bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neuroprotective effects of Quinpirole in lithium chloride pilocarpine-induced epilepsy in rats and its underlying mechanisms
Authors: Wang Hui , Zhao Yongheng , Zhang Dongqing , Li Jun , Yang Kun , Yang Junli , Li Baomin 
Published: December 2023

Abstract
Epilepsy is a common neurological disorder that presents with challenging mechanisms and treatment strategies. This study investigated the neuroprotective effects of Quinpirole in lithium chloride pilocarpine-induced epileptic rats and explored its potential mechanisms.
Methods:
Lithium chloride pilocarpine was used to induce an epileptic model in rats, and the effects of Quinpirole on seizure symptoms and cognitive function were evaluated. The Racine scoring method, electroencephalography, and Morris water maze test were used to assess the severity of the seizures as well as learning and memory function in the group of rats with epilepsy. Additionally, immunohistochemistry and Western blot techniques were used to analyze the expression levels and morphological changes in glutamate receptor 2 (GluR2; GRIA2), BAX, and BCL2 proteins in the hippocampi of the group of rats with epilepsy.
Results:
First, it was confirmed that the symptoms in the group of rats with epilepsy were consistent with features of epilepsy. Furthermore, the group of rats with epilepsy demonstrated decreased learning and memory function in the Morris water maze test. Additionally, gene and protein levels of GluR2 in the hippocampi of the group of rats with epilepsy were significantly reduced.
Treatment with Quinpirole significantly delayed seizure onset and decreased the mortality rate after the induction of a seizure. Furthermore, electroencephalography showed a significant decrease in the frequency of the spike waves. In the Morris water maze test, rats from the Quinpirole treatment group demonstrated a shorter latency period to reach the platform and an increased number of crossings through the target quadrant. Network pharmacology analysis revealed a close association between Quinpirole and GluR2 as well as its involvement in the cAMP signaling pathway, cocaine addiction, and dopaminergic synapses.
Furthermore, immunohistochemistry and Western blot analysis showed that Quinpirole treatment resulted in a denser arrangement and a more regular morphology of the granule cells in the hippocampi of the group of rats with epilepsy. Additionally, Quinpirole treatment decreased the expression of BAX protein and increased that of BCL2 protein.
Conclusion:
The current study demonstrated that Quinpirole had neuroprotective effects in the epileptic rat model induced by lithium chloride pilocarpine. Further, it improved the symptoms of seizures as well as the learning and memory function of the rats and was associated with the modulation of the expression of GluR2, BAX, and BCL2 proteins. These findings provided clues that would be important for further investigation of the therapeutic potential of Quinpirole and its underlying mechanisms for epilepsy treatment.
Microvesicles derived from mesenchymal stem cells inhibit ARDS pulmonary fibrosis partly through HGF
Authors: Chen Qi-Hong , Zhang Ying , Gu Xue , Yang Penglei , Yuan Jun , Yu Lina , Chen Jianmei 
Published: December 2023

Abstract
Objective
We investigated the effects and mechanisms of mesenchymal stem cell (MSC-MVs) derived microvesicles on pulmonary fibrosis in acute respiratory distress syndrome (ARDS) mice models.
Methods
The MSC-MVs with low hepatocyte growth factor (HGF) expressions (siHGF-MSC-MVs) were obtained by Lentivirus transfection. The ARDS pulmonary fibrosis mice models were established by injecting them with lipopolysaccharide (LPS) three times, after which MSC-MVs or siHGF-MSC-MVs were injected via the tail veins of ARDS pulmonary fibrosis mice. Homing of MSC-MVs in lung tissues were investigated by near-infrared live imaging. Immunohistochemical, Western blot, enzyme linked immunosorbent assay (ELISA) and other methods were used to detect the expressions of pulmonary fibrosis-related proteins, and to compare the effects of MSC-MVs on pulmonary fibrosis and fibrosis-related indicators in ARDS pulmonary fibrosis mice models.
Results
After tail vein injection, the MSC-MVs gradually migrated and homed to damaged lung tissues in ARDS mice models. Treatment of pulmonary fibrosis mice with MSC-MVs significantly reduced the lung injury and pulmonary fibrosis scores. However, low expressions of HGF (siHGF-MSC-MVs) significantly inhibited the roles of MSC-MVs (p < 0.05). Compared with the ARDS pulmonary fibrosis group, the MSC-MVs group exhibited suppressed expressions of type I collagen antigen, type III collagen antigen, TGF-β and α-SMA proteins while the siHGF-MVs group exhibited significantly elevated expressions of the proteins. In addition, compared to the pulmonary fibrosis group, pulmonary compliance (Cydn) and PO2/FiO2 were significantly low in the MSC-MVs group, and the effects of MSC-MVs were significantly inhibited after low expressions of HGF (all p < 0.05).
Conclusion
The MSC-MVs inhibited pulmonary fibrosis in ARDS mice partly via HGF mRNA transfer.
Use of mobile fitness app to improve pelvic floor muscle training in puerperal women with gestational diabetes mellitus: a randomized controlled trial
Authors: He Xiaocheng , Xie Yaping , Xie Baoyuan , Zhao Meijing , Zhang Honghui , Zhao Xiaoshan , Zhao Huifen 
Published: December 2023

