bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linking the trickle-down effect of supervisor incivility to turnover intentions in the context of SMEs: a serial mediation model
Authors: Theriou Georgios , Tasoulis Konstantinos , Kravariti Foteini 
Published: October 2024

PurposeBased on conservation of resources theory and incivility spiral theory, this study examines the relationship between supervisor incivility and turnover intentions by investigating the mediating roles of co-worker incivility and emotional exhaustion.Design/methodology/approachA serial mediation model was empirically tested with a sample of 600 employees in Greek small and medium-sized enterprises (SMEs).FindingsResults indicate that supervisor incivility is linked to turnover intentions in three main ways. Directly, indirectly through emotional exhaustion, and indirectly through the serial mediation of co-worker incivility and emotional exhaustion.Research limitations/implicationsPossible limitations of the study include the sampling techniques and the self-reporting measures for data collection.Practical implicationsFindings attest that owners and managers of SMEs should take note of the trickle-down effect, namely that their own misbehavior will fuel co-worker incivility. Supervisor incivility has cumulative, deleterious consequences on employees, depleting their emotional resources and enhancing turnover intentions, and should therefore be restricted through appropriate practices.Originality/valueFew empirical studies have explored the effects of supervisor incivility on the incivility manifested by others and their impact on turnover intentions. The study enriches the trickle-down effect literature by identifying an additional indirect path linking supervisor incivility and turnover intentions. Further, this study took place in SMEs, a context with limited research in abusive supervision and incivility yet highly pertinent, given that SMEs informality might leave such behavior unchecked.
Does robotic service quality determine robotic restaurant diners’ engagement behaviors? Role of customer engagement and attachment to the restaurant
Authors: Veera Raghavan Deepak Ramanan 
Published: October 2024

PurposeRobotic restaurants are very novel, and service robots in these restaurants are identified as offering unique advantages in terms of efficiency, tireless service and potentially lower operational costs. However, studying customer engagement with the robots can reveal aspects of robotic service that resonate with diners. Understanding how diners interact with robots can help create a more engaging and enjoyable atmosphere, bringing more business to restaurants. Building on the stimulus-organism-response (SOR) theory and place attachment theory, the purpose of this paper is to study the impact of the robotic service quality (RSQ) on the customer attachment to the robotic restaurant with the mediating role of the different dimensions of the customer engagement, like the Absorptive Attention, Enthusiastic Participation and Social Connection. Subsequently, the impact of the customer attachment to the robotic restaurant on different dimensions of customer engagement behaviors like augmenting, co-developing, influencing and mobilizing behaviors was also studied.Design/methodology/approachThe cross-sectional data from 786 robotic restaurant diners in India who answered the self-administered structured questionnaires is utilized for this descriptive study. The study employed a purposive sampling strategy. The SMART-PLS 4.0 program was used to run structural equation modeling and analyze the data.FindingsThe results indicate that customer engagement dimensions like Absorptive Attention, Enthusiastic Participation and Social Connection differentially mediate the relationship between RSQ and customer attachment with the robotic restaurant. Customer attachment to the restaurant and the robotic services subsequently positively impact customer engagement behaviors.Research limitations/implicationsThe study relied upon cross-sectional data from the Indian population above 18 years to test the proposed model. Further studies could test the model across different populations to generalize the study results.Originality/valueThis study addresses the need to investigate robotic restaurant diners’ engagement behaviors. By testing place attachment theory and the SOR framework, this study is the first to show that RSQ will impact the customer attachment with the robotic restaurant and that different dimensions of customer engagement mediate the relationship. It also validates the previous research findings that customer engagement is not a single global construct, and different sub-dimensions are to be explored. This study is also the first to show customer attachment to the robotic restaurant will impact customer engagement behaviors differently.
Removing random noise and improving the resolution of seismic data using deep‐learning transformers
Authors: Sun Qifeng , Feng Yali , Du Qizhen , Gong Faming 
Published: October 2024

