Imperatives of sustainable university excellence: A conceptual framework

This study envisions the architecture of a modern era university with sustainable standards of excellence. Sustainable University Excellence is herein defined as the ability of a university to excel in the core areas of knowledge creation and dissemination via the mobilization of human, social, intellectual, and financial capital to serve on socioeconomic and environmental fronts ethically, efficiently, and effectively to secure a sustainable future. Seven core performance domains of a university are explored: Teaching quality, research culture, technological capability building, accessibility, community engagement, internationalization, and environment, which should be prioritize in order to continuously advance along a sustainable excellence continuum. This study provides a self-assessment methodology that universities can employ to compare their performance relative to the aforementioned domains, thus enabling the identification of performance gaps, the knowledge of which is crucial to the formulation of more targeted strategy. This approach allows decision makers to form a more coherent vision for sustainability within institutional and broader contexts. The approach proposed herein incorporates the three aspects of sustainability that form the so-called Triple Bottom Line (TBL)

Regional Frequency Analysis of Extremes Precipitation Using L-Moments and Partial L-Moments

Extremes precipitation may cause a series of social, environmental, and ecological problems. Estimation of frequency of extreme precipitations and its magnitude is vital for making decisions about hydraulic structures such as dams, spillways, and dikes. In this study, we focus on regional frequency analysis of extreme precipitation based on monthly precipitation records (1999–2012) at 17 stations of Northern areas and Khyber Pakhtunkhwa, Pakistan. We develop regional frequency methods based on L-moment and partial L-moments (L- and PL-moments). The L- and PL-moments are derived for generalized extreme value (GEV), generalized logistic (GLO), generalized normal (GNO), and generalized Pareto (GPA) distributions. The Z-statistics and L- and PL-moments ratio diagrams of GNO, GEV, and GPA distributions were identified to represent the statistical properties of extreme precipitation in Northern areas and Khyber Pakhtunkhwa, Pakistan. We also perform a Monte Carlo simulation study to examine the sampling properties of L- and PL-moments. The results show that PL-moments perform better than L-moments for estimating large return period events

Natural disasters and economic losses: controlling external migration, energy and environmental resources, water demand, and financial development for global prosperity

The objective of the study is to examine the impact of natural disasters on external migration, price level, poverty incidence, health expenditures, energy and environmental resources, water demand, financial development, and economic growth in a panel of selected Asian countries for a period of 2005–2017. The results confirm that natural disasters in the form of storm and flood largely increase migration, price level, and poverty incidence, which negatively influenced country’s economic resources, including enlarge healthcare expenditures, high energy demand, and low economic growth. The study further presented the following results: i) natural resource depletion increases external migration, ii) FDI inflows increase price level, iii) increase healthcare spending and energy demand decreases poverty headcount, iv) poverty incidence and mortality rate negatively influenced healthcare expenditures, v) industrialization increases energy demand, and vi) agriculture value added, fertilizer, and cereal yields required more water supply to produce greater yield. The study emphasized the need to magnify the intensity of natural disasters and create natural disaster mitigation unit to access the human and infrastructure cost and attempt quick recovery for global prosperity

Bayesian network based procedure for regional drought monitoring:The Seasonally Combinative Regional Drought Indicator

Drought is a complex natural hazard. It occurs due to a prolonged period of deficient in rainfall amount in a certain region. Unlike other natural hazards, drought hazard has a recurrent occurrence. Therefore, comprehensive drought monitoring is essential for regional climate control and water management authorities. In this paper, we have proposed a new drought indicator: the Seasonally Combinative Regional Drought Indicator (SCRDI). The SCRDI integrates Bayesian networking theory with Standardized Precipitation Temperature Index (SPTI) at varying gauge stations in various month/seasons. Application of SCRDI is based on five gauging stations of Northern Area of Pakistan. We have found that the proposed indicator accounts the effect of climate variation within a specified territory, accurately characterizes drought by capturing seasonal dependencies in geospatial variation scenario, and reduces the large/complex data for future drought monitoring. In summary, the proposed indicator can be used for comprehensive characterization and assessment of drought at a certain region

Sampler’s strategies vs .

<p>Sampler’s strategies vs .</p

Payoff Matrix of sampler.

<p>Payoff Matrix of sampler.</p

Test and Interview marks Master of Philosophy, Dept. of Statistics, QAU Islamabad, Fall 2014.

<p>Test and Interview marks Master of Philosophy, Dept. of Statistics, QAU Islamabad, Fall 2014.</p