Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets

This study analyzed the time–frequency relationship between oil price and exchange rate for Pakistan by using measures of continuous wavelet such as wavelet power, cross-wavelet power, and cross-wavelet coherency (WTC). The results of cross-wavelet analysis indicated that covariance between oil price and exchange rate is unable to give clear-cut results, but both variables have been in phase and out phase (i.e. they are anti-cyclical and cyclical in nature) in some or other durations. However, results of squared wavelet coherence disclose that both variables are out of phase and real exchange rate was leading during the entire period studied, corresponding to the 10–15 months’ scale. These results are the unique contribution of the present study, which would have not been drawn if one would have utilized any other time series or frequency domain-based approach. This finding provides evidence of anti-cyclical relationship between oil price and real effective exchange rate; however, in most of the period studied, real exchange rate was leading and passing anti-cycle effects on oil price shocks which is the major contribution of the study.     

Habitat selection by an avian top predator in the tropical megacity of Delhi: human activities and socio-religious practices as prey-facilitating tools

Research in urban ecology is growing rapidly in response to the exponential growth of the urban environment. However, few studies have focused on tropical megacities, and on the interplay between predators’ habitat selection and human socio-economic aspects, which may mediate their resilience and coexistence with humans. We examined mechanisms of breeding habitat selection by a synanthropic raptor, the Black Kite Milvus migrans, in Delhi (India) where kites mainly subsist on: (1) human refuse and its associated prey-fauna, and (2) ritualised feeding of kites, particularly practised by Muslims. We used mixed effects models to test the effect of urban habitat configuration and human practices on habitat selection, site occupancy and breeding success. Kite habitat decisions, territory occupancy and breeding success were tightly enmeshed with human activities: kites preferred areas with high human density, poor waste management and a road configuration that facilitated better access to resources provided by humans, in particular to Muslim colonies that provided ritual subsidies. Furthermore, kites bred at ‘clean’ sites with less human refuse only when close to Muslim colonies, suggesting that the proximity to ritual-feeding sites modulated the suitability of other habitats. Rather than a nuisance to avoid, as previously portrayed, humans were a keenly-targeted foraging resource, which tied a predator’s distribution to human activities, politics, history, socio-economics and urban planning at multiple spatio-temporal scales. Many synurbic species may exploit humans in more subtle and direct ways than was previously assumed, but uncovering them will require greater integration of human socio-cultural estimates in urban ecological research.

     

On the assessment of various factors effecting the improvement in CD4 count of aids patients undergoing antiretroviral therapy using generalized Poisson regression

An important marker for identifying the progression of human immunodeficiency virus (HIV) infection in an individual is the CD4 cell count. Antiretroviral therapy (ART) is a treatment for HIV/AIDS (AIDS, acquired immune-deficiency syndrome) which prolongs and improves the lives of patients by improving the CD4 cell count and strengthen the immune system. This strengthening of the immune system in terms of CD4 count, not only depends on various biological factors, but also other behavioral factors. Previous studies have shown the effect of CD4 count on the mortality, but nobody has attempted to study the factors which are likely to influence the improvement in CD4 count of patients diagnosed of AIDS and undergoing ART. In this paper, we use Poisson regression model (GPR) for exploring the effect of various socio-demographic covariates such as age, gender, geographical location, and drug usage on the improvement in the CD4 count of AIDS patients. However, if the CD4 count data suffers from under or overdispersion, we use GPR model and compare it with negative binomial distribution. Finally, the model is applied for the analysis of data on patients undergoing the ART in the Ram Manohar Lohia Hospital, Delhi, India. The data exhibited overdispersion and hence, GPR model provided the best fit.     

Like Mother(-in-Law) Like Daughter? Influence of the Older Generation’s Fertility Behaviours on Women’s Desired Family Size in Bihar,India

This paper investigates the associations between preferred family size of women in rural Bihar, India and the fertility behaviours of their mother and mother-in-law. Scheduled interviews of 440 pairs of married women aged 16–34 years and their mothers-in-law were conducted in 2011. Preferred family size is first measured by Coombs scale, allowing us to capture latent desired number of children and then categorized into three categories (low, medium and high). Women’s preferred family size is estimated using ordered logistic regression. We find that the family size preferences are not associated with mother’s fertility but with mother’s education. Mother-in-law’s desired number of grandchildren is positively associated with women’s preferred family size. However, when the woman has higher education than her mother-in-law, her preferred family size gets smaller, suggesting that education provides women with greater autonomy in their decision-making on childbearing.     

