bluuebunny/crossref_metadata_2025_split huggingface dataset:
A configuration analysis of the driving path of corporate physical investments: Necessary condition analysis and qualitative comparative analysis based on fuzzy sets
Authors: Wang Zhen , Zhao Hongyan 
Published: October 2024

AbstractCorporate physical investments are an important foundation for sustainable economic development. This study investigates the causal relationships and complex mechanisms of the five antecedent conditions under the “Merger–Organization–Environment” (MOE) framework with corporate physical investments. These antecedent conditions include green mergers and acquisitions (M&A), financing constraints, main business performance, corporate governance capacities, and government environmental concerns. Combining the necessary condition analysis and fuzzy‐set qualitative comparative analysis, a sample of 40 heavily polluting listed enterprises in China's A‐share market from 2016 to 2020 is used. The results found that (1) individual antecedent conditions do not constitute the necessary conditions for corporate physical investments; (2) there are four configurations that generate high corporate physical investments, i.e., proactive change‐oriented type, main business focus‐oriented type, performance‐driven oriented type, and passive change‐oriented type; and (3) there are four configurations that generate non‐high corporate physical investments, i.e., performance‐bound type, funding‐bound type, M&A‐bound type, and strategically bound type. This demonstrates the asymmetry of the configurations that generate high and non‐high corporate physical investments, which helps to formulate incentives for corporate physical investments from multiple perspectives.
Drivers of sub‐seasonal extreme rainfall and their representation in ECMWF forecasts during the Eastern African March‐to‐May seasons of 2018–2020
Authors: Gudoshava Masilin , Nyinguro Patricia , Talib Joshua , Wainwright Caroline , Mwanthi Anthony , Hirons Linda , de Andrade Felipe , Mutemi Joseph , Gitau Wilson , Thompson Elisabeth , Gacheru Jemimah , Marsham John , Endris Hussen Seid , Woolnough Steven , Segele Zewdu , Atheru Zachary , Artan Guleid 
Published: October 2024

AbstractIn recent years, Eastern Africa has been severely impacted by extreme climate events such as droughts and flooding. In a region where people's livelihoods are heavily dependent on climate conditions, extreme hydrometeorological events can exacerbate existing vulnerabilities. For example, suppressed rainfall during the March to May 2019 rainy season led to substantial food insecurity. In order to enhance preparedness against forecasted extreme events, it is critical to assess rainfall predictions and their known drivers in forecast models. In this study, we take a case study approach and evaluate drivers during March to May seasons of 2018, 2019 and 2020. We use observations, reanalysis and predictions from the European Centre for Medium‐Range Weather Forecasts (ECMWF) to identify and evaluate rainfall drivers. Extreme rainfall during March to May 2018 and 2020 was associated with an active Madden–Julian Oscillation (MJO) in Phases 1–4, or/and a tropical cyclone to the east of Madagascar. On the other hand, the dry 2019 March to May MAM season, which included a delayed rainfall onset, was associated with tropical cyclones to the west of Madagascar. In general, whilst ECMWF forecasts correctly capture temporal variations in anomalous rainfall, they generally underestimate rainfall intensities. Further analysis shows that underestimated rainfall is linked to a weak forecasted MJO and errors in the location and intensity of tropical cyclones. Taking a case study approach motivates further study to determine the best application of our understanding of rainfall drivers. Communicated effectively, knowledge of rainfall drivers and forecast uncertainty will inform preparedness actions and reduce climate‐driven social and economic consequences.
Identification of spatio‐temporal patterns in extreme rainfall events in the Tropical Andes: A clustering analysis approach
Authors: Urgilés Gabriela , Célleri Rolando , Bendix Jörg , Orellana‐Alvear Johanna 
Published: October 2024

