Mapping inequalities in exclusive breastfeeding in low- and middle-income countries, 2000–2018

Exclusive breastfeeding (EBF)-giving infants only breast-milk for the first 6 months of life-is a component of optimal breastfeeding practices effective in preventing child morbidity and mortality. EBF practices are known to vary by population and comparable subnational estimates of prevalence and progress across low- and middle-income countries (LMICs) are required for planning policy and interventions. Here we present a geospatial analysis of EBF prevalence estimates from 2000 to 2018 across 94 LMICs mapped to policy-relevant administrative units (for example, districts), quantify subnational inequalities and their changes over time, and estimate probabilities of meeting the World Health Organization's Global Nutrition Target (WHO GNT) of ≥70% EBF prevalence by 2030. While six LMICs are projected to meet the WHO GNT of ≥70% EBF prevalence at a national scale, only three are predicted to meet the target in all their district-level units by 2030.This work was primarily supported by grant no. OPP1132415 from the Bill & Melinda Gates Foundation. Co-authors used by the Bill & Melinda Gates Foundation (E.G.P. and R.R.3) provided feedback on initial maps and drafts of this manuscript. L.G.A. has received support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), Código de Financiamento 001 and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grant nos. 404710/2018-2 and 310797/2019-5). O.O.Adetokunboh acknowledges the National Research Foundation, Department of Science and Innovation and South African Centre for Epidemiological Modelling and Analysis. M.Ausloos, A.Pana and C.H. are partially supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project no. PN-III-P4-ID-PCCF-2016-0084. P.C.B. would like to acknowledge the support of F. Alam and A. Hussain. T.W.B. was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research. K.Deribe is supported by the Wellcome Trust (grant no. 201900/Z/16/Z) as part of his international intermediate fellowship. C.H. and A.Pana are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project no. PN-III-P2-2.1-SOL-2020-2-0351. B.Hwang is partially supported by China Medical University (CMU109-MF-63), Taichung, Taiwan. M.Khan acknowledges Jatiya Kabi Kazi Nazrul Islam University for their support. A.M.K. acknowledges the other collaborators and the corresponding author. Y.K. was supported by the Research Management Centre, Xiamen University Malaysia (grant no. XMUMRF/2020-C6/ITM/0004). K.Krishan is supported by a DST PURSE grant and UGC Centre of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India. M.Kumar would like to acknowledge FIC/NIH K43 TW010716-03. I.L. is a member of the Sistema Nacional de Investigación (SNI), which is supported by the Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT), Panamá. M.L. was supported by China Medical University, Taiwan (CMU109-N-22 and CMU109-MF-118). W.M. is currently a programme analyst in Population and Development at the United Nations Population Fund (UNFPA) Country Office in Peru, which does not necessarily endorses this study. D.E.N. acknowledges Cochrane South Africa, South African Medical Research Council. G.C.P. is supported by an NHMRC research fellowship. P.Rathi acknowledges support from Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India. Ramu Rawat acknowledges the support of the GBD Secretariat for supporting the reviewing and collaboration of this paper. B.R. acknowledges support from Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal. A.Ribeiro was supported by National Funds through FCT, under the programme of ‘Stimulus of Scientific Employment—Individual Support’ within the contract no. info:eu-repo/grantAgreement/FCT/CEEC IND 2018/CEECIND/02386/2018/CP1538/CT0001/PT. S.Sajadi acknowledges colleagues at Global Burden of Diseases and Local Burden of Disease. A.M.S. acknowledges the support from the Egyptian Fulbright Mission Program. F.S. was supported by the Shenzhen Science and Technology Program (grant no. KQTD20190929172835662). A.Sheikh is supported by Health Data Research UK. B.K.S. acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal for all the academic support. B.U. acknowledges support from Manipal Academy of Higher Education, Manipal. C.S.W. is supported by the South African Medical Research Council. Y.Z. was supported by Science and Technology Research Project of Hubei Provincial Department of Education (grant no. Q20201104) and Outstanding Young and Middle-aged Technology Innovation Team Project of Hubei Provincial Department of Education (grant no. T2020003). The funders of the study had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. All maps presented in this study are generated by the authors and no permissions are required to publish them

