bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Mobile Post Anesthesia Care Unit Order Reminder System Improves Timely Order Entry
Authors: Clifton Jacob C. , Ende Holly B. , Rathnam Chandramouli , Freundlich Robert E. , Sandberg Warren S. , Wanderer Jonathan P. 
Published: June 2024

Abstract
Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist’s receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.
Impact of autonomous vehicles on discretionary activities: an agent-based model with space–time accessibility constraints
Authors: Feng Lewen , Betts John M. , Kamruzzaman Liton , Vu Hai L. 
Published: June 2024

AbstractThe extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.
An overview of the occurrence, impact of process parameters, and the fate of antibiotic resistance genes during anaerobic digestion processes
Authors: Pourrostami Niavol Kasra , Bordoloi Achinta , Suri Rominder 
Published: June 2024

AbstractAntibiotic resistance genes (ARGs) have emerged as a significant global health threat, contributing to fatalities worldwide. Wastewater treatment plants (WWTPs) and livestock farms serve as primary reservoirs for these genes due to the limited efficacy of existing treatment methods and microbial adaptation to environmental stressors. Anaerobic digestion (AD) stands as a prevalent biological treatment for managing sewage sludge and manure in these settings. Given the agricultural utility of AD digestate as biofertilizers, understanding ARGs’ fate within AD processes is essential to devise effective mitigation strategies. However, understanding the impact of various factors on ARGs occurrence, dissemination, and fate remains limited. This review article explores various AD treatment parameters and correlates to various resistance mechanisms and hotspots of ARGs in the environment. It further evaluates the dissemination and occurrence of ARGs in AD feedstocks and provides a comprehensive understanding of the fate of ARGs in AD systems. This review explores the influence of key AD parameters such as feedstock properties, pretreatments, additives, and operational strategies on ARGs. Results show that properties such as high solid content and optimum co-digestion ratios can enhance ARG removal, while the presence of heavy metals, microplastics, and antibiotics could elevate ARG abundance. Also, operational enhancements, such as employing two-stage digestion, have shown promise in improving ARG removal. However, certain pretreatment methods, like thermal hydrolysis, may exhibit a rebounding effect on ARG levels. Overall, this review systematically addresses current challenges and offers future perspectives associated with the fate of ARGs in AD systems.
Graphical Abstract
The Holonomy of Spherically Symmetric Projective Finsler Metrics of Constant Curvature
Authors: Asma Mezrag , Zoltan Muzsnay 
Published: June 2024

AbstractIn this paper, we investigate the holonomy group of n-dimensional projective Finsler metrics of constant curvature. We establish that in the spherically symmetric case, the holonomy group is maximal, and for a simply connected manifold it is isomorphic to $${\mathcal {D}}i\!f \hspace{-3pt} f_o({\mathbb {S}}^{n-1})$$

D
i

f

f
o

(

S

n
-
1

)

, the connected component of the identity of the group of smooth diffeomorphism on the $${n-1}$$

n
-
1

-dimensional sphere. In particular, the holonomy group of the n-dimensional standard Funk metric and the Bryant–Shen metrics are maximal and isomorphic to $${\mathcal {D}}i\!f \hspace{-3pt} f_o({\mathbb {S}}^{n-1})$$

D
i

f

f
o

(

S

n
-
1

)

. These results are the firsts describing explicitly the holonomy group of n-dimensional Finsler manifolds in the non-Berwaldian (that is when the canonical connection is non-linear) case.
Extraction of the outer membrane protein pertactin from Bordetella pertussis with urea for the production of acellular pertussis vaccine
Authors: Moon Jae Hoon , Park Jong Kwan , Park Bu Young , Jeon Hyung Jin , Choi Gi Sub , Lee Gyun Min 
Published: June 2024

AbstractPertactin (PRN), a non-fimbrial outer membrane protein of Bordetella pertussis, is the limiting component of the acellular pertussis vaccine because of its low concentration. This study aimed to develop a large-scale urea-based process for PRN extraction from B. pertussis. Cell pellet processing conditions, including freezing and thawing, were found to substantially affect PRN yield. A single cycle of rapid freezing of the cell pellet at − 30 °C with slow thawing at 5 ± 3 °C resulted in up to fivefold higher PRN yield than condition without freezing and thawing. The search for urea treatment conditions was also conducted, and 5 M urea treatment for 2 h was the optimal condition. The developed urea-based process was applied to 50 L culture scale, and residual impurities were removed by sequential anion exchange, hydrophobic interaction and gel filtration chromatography and resulted in PRN with a purity of over 95% at a yield of 33.2%. From 50 L culture broth, the final yield of PRN per cell pellet was 0.23 mg/g (wet weight). Thus, a large-scale production process for high-quality PRN from B. pertussis was developed based on urea extraction process. The results may serve as a reference for production of other membrane proteins.
CRISPR/Cas9 Mediated Deletion of the Uox Gene Generates a Mouse Model of Hyperuricemia with Multiple Complications
Authors: Zeng Linzi , Shali Shalaimaiti , Gao Yabiao , Du Xingchen , Zhu Xiaoxia , Li Lin , Dai Yuxiang , Zhou Ping 
Published: June 2024

