bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Irrigation Performance of Raised Bed Furrow Using WinSRFR Model
Authors: Shah Muhammad Ayub , Waseem Muhammad , Iqbal Mudassar , Nabi Ghulam , Tariq Muhammad Atiq Ur Rehman , Arshed Abu Bakar , Sultan Umar , Laraib Muhammad , Abbas Khawar 
Published: July 2024

AbstractAgricultural productivity is intricately tied to efficient water management strategies, with raised bed furrow systems being a prevalent method for irrigation. However, the optimization of these systems remains a critical area of exploration. The border irrigation method is commonly employed in developing countries for irrigation and leads to significant water loss, reduced irrigation efficiency, and increased irrigation durations. In contrast, raised bed furrow irrigation represents an improved surface irrigation technique that optimizes water usage in irrigated systems. This study seeks to assess the irrigation performance of raised bed furrows, encompassing deep percolation loss, distribution uniformity, adequacy, and application efficiency. The evaluation will be conducted for both existing conditions and an optimized scenario achieved through the application of the WinSRFR model. Field data facilitated the numerical simulation and the model was calibrated to reflect the existing irrigation system dynamics accurately. The performance of the model was assessed by utilizing the statistical indicator of root mean square error (RMSE) and revealed good agreement between advance and recession time. Results revealed that existing raised bed furrow irrigation exhibited up to 40% deep percolation loss, 80% distribution uniformity, and 60% application efficiency. Increasing furrow length had adverse effects; decreased application efficiency and distribution uniformity; and increased deep percolation losses. In contrast, reducing the furrow length and cutoff time by up to 33% and 40%, respectively, and increasing the width and inflow rate by up to 55% and 100%, respectively, enhanced the application efficiency and distribution uniformity, and minimized deep percolation loss. Overall, improved raised bed furrow irrigation provides a more efficient option and is encouraged to adopt for irrigation.
From time series to networks in R with the ts2net package
Authors: Ferreira Leonardo N. 
Published: July 2024

AbstractNetwork science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents a detailed revision of the main transformation methods in the literature and their implementation in the package in . The package provides time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, permits the use of network science and graph mining tools to extract information from time series.
Analyzing breast cancer comorbidities: a network approach using community detection algorithms
Authors: Permana Angga A. , Yaputra Reynard M. 
Published: July 2024

AbstractBreast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. To help improve the current breast cancer comorbidity identification by medical personnel and ultimately, lower the number of death cases from breast cancer comorbidity, this research aims to discover the breast cancer comorbidity community, do modularity and similarity-based evaluation, suggest the best semantic similarity measurement and threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden–Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain–Leiden (0.99), RB Pots–Leiden (0.97), and RB Pots–RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed by four algorithms, two diseases are agreed by three algorithms, a disease is agreed by two algorithms, and ten diseases are agreed by an algorithm.
Dynamics of economic growth in ASEAN-5 countries: a panel ARDL approach
Authors: Hidthiir Mohammad Helmi bin , Ahmad Zaki , Junoh Mohd Zukime Mat , Yusof Mohd Faizal Bin 
Published: July 2024

AbstractThis research explores economic growth dynamics in five ASEAN countries (1980–2020), analyzing the short and long-term impact of financial development (FD), oil prices, investment (INV), and inflation on GDP growth. Panel ARDL analysis reveals a transient positive link between FD and long-term GDP growth, necessitating a holistic approach for short-term stability. Similarly, oil prices exhibit long-term volatility, urging diversification strategies. In contrast, consistent and significant relationships exist between INV and GDP growth in both time frames, underscoring investment's pivotal role. The study emphasizes managing inflation for sustained growth, offering vital insights for policymakers, economists, and analysts in fostering ASEAN's economic stability.
Risk of Cardiovascular Disease Hospitalization After Common Psychiatric Disorders: Analyses of Disease Susceptibility and Progression Trajectory in the UK Biobank
Authors: Han Xin , Zeng Yu , Shang Yanan , Hu Yao , Hou Can , Yang Huazhen , Chen Wenwen , Ying Zhiye , Sun Yajing , Qu Yuanyuan , Wang Junren , Zhang Wei , Fang Fang , Valdimarsdóttir Unnur , Song Huan 
Published: July 2024

