Sustainable luxury: from an oxymoron to a tautology – the case of the Indian luxury hospitality industry

PurposeIn the context of heightened awareness and understanding of responsible tourism, it has become increasingly imperative for luxury hotels to introspect on the impact of their operations on environmental and social sustainability. This study aims at examining the prevalence and growth of sustainability practices in the Indian luxury hotel segment as it increasingly competes on a global platform.Design/methodology/approachThe research approach adopted in this paper is qualitative and emic. Primary data is gathered for the study through semi-structured interviews with select luxury hotel general managers from four hotel chains of Indian origin – ITC Luxury Hotels, Oberoi Hotels and Resorts, Taj Hotels and Palaces and The Leela – to gain an insight into sustainability initiatives adopted in the Indian hospitality industry. Secondary research data regarding the eco-friendly, green measures implemented in these hotels is collected primarily from the websites of the respective hotel chains and supplemented by review of academic literature, media articles, industry reports and company press releases.FindingsThe findings of the study reveal that Indian luxury hotel brands are increasingly adopting green products and integrating innovative sustainability practices in their day-to-day operations. However, several of these initiatives are in the non-customer-facing domains such as the engineering and back-of-the-house operations. Very few Indian hotel chains are building these sustainability initiatives into their core philosophy and embedding it in the front-line customer service experience at their properties to reinforce the green image of the hotels.Originality/valueThe paper also proposes the 6Cs Framework of Sustainability that can be utilised to categorise the green sustainable practices adopted in responsible hotels in a simplistic manner under six broad verticals. Practitioners, researchers and educationists in the hospitality industry would find the implications of this study useful in the context of a post-pandemic world where sustainability is influencing consumer choices across industries in today's eco-conscious and enlightened business and marketing environment.

Figurative Language Used by Australian Facebookers During COVID-19 Pandemic

This study investigated the metaphorical speech acts used by Australian commentators on Facebook during COVID-19. The sample contained 50 Facebook comments that were analyzed qualitatively. The study adopted Searle taxonomy of speech acts, namely, directive, assertive, expressive, commissive, and declarative. This helped in identifying the different functions of the metaphorical speech acts. Moreover, Austin taxonomy of speech act forms, namely, locutionary, illocutionary, and perlocutionary, was adopted. The findings revealed that the most common types of metaphors that were used by the Australian commentators were war and conflict metaphors, followed by psychological status metaphors and irony metaphors. The study found that war and conflict metaphors were the most commonly used forms of speech by the commentators. This is in alignment with the literature that also highlights how the advent of a crisis, such as COVID-19, results in excessive use of war and militarized metaphors. The study found that the most common speech act was directive, while expressive was the least common form used by Australian commentators during the COVID19 pandemic.

Lexico-Semantic Change in the Kazakh Language of the COVID Era

This article discusses lexical and semantic changes during the COVID-19 pandemic. In this article, we describe semantic shifts, new concepts, and neologisms associated with the COVID-19 pandemic based on the results of an associative survey. A total of 142 respondents voluntarily participated in our online survey. The term 'coronavirus' was taken as a stimulus word. Respondents had to answer what colour and number the word 'coronavirus' is associated with. The results of the study show that the stimulus 'coronavirus' in the minds of people activates the colours red, green, black, blue, yellow and very weakly causes associations with brown, white, gold, purple, colourless, as well as the frequency of the number 19. Additionally, according to the results of the study, it can be said that during the COVID-19 pandemic, negative meanings of colourative vocabulary were actualized (except green, because this colour began to symbolize safety), and numbers and some new concepts that have a nonpositive colouring appeared.

Les mots du COVID: en créole martiniquais et créole guadeloupéen par Raphaël Confiant et Hector Poullet (review)

The first entry is quite familiar in the COVID-19 era—the French term "Antivax," which comes out in Creole as "Moun ki kont vaksen ora-kou" (15). Many of the terms are similarly recognizable at a global scale and even across many languages, from comorbidité ("malady-batjé") to déconfinement ("dézankazaj") to pandémie ("maladi-tout-wonlatè") to quarantaine ("karantjou-fèmen"). In all, the forty-five terms that the authors present in Creole, with examples in both Creole and French translation, contribute to their effort to enrich their Creole languages, their intent "d'apporter [End Page 169] notre pierre aux efforts d'enrichissement lexical de nos créoles sans lesquels ces derniers n'ont pas d'avenir" (11).

Multitasking: Is It Worth the Risk?

