Multiple Brands Research Articles

Micromolar fluoride contamination arising from glass NMR tubes and a simple solution for biomolecular applications.

Fluorine (19F) NMR is emerging as an invaluable analytical technique in chemistry, biochemistry, structural biology, material science, drug discovery, and medicine, especially due to the inherent rarity of naturally occurring fluorine in biological, organic, and inorganic compounds. Here, we revisit the under-reported problem of fluoride leaching from new and unused glass NMR tubes. We characterised the leaching of free fluoride from various types of new and unused glass NMR tubes over the course of several hours and quantify this contaminant to be at micromolar concentrations for typical NMR sample volumes across multiple glass types and brands. We find that this artefact is undetectable for samples prepared in quartz NMR tubes within the timeframes of our experiments. We also observed that pre-soaking new glass NMR tubes combined with rinsing removes this contamination below micromolar levels. Given the increasing popularity of 19F NMR across a wide range of fields, increasing popularity of single-use screening tubes, the long collection times required for relaxation studies and samples of low concentrations, and the importance of avoiding contamination in all NMR experiments, we anticipate that our simple solution will be useful to biomolecular NMR spectroscopists.

International trends in prescribing silicone hydrogel contact lenses for daily wear (2000–2023): An update

PurposeIntroduced around the turn of the 21st century, silicone hydrogel contact lenses alleviated hypoxic anterior eye complications due to their high oxygen transmissibility. The purpose of this work is to update earlier surveys by describing international trends in silicone hydrogel daily wear contact lens fitting between 2000 and 2023. MethodAn annual contact lens prescribing survey was sent to eye care practitioners in up to 71 countries between 2000 and 2023. Data relating to 260,144 daily wear soft contact lens fits undertaken in 20 countries returning reliable longitudinal data were analysed in respect of silicone hydrogel daily wear contact lens fitting. ResultsThere has been a dramatic increase in silicone hydrogel daily wear lens fits (p < 0.0001), increasing from 2.8 % of all daily wear soft lens fits in 2000 to 73.7 % in 2023. Of all daily wear soft contact lenses prescribed to males, 44.6 % were silicone hydrogel lenses, compared with 43.5 % for females (p = 0.0146). The mean age of those wearing silicone hydrogel daily wear lenses was 32.0 ± 14.5 years, compared to 30.4 ± 13.6 years for those wearing daily wear hydrogel lenses (p < 0.0001). Between 2019–2023, the average percentage of fits was – (a) material type: silicone hydrogel – 73 %; mid-water content hydrogels – 13 %; high water content hydrogels – 9 %; and low water content hydrogels – 5 %, and (b) lens design: spherical – 44 %, toric – 32 %, multifocal – 17 %, monovision – 4 %, and ‘other’ – 3 %. ConclusionThe dramatic increase in silicone hydrogel contact lens prescribing for daily wear has been commensurate with the introduction of multiple lens brands and an ongoing expansion of lens designs, parameters and replacement frequency options. The balance between silicone hydrogel and hydrogel lens prescribing is perhaps starting to approach an equilibrium.

The negative effect of virtual endorsers on brand authenticity and potential remedies

Although more and more brands are partnering with virtual endorsers for marketing, little is known about whether this approach benefits brands. Drawing on Entity-Referent Correspondence (ERC) Framework of VI Authenticity and Meaning Transfer Theory, this study explores the effect of endorser type (human vs. virtual endorser) on consumers’ perceptions of brand authenticity. Based on textual analysis and four lab experiments, our results show that consumers perceive brands that are endorsed by virtual endorsers as less authentic than those with human endorsers because they perceive virtual endorsers to be less authentic. We explore the boundary conditions of this finding by testing the moderating roles of aesthetic imperfection and multiple brand endorsements. When endorsers are designed to be aesthetically imperfect, the negative effect of virtual endorsers on brand authenticity is attenuated. When endorsers cover an excessive amount of brands, the negative effect of virtual endorsers on brand authenticity is reversed. This study contributes to the literature on virtual influencers and provides companies with practical guidelines for advertising strategies in the era of the metaverse.

