bluuebunny/crossref_metadata_2025_split huggingface dataset:
Root productivity contributes to carbon storage and surface elevation adjustments in coastal wetlands
Authors: Conroy Brooke M , Kelleway Jeffrey J , Rogers Kerrylee 
Published: November 2024

Abstract

Background and aims: Additions of organic matter in coastal wetlands contributes to blue carbon sequestration and adjustment to sea-level rise through vertical growth of substrates. To improve models of carbon sequestration and adaptation to sea-level rise, data of root mass and volume additions across tidal gradients are required. This study aims to characterise the influence of vegetation zonation and tidal position on root mass and volume dynamics within substrates. Methods: The root ingrowth technique was coupled with sediment cores to quantify below-ground root mass and volume production, standing stocks and turnover across two years to 90 cm depth at Kooweerup, Victoria, Australia. Measurements of vertical accretion quantified mineral sediment additions at the surface. Results: The results indicate a complex non-linear relationship between root production and tidal position, which is driven by variation in vegetation structure across mangrove (442–3427 g fine root mass m-2 yr-1), saltmarsh (540–860 g m-2 yr-1) and supratidal forest (599 g m-2 yr-1) zones. Fine root volume additions ranged from 274 to 4055 cm3 m-2 yr-1 across sampling locations. Root production was greatest for older mangroves and tidally defined optimal zones of production were evident for mangrove and saltmarsh. The live rooting zone extended beyond depths typically measured in studies, and for forested sampling locations, live roots were found as deep as 1.0 m. Conclusion: These data can be used to improve highly parameterised models accounting for carbon sequestration and substrate vertical adjustment across an intertidal gradient by quantifying both root mass and volume additions across the live rooting zone.
Motivate or suppress: The dual effects of leader bottom-line mentality on employee innovation behavior
Authors: Luo shiwen , Tong David Yoon Kin 
Published: November 2024

Abstract

Background
In the VUCA era, employee innovation behavior is critical to a enterprise success. In China's high power distance and collectivist culture, employee innovation behavior is often influenced by leadership authority, leading to a division between proactive and reactive innovation behavior, with each having a different effect on innovation performance. Innovation is influenced not only by leadership type but also by leadership mindset. As a unidimensional approach focused on bottom-line profits while neglecting other important factors, leader bottom-line mentality (LBLM) and its effect on employee innovation behavior, especially on proactive and reactive innovation, has yet to be thoroughly explored. Understanding this dual influence can help balance short-term profit goals with long-term innovation development.
Methods
This study employed a two-stage survey method, conducting a questionnaire survey with employees from 13 innovation-driven enterprises, ultimately collecting 351 valid responses. Using SPSS and MPLUS software, the data were analyzed through reliability and validity tests, confirmatory factor analysis, descriptive statistics, and linear regression to validate the proposed research hypotheses.
Results
the study finds that LBLM has a significantly negative effect on employee proactive innovation behavior but a positive effect on reactive innovation behavior. Moreover, employees' willingness to take risks mediates the relationship between LBLM and employee innovation behavior. At the same time, intrinsic work values negatively moderate the relationship between LBLM and Willingness to take risks, enhancing proactive innovation behavior and reducing reactive innovation behavior. On the other hand, extrinsic work values positively moderate the relationship, enhancing reactive innovation behavior and reducing proactive innovation behavior.
Conclusion
As a unidimensional mentality, LBLM has a double-edged effect on employee innovation behavior: it positively predicts reactive innovation behavior while negatively predicting proactive innovation behavior. However, this effect is moderated by work values; intrinsic work values weaken the double-edged effect, whereas extrinsic work values enhance its influence.
Microscopic parametrizations for gate set tomography under coloured noise
Authors: Viñas Pablo , Bermudez Alejandro 
Published: November 2024

Abstract

Gate set tomography (GST) allows for a self-consistent characterization of noisy quantum information processors (QIPs). The standard device-agnostic approach treats the QIPs as black boxes that are only constrained by the laws of physics, attaining full generality at aconsiderable resource cost: numerous circuits built from the gate set must be run in order to amplify each of the gate set parameters. In this work, we show that a microscopic parametrization of quantum gates under time-correlated noise on the driving phase, motivated by recent experiments with trapped-ion gates, reduces the required resources enabling a more efficient version of GST. By making use of the formalism of filter functions over the noise spectral densities, we discuss the minimal parametrizations of the gate set that include the effect of finite correlation times and non-Markovian quantum evolutions during the individual gates. We compare the estimated gate sets obtained by our method and the standard long-sequence GST, discussing their accuracies in terms of established metrics, as well as showcasing the advantages of the parametrized approach in terms of the sampling complexity for specific examples.
Intelligent Adaptive Service Grid Architecture Design and Performance Optimization for Future Networks
Authors: Hu YuFeng , Liu MinMin 
Published: November 2024

