bluuebunny/crossref_metadata_2025_split huggingface dataset:
Suspect screening-data independent analysis workflow for the identification of arsenolipids in marine standard reference materials
Authors: Bhattacharjee Shubhra , Chacon-Teran Miguel A. , Findlater Michael , Louie Stacey M. , Bailoo Jeremy D. , Deonarine Amrika 
Published: September 2024

AbstractThere has been limited research into arsenolipid toxicological risks and health-related outcomes due to challenges with their separation, identification, and quantification within complex biological matrices (e.g., fish, seaweed). Analytical approaches for arsenolipid identification such as suspect screening have not been well documented and there are no certified standard reference materials, leading to issues with reproducibility and uncertainty regarding the accuracy of results. In this study, a detailed workflow for the identification of arsenolipids utilizing suspect screening coupled with data independent analysis is presented and applied to three commercially available standard reference materials (Hijiki seaweed, dogfish liver, and tuna). Hexane and dichloromethane/methanol extraction, followed by reversed-phase high-performance liquid chromatography-inductively coupled plasma mass spectrometry and liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry. Using the workflow developed, mass fragmentation matching, mass error calculations, and retention time matching were performed to identify suspect arsenolipids. Arseno-fatty acids (AsFAs), arsenohydrocarbons (AsHCs), and arsenosugar phospholipids (AsSugPLs) were identified with high confidence; AsHC332, AsHC360, and AsSugPL720 in seaweed, AsHC332 in tuna, and AsFA474 and AsFA502 in the dogfish liver. AsHC332, AsHC360, and AsFA502 were identified as promising candidates for further work on synthesis, quantification using MS/MS, and toxicity testing.
The complex swarming dynamics of malaria mosquitoes emerges from simple minimally-interactive behavioral rules
Authors: Cribellier Antoine , Poda Bèwadéyir Serge , Dabiré Roch Kounbobr , Diabaté Abdoulaye , Roux Olivier , Muijres Florian T. 
Published: September 2024

AbstractComplex coordinated group behaviors such as bird flocking and fish schooling often rely on intricate interactions among individuals. In these groups, neighbors exhibit strong mutual attraction, alignment, and collision avoidance. Here, we tested whether insect swarms arise from similar simple behavioral rules. Using high-speed videography and agent-based modeling, we identified the behavioral rules governing mating swarms of flying malaria mosquitoes, marking the first such analysis for any insect species. We found that, unlike birds and fish, mosquito swarming behavior is driven primarily by environmental cues like the sunset horizon and ground objects. In fact, interactions between conspecifics are limited to only close-range collision avoidance behaviors. Thus, insect swarming seems fundamentally distinct from bird flocking and fish schooling.
Multi-scale and multi-context interpretable mapping of cell states across heterogenous spatial samples
Authors: Martin Patrick C.N. , Wang Wenqi , Kim Hyobin , Holze Henrietta , Won Kyoung Jae 
Published: September 2024

AbstractIn clinical applications, spatial data collected under varying conditions, time points, or patients often lack discernible structural alignment. Computational tools designed to align adjacent tissue sections are unsuited for dealing with this structural heterogeneity. There is a growing demand for methods that can effectively align and compare spatial data in the absence of obvious visual correspondence. To address this challenge, we developed an interpretable cell mapping strategy by considering spatial context at various scales. Our approach outperforms existing mapping tools in dealing with heterogeneous samples and is flexible enough to map cells across samples, technologies, resolutions, developmental and regenerative time. Using our approach, we showed spatiotemporal decoupling of cells during development. We even performed alignment for a population of spatial data from cancer patients to identify sub- populations. Our interpretable mapping approach facilitates systemic comparison and analysis of heterogeneous spatial data.
Secretome analysis by proximal labeling in Golgi apparatus for identifying fat body derived humoral factors inDrosophiladisc regeneration
Authors: Yoshida Yutaka , Kashio Soshiro , Miura Masayuki 
Published: September 2024

AbstractHumoral factors act as inter-tissue mediators and regulate various organismal physiologies. Although the importance of humoral factors in tissue repair has been recognized recently, our understanding of how humoral proteins regulate tissue repair remains limited. Glycosylation is an important modification of the conventional secretory pathway, and we have demonstrated that N-glycosylation in the Golgi apparatus of theDrosophilafat body (FB), the major secretory tissue equivalent to the mammalian liver and adipose tissue, remotely contributes to epithelial tissue repair. To identify humoral factors that contribute to repair via the Golgi apparatus, we constructed a Golgi-specific protein biotinylation system and performed hemolymph proteomics. By combining genetic analyses, we found that FB-derived innate immune regulators and iron mediators affect tissue repair. Altogether, our Golgi-specific labeling system has the potential to identify Golgi-mediated secreted factors that regulate inter-organ communication.
The more followers the better? The impact of food influencers on consumer behaviour in the social media context
Authors: Misra Ankita , Dinh Tam Duc , Ewe Soo Yeong 
Published: September 2024

