bluuebunny/crossref_metadata_2025_split huggingface dataset:
Agenda Formation and Prediction of Voting Tendencies for European Parliament Election using Textual, Social and Network Features
Authors: Shahi Gautam Kishore , Basyurt Ali Sercan , Stieglitz Stefan , Neuberger Christoph 
Published: December 2024

AbstractAs per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.
Parental Burnout in Israeli Parents of Children with ASD During Wartime: The Role of Child Behavior, Parental Emotion Regulation, Stress, and Social Support
Authors: Aviad Shani , Shnitzer-Meirovich Shlomit , Gur Ayelet 
Published: December 2024

AbstractDuring emergencies like wartime, parents of children with Autism Spectrum Disorder (ASD) face heightened challenges, potentially leading to Parental Burnout (PB). Wartime conditions can exacerbate children’s behavioral difficulties, contributing to PB. Protective factors such as successful Emotional Regulation (ER) and perceived social support may mitigate PB. This study aims to compare child behavioral problems, parental ER difficulties, perceived social support, stress, and PB between parents of children with ASD and Typical Development (TD) during wartime. It also investigates how ER difficulties, perceived social support, and stress moderate the relationship between child behavioral problems and PB. The study included 213 Israeli parents: 101 parents of children diagnosed with ASD and 112 parents of TD children. Participants were recruited during the “Swords of Iron” War using purposive sampling via online platforms. Findings indicate that children with ASD exhibited higher levels of behavioral problems, and their parents experienced increased difficulties in ER, stress, and PB, alongside decreased perceived social support compared to parents of TD children. Moderation analysis revealed that ER difficulties, perceived social support, and stress moderated the direct association between child behavioral problems and PB specifically among parents of TD children. This study underscores the heightened vulnerability of parents with ASD-diagnosed children during wartime, emphasizing the importance of understanding how these circumstances impact parental well-being and available resources. Effective interventions should target enhancing ER, social support, and addressing parental stress, PB, and child behavioral issues, underscoring the need for prioritized services despite wartime challenges.
ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images
Authors: R Rashmi , S Girisha 
Published: December 2024

AbstractDigital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation.
68Ga-pentixafor PET/CT Is a Supplementary Method for Primary Aldosteronism Subtyping Compared with Adrenal Vein Sampling
Authors: Yi Tieci , Lu Difei , Cui Yonggang , Zhang Zheng , Yang Xing , Zhang Jianhua , Qiu Lin , Weng Haoyu , Liu Lin , Duan Xiaojiang , Zhao Guangyu , Ma Wei , Gao Ying , Fan Yan 
Published: December 2024

Abstract

Purpose
To investigate the diagnostic efficacy of 68Ga-pentixafor positron emission tomography/computed tomography (PET/CT) in primary aldosteronism (PA) subtyping and lateralization of aldosterone secretion in PA patients.

Procedures
37 patients who were diagnosed with PA, were prospectively enrolled in the study, and underwent adrenal vein sampling (AVS) after 68Ga-pentixafor PET/CT was conducted. Lateralization index (LI), defined as aldosterone/cortisol ratio in the dominant side to the contralateral adrenal vein when bilateral adrenal vein catheterization succeeded, and the aldosterone/cortisol ratio in the left adrenal vein to IVC (LAV/IVC) when the catheterization of right adrenal vein failed, were applied to determine lateralization side. Statistical analysis was performed using SPSS 21.0.

Results
The female proportion of all patients with PA was 32.4% (12/37), and the mean age was 51.3 ± 10.9 years. Patients with bilateral adrenal mass accounted for 54.1% (20/37), and 10 of them (27.0%) had adrenal hyperplasia or adrenal nodules ≤ 1.0 cm. In all 37 patients, the sensitivity, specificity and accuracy of 68Ga-pentixafor PET/CT in distinguishing lateralization by visualization were 89.3%, 77.8% and 86.5%, respectively. The area under the ROC curve for detecting positive lateralization based on the value of 68Ga-pentixafor SUVmax was 0.750 (95%CI 0.578–0.922, p = 0.026). The optimum SUVmax cut-off value was 6.86, with the sensitivity of 78.6%, specificity of 66.7%, and accuracy of 78.4%. Defining SUV ratio as SUVmax/SUV of contralateral adrenal gland, the area under the ROC curve for identifying lateralization based on the SUV ratio was 0.710 (95%CI 0.500–0.921, p = 0.061). The optimum SUV ratio cut-off was 2.40, with the sensitivity of 60.7%, specificity of 88.9%, and accuracy of 67.6%. The consistency of 68Ga-pentixafor PET/CT with AVS was of no significant difference between patients with bilateral adrenal lesions (80.0%, 16/20) and unilateral lesion (94.1%, 16/17; p = 0.737), and no significance was revealed in the consistency between patients with adrenal hyperplasia or adrenal lesion of diameter ≤ 1 cm (81.8%, 9/11) and those with adrenal lesions > 1 cm (88.5%, 23/26; p = 0.884).

