bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advanced Numerical Algorithm for Non-smoothness Differential Equations: Integrating Fractional Interpolation with Predictive- Corrective Techniques
Authors: Ye L. T. , Chen Y. M. , Liu J. K. , Liu Q. X. 
Published: May 2024

Abstract

In this study, we investigate numerical methods for non-smooth differential equations (NSDEs), which are pivotal in simulating abrupt phenomena in natural and engineering systems. We introduce the fractional interpolation method (FIM), a novel technique that utilizes fractional power functions to approximate solutions at points where derivatives are infinite. This method’s principal innovation is its adept handling of NSDEs' inherent discontinuities, offering a stable and convergent solution framework. Our findings confirm that FIM is both theoretically sound and practically reliable. Through rigorous numerical experiments, we have demonstrated its superior performance compared to conventional high-order numerical methods and MATLAB’s built-in functions. To further affirm FIM’s practicality, we applied it to two distinct non-smooth system types: systems with dry friction and binary wing systems with clearances. These applications substantiate the effectiveness of FIM and highlight its potential to tackle real-world challenges. Furthermore, this research equips scientists and engineers with a robust new tool for addressing NSDEs, setting the stage for further exploration and practical uses, especially in scenarios requiring accurate simulation of abrupt system behaviors. We anticipate the broader application of FIM in analyzing and designing non-smooth systems and are enthusiastic about its role in enhancing our understanding and prediction of complex dynamics across various natural and technical systems.
Spectral Analysis of Cellular Neural Network: Unveiling Network Parameters and Graph Characteristics
Authors: Saravanan Nithya , Ganesan Manju 
Published: May 2024

Abstract

Cellular Neural Network (CNN) finds application in a wide array of parallel processing tasks, such as image processing, non-linear operations, generating geometric maps, and high-speed computations. Functioning as an analogue paradigm, CNN comprises an array of cells interconnected at a local level. These cells can be arranged in an array of $m \times n$ identical cells in a rectangular grid. Graphical representation is achieved by representing cells as vertices and their connections as edges, denoted as $\Gamma_{m,n}$. This paper aims to compute spectrum based results for the CNN. To accomplish this, we determine the Laplacian and the signless Laplacian eigenvalues of $\Gamma_{m,n}$. Utilising these results, we derive a comprehensive set of network-related parameters, including the Kirchhoff index, average path length, mean first passage time, the number of spanning trees, the Estrada index, and the resolvent Estrada index. Furthermore, we delve into graph-related parameters encompassing graph energy and spectral radius to gain deeper insights into the characteristics of $\Gamma_{m,n}$. To streamline our analysis, we design an algorithm to calculate the network-related parameters, as mentioned earlier. Moreover, results are enhanced by the inclusion of numerical tables and vibrant 3D plots that visually depict Laplacian and signless Laplacian energies for specific configurations. Finally, we underscore the application of energy for image segmentation by selecting suitable CNN sizes based on energy, which provides precise results that reduce evaluation time and significantly enhance diagnostic accuracy in image processing.
2020 Mathematics Subject Classification. 05C50, 05C76, 05C90, 68R10, 94C15.
Optimization of the extraction process of bioactive compounds from red fruits of Stenocereus stellatus: Response Surface Methodology (RSM)
Authors: Cervantes-Arista Clara , García-Jiménez Abraham , Trujillo-Ramírez Daniel , León-Sánchez Fernando Díaz , Mendoza-Espinoza José Alberto , Román-Guerrero Angélica , Martínez-Velasco Alejandro 
Published: May 2024

