bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facial Expression Recognition for Probing Students’ Emotional Engagement in Science Learning
Authors: Tang Xiaoyu , Gong Yayun , Xiao Yang , Xiong Jianwen , Bao Lei 
Published: August 2024

Abstract
Student engagement in science classroom is an essential element for delivering effective instruction. However, the popular method for measuring students’ emotional learning engagement (ELE) relies on self-reporting, which has been criticized for possible bias and lacking fine-grained time solution needed to track the effects of short-term learning interactions. Recent research suggests that students’ facial expressions may serve as an external representation of their emotions in learning. Accordingly, this study proposes a machine learning method to efficiently measure students’ ELE in real classroom. Specifically, a facial expression recognition system based on a multiscale perception network (MP-FERS) was developed by combining the pleasure-displeasure, arousal-nonarousal, and dominance-submissiveness (PAD) emotion models. Data were collected from videos of six physics lessons with 108 students. Meanwhile, students’ academic records and self-reported learning engagement were also collected. The results show that students’ ELE measured by MP-FERS was a significant predictor of academic achievement and a better indicator of true learning status than self-reported ELE. Furthermore, MP-FERS can provide fine-grained time resolution on tracking the changes in students’ ELE in response to different teaching environments such as teacher-centered or student-centered classroom activities. The results of this study demonstrate the validity and utility of MP-FERS in studying students’ emotional learning engagement.
Development of a holistic framework for the description and characterization of company-individual Manufacturing Change Management processes
Authors: Rammo Jan-Philipp , Arifagic Almin , Agyekum Ellen , Zaeh Michael F. 
Published: August 2024

AbstractIn today’s manufacturing industry, companies operate in a complex and rapidly changing environment, resulting in an increasing number and variety of Manufacturing Changes (MCs). To ensure long-term competitiveness, companies must respond quickly, flexibly, and effectively to various types of MCs. While many companies have established processes to manage MCs, these processes often lack flexibility and a comprehensive methodological and digital support. Existing approaches to support these processes are often generic and difficult to transfer to a company’s unique Manufacturing Change Management (MCM) process. For this purpose, a framework and an associated application method have been developed in which MCM processes are abstracted and mathematically modeled using descriptive and characterizing attributes. To achieve a comprehensive view of the processes, the holistic framework intends to model people’s activities involved in the process, process steps, and the occurrence of information inside the process. The framework was developed through a three-phase approach, with attributes derived from the literature, case studies, and expert interviews. The framework and the associated application method were tested in a first industrial use case. In conclusion, the framework developed in this contribution supports practitioners and researchers to consistently describe and to flexibly adapt generally valid theoretical and practical approaches to company-individual MCM processes.
Experiences of unemployed and/or work-disabled cancer survivors who have pursued to return to paid employment: a focus group study
Authors: Greidanus M. A. , van Ommen F. , de Boer A. G. E. M. , Coenen P. , Duijts S. F. A. 
Published: August 2024

Abstract
Purpose
To explore experiences of unemployed and/or work-disabled cancer survivors who have pursued to return to paid employment.

Methods
Four digital focus group interviews were conducted with 16 cancer survivors (< 10 years post-diagnosis) who have pursued to return to work within the last 2 years. Interview topics included motivations, facilitators of and barriers to job seeking, and returning to and maintaining paid employment. Interview audio recordings were transcribed verbatim and analyzed using conventional content analyses.

Results
Participants were mostly female (94%), and the majority had successfully returned to paid employment (56%). Both intrinsic factors (e.g., sense of purpose, social interactions) and extrinsic factors (e.g., financial necessity) motivated their return to paid employment. During job seeking, participants experienced facilitators including support, personal qualities (e.g., life experience), and trial workplaces. Barriers included inadequate support, perceived employer discrimination, and work ability uncertainty. Returning to and maintaining employment was facilitated by flexible work, supportive colleagues, and intrinsic drive, while barriers included side effects (e.g., fatigue) and overly demanding work.

Conclusions
Unemployed and/or work-disabled cancer survivors are generally motivated to return to paid employment by both intrinsic and extrinsic factors, but uncertainty about their ability and inadequate support may hinder this. These findings highlight the need for trial workplaces, support during every phase of return to paid employment, and a flexible, supportive workplace.

