bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Tool Alignment of an Eye-in-Hand Manipulator for Overhead Line Insulation Cleaning
Authors: Vohra Mohit , Panda Amruta , Subramaniam Prashanth , Althani Thani , Rezk Mahmoud 
Published: January 2025

Abstract
The maintenance of electrical insulators is critical for ensuring the reliability and safety of power transmission networks. Traditional manual cleaning methods, while effective, pose safety risks to workers and require considerable time, compromising operational efficiency. Addressing these challenges, this paper introduces a dual-mode, eye-in-hand depth camera-based robotic system tailored for the cleaning of insulators. The system leverages a combination of automatic and teleoperated modes to optimize both safety and cleaning efficacy. In the automatic mode, the system uses advanced visual servoing techniques to detect and perform coarse alignment of the robotic end-effector with the target insulator. Following this initial alignment, control is seamlessly transitioned to the teleoperated mode, enabling precise manipulation of the cleaning tools by an operator from a safe distance. This dual-mode approach merges the reliability and speed of automated technologies with the precision and flexibility of manual control. The paper provides an in-depth analysis of the system’s architecture, details the integration of its control modes, and describes the operational procedures involved. Experimental results are presented to demonstrate the effectiveness of the entire system.
The Effect of Meniscal Tears on Knee Biomechanics: a Finite Element Analysis Investigation
Authors: Ghidotti Anna , Penzo Michela , Landi Daniele , Regazzoni Daniele , Rizzi Caterina 
Published: January 2025

Abstract
Meniscal tears may adversely impair knee function and biomechanics, with effects varying based on tear type and location. There are several methods for studying meniscal injuries, including human cadavers, and computational models, such as Finite Element Analysis (FEA). Previous studies have used the FEA technique to examine the effects of artificially replicating meniscal tears. The aim of this study is to investigate the effect of meniscal injury on knee biomechanics, by means of FEA, starting with real meniscal tears.
Based on Magnetic Resonance (MR) images of 12 patients, 3D models of the distal femur, proximal tibia, cartilage, and menisci were obtained for each patient through the segmentation process. A preprocessing phase was then necessary to prepare the models for FEA. Finally, simulations were conducted to study the effects of lesions on stress, contact pressure, and meniscal extrusion.
The simulation results showed that meniscal lesions, especially radial tears, increase the pressure between menisci and tibial cartilage, increase the maximum shear stress on menisci, and lead to meniscal extrusion. The integration of FEA with clinical data may provide surgeons with detailed insight, enabling them to decide whether surgical intervention is necessary.
Unsupervised Classification of Perovskite Crystal Structures From XRD Data
Authors: Mathew Ansu , A. B. Baloch Ahmer , Mohammed Yakasai Alamin , Mittal Hemant , Alberts Vivian , Vandavasi Karunamurthy Jayakumar 
Published: January 2025

Abstract
Perovskite materials hold immense promise for applications in photovoltaics, optoelectronics, and photonics. However, accurately characterizing their crystal structures, which directly influence these properties, remains challenging. Traditionally, analyzing these structures from X-ray diffraction (XRD) data relies on labor-intensive steps like crystallographic analysis and phase identification. Hence, this work explores the potential of unsupervised machine learning (ML) for automated and efficient classification of perovskite compounds. By applying unsupervised ML algorithms to raw XRD data, we directly classify crystal structures, bypassing the need for complex traditional procedures. PyMatgen was employed to extract data for 4008 perovskite compounds from the Materials Project repository, followed by initial preprocessing to prepare it for machine learning models. We identified information underlying XRD data using PCA before K-means clustering (Approach 1) and K-means clustering before PCA (Approach 2), which are some typical approaches in machine learning for data analysis and clustering. Elbow method was used to determine the optimal number of crystal structure clusters for perovskites, which was 7 for both approaches. We then employed the silhouette score, Davies-Bouldin Score, and Calinski-Harabasz score to assess our clustering methodology’s effectiveness and identified that approach 1 depicts better quality in clustering. We mapped the clusters to crystal structure classes and evaluated the classification metrics. Both approaches generate similar classification metrics, indicating that the underlying structure of the data is exceptionally robust and can be captured effectively using either method.
An Exploration of 3D Printing’s Contribution to Responsible Consumption and Production (SDG 12)
Authors: Hasaballah Maryam , Pervaiz Salman 
Published: January 2025

