bluuebunny/crossref_metadata_2025_split huggingface dataset:
Soil capacity to biomass production in sandy loam soils following cover crop farming systems in tropical conditions in the Northeast region of Brazil
Authors: Nascimento Gislaine dos Santos , Souza Tancredo , da Silva Lucas Jónatan Rodrigues , Santos Djail 
Published: August 2024

Context Soil capacity to biomass production (SC) sheds light on soil health and its capacity to sustain high primary production. However, the currently existing models are based on rather local data, and thus there is a lack of predictive tools to monitor SC. Aims Our aim was to assess the influence of cover crops on soil chemical properties by considering their different behaviours (biomass production and root density). Methods We characterised soil chemical properties, and plant biomass production during 6 years in field conditions at a tropical soil. Key results Our findings suggest that: (1) green manure practice improved Ca2+, K+, and soil organic carbon (SOC) stock, but none of the studied cover crops were able to improve soil P content; and (2) we found three groups of green manure by considering the SC reduction, namely G1 (low SC reduction) that comprises Crotalaria ochroleuca and Neonotonia wightii, G2 (medium SC reduction) that comprises Brachiaria decumbens, Pennisetum glaucum, and Crotalaria juncea, and G3 (high SC reduction) that comprises Mucuna pruriens, Canavalia ensiformis, Crotalaria spectabilis, Dolichos lablab, and Stizolobium aterrimum. Conclusion We concluded that after 3 years during which soil parameters and the production of most, but not all, cover crops have increased, the deficiency in P did not allow plants to grow well anymore. Implications This study highlights the importance to consider predictive models as a tool to be used in soil management. Our study also provides a deeper view about the use of green manure and their influence on soil capacity to biomass production.
Brushtail possums (Trichosurus vulpecula) dominate dama wallabies (Notamacropus eugenii) and Bennett’s wallabies (Notamacropus rufogriseus) at bait feeders: implications for invasive species management
Authors: Hickling Graham J. , Day Tim D. 
Published: August 2024

Context Dama wallaby (Notamacropus eugenii) and Bennett’s wallaby (N. rufogriseus) are invasive pests on the New Zealand mainland and consequently are subject to ongoing control measures that include deployment of toxic baits in feeding stations. Aim We investigated whether behavioural interactions between wallabies and introduced brushtail possums (Trichosurus vulpecula) at bait feeders are likely to reduce the efficacy of this method for wallaby control. Methods Wallaby and possum visits and encounters at cereal bait feeders were monitored with trail cameras for several months in dama wallaby habitat near Rotorua, and in Bennett’s wallaby habitat in South Canterbury. The response of Bennett’s wallabies to possum carcases placed at feeders was also assessed. Key results The diurnal activity of wallabies and possums at the feeders overlapped extensively, although Bennett’s wallabies exhibited more daytime activity than the other two species. Thousands of visits by wallabies and possums were recorded but close encounters between the species at feeders were infrequent (N = 251). When encounters did occur, the wallaby was usually excluded from the feeder (72% of 229 encounters at Titoki Estate, near Rotorua, and 95% of 22 encounters at Blue Cliffs, South Canterbury) regardless of which species arrived at the feeder first. Zero instances of a wallaby excluding a possum from a feeder were recorded. When possum carcases were placed beside feeders, visitations by Bennett’s wallabies reduced significantly, by 86% during the first week when a possum carcase was present. This effect was short lived, however, because the carcase soon decayed or was scavenged by other wildlife. Conclusions Despite their smaller body size, possums are strongly behaviourally dominant over both wallaby species. Consequently, possums may empty feeders of bait before visiting wallabies have an opportunity to feed. Furthermore, interruption and exclusion of feeding wallabies by possums will increase the risk of sublethal toxic dosing and consequent bait shyness. Implications Since possums are common throughout most wallaby habitat in New Zealand, their dominance behaviour is likely to reduce the efficacy of bait feeder control of wallabies at many sites. Possum population suppression is, therefore, likely to increase the effectiveness of wallaby bait feeder programs but will be costly to achieve.
AFRIMETS supplementary comparison on calibration of feeler gauges
Authors: ElMelegy Ahmed , Mechkour Lhossain , Obayi Bede , James Joseph , Charles Angela , Mutale Daniel , Gandah Burnhard , Kame Modiriemang , Ntima Pamidzani , Nzimande Zanele , Masina Patrick 
Published: August 2024

