bluuebunny/crossref_metadata_2025_split huggingface dataset:
Individual and Team Cognitive Biases That May Affect Engineers’ Assessments and Decisions on Risk
Authors: Gernand Jeremy M. 
Published: January 2025

Abstract
Cognitive biases play a significant role in decision-making processes within engineering, particularly when it comes to assessing risks associated with technology and making decisions regarding the mitigation of those risks, but while these topics have been explored in a variety of contexts like economics and medicine, current research of these processes in engineering is limited. This paper presents a review of research into engineering decision making and the effects that cognitive biases have on this process. Seventeen experimental studies were reviewed for their insights on these critical questions. While sample sizes are small, and professional engineers have rarely been involved in these experiments, several risk-relevant cognitive biases were confirmed, and some avenues for mitigation of those biases have been explored. These results highlight the critical need for further research in this area, and that more fundamentally sound organization, increasing diversity of research funding and management are needed. These include the investigation of engineering teams, of which only 4 of the 17 included studies have so far explored, with the functioning of teams in the identification and mitigation of risk being current standard engineering practice and critical to the continued safe implementation of technologies to make the world a better place.
An Adaptive Spatial-Terminal Iterative Learning Strategies in Roll-to-Roll Control Problems
Authors: Wang Zifeng , Jin Xiaoning 
Published: January 2025

Abstract
Roll-to-Roll (R2R) systems, featuring a series of motorized or idle rollers, are pivotal for high-volume, continuous production of flexible substrates across various industrial applications. One of the significant challenges in R2R printing processes lies in maintaining tight alignment tolerances, as specified by the device resolution for multi-layer printed electronics. The alignment of the printed patterns in different layers is known as registration. However, the inherent deformability of flexible substrates, coupled with the complicated dynamics of rollers result in significant difficulties for high-precision registration control. The registration error (RE) is essentially the positional or dimensional deviation caused by undesired variations in substrate tensions and substrate speeds. Despite the employment of real-time feedback controllers such as Proportional-Integral-Derivative (PID) or Model Predictive Control (MPC) for R2R tension control, they are not responsive enough to transient disturbances and they fail to continually enhance performance in the presence of angle-periodic disturbances, a common scenario in R2R systems due to roller rotations. In this study, we introduce a Spatial-Terminal Iterative Learning Control (STILC) method, incorporating an adaptive basis function, aimed at eradicating RE in an R2R gravure printing process. This basis function, alongside the registration error, is utilized in the update of the STILC compensation profile via a P-type Iterative Learning Control law. Our numerical experiments show that the STILC with the adaptive basis function effectively eliminates the RE caused by roller-motor axis mismatches. Additionally, we also verify by simulations that our method provides better convergence performance than the STILC with an invariant basis function. This novel STILC approach is promising for various industrial applications involving spatially periodic disturbances, especially those with unknown dynamics and without given state trajectories to track rigorously, which is common in engineering practice.
Deep Q-Learning Based Optimal Energy Management of a Plug-in Hybrid Electric Vehicle
Authors: Narang Vikas , Neema Kartavya , Anwar Sohel 
Published: January 2025

Abstract
The Energy Management Strategy (EMS) of PHEV controls the motor power & engine power to reduce overall energy consumption and manage the battery state of charge (SOC). EMS strategies for PHEV are typically divided into two categories rule-based and optimization based. Rule-based EMS are easy to implement and simple to understand. However, their application is limited to specific driving cycles which makes it difficult to achieve the optimal fuel economy. Optimization based on the other hand requires a system model to compute the optimal solution with given constraints. Here we propose a value-based reinforcement learning approach called Deep Q learning-based (DQN) strategy to determine the optimal action to maximize the fuel economy, without the need to have a system model. A DQN algorithm is trained on a UDDS cycle using a PHEV simulation model. The agent interacts with the PHEV environment during training and stores the transactions St, at, rt, St+1, then random mini-batches are sampled to calculate the state-action values Q(s, a; θi), and the target network is used to generate target Q value. These two are then used to compute loss function to update neural network weights. Fuel economy and convergence characteristics of DQN are discussed and compared with rule-based EMS and performance is analyzed on unseen HWFET cycle.
Early Detection of Faulty Operation of Air Conditioning Systems in High Energy Consuming Manufacturing Sites
Authors: Allen-Magande Hugh , Khazaii Javad , Esmaeili Amin , Khazaei Ali 
Published: January 2025

