bluuebunny/crossref_metadata_2025_split huggingface dataset:
SAILOR: perceptual anchoring for robotic cognitive architectures
Authors: González-Santamarta Miguel Á. , Rodrıguez-Lera Francisco J. , Matellan-Olivera Vicente , del Castillo Virginia Riego , Sánchez-González Lidia 
Published: January 2025

AbstractSymbolic anchoring is an important topic in robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors and maintain the link between that knowledge and the sensory data. In cognitive-based robots, this process of transforming sub-symbolic data generated by sensors to obtain and maintain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for symbolic anchoring integrated into ROS 2. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper describes the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2) and the validation of SAILOR using public datasets and a real-world scenario.
Corporate genome screening India (CoGsI) identified genetic variants association with T2D in young Indian professionals
Authors: Husami Shah Fahad , Kaur Tavleen , Gupta Love , Rastogi Garima , Singh Lakhvinder , Meena Pooja , Sharma Indu , Singh Hemender , Sharma Varun 
Published: January 2025

AbstractRising cases of type 2 diabetes (T2D) in India, especially in metropolitan cities is an increasing concern. The individuals that were most affected are young professionals working in the corporate sector. However, the corporate sector has remained the least explored for T2D risk predisposition. Considering corporate employees’ lifestyles and the role of gene-environment interaction in T2D susceptibility, the study aims to find genetic variants associated with T2D predisposition. In this first kind of study, 680 young professionals (284 T2D cases, and 396 controls) were diagnosed and screened for 2658 variants on an array designed explicitly for the CoGsI study. The variant filtering was done at Bonferroni p-value of 0.000028. The genetic data was analysed using PLINK v1.09, SPSS, R programming, VEP tool, and FUMA GWAS tool. Interestingly, 42 variants were associated with the T2D risk. Out of 42, three missense variants (rs1402467, rs6050, and rs713598) in Sulfotransferase family 1 C member 4 (SULT1C4), Fibrinogen Alpha Chain (FGA), and Taste 2 Receptor Member 38 (TAS2R38) and two untranslated region (UTR) variants (rs1063320 and rs6296) in Major Histocompatibility Complex, Class I, G (HLA-G) and 5-Hydroxytryptamine Receptor 1B (HTR1B) were associated with the T2D risk. CoGsI identified potential genomic markers increasing susceptibility to the early onset of T2D. Present findings provide insights into mechanisms underlying T2D manifestation in corporate professionals due to genetics interacting with occupational stress and urban lifestyles.
Aboveground and belowground trait coordination across twelve boreal forest tree species
Authors: Spitzer Clydecia M. , Jämtgård Sandra , Larsson Marcus J. , Gundale Michael J. 
Published: January 2025

AbstractThe existence of trait coordination in roots and leaves has recently been debated, with studies reaching opposing conclusions. Here, we assessed trait coordination across twelve boreal tree species. We show that there is only partial evidence for above-belowground coordination for “fast-slow” economic traits across boreal tree species, i.e., while N content in leaves and roots were positively correlated, as well as dry matter content, root dry matter content and leaf N had no significant relationship. For resource acquisition traits (i.e. related to light capture and nutrient uptake) we did not find strong evidence for trait coordination, as specific root length and specific leaf area were not positively correlated. We further show that site only explained between 0 and 7% of the total trait variation, while within-site variation contributed substantially to the total trait variation for a large number of traits (1.6–96%), and more so for morphological root traits than leaf traits. This likely influences the strength of above-belowground trait coordination found across species in our study. Understanding sources of trait variation and above-belowground trait relationships can contribute to improving global and regional C cycling models. However, fine-scale environmental variability should be accounted for given its importance for driving trait variation.
Evaluation of ovarian stiffness and its biological mechanism using shear wave elastography in polycystic ovary syndrome
Authors: He Yifang , Deng Shuangping , Wang Yanli , Wang Xiali , Huang Qingqing , Cheng Jing , Wang Dandan , Lyu Guorong 
Published: January 2025