Conclusions

68Ga-pentixafor PET/CT showed at least 80% consistency for the lateralization in patients with PA compared with AVS, even in those presented with bilateral adrenal hyperplasia. Visual analysis exhibited better diagnostic efficacy compared with SUVmax or SUVmax/SUV of the contralateral adrenal gland.( ChiCTR2300073049. Registered 30 June 2023. Retrospectively registered)
Tuning the Microstructure and Mechanical Properties of Al-Co-Cr-Fe-Ni-Ti Compositionally Complex Alloys Manufactured by Means of L-DED
Authors: Gerdt L. , Hilhorst A. , Müller M. , Heidowitzsch M. , Kaspar J. , Lopez E. , Zimmermann M. , Jacques P. J. 
Published: December 2024

Abstract
In the current study, a combinatorial high-throughput screening approach based on CALPHAD simulations and experimental validation has been utilized to explore a Co2CrFeNi2-Al-Ti CCA-system. This technique, introduced by Kaspar et al. (High Entropy Alloys Mater. https://doi.org/10.1007/s44210-023-00023-x), allows to perform an accelerated alloy development within a wide compositional range and automated fabrication of graded components with varying chemical composition and microstructure. Extended by semi-automated analytical characterization of the produced samples, this approach enables to design novel compositionally complex alloys (CCAs) with promising properties for specific requirements. In our current work, a multiphase design of L12 γ′-strengthened Co2CrFeNi2-Al-Ti CCAs partially tolerating the disordered BCC-A2 or ordered B2 phases in the alloy microstructure has been utilized. The samples with three different chemical compositions were manufactured by means of laser directed energy deposition (L-DED). By subsequent two-step heat treatment, different phase compositions and microstructures have been realized with a main objective to achieve a high volume fraction of L12 γ′ precipitations. Mechanical properties of investigated alloys were characterized by means of tensile tests. Depending on chemical and phase composition of the alloys, the ultimate tensile strength varied in the range of 1060-1150 MPa. The formation of BCC-B2 phase led to decreased yield to tensile strength and showed a detrimental effect on ductility of investigated alloys.
Ammonium Sensing Patch with Ultrawide Linear Range and Eliminated Interference for Universal Body Fluids Analysis
Authors: Huang Mingli , Ma Xiaohao , Wu Zongze , Li Jirong , Shi Yuqing , Yang Teng , Xu Jiarun , Wang Shuhan , Lv Kongpeng , Lin Yuanjing 
Published: December 2024

AbstractAmmonium level in body fluids serves as one of the critical biomarkers for healthcare, especially those relative to liver diseases. The continuous and real-time monitoring in both invasive and non-invasive manners is highly desired, while the ammonium concentrations vary largely in different body fluids. Besides, the sensing reliability based on ion-selective biosensors can be significantly interfered by potassium ions. To tackle these challenges, a flexible and biocompatible sensing patch for wireless ammonium level sensing was reported with an ultrawide linear range for universal body fluids including blood, tears, saliva, sweat and urine. The as-prepared biocompatible sensors deliver a reliable sensitivity of 58.7 mV decade−1 in the range of 1–100 mM and a desirable selectivity coefficient of 0.11 in the interference of potassium ions, attributed to the cross-calibration within the sensors array. The sensor’s biocompatibility was validated by the cell growth on the sensor surface (> 80%), hemolysis rates (< 5%), negligible cellular inflammatory responses and weight changes of the mice with implanted sensors. Such biocompatible sensors with ultrawide linear range and desirable selectivity open up new possibility of highly compatible biomarker analysis via different body fluids in versatile approaches.
How Digital Twins Impact Responsiveness: A Dynamic Fit Approach to Information Processing for High-Involvement Product Demand Management
Authors: Oehlschläger Dominik , Glas Andreas H. , Eßig Michael 
Published: December 2024

Abstract
In an era of rapidly evolving customer demands and technological advancements, organisations face mounting pressure to enhance their responsiveness. Digital Twins offer novel capabilities by providing comprehensive transparency on customer demands through dynamic and interactive links between real-world entities and their digital counterparts. As a result, Digital Twins have the potential to enable organisations to respond more swiftly and effectively to fluctuating customer demands across volume, process, and product dimensions. However, Digital Twins still lack empirical explorations and theoretical embedment, leading to conceptual ambiguity. Through a case study approach, the study integrates Digital Twins into the framework of Organisational Information Processing Theory (OIPT). Empirical evidence proposes that Digital Twins offer a spectrum of capabilities rather than a one-size-fits-all solution. The findings further emphasise the need for aligning Digital Twin sophistication levels with Information Processing Needs and Constraints, highlighting that the dynamic interplay between Digital Twin advancements, Information Processing Constraints, and Information Processing Capabilities creates a dynamic cycle of adaptation and optimisation. On the whole, the study presents four key implications: (1) Awareness about the versatile application areas of Digital Twins is created. (2) The positive impact of Digital Twins on volume, process, and product responsiveness is outlined. (3) The concept of Information Processing Constraints is introduced to OIPT. And (4) a dynamic cycle of aligning Digital Twin sophistication with Information Processing Capabilities and Constraints is suggested. Therefore, the study contributes not only to a deeper understanding of Digital Twins in demand management but also proposes a new dynamic fit perspective on OIPT.
Religiöse Vielfalt und Judentum im islamischen Religionsunterricht: Empirische Befunde und Potenziale zur Antisemitismusprävention
Authors: Güzel Selcen 
Published: December 2024

Zusammenfassung
Der Beitrag befasst sich aus islamisch religionspädagogischer Perspektive mit religiöser Vielfalt und Judentum im islamischen Religionsunterricht/Islam-Unterricht und seinen Potenzialen zur Antisemitismusprävention. Dazu wird ausgehend von den Haltungen muslimischer Schüler*innen gegenüber religiöser Vielfalt erörtert, inwieweit der islamische Religionsunterricht – vor dem Hintergrund grundsätzlich wachsender Vielfaltskonflikte und antisemitischer Vorfälle an Schulen – die Pluralitätsfähigkeit als Orientierungskompetenz fördern kann. Daran anschließend werden religionspädagogische Perspektiven und didaktische Handlungsmöglichkeiten skizziert, wie menschenfeindliche Haltungen im islamischen Religionsunterricht konstruktiv-kritisch aufgegriffen werden können. Der Beitrag basiert auf einer empirischen Studie, in der die Einstellungen von Schüler*innen aus dem bayerischen Schulversuch zum Islam-Unterricht untersucht wurden. Dazu wurden 30 qualitative Interviews mit muslimischen Schüler*innen aus dem schulischen Islam- und Ethik-Unterricht geführt und inhaltsanalytisch ausgewertet.
Heterogeneous many-core optimization for Monte Carlo path-tracing on new generation Sunway HPC system
Authors: Wang Xinjie , Ma Guanghao , Song Jiaying , Geng Mingyao , Hu Wenhui , Duan Xi , Wang Zhigang , Xu Jiali , Jin Xiaogang , Li Fang , Chen Dexun , Yu Maoxue 
Published: December 2024

Abstract
We present swRender, a new parallel rendering pipeline based on the new Sunway many-core architecture (SW26010P) for the Monte Carlo path-tracing algorithm. Previous parallel rendering schemes are unsuitable for our task due to issues such as vast differences in hardware architectures and bottlenecks in I/O communication efficiency. To that end, we create a new two-level parallel tile rendering framework to fully utilize the Sunway computing resources, a practical tile-grouping load-balancing method to maintain the framework’s stability, and a novel many-core acceleration optimization to improve the rendering performance at the pixel level. Our method achieves (1) an average speedup of 16x in multiple benchmarks when compared to the baseline path-tracing model on the Sunway architecture, and (2) an average speedup of 2x when compared to state-of-the-art CPU, co-processor, and GPU-based parallel rendering approaches. Moreover, we scale swRender to run on 15 million cores and obtain high scalable parallel efficiency of 92%.
“I have always found the whole area a minefield”: Wikidata, historical lives, and knowledge infrastructure
Authors: Baker James , Mahal Ammandeep K. 
Published: December 2024

AbstractThe rise of Wikidata represents a quiet revolution in knowledge infrastructure. This paper enquires into this knowledge base as an infrastructure and considers the implications of its centrality within our contemporary knowledge ecosystem. Rather than read Wikidata at scale, we employ of a narrow frame through which to explore the ideologies Wikidata has adopted and reproduces. This frame is Beyond Notability, a knowledge base that seeks to document women’s work in archaeology, history, and heritage between 1870 and 1950 through original archival research. Beyond Notability draws on and responds to the Wikidata data model, and this paper emerges from our experiences interacting with Wikidata to produce linked data biography. In foregrounding the tensions between historically specific phenomena and classificatory logics, our work stresses the value of using practice-based ontology development to investigate large-scale knowledge infrastructures at a time when the fabric of knowledge is at stake.
VAGen: waterbody segmentation with prompting for visual in-context learning
Authors: Zhao Jiapei , Yabuki Nobuyoshi , Fukuda Tomohiro 
Published: December 2024