Deep Learning and Minimally Invasive Endoscopy: Panendoscopic Detection of Pleomorphic Lesions

Introduction: Capsule endoscopy (CE) is a minimally invasive exam suitable of panendoscopic evaluation of the gastrointestinal (GI) tract. Nevertheless, CE is time-consuming with suboptimal diagnostic yield in the upper GI tract. Convolutional neural networks (CNN) are human brain architecture-based models suitable for image analysis. However, there is no study about their role in capsule panendoscopy. Methods: Our group developed an artificial intelligence (AI) model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers, and erosions). 355,110 images (6,977 esophageal, 12,918 gastric, 258,443 small bowel, 76,772 colonic) from eight different CE and colon CE (CCE) devices were divided into a training and validation dataset in a patient split design. The model classification was compared to three CE experts’ classification. The model’s performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results: The binary esophagus CNN had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric CNN identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel CNN distinguished pleomorphic lesions with different hemorrhagic potentials with 97.6% accuracy. The trinary colonic CNN (detection and differentiation of normal mucosa, pleomorphic lesions, and hematic residues) had 94.9% global accuracy. Discussion/Conclusion: We developed the first AI model for panendoscopic automatic detection of pleomorphic lesions in both CE and CCE from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.

Brand and Country of Origin Towards Consumer Attitudes in Choosing Smartphones

As technology advances, smartphone devices become a fascinating topic for conversation. The growth of multiple smartphone brands as choices appears to be inextricably linked to the relevance of customer attitudes in deciding which smartphone to use. The growth of multiple smartphone brands as choices appears to be inextricably linked to the relevance of customer attitudes in deciding which smartphone to use. Furthermore, when choosing a smartphone, shoppers consider the product's nation of origin. This study uses a quantitative approach method. Involving the 100-person Dumai City community. The findings of the partial analysis indicate that brands have a positive and significant effect on consumer attitudes and country of origin variables have a positive but not significant effect on consumer attitudes. Brand and country of origin together have a positive and significant effect on consumer attitudes in the Dumai City community. The result of the Determination Coefficient is 21.3%, with the remaining 78.7% impacted by other variables.

Exploring the Influence Factors of Purchase Intention of College Students by Short Video Opinion Leaders in Field of the Beauty

Over the past decade, the rapid development of new media and e-commerce have triggered the emergence of online opinion leaders. Simultaneously, consumers’ purchase intention has been swayed by some opinions expressed by online opinion leaders. Therefore, this paper aims to delve into the factors influencing the consumption of college students by opinion leaders in field of the beauty. It aims to explore the influence degree of short video opinion leaders on the purchase intention of college student consumers in three dimensions, including short video production, opinion leaders’ characteristics and short video content. Employing a questionnaire for data collection and SPSS 29.0 for analysis, the study uses methods such as reliability analysis, exploratory factor analysis, and regression analysis to validate the model and research hypotheses. The results of the study show that in the dimension of opinion leaders’ short video production, the attractiveness of the title and editing format of the short videos have a significant positive impact on the purchase intention of college students. Regarding opinion leaders’ characteristics, their popularity, professionalism, and interactivity exert a substantial positive influence on college students’ purchase intentions, whereas timeliness shows no significant impact. In terms of opinion leaders short video content, comparing products with multiple brands, promoting affordable substitutes, and vivid and interesting promotion discourse of opinion leaders all have a significant positive impact on the purchase intention. Finally, this paper provides suggestions for short video opinion leaders in field of the beauty.

The Rivalry Reference Effect: Referencing Rival (vs. Nonrival) Competitors in Public Brand Messages Increases Consumer Engagement

Brands often make references to competitors in their public communications. Yet not all competitors are equal—over time, certain pairs of brands may develop special rivalry relationships. This research examines whether these relationships can alter the effects of interbrand communications. Drawing on rivalry theory, the authors distinguish between brand rivalry, a special competitive relationship between brands based on a shared history, and competition, a situation where brands merely have currently opposing goals. A series of studies using complementary methods (two archival studies using large-scale Twitter data and three preregistered experiments) provide evidence for the “rivalry reference effect” across multiple brands and product categories. That is, referencing a rival (vs. a nonrival) competitor in a public brand message increases consumer engagement. The effect is mediated by increased story embeddedness, defined as the perception that the reference is embedded within an ongoing story. The authors also show positive downstream effects on purchase intentions and test two moderators: brand preference (loyal vs. neutral consumers) and message valence (negative vs. positive). This research highlights the potential appeal of brand rivalry to consumers, illustrating how and under what conditions brands can use their rivalries to their advantage.