AbstractPolycystic ovary syndrome (PCOS) is a common endocrine disorder with various contributing factors. Shear wave elastography (SWE) is a contemporary noninvasive imaging technique that reports on the elasticity of tissues. This study aimed to evaluate ovarian stiffness in patients with PCOS using transvaginal SWE, and investigate the potential biological mechanisms underlying increased ovarian stiffness. Patients with PCOS and healthy controls underwent transvaginal 2D ultrasound and SWE to measure the number of follicles, ovarian volume, and ovarian elasticity. Multivariate logistic regression analysis was conducted to identify risk factors for PCOS. A rat model of PCOS was established to further investigate the biological basis of increased ovarian stiffness. Histological analysis, enzyme-linked immunosorbent assay, quantitative reverse transcription-polymerase chain reaction, western blotting, transcriptomics, and proteomics were performed to assess alterations in fibrosis and basement membrane (BM) gene expression. The results demonstrated that patients with PCOS (n = 59) showed an increased number of follicles, ovarian volume, and SWE (mean and max) compared with controls (n = 56; P < 0.001). The number of follicles, ovarian volume, and SWE_mean were identified as independent risk factors for PCOS (P < 0.05). SWE_mean ≥ 12.5 kPa demonstrated an area under the curve of 0.816 for PCOS diagnosis and was positively correlated with AMH levels (r = 0.6776, P < 0.0001). In the rat model, increased ovarian stiffness was associated with significant fibrosis and altered expression of fibrosis-related markers. Transcriptomic and proteomic analyses revealed that BM gene alterations were correlated with ovarian stiffness, which was further validated using PCOS patient data from the Gene Expression Omnibus database. In conclusion, SWE is a valuable technique for diagnosing PCOS by detecting increased ovarian stiffness, which may be associated with alterations in the expression of BMs, thereby mediating ovarian fibrosis.
Superconductivity in ordered Li–Al–B compounds
Authors: Hussain K. , Donaldson S. J. , Karaca E. , Byrne P. J. P. , Hasnip P. J. , Probert M. I. J. 
Published: January 2025

AbstractUsing first principles calculations, we show that $$\hbox {Li}_x\hbox {Al}_y\hbox {B}_{2(x+y)}$$ materials have strong electron-phonon coupling, with many having a superconducting critical temperature ($$T_c$$) that exceeds that of the more familiar $$\hbox {MgB}_2$$ at ambient pressure. In particular, we find that $$\hbox {LiAlB}_4$$ is the most stable member of the family, with $$T_c > 44\,\hbox {K}$$ whilst the peak $$T_c$$ is with $$\hbox {Li}_3\hbox {AlB}_8$$ which has $$T_c > 77\,\hbox {K}$$. Our results reveal that these materials are both thermodynamically and dynamically stable, with strong electron-phonon coupling, indicating significant potential for practical superconducting applications.
Development of the membrane ceiling method for in vitro spermatogenesis
Authors: Kamoshita Maki , Shirai Hiroki , Nakamura Hiroko , Kishimoto Tetsuya , Hatanaka Yuki , Mashiko Daisuke , Esashika Katsuhiro , Yang Jingjing , Yamasaki Satoshi , Ogawa Takehiko , Kimura Hiroshi , Ikawa Masahito 
Published: January 2025

