Group testing has been widely used in epidemiology and related fields to estimate prevalence of rare diseases. Parametric binary regression models are used in group testing to estimate the covariate adjusted prevalence. Unlike the standard binary regression model (viz., logistic, complementary log–log, etc.), the regression model for group testing data connects the maximum of a group of independent binary responses to its covariate values. Recently, in group testing literature, it has been extensively used for estimating covariate adjusted prevalence of a disease making the tacit assumptions that (i) the regression model is identifiable, and (ii) the standard asymptotic theory is valid for the maximum likelihood estimators of the regression parameters. Verifying these assumptions is found to be non-trivial theoretical issues. In this paper, we give theoretical proofs of these assumptions under a set of simple sufficient conditions, thus, providing a theoretical justification for likelihood based inference on the regression parameters. We also provide an outline of the proof extending the asymptotic theory to the data obtained by Dorfman retesting, where all subjects in a positive group are retested. © 2019 Elsevier B.V.

Working lives face several interruptions, either due to employers’ decisions, new regulations or individuals’ choices, resulting in breaks, slumps, stagnations or even exits. Several types of job seekers thus arise due to such interruptions, of which one category comprises individuals seeking to re-enter the workforce after a period of voluntary exit. Most South Asian countries face a challenge of low labor force participation of women with the exit of women significantly high in middle and senior levels of an organization. The Indian workforce reports a reduction in the number of women at higher organizational positions. While the percentage of women employees hired at the entry level across industries is 21, it shrinks to 15% at higher positions; of these 28% quit their jobs at the executive levels. One of the primary reasons for women leaving the workforce is child-birth. However, recent years have seen such women seeking to return to full-time work after a voluntary exit. This chapter shall focus on the experiences of women who left their jobs post child-birth and returned to the industry after a prolonged absence. We use the term re-entry women to describe such women and seek to understand their experiences of re-entry. Based on interviews with 15 women in various professions, we present the work and family expectations as perceived by women and try to understand how they navigate these during re-entry. © Springer Nature Switzerland AG 2020.

Two-stage regression methods are typically used for handling endogeneity in the simultaneous equations models in economics and other social sciences. However, the problem is challenging in the presence of incomplete response and/or incomplete endogenous covariate(s). We propose a Bayesian approach for the joint modelling of incomplete longitudinal continuous response and an incomplete count endogenous covariate, where the incompleteness is caused by the censorship through a selection mechanism. We define latent continuous variables which are left-censored at zero and develop a Gibbs sampling algorithm for the simultaneous estimation of the model parameters. We consider partially varying coefficients regression models containing covariates with fixed and time-varying effects on the response. Our work is motivated by a sample dataset from the Health and Retirement Study (HRS) for modelling the out-of-pocket medical cost, where the number of hospital admissions is considered as an endogenous covariate. Our analysis addresses some of the previously unanswered questions on the physical and financial health of the older population based on HRS data. Simulation studies are performed for assessing the usefulness of the proposed method compared to its competitors. © 2018 SAGE Publications.

With a population of 1.252 billion, India holds the second position in the world and is one of the fastest developing countries as well. However, like every good thing has to pay a certain cost, the country is now facing an alarming threat of health and environmental problems due to its poor waste management practices. This paper presents a theoretical framework of an integrated approach of solid waste management system in which a waste segregation layout is shown including waste to energy (WtE) application of the refused derived fuel formed in the process. Real-time monitoring of the segregation process and energy extraction process is done using internet of things (IoT) application. Segregation of waste before processing gives an efficient way of waste management and with this framework, one can easily monitor and acquire various information obtained during different stages of segregation and energy extraction process. © 2019 Inderscience Enterprises Ltd.

A cutting-plane approach is developed for the problem of optimally scheduling jobs with arbitrary precedence constraints on unrelated parallel machines to minimize weighted completion time. While the single machine version of this problem has attracted much research efforts, enabling solving problems with up to 100 jobs, not much has been done on the multiple machines case. A novel mixed-integer programming model is presented for the problem with multiple machines. For this model, many classes of valid inequalities that cut off fractional linear programming solutions are developed. This leads to an increase of the linear programming lower bound from 89.3 to 94.6% of the corresponding optimal solution, and a substantial reduction in the computational time of an optimal branch-and-bound algorithm for this problem. This enables us to report optimal solutions for problem instances with up to 25 jobs and 5 machines, which is more than twice the size of problems for which optimal solutions have been reported in the literature thus far. For a special case of the problem—that of minimizing makespan—application of our model helps solve 18 of 27 previously unsolved problem instances to optimality. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Purpose: This study aims to examine a dual-pathway model that recognizes two distinct (formal and informal) but complementary mechanisms of knowledge exchanges – knowledge sharing and knowledge helping. It also investigates how team members use their limited human and psychosocial capital for prosocial knowledge effectiveness. Design/methodology/approach: A survey-based approach was used to examine the hypotheses of the study. A moderated-mediation model was proposed and tested using bootstrap approach. Findings: Knowledge sharing and knowledge helping were found to be the significant links through which human capital (capability) and psychosocial capital (motivation and efficacy) significantly predict prosocial knowledge effectiveness. Post hoc analysis suggests that human capital through knowledge sharing influences team learning, whereas the psychosocial capital through knowledge helping influences team leadership. Originality/value: The present study found two distinct but complementary and yet necessary mechanisms of knowledge exchanges to be linked as the important outlay for the human and psychosocial capital to be effective in the prosocial knowledge behaviours. © 2019, Emerald Publishing Limited.