Implications for Cancer Survivors
Tailored interventions addressing the needs identified in this study are urgently needed. The recommendations provided offer strategies for various stakeholders to enhance support for unemployed and work-disabled cancer survivors.
Inquiry into the validity of the Digital Services Act and the role of the representative associations under that regulation
Authors: Pacuła Krzysztof 
Published: August 2024

AbstractThe Digital Services Act (DSA) tackles challenges arising from the evolution of online intermediary services and inevitably brings forth a multitude of legal issues regarding its operation. While the first rulings of the EU courts, delivered in incidental proceedings, have to be read in the specific context in which they were rendered, these decisions could serve as a noteworthy reference point for future discussions on the broader understanding of the DSA. An intriguing aspect of these rulings is their potential to illustrate two prospective approaches that may emerge in future case law concerning the DSA at both EU and national levels.
Life Project Scale: a new measure to assess the coherence of the intended future
Authors: Coscioni Vinicius , Teixeira Marco Antônio Pereira , Sacramento Adriana Malheiros , Paixão Maria Paula 
Published: August 2024

AbstractThis article identifies different types of validity and reliability evidence of the Life Project Scale (LPS), a new psychometric measure to assess the coherence of the intended future. It comprises three studies involving five samples that accounted for 2,808 participants from Brazil and Portugal. The final version of the LPS contains two factors with four items each. The factors assess identification (i.e., awareness of one’s intended future) and involvement (i.e., enactment of plans and actions in favor of one’s intended future). The two factors are indicators of the coherence of life projects’ intentional structures and action. Altogether, the three studies provide validity and reliability evidence based on exploratory and confirmatory factor analyses, the assessment of ceiling and floor effects, four types of reliability coefficients, relations to other measures (authenticity and meaning in life), and measurement invariance models. Therefore, the LPS is ready for further use, development, and testing in other research and practice contexts.
Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy
Authors: Takagi Hisamichi , Takeda Ken , Kadoya Noriyuki , Inoue Koki , Endo Shiki , Takahashi Noriyoshi , Yamamoto Takaya , Umezawa Rei , Jingu Keiichi 
Published: August 2024

AbstractUrinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate’s position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.
Low-light enhancement method with dual branch feature fusion and learnable regularized attention
Authors: Sun Yixiang , Ni Mengyao , Zhao Ming , Yang Zhenyu , Peng Yuanlong , Cao Danhua 
Published: August 2024

AbstractRestricted by the lighting conditions, the images captured at night tend to suffer from color aberration, noise, and other unfavorable factors, making it difficult for subsequent vision-based applications. To solve this problem, we propose a two-stage size-controllable low-light enhancement method, named Dual Fusion Enhancement Net (DFEN). The whole algorithm is built on a double U-Net structure, implementing brightness adjustment and detail revision respectively. A dual branch feature fusion module is adopted to enhance its ability of feature extraction and aggregation. We also design a learnable regularized attention module to balance the enhancement effect on different regions. Besides, we introduce a cosine training strategy to smooth the transition of the training target from the brightness adjustment stage to the detail revision stage during the training process. The proposed DFEN is tested on several low-light datasets, and the experimental results demonstrate that the algorithm achieves superior enhancement results with the similar parameters. It is worth noting that the lightest DFEN model reaches 11 FPS for image size of 1224×1024 in an RTX 3090 GPU.
Graphical Abstract
The Child Factor in Child–Robot Interaction: Discovering the Impact of Developmental Stage and Individual Characteristics
Authors: Rudenko Irina , Rudenko Andrey , Lilienthal Achim J. , Arras Kai O. , Bruno Barbara 
Published: August 2024

AbstractSocial robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily life. Child–Robot Interaction (CRI), as any domain involving children, inevitably faces the major challenge of designing generalized strategies to work with unique, turbulent and very diverse individuals. Addressing this challenging endeavor requires to combine the standpoint of the robot-centered perspective, i.e. what robots technically can and are best positioned to do, with that of the child-centered perspective, i.e. what children may gain from the robot and how the robot should act to best support them in reaching the goals of the interaction. This article aims to help researchers bridge the two perspectives and proposes to address the development of CRI scenarios with insights from child psychology and child development theories. To that end, we review the outcomes of the CRI studies, outline common trends and challenges, and identify two key factors from child psychology that impact child-robot interactions, especially in a long-term perspective: developmental stage and individual characteristics. For both of them we discuss prospective experiment designs which support building naturally engaging and sustainable interactions.
A user study of visualisations of spatio-temporal eye tracking data
Authors: Claus Marcel , Hermens Frouke , Bromuri Stefano 
Published: August 2024