Abstract
Background
Gestational diabetes mellitus (GDM) is a risk factor for postpartum urinary incontinence. Pelvic floor muscle training (PFMT) improves pelvic floor dysfunction in puerperal women, but patient compliance is low. Mobile Health(mHealth)is a promising solution.
Objective
To investigate PFMT compliance and effects on pelvic floor muscles in GDM puerperal women guided by the mobile fitness app KEEP.
Methods
This randomized controlled trial included puerperal women with GDM (n = 72) who were delivered at a tertiary general hospital, selected from November 2021 to April 2022 using convenience sampling, and randomly divided into control (n = 36) and experimental (n = 36) groups. The control group performed PFMT based on routine postpartum PFMT training instruction. The experimental group performed PFMT based on KEEP. Both groups had a 4-week intervention period. The PFMT compliance, International Consultation on Incontinence Questionnaire Short Form (ICIQ-SF), Pelvic Muscle Self-efficacy Scale, and the Knowledge, Attitude, Belief, and Practice (KAP) scores of PFMT in puerperal women in the groups were compared pre- and post-intervention. Pelvic floor surface electromyographic biofeedback was used to compare the post-intervention pelvic floor muscle strength between the two groups.
Results
Compared with the control group, the test group had higher post-intervention maternal PFMT compliance, pelvic floor muscle strength, pelvic floor muscle self-efficacy, and KAP scores (p < 0.05); incontinence scores were lower (p < 0.05). Pelvic floor muscles in both groups recovered better post-intervention (p < 0.05).
Conclusion
The KEEP app can improve PFMT adherence, urinary incontinence, KAP scores, self-efficacy, and pelvic floor muscle strength in GDM puerperal women and promote pelvic floor rehabilitation after delivery.
A Multi-objective Grey Wolf Optimizer for Power Scheduling Problem in Smart Home Using Renewable Energy Systems
Authors: Makhadmeh Sharif Naser , Al-Betar Mohammed Azmi , Al-Obeidat Feras , Alomari Osama Ahmad , Abasi Ammar Kamal , Tubishat Mohammad , Elgamal Zenab , Alomoush Waleed 
Published: December 2023

Abstract
This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users' comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new power and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure optimizing EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives.
AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection
Authors: Bhure Chaitanya , Nicholas Geraldine Shirley , Ghosh Shajib , Asadi Navid , Saqib Fareena 
Published: December 2023

Abstract
The global electronic supply chain industry is a complex and interconnected network of companies, organizations, and individuals that collaborate to produce and distribute electronic devices and components. It plays a critical role in the global economy, as electronic devices have become an essential part of modern life. Electronic components and devices are produced and distributed across the world, with different regions and countries specializing in different aspects of the supply chain. This global nature of the electronic supply chain industry also poses challenges, particularly in terms of quality control and supply chain transparency involving counterfeit components owing to its interconnected nature. Counterfeiting of integrated circuits (ICs) and semiconductor devices is a significant challenge that poses threats to the safety and reliability of electronic devices, the global economy, intellectual property rights, and the overall sustainability of the electronic supply chain industry. To address this challenge, we propose a novel autoencoding architecture for counterfeit IC detection using a labeled database. The proposed architecture achieves an overall accuracy of 83% which is approximately ≈ 20% more than the existing transfer learning approaches.
Study of research hotspots and development trends of image security under the background of deep learning
Authors: Guo Yuan , Liu Ziqi 
Published: December 2023

