bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Polyphenolic Compounds in Different Amaranthus Species: Influence of Genotypes and Harvesting Year
Authors: Bang Jun-Hyoung , Jo Ick-Hyun , Sebastin Raveendar , Jeong Won Tea , Oh Sangtaek , Heo Tae-Young , Sung Jeehye , Hyun Tae kyung , So Yoon-Sup , Yu Ju-Kyung , AlGarawi Amal Mohamed , Hatamleh Ashraf Atef , Sung Gi-Ho , Chung Jong-Wook
Published: April 2024

Amaranth is a nutritionally valuable crop, as it contains phenolic acids and flavonoids, yielding diverse plant secondary metabolites (PSMs) like phytosterol, tocopherols, and carotenoids. This study explored the variations in the contents of seventeen polyphenolic compounds within the leaves of one hundred twenty Amaranthus accessions representing nine Amaranthus species. The investigation entailed the analysis of phenolic content across nine Amaranthus species, specifically A. hypochondriacus, A. cruentus, A. caudatus, A. tricolor, A. dubius, A. blitum, A. crispus, A. hybridus, and A. viridis, utilizing ultra performance liquid chromatography with photodiode array detection (UPLC-PDA). The results revealed significant differences in polyphenolic compounds among accessions in which rutin content was predominant in all Amaranthus species in both 2018 and 2019. Among the nine Amaranthus species, the rutin content ranged from 95.72 ± 199.17 μg g−1 (A. dubius) to 1485.09 ± 679.51 μg g−1 (A. viridis) in 2018 and from 821.59 ± 709.95 μg g−1 (A. tricolor) to 3166.52 ± 1317.38 μg g−1 (A. hypochondriacus) in 2019. Correlation analysis revealed, significant positive correlations between rutin and kaempferol-3-O-β-rutinoside (r = 0.93), benzoic acid and ferulic acid (r = 0.76), and benzoic acid and kaempferol-3-O-β-rutinoside (r = 0.76), whereas gallic acid showed consistently negative correlations with each of the 16 phenolic compounds. Wide variations were identified among accessions and between plants grown in the two years. The nine species and one hundred twenty Amaranthus accessions were clustered into six groups based on their seventeen phenolic compounds in each year. These findings contribute to expanding our understanding of the phytochemical traits of accessions within nine Amaranthus species, which serve as valuable resources for Amaranthus component breeding and functional material development.
Online Model Adaption for Energy Management in Fuel Cell Electric Vehicles (FCEVs)
Authors: Novella Ricardo , Plá Benjamín , Bares Pau , Pinto Douglas
Published: April 2024

The growing interest in low-impact mobility technologies has elevated the significance of fuel cell electric vehicles (FCEVs) in the automotive sector. Given the complexity of the resulting powertrain, the need for an effective energy management strategy (EMS) becomes essential to optimize efficiency and energy consumption in vehicles with diverse energy sources. Model-based control is the main approach to address the EMS in electrified vehicles. In particular, fuel cell power is commonly represented through a 1D look-up table using the current demand as input to simplify the implementation in a vehicle control unit. Uncertainties that may be implemented in maps due to simplifying hypotheses, dynamics, ageing, etc., can be propagated to powertrain control, motivating the adoption of adaptive look-up tables for FC modelling. In this study, an extended Kalman filter (EKF) is proposed to adapt the look-up table to actual FC behaviour by measuring its power and gradually correcting calibration errors, drift, and ageing. Subsequently, a standard equivalent consumption minimization strategy (ECMS) is employed to control the FCEV. The fuel cell model is calibrated with experimental data from an FCEV. The results demonstrate that the adaptive strategy outperforms the base calibration. Following an extensive simulation campaign, an improvement of 1.1% in fuel consumption was observed. Remarkably, after just one hour of operation, there was a notable 85% reduction in fuel cell power estimation error, even when the EMS was initially fed a biased look-up table.
Refinement and Computation Method for Line/Body Topological Relationships
Authors: Zhou Xiaoguang , Wang Xiaohan , Hou Dongyang , Kang Qiankun , Ali Nawaz
Published: April 2024

