bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Challenge Based Teaching Model for Applied Mechanics Courses With Flipped Learning and Artificial Intelligence Elements
Authors: Rodriguez-Paz Miguel X. , Gonzalez-Mendivil Jorge A. , Zamora-Hernandez Israel 
Published: January 2025

Abstract
In this paper we present a challenge based teaching model for Applied Mechanics courses, particularly a Solid Mechanics Course, within the framework of our education model, namely (blinded for review). The model also includes the use of some Artificial Intelligence tools during the course in some activities. The selected course for this paper is considered a difficult one by our students and sometimes the professors and students find it complex and some principles and concepts are difficult for students to associate with a real life application. The contents of the course include a review of Equilibrium, Stress definitions, Structural Analysis of statically determinate beams and two-dimensional trusses. The main component of the model is the solution of a challenge for which, students work in teams. The challenge presented to students at the beginning of an intense five week period consists on the design of the truss structure for the roof of an industrial warehouse. The teams of students have to present a solution applying the concepts seen in the course as well as some basic building code requirements. The course is taught by two professors and the participation of people from a chosen industry partner, that for the case of this course, it is a professional and chartered structural engineering firm. For the solution of the challenge, the participation of professional engineers from the industry partner was crucial, as they provided advice and recommendations to students in several meetings throughout the five week period. The results included in this paper are from two different implementations of the model in one campus of our university system. During each week the teams have some sessions where they work on the solution of the challenge. The use of professional software for structural analysis is also taught to students during those sessions where some elements of Artificial Intelligence tools are allowed. Students’ motivation was high, as expected during the five week period and with the participation of the industry partner at different stages of their solution, students got a sense of what is like to work in real life projects in their region.
Some results on students’ perceptions are included as well as those of students’ satisfaction and the type of activities that they enjoyed the most are also presented. This work can be used to design new courses related to Applied Mechanics including the challenge-based model with elements of Flipped-learning and AI tools.
Kinetic Modeling of a Conceptual Hydrogen Production Plant for Regional Hydrogen Resiliency
Authors: Shafquat Safwan , Itul Afsana Mustari , Chanda Sumit , Udoy Md Ismail Hassan , Tasnim Anika Farhat , Ryen Ahmed Ann Noor , Choudhuri Ahsan , Islam Nawshad Arslan 
Published: January 2025

Abstract
The current article explores a conceptual hydrogen (H2) production plant with an integrated gasification combined cycle (IGCC) designed in a commercially available simulation software to meet the auxiliary power requirements of the plant and a portion of the local electricity demand. A kinetic reactor model was used to carry out the gasification reactions. A major goal was to understand the capability of locally available feedstocks in El Paso, TX, such as pecan shells, cotton gin trash, and MSW, in hydrogen production. Both single feedstock gasification and co-gasification (municipal solid waste and pecan shell blend) methods were investigated to observe feed sensitivity to hydrogen production. A process parametric sensitivity analysis is conducted, primarily focusing on equivalence ratio and temperature. Initial results show that a lower equivalence ratio (ER) provided more H2 and CO yield and then decreased as ER increased from 0.18 to 0.36. However, a slightly different trend is seen when cotton gin trash (CGT) is used as feedstock. The sensitivity study found that the highest H2 yield was from pecan shell gasification at 950°C temperature. Additionally, co-gasification of pecan shells and MSW provided better syngas yield results with approximately 31% H2 and 32% CO, respectively, compared to only MSW gasification.
Electrifying Rural Life: A Socio-Technical Framework for Power Distribution in Microgrids
Authors: Pangia Andrew , Suk Hailie , Yaghoubirad Maryam , Khan Taufiquar , Hall John 
Published: January 2025

