bluuebunny/crossref_metadata_2025_split huggingface dataset:
A systematic review on research utilising artificial intelligence for open source intelligence (OSINT) applications
Authors: Browne Thomas Oakley , Abedin Mohammad , Chowdhury Mohammad Jabed Morshed 
Published: June 2024

AbstractThis paper presents a systematic review to identify research combining artificial intelligence (AI) algorithms with Open source intelligence (OSINT) applications and practices. Currently, there is a lack of compilation of these approaches in the research domain and similar systematic reviews do not include research that post dates the year 2019. This systematic review attempts to fill this gap by identifying recent research. The review used the preferred reporting items for systematic reviews and meta-analyses and identified 163 research articles focusing on OSINT applications leveraging AI algorithms. This systematic review outlines several research questions concerning meta-analysis of the included research and seeks to identify research limitations and future directions in this area. The review identifies that research gaps exist in the following areas: Incorporation of pre-existing OSINT tools with AI, the creation of AI-based OSINT models that apply to penetration testing, underutilisation of alternate data sources and the incorporation of dissemination functionality. The review additionally identifies future research directions in AI-based OSINT research in the following areas: Multi-lingual support, incorporation of additional data sources, improved model robustness against data poisoning, integration with live applications, real-world use, the addition of alert generation for dissemination purposes and incorporation of algorithms for use in planning.
Enhancing Skin Cancer Diagnosis Using Swin Transformer with Hybrid Shifted Window-Based Multi-head Self-attention and SwiGLU-Based MLP
Authors: Pacal Ishak , Alaftekin Melek , Zengul Ferhat Devrim 
Published: June 2024

AbstractSkin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning–based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature.
Asymptotically uniform functions: a single hypothesis which solves two old problems
Authors: Gabriel J.-P. , Berrut J.-P. 
Published: June 2024

Abstract
The asymptotic study of a time-dependent function ƒ as the solution of a differential equation often leads to the question of whether its derivative

$$f'$$

f
′

vanishes at infinity. We show that a necessary and sufficient condition for this is that

$$f'$$

f
′

is what may be called asymptotically uniform. We generalize the result to higher order derivatives. We also show that the same property for ƒ itself is also necessary and sufficient for its one-sided improper integrals to exist. The article provides a broad study of such asymptotically uniform functions.
Maternal Depression and Sleep Problems in Early Childhood: A Meta-Analysis
Authors: Orton Owen , Bilgin Ayten 
Published: June 2024

AbstractBoth prenatal and postnatal maternal depression have been associated with increased sleep problems in early childhood. However, this association is less consistent for postnatal depression, and the strength of the association remains unclear. The aim of the current study was to provide a quantitative synthesis of the literature to estimate the magnitude of the association between maternal depression and sleep problems in early childhood. Medline, PsycINFO, PsycARTICLES, Web of Science, and Scopus were searched for prospective longitudinal studies from 1970 to December 2022. Of 117 articles screened, 22 studies met the inclusion criteria. Both prenatal depression (OR = 1.82; 95% CI = 1.28–2.61) and postnatal depression (OR = 1.65; 95% CI = 1.50–1.82) were associated with increased likelihood of sleep problems in early childhood. The heterogeneity between the studies was significant and high both for prenatal (Q = 432.323; I2 = 97.456, P < .001) and postnatal depression (Q = 44.902, I2 = 65.594, P < .001), which mean that conclusions are tentative and need to be considered within the possible influence of unmeasured confounding. However, mitigating depression symptoms in mothers both during pregnancy and in the postnatal period would be an effective strategy for reducing sleep problems in children.
A computer vision system for detecting motorcycle violations in pedestrian zones
Authors: Hernández-Díaz Nicolás , Peñaloza Yersica C. , Rios Y. Yuliana , Martinez-Santos Juan Carlos , Puertas Edwin 
Published: June 2024