Abstract

Stenocereus stellatus is one of the top 10 most important cactus species in Mexico. Its fruits contain bioactive components like betalains and phenolic compounds. In this study, the extraction conditions were optimized through response surface methodology to maximize the concentration of betalains (betacyanins and betaxanthins), total phenolic compounds (TPC), as well as antioxidant capacity (AC). Various factors were assessed to optimize the extraction of betalains, including pH (W), extraction temperature (X), extraction time (Y), and water: ethanol solvent ratio (Z). TPC and AC were also evaluated using the same factors, except for Z. All these factors had an impact on the concentration of betalains. After evaluating these factors, the optimal extraction conditions were found to be pH 4.2, extraction temperature of 10°C, extraction time of 60 minutes, and a water: ethanol solvent ratio of 55%. Under these conditions, 0.51 mg per g total betalains (BT) were obtained, of which 0.25 mg per g corresponded to BC and 0.26 mg per g to BX. In evaluating TPC and AC, only pH and temperature affected these determinations. The optimal conditions for both variables were pH of 4 and extraction temperature of 60°C, under these conditions 2.34 mg of gallic acid equivalents per g and 18.60 µmol of Trolox equivalents per g were obtained. The results suggest that Stenocereus stellatus could be a promising source for these compounds, which have potential applications in the food and pharmaceutical industries due to their excellent stability under these conditions.
Future cardiovascular events prediction from invasive coronary angiography: A graph representation learning perspective
Authors: Sun Xiaowu , Belmpas Theofilos , Senouf Ortal , Abbé Emmanuel , Frossard Pascal , De Bruyne Bernard , Auberson Denise , Müller Olivier , Fournier Stephane , Mahendiran Thabo , Thanou Dorina 
Published: May 2024

Abstract

Improving risk stratification for coronary artery disease, the leading cause of death worldwide, continues to present a daily challenge in clinical practice, highlighting the urgent need for innovative approaches to early prediction of future cardiovascular events. In this work, we propose AngioGraphCAD, a deep learning based framework that employs graph neural networks to leverage geometry features and a masked attention to fuse geometry features from multiple coronary stenoses for future events prediction at both lesion and patient level from invasive coronary angiography. AngioGraphCAD is evaluated across two clinical cohorts at the lesion level and one datatset at the patient level, achieving superior performance compared to clinical measures. This is the first study that highlights the importance of geometry information in advancing future events prediction from invasive coronary angiography. Given the significance of the clinical question and the innovative nature of the proposed methodology, this work could pave the way for the development of an AI framework fueled by patient-specific data in cardiology, potentially revolutionizing personalized decision-making in managing coronary artery diseases for individual patients.
Estimating risk attitude impact on allocative efficiency under different plantain-based cropping systems: A latent factor model with instrumental variables
Authors: ABIOLA Adikath , ADEGBOLA Ygué Patrice , ZANDJANAKOU-TACHIN Martine , ZOSSOU Roch , BIAOU Gauthier 
Published: May 2024

Abstract

Plantain production is subject to risk, emerging from diverse sources. Consequently, risk modifies farmers behavior, affecting their decision making and thereby their future results. These choices are not only related to investments, crop to produce, and technology use but also to resource allocation. The objective of this study is to analyze the impact of farmers’ risk attitude on allocative efficiency in plantain production. Primary data from 584 farmers, operating on 707 plots, selected through a four-level stratified random sampling was used. The study used the Latent factor models with instrumental variables to account for endogeneity and selection bias. We apply the causal inference methods to assess the impact of risk attitude on allocative efficiency. The result highlights a negative impact of risk attitude on the allocative efficiency. Compared to non-averse farmers, risk-averse farmers are less efficient. Also, as the level of risk aversion increases, the negative impact of risk aversion is more pronounced (-49.1% for extreme risk aversion). Furthermore, stronger impact of risk attitude regardless the level of risk aversion is found for the type 3 which stands for the intensive cropping system. The study provides empirical evidence of the extent of the impact of risk attitude on the allocative efficiency and confirms the heterogeneity of impact assumption. It also provides a useful insight into the key role risk attitude plays in productivity and efficiency in agriculture. These findings have critical policy implications and can help policymakers and practitioners make informed decisions and devise interventions to improve farmers allocative efficiency.
An ecological study of the association between childhood stunting, water, sanitation, and protein access, 2001-20
Authors: Wang Yongsheng , Olukunle Jurido , Wallach Thomas , Rosenbaum Janet 
Published: May 2024

