bluuebunny/crossref_metadata_2025 huggingface dataset:
Navigating “tip fog”: Embracing uncertainty in tip measurements
Authors: Beaulieu Jeremy M. , O’Meara Brian C.
Published: August 2024

Nature is full of messy variation, which serves as the raw material for evolution. However, in comparative biology this variation is smoothed into averages. Overlooking this variation not only weakens our analyses but also risks selecting inaccurate models, generating false precision in parameter estimates, and creating artificial patterns. Furthermore, the complexity of uncertainty extends beyond traditional “measurement error,” encompassing various sources of intraspecific variance. To address this, we propose the term “tip fog” to describe the variance between the true species mean and what is recorded, without implying a specific mechanism. We show why accounting for tip fog remains critical by showing its impact on continuous comparative models and discrete comparative and diversification models. We rederive methods to estimate this variance and use simulations to assess its feasibility and importance in a comparative context. Our findings reveal that ignoring tip fog substantially affects the accuracy of rate estimates, with higher tip fog levels showing greater biases from the true rates, as well as affecting which models are chosen. The findings underscore the importance of model selection and the potential consequences of neglecting tip fog, providing insights for improving the accuracy of comparative methods in evolutionary biology.
UnigeneFinder: An automated pipeline for gene calling from transcriptome assemblies without a reference genome
Authors: Xue Bo , Prado Karine , Rhee Seung Yon , Stata Matt
Published: August 2024

For most species in nature, transcriptome data is much more readily available than genome data. Without a reference genome, however, gene calling is cumbersome and inaccurate due to the high degree of redundancy inde novotranscriptome assemblies. To simplify and increase the accuracy ofde novotranscriptome assembly in the absence of a reference genome, we developed UnigeneFinder. Combining several clustering methods, UnigeneFinder substantially reduces the redundancy typical of raw transcriptome assemblies. This pipeline offers an effective solution to the problem of inflated transcript numbers, achieving a closer representation of the actual underlying genome. UnigeneFinder performs comparably or better, compared to existing tools, on plant species with varying genome complexities. UnigeneFinder is the only available transcriptome redundancy solution that fully automates the generation of primary transcript, coding region, and protein sequences, analogous to those available for high quality reference genomes. These features, coupled with the pipeline’s cross-platform implementation and focus on automation and an accessible user interface, make UnigeneFinder a useful tool for many downstream sequence-based analyses in non-model organisms lacking a reference genome, including differential gene expression analysis, accurate ortholog identification, functional enrichments, and evolutionary analyses. UnigeneFinder also runs efficiently both on high-performance computing (HPC) systems and personal computers, further reducing barriers to use.
Induction of Ferroptosis by an Amalgam of Extracellular Vesicles and Iron Oxide Nanoparticles Overcomes Cisplatin Resistance in Lung Cancer
Authors: Paramanantham Anjugam , Asfiya Rahmat , Manjunath Yariswamy , Xu Lei , McCully Grace , Das Siddharth , Yang Hu , Kaifi Jussuf T. , Srivastava Akhil
Published: August 2024

Extracellular vesicles (EVs) hold potential as effective carriers for drug delivery, providing a promising approach to resolving challenges in lung cancer treatment. Traditional treatments, such as with the chemotherapy drug cisplatin, encounter resistance in standard cell death pathways like apoptosis, prompting the need to explore alternative approaches. This study investigates the potential of iron oxide nanoparticles (IONP) and EVs to induce ferroptosis—a regulated cell death mechanism—in lung cancer cells. We formulated a novel EV and IONP-based system, namely ‘ExoFeR’, and observed that ExoFeR demonstrated efficient ferroptosis induction, evidenced by downregulation of ferroptosis markers (xCT/SLC7A11 and GPX4), increased intracellular and mitochondrial ferrous iron levels, and morphological changes in mitochondria. To enhance efficacy, tumor-targeting transferrin (TF)-conjugated ExoFeR (ExoFeRTF) was developed. ExoFeRTFoutperformed ExoFeR, exhibiting higher uptake and cell death in lung cancer cells. Mechanistically, nuclear factor erythroid 2-related factor 2 (Nrf2)—a key regulator of genes involved in glutathione biosynthesis, antioxidant responses, lipid metabolism, and iron metabolism—was found downregulated in the ferroptotic cells. Inhibition of Nrf2 intracellular translocation in ExoFeRTF-treated cells was also observed, emphasizing the role of Nrf2 in modulating ferroptosis-dependent cell death. Furthermore, ExoFeR and ExoFeRTFdemonstrated the ability to sensitize chemo-resistant cancer cells, including cisplatin-resistant lung cancer patient-derived tumoroid organoids. In summary, ExoFeRTFpresents a promising and multifaceted therapeutic approach for combating lung cancer by intrinsically inducing ferroptosis and sensitizing chemo-resistant cells.
Sawfish: Improving long-read structural variant discovery and genotyping with local haplotype modeling
Authors: Saunders Christopher T , Holt James M , Baker Daniel N , Lake Juniper A , Belyeu Jonathan R , Kronenberg Zev , Rowell William J , Eberle Michael A
Published: August 2024