AbstractThis paper presents a system that relies on computer vision to identify instances of motorcycle violations in crosswalks utilizing CNNs. The system was trained and evaluated on a novel public dataset published by the authors, which contains traffic images classified into four categories: motorcycles in crosswalks, motorcycles outside crosswalks, pedestrians in crosswalks, and only motorbike outside. We demonstrate the viability of leveraging deep learning models such as YOLOv8 for this purpose and provide details on the training and performance of the model. This system has the potential to enable intelligent traffic enforcement to mitigate accidents in pedestrian zones; to develop the system, a dataset comprising over 6,000 images was amassed from publicly available traffic cameras and subsequently annotated. Several models, including YOLOv8, SSD, and MobileNet, were trained on this dataset. The YOLOv8 model attained the highest performance with a mean average precision of 84.6% across classes. The study presents the system architecture and training process. Results illustrate the potential of utilizing deep learning to detect traffic violations in pedestrian zones, which can promote intelligent traffic enforcement and improved safety.
Extratesticular masses focusing on MRI findings
Authors: Edo Hiromi , Yagi Fumiko , Mizuno Mariko , Okada Masahiro , Hyoe Eiko , Ozaki Ippei , Akita Hirotaka , Jinzaki Masahiro , Shinmoto Hiroshi 
Published: June 2024

AbstractScrotal masses, whether cystic or solid lesions, are routinely evaluated using ultrasonography. Magnetic resonance imaging (MRI) may be used for further investigation in cases with atypical findings, difficult diagnoses, large masses, and/or unclear relationships with the surrounding tissues. Scrotal solid masses are divided into intra- and extra-testicular masses. A staggering 90% of the intratesticular masses are malignant, whereas 75% of extratesticular masses are benign. Extratesticular masses are less common than intratesticular masses; however, some extratesticular masses present characteristic MRI findings. Familiarity with these specific MRI features of extratesticular masses is beneficial to radiologists, as appropriate diagnoses can help avoid unnecessary invasive treatments such as orchiectomy. In this review, we describe fibrous pseudotumors, polyorchidism, adenomatoid tumors, and scrotal leiomyoma as benign paratesticular masses, focusing on their characteristic imaging features on MRI. Although these tumors are extremely rare, their MRI findings are distinctive, and accurate diagnoses can prevent unnecessary orchiectomy. In addition, to demonstrate the pitfalls of diagnosing extratesticular masses, we present a case of seminoma misidentified as extratesticular masses due to large extensions outside the testis. Spermatic cord sarcoma, including rhabdomyosarcoma, leiomyosarcoma, and liposarcoma, and metastasis to the spermatic cord are described as malignant extratesticular masses. This review focused on extratesticular masses and elaborates the imaging findings that can aid in the accurate diagnosis using MRI.
Adhipati, Yogācāra Intersubjectivity, and Soteriology in Kuiji’s Commentaries
Authors: Zu Jessica X. 
Published: June 2024

AbstractThis study sheds light on a key concept of Yogācāra intersubjectivity that played a significant role in medieval Chinese Yogācāra. Specifically, it analyzes how Kuiji 窺基 (632–682) reinterprets adhipati (activating and amplifying influence; Ch: zengshang 增上 or zengshang li 增上力 or zengshang yuan 增上緣) to account for intersubjective karmic interactions across different lifeworlds in the events of teaching and killing.As this line of investigation shows, Kuiji’s theory of adhipati attempts to sidestep the entanglement of the problems of intersubjectivity and incommensurable worlds in Yogācāra. Extant scholarship on intersubjectivity often presumes one spatio-temporal world populated by sentient beings and insentient objects, which is fundamentally at odds with the Yogācāra tenet that each mental stream generates its distinct lifeworld. To address the pragmatic question of how different mental continuums of consciousnesses can impinge into each other’s lifeworlds, Kuiji employed adhipati in a specific way to account for intersubjective interplay across different, potentially incommensurable worlds. In this framing of intersubjectivity, the problem of one world or many worlds is explained non-dualistically: these lifeworlds are neither the same nor different, neither one nor many, but karmically interconnected.Expanding on recent discussions of the puzzle of intersubjectivity in Yogācāra, this paper shows that Kuiji’s deployment of adