Role of Global Value Chains and Exchange Rate: An Empirical Examination in case of Pakistan

Pakistan’s economy has a history of facing continuous external sector shocks that often resulted in large exchange rate depreciations. Whether these depreciations have supported growth in exports from Pakistan or do more harm than providing any benefit to the economy is always a matter of domestic debate with inconclusive results. One major apprehension sighted in this regard is the role of intermediate imported goods that become expensive after depreciations and thus offset any competitive gains expected to be achieved from the exchange rate adjustment. To empirically investigate this argument, we evaluate that whether and how the Global Value Chains (GVCs) participation, i.e. the export and import of intermediate goods, affects the REER elasticity for exports in Pakistan using input-output model techniques. We find that, like elsewhere, REER elasticity of exports has declined in Pakistan overtime. However, only around 16 percent of this decline in REER elasticity is explained by the role of GVCs participation. One major reason for this lower impact could be coming from the fact that, unlike other emerging economies and in contrast to general perception, role of backward participation (i.e. use of imported inputs to produce exports) is one of the lowest in Pakistan. While the results still signify the role of PKR exchange rate in external adjustment, the low backward participation is not helping the exports to become competitive overtime

Welfare of Pet Birds and Potential Zoonoses

The human-animal interaction had long been established and currently emerged in multiple aspects including housing of animals for food and as pets. The “pet birds” are the wild or exotic birds having high genetic value and are housed under captivity as companions or for ornamental purposes. The commonly housed pet birds are either passeriformes or psittaciformes. These birds are housed under conditions to meet standard requirements for welfare of pet birds. Besides the pet birds and human relationship, these birds are potential carriers or transmitters of several pathogens considered responsible for zoonotic diseases. The range of the zoonotic diseases consisted of bacterial, viral, parasitic and fungal diseases. The mode of transmission is also an important entity for understanding the spread mechanism of zoonotic diseases. The transmission and spread is predominantly through the direct contact and in the few conditions through the vectors; termed as vector-borne transmission. Altogether, in this chapter, the authors have discussed different aspects of welfare of pet birds, categories of zoonotic diseases along with mode of transmission and spread of zoonoses. At the last, few aspects of welfare of pet birds and prevention and control guidelines of zoonoses are suggested for the personal biosafety and public health

A novel design of fractional Mayer wavelet neural networks with application to the nonlinear singular fractional Lane-Emden systems

In this study, a novel stochastic computational frameworks based on fractional Meyer wavelet artificial neural network (FMW-ANN) is designed for nonlinear-singular fractional Lane-Emden (NS-FLE) differential equation. The modeling strength of FMW-ANN is used to transformed the differential NS-FLE system to difference equations and approximate theory is implemented in mean squared error sense to develop a merit function for NS-FLE differential equations. Meta-heuristic strength of hybrid computing by exploiting global search efficacy of genetic algorithms (GA) supported with local refinements with efficient active-set (AS) algorithm is used for optimization of design variables FMW-ANN., i.e., FMW-ANN-GASA. The proposed FMW-ANN-GASA methodology is implemented on NS-FLM for six different scenarios in order to exam the accuracy, convergence, stability and robustness. The proposed numerical results of FMW-ANN-GASA are compared with exact solutions to verify the correctness, viability and efficacy. The statistical observations further validate the worth of FMW-ANN-GASA for the solution of singular nonlinear fractional order systems.This paper is partially supported by Ministerio de Ciencia, Innovación y Universidades grant number PGC2018-097198-BI00 and Fundación Séneca de la Región de Murcia grant number 20783/PI/18

