bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consumers’ perceptions on indigenous fruit value-added products in Bushbuckridge, Mpumalanga, South Africa
Authors: Maluleke Mdungazi K. , Mathibe Noluvuko , Mthombeni Danisile Leonah 
Published: August 2024

AbstractMost rural communities use indigenous fruits as an integral part of their diets by processing them into value-added products such as alcoholic beverages, jam, juice, and nuts/dried fruits. Despite the claim that these products are rich in vitamins and energy, they are still relatively underutilised due to their low availability at mainstream retailers. The data was collected from 384 participants in Bushbuckridge in Mpumalanga Province and the Statistical Package for the Social Sciences (SPSS) was used to analyse the consumers’ perceptions on indigenous fruit value-added products. Principal component analysis was applied, and the study results revealed that consumers preferred buying indigenous fruit value-added products over exotic fruit value-added products. Data on indigenous fruit value-added products’ market availability and accessibility was extracted and the data showed that they were perceived as important variables by consumers. Furthermore, consumers (96.4%) indicated that they would purchase marula fruit value-added products instead of other products (wild sour plum, orange monkey and wild medlar). Regarding access to indigenous value-added products, 24.7% of the participants strongly agreed and 27.6% agreed that they do have access. Therefore, it is recommended that interventions to create awareness and promote the sale and consumption of the indigenous fruit value-added products should be prioritised at events such as indigenous festivals, as well as at local retailers.
A governance and legal framework for getting to “yes” with enterprise-level data integration
Authors: Hawn Nelson Amy , Hogle Paul , Zanti Sharon , Proescholdbell Scott , Tenenbaum Jessica D. 
Published: August 2024

Abstract
Public agencies routinely collect administrative data that, when shared and integrated, can form a rich picture of the health and well-being of the communities they serve. One major challenge is that these datasets are often siloed within individual agencies or programs and using them effectively presents legal, technical, and cultural obstacles. This article describes work led by the North Carolina Department of Health and Human Services (NCDHHS) with support from university-based researchers to establish enterprise-wide data governance and a legal framework for routine data sharing, toward the goal of increased capacity for integrated data analysis, improved policy and practice, and better health outcomes for North Carolinians. We relied on participatory action research (PAR) methods and Deliberative Dialogue to engage a diverse range of stakeholders in the co-creation of a data governance process and legal framework for routine data sharing in NCDHHS. Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy road map, created a data sharing guidebook to operationalize legal and ethical review of requests, staffed the Data Office, and implemented a legal framework. In addition to describing how these ongoing streams of work support data use across a large state health and human services agency, we provide three use cases demonstrating the impact of this work. This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, integration, and use.
What does a record have to do with it? Re-situating records management within Indian public data governance and policy
Authors: Sen Srijoni , Jindal Trishi 
Published: August 2024

Abstract
A number of data governance policies have recently been introduced or revised by the Indian Government with the stated goal of unlocking the developmental and economic potential of data. The policies seek to implement standardized frameworks for public data management and establish platforms for data exchange. However, India has a longstanding history of record-keeping and information transparency practices, which are crucial in the context of data management. These connections have not been explicitly addressed in recent policies like the Draft National Data Governance Framework, 2022. To understand if record management has a role to play in modern public data governance, we analyze the key new data governance framework and the associated Indian Urban Data Exchange platform as a case study. The study examines the exchange where public records serve as a potential source of data. It evaluates the coverage and the actors involved in the creation of this data to understand the impact of records management on government departments’ ability to publish datasets. We conclude that while India recognizes the importance of data as a public good, it needs to integrate digital records management practices more effectively into its policies to ensure accurate, up-to-date, and accessible data for public benefit.
Assessing the Optimal Tsunami Inundation Modeling Strategy for Large Earthquakes in Subduction Zones
Authors: Scala A. , Lorito S. , Escalante Sánchez C. , Romano F. , Festa G. , Abbate A. , Bayraktar H. B. , Castro M. J. , Macías J. , Gonzalez‐Vida J. M. 
Published: August 2024