Three-dimensional topological relationships serve as a theoretical foundation for quality control, update processing, and spatial analysis of three-dimensional spatial data in real-world three-dimensional GIS. The existing 3D topological relationship models are all basic relationship models that cannot distinguish the refined topological relationship between the line and the body with multiple intersections. In this study, we develop a 3D refined topological relationship description framework that draws from the two-dimensional refined topological relationship model, defines the unit intersection between the line and the body based on manifold topology, and proposes a method for describing the unit intersections between the line and the body considering Euler numbers and adjacency types. In total, 23 basic types between the line and the body are deduced. An example is provided to illustrate the distinguished refined topological relationship between the line and the body with multiple intersections. Subsequently, an algorithm for determining the basic type of line/body is developed. Finally, a line/body refined topological relationship computation prototype system is developed using the Nef polyhedron model, C++ language, and an open-source geometric algorithm library, and the effectiveness of our method is verified using actual building and pedestrian data.
Scaling Implicit Bias Analysis across Transformer-Based Language Models through Embedding Association Test and Prompt Engineering
Authors: Bevara Ravi Varma Kumar , Mannuru Nishith Reddy , Karedla Sai Pranathi , Xiao Ting
Published: April 2024

In the evolving field of machine learning, deploying fair and transparent models remains a formidable challenge. This study builds on earlier research, demonstrating that neural architectures exhibit inherent biases by analyzing a broad spectrum of transformer-based language models from base to x-large configurations. This article investigates movie reviews for genre-based bias, which leverages the Word Embedding Association Test (WEAT), revealing that scaling models up tends to mitigate bias, with larger models showing up to a 29% reduction in prejudice. Alternatively, this study also underscores the effectiveness of prompt-based learning, a facet of prompt engineering, as a practical approach to bias mitigation, as this technique reduces genre bias in reviews by more than 37% on average. This suggests that the refinement of development practices should include the strategic use of prompts in shaping model outputs, highlighting the crucial role of ethical AI integration to weave fairness seamlessly into the core functionality of transformer models. Despite the basic nature of the prompts employed in this research, this highlights the possibility of embracing structured prompt engineering to create AI systems that are ethical, equitable, and more responsible for their actions.
Method for the Experimental Identification of Variables and Configurations That Modify the Shape of the Macroscopic Fundamental Diagram
Authors: Carrillo-González José Gerardo , López-Maldonado Guillermo
Published: April 2024

In this paper, we propose a method for establishing if a variable is capable of modifying the Macroscopic Fundamental Diagram (MFD) of a street network. The variables have many different configurations, and a simulation is performed for each one. Then, based on the output data of each simulation, the representative speed, density, and flow of the network are calculated. We use three metrics to establish if a variable affects the MFD: the first establishes a distance between the compared density and speed patterns, the second establishes a distance between capacities, and the third establishes a distance between critical densities. We select four variables to test our method: the precision of driving, the vehicles’ top speeds distribution, the procedure for selecting routes, and the procedure for selecting destinations; we determine whether each of these variables can modify the MFD shape. Additionally, we detect which configurations of a variable are able to reach and exceed the critical density (causing congestion) so we can establish which configurations are sustainable and which are not. The novelties of this work are twofold: (1) we introduce a method to detect if a variable modifies the MFD; (2) we establish if the selected variables modify the MFD.
Selected Plants as Sources of Natural and Active Ingredients for Cosmetics of the Future
Authors: Kowalczyk Sławomir , Grymel Mirosława , Bilik Janusz , Kula Wiktoria , Wawoczny Agata , Grymel Paulina , Gillner Danuta
Published: April 2024

A clear trend of replacing synthetic cosmetic ingredients with natural ones can be observed in modern cosmetology. This entails the need to search for bioactive ingredients in the natural environment, especially in plants. This paper presents a comprehensive overview of dermatological, cosmetic, and pharmacological properties of highly potent plants, namely Acmella oleracea (A. oleracea), Centella asiatica (C. asiatica), Psoralea corylifolia (P. corylifolia), Plantago lanceolata L. (P. lanceolata L.), and Solidago virgaurea L. (S. virgaurea L.). Biological activity and phytochemical constituents are presented for all plants, but special attention is paid to ingredients of particular value to the cosmetics industry. The advantages of spilanthol and bakuchiol as a replacement for the popular botulinum toxin and retinol are discussed. Natural habitats, ethnomedical importance, cultivation area, as well as extraction methods of active plant ingredients are presented in detail. A wide spectrum of biological activity indicates the enormous potential of the presented plants in formulating new cosmetic and dermatological preparations.
Design of Series-Fed Circularly Polarized Beam-Tilted Antenna for Microwave Power Transmission in UAV Application
Authors: Park Mok Yoon , Kim Jun Hee , Yi Sang-hwa , Lim Wonseob , Yang Youngoo , Hwang Keum Cheol
Published: April 2024