Main text
National Metrology Institutes from 8 African countries, namely Egypt, Morocco, Nigeria, Tanzania, Zambia, Zimbabwe, Botswana, and South Africa have participated in an international supplementary comparison on the calibration of feeler gauges.
This comparison was a part of larger supplementary comparison between 13 African countries for the calibration of hand measuring instruments. This larger comparison which was carried out during the period between December 2019 - December 2022 has been piloted by NIS, Egypt and has been registered in BIPM-KCDB database on December 2019. The artifacts have been prepared by NIS, Egypt and measured before sent to circulate between all participant countries in round-robin scheme and returned back again for NIS, Egypt where a final measurement was made for stability check.
The main purpose of these comparisons is to support submission of CMCs for calibration of hand length measuring instruments in BIPM-KCDB.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCL, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Report on key comparison AFRIMETS.AUV.V-K5 and linking to CCAUV.V-K5
Authors: Veldman C S , Garg Dr Naveen , Rattanangkul Mr Pairoj , Serrano Lic Federico A 
Published: August 2024

Main text
Following the completion of the fifth CIPM key comparison (KC) in the area of vibration concerning sinusoidal acceleration, participating Regional Metrology Organizations (RMOs) have the responsibility to disseminate the Key Comparison Reference Values (KCRV) to non-participating National Metrology Institutes (NMIs), thereby establishing a traceable link between NMIs participating in regional comparisons and the KCRVs. The comparison results reported here have that objective. The comparison was piloted by AFRIMETS with participants from three NMIs, situated in two RMOs, namely SIM and APMP.
The respective Degrees of Equivalence, linked to the KCRVs, are reported. The final report has been peer-reviewed and approved for publication by the CCAUV, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCAUV, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Measurement of medical haemodilatation based on statistical analysis of big data
Authors: Wang Xin , Jiang Qiang , Chu Huixiao , Pu Xudong , Cheng Tong 
Published: August 2024

Abstract
This study aims to explore the association between hematoma expansion, edema development, and patient prognosis in hemorrhagic stroke patients. Utilizing clinical information, imaging characteristics, and prognostic data from 160 hemorrhagic stroke patients, several predictive models were constructed to examine the pathological progression and outcomes of these patients. Specifically, data preprocessing was employed to calculate the time intervals between each examination and select data within 48 hours for analyzing changes in hematoma volume and its percentage. This facilitated the determination of hematoma expansion within the first 48 hours post-onset, with results documented in a dedicated table. Employing the XGBoost model, both the test and training datasets were trained to develop a predictive model for hematoma expansion. Upon evaluation, the model demonstrated a 75% accuracy rate in predicting hematoma expansion across all patients (sub001-sub160). This study underscores the potential of using advanced predictive modeling, such as XGBoost, to enhance the prognosis assessment and clinical decision-making in hemorrhagic stroke care.
Natural language processing applications in social network analysis: a data mining approach
Authors: Yu Yunxia , Guan Yibo , Hu Yurong 
Published: August 2024

Abstract
Social media texts, such as tweets and user profiles, reflect the opinions, emotions, preferences, and behaviors of social media users, as well as the structure and dynamics of their social networks. Analyzing social media texts can provide valuable insights and applications for various domains and stakeholders, such as marketing, politics, healthcare, and education. However, analyzing social media texts poses significant challenges, as they are often noisy, informal, unstructured, and heterogeneous. Moreover, social media texts are not only influenced by the individual characteristics and contexts of the users but also by the complex and evolving interactions and relationships among them. Therefore, traditional methods and techniques for natural language processing (NLP), social network analysis (SNA), and data mining (DM) may not be sufficient or effective for social media text analysis. In this paper, we propose a data mining approach that integrates and leverages NLP, SNA, and DM methods and techniques for social media text analysis. We demonstrate the feasibility and effectiveness of our approach on a large and comprehensive data set of tweets and user profiles related to the domain of consumer spending. We show that our approach can provide comprehensive and effective solutions and applications for various domains and stakeholders, such as marketing, and education. We also compare and contrast our results and outcomes with the existing works, and show the advantages and improvements of our approach over the existing works.
Identification of low voltage ride-through parameters of DFIG based on improved particle swarm optimization algorithm
Authors: Xu Zhonglin , Chen Zhong , Yao Wang , Wu Yongkang 
Published: August 2024

Abstract
To improve the accuracy of LVRT control parameter identification for DFIG, a method is proposed based on an improved particle swarm optimization (PSO) algorithm. Firstly, a single DFIG connected to an infinite bus system model is established using the MATLAB/Simulink simulation platform. Subsequently, logistic chaos mapping is employed to initialize the population based on the standard PSO algorithm. An adaptive parameter adjustment mechanism is introduced, along with the Lévy flight strategy integrated into the algorithm’s flow. Finally, based on the residual measured operating conditions, an analysis and verification of the identified parameter accuracy are conducted. Simulations demonstrate the effectiveness and accuracy of the proposed parameter identification method. The results indicate that the proposed approach can rapidly and accurately identify the LVRT control parameters of DFIG. This method utilizes advanced signal processing techniques and machine learning algorithms to analyze and model real-time data from the DFIG system, enabling the rapid and accurate identification of the DFIG low-voltage ride-through saturation control parameters. This provides researchers and engineers with an effective tool to better understand and optimize the performance of DFIG systems, thereby driving greater progress in research and practice in this field.
Blockchain-based anonymous (IOT) data integrity auditing scheme in cloud environments
Authors: Zhang Jianhong , Zhang Jianan 
Published: August 2024