AbstractTsunamis are rare events involving several complex physical phenomena. Due to this complexity and the relative scarcity of observations, tsunami research makes extensive use of numerical simulations. For seismogenic tsunamis, the source is often modeled as an instantaneous sea‐floor displacement (IS), while the tsunami propagation and inundation is computed through a shallow water approximation (SW). Here, we investigate what is the best tsunami inundation modeling strategy for different realistic earthquake source size and duration. We use 1D earthquake‐tsunami coupled simulations of large M > 8 earthquakes in Tohoku‐like subduction zone to test for which conditions the IS and/or the SW approximations can simulate with enough accuracy the tsunami evolution. We use as a reference a time‐dependent (TD), multi‐layer, non‐hydrostatic (NH) 1D model. Source duration, and size, are based on 1D dynamic rupture simulations with realistic stress drop and rigidity. We show that slow ruptures, generating slip in the shallow part of subduction zones (e.g., tsunami earthquakes), and very large events, with an along‐dip extent comparable with the trench‐coast distance (as occurs for megathrust events) require a TD‐NH modeling, especially for regions with steep coastal bathymetry. Conversely, deeper, higher stress‐drop events can be modeled through an IS‐SW approximation. We finally show that: (a) steeper bathymetries generate larger runups and, (b) a resonant mechanism emerges with runup amplifications associated with larger source size on flatter bathymetries. These results, obtained with 1D modeling, can serve as a guide for the appropriate 2/3D simulation approach for applications ranging from fundamental tsunami science to computational‐intensive hazard assessments.
Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models
Authors: Gurumurthy Gerard , Gurumurthy Juditha , Gurumurthy Samantha 
Published: August 2024

Abstract
Background
Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limitations in this area.

Methods
A literature search was conducted through Ovid. Studies included focused on ML models in paediatric patients with haematological malignancies. Studies were categorised into thematic groups for analysis.

Results
Twenty studies, primarily on leukaemia, were included in this review. Studies were organised into thematic categories such as prognoses, treatment responses and toxicity predictions. Prognostic studies showed AUC scores between 0.685 and 0.929, indicating moderate-high predictive accuracy. Treatment response studies demonstrated AUC scores between 0.840 and 0.875, reflecting moderate accuracy. Toxicity prediction studies reported high accuracy with AUC scores from 0.870 to 0.927. Only five studies (25%) performed external validation. Significant heterogeneity was noted in ML tasks, reporting formats, and effect measures across studies, highlighting a lack of standardised reporting and challenges in data comparability.

Conclusion
The clinical applicability of these ML models remains limited by the lack of external validation and methodological heterogeneity. Addressing these challenges through standardised reporting and rigorous external validation is needed to translate ML from a promising research tool into a reliable clinical practice component.

Impact

Key message: Machine Learning (ML) significantly enhances predictive models in paediatric haematological cancers, offering new avenues for personalised treatment strategies. Future research should focus on developing ML models that can integrate with real-time clinical workflows.

Addition to literature: Provides a comprehensive overview of current ML applications and trends. It identifies limitations to its applicability, including the limited diversity in datasets, which may affect the generalisability of ML models across different populations.

Impact: Encourages standardisation and external validation in ML studies, aiming to improve patient outcomes through precision medicine in paediatric haematological oncology.
Exploring impact of green finance and natural resources on eco-efficiency: case of China
Authors: Fang Xu , Khalaf Osamah Ibrahim , Guanglei Wu , Cristia Juan Felipe Espinosa , Almasabi Salwa 
Published: August 2024

AbstractChina ranks 160 out of 180 countries in terms of ecological efficiency, with an EPI score of 28.40 and a 10-year average change in score of 11.40. This article examines the impact of green finance and China’s natural resources on regional ecological efficiency using the Tobit regression model. The study uses the average yearly exchange rate to normalize dollar-related values and GDP to 2012 RMB using the price deflator. Variables used as explanatory tools include green financing, the availability of natural resources, and regional eco-efficiency. The results of the study imply that natural resources in eastern region of China are better managed as and have avoided the resource curse as compared to central and western regions. Resources temporarily support area economic and social growth. However, resource agglomeration locks many elements in the resource industry and degrades regional industrial development, generating environmental and social difficulties that may hinder regional economic progress. Given that Foreign Direct Investment (FDI) increases regional eco-efficiency after accounting for adjustment. The FDI positively correlated with ecological efficiency in the east zone, while central and western zones have negative correlations. The industrial development of the nation negatively impacts ecological efficiency in the East, Midwest, and West regions. Western results are distinctive, with ecological efficiency and regional economic growth frequently going hand in hand.
CTNet: a convolutional transformer network for EEG-based motor imagery classification
Authors: Zhao Wei , Jiang Xiaolu , Zhang Baocan , Xiao Shixiao , Weng Sujun 
Published: August 2024