PurposeThe study explores the impact of food influencers on consumer behaviour in the social media context. It assesses the interplay between the number of followers an influencer has and the type of content this influencer communicates to the audience. Doing so, the research contributes to the strategic refinement of influencer marketing practices, especially in the food industry.Design/methodology/approachThe study employed an experimental between-subject design 2 (influencer type: micro vs macro) x 2 (content type: informational vs entertaining). It recruited 197 Prolific participants (45.7% female, Mage = 45.076), testing their perceptions towards the influencer and the endorsed product in the social media post.FindingsThere was a significant interaction between influencer type and content type on consumers’ attitudes towards and their willingness to buy the advertised product. Specifically, the notion that “the more followers, the better” may only be applicable when consumers peruse the content for entertainment purposes, whereas while they read it for information purposes, a micro influencer (with hundreds to thousands of followers) may have as much impact on consumer behaviour as a macro influencer (with hundreds of thousands to a million followers).Originality/valueOur findings offer a nuanced understanding into the conventional wisdom that people often follow crowd behaviour. Using the Heuristic-Systematic Model (HSM), we explicate when the number of followers matters and when the content type prevails.
Sustainability index development by integrating lean green and Six Sigma tools: a case study of the Indian manufacturing industry
Authors: Gandhi Jaiveshkumar D. , Thanki Shashank 
Published: September 2024

PurposeIndia’s manufacturing sector employs about 12% of the labour force and contributes to about 17% of the nation’s GDP. The Indian government intends to implement several initiatives under the “Make in India” and Atma Nirbhar Bharat banners to increase the manufacturing sector’s share of the nation’s GDP to 25% by 2025. Applying lean manufacturing, green manufacturing and Six Sigma is crucial to ensure that India’s manufacturing sectors grow sustainably in international markets. This study aims to identify sustainability indicators and ascertain their respective weights to evaluate the sustainability performance of the Indian manufacturing industry.Design/methodology/approachThis research identifies 25 sustainability indicators and classifies them into the triple bottom line of sustainability based on an evaluative literature review and expert opinion. The Best Worst Method was utilised to determine the weights of the sustainability indicators. The sustainability index was developed to evaluate economic, social and environmental sustainability.FindingsThe sustainability performance of a foundry in a significant Western Indian State city was assessed by applying the developed sustainability index. After the adoption of integrated lean, green and Six Sigma (LG&SS) strategies and related practices in the foundry, there has been a notable improvement of 68.03% in the economic index, 61.62% in the social index and 13.24% in the environmental index.Research limitations/implicationsThe proposed sustainability index is applied and evaluated specifically for assessing the sustainability performance of Indian manufacturing SMEs. It can be used to substantiate firm’s sustainability performance and also to assess the improvement in firm’s performance in economic, environmental and social dimensions after implementing various operational excellence practices. However, it cannot serve as a benchmark tool across similar companies or organisations.Practical implicationsThe developed sustainable index can be used to analyse the company or organisation’s sustainability performance and see how various strategies have improved things. Practitioners can use this index to assess social, economic and environmental performance and focus on areas that need improvement.Social implicationsThe proposed sustainability index serves as a vital tool for monitoring a firm’s progress in triple bottom line (TBL) dimensions of sustainability, tracking a diverse range of indicators and encouraging sustainable organisational practices.Originality/valueThis study attempts to assess the economic, social and environmental performance of Indian Manufacturing SMEs by proposing a sustainability index.
Microstructures and wear properties of CoCrFexNi (x = 0, 1) medium-entropy alloy coatings prepared by laser cladding
Authors: Wang Yinghu , Wang Zhiyuan , Xu Jianyan 
Published: September 2024

Purpose
High-nitrogen steel is a common material for the manufacture of drilling equipment such as drill collars. However, its poor surface properties often limit its applications. The purpose of this paper is to find a way to enhance the surface performance of high-nitrogen steel, which is expected to improve the wear resistance of high-nitrogen steel.

