bluuebunny/crossref_metadata_2025_split huggingface dataset:
An anomalous traffic detection method for distributed photovoltaic safety state baseline
Authors: Mu Chen , Nige Li , Yong Li , Bo Zhang , Yongcai Xiao , Tengyan Wang , Ziang Lu 
Published: September 2024

Abstract

With the rapid growth of resource integration in modern power systems, these resources are diverse, large-scale, and situated in complex and open physical environments, making them relatively vulnerable to cyber-attacks due to weaker security measures. To address this challenge, this paper proposes an identity authentication architecture system that integrates software and hardware. In the software fingerprint section, we extract packet characteristics and statistical features through network probing, and combine them with time difference sequence features obtained from side-channel monitoring to generate the software fingerprint of the power smart terminal by direct concatenation. This method incorporates various characteristic information, enhancing the recognition accuracy of the fingerprint features. In the hardware fingerprint section, we generate hardware fingerprints by extracting the preamble signal and performing statistical feature analysis. Finally, using an ensemble learning method, we integrate the software and hardware fingerprints to generate device fingerprint features. This approach effectively addresses the security authentication issue of power equipment based on High-Level Power Line Communication (HPLC), achieving a recognition rate of over 95% under most machine learning classification methods.
Hierarchy-based domain adversarial neural network for bearing fault diagnosis under variable working conditions
Authors: Zheng Yuanlin , Wang Ting , Liu Jie , Zhang Xueqian , Xie Jingsong 
Published: September 2024

Abstract

The bearing fault is one of the primary factors affecting the safe and stable running of mechanical systems. To guarantee the normal and reliable running of the entire equipment, it is crucial to promptly and accurately monitor the operating conditions of bearings. Conventional fault diagnosis methods usually depend upon the assumption that the training and test data are consistently distributed and independent. However, this premise poses challenges to the resolution of fault diagnosis issues for changeable running conditions. To tackle the aforementioned problem, a novel hierarchy-based domain adversarial neural network (H-DANN) is introduced in this paper. For the proposed H-DANN model, it is mainly constructed based on the DANN. The domain discriminator enables the feature extractor to abstract domain-independent features and allows classifier transfer across different operating environments. Furthermore, to extract rich discriminative features, a hierarchy-based feature extractor is proposed based on a novel feature pyramid network (FPN) modified by the CNN-BiLSTM network. Finally, the results of two bearing datasets indicate that the H-DANN model is adept at precisely recognizing bearing fault categories under different running environments, outperforming some state-of-the-art models.
CO2 capture using biochar derived from municipal residual sludge conditioned with chitosan
Authors: Yu Yue , Li Tongqing , Gui Jiacheng , Chen Ming , Zheng Qiushi , Liao Yang , Yang Yueyue , Wu Yan , Liu Chang 
Published: September 2024

Abstract

To achieve the dual objectives of pollution reduction and carbon mitigation, this study examined the effects of biochar derived from urban residual sludge conditioned with chitosan (SBCC) on the CO2 capture capacity. Using raw sludge biochar (RSB) as the control group, and focusing on the preparation temperature of the biochar and the adsorption temperature of CO2 as main parameters, the study explored the CO2 adsorption performance of modified sludge biochar. The mechanism of CO2 adsorption by SBCC was elucidated through the analysis of the surface morphology, elemental composition, functional groups, and surface area of the sludge biochar. Results indicate that the optimal preparation and adsorption temperatures for RSB are 800°C and 30°C, respectively, with a maximum CO2 adsorption capacity of 28.36 mg/g. For SBCC, the optimal temperatures are 600°C and 30°C, respectively, achieving a maximum CO2 adsorption of 89.88 mg/g. Compared to RSB, SBCC primarily exhibits a chemisorption process, with its adsorption mechanism involving strong dipole-quadrupole interactions between nitrogen atoms and CO2. In the sludge, chitosan undergoes partial hydrolysis through alkalinization, forming carboxyl groups. These carboxylic functional groups facilitate the formation of hydrogen bonds between CO2 and the carbon surface, as well as condensation reactions between alkaline functional groups and CO2. Moreover, SBCC also demonstrates good reusability. After five cycles of adsorption and desorption, it still retains up to 75% of its initial CO2 adsorption capacity.
Serum levels of auto-antibodies (rheumatoid factor and anti-double stranded DNA) in Nigerian COVID-19 patients as predictions of developing future autoimmune disorders
Authors: Oladokun Haleemat Yejide , Rahamon Sheu Kadiri , Surajudeen Yaqub Adebayo , Onifade AbdulFattah Adekunle , Arinola Ganiyu Olatunbosun 
Published: September 2024