AbstractPost‐stack data are susceptible to noise interference and have low resolution, which impacts the accuracy and efficiency of subsequent seismic data interpretation. To address this issue, we propose a deep learning approach called Seis‐SUnet, which achieves simultaneous random noise suppression and super‐resolution reconstruction of seismic data. First, the Conv‐Swin‐Block is designed to utilize ordinary convolution and Swin transformer to capture the long‐distance dependencies in the spatial location of seismic data, enabling the network to comprehensively comprehend the overall structure of seismic data. Second, to address the problem of weakening the effective signal during network mapping, we use a hybrid training strategy of L1 loss, edge loss and multi‐scale structural similarity loss. The edge loss function directs the network training to focus more on the high‐frequency information at the edges of seismic data by amplifying the weight. Additionally, the verification of synthetic and field seismic datasets confirms that Seis‐SUnet can effectively improve the signal‐to‐noise ratio and resolution of seismic data. By comparing it with traditional methods and two deep learning reconstruction methods, experimental results demonstrate that Seis‐SUnet excels in removing random noise, preserving the continuity of rock layers and maintaining faults as well as being strong robustness.
Addressing cycle‐skipping in full‐waveform inversion using acoustic wave energy
Authors: Li Zhonglei , Yao Gang 
Published: October 2024

AbstractLimitations in acquisition technologies lead to insufficient low‐frequency signals in field seismic data. Local optimization methods are the common approaches for full‐waveform inversion. Inaccurate initial velocity models and lack of low‐frequency signals in seismic data typically cause the local‐gradient‐based full‐waveform inversion to converge to a local minimum due to cycle‐skipping. The existing energy‐based objective functions can generate artificial low‐frequency signals successfully by squaring the pressure but overlook the law of energy conservation, which may mislead model updates. To overcome this issue, we combine acoustic wave potential energy and kinetic energy to develop a new objective function that fits the acoustic wave energy. The new acoustic‐wave‐energy‐based full‐waveform inversion considers the law of energy conservation. The new system creates low‐frequency signals to avoid cycle‐skipping and produce an accurate smooth background velocity model, which provides a sufficient starting model for conventional full‐waveform inversion. Numerical examples demonstrate that the combination of acoustic‐wave‐energy‐based full‐waveform inversion and conventional full‐waveform inversion can deliver more faithful and accurate final results than conventional full‐waveform inversion alone.
Koalas, friends and foes—The application of airborne eDNA for the biomonitoring of threatened species
Authors: Frere Celine , Jackson Nicola , Moreno Jarred , Oliveros Sandino Alejandro , Ball Sarah , Powell Daniel 
Published: October 2024

Abstract

Curbing global wildlife population declines will necessitate the protection of their habitat, and subsequent robust baseline information about wildlife that use and occupy it. Collating such information, however, remains a challenging and costly endeavour. Little did we know that traces of wildlife presence float in the air and can be detected through traces of DNA.

Here, we deployed samplers to test the applicability of collecting airborne eDNA for the detection of a threatened species, the koala, and its co‐occurring terrestrial mammalian community.

We develop a novel species specific qPCR assay to detect the presence of koalas and applied this in concert with a meta‐barcoding approach to detect the co‐occurring mammalian community.

Through sampling of airborne particles, we successfully detected koala presence accurately to habitat patch level, alongside 16 unique taxonomic assignments, successfully assigning 11 of these to species level including detections belonging to the wallaby, antechinus, members of possum family and invasive species such as foxes, domestic dogs and hares.

Synthesis and applications: We demonstrate the potential of airborne eDNA for the detection of threatened terrestrial wildlife and their surrounding ecological community under natural conditions. With achievable optimisations we detail how airborne eDNA may be applied for the management and monitoring of threated species for enhanced conservation efforts.
Specialization of Alcanivorax species in colonizing diverse plastics
Authors: Mattelin Valérie , Rombouts Astrid , Van Landuyt Josefien , Scoma Alberto , Boon Nico 
Published: October 2024

AbstractRecently, there has been increased attention to hydrocarbonoclastic bacteria in the plastisphere. One particular genus, Alcanivorax, is reported in the biodegradation of several polymers in the literature. In this study, we further explored the role of Alcanivorax in the early colonization of poly(3‐hydroxybutyrate‐co‐3‐hydroxyhexanoate) (PHBH), nylon 6/69, and a novel plastic B4PF01. Starting from enrichments of a one‐year experiment with a maximum relative abundance of 58.8% of Alcanivorax, two parallel experiments were set up. One experiment followed growth and activity during the first 21 days of plastic incubations, and the other followed the same parameters on the different material fractions of the plastics, such as leachables and pure polymer. For all plastic types, the highest microbial growth was associated with the total plastics compared to the other material fractions. A relative abundance of 62.7% of Alcanivorax in the nylon 6/69‐enriched community was observed. This, combined with data on activity, suggests that nylon 6/69 is potentially degraded by this genus. Two isolates were obtained, closely related to A. borkumensis SK2 and Alcanivorax sp. DG881. The activity and growth of the isolates as axenic cultures resemble their abundance in the community. In conclusion, this study contributes to the knowledge of the role of Alcanivorax in plastic‐enriched communities.
Attenuated total reflection Fourier transform infrared spectroscopy (ATR‐FTIR) analysis of human nails: Implications for age determination in forensics
Authors: Mitu Bilkis , Trojan Václav , Hrib Radovan , Halámková Lenka 
Published: October 2024

