bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the integration of artificial intelligence in math education: Preservice Teachers’ experiences and reflections on problem‐posing activities with ChatGPT
Authors: Kim Young Rae , Park Mi Sun , Joung Eunmi 
Published: January 2025

AbstractThis study addresses the need for research that incorporates educational perspectives and theories to understand the impact of Artificial Intelligence (AI) on learning and teaching. Utilizing AI‐integrated mathematical problem‐posing (AIM) activities and the AI‐powered scaffolding (AIS) strategy, the research investigated preservice teachers’ (PTs’) experiences with AI and their capacity for error recognition and correction in problems posed by ChatGPT. The findings reveal that while the PTs excelled at verifying error‐free problems, they struggled significantly with identifying and correcting errors, indicating a gap in their instructional preparedness. The study demonstrates that AIM activities are effective tools for assessing and developing PTs’ error recognition and correction skills. Additionally, AIM activities support the transfer of mathematical knowledge to pedagogical and instructional practices, contributing to PTs’ growth as adaptable educators. The research highlights the need to integrate AI‐based activities into PT training to build robust mathematical knowledge and teaching skills. Focusing on learning, pedagogy, and the human aspects of technology use, AIM activities and the AIS strategy enable PTs to engage critically with AI outputs and enhance their metacognitive skills. These insights emphasize the importance of incorporating AI‐integrated methods into teacher preparation programs to better equip future educators for an AI‐driven educational landscape.
Exploring the interactions among instructor, prospective teachers and AI in facilitating mathematics learning
Authors: Yılmaz Zuhal , Galanti Terrie M. , Naresh Nirmala , Kanbir Sinan 
Published: January 2025

AbstractMathematics teacher educators (MTEs) have an important obligation to prepare prospective elementary mathematics teachers (PEMTs) to use artificial intelligence (AI) in their classrooms. To better understand how AI can mediate the interactions between students, teachers, and mathematics, we examined PEMTs' perceptions of AI‐integrated learning sessions in a foundational content course. We employed inductive and deductive thematic analysis to categorize these perceptions along three edges of a didactic tetrahedron for teaching and learning (AI–PEMT interactions, PEMT–instructor interactions, and AI–instructor interactions). The instructor's use of Khanmigo as an AI tutor to facilitate small‐group problem‐solving yielded several experiences categorized along the AI–PEMT and instructor–PEMT edges of the didactic tetrahedron. AI–PEMT interactions created robust opportunities to communicate and construct mathematical knowledge with Khanmigo. Instructor–PEMT interactions provided a mathematical and technological “safety net”, mitigating challenges PEMTs faced in communicating with AI. Limited evidence of PEMT descriptions of AI–instructor interactions suggests MTEs should intentionally model their instructional decision‐making and partner with AI to facilitate their lessons. PEMTs can benefit from discussing how teachers foster meaningful student engagement with AI beyond getting answers to problems. Implications for recognizing challenges and realizing the affordances of using AI to deepen mathematics learning in content courses for PEMTs are offered.
Full Genomic Sequence of the HLA‐DQB1*06:304N Allele Identified by Next Generation Sequencing
Authors: Liacini Abdelhamid , Geier Steven 
Published: January 2025

ABSTRACTThe full‐length sequence of HLA‐DQB1*06:304N covers the 5′‐untranslated region (UTR), all introns and exons, and the 3′ UTR.
Exploring student profiles in statistical performance based on self‐regulation, attitudes toward statistics and computer self‐efficacy
Authors: O'Connor Patrick A. , Graham Agnieszka J. 
Published: January 2025