Abstract

Background
A deeper knowledge of the roles played by severe, acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the development of autoantibodies is vital to understand the relationship between COVID-19, autoimmune disorders and proper management of the two diseases. Therefore, serum levels of rheumatoid factor (RF) and anti-double stranded DNA antibody (dsDNA ab) which are important factors of autoimmune disorders were determined in COVID-19 patients and apparently healthy control.
Methods
Serum levels of rheumatoid factor were determined using turbidimetric immunoassay. Serum anti-dsDNA antibody levels were determined using enzyme linked immunosorbent assay (ELISA) in both COVID-19 patients and healthy controls.
Results
Serum levels of RF and anti-dsDNA ab were similar in COVID-19 patients compared with controls. A significant positive correlation was observed between RF and anti-dsDNA ab in healthy controls. The correlation between RF and anti-dsDNA ab in COVID-19 patients was not significant.
Conclusion
SARS-CoV-2 infection did not have a profound effect on serum RF and anti-dsDNA ab levels in these infected individuals. Therefore, autoimmune disorders are not implicated in these COVID-19 patients and that the use of therapeutic antagonists to auto-antibodies may not be useful in the management of COVID patients, since they may not develop COVID-19 based autoimmune disorder.
The paradox of climate policy diffusion
Authors: Savin Ivan , Mundt Philipp , Bellanca Margherita 
Published: September 2024

Abstract

Prior research produced contradicting evidence regarding the role of international influence in the diffusion of climate policies. To unravel this puzzle, we examine various policy instruments adopted by G20 countries, demonstrating that peer pressure stimulates convergence in the number of new policies adopted but divergence in their stringency. This suggests that policymakers emulate the appearance of their peers but not the rigor of regulation, creating opportunities for carbon leakage.
JEL Codes: C21, F18, F42, F64, Q56
Adaptive Real-Time Big Data Processing Framework: A Machine Learning and Reinforcement Learning Approach Using Random Forest and Q-Learning for Dynamic Resource Management
Authors: Hooda Akash , Hooda Arju , Yadav Disha 
Published: September 2024

Abstract

Real-time big data analytics is crucial for industries that handle high-velocity data streams, such as IoT, finance, e-commerce, and social media. Traditional big data processing frameworks often struggle to adapt to the dynamic nature of real-time data, leading to inefficiencies, increased latency, and higher operational costs. This research proposes an Adaptive Real-Time Big Data Processing Framework that integrates a Random Forest model for load prediction with Q-learning for dynamic resource allocation. The framework adjusts in real-time based on predictive insights and learned policies, optimizing system performance while minimizing costs.
Experimental results demonstrate that the framework reduces latency by 15%, improves throughput by 20%, compared to traditional methods. The Random Forest model achieved an efficiency of 93.62%, while the Q-learning approach further achieved 94.86%, reflecting superior resource utilization. These improvements indicate the framework's effectiveness in dynamically adapting to real-time data, ensuring high performance and cost-efficiency.
Machine Learning Approaches for Classifying and Characterizing Coral Diseases
Authors: Buren Emily W. Van , Beavers Kelsey M. , Cornelio Mariah N. , Stokes Alexia , Emery Madison , Demuth Jeffery P. , Wang Li , Mydlarz Laura D 
Published: September 2024

Abstract

Background: Anthropogenic climate change has had devastating effects on the Florida and Caribbean reef systems, primarily due to increased disease outbreaks. Climate change contributes to the rising frequency of marine diseases by expanding pathogen ranges and heightening host susceptibility due to environmental stress. Specifically, there has been a stark rise in marine disease events targeting multiple coral species, resulting in high mortality rates and declining reef biodiversity. Although many of these diseases present similar visual symptoms, they exhibit varying mortality rates and require distinct treatment protocols. Advances in coral transcriptomics research have enhanced our understanding of coral responses to different diseases, but more sophisticated methods are required to classify diseases that appear visually similar.
Results: This study provides the first machine learning algorithm that can classify two common coral diseases: stony coral tissue loss disease (SCTLD) and white plague (WP). This algorithm also identifies 463 biomarkers, with 275 unique to SCTLD and 167 unique to WP. These biomarkers highlight the differences in immune responses to the different disease outbreaks. These algorithms were tested and validated with samples collected in situ, supporting the biomarker efficacy of the identified biomarkers for disease classification. The final model was built with partial least squares discriminant analysis and the 463 biomarkers, resulting in a highly predictive model with an AUC of 0.9895 and low error rates.
Conclusion: This study provides a diagnostic tool that reliably distinguishes between phenotypically similar tissue loss diseases and identifies biomarkers that provide characterizations of SCTLD and WP.
Gender equality in academic medicine before, during and after COVID: what have we learned? A systematic review.
Authors: Burke Elaine , Darker Catherine , Godson-Treacy Isabelle Molly , Kavanagh Columb , Mockler David , Slattery Natasha , Hennessy Martina 
Published: September 2024