Abstract

With the development of future networks, the popularization of technologies such as 5G and the Internet of Things (IoT) has led to new characteristics of network environments with high dynamics, low latency, and complexity. Traditional service grid architectures have limitations in coping with the dynamics and complex traffic management of future networks, such as increased latency and inflexible traffic management. This study aims to design an intelligent and adaptive service grid architecture to optimize the performance to cope with the new challenges in future networks. To address the network routing optimization problem, this study uses deep Q-learning algorithms to achieve intelligent routing, which effectively reduces the network delay and packet loss rate. To address the complexity of traffic management in large-scale distributed systems, a dynamic traffic management module combining convolutional neural networks and long and short-term memory networks is designed to improve the accuracy of traffic prediction. To enhance network adaptability and fault recovery, the study introduces a network resilience enhancement module, which ensures service continuity under high load and fault conditions. In addition, efficient transmission and low resource consumption under multiple network protocols are realized by the design of protocol adaptive module. Experimental evaluation of the entire optimized architecture shows that this intelligent service grid architecture exhibits excellent performance in future network environments. The intelligent routing module effectively reduces network delay and packet loss, the dynamic traffic management module improves the accuracy of traffic prediction, the network resilience enhancement module ensures service continuity under high load and fault conditions, and the protocol adaptation module demonstrates efficient transmission and low resource consumption under multiple network protocols. Through the optimal design of the service grid architecture and the introduction of intelligent technologies, the performance and adaptability of service grids in future network environments are successfully enhanced, and the deficiencies faced by traditional service grids in dynamic network environments are addressed. This research provides important technical support and development direction for intelligent service grids in future networks.
Predicting 90-day mortality in patients with HBV-ACLF using machine learning tools
Authors: Liu Juan , Zhu Wentao , Deng Ting , Zhu Yin , Xu Qinglang , Shi Pei , Liang An , Wang Yuna , Fu Jiwei , Wu Xiaoping 
Published: November 2024

Abstract

Background
Acute chronic liver failure (ACLF) is characterized by a systemic inflammatory response, mainly associated with hepatitis B virus (HBV) in the Asia-Pacific region, and has a high mortality rate. We aimed to develop a stable and feasible prognostic prediction model based on machine learning (ML) tools to predict 90-day mortality in patients with hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF).
Method
Clinical data from 573 patients with HBV-ACLF across two hospitals were retrospectively collected. Prognostic models of HBV-ACLF were constructed using support vector machine (SVM), decision tree (DT), random forest (RF), K nearest neighbour (KNN), least absolute shrinkage selection operator (LASSO), and logistic regression (LR). Model performance metrics included accuracy, area under the (AUC) receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
Results
In the training cohort, the RF prediction model demonstrated significantly higher AUC, sensitivity, specificity, PPV, and NPV than the LASSO, LR, SVM, DT, and KNN prediction models. However, the AUC of RF in the validation cohort was 0.728, with a decline in accuracy, specificity, and PPV to 0.688, 0.545, and 0.655, respectively. In the training cohort, the LASSO model had the lowest PPV at 0.739, while the KNN model had the lowest sensitivity at 0.694. In the testing and validation cohorts, the SVM and DT models exhibited the lowest sensitivity, both at 0.581. Although LR performed less effectively than RF in the training cohort, it outperformed the RF model in the testing and validation cohorts.
Conclusions
In summary, the LR predictive model demonstrates higher predictive efficacy and greater stability, making it more practical for clinical treatment decision-making.
Alcohol consumption in patients with hypertension in primary care comparing Phosphatidylethanol and AUDIT – Cross-sectional study
Authors: Thurfjell Åsa , Hagströmer Maria , Ivarsson Charlotte , Norrman Anders , Adami Johanna , Lundh Lena , Hasselström Jan 
Published: November 2024

