Supporting Asian Deaf Young People and their Families: the role of professionals and services

This paper discusses how Asian deaf young people and their families engage with welfare provision. Our findings, based on group and individual interviews with young deaf people and individual interviews with their parents, explore the assumptions underlying current provision and how they influence the options available to young people and their families. The paper suggests that the welfare state exerts a form of social control where professional help, although well intended, may disempowers Asian deaf people by privileging 'oralism' over sign language, and western norms over other cultural values. On the other hand, positive constructions of deafness privilege Deaf identity while failing to accommodate ethnic or religious diversity, resulting in Asian deaf young people and their families having an ambivalent relationship with the Deaf community. We argue that services need to recognise and address the reasons for this ambivalence if they are to adequately engage Asian deaf people and their families.     

Impacts of institutional pressures and the strength of market supporting institutions in the host country on the ownership strategy of multinational enterprises: theoretical discussion and propositions

The ownership strategy of multinational enterprises (MNEs) i.e. the choice between wholly owned subsidiary (WOS) or international joint venture (IJV) is an extensively studied topic in international business studies. Many past studies concentrated on transactional, economic, location attraction, resource seeking and learning rationales for the ownership strategy of MNEs. However, it is important to note that MNEs enter a new institutional environment also when they enter new international markets. Therefore, the ownership strategy of MNEs is also influenced by the external as well as internal institutions. This paper attempts to address the impacts of different institutional pressures on ownership strategy of MNEs using the arguments from institutional theory. The paper also includes discussion about the strength of market supporting institutions in the host country and its impacts on the ownership strategy of MNEs. On the basis of the theoretical discussion, several propositions are developed in this paper. These propositions relate the ownership strategy of MNEs with the impacts of internal and external institutional pressures on it. It is also proposed to consider the impact of strength of market supporting institutions in the host country on the ownership strategy specifically. When the host country has well developed institutions supporting market conforming values, they can moderate the impacts of coercive and normative pressures from external institutions on the MNEs.     

Enterprise systems and performance of future city logistics

Abstract

Future cities are driven by the developments in Information and Communication Technology to support the triple bottom line requirements of sustainability. Logistics will play a critical role in future cities due to the increasingly micro-dynamic nature of socio-economics of these cities and globalised production and consumption patterns. Enterprise systems (ES), the founding applications to automate and integrate different business processes, will be the key enablers for providing the necessary support required by the future city logistics. However, the implementation of ES and realisation of proposed benefits within the estimated time frame is challenging due to the huge resource requirements in terms of manpower, budget and time. This study takes the antecedent (critical success factors) approach for the implementation process to establish a success predictive model for the realisation of ES benefits. The partial least square regression has been used to test the model. Moreover, this study explores the impact of ES benefits on the logistics performance indicators to explore the synergies between them. This is fundamentally important because future city logistics will rely heavily on appropriately designed and implemented (enterprise) information systems.     

Determining the optimal allocation of testing resource for modular software system using dynamic programming

Abstract

Reliability is a major concern in the process of software development because unreliable software can cause failure in the computer system that can be hazardous. A way to enhance the reliability of software is to detect and remove the faults during the testing phase, which begins with module testing wherein modules are tested independently to remove a substantial number of faults within a limited resource. Therefore, the available resource must be allocated among the modules in such a way that the number of faults is removed as much as possible from each of the modules to achieve higher software reliability. In this article, we discuss the problem of optimal resource allocation of the testing resource for a modular software system, which maximizes the number of faults removed subject to the conditions that the amount of testing-effort is fixed, a certain percentage of faults is to be removed and a desired level of reliability is to be achieved. The problem is formulated as a non linear programming problem (NLPP), which is modeled by the inflection S-shaped software reliability growth models (SRGM) based on a non homogeneous Poisson process (NHPP) which incorporates the exponentiated Weibull (EW) testing-effort functions. A solution procedure is then developed using a dynamic programming technique to solve the NLPP. Furthermore, three special cases of optimum resource allocations are also discussed. Finally, numerical examples using three sets of software failure data are presented to illustrate the procedure developed and to validate the performance of the strategies proposed in this article. Experimental results indicate that the proposed strategies may be helpful to software project managers for making the best decisions in allocating the testing resource. In addition, the results are compared with those of Kapur et al. (2004), Huang and Lyu (2005), and Jha et al. (2010) that are available in the literature to deal the similar problems addressed in this article. It reveals that the proposed dynamic programming method for the testing-resource allocation problem yields a gain in efficiency over other methods.     

