‘King's inheritors': understanding the ethnic discourse on the Rajbanshi as an indigenous community

The rise of ethnic struggles in various parts of the world, particularly in the post-colonial period, is an intriguing phenomenon. Having the consciousness of primordial origins, indigenous communities have pursued ethnic mobilizations along different lines in order to achieve the goals of social and economic uplift. This paper focuses on the Rajbanshi, one such community living in northeast India, as they offer an opportunity to study history and ethnic identity formation as the dynamics behind their current situation. From the standpoint of applied anthropology, processes of social change and activism intended to improve the lot of Rajbanshi communities are evaluated. Given their obscure origins, cultural diversity and divided political struggle, the Rajbanshi are far from achieving their goal of pursuing better lives. Strategies of Sanskritization and ‘sons-of-the-soil’ indigeneity have not reaped the desired results in terms of social and economic development. Furthermore, discourses rooted in immigrant-aboriginal binaries and theoretical dichotomies of primordialism–constructivism fail to make sense of this community's experience and are not helpful in guiding them toward meaningful and fruitful political and social change.     

Total edge irregularity strength of accordion graphs

An edge irregular total k-labeling \(\varphi : V\cup E \rightarrow \{ 1,2, \dots , k \}\) of a graph \(G=(V,E)\) is a labeling of vertices and edges of G in such a way that for any different edges xy and \(x'y'\) their weights \(\varphi (x)+ \varphi (xy) + \varphi (y)\) and \(\varphi (x')+ \varphi (x'y') + \varphi (y')\) are distinct. The total edge irregularity strength, tes(G), is defined as the minimum k for which G has an edge irregular total k-labeling. We have determined the exact value of the total edge irregularity strength of accordion graphs.     

Impact of Retailers with Knowledge of Supplier's Inventory on Supply Chain Performance

We model a supply chain consisting of a supplier and multiple retailers facing deterministic demand. We denote some retailers as strategic in the sense that given the supplier inventory information, they will implement the optimal stocking policy by incorporating such information. On the other hand, some retailers are denoted as naïve in the sense that they ignore supply information and resort to a simplistic ordering policy. Naïve retailers learn the optimal policy over time and adjust their orders accordingly. We study the dynamics of this game and investigate the impact of such strategic and naïve retailers on the cost, ordering pattern and stocking policies of all parties. We analyze the supply chain under two scenarios: the centralized supply chain where the objective is to minimize the total supply chain cost, and the decentralized supply chain where each self‐interested player minimizes its own cost in a Stackelberg game setting. We fully characterize the optimal policies under both centralized and decentralized scenarios and show that, surprisingly, the supply chain might be better off by virtue of naïve retailers. The result is driven by the fact that strategic and naïve players’ decisions shift the positioning of inventory in the supply chain with its final impact being determined by the relative costs of different retailer‐types. Our results also offer managerial insights into how access to supply information can improve supply chain performance.     

Comparison between Bayesian approach and frequentist methods for estimating relative risk in randomized controlled trials: a simulation study

Relative risks (RRs) are often considered as preferred measures of association in randomized controlled trials especially when the binary outcome of interest is common. To directly estimate RRs, log-binomial regression has been recommended. Although log-binomial regression is a special case of generalized linear models, it does not respect the natural parameter constraints, and maximum likelihood estimation is often subject to numerical instability that leads to convergence problems. Alternative methods for solving log-binomial regression convergence problems have been proposed. A Bayesian approach also was introduced, but the comparison between this method and frequentist methods has not been fully explored. We compared five frequentist and one Bayesian methods for estimating RRs under a variety of scenario. Based on our simulation study, there is not a method that can perform well based on different statistical properties, but COPY 1000 and modified log-Poisson regression can be considered in practice.     

Lead Time Management through Expediting in a Continuous Review Inventory System

We consider a continuous review inventory system where delivery lead times can be managed by expediting in‐transit orders shipped from the supplier. First, we propose an ordering/expediting policy and derive expressions for evaluating the operating characteristics of such systems. Second, using extensive numerical experiments, we quantify the benefits of such an expediting policy. Third, we investigate a number of managerial issues. Specifically, we analyze the impact of the number of expediting hubs and their locations along the shipment network on the performance of such systems and offer insights into the design of the shipment network. We show (i) a single expediting hub that is optimally located in a shipment network can capture the majority of cost savings achieved by a multi‐hub system, especially when expediting cost is not low or demand variability is not high; (ii) when expediting time is proportional to the time to destination, for small‐enough or large‐enough demand variations, a single expediting hub located in the middle of the shipment network can capture the majority of cost savings of an optimally located hub; and (iii) in general, hubs close to the retailer significantly drive down costs, whereas hubs close to the supplier may not offer much cost savings.     

Objective Bayesian Analysis for Log-logistic Distribution

In this article, Bayesian approach is applied to estimate the parameters of Log-logistic distribution under reference prior and Jeffreys’ prior. The reference prior is derived and it is found that the reference prior is also a second-order matching priors as for the case of any parameter of interest. The Bayesian estimators cannot be obtained in explicit forms. Metropolis within Gibbs sampling algorithm is used to obtain the Bayesian estimators. The Bayesian estimates are compared with the maximum likelihood estimates via simulation study. A real dataset is considered for illustrative purposes.     

Deciphering the modern Janus: societal multiplicity and nation-formation

ABSTRACT

How to theorize the nation’s Janus-like form, its simultaneous modernity and antiquity? This paper provides an original answer to this longstanding question. It argues that nations arise from the interaction of ‘societal multiplicity’ and the expansionist tendency of historical capitalism. The emergence of capitalism super-adds a modern inflection to the inherently relational process of collective identity formation by generating modern sovereignty as an abstract form of rule. Crucially however, just like its emergence, capitalism’s expansion also refracts through societal multiplicity. Non-capitalist societies are therefore pressured into ‘nationalist’ projects of emulative self-preservation in which the nation’s political form (i.e. the sovereign state) is forged before its sociological content (i.e. primitive accumulation). Thus, the original site of this process, France, produced the modular nation-form that unlike Britain’s imperial nationhood could be globalized. The paper therefore shows that IR’s premise of multiplicity may be the key to one of social sciences’ most enduring puzzles.     

An Evaluation of Progress Toward European Monetary Union Using Fuzzy Analysis

European Monetary Union (EMU) is scheduled to commence by the end of the century. Currently, Europeans are engaged in a grand debate over EMU. The controversy is not only over the timetable and stringency of criteria for macroeconomic convergence between member countries, but also over the desirability of the whole EMU enterprise. The European Union (EU) has three choices, to abandon the idea of EMU and opt for independent national monetary policy for each member, to venture the unknown by adopting the EMU regardless of the conditions of its members, or to measure and monitor the fulfillment of EMU preconditions carefully and take the final step when there is a consensus that convergence is reasonably attained. Our paper is a contribution to a systematic quantification and measurement of the fulfillment of the EMU preconditions. We provide a general framework for policy evaluation based on fuzzy logic. We use fuzzy analysis to assess the degree to which each goal is attained, to evaluate the performance of different countries, and to determine the overall progress of the EU in meeting the Maastricht criteria.     

The Impact of Estimation: A New Method for Clustering and Trajectory Estimation in Patient Flow Modeling

The ability to accurately forecast and control inpatient census, and thereby workloads, is a critical and long‐standing problem in hospital management. The majority of current literature focuses on optimal scheduling of inpatients, but largely ignores the process of accurate estimation of the trajectory of patients throughout the treatment and recovery process. The result is that current scheduling models are optimizing based on inaccurate input data. We developed a Clustering and Scheduling Integrated (CSI) approach to capture patient flows through a network of hospital services. CSI functions by clustering patients into groups based on similarity of trajectory using a novel semi‐Markov model (SMM)‐based clustering scheme, as opposed to clustering by patient attributes as in previous literature. Our methodology is validated by simulation and then applied to real patient data from a partner hospital where we demonstrate that it outperforms a suite of well‐established clustering methods. Furthermore, we demonstrate that extant optimization methods achieve significantly better results on key hospital performance measures under CSI, compared with traditional estimation approaches, increasing elective admissions by 97% and utilization by 22% compared to 30% and 8% using traditional estimation techniques. From a theoretical standpoint, the SMM‐clustering is a novel approach applicable to any temporal‐spatial stochastic data that is prevalent in many industries and application areas.     

Analysis of Frechet Distribution Using Reference Priors

This article develops the Bayesian estimators in the context of reference priors for the two-parameter Frechet distribution. The general forms of the second-order matching priors are also derived in case of any parameter of interest and concluded that the reference prior is also a second order matching prior. Since the Bayesian estimators cannot be obtained in closed form, they are obtained using Monte Carlo simulation and Laplace approximation. The Bayesian and maximum likelihood estimates are compared via simulation study. Two real-life data sets are analyzed for illustration and comparison purpose.