bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Analysis of Non-Pneumatic Tire Spoke Designs for On-Road Applications: A Traction Force Perspective
Authors: Sidhu Charanpreet , El-Sayegh Zeinab 
Published: November 2024

Abstract
This study explores the traction performance and structural integrity of Non-pneumatic (NP) tires, which are being explored as alternatives to traditional air-filled tires, offering enhanced durability, reduced maintenance, and improved eco-friendliness. This study investigates the influence of four spoke shapes (honeycomb, modified honeycomb, re-entrant honeycomb, and straight spokes) on NP tire traction performance for on-road applications. Using Finite Element Analysis (FEA), the study considers the influence of longitudinal speed and vertical load on the traction performance and stress distribution of the NP tire designs. This assessment of traction coefficient under the constant torque will reveal distinct variations in performance across the four-spoke designs. By systematically varying these parameters, the study aims to provide a detailed understanding of how different design factors influence the overall performance of NP tires. Furthermore, the traction coefficient measurements helped to identify spoke configurations that optimize tire-road interaction performance. Comprehensive comparison and evaluation of the four NP tire spoke designs highlight their respective advantages and limitations for on-road applications. By optimizing spoke shapes and considering the effects of speed and load, manufacturers can develop NP tire designs that offer improved traction, durability, and efficiency for a wide range of automotive applications. These findings provide valuable insights for optimizing NP tire designs, aiding decision-making for manufacturers in the automotive industry.
Towards Novel Standard-Based Approach to Flexible Supply Chain Integration
Authors: Jelisic Elena , Kulvatunyou Boonserm , Ivezic Nenad , Oh Hakju 
Published: November 2024

Abstract
Global supply chain digitalization must support diverse and incompatible formats, protocols, and structures of the Data Exchange Standards (DESes). The complexity of the global supply chain network has led to the need for increased supply chain flexibility, agility, and resilience. This paper reviews different approaches to resolving the DES heterogeneity between business systems and their trade-offs in supporting flexible supply chain integration. A novel approach is then proposed to better manage and reuse mapping specifications and enable more efficient translation of messages from one DES to another and, consequently, more flexibility in supply chain networking. The approach represents an extension of the existing platform for DES management. It is based on previously documented federated DES development and management, which adopts the federated learning approach for building the DES library. Central to the proposed approach is the concept of a super data model as an “interoperable” layer that enables semantics match between different DESes. Another important concept is Business Context, which is envisioned as a basis for the identification and reuse of existing mapping specifications. Our initial evaluation shows the feasibility and benefit of the proposed solution. The paper identifies a list of research directions that will be addressed in the future.
An Augmented Reality Platform for Real-Time Ergonomic Assessment and Biomechanical Simulation
Authors: Vitali Andrea , Lanzoni Daniel , Ferrari Davide , Regazzoni Daniele , Catalano Manuel Giuseppe , Rizzi Caterina 
Published: November 2024

Abstract
Despite the existence of numerous innovative solutions aimed at enhancing the objectivity and efficiency of physical ergonomics analysis, traditional methods are still widely used among ergonomists, relying on direct observation of workers and their task performances. This research addresses this gap by introducing AR4Ergo, an Augmented Reality platform specifically designed to assist ergonomists by computing real-time standard indexes and simulating musculoskeletal loads using biomechanical virtual models.
Xsens inertial motion capture system is employed to monitor human movements, while the OpenSim Application Programming Interface has been integrated into AR4Ergo for the simulation of musculoskeletal loads. Microsoft Hololens v2 is used to visualize human and workplace digital twins with information relative to ergonomic indexes and biomechanical simulations during the observation of the tasks performed by workers.
A test was conducted to evaluate the functionalities of the platform, with three testers taking turns performing the roles of both the ergonomists and the operators. The results demonstrate how real-time analysis with a visual interface can be performed to augment ergonomists’ experience without modifying the basic observational procedure. Even if the feedback is positive, some improvements have been identified in order to allow the ergonomist to use AR4Ergo in daily practice for different industrial applications.
Dynamic State Tracking of Overhead Powerlines With Real Time State Estimation: a Data-Driven Approach
Authors: Nambiar Nipun , Barry Oumar 
Published: November 2024

