were found highly plastic to the improvements in the production environment under timely sown conditions.Not Availabl

Autoimmunity gene IRGM suppresses cGAS-STING and RIG-I-MAVS signaling to control interferon response

Activation of the type 1 interferon response is extensively connected to the pathogenesis of autoimmune diseases. Loss of function of Immunity Related GTPase M (IRGM) has also been associated to several autoimmune diseases, but its mechanism of action is unknown. Here, we found that IRGM is a master negative regulator of the interferon response. Several nucleic acid‐sensing pathways leading to interferon‐stimulated gene expression are highly activated in IRGM knockout mice and human cells. Mechanistically, we show that IRGM interacts with nucleic acid sensor proteins, including cGAS and RIG‐I, and mediates their p62‐dependent autophagic degradation to restrain interferon signaling. Further, IRGM deficiency results in defective mitophagy leading to the accumulation of defunct leaky mitochondria that release cytosolic DAMPs and mtROS. Hence, IRGM deficiency increases not only the levels of the sensors, but also those of the stimuli that trigger the activation of the cGAS‐STING and RIG‐I‐MAVS signaling axes, leading to robust induction of IFN responses. Taken together, this study defines the molecular mechanisms by which IRGM maintains interferon homeostasis and protects from autoimmune diseases