Abstract

Background
Hazardous alcohol use often goes unidentified in management of hypertension. The aim was to describe alcohol consumption using the alcohol biomarker Phosphatidylethanol (PEth) and the Alcohol Use Disorders Identification Test (AUDIT) in relation to hypertension (controlled, uncontrolled, and treatment resistant). Additionally, analyse the correlation between PEth and AUDIT and compare their performance in identifying hazardous alcohol use.
Methods
Cross-sectional study of patients with hypertension (ICD-code I10.9) from primary care in Sweden (2022–2024). A random sample of 270 participants from three groups of hypertension, mmHg, (controlled < 140/<90, uncontrolled ≥ 140/≥90, and treatment resistant ≥ 140/≥90 with at least three antihypertensive drugs regardless of class) was included. Data on alcohol consumption were collected during clinical hypertension check-up. In relation to three groups of hypertension, prevalence of alcohol consumption with PEth µmol/L (< 0.05, 0.05–0.3, and > 0.3) and AUDIT points (0–7, 8–15, and 16–40) and hazardous alcohol use with PEth (≥ 0.120 µmol/L) and AUDIT (8p), was calculated. The Spearman rank correlation coefficient was calculated between PEth values and AUDIT scores.
Results
Mean age 67 ± 11 years, 42% were women. Using PEth, approximately 40% in all groups of hypertension had “no/low/sporadic” consumption according to PEth. Using AUDIT, about 85% in all groups of hypertension did not have hazardous alcohol use. With PEth, the prevalence of “high and regular consumption” was 6.4% in controlled hypertension, 5.3% in uncontrolled hypertension, and 19.2% in treatment-resistant hypertension (p = 0.027 controlled hypertension vs treatment resistant hypertension, p = 0.013 uncontrolled hypertension vs treatment resistant hypertension). In the entire study population, the prevalence of hazardous alcohol with PEth was 23.7% (men 27%, women 19.1%, p = 0.138) and with AUDIT 12.4% (men 17.1%, women 6.1%, p = 0.008). The Spearman rank correlation coefficient between PEth values and AUDIT scores was 0.590, p < 0.001. PEth identified 14.7% with hazardous alcohol use that AUDIT missed, while AUDIT identified 3.5% that PEth did not identify.
Conclusions
PEth identified higher alcohol consumption and higher prevalence of hazardous alcohol use, compared to AUDIT. The results suggest that PEth provides valuable information about alcohol consumption, and highlights the importance of identifying alcohol use, especially in patients with treatment resistant hypertension and in men.
Trial registration
Retrospectively registered in Clinical Trials, SLSO2022-0143, 2022-12-10.
Holocene shifts in marine mammal distributions around Northern Greenland revealed by sedimentary ancient DNA
Authors: Schreiber Lennart , Ribeiro Sofia , Jackson Rebecca , Kvorning Anna , Nota Kevin , O'Regan Matt , Pearce Christof , Seersholm Frederik , Seidenkrantz Marit-Solveig , Zimmermann Heike , Lorenzen Eline 
Published: November 2024

Abstract

Arctic marine ecosystems have undergone notable reconfigurations in response to Holocene environmental shifts. Yet our understanding of how marine mammal occurrence was impacted remains limited, due to their relative scarcity in the fossil record. We reconstructed the occurrence of marine mammals across the past 12,000 years through genetic detections based on sedimentary ancient DNA from four marine sediment cores collected around Northern Greenland, and integrated the findings with local and regional environmental proxy records. Our findings indicate a close association between the establishment of marine mammals at densities detectable in marine sediments and the deglaciation of marine environments at the onset of the Holocene. Further, we identified air temperature as a significant driver of community change across time. Several marine mammals were detected in the sediments earlier than in the fossil record, for some species by several thousands of years. During the Early-to-Mid Holocene, a period of past warmer climate, we recorded northward distribution shifts of temperate and low-arctic marine mammal species. Our findings provide unique, long-term baseline data on the occurrence of marine mammals around Northern Greenland, providing novel insights into past community dynamics and the effects of Holocene climatic shifts on the region’s marine ecosystems.
Investigating the intrinsic factors related with anterior disc displacement in temporomandibular disorder: a preliminary MRI study
Authors: Wang Botao , Wu Mei , Chen Zhiye 
Published: November 2024

Abstract

To observe the structure of temporomandibular joint (TMJ) in patients with TMD by magnetic resonance imaging (MRI), and to investigate the related factors of anterior disc displacement in patients with TMD.Which were classified into TMD without displacement group (97 TMJs), anterior displacement (147TMJs, including anterior displacement with reduction 69 TMJs and without reduction 78 TMJs), the observed variables were focused on the bone structures of TMJ ( the depth, the anteroposterior diameter, the index of TMJ fossa, the morphology, location, medial-lateral diameter, anteroposterior diameter and the index of TMJ condyle) and the other structures indexes involved in the temporomandibular movement (mouth opening movement range, soft tissue of bilaminar region, superior band of the bilaminar zone, morphology and signal of the lateral pterygoid muscle(LPM), joint effusion, osteoarthritis). The three groups were subjected to one-way analysis of variance and chi-square test, while binary logistic regression analysis was employed to establish a regression model and construct the receiver operating characteristic curve. The present study demonstrated that various structural and clinical factors, particularly the altered bone structure of the TMJ, hypertrophy of the LPM, and joint effusion, significantly contribute to the ADD in patients with TMDs.
Perceived teacher support profiles and mathematics engagement, mathematics anxiety, mathematics attitude: A latent profile analysis approach
Authors: Zhou Yu , Jing Bin , Pi Zhongling , Zhang Jing , Han Yifei , Ma Hongliang 
Published: November 2024