AbstractHyperuricemia is a common metabolic disorder with severe complications. We aimed to develop a mouse model for spontaneous hyperuricemia. Uox-/- mouse model was generated on C57BL/6J background by deleting exon 2-4 of Uox using the CRISPR/Cas9 system. The prototypic Uox-/-mice had 5.5-fold increased serum uric acid (1351.04±276.58μmol/L) as compared to the wild type mice (P<0.0001), but died by 4 weeks. After allopurinol (3ug/g) intervention, they all survived > 8 weeks. The serum uric acid was 612.55±146.98μmol/L in the 8-week-old allopurinol-rescued Uox-/-mice, which manifested multiple complications including severe renal insufficiency, hypertension, left ventricular remodeling and systolic dysfunction, aortic endothelial dysfunction, hepatic steatosis and elevated liver enzymes, as well as hyperglycemia and hypercholesteremia. The present Uox-/- mice developed spontaneous hyperuricemia complicated with urate nephropathy, cardiovascular disease and cardiometabolic disorders, and may provide a novel tool to study hyperuricemia associated early-onset cardiovascular disorders in human.
Graphical Abstract
A mouse model of hyperuricemia with multiple complications constructed by knocking out of urate oxidase (Uox) using CRISPR/Cas9 technology. Uox-/-: homozygous; Uox+/-: heterozygous; SUA: serum uric acid; ALT: alanine aminotransferase; AST: aspartate aminotransferase.
The Relationship Between Academic Stress, Sleep Quality, and Psychological Wellbeing in Pasifika and New Zealand European Students at the University of Otago
Authors: Time Willie Solomona , Fakapulia Ilaisaane Foli , Samalia Latika , Wibowo Erik 
Published: June 2024

Abstract
Background
Pasifika students in New Zealand are overrepresented in poor academic outcomes, and their academic challenges may potentially influence their wellbeing. We aim to: 1) compare the academic stress, sleep quality, and psychological wellbeing of Pasifika and New Zealand European (NZE) anatomy students at the University of Otago, and 2) determine if academic stress mediates the association between their psychological wellbeing and sleep quality.

Methods
We launched a brief online survey to Pasifika and NZE students in our department. The survey included basic demographics, Perception of Academic Stress Scale, Pittsburgh Sleep Quality Index, Hospital Anxiety and Depression Scale, a short loneliness scale, a reduced morningness-eveningness scale, and the Epworth Sleepiness Scale.

Results
Perceived academic stress were comparable between NZE and Pasifika students, but Pasifika students reported poorer sleep quality than NZE students (t(113) = 14.41, P < .001). In addition, Pasifika students reported more loneliness (t(119) = 8.933, P < .001), less anxiety symptoms (t(120) = 2.469, P = .015), and less of a morning person (t(121) = 2.618, P = .010) than NZE students, but they had comparable depressive symptoms and daytime sleepiness. After controlling for age, ethnicity and gender, we found that academic stress fully mediated the relationship between anxiety or depressive symptoms and poor sleep quality. Furthermore, academic stress partially mediated the relationship between loneliness and poor sleep quality in our cohort.

Conclusion
Our findings highlight the importance for academic institutions to support students’ wellbeing, including ethnic minority students such as Pasifika students in New Zealand.
Long-term student performance prediction using learning ability self-adaptive algorithm
Authors: Ren Yi , Yu Xinjie 
Published: June 2024