Abstract

Background
Adequate height attainment during early childhood foreshadows lifelong health. Stunting may be caused by environmental enteropathy (EE), but EE has persisted after improvements in water and sanitation access. Recent studies suggest tryptophan and other amino improve height attainment.
Methods
This study used ecological data from 94 countries over 21 years (2001-20) (n = 1836) to evaluate the association between stunting and environmental variables: the percentage with access to adequate water and sanitation, country-level supply of animal protein and fish protein access, and per capita real gross domestic product. We used mixed effects linear models with varying intercepts by country after verifying residuals were uncorrelated and had uniform variance.
Results
Water/sanitation access and animal protein supply are associated with lower stunting: each 1 standard deviation change in water/sanitation was associated with 1 lower standard deviation of stunting, and each 1 standard deviation change in animal protein supply was associated with 0.2 lower standard deviations of stunting. Controlling for year, each 1 standard deviation change in water/sanitation was associated with 0.8 lower standard deviation of stunting, and each 1 standard deviation change in animal protein supply was associated with 0.1 lower standard deviations of stunting.
Conclusions
Improvements in water/sanitation infrastructure and animal protein access are both important interventions to reduce childhood stunting. These findings complement the 2023 FAO report conclusions regarding the importance of land animal protein to childhood growth.
Chinese ponds exhibit larger carbon sequestration but smaller carbon emissions than lakes and reservoirs
Authors: Lv Mingquan , Lv Mingquan , Yang Hong , Yang Hong , Thompson Julian R. , Thompson Julian R. , Huang Ping , Huang Ping , Gao Xin , Gao Xin , Chen Jilong , Chen Jilong , Wu Shengjun , Wu Shengjun 
Published: May 2024

Abstract

China's ponds, which have proliferated in number due to agricultural expansion and population growth, serve as both carbon sinks and sources due to large nutrient influxes. Despite their significance, the role of ponds in the carbon cycle has remained largely unknown, owing to their small size, vast number and widespread distribution. This study integrates carbon emissions and burial across 14.6 million Chinese ponds, revealing their substantial carbon sequestration potential. Ponds emit 5.29 Tg CO2-C yr-1 and 0.86 Tg CH4-C yr-1 but sequester 6.49 Tg C yr-1 of which 82% is organic carbon (OC). In contrast, lakes and reservoirs sequester fluxes of 1.77 Tg C yr-1 and 4.64 Tg C yr-1 of OC, respectively. Considering the high OC-burial/C-emissions ratio (0.86), China’s ponds are more effective as C sinks than lakes and reservoirs. Our results offer a more comprehensive estimate of the C budgets of China's ponds and enhance understanding of their contribution to the global C cycle.
Out-diffusion and uphill-diffusion of Mg in Czochralski-grown (100) β-Ga2O3 under high-temperature annealing and its influence on lateral MOSFET devices
Authors: Chou Ta-Shun , Tran Thi Thuy Vi , Peelaers Hartwin , Tetzner Kornelius , Hilt Oliver , Rehm Jana , Anooz Saud Bin , Fiedler Andreas , Galazka Zbigniew , Albrecht Martin , Popp Andreas 
Published: May 2024

Abstract

In this work, we report the out-diffusion and uphill-diffusion of Mg inside the (100) β-Ga2O3 epilayer and substrate. The Mg accumulates towards the (100) surface upon annealing under an oxidizing environment, whereas the concentration profile changes with annealing temperature and time. Furthermore, the out-diffusion of Mg from the substrate into the epilayer is observed at temperatures above 800 ℃, which continues during the film growth. The substitutional-interstitial-diffusion (SID) mechanism is suggested to be the driving mechanism for the former, and the latter is related to the diffusion of mobile Mg interstitials. The accumulation profile of Mg can be used to identify the interface between the epilayer and the substrate, which shows that the commonly observed interfacial Si peak might be a heavily doped layer at the beginning of the epitaxy process. Furthermore, significant differences in device performance are observed for power transistors fabricated on annealed and non-annealed epitaxial β-Ga2O3 wafers. Increased breakdown voltages of annealed samples are attributed to the Mg diffusion into the first few nanometers of the epitaxial layer close to the interface to the semi-insulating substrate, leading to a thorough compensation of residual dopants in that region.
WITHDRAWN: Phosphate-Driven Interfacial Self-Assembly of Silk Fibroin for Continuous Non-Covalent Growth of Nanothin Defect-Free Coatings
Authors: Wigham Caleb , Fink Tanner D. , Sorci Mirco , O'Reilly Padraic , Park Sung , Kim Jeongae , Zha R. Helen 
Published: May 2024