Abstract
The Internet of Things (IoT) facilitates the interaction, data collection, and exchange between devices and the network. With an increasing proliferation of IoT devices, the volume of data produced escalates at an exponential rate. Considering loT’s constrained computational and storage capabilities, the necessity for cloud data storage becomes imperative to enhance service quality. Scholars have introduced various auditing mechanisms, within which the role of a third-party auditor (TPA) emerges as a distinctive attribute in certain methodologies. Nonetheless, assuring the security of a centralized TPA poses significant challenges, a gap that blockchain technology seeks to bridge. Addressing this issue, our proposition is the Blockchain-based Anonymous Data Integrity Auditing (BADIA) for IoT within cloud environments, employing a federated blockchain approach. Our scheme has the following characteristics: conditional anonymity, fairness, accountability, and security. In conclusion, a comprehensive assessment, considering both security and performance metrics, demonstrates that the proposed scheme is robust and effectively alleviates the computational load for data proprietors who possess limited processing capabilities. This analysis ensures that our methodology not only adheres to stringent security standards but also optimizes the efficiency of data management for those with constrained computational resources.
Analysis of the functional regression problem of temperature on Ozone concentration
Authors: Gou Yanqing , Zi Xuemin 
Published: August 2024

Abstract
With the continuous improvement of computer storage capabilities and online observation technology, functional data emerged as the times required. Among the various research methods, regression analysis of functional data stands out as one of the most commonly employed techniques. This study employed Fourier spline basis functions to smooth the gathered data and preprocess it to establish a functional linear regression model with scalar response. Specifically, we investigated temperature regression on Ozone concentration across 17 cities in Henan Province in 2022. In our functional regression prediction, we compared the results obtained with and without the addition of a roughness penalty. Through this comparison, we observed that the absence of a roughness penalty led to overfitting, while including the penalty yielded more detailed results. Moreover, the coefficient of determination R
2 approached one more closely when using the roughness penalty, indicating a better-fitting regression effect. Lastly, for those interested in monitoring the regression residuals of the estimated Ozone concentration, we provide a detailed flowchart outlining the process for monitoring these residuals.
Evading model ownership resolution through reconstructive neuron pruning
Authors: Wu Jun 
Published: August 2024

Abstract
Due to training a deep learning model requires a lot of resources, we regard the trained model as the intellectual property (IP) of its trainer. However, researchers have shown that deep learning models are susceptible to model-stealing attacks. Recently, a method to detect theft by embedding external features has been proposed. We argue that this method is similar to backdooring the model and still has deficiencies in protecting model IP. In this paper, we present a method that removes external features to evade such detection. Initially, we get clean data by employing model inversion and adaptive selection. Subsequently, we utilize this data to exclude neurons associated with external features, while simultaneously guaranteeing that the model’s accuracy remains relatively unchanged. The empirical data demonstrates that our method can help different types of adversaries evade ownership verification of external features. In addition, our method can further enhance the attack effect under watermark-based defense.
Research on PM2.5 concentration prediction based on transformer
Authors: Dai Zhiqiang , Ren Ge , Jin Yongming , Zhang Jintao 
Published: August 2024

Abstract
The pollution caused by PM2.5 can have serious consequences for human health and the environment, making timely prediction of its concentration important. This study proposes a Spatial-Temporal Air quality Transformer (STAFormer) for forecasting PM2.5 concentration. In the dimension of time, this study designs a novel multi-head attention mechanism that can discover multi-periodicity in time series, thereby extracting more complex temporal features. In terms of spatial analysis, this study designs a graph convolutional neural network that incorporates distance and wind direction information to account for the impact of surrounding cities on PM2.5 concentration prediction. Additionally, it dynamically captures spatial correlations using an attention mechanism. The prediction results for Zhengzhou show that the model suggested in this study performs better. The RMSE for forecasting PM2.5 levels for the next hour is 3.51 μg/m3, and MAE is 2.38 μg/m3.
A time series data regression based on a B-spline and stacking framework
Authors: Li Ruizhe , Cui Jingyu , Fu Daobo 
Published: August 2024