To combat the critical stresses of rising urbanization, the government acquires land from private owners using the power of eminent domain. This land assembly causes negative externalities such as increasing social tension and injustice that may impose a long-term threat to stability and sustainable development. Therefore, there is a need to make the land acquisition process more transparent and just. Considering the unique Indian context where informality has a strong presence, we propose an approach based on game theory that models the bargain through a three-stage Nash equilibrium game. Four agents – the government, the private developer, the landowner, and the free rider – are considered. We provide conditional solutions for the generalizable Case and proceed to model different stakeholder behavior patterns through two utility functional forms – linear and exponential. In the linear case, we find that the free rider obtains half of the revenue of the project, whereas the landowner gains between one-fourth and one-half of the revenue. Thus, we highlight the undeniably crucial role free riders play in land acquisition negotiations. However, closed form solutions cannot be obtained for the exponential form, due to which we use simulations to demonstrate a solution procedure. We conclude by stating that the proposed model can be useful in formulating future land policies in a sustainable and inclusive manner, with optimal utility derivations for all concerned stakeholders. Our model can also be extended to other spatial contexts where informality features heavily in the land market, especially in the Global South. © 2019 Elsevier Ltd

In this paper, a multi-objective manufacturing cell design problem is studied. A goal programming (GP) embedded real-coded genetic algorithm (GA) is designed for solving this problem. Initially, the GA is used to obtain the individual minimum of each objective. Thereafter, utilising the concepts of GP, an equivalent problem is derived, and the sum of deviation variables associated with the objectives are minimised. The GA is used further to obtain the optimal cell design. A software toolkit is developed based on the proposed technique using C Sharp.net to ensure its use in a larger scale. The effectiveness of the technique is judged based on a set of test problems of different sizes. The proposed technique is found to be better in terms of the performance measure over the existing ones. Copyright © 2019 Inderscience Enterprises Ltd.

Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

This study develops a new research model to understand and predict the key determinants influencing the adoption of mobile payment services in a Middle Eastern country, Oman. The research model was tested using a hybrid structural equation modeling (SEM) and neural network (NN) modeling. The findings suggest valuable insights into the mobile payment service providers in the development of an appropriate and effective strategy to raise the number of new consumers in Oman. © 2019, © 2019 Taylor & Francis.

We develop a constrained extremely zero inflated joint (CEZIJ) modeling framework for simultaneously analyzing player activity, engagement, and dropouts (churns) in app-based mobile freemium games. Our proposed framework addresses the complex interdependencies between a player’s decision to use a freemium product, the extent of her direct and indirect engagement with the product and her decision to permanently drop its usage. CEZIJ extends the existing class of joint models for longitudinal and survival data in several ways. It not only accommodates extremely zero-inflated responses in a joint model setting but also incorporates domain-specific, convex structural constraints on the model parameters. Longitudinal data from app-based mobile games usually exhibit a large set of potential predictors and choosing the relevant set of predictors is highly desirable for various purposes including improved predictability. To achieve this goal, CEZIJ conducts simultaneous, coordinated selection of fixed and random effects in high-dimensional penalized generalized linear mixed models. For analyzing such large-scale datasets, variable selection and estimation are conducted via a distributed computing based split-and-conquer approach that massively increases scalability and provides better predictive performance over competing predictive methods. Our results reveal codependencies between varied player characteristics that promote player activity and engagement. Furthermore, the predicted churn probabilities exhibit idiosyncratic clusters of player profiles over time based on which marketers and game managers can segment the playing population for improved monetization of app-based freemium games. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. © 2019, © 2019 American Statistical Association.

