bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Practices and Health Risks
Authors: Burton-Jeangros Claudine 
Published: February 2025

Abstract
Risk implies choices over different scenarios to avert future harm. In parallel to the norms formulated on predictions defining a preferred course of action, personal responsibility toward health is regularly emphasized. The analysis of how recommendations reflecting risk evidence are actually endorsed, or not, by individuals provides insights into the respective role of social structures and their agency. In their health practices, people and communities indeed express various forms of autonomy when they select which risks they want to avoid and which ones they are ready to take or ignore. I examine the range of justifications they use to align with or to question public health recommendations. These include references to pleasure, moderation, a sense of personal immunity, and critiques of institutions, which helps us understand the multiple forms of practical responses adopted in the face of health risks.
Social Structures as Determinants of Health Risks
Authors: Burton-Jeangros Claudine 
Published: February 2025

Abstract
Experiences of health risks are shaped by social structures. Exposure to danger and resources to avoid or cope with adverse events are not evenly distributed across society, as evidenced by health inequality research. This chapter examines the role of social stratification factors in health risk experiences, bringing together empirical findings regarding their influence on risk interpretations and actions. Variations in responses to health risks across social groups also reflect power relations between those who define risks and implement mitigation measures and those who experience them amidst their daily circumstances. The discussion suggests that by not considering the influence of social positions and resources, the dominant risk model exacerbates inequalities rather than reducing them.
The Who, What, When, Where and Why of Social Impact Data
Authors: Aebi Carol 
Published: February 2025

Abstract
This chapter delves into the interconnection of social sustainability assessment information and data. The chapter starts by introducing the interconnection of Social Impact and Sustainability within assessments providing context to societal automation. The remainder of the chapter is structured in accordance with a landscape theory review of existing social value and social impact assessment literature including theories, metrics, measurements, and tools. This investigations considers social value for business and or societal institutions. It highlights the challenges of inconsistency when machines attempt to process and apply the data then unravels and unifies definitions to address this challenge. The investigation further explores the relationships between the data and maps the symbiotic relationship between the data and stakeholders measuring social impact. This chapter concludes with requirements for the systemic alignment of social sustainability information in a technological network that are further applied to the conceptual modeling of the Stigmergic Information System Network for Social Sustainability Information elaborated in subsequent chapters.
From Concepts to Coherence: Modeling Social Impact Data for Societal Systemic Alignment
Authors: Aebi Carol 
Published: February 2025

Abstract
This chapter presents a conceptual model of a stigmergic information system network for social sustainability data. It recognizes the omnipresence of technology in contemporary society and acknowledges the dynamic landscape of society. The interaction between humans and systems can be harmonized, leveraging collective intelligence to foster social sustainability. The Conceptual Model for a Stigmergic Information System Network for Social Sustainability proposes a structure that unifies and leverages social sustainability data from diverse societal activities to support alleviating social problems by design. This base model considers predictive, corrective, and retrospective analytics with system dynamic feedback loops applied against control module parameters to act as systemic thermostats for social stability, reduce perception bias, and monitor exploitative effects. The distributed architecture operates with self-organizing communication micro-processes. It introduces the capacity for increased transparency, data comparability, and social information sharing. The intended audience includes academics, business managers, policymakers, computer scientists, and sustainability practitioners, indicating the proposed concepts’ diverse applicability and relevance.
Panoptic Semantic Mapping Method for Tomato Growing Environment Based on K-Net and OctoMap
Authors: Zhang Junxiong , Zhang Yu , Xie Jinyi , Zheng Xiajun , Zhang Fan , Rao Weijie , Guo Jiayang 
Published: February 2025

Abstract
In modern greenhouses, complicated tasks and unstructured environments generate the imperious demand for advanced semantic information about each object at work scenes. A significant problem that mainstream methods intend to resolve is that the refinement and understanding of environmental information cannot efficiently cover the entire task in real time. Therefore, this paper proposes a panoptic semantic mapping method to identify each object that is supposed to be concerned in greenhouses. This method builds grid maps with advanced semantic information based on RGB and depth images. For the agricultural task with tomato as the working object, the categories of various objects in the grid map are divided into four groups: fruits, pedicels, stems and obstacles. This method consists of three steps: semantic segmentation from RGB images with K-Net, reconstruction of point cloud data based on depth images and semantic masks and transformation of the point cloud data into OctoMap. Experimental results show the semantic segmentation algorithm reaches a mean precision of semantic segmentation of 93.83%, a mean IoU of 88.39% and an average accuracy of 98.28%. Meanwhile, the refresh frequency of publishing point cloud data with advanced semantic information holds steady at 2 Hz with the resolution of 8 mm.
Research and Analysis of the Green Vision Rate of Street Space Based on Information Visualization Technology
Authors: Wang Jun , Zhang Hua , Yan Zhulong 
Published: February 2025

