Improved strategy for computation of population mean under double stratified sampling framework

The article contains a new technique to estimate the mean of the variate of the interest of the finite population with the help of two auxiliary variates. The technique complies well with the stratified population in which each strata proportion is predicted by taking an initial sample called the first phase sample. When the first phase sample is taken, a second sample is taken from the first sample which is called the second phase sample which is used to estimate the mean of the variate of the interest. In our study, we have considered the population which has two correlated auxiliary variates that pass almost through the origin. In such a situation ratio estimation technique and product estimation technique that provides improved estimates of the mean of the variate of the interest. Our technique considers a ratio-product type exponential estimator of which we have established efficiency theoretically as well as empirically.

The educational e-portfolio: preliminary evidence of its relationship with student’s self-efficacy and engagement

The educational use of portfolios has been increasing in the last few years, especially as technology has also developed electronic versions of portfolios. Although there is abundant information about their benefits and practice description, few studies provide empirical evidence of their implementation. The objective of this study was to provide initial evidence about the use of the portfolio in higher education. Concretely, we aimed 1) to explore the correlation between students’ performance on the portfolio and their performance on more traditional assessment methods 2) to explore whether student’s personal variables predict performance in key elements of the e-portfolio, such as individual reflections, and if these contribute to general academic performance in the course, and 3) to evaluate whether the use of the e-portfolio during a semester changes the students’ self-efficacy and engagement. For this purpose, an initial sample of 73 students were recruited, and an e-portfolio (based on Mahara) was implemented over a semester. The results showed that performance on the portfolio correlated with the score obtained on multiple choice tests. There was an increase in self-efficacy after one semester of e-portfolio implementation, and engagement proved to be an important predictor of the final course grade through the mediation of individual reflections. These results offer preliminary and promising evidence about the relationship of a specific element of e-portfolios, individual reflections with several variables related to academic achievement such as self-efficacy and engagement.

Tourism, knowledge management and its processes

When it comes to knowledge management (KM), one of the ways to classify it is through its processes. When it comes to tourism, it is the sectors that reveal its practical development. At this juncture, this article aims to analyze which are the tourism sectors that are considering KM in their research, as well as which KM processes are most used in tourism studies. To this end, this research is supported by an integrative literature review and follows the guidelines of the PRISMA recommendation. Due to the research protocol established and using the Scopus and Web of Science databases, an initial sample of 376 articles was obtained, of which 107 met the eligibility criteria. The research results are: (1) the most representative sectors are macro tourism and the accommodation services segment; (2) there is an emphasis on knowledge sharing and transfer processes, which are KM concerns also in other areas; (3) the researches highlight tacit knowledge, given its management complexity and the competitive differential it supports; (4) the researches on KM in tourism received criticism for low quality, complexity of scientific language, or disconnection with the managers and operators in the private and public sectors.

Development and Validation of a Multi-Dimensional Measure of Activity-Based Working Behaviors

Most work on activity-based working centers on the physical environment and digital technologies enabling flexible working. While important, we believe the key components for implementing activity-based working are employee and manager behaviors. To measure the degree of enactment of activity-based work, based on workshops with experienced practitioners as well as previous literature, we have developed and validated a behavior-focused measure of activity-based working behaviors. In our initial sample (Sample 1, N = 234), three subscales were identified: task – environment crafting, workday planning, and social needs prioritization. In the replication sample (Sample 2, N = 434), this model also showed adequate fit. Moreover, task – environment crafting was related to general health and lower stress in sample 1 (multi-organization sample), but not in the single-organization sample (sample 2). Workday planning was associated with higher concentration in both samples and in the second sample with general health and work engagement; the latter was also related to social needs prioritization.

Global Travel Restriction's Effect on The Number of Deaths from COVID - 19: An Analysis on Global COVID-19 Data

<p><a>Our initial sample size includes 221 countries which includes 195 United Nations (UN) member countries and some dependent territories for which independent data are available. </a>Some of the countries included in the analysis are dependent territories under some independent countries such as The United Kingdom, which has 14 overseas territories under its jurisdiction. Though they are not independent countries but most of them are internally self-governing with the UK keeping responsibility for protection and foreign affairs. However, we have excluded two countries as the data were missing for many variables for these two territories. The two omitted countries are MS Zaandam and Diamond Princess. So, the final sample size includes 219 countries and territories for which we collected data on COVID-19 as well as their geographic and demographic aspects. More details of the variables included are provided in the table 1. A list of all countries included in the analysis is provided in the appendix. All data from all sources including Wikipedia, World population review and Worldometer are collected during the period of Feb 1, 2021, to Feb 07, 2021.</p>

Global Travel Restriction's Effect on The Number of Deaths from COVID - 19: An Analysis on Global COVID-19 Data