A nonlinear transformation based hybrid evolutionary method for MINLP solution

In the recent past, some of the population based stochastic direct search methods, like genetic algorithms and differential evolution (DE), have been increasingly applied for solving complex optimization problems in diverse applications. Most of the times, Cthough global optimal solutions are obtained, these stochastic methods have slow convergence and take long computational times. The handling of discrete variables has been quite ad hoc; for instance in DE, the algorithm works assuming them as continuous variables during all the steps but only for the objective function evaluation, a truncation operation is used for forcing the integrality requirements. In this paper, we address both, the convergence issues and improved ways of handling discrete variables. A nonlinear transformation proposed in the literature for representing the discrete variables as continuous variables has been explored for alternate ways of solving MINLP problems to global optimality through conversion of MINLP problems into equivalent NLPs. For finding global optimal solutions to the resulting nonconvex NLP and to improve the convergence rate of DE closer to the optimum, in this work a hybrid method combining stochastic and deterministic approaches has been proposed, which seems to be promising within the scope of the case studies considered, though guarantee of the global optimality still remains an issue.© Elsevie

MISO structure based control-relevant identification of MIMO systems

The prefilter based open loop control-relevant identification scheme proposed for single input single output (SISO) systems by D.E. Rivera et al. (1992) is extended for multi input multi output (MIMO) systems using a multi input single output (MISO) structural framework. By selecting input output pair for control, the MIMO system is partitioned into individual SISO systems with the interacting branches acting on each SISO loop being treated as structured measured disturbances (in addition to the regular disturbance at each output). With this consideration, a methodology is proposed for design of separate prefilters for the individual channels, taking into account the performance specifications for each loop. The use of an uncorrelated set of inputs is proposed for obtaining accurate estimation of individual channel elements. The prefiltering for each individual loop has been shown to give good estimates of the control-relevant model for the direct as well as the interacting branches. Closed loop simulations, using decoupled internal model controllers, involving representative processes taken from the literature demonstrate the validity of the approach.© IEE

A multilevel, control-theoretic framework for integration of planning, scheduling, and rescheduling

In this paper, an integrated multilevel, control-theoretic framework has been proposed for effectively handling integration of planning, scheduling, and rescheduling. A general resource-constrained, multistage, multiproduct plant operating as a hybrid flowshop facility has been considered. The proposed approach is based on the inherent hierarchical decomposition of the overall decision-making process that is a typical characteristic of large enterprises. The overall problem is segregated into three levels with different horizons, wherein planning over multiperiods is at the top level followed by scheduling for a single period at the middle level and a detailed inventory management schedule for the operator at the lower level. In the hierarchical decomposition, the upper levels are equipped with abstractions of the lower levels and proactiveness for reactive scheduling. The integration. of reactive scheduling is motivated by some of the process control principles like cascade control and the concepts of receding horizon. Using the philosophy of decentralized decision-making, it is demonstrated that the lower levels with accurate models have the flexibility and amenability for rescheduling without upsetting the global performance. As an illustrative case study, cyclic scheduling of a simple refinery flow sheet involving continuous lube production in a resource constrained hybrid flowshop is presented to demonstrate the proposed methodology

Identification for decentralized model predictive control

The problem of identifying interaction dynamics that exist between units operating in a decentralized control scheme is addressed. Identification of such interaction relationships is crucial to the deployment of coordinated decentralized control. The proposed methodology is based on a variant of the two-step, closed-loop identification methods proposed earlier in the literature. Alternative identification schemes relevant for this scenario are theoretically analyzed and are also evaluated based on the criteria of a priori knowledge necessary about the controller and the plant, as well as the applicability of the methods for the constrained controller case. Validation studies on representative systems taken from literature are presented to demonstrate the efficacy of the proposed schemes. (c) 2006 American Institute of Chemical Engineer