AbstractA person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT‐IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT‐IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS‐R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS‐DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof‐of‐concept study highlights the potential of ATR FT‐IR spectroscopy as a non‐destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.
Physical activity modifies the association between atherogenic index of plasma and prediabetes and diabetes: A cross‐sectional analysis
Authors: Yang Shenglan , Gou Xinyu , Dong Hui , Chen Limei , Wang Yiyan , Wu Jing 
Published: October 2024

AbstractBackgroundAlthough research has explored the association between atherogenic index of plasma (AIP) and prediabetes and diabetes, there is still not sufficient available evidence the role of physical activity (PA) in this relationship. Our purpose is to examine the complex connections between AIP, PA, and prediabetes and diabetes in a young and middle‐aged population.MethodsThis study included 2220 individuals from the general population, aged 20–60 years. AIP was calculated from the logarithm of the triglyceride (TG) to high‐density lipoprotein cholesterol (HDL‐C) ratio. PA was assessed depending to the American Heart Association (AHA) criteria and categorized into medium‐high and low PA levels. We used binary logistic regression to explore associations and subsequently performed sensitivity and subgroup analyses.ResultsThe 2220 participants had a mean age of 38 years, with a mean AIP of −0.1185, and a prediabetes and diabetes prevalence of 7.2%. After adjusting for auxiliary variables, AIP was positively correlated with prediabetes and diabetes (odds ratio [OR]: 3.447, 95% confidence interval [CI]: 1.829–6.497). In the low PA population, the prevalence of prediabetes and diabetes raised significantly with higher AIP (OR: 3.678, 95% CI: 1.819–7.434). This association was not meaningful in the medium to high PA population (OR: 1.925, 95% CI: 0.411–9.007). Joint and sensitivity analyze results also showed agreement. Restricted cubic spline identified a linear relationship between AIP and the prevalence of prediabetes and diabetes. Notably, the prevalence significantly increases when AIP values exceed −0.16 (p for linearity <0.05). The findings revealed heterogeneity across subgroups stratified by sex and age.ConclusionsPA may modify the link as regards AIP with prediabetes and diabetes in young and middle‐aged populations. Adherence to PA prevents the adverse effects of abnormal glucose metabolism caused by dyslipidemia, particularly in women.image
Elevations in interleukin‐8 levels in individuals with alcohol use disorder and clinical insomnia symptoms
Authors: Grodin Erica N. , Baskerville Wave‐Ananda , McManus Kaitlin R. , Irwin Michael R. , Ray Lara A. 
Published: October 2024

AbstractBackgroundInsomnia commonly co‐occurs with alcohol use disorder (AUD) and predicts poorer outcomes for those with AUD. Insomnia and AUD are individually associated with increases in systemic inflammation. Insomnia and inflammation both serve as risk factors for relapse in AUD. However, little is known about the relationship between insomnia and systemic inflammation in individuals with AUD. Therefore, the present study examined the relationship between the severity of insomnia symptoms and plasma levels of inflammatory cytokines in a sample of treatment‐seeking individuals with an AUD.MethodsThis secondary analysis included 101 (61M/40F) individuals with an AUD. Participants were categorized into groups based on their scores on the Insomnia Severity Index: no insomnia (n = 47), subthreshold insomnia (n = 37), and clinical insomnia (n = 17). Participants provided blood samples to measure plasma levels of four peripheral markers of inflammation (IL‐6, IL‐8, TNF‐α, and CRP). Inflammatory marker levels were compared between groups. Interactive effects of sex and AUD severity were examined.ResultsThere was a significant main effect of insomnia group on log IL‐8 levels (F = 6.52, p = 0.002), such that individuals with AUD and clinical insomnia had higher log IL‐8 levels compared to both the no insomnia and subthreshold insomnia groups (ps ≤ 0.05). Sex and AUD severity interacted with this relationship, such that men with clinical insomnia and AUD and individuals with severe AUD had higher log IL‐8 levels. There were no significant effects of insomnia on IL‐6, TNF‐α, or CRP levels.ConclusionThe present study identified a specific elevation in IL‐8 levels in individuals with an AUD and clinical insomnia that was not identified in other markers of peripheral inflammation (IL‐6, TNF‐α, CRP). Sex and AUD severity interacted with insomnia symptoms, indicating that those with clinical insomnia and severe AUD or male sex may be the most vulnerable to the inflammatory consequences associated with AUD and clinical insomnia symptoms.
Eighteen‐year trends in the rates of intra‐operative cardiac arrest and associated mortality at a public university hospital in Brazil*
Authors: Morais Arthur C. , Braz Jose R. C. , Soares Joao Vitor A. , Pessoto Jessica G. J. , Tanabe Matheus R. , Pignaton Wangles , de Carvalho Lidia R. , Braz Mariana G. , Braz Leandro G. 
Published: October 2024