AbstractUnderstanding factors influencing students' success in statistics is crucial, as many psychology students struggle with the subject, affecting their academic confidence and career readiness. This study examines profiles of undergraduate psychology students learning statistics, focusing on attitudes, IT self‐efficacy, and statistical performance. A sample of 175 first‐year students completed assessments on attitudes toward statistics, academic delay of gratification, test anxiety, and computer self‐efficacy, plus an end‐of‐year exam. Latent profile analysis identified two groups: “Motivated Performers” with higher positive attitudes toward statistics, greater computer self‐efficacy, and lower statistical anxiety, and “Anxious Learners,” who scored lower in these areas. Although Motivated Performers outperformed Anxious Learners on the exam, the difference was nonsignificant. Academic delay of gratification was the only significant predictor of performance, regardless of profile membership. These findings emphasize promoting positive attitudes and I.T. confidence in statistics and highlight the importance of self‐regulatory academic behaviors in preparing students for statistics exams.
Spatial Performance Approach to Machine Learning Algorithms: A GIS‐Based Comparison Analysis for Real Estate Valuation
Authors: Genc Nihal , Colak H. Ebru , Ozbilgin Ferdi 
Published: January 2025

ABSTRACTMachine learning algorithms are frequently used in mass valuation activities today. These algorithms are believed to perform differently depending on various factors such as different functions, model parameters, data quantity, and quality. Another factor that is often overlooked is location. In this study, the performance of a set of machine learning algorithms in predicting real estate values was evaluated from a spatial perspective. To highlight the spatial performance of the algorithms, a study area was chosen where the factors affecting value are spatially heterogeneous. Value predictions were made using Linear Regression, Random Forest, Support Vector Machine, Regression Trees, Gaussian Process Regression, Artificial Neural Network, and Least‐Squares Boosting algorithms. Bayesian Optimization was used to improve the performance of the algorithms. The performances of the algorithms were compared using R2, RMSE, MSE, MAPE, and MAE metrics. The Random Forest model showed the best performance according to these metrics. Spatial predictions were made in the GIS environment using the IDW spatial interpolation method based on test data. The spatial distribution of the predictions and actual housing values were shown by producing maps. The spatial distribution of the algorithms was compared both with the actual values and with each other. It was found that, aside from the Random Forest model, other models also performed quite well in some locations and even produced better results than the Random Forest model. This demonstrates that location affects the performance of ML algorithms and that performance may vary depending on location. This study is expected to contribute to many fields of science where ML algorithms are used, especially in real estate valuation, and to bring a new perspective to the literature.
An experimental comparison and user evaluation of three different dried plasma products
Authors: Ehn Kristina , Skallsjö Gabriel , Romlin Birgitta , Sandström Göran , Sandgren Per , Wikman Agneta 
Published: January 2025

AbstractBackground and ObjectivesAccess to blood components in pre‐hospital bleeding resuscitation is challenging. Dried plasma is a logistically superior alternative, and new products are emerging. Therefore, we aimed to evaluate laboratory and practical differences in three differently produced dried plasma products.Materials and MethodsSingle‐donor lyophilized LyoPlas®, pooled‐donor, lyophilized and pathogen‐reduced OctaplasLG Powder®, and single‐donor sprayed‐dried FrontlineODP™ along with fresh plasma (in‐house, pre‐FrontlineODP and OctaplasLG) as controls were analysed (n = 8). Laboratory tests included measurements of various coagulation factors and thromboelastography. The practical evaluation of the dried plasma products included preparation time, time to dissolve the dried plasma and total time, together with subjective opinions from eight clinical users.ResultsThe coagulation factor content was within human reference ranges for all dried plasma, with approximately 10%–20% loss compared with fresh plasma. More variations were observed in the single‐donor products compared with the pooled products. Clot formation analysed by thromboelastography showed normal graphs. Reconstitution time was similar, ranging from on average 7–9 min. In the user evaluation, the reconstitution time and the possibility of using a plastic bag for the transfusion were emphasized as important, the latter fulfilled by two of the products.ConclusionThe study supports that dried plasma may be produced with lyophilization or spray‐drying technique, as well as with the addition of pathogen reduction, with preserved coagulation capability. The products were reconstituted in acceptable time and deemed feasible for pre‐hospital use by eighth test users.
Implementation of Flow Control Methods for Design of Connecting Duct of Annular Sector Cascade Tunnel
Authors: Bhavsar Hardikkumar , Mistry Chetan S. 
Published: January 2025