Fuzzy segregation-based identification and control of nonlinear dynamic systems

In this work, we propose a new method to model and control complex nonlinear dynamic systems. The suggested scheme employs an output curve methodology to determine the initial set of dynamic clustering spaces. The choice of the optimal dynamic clustering space is made through an analysis of cross validation performance and other indicative indices. A fuzzy clustering methodology for dynamic-model building is proposed. The relative advantages of the proposed adaptive fuzzy clustering method over other segregation methods are highlighted. For online control, a smooth internal model-switching strategy based on fuzzy methods is proposed and shown to be superior to other methods hitherto proposed in the literature. Two control structures based on the proposed methodology are discussed. The efficacy of the proposed fuzzy modeling and control schemes are demonstrated through several representative illustrative examples and by application to a high-purity distillation process

Control-relevant identification for two degrees of freedom control

The prefilter-based control-relevant identification scheme for single-input single-output (SISO) systems proposed earlier is useful for a one degree of freedom (DOF) control design. In this paper, we propose a control-relevant identification scheme for the synthesis of control-relevant, model-based, two-DOF controller. We propose a systematic method for the design of the two prefilters required for the estimation of the two distinct control relevant models. While one of the prefilters is based on the nature of disturbance characteristics and disturbance regulation specification, the second is based on the nature of set point signal and the specification for the set point tracking. The closed loop performance achieved with control relevant models and direct estimated model, using the internal model control (IMC)based two-DOF controller, is analysed. The results obtained validate the significance of proposed control-relevant model-based two-DOF control, when the nature of set point/disturbance signals and the respective specifications for tracking and regulation are different

MISO-structure-based control-relevant identification of MIMO systems with steady-state gain matching

The prefilter-based control-relevant identification (CRI) scheme proposed by Rivera et al. [IEEE Trans. Autom. Control 1992, 37 (7), 964-974] for single-input/single-output (SISO) systems estimates a model of the system that matches the true system dynamics in a range of frequencies that are important and relevant for closed-loop performance. In this paper, an extension of this approach is proposed for the CRI of multi-input/multi-output (MIMO) systems by partitioning the overall system into individual multi-input/single-output (MISO) structures. Furthermore, CRI methodologies proposed hitherto have not examined the issue of accurate identification of the steady-state gain. In this paper, the importance of achieving good steady-state gain match is emphasized, and a method is proposed to identify a control-relevant model that has a good match, at steady state (zero frequency), as well as in the control-relevant frequency band, with the true plant. The effect of the proposed control-relevant prefiltering on the identification of the high- and low-gain directions is also analyzed in this paper. The proposed methodology is validated using simulations on representative problems from the literature and on a paper machine problem

Perturbation signal design for neural network based identification of multivariable nonlinear systems

The paper focuses on issues in experimental design for identification of nonlinear multivariable systems. Perturbation signal design is analyzed for a hybrid model structure consisting of linear and neural network structures. Input signals, designed to minimize the effects of nonlinearities during the linear model identification for the multivariable case, have been proposed and its properties have been theoretically established, The superiority of the proposed perturbation signal and the hybrid model has been demonstrated through extensive cross validations. The utility of the obtained models for control has also been proved through a case study involving MPC of a nonlinear multivariable neutralization plant

Analysis of pre-filter based closed-loop control-relevant identification methodologies

Control-relevant identification strategies have been variously proposed for open loop model building. In this paper, issues related to control-relevant model building in closed-loop schemes are discussed. Various important aspects such as pre-filter design and plant friendliness of the perturbation signals have been examined. Simulations involving representative problems have been considered from chemical engineering literature to highlight the applicability of the proposed methods