MotivationStructural variants (SVs) play an important role in evolutionary and functional genomics but are challenging to characterize. High-accuracy, long-read sequencing can substantially improve SV characterization when coupled with effective calling methods. While state-of the-art long-read SV callers are highly accurate, further improvements are achievable by systematically modeling local haplotypes during SV discovery and genotyping.ResultsWe describe sawfish, an SV caller for mapped high-quality long reads incorporating systematic SV haplotype modeling to improve accuracy and resolution. Assessment against the draft Genome in a Bottle (GIAB) SV benchmark from the T2T-HG002-Q100 diploid assembly shows that sawfish has the highest accuracy among state-of-the-art long-read SV callers across every tested SV size group. Additionally, sawfish maintains the highest accuracy at every tested depth level from 10 to 32-fold coverage, such that other callers required at least 30-fold coverage to match sawfish accuracy at 15-fold coverage. Sawfish also shows the highest accuracy in the GIAB challenging medically relevant genes benchmark, demonstrating improvements in both comprehensive and medically relevant contexts. When joint-genotyping 10 samples from CEPH-1463, sawfish has over 9000 more pedigree-concordant calls than other state-of-the-art SV callers, with the highest proportion of concordant SVs (78%) as well. Sawfish's quality model can be used to select for an even higher proportion of concordant SVs (86%), while still calling over 5000 more pedigree-concordant SVs than other callers. These results demonstrate that sawfish improves on the state-of-the-art for long-read SV calling accuracy across both individual and joint-sample analyses.AvailabilitySawfish is released as a pre-compiled Linux binary and user guide on GitHub: https://github.com/PacificBiosciences/sawfish.
Feminization of social play behavior depends on microglia
Authors: VanRyzin Jonathan W , Marquardt Ashley E , McCarthy Margaret M
Published: August 2024

Many sex differences in brain and behavior are established developmentally by the opposing processes of feminization and masculinization, which manifest following differential steroid hormone exposure in early life. The cellular mechanisms underlying masculinization are well-documented, a result of the fact that it is steroid-mediated and can be easily induced in newborn female rodents via exogenous steroid treatment. However, the study of feminization of particular brain regions has largely been relegated to being “not masculinization” given the absence of an identified initiating trigger. As a result, the mechanisms of this key developmental process remain elusive. Here we describe a novel role for microglia, the brain’s innate immune cell, in the feminization of the medial amygdala and a complex social behavior, juvenile play. In the developing amygdala, microglia promote proliferation of astrocytes equally in both sexes, with no apparent effect on rates of cell division, but support cell survival selectively in females through the trophic actions of Tumor Necrosis Factor α (TNFα). We demonstrate that disrupting TNFα signaling, either by depleting microglia or inhibiting the associated signaling pathways, prevents the feminization of astrocyte density and increases juvenile play levels to that seen in males. This data, combined with our previous finding that male-like patterns of astrocyte density are sculpted by developmental microglial phagocytosis, reveals that sexual differentiation of the medial amygdala involves opposing tensions between active masculinization and active feminization, both of which require microglia but are achieved via distinct processes.