Abstract
Manufacturing facilities that produce high-precision electronics, solar panels, pharmaceuticals, and other critical products demand a controlled environment with minimal airflow velocities at high airflow volumes to prevent product contamination. Consequently, the energy consumption of these facilities’ heating, ventilation, and air conditioning (HVAC) systems is substantial. This study focuses on assessing and enhancing the energy efficiency of HVAC systems in such high energy consuming manufacturing sites, aligning with ASHRAE guidelines using early fault prediction. The authors have developed a model of typical manufacturing site and have utilized pattern recognition approach to predict the faulty operation of the HVAC system in one type of such facilities that are generally referred to as clean space as soon as it happens. This will protect the system from lengthy and costly faulty operation, before it is recognized by the maintenance personnel, during pre-scheduled service plans. The findings of this research can significantly expedite the detection of HVAC system malfunctions when integrated into the manufacturing site’s automatic control system.
Formulation of Loosening Phenomena in Actual Machines and a Consideration of Loosening Visibility
Authors: Hareyama Soichi , Kobayashi Satoshi , Ozawa Kazumi , Tsuji Kenji , Yamamoto Shinya 
Published: January 2025

Abstract
The results of the previous study showed that the loosening phenomenon of bolted joints used in actual equipment has a strong linear relationship on both logarithmic coordinates. This linear relationship is also observed in the tendency of axial tension reduction in Junker-type loosening test machine. This relationship can be displayed as a regression line and regression equation, and can be utilized for loosening lifetime estimation. The axial tension reduction phenomenon usually can’t be measured without using ultrasonic measurement methods or strain gauges etc. Therefore, measurement required preparatory work, and loosening can’t be directly observed. In this study, the possibility of visualization of loosening on a practical level is examined using a bolt with an axial force indicator. At first, static tightening and loosening tests are conducted and the indications of the indicator on the bolt are in good agreement with the axial tension from the tightening side to the loosening side. In the dynamic loosening test, the test results are organized on both logarithmic coordinates, and a regression line can be drawn, confirming a strong linear relationship similar to the measurement results on the actual machine. The axial force indicator values measured intermittently can also be plotted on the regression line. We confirmed that the use of bolts with axial force indicators can be used to accurately grasp loosening (tendency in axial tension decrease) and that the regression line easily obtained by continuous measurement can also be obtained.
Experimental Study of Composite Prepreg Layup and Fused Filament Fabricated Hydrodynamic Thrust Bearings
Authors: Ali Muhammad , Yasko Isaiah 
Published: January 2025

Abstract
This research experimentally investigates the operating performance of composite hydrodynamic thrust bearings manufactured using prepreg layup and three-dimensional fused filament fabrication methods. Literature on the specific composite formulations being experimentally tested as hydrodynamic thrust bearing stock material in this study is not publicly available. Experimental analysis was conducted on a traditional aluminum-based, bisphenol resin and carbon fiber prepreg layup, and a three-dimensional fused filament fabricated chopped carbon fiber filled nylon hydrodynamic tapered-land thrust bearing. Each bearing was subjected to specific loads and sliding speeds ranging from 0.18–0.72 MPa and 4.11–8.23 m/s, respectively. Oil type and temperature was held constant throughout the analysis. Film pressure distribution, film thickness, and the change in bearing temperature due to frictional heating were recorded for each test. Results show that both composite bearings operated with improved performance when compared to a standard aluminum bearing. The composite layup bearing operated with pressures, minimum films, and bearing temperature elevations 43% higher, 30.5% thicker, and 25% lower than the aluminum bearing, respectively. The composite fused filament fabricated bearing operated with pressures, minimum films, and bearing temperature elevations 254% higher, 113% thicker, and 34% lower than the aluminum bearing, respectively. The results presented here provide justification for further research and potential deployment of composite layup and FFF materials in the hydrodynamic thrust bearing industry.
Prediction Current Control of Tubular Linear Permanent Magnet Synchronous Motor With Variable Coupling Length
Authors: Gao Qiantong , Li Bingran , Ye Peiqing 
Published: January 2025