Abstract

Silk fibroin is a fiber-forming protein derived from the thread of Bombyx mori silkworm cocoons. This biocompatible protein, under the kosmotropic influence of potassium phosphate, can undergo supramolecular self-assembly driven by a random coil to β-sheet secondary structure transition. By leveraging concurrent non-specific adsorption and self-assembly of silk fibroin, we demonstrate an interfacial phenomenon that yields adherent, defect-free nano-thin protein coatings that grow continuously in time, without observable saturation in mass deposition. This non-covalent growth of silk fibroin coatings is a departure from traditionally studied protein adsorption phenomena, which generally yield adsorbed layers that saturate in mass with time and often do not completely cover the surface. Here, we explore the fundamental mechanisms of coating growth by examining the effects of coating solution parameters that promote or inhibit silk fibroin self-assembly. Results show a strong dependence of coating kinetics and structure on solution pH, salt species, and salt concentration. Moreover, coating growth was observed to occur in two stages: an early stage driven by protein-surface interactions and a late stage driven by protein-protein interactions. To describe this phenomenon, we developed a kinetic adsorption model with Langmuir-like behavior at early times and a constant steady-state growth rate at later times. Structural analysis by FTIR and photo-induced force microscopy show that small β-sheet-rich structures serve as anchoring sites for absorbing protein nanoaggregates, which is critical for coating formation. Additionally, β-sheets are preferentially located at the interface between protein nanoaggregates in the coating, suggesting their role in forming stable, robust coatings.
The Art of Seeing: A Computer Vision Journey into Object Detection
Authors: Khan Mohammad Salman , Imran Ayesha 
Published: May 2024

Abstract

Object detection is a fundamental task in computer vision with numerous applications ranging from surveillance and autonomous driving to medical imaging and augmented reality. In recent years, deep learning approaches have significantly advanced the state-of-the-art in object detection, enabling remarkable progress in accuracy, robustness, and efficiency. This paper presents a comprehensive review of recent research developments in computer vision and object detection. We discuss key concepts, methodologies, and challenges in object detection, focusing on deep learning-based approaches. Additionally, we explore emerging trends such as instance segmentation, few-shot learning, and privacy-preserving techniques in object detection. Furthermore, we discuss benchmark datasets, evaluation metrics, and open research challenges in the field. By providing an overview of the current landscape of object detection research, this paper aims to guide researchers, practitioners, and enthusiasts towards understanding the latest advancements and future directions in this exciting area of computer vision. We discuss key topics such as image classification, object detection, image segmentation, and scene understanding. The rapid progress in deep learning has revolutionized computer vision, enabling models to learn hierarchical representations directly from data. We review prominent deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), and their applications in various computer vision tasks. Furthermore, we explore recent developments in multi-modal and cross-modal learning, domain adaptation, and interpretability in computer vision models. Additionally, we discuss challenges such as data bias, ethical considerations, and scalability issues faced by the field. By providing a comprehensive overview, this paper aims to inspire further research and innovation in computer vision, advancing its capabilities and broadening its impact on society.
Accessibility, Usage Patterns of Forest Parks, and Their Relation to Residents' Behaviors based on the Theory of Planned Behavior - Evidence from Suqian, China
Authors: Aziz Faziawati Abdul 
Published: May 2024