Design/methodology/approach
The CoCrNi and CoCrFeNi medium-entropy alloy (MEA) coatings were prepared on high-nitrogen steel substrate by laser cladding technology. The microstructure, phase composition and element distribution of the fabricated coatings were investigated using scanning electronic microscopy, electron backscatter diffraction and X-ray diffraction. The phase structure, phase stability and structural stability of the CoCrNi and CoCrFeNi MEAs were investigated in combination with phase diagram calculation and molecular dynamics. Then the wear tests were carried out for coatings.

Findings
The results show that both prepared MEA coatings have good quality and contain a single face-centered cubic phase. The wear performance of MEA coatings is improved by the refinement of grain size and the increase of dislocation density. Due to the addition of Fe atoms, the lattice distortion of the CoCrFeNi system increased, resulting in a higher dislocation density of the coating. Cr atoms in the CoCrFeNi system are the largest, and the local lattice distortions induced by them are greater. Through this study, MEA coatings with high hardness can be expanded to drilling field applications.

Originality/value
CoCrFeNi and CoCrNi MEA coatings were successfully prepared on the surface of high-nitrogen steel for the first time without obvious defects. The micromorphology and grain orientation of the different kinds of coatings were discussed in detail. The hardness-strengthening mechanism and structure stability of the coatings were illustrated by experiments and molecular dynamics simulations.

Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0116/
The impact of social support chatbots on patients’ value co-creation behavior in online health communities: a moderated mediation model
Authors: Latif Muhammad Salman , Wang Jian-Jun , Shahzad Mohsin , Mursil Muhammad 
Published: September 2024

PurposeOnline health communities (OHCs) have emerged on the Internet, substantially changing the conventional healthcare delivery model. Despite this emergence, the lack of patient participation and contribution always limits the success and sustainability of OHCs. Previous studies have disclosed that patients’ value co-creation behavior (VCB) helps organizations sustain OHCs. However, how the recent surge in artificial intelligence (AI) tools, such as social support chatbots (SSCs), drives patients’ VCB is still unknown. Therefore, this study examines the complex mechanism behind patients’ VCB to establish sustainable OHCs.Design/methodology/approachUsing value co-creation and social support theories, the author develops a moderated mediation model and analyzes survey data from 338 respondents using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods.FindingsResults demonstrate that perceived social support (PSS) from SSCs positively affects VCB directly and indirectly via patient learning (PL). This indirect effect is stronger when patient ability/readiness (PAR) is high. ANN findings highlight the model’s robustness and the significant role of PAR in VCB.Originality/valueThis study’s integrated framework offers unique insights into key drivers of patients’ VCB in OHCs. The findings indicate that PSS from SSCs enhances PL and VCB, with PAR influencing the strength of these relationships. Understanding these dynamics can inform user-centric interventions to promote effective learning and collaboration in OHCs.
Does speculation impair informational efficiency? New evidence from the Indian agricultural commodity market
Authors: Pullaykkodi Sreekha , Acharya Rajesh H. 
Published: September 2024

Purpose This study explores the association between market efficiency and speculation. The government of India temporarily banned the futures trading of various commodities several times citing the presence of speculation. Many controversies exist about this topic; thus, this study clarifies the association between market efficiency and speculation and investigates whether market reforms altered this association.Design/methodology/approach The data for nine commodities is collected from the National Commodity and Derivative Exchange (NCDEX) for 2005–2022. Regression analysis and Automatic Variance Ratio (AVR) were adopted to inspect the informational efficiency and influence of speculation in the commodity market. Furthermore, this study uses different sub-samples to understand the changes in the market microstructure and its effects on market quality.Findings The results confirm an inverse and significant relationship between information efficiency and speculation and a deviation from the random walk process observed. Therefore, return predictability exists in the market. This study confirms that market reforms do not reduce the influence of speculation on market efficiency. The study concludes that the market is not weak-form efficient.Research limitations/implications This study has certain limitations, since this study is empirical in nature, it may possess the limitations of empirical research.Originality/value This paper has dual novelty. First, this study investigates the effects of market reforms. Second, this study captures the influence of speculation in the Indian agricultural commodity market by considering the market microstructure aspects.
Buyer-initiated knowledge payment facilitates free knowledge contribution in knowledge-sharing platforms: an integration of relational signaling theory and attribution theory
Authors: Jiang Shan , Shi Daqian , Cheng Yihang 
Published: September 2024

Purpose
The model of pay-for-knowledge incentivizes individuals with financial rewards for sharing their expertise, facilitating a transactional exchange between knowledge providers (sellers) and seekers (buyers). While this model is effective in promoting paid contributions, its influence on free knowledge exchanges remains ambiguous, creating uncertainty about its overall impact on platform knowledge ecosystems. This study aims to explore the mechanim of how knowledge payment influences free knowledge contribution. Based on relational signaling theory, this study posits that a buyer’s payment for knowledge acts as a positive relational signal in the buyer–seller relationship and examines how the signaling effect varies across different social contexts through attribution theory.