AbstractHigh spatio‐temporal variability is a characteristic of extreme rainfall. In mountainous regions like the Tropical Andes, where intricate orography and mesoscale atmospheric dynamics greatly impact rainfall systems, this particularly holds for mountain areas like the Tropical Andes. Thus, the absence of operational rainfall monitoring networks with high spatio‐temporal resolution has imposed difficulties for a proper analysis of extreme rainfall events in the Ecuadorian Andes. Nowhere, we present our improved knowledge on rainfall extremes based on newly available rainfall radar data of this region. In our study we employ a clustering approach to identify types of extreme rainfall events and analyze their spatio‐temporal characteristics. Based on 3 years of data obtained from an X‐band scanning weather radar data, the study was conducted in the southern Ecuadorian Tropical Andes at 4450 m a.s.l. By applying a rainfall threshold, 67 extreme rainfall events were selected. The rainfall characteristics of each extreme rainfall event, such as the amount of rain, its duration, its hour, and month of occurrence were determined and used as input variables of a k‐means clustering analysis to group the events into different classes. The result revealed three main classes of extreme rainfall events. The first class is characterized by highest rain intensity and lowest duration. The second class is characterized by its month of occurrence, during the first 5 months of the year. The third class showed lowest rain intensity and highest duration mainly occurred at higher elevations. The typology of events advances our understanding of the spatio‐temporal characteristics of extreme rainfall in the Tropical Andes.
A preliminary observational study on the characteristics of surface turbulent fluxes over the South China Sea Islands
Authors: Zhou Qianjin , Li Lei , Chan Pak Wai , Gao Zhongming , Huang Xiaodong , Ouyang Xiwen , Fan Shaojia 
Published: October 2024

AbstractIn recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high‐frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.
Fast 3D31P B1+ mapping with a weighted stack of spiral trajectory at 7 T
Authors: Widmaier Mark Stephan , Kaiser Antonia , Baup Salomé , Wenz Daniel , Pierzchała Katarzyna , Xiao Ying , Huang Zhiwei , Jiang Yun , Xin Lijing 
Published: October 2024

AbstractPurposePhosphorus MRS (31P MRS) enables noninvasive assessment of energy metabolism, yet its application is hindered by sensitivity limitations. To overcome this, often high magnetic fields are used, leading to challenges such as spatial inhomogeneity and therefore the need for accurate flip‐angle determination in accelerated acquisitions with short TRs. In response to these challenges, we propose a novel short TR and look‐up table–based double‐angle method for fast 3D 31P mapping (fDAM).MethodsOur method incorporates 3D weighted stack‐of‐spiral gradient‐echo acquisitions and a frequency‐selective pulse to enable efficient mapping based on the phosphocreatine signal at 7 T. Protocols were optimized using simulations and validated through phantom experiments. The method was validated in the human brain using a 31P 1Ch‐trasmit/32Ch‐receive coil and skeletal muscle using a birdcage 1H/31P volume coil.ResultsThe results of fDAM were compared with the classical DAM. A good correlation (r = 0.95) was obtained between the two maps. A 3D 31P mapping in the human calf muscle was achieved in about 10:50 min using a birdcage volume coil, with a 20% extended coverage (number of voxels with SNR > 3) relative to that of the classical DAM (24 min). fDAM also enabled the first full‐brain coverage 31P 3D mapping in approximately 10:15 min using a 1Ch‐transmit/32Ch‐receive coil.ConclusionfDAM is an efficient method for 31P 3D mapping, showing promise for future applications in rapid 31P MRSI.
Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus
Authors: Gong Chen , Cai Tingting , Wang Ying , Xiong Xuelian , Zhou Yunfeng , Zhou Tingting , Sun Qi , Huang Huiqun 
Published: October 2024

ABSTRACTAimTo develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus.DesignA retrospective study.MethodsWe collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov–Smirnov test.ResultsA total of 4015 continuous glucose‐monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day.Patient or Public ContributionNo Patient or Public Contribution.
Using surrogate models to accelerate load step methods for nonlinear finite element problems in hyperelasticity
Authors: Fesefeldt Lina , Le Borne Sabine , Düster Alexander , Radtke Lars 
Published: October 2024

AbstractFinite element methods for displacement problems in hyperelasticity lead to systems of nonlinear equations. These equations are usually solved with Newton's method or a related method. The convergence of Newton's method depends heavily on the proximity of the initial guess to the numerical solution. Load step methods overcome problems with divergence by applying the load in increments, leading to a sequence of sub‐problems with initial guesses closer to the numerical solution of each sub‐problem, supporting the convergence. The downside of this approach is the high computational effort needed to solve the load steps. Based on a benchmark problem in high‐order FEM, we extend traditional load step methods to a new approach exploiting the hierarchical basis used for the spatial discretization of the problem and saving up to 50% of computation time (vs. benchmark).
Sparsity comparison of polytopal finite element methods
Authors: Lehrenfeld Christoph , Stocker Paul , Zienecker Maximilian 
Published: October 2024