Abstract
This study focuses on laying the groundwork for the effective vibration suppression of power lines using mobile damping robots (MDR). Earlier research shows that effective vibration suppression is achieved by positioning the MDR at the anti-nodes of the power line. This study focuses on accurately estimating the dynamic state of the power line using a data-driven approach, hence identifying the position of the antinode. The entire dynamics of the vibration of the system is estimated from the displacement data of the power line using Dynamic Mode Decomposition (DMD) and the resulting system is stabilized with Tikhonov Regularization. The stabilized system is then used in conjunction with a Kalman Filter to accurately estimate the dynamic state of the power line using minimal displacement. All displacement data used in this study is acquired from a Galerkin model of the power line. This study shows that this method is a viable alternative to existing numerical methods which are often computationally expensive and time-consuming.
Exploring Deep Learning Models for Pathological Tremors Prediction Using EMG and Kinematic Measurements
Authors: Samal Subham , Barry Oumar 
Published: November 2024

Abstract
Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses, motivating us to explore better modeling of tremors for long-term predictions and analysis. This study is a preliminary step towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20–100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinsonian tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. The motivation behind hybrid CNN-LSTM models is to exploit both the temporal and spatial dependencies using CNN and LSTM respectively. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions.
Leveraging Latent Textual Topology for Standard Identification in Engineering Design
Authors: Bowen Matthew , Smith Logan , Carroll Cody , Morkos Beshoy 
Published: November 2024

Abstract
In engineering design, standards are instrumental in providing technical definitions and guidelines to designers, manufacturers, and users, reflecting best practices that are widely recognized. These standards, numbering in the hundreds of thousands, encapsulate the current state of technological expertise and aim to promote safety, reliability, productivity, and efficiency in both component and system design. Despite the clear value of standards, the vast quantity and diversity of these documents present significant challenges for designers in retrieving and implementing the appropriate standards for their projects. Moreover, the significant amount of time design engineers exhaust navigating through technical documentation highlights the necessity for more effective methods of engaging with the textual knowledge base represented by engineering standards, as the current manual process is inefficient and time-intensive. This presents an even greater challenge for small and medium-sized enterprises (SMEs) lacking the requisite personnel to adeptly navigate evolving standards. To bridge this gap, this research develops a fundamental understanding of language embeddings and large language models to assist in navigating engineering standards. This work examines the efficacy of three semantic-based approaches for engineering standard retrieval and further validates these approaches through an industry case study. Additionally, the Standards Augmented Requirements Generator (SARG) tool is presented, which provides a domain-specific approach to leveraging Retrieval Augmented Generation (RAG) in design contexts.
A Star Based Path Planning Strategy for Off-Road Vehicle Applications With Considering Wheel Mobility Performance
Authors: Fan Huashuai , Zhang Siyuan , Patel Parth , Moradi Lee , Vantsevich Vladimir 
Published: November 2024

Abstract
Most current path planning algorithms, used in various applications such as robotics and unmanned ground vehicles, aim to find the shortest path. However, they have not yet incorporated terrain-dependent speed limits into these calculations, which could affect the practicality and safety of the resulting paths. This study investigates the integration of mobility considerations into vehicle path planning algorithms within off-road terrain environments. In this context, the Wheel Mobility Performance (WMP) index is introduced as a novel metric designed to assess the mobility performance of off-road vehicles across varied terrain conditions. An optimized version of the A* algorithm, which integrates the WMP index, has been developed and implemented to navigate challenging terrains. Simulation results demonstrate that the optimized algorithm effectively reduces traversal time by selecting paths with enhanced mobility performance. These findings highlight the potential of integrating domain-specific metrics like WMP to improve path planning efficiency for off-road vehicles, with implications for autonomous navigation and robotics applications.
Generative Design of Planar Four-Bar Motions Using Conditional Variational Autoencoder
Authors: Halwah Amro , Nurizada Anar , Purwar Anurag 
Published: November 2024

Abstract
This paper introduces a novel deep generative approach to the approximate motion synthesis of planar four-bar mechanisms. Existing motion synthesis approaches for synthesizing four-bar mechanisms have generally focused on representing the motion as five or less discrete poses while not providing any guarantees on the practicality of the solutions, such as ones without circuit-, branch-, and order-defects. This paper presents a Conditional Variational Autoencoder (C-VAE) based approach for synthesizing practical mechanisms that can capture the overall motion requirement while generating a continuous motion approximately. The mathematical machinery in our work uses quaternion and kinematic mapping to represent the geometric constraints of dyads of four-bar mechanism as algebraic constraint manifolds in a three-dimensional projective space. The parameters describing the manifolds encode information about the type and dimensions. These parameters are used as input to a C-VAE that uses an arbitrarily large number of input poses approximating a motion as the condition. A procedure is presented for normalizing input motions such that they are invariant with respect to rotation, scaling, translation and reflection. The trained model can generate several pairs of constraint manifolds from which four-bar mechanisms with different parameters can be retrieved. These mechanisms’ coupler poses approximate the input’s coupler poses to varying degrees of error. A method for filtering mechanisms by the quality of match of their output motion to the input motion is also proposed. Different metrics for quantifying the similarity of an output mechanism’s motion with the input motion are presented for path and orientation separately.
Real-Time Multibody Co-Simulation of Thermal and Electromechanical Systems in an Electric Wheel Loader Model
Authors: Ikäheimo Eero , Khadim Qasim , Choudhury Tuhin , Kurvinen Emil 
Published: November 2024