Abstract

BackgroundGender disparity at senior levels in academic medicine has been recognised for decades, but progress has been slow and confounded further by the COVID pandemic. While there are many papers describing this problem, there is little evidence for potential solutions. We aimed to describe the current evidence for interventions to enhance gender equality in academic medicine, and to compare interventions pre, during and post-COVID-19. We also wished to characterise the nature of the interventions, who delivered them, and whether they seek to “fix the women”, or target issues at organisational and systemic levels.MethodsWe searched five electronic databases in November 2022 and August 2023 and undertook hand-searching. We extracted data using a form developed for the study, and applied the TIDieR and Morahan frameworks to describe and characterise interventions. We used the QUADs tool to critically appraise included studies.ResultsThe search of electronic databases yielded 1,747 studies. A further 62 were identified through hand-searching. Following removal of duplicates, 764 articles were screened for eligibility, and 199 full-text articles were screened. Of these, 27 met the inclusion criteria. The most commonly reported interventions were career development or leadership skills programmes, followed by mentorship and multi-faceted interventions. Most papers reported positive findings, but many relied on subjective measures. Robustly designed studies often reported mixed findings. The majority of interventions aimed to “fix the women”, with few addressing inequality at organisational level. We found no studies describing interventions aimed specifically at mitigating the effects of the COVID pandemic, and none describing the effects of the pandemic on their interventions.ConclusionAcknowledging the possibility of publication delay, we found that despite strong evidence of the negative effects of the pandemic on women’s research productivity, there were no new interventions designed to mitigate this. Many existing interventions create “institutional housekeeping” by relying on women for their delivery, this can result in failure, especially during a crisis like COVID. Most studies were low to moderate quality. More robust research, and a more holistic approach is needed, moving away from “fixing the women” to address the organisational and systemic structures which underpin inequality.
Evidence of Human-designated Antiretroviral (ARV) Drug Residues in Broiler Chicken, Domestic Pigs, and Animal Feeds in Tanzania
Authors: Kimera Zuhura , Shimo Peter , Ballandya Emmanuel , Matee Mecky Isaac , Adams Lisa 
Published: September 2024

Abstract

Background: Recent reports have indicated the use of antiretroviral (ARV) drugs to boost animal production in neighboring Uganda, with further reports of use in several African countries.
Methods: This cross-sectional study was conducted in nine districts in Tanzania, and involved screening for the presence of three first-line ARVs (lamivudine, nevirapine, and efavirenz) residues in the muscle and blood of domestic pigs and broiler chickens, and in sampled animal feed and water. Residues were determined using liquid chromatography and mass spectrometry (LC-MS/MS). The method involved calibration of the lower limit of quantitation (LLOQ) and limit of detection (LOD). ARVs were detected and quantified using the Multiple Reaction Monitoring (MRM) system.
Results: 131 (66.8%) of the 196 samples of muscle, blood, and animal feed were found to contain lamivudine residues, with the highest concentration detected in domestic pig blood and muscle (7.58mg/kg) and the lowest concentration (0.01 mg/kg) in broiler chicken feed. There was a significant relationship between the presence of lamivudine by sample type and sample origin (p=0.000). Nevirapine and efavirenz drugs were not detected in any of the collected samples. No ARV residues were detected in water samples (n=37).
Conclusion:This study confirms the use of ARVs in animal production in Tanzania as evidenced by the presence of residues in animal feeds. We found lamivudine residues in domestic pigs and broiler chickens at concentrations higher than those recently reported in other East African studies. Farmers living with HIV may be using ARVs from their prescribed medications, which may lead to poor adherence and the emergence of drug resistance. Besides direct human and animal health issues, these residues in animal feeds and animal excreta can lead to environmental contamination leading to several negative impacts. We recommend a total ban on human-designated ARVs in animal production and advocate for comprehensive studies and monitoring systems across African countries to reveal potential societal and other reasons for their use and provide comprehensive solutions using One Health approaches.
The Impact of Oil Prices on Import Demand in an Oil-Rich Country: A Multisectoral Bayesian Approach
Authors: Ozturk Ozcan , Canga Miranda 
Published: September 2024