AbstractPredicting student performance is crucial for both preventing failure and enabling personalized teaching-and-learning strategies. The digitalization of educational institutions has led to the collection of extensive student learning data over the years. Current research primarily focuses on short-term data, e.g. a single year or semester. In contrast, long-term data has the potential to offer a deeper insight into student behavior, thereby increasing the accuracy of predictions. However, the direct application of long-term data in prediction models assumes consistent data distributions over time. In the real world, evolutions in course content and structure can lead to variations in feature spaces (heterogeneity) and distribution shifts across different academic years, compromising the effectiveness of prediction models. To address these challenges, we introduce the Learning Ability Self-Adaptive Algorithm (LASA), which can adapt to the evolving feature spaces and distributions encountered in long-term data. LASA comprises two primary components: Learning Ability Modeling (LAM) and Long-term Distribution Alignment (LTDA). LAM assumes that students’ responses to exercises are samples from distributions that are parameterized by their learning abilities. It then estimates these parameters from the heterogeneous student exercise response data, thereby creating a new homogeneous feature space to counteract the heterogeneity present in long-term data. Subsequently, LTDA employs multiple asymmetric transformations to align distributions of these new features across different years, thus mitigating the impact of distribution shifts on the model’s performance. With these steps, LASA can generate well-aligned features with meaningful semantics. Furthermore, we propose an interpretable prediction framework including three components, i.e. LASA, a base classifier for outcome predictions, and Shapley Additive Explanations (SHAP) for elucidating the impact of specific features on student performance. Our exploration of long-term student data covers an eight-year period (2016-2023) from a face-to-face course at Tsinghua University. Comprehensive experiments demonstrate that leveraging long-term data significantly enhances prediction accuracy compared to short-term data, with LASA achieving up to a 7.9% increase. Moreover, when employing long-term data, LASA outperforms state-of-the-art models, ProbSAP and SFERNN, by an average accuracy improvement of 6.8% and 6.4%, respectively. We also present interpretable insights for pedagogical interventions based on a quantitative analysis of feature impacts on student performance. To the best of our knowledge, this study is the first to investigate student performance prediction in long-term data scenarios, addressing a significant gap in the literature.
Asymptotic dynamic graph order evolution analysis
Authors: Bridonneau Vincent , Guinand Frédéric , Pigné Yoann 
Published: June 2024

AbstractIn this work, we investigate the analysis of generators for dynamic graphs, which are defined as graphs whose topology changes over time. We focus on generated graphs whose order (number of nodes) varies over time. We use a concept called “sustainability” to qualify the long-term evolution of dynamic graphs. A dynamic graph is considered sustainable if its evolution does not result in a static, empty, or periodic graph. To illustrate how the analysis can be conducted, a parameterized and probability-based generator, named D3G3 (Degree-Driven Dynamic Geometric Graphs Generator), has been introduced in a recent work. From this model, we derive multiple scenarios that correspond to three trends in graph order evolution. Our central contribution lies in a mathematical framework that provides an expectation of the order of the graph at time step $$t+1$$

t
+
1

, given its order at time step t. Nevertheless, our analysis underscores the challenge of characterizing the sustainability of dynamic graphs, even when a formal mathematical model for graph order evolution is known.
Background music varying in tempo and emotional valence differentially affects cognitive task performance: experimental within-participant comparison
Authors: Hofbauer Lena Maria , Lachmannn Thomas , Rodriguez Francisca Saveria 
Published: June 2024

AbstractThe degree of stimulation provided by background music appears to affect performance on cognitive tasks. Moreover, individual differences influence what degree of stimulation is beneficial or detrimental. In a within-subject design, 40 participants (Mean Age: 26.15, SD: 2.99) completed cognitive tasks (immediate and delayed recall, phonemic fluency, trail-making) under varying (2 tempi: fast/slow×2 valences: positive/negative) musical background conditions. Further, they completed questionnaires on individual differences (extraversion, noise sensitivity, annoyance/distraction by background noise). Performance was assessed using analyses of variance and mixed-effect models. Sensitivity analyses adjusted for stimulus liking and further individual characteristics. Fast (vs. slow) tempo was associated with better immediate recall (p = .002, η2 = .08) and phonemic fluency (p < .001, η2 = .16). Positive (vs. negative) valence was also associated with better immediate recall (p < .001, η2 = .10) and phonemic fluency (p < .001, η2 = .10). The association of positive valence with phonemic fluency was attenuated in those with above average Annoyance/Distraction by Background Noise. The latter also had a slower performance in the trail making test under positive background music. The association of fast tempo with verbal fluency was stronger among those scoring high in Noise Sensitivity. Overall, our results suggest that, with regard to concurrent cognitive performance, fast tempo, positively valenced background music is preferable over slow, negatively valenced background music. A deeper understanding of inter-individual differences could allow further individualisation of background music for cognitive task performance.
Advanced design of triboelectric nanogenerators for future eco-smart cities
Authors: Tang Yun , Fu Hong , Xu Bingang 
Published: June 2024