Abstract
In 2015, the United Nations (UN) General Assembly established seventeen different sustainable development goals (SDGs) that outlined principles and objectives which tackled challenges of diverse nature around the world and achieve the set-out targets by 2030. On the other hand, 3D printing has been attributed as a game changer for the manufacturing sector. It is due to the disruptive nature of this technology where conventional design to manufacture principles are not strictly applied. It has been observed that inherent nature of 3D printing can positively contribute towards achieving these global challenges. The 3D printing contributes towards achieving multiple goals from the SDGs in many interactions.
This article examines the role of 3D printing technology in contributing to Sustainable Development Goal 12 (SDG 12) — responsible consumption and production. Through a comprehensive literature review and empirical analysis, the study explores various aspects of 3D printing’s impact on sustainability, including resource efficiency, waste reduction, circular economy practices, localized production, customization, longevity, and energy efficiency. Key findings suggest that 3D printing offers significant potential for improving sustainability across industries such as construction, packaging, supply chain management, healthcare, and manufacturing. The synthesis of findings reveals a high impact relationship between 3D printing and SDG 12, with measurable effects on efficiency improvements, waste reduction, and environmental conservation. Challenges and considerations for future research are also discussed, highlighting the need for long-term environmental impact assessments, integration of social sustainability factors, regulatory frameworks, technological advancements, and economic accessibility. By addressing these research areas, the article highlights the potential of 3D printing to contribute to sustainable development goals and foster a more stable future.
Empowering Mobile, Energy-Efficient and Sustainable 3D Printing Systems for Emergencies and Rural Communities
Authors: Linke Barbara , Bashayan Kholoud , Ramírez-Cedillo Erick , Armendariz-Rodriguez Maximiliano , Habibi Mohsen , Vargas Martínez Adriana , Ramírez Ricardo A. 
Published: January 2025

Abstract
Natural disasters and emergencies create pain, diseases, and death. Resources at the affected sites might be sparse, yet the availability of simple parts can be crucial to ease suffering and restore critical infrastructure. Additive manufacturing, also commonly called 3D printing, can provide a fast and flexible way to produce items locally and help provide customized components in rural areas, support Do-It-Yourself users, and address product shortages.
This study therefore addresses an urgent and important need in today’s world by designing mobile, energy-efficient, and sustainable 3D printing systems that can be used in emergencies and in rural communities to print essential medical and mechanical parts. To the best of our knowledge, prior literature on mobile 3D printing systems does not consider critical conditions imposed by solar radiation availability based on both, location and season, days of autonomy, and how the combination of these critical factors affects the sizing and hence mobility of the system. In this study, we aim to bridge this knowledge gap by a literature study and synthesize relevant information into graphs and a step-by-step guide with equations, to design a solar-powered 3D printing system from commercially available solutions. This paper shows a first approach for setting up such a system that can work under critical conditions while still being reasonably sized and walks through a case study.
Assessing Hemodynamic Changes in Cerebral Aneurysms Post Coil Embolization: A Preliminary Investigation
Authors: Monfared Mohammadali , Hollingsworth Luke , Thibbotuwawa Gamage Peshala , Taebi Amirtahà 
Published: January 2025

Abstract
A cerebral aneurysm is a weakened area in the wall of a blood vessel within the brain that causes the vessel to bulge outwards. The choice of treatment depends on various factors including the size and location of the aneurysm and the risk of rupture. The noninvasive methods are watchful waiting and medication. The invasive methods include surgical clipping, flow diversion, and endovascular embolization. Treatment for aneurysms should be tailored to individual situations rather than following a universal approach due to their variability among individuals. This approach provides the possibility of choosing precise treatment plans that are proper for each patient’s circumstances. The utilization of computational fluid dynamics (CFD) allows for the prediction of after-surgery hemodynamic changes before any surgical approach. The goal of this research was to investigate the flow characteristics and hemodynamic parameters in cerebral arteries before and after endovascular embolization treatments using CFD in three patient-specific geometries with at least one aneurysm at the middle cerebral artery’s bifurcation. To model the coiling, the computational domain was divided into two fluid domains, a general fluid domain consisting of the parent arteries and a porous domain inside the aneurysm. CFD modeling was conducted to simulate blood flow in pre- and post-coiling scenarios. Results showed that the coiling model with different porosity values led to a redirection of blood flow away from the aneurysm sac. Additionally, changes in shear stress indicated potential alterations in susceptibility to vascular remodeling. In conclusion, these findings indicated that utilizing CFD modeling for simulating blood flow in patient-specific cerebral arteries could be utilized to predict hemodynamic parameters before any treatment.
Finite Element Modeling of Ballistic Impact for Steel Plates Using Two Constitutive Models
Authors: Johnson Daniel , Peterson Luke , Sharma Gehendra , Mun Sungkwang , Hammi Youssef , Stone Tonya , Sherburn Jesse , Rios-Estremera Daniel 
Published: January 2025