Abstract
In the realm of rural electrification, renewable-energy microgrids represent a pivotal advancement in sustainability, providing essential power solutions for areas lacking traditional grid connectivity. This study explores a relationship to control microgrid power on the basis of the quality of life (QOL) in rural electrification. It introduces a socio-technical framework designed to optimize power distribution in microgrid systems, particularly those with constrained production capacities.
Central to our research is the integration of social needs with technical capabilities. The framework utilizes a decision-making process that evaluates QOL parameters — such as water safety, education, and leisure/social activities — which are vital for enhancing community well-being. These indicators are crucial for prioritizing power distribution, demonstrating how microgrids can significantly enhance rural development by aligning electrification with QOL improvements.
Our previous work utilized the compromise decision support problem (cDSP) to aid in human decision-making for microgrid design and operation. This study introduces a programming technique that automates the power management process. Our methodology develops a mathematical program that incorporates QOL parameters directly into the objective function, defining a prioritization strategy for power distribution based on community-importance and energy-dependence scores. This automation is crucial for implementing control devices capable of managing microgrid power in real-time. The shift from manual to automated decision-making streamlines the distribution of limited power resources, ensuring optimal allocation across essential community needs and enhancing the system’s adaptability and efficiency.
The efficacy of the framework is demonstrated through two examples based on data from our previous work. The results highlight the benefits of prioritizing power allocation based on QOL parameters, leading to more impactful energy management within microgrids. The study also emphasizes the importance of including community perspectives in microgrid design and operation, ensuring that energy solutions are closely aligned with the most pressing community needs. We present this formulation for rural microgrids, but the framework is scalable.
Unveiling the Voltage-Current Characteristics of Dielectric Barrier Discharge Plasma in Interaction With Water Droplets at Varied Temperatures
Authors: Sarwar MD Sohaib Bin , Ahumada Lazo Jorge , Lelikov Petr , Liu Yang 
Published: January 2025

Abstract
Plasma-droplet interaction is a widely observed phenomenon in various applications, such as environmental cleanup, additive manufacturing, aviation, and fusion energy. The characteristics of plasma discharge can be greatly altered during these complex multiphase interactions. In this paper, we aim to investigate the voltage-current (V-I) properties of dielectric barrier discharge (DBD) plasma actuator when interacting with a droplet impacting on the plasma surface. Specifically, this study explores the complex interactions between DBD plasma and water droplets at ambient temperature and in a 0°C temperature condition. The V-I profiles of the DBD plasma actuator are recorded throughout the entire process of droplet-plasma interaction, i.e., before, during, and after droplet impact. Lissajous graphs are also plotted to reveal the distinct discharge profiles of the DBD plasma actuator during droplet-plasma interactions. Our findings show that the impact of water droplets led to an increase in current magnitude while voltage magnitude remained the same. The results highlight that the interactions between liquid droplets and DBD plasma bring changes in the current amplitude before and after impact, and this fluctuation in current amplitude becomes more evident at higher input voltages. The Lissajous figures and time-dependent current profiles revealed that the plasma’s reaction to water droplets is significantly different depending on the temperature of the droplets. Specifically, droplets at room temperature resulted in more noticeable alterations in the behavior of the plasma discharge.
Creating Equitable Spaces: Harnessing Dynamic Modeling for Gender Balance in Engineering Education
Authors: Gonzalez-Mendivil Jorge A. , Rodriguez-Paz Miguel X. , Zamora-Hernandez Israel , Caballero-Montes Eduardo 
Published: January 2025