AbstractEco-smart cities follow the ecological principles, utilize smart information technologies (Internet of Things, artificial intelligence, cloud computing) to build an efficient, harmonious, resilient, and sustainable habitable environment in the form of informatization. Triboelectric nanogenerators (TENGs) offer the benefits of being self-powered, affordable, extremely customizable, and multi-scenario applications. The researches depict that TENGs are well positioned to support the digitization, intellectualization and sustainable urban services, since they have been repeatedly demonstrated as renewable power providers and self-powered sensors. In this assessment, the most recent applications of TENGs technology in eco-smart cities over the past two years in various categories are investigated, including renewable energy supply (water, wind, solar and raindrop energy, etc.), human–machine interaction, intelligent healthcare, intelligent transportation, intelligent agriculture, intelligent industry and intelligent environmental protection. There is additional sketch of the distinctions in TENG materials, architectures, working modes, and contact modes for serving diverse living usage scenarios of the eco-smart cities. This review will promote and popularize the utilization of TENG in smart ecological cities, as well as provide instruction of its construction for future smart cities and eco-cities.
Effects of Operational and Environmental Conditions on Estimated Dynamic Characteristics of a Large In-service Wind Turbine
Authors: Ozturkoglu Onur , Ozcelik Ozgur , Günel Serkan 
Published: June 2024

Abstract
Purpose
The reliable and continuous operation of wind turbines is of utmost importance, making it necessary to thoroughly understand their dynamic behavior under various operational and environmental conditions.

Methods
To achieve this, a data acquisition system distributed throughout the tower height is designed. The system records data such as the acceleration, temperature, and relative humidity from the sensors, along with the rotor speed, wind speed, temperature, pitch angle, nacelle direction, and wind direction from the data acquisition system of the turbine. The acquired data is synchronized and processed by Autonomous and Continuous System Identification system based on the poly-reference Least Squares Complex Frequency method. The extensive dataset, gathered over a 7-month period, allows for the estimation of modal parameters of the wind turbine. The modal parameters are then correlated with the operational and environmental conditions that were recorded. The relationships between these conditions are thoroughly analyzed and explained. Additionally, the operational principles of the wind turbine are elucidated in detail. The correlations between the modal parameters and operational or environmental factors are presented and interpreted, shedding light on the complex interplay between wind turbine dynamics and external conditions.

Conclusion
It can be said that changes in operational and environmental conditions affect the modal parameters of the wind turbine differently across various structural modes. Without considering these effects, structural health monitoring systems may produce false alarms. Failure to consider these effects in the development of structural health monitoring systems may lead to incorrect damage alarms.
Nanoparticles in revolutionizing crop production and agriculture to address salinity stress challenges for a sustainable future
Authors: Singh Abhishek , Agrawal Shreni , Rajput Vishnu D. , Ghazaryan Karen , Yesayan Aleksandr , Minkina Tatiana , Zhao Yufei , Petropoulos Dimitrios , Kriemadis Athanasios , Papadakis Marios , Alexiou Athanasios 
Published: June 2024

AbstractAcross the world, salinity is one of the most detrimental environmental stresses that severely reduces agricultural yield and productivity. More than 20% of the world's agricultural soils endure the adversity of salinity stress, and such saline-prone regions are constantly rising, due to human and natural activity. This review paper explores the promising role of nanoparticles (NPs) in mitigating the adverse effects of salinity stress on crops and offers insights into their potential applications in sustainable agriculture. Salinity stress, primarily caused by soil salinization, disrupts plant growth and development, leading to reduced crop yields and quality. NPs, with their unique physicochemical properties and nanoscale dimensions, have demonstrated remarkable potential in ameliorating the effects of salinity stress. This review provides an in-depth analysis of the various types of NPs, including metallic, metal oxide, and carbon-based NPs, and their effects on plant hormone signalling networks (auxin, giberellins, abscisic acid (ABA) and jasmonic acid (JA), and applications in enhancing salt tolerance in diverse crop species. This review article examines the physiochemical, biochemical, and molecular mechanisms of plants by which NPs alleviate salinity stress, encompassing ion homeostasis, osmotic regulation, and antioxidant defence systems. Moreover, this paper critically evaluates the toxic effects of NPs in plants and outlines the challenges and concerns associated with their widespread adoption. By understanding the potential benefits and limitations of NP applications, we can pave the way for sustainable agriculture practices that improve crop resilience, mitigate soil salinization, and contribute to global food security in an era of increasing environmental stressors.