In response to the increasing deployment of unmanned aerial vehicles (UAVs) across various sectors, the demand for efficient microwave power transmission (MPT) systems for UAVs has become paramount. This study introduces series-fed circularly polarized (CP) and passively beam-tilted patch array antennas designed to enhance MPT in UAV applications, with the intention of addressing the needs related to extending flight times and improving operational efficiency. The radiating element of the proposed antennas employs the conventional model of the patch with truncated corners for CP operation, with transmission line lengths optimized for beam tilt to ensure precise energy transfer. Additionally, an open stub is integrated into the broadside series-fed antenna to improve impedance matching, which is crucial for maintaining signal integrity. The proposed design achieves right-hand circular polarization (RHCP) with an axial ratio (AR) below 3 dB across the operating band, indicative of its effectiveness in diverse UAV operational contexts. Prototypes of each proposed antenna were fabricated and measured according to the beam tilting angle. The measured RHCP realized gains of the proposed antennas are 14.59, 13.09, 13.07, and 10.71 dBic at the tilted angles of 0°, 15°, 30°, and 45°, respectively, at 5.84 GHz.
Two-Dimensional Ultra Light-Weight Infant Pose Estimation with Single Branch Network
Authors: Nguyen Viet Dung , Nguyen-Quang Thinh , Nguyen Minh Duc , Phan Dang Hung , Bui Ngoc Dung
Published: April 2024

Motivated by the increasing interest in clinical studies focused on infant movements and poses, this research addresses the limited emphasis on speed and efficiency in existing 2D and 3D pose estimation methods, particularly concerning infant datasets. The scarcity of publicly available infant data poses a significant challenge. In response, we aim to develop a lightweight pose estimation model tailored for edge devices and CPUs. Drawing inspiration from the OpenPose-2016 approach, we refine the algorithm’s architecture, focusing on 2D image training. The resulting model, with 4.09 million parameters, features a single-branch structure. During execution, it achieves an algorithmic complexity of 8.97 giga floating-point operations per second (GFLOPS), enabling operation at approximately 23 frames per second on a Core i5-10400f processor.Notably, this approach balances compact dimensions with superior performance on our self-collected infant dataset. We anticipate that this pragmatic methodology establishes a robust foundation, addressing the need for speed and efficiency in infant pose estimation and providing favorable conditions for future research in this application.
Efficient Eye State Detection for Driver Fatigue Monitoring Using Optimized YOLOv7-Tiny
Authors: Chang Gwo-Ching , Zeng Bo-Han , Lin Shih-Chiang
Published: April 2024

This study refines the YOLOv7-tiny model through structured pruning and architectural fine-tuning, specifically for real-time eye state detection. By focusing on enhancing the model’s efficiency, particularly in environments with limited computational resources, this research contributes significantly to advancing driver monitoring systems, where timely and accurate detection of eye states such as openness or closure can prevent accidents caused by drowsiness or inattention. Structured pruning was utilized to simplify the YOLOv7-tiny model, reducing complexity and storage requirements. Subsequent fine-tuning involved adjustments to the model’s width and depth to further enhance processing speed and efficiency. The experimental outcomes reveal a pronounced reduction in storage size, of approximately 97%, accompanied by a sixfold increase in frames per second (FPS). Despite these substantial modifications, the model sustains high levels of precision, recall, and mean average precision (mAP). These improvements indicate a significant enhancement in both the speed and efficiency of the model, rendering it highly suitable for real-time applications where computational resources are limited.
Removal of Contaminants in Water with Scallop Shell Waste
Authors: Sore Pablo Zavala , Mera Adriana C. , Concepción Armando Díaz , Rivera José Luis Valin , Valin Fernández Meylí , Navarrete Rojas Carlos Arturo , Alfonso-Alvarez Alexander
Published: April 2024

Currently, the cultivation and harvesting of mollusks is a crucial activity worldwide. However, this industry generates a large amount of mollusk shell waste disposed of in landfills, causing environmental pollution. In addition, the companies linked to this item allocate large sums of money to depositing the shells in authorized landfills. In South America, Chile is one of the leading producers worldwide of scallop shell (Argopecten purpuratus) waste, creating a growing environmental and financial problem in the country, especially considering that there has yet to be progress in the development of new technologies that may reuse this waste in Chile. This study used different techniques to completely characterize the northern Chile scallop shell waste’s physical and chemical properties for the first time. The XRD result corresponded with calcite crystal structures (CaCO3), and the XFR showed 97.68% purity. Three particle sizes were obtained: BS (595–100 µm), MS (250–595 µm), and SS (<250 µm). In addition, the potential use of these wastes to remove contaminants present in water from the wine industry (caffeic acid) and some drinking water (arsenic(III)) was evaluated. The powder with the smallest particle size (SS), which has a surface area of 1 m2/g, 0.0050 m3/g of pore volume and pore diameter of 18.0 nm, removed 100.0% of CA and 23.0% As(III) in a pH condition of 4.6. The results show that scallop shell waste can be used to treat water and reinforce polymeric matrix composite materials to improve mechanical properties.
Hub Airport End-Around Taxiway Construction Planning Development: A Review
Authors: Jiang Xiaoxi , Hao Peiwen
Published: April 2024

Hub airports typically have multiple parallel runways, requiring aircraft to transfer between them. This increases the risk of runway incursions. End-around taxiways (EATs) mitigate such risk by enabling bypassing without runway crossings. This review summarizes 15 EAT layouts worldwide and presents two classification methods: by configuration (N-type, M-type, Large N-type) and by operational relationship with runways (back-around, runway end-around, start-around). The key benefits of EATs were manifold, including the reduction of runway incursions, controller workload, and delays while improving communication safety, capacity, and efficiency. However, drawbacks such as increased land use, construction costs, and potentially longer taxi times and distances should be considered. Therefore, the optimization of the EAT layout is essential. In pursuit of optimal design, considerations should encompass limiting obstacle surfaces, flight procedures, navigation/lighting facilities, jet blast, and visual obstructions from end-around aircraft to departing aircraft. Notably, challenges arise in reducing distances between EATs and thresholds/ends. The given solutions include displacing thresholds, creating “sunken” lowered elevation EATs, and utilizing terrain shielding instead of metallic visual screens. A case study was introduced to demonstrate the successful improvement of the EAT via sunken EATs and terrain shielding at Guiyang Airport. The conclusion encourages further optimization of EAT layouts that balance safety and efficiency.
The Effect of Laser Settings and Ureteral Access Sheath Size on Intrapelvic Temperature during Holmium Laser Lithotripsy
Authors: Balawender Krzysztof , Dybowski Bartosz
Published: April 2024

Introduction: Thermal damage to the kidney during holmium laser lithotripsy is a serious complication, which cannot always be prevented considering the diversity of conditions in the kidney and the lack of technical capability to measure intrarenal temperature in real-time. The aim of this study was to analyze the impact of different power settings of the holmium laser and the flow rate of irrigation fluid conditioned by the size of the sheath on the fluid temperature in a model of the pelvicalyceal system during lithotripsy. Materials and Methods: A lithotripsy of artificial stones was performed in a 3D-printed model of the renal pelvicalyceal system with a volume of 20.5 mL. A gravity-driven fluid flow was applied (p = 60 cm H2O). A 9.2Fr ureteroscope was introduced into the model through a ureteral access sheath of either 10/12Fr or 12/14Fr. Lithotripsy power ranged from 12 to 25 W. Temperature was measured using thermography. Results: For the 10/12Fr ureteral access sheath, the fluid flow rate was 2.7 mL/min, with the maximum temperature reached within 20 min ranging from 45 to 50 °C. For the 12/14Fr ureteral access sheath, the fluid flow rate was 9.8 mL/min, with the achieved temperature ranging from 35 to 45 °C. Laser power influenced the rate of temperature increase and the maximum temperature reached. During the first 5 min, the rate of temperature increase was solely dependent on laser power, not on the flow rate. Temperatures of 40 °C were only not exceeded with high flow rates (12/14Fr sheath) and power below 20 W. Conclusions: In a short period, the rate of temperature increase depends solely on laser power, not on the irrigation flow rate. During prolonged lithotripsy, the maximum temperature value depends on both parameters.
A Meta-Analysis of the Effect of Moisture Content of Recycled Concrete Aggregate on the Compressive Strength of Concrete
Authors: Cho Sung-Won , Cho Sung Eun , Brand Alexander S.
Published: April 2024