Identifying design criteria for urban system ‘last-mile’ solutions – a multi-stakeholder perspective

Abstract

This study presents a novel approach to design and evaluate ‘last-mile’ solutions – encompassing the social and economic perspectives of key stakeholders. While urban system initiatives have been implemented in practice, theoretical gaps remain at the operational design level. A theoretical framework is developed, based on design criteria identified from a critical synthesis of supply chain and operations management literature, and ‘operationalised’ using an in-depth case study demonstrating implementation of a Consumer Choice Portal-Package Consolidation Centre solution, within a densely populated urban geography. Findings suggest that there is a need to re-define the role of institutional actors beyond that of the traditional governance task, to one of being able to facilitate performance outcomes. Similarly, industrial efficiency dimensions need to be reorientated to include consumer participation, social considerations and multi-stakeholder service outcomes. Finally, implications for operations theory and practising managers in city logistics are highlighted, with suggested directions for future research.     

Acquisition of Telecommunications Bandwidth Under Economies of Scale in Size and Duration of Contracts*

Significant advances in information technology have brought about increased demand for bandwidth. Buyers of bandwidth often encounter bandwidth prices that are decreasing over time. Additionally, bandwidth prices at any point in time are decreasing in total bandwidth purchased and length of contracts. Therefore, buyers face complex decisions in terms of the number of contracts to buy, their bandwidth, and their lengths. In this article, we formulate models for the acquisition of bandwidth from a buyer's perspective. We begin with a model that allows varying contract durations under deterministic demand and without allowing shortages or overlapping contracts. We then formulate a simpler model, which restricts contract lengths over the planning horizon to be equal. We also solve the problem under probabilistic demand and allowing for shortages, which are satisfied by buying additional bandwidth at a premium. We perform numerical sensitivity analysis to compare the results of the models and illustrate the results with numerical examples. The numerical analyses illustrate that using relatively simple equal‐length contracts produces approximately the same results as the more complicated unequal‐length contract strategy.     

Uncertainty Analysis Based on Probability Bounds (P-Box) Approach in Probabilistic Safety Assessment

A wide range of uncertainties will be introduced inevitably during the process of performing a safety assessment of engineering systems. The impact of all these uncertainties must be addressed if the analysis is to serve as a tool in the decision-making process. Uncertainties present in the components (input parameters of model or basic events) of model output are propagated to quantify its impact in the final results. There are several methods available in the literature, namely, method of moments, discrete probability analysis, Monte Carlo simulation, fuzzy arithmetic, and Dempster-Shafer theory. All the methods are different in terms of characterizing at the component level and also in propagating to the system level. All these methods have different desirable and undesirable features, making them more or less useful in different situations. In the probabilistic framework, which is most widely used, probability distribution is used to characterize uncertainty. However, in situations in which one cannot specify (1) parameter values for input distributions, (2) precise probability distributions (shape), and (3) dependencies between input parameters, these methods have limitations and are found to be not effective. In order to address some of these limitations, the article presents uncertainty analysis in the context of level-1 probabilistic safety assessment (PSA) based on a probability bounds (PB) approach. PB analysis combines probability theory and interval arithmetic to produce probability boxes (p-boxes), structures that allow the comprehensive propagation through calculation in a rigorous way. A practical case study is also carried out with the developed code based on the PB approach and compared with the two-phase Monte Carlo simulation results.     

Production knowledge and its impact on the mechanisms of governance

A framework is developed outlining how production knowledge and capabilities influence firm boundaries by impacting the transaction costs of markets and hierarchies. A central implication of the framework is that at lower levels of these capabilities the transaction costs of markets decline at a faster rate than the costs of hierarchy, while at higher levels of these capabilities the transaction costs of hierarchy decline at a faster rate than the costs of markets. The discriminating role of production capabilities arises because markets and hierarchies utilize different types of control (prices and output control versus authority and behavior control), and hence require different levels of knowledge to be efficient. The analysis suggests firms often maintain some production knowledge when contracting for various inputs since it not only reduces transactional hazards in markets, but also because in comparative institutional terms, initial gains in knowledge make markets more efficient than internal organization. In addition, the analysis suggests that there would be a U shaped relationship between the propensity to integrate vertically and the extent of production capabilities as opposed to a monotonically increasing relationship. I find support for the U shaped relationship in a cross sectional sample of 1553 manufacturing firms.     

Efficient formation of storage classes for warehouse storage location assignment: A simulated annealing approach

Class-based storage policy distributes products among a number of classes and for each class it reserves a region within the storage area. The procedures reported in the literature for formation of storage classes primarily consider order-picking cost ignoring storage-space cost. Moreover, in these procedures items are ordered on the basis of their cube per order index (COI), and items are then partitioned into classes maintaining this ordering. This excludes many possible product combinations in forming classes which may result in inferior solutions. In this paper, a simulated annealing algorithm (SAA) is developed to solve an integer programming model for class formation and storage assignment that considers all possible product combinations, storage-space cost and order-picking cost. Computational experience on randomly generated data sets and an industrial case shows that SAA gives superior results than the benchmark dynamic programming algorithm for class formation with COI ordering restriction.