Abstract
A comparison of Internal State Variable (ISV) and Johnson Cook material models for Finite Element (FE) simulations of ballistic impact of A36 structural steel plates by 4340 steel Fragment Simulating Projectiles (FSP) is performed using the Lagrangian Abaqus Explicit FE solver. A novel ISV plasticity-damage material model calibration for A36 steel is presented and validated through comparison of residual velocity and perforation characteristics of simulated ballistic FSP impacted 12.7 mm plates to experiment data. The ISV and Johnson Cook models’ ability to accurately predict residual velocity and changing perforation characteristics is assessed for a range of target plate thicknesses and impact velocities. Generally, each model shows satisfactory prediction of residual velocity, however the Johnson Cook model tends to inaccurately predict target fragmentation at high impact velocities. Both the ISV and Johnson Cook models predict shear plugging near the ballistic limit, which matches experimental data. At higher impact velocities, both models show varying amounts of fragmentation of target material. Underlying differences in damage evolution during ballistic impact events (and subsequent differences in predicted perforation modes) between the two constitutive models are discussed.
Feasibility of Left Ventricular Function Assessment via Precordial Vibrations in Heart Failure Patients
Authors: Ahmed Mohammad H. , Weerasinghe Yasith , Grillo Michael , Taebi Amirtaha , Kaya Mehmet , Thibbotuwawa Gamage Peshala 
Published: January 2025

Abstract
Heart failure (HF) is a prevalent and potentially life-threatening condition characterized by the heart’s inability to pump blood effectively. Managing HF requires careful monitoring of left ventricular function (LVF), necessitating timely interventions to prevent adverse outcomes. Traditional methods of monitoring LVF can be invasive, costly, and limited to clinical settings which poses challenges for continuous monitoring and early detection of HF progression. This study aims to evaluate the feasibility of estimating left ventricular ejection fraction (LVEF) from precordial vibration signals. Precordial vibration data from 70 patients with varying levels of HF were analyzed. Seismocardiography (SCG) signals were processed to extract features and train machine-learning regression models. The predicted LVEF values from the models were compared with LVEF data obtained from echocardiography, the gold standard for LVF assessment. Among trained machine learning models, Gaussian process regression indicated the greatest correlation between the estimated and obtained values with the least mean squared error. Variations in the precordial vibrations were observed to correlate with the severity of valvular dysfunction, highlighting the sensitivity of this approach. In conclusion, precordial vibration signals have the potential to offer valuable insights into both myocardial and valvular function in HF patients. Precordial vibration analysis shows promise as a non-invasive, cost-effective method for continuous monitoring of LVF in HF patients. Further research and validation are warranted to explore its clinical applicability and potential integration into routine HF management protocols, with the ultimate goal of improving patient outcomes and quality of life.
Numerically Driven Geometrical Modeling and Refinement Approaches to Streamline the Design of Topology Optimized Parts: A Comparative Study
Authors: Dalpadulo Enrico , Pini Fabio , Leali Francesco 
Published: January 2025

Abstract
The integration of structural optimization into product design processes has been a focus of extensive research, leveraging advances in computing technology to embed numerical techniques within simulation-driven approaches. Topology optimization, particularly enabled by Additive Manufacturing, has emerged as a powerful tool for generating structurally efficient geometries. However, practical implementation poses challenges, often requiring manual redesign and interpretation of results. To address these issues, this study explores the potential of numerical optimization techniques, specifically parametric and shape optimization, to refine topology optimized structures automatically. The final aim is to develop a systematic workflow integrating these tools to produce ready-to-print geometries with minimal user intervention. The investigation, based on simple geometries resembling topology optimized structures, investigates the tools for simulation-driven design methods utilizing multi-step numerical optimization within an integrated CAD platform, aiming for a direct, efficient, and mathematically-driven design process. Advantages include faster iterations, reduced errors, and independence from user expertise, while challenges such as computational complexity, sensitivity to initial conditions, and need for specific design expertise are acknowledged. The potential further systematic approaches for broader applications, particularly in additive manufacturing and structural engineering, underscore the significance of further research in this area.
Preparing Generation Z for the Future: A Challenge-Based Learning Approach in Higher Education
Authors: Leão Celina P. , Alves Anabela C. , Silva Vinícius 
Published: January 2025