To reduce the environmental impact of concrete, recycled aggregates are of significant interest. Recycled concrete aggregate (RCA) presents a significant resource opportunity, although its performance as an aggregate in concrete is variable. This study presents a meta-analysis of the published literature to refine the understanding of how the moisture content of RCA, as well as other parameters, affects the compressive strength of concrete. Seven machine learning models were used to predict the compressive strength of concrete with RCA, including linear regression, support vector regression (SVR), and k-nearest neighbors (KNN) as single models, and decision tree, random forest, XGBoost, and LightGBM as ensemble models. The results of this study demonstrate that ensemble models, particularly the LightGBM model, exhibited superior prediction accuracy compared to single models. The LightGBM model yielded the highest prediction accuracy with R2 = 0.94, RMSE = 4.16 MPa, MAE = 3.03 MPa, and Delta RMSE = 1.4 MPa, making it the selected final model. The study, employing feature importance with LightGBM as the final model, identified age, water/cement ratio, and fine RCA aggregate content as key factors influencing compressive strength in concrete with RCA. In an interaction plot analysis using the final model, lowering the water–cement ratio consistently improved compressive strength, especially between 0.3 and 0.4, while increasing the fine RCA ratio decreased compressive strength, particularly in the range of 0.4 to 0.6. Additionally, it was found that maintaining moisture conditions of RCA typically between 0.0 and 0.8 was crucial for maximizing strength, whereas extreme moisture conditions, like fully saturated surface dry (SSD) state, negatively impacted strength.
Research on Optimization of an Open-Bench Deep-Hole Blasting Parameter Using an Improved Gray Wolf Algorithm
Authors: Zhao Li , Su Dengfeng , Li Zhengguo , Chen Banghong , Wang Rui , Chen Rongkai
Published: April 2024

The blasting quality of open-pit mining can be enhanced and the production cost of stope reduced by establishing a mathematical model for step drilling and blasting costs based on stope consumption. By enhancing the Gray Wolf algorithm, the parameters for step drilling and blasting are optimized, resulting in improved effectiveness for step blasting mining, as demonstrated through modeling and calculation. The enhanced Gray Wolf algorithm effectively enhances the blasting performance, reduces production costs, and increases production efficiency. Taking a limestone mine as an example, the optimized drilling and blasting parameters are as follows: hole spacing of 4.62 m, row spacing of 4 m, and explosive consumption rate of 0.23 kg/t; based on these parameters, the stope’s production cost is reduced to CNY 7.7.
Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
Authors: Romero Raphaël , Buyl Maarten , De Bie Tijl , Lijffijt Jefrey
Published: April 2024

Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pipelines usually calculate ranking or binary classification metrics, where the scores of observed interactions (positives) are compared with those of randomly generated ones (negatives). However, a single metric is not sufficient to fully capture the differences between DLP algorithms, and is prone to overly optimistic performance evaluation. Instead, an in-depth evaluation should reflect performance variations across different nodes, edges, and time segments. In this work, we contribute tools to perform such a comprehensive evaluation. (1) We propose Birth–Death diagrams, a simple but powerful visualization technique that illustrates the effect of time-based train–test splitting on the difficulty of DLP on a given dataset. (2) We describe an exhaustive taxonomy of negative sampling methods that can be used at evaluation time. (3) We carry out an empirical study of the effect of the different negative sampling strategies. Our comparison between heuristics and state-of-the-art memory-based methods on various real-world datasets confirms a strong effect of using different negative sampling strategies on the test area under the curve (AUC). Moreover, we conduct a visual exploration of the prediction, with additional insights on which different types of errors are prominent over time.
Can Windows 11 Stop Well-Known Ransomware Variants? An Examination of Its Built-in Security Features
Authors: Al-Awadi Yousef Mahmoud , Baydoun Ali , Ur Rehman Hafeez
Published: April 2024

The ever-evolving landscape of cyber threats, with ransomware at its forefront, poses significant challenges to the digital world. Windows 11 Pro, Microsoft’s latest operating system, claims to offer enhanced security features designed to tackle such threats. This paper aims to comprehensively evaluate the effectiveness of these Windows 11 Pro, built-in security measures against prevalent ransomware strains, with a particular emphasis on crypto-ransomware. Utilizing a meticulously crafted experimental environment, the research adopted a two-phased testing approach, examining both the default and a hardened configuration of Windows 11 Pro. This dual examination offered insights into the system’s inherent and potential defenses against ransomware threats. The study’s findings revealed that Windows 11 Pro does present formidable defenses. This paper not only contributes valuable insights into cybersecurity, but also furnishes practical recommendations for both technology developers and end-users in the ongoing battle against ransomware. The significance of these findings extends beyond the immediate evaluation of Windows 11 Pro, serving as a reference point for the broader discourse on enhancing digital security measures.
Automatic Object Detection in Radargrams of Multi-Antenna GPR Systems Based on Simulation Data for Railway Infrastructure Analysis
Authors: Lahnsteiner Lukas , Größbacher David , Bürger Martin , Zauner Gerald
Published: April 2024

Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth—accurate real-world data used to validate machine learning models—thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations.
Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism
Authors: Piao Yinghao , Zhang Jin-Xi
Published: April 2024

In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks.
Hypergraph Position Attention Convolution Networks for 3D Point Cloud Segmentation
Authors: Rong Yanpeng , Nong Liping , Liang Zichen , Huang Zhuocheng , Peng Jie , Huang Yiping
Published: April 2024

Point cloud segmentation, as the basis for 3D scene understanding and analysis, has made significant progress in recent years. Graph-based modeling and learning methods have played an important role in point cloud segmentation. However, due to the inherent complexity of point cloud data, it is difficult to capture higher-order and complex features of 3D data using graph learning methods. In addition, how to quickly and efficiently extract important features from point clouds also poses a great challenge to the current research. To address these challenges, we propose a new framework, called hypergraph position attention convolution networks (HGPAT), for point cloud segmentation. Firstly, we use hypergraph to model the higher-order relationships among point clouds. Secondly, in order to effectively learn the feature information of point cloud data, a hyperedge position attention convolution module is proposed, which utilizes the hyperedge–hyperedge propagation pattern to extract and aggregate more important features. Finally, we design a ResNet-like module to reduce the computational complexity of the network and improve its efficiency. We have conducted point cloud segmentation experiments on the ShapeNet Part and S3IDS datasets, and the experimental results demonstrate the effectiveness of the proposed method compared with the state-of-the-art ones.
Optimization of Well Placement in Carbon Capture and Storage (CCS): Bayesian Optimization Framework under Permutation Invariance
Authors: Fotias Sofianos Panagiotis , Ismail Ismail , Gaganis Vassilis
Published: April 2024

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations being already an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placement in CCS operations, employing a derivative-free method known as Bayesian Optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian Optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian Optimization in the context of optimizing well placement for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.
Emerging Technologies for Automation in Environmental Sensing: Review
Authors: Borah Shekhar Suman , Khanal Aaditya , Sundaravadivel Prabha
Published: April 2024

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.
Resonating Reflections: A Critical Review of Ethnosymbolic Dynamics in Les Six’s Music Nationalism Movement
Authors: Chang Xuewei , Salleh Marzelan Bin , Sun Jifang
Published: April 2024

Les Six and their mentors stirred a debatement of French nationalist music in the early 20th century. However, this movement faced serious criticism and mockery from various quarters and eventually fell apart amid challenges. This critical review explores the ethnosymbolic dynamics within the nationalism music movement of Les Six, and drawing upon ethnomusicological perspectives, the study examines how their compositions reflected and resonated with French national identity and cultural heritage. By analyzing primary sources, scholarly literature, and musical compositions, this article meticulously uncovers the chain reactions generated in the process of constructing national identity and cultural identity within this movement by examining the French societal backdrop, musical traditions, as well as the relationships and attitudes among relevant figures in this movement. The conclusions highlight the multifaceted nature of ethnosymbolism in their work, shedding light on the complexities of national identity construction through music.
Meteor Radar for Investigation of the MLT Region: A Review
Authors: Reid Iain M.
Published: April 2024