Abstract

As urbanization accelerates, there is an increasing desire for access to green spaces to promote human health; however, there is a scarcity of evidence documenting the characteristics that green spaces must have to provide genuine benefits. This research investigates the connection between greenery in public places and residents' health, especially after the COVID-19 pandemic, which has boosted the need for such spaces. The focus is on understanding how forest parks are utilized and perceived by people in their daily activities, within the cultural context of China, with a specific emphasis on the constraints faced in China. The study examines the attributes of green spaces and the ecological characteristics of forest parks as accessible urban greenery, as well as their impact on the behavior of visitors. A survey of 338 residents visiting 5 forest parks from Suqian was conducted, utilizing a random sampling method for respondent selection. The results indicate that respondents use urban forest parks for relaxation, stress reduction, and enjoyable activities with family and friends. The research also reveals a strong connection between visitor usage patterns and forest park characteristics, including factors such as distance, safety, playground quality, natural elements, and quietness. Primary constraints that hinder the utilization of urban forest parks in Suqian were identified as the lack of time and long distances. Respondents expressed a higher interest in visiting forest parks more frequently if safety and accessibility were improved, along with the addition of extra amenities. These findings provide valuable insights into accessibility patterns and constraints related to the utilization of urban forest parks by Suqian residents. The research also offers suggestions for addressing public security and safety concerns based on residents' visiting habits. The outcomes of this research will be beneficial for future park development, design, and management.
Estimating groundwater resources in an equatorial forested basin using geographic information system and remote sensing techniques (case of Nyong, South Cameroon)
Authors: Ebode Valentin Brice , Nnomo Bernadette Nka 
Published: May 2024

ABSTRACT

This article aims to produce a groundwater potential zone (GWPZ) map of the Nyong basin using an integrated analytical hierarchy process driven in a geographic information system (GIS) environment. For this, seven parameters (rainfall, geology, slopes, drainage density, land use and land cover, lineament density, and soil) known to influence the availability of groundwater were considered. The results obtained show that the Eastern part of the basin holds the largest quantities of groundwater. The produced map shows that this region includes high and very high GWPZs (yield &gt; 6 l/s). It is essentially characterized by abundant rainfall (on average 135–136 and 131–134 mm), low slopes, significant drainage and lineament densities, and significant forest cover. On the other hand, the opposite region (west), which includes the low and very low GWPZs, is characterized by less abundant precipitation (on average 121–125 and 125–128 mm), significant slopes, lower drainage and lineament densities, and greater impervious areas. With a success rate of 80%, confidence deserves to be placed in the GWPZs map produced. It can be considered in water supply projects in this basin. Such work has never been done in this basin before.
Simulation and prediction of precipitation and temperature under RCP scenarios
Authors: Chengcheng Xu , Kaixuan Guo , Jama Abdiqani Hussein , Chuiyu Lu , Qingyan Sun , Xu Li , Lingjia Yan 
Published: May 2024

ABSTRACT

Climate change has an immense impact on the environment, ecology, agriculture, and economy. As the most influential climate prediction platform in the world, the Intergovernmental Panel on Climate Change provides important data on predicted future climate change trends. Based on 10 models under the RCP8.5 and RCP4.5 emission scenarios, in this study, we obtained 0.5 × 0.5° data through down-scaling data processing and predicted future rainfall and temperature changes in the Beijing–Tianjin–Hebei region of China. Combined with historical observed data of 198 meteorological stations in the region, the relationships between the predicted values of the model and the measured values were analyzed using the Taylor diagram method. The results show that (1) the future precipitation capacity assessed by the global climate system is generally higher than observed data; (2) for assessment of future temperatures, RCP8.5 shows a larger increase than RCP4.5; (3) for the Beijing–Tianjin–Hebei region, our results show that the Atmospheric Chemistry Coupled Version of Model for Interdisciplinary Research on Climate-Earth System (MIROC-ESM-CHEM) model is more consistent with the evaluation of future precipitation capacity, the Hadley Centre Global Environment Model, version 2-Earth System(HadGEM2-ES) model is more consistent with the future RCP4.5 scenario temperature evaluation, and the MIROC-ESM-CHEM model is more consistent with the future RCP8.5 scenario temperature evaluation.