Abstract
Taking the research results of China National Knowledge Network (CNKI) from 1998 to 2023 as the data source, with the help of CiteSpace V and VOSviewer digital technology, the knowledge map on the research of China street space is drawn. The visualization technology shows the structure, evolution of street space, and the interrelationship of green vision rate research, and intuitively shows the research hotspot and frontier dynamics of green vision rate of street space in China. Research has learned that before 2017, the green vision rate of street space was in the bud, and then began to develop rapidly. At present, the street space green rate is mainly concentrated in the street roaming, street walkability, measurement method, evaluation index, healing environment, restorative environment, public space security, street image research, VR virtual reality research and green rate, green rate, green rate, etc. Through the visual map analysis of the green vision rate literature in the street space, it shows that the use of new technologies and cross-field integration will become the future research trend.
Research on Intelligent Monitoring System for Double-Boom Floor-Standing Derrick Based on Distributed Wireless Monitoring Technology
Authors: Sun Lijiang , Xia Yongjun , Meng Fanhao , Zhou Wei 
Published: February 2025

Abstract
Employing double-boom floor-standing derrick in tower erection construction involves complexities in tension and challenges in achieving real-time perception, potentially complicating construction tasks or leading to accidents. In order to enhance the safety of tower erection construction with the use of double-boom floor-standing derrick, this paper proposes an intelligent, information-based wireless monitoring system based on distributed wireless monitoring technology. Through an analysis of the tower erection construction process with the use of double-boom floor-standing derrick, this study identifies safety factors and monitoring parameters that impact the working conditions of double-boom floor-standing derrick. Given the extensive and dispersed nature of monitoring points within the holding pole system, a wireless, distributed, and multi-level monitoring scheme is adopted, which can satisfy the construction requirements on tower erection construction sites while ensuring safety and stability throughout the construction process.
A Configurable and Automated Testing Framework for Hardware Trojan Detection in FPGAs
Authors: Li Xiaodong , Chai Song , Wang Liwei , Wang Hua 
Published: February 2025

Abstract
With the widespread adoption of FPGA, the inevitable rise of security threats, including Hardware Trojan, poses significant risks. Detecting such trojan is crucial as they can lead to severe consequences. Consequently, various detection methods have been proposed. However, conventional approaches typically require extensive datasets for model training. To tackle this issue, this paper presents a customizable Hardware Trojan detection framework. This framework is designed to generate ample samples for FPGA hardware security testing, ensuring robust detection capabilities.
A Cache Scheduling Method Based on Adaptive Expiration for Data Process System
Authors: Fang Bin , Zhong Yibo , Sun Yizhen , Tu Jinjin , Jiang Guang , Xiao Yao 
Published: February 2025

Abstract
Cache data plays a crucial role in the operation of the power system. In practical applications, the demand for retrieval and queries has surged due to the vast amount of terminal data collected throughout the province. Placing data in the cache effectively improves query speed [1]. However, cache space is limited, and the amount of storable data is small [2]. Without proper control over the data placed in the cache, the hit rate may decrease [3]. Thus this paper proposes a new control method through adaptively setting the expiration time of cached data, which considers the historical traffic distribution and real-time query distribution. The simulation results show that the proposed method can significantly reduce the cache hit rate, which leads to the improvement of cache utilization. In addition, it avoids frequent cache replacements and reduces I/O overhead.
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
Authors: Zhang Jingyu , Cao Jin , Chang Jinghao , Li Xinjin , Liu Houze , Li Zhenglin 
Published: February 2025

Abstract
This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future potential.
Classifying Triebel–Lizorkin Capacities in Metric Spaces
Authors: Lehrbäck Juha , Mohanta Kaushik , Vähäkangas Antti V. 
Published: February 2025

Abstract
We study nonlocal or fractional capacities in metric measure spaces. Our main goal is to clarify the relations between relative Hajłasz–Triebel–Lizorkin capacities, potentional Triebel–Lizorkin capacities, and metric space variants of Riesz capacities. As an application of our results, we obtain a characterization of a Hajłasz–Triebel–Lizorkin capacity density condition, which is based on an earlier characterization of a Riesz capacity density condition in terms of Hausdorff contents.
Advantages of 3D printed patient-individual moulds in brachytherapy for facial skin cancer
Authors: Licher Jörg , Achenbach Julia , Köhn Janett , Diefenhardt Markus , Fleischmann Maximilian , Rödel Claus , Tselis Nikolaos , Ramm Ulla , Scherf Christian 
Published: February 2025

Abstract

Purpose

Facial skin cancer of 42 elderly frail patients was treated with individualised 3D-printed mould applicators for high-dose-rate (HDR) brachytherapy. The dosimetric outcome was compared to conventionally manufactured individual moulds used before.