Relationship qualities of brand heritage: warmth, competence and the moderating roles of anthropomorphic factors

PurposeResearch has started exploring how brand heritage perceptions affect people. However, little attention has been paid to the underlying mechanisms and the link between brand heritage and relational outcomes. This study aims to integrate research on brand heritage with the stereotype-content model (SCM) to offer a novel explanation of why and when consumers identify with heritage brands.Design/methodology/approachTwo quasi-experimental studies with consumers in Germany (N = 312 and N = 300) focus on multiple real brands to test the mediating roles of warmth and competence. Given the central role of anthropomorphism in brand applications of the SCM, two corresponding variables are examined as moderators, one relating to the brand (brand anthropomorphism) and the other relating to the individual (a person’s feeling of loneliness). Category involvement, state anxiety, brand familiarity, past orientation and consumer age are included as controls.FindingsThe findings indicate that warmth and competence mediate the brand heritage consumer–brand identification relationship. In addition, they highlight the moderating role of brand anthropomorphism and loneliness.Research limitations/implicationsThis study offers a novel process explanation for how brand heritage perceptions influence consumer–brand relationships, contingent upon loneliness and anthropomorphism.Practical implicationsThe findings help marketers better understand how and when warmth and competence transmit positive brand heritage effects, resulting in more favorable responses.Originality/valueTo the best of the authors’ knowledge, this work is among the first to adopt a stereotype-content and anthropomorphic perspective on consumer responses to brand heritage perceptions.

Air Conditioner Sales Prediction Using CTGAN, XGBoost and SHAP

In today's modern world, air conditioners (ACs) have become a vital part of daily life due to rising temperatures and increasing disposable incomes, leading to a surge in AC sales. With numerous companies competing in this industry, businesses face challenges in selecting the most suitable AC brand for their needs, managing inventories across multiple brands. To address this issue, this research paper presents a machine learning model designed to recommend the optimal AC brand based on size, price, and predicted sales potential. Leveraging data from Amazon, the leading AC retailer in the Indian market, the model facilitates informed decision-making for businesses seeking efficient AC procurement strategies. Key Words: Sales Prediction, Amazon, AC, XGBoost, CT-GAN, SHAP, KNN

Are brands that perform gendered product differentiation perceived as sexist? An experimental investigation

PurposeThe purpose of this paper is to examine the consumer response to brands offering gendered product differentiation (i.e. products “for her” or “for him”).Design/methodology/approachAcross three experiments, the effect of gendered (vs gender-unrelated) product differentiation on perceived brand sexism and word-of-mouth intention was tested. The moderating effects of feminist identity (Studies 1 and 2), endorsement of sexist beliefs (Study 2) and basis (stereotypical vs biological) for product differentiation (Study 3) were also tested.FindingsConsumers perceive brands that offer gendered product differentiation as sexist, which in turn leads to negative word-of-mouth intention. Moreover, consumers with a strong feminist identity are more likely to perceive brands that offer gendered product differentiation as sexist, whereas consumers who endorse sexist beliefs are less likely to do so. Finally, consumers respond negatively when the gendered product differentiation is based on a gender stereotype, but much less so when it seems based on a biological difference between sexes.Originality/valueAlthough multiple brands offering gendered products have been denounced by consumers as sexist, no research has examined this phenomenon. This paper pioneers in examining the consumer response to brands offering gendered product differentiation and in demonstrating that consumers perceive such brands as sexist.

Adaptive Content-Aware Influence Maximization via Online Learning to Rank

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model , which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies ) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.