Abstract
The tubular linear permanent magnet synchronous motor (TLPMSM) can directly provide large thrust without the need for transmission mechanisms like screws. However, the coupling length between the stator and mover in TLPMSM may change during operation, leading to parameter changes and thrust fluctuations. In this paper, we propose an improved deadbeat prediction current control method for linear motors with variable coupling length (VC-DPCC). To solve the model mismatch and parameter mismatch problems, we first analyze the variation of inductance and flux linkage and calibrate the fundamental component of the parameters based on the analytical model. Then, our proposed method suppresses the impact of harmonic components of parameters and eliminates the current prediction error caused by magnetic field distortion. Finally, our proposed method is verified on an experimental prototype. Compared to traditional control methods, the proposed method offers faster response and reduced current fluctuations, significantly improving the performance of TLPMSM in terms of current and thrust control.
Mechanical Properties of FDM Printed PLA-MXene Composites
Authors: Srinivas Pooja , Barsoum Imad , K. Abu Al-Rub Rashid , Zaki Wael 
Published: January 2025

Abstract
This study explores the integration of MXene nanomaterials with Polylactic Acid (PLA) for 3D printing via fused deposition modeling (FDM). Incorporating MXene at concentrations of 0.125, 0.25, 0.5, and 1 wt% into PLA was carried out to enhance the mechanical and thermal properties of the resultant composites. Mechanical testing, including tensile and dynamic mechanical analysis (DMA), showed notable improvements in mechanical strength with MXene inclusion. The most significant enhancements were observed at 0.25 wt% MXene, with an increase in tensile strength and DMA revealed increased stiffness and energy dissipation. Differential scanning calorimetry indicated minimal changes in transformation temperatures across all composites. SEM analysis of fracture surfaces confirmed the transition from brittle to ductile fracture modes with increasing MXene content, particularly evident at intermediate concentrations. However, further increases in MXene content (1 wt%) led to decreased tensile properties, likely due to agglomeration effects. These findings underscore the potential of PLA-MXene composites in applications requiring improved mechanical performance and vibration damping, demonstrating the effectiveness of 2D materials in enhancing polymer capabilities through additive manufacturing.
Comparison of Mechanical Behavior of Macro and Micro-Sized Ecoflex 0030 and 0031
Authors: Janardhana Ranjith , Guler Zeynel , Akram Fazli , Jackson Nathan 
Published: January 2025

Abstract
Dynamic system applications such as soft robotics, energy harvesters, smart skin, and wearable sensors, require highly stretchable elastomer materials. Ecoflex is a silicone elastomer that is increasing in demand but, the mechanical properties at the micro-scale for Micro Electronic Mechanical Systems (MEMS) are not fully understood. Ecoflex also has different formulations which have varying properties, and the material can be mixed with composites or varying ecoflex formulations to alter the mechanical properties. In this work, we illustrate the comparison of mechanical properties of micro and macro-sized samples of ecoflex 0030 and 0031 under uniaxial tensile test. Additionally, the mechanical response of the micro-sized specimens is studied by varying mixing ratios of two different formulations of ecoflex (0030 and 0031). Material properties of produced samples are investigated by subjecting samples to batch-to-batch repeatability test, cyclic loading, Mullins effect, and stress recovery test. Results demonstrate that the micro-sized samples exhibit higher stiffness in comparison to macro-sized samples. Blending ecoflex 0030 with higher a concentration of 0031 enhanced the ultimate strength and stretchability of the micro-sized sample in case of 1: 3 and 2: 3 (based on the 0030: 0031 ecoflex).
Uncertainty Analysis Methodology for Fluid Dynamic Gauging Characterization
Authors: Olufade Adesola 
Published: January 2025

