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The purpose of this paper is to explain how emotional intelligence (EI) influences intercultural competence (IC), which in turn may influence online instruction. The authors further explored the varying attributes of EI and the extent to which it intersects with IC in the workplace.

Literature on EI and IC from the fields of education, business and leadership was reviewed. The search entailed articles related to EI and IC using the following databases: Business Search Premier, ERIC, JSTOR and ProQuest. The authors used the following key search terms in researching the articles: EI, IC, learning and online instruction. Title and abstract analyses judged each article’s suitability for the study.

To better perceive, understand and appreciate others and their cultures, we need to understand our own emotions and the way we interact with others. EI is thus the foundation on which IC can be built. It takes a higher level of EI to develop higher IC quotient. An online instructor should be cognizant about the emotional issues involved in the online learning and suitably modify the instruction to improve learner engagement to ensure better and improved student learning.

Findings of this study should provide useful information for theory building and practice. Further, it is hoped the findings of this study will stimulate more scholarly interest in this relatively untapped research area exploring how EI can influence IC and ultimately influence online instruction and improve student learning.

The findings will serve as useful pointers for instructors and scholars who strive to improve ICs and appreciate the nuances that enable an emotionally intelligent instructor to perform better and connect with learners from a different culture.

Based on empirical literature reviewed, EI is the ability to perceive, understand and control our own emotions to better connect and relate with other individuals. It is the ability to recognize the emotional cues and change our behavior accordingly. IC is the ability to understand and appreciate the cultural differences to better function in a culture different from our own. The two constructs are therefore interrelated and have a significant overlap. However, while EI has been studied exclusively in different contexts, surprisingly, the researchers have not given adequate attention to the important theme of using EI in improving IC or even the role EI can play in improving instructors’ IC. Moreover, the interrelationship between EI, IC and online learning has not been explored previously. This paper seeks to address this gap.

The purpose of this study is to increasing the gauge factor, reducing the hysteresis error and improving the stability over cyclic deformations of a conductive polylactic acid (CPLA)-based 3D-printed strain sensor by modifying the sensing element geometry.

Five different configurations, namely, linear, serpentine, square, triangular and trapezoidal, of CPLA sensing elements are printed on the thermoplastic polyurethane substrate material individually. The resistance change ratio of the printed sensors, when loaded to a predefined percentage of the maximum strain values over multiple cycles, is recorded. Finally, the thickness of substrate and CPLA and the included angle of the triangular strain sensor are evaluated for their influences on the sensitivity.

The triangular configuration yields the least hysteresis error with high accuracy over repeated loading conditions, because of its uniform stress distribution, whereas the conventional linear configuration produces the maximum sensitivity with low accuracy. The thickness of the substrate and sensing element has more influence over the included angle, in enhancing the sensitivity of the triangular configuration. The sensitivity of the triangular configuration exceeds the linear configuration when printed at ideal sensor dimensional values.

The 3D printing parameters are kept constant for all the configurations; rather it can be varied for improving the performance of the sensor. Furthermore, the influences of stretching rate and nozzle temperature of the sensing material are not considered in this work.

The sensitivity and accuracy of CPLA-based strain sensor are evaluated for modification in its geometry, and the performance metrics are enhanced using the regression modelling.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

This study aims to provide a methodology for constructing farm household-level adaptation metrics for agriculture and water sectors. The livelihood of farm households is at risk now and in the foreseeable future, as both agriculture and water sectors are vulnerable to climate variability, particularly in developing nations. Adaptation is critical to protect their livelihood. Vulnerable farmers have adopted various adaptation mechanisms to counteract negative impacts of climate variability, though the extent varies temporally and spatially.

It is, therefore, imperative to understand current adaptation practices for successfully implementing them. A few studies have emerged so far in this context, investigating different issues associated with micro-level adaptation strategies related to agriculture and water sectors, e.g. output and cost-effectiveness, and constraints related to farm, household and institutional levels.

While such analysis is critical to enhance micro-level adaptation measures, there is a felt need to formulate adaptation metrics that can investigate the underlying factors in an integrated manner. For empirical assessment, 146 farmers were interviewed from different agro-ecological zones of Tamil Nadu, India, regarding seven adaptation measures, such as micro-irrigation, rainwater harvesting, resistant crops, use of bio-fertilisers, crop insurance, income diversification and community-based efforts.