AbstractSpermatogenesis is one of the most complex processes of cell differentiation and its failure is a major cause of male infertility. Therefore, a proper model that recapitulates spermatogenesis in vitro has been long sought out for basic and clinical research. Testis organ culture using the gas-liquid interphase method has been shown to support spermatogenesis in mice and rats. However, the conventional method using agarose gel has limitations including medium replacement efficiency and live imaging because agarose absorbs medium and is not transparent. To overcome this issue, we developed a new device using microporous membranes and oxygen-permeable materials. Mouse testes sandwiched between a microporous polyethylene terephthalate (PET) membrane on top and an oxygen-permeable 4-polymethyl-1-pentene polymer (PMP) membrane base maintained spermatogenesis over months. The chamber volume was minimized to 0.1% of the culture medium. Weekly time-lapse live imaging enabled us to observe transgenically fluorescent acrosome and nuclear shape formation throughout spermatogenesis. Finally, we obtained healthy fertile offspring from spermatozoa generated in our system. The device could be used not only for basic research to understand spermatogenesis but also for applied research, such as diagnosing and treating male infertility.
A review of deep learning for brain tumor analysis in MRI
Authors: Dorfner Felix J. , Patel Jay B. , Kalpathy-Cramer Jayashree , Gerstner Elizabeth R. , Bridge Christopher P. 
Published: January 2025

AbstractRecent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities.
Impaired arbitration between reward-related decision-making strategies in Alcohol Users compared to Alcohol Non-Users: a computational modeling study
Authors: Ramakrishnan Srinivasan A. , Shaik Riaz B. , Kanagamani Tamizharasan , Neppala Gopi , Chen Jeffrey , Fiore Vincenzo G. , Hammond Christopher J. , Srinivasan Shankar , Ivanov Iliyan , Chakravarthy V. Srinivasa , Kool Wouter , Parvaz Muhammad A. 
Published: January 2025

AbstractReinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. Greater model-based control is typically used under highly rewarding conditions to minimize risk and maximize gain. Although prior studies have shown impairments in sensitivity to reward value in individuals with frequent alcohol use, it is unclear how these individuals arbitrate between model-free and model-based control based on the magnitude of reward incentives. In this study, 81 individuals (47 frequent Alcohol Users and 34 Alcohol Non-Users) performed a modified 2-step learning task where stakes were sometimes high, and other times they were low. Maximum a posteriori fitting of a dual-system reinforcement-learning model was used to assess the degree of model-based control, and a utility model was used to assess risk sensitivity for the low- and high-stakes trials separately. As expected, Alcohol Non-Users showed significantly higher model-based control in higher compared to lower reward conditions, whereas no such difference between the two conditions was observed for the Alcohol Users. Additionally, both groups were significantly less risk-averse in higher compared to lower reward conditions. However, Alcohol Users were significantly less risk-averse compared to Alcohol Non-Users in the higher reward condition. Lastly, greater model-based control was associated with a less risk-sensitive approach in Alcohol Users. Taken together, these results suggest that frequent Alcohol Users may have impaired metacontrol, making them less flexible to varying monetary rewards and more prone to risky decision-making, especially when the stakes are high.
Urban growth strategy in Greater Sydney leads to unintended social and environmental challenges
Authors: Ríos-Ocampo Juan Pablo , Gary Michael Shayne 
Published: January 2025

AbstractCities have advanced in terms of economic and social status over the past five decades, improving the living conditions of hundreds of millions of people. However, population growth and urban expansion have put pressure on social and environmental conditions. This study examines urban policymakers’ perceptions about causal relationships in the urban system as revealed in urban planning reports. Here we analyzed 500 pages from published urban plans of Greater Sydney between 1968 and 2018 and coded the text into causal maps. The findings show that policymakers adopted a dominant urban development strategy over the past 50 years to pursue economic and public infrastructure growth. Over time, this growth strategy resulted in a number of social and environmental challenges that negatively impacted societal well-being. Although policymakers eventually recognized the seriousness of social and environmental challenges, they never attempted to fundamentally change the dominant growth strategy. Instead, policymakers sought to address the challenges (that is, symptoms) by responding to each issue piecemeal.
Center of Rotation and Hysteresis Quantification in the Wrist Utilizing Four-Dimensional Computed Tomography
Authors: Norman Elizabeth , Robinson Sydney , Chambers Spencer , Rainbow Michael , Suh Nina , Lalone Emily 
Published: January 2025