Abstract
Gender equality in engineering is crucial for several reasons: The equal participation of men and women in engineering ensures a greater diversity of perspectives, ideas, and approaches. This enriches the design, development, and problem-solving process by considering a variety of experiences and knowledge. Gender diversity in engineering encourages innovation and creativity, as different points of view can lead to more complete and effective solutions. The lack of gender diversity in engineering can lead to the creation of products and services that do not meet the needs of all people. By having an equal representation of men and women in the field of engineering, more inclusive and appropriate solutions can be designed for all users, regardless of gender. This applies to fields like technology, infrastructure, healthcare, energy, and many others.
This paper aims to explore the use of dynamic modeling as a pathway to achieving gender equality in engineering education. By addressing the gaps in current approaches, it seeks to provide insights and guidance for educational institutions, policymakers, and stakeholders to effectively advance gender equality and create a more inclusive and diverse engineering education ecosystem. A dynamic model that describes the enrollment of women in an engineering academic program can have several uses and applications. This would provide an understanding of historical trends and patterns and allow estimates of the number of women who will enroll in a specific program in the future. Early results of the model demonstrate that women have a very different scheme in making decisions about the academic program to choose. While the male gender decides more based on what they know can be successful in the future, the female gender goes further and takes into account aspects such as the sense of fulfillment and belonging. The model also shows that if, during the student recruitment stage, activities designed specifically for what women are most attracted to in engineering were used, more women would enroll in said academic programs.
Design of a Novel Passenger Car Axle Housing Mount Using Alternative Geometry and Material
Authors: Burns Jeremy , Kocharian Tikran 
Published: January 2025

Abstract
The axle housing, typically manufactured from robust metal alloys like cast iron, encloses essential rear axle components in a vehicle and connects to the suspension system, facilitating independent wheel movement while supporting power transmission, weight, stability, and shock absorption. Reverse engineering, a valuable technique in industry, involves understanding existing product functionality through deductive analysis, enabling documentation, replication, enhancement, or adaptation. In the automotive aftermarket, there is significant potential for enhancing products to replace current vehicle components, often necessitating modifications and integration of new hardware. Reverse engineering preserves crucial design specifications while tailoring products for alternative applications. This study focused on reverse engineering and redesigning a passenger car axle housing mount, utilizing practices such as 3-D scanning, hybrid modeling, and 3-D printing. The primary objective was to evaluate the modified axle assembly’s durability under increased loads while minimizing weight gain, utilizing Finite Element Analysis (FEA) to simulate real-life conditions. The innovative mount design successfully met functional requirements, with simulations showing acceptable stress levels. A prototype was fabricated and installed on an existing axle housing, resulting in a positive fit without adjustments. This research offers a versatile solution for passenger cars, promising improved performance and efficiency across models, with an adaptable design poised to expand automotive technology.
Advancements and Challenges in Soft Tissue Engineering: A Comprehensive Review of Additive Manufacturing Technologies
Authors: Azari Fatemeh , Jazaei Robabeh 
Published: January 2025

Abstract
In contemporary biomedical innovation, Additive Manufacturing (AM) has been pivotal since the 1980s, crafting complex structures for bioengineering, biomedicine, and pharmacology [1]. Particularly in pharmacology, AM, notably through Stereolithography (SLA), has transformed drug delivery, enhanced dosage precision and advancing personalized therapies [2]. In tissue engineering, AM is crucial for bioprinting scaffolds and tissue constructs using bio-inks to mimic human tissues, facilitating significant advances in medical treatments [3]. (i) Key techniques include SLA achieving 30-micrometer resolution [4–6], Fused Deposition Modeling (FDM) is an evolving drug delivery technique [9], and Powder Bed Fusion (PBF) 70% to 90% had minimal impact on osteogenesis when using a 700-μm pore size [41]. (ii) Applications span tissue engineering with materials like PLA and PGA [16], achieving 97.2 ± 48.4 implant fit accuracy using MRI and CT scans [40–41]. Drug delivery systems, such as multi-layered tablets, enable sequential release [22]. Personalized medicine benefits from custom-made implants based on patient data [25].
(iii) Challenges include replicating intricate organ structures like the bladder and liver, ensuring vascularization, and integrating immune components [28]. Incorporating vascular endothelial growth factor (VEGF) into bioinks increases angiogenesis [31]. Material limitations and regulatory hurdles persist [34]. (iv) Future directions focus on improving vascularization, immune integration, and personalized oncological treatments [37]. Continued research and technological innovations are essential for accurate tissue replication [40]. (v) In conclusion, AM techniques like SLA and FDM revolutionize drug delivery and tissue bioprinting, marking a transformative period in biomedicine with significant potential for personalized medicine and regenerative therapies, promising breakthroughs in disease modeling and drug testing[41].
Accelerating Microstructure and Melt Pool Prediction in Laser Powder Bed Fusion Processes Using Data Driven Neural Network
Authors: Qadeer Abdul , Akhtar S. Sohail , Abubakar Abba A. , Arif Abul Fazal M. , Mekid Samir , Al-Athel Khaled S. 
Published: January 2025