Abstract
The present study discusses the effect of various flow control methods implemented to develop an intermediate turbine duct (ITD), providing a continuous flow path between the contraction duct of the wind tunnel and the annular sector cascade (ASC) test section to deliver the required flow. The S-shaped diverging passage of the ITD with continuously increasing mean radius imposes a major challenge to achieving the required inlet flow angle with periodicity at the entry of the ASC. The numerical study was performed in ansyscfx to analyze the effect of active and passive flow control methods on the ITD exit flow delivered to the ASC test section. It was found that inlet guide vanes (IGVs), incorporated within the ITD as a passive flow control method, direct the flow through ITD, and approximately required inlet flow condition with reasonable periodicity was achieved at the entry of the ASC test section. However, the opening slot on the casing endwall, an active flow control method, shows the adverse effect on the flow angle distribution at the entry of the ASC test section. The change in axial spacing between IGVs and the test vane cascade shows a negligible effect on the cascade entry flow field. The experimental flow field measurement performed using a five-hole pressure probe showed that IGVs successfully directed the flow through the ITD, precisely meeting the requirements at the test section. The surface oil flow visualization confirms the formation of various flow features captured in the computational study.
A Force-Sensor-Less Approach for Rapid Young’s Modulus Identification of Heterogeneous Soft Tissue
Authors: Wang Zhen , Xu Tian , Shen Mengruo , Lei Yong 
Published: January 2025

Abstract
Due to individual differences, accurate identification of tissue elastic parameters is essential for biomechanical modeling in surgical guidance for hepatic venous injections. This paper aims to acquire the absolute Young’s modulus of heterogeneous soft tissues during endoscopic surgery with two-dimensional (2D) ultrasound images. First, we introduced a force-sensor-less approach that utilizes a precalibrated soft patch with a known Young’s modulus and its ultrasound images to calculate the external forces exerted by the probe on the tissue. Second, we introduced a Kriging-based inverse algorithm to identify the relative Young’s modulus (RYM) between the inclusion and the background tissue. The RYM was estimated based on 2D plane strain approximation and mapped to the RYM of three-dimensional (3D) soft tissue through a trained Kriging model. Finally, we developed a direct method to identify the background Young’s modulus (BYM) based on calculated external forces and RYM. The simulation results demonstrate the high efficiency and robustness of the Kriging-based inverse algorithm in identifying RYM. Physical experiments on the three phantoms show that the errors of the identified BYM and RYM are all below 15%. The proposed methodology for Young’s modulus identification is feasible and achieves satisfactory accuracy and computational efficiency in both simulations and physical experiments.
Improved Augmented Lagrangian Relaxation-Assisted Analytical Target Cascading for Multidisciplinary Design Optimization
Authors: Zhou Xiaowei , Li Wei 
Published: January 2025

Abstract
Analytical target cascading (ATC) is an optimization strategy designed for multilevel and multidisciplinary design optimization (MDO) problems. Its core aim is to enhance the overall convergence of the system by reducing the inconsistency between different levels. In previous studies, although the augmented Lagrangian relaxation method can achieve rapid and accurate positioning of the optimal solution, it is highly sensitive to the penalty parameters, which can easily cause instability in numerical calculation. In contrast, the Lagrangian duality theory effectively reduces the numerical instability by adaptively adjusting the parameters, but this method is at the expense of increasing the computational time and the number of iterations. This study proposes an analytical target cascading combined with the Maclaurin series method (ATC-MAC), which aims to reduce parameter sensitivity and accelerate convergence to the optimal solution. The method uses the Maclaurin series to approximate the cross terms in the augmented Lagrangian function to reduce its interference with the main function. Meanwhile, the step size updating strategy is optimized by combining the duality theory and the multiplier method. Through the application in three numerical cases and one engineering case, the effectiveness and practicability of the ATC-MAC method are verified.
Compliant Toggle-Linkage Mechanisms: A New Type of Displacement–Force Amplification Principle
Authors: Wu Shilei , Ling Mingxiang 
Published: January 2025