Abstract

Background
The critical role of perceived teacher support in mathematics learning has been widely recognized, but individual student differences have often been overlooked in previous studies.
Methods
This study adopts a person-centered approach to study the perceived teacher support in the process of mathematics learning, uses latent profile analysis to classify 1314 students, and uses one-way analysis of variance to explore the differences between students with different profiles. Further, a mediation model is established to explore the relationship between different perceived teacher support profiles and and mathematics engagement, mathematics anxiety, and mathematics attitude.
Results
Latent profile analysis identified three profiles: low (5.78%), medium (44.29%), and high perceived teacher support (49.93%). There were no significant differences in gender or grade among students with different profiles. But they show significant differences in mathematics engagement, mathematics anxiety, and mathematics attitude. Further analysis revealed that there are differences in behavioral engagement, cognitive engagement, emotional engagement, classroom anxiety, learning motivation, and learning strategies. The mediation effect analysis showed that students with higher perceived teacher support exhibited stronger mathematics attitudes, which in turn increased their mathematics engagement. However, mathematics anxiety did not serve as a mediating factor.
Conclusion
The study showed that individual differences in perceived teacher support affect students’ mathematics engagement mathematics anxiety, and mathematics attitudes. Students who perceive a higher level of teacher support show a more positive attitude toward mathematics, which promotes mathematics engagement. This study provides empirical evidence for teachers to implement personalized support.
Effect of the heterocyclic ring-fusion on the photochemical reactivity of 1,4- dihydropyridines, polyhydroquinolines, and decahydroacridine-1,8-diones
Authors: Hosseininasab Fatemeh S. , Memarian Hamid R. , Asgari Mehrnoosh 
Published: November 2024

Abstract

Photo-oxidation of 1,4-dihydropyridine, polyhydroquinoline, and decahydroacridine-1,8-dione derivatives was investigated by exposing them to the UV light to elucidate the extent of the ring-fusion and the impact of molecular rigidity on the photo-oxidation completion time. The results indicated that the extent of the ring-fusion affects the time of irradiation, which is mostly due to the rigidity of the heterocyclic ring core regarding the freedom of rotation of the attached CO-groups and the effective delocalization of the nitrogen lone pair towards the attached conjugated part. Furthermore, the results showed the impact of the steric and electronic effects of the C-aryl substitution on the rate of the photo-oxidation. Among these three series of compounds, decahydroacridine-1,8-diones, and 1,4-dihydropyridines have the largest and lowest rate of the photo-oxidation, respectively. The geometry of the considered heterocyclic compounds and their corresponding photo-oxidation intermediates was optimized using the density functional theory (DFT) B3LYP method with 6-311 + + G(d,p) basis set. Computational results explained that the deviations of the N1- and C4-atoms of the dihydropyridine ring core from the ring boat plane can affect the rate of the photoreaction of these three series of compounds. These deviations are dependent on the preferred orientation of the CO group towards the heterocyclic ring, the rigidity of the molecule, and the type, position, and electronic nature of the additional substituent on the aryl ring. The dihedral angle scanning clarified the intramolecular interaction of unbounded atoms in the molecules besides the extent of the electron delocalization on the total energy content of the molecules.
Plants diseases diagnosis based on MobileNet and Inception Architectures
Authors: Halidou Aminou , Olle Daniel Georges Olle , Von Kallon Daramy Vandi , Baraza William John , Akou Obam , Melvin Fokam Ninyim Astride 
Published: November 2024

Abstract

Food shortages are some of the direct consequences of plant diseases in Developing countries. Artificial intelligence combined with image processing can help respond to these threats. In this paper, Inception v3 and MobileNet v2 are presented for plant disease detection using smartphones. We implemented the proposed methods and conducted the detection experiments carried out on an open dataset of 54305 images of leaves containing 14 species, spread over 38 distinct classes of combinations [plants, diseases], including healthy plants. The models were also run on the improved version of this dataset including 87867 images, and where there was no imbalance between classes. The results show that the Inception v3 method outperforms MobileNet v2, a state-of-the art model in terms of precision both on the original dataset and its augmented version, which makes it a very useful advisory or early warning tool. It should be noted that the presented method could be further improved by integrating several other varieties of plant 1 species and diseases taken under real culture conditions from several geographical areas.