AbstractWhether associations between psychiatric disorders and hospitalization for cardiovascular diseases (CVDs) can be modified by disease susceptibility and the temporal pattern of these associated CVDs remain unknown. In our study, we conducted a matched cohort study of the UK Biobank including 44,430 patients with common psychiatric disorders (anxiety, depression, and stress-related disorders) between 1997 and 2019, together with 222,150 sex-, Townsend deprivation index-, and birth year- individually matched unexposed individuals. The hazard ratios (HRs) for CVD hospitalization associated with a prior psychiatric disorder were derived from Cox models, adjusted for multiple confounders. We then stratified the analyses by self-reported family history of CVD and CVD polygenic risk score (PRS) calculated based on summary statistics of independent genome-wide association studies. We further conducted disease trajectory analysis and visualized the temporal pattern of CVDs after common psychiatric disorders. During a mean follow-up of 12.28 years, we observed an elevated risk of CVD hospitalization among patients with psychiatric disorders, compared with matched unexposed individuals (hazard ratios [HRs] = 1.20, 95% confidence interval [CI]: 1.18–1.23), especially during the first six months of follow-up (1.72 [1.55–1.91]). The stratification analyses by family history of CVD and by CVD PRS obtained similar estimates between subgroups with different susceptibilities to CVD. We conducted trajectory analysis to visualize the temporal pattern of CVDs after common psychiatric disorders, identifying primary hypertension, acute myocardial infarction, and stroke as three main intermediate steps leading to further increased risk of other CVDs. In conclusion, the association between common psychiatric disorders and subsequent CVD hospitalization is not modified by predisposition to CVD. Hypertension, acute myocardial infarction, and stroke are three initial CVDs linking psychiatric disorders to other CVD sequelae, highlighting a need of timely intervention on these targets to prevent further CVD sequelae among all individuals with common psychiatric disorders.
Local population decline of the threatened Lesser Grey Shrike Lanius minor is linked to the modernisation of the rural landscape
Authors: Krištín Anton , Hoi Herbert , Kaňuch Peter 
Published: July 2024

Summary
Landscape changes and the intensification of agriculture in recent centuries were largely responsible for the dramatic decline in the biodiversity of farmlands. Rural settlements have also been subject to radical changes due to modernisation, but their impact on bird populations is poorly quantified. The Lesser Grey Shrike Lanius minor is a threatened farmland bird and already extinct in many areas. We monitored a population of this long-distance migrant in a traditional farming area in the Poľana Mountains (central Slovakia) in three breeding seasons (1996, 2016, and 2021). We analysed the impact of the increase in number of modern habitations and the decrease in traditional farmsteads on the population decline. The number of breeding territories decreased from 73 in 1996 to 38 in 2016 and 22 in 2021. As the population has declined, the breeding area has also shrunk significantly. While there were no modern homesteads in the breeding territories in 1996, by 2021 their number had increased to the number of traditional farmsteads. Building a single modern house in a territory reduced the probability of nesting to about 6%, and this effect was also seen when one or two farms were still present (17% and 40%, respectively). An additional modern homestead in the territory reduced the nesting probability to almost zero, even if a farmstead was already present. In this long-term empirical study, we identified these changes as a local threat factor for the species studied. The results presented can help in the design and implementation of conservation measures in traditional farming landscapes.
Integrating a Water Tracer Model Into WRF‐Hydro for Characterizing the Effect of Lateral Flow in Hydrologic Simulations
Authors: Hu Huancui , Leung L. Ruby , Dominguez Francina , Gochis David , Chen Xingyuan , Good Stephen , Dugger Aubrey , Larsen Laurel , Barlage Michael 
Published: July 2024

AbstractMost current land models approximate terrestrial hydrological processes as one‐dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer‐scale land models. To test the effect of incorporating lateral flow toward three‐dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF‐Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic‐tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF‐Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event‐water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event‐water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration‐excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope‐derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.
Insights on Source Lithology and Pressure‐Temperature Conditions of Basalt Generation Using Machine Learning
Authors: Cheng Lilu , Yang Zongfeng , Costa Fidel 
Published: July 2024

AbstractIdentifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user‐friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds.
MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction
Authors: Wang Haoran , Liu Haijun , Yuan Jing , Le Huijun , Shan Weifeng , Li Liangchao 
Published: July 2024

AbstractThe high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.
Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models
Authors: Prior Elizabeth M. , Michaelson Nathan , Czuba Jonathan A. , Pingel Thomas J. , Thomas Valerie A. , Hession W. Cully 
Published: July 2024

