The Sources of Income of English and Welsh Charities: An Organisation-Level Perspective

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Despite the prominence of voluntary organisations in public life and their high policy profile, there is a need for...     

The Role of Operational Capabilities in Enhancing New Venture Survival: A Longitudinal Study

We investigate relationships between operational capabilities and new venture survival. On the basis of operations management and entrepreneurship literature, we develop a contingency framework of operational capabilities especially appropriate at different life phases of a new venture's evolution. We expect that in the first years of a new venture's life, entrepreneurs should emphasize high inventory turnover to preserve working capital, support customer responsiveness, and aid firm adaptability. As new ventures grow, entrepreneurs should emphasize internal working capital generation via larger gross margins to support production ramp‐up. Later, new venture entrepreneurs should emphasize employee productivity to buttress sustainable volume production. We analyze a 6‐year longitudinal sample of 812 Swedish manufacturing new ventures using a gamma frailty‐based Cox regression. The findings show that specific operational capabilities, while always supporting new venture survival, have exceptional influence in specific new venture life phases. The three hypotheses are confirmed, suggesting that higher inventory turnover, gross margin, and employee productivity further increase new venture survival likelihoods, respectively, in the venture's start‐up, growth, and stability phases. This suggests a phased‐capabilities approach to new venture survival. This study contributes to operations management and entrepreneurship theory and practice, and sets a foundation for future research on operations strategy for new ventures.     

Three methods of constructing pbib designs

Three construction methods of two- or three-associate partially balanced incomplete block (PBIB) designs are presented.     

Unbiased statistical estimation functions for parameters in presence of nuisance parameters

We consider the problem of estimating a vector interesting parameter in the presence of nuisance parameters through vector unbiased statistical estimation functions (USEFs). An extension of the Cramer—Rao inequality relevant to the present problem is obtained. Three possible optimality criteria in the class of regular vector USEFs are those based on (i) the non-negative definiteness of the difference of dispersion matrices (ii) the trace of the dispersion matrix and (iii) the determinant of the dispersion matrix. We refer to these three criteria as M-optimality, T- optimality and D-optimality respectively. The equivalence of these three optimality criteria is established. By restricting the class of regular USEFs considered by Ferreira (1982), we study some interesting properties of the standardized USEFs and establish essential uniqueness of standardized M-optimal USEF in this restricted class. Finally some illustrative examples are included.     

A note on a strong renewal theorem

Let {S_n, n ≥ 1} be a sequence of partial sums of independent and identically distributed non-negative random variables with a common distribution function F. Let F belong to the domain of attraction of a stable law with exponent α, 0 < α < 1. Suppose H(t) = ? N(t), t ? 0, where N(t) = max(n : S_n ≥ t). Under some additional assumptions on F, the difference between H(t) and its asymptotic value as t → ∞ is estimated.     

Atmospheric Dispersion of Inflammable Substances for Estimating Vulnerable Zones in Hydrocarbon Industry

The present study utilizes an operational model as well as simple empirical relationships for estimating hazard zones due to fire, explosion, and toxic vapor cloud dispersion. The empirical relationships are based on giving appropriate weightage to each of the parameters on which the hazard in question (viz, fire, explosion, toxic vapour dispersion) is dependent. Results from these two approaches [i.e., an operational model FLAMCALC of U.K. Health and Safety Executive (HSE) and an empirical model named FIREX] have been compared with the data obtained from the Mexico City disaster in 1984. In general, results from the empirical approach and FLAMCALC are comparable to the observed effects.     

Estimation of Vulnerable Zones Due to Accidental Release of Toxic Materials Resulting in Dense Gas Clouds

Heavy gas dispersion models have been developed at IIT (hereinafter referred as IIT heavy gas models I and II) with a view to estimate vulnerable zones due to accidental (both instantaneous and continuous, respectively) release of dense toxic material in the atmosphere. The results obtained from IIT heavy gas models have been compared with those obtained from the DEGADIS model [Dense Gas Dispersion Model, developed by Havens and Spicer (1985) for the U.S. Coast Guard] as well as with the observed data collected during the Burro Series, Maplin Sands, and Thorney Island field trials. Both of these models include relevant features of dense gas dispersion, viz., gravity slumping, air entrainment, cloud heating, and transition to the passive phase, etc. The DEGADIS model has been considered for comparing the performance of IIT heavy gas models in this study because it incorporates most of the physical processes of dense gas dispersion in an elaborate manner, and has also been satisfactorily tested against field observations. The predictions from IIT heavy gas models indicate a fairly similar trend to the observed values from Thorney Island, Burro Series, and Maplin experiments with a tendency toward overprediction. There is a good agreement between the prediction of IIT Heavy Gas models I and II with those from DEGADIS, except for the simulations of IIT heavy gas model-I pertaining to very large release quantities under highly stable atmospheric conditions. In summary, the performance of IIT heavy gas models have been found to be reasonably good both with respect to the limited field data available and various simulations (selected on the basis of relevant storages in the industries and prevalent meteorological conditions performed with DEGADIS). However, there is a scope of improvement in the IIT heavy gas models (viz., better formulation for entrainment, modification of coefficients, transition criteria, etc.). Further, isotons (nomograms) have been prepared by using IIT heavy gas models for chlorine, which provide safe distance for various storage amounts for 24 meteorological scenarios prevalent in the entire year. These nomograms are prepared such that a nonspecialist can use them easily for control and management in case of an emergency requiring the evacuation of people in the affected region. These results can also be useful for siting and limiting the storage quantities.     

论闲暇教育对成人教育的意义

我国成人教育应该重视闲暇教育在提高成人素质和人的全面发展中的重要作用,积极开发闲暇教育的课程内容。这样既能满足人的精神需求,同时又潜在的提高了人的素质。     

Capacity Planning and Allocation for Web‐Based Applications

Motivated by the technology division of a financial services firm, we study the problem of capacity planning and allocation for Web‐based applications. The steady growth in Web traffic has affected the quality of service (QoS) as measured by response time (RT), for numerous e‐businesses. In addition, the lack of understanding of system interactions and availability of proper planning tools has impeded effective capacity management. Managers typically make decisions to add server capacity on an ad hoc basis when systems reach critical response levels. Very often this turns out to be too late and results in extremely long response times and the system crashes. We present an analytical model to understand system interactions with the goal of making better server capacity decisions based on the results. The model studies the relationships and important interactions between the various components of a Web‐based application using a continuous time Markov chain embedded in a queuing network as the basic framework. We use several structured aggregation schemes to appropriately represent a complex system, and demonstrate how the model can be used to quickly predict system performance, which facilitates effective capacity allocation decision making. Using simulation as a benchmark, we show that our model produces results within 5% accuracy at a fraction of the time of simulation, even at high traffic intensities. This knowledge helps managers quickly analyze the performance of the system and better plan server capacity to maintain desirable levels of QoS. We also demonstrate how to utilize a combination of dedicated and shared resources to achieve QoS using fewer servers.     

Estimation of extinction probability of a multitype markov branching process

Maximum likelihood estimation of the probability of ultimate extinction of a multitype Markov branching process is studied when the process is observed completely over a fixed time interval [0,t]. The asymptotic properties of the estimator are discussed.