Abstract
Laser powder bed fusion (LPBF) is a pivotal additive manufacturing process characterized by selective melting and solidification of powder layers to form complex parts. The microstructure of the final part plays a crucial role in determining its properties, which are heavily influenced by process parameters such as laser power, scanning speed, powder layer thickness, and material properties. Understanding the influence of process parameters on melt pool geometry is crucial for minimizing defects due to unmelted powder regions and optimizing hatch spacing. Additionally, the microstructure plays a vital role, as process parameters can lead to the formation of columnar grains, promoting anisotropy in the fabricated parts. Therefore, understanding microstructural characteristics is key to ensuring the reliability of manufactured parts. Predicting the microstructure and melt pool geometry in laser powder bed fusion (LPBF) processes via finite element modeling is computationally demanding and time-intensive. To mitigate these challenges, we propose the development of a machine learning model capable of accurately predicting melt pool geometry and microstructure based on process parameter variation, thereby reducing time in the identification of optimal process parameters for further experimental investigation. This study utilizes a physics-based numerical model within ANSYS Additive to predict melt pool geometry and microstructure in the LPBF process. The model’s accuracy was validated using single-track metal additive manufacturing (AM) experiments extracted from relevant literature. A comprehensive database was compiled by numerical simulation, including melt pool geometry, microstructure, process parameters, and material IDs. This dataset was then employed to train a data-driven model, with process parameters and material IDs serving as inputs and melt pool width, depth, and microstructure images as outputs. Additionally, the images were resized to 100 × 200 pixels for computational efficiency. Various neural network architectures, including Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN), were utilized to enhance the predictive capabilities of the model. These architectures were trained and fine-tuned to achieve precise predictions that closely matched the actual dataset. The study showcased the efficiency of neural networks in accurately predicting melt pool geometry and microstructure, significantly reducing prediction times compared to conventional numerical simulations by several orders of magnitude. Future research direction will focus on incorporating physics constraints into the loss function to accelerate the training process, thereby reducing the number of epochs required. However, the development of a rapid data-driven model with minimal mean absolute error is vital for enhancing part optimization and promoting the widespread adoption of MAM.
Characterization of Droplet Dynamics Under Impact of a Shockwave Interacting With a Solid Surface
Authors: Giacoma Jordan , Ahumada Lazo Jorge , Zhang Haipeng , Liu Yang 
Published: January 2025

Abstract
This paper presents an experimental study on the transient deformation and breakup morphology of a water droplet as it approaches a solid surface driven by an interaction with a shockwave. A droplet injection system integrated into the top of the Shock Tube Test Section produces consistently sized water droplets of a prescribed diameter. A super-sonic shockwave is then shot at the falling water droplet. A high-speed imaging system, consisting of two high-speed cameras, a high-intensity LED point light source, and a series of optics, is used to visualize the complex shock-droplet-surface interaction. One of the high-speed cameras witnesses the impact through Schlieren conditions, to clearly visualize the large alterations of the density field generated by the shock wave. The other receives a collimated light beam through a cubic beam splitter, more clearly able to visualize the breakup morphology of the droplet. After the shockwave impacts the droplet, it encounters a metal plate and rebounds and impacts the falling water droplet a second time, causing significant shockwave propagation within the droplet. It is found that the duration of time between impacts due to the droplets initial distance from the plate, influences the duration of time under which the droplet deforms before failing.
A Framework for Surface Scanning to Additive Repairing Using Point Cloud Data and Surface Reconstruction Algorithms
Authors: Ahmed Radif Uddin , Nabi Iftesam , Alam Chowdhury Sadid , Chen Yun , Rahman M. Shafiqur 
Published: January 2025

