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The purpose of this paper is to examine the nature of change readiness and resolve as a principal feature of strategic teams. Change leadership and management in the context of strategy, talent and culture provide the organization with the capacity for navigating change at the process, category and business levels. This paper provides a practical look at the cultivation of strategic teams as agents for making change strategy happen.

The authors have examined a series of variables that inform the strategic agenda, talent blocks and beams, and the cultural agenda for organizations large and small. They have built a general framework for mapping and engaging what serves as a strong mind-set of change readiness and resolve, along with specific team-building elements for learning and development.

Strategic teams that are part of the structure and culture of the organization serve as development grounds for change competence and capacity at the group and individual levels. Further, because there are several types of strategic teams with diverse roles and functions, the change readiness and resolve mechanics are subject to experimentation and adaptation. This results in a strong mind-set for change leadership and management and the ability to deploy effectively across a range of situations, needs and challenges.

This work offers a practical set of views on change and adaptive capacity, and the development pathways that afford an organization the ongoing preparation of individuals and groups for changes in process and policy, programs and categories and business models through the most demanding transformation journeys.

The purpose of this paper is to examine motivation in a team setting, how engagement in the organization's value proposition and strategic agenda, better-matched talent, solid enterprise culture and accountability all play a role in motivating groups of people to become agents of making strategy happen. This paper also provides practical themes for team leadership.

For over 25 years, the authors have observed team function and dysfunction on all levels of business. They have defined a series of conditions that open the gates for motivation to flourish. Using developmental content, questions and frameworks, they provide a roadmap for the motivation of strategic teams in different settings, with different charters.

The authors find that teams whose members have a clear focus on their purpose within the organization's over-arching strategic plans, are well matched with specific talents of members and leaders of the team, and serve within a strong enterprise culture, are more highly motivated as agents of making strategy happen.

This study offers an expanded viewpoint on strategic team motivation with some themes for building strategic team competence, resilience and adaptive capacity across a range of organizational settings.

Although the particular policies that groups establish may serve to differentiate those groups from others in the broader community, policies are better envisioned as aspects of group life in the making than as structures or rules that define the character or operations of the groups under consideration. Addressing instances of policy as humanly engaged ventures, this statement attempts to demystify policy by (a) examining organizational directives in process terms, (b) explicitly incorporating people into the study of the policy‐making process. This paper also addresses policy in ways that (c) are more amenable to ethnographic research on actual instances of policy and (d) contribute to a sustained, comparative analysis of “policy in the making”.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The purpose of this paper is to reveal a symbol – “however” that authors are very interested in, but few research studies pay attention to the existing literature. The authors aim to further insight its function.

In this research, the authors selected 3,329 valid comments on articles published in the British Medical Journal (BMJ) from 2015 to 2020 as the research objects. The authors showed the length distribution of reviewers' comments. In what follows, the authors analyzed the general distribution of words in comments and reviewer comments’ position to understand reviewers' comments qualitatively in word dimension. Specially, the authors analyzed functions of “however” and “but”, words that authors are most concerned with. In addition, the authors also discussed some factors, which may be related to “however,” that reflect reviewers' praise through regression analysis.

The authors found that there are marked differences in the length of reviewers' comments under different review rounds. By mapping the reviewers' comments to different sections, the authors found that reviewers are deeply concerned to methods section. Adjectives and adverbs in comments written in different sections of the manuscripts also have different characteristics. The authors tried to interpret the turning function of “however” in scientific communication. Its frequency of use is related to reviewers' identities, specifically academic status. More precisely, junior researchers use “however” in praise more frequently than senior researchers do.

The linguistic feature and function of “however” and “but” in the reviewers' comments of the rejected manuscripts may be different from accepted papers and also worth exploring. Regrettably, the authors cannot obtain the peer review comments of rejected manuscripts. This point may limit the conclusion of the investigation of this article.

Overall, the survey results revealed some language features of reviewers' comments, which could provide a basis of future endeavors for many reviewers in open peer review (OPR) field. Specially, the authors also put forward an interesting symbol to examine the review comments, “however”, for the first time.

This study aims to use a theoretical-based case study of two distinct ownership groups of the Jack Daniel’s brand to explore how rhetorical history (i.e. malleability of the past for strategic goals) may evoke and capitalize on different forms of nostalgia. Within, the authors configure four forms of nostalgia (i.e. personal, historical, collective and cultural) from the individual or collective interaction and level of direct experience one has with the past as lived or happened.