Abstract
The electrification of on-road and off-road vehicles is increasing due to the depletion of fossil fuels and emissions from internal combustion engines (ICE). Real-time simulation analysis of electric vehicles can aid in both the design and analysis processes, enabling users to actively participate in the design phase. However, the computational costs associated with various subsystems, such as electro-thermal dynamics, hydraulics, tires, friction, and contacts etc. make real-time computational efficiency of electric vehicles cumbersome. To this end, this study introduces the real-time multibody co-simulation of multibody, hydraulics and electro-thermal dynamic systems. In the co-simulation approach, the multibody and hydraulics are solved within one solver, while the electro-thermal system is solved within a different solver. The communication between different solvers is performed using the TCP/IP (Transmission Control Protocol/Internet Protocol) connection. The proposed approach is implemented on a four degrees of freedom electric wheel loader. This system was modelled using the standard multibody system dynamics, lumped fluid theory, tyre model, Li-ion Thevenin battery equivalent circuit model and power transmission systems. The dynamic and electro-thermal performance of this system was tested in a simulation experiment under different temperatures of +0 °C, 15 °C and +30 °C. The results confirm the real-time computational performance of the electric wheel loader, even considering the computation of various subsystems. Further, the state of charge and temperature of the battery can be estimated during the vehicle working cycle. This study can contribute to efforts aimed at optimizing the design of high-efficiency electric machinery and developing intelligent electric vehicles through real-time simulations, thereby increasing the lifespan of the vehicles.
Exploring the Use of Immersive VR for Human-Robot Collaboration in Construction: An Interview Study With Industry Experts and University Faculty
Authors: Kim Ruby Yeeun , Cai Jiacheng , Chandran Swati , Liu Jieru , Peng Aoran , Miller Scarlett R. 
Published: November 2024

Abstract
The construction industry is increasing its adaptation of automation technologies such as robots to improve productivity and efficiency. However, human-robot collaboration requires new skills and knowledge from both workers and engineers. However, training for these new skills might be difficult due to the high-risk nature of construction environments, the complexity of the tasks involved, and the need for precise and safe coordination between humans and robots. This research proposes an immersive virtual reality-based learning environment that provides a safe and engaging space for learning and practicing human-robot collaboration skills. The learning environment aims to simulate realistic construction site scenarios where human-robot teams collaborate. The research explores the effectiveness of the virtual learning environment in terms of learning outcomes, usability, and user satisfaction. To do so, eight interviews were conducted with industry experts and university faculty. The results of the interviews showed that there is a significant potential for the immersive virtual reality-based learning environment to improve workers’ skills, increase their confidence, and promote collaboration in the construction industry. Experts emphasized the importance of realistic simulations that closely mimic actual construction scenarios to enhance the learning experience. The interview results were then used to develop a medium-fidelity prototype. Usability testing of the prototype with undergraduate Construction Engineering and Management (CEM) students revealed that the students found the virtual environment engaging and beneficial for understanding the dynamics of human-robot collaboration. They reported an increased awareness of safety protocols and improved their ability to anticipate and coordinate with robot actions. However, some students noted challenges with the interface, suggesting areas for improvement in the system’s intuitiveness and user guidance. The findings of this research have implications for the development of immersive virtual reality-based learning environments and can contribute to the improvement of human-robot collaboration in the construction industry. Further, the technology’s immediate advantages, such as enhancing the safety and effectiveness of human-robot collaboration in construction, make it an important tool for companies in this sector trying to streamline their procedures and develop their staff. This research suggests that immersive VR training can be a valuable component of a broader strategy to integrate automation technologies into the construction industry, ensuring that workers are equipped with the necessary skills and confidence to work effectively alongside robots.
Exploring the Impact of CRAH Unit Partial Failure in Hybrid-Cooled Data Centers
Authors: Heydari Ali , Gharaibeh Ahmad R. , Tradat Mohammad , Soud Qusai , Manaserh Yaman , Radmard Vahideh , Eslami Bahareh , Rodriguez Jeremy , Sammakia Bahgat 
Published: November 2024

