bluuebunny/crossref_metadata_2025_split huggingface dataset:
ANN-UKF-Based Estimator for Landing Forces in Quadruped Robots
Authors: Huynh Ba-Phuc , Bae Joonbum 
Published: November 2024

Abstract

Estimation of landing forces is essential for legged robots to execute precise and effective movements, particularly when navigating complex and unfamiliar terrains. Traditionally, this estimation is achieved using force sensors mounted on the robot's legs. This paper introduces a novel approach that eliminates the need for force sensors by employing a force-sensorless estimator. The proposed method utilizes an artificial neural network (ANN) to forecast joint torques at the legs at specific points along the motion trajectory. This ANN model functions as a predictor within an Unscented Kalman Filter (UKF), which refines and stabilizes the force measurements. By analyzing the differences between predicted and filtered values, the method estimates both the state and magnitude of the robot’s foot impact with the terrain. The effectiveness of this approach has been validated through simulations and practical tests on a quadruped robot.
Assessment of the Robustness and Resilience of the Water System in the Bogotá River Basin: A Multi-Objective Optimization Approach
Authors: Muñoz Dairo , Rueda William J. Guerrero , Rodríguez Leonardo José González 
Published: November 2024

Abstract

Extreme climatic phenomena, such as the El Niño/Southern Oscillation (ENSO), have significant consequences for human life and global ecosystems. This phenomenon manifests in two forms: La Niña and El Niño. In Colombia, La Niña brings seasons of intense rainfall and flood risks, while El Niño induces warm and dry conditions, reducing river flow rates, reservoir levels, and causing water scarcity. In the Bogotá River basin, located in the Cundinamarca department, Colombia, these phenomena have resulted in severe floods and water and energy rationing in the past. Understanding and monitoring these events are fundamental for risk management and adaptation to climate change. This study focuses on evaluating the robustness and resilience of the hydrological system in the Bogotá River basin in the face of flooding and water scarcity events, analyzing strategic control points throughout the basin. A multi-objective linear programming model was implemented to represent the functioning of the hydrological system, covering 22 hydrographic sub-basins, 9 reservoirs, and 25 monitoring stations distributed along the Bogotá River. The findings of the study are crucial for risk planning and mitigation, promoting more effective management in response to extreme climatic events. However, the results indicate that the hydrological system lacks the necessary robustness and resilience to operate effectively under extreme conditions of rainfall or drought, highlighting the need to adopt strategies that improve its operational performance and meet robustness and resilience requirements, thus ensuring its functionality even under adverse climatic scenarios.
Synergistic inhibitory effect of atmospheric pressure plasma and berberine on non‑small cell lung cancer cells via inducing apoptosis
Authors: Ming Huiyun , Lu Tingting , Zhou Han , Song Wencheng , Dai Haiming 
Published: November 2024

Abstract

Background
Non-small cell lung cancer (NSCLC) is a type of lung cancer, the incidence and mortality rate have been high, and the use of monotherapy is easy to make patients develop tolerance. Atmospheric pressure plasma (APP) is an emerging technology for killing cancer cells in recent years, and there are fewer studies related to the combination of berberine and the mechanism of action has not been fully elucidated for NSCLC. The article primary goal is to investigate the effect of combination on NSCLC and its associated characterization.
Methods and results
Antiproliferative effects were detected by MTT assay and colony formation, and flow cytometry analysis of apoptosis and cycling showed that the combination synergistically induced apoptosis. Then, extracellular ROS levels and DCFH-DA-based kits examined intracellular ROS levels, and their effects on mitochondrial membrane potential were measured. Study reveals that co-induced apoptosis is associated with ROS accumulation. Subsequently, Western blotting (WB) detected the expression of EGFR, and the important signaling pathway proteins Ras / ERK and AKT / mTOR. Results showed that it could downregulation of EGFR protein expression and inhibit of activation of ERK/ AKT signaling pathways. Simultaneous wound healing assay and EMT marker detection were performed for the assessment of migration and EMT ability of NSCLC cells. Combination therapy inhibited migration and EMT of NSCLC cells.
Conclusion
The results of this study show that the combination can synergistically induce apoptosis of NSCLC by regulating ROS production. EGFR downregulation and AKT/ERK signaling pathway inhibition are linked to the synergistic effect.
Investigation antibacterial activity of synthesized GO-SiO 2 nano hybrid against Escherichia coli as a bacterial index in water
Authors: Mohammadzadeh Alireza , Kalankesh Laleh R. , Karbalayi Mohadeseh , Hoshanghi Zahra 
Published: November 2024