This study uses an historical research approach which involved the identification of primary and secondary sources, facility tour, source criticism and triangulation to create themes of rhetorical history infused with nostalgic narratives using compelling evidence through rich description of this fusion.

The findings reveal how nostalgia-driven narratives reflecting different collective longing for the re-creation of an American Paradise Lost used by Jack Daniel (i.e. the man) and later but differently by Brown-Forman. This study uncovers how the company’s inherited past was used rhetorically throughout its history, beginning with the nostalgic story of Jack Daniel and the distillery’s nostalgically choreographed location in Lynchburg, Tennessee. This study delves into this setting to highlight the importance of symbols, details, emotional appeals and communications for collective memory and identity development and to showcase the ways in which they are influenced by different types and forms of nostalgia.

This study adds to a limited number of studies focused on understanding the impact of founders on an organization’s brand and how that is malleable. This study responds to scholarly calls to study the influence of sequenced historical rhetoric on an organization and highlight the relevance of social emotions such as nostalgia for rhetorical history. Finally, the theoretical contribution involves the advancing and construction of a theory typology of nostalgia previously proposed by Havlena and Holak in 1996.

The purpose of this paper is to provide a critical and in‐depth review of the present status and recent developments in synthetic methodologies, reaction engineering, process design and quality control aspects associated with the manufacture of mono and multifunctional acrylate monomers.

This paper reviews commercially important UV cure mono and multifunctional acrylate monomers. It covers their synthesis, catalyst, and appropriate solvents for azeotropic removal of byproducts. The detail discussion on catalysis, basis of design of reactors and commercial plant and the process engineering associated with the manufacture has been supported through citation of synthesis of various acrylate monomers. The methodologies adopted for determination of physical, chemical and compositional characterisation of acrylate monomers have been presented. In addition, the guidelines regarding the bulk storage and commercial handling of acrylates have been reviewed.

The reaction engineering of esterification reaction between acrylic acid and polyol has been worked out to provide the basis for selection of reactors. The reaction has been modeled as a series – parallel complex reaction for providing explanation for generation of various byproducts/adducts and multiple esters.

The detailed discussion on formation, characterisation and treatment of Michael adducts and purification of acrylate monomers will be relevant for new researchers for further development. A review of guidelines on selection of homogenous and heterogeneous catalysts for synthesis of acrylate monomers has been presented.

Since the related literature on acrylate monomers is scarce, scattered and proprietary, the consolidated coverage in one paper will be useful.

This paper aims to quantify the quality of peer reviews, evaluate them from different perspectives and develop a model to predict the review quality. In addition, this paper investigates effective features to distinguish the reviews' quality. 

First, a fine-grained data set including peer review data, citations and review conformity scores was constructed. Second, metrics were proposed to evaluate the quality of peer reviews from three aspects. Third, five categories of features were proposed in terms of reviews, submissions and responses using natural language processing (NLP) techniques. Finally, different machine learning models were applied to predict the review quality, and feature analysis was performed to understand effective features.

The analysis results revealed that reviewers become more conservative and the review quality becomes worse over time in terms of these indicators. Among the three models, random forest model achieves the best performance on all three tasks. Sentiment polarity, review length, response length and readability are important factors that distinguish peer reviews’ quality, which can help meta-reviewers value more worthy reviews when making final decisions.

This study provides a new perspective for assessing review quality. Another originality of the research lies in the proposal of a novelty task that predict review quality. To address this task, a novel model was proposed which incorporated various of feature sets, thereby deepening the understanding of peer reviews.

Social comments are rich in information and useful in evaluating, ranking or retrieving different kinds of materials. However, their merits in representing or providing added values to scientific articles have not yet been studied. Therefore, the present study investigates the informativeness of open review reports as a kind of social comments in a scholarly setting.

A test collection was built consisting of 100 randomly selected queries, 1,962 reviewed documents and their reviewers' open reports from F1000Research. They were analyzed using natural language techniques. The comments' salient words were compared to the documents' and also to the Medical Subject Headings (MeSH) salient words. The receiver operating characteristic (ROC) curve was used to test the accuracy of the comments in representing their related articles.

The papers' contents and comments have a considerable number of salient words in common. The comments' salient words are also largely found in the MeSH, signifying their consistency with the knowledge tree and their potential to add some complementary features to their related items. The ROC curves confirm the accuracy of the comments in retrieving their related papers.

This research is the first to reveal the merits of open review reports on scientific papers, in terms of their relatedness to their mother articles, in specific, and to the knowledge tree, in general. They are found informative in not only representing the reviewed papers but also in adding values to the contents of the papers.