AbstractBrain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the translation of thought into actionable commands for interactive and assistive technologies. However, the constrained decoding performance of brain signals poses a limitation to the broader application and development of BCI systems. In this study, we introduce a convolutional Transformer network (CTNet) designed for EEG-based MI classification. Firstly, CTNet employs a convolutional module analogous to EEGNet, dedicated to extracting local and spatial features from EEG time series. Subsequently, it incorporates a Transformer encoder module, leveraging a multi-head attention mechanism to discern the global dependencies of EEG's high-level features. Finally, a straightforward classifier module comprising fully connected layers is followed to categorize EEG signals. In subject-specific evaluations, CTNet achieved remarkable decoding accuracies of 82.52% and 88.49% on the BCI IV-2a and IV-2b datasets, respectively. Furthermore, in the challenging cross-subject assessments, CTNet achieved recognition accuracies of 58.64% on the BCI IV-2a dataset and 76.27% on the BCI IV-2b dataset. In both subject-specific and cross-subject evaluations, CTNet holds a leading position when compared to some of the state-of-the-art methods. This underscores the exceptional efficacy of our approach and its potential to set a new benchmark in EEG decoding.
Thermokarst landscape exhibits large nitrous oxide emissions in Alaska’s coastal polygonal tundra
Authors: Hashemi Josh , Lipson David A. , Arndt Kyle A. , Davidson Scott J. , Kalhori Aram , Lunneberg Kyle , van Delden Lona , Oechel Walter C. , Zona Donatella 
Published: August 2024

AbstractGlobal atmospheric concentrations of nitrous oxide have been increasing over previous decades with emerging research suggesting the Arctic as a notable contributor. Thermokarst processes, increasing temperature, and changes in drainage can cause degradation of polygonal tundra landscape features resulting in elevated, well-drained, unvegetated soil surfaces that exhibit large nitrous oxide emissions. Here, we outline the magnitude and some of the dominant factors controlling variability in emissions for these thermokarst landscape features in the North Slope of Alaska. We measured strong nitrous oxide emissions during the growing season from unvegetated high centered polygons (median (mean) = 104.7 (187.7) µg N2O-N m−2 h−1), substantially higher than mean rates associated with Arctic tundra wetlands and of similar magnitude to unvegetated hotspots in peat plateaus and palsa mires. In the absence of vegetation, isotopic enrichment of 15N in these thermokarst features indicates a greater influence of microbial processes, (denitrification and nitrification) from barren soil. Findings reveal that the thermokarst features discussed here (~1.5% of the study area) are likely a notable source of nitrous oxide emissions, as inferred from chamber-based estimates. Growing season emissions, estimated at 16 (28) mg N2O-N ha−1 h−1, may be large enough to affect landscape-level greenhouse gas budgets.
Intravascular ICG-enhanced NIRF-IVUS imaging to assess progressive atherosclerotic lesions in excised human coronary arteries
Authors: Rauschendorfer Philipp , Lenz Tobias , Nicol Philipp , Wild Léa , Beele Alicia , Sabic Emina , Klosterman Grace , Laugwitz Karl-Ludwig , Jaffer Farouc A. , Gorpas Dimitris , Joner Michael , Ntziachristos Vasilis 
Published: August 2024