AbstractTopography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
Synchronization bandwidth enhancement induced by a parametrically excited oscillator
Authors: Song Jiahao , Xu Yutao , Yang Qiqi , Huan Ronghua , Wei Xueyong 
Published: July 2024

AbstractThe synchronization phenomenon in nature has been utilized in sensing and timekeeping fields due to its numerous advantages, including amplitude and frequency stabilization, noise reduction, and sensitivity improvement. However, the limited synchronization bandwidth hinders its broader application, and few techniques have been explored to enhance this aspect. In this paper, we conducted theoretical and experimental studies on the unidirectional synchronization characteristics of a resonator with phase lock loop oscillation. A novel enhancement method for the synchronization bandwidth using a parametrically excited MEMS oscillator is proposed, which achieves a remarkably large synchronization bandwidth of 8.85 kHz, covering more than 94% of the hysteresis interval. Importantly, the proposed method exhibits significant potential for high-order synchronization and frequency stabilization compared to the conventional directly excited oscillator. These findings present an effective approach for expanding the synchronization bandwidth, which has promising applications in nonlinear sensing, fully mechanical frequency dividers, and high-precision time references.
The combination of temozolomide and perifosine synergistically inhibit glioblastoma by impeding DNA repair and inducing apoptosis
Authors: Zhao Wenpeng , Zhou Liwei , Zhao Wentao , Yang Huiying , Lu Zhenwei , Zhang Liang , Zhang Yaya , Xie Yuanyuan , Lu Hanwen , Han Wanhong , He Jiawei , Qiu Xiansheng , Jia Fang , Zhao Wujie , Zhang Bingchang , Wang Zhanxiang 
Published: July 2024

AbstractTemozolomide (TMZ) is widely utilized as the primary chemotherapeutic intervention for glioblastoma. However, the clinical use of TMZ is limited by its various side effects and resistance to chemotherapy. The present study revealed the synergistic inhibition of glioblastoma through the combined administration of TMZ and perifosine. This combination therapy markedly diminished BRCA1 expression, resulting in the suppression of DNA repair mechanisms. Furthermore, the combination of TMZ and perifosine elicited caspase-dependent apoptosis, decreasing glioblastoma cell viability and proliferation. The observed synergistic effect of this combination therapy on glioblastoma was validated in vivo, as evidenced by the substantial reduction in glioblastoma xenograft growth following combined treatment with TMZ and perifosine. In recurrent glioma patients, higher BRCA1 expression is associated with worse prognosis, especially the ones that received TMZ-treated. These findings underscore the potent antitumor activity of the AKT inhibitor perifosine when combined with TMZ and suggest that this approach is a promising strategy for clinical glioblastoma treatment.
Computational gastronomy: capturing culinary creativity by making food computable
Authors: Bagler Ganesh , Goel Mansi 
Published: July 2024

AbstractCooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet. Computational Gastronomy is a novel approach for investigating food through a data-driven paradigm. It offers a systematic, rule-based understanding of culinary arts by scrutinizing recipes for taste, nutritional value, health implications, and environmental sustainability. Probing the art of cooking through the lens of computation will open up a new realm of possibilities for culinary creativity. Amidst the ongoing quest for imitating creativity through artificial intelligence, an interesting question would be, ‘Can a machine think like a Chef?’ Capturing the experience and creativity of a chef in an AI algorithm presents an exciting opportunity for generating a galaxy of hitherto unseen recipes with desirable culinary, flavor, nutrition, health, and carbon footprint profiles.
Co-evolutionary dynamics of mammalian brain and body size
Authors: Venditti Chris , Baker Joanna , Barton Robert A. 
Published: July 2024

AbstractDespite decades of comparative studies, puzzling aspects of the relationship between mammalian brain and body mass continue to defy satisfactory explanation. Here we show that several such aspects arise from routinely fitting log-linear models to the data: the correlated evolution of brain and body mass is in fact log-curvilinear. This simultaneously accounts for several phenomena for which diverse biological explanations have been proposed, notably variability in scaling coefficients across clades, low encephalization in larger species and the so-called taxon-level problem. Our model implies a need to revisit previous findings about relative brain mass. Accounting for the true scaling relationship, we document dramatically varying rates of relative brain mass evolution across the mammalian phylogeny, and we resolve the question of whether there is an overall trend for brain mass to increase through time. We find a trend in only three mammalian orders, which is by far the strongest in primates, setting the stage for the uniquely rapid directional increase ultimately producing the computational powers of the human brain.