Design and Implementation of a Tailored Dealer Management System (DMS) for the Automotive Industry

This paper presents a thorough investigation into the design and implementation of a Dealer Management System tailored for the automotive industry. The DMS is intended to streamline after-sales processes such as inventory and expense management, service order creation, and financial data monitoring. The system integrates with a variety of other platforms, including ERP, CRM, and payment systems, which improves collaboration between dealers and distributors. The architecture of DMS, known as Copilot Next, is detailed, with a focus on the backend, frontend, and integration components. Sentinet and Dell Boomi are integration tools that allow for seamless connectivity with other systems. The findings show that the DMS effectively meets the needs of multiple vehicle brands, improves part ordering accuracy with AI tools, and ensures data consistency across systems. This study offers useful insights into the design and implementation of a DMS that can significantly improve dealership operations and customer service in the automotive industry.

Comparison of long-term outcomes between a single versus a multiple stent brand strategy during �full metal jacket� procedures

Comparison of long-term outcomes between a single versus a multiple stent brand strategy during �full metal jacket� procedures

P469 Deep learning and minimally invasive endoscopy – panendoscopic detection of pleomorphic lesions

Abstract Background Capsule endoscopy is a minimally invasive exam capable of performing a panendoscopic evaluation of the gastrointestinal tract. Nevertheless, capsule endoscopy is a time-consuming exam, revealing suboptimal diagnostic yield when considering the upper gastrointestinal tract. Convolutional neural network are models based on the human visual cortex architecture, suitable for image analysis. However, there is still an absence of studies about their role in capsule panendoscopy. Methods Our group developed a deep Learning model for panendoscopic automatic detection of pleomorphic lesions (namely vascular lesions, protuberant lesions, hematic residues, ulcers and erosions). In order to develop the convolutional neural network, 355 110 images (6 977 oesophageal, 12 918 gastric, 258 443 enteric, 76 772 colonic) from eight different capsule endoscopy and colon capsule endoscopy devices were divided in a training and validation dataset in a patient split design. The model’s output was compared to three CE experts’ classification. The model performance was evaluated by its sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the precision-recall curve. Results The panendoscopic convolutional neural network was composed by organ-specific neural networks. The binary oesophagus convolutional neural network had a diagnostic accuracy for pleomorphic lesions of 83.6%. The binary gastric network identified pleomorphic lesions with a 96.6% accuracy. The undenary small bowel convolutional neural network distinguished pleomorphic lesions with different potential with 97.6% accuracy. The trinary colonic model (detection and differentiation of normal mucosa, pleomorphic lesions and hematic residues) had 94.9% global accuracy. Conclusion Our group developed the first deep learning model for panendoscopic automatic detection of pleomorphic lesions in both small bowel and colon capsule endoscopy devices from multiple brands, solving a critical interoperability technological challenge. Deep learning-based tools may change the landscape of minimally invasive capsule panendoscopy.

Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare

Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.

Variable Importance Measures for Multivariate Random Forests

Multivariate random forests (or MVRFs) are an extension of tree-based ensembles to examine multivariate responses. MVRF can be particularly helpful where some of the responses exhibit sparse (e.g., zero-inflated) distributions, making borrowing strength from correlated features attractive. Tree-based algorithms select features using variable importance measures (VIMs) that score each covariate based on the strength of dependence of the model on that variable. In this paper, we develop and propose new VIMs for MVRFs. Specifically, we focus on the variable’s ability to achieve split improvement, i.e., the difference in the responses between the left and right nodes obtained after splitting the parent node, for a multivariate response. Our proposed VIMs are an improvement over the default naïve VIM in existing software and allow us to investigate the strength of dependence both globally and on a per-response basis. Our simulation studies show that our proposed VIM recovers the true predictors better than naïve measures. We demonstrate usage of the VIMs for variable selection in two empirical applications; the first is on Amazon Marketplace data to predict Buy Box prices of multiple brands in a category, and the second is on ecology data to predict co-occurrence of multiple, rare bird species. A feature of both data sets is that some outcomes are sparse — exhibiting a substantial proportion of zeros or fixed values. In both cases, the proposed VIMs when used for variable screening give superior predictive accuracy over naïve measures.

