Relationship of Social Progress Index (SPI) with Gross Domestic Product (GDP PPP per capita): The moderating role of Corruption Perception Index (CPI)

This study investigated the impact of social progress on economic development in 119 countries, while taking their individual corruption perception into consideration. Simple linear regression was use on the secondary data for 119 countries and 5 continents while the SPSS PROCESS macro was used to test the moderating effect of corruption perception. As hypothesized, a positive relationship of the social progress index (SPI) with gross domestic product (GDP) PPP per capita was observe. This means that countries, which fulfill basic human needs, foundations of wellbeing and foster availability of opportunities have enhanced economic development. Moreover, the moderating role of corruption perception between the relationship of social progress and economic development was confirmed; thus indicating that countries with better corruption perception rating possess a stronger relationship of SPI and GDP (PPP) per capita and vice versa. When checked for continents, moderation results showed that the continents that have higher values of corruption perception index (CPI) are more socially and economically developed

QoT Estimation for Light-path Provisioning in Un-Seen Optical Networks using Machine Learning

We propose the use of machine-learning based regression model to predict the quality of transmission (QoT) of an un-established lightpath (LP) in an un-seen network prior to its actual deployment, based on telemetry data of already established LPs of different network. This advance prediction of the QoT of un-established LP in an un-seen network has a promising factor not only for the optimal designing of this network but also enables the possibility to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the Generalized Signal-to-Noise Ratio (GSNR) which includes the effect of both Amplified Spontaneous Emission (ASE) noise and Non-Linear Interference (NLI) accumulation. In the response of present simulation scenario, the real field telemetry data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated data set, a machine-learning technique is applied, demonstrating the GSNR prediction of an un-established LP in an unrevealed network with maximum error of 0.53 dB

Prediction of cognitive and intellectual competence in kindergarten schools associated with general measures of health: a study on children with age ranges between 4 to 7 years

BACKGROUND Stunting refers to the low “height-for-age” measurement. Literature suggests that it is associated with delayed or diminished physical development, cognition and intellectual abilities. OBJECTIVES: This study aimed to estimate the physical growth measures among children under 4 to 7 years of age and to determine its relationship with cognitive deficits & intellectual performances

Cross-feature trained machine learning models for QoT-estimation in optical networks

The ever-increasing demand for global internet traffic, together with evolving concepts of software-defined networks and elastic-optical-networks, demand not only the total capacity utilization of underlying infrastructure but also a dynamic, flexible, and transparent optical network. In general, worst-case assumptions are utilized to calculate the quality of transmission (QoT) with provisioning of high-margin requirements. Thus, precise estimation of the QoT for the lightpath (LP) establishment is crucial for reducing the provisioning margins. We propose and compare several data-driven machine learning (ML) models to make an accurate calculation of the QoT before the actual establishment of the LP in an unseen network. The proposed models are trained on the data acquired from an already established LP of a completely different network. The metric considered to evaluate the QoT of the LP is the generalized signal-to-noise ratio (GSNR), which accumulates the impact of both nonlinear interference and amplified spontaneous emission noise. The dataset is generated synthetically using a well-tested GNPy simulation tool. Promising results are achieved, showing that the proposed neural network considerably minimizes the GSNR uncertainty and, consequently, the provisioning margin. Furthermore, we also analyze the impact of cross-features and relevant features training on the proposed ML models’ performance

Effect of phase angle on the efficiency of beta type Stirling engine

A Stirling Engine is a mechanical device, which operates on a closed regenerative cycle, based on cyclic compression and expansion through a piston and a displacer respectively. It can be widely used for many thermodynamic purposes such as stationary power generation, heat pumps or co-generation systems. Due to the external supply of heat and increasing scope of solar energy utility in Pakistan, this engine can be operated successfully with this useful source of energy. Phase angle is an important parameter of the Stirling engine and is one of the key factors on which performance of the engine depends. It is the angle by which expansion space volume leads the compression space with respect to the volume variations in the engine cycle. This paper describes the optimization and modelling of the phase angle of a single cylinder beta Stirling Engine with Helium as the working fluid. Schmidt analysis is considered to be the standard during this research for analysing the output efficiency of the engine. The volume and pressure variations are computed at different values of phase angle for a complete cycle and ultimately values chart and pressure-volume diagrams are prepared. The work done for each case is calculated for finding the optimum phase angle. It is calculated that the best suitable phase angle for the maximum efficiency of the engine is around 90°. Along with maximum and minimum pressure inside the engine, the overlap volume in beta type Stirling engine plays a vital role and efficiency increases with increase in overlap region.&nbsp

Time Complexity of Color Camera Depth Map Hand Edge Closing Recognition Algorithm

The objective of this paper is to calculate the time complexity of the colored camera depth map hand edge closing algorithm of the hand gesture recognition technique. It has been identified as hand gesture recognition through human-computer interaction using color camera and depth map technique, which is used to find the time complexity of the algorithms using 2D minima methods, brute force, and plane sweep. Human-computer interaction is a very much essential component of most people's daily life. The goal of gesture recognition research is to establish a system that can classify specific human gestures and can make its use to convey information for the device control. These methods have different input types and different classifiers and techniques to identify hand gestures. This paper includes the algorithm of one of the hand gesture recognition “Color camera depth map hand edge recognition” algorithm and its time complexity and simulation on MATLAB

Evaluating Cross- feature Trained Machine Learning Models for Estimating QoT of Unestablished Lightpaths

The rapid increase in bandwidth-driven applications has resulted in exponential internet traffic growth, especially in the backbone networks. To address this growth of internet traffic, operators always demand the total capacity utilization of underlying infrastructure. In this perspective, precise estimation of the quality of transmission (QoT) of the lightpaths (LPs) is vital for reducing the margins provisioned by uncertainty in network equipment's working point. This article proposes and compares several data-driven Machine learning (ML) based models to estimate QoT of unestablished LP before its deployment in the future deploying network. The proposed models are cross-trained on the data acquired from an already established LP of an entirely different in-service network. The metric considered to evaluate the QoT of LP is the Generalized Signal-to-Noise Ratio (GSNR). The dataset is generated synthetically using well tested GNPy simulation tool. Promising results are achieved to reduce the GSNR uncertainty and, consequently, the provisioning margin