Statistical inference for discrete middle-censored data

In this paper we consider the discrete middle censoring where lifetime, lower bound and length of censoring interval are variables with geometric distribution. We obtain the likelihood function of observed data and derive the MLE of the unknown parameter using EM algorithm. Also we obtain the Bayes estimator of the unknown parameter under squared error loss (SEL) function and credible interval of unknown parameter using Monte Carlo methods.     

Robust Bayesian methodology with applications in credibility premium derivation and future claim size prediction

Robust Bayesian methodology deals with the problem of explaining uncertainty of the inputs (the prior, the model, and the loss function) and provides a breakthrough way to take into account the input’s variation. If the uncertainty is in terms of the prior knowledge, robust Bayesian analysis provides a way to consider the prior knowledge in terms of a class of priors \(\varGamma \) and derive some optimal rules. In this paper, we motivate utilizing robust Bayes methodology under the asymmetric general entropy loss function in insurance and pursue two main goals, namely (i) computing premiums and (ii) predicting a future claim size. To achieve the goals, we choose some classes of priors and deal with (i) Bayes and posterior regret gamma minimax premium computation, (ii) Bayes and posterior regret gamma minimax prediction of a future claim size under the general entropy loss. We also perform a prequential analysis and compare the performance of posterior regret gamma minimax predictors against the Bayes predictors.     

Optimal designing of an SkSP-R double sampling plan

In this article, we propose a method of planning and determining the optimum parameters of a SkSP-R skip-lot sampling plan by using the attribute double sampling plan as the reference plan. The SkSP-R plan is a new type of skip-lot sampling plan which has a provision for re-inspecting the submitted lots. The optimal plan parameters of the suggested sampling plan are estimated with the target that the average sample number be minimized and satisfying both the specified producer's as well as the consumer's risks simultaneously. In order to obtain the optimum parameters, tables are also built for different combinations of the acceptable quality level and the limiting quality level in conjunctions with different producer's and consumer's risks. An illustrative example is provided for the implementation of the suggested plan. The advantages of the suggested plan over the existing conventional sampling plans and other existing skip-lot sampling plans are also described.     

Fitting a multiple regression function

Consider the p-dimensional unit cube [0,1]^p, p≥1. Partition [0, 1]^p into n regions, R_1,n,…,R_n,n such that the volume Δ(R_j,n) is of order n^?1,j=1,…,n. Select and fix a point in each of these regions so that we have x⁽ⁿ⁾₁,…,x⁽ⁿ⁾_n. Suppose that associated with the j-th predictor vector x⁽ⁿ⁾_j there is an observable variable Y⁽ⁿ⁾_j, j=1,…,n, satisfying the multiple regression model $\text{Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{=g(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{)+e}{}^{\mathrm{(n)}}{}_{\mathrm{j}}$, where g is an unknown function defined on [0, 1]^pand {e⁽ⁿ⁾_j} are independent identically distributed random variables with Ee⁽ⁿ⁾₁=0 and Var e⁽ⁿ⁾₁=σ²<∞. This paper proposes as an estimator of g(x), where k(u) is a known p-dimensional bounded density and {a_n} is a sequence of reals converging to 0 asn→∞. Weak and strong consistency of g_n(x) and rates of convergence are obtained. Asymptoticnormality of the estimator is established. Also proposed is $\text{σ}{}^2{}_{\mathrm{n}}\text{=n}{}^{\mathrm{?1}}\text{Σ}{}^{\mathrm{n}}{}_{\mathrm{j=1}}\text{(Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{?g}{}_{\mathrm{n}}\text{(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{))}{}^2$ as a consistent estimate of σ².