Significance Statement

The cellular mechanisms by which sex differences in the brain arise provide insight into the cellular basis of behavior. Most mechanistic studies have focused on the process whereby regions of the male brain are differentiated from the female in response to elevated gonadal steroid in development due to the tractability of inducing masculinization by blocking steroid action in males or providing exogenous steroids to newborn females. As such, feminization is usually defined as “not masculinized”. Here, we demonstrate the active feminization of astrocyte density in a brain region modulating complex social behavior, rough-and-tumble play in juveniles. These findings indicate that lower levels of playfulness in females is an actively regulated process as opposed to simply being a lack of masculinization.
Blocking toxin function and modulating the gut microbiota: caffeic acid phenethyl ester as a potential treatment forClostridioides difficileinfection
Authors: Guo Yan , Zhang Yong , Wang Guizhen , Liu Hongtao , Wang Jianfeng , Deng Xuming , He Liuqing , Qiu Jiazhang
Published: August 2024

Clostridioides difficileinfection (CDI) is the leading cause of hospital-acquired diarrhea that seriously threatens public health. The disruption of normal gut microbiota by the use of broad-spectrum antimicrobial agents enables C. difficile to proliferate in the colon. The emergence and prevalence of hypervirulent C. difficile strains result in increased morbidity, mortality, and high recurrence rates of CDI, thus creating a pressing need for novel therapeutics. The multi-domain toxins TcdA and TcdB are the primary determinants of CDI pathogenesis, rendering them ideal drug targets in the anti-virulence paradigm. In this study, we identified caffeic acid and its derivatives from natural compounds library as active inhibitors of TcdB via a cell-based high-throughput phenotypic screening. Further mechanistic investigations revealed that caffeic acid phenethyl ester (CAPE) could directly bind to TcdB, thus suppressing InsP6-induced autoproteolysis and inhibiting glucosyltransferase activity. CAPE treatment remarkably reduces the pathology of CDI in a murine infection model in terms of alleviated diarrhea symptoms, decreased bacterial colonization, and relieved histopathological lesions. Moreover, CAPE treatment ofC. difficile-challenged mice induces a remarkable increase in the diversity and composition of the gut microbiota (e.g.,Bacteroidesspp.) and alterations of gut metabolites (e.g., adenosine, D-proline, and melatonin), which might partially contribute to the therapeutic outcomes of CAPE against CDI. Our results reveal the potential of CAPE as a therapeutic for the management of CDI, or CAPE might serve as a lead compound for the development of antivirulence drugs targeting TcdB.
IGPR-1 is phosphorylated on the immunoreceptor tyrosine-based motif, stimulates AKT pathway and supports melanoma growth
Authors: Rahimi Nader , Potluri Sreesaisowmya , Chitalia Vipul
Published: August 2024

Melanoma is a lethal form of skin cancer that impacts one out of every five Americans and ranks as the fifth most prevalent cancer among men and women in the United States. Immunoglobulin (Ig) and Proline-rich Receptor-1 (IGPR-1, also called TMIGD2/CD28H) is closely related to immune checkpoint, CD28/PDL1 family receptors. It controls important cellular processes including, immune cell regulation, cell-cell adhesion, mechanosensing, autophagy, and angiogenesis, and its activity is associated with multiple human malignancies. However, the role and signaling mechanism of IGPR-1 in melanoma remains largely undefined. Here, we report that IGPR-1 is mutated or upregulated in nearly 13% of melanoma and its pro-tumor signaling in melanoma cells is mediated by phosphorylation of immunoreceptor tyrosine-based activation motif (ITAM) tyrosine (Y222). IGPR-1 is phosphorylated at Y222 in human melanoma and cell culture. Phosphorylation of Y222 is context-dependent and is catalyzed by EGFR and Src kinase. Inhibition of EGFR by pharmacological and shRNA strategies inhibited phosphorylation of Y222, whereas stimulation with EGF promoted phosphorylation of Y222 in vivo and recombinant active EGFR catalyzed its phosphorylation in anin vitrokinase assay.In vivoco-immunoprecipitation andin vitroGST-pull-down assays demonstrated that phospho-Y222 facilitates the binding of IGPR-1 with the SH2 domain-containing proteins, SHC1 and SHP2. IGPR-1 stimulates multiple key signal transduction pathways relevant to tumorigenesis, including AKT, mTOR, and MAPK. Mutation of Y222 blocked IGPR-1-mediated activation of AKT and MAPK leading to inhibition of 3D-spheroid tumor growth. By investigating the immunoreceptor tyrosine-based motif signaling of IGPR-1, this study uncovers new findings that could have significant diagnostic and therapeutic implications for melanoma.
The structural basis for RNA slicing by human Argonaute2
Authors: Mohamed Abdallah A. , Wang Peter Y. , Bartel David P. , Vos Seychelle M.
Published: August 2024

