bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Lithium-Ion Battery Fast Charging Algorithm Based on Electrochemical Model: Experimental Results
Authors: Anwar Sohel , Pramanik Sourav , Amini Ali 
Published: January 2025

Abstract
Lithium-Ion batteries have become the principal battery technology for EVs to date. However, one of the principal factors limiting the widespread usage of the EVs is the length of charging times for the lithium-ion battery packs. The appropriate charging algorithm is critical to shorten the battery charging times while keeping the battery safe. In our earlier work, we proposed a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model using A performance index that aimed at achieving a faster charging rate while maintaining safe limits for various battery parameters. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal charging algorithm as opposed to the conventional equivalent circuit models. Simulation results showed that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium-ion cell by as much as 30% when compared with the standard constant current charging. Here we present the results from a number of experiments using Lithium-Ion cylindrical cells that were charged using the proposed algorithm and compared the charging times with the standard constant current-constant voltage (CC-CV) charging algorithms. A Maccor Series 4300 battery testing system was used to carry out the experiments. The experimental results showed that the proposed algorithm offered shorter charging times by up to 16% when compared to the CC-CV charging algorithms under the same battery initial conditions such as SOC and temperature of the cells.
Development of a Microfluidic Device for Larval Zebrafish Culture to Understand the Connection Between Microbiome and Neurological Disease
Authors: Nath Tushar , Tan Hua , Coffin Allison , Rudman Seth 
Published: January 2025

Abstract
Small multicellular model organisms such as zebrafish (Danio rerio) have significantly advanced modern biomedical research methodologies. The substantial genetic and physiological similarities of zebrafish to other animals in the Chordata phylum, including humans, and a fast generation time, makes them an excellent model system for many biomedical studies. Despite progress using zebrafish as a model organism, challenges persist in precisely manipulating the aquatic environment of zebrafish embryos or larvae under different conditions. In recent decades, microfluidic technologies have revolutionized experimental setups by offering precise control over fluid dynamics and material exchange in the fluid systems. These advancements have paved the way for the development of microfluidic fish-on-a-chip (FOC) devices tailored for embryonic and larval zebrafish studies. This study focuses on the fabrication of a novel FOC device designed for culturing zebrafish larvae to investigate the influence of the microbiome on neurodegenerative conditions, hearing loss, and neuropsychiatric disorders. Our device enables controlled manipulations of the microbiome in the fish larva, including precise probiotic treatments, essential for quantitative understanding of the impact of microbiomes on host physiology and disease. Fabrication of the device involved soft lithography with a replica molding technique, resulting in a structure composed of a flat glass substrate and polydimethylsiloxane (PDMS) fluidic channels. The device comprises 24 culturing chambers with separate inlet and outlet channels to minimize cross-contamination among chambers, integrating bacterial culture systems for precise dosing within each chamber. Design specifications were realized using SolidWorks CAD software, with the Delrin mold produced through computerized numerical control (CNC) machining. The PDMS replica was bonded to the glass substrate using oxygen plasma treatment to ensure a leak-proof assembly. Optimal device integrity was achieved through meticulous parameter adjustments during plasma treatment. The device dimensions were checked against the design for validating the accuracy of the fabrication process. Variation of experimental flowrate through the device with change in the hydrostatic pressure in the liquid reservoir was compared against analytical solutions to validate device performance. Also, the initial growth rate of embryos in selected chambers of the device were compared against the controls.
Optimized Cutting Ball Design for a Subsea Valve
Authors: Shenoy Mahesh , Bansal Gaurav , Abnave Vivek , Esenyyazov Hemra 
Published: January 2025

Abstract
Subsea valves are an integral part of the in-riser landing strings that are used for test and cleanup operations from floating rigs. The ball valve is used as a controlled barrier to secure the well, by containing wellbore fluid flow at the subsea blowout preventer (BOP). The ball valve needs to provide cutting capability while still maintaining the sealing integrity even when intervention media such as wireline, slickline or coiled tubing reside across the valve.
In the past, incorporating the shear and seal mechanism using a floating ball valve was especially difficult in the limited available radial space around the tubing inside the riser. Alternative solutions such as using a separate cutter module for the shearing function have not always been feasible due to the length constraints imposed by the BOP spaceout. Therefore, the ball design needs to be optimal to cut the largest size of tubing, while still ensuring that the seals and associated interfaces are not compromised.
This paper focuses on the development of a cutting ball that can shear different wireline, slickline, and coiled tubing media, while maintaining the seal integrity. The sealing functionality is heavily governed by the localized damage characteristics of the cutting edges. This is a function of the intrinsic material properties of the cutting surfaces and the intervention media sheared. Other factors such as geometry and cutting efficiency also play an important role. The paper provides a solution by using a combination of experiments and computational simulations to quantify the influence of these parameters on the cutting efficiency and design an optimal shear and seal valve. Parameters that are most relevant to cutting and sealing functionalities were characterized to design an optimal valve mechanism with improved reliability and performance.
Enhancing Sound Absorption of Curvature Damped Membrane With Heterogenous Microperforated Panel
Authors: Kumar Raja , Mohanty Amiya Ranjan 
Published: January 2025