These adaptation measures were evaluated through an Analytical Hierarchy Process using four criteria: effective awareness, economic viability, individual and institutional compatibility and flexibility and independent benefits.

The present study provides a methodology to identify barriers that limit implementation of adaptation measures, and enable target-oriented policy measures to promote appropriate adaptation strategies at the local level.

The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.

The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.

Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.

Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.

The purpose of this paper is to investigate both long-run and short-run dynamics among the software and services export, investment in information technology (IT) and GDP in India and to investigate the direction of the relationship among the given three macro-economic variables.

The time series data have been taken to investigate the long-run relationship exists among the variables. Annual data were collected from the NASSCOM Annual Reports, Planning Commission of India and Reserve Bank of India during the period 1980–2016. Cointegration and vector error correction model have been used for analyzing the causal relationship among investment in IT, software exports and GDP in India.

Cointegration results confirm that software and services export, investment in IT and GDP are cointegrated, implying that there exists the long-run equilibrium relationship among the given three macro-economic variables. Similarly, vector error correction mechanism Granger causality results hold that there is uni-directional long-run causality running from software and services export and investment in IT to GDP, implying that software and services export is an important determinant of economic growth in India.

The limitations of the paper are generalization of the results and proxy variable for IT investments.

The paper has implications for the expansion of market concentration, diversification of software and service exports, and investments in R&D for increasing competitiveness of the industry in the global market.

This paper focuses on originality in the analysis of the relationship among the given variables software exports, investment in the IT sector and GDP in India. All the work has been done in original by the authors and the work used have been acknowledged properly.

Using data on 51 firms traded in the Egyptian Exchange from 2014 to 2016, this paper aimed to assess the efficiency of corporate governance (CG) and intellectual capital (IC) practices and to explore their influence on the probability of a firm's financial distress.

The relative efficiency of CG and IC practices has been measured under the Malmquist data envelopment analysis model. A modified Z-score model was applied to assess firms' financial distress.

The Wilcoxon signed-rank test revealed almost insignificant evidence regarding the improvement of CG and IC efficiency over the study period. The efficiency score of CG practices had no impact on the likelihood of financial distress. However, the efficiency score of IC negatively affected the probability of financial distress.

The integration of data envelopment analysis with Tobit regression was required for identifying the significant drivers of efficient CG and IC.

The findings shed light on the role of CG and IC in alleviating the degree of financial distress in Egypt as an emerging market, especially the need to raise firms' compliance with the Egyptian CG code from a voluntary to mandatory status.

This study, using Malmquist data envelopment analysis, is among the first attempts to assess the relative efficiency of CG and IC practices and their effects on financial distress.

The purpose of this paper is to examine the potential of biogas in India for energy conservation and its potential in emission reduction through proper manure management and utilizing the tappable droppings while replacing the conventional and non-conventional fuel.

The authors have looked at the production, use and tappability of animal excreta for possible biogas generation and reduction in fuelwood consumption on one hand and emission reduction across the Indian states on the other. The average percentage of Biogas potential is created in the GIS database for analyzing the data set in the spatial domain using ArcGIS 9.2 software.

The paper examines that unused manure contributes to the greenhouse gas (GHG) and has huge potential of reducing the emission through proper utilization across the Indian states. Keeping current climate change dilemma in view this could be one of the feasible options to cope with the climate change and mitigating the threats.

A comprehensive data regarding methane emission from various sources is not readily available so far. With the help of this research work the authentic data has been collected from different government departments’ data banks and past research work. However, the authors have limited to few conversion aspects in quantifying the emission factor due to complexity of the various data sets.

Looking at the availability of usable animal excreta in different climatic regions, the attempt has been made in demarcating economically viable and technically feasible areas for biogas generation in India. An environmental and economic cost benefit analysis for adopting this renewable energy source has also been incorporated within this research.

The paper examined the GHG contribution of unused manure and the possibility of reducing it through proper utilization. The adverse environmental consequences of the use of conventional and non-conventional cooking fuels have also been analyzed in terms of GHG emissions. The same was assessed for the whole lifecycle of liquefied petroleum gas, which is commonly assumed as a clean fuel.

Gives a bibliographical review of the error estimates and adaptive finite element methods from the theoretical as well as the application point of view. The bibliography at the end contains 2,177 references to papers, conference proceedings and theses/dissertations dealing with the subjects that were published in 1990‐2000.