Argonaute (AGO) proteins associate with guide RNAs to form complexes that slice transcripts that pair to the guide. This slicing drives post-transcriptional gene-silencing pathways that are essential for many eukaryotes and the basis for new clinical therapies. Despite this importance, structural information on eukaryotic AGOs in a fully paired, slicing-competent conformation—hypothesized to be intrinsically unstable—has been lacking. Here we present the cryogenic-electron microscopy structure of a human AGO−guide complex bound to a fully paired target, revealing structural rearrangements that enable this conformation. Critically, the N domain of AGO rotates to allow the RNA full access to the central channel and forms contacts that license rapid slicing. Moreover, a conserved loop in the PIWI domain secures the RNA near the active site to enhance slicing rate and specificity. These results explain how AGO accommodates targets possessing the pairing specificity typically observed in biological and clinical slicing substrates.
Multi-Modal Large Language Model Enables Protein Function Prediction
Authors: Huo Mingjia , Guo Han , Cheng Xingyi , Singh Digvijay , Rahmani Hamidreza , Li Shen , Gerlof Philipp , Ideker Trey , Grotjahn Danielle A. , Villa Elizabeth , Song Le , Xie Pengtao
Published: August 2024

Predicting the functions of proteins can greatly accelerate biological discovery and applications, where deep learning methods have recently shown great potential. However, these methods predominantly predict protein functions as discrete categories, which fails to capture the nuanced and complex nature of protein functions. Furthermore, existing methods require the development of separate models for each prediction task, a process that can be both resource-heavy and time-consuming. Here, we present ProteinChat, a versatile, multi-modal large language model that takes a protein’s amino acid sequence as input and generates comprehensive narratives describing its function. ProteinChat is trained using over 1,500,000 (protein, prompt, answer) triplets curated from the Swiss-Prot dataset, covering diverse functions. This novel model can universally predict a wide range of protein functions, all within a single, unified framework. Furthermore, ProteinChat supports interactive dialogues with human users, allowing for iterative refinement of predictions and deeper exploration of protein functions. Our experimental results, evaluated through both human expert assessment and automated metrics, demonstrate that ProteinChat outperforms general-purpose LLMs like GPT-4, one of the flagship LLMs, by over ten-fold. In addition, ProteinChat exceeds or matches the performance of task-specific prediction models.
Membrane microdomains are crucial forMycobacterium marinumEsxA-dependent membrane damage, escape to the cytosol and infection
Authors: Bosmani Cristina , Perret Angélique , Leuba Florence , Guého Aurélie , Hanna Nabil , Soldati Thierry
Published: August 2024

Infection by pathogenic mycobacteria such asMycobacterium tuberculosisdisrupts the membrane of the Mycobacterium-Containing Vacuole (MCV). The key effector EsxA, secreted via the ESX-1 type-VII system, is pivotal in this process, yet its membranolytic activity is not fully elucidated. Infecting the amoebaDictyostelium discoideumwithMycobacterium marinum, we demonstrate that the composition of the MCV membrane, notably its sterol-rich microdomains, significantly influences damage and rupture. Disruption of these microdomains through the knockout of organizing proteins, termed vacuolins, or through sterol depletion, markedly diminishesM. marinum-induced membrane damage and cytosolic escape, thereby increasing cellular resistance to infection. Furthermore, we establish that vacuolins and sterols are essential for thein vitropartitioning of EsxA within membranes. Extending our findings to mammalian cells, we show that the role of microdomain organizers and sterols is evolutionarily conserved; specifically, flotillin knockdown and sterol depletion enhance the resistance of murine microglial cells toM. marinuminfection. Our results underscore the critical role of host membrane microdomains in facilitating mycobacterial membranolytic activity and subsequent cytosolic access, pivotal for a successful infection.
Topology of
SmB6
revisited by means of topological quantum chemistry
Authors: Iraola Mikel , Robredo Iñigo , Neupert Titus , Mañes Juan L. , Valentí Roser , Vergniory Maia G.
Published: August 2024

The mixed-valence compound SmB6 with partially filled samarium 4f flat bands hybridizing with 5d conduction bands is a paramount example of a correlated topological heavy-fermion system. In this study, we revisit the topology of SmB6 within the formalism of topological quantum chemistry and symmetry-based indicators. Based on this approach, we reach a detailed classification of the material's topology, and we reformulate the origin of topology in terms of band representations. We corroborate these aspects by constructing a minimal TB model that is consistent with all the symmetries of the system and is able to reproduce the topology of the material. We finally unveil the previously overlooked formation of multiple topological gaps, and we discuss its implications for experiments.