Abstract
As an important carrier of information transmission, images are widely used in human-computer interaction, digital twins, cloud computing, and even people’s daily life. Image security is directly related to personal privacy, business secrets and national security. It is of great significance to strengthen the research of image security. Deep learning is a machine learning method based on neural networks, which enables the processing and analysis of complex data by simulating the way human brain neurons work and provides strong support for research in the field of image security. With the rapid development of deep learning technology, its application in the field of image processing and security is increasingly extensive. To gain a deeper understanding of the current situation and cutting-edge trends of deep learning-based image security research. This paper uses CiteSpace, a bibliometric tool, to analyze the 2009-2022 research results on "image security" and "deep learning" in the core database of Web of Science. Accurately grasp the stage of development and characteristics of image security research in the world through data analysis. It is found that the research hotspots mainly cover image encryption, image steganography, image watermarking, and other technical applications. In the past decade, great progress has been made in the field of image security. The research on techniques such as synthetic image identification, image tampering detection, and adversarial samples is rapidly emerging. Future research on image security deep learning-based will focus on the directions of anti-attacks, robustness enhancement, privacy protection, and data sharing. We hope this study will provide new research ideas and thinking perspectives to draw on for the future direction of image security.
Trend patterns of nitrogen dioxide: OMI measurements and Machine Learning to understand the global anthropogenic emissions
Authors: Murillo-Tovar Mario Alfonso , Herrera-López Enrique Jaime , Saldarriaga-Noreña Hugo Albeiro , Díaz-Torres José de Jesús 
Published: December 2023

Abstract
Tropospheric dioxide nitrogen is one of the criteria pollutants considered a toxic gas that contributes to climate change and affects public health. Anthropogenic activities are the primary NO2 sources affecting the planetary ecosystems. The Ozone Monitoring Instrument (OMI) on board AURA Missions is one of the most robust projects contributing to the NO2 investigation. A methodological coupling based on spatial analysis, clustering, machine learning, and statistical validation helped to analyze the OMI satellite data and its interactions with socioeconomic factors. Spatial contrasts show differences between continental and marine domains, highlighting the influence of coastal urban centers on the near marine areas; differences between hemispheres and latitudinal changes overall in the Pacific and Atlantic oceans; contrasts between urban and rural areas in continents; and outstanding regions by their high NO2 emissions. The trend analysis outlined regional contrasts that contribute to understanding the impact of economic activities and environmental policy implementation. From 2005 to 2021, trend patterns characterization established the framework to correlate the population size and the GDP of more than 250 developed urban centers worldwide. Prominent maximum NO2 densities between 2011 and 2013 stand out among the four trend patterns, outlining a point inflection (peak component) in the trend direction of several regions. The correlation, including all cities, followed a significant moderate relationship (R=0.573, p~0.000) where the population explained 33.7% of the productivity. However, the correlations by subgroups considering trend pattern classification indicated significant moderate to strong relationships for almost all trend types (R from 0.689 to 0.814, p~0.000), where the population explains 47.5 to 66.2% of the productivity. These results partially show the direct cause-effect relationship between the high NO2 emissions and development levels in urban centers. Conversely, the wide scattering in such correlations suggests the gradual and positive effects of Environmental policies in favor of better air quality, different from the sudden decrease in NO2 densities caused by confinement and preventive measures against COVID-19.
A Multi-layer Graph Neural Network Framework for Cold Start Recommendation
Authors: Whitman Ignacio , Havisham Booker , Hurst Greta , Serrano Ximena 
Published: December 2023

Abstract
Traditional recommendation algorithms often face challenges such as data sparsity or cold start issues and may not effectively explore the correlation between rating information and auxiliary data. Convolutional Graph Neural Networks are broadly divided into two mainstream categories: spectral-domain-based convolution methods and spatial-domain-based convolution methods. In 2013, Bruna et al. published the first study on graph neural networks based on spectral-domain convolution. This study defined a specific way of graph convolution based on spectral graph theory. Since then, spectral-domain-based convolutional graph neural networks have received increasing attention . A series of spectral methods based on graph Fourier transform have been proposed. In these methods, spectral convolutional neural networks assume that the filters in the Fourier transform are a set of trainable parameters, laying the theoretical foundation for subsequent research. Following this trend, Kipf et al. proposed the groundbreaking Graph Convolutional Network (GCN), which efficiently approximates neighborhood convolution and aggregation by using Chebyshev polynomials to achieve a first-order approximation. The aim of this paper is to address the cold start problem using graph neural network technology while ensuring that the model retains the ability to leverage node content features. Additionally, the goal is to enhance the recommendation performance of the model on this basis, aiming to achieve or surpass the effectiveness of existing methods.
A study on ceramic composite filters utilizing rice husk for enhancing groundwater quality
Authors: Susanto Iwan , Agsya Raihan Trinanda , Zainuri Fuad 
Published: December 2023