AbstractIndocyanine green (ICG)-enhanced intravascular near-infrared fluorescence (NIRF) imaging enhances the information obtained with intravascular ultrasound (IVUS) by visualizing pathobiological characteristics of atherosclerotic plaques. To advance our understanding of this hybrid method, we aimed to assess the potential of NIRF-IVUS to identify different stages of atheroma progression by characterizing ICG uptake in human pathological specimens. After excision, 15 human coronary specimens from 13 adult patients were ICG-perfused and imaged with NIRF-IVUS. All specimens were then histopathologically and immunohistochemically assessed. NIRF-IVUS imaging revealed colocalization of ICG-deposition to plaque areas of lipid accumulation, endothelial disruption, neovascularization and inflammation. Moreover, ICG concentrations were significantly higher in advanced coronary artery disease stages (p < 0.05) and correlated significantly to plaque macrophage burden (r = 0.67). Current intravascular methods fail to detect plaque biology. Thus, we demonstrate how human coronary atheroma stage can be assessed based on pathobiological characteristics uniquely captured by ICG-enhanced intravascular NIRF.
Prevalence and assemblage identified of Giardia duodenalis in zoo and farmed Asiatic black bears (Ursus thibetanus) from the Heilongjiang and Fujian Provinces of China
Authors: Chen Jiani , Zhou Liyuting , Cao Wenjie , Xu Junchen , Yu Kuai , Zhang Ting , Wang Yiqing , Wang Jiayan , Huang Huicong , Zhao Wei 
Published: August 2024

Captive and free-living wildlife serve as significant hosts for Giardia duodenalis. Asiatic black bears, valued for their economic and medicinal importance, are extensively farmed in China and also prevalent in zoos. However, studies on G. duodenalis in these animals in China are limited. Here, 218 feces samples of Asiatic black bears were collected: 36 from a zoo in Heilongjiang Province, and 182 from a farm in Fujian Province. Nested PCR of the SSU rRNA gene, followed by sequencing, was employed to determine the frequency and assemblage distribution of G. duodenalis. Positive samples underwent further analysis through multilocus genotyping (MLG) by amplifying the genes for glutamate dehydrogenase (gdh), β-giardin (bg), and triosephosphate isomerase (tpi). Of the 218 samples, G. duodenalis was detected in 22 cases at the SSU rRNA gene locus, including three from Heilongjiang and 19 from Fujian. Three assemblages were identified: A (n = 1), B (n = 16), and E (n = 2) in Fujian; and B (n = 3) in Heilongjiang. Out of the 22 positive samples, 20, 19, and 9 were effectively amplified and sequenced across the tpi, gdh, and bg loci, respectively. Seven samples were genotyped successfully at all three loci, identifying MLG-B1 (n = 1), MLG-B2 (n = 1), and MLG-B3 (n = 1), MLG-B4 (n = 1), MLG-B5 (n = 2), and MLG-B6 (n = 1) as the six assemblage B MLGs. This study marks the first documentation of G. duodenalis in Asiatic black bears in captivity in Fujian and Heilongjiang. The identification of zoonotic assemblages A and B, along with E, underscores potential public health concerns.
A Narrowband Burst from FRB 20190520B Simultaneously Observed by FAST and Parkes
Authors: Zhu 朱 Yuhao 禹豪 , Niu 牛 Chenhui 晨辉 , Dai 代 Shi 实 , Li 李 Di 菂 , Wang 王 Pei 培 , Feng 冯 Yi 毅 , Wu 吴 Jingwen 京文 , Zhang 张 Yongkun 永坤 , Cui 崔 Xianghan 翔翰 , Zhang 张 Junshuo 钧硕 , Cao 曹 Jinhuang 锦煌 
Published: August 2024

Abstract
Fast radio bursts (FRBs) are short-duration radio transients with mysterious origins. Since their uncertainty, there are very few FRBs observed by different instruments simultaneously. This study presents a detailed analysis of a burst from FRB 20190520B observed by FAST and Parkes at the same time. The spectrum of this individual burst ended at the upper limit of the FAST frequency band and was simultaneously detected by the Parkes telescope in the 1.5–1.8 GHz range. By employing spectral energy distribution (SED) and spectral sharpness methods, we confirmed the presence of narrow-band radiation in FRB 20190520B, which is crucial for understanding its radiation mechanisms. Our findings support the narrow-band characteristics that most repeaters exhibit. This work also highlights the necessity of continued multiband observations to explore its periodicity and frequency-dependent properties, contributing to an in-depth understanding of FRB phenomena.
NaOH-assisted hydrothermal reduction of graphene oxide
Authors: Kotsyubynsky Volodymyr , Khimyak Yaroslav Z , Zapukhlyak Ruslan , Boychuk Volodymyra , Turovska Liliia , Hoi Vladyslav 
Published: August 2024