Abstract
This study represents an additive repairing method for solid bodies by accurately scanning and reconstructing three-dimensional (3D) surfaces inspired by the additive manufacturing (AM) technique. The proposed methodology utilizes a laser profiler (Keyence LJX 8200) to scan an arbitrary surface and obtain point cloud data (PCD). The laser profiler functions as a line scan device that can provide 3200 data points for a single two-dimensional XZ plane. To extend the scanning capabilities beyond a single line, a computer-controlled gantry system driven by a stepper motor is integrated to offer significant information on the Y-axis. The scanned PCD is then evaluated for quality consistency by implementing various preprocessing techniques, including outlier elimination and data sorting using Python. The preprocessed data is exported into the final PCD file, which contains crucial information about the vertices of the scanned surface in terms of coordinates; however, it does not provide information about the faces of the actual surface. To prepare the actual surfaces effectively from the vertices and normal to the faces, several surface reconstruction techniques, including Delaunay Triangulation, Ball Pivoting Algorithm, and Poisson’s Surface Reconstruction, are applied to the point cloud dataset. Results show that Poisson’s Surface Reconstruction consistently produces the highest level of accuracy among the tested algorithms, making it the optimal choice for surface reconstruction in additive repair applications. The 3D surface produced through this process is exported as a Standard Tessellation Language (STL) file, which can be printed via any 3D printer. The precision of the surface reconstruction algorithm is experimentally evaluated by comparing the dimensions of the scanned item with that of the 3D-printed item.
Enhancing Energy Absorption Capacity of Thin-Walled Box Structures Through the Integration of Auxetic Materials
Authors: Narayana Peddi Sai Rama , Prakash Raghu V. , Gunti Srinivas , Kanugula Raghu 
Published: January 2025

Abstract
In frontal impacts, the usage of thin-walled box structures in the front-end of vehicles is crucial for effectively absorbing collision energy. However, achieving maximum energy absorption while considering constraints such as available space, weight, cost, and maintaining occupant safety is a challenging task. Previous studies have indicated that the structural shape, utilization of high strength materials, and incorporating foam-filled structures can significantly enhance energy absorption capacity. This paper presents a novel approach to optimize the energy absorption capacity with a combination of conventional and auxetic materials. Auxetic materials possess a unique characteristic of exhibiting a negative Poisson’s ratio, which means they contract when compressed and expand when subjected to tension. These distinctive properties enable auxetic structures to achieve higher stiffness and improved impact resistance while remaining lightweight.
In this paper, a Finite Element Analysis (FEA) methodology has been formulated with a simple crush box, and with multiple materials combinations of conventional materials such as high strength steels, Aluminum and auxetic materials, to improve the energy absorption capacity. The significant factors affecting the energy absorption which are critical to occupant safety have been compared between the simulations. It is demonstrated that the optimal blend of conventional materials such as Aluminum and Ultra High Strength Steels, when integrated with auxetic structures, exhibits a higher potential for maximizing the specific energy absorption capacity of these structures. The findings reveal that the optimized combination of the above-mentioned materials can increase the specific energy absorption (SEA) capacity by up to 256%. Additionally, the practical application of these findings is demonstrated through full vehicle level crash analyses aimed at achieving light weighting. The research findings were implemented on the Ford Taurus FE model, available online and validated experimentally that shows benefits of 7.83kg weight reduction for the same crash performance of a standard material taken as baseline.