Summary: This study addresses the persistent reliance of certain communities on groundwater for drinking purposes, despite its failure to meet quality standards due to the presence of harmful coliform bacteria. To meet these standards, a cost-effective and environmentally friendly filtration method is proposed, utilizing clay and rice husk as formative elements. The research focuses on creating a ceramic composite filter by blending rice husk with clay in varying compositions (30 %, 40 %, and 50 %). The mixture is processed into a paste, shaped into a green body, air-dried, and fired at different temperatures (875 °C, 900 °C, and 925 °C). Compression, porosity, and flow tests are conducted to evaluate the filter's performance. An increase in rice husk composition leads to higher flow rates, indicating improved water flow through the filter, suggests a positive influence of rice husk on the hydraulic characteristics of the filter material. The ceramic composite filter effectively filters coliform bacteria, achieving a 93 % reduction, although it falls short of meeting drinking water quality standards. The optimal sintering temperature is identified at 900 °C with a forming pressure of 30 MPa. This study emphasizes the potential of clay and rice husk-based ceramic filters as a viable option for improving groundwater quality, acknowledging their positive impact on porosity and flow characteristics. However, further refinement is required to fully align with established drinking water quality standards, suggesting avenues for future research and development in this critical field. This research explores the potential development of technology ceramic composite filter with the hope of making a positive contribution to advancing clean and sustainable energy solutions in the future
Water quality prediction: a data-driven approach exploiting advanced machine learning algorithms with data augmentation
Authors: K Karthick , Krishnan S. , Manikandan R. 
Published: December 2023

Abstract

Water quality assessment plays a crucial role in various aspects, including human health, environmental impact, agricultural productivity, and industrial processes. Machine learning (ML) algorithms offer the ability to automate water quality evaluation and allow for effective and rapid assessment of parameters associated with water quality. This article proposes an ML-based classification model for water quality prediction. The model was tested with 14 ML algorithms and considers 20 features that represent various substances present in water samples and their concentrations. The dataset used in the study comprises 7,996 samples, and the model development involves several stages, including data preprocessing, Yeo–Johnson transformation for data normalization, principal component analysis (PCA) for feature selection, and the application of the synthetic minority over-sampling technique (SMOTE) to address class imbalance. Performance metrics, such as accuracy, precision, recall, and F1 score, are provided for each algorithm with and without SMOTE. LightGBM, XGBoost, CatBoost, and Random Forest were identified as the best-performing algorithms. XGBoost achieved the highest accuracy of 96.31% without SMOTE and had a precision of 0.933. The application of SMOTE enhanced the performance of CatBoost. These findings provide valuable insights for ML-based water quality assessment, aiding researchers and professionals in decision-making and management.
Analyzing the relationship between meteorological changes and evapotranspiration trends in Gia Lai province, Central Highlands of Vietnam
Authors: Ha Phan Thi , Hai Le Minh , Liem Nguyen Duy , Dat Nguyen Le Tan , Huyen Nguyen Thi , Thuy Nguyen Ngoc , Kim Loi Nguyen 
Published: December 2023

Abstract

This study aims to analyze the historical trends of evapotranspiration at annual and seasonal scales and assess the sensitivity to various meteorological factors in Gia Lai province from 1980 to 2019. The modified innovative-Şen trend method and Sobol analysis are employed for trend identification and sensitivity assessment, respectively. The results obtained indicate significant downward trends using both the innovative-Şen trend analysis and the improved version of the innovative trend analysis method across different stations and time scales. Furthermore, in the low-value subgroups of the rainy season at Pleiku and An Khe stations, there were notable increasing trends with quantitative values of approximately 6%. Sensitivity analysis reveals that evapotranspiration is most sensitive to sunshine duration across most stations on annual and seasonal time scales, followed by relative humidity and minimum temperature. Overall, this research provides valuable insights into the relationship between evapotranspiration and relevant climate variables, contributing to the assessment of water demand for agricultural irrigation.
Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley
Authors: Chen Hongzhuan , Yin Xinhuai , Huang Xiaoyu , Zhao Enrong , Ou Xiaofeng , Ye Chengzhi 
Published: December 2023

Abstract

Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.