Design/methodology/approach
This paper empirically tests the hypotheses by analyzing a data set comprising 630 instances from 359 unique knowledge sellers on Zhihu, a prominent knowledge-sharing platform in China. This paper use zero-inflated negative binomial models to conduct this analysis.

Findings
The findings reveal that when buyers pay for knowledge, this action positively influences sellers to contribute knowledge for free. However, the strength of this influence is moderated by the platform’s social functions: appreciation feedback tends to weaken this effect, while social network ties enhance it.

Originality/value
Prior research has predominantly focused on the financial incentives of pay-for-knowledge and its spillover effects on unpaid users’ activities. This study shifts the focus to the social dimensions of pay-for-knowledge, arguing that buyer-initiated knowledge payments signal buyers’ commitment to foster reciprocal relationships with sellers. It expands the literature on the relationship between knowledge payment and contribution, moving beyond financial incentives to include social factors, thus enriching our understanding of the interplay between paid and free knowledge activities. Additionally, the empirical evidence supports the efficacy of pay-for-knowledge in promoting both free and paid contributions within knowledge-sharing platforms.
Bayesian-optimized extreme gradient boosting models for classification problems: an experimental analysis of product return case
Authors: Bhattacharjee Biplab , Unni Kavya , Pratap Maheshwar 
Published: September 2024

Purpose
Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

Design/methodology/approach
An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

Findings
A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Research limitations/implications
Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

Originality/value
There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.
Generational persona and entrepreneurial behavior among millennial entrepreneurs: the mediating role of adaptability tendencies
Authors: Sirajje Yiga , Abaho Ernest , Nsereko Isa , Basalirwa Edith Mwebaza , Muhammed Ngoma , Balunywa Juma Wasswa 
Published: September 2024

PurposeThe purpose of this study is to examine the relationship between generational persona, adaptability tendencies and entrepreneurial behavior. The paper also aims at testing the mediating role of adaptability tendencies in the relationship between generational persona and entrepreneurial behavior among millennial entrepreneurs in an African setting.Design/methodology/approachThe study adopts a quantitative methodological approach with a cross-sectional, questionnaire survey and correlational design where hypotheses were statistically tested using Structural Equation Modelling based on survey data (n = 382) from millennial entrepreneurs in Kampala Uganda.FindingsDrawing on the sample of 382 millennial entrepreneurs in Kampala, findings show that both generational persona and adaptability tendencies are positively and significantly associated with entrepreneurial behavior. Results further indicate that adaptability tendencies partially mediates the relationship between generational persona and entrepreneurial behavior among millennial entrepreneurs.Research limitations/implicationsThis study focused only on millennial entrepreneurs in Kampala Uganda ignoring other equally important groups of entrepreneurs like the baby boomers, generation Xers, generation Y and others. As such, the findings of this research do not entirely apply to all entrepreneurs in the country and this may have affected the generalizability of the results. Therefore, future studies can be done on the entrepreneurial behavior focusing on all entrepreneurs from all generations. Also, the study used a quantitative approach, future studies should consider a mixed methodology, which may give a more holistic understanding of entrepreneurial behavior.Practical implicationsIn practice, millennial entrepreneurs may use the results of the study to see how they can improve their performance for their businesses to benefit. Specifically, they ought to focus on adaptability, and generational persona to exhibit those entrepreneurial behaviors which will generally lead to the improvement of their businesses.Originality/valueTo the authors’ knowledge, this study provides a shred of initial empirical evidence on the relationship between generational persona, adaptability tendencies and entrepreneurial behavior using evidence from a low developed African country Uganda. Mostly, this study provides initial evidence of the mediating role of adaptability tendencies in the relationship between generational persona and entrepreneurial behavior. This study incorporates the Generational Cohort Theory and the Complex Adaptive Systems Theory into an applied theoretical framework that explains entrepreneurial behavior. More still, this study answers the call for more empirical studies on entrepreneurial behavior.