SummaryBackgroundIntra‐operative cardiac arrest is a rare but life‐threatening event. Over the past two decades, various initiatives have improved the care of patients undergoing surgery at our quaternary teaching hospital in Brazil. We aimed to evaluate the epidemiology of intra‐operative cardiac arrest and associated 30‐day mortality over an 18‐year period. A secondary aim was to identify associated risk factors.MethodsWe conducted a retrospective observational study using data collected from 1 January 2005 to 31 December 2022. Factors associated with cardiac arrest and mortality were identified using multivariable logistic regression analysis.ResultsAmong the 154,178 cases, the overall rates of intra‐operative cardiac arrest (n = 297) and associated 30‐day mortality (n = 248) were 19.3 (95%CI (16.6–21.9)) and 16.1 (95% CI 13.9–18.3) per 10,000 anaesthetics, respectively. These decreased over time (2005–2010 vs. 2017–2022) from 26.3 (95%CI 21.0–31.6) to 15.4 (95%CI 12.0–18.7) per 10,000 anaesthetics, and from 23.4 (95%CI 18.8–28.1) to 13.7 (95%CI 10.8–16.7) per 10,000 anaesthetics, respectively. Factors associated with intra‐operative cardiac arrest included children aged < 1 year (adjusted OR (95%CI) 3.51 (1.87–6.57)); ASA physical status 3–5 (adjusted OR (95%CI) 13.85 (8.86–21.65)); emergency surgery (adjusted OR (95%CI) 10.06 (7.85–12.89)); general anaesthesia (adjusted OR (95%CI) 8.79 (4.60–19.64)); surgical procedure involving multiple specialities (adjusted OR (95%CI) 9.13 (4.24–19.64)); cardiac surgery (adjusted OR (95%CI) 7.69 (5.05–11.71)); vascular surgery (adjusted OR (95%CI) 6.21 (4.05–9.51)); and gastrointestinal surgery (adjusted OR (95%CI) 2.98 (1.91–4.65)).DiscussionWe have shown an important reduction in intra‐operative cardiac arrest and associated 30‐day mortality over an 18‐year period. Identification of relative risk factors associated with intra‐operative cardiac arrest can be used to improve the safety and quality of patient care, especially in a resource‐limited setting.
Associations between birth characteristics, pubertal timing and adult height
Authors: Skogastierna Carin , Kalay Nazli , Swolin‐Eide Diana , Holmgren Anton 
Published: October 2024

AbstractAimThis study investigated the association between gestational age and birth size with pubertal timing, measured as peak height velocity, and adult height.MethodsThis retrospective, population‐based study was conducted in Sweden in 2023. A sub‐group of the 1974 and 1990 GrowUp Gothenburg cohorts was used (n = 4700, 50% males). The subgroup consisted of healthy individuals with Nordic ethnicity, known parental heights and measured adult heights. Data on birth characteristics (gestational age, birth length, birth weight) were collected. Pubertal timing was assessed as age at peak height velocity. Univariable linear regression analysis and bivariate correlations were conducted to answer the research questions.ResultsGestational age was not associated with age at peak height velocity or adult height. Birth length and weight could explain 12% and 8% (p ≤ 0.001) respectively of attained adult height. However, birth length and birth weight could only explain the variation in age at peak height velocity to a small degree.ConclusionThis study showed that gestational age is not associated with age at peak height velocity or adult height. A positive relationship was found between birth size, particularly birth length, and adult height. No strong associations were found between birth size and age at peak height velocity.
Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition
Authors: Bharadwaj Shruti , Deepika Kumari , Kumar Asim , Jaiswal Shivani , Miglani Shaweta , Singh Damini , Fartyal Prachi , Kumar Roshan , Singh Shareen , Singh Mahendra Pratap , Gaidhane Abhay M. , Kumar Bhupinder , Jha Vibhu 
Published: October 2024

ABSTRACTThe technological revolutions in computers and the advancement of high‐throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost‐friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit‐to‐lead optimization, analyzing drug–receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state‐of‐the‐art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.