Abstract

Graphene oxide-silicon dioxide (GO-SiO2) nanocomposites are known for their potential antibacterial activity and unique characteristics. This study was aimed to investigate antibacterial activity of GO-SiO2 nanocomposites in various ratios. Scanning electron microscope (SEM) method was used to characterize morphological structure of GO-SiO2. Disk diffusion, Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) method was used to investigation antibacterial activity in different nanoparticle ratios. The GO-SiO2 ratio of 1:1 exhibited the strongest inhibition zone against E. coli (20 mm), surpassing the individual antibacterial activity of GO and SiO2 alone. MIC and MBC tests provided valuable insights into the effectiveness of GO-SiO2 nano-hybrids against E. coli, with a MIC value of 6%, 2% and 2% for GO-SiO2 1:1 SiO2 and GO nanoparticles, respectively. MIC for GO-SiO2 in 1:2 and 2:1 ration >6% and >10% was resulted. Additionally, MBC of 12%, 4% and 4% was obtained for GO-SiO2 1:1 SiO2 and GO nanoparticles, respectively. This study successfully investigated the antibacterial properties of SiO2-GO nano-hybrids prepared through a novel imprinting route. The results demonstrate their potential as effective antibacterial agents, offering valuable insights for the development of advanced antibacterial materials.
Figure-of-eight Knot Technique combined with Subtemporal Placement for Prevention of Silicone Oil Blockade in Ahmed Glaucoma Valve：A Novel Surgical Technique for the Treatment of Refractory Glaucoma after silicone oil endotamponade
Authors: Zhang Lishuai , Chu Yanzi , Xi Huiyu , Chen Xinxia , Wei Jiajun , Zhao Jinqiao , Liu Haiyang , Chen Xiaoya 
Published: November 2024

Abstract

Background
In recent years, glaucoma valves have played an important role in the management of SOE-related glaucoma. However, the occurrence of tube blockage with SO has emerged as a significant factor contributing to surgical failure in these patients. One reason is that the opening of tube is too close to silicon oil, and the other is the early excessive drainage of aqueous humor. Therefore, we developed this technique to manipulate the positioning of the valve body and restrict early aqueous humor outflow and subsequently assessed its safety and efficacy during an over one-year follow-up.
Methods
After the glaucoma valve was implanted in the subtemporal region, a figure-of-eight knot was utilized to encircle the tube, forming a knot of continuous double loops. This knot gently compresses the tube, thereby constricting its lumen and restricting drainage of aqueous humor. The IOP, BCVA, and complications in all patients were observed and recorded.
Results
There are 6 eyes with secondary glaucoma received this surgical treatment. The mean of preoperative IOP was 45.5 ± 6.8mmHg, and decreased to 13.5 ± 3.0mmHg after surgery (P = 0.000). During the follow-up(17.7 ± 4.3months), we observed stable IOP, and none of the patients exhibited tube blockage caused by SO.
Conclusions
This technique can effectively prevent SO blockage in AGV.
Rediscovering the climate analogue method: insights from a data-driven approach
Authors: Chen Pei-Chun , Wang Li-Pen , Kim Dongkyun 
Published: November 2024

Abstract

Analogue methods are widely used in atmospheric science for weather forecasting and climate risk studies. The concept of weather/climate analogues is straightforward: it quantifies the similarity between weather conditions at target and candidate time periods by computing a distance using relevant atmospheric variables (e.g., temperatures and geopotential heights). Traditionally, Euclidean distance (ED) is used for this task, computed over the original value space of the variables. In recent years, with the rapid advance in deep learning (DL) techniques, the concept of ’features’ (or representations) has emerged, providing an alternative for analogue-related applications. This study aims to explore the potential of DL in climate- scale applications. Specifically, we propose a data-driven climate analogue approach, which involves constructing a DL-based climate dynamics model, ClimaDist. This model identifies features relevant to climate dynamics, and we use these features, instead of the original input atmospheric variables, to search for analogues. In addition to the traditional ED, we explored two other distance measures that were not widely used in the atmospheric community: the image Euclidean distance (IMED) and the structural similarity index metric (SSIM). A range of different combinations of models, distance measures and input data settings were tested using ERA5 reanalysis data, and our results suggest that the proposed ClimaDist outperforms all other model combinations in terms of the ability to reproduce both the observed distributions and variations. Finally, we employed explainable AI (XAI) techniques to analyse the effectiveness of ClimaDist in identifying climate analogues. The results indicate that the importance of climate features varies both spatially and temporally, a factor that should be accounted for in analogue identification but is often overlooked in traditional methods.
Simulation comparison of the effects of missing data imputation methods on classification performance in high dimensional data
Authors: VAROL Bugra , Omurlu Imran Kurt , Ture Mevlut 
Published: November 2024