Methods
Tumour-adapted HDR brachytherapy source paths were pre-planned and dosimetrically optimised in the brachytherapy treatment planning system (TPS) using computed tomography (CT) data and considered in the design of the patient-individual moulds. Dosimetric outcome for the planning target volumes and organs at risk were statistically evaluated and compared for pre-planning, final clinical treatment planning with TG-43 formalism and retrospective tissue, material and CT density related TG-186 calculations.

Results

Pre-planning allows reliable brachytherapy source paths design to achieve intended dosimetric clinical goals. The 3D-printed patient-specific moulds show a clear advantage in the dosimetric coverage of the target volume (improving D90 from 98.3% to 104.3%) and the protection of the relevant organs at risk (reduction up to 30% of maximum Dose). With the 3D-printed moulds only minor deviations were observed for TG-43 and TG-186 dose recalculations of the treated plans.

Conclusion
Customised 3D printed moulds offer a safe and efficient technique to treat facial skin cancer in critical locations and complex clinical situations with HDR brachytherapy. The two-step planning process results in reliable PTV dose coverage and efficient sparing of eye lenses and eyeballs. Dosimetric outcome and interfractional position reproducibility with 3D printed moulds were superior to conventionally manufactured facial moulds with respect to the clinical goals.
Exploring the correlation between design of experiments and machine learning prediction accuracy in ultra-precision hard turning of AISI D2 with CBN insert: a comparative study of Taguchi and full factorial designs
Authors: Adizue Ugonna Loveday , Takács Márton 
Published: February 2025

Abstract
Efficient optimization of processes involves excellent knowledge of the influence cum contributing effect on product quality, reliability, and improved productivity via precision machining in modern manufacturing technology. Thus, this research explores the correlation between design of experiment, process optimization, and predictive accuracy of machine learning models. An ultraprecision hard turning finishing experiment was carried out on an AISI D2 of 62 HRCs via a CBN insert with two different experimental designs, namely, the Taguchi design and the full factorial design. The process parameters, cutting speed (v

c
), feed (f), and depth of cut (ɑ

p
) were investigated with signal‒to‒noise ratios (S/N) and the response surface method (RSM), which are suitable for each experimental design for response parameters: surface roughness (R

ɑ
) and the material removal rate (MRR), respectively. The Bayesian regularization neural network (BRNN)–based machine learning model was implemented to estimate the surface roughness with data from each experimental design. The results show that surface roughness was strongly influenced by the feed, whereas the material removal rate was affected by all process parameters. The model performance significantly improved as additional process parameters were introduced in the full factorial design, with an R

2
of 0.99% and a MAPE of 8.14%. An empirical equation for estimating R

ɑ
is expressed in matrix form using the weights and biases from the BRNN. For the integration of the proposed model in real-time manufacturing and decision-making, an additional experimental test was performed to validate the models with a new dataset. The results show that the full factorial design has an improvement of 36% in predictive accuracy with minimum error over the Taguchi design and provides excellent interpretability of the process parameters. A dual assessment metric criterion was employed to ascertain the credibility of the models with corresponding designs.
Is there still a place for autologous salvage transplantation in relapsed/refractory multiple myeloma in the era of novel therapies?
Authors: Karp Simone , Trautmann-Grill Karolin , Warncke Paul , Zolnowski Dominik , Röllig Christoph , Pannach Marcel , Zinn Jessica , Kroschinsky Frank , Morgner Anke , von Bonin Malte , Hänel Annette , Herbst Regina , Fricke Stephan , Bornhäuser Martin , Hänel Mathias , Teipel Raphael 
Published: February 2025