Abstract
Integrating a membrane layer atop microperforated panels (MPP) prevents dust accumulation within the pores, thus preserving their acoustic performance. The induced tension and curvature-induced damping of the membrane on the fixed rim affect the sound absorption. The sound absorption caused by the MPP mostly results from thermal and viscous losses occurring in an array of micrometer-sized holes. The objective of this study is to develop a multilayer sound absorber with a damped membrane coupled with a heterogenous microperforated panel using the two-point impedance with a graph theory-based approach. Further, the effect of the influencing parameters of the membrane, i.e., tension, surface density, damping coefficient of the membrane, and hole diameter, thickness, and perforation porosity of the MPP, is studied. The parametric studies showed how to select the design for a particular frequency to target. The findings demonstrated that including the membrane significantly improved the acoustic characteristics. The presence of air cavities between the membrane and MPP, as well as between MPP and the rigid wall, determines the extent to which the sound absorption peak is shifted.
Evaluating the Aerodynamics of a Rooftop Greenhouse
Authors: van Ginkel Cailin , Kruger Sunita , Pretorius Leon 
Published: January 2025

Abstract
In this article the aerodynamics of a rooftop greenhouse is evaluated using Computational Fluid Dynamics (CFD). This entails using a scale model and wind tunnel tests, as well as understanding the effects of design changes of the rooftop greenhouse on the aerodynamics. A wind tunnel test was conducted using a reduced scale model based on the full-scale rooftop greenhouse. Using these experimental results, a numerical model was developed in Star-CCM+ and the flow around the rooftop greenhouse was simulated. Once the numerical model results were found to be in good comparison to the experimental results, the greenhouse was redesigned to improve the aerodynamics. A numerical model was developed for the redesigned greenhouse and simulated again using CFD. The results indicated a reduction in the high-pressure points that were seen in the original model and less circulating patterns occurred which reduces the damage potential on the greenhouse due to wind flow. This article therefore provides insight into the design of a greenhouse to withstand high pressure from the wind on top of the roof of a building thus being more aerodynamically efficient.
Improving Contouring Accuracy of CNC Systems by Improved RNN Prediction and Pre-Compensation
Authors: Song Jiamu , Li Bingran , Ye Peiqing 
Published: January 2025

Abstract
Contour motion control is a crucial factor in ensuring the profile precision of workpieces during CNC machining. Existing methods heavily rely on accurate contour error prediction and effective compensation algorithms. This paper employs an improved Recurrent Neural Network (RNN) to predict the contour error and proposes a contour error pre-compensation algorithm considering vibration suppression of machine tools.
Initially, an improved RNN is proposed to construct an input-output mapping model for single-axis servo feed systems, which is grounded in the examination of the primary factors influencing tracking error, the construction of the dataset, and the network structure. The model predicts the expected motion positions of each feed axis, and the predicted trajectory contour error is then obtained via the Newton numerical method.
On the basis of the accurate prediction of trajectory contour error, an FIR filter is suggested to smooth the motion trajectory after mirror compensation of the contour error. This overall trajectory smoothing method reduces high-frequency noise in the compensated trajectory and decreases the vibration during processing while ensuring contour accuracy.
Finally, validation experiments were performed on a multi-axis CNC experimental platform. The proposed method achieved an average tracking error prediction deviation of less than 3μm for each axis of the test trajectory. The root mean square value of the compensated trajectory contouring error was reduced by half, and the maximum value was effectively suppressed. Compared to traditional compensation methods, the proposed pre-compensation method effectively suppressed the vibration caused by the compensated trajectory, while sacrificing some contouring accuracy.
Extended Reality and Motion Capture for Ergonomics in Industrial Contexts
Authors: Lanzoni Daniel , Nava Marco , Vitali Andrea , Regazzoni Daniele , Rizzi Caterina 
Published: January 2025