AbstractIn this work we compare crucial parameters for efficiency of different finite element methods for solving partial differential equations on polytopal meshes. We consider the virtual element method (VEM) and different discontinuous Galerkin (DG) methods, namely, the Hybrid DG and Trefftz DG methods. The VEM is a conforming method, that can be seen as a generalization of the classic finite element method to arbitrary polytopal meshes. DG methods are non‐conforming methods that offer high flexibility, but also come with high computational costs. Hybridization reduces these costs by introducing additional facet variables, onto which the computational costs can be transferred to. Trefftz DG methods achieve a similar reduction in complexity by selecting a special and smaller set of basis functions on each element. The association of computational costs to different geometrical entities (elements or facets) leads to differences in the performance of these methods on different grid types. This paper aims to compare the dependency of these approaches across different grid configurations.
Exploring reward shaping in discrete and continuous action spaces: A deep reinforcement learning study on Turtlebot3
Authors: Nath Aswin , Oveisi Atta , Pal Amit Kumar , Nestorović Tamara 
Published: October 2024

AbstractIn robotics, reinforcement learning can train controllers or agents to find optimal solutions for complex tasks by enabling the robot to interact repeatedly with the environment. The reward function is an important aspect that guides any reinforcement algorithm to find the desired solution successfully. This work examines two deep reinforcement learning approaches, one uses a Deep Q‐Network(DQN) and the other a deep deterministic policy gradient (DDPG) algorithm, applicable to navigation tasks for mobile robots. Comparison between different reward schemes for both algorithms is one of the main focuses in this work. The methodology is implemented in the simulation for a mobile robot called TurtleBot3. The task for the robot is to navigate through obstacles from an initial location to a goal position. Finally, the trained end‐to‐end navigation stack is also implemented on the actual TurtleBot3 in a real environment. The robot uses a Lidar sensor to detect obstacles. The Lidar measurements and the relative position and angle of the robot to the target location are the inputs to the controller. The TurtleBot3 also utilizes distance information from its Lidar sensor to create an environmental map using the simultaneous localization and mapping (SLAM) technique. Additionally, given an initial position, the robot employs its inertial measurement unit (IMU) sensor and encoders for precise localization.
Greater impact of COVID‐19 on peer‐supported addiction services than government‐owned services for addiction in Japan: A nationwide 3‐year longitudinal cohort study
Authors: Katayama Munenori , Fujishiro Sou , Sugiura Kanna , Konishi Jun , Inada Ken , Shirakawa Norihito , Matsumoto Toshihiko 
Published: October 2024

AbstractAimThis study aims to investigate how the coronavirus disease (COVID‐19) pandemic impacted service provision of peer‐supported addiction services, such as self‐help groups or recovery houses and government‐owned addiction healthcare services, in Japan, as well as the quality of their interaction.MethodsAn online survey was distributed to all 69 public regional healthcare facilities (Mental Health and Welfare Centers [MHWCs]) across the 47 prefectures of Japan annually from 2021 to 2023, totaling three surveys. The survey asked about: (1) the current status of addiction healthcare services at each center, (2) the current status of peer‐supported addiction services within each center's area, and (3) changes in connectivity between centers and peer‐supported addiction services.ResultsAll 69 centers participated in the survey each year. Following the second year of the pandemic, both MHWCs and peer‐supported services experienced service closures and restrictions; however, peer‐supported services were notably more affected nationwide, such as downsizing of services, decreased number of users, shortage of operation funds, and inability to connect with other services being widely reported (p < 0.0001). Despite the easing of most restrictions by the fourth year, MHWCs in 18 out of 47 prefectures reported at least one sustained negative impact on their service provision, while peer‐supported services struggled to recover, with 40 out of 47 prefectures still observing difficulties.ConclusionThe COVID‐19 pandemic significantly affected both types of service, with peer‐supported services facing greater challenges in recovery. Additional support is essential to restore these services to normal operation.
A Molecular Dynamics Modeling Framework for Shape Memory Vitrimers
Authors: Wick Collin D. , Peters Andrew J. , Li Guoqiang 
Published: October 2024

ABSTRACTVitrimers with self‐healing, recycling, and remolding capabilities are changing the paradigm for thermoset polymer design. In the past several years, vitrimers that exhibit shape memory effects and are curable by ultraviolet (UV) light have made significant progress in the realm of 4D printing. Herein, we report a molecular dynamics (MD) modeling framework to model UV curable shape memory vitrimers. We used our framework and compared our modeling results with one UV curable shape memory vitrimer found in the literature, bisphenol A glycerolate dimethacrylate. The comparison showed reasonable agreement between the modeling and experimental results in terms of thermomechanical and shape memory properties, along with self‐healing efficiency. It was found that during recycling, it was important for the network to percolate through a majority of the system to get reasonably high recovery stress and recycling efficiency. Once this was achieved, a topological descriptor that was found to represent the compactness of the network was identified as having a very high correlation with recovery stress and recycling efficiency for networks that percolated 70% or more of the monomers in a system.
Coping Strategies of Patients With Advanced Lung or Colorectal Cancer Over Time: Insights From the International ACTION Study
Authors: Luu K. L. , Mager P. , Nieboer D. , Witkamp F. E. , Jabbarian L. J. , Payne S. , Groenvold M. , Pollock K. , Miccinesi G. , Deliens L. , van Delden J. J. M. , van der Heide A. , Korfage I. J. , Rietjens J. A. C. ,   the ACTION Consortium
Published: October 2024

ABSTRACTObjectiveA comprehensive understanding of coping strategies of patients with advanced diseases can contribute to providing supportive care that meets patients' needs. However, insight into how coping of this population develops over time is lacking. We examined coping strategies of patients with advanced cancer over time and identified distinct trajectories and their predictors.MethodsData from 675 patients of the control group from the ACTION cluster‐randomized trial were analyzed. Patients with lung or colorectal cancer from six European countries (Belgium, Denmark, Italy, the Netherlands, Slovenia and the United Kingdom) completed questionnaires at baseline, 12 and 20 weeks. Measures included Denial, Acceptance, and Problem‐focused coping (COPE, Brief COPE inventory; scores 4–16 per scale). We used linear mixed models to analyze the data and latent class mixed models to identify stable (within patient change < 2) coping strategies.ResultsAt baseline, patients reported low use of Denial (6.6) and greater use of Acceptance (12.6) and Problem‐Focused coping (12.2). These scores did not significantly change. We found four distinct trajectories for the use of Denial, three for Acceptance and five for Problem‐Focused coping strategies. Stable trajectories were found in 513 (77%) patients for Denial, 645 (96%) for Acceptance and 602 (91%) for Problem‐Focused coping. All coping strategies were stable in 447 (68%) patients and two were stable in 181 patients (28%).ConclusionsOverall, the use of coping strategies was rather stable in the majority of patients with advanced cancer. However, for each of the coping strategies subgroups of patients reported fluctuating coping trajectories.
One‐Step Nitrogen Fixation by a Magnetic Field Stabilized Glow Discharge (MSGD) With Ethanol Solution and Nitrogen
Authors: Liu Y. , Li Z. , Yang L. , Nie L. , Liu D. , Lu X. 
Published: October 2024

ABSTRACTThis article presents a one‐step nitrogen fixation (NF) process using ethanol solution and nitrogen as raw materials and a magnetic field stabilized glow discharge (MSGD) as a plasma reactor, in which the plasma is stabilized at a fixed position under the opposing forces of the Lorentz force and airflow. Compared to deionized water, the participation of ethanol promotes the generation of NH3 and NOx. This is because the addition of ethanol not only serves as a source of hydrogen but also facilitates the generation of OH, which further reacts to produce NO through , thereby increasing NOx concentration and reducing energy consumption. Additionally, with an increase in ethanol volume fraction, the concentration of NF products also increases, reaching saturation when the ethanol volume fraction reaches 50%. At an ethanol volume fraction of 50%, the energy consumption for NF is lowest at a discharge current of 40 mA, at approximately 4.92 MJ/mol. The range of the reduced electric field strength of MSGD is calculated to be approximately 31.9 ~ 39.7 Td, at which about 90% of the electrical energy is converted into N2(v) vibrational states. At this point, NO is primarily formed by the Zeldovich mechanism (), which is the main reason for the lower energy consumption of NF in MSGD compared with the other two scenarios (opposite magnetic field and no magnetic field). Finally, the lower energy consumption of MSGD compared with the other two scenarios is discussed, highlighting that this is mainly due to its high gas utilization rate, high N2 vibrational energy, and high electrical energy utilization efficiency.