Abstract
For patients (pts) with relapsed or refractory multiple myeloma (RRMM) after previous autologous hematopoietic cell transplantation (AHCT), novel agents, cellular and immunotherapies are increasingly available. Options for second-line treatment mostly include triplet regimens based on proteasome inhibitors, immunomodulatory drugs and anti-CD38 monoclonal antibodies and since recently also CAR T cells. The importance of autologous salvage transplantation (retransplantation, Re-AHCT) has significantly decreased in recent years due to the availability of many new treatment options. Therefore, we performed a retrospective analysis of 171 pts cases with RRMM who received Re-AHCT between 2002 and 2021. With a median follow-up of 74.7 months, the 5-year rates of progression-free survival (PFS) and overall survival (OS) were 18% (median 20.6 months) and 57% (median 65.0 months), respectively, the 100-day mortality rate was 4%. Multivariate analysis identified R-ISS stage and duration of previous response (DoR) as independent prognostic factors for PFS and OS. While the revealed high-risk population (R-ISS stage II/III, DoR ≤ 24 months) was associated with a significantly worse PFS (HR 2.728) and OS (HR 3.129), the low-risk group (R-ISS I, DoR > 24 months) achieved a median PFS and OS of 45.0 months and 80.2 months, respectively. Therefore, Re-AHCT could remain an option in such prognostically favorable pts with RRMM even in the era of novel therapies especially when more potent treatment modalities are not available.
Fizz-computed tomography as a novel modality of objective esophageal hiatal assessment
Authors: Chan Daniel Leonard , Yin Grace Huan , Chug Manish , Hennessy Annemarie , Iliopoulos Jim , Talbot Michael Leonard 
Published: February 2025

Abstract

Background
Traditional investigations of esophageal hiatal assessment for reflux disease and hiatal hernia (HH), such as endoscopy and barium swallow are subjective. High resolution manometry (HRM) limits hiatal hernia assessment to vertical length. We report a novel use of 3D volumetric Computed Tomography with effervescent oral contrast (Fizz-CT) as a means of preoperative HH diagnosis.

Methods
A pilot series of 12 consecutive patients who underwent preoperative Fizz-CT assessment, as well as a combination of traditional investigations for HH (five primary, seven revisional HH).

Results
The median age was 70years (IQR 57.5-76.8years) and median BMI 28.62 kg/m2 (IQR 24.9–34.1 kg/m2). Seven patients (58%) had a recurrent HH and five patients (42%) had a primary hiatus hernia. Fizz-CT was able to diagnose the HH in all cases. The median oesophageal hiatal surface area (HSA) was 9.46cm2 (IQR 4.66-13.79cm2). The median HH sac volume was 36.3cm3 (IQR 26.0-80.3cm3). All patients had a least one other investigation that has been traditionally used to diagnose HH. Seven of the 12 patients subsequently underwent laparoscopic HH repair surgery with intraoperative findings further confirming the radiological diagnosis of hiatus hernia.

Conclusion
Fizz-CT imaging is a novel and accurate means of objective esophageal hiatal assessment in both primary and revisional HH patients. Vertical and radial measures of hiatal defects as well as hernia volumetry can be obtained. In post-surgical patients the relationship between the esophago-gastric junction and an infra- or supra-diaphragmatic fundoplication can also be assessed.
WOVOdat web service data retrieval system for comprehensive volcano monitoring
Authors: Nang Thin Zar Win , Widiwijayanti Christina , Espinosa-Ortega Tania , De Groote Julie , Taisne Benoit 
Published: February 2025

Abstract
WOVOdat, the World Organization of Volcano Observatories database, serves as a repository for global volcano unrest data, housed within a MySQL structure and accessible via the user-friendly web interface, wovodat.org. This paper introduces a novel web service tool designed to facilitate seamless access to this wealth of data, streamlining the search process through structured query scripts. This tool enables users to effortlessly modify variable values to suit their specific needs, enhancing the efficiency of data retrieval. We explore the numerous benefits of employing web services, highlighting their convenience and effectiveness, particularly in handling large datasets. By leveraging the Web Service Interface (WSI), users can retrieve data by specifying key parameters such as volcano name, data type, and time range in a standardized format over HTTP. Additionally, the Application Programming Interface (API) endpoint facilitates the integration of functionalities into scripts, enabling automated data retrieval for further analysis. By offering flexible access options to WOVOdat, the web service tool simplifies the exploration of its comprehensive global volcano unrest data. Leveraging the wealth of compiled unrest datasets, researchers can identify key precursory patterns and their correlations, thereby improving statistical analysis capabilities for eruption forecasts. The database also enables researchers to understand volcanic processes, compare volcanic behavior across diverse volcanoes worldwide, and conduct analogue studies, contributing to a deeper understanding of volcanic activity and its associated hazards.