AbstractEffective water management and flood prevention are critical challenges encountered by both urban and rural areas, necessitating precise and prompt monitoring of waterbodies. As a fundamental step in the monitoring process, waterbody segmentation involves precisely delineating waterbody boundaries from imagery. Previous research using satellite images often lacks the resolution and contextual detail needed for local-scale analysis. In response to these challenges, this study seeks to address them by leveraging common natural images that are more easily accessible and provide higher resolution and more contextual information compared to satellite images. However, the segmentation of waterbodies from ordinary images faces several obstacles, including variations in lighting, occlusions from objects like trees and buildings, and reflections on the water surface, all of which can mislead algorithms. Additionally, the diverse shapes and textures of waterbodies, alongside complex backgrounds, further complicate this task. While large-scale vision models have typically been leveraged for their generalizability across various downstream tasks that are pre-trained on large datasets, their application to waterbody segmentation from ground-level images remains underexplored. Hence, this research proposed the Visual Aquatic Generalist (VAGen) as a countermeasure. Being a lightweight model for waterbody segmentation inspired by visual In-Context Learning (ICL) and Visual Prompting (VP), VAGen refines large visual models by innovatively adding learnable perturbations to enhance the quality of prompts in ICL. As demonstrated by the experimental results, VAGen demonstrated a significant increase in the mean Intersection over Union (mIoU) metric, showing a 22.38% enhancement when compared to the baseline model that lacked the integration of learnable prompts. Moreover, VAGen surpassed the current state-of-the-art (SOTA) task-specific models designed for waterbody segmentation by 6.20%. The performance evaluation and analysis of VAGen indicated its capacity to substantially reduce the number of trainable parameters and computational overhead, and proved its feasibility to be deployed on cost-limited devices including unmanned aerial vehicles (UAVs) and mobile computing platforms. This study thereby makes a valuable contribution to the field of computer vision, offering practical solutions for engineering applications related to urban flood monitoring, agricultural water resource management, and environmental conservation efforts.
Potential of co-application of microbial enriched winery solid waste compost and inorganic fertilizers to enhance maize growth and yield parameters
Authors: Masowa Manare Maxson , Babalola Olubukola Oluranti , Mulidzi Azwimbavhi Reckson , Kutu Funso Raphael 
Published: December 2024

AbstractThe present study was conducted to assess the effects of co-application of winery solid waste compost (WWC) and synthetic nitrogen (N) and phosphorus (P) fertilizers (SF) on maize growth and yield parameters. The field experiment was conducted during the 2017/18 and 2018/19 summer cropping seasons. The WWCs (microbially inoculated and uninoculated) and SF were combined at ratios: 0:0, 25:75, 50:50, 75:25, and 100:0 (wt/wt) to attain the amount of N and P supplied by the predicted rates of compost. The optimum SF rate (90 kg/ha P and 200 kg/ha N) for maize was used as a positive control. The non-significant (p ≤ 0.05) effect of compost type on growth and yield parameters indicated that the microbial inoculation during compost production has no effect on compost quality. Compared with control, WWC-SF combination (50:50) improved grain weight per cob by 18.6% in 2017/18. The increase in the measured yield parameters was quantitatively higher in treatment with the 50:50 WWC-SF mix ratio than in other treatments. Significant and positive correlations exist between growth and yield parameters. In conclusion, the study findings suggest that the combined application of WWC and SF has great potential to enhance the maize growth and yield attributes.
Enhancing performance and reducing carbon emissions in sprayed concrete using green silica-lignin admixture and recycled aggregates
Authors: Dong Weijian , Kong Fanyu , He Ting , Liu Mengke , Wu Jingang , Zhang Lilong 
Published: December 2024

AbstractThe substitution of low-carbon and carbon-negative materials is an important pathway and a fundamental means of reducing carbon emissions in the construction sector. This work utilized the pulping wastewater (black liquor (BL)) produced from rice straw alkaline-oxygen cooking to synthesize a green concrete silica-lignin (SL) admixture through acid regulation. This novel admixture was employed to replace conventional high-carbon alkali powder, and its integration with recycled aggregates significantly enhanced the performance of sprayed concrete. The results show that the addition of the silica-lignin admixture increased the slump and compressive strength of sprayed concrete by 46.3% and 28.5%, respectively, while reducing the rebound ratio by 67.3%. Moreover, the use of recycled aggregates effectively reduces the global warming potential (GWP) of sprayed concrete production. When the substitution rate of recycled aggregates reaches 50%, carbon emissions are reduced by 44.7%, to only 183 kgCO2eq/m3. The sprayed concrete with added silica-lignin admixture not only exhibited increased compressive strength but also contributed to a reduction in CO2 emissions, decreased the amount of concrete used in building structures, and achieved the goal of carbon reduction. This work provides valuable insights for advancing sustainable practices in the construction industry.