Abstract
In recent years, operator ergonomics has been playing an important role, driven by workplace regulations and worker safety in manufacturing settings characterized by physical demands and injury risks. Traditional ergonomic evaluation methods rely mainly on observation and manual measurements, which are subjective, reactive, and sometimes challenging to perform. Technologies, such as virtual reality devices and motion capture systems, offer opportunities to streamline data collection and enhance objectivity. Moreover, Industry 4.0 introduced digital twins, facilitating ergonomic evaluations in virtual environments.
This paper presents a framework that integrates Virtual Reality technologies and motion capture analysis to support ergonomists in evaluating operators’ working conditions in manufacturing settings. The operator wears a head mounted display to simulate working tasks in a virtual replica of the real plant. An inertial motion capture system is employed to automate data gathering and processing for an objective evaluation of the operator’s postural condition. A Virtual Wall system is exploited by the ergonomist for the evaluation of the tasks using the motion captured data.
A case study has been considered to test the functionality of the platform. A tester repeats a sequence of tasks both in real and virtual scenarios in order to compare the results. Ergonomics is evaluated through three standard indexes (i.e., RULA, REBA, OWAS), resulting in a strong correlation between data acquired interacting with physical and virtual environments.
Adaptive and Scalable Approach for Time Series Anomaly Detection Using Transformer
Authors: Hu JieYi , Ding Sheng , Grimmeisen Philipp , Morozov Andrey 
Published: January 2025

Abstract
This paper introduces an innovative approach to time series anomaly detection, focusing on addressing the challenge of adapting to concept drift within input time series. Traditional time series anomaly detection methods often assume a stable environment for modeling. However, in practical applications, time series data may be influenced by concept drift, where the data distribution and features change over time. To effectively tackle this issue, we propose a dynamic adaptive model capable of real-time detection and adaptation to concept drift within time series data. Our algorithm leverages advanced time series anomaly detection techniques, particularly a Transformer-based online learning model, utilizing prediction errors as the basis for anomaly scores. Notably, the model incorporates a mechanism for concept drift detection and adaptation, continuously monitoring changes in the time series to adjust the learning rate accordingly. Additionally, our approach enhances the self-attention mechanism within the Transformer model by employing sparse self-attention, improving computational efficiency. Experimental results demonstrate that our algorithm exhibits superior accuracy in the presence of concept drift compared to traditional time series anomaly detection methods. This makes our approach particularly valuable in practical applications, especially in dynamic environments requiring adaptability. The combination of online learning, concept drift adaptation, and optimized self-attention contributes to the algorithm’s effectiveness in handling real-world time series data.
A Practical Approach to Evaluating the Adversarial Distance for Machine Learning Classifiers
Authors: Siedel Georg , Gupta Ekagra , Morozov Andrey 
Published: January 2025

Abstract
Robustness is critical for machine learning (ML) classifiers to ensure consistent performance in real-world applications where models may encounter corrupted or adversarial inputs. In particular, assessing the robustness of classifiers to adversarial inputs is essential to protect systems from vulnerabilities and thus ensure safety in use. However, methods to accurately compute adversarial robustness have been challenging for complex ML models and high-dimensional data. Furthermore, evaluations typically measure adversarial accuracy on specific attack budgets, limiting the informative value of the resulting metrics. This paper investigates the estimation of the more informative adversarial distance using iterative adversarial attacks and a certification approach. Combined, the methods provide a comprehensive evaluation of adversarial robustness by computing estimates for the upper and lower bounds of the adversarial distance. We present visualisations and ablation studies that provide insights into how this evaluation method should be applied and parameterised. We find that our adversarial attack approach is effective compared to related implementations, while the certification method falls short of expectations. The approach in this paper should encourage a more informative way of evaluating the adversarial robustness of ML classifiers.
Damage Detection and Imaging for Composite Storage Tanks Utilizing Ultrasonic Guided Waves
Authors: Jiang Houfu , Shen Yanfeng , Qu Yegao 
Published: January 2025