Offshore Wind Power Policy in The U.S. And China: Comparison And Analysis

This paper compares and analyzes different offshore wind power policies between two countries, follows the timeline for the development phase, and explores deep reasons for framework differences in three aspects (Policy Environment, Financial Incentives, and Permit Processes). The key findings are that policy is the primary driver to boost offshore wind energy. A stable policy environment with long-term goals could revise developments from experience, support SOEs to raise capital and develop experiments for provincial and local governments and increase international collaborations. Differentiating FITs payments contribute to renewable electricity generation, promote technology price decreases, and ensure market certainty. Streamlined permitting accelerates speed construction and creates an energy innovation system with multiple brands and technologies. In the end, suggestions are given separately to help China and US further develop the offshore wind power industry.

Reselling or marketplace channel? A manufacturer’s strategic decisions for differentiated brands considering channel’s service level

Brand differentiation is an effective strategy commonly used by large-scale brand manufacturers to produce multiple brands of a particular product type. To avoid undesirable inter-brand competition, a brand manufacturer prefers to adopt multiple online sales channels (OSCs) with different channel’s service levels provided by the online retailer in selling such differentiated brands. Considering both brand and channel’s service differentiation, this joint decision of the brand manufacturer and online retailer achieves the coordination of retail supply chain. Our study is based upon a model that consists of a responsible brand manufacturer(he) that aims to maximise profits and enhance consumer surplus (CS). He offers two brands with differentiated consumer acceptances, a premium and an economy brand. Also, he chooses between two OSCs (a reselling and a marketplace channel) to provide different levels of services. The brand manufacturer’s OSC strategies are investigated according to the interplay between brand and service differentiation, and profits and CS, respectively. The results show that the brand manufacturer prefers differentiated OSCs for two brands as service differentiation decreases. Also, at low brand differentiation and alternatively, high brand differentiation and low service differentiation, the brand manufacturer and consumers show different OSC preferences. Finally, a case study is performed to validate our results.

Ramping up the food park business: a case study of Urban Chowk

Learning outcomes After completion of the case study, students will be able to critically appraise the strategic growth opportunities of an “eating out” industry brand; evaluate the role of customer relationship and retention management in sustaining a brand; and analyse the importance of developing core competencies for long-term business viability. Case overview/synopsis Since its inception in 2017, Urban Chowk was one of the pioneer places that successfully managed to create a hospitality format called “food and lifestyle park” which combined multiple food brands with good ambience and entertainment, all under the same roof. Although Urban Chowk was one of the popular brands in the given format, its USP was not difficult to challenge or copy. The owner, Mr Kartikey Rajput, anticipated a deluge of similar establishments in the near future. Urban Chowk led the pack in terms of footfalls compared to similar establishments, with Rajput looking to expand his brand beyond Ahmedabad. The case took 2022 as the timeline and brought forth the challenge that Urban Chowk faced. The primary challenge was retaining the existing customer base along with adding newer ones. Second, with the industry being extremely dynamic, it was important to determine how marketing analytics could be used to collect customer data and convert them into repeat customers, thus building a loyal customer base. Complexity academic level This case is applicable for teaching at undergraduate and graduate levels for understanding the concepts of brand development and strategic growth as well as creating and maintaining customer relationships, also helping the students in assimilating these concepts as a part of brand building. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Insecurity, deportability and authority

Security is more than ever a central theme in the study of international migration. For the past twenty years, research on the securitization of migration has burgeoned. While these initiatives are to be applauded, we believe they may also have misdiagnosed the problem. For example, it may not be that the concept of ‘security’ needs to be ‘humanized’ in order to be more in tune with migrants’ concerns. Rather, the problem may lie in the use of the ‘migrant’ as an analytical category. The ‘migrant’ remains an inherently statist construct. The starting premise for the collection of articles in this special issue is that it is the tendency of academic research to mistake the statist category of the ‘migrant’ as an analytical category that has prevented the literature on the migration–security nexus from meaningfully reflecting the lived experience and aspirations of its human respondents, particularly as regards their encounters with forms of institutional authority, practices, resistance and resilience. We use the rubric of deportability to open up a variety of ways of thinking and talking about migration and security that do not fall back upon statist tropes. The authors in this collection take up this challenge by framing and employing concepts such as statelessness, sedentariness and expulsion to redefine our understanding of the relationship between movement and order. They take inspiration from multiple brands of social and political theorizing where conditions of violence and forced removal qualitatively differentiate the experiences and encounters of a particular group.