Abstract
The escalating information technology (IT) loads in modern data centers (DCs) present formidable challenges for traditional room-conditioning systems. The heat dissipated from IT equipment has witnessed a significant surge due to the rapid development of data processing, retrieval, and storage, driven by changing technology trends and the growing demand for online services. This evolving landscape poses a substantial burden on air-cooling systems, pushing them to their limits, especially with the prevailing trend of rising power densities in microprocessors and the emergence of hot spots. Amidst these challenges, single-phase cold plate cooling is gaining traction as IT power densities experience a dramatic climb. However, the widespread adoption of this cooling method faces impediments such as the limited availability of chilled water supplies, constrained air distribution pathways, and the absence of elevated floors in many older DCs. In response to these limitations, liquid-to-air (L2A) cooling distribution units (CDUs) have emerged as an alternative method. By incorporating hybrid air and liquid cooling technologies, the industry aims to achieve precise, on-demand cooling through the utilization of various techniques. In the realm of hybrid cooling systems that integrate both air and liquid cooling technologies, a partial failure of the Computer Room Air Handlers (CRAH) introduces unique challenges. Such a failure has the potential to disrupt the delicate balance between air and liquid cooling components, leading to uneven heat dissipation. Moreover, the interdependence of liquid and air cooling in hybrid systems means that even a partial failure can trigger a domino effect, reducing the overall cooling efficiency of the system. This comprehensive study delves into the implications of partial failure in the CRAH unit within the high-power density racks of a hybrid-cooled DC. The investigation explores how this partial failure impacts various critical parameters, including cooling capacity (CC), supply air temperature (SAT), air flow rate, supply fluid temperature (SFT), and thermal testing vehicle (TTV) heater case temperatures. For the purposes of this study, two L2A in-row CDUs were utilized, with a combined total heat load of 129 kW supplied to three racks. The experimental setup is meticulously equipped with the necessary instruments for monitoring and assessing tests on both the liquid coolant and air sides. By addressing these issues, the research contributes valuable insights to the ongoing efforts to optimize data center cooling solutions in the face of evolving IT demands and technological advancements.
Thermophysical Property Measurement of GaN-on-AlN Wafers for Next-Generation RF Device Technologies
Authors: Walwil Husam , Shoemaker Daniel C. , Song Yiwen , Kang Kyuhwe , McIlwaine Nathaniel S. , Schuette Michael L. , Tweedie James S. , Sheppard Scott T. , Maria Jon-Paul , Choi Sukwon 
Published: November 2024

Abstract
Gallium nitride (GaN) high electron mobility transistors (HEMTs) play a crucial role in modern radio frequency (RF) power amplifiers. However, the performance and reliability of GaN HEMTs are negatively impacted by device self-heating. Hence, it’s imperative to thoroughly characterize the entire thermal resistance network within the material stack to pinpoint any bottleneck resistance components. In this study, a multi-frequency, multi-spot time-domain thermoreflectance measurement approach was used to measure the thermal resistance associated with the constituent layers and interfaces within a GaN-on-AlN wafer stack. The thermal conductivity of the GaN layer and the AlN substrate in both through-plane and in-plane directions were determined. The GaN/AlN interface exhibits a high thermal boundary conductance (TBC) of 500 MW/m2·K. This surpasses previously reported TBC values across GaN/SiC and GaN/diamond interfaces, highlighting the potential for improved thermal management of GaN HEMTs.
Effect of Die Parameters on the Thermomechanical Performance of PBGA Packages With Hybrid SAC-LTS Interconnects
Authors: Chakraborty Souvik , Mondal Debabrata , Suhling Jeffrey C. , Lall Pradeep 
Published: November 2024

Abstract
Low-temperature solder (LTS) pastes are increasingly used in surface mount technology, replacing the conventional SAC (Sn-Ag-Cu) solder pastes, due to their lower melting point, reducing reflow energy consumption as well as their ability to significantly reduce warpage during package reflow. However, second-level interconnects (solder joints) still remain prone to failure, influenced by factors like peak reflow temperature, time above liquidus and paste-to-ball volume ratio. Besides, understanding the effects of die parameters on Thermal Fatigue Life (TFL) in hybrid SAC-LTS solder joints is crucial for ball grid array (BGA) package reliability. In this study, finite element analyses of plastic ball grid array (PBGA) packages with hybrid SAC-SnBi solder joints were conducted, with varying LTS paste to SAC sphere volume ratio, under thermal cycling boundary conditions. The nonlinear plastic work accumulations in the critical joints were used to compare thermal fatigue life. Once an optimal LTS paste to SAC sphere volume ratio was selected, a series of finite element analyses were conducted with varying die-dimensions (size and thickness) to capture their effects on thermomechanical performance of hybrid joints. This study provides insights for designing reliable PBGA packages, emphasizing the significance of paste-to-ball volume ratio along with die configuration.