Abstract
Background Wrist injuries limit function and can lead to long-term pain and disability. Current interventions aim to decrease pain and restore healthy function; however, healthy function during dynamic motion has not been fully characterized. Four-dimensional computed tomography (4DCT) can capture dynamic bony motion providing new insight to the dynamic wrist joint.
Materials and Methods Ten young healthy participants were scanned using a 4DCT scanner and three-dimensional (3D) models were made of the radius and carpal bones. Using helical axes and local coordinate systems for the radius, rotations of each bone were measured, and center of rotation was determined. In addition, hysteresis was quantified by measuring hysteresis area, the area between the curves of the forward and reverse motion of the wrist joint.
Results The results showed that the wrist axis of rotation was located more proximal (−15.2 ± 1.3 mm distal) and dorsal (−0.8 ± 2.6 mm dorsal) in extension as compared with the flexion positions (−20.4 ± 1.6 mm distal; 3.9 ± 2.5 mm volar). In addition, no statistical differences were identified between the different directions of motion at each angle of wrist motion, with the exception of the trapezium at 30 degrees of extension. The largest hysteresis effect was noted in the trapezoid (112.8 deg2) and the smallest effect identified in the lunate (46.0 deg2).
Conclusion The results of this study may provide a better understanding of the dynamic nature of the wrist joint for identification of ligamentous injuries and replication of joint motion after surgical intervention.
Understanding teacher identity construction through hybridity theory: a case study of a Chinese-Canadian teaching English in China
Authors: Zhao Angel , Xiao Yuehai 
Published: January 2025

AbstractGlobalization has given rise to a new generation of English language teachers, including transnational teachers, who have complex teacher identities that the traditional “native” versus “non-native” English teacher dichotomy fails to capture adequately. Researchers have problematized this dichotomy by applying theories such as poststructuralism. However, there is a lack of consensus on a theory that can challenge the monolingual model and the language hierarchy to support the development of teacher identity. Consequently, this case study draws on the theory of hybridity from sociology and uses narrative inquiry and interviews to explore the evolution of a Chinese-Canadian English teacher, Sally, in constructing her teacher identity, which includes both achievements and challenges. The study contributes to the literature by attempting to lessen the impact of the language hierarchy and the “native” versus “non-native” dichotomy through empirical evidence supporting hybridity theory. The findings reveal the complexity of Sally’s teacher identity development across various contexts, resulting in numerous struggles. This complexity aligns with hybridity theory. Moreover, Sally outlines her coping strategies, which could help transnational teachers overcome obstacles and successfully broaden their teacher identities. The study discusses theoretical and pedagogical implications for the development of English language teacher identity and teacher training.
IC-GLI: a real-time, INS-centric GNSS-LiDAR-IMU localization system for intelligent vehicles
Authors: Zhang Yong , Li Xudong , Zhang Wei , Zhao Fengkui 
Published: January 2025

Abstract
The integration of multiple sensors plays a vital role in achieving accurate vehicle positioning. However, light detection and ranging (LIDAR) and global navigation satellite system (GNSS) are susceptible to environmental effects, unlike inertial navigation system (INS), which remains unaffected by external factors. To enhance the precise positioning of intelligent vehicles in diverse environments and maximize the use of inertial measurement unit (IMU) data, this study presents a LIDAR, IMU, and GNSS-based positioning method centered around INS. The proposed method employs an IMU pre-integration model that includes the Earth’s rotation and utilizes a factor graph optimization framework to fuse the measurement data from LIDAR, IMU, and GNSS. By assigning appropriate weights to the residuals and dynamically adjusting the contributions of each sensor based on the prevailing environmental conditions, the optimal localization results are obtained through the solution of the maximum a posteriori estimate and the update of the INS state. Finally, the proposed system’s localization performance is validated using intelligent vehicles in the Robot Operating System framework. The results demonstrate that the system achieves high accuracy and robustness across various environments.
Capacitance dynamics in W-shaped silicon substrate MEMS double-touch mode pressure sensor
Authors: Rukshana Bi Gajula , Chhetri Biraj , Upreti Akriti , Jindal Sumit Kumar , Patil Hemprasad Yashwant 
Published: January 2025