Abstract
A compliant toggle-linkage mechanism with the dual functions of displacement amplification and reduction is proposed. A series of compliant amplification mechanisms are examined by combining multi-group flexure toggle linkages, showcasing a flexible design with modular flexure toggle-linkage building blocks. A compliant toggle-lever mechanism is then investigated in detail as a displacement amplifier and a reducer for piezoelectric actuators by exchanging the input and output ports. Its kinetostatic and dynamic performances are captured using an improved dynamic stiffness matrix modeling procedure with high prediction accuracy and robustness, which shifts the end nodes of all adjacent flexure hinge/beam members to be coincident in order to deal with the irregular connection of building blocks. It is interestingly observed that the output displacements with forward and reverse motion directions can be simultaneously achieved with such a compliant toggle-lever mechanism by changing the structural parameters. Finally, the relationship of displacement amplification and reduction ratios between the input and output ports is clarified and discussed by comparing the results among the dynamic stiffness matrix model, finite element simulation, and experimental testing.
Maximum Stress Minimization Via Data-Driven Multifidelity Topology Design
Authors: Kato Misato , Kii Taisei , Yaji Kentaro , Fujita Kikuo 
Published: January 2025

Abstract
The maximum stress minimization problem is among the most important topics for structural design. The conventional gradient-based topology optimization methods require transforming the original problem into a pseudo-problem by relaxation techniques. Since their formulation methods and the parameter settings significantly influence optimization, a method is required to accurately solve the original maximum stress minimization problem without using relaxation techniques. This paper focuses on this challenge and compares solutions obtained by gradient-based topology optimization with those obtained by solving the original maximum stress minimization problem without relaxation techniques. We employ data-driven multifidelity topology design (MFTD), a gradient-free topology optimization based on evolutionary algorithms. The basic framework involves generating candidate solutions by solving a low-fidelity optimization problem, evaluating these solutions through high-fidelity forward analysis, and iteratively updating them using a deep generative model without sensitivity analysis. In this study, data-driven MFTD incorporates the optimized designs obtained by solving a gradient-based topology optimization problem with the p-norm stress measure in the initial solutions and solves the original maximum stress minimization problem based on a high-fidelity analysis with a body-fitted mesh. We demonstrate the effectiveness of our proposed approach through the benchmark of L-bracket. As a result of solving the original maximum stress minimization problem with data-driven MFTD, a volume reduction of up to 22.6% was achieved under the same maximum stress value, compared to the initial solution.
A Robust Algorithm for Solving Heterogeneous Systems of Equations Arising in Kinetostatics of Compliant Mechanisms
Authors: Tari Hafez 
Published: January 2025

Abstract
Design and analysis of compliant mechanisms draw constraints from both kinematics and statics, resulting in heterogenous systems of equations. This heterogeneity makes the system numerically ill conditioned and not amenable to the entire solution set (conversely seen for rigid mechanisms), thus burdening Jacobian-based solution procedures and the underlying design or analysis. This article proposes a robust restrained line search for Newton’s method for the numerical solution of such heterogeneous systems. This novel line search (i) prioritizes accuracy of the underlying function approximation over the widely used minimization of the functional residual error and (ii) employs a dual objective function definition with closed-form Hessian formulation to maximize performance. It addresses gaps existing in the classical Newton’s method and makes the method truly localized, even more than the MATLAB’s nonlinear solver “fsolve.” Examples from 2D inverse and 14D coupled kinetostatics of compliant mechanisms are presented to demonstrate the robustness of the proposed algorithm, rendering it into a black-box solver for kinetostatic problems.
Soft Robot Kinematic Control Via Manipulability-Aware Redundancy Resolution
Authors: Azizkhani Milad , Ha Junhyoung , Gunderman Anthony L. , Chen Yue 
Published: January 2025

Abstract
This article addresses the kinematic control of a redundant soft robotic arm. Full pose kinematic control of soft robots is challenging because direct application of the classical controllers developed based on rigid robots to soft robots could lead to unreliable or infeasible motions. In this study, we explore the manipulability property of a soft robotic arm and develop an advanced resolved-rate controller that prioritizes position over orientation control and switches its modes and gains based on position and orientation manipulabilities, enabling stable motion even when the robot is close to the singular configurations. The simulation and experimental results indicate that our proposed method outperforms previous methods in terms of both accuracy and smoothness during operation.
Enhanced Thermal Regulation of Lithium-Ion Batteries Using Composite Phase Change Material–Fin Structures for High Heat Dissipation
Authors: Shehabaz S. M. D. , Gugulothu S. K. , Muthyala Raju , Barmavatu Praveen 
Published: January 2025