Abstract

The study aims to examine the performance of different missing data imputation methods on accurately estimating missing data in high dimensional datasets and their impact on classification using extreme learning machines (ELM). Random datasets were generated with n = 150 observations, p = 500 independent variables, and different missing data rates. Various imputation methods were used, including mean, median, random, k-nearest neighbors (KNN), missing value imputation with random forests (I-RF), multivariate imputations by chained equations with classification and regression trees (MICE-CART), as well as direct and indirect use of regularized regression (DURR and IURR) methods specifically developed for high dimensional data. The performance of the methods was evaluated based on their proximity to the reference classification scores obtained using ELM. I-RF, MICE-CART, DURR, and IURR, followed by KNN methods, exhibited better performance at low missing rates, while DURR and IURR methods stood out at high missing rates.
Improved R-Peak Detection in Long-Term ECGs: Leveraging Hybrid Linearization and LSTM with Grey Wolf Optimization
Authors: VELUSAMY SARAVANAN , MURUGAN PALLIKONDA RAJASEKARAN 
Published: November 2024

Abstract

The precise interpretation of the ECG signal can reveal the condition of the heart. ECG signal analysis can assist in identifying any abnormalities or arrhythmias in the heart. Premature Ventricular Contractions (PVCs) are irregular heartbeats that may signal the presence of a heart ailment. Long-term ECGs are commonly utilized in clinical practice to diagnose PVCs. However, analyzing these long-term ECGs is time-consuming for cardiologists and requires human involvement. This research proposes a robust approach for detecting R peaks in QRS complexes using a recurrent neural network. Our proposed methodology was applied to the well-known MIT-BIH Arrhythmia Database (MIT-DB) dataset and the China Physiological Signal Challenge (2020) database, which contains over a million beats. The hybrid linearization technique uses an adaptive filter and discrete wavelet transform (DWT) to remove noise from the ECG signal. The next step is to use principle component analysis (PCA) to extract characteristics from the ECG data. Lastly, the R peak signals are classified using long short-term memory (LSTM) to improve accuracy through optimization techniques like Grey Wolf optimization (GWO). The algorithm's performance was also evaluated using the MIT-BIH Arrhythmia database and the China Physiological Signal Challenge (2020). The suggested formal technique yields the best results for R-peak detection on CPSC-DB, with F1-score of 95.3%, recall of 96.8%, accuracy of 99.5%, and precision of 95.3%. The F1-score, recall, and precision of the algorithms on MIT-DB are all equivalent to, or better than, those of the competing methods.
Hybrid Cloud-Edge AI Framework for Real-Time Predictive Analytics in Digital Twin Healthcare Systems
Authors: Jameil Ahmed K , Al-Raweshidy Hamed 
Published: November 2024

Abstract

The development of Digital Twin (DT) systems for healthcare introduces new opportunities and challenges, particularly in the areas of real-time processing and predictive analytics. This research presents a hybrid cloud-edge architecture designed to improve the efficiency and responsiveness of remote healthcare monitoring. By integrating edge computing with cloud resources, the system facilitates real-time data transfer and decision-making based on live patient met-rics. A hybrid machine learning model, combining Multi-Layer Perceptron (MLP) and XGBoost, was applied to predict key health indicators, such as heart rate, blood oxygen saturation, body temperature, and diabetes mellitus. Far-edge and near-edge devices were deployed to handle data processing at various stages, reducing latency and improving overall system performance. The results demonstrate significant improvements, with a 40% reduction in latency, a 15% increase in efficiency, and 30% improvement in throughput compared to non-DT systems. The hybrid AI model exhibited robust performance, with cross-validation accuracy reaching 97.48% and real-time accuracy measured at 95.45%. Additionally, it reduced testing time by 58.4%, highlighting its efficiency for real-time healthcare applications. This framework enhances the scalability, efficiency, and reliability of remote healthcare systems, offering a practical solution for real-time patient monitoring in healthcare applications.
NDVI and vegetation volume as predictors of urban bird diversity
Authors: Fairbairn Andrew J. , Katholnigg Sophia , Leichtle Tobias , Merkens Lisa , Schroll Louis , Weisser Wolfgang W. , Meyer Sebastian T. 
Published: November 2024

Abstract

Urban expansion and densification pose a challenge to urban biodiversity. Rapid estimation of biodiversity could help urban planners balance development and conservation goals. While the Normalised Difference Vegetation Index (NDVI) has proven useful for predicting urban bird diversity, new products derived from remote sensing, such as vegetation volume, could provide more detailed descriptions of available habitat, potentially improving biodiversity predictions. We evaluated the effectiveness of NDVI and vegetation volume as predictors of urban bird diversity and local community composition for different buffers around 86 sampling points in Munich, Germany. Using linear models, we showed that a 100 m buffer best described bird diversity (highest r2) for both NDVI and vegetation volume compared to the other buffers. Contrary to expectations, NDVI was superior to vegetation volume in predicting bird diversity (mean r2 NDVI = 0.45, mean r2 vegetation volume 0.37). We found a shift in community composition from species associated with human-modified landscapes to those associated with forests along an urban greenness gradient. In contrast to diversity, we found that vegetation volume was slightly better at predicting community composition. Using NDVI to predict bird diversity across Munich, we demonstrated its potential for predicting city-wide bird diversity. We discuss how such predictive maps can be used for urban planning and conservation. As urbanisation continues to impact global biodiversity, refining ecological models for urban planning will be crucial to developing more biodiverse urban environments.
Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation
Authors: Urfels Anton , McDonald Andrew , Ajay Anurag , Arenas-Calle Laura , Craufurd Peter , Deb Proloy , De Graaf Inge E.M. , Halsema Gerardo Van , Karki Saral , Kishore Avinash , Kumar Virender , Mkondiwa Maxwell , Rossiter David , Seijgers Chris , Nayak Hari , Sherpa Sonam , Srivast Amit , Laborte Alice 
Published: November 2024