Abstract
The in-situ characterization of soft deposits in liquid media is important in several scientific and industrial applications but presents substantial technological challenges. Nevertheless, fluid dynamic gauging (FDG), which is based on the principle of fluid flow through a nozzle that is in close proximity to a wall, can be used to estimate the thickness of wet deposits but its uncertainty analysis has not been rigorously addressed in the literature. This paper reports the development of a comprehensive device-agnostic methodology that can be used to characterize the uncertainty of FDG instruments. Furthermore, the uncertainty analysis of a calibrated instrument that has a horizontal configuration, in which the flow rate is controlled but the pressure drop is measured, is presented. The results indicate that the calibrated instrument can be used to estimate the thickness of deposits to ±81 μm at a 95% confidence. In addition, the uncertainty in thickness was found to be dependent on the confidence level of the expanded uncertainty coefficient, magnitude of the estimated thickness, and final clearance or height of the nozzle from the wall. The highest sensitivities of the instrument to the systematic uncertainties in the flow rate and pressure drop were observed at relatively low and high thickness estimates, respectively. An important contribution of the paper is the introduction of an uncertainty component from external mechanisms that may be used for independent displacement measurements or position control in the course of operating FDG instruments.
Evaluation of Biochar Production Through Slow Pyrolysis of Corn Agroindustry Residues in the Department of Córdoba, Colombia
Authors: Rhenals Hoyos Jorge Emilio , Cogollo Torres Cristina Isabel , Bula Antonio , Gómez Vásquez Rafael David , Rhenals Julio Jesús David , Mendoza Fandiño Jorge Mario , Arrieta Almario Álvaro Ángel 
Published: January 2025

Abstract
Biomass emerges as a promising source for bioenergy generation and valuable by-products, offering a sustainable alternative for utilizing agro-industrial waste. Among thermochemical conversion techniques, pyrolysis stands out for its capacity to transform biomass into three main products: Syngas, Bio-oil, and Biochar. Biochar, the result of biomass conversion, can represent between 4% and 40% of the original mass, depending on the conditions of the pyrolysis process. This solid material is of particular interest due to its multiple potential applications, which are regulated by international standards such as the International Carbon Standard and European EBC certification. In this study, a predictive model was developed to characterize the properties of corn cob biochar produced at different pyrolysis temperatures. The model is based on experimental thermogravimetric analysis and elemental analysis of the biomass, using empirical correlations to predict H/C ratios, OC content, inorganic content, heating value, and formation enthalpy. The results show that the pyrolysis temperature significantly affects the characteristics of biochar. It was observed that the thermochemical transformation of biomass reaches a maximum in gross calorific value at temperatures of 500°C, with an H/C ratio of 0.3 and an O/C ratio of 0.1. The formation enthalpies of biochar increase with the pyrolysis temperature, associated with the increase in organic carbon concentration. Furthermore, it is estimated that biochar begins to form at temperatures above 380°C, achieving a minimum H/C ratio of less than 0.7.
An Optimized Laser-Induced Graphene-Based Electrochemical Biosensor Modified With Graphene-Conductive Ink for Alpha-Fetoprotein Detection
Authors: Tabassum Ridma , Sarkar Pritu Parna , Jalal Ahmed Hasnain , Ashraf Ali , Islam Nazmul 
Published: January 2025

