scholarly journals Altitudinal Variation in Alkaloid Composition of Hyoscyamus niger: A study with reference to Kashmir region of Himalayas

2018 ◽  
Vol 4 (3) ◽  
pp. 13
Author(s):  
Sheema Zaffar ◽  
Ieba Khan ◽  
Asma Zaffar ◽  
Azra N Kamili
Keyword(s):  
Resource Allocation ◽  
Hyoscyamus Niger ◽  
Kashmir Valley ◽  
Altitudinal Variation ◽  
Alkaloid Composition ◽  
Commercial Cultivation ◽  
Distributional Range ◽  
The Impact ◽  
Hyoscyamine And Scopolamine

The present study has been undertaken to understand the impact of altitude on the synthesis of alkaloids in Hyoscyamus niger found in Kashmir valley of J&K state in India. Given its wide distributional range, the study aimed to quantify the alkaloids (hyoscyamine and scopolamine) in Hyoscyamus niger grown at various altitudes in Kashmir valley. The sampling of populations at varying altitudes allowed assessing the intraspecieac variation and ecological trends of accumulation of alkaloids in the plant. Furthermore the resource allocation in the parts of the plant has also been shown. The four different sites were Gulmarg, Pahalgam, Gurez and Qazigund in Kashmir valley. The study was able to identify the elite genotype and the best suited habitat (altitude) for commercial cultivation of the species with higher quantity of alkaloids.

Computers in Medical Care: A Review

1984 ◽  
Vol 23 (02) ◽  
pp. 63-74 ◽  
Author(s):  
Hans W. Gottinger
Keyword(s):  
Resource Allocation ◽  
Medical Care ◽  
Hospital Care ◽  
The Impact

SummaryThis survey provides an overview of major developments on the impact of computers in medical and hospital care over the last 25 years. Though the review emphasizes developments in the U. S. and their multi-faceted impacts upon resource allocation and regulation, a serious attempt is made to track those impacts being universally true in multinational environments.

scholarly journals The impact of predictability on dual-task performance and implications for resource-sharing accounts

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Laura Broeker ◽  
Harald Ewolds ◽  
Rita F. de Oliveira ◽  
Stefan Künzell ◽  
Markus Raab
Keyword(s):  
Resource Allocation ◽  
Task Performance ◽  
Dual Task ◽  
Resource Sharing ◽  
Visual Information ◽  
Allocation Policy ◽  
Capacity Sharing ◽  
Dual Task Performance ◽  
Sound Sequence ◽  
The Impact

AbstractThe aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

scholarly journals Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

2021 ◽  
Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron
Keyword(s):  
Resource Allocation ◽  
Discrete Event Simulation ◽  
Elective Surgery ◽  
Discrete Event ◽  
Specific Patient ◽  
Event Simulation ◽  
The Impact ◽  
Allocation Decisions ◽  
Allocation Strategies ◽  
Resource Allocation Decisions

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

scholarly journals How Does Environmental Regulation Affect Industrial Transformation? A Study Based on the Methodology of Policy Simulation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Wei Liu ◽  
Jian Tong ◽  
Xiaohang Yue
Keyword(s):  
Resource Allocation ◽  
Environmental Regulation ◽  
Spillover Effect ◽  
Policy Simulation ◽  
Industrial Transformation ◽  
Input Structure ◽  
Distortion Effect ◽  
The Difference ◽  
The Impact ◽  
Factor Input

The difference of factor input structure determines different response to environmental regulation. This paper constructs a theoretical model including environmental regulation, factor input structure, and industrial transformation and conducts a policy simulation based on the difference of influencing mechanism of environmental regulation considering industrial heterogeneity. The findings show that the impact of environmental regulation on industrial transformation presents comparison of distortion effect of resource allocation and technology effect. Environmental regulation will promote industrial transformation when technology effect of environmental regulation is stronger than distortion effect of resource allocation. Particularly, command-control environmental regulation has a significant incentive effect and spillover effect of technological innovation on cleaning industries, but these effects do not exist in pollution-intensive industries. Command-control environmental regulation promotes industrial transformation. The result of simulation showed that environmental regulation of market incentives is similar to that of command-control.