Abstract
In response to the growing demand for personal skills essential for success in technological careers and the imperative to address Sustainable Development Goals (SDGs), this paper presents the design and implementation of the elective course, “Preparing Generation Z: beyond technologies (Z-Tec+)”, applying challenge-based learning (CBL) methodology. Introduced in the 2021–2022 academic year, Z-Tec+ aims to equip students with skills crucial for a thoughtful transition into the job market while addressing key SDGs, including Quality Education (SDG 4), Gender Equality (SDG 5), Sustainable Cities and Communities (SDG 11), Responsible Consumption and Production (SDG 12), and Partnerships for the Goals (SDG 17). This paper outlines the rationale for adopting CBL and its alignment with the SDGs, details its integration into the course curriculum, and presents the methodology’s application in facilitating student learning and skill development in the context of sustainability challenges. The central challenge presented to students was to mitigate the “fast-fashion” phenomenon. The results, drawn from student-generated solutions over the Z-Tec+ semester, are informed by student perceptions gathered at the start of each semester, their engagement in various CBL activities, and feedback collected at the semester’s end. The study captures insights from 33 students across diverse academic disciplines, including Engineering, Economics, Marketing, Sociology, and International Relations. Each student team utilized Padlet to document their project journey and solution development process, creating a comprehensive portfolio that facilitated collaborative reflection on their collective efforts. By integrating CBL with a focus on SDGs, Z-Tec+ fosters the development of socially responsible professionals equipped to tackle complex global challenges. This study underscores the importance of fostering collaboration between academia, industry, and civil society to scale sustainable initiatives and equip students with the skills and awareness necessary to address these complex challenges effectively.
In Situ Thermal Measurement of Polymers
Authors: Powers Carson , Bratescu Simon , Bearden Peter , Dorsey Fahim , Trinh Jessica , Kim Dal Hyung , Park Jungkyu 
Published: January 2025

Abstract
In this research, we conducted a detailed experimental investigation into how strain affects the thermal conductivity of Ecoflex elastomer, utilizing a newly developed method for measuring thermal conductivity under mechanical strain for the first time. In situ thermal conductivity measurement apparatus was developed by combining KLA T150 nanoscale tensile tester and a custom fabricated thermal measurement sensor. The development of experimental method for measuring the thermal conductivity of nanomaterials under mechanical testing simultaneously will contribute to the development of novel materials for flexible electronics by helping us to better understand the strain effect on their thermal performance. Interestingly, the thermal conductivity of Ecoflex elastomer is shown to increase with an increase in tensile strain until the engineering strain reaches 20%. This is understood to be due to the straightened polymer chains, which makes the phonon transport to be more efficient through the stiffened polymer chains. At very high degrees of mechanical strain, the thermal conductivity may decrease due to the disruption of filler-to-filler connections and increased phonon-boundary scattering between polymer chains and magnetic powders. This is caused by the reduced spacing resulting from the increased mechanical strain. The findings from this study are expected to propel the advancement of future flexible electronics by facilitating the creation of a foundational elastomer.
Benchmarking the Efficacy of Feedback Guided Self Balance Equipment With Variable Motion Regimens for Physical Therapy and Elite Athletes
Authors: Tatoglu Akin , Pelczarski Brennen , Balaji Dhananjay , Pezzolesi Justin , Campana Claudio , Asaki Takafumi , Altuger-Genc Gonca 
Published: January 2025

Abstract
We designed and developed a feedback-guided balance device that can be used for patient rehabilitation, training, and athletic conditioning, including for elite athletes. This system is cross-platform, enabling users to connect via Bluetooth using any computer or a mobile device. The user interface is intentionally minimalist to facilitate ease of use. Users can independently adjust the system’s complexity and exercise types. Performance data can be accessed by professionals, such as physical therapists or coaches, who can subsequently tailor recommendations based on this data. The system can also be utilized to monitor patient recovery and ensure correct execution of exercises within prescribed repetition ranges. As exercises are modifiable based on patient age and condition, a framework is being developed to allow clinicians to easily design and implement new exercises on the system.
Markerless Pose Estimation and Wearable Sensor Technology for Enhanced Diagnosis and Monitoring of Idiopathic Toe Walking
Authors: Weerasinghe Yasith , Grillo Michael , Ahmed Mohammad H. , Sheppard Christina , Taebi Amirtaha , Wilder David , Kaya Mehmet , Thibbotuwawa Gamage Peshala 
Published: January 2025

Abstract
Idiopathic Toe Walking (ITW) is a common condition characterized by an individual’s preference for walking on their toes without any identifiable medical reasons. Children usually outgrow this habit; however, those who don’t are often affected by certain conditions such as cerebral palsy, muscular dystrophy, and autism spectrum disorder. If not addressed, ITW can lead to physiological complications such as skeletal and muscular deformations. Identifying and monitoring toe walking patterns is only accessible through qualitative observational gait analysis and electromyography (EMG). This approach’s main objective is to provide quantitative means to analyze ITW and diagnose and monitor patient rehabilitation. The markerless pose estimation approach analyzes the ankle’s range of motion to identify normal and abnormal gait patterns. In addition to the markerless pose estimation, a 6-axis accelerometer and gyroscope inertial measurement unit (IMU) allowed for the continuous monitoring of motion values, which constitute the identification of idiopathic toe walking. Both noninvasive approaches provided quantifiable angles, allowing for convenient and continuous ITW monitoring. By employing markerless pose estimation coupled with a wearable sensor, ITW could be monitored, enhancing the accessibility of continuous monitoring for gait irregularities.