Published by the American Physical Society
2024
The influence of perceived streamer characteristics and product characteristics on consumer behavior inertia in livestreaming contexts
Authors: Yuan Chunlin , Yin Yajing , Zhang Zeran , Wang Shuman , Moon Hakil
Published: August 2024

Purpose

This study aims to develop a chain-mediated model to examine how streamer and product characteristics influence consumer behavior inertia (CBI) based on an extended cognitive-affective-conative (CAC) perspective.

Design/methodology/approach

This study used an online survey concerning the livestreaming activities of 250 experienced shoppers to measure the proposed model.

Findings

Streamer (product) attractiveness mediates the relationship between streamer (product) characteristics and shopping experience memory. Streamer (product) attractiveness and shopping experience memory have a chain mediating effect between the streamer (product) characteristics and CBI.

Originality/value

Theoretically, the study demonstrates the critical role of attractiveness and shopping experience memory in the formation of consumer inertia and complements current explanations of consumer continuance of buying through livestreaming. From a managerial standpoint, the findings provide guidelines for enterprises carrying out livestreaming business on how to improve inertia to realize a stable profit source.
Influencer-generated content on social media: how does influencers’ self-disclosure affect followers’ purchase intentions?
Authors: Zhang Honghong
Published: August 2024

Purpose

This study investigates the effects of two types of self-disclosure by influencers (i.e. personal self-disclosure and professional self-disclosure) on followers’ parasocial relationships with them and online engagement with their content, which eventually affect followers’ purchase intentions.

Design/methodology/approach

This study collected data based on a cross-sectional survey of 823 social media users. Structural equation modeling analysis was used to test the overall structural model and the mediating roles of parasocial relationships and engagement.

Findings

This study reveals that influencers’ personal self-disclosure has a positive impact on followers’ parasocial relationships with them and online engagement with their content. Interestingly, the results indicate an inverted U-shaped relationship between influencers’ professional self-disclosure and followers’ parasocial relationships, as well as online engagement with the influencers’ content. Furthermore, followers’ parasocial relationships and engagement partially mediate the impact of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

Research limitations/implications

This study contributes to the literature by revealing the underlying mechanisms of the differential effects of influencers’ personal and professional self-disclosure on followers’ purchase intentions.

Practical implications

The findings will assist marketers in leveraging influencer-generated content to enhance influencer marketing effectiveness.

Originality/value

This research provides a better understanding of the potential linear and nonlinear effects of influencers’ self-disclosure on followers’ parasocial relationships and engagement in social media marketing.
Navigating the innovation frontier: ambidextrous strategies, knowledge creation, and organizational agility in the pursuit of competitive excellence
Authors: Waseel Abdul Hakeem , Zhang Jianhua , Shehzad Muhammad Usman , Hussain Sarki Irshad , Kamran Muhammad Wajid
Published: August 2024

Purpose

This study examines the link between the knowledge creation process, ambidextrous innovation, and competitive advantage. Further, this study also tested the moderating role of organizational agility on the relationship between the knowledge creation process and ambidextrous innovation.

Design/methodology/approach

The empirical study’s data were collected by surveying 306 respondents employed in 140 Pakistani Small and Medium Enterprises (SMEs). The questionnaire was designed according to the study’s requirements and was based on theoretical knowledge and findings from previous research on the knowledge-creation process, ambidextrous innovation, and competitive advantage. All hypotheses were tested using a structured regression method.

Findings

The study indicates that the knowledge creation process significantly impacts a firm’s competitive advantage. Additionally, this study demonstrates that ambidextrous innovation can moderate the relationship between the knowledge-creation process and competitive advantage.

Research limitations/implications

Future studies should examine mediating factors, such as organizational culture, leadership style, and industry characteristics, as well as moderating variables, such as environmental turbulence.

Practical implications

This study guides SME leaders on the importance of knowledge creation and ambidextrous innovation in achieving operational success and gaining a competitive advantage.