Abstract
In this work, a label-free electrochemical immunosensor was fabricated for the detection of alpha-fetoprotein (AFP) based on Laser-Induced Graphene (LIG) and modified with developed graphene polyaniline (G-PANI) ink. Alpha-fetoprotein (AFP) is a prominent biomarker for the diagnosis of primary liver cancer. For the prevention and treatment of hepatocellular carcinoma (HCC), it is essential to ensure the accurate detection of AFP. The performance of our designed immunosensor was evaluated by differential pulse voltammetry (DPV) and Chronoamperometry (CA) techniques were used to detect cancer proteins in the phosphate buffer saline (PBS). Both electrochemical analyses in different concentrations of AFP resulted in a linear relationship between peak current and the logarithmic plot of the concentrations with a correlation coefficient, R2 = 0.99. The limit of detection was also calculated to be ∼0.73 ng/mL in the range of 4 ng/mL-500 ng/mL. The nearly identical current outputs in the DPV measurements were obtained consecutive ten times which represents the sensor’s stability with an RSD of 1.66%. The results showed that the proposed immunosensor can be successfully used to detect AFP.
Enhancing Safety in Collaborative Workspaces: Defining Attention and Avoidance Zones Using Path Planning With Mobile Robotic Systems
Authors: Cheng Marvin H. , Camargo Hugo E. , Haney Justin M. 
Published: January 2025

Abstract
Ensuring the safety of human workers who work alongside mobile robots is extremely important. This study defines and delineates attention zones and avoidance zones for human workers to include both visual and audible aspects. Attention zones aims to keep workers aware of potential hazards. Specifically, visual attention zones focus on field of view and audible attention zones consider noise levels in the workspace. By establishing these zones, mobile robots can generate warning signals as human workers approach to minimize the risk of collisions. After determining optimal attention zones, researchers identify path planning strategies that can be adjusted based on visual and audible warnings to avoid potential accidents. The proposed attention zone-based warning system and the contact avoidance zone represent a critical advancement in enhancing worker safety in collaborative workspaces to promote a safer workspace.
Identification of Maxwell Model Parameters of Viscous Dampers With Elastic Traits
Authors: Farran Abdulrhman Mohmmed H. , Kashani Reza 
Published: January 2025

Abstract
Two methodologies for determining Maxwell model parameters for a viscous damper are introduced. Both approaches rely on experimental identification employing harmonic displacement loading (sinusoidal loading) across varied excitation frequencies. This entails simultaneous measurement of the damper’s displacement and the resultant force to construct its hysteresis curve. By integrating the force with respect to displacement along the hysteresis closed path — representing the area within this path — the energy dissipation of the damper is elucidated.
The first method involves starting the modeling process at each frequency with Kelvin-Voigt (K-V) model. This entails evaluating the parameters of the K-V model and subsequently relating them back to the parameters of the Maxwell model. The second method involves determining the phase difference between the measured force and displacement within one cycle. This, coupled with the utilization of the hysteresis curve, enables the direct extraction of Maxwell model parameters. Both methodologies have been employed in identifying the parameters of an eddy current damper at two frequencies, yielding similar results.
Adjustable Pedals in Automotive Design: a Digital Human Modeling-Based Clearance, Clash, and Reach Design Study
Authors: Joffe Gabrielle B. , Demirel H. Onan 
Published: January 2025

Abstract
Although many interior vehicle components, including seats, steering wheels, and mirrors, are commonly adjustable in modern cars, many drivers and passengers still experience a poor occupant-vehicle fit. Subpopulations such as short, tall, and pregnant occupants encounter comfort- and safety-related concerns in the form of increased risk of airbag injury, a lack of legroom, and inadequate abdomen-steering wheel clearance. Despite the fact that pedal placement is a crucial factor that directly impacts a driver’s choice of seating position, pedal adjustability is only featured in a limited selection of modern vehicles. The early design approach proposed in this research work uses digital human modeling (DHM) to assess improvements to automotive occupant packaging as a result of implementing adjustable pedals. Three case studies are presented, depicting “what-if” scenarios that analyze clearance, clash, and pedal reach in generic, low-fidelity computer-aided design (CAD) vehicle models with short, tall, and pregnant occupants. The results of this study demonstrate how adjustable pedals benefit occupants and highlight how DHM can be used proactively to improve occupant-vehicle fit in the early design phases.