[...]the nurse may be triaging a patient who is reporting symptoms and at the same, a staff member gives the nurse a critical lab result. Despite the perception that multitasking is an impressive skill, it is actually detrimental and, in some cases, risky. Since human multitasking became a phenomenon, it has been studied by many scientists. For the well-being of you and your patients, slow down, pause, and focus. * Kathryn Koehne, DNP RN, AMB-BC, C-TNP is Director of Nursing and Operations, Crescent Cove, Minneapolis, MN;Consultant and Presenter, Telephone Triage Consulting, Inc.;and Adjunct Faculty, Viterbo University, La Crosse, WI. Sg2 Health Care Intelligence. https://www.sg2.com/health-care-intelligence-blog/2021/06/sg2-2021-impact-ofchange-forecast/ Merriam-Webster.

Clarifying Organizational Generativity: A Future Forming Perspective for OD Practitioners and Researchers

As the catastrophic consequences of the COVID-19 pandemic have made clear, both the practice and research of organizational development (OD) urgently need alternative pathways to the future. Organizational generativity (OG) offers one such promising alternative. While much of OD practice and research are focused on enabling organizations to better prepare for an unknown future, OG accommodates new ways for organizations to proactively create their own future. As a nascent field of inquiry, however, research on OG is underdeveloped and characterized by a lack of clarity. Therefore, the purpose of this paper is to clarify the construct of organizational generativity to be more actionable by OD practitioners, researchers, and managers alike. Using grounded theory, we review and critique the literature on generativity, from the "ancestral" writers in psychology to current OD authors. Through successive rounds of inquiry, we reveal the syntax, the semantics, and the inherent processual nature of organizational generativity. We then derive a conceptual framework describing seven manifestations of generative organizational processes: relational, transformational, disruptive, future-focused, idea-giving, actionable, and procreative. Finally, we discuss implications for OD practice and opportunities for future research.

Using Python and AWS for NoSQL in a BI Course

This article presents a multi-stage guided technical project coding Python scripts for utilizing Amazon Web Services (AWS) to work with a document-store database called DynamoDB. Students doing this project should have taken an introductory programming class (ideally in Python) and a database class to have experience with Python coding and database manipulation/querying in a relational environment. Students learn new data formats (Python dictionaries, JSON text data, keyvalue storage structures) and learn how to transform data from one format to another. They also gain experience with data visualization. The project was first carried out in a business intelligence (BI) course during Spring 2020 semester in the midst of COVID and included video tutorials. Since then, it has been refined and used each semester the BI course is taught.

Artificial intelligence in retail – a systematic literature review

PurposeThe purpose of this study is to present a systematic literature review of academic peer-reviewed articles in English published between 2005 and 2021. The articles were reviewed based on the following features: research topic, conceptual and theoretical characterization, artificial intelligence (AI) methods and techniques.Design/methodology/approachThis study examines the extent to which AI features within academic research in retail industry and aims to consolidate existing knowledge, analyse the development on this topic, clarify key trends and highlight gaps in the scientific literature concerning the role of AI in retail.FindingsThe findings of this study indicate an increase in AI literature within the field of retailing in the past five years. However, this research field is fairly fragmented in scope and limited in methodologies, and it has several gaps. On the basis of a structured topic allocation, a total of eight priority topics were identified and highlighted that (1) optimizing the retail value chain and (2) improving customer expectations with the help of AI are key topics in published research in this field.Research limitations/implicationsThis study is based on academic peer-reviewed articles published before July 2021;hence, scientific outputs published after the moment of writing have not been included.Originality/valueThis study contributes to the in-depth and systematic exploration of the extent to which retail scholars are aware of and working on AI. To the best of the author's knowledge, this study is the first systematic literature review within retailing research dealing with AI technology.

Masked Face Recognition System Based on Attention Mechanism

With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recognition model and proposes an improvement of Facenet. We use ConvNeXt-T as the backbone of the network model and add the ECA (Efficient Channel Attention) mechanism. This enhances the feature extraction of the unobscured part of the face to obtain more useful information, while avoiding dimensionality reduction and not increasing the model complexity. We design new face recognition models by investigating the effects of different attention mechanisms on face mask recognition models and the effects of different data set ratios on experimental results. In addition, we construct a large set of faces wearing masks so that we can efficiently and quickly train the model. Through experiments, our model proved to be 99.76% accurate for real faces wearing masks. A combined accuracy of 99.48% for extreme environments such as too high or lousy contrast and brightness.

Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics;its correlation dimension indicates that it contains obvious fractal characteristics;the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.