Originality/value

This study explores how the knowledge creation process directly and indirectly, enhances organizational capacity for competitive advantage through the mediating roles of ambidextrous innovation and the moderating role of organizational agility.
Exploring factors influencing smart tourism destination visiting behaviors in a historic country: a theory of e-consumption behavior
Authors: Hasan Abdulla Al-Towfiq , Rahman Md. Masudur , Gani Mohammad Osman
Published: August 2024

Purpose

This study aims to explore factors that influence tourists’ visiting behaviors towards smart tourism destinations (STDs) by extending the theory of planned behavior.

Design/methodology/approach

Partial least squares structural equation modeling (PLS-SEM) technique is used to test the proposed hypotheses by analyzing 413 usable responses that are collected through personal interviews. Moreover, data are collected using structured interviews and analyzed by SmartPLS, 3.3.3.

Findings

This study results reveal that STD visit intentions significantly influence STD visiting behaviors among international and domestic tourists in Bangladesh. Moreover, the results show that e-attitude, resource efficiency norms and perceived environmental sustainability have significant impacts on STD visit intentions.

Practical implications

The study findings indicate that destinations’ electronic flowcharts of places, usage of low impact and biodegradable materials and history and culture presented in the forms of games and stories increase travelers’ motivation to visit STDs.

Originality/value

The study provides empirical evidence to support the importance of factors enhancing travelers’ STD visiting behaviors by integrating e-attitude, resource efficiency norms, perceived environmental sustainability and STD visit intentions.
Low-resource multi-granularity academic function recognition based on multiple prompt knowledge
Authors: Liu Jiawei , Xiong Zi , Jiang Yi , Ma Yongqiang , Lu Wei , Huang Yong , Cheng Qikai
Published: August 2024

Purpose

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Design/methodology/approach

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Findings

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

Originality/value

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.
The determinants of digital competencies in accounting: an empirical study in Vietnam
Authors: Pham Tra Lam , Tran Thi Phuong Thanh , Dau Thi Kim Thoa , Tran Anh Hoa
Published: August 2024

Purpose

This study investigates the determinants of digital competencies (DC) of students and professionals in accounting.

Design/methodology/approach

A total of 278 valid questionnaires were collected from 145 accounting students and 133 professionals to test the relationship between some characteristics of learners through the t-test and two-way ANOVA test.

Findings

The results showed a difference in DC between students and professionals. Whereas the characteristics of students do not influence their DC, gender and the number of short courses significantly affect their DC. Information and communication technologies (ICTs) should be provided in accounting programs as core competencies.

Research limitations/implications

A sample size that is not too large and does not include learners and professionals in the central and northern areas is problematic.

Practical implications

The key findings support the development of policies for digital transformation in accounting.

Originality/value

Accounting is one of the professions that must keep up with the changes in the world, which is in the process of rapid digitalization. Hence, this study is valuable for accounting practitioners, higher education institutions and managers to enhance DC continuously.
The conceptualisation of employability among higher education administrators in Sri Lanka’s turbulent labour market
Authors: Samaradivakara Chandrani , Bell Robin , Raghubansie Antonius
Published: August 2024

Purpose

Employability is an established research theme in Western literature; however, in developing economies, the concept remains unclear and underresearched. This study addresses this lacuna by exploring how Sri Lankan higher education (HE) administrators conceptualise employability and which capitals they perceive as needing development to achieve employability.

Design/methodology/approach

The research utilised semi-structured interviews with an expert panel responsible for leading the development of the employment agenda within Sri Lankan HE. The purposive sample included four Sri Lankan vice chancellors (VCs), representing leadership at 27% of the country’s publicly funded universities and the HE funding commission. The qualitative data collected were thematically analysed to identify how employability was conceptualised and the perceived employability skills and capitals required.

Findings

The findings demonstrated that employability was conceptualised as requiring transferable skills and job-specific occupational skills. This view of employability represents a narrow definition with an emphasis on skill development rather than longer-term capability building. The results show the applicability of Bourdieu’s (1986) capitals in the Sri Lankan HE context, with cultural and social capital and proficiency in the English language critical to meeting employability objectives.

Originality/value

This research addresses the shortage of research about the concept and requirements of employability in developing countries. Most employability studies have drawn on samples from students, lecturers and employers, whilst this study considers the phenomenon from the perspectives of strategic administrators in HE who guide the employability agenda in practice. These insights are important in informing policymakers to calibrate a more balanced approach by incorporating employability into the Sri Lankan HE curriculum and sector strategy.
Impact of digital logistics platforms to facilitate more regional food in the hospitality sector and communal catering
Authors: Mendoza Pardo Christine , Fikar Christian
Published: August 2024

Purpose

This paper studies digitalization projects aiming to increase the share of regional food in the hospitality sector and communal catering. The focus is set on influencing factors and underlying feedback structures that arise through the digitalization of regional food supplies. The results can guide stakeholders to get a better understanding of key influencing factors and complexities from a holistic perspective.

Design/methodology/approach

A systems thinking approach is employed to model regional food networks. The influencing factors were found in the literature and in two model regions. Feedback loops and underlying structures were explained and validated through semi-structured interviews. Findings are visualized in causal loop diagrams (CLDs) and are used for theory development.

Findings

The presentation in CLDs helps understanding the influence of digital logistics platforms on the entire system of regional food supply and not just on isolated parts. Among others, cooperation, trust and digital solutions were identified as key success factors when aiming to increase the share of regional food in the hospitality sector and communal catering.

Research limitations/implications

The work focused on the underlying feedback structures occurring in regional food supply in two, rural Bavarian regions in Germany with their unique geographical settings. Findings may, consequently, not be entirely transferable to other regions with varying characteristics. Further research needs to be done to see how much these regional parameters influence digital logistics platforms.

Originality/value

The paper contributes to the existing scientific literature by showing the impact coming from digital logistics platforms on regional food supply systems. Developing CLDs provides a basis for future work and facilitates discussion for researchers and practitioners to support future real-world implementations.
Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method
Authors: Delgoshaei Aidin , Ariffin Mohd Khairol Anuar Mohd
Published: August 2024

Purpose

Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system).

Design/methodology/approach

The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain.

Findings

The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains.

Practical implications

The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns.

Originality/value

In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network.
Structural integrity assessment of an offshore platform using RB-FEA
Authors: Di Nicola Felice , Lonardi Graziano , Fantuzzi Nicholas , Luciano Raimondo
Published: August 2024

Purpose

The paper aims to analyze the structural integrity of an existing offshore platform located in the Northern Adriatic Sea, followed by the topside decommissioning and the re-utilization of the jacket as a wind turbine support. The structural integrity assessment against the in-place and the long-term actions is accomplished by using a reduced basis finite element method (RB-FEA) software program assessing the capability of the jacket to be used as a support for wind turbines at the end of its life cycle as oil and gas (O&G) platform.

Design/methodology/approach

The project starts by modeling the jacket, and subsequently, the structural analyses for the in-place loads in operative and extreme conditions are performed. Then, the fatigue analysis is carried out in order to define the cumulative damage necessary to evaluate the possibility to use the jacket as a wind turbine support.

Findings

The results show that the jacket, at the end of the service life as O&G platform, is able to withstand the loads produced by the installation of the wind turbine since the analyses are satisfied even with the conservative approach used which overestimates the thickness loss assuming a linear increasing value during the service life.

Research limitations/implications

Because of the chosen approach, the study presents some limitations, especially concerning the real state of the platform which has been defined considering the thickness loss only. Additionally, a 1D model was used to perform the analyses, and hence, a 3D model could help in evaluating the critical points with higher precision.

Practical implications

The assessment of the structure could be improved by modeling a digital twin of the asset allowing a real-time monitoring which, however, involves a huge amount of data to be processed, so a suitable simulation technology must be used.

Originality/value

The RB-FEA proposed by Akselos is suitable to perform the analyses speeding up the processing of the data even in real time.
“You are you, and I am me”: investigating inter-organisational collaboration between multiple emergency services
Authors: Zaghloul Fatema
Published: August 2024

Purpose

In the past decade, financial austerity has brought significant pressure upon emergency services in the UK. For the British Government, one approach to alleviate this pressure was to increase collaborative efforts in core functions (e.g. information systems and technology) between multiple organisations. Despite the consensus that collaborations are key in addressing complex problems, the majority fail or become discontinued. This research explores the development of collaborative information infrastructures between one Police Force and two Fire and Rescue Services in the UK, with a specific focus on how the difference in culture, identities and rules and norms, can work in a collaborative emergency service environment.