Abstract
Eye movements have a spatial (where people look), but also a temporal (when people look) component. Various types of visualizations have been proposed that take this spatio-temporal nature of the data into account, but it is unclear how well each one can be interpreted and whether such interpretation depends on the question asked about the data or the nature of the dataset that is being visualised. In this study, four spatio-temporal visualization techniques for eye movements (chord diagram, scan path, scarf plot, space-time cube) were compared in a user study. Participants

$$(N = 25)$$

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answered three questions (what region first, what region most, which regions most between) about each visualization, which was based on two types of datasets (eye movements towards adverts, eye movements towards pairs of gambles). Accuracy of the answers depended on a combination of the dataset, the question that needed to answered, and the type of visualization. For most questions, the scan path, which did not use area of interest (AOI) information, resulted in lower accuracy than the other graphs. This suggests that AOIs improve the information conveyed by graphs. No effects of experience with reading graphs (for work or not for work) or education on accuracy of the answer was found. The results therefore suggest that there is no single best visualisation of the spatio-temporal aspects of eye movements. When visualising eye movement data, a user study may therefore be beneficial to determine the optimal visualization of the dataset and research question at hand.

Graphical abstract
Translation and Linguistic Validation of BIS (Body Image Scale) for Breast Cancer Patients in India
Authors: Belani Preeti , Wadasadawala Tabassum , Sarin Rajiv , Pathak Rima , Krishnamurthy Revathy , Syeda Naseera , Chavan Sonal 
Published: August 2024

AbstractA number of PROMs (patient-reported outcome measures) have been developed and validated for English-speaking populations but with limited utility in non-English-speaking countries. Body image scale (BIS) is a type of PROM which assesses the changes in the body image of a patient diagnosed with cancer. The current study was carried out to translate the BIS in Hindi and Marathi. Out of the 10 questions in the BIS questionnaire, Hindi and Marathi translations were already available for 4 questions (Questions no. 2, 4, 5, and 9) from the EORTC BR23 questionnaire. Remaining questions were translated using the EORTC translation manual, which includes forward translation, reconciliation, backward translation, proofreading, and pilot testing. Pilot testing done on 10 patients each for Hindi and Marathi showed at least 80% acceptability for all the questions for both Hindi and Marathi translations. Both the translated versions will be used for validation in a large cohort of breast cancer patients. The Hindi and Marathi versions of the BIS have very good acceptability and can be utilized for clinical trials once validated in cancer patients.
Endovascular transvenous treatment of carotid–cavernous fistula
Authors: Villanova Ilaria , Lorusso Giovanni , Greco Chiara Noemi , Tarantino Francesca , Laera Marika , Lucarelli Nicola Maria , Zimatore Domenico Sergio , Morelli Chiara , Maggialetti Nicola , Ianora Amato Antonio Stabile 
Published: August 2024

AbstractCarotid–cavernous fistulas represent abnormal communication between the carotid circulation and the cavernous sinus. They can be classified as direct or indirect. The Barrow classification is the most widely used system that categorizes the carotid–cavernous fistulas based on their arterial supply (type A–D). The mainstay of therapy for carotid–cavernous fistulas consists of transarterial or transvenous embolization, while other treatment options such as open surgery or radiosurgery are still utilized as second-line or adjuvant therapeutic options. We present the case of a 72-year-old female patient with conjunctival chemosis and exophthalmos. Computed tomography angiography detected a carotid–cavernous fistula that was confirmed with catheter angiography (Barrow type C). Through right femoral vein catheterization, detachable coils were deployed in the right cavernous sinus, adjacent to the fistula shunt point. The procedure was completed with N-butyl cyanoacrylate injection (dilution 1:2). At post-procedural digital subtraction angiography, the fistula was excluded. Endovascular transvenous treatment is a valid therapeutic option for indirect carotid–cavernous fistulas.
The Role of Forcing and Parameterization in Improving Snow Simulation in the Upper Colorado River Basin Using the National Water Model
Authors: Gan Yanjun , Zhang Yu , Kongoli Cezar , Pan Ming 
Published: August 2024