Abstract
The influence of the pH of the reaction medium on the structural characteristics of hydrothermally reduced graphene oxide, synthesized by the tour method, has been investigated. Varying the pH of the reaction medium within the range of 8.0, 10.0 and 12.0 (adjusted with NaOH) has revealed distinct effects on the morphology and properties of the resulting reduced graphene oxide. At a pH of 8.0 the hydrothermal treatment yielded reduced graphene oxide comprising of two particle fractions with a thickness equivalent to 4–5 graphitic layers each. In contrast, pH of 10.0 resulted in two particle fractions corresponding to 2–3 and 4 layers, respectively, while pH of 12.0 produced a single fraction with a particle thickness of 0.70 nm, encompassing 3 graphitic layers. Increasing the pH led to a decrease in the average lateral size of reduced graphene oxide particles to about 8 nm. All rGOs had micro- and mesopores with a specific surface area up to 226 m2 g−1, showing a proportional increase in mesopores with increasing pH. Analysis of slit-like micropores revealed a minimum fractal dimension (D = 2.18) at pH = 8.0. The obtained results provide valuable insights into tailoring the structural properties of hydrothermally reduced graphene oxide by controlling the pH of the reaction medium, offering potential applications in various fields, including nanotechnology and materials science.
Achievement of the first tokamak plasma with low inductive electric field in JT-60SA
Authors: Wakatsuki T. , Urano H. , Yoshida M. , Tsujii N. , Inoue S. , Kojima S. , Nakano T. , Fukumoto M. , Ohtani Y. , Sano R. , Ide S. 
Published: August 2024

Abstract
The world’s largest superconducting tokamak, JT-60SA, has successfully achieved its first plasma operation under the constraint of a low toroidal inductive electric field of 0.15 V m−1. A plasma start-up scenario, leveraging the effective confinement of electrons accelerated by the electron cyclotron wave, proved to be instrumental in reaching this milestone under the challenging conditions. The demonstration of plasma start-up using second harmonic electron cyclotron heating, with an applied toroidal electric field of 0.15 V m−1, strongly validates the feasibility of achieving first plasma operation in ITER.
Stochastic thermodynamics of micromagnetics with spin torque
Authors: Ding Mingnan , Wu Jun , Xing Xiangjun 
Published: August 2024

Abstract
In this work, we study the stochastic dynamics of micro-magnetics interacting with a spin-current torque. We extend the previously constructed stochastic Landau–Lifshitz equation to the case with spin-current torque, and verify the conditions of detailed balance. Then we construct various thermodynamics quantities such as work and heat, and prove the second law of thermodynamics. Due to the existence of spin-torque and the asymmetry of the kinetic matrix, a novel effect of entropy pumping shows up. As a consequence, the system may behave as a heat engine which constantly transforms heat into magnetic work. Finally, we derive a fluctuation theorem for the joint probability density function of the pumped entropy and the total work, and verify it using numerical simulations.
Research on Heating Load Forecasting Based on Support Vector Machine and Multiple Linear Regression
Authors: Mu Dongdong , Sang Zhendong , Du Guofei , Fan Yunsheng 
Published: August 2024

Abstract
Various prediction models from the perspectives of short-term and long-term heat load prediction are summarized and verified for centralized heating heat load prediction. Firstly, historical data is preprocessed by removing abnormal data and standardizing the sample dataset. Secondly, support vector machine regression (SVM) model and multiple linear regression (MLR) are chosen for modeling and predictive analysis. Then, the mean square error (MSE) criteria are applied to evaluate the model performance by calculating the MSE values corresponding to different prediction results. Finally, historical production data of a heating company in Dalian City are used to verify the applicability of different prediction model.