Dengue Fever: A General Perspective

Dengue Fever or commonly known as Dengue, a mosquito-borne arboviral infection has emerged as havoc around the globe. Annually, about 50 million infections are reported, resulting in 22,000 deaths and almost 2.5 billion people are reported living at risk. Dengue infection is caused by Dengue Virus (DENV), which is a member of genus Flavivirus and comprised of ten proteins; three proteins, capsid (C), membrane (M), and envelope (E), play structural role and seven are identified as non-structural that direct DENV replication. Four distinct serotypes: DENV-1, DENV-2, DENV-3 and DENV-4 are transmitted via Aedes mosquitoes. Clinically, Dengue patients can be categorized into three groups according to WHO 2009 revised classification. Typical symptoms of dengue include: extreme fatigue; sudden fever (from 3-7 days), headache, joint, muscle, and back pain; vomiting and diarrhea, appetite loss; skin rash along minor bleeding. Aedes aegypti is geographically distributed in tropical areas and breeds in artificially filled water containers i.e. drums, tyres, flower vases plastic food containers, tin cans, etc. Due to four viral serotypes and non-availability of the model animal for dengue, producing vaccines is a challenging task. Thus, Dengue can be managed using various vector control strategies through physical, chemical and biological means

Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach

A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio

Design of neuro-swarming computational solver for the fractional Bagley–Torvik mathematical model

This study is to introduce a novel design and implementation of a neuro-swarming computational numerical procedure for numerical treatment of the fractional Bagley–Torvik mathematical model (FBTMM). The optimization procedures based on the global search with particle swarm optimization (PSO) and local search via active-set approach (ASA), while Mayer wavelet kernel-based activation function used in neural network (MWNNs) modeling, i.e., MWNN-PSOASA, to solve the FBTMM. The efficiency of the proposed stochastic solver MWNN-GAASA is utilized to solve three different variants based on the fractional order of the FBTMM. For the meticulousness of the stochastic solver MWNN-PSOASA, the obtained and exact solutions are compared for each variant of the FBTMM with reasonable accuracy. For the reliability of the stochastic solver MWNN-PSOASA, the statistical investigations are provided based on the stability, robustness, accuracy and convergence metrics.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper has been partially supported by Fundación Séneca de la Región de Murcia grant numbers 20783/PI/18, and Ministerio de Ciencia, Innovación y Universidades grant number PGC2018-0971-B-100

Neuro-swarm computational heuristic for solving a nonlinear second-order coupled Emden–Fowler model

The aim of the current study is to present the numerical solutions of a nonlinear second-order coupled Emden–Fowler equation by developing a neuro-swarming-based computing intelligent solver. The feedforward artificial neural networks (ANNs) are used for modelling, and optimization is carried out by the local/global search competences of particle swarm optimization (PSO) aided with capability of interior-point method (IPM), i.e., ANNs-PSO-IPM. In ANNs-PSO-IPM, a mean square error-based objective function is designed for nonlinear second-order coupled Emden–Fowler (EF) equations and then optimized using the combination of PSO-IPM. The inspiration to present the ANNs-PSO-IPM comes with a motive to depict a viable, detailed and consistent framework to tackle with such stiff/nonlinear second-order coupled EF system. The ANNs-PSO-IP scheme is verified for different examples of the second-order nonlinear-coupled EF equations. The achieved numerical outcomes for single as well as multiple trials of ANNs-PSO-IPM are incorporated to validate the reliability, viability and accuracy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors have not disclosed any funding

Hurdles in Vaccine Development against Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) infection is a major cause of severe respiratory disease in infants and young children worldwide and also forms a serious threat for the elderly. Vaccination could significantly relieve the burden of the RSV disease. However, unfortunately there is no licensed vaccine available so far. This is partly due to disastrous outcome of a clinical trial of formalin-inactivated RSV (FI-RSV) in children in 1960s; leading to enhanced respiratory disease upon natural infection. These findings contributed significantly to the delay of RSV vaccine development. Other key obstacles in development of RSV vaccine such as a peak of severe disease at 2–3 months of age, challenging biochemical behavior of key vaccine antigens and dependence on animal models that may not truly reflect human disease processes. These challenges could be overcome through maternal immunization, structure-based engineering of vaccine antigens, the design of a novel platform for safe infant immunization, and the development of improved animal models. Currently, several vaccine candidates are in pre-clinical and clinical trials targeting the diverse age groups; young children or older adults from the infection or can reduce incidence, mortality and morbidity among the RSV infected individuals