Explainable Deep Learning for Medical Imaging Models Through Class Specific Semantic Dictionaries

Explainability is important in the design and deployment of neural networks. It allows engineers to design better models and can give end-users an improved understanding of the outputs. However, many explainability methods are unsuited to the domain of medical imaging. Saliency mapping methods only describe what regions of an input image contributed to the output, but don't explain the important visual features within those regions. Feature visualization methods have not yet been useful in the domain of medical imaging due to the visual complexity of images generally resulting in un-interpretable features. In this work, we propose a novel explainability technique called 'Class Specific Semantic Dictionaries'. This extends saliency mapping and feature visualisation methods to enable the analysis of neural network decision-making in the context of medical image diagnosis. By utilising gradient information from the fully connected layers, our approach is able to give insight into the channels deemed important by the network for the diagnosis of each particular disease. The important channels for a class are contextualised by showing the highly activating examples from the training data, providing an understanding of the learned features through example. The explainability techniques are combined into a single User Interface (UI) to streamline the evaluation of neural networks. To demonstrate how our new method overcomes the explainability challenges of medical imaging models we analyse COVID-Net, an open source convolutional neural network for diagnosing COVID-19 from chest x-rays. We present evidence that, despite achieving 96.3% accuracy on the test data, COVID-Net uses confounding variables not indicative of underlying disease to discriminate between COVID-Positive and COVID-Negative patients and may not generalise well on new data. © 2022 IEEE.

The Richness of English Language during COVID-19 Pandemic: Selected Words and Expressions That Can Be Taught to EFL Students at the Colleges of Health Science and Colleges of Nursing in KSA and Kuwait

The COVID-19 crisis has made the years 2020 and 2021 an unpolitical and spiritual crisis. It has affected virtually everybody in the world and introduced a new normal. Since the beginning of the COVID-19 outbreak, people have been hooked on consuming news media to follow the development of this unprecedented disease. Subsequently, a new language with vocabulary, expressions, and metaphors has appeared in various languages, including English and Arabic. Dictionaries have added new words in English and revised others, they are now fully integrated into our everyday vocabularies. COVID-19 has changed the English language in many ways: it has brought previously obscure medical words to the forefront of everyday speech, made terms related to social isolation more common, and witnessed a shift in meaning in other terms. As linguists, researchers, and teachers gradually return to their classrooms next term (Spring, 2022) we undertook this study to identify 57 English terms, expressions, and metaphors that emerged during the COVID-19 pandemic, either in English-speaking countries or Arabic-speaking countries where English is a first or second language. We deemed the new terminologies necessary for EFL learners in the Gulf Cooperation Countries (GCC) countries. It can serve the purpose of making a list of these words and expressions to be taught to our EFL students at colleges of nursing and health science in Kuwait or Saudi Arabia or any other equivalent colleges in the Arab World. The terms and expressions came from articles, magazines, and English and Arabic dictionaries published during the COVID-19 pandemic.

Investigating the Adaptation of Saudi High School Students to Electronic Dictionaries as Language Learning Tools

Following the COVID-19 pandemic, the traditional education system has been moved to alternative online solutions worldwide. This research aims to uncover the experiences of Saudi secondary school students in using electronic dictionaries as an assistive language learning tool in the Madrasati online learning platform for English. Mixed methods research is employed to understand students' experiences, knowledge, expectations, and thoughts about the electronic dictionaries they used during the COVID-19 crisis and the sudden and unplanned movement to online teaching tools in their language learning and practices. A total of 145 male students enrolled in a secondary school in the Ar-Rass educational directorate were asked to respond to the questionnaire, and 5 of them were randomly chosen to participate in the semi-structured interviews. Findings showed that a majority of the participants dislike the dictionary currently available on the Madrasati platform. They stated that they either favored using free dictionaries available on their mobile phone app stores or other online dictionaries. They consulted their dictionaries mainly to check the meanings of the new words because as compared to other language skills, they engaged more in reading. The data showed that a majority of the students neither sought the help of their teachers about the unknown words nor their friends. They also thought that the pandemic drastically altered their style of learning. Data also showed some disadvantages, difficulties, and concerns of using electronic dictionaries during the virtual classes through Madrasati. Copyright for this article is retained by the author(s), with first publication rights granted to the journal.

COVID-19 PANDEMIC AND ADMINISTRATION OF CRIMINAL JUSTICE IN NIGERIA: A CALL FOR A BETTER APPLICATION

The world is moving into an uncertain future and one does not need a crystal ball to agree that uncertainty stares the whole world in the face;one only need reference the global pandemic caused by the dreaded corona virus. Famously, for a long time it has been known that when America sneezes the whole world catches a cold, but this time china Coughed and the whole globe was affected. Nigeria was not spared and neither were its security apparatus spared nor the Criminal Justice Administration (CJA) which struggled to cope with the new challenges that surfaced due to the pandemic. In this entire viral hurricane witnessed globally how did the Criminal Justice Administration (CJA) and its attendant legal framework in Nigeria fare? This paper has as its focal point to review said legal framework viz a viz attendant challenges leading to a call for better application of the legal framework to the administration of criminal justice in Nigeria hoping that lessons were learnt from mistakes made.

LEXICOGRAPHY FOR SOCIETY AND WITH SOCIETY – COVID-19 AND DICTIONARIES

Not only professional lexicographers, but also people without a professional background in lexicography, have reacted to the increased need for information on new words or medical and epidemio-logical terms being used in the context of the COVID-19 pandemic. In this study, corona-related glossaries published on German news websites are presented, as well as different kinds of responses from professional lexicography. They are compared in terms of the amount of encyclopaedic information given and the methods of definition used. In this context, answers to corona-related words from a German question-answer platform are also presented and analyzed. Overall, these different reactions to a unique challenge shed light on the importance of lexicography for society and vice versa. © 2022, European Association for Lexicography. All rights reserved.

ENGLISH AND ROMANIAN PANDEMIC-RELATED TERMS

According to Singh (2005: 25) any change in a language goes through two phases: the innovation itself, and then dissemination. According to him, the first step is enriching the lexicon of a language, that is to say transforming it, making it as rich and as diverse as possible. [...]these remarks are commonsensical because if we are to look at what happens during the history of any language or rather if we are to follow the evolution of any lexicon, we are going to notice that language does not rush to come up with new words or coin new terms. At present one can notice a tendency towards foreignization which comes from the loan translation of structures from the source language or as a consequence of lexical borrowing. [...]certain terms may have come to the stage that Pym (2004:37) calls glocalized. (1) dinosaur = Greek dino 'terrible' + Greek saur 'lizard' (2) submarine = Latin sub 'under' + Latin marin- 'sea' (3) telephone = Greek tele 'far' + Greek phone 'voice' (4) telescope = Greek tele 'far' + Greek scope 'watcher' (5) stethoscope = Greek stetho 'breast/chest' + Greek scope 'watcher' (6) bronchoscope = Greek broncho 'windpipe' + Greek scope 'watcher' Both tele and scope have become part of hybrid compounds such as television and flouroscope, which include words borrowed from Old French (vision ultimately from Latin) and Latin (flouro from Latin fluere 'to flow').

Covid-19 Vaccines: Is the Seriousness Criterion "Medically Important" Applied Correctly in Reports and Are There Differences between HCPs and Non-HCPs?: An International Journal of Medical Toxicology and Drug Experience

Introduction: During the Covid-19 vaccination campaign, Swissmedic received approximately 50% of the spontaneous reports from health care professionals (HCP) and 50% directly from patients/consumers. The rate of adverse drug reactions (ADRs) following Covid-19 vaccination categorized as "serious" by the reporters is approximately 35% in Switzerland and thus significantly higher than e.g. in the US (6.6%), while rates of reports with fatal outcome are comparable (1.4% vs. 1.3%) (1). A high proportion of the cases labelled as "serious" in Switzerland was classified based on the category "medically important". Since this criterion is used very commonly but is less distinct than the other seriousness criteria, we aimed to analyze whether it is used correctly by the reporters and whether differences between healthcare professionals and patients/consumer can be identified. Objective: To evaluate the appropriate usage of the seriousness criterion "medically important" in spontaneous ADR reports submitted by health care professionals compared to non-health care professionals following immunization with a Covid-19 vaccine. Methods: All serious ADR reports received between the 1st of January 2021 and 31st of December 2021 following immunization with a Covid-19 vaccine were extracted from the Swiss database. Cases categorized as "medically important" were further analyzed. We extracted the preferred terms (PT) according to the medical dictionary for regulatory activities (MedDRA) (2) of the reported ADRs and matched them with the important medical event terms list (IME list) (3). Results: From a total of 11,115 ADR reports, 4,125 (37.1%) were classified as "serious". 2,773 (67.2%) of the serious cases were reported by HCPs and 1,352 (32.8%) by non-HCPs. In 2,260 (55%) reports, the seriousness was based solely on the criterion "medically important". 755 (33.4%) of these reports would also be classified as "serious" according to the IME List. 498 (39%) reports by HCP and 257 (26%) by non-HCP match with the IME list. The proportion of correctly categorized ADRs is significantly higher (p < 0.0001) in reports from HCPs compared to non-HCPs. Conclusion: Only approximately one third of the cases, which were classified as "medically important" and thus reported as "serious", would also be classified as such according to the IME list. The proportion of correctly categorized ADRs is significantly higher in reports from HCPs. Additional information and training for HCPs appears necessary to achieve a higher rate of appropriate seriousness categorization in ADR reports. The usage of the category "medically important" in reports by patients/consumers requires general revision.

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.