bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cooperative Image Orientation with Dynamic Objects
Authors: Trusheim Philipp , Mehltretter Max , Rottensteiner Franz , Heipke Christian 
Published: July 2024

AbstractUsing images to supplement classical navigation solutions purely based on global navigation satellite systems (GNSSs) has the potential to overcome problems in densely built-up areas. These approaches usually assume a static environment; however, this assumption is not necessarily valid in urban areas. Therefore, many approaches delete information stemming from moving objects in a first processing step, but this results in information being lost. In this paper, we present an approach that detects and models so-called dynamic objects based on image sequences and includes these object models into a bundle adjustment. We distinguish dynamic objects that provide information about their position to others (cooperating objects) and those that do not (non-cooperating objects). Dynamic objects that observe the environment with the help of sensors in order to determine their position are called observing objects. In the experiments discussed here, the observing object is equipped with a stereo camera and a GNSS receiver. We show that cooperating objects can have a positive effect on the exterior orientation of the observing object after the bundle adjustment, both in terms of precision and accuracy. However, we found that introducing non-cooperating objects did not result in further improvements, probably because in our case the photogrammetric block was already stable without them due to the large number and good distribution of static tie points.
Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud
Authors: Gadad Veena , Sowmyarani C. N. , Dayananda P. 
Published: July 2024

AbstractDigital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones.
Hybrid RNN Based Text Classification Model for Unstructured Data
Authors: Sunagar Pramod , Sowmya B. J. , Pruthviraja Dayananda , Supreeth S , Mathew Jimpson , Rohith S , Shruthi G 
Published: July 2024

AbstractThe volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others.
Evolution of international sentiment towards climate change on Twitter
Authors: Rivera Alejandro Guzmán , Jemielniak Dariusz 
Published: July 2024

AbstractThe objective of the study was to examine a vast dataset of over 11 million English-language tweets concerning climate change gathered over an eleven-year period. The overarching aim was to illuminate the trajectory and geographical variations in sentiment throughout this timeframe. The authors conducted an exhaustive study of tweets adorned with hashtags related to climate change, using the Valence Aware Dictionary and Sentiment Reasoner (VADER) as a tool to assign sentiment scores and determine the polarity of tweets. Concurrently, it was traced the geographical dispersion of these tweets and their evolution over the duration of the study. The findings unveiled noticeable shifts in sentiment aligning with major global events. For instance, the United Nations' endorsement of the Sustainable Development Goals in 2015 stimulated a rise in positive sentiment toward climate discourse, while the emergence of the COVID-19 pandemic from 2019 to 2021 triggered a significant fall in sentiment scores. The study also detected an ascending trend of positive discourse in the United States and Europe, with Central Africa sustaining the highest average annual sentiment score. On the other hand, the sentiment in New Zealand and India was extraordinarily volatile, exhibiting dramatic changes from one year to the next. In contrast, Australia and New Zealand consistently registered the lowest sentiment averages. Overall, our findings highlight a complex mosaic of sentiment pertaining to climate change discourse across diverse global regions.
Does energy innovation heterogeneously affect renewable energy production?
Authors: Esmaeilpour Moghadam Hadi , Karami Arezou 
Published: July 2024

AbstractAchieving long-term sustainable development is a critical global imperative, and the adoption of renewable energy sources plays a pivotal role in this endeavor, aided by the advancements of the Fourth Industrial Revolution. As a result, energy innovation has emerged as a crucial factor in realizing sustainable development goals. This study utilizes panel quantile regression analysis to investigate the relationship between energy innovation and renewable energy production in the Middle East and North Africa (MENA) region from 1985 to 2019. The findings suggest that energy innovation can effectively enhance renewable energy production, particularly in countries with higher proportions of renewable energy generation, such as Egypt, Morocco, and Tunisia. However, countries like Saudi Arabia, which have lower ratios of renewable energy production, may not experience a significant impact from energy innovation. Consequently, the study recommends implementing systematic and integrated measures that focus on raising awareness and improving conditions for the adoption of renewable energy. These measures can drive the transition towards a more sustainable energy landscape in the MENA region. Policymakers should prioritize research and development investment and incentives in countries with higher levels of renewable energy generation, while simultaneously addressing barriers and fostering knowledge sharing between countries with differing levels of renewable energy production.
Structural design and molecular beam epitaxy growth of GaAs and InAs heterostructures for high mobility two-dimensional electron gas
Authors: Wang Tiantian , Song Huading , He Ke 
Published: July 2024