<p><a>Our initial sample size includes 221 countries which includes 195 United Nations (UN) member countries and some dependent territories for which independent data are available. </a>Some of the countries included in the analysis are dependent territories under some independent countries such as The United Kingdom, which has 14 overseas territories under its jurisdiction. Though they are not independent countries but most of them are internally self-governing with the UK keeping responsibility for protection and foreign affairs. However, we have excluded two countries as the data were missing for many variables for these two territories. The two omitted countries are MS Zaandam and Diamond Princess. So, the final sample size includes 219 countries and territories for which we collected data on COVID-19 as well as their geographic and demographic aspects. More details of the variables included are provided in the table 1. A list of all countries included in the analysis is provided in the appendix. All data from all sources including Wikipedia, World population review and Worldometer are collected during the period of Feb 1, 2021, to Feb 07, 2021.</p>

Thermal Decomposition and Kinetics of Pentlandite-Bearing Ore Oxidation in the Air Atmosphere

The roasting of sulfide ores and concentrates is one of the most important steps in pyrometallurgical metal production from primary raw materials, due to the necessity of excess sulfur removal, present in the virgin material. Pentlandite is one of the main sources for nickel pyrometallurgical production. The knowledge of its reaction mechanism, products distribution during oxidation and reaction kinetics is important for optimizing the production process. Raw pentlandite-bearing ore from the Levack mine (Ontario, Canada) was subjected to oxidative roasting in the air atmosphere. A chemical analysis of the initial sample was conducted according to EDXRF (Energy-Dispersive X-Ray Fluorescence) and AAS (Atomic Adsorption Spectrometry) results. The characterization of the initial sample and oxidation products was conducted by an XRD (X-ray Diffraction) and SEM/EDS (Scanning Electron Microscopy with Energy Dispersive Spectrometry) analysis. Thermodynamic calculations, a phase analysis and construction of Kellogg diagrams for Ni-S-O and Fe-S-O systems at 298 K, 773 K, 923 K and 1073 K were used for proposing the theoretical reaction mechanism. A thermal analysis (TG/DTA—Thermogravimetric and Differential Thermal Analyses) was conducted in temperature range 298–1273 K, under a heating rate of 15° min−1. A kinetic analysis was conducted according to the non-isothermal method of Daniels and Borchardt, under a heating rate of 15° min−1. Calculated activation energies of 113 kJ mol−1, 146 kJ mol−1 and 356 kJ mol−1 for three oxidation stages imply that in every examined stage of the oxidation process, temperature is a dominant factor determining the reaction rate.

Insta-learn: Face Stereotypes Emerge and Persist through Insufficient Statistical Learning

Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.

Face Stereotypes from Insufficient Statistical Learning

Social stereotypes are prevalent and consequential, yet sometimes inaccurate. How do people learn these inaccurate beliefs in the first place and why do these beliefs persist in the face of counter evidence? Building on past research on cognitive limitations and environmental sample biases, we propose an integrative perspective: Insufficient statistical learning (Insta-learn). Instalearn posits that humans are active learners of the environment. Starting from a small sample, people are able to extract statistical patterns within the sample accurately and quickly. However, people do not continue sampling sufficiently. If they decide not to collect more samples once they are (prematurely) satisfied, inaccurate stereotypes can emerge even when more data would show otherwise. We investigated this hypothesis across six online experiments (N = 1565), using novel pairs of computer-generated faces and social behaviors. Fixing the population level statistics of face-behavior associations to zero and varying the initial sample statistics, we found that participants quickly learned the initial sample statistics (from as few as three examples) and persisted in using such spurious associations in their final decisions. Granting the sampling power to participants — samples were endogenously generated by participants and not defined by the experimenters — we found insufficient sampling caused spurious associations to persist. Insta-learn provides a domain-general framework for a mechanistic explanation of the emergence and persistence of social stereotypes.

Disaster management and emerging technologies: a performance-based perspective

Purpose This paper aims to analyse how emerging technologies (ETs) impact on improving performance in disaster management (DM) processes and, concretely, their impact on the performance according to the different phases of the DM cycle (preparedness, response, recovery and mitigation). Design/methodology/approach The methodology is based on a systematic review of the literature. Scopus, ProQuest, EBSCO and Web of Science were used as data sources, and an initial sample of 373 scientific articles was collected. After abstracts and full texts were read and refinements to the search were made, a final corpus of 69 publications was analysed using VOSviewer software for text mining and cluster visualisation. Findings The results highlight how ETs foster the preparedness and resilience of specific systems when dealing with different phases of the DM cycle. Simulation and disaster risk reduction are the fields of major relevance in the application of ETs to DM. Originality/value This paper contributes to the literature by adding the lenses of performance measurement, management and accountability in analysing the impact of ETs on DM. It thus represents a starting point for scholars to develop future research on a rapidly and continuously developing topic.