The aim of this study is to identify an intention towards work after retirement. This article examines the multilevel sequential mediating role of job satisfaction and affective commitment in the relationship between passion for work and intention towards work. This study has used 310 respondents retired from government jobs and data are collected through a well-administered questionnaire. Smart partial least square-structural equation modelling (SmartPLS-SEM) was used to analyse data. Path analysis of the structural model revealed that passion for work has a significant relationship with job satisfaction, affective commitment and intention towards work. The findings of the analysis show that the multilevel mediator model is significant (? = 0.08, p < 0.05) at the 5 per cent level of significance. This study supports the satisfaction-commitment model and creates intention towards work after retirement. This research is unique in the sense that it relates passion for work and satisfaction-commitment model with the work intentions of the retired persons. © 2019 Management Development Institute.

Purpose: Research in entrepreneurial cognition has called for a better understanding of interactions between contextual variables and cognitive processes. Based on previous work done on organizational learning and social networks, the purpose of this paper is to propose a formal model in which information acquisition, distribution and interpretation are tested as a function of cognition-based trust, perceived expertise and tie strength between organizational members in two different corporate entrepreneurship (CE) types. Design/methodology/approach: The authors conduct a quantitative analysis based on network data in two companies located in India. Special procedures known as quadratic assignment procedure and multiple regression quadratic assignment procedure were used to run the correlations and multiple regressions, respectively. The authors complement this analysis with interviews and qualitative information to build a rich description in each of these cases. Findings: The results indicate moderate support for the model. The evidence suggests that between both types of CE types, domain redefinition requires higher levels of tie strength, trust and perceived expertise. Sustained regeneration shows moderate significant results in tie strength, and cognition-based trust. Originality/value: The authors combined insights on social network and organizational cognitive processes to analyze interactions between context and cognition. The authors were also able to compare two different companies. The authors found consistent results regarding tie strength, but the authors also found differences between both companies, which suggest that different CE types tend to require different dynamics between context and cognitive processes. © 2019, Emerald Publishing Limited.

Returns of risky assets and their time-dependent volatility are often jointly modelled by stochastic volatility models (SVMs). Over the last few decades, several SVMs have been proposed to adequately capture the features of the relationship between the return and its volatility. The earliest SVM considers a hierarchical model, with current return as a function of the current latent volatility, which is further modelled as an auto-regressive process. In an attempt to make SVM more appropriate for complex realistic market behaviour, leverage parameter was introduced, which however led to violation of martingale difference property of the risky part of return (a necessary mean-zero condition that prevents arbitrage opportunities under mild regularity conditions). Subsequently, alternative SVMs had been developed and are currently in use. In this article, we propose mean-corrections for several generalizations of SVM with leverage that capture the complex market behaviour as well as satisfy martingale difference property of returns. We also establish a few theoretical results to characterize the key desirable features of these models, and present comparison with other popular competitors. Furthermore, three real-life examples (CITI bank stock price, euro–USD rate and S&P 500 index returns) have been used to demonstrate the performance of this new class of SVMs. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

We present a new disaggregated formulation of the Capacitated Concentrator Location Problem (CCLP) using the notion of cardinality of terminals assigned to a concentrator. This formulation consists of O(mnn) variables and constraints, where m denotes the number of concentrators and n the number of terminals, respectively. We prove that this extended formulation is stronger than the traditional one. We also present two classes of inequalities exploiting the cardinality effect of the extended formulation. The first class is a generalization of the well-known Cover and (1, k)-Configuration inequalities, which collectively are stronger than the original Cover and (1, k)-Configuration inequalities. The second class, called the 2-Facility Cardinality Matching Inequality, holds for the uncapacitated version of the Concentrator Location Problem and can be lifted to become a strong inequality for CCLP. We solve the LP relaxation of the extended formulation and use separation heuristics to identify and sequentially add the previous valid inequalities to improve the lower bound. This approach is embedded in a branch-and-bound and results in a branch-and-cut approach. We test our solution approach on a large set of benchmark problems. The experimentation shows that we can identify the optimal solution at the root node in most of the problem instances with up to 50 concentrators and 50 terminals. For larger sized test problems with up to 100 concentrators and 1000 terminals, the branch-and-cut procedure using the disaggregated formulation outperforms the branch-and-cut procedure applied to the traditional formulation both in terms of CPU and the required number of branch-and-bound nodes. © 2019 Elsevier B.V.

We propose a new order of financing investments based on the considerations of control and financial constraints in a market with the presence of business groups. We base our analysis on a sample of acquisitions, one of the largest forms of investments, made by India's publicly listed firms from 1997 through 2016. We test the relative propensity of group-affiliated firms, as well as that of standalone (non-affiliated) firms, to finance their investments with stock on the one hand, and either cash or debt on the other. We find that group-affiliated bidders have the greatest propensity to finance their investments with stock when taking over firms affiliated with the same business group (within-group acquisitions), followed by standalone firms making acquisitions (standalone acquisitions). Finally, group-affiliated bidders acquiring either standalone firms or firms not affiliated with their group (outside-group acquisitions) have the lowest propensity to finance their investments with stock. The evidence of higher stock-financing of within-group acquisitions is robust to alternative explanations of tunneling and propping up in business groups. © 2019 Elsevier B.V.