AbstractThis review aims to provide a comprehensive overview of the development and current understanding of GaAs and InAs heterostructures, with a special emphasis on achieving high material quality and high-mobility two-dimensional electron gases (2DEGs). The review discusses the evolution of structural designs that have significantly contributed to the enhancement of electron mobility, highlighting the critical considerations of scattering mechanisms of the 2DEGs. In addition, this review examines the substantial contributions of Molecular Beam Epitaxy (MBE) to these developments, particularly through advancements in vacuum technology, source material purification, and precision control of growth conditions. The intent of this review is to serve as a useful reference for researchers and practitioners in the field, offering insights into the historical progression and technical details of these semiconductor systems.
Farmers’ motivational factors for medicinal plant production in the southwestern region of Bangladesh
Authors: Hossain Mohammad Ismail , Saiyem Md. Abu , Begum Mst. Fatema , Begum Mst. Esmat Ara 
Published: July 2024

AbstractMedicinal plants (MPs) generate cash income and save millions of people in Bangladesh. The aim of this study is to identify farmers’ motivational factors to cultivate MPs. This study is based on primary data collected from 196 sample households from two MP-growing districts of Bangladesh. Three MPs, namely, Aloe vera, Bombax ceiba, and Withania somnifera, were considered for this study. Principal component analysis (PCA), the z test, the Wilcoxon signed-rank test and factor analysis (FA) were used in this study. The results showed that household size and male-headed household status are driving socioeconomic factors. Ownership of water pumps, number of rooms in the house, access to contract marketing, and off-farm income are significant economic factors encouraging households to cultivate MPs. Farming experience, scientific knowledge, access to high land and access to loamy land are significant biophysical factors motivating farmers to cultivate MPs. More technical support and institutionalized contract marketing systems should be continued and established for the sustainability of MP cultivation.
Critical evaluation of borehole ovalization analysis against hydrofracturing profiles in argillaceous formations
Authors: Galera Jose Miguel , Alonso-Jiménez Antonio , Suarez-Fino Jose Francisco 
Published: July 2024

AbstractThis paper introduces and evaluates the effectiveness of borehole ovalization analysis, a methodology designed to predict the natural stress field by examining deformations in vertical boreholes. The reliability of this approach has undergone rigorous testing across multiple boreholes within soft argillaceous formations, revealing several key observations. First, the results obtained demonstrate a notable correlation with hydrofracturing measurements, enhancing the overall credibility of the methodology. Second, the ratio of horizontal to vertical stress (σH/σh) is found to be contingent on the selected value of the breakout angle (θ), introducing a nuanced variable into the predictive framework. This insight emphasizes the importance of considering breakout angle variability in stress field predictions. Finally, the cohesion of the rock mass emerges as a pivotal factor that significantly influences the estimation of the horizontal stress magnitude (σH). This finding underscores the necessity of accounting for rock mass cohesion when applying borehole ovalization analysis for stress field predictions. Additionally, this paper conducts a meticulous comparative analysis by contrasting its results with findings from three hydrofracturing profiles conducted in distinct boreholes. Through this comparative approach, a more comprehensive understanding of the methodology's strengths and limitations is unveiled, contributing to the ongoing discourse on accurate stress field predictions in subsurface geomechanics.
Training Warm‐Rain Bulk Microphysics Schemes Using Super‐Droplet Simulations
Authors: Azimi Sajjad , Jaruga Anna , de Jong Emily , Arabas Sylwester , Schneider Tapio 
Published: July 2024

AbstractCloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano‐ and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm‐rain bulk schemes using high‐fidelity super‐droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super‐droplet simulations. We demonstrate the framework's effectiveness by calibrating a single‐moment bulk scheme, resulting in a reduction of data‐model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling.
Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM
Authors: Sun Y. Qiang , Pahlavan Hamid A. , Chattopadhyay Ashesh , Hassanzadeh Pedram , Lubis Sandro W. , Alexander M. Joan , Gerber Edwin P. , Sheshadri Aditi , Guan Yifei 
Published: July 2024

AbstractNeural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO2). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.
Shear Behaviors of Confined Flow: Insights for Understanding the Influences of Fractal Particle Size Distribution on High Mobility of Granular Flows
Authors: Ding Zhaowei , Hu Wei , Chang Chingshung , Li Yan , Wang Gonghui 
Published: July 2024

AbstractGranular flows, such as rockslide debris flows, can reach high velocities and travel vast distances, but mechanisms behind this high mobility remain elusive. Using the Discrete Element Method, we implement confined shear flow with fractal particle size distribution to study the physical mechanisms inside the highly localized basal zones. Our results show that the shear strength of confined flow decreases at large fractal dimensions, and a notable transition in shear behavior also occurs with increasing fractal dimension: large particles exhibit low granular temperature and act as solid phase within a semi‐diluted suspension, and small particles enter the rolling regime. The small particles behave as interstitial fluids and reduce the shear strength of confined flow. We suggest that the reduction of shear strength stemming from the fractal particle size distribution may provide insights into the mechanisms within the highly localized basal zones, ultimately contributing to the high mobility of granular flows.
First Assessment of Cloud‐Land Coupling in LASSO Large‐Eddy Simulations
Authors: Zhang Haipeng , Su Tianning , Zheng Youtong , Li Zhanqing 
Published: July 2024