Abstract

This study investigates the import demand function for Qatar across sixteen economic sectors. Using data from 2012 to 2022 and employing a Bayesian estimation approach, we derive precise elasticity estimates to quantify the impact of changes in import prices, national income, and oil prices on the demand for imports. The findings indicate that import demand in Qatar is predominantly price inelastic, with price elasticities ranging from − 0.079 to -0.21 across different sectors. This indicates that even substantial changes in import prices result in relatively small adjustments in the quantity of imports demanded. This suggests that Qatar heavily relies on imported goods with a few domestic alternatives. This reliance highlights the country’s vulnerability to global price fluctuations and the challenges in reducing imports through price mechanisms alone. Income elasticities, on the other hand, are highly elastic, ranging from 4.582 to 6.353 across all sectors. This suggests that as national income increases, import demand grows substantially across all sectors. Moreover, oil prices positively influence import demand in specific sectors such as Metals and Machinery/Electrical. This implies that higher oil prices, which often correlate with increased government revenues, lead to increased import demand in sectors tied to infrastructure and industrial development. However, this dependence on oil revenues also introduces a level of economic risk, as fluctuations in global oil prices can lead to volatility in import demand, potentially destabilizing these key sectors. These findings have significant policy implications for Qatar. The inelastic response to price changes suggests that price-based policies may be insufficient to control import volumes, necessitating more structural strategies, such as diversifying the economy and enhancing domestic production and supply chain resilience. Investment in local industries, especially in high-import sectors like Foodstuffs, Transportation, and Machinery/Electrical, could reduce reliance on imports and improve economic resilience. Moreover, the high-income elasticities highlight the need for robust infrastructure planning, particularly in logistics and port facilities, to accommodate the expected surge in import volumes as the economy grows. Finally, the strong link between oil prices and import demand in specific sectors emphasizes the necessity of reducing economic dependency on oil revenues. This could be achieved by promoting energy diversification, investing in renewable energy, and encouraging growth in non-oil sectors.
Label-free and TMT-labeled Proteomics Methods to Compare Differences
on Normal and Attached Livers of Glyptosternum maculatum
Authors: Sun Wu , Yan Yingying , Muhammad Zafarullah , Guo Yufeng , Zhang Guoqiang 
Published: September 2024

Background:
The aim of this study is to compare the molecular differences between
the normal liver (NG) and attached liver (WG) of Glyptosternum maculatum (G. maculatum)
based on label-free and TMT-labeled proteomics data. It provides a theoretical basis for the adaptation of G. maculatum in the plateau area with low temperature and low oxygen

Methods:
The differentially expressed proteins (DEPs) of label-free and TMT-labeled proteomics
data were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein structural domain (PSD) enrichment, and protein network interactions
were constructed to reveal protein characteristics.

Results:
detected a total of 643 and 107 DEPs; 7
common DEPs (co-DEPs) were selected through PPI network information. Higher expression of
evm.model.chr5.73, evm.model.chr18.610, evm.model.chr10.1201, and HIF-1 signaling pathway
were associated with a hypoxic environment. evm.model.chr15.573, evm.model .chr5.320, evm.-
model.chr6.360, and evm.model.chr18.610 were associated with WG production. Significant expression of Ubiquitin-like protein 4A and Heat shock 70 kDa protein protects liver cells from injury in a hypothermic and hypoxic environment.

Conclusion:
In terms of the number of DEPs, the labelling-free method is superior to the TMT-labeled method. The TMT-labeled method is better in terms of repeatability and accuracy. The
myosin11 not only responds to low temperature and low oxygen but may also lead to WG production through Tight junctions. The NG may be more sensitive than WG in stress response to cancer
and viral infections.
A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome
Authors: Saranya R. 
Published: September 2024

Introduction:
This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from
ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are
increasingly finding reproductive disorders, namely PCOS.

Methods:
The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound
scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis.

Results:
This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in
Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the
optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been
compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO.

Conclusion:
Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.
Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis
Authors: Kukreja Vinay , Dogra Ayush , Kaushal Rajesh Kumar , Mehta Shiva , Vats Satvik , Goyal Bhawna 
Published: September 2024

Background:
Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be
precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data.

Objective:
This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks
(CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on
preprocessed MRI scan pictures that have already been categorized.

Methods:
In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The
processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy
and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy.

Results:
The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and
weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to
those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert
individual model insights into a consistent global model while keeping all personal data private.

Conclusion:
The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving
approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in
improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.