Planning and scheduling transportation operations is considered computationally hard and time demanding. We consider a complex network of interconnected links across a set of locations and a set of vehicle movements on these links to fulfil some traffic demands. In such a set-up, basically two types of computational solutions are sought: one is a decision making, if a link (and/or a vehicle) is to be included in a route and second, is computation of a specific time duration during which a link and a vehicle are allocated to fulfil a traffic demand. Such planning efforts are undertaken at a strategic level. At the operational level, the planned schedules are executed. For any unforeseen reasons, if a planned schedule gets disrupted, while en-route, the entire planning efforts turn futile. The situation turns an emergency, as the available time to restore the original schedule is very small and it also knocks down other transport schedules that follow. Using an available approach to reschedule disrupted transportation services, we developed a learning architecture that is capable of learning and creating a knowledge base from the solved instances of disruption resolution. When a new disruption happens, the learner deduces the applicability of any solved instances in the knowledge base to quickly resolve the disruption. If not, the disruption is resolved completely and the resolution is added to the existing knowledge base. The combined abilities of the agents based architecture include monitoring, identification, deduction, decision making, and resolution execution, by learning, storing solution seeds and facilitating quick resolutions. The architecture and the application were developed using JADE Giovanni et al. (2007) toolkit on Ubuntu OS; we verified the deployability of the system on a Windows machine. The agent architecture and empirical results are presented in this paper. Our proposed architecture is faster than traditional methods and novel as an evidence of agent capabilities in solving real-life complex problems and safety-critical domains. © 2018 IEEE.

A new method of pricing, known as pay-what-you-want (PWYW) has been successfully used in many contexts in the recent past. Success stories are in restaurants, museums, music albums, software, etc. The method where the buyer selects the price, possibly a zero price, and the seller accepts it, is a bit counter intuitive. Quite surprisingly, the PWYW pricing method has been used successfully, and some businesses have adopted it for selling their goods. We study the economic rationale behind the success of the PWYW pricing method, and analyse different situations using a simple model. Results show that the PWYW pricing method can be successful when there is a large proportion of fair-minded consumers, and a lesser proportion of free-loaders. Also, though a low marginal cost is beneficial for a PWYW method, it is not necessary. Sellers can choose to use PWYW pricing with reference prices that induce, but not bind, consumers to pay an appropriate price. Also, setting a minimum threshold price alleviates the problem of free-loaders, and the PWYW pricing method can be used profitably in the long run. © 2019 Inderscience Enterprises Ltd.

Purpose: In the recent years, the two increasingly popular topics, namely, Industry 4.0 and circular economy (CE) have attracted the attention of many academicians and practitioners. However, the connection between CE and Industry 4.0 has not been much investigated in the literature. Motivated by this gap, the purpose of this paper is to integrate these two streams and attempt to understand the new paradigm of Industry 4.0 for resolving the issues pertaining to CE principles. Design/methodology/approach: The study uses situation, actor, process, learning, action, performance (SAP-LAP) linkages framework to analyze the applications of Industry 4.0 mechanisms in realizing the issues of current CE business models. This is done through the interpretation of the cross-interaction and self-interaction relationships among the different interfaces/elements of SAP-LAP. Findings: The results suggest that top managers are the most essential actors for integrating the use of Industry 4.0 to achieve sustainability, in the light of CE. In addition, advanced technologies such as Internet of Things and cyber-physical systems are the most important Industry 4.0 actions that help in improving the CE performance parameters. Research limitations/implications: This qualitative study is an attempt to analyze and assess the strategic issues pertaining to Industry 4.0 standards in CE. The study identifies learnings (challenges/opportunities) and the corrective actions which are imperative toward achieving CE principles. This study will guide managers and policymakers to understand the importance of implementing Industry 4.0 for accomplishing CE principles. Originality/value: This study integrates two important streams of literature – Industry 4.0 and CE. Thus, this paper offers insights about the importance of Industry 4.0 standards in achieving CE principles. © 2019, Emerald Publishing Limited.

The digital world is engulfed by the imminent threat from digital perpetrators. Cryptography and authentication is necessary for digital communication to make sure that connecting parties are the authorized ones to have access to the exclusive data or information. It can be enforced by using cryptography and authentication algorithms. We present a study of the data threats and the role of symmetric and asymmetric key algorithms which may be used to circumvent such threats. A comparison is presented to identify the pros and cons of these algorithms. It is observed that these algorithms are still in a nascent phase and are improving gradually. © 2018 IEEE.