Abstract
This paper proposes a new time series regression method based on the combination of B-spline basis expansion and distance covariance weighted model. This paper considers the case that the predictor variable is a time series and the response variable is a continuous scalar. In order to solve these problems of high dimension and high noise of time series, this proposed method uses a B-spline basis to reduce the dimension and extract the function information of the original sequence. Considering that there is still some noise in the reduced dimension random variables, this proposed method considers using the stacking framework to enhance the data. Since the importance of each basis model is unknown, this paper uses the distance covariance to measure the importance of the basis model and develop a weighted stacking framework, which can adaptively weigh the bias and variance of the predictor model. In addition, each basis estimator in ensemble learning is model-free. The real data analysis shows that the proposed method is competitive with some existing methods. Finally, the proposed method can be directly applied to other fields such as environmental science, medical science and speech recognition et al.
Impact of tropical waves on extreme rainfall events during coastal El Niño
Authors: Mayta Víctor C , Lin Qiao-Jun , Adames Corraliza Ángel F , Chavez Mayta Erika 
Published: August 2024

Abstract
We examine the relationship between convectively coupled waves, the Madden–Julian Oscillation (MJO), and extreme precipitation over the western coast of South America during Coastal El Niño (COEN) events for the period spanning 1980–2023. Two types of COEN can be distinguished: (i) that occur in association with large-scale El Niño Southern Oscillation (ENSO) (e.g. 1982/83, 1997/98), and (ii) more ‘local’ COEN–when anomalous sea surface temperature take place over the far-eastern equatorial Pacific only (e.g. 2017, 2023). During both types of COEN events, increased rainfall along the western coast of South America is associated with intense Kelvin wave activity. In addition, westward inertio-gravity (WIG) waves, Rossby waves, and the MJO exhibit increased activity during local COEN events. During the recent extreme COEN 2017 and 2023, heavy rainfall occurred alongside significant WIGs, Kelvin, Rossby, and MJO events with unprecedented amplitudes propagating along western South America. Our results suggest that the probability of extreme precipitation under Coastal ENSO in western South America is strongly modulated by wave activity.
Investigation of a cross-flow wind turbine with frontal deflector
Authors: Iliev Rossen 
Published: August 2024

Abstract
The publication presents the results of the study on vertical cross-flow wind turbine with frontal arc-shaped and flat deflectors that increase the turbine’s power coefficient by up to 0.08. The optimal development angle of the arc-shaped deflector has been determined. The obtained results are compared with those of numerical and physical studies published in various reputable journals. Relevant fields of application are discussed. The research is carried out at test bench No. 7C (wind turbines) in the Laboratory of Hydropower and Hydraulic Turbomachinery at the Technical University of Sofia.
Modelling indoor pollutant exposure from steady-state and pulse sources
Authors: Pitchurov George , Fantke Peter , Markov Detelin , Jolliet Olivier , Ivanov Martin 
Published: August 2024

Abstract
The distribution of fine particulate matter (PM2.5) around a thermal manikin was simulated using the model of a passive scalar under constant inhalation mode. The effect of the generation mechanism and the distance to receptor of the pollutant was addressed by simulating diffusive and momentum sources in steady-state and pulse sources in transient mode. The inhaled fraction of pollutant was nearly identical for all cases with the exception when the pollutant was emitted from the body surface and therefore entrained by the thermal convective plume. The reason for this uniformity was attributed to the fairly good degree of mixing and relatively large nominal time constant, which helped to distribute the pollutant uniformly.
Influence of internal and external air leakages on the real performance of an air handling unit with two-stage heat recovery
Authors: Dimchev Ivan 
Published: August 2024

Abstract
The purpose of this study is to evaluate the influence of internal and external air leakages, which appears to be small but actually leads to a serious discrepancy between the theoretical and real performance of air handling units with two-stage heat recovery of the exhaust air. The results show that the overall efficiency of the air handling unit is up to 12.4% lower than theoretical for Class 3 equipment. With increasing airtightness, the efficiency of the AHU, expressed with COP, can be increased by about 3.2% as the class increases by one level.
Forecasting thermophysiological comfort of clothing ensembles designed for work in low temperatures
Authors: Borisova Elena , Angelova Radostina A. 
Published: August 2024

Abstract
The study investigates the relationship between clothing insulation, metabolic rate, and exposure to low temperatures, with a focus on predicting thermophysiological comfort according to ISO 11079. Results from predicting thermophysiological comfort, including IREQmin, IREQneutral, DLEmin, and DLEneutral, are presented, indicating the thermophysiological comfort of a person wearing two ensembles of clothing designed for work in low temperatures. Predictive models are proposed as valuable tools for determining clothing needs in various conditions. Analysis of data reveals that both required clothing insulation and minimum required insulation increase with higher metabolic rates and lower temperatures. Linear relationships are identified between clothing insulation and metabolic rate, particularly at 5°C and -15°C. Understanding these relationships aids in selecting appropriate clothing to maintain thermal comfort and mitigate cold-related risks in occupational settings.