AbstractTo enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future.
The Culprit of Bias for Paleointensity Estimation in the Shaw‐Type Protocol and an Innovative Calculation Method
Authors: Qi Kaixian , Cai Shuhui , Qin Huafeng , Yamamoto Yuhji , Deng Chenglong , Pan Yongxin , Cheng Xin , Wu Hanning , Zhu Rixiang 
Published: July 2024

AbstractPaleointensity records are vital for understanding the Earth's evolution, but obtaining accurate paleointensity is a challenging task. The Shaw‐type method, a widely‐used paleointensity protocol, produces biased results occasionally despite strict selection criteria. By examining the relationships between paleointensities and rock magnetic parameters from a pseudo‐Tsunakawa‐Shaw experiment, we ascertain that changes in the ratio of thermal to anhysteretic remanent magnetization (R) are proportional to bias in paleointensity, especially prominent in samples with hundred‐nanometer‐scale magnetic grains, and thus proved to be the culprit for the bias. Furthermore, we develop a method exploiting the Linear regression of R with Diverse cut‐off coercivity intervals for estimating Shaw‐type paleointensities (LoRD‐Shaw). Combined with a curve fit technique for samples with “folding" phenomenon, the LoRD‐Shaw method yields high‐accuracy results in all tested samples, demonstrating its efficiency in mitigating paleointensity bias from thermal alteration. The new method will enhance acquisition of high‐precision paleointensities for constraining the geodynamo evolution.
Patient and healthcare practitioner evaluation of patient-reported outcomes in bariatric surgery – a modified Delphi study
Authors: Budin Alyssa J. , Sumithran Priya , MacCormick Andrew D. , Caterson Ian D. , Brown Wendy A. 
Published: July 2024

Abstract
Background
Patient-reported outcomes are an important emerging metric increasingly utilised in clinical, research and registry settings. These outcomes, while vital, are underutilised and require refinement for the specific patient population of those undergoing bariatric surgery. This study aimed to investigate and compare how pre-surgical patients, post-surgical patients, and healthcare practitioners evaluate patient-reported outcomes of bariatric surgery to identify outcomes that are considered most important.

Methods
A modified Delphi survey was distributed to patients pre- and post-surgery, and to a variety of healthcare practitioners involved in bariatric care. Across two rounds, participants were asked to rate a variety of physical and psychosocial outcomes of bariatric surgery from 0 (Not Important) to 10 (Extremely Important). Outcomes rated 8–10 by at least 70% of participants were considered highly important (prioritised). The highest-rated outcomes were compared between the three groups as well as between medical and allied health practitioner subgroups.

Results
20 pre-surgical patients, 95 post-surgical patients, and 28 healthcare practitioners completed both rounds of the questionnaire. There were 58 outcomes prioritised, with 21 outcomes (out of 90, 23.3%) prioritised by all three groups, 13 (14.4%) by two groups, and 24 (26.7%) prioritised by a single group or subgroup. Unanimously prioritised outcomes included ‘Co-morbidities’, ‘General Physical Health’, ‘Overall Quality of Life’ and ‘Overall Mental Health’. Discordant outcomes included ‘Fear of Weight Regain’, ‘Suicidal Thoughts’, ‘Addictive Behaviours’, and ‘Experience of Stigma or Discrimination’.

Conclusion
While there was considerable agreement between stakeholder groups on many outcomes, there remain several outcomes with discordant importance valuations that must be considered. In particular, healthcare practitioners prioritised 20 outcomes that were not prioritised by patients, emphasising the range of priorities across stakeholder groups. Future work will consider these priorities to ensure resulting measures encompass all important outcomes and are beneficial and valid for end users.
Local-distortion-informed exceptional multicomponent transition-metal carbides uncovered by machine learning
Authors: Zhang Jun , He Liu , Xiong Yaoxu , Huang Shasha , Xu Biao , Ma Shihua , Xiang Xuepeng , Fu Haijun , Kai Jijung , Wu Zhenggang , Zhao Shijun 
Published: July 2024

AbstractDeveloping high-performance multicomponent ceramics, which are promising in solving challenges posed by emerging technologies, shows grand difficulties because of the immense compositional space and complex local distortions. In this work, an accurate machine learning (ML) model built upon an ab initio database is developed to predict the mechanical properties and structural distortions of multicomponent transition metal carbides (MTMCs). The compositional space of MTMCs is thoroughly explored by the well-trained model. Combined with electronic and geometrical analysis, we show that the elemental adaptability to the rock-salt structure elegantly elucidates the mechanical characteristics of MTMCs, and such adaptability can be quantified by local lattice distortions. We further establish new design principles for high-strength MTMCs, and V–Nb–Ta-based MTMCs are recommended, which are validated by the present experiments. The proposed model and design philosophy pave a broad avenue for the rational design of MTMCs with exceptional properties.