scholarly journals SMALLHOLDER FARMERS’ RESOURCE ALLOCATION DECISIONS IN A MAIZE-FARMING SYSTEM UNDER CLIMATE RISKS IN MALAWI

AGROFOR ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruth MAGRETA ◽  
Henderson NG’ONG’OLA ◽  
Julius MANGISONI ◽  
Kennedy MACHILA ◽  
Sika GBEGBELEGBE
Keyword(s):  
Resource Allocation ◽  
Smallholder Farmers ◽  
Farming Systems ◽  
The Body ◽  
Research Station ◽  
Climatic Data ◽  
Climatic Risk ◽  
The Impact ◽  
Allocation Decisions ◽  
Resource Allocation Decisions

Using household data from Lilongwe districts, along with crop phenology, agronomic management and climatic data from Chitedze Research Station, the Target-MOTAD and DSSAT-CSM models examined the resource allocation decisions of smallholder farmers in maize farming systems under climate risk in Malawi. Specific aims were to evaluate the ability of DSSAT to predict and collate DTM and non-DTM yields under climatic risk and to use a bio-economic procedure developed using DSSAT and Target-MOTAD to explore the impact of climatic risk on allocation of resources to DTM and non-DTM production. The paper argues that higher average yields observed from DTM varieties make it the most optimal maize production plan, in maximizing household incomes, food security, and minimizing deviations from the mean while meeting the set target incomes of farmers compared to non-DTM varieties. The multidisciplinary nature of this paper has contributed to the body of research by providing a powerful analytical procedure of modelling farmers’ resource allocation decisions in maize based farming systems in Malawi. This study necessitates the use of a combination of biophysical and economic procedures when evaluating promising lines prior to variety release in order to identify the high yielding variety that will continuously bring sustained profits to the farmers amidst climate change.

scholarly journals Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Yang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of the most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

scholarly journals Resource allocation and task admission control in cloud systems

2021 ◽  
Author(s):  
Haleh Khojasteh
Keyword(s):  
Resource Allocation ◽  
Performance Metrics ◽  
Cloud Model ◽  
Blocking Probability ◽  
Arrival Rate ◽  
Cloud System ◽  
Wide Range ◽  
Priority Scheme ◽  
The Impact ◽  
And Task

The focus of this thesis is solving the problem of resource allocation in cloud datacenter using an Infrastructure-as-a-Service (IaaS) cloud model. We have investigated the behavior of IaaS cloud datacenters through detailed analytical and simulation models that model linear, transitional and saturated operation regimes. We have obtained accurate performance metrics such as task blocking probability, total delay, utilization and energy consumption. Our results show that the offered load does not offer complete characterization of datacenter operation; therefore, in our evaluations, we have considered the impact of task arrival rate and task service time separately. To keep the cloud system in the linear operation regime, we have proposed several dynamic algorithms to control the admission of incoming tasks. In our first solution, task admission is based on task blocking probability and predefined thresholds for task arrival rate. The algorithms in our second solution are based on full rate task acceptance threshold and filtering coefficient. Our results confirm that the proposed task admission mechanisms are capable of maintaining the stability of cloud system under a wide range of input parameter values. Finally, we have developed resource allocation solutions for mobile clouds in which offloading requests from a mobile device can lead to forking of new tasks in on-demand manner. To address this problem, we have proposed two flexible resource allocation mechanisms with different prioritization: one in which forked tasks are given full priority over newly arrived ones, and another in which a threshold is established to control the priority. Our results demonstrate that threshold-based priority scheme presents better system performance than the full priority scheme. Our proposed solution for clouds with mobile users can be also applied in other clouds which their users’ applications fork new tasks.

Bottleneck-Aware Resource Allocation for Service Processes

2021 ◽  
Vol 18 (3) ◽  
pp. 1-21
Author(s):  
Shoulu Hou ◽  
Wei Ni ◽  
Ming Wang ◽  
Xiulei Liu ◽  
Qiang Tong ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Service Rate ◽  
Service Models ◽  
Service Rates ◽  
Simulation Results ◽  
5G Systems ◽  
Generic Service ◽  
The Impact ◽  
Process Simulator ◽  
Allocation Approach

In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.

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