Design/methodology/approach

This study investigated an example of successful development of a collaborative information infrastructure within the context of public safety and, specifically, the technology-based structures that underpin information management. A case study approach was taken, combining semi-structured interviews, document analysis and site visits. The study used activity theory as a theoretical and analytical framework.

Findings

The research revealed that creating a shared identity is not essential in emergency services collaboration, and organisations may maintain their separate identity, given they address other elements of the activity system (e.g. leadership, motivated subjects). However, pursuing this strategy will create multiple tensions throughout the collaboration process.

Originality/value

The study contributes to the information systems literature concerning inter-organisational collaborations in the public sector providing a novel view to the implications of maintaining separate identities, and the significance of misalignment between interacting activity systems.
Why do Indian exporting firms borrow in foreign currency?
Authors: Bhoi Lilu
Published: August 2024

Purpose

Since the liberalization policy of 1991, India has focused on export-led growth. However, the performance of international trade remains poor. This study aims to examine the role of credit constraints on the choice of Indian manufacturing firms to borrow in foreign currency. First, it explores the role of export activities in foreign currency borrowing (FCB). Second, it investigates how credit constraints forced these firms for foreign currency loans.

Design/methodology/approach

The study analysed data from 1,412 firms listed on the Bombay Stock Exchange in the manufacturing sector, covering the period from 1991 to 2022. A random effects probit model was used to examine the role of credit constraints on FCB, incorporating the influence of micro, small and medium enterprises (MSMEs) status and export activities. Additionally, a two-step system-generalized method of moment was used for robustness checks.

Findings

Export activities significantly influence FCB, with exporting firms showing a higher propensity to borrow foreign currency compared to domestically operating firms because of the increased funding needs of export activities. Larger firms are more likely to secure FCB than MSMEs, benefiting from collateral advantages. MSME exporting firms exhibit a higher tendency to borrow in foreign currency compared to large exporting firms.

Research limitations/implications

This study focuses on firm-level data and considers only demand-side credit constraints. It does not examine supply-side credit constraints affecting FCB.

Social implications

This study underscores the credit constraints faced by MSME exporters in the domestic market, leading them to rely on FCB. These insights are valuable for policymakers aiming to reduce MSMEs' dependency on FCB and enhance their export performance.

Originality/value

The findings highlight that MSME exporting firms are more inclined to borrow in foreign currency than their larger counterparts. This tendency is driven by the credit constraints MSMEs face because of asymmetric information and underdeveloped financial markets, which compel them to seek FCB.
Forecasting international tourist arrivals in South Korea: a deep learning approach
Authors: Zhang Siyu , Lin Ze , Yhang Wii-Joo
Published: August 2024

Purpose

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

Design/methodology/approach

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

Findings

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

Originality/value

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.
Assessment of academic burnout among undergraduate medical and health sciences students: a survey study from the United Arab Emirates
Authors: Salama Rasha Aziz Attia , Iqbal Saima Javaid , Mohsin Mariam Mehwish , Mohsin Aisha Iram , Wadid Nihal Amir
Published: August 2024

Purpose

This study aims to assess the prevalence of burnout among undergraduate medical and health science students and to identify the factors associated with its development.

Design/methodology/approach

A cross-sectional study was conducted involving students from Ras Al Khaimah Medical and Health Science University. The Maslach Burnout Inventory-Student Survey was used to evaluate three subscales of burnout. Ethical considerations were met, and informed written consent was obtained from participants. Bivariate and multiple logistic regression analyses were conducted to examine the relationship between demographic, personal and school environment factors and burnout.

Findings

Out of 453 students surveyed, the majority were females (74.8%) aged 18–20 years (60%). Significant levels of burnout were reported, with emotional exhaustion, depersonalization and reduced personal accomplishment affecting over half of the participants (69%). Factors such as being in the clinical years, long-distance commuting challenges and involuntary college selection were identified as predictors of burnout.

Research limitations/implications

While the study provides valuable insights, limitations may include the specific context of the university and potential biases in self-reported data.

Practical implications

The findings underscore the importance of raising awareness, maintaining work-life balance and implementing coping strategies to support student well-being and foster a positive learning environment. Suggestions include time management and seeking guidance from mentors or mental health professionals.

Originality/value

This study contributes to the understanding of burnout among medical and health science students, especially within the unique context of the United Arab Emirates, given the convergence of students from diverse nationalities and cultural backgrounds.