AbstractThis study assesses snow water equivalent (SWE) simulation uncertainty in the National Water Model (NWM) due to forcing and model parameterization, using data from 46 Snow Telemetry (SNOTEL) sites in the Upper Colorado River Basin (UCRB). We evaluated the newly developed Analysis of Record for Calibration (AORC) forcing data for SWE simulation and examined the impact of bias correction applied to AORC precipitation and temperature. Additionally, we investigated the sensitivity of SWE simulations to choices of physical parameterization schemes through 72 ensemble experiments. Results showed that NWM driven by AORC forcings captured the overall temporal variation of SWE but underestimated its amount. Adjusting AORC precipitation with SNOTEL observations reduced SWE root‐mean‐square error (RMSE) by 66%, adjusting temperature trimmed it by 10%, and adjusting both decreased it by 69%. Among the physical processes, the snow/soil temperature time scheme (STC) demonstrated the highest sensitivity, followed by the surface exchange coefficient for heat (SFC), snow surface albedo (ALB), and rainfall and snowfall partitioning (SNF), while the lower boundary of soil temperature (TBOT) proved to be insensitive. Further optimization of the parameterization combination resulted in a 12% SWE RMSE reduction. When combined with the bias‐corrected AORC precipitation and temperature, this optimization led to a remarkable 78% SWE RMSE reduction. Despite these enhancements, a persistent slow and late spring ablation suggests model deficiencies in snow ablation physics. The study emphasizes the critical need to enhance the accuracy of forcing data in mountainous regions and address model parameterization uncertainty through optimization efforts.
A Continuous Root Water Uptake Isotope Mixing Model
Authors: Fu Han , Neil Eric John , Liu Juxin , Si Bingcheng 
Published: August 2024

AbstractThe depth‐wise distribution of root water uptake is typically inferred through linear mixing models that utilize knowledge of stable water isotopes in soil and plants. However, these existing models often represent the water uptake profile in discrete segments, potentially introducing significant uncertainty and bias into results. In this study, we introduced a novel root water uptake mixing model that combines a Bayesian linear mixing framework with a continuous root water uptake pattern, named CrisPy. To evaluate the performance of CrisPy, we conducted virtual and field‐based tests under several types of prior information. CrisPy showed accurate and robust reconstruction of the true root water uptake profile under various prior information settings in the virtual test. By contrast, the discrete mixing model, MixSIAR was greatly influenced by the prior information and deviated from the true profile. The root mean squared error of the uptake proportions from CrisPy ranged from 3.6% to 7.4%, while MixSIAR exhibited values of 6.3%–15.2%. Furthermore, posterior predictive checking indicated that CrisPy effectively reconstructed the mean and standard deviations of plant water isotopic compositions in both virtual and field‐based tests. MixSIAR, however, underestimated the mean and overestimated the standard deviation of these compositions. These findings collectively support the enhanced accuracy, greater robustness, and reduced uncertainty of CrisPy in comparison to MixSIAR. Therefore, CrisPy provides a powerful tool for partitioning plant water sources.
Association between fast eating speed and metabolic dysfunction-associated steatotic liver disease: a multicenter cross-sectional study and meta-analysis
Authors: Zhang Miao , Sun Xiaoyang , Zhu Xiaopeng , Zheng Lili , Bi Yufang , Li Qiang , Sun Lirong , Di Fusheng , Xu Yushan , Zhu Dalong , Gao Yanyan , Bao Yuqian , Wang Yao , He Lanjie , Fan Chenmin , Gao Xin , Gao Jian , Xia Mingfeng , Bian Hua 
Published: August 2024

Abstract
Background
With the fast pace of modern life, people have less time for meals, but few studies have examined the association between the habit of fast eating and metabolic diseases.

Objective
Combining the results of the current study and the prior ones, we aimed to investigate the possible relationship between fast eating and the risk of metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods
This is a sub-analysis of a multicenter cross-sectional study of 1965 participants investigated the association between fast eating and MASLD in Chinese. Fast eating was defined as meal time less than five minutes and participants were divided into three categories based on their self-reported frequency of fast eating: ≤1 time/month, ≤1 time/week and ≥2 times/week. We further conducted a literature search for available studies published before November, 2023 as well as a meta-analysis to investigate the association between fast eating and MASLD.

Results
The proportion of MASLD was 59.3%, 50.5%, and 46.2% in participants with fast eating ≥2 times/week, ≤1 time/week and ≤1 time/month, respectively (P for trend <0.001). The frequency of fast eating was independently associated with risk of MASLD after multiple adjustment for sex, age, demographics, smoking and drinking status, BMI and clinical metabolic parameters (OR, 1.29; 95%CI, 1.09–1.53). Participants who ate fast frequently (≥2 times/week) had 81% higher risk of MASLD (P = 0.011). A meta-analysis of five eligible studies confirmed that frequent fast eating was associated with increased risk of MASLD (pooled OR, 1.22; 95%CI, 1.07–1.39).

Conclusions
Frequent fast eating was associated with an increased risk of MASLD.