Abstract

Groundwater irrigation supports over 40% of global crop production and stabilizes yields amidst climatic change. Yet, over-abstraction can cause water scarcity, disrupt ecosystems, and increase greenhouse gas emissions. Governments and international financial institutions have made significant investments in sustainable groundwater irrigation but require enhanced spatial targeting to increase impact. In response, this study employs an agro-hydrological machine-learning approach to analyze spatial patterns of (i) crop yield responses to increased irrigation and (ii) groundwater sustainability in South Asia – characterized by smallholder farming, increasing groundwater dependence, and post-green revolution sustainability challenges. We show that modestly increasing irrigation intensity in groundwater-rich areas with high yield responses could boost rice production by 2.22Mt annually – sufficient to feed over 33 million people with little anticipated risks of groundwater depletion. However, current investments overlook these areas. Our approach can be globally applied to catalyze sustainable irrigation through integrated use of expanding agricultural and hydrological datasets.
Hormones and science learning: New insights into adolescent motivation
Authors: Ofek-Geva Ella , Kubsch Marcus , Batushansky Albert , Mehlman Tevie , Shuster Michal Vinker , Yeshayahu Yonatan , Brandis Alexander , Fortus David 
Published: November 2024

Abstract

The decline in students' motivation to learn science during adolescence is a widespread phenomenon documented across many countries. While social and educational factors affecting this decline are well-studied, the role of pubertal physiological changes remains unexplored. Here we show that changes in dehydroepiandrosterone sulfate (DHEA-S) and testosterone levels correlate with shifts in students' science learning motivation, particularly during elementary school. Our study of elementary and junior high school students reveals that higher levels of these hormones associate with lower overall motivation in science during elementary years, primarily affecting personal motivation and perceived parental emphasis. This relationship becomes less pronounced in junior high school, suggesting a critical window for hormone-sensitive motivational changes in early adolescence. These findings bridge endocrinology and education research, demonstrating how biological development influences academic motivation during puberty. Our research provides a new framework for understanding the complex interplay between physiological changes and educational outcomes, suggesting the need for developmentally-informed approaches to maintaining students' engagement with science.
Геоисторические компоненты этнополитических концепций в идеологических доктринах коммунистических режимов Румынии, Болгарии и Албании (конец 60-х – 70-е годы ХХ в.)
Authors: Ар.А. Улунян 
Published: November 2024

Коммунистические режимы Румынии, Болгарии и Албании активно использовали концепции появления иразвития народов этих стран в своих идейно-политических целях как для реализации внутриполитических установок, так и внешнеполитических задач. Историческая география и этногенез румын, болгар и албанцев превращались в инструмент формирования национальной идентичности при активном участии властей в этом процессе. В конце 60-х начале 70-х годов ХХ в. он активизировался в силу объективных общественно-политических причин. Научные концепции становились основой геоисторических конструктов, призванных объяснить и закрепить в общественном сознании этих стран, атакже в международном мнении вполне конкретные представления как о государствах, имеющих историческое прошлое, тесно связанное не только с Балканским регионом, но иЕвропой в целом. Несмотря на особенности выдвигавшихся концепций, все они подчеркивали автохтонный характер народов трех стран и их особый вклад в европейское историческое развитие.
The communist regimes of Romania, Bulgaria and Albania actively used the concepts of the emergence and development of the peoples of these countries for their ideological and political purposes both for the implementation of domestic political objectives and foreign policy tasks. The historical geography and ethnogenesis of the Romanians, Bulgarians, and Albanians turned into an instrument of forming a national identity with the active participation of the authorities in this process. In the late 1960s early 1970s it became active for objective socio-political reasons. Scientific concepts became the basis of geohistorical constructs designed to explain and consolidate in the public consciousness of these countries, as well as in international opinion, ideas about them as states having a historical past, closely connected not only with the Balkan region, but also with Europe as a whole. Despite the particularities of the concepts put forward, they all emphasized the autochthonous nature of the peoples of the three countries and their special contribution to European historical development.