Abstract
Poor thermal conductivity is common in batteries that use phase change material (PCM)-based thermal management systems (BTMS). This study introduces cylindrical rings and longitudinal fins to improve heat transfer. The thermal performance of several BTMS configurations is the primary focus of this analysis. The results show that the PCM–fin system offers superior temperature management compared to both pure PCM and pure battery systems. To understand the mechanisms, numerical simulations were compared against experimental data available in the literature. Numerical analysis shows that the fin shapes increase heat transmission and create a thermal conductive network inside the PCM, improving battery thermal performance. The study indicates that the effectiveness of thermal management is influenced by the number of extended surfaces and rings, as well as the heat generation rate. One cylindrical ring and eight longitudinal fins, with a dimensionless spacing of 0.2 between the battery and the ring, are recommended. The PCM–fin technology also effectively controls the battery's temperature rise at 20 W, proving its durability in high-heat conditions.
Evaluation of CRDi Engine Fueled With Binary and Ternary Blends of Biodiesel, Butanol, and Diesel
Authors: Amba Prasad Rao G. , Vinod Babu M. 
Published: January 2025

Abstract
An automotive-type four-cylinder turbocharged common rail direct injection (CRDi) engine has been considered to utilize a higher proportion of biodiesel beyond 20% while effectively addressing the NOx–soot engine performance trade-off with biodiesel, butanol, and petro-diesel fuel blends. Mixed feedstock obtained from Sterculia foetida oil and used palm oil in equal proportions to prepare biodiesel, and three binary and three ternary blends, prepared in different proportions, have been considered. Fuels were prepared with butanol blended with biodiesel and petro-diesel in different proportions. Investigations were carried out at various speed and load conditions similar to idling, urban, and highway drive conditions by varying the main injection timing and boost pressure, keeping other parameters at optimized values. The speed test conditions are chosen from a modified Indian driving cycle to simulate real driving conditions. The results obtained with different blends are compared against engine operation with neat petro-diesel operation. The engine is incorporated with a variable geometry turbine for increasing intake air and an open electronic control unit (ECU) for setting the operating parameters. For facilitating split injections, a dwell period (time between start of pilot and start of main injection) of 27° crank angle (CA) [i.e., with pilot injection timing (PIT) at 28 deg before top dead center (bTDC) and main injection timing (MIT) at 1 deg bTDC], pilot injection quantity (PIQ) of 11%, and fuel injection pressure (FIP) of 61.4 MPa are recommended for the speed and load conditions under consideration and the given engine configuration. Among the tested ternary blends, B20Bu10D70 and B30Bu10D60 showed improvement in NOx and smoke emissions, with low-temperature combustion-like conditions observed. The use of renewable fuels will effectively address UNO's sustainable development goals.
Restoring ceftolozane susceptibility: a role for diazabicyclooctane β-lactamase inhibitors?
Authors: Dorazio Ava J. , Kline Ellen G. , Squires Kevin M. , Pogue Jason M. , Van Tyne Daria , Shields Ryan K. 
Published: January 2025

ABSTRACT

Paired baseline and post-exposure isolates from 34 patients who developed ceftolozane–tazobactam (TOL-TAZ) resistance following treatment of multidrug-resistant (MDR)
Pseudomonas aeruginosa
infections were analyzed to determine if ceftolozane with an alternative β-lactamase inhibitor could restore susceptibility. The median baseline TOL-TAZ MIC was 2 mg/L; 88% of post-exposure isolates harbored new
ampC
mutations. Median MIC fold-increase from baseline was 32-, 24-, 16-, and 6-fold for ceftolozane–tazobactam, ceftolozane–avibactam (AVI), ceftolozane–relebactam (REL), and ceftolozane–durlobactam (DUR), respectively. Enhanced ceftolozane–durlobactam activity was evident in specific
ampC
mutations.