This is an introductory review of modern meteor radar and its application to the measurement of the dynamical parameters of the Mesosphere Lower Thermosphere (MLT) Region within the altitude range of around 70 to 110 km, which is where most meteors are detected. We take a historical approach, following the development of meteor radar for studies of the MLT from the time of their development after the Second World War until the present. The application of the meteor radar technique is closely aligned with their ability to make contributions to Meteor Astronomy in that they can determine meteor radiants, and measure meteoroid velocities and orbits, and so these aspects are noted when required. Meteor radar capabilities now extend to measurements of temperature and density in the MLT region and show potential to be extended to ionospheric studies. New meteor radar networks are commencing operation, and this heralds a new area of investigation as the horizontal spatial variation of the upper-atmosphere wind over an extended area is becoming available for the first time.
Bone-Anchored Hearing Aid Effects on Vestibular Function: A Preliminary Report
Authors: Pollastri Federica , Giannoni Beatrice , Marcelli Vincenzo , Spadavecchia Giulia , Pecci Rudi
Published: April 2024

Objective: Cochlear receptors are sensitive to vibratory stimuli. Based on this sensibility, bone-anchored hearing aids have been introduced to correct unilateral or bilateral conductive or mixed hearing loss and unilateral deafness. The vestibular system is also sensitive to the vibratory stimulus and this type of response is used in clinics to test its functionality. Being aware of this double separated sensibility, we wondered whether bone vibration, which activates the acoustic receptors of patients with bone conduction aids, can also influence the functionality of the vestibular system. Methods: To this end, we recruited 12 patients with a bone-anchored hearing aid and evaluated their vestibular function with and without an activated vibratory acoustic device. Results: Our results show that the vibratory stimulus delivered by the bone conduction aid also reaches and stimulates the vestibular receptors; this stimulation is evidenced by the appearance or modification of some nystagmus findings during bedside vestibular testing. Despite this, none of these patients complained of dizziness or vertigo during prosthesis use. Nystagmus that appeared or changed during acoustic vibratory stimulation through the prosthesis was almost all predominantly horizontal, unidirectional with respect to gaze or body position, inhibited by fixation, and most often consistent with vestibular function tests indicating peripheral vestibular damage. Conclusions: The findings of sound-evoked nystagmus seem to indicate peripheral rather than central vestibular activation. The occurrence of some predominantly horizontal and high-frequency induced nystagmus seems to attribute the response mainly to the utricle and lateral semicircular canal.
Monotonic Random Variables According to a Direction
Authors: Quesada-Molina José Juan , Úbeda-Flores Manuel
Published: April 2024

In this paper, we introduce the concept of monotonicity according to a direction for a set of random variables. This concept extends well-known multivariate dependence notions, such as corner set monotonicity, and can be used to detect dependence in multivariate distributions not detected by other known concepts of dependence. Additionally, we establish relationships with other known multivariate dependence concepts, outline some of their salient properties, and provide several examples.
Evidential-Reasoning-Type Multi-Attribute Large Group Decision-Making Method Based on Public Satisfaction
Authors: Cai Chenguang , Wang Yuejiao , Wang Pei , Zou Hao
Published: April 2024

To address public participation-oriented, large group decision-making problems with uncertain attribute weights, we propose a multi-attribute decision-making method considering public satisfaction. Firstly, a large group is organized to provide their opinions in the form of linguistic variables. Public opinions can be categorized into two types based on their content: one reflects the effectiveness of an alternative implementation and the other reflects the public expectations. Secondly, the two types of public opinions are sorted separately by linguistic variables. The evaluation of alternatives and the evaluation of expectations in different attributes are determined, both of which are expressed in the form of linguistic distributions. These two evaluations are then compared to determine the public satisfaction of the attributes in different alternatives. Thirdly, based on the deviation of public satisfaction in different attributes, a weight optimization model is constructed to determine the attribute weights. Fourthly, leveraging the interval credibility of attribute satisfaction for various alternatives, an evidential reasoning non-linear optimization model is established to obtain the comprehensive utility evaluation value for each alternative, which is used for ranking. Finally, a numerical example is employed to validate the feasibility and effectiveness of the proposed approach. According to the results of the numerical example, it can be concluded that the proposed approach can be effectively applied to large group decision-making problems that consider public satisfaction. Based on the comparison of methods, the proposed approach has certain advantages in reflecting public opinions and setting reference points, which can ensure the reliability of the decision results.
A Novel Differentiated Control Strategy for an Energy Storage System That Minimizes Battery Aging Cost Based on Multiple Health Features
Authors: Xiao Wei , Jia Jun , Zhong Weidong , Liu Wenxue , Wu Zhuoyan , Jiang Cheng , Li Binke
Published: April 2024