Abstract
Storage tanks, widely used for containing hazardous or high-pressure gases, are susceptible to damage due to factors like corrosion, cyclic inflations, and external impacts. The failure to promptly and accurately detect such damage can lead to catastrophic events, including leaks, explosions, and environmental contamination, posing severe risks to public health and safety. Moreover, timely detection plays a crucial role in maintaining the operational efficiency of storage facilities, avoiding costly downtime and ensuring compliance with strict safety regulations. Therefore, the detection of damage in storage tanks holds paramount importance in the realm of industrial safety and environmental conservation. Thus, this paper proposes the advancement of damage detection technology, utilizing the guided waves by detecting the variation of ultrasonic features for different damage conditions.
By carrying out an in-depth theoretical analysis via Finite Element Modeling (FEM), the sensitivity of the probing ultrasonic wave characteristics is studied systematically. The effective generation of guided wave modes is investigated, where the pure wave mode excitation is accomplished through a tuning procedure. To realize the damage localization, a three-dimensional numerical model is established by considering random defects placed on the surface. Two arrays of active sensors are placed on the tank, in order to record and evaluate the signal amplitude variation, while the propagating waves are travelling along the composite structure. Consequently, via the comprehensive consideration of transducer dimensions, center frequency, and propagating wave modes, the most effective damage detection setup is achieved and the sensing signals are recorded by the piezoelectric wafer active sensors (PWAS). Simulation results show that significant features such as the variation of the signal amplitude are fully captured, which are related to the presence of the damage along the wave path. Additionally, the Time of Flight (TOF) of sensing signals could further provide indicative information for localizing the particular defect on the tank. By changing the size, location, and quantities of defects, the feasibility and accuracy of this damage detection method is verified through the numerical analysis. The characteristic features extracted from multiple received signals are combined to reconstruct a color map of the damage condition on the storage tank. Finally, based on the quantification algorithm, damage can be imaged, utilizing crossing propagation paths of guided waves. The findings of this research possess superb application potential for the damage detection in composite tanks for enhancing the safety of storage systems. This paper finishes with summary, concluding remarks, and suggestions for future work.
Enhancing the Performance Measures of Abrasive Water Jet Machining on Cutting Titanium Alloy Specimens
Authors: Shanmugam Ragavanantham , Thangaraj Muthuramalingam , Ramoni Monsuru , Gaikwad Mahendra 
Published: January 2025

Abstract
The Titanium alloy is widely utilized in many bio medical application industries due its higher corrosion resistance and lower density. Since the Abrasive water jet machining (AWJM) process is used to cut such materials, it is essential to enhance the process mechanism as much as possible. The optimization of the AWJM technique can effectively improve the perfromance measures. In the present approach, an attempt was made to improve the performance measures of AWJM process with 40 mesh garnet particles while machining titanium (Ti-6Al-4V) specimens. The effect of input parameters such Water pressure (P), standoff distance (SD), abrasive particle flow rate (QA), and traversal speed (F) have been evaluated on performance measures such as surface roughness and material removal rate using Taguchi DEAR optimization approach. The optimal arrangement of input parameters in AWJM process were found as 1000 bar (P), 400 mg/min (QA), 4 mm (SD), and 400 mm/min (F) among the chosen factors with the error of 1.5%. It was also observed that the traversal speed possesses higher influential character on determining quality measures owing to its ability on modifying the impact energy.
A Conceptual Costing Software Tool Based on Machine Learning for Turbomachines
Authors: Sartini Mikhailo , Manuguerra Luca , Menchi Giacomo , Mandolini Marco , Lo Presti Giulio Marcello , Pescatori Francesco 
Published: January 2025

Abstract
The contemporary design landscape emphasizes the importance of product cost alongside performance, sustainability, and quality. Early determination of production costs, particularly in preliminary design phases like conceptual design, is crucial for maintaining competitiveness. Parametric cost estimation methods are preferred during these stages, leveraging identifiable relationships between design variables (cost drivers) and costs. Industry 4.0 innovations offer solutions to challenges posed by traditional Cost Estimation Relationship methods, with machine learning (ML) applications emerging as efficient tools for industrial processes. In the scientific literature, several approaches aim to create cost models using regression analysis, neural networks, and decision trees. The methods are implemented using data analysis tools that are not entirely suitable for product designers or cost engineers. The potential benefits of ML methods are not wholly exploited due to the limited effectiveness of the software tools.
Addressing this gap, the paper introduces a software tool with an administrative module enabling cost engineers to develop parametric cost models using ML algorithms based on the CRISP-DM method. The tool facilitates data collection, model training, evaluation, and benchmarking. Cost models are stored in a database accessible to design engineers, allowing quick and accurate cost estimations by providing relevant cost drivers. Feature importance algorithms highlight critical cost drivers.
The tool was tested in collaboration with a company that designs turbomachinery. During the experimentation, cost models were developed for one of the main rotor parts of an axial compressor (discs and spacer). The tool is a prototype to be further developed to manage an entire bill of materials, integrating risk management concepts.