Abstract
Capacitive pressure sensors (CPSs) have gained significant attention due to their superior sensitivity and linearity compared to piezoresistive alternatives. This paper presents a detailed analysis of a novel W-shaped silicon substrate micro electromechanical system double touch mode CPS with a flexible circular diaphragm. The innovative W-shaped substrate design increases the touch area with the substrate, enhancing the sensor’s linearity and sensitivity. This study provides a comprehensive examination of the sensor’s capacitive response, incorporating the deflection pattern of the circular diaphragm based on small deflection theory to improve linearity. The analytical model of capacitance, capacitive sensitivity, and mechanical sensitivity is verified through MATLAB simulations, while COMSOL Multiphysics is employed to validate the diaphragm deflection. Compared to earlier models, the capacitance shows significant improvement, reaching 14.7 pF with a sensitivity of 3.0 aF Pa−1 and a resolution 3.33 Pa over a pressure range of 0–2 MPa. The maximum center deflection observed in COMSOL simulations was 2.60 μm, while MATLAB calculations yielded a slightly lower value of 2.518 μm. Both results are consistent with small deflection theory, as they are less than one-fifth of the diaphragm’s thickness. This study contributes to the advancement of CPSs for various applications, including medical, automotive, aviation, and industrial fields.
Real-time tool wear monitoring and multi-step forward prediction based on multi-information fusion
Authors: Bao Saixiyalatu , Chao Bao , Zhang Chaoyong , Li Yong , Li Jingliang 
Published: January 2025

Abstract
Accurate diagnosis of tool wear is essential for enhancing both product quality and production efficiency. However, most existing diagnostic methods for tool wear fail to integrate both monitoring and prediction, with the majority of prediction approaches focusing on short-term prediction, often leading to significant cumulative errors. This study proposes a real-time tool wear monitoring and multi-step forward tool wear prediction (MFTWP) method based on multi-information fusion. First, an automated data acquisition platform is established to collect milling tool wear images and multi-information. Second, the segmenting objects by locations v2 model is trained to segment the wear area, and the maximum flank wear width of the milling tool is obtained to provide labeled data for monitoring and prediction models. Simultaneously, multi-domain features from the multi-sensor data are extracted, followed by fusion and post-processing. Kernel principal component analysis is then applied to extract the most representative and interpretable input features for the monitoring model. Subsequently, the Kolmogorov-Arnold Networks monitoring model is constructed to establish the relationship between multi-sensor features and real-time tool wear values. The output sequence is used as input to the Transformer prediction model to determine the tool wear sequence for future time steps. Following this, a MFTWP pattern is proposed, along with a real-time correction strategy to address the issue of cumulative errors. Finally, A large number of experiments are conducted, demonstrating the outstanding performance and real-time efficiency of the proposed method.
Model for the X-point radiator height and its energetic coupling to the plasma edge
Authors: Stroth U , Bernert M , Lunt T , Pan O , Wolfrum E ,   ASDEX Upgrade Team
Published: January 2025

Abstract
The physics governing the height of the X-point radiator (XPR) and the influence of the related radiation losses on the edge temperature profiles is studied with a reduced power balance model, which is an extension of a model developed to study XPR access. For ASDEX Upgrade parameters with a heating power of 10  MW, the model reproduces that the X-point radiator can be moved from the X-point up to 14  cm into the confined plasma, dissipating up to 70% of the heating power. The key parameter is the neutral density in the X-point region. A reduction of the edge kinetic pressure gradient of about 50% is found which could explain the suppression of edge localized modes observed in experiment. The calculated edge temperature response is consistent with experimental data.