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, and the operating boundaries for both depth of discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.
Biomechanical Considerations in the Orthodontic Treatment of a Patient with Stabilised Stage IV Grade C Generalised Periodontitis: A Case Report
Authors: Sum Fung Hou Kumoi Mineaki Howard , Shan Zhiyi , Chan Yat Him Dave , Chu Ryan Julian Dick Hei , Pelekos George , She Tsang Tsang
Published: April 2024

Orthodontic treatment of periodontally compromised patients presents unique challenges, including controlling periodontal inflammation, applying appropriate force, designing an effective dental anchorage, and maintaining treatment results. Deteriorated periodontal support leads to alterations in the biological responses of teeth to mechanical forces, and thus orthodontists must take greater care when treating patients with periodontal conditions than when treating those with a good periodontal status. In this article, we report the case of a 59-year-old woman with stabilised Stage IV grade C generalised periodontitis characterised by pathological tooth migration (PTM). The assessment, planning, and treatment of this patient with orthodontic fixed appliances is described. Moreover, the anchorage planning and biomechanical considerations are detailed. Specific orthodontic appliances were employed in this case to produce force systems for achieving precise tooth movement, which included a cantilever, mini-screws, and a box loop. Careful application of those appliances resulted in satisfactory aesthetic and functional orthodontic outcomes in the patient. This case highlights the importance of multidisciplinary collaboration in the treatment of patients with severe periodontitis and the potential for tailored biomechanical approaches in orthodontic treatment to furnish good outcomes.
An Investigation of Running Kinematics with Recovered Anterior Cruciate Ligament Reconstruction on a Treadmill and In-Field Using Inertial Measurement Units: A Preliminary Study
Authors: Hill Matteo , Kiesewetter Pierre , Milani Thomas L. , Mitschke Christian
Published: April 2024

Anterior cruciate ligament reconstruction (ACLR) may affect movement even years after surgery. The purpose of this study was to determine possible interlimb asymmetries due to ACLR when running on a treadmill and in field conditions, with the aim of contributing to the establishment of objective movement assessment in real-world settings; moreover, we aimed to gain knowledge on recovered ACLR as a biomechanical risk factor. Eight subjects with a history of unilateral ACLR 5.4 ± 2.8 years after surgery and eight healthy subjects ran 1 km on a treadmill and 1 km on a concrete track. The ground contact time and triaxial peak tibial accelerations were recorded using inertial measurement units. Interlimb differences within subjects were tested and compared between conditions. There were no significant differences between limbs in the ACLR subjects or in healthy runners for any of the chosen parameters on both running surfaces. However, peak tibial accelerations were higher during field running (p-values < 0.01; Cohen’s d effect sizes > 0.8), independent of health status. To minimize limb loading due to higher impacts during field running, this should be considered when choosing a running surface, especially in rehabilitation or when running with a minor injury or health issues.
How the Effect of Virtual Reality on Cognitive Functioning Is Modulated by Gender Differences
Authors: Righi Stefania , Gavazzi Gioele , Benedetti Viola , Raineri Giulia , Viggiano Maria Pia
Published: April 2024

Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a computer, we can concentrate, enter a state of flow, and still maintain control over our surrounding world. Differently, VR is a very immersive experience which could be a challenge for our ability to allocate divided attention to the environment to perform executive functioning tasks. This may also have a different impact on women and men since gender differences in both executive functioning and the immersivity experience have been referred to by the literature. The present study aims to investigate cognitive multitasking performance as a function of (1) virtual reality and computer administration and (2) gender differences. To explore this issue, subjects were asked to perform simultaneous tasks (span forward and backward, logical–arithmetic reasoning, and visuospatial reasoning) in virtual reality via a head-mounted display system (HDMS) and on a personal computer (PC). Our results showed in virtual reality an overall impairment of executive functioning but a better performance of women, compared to men, in visuospatial reasoning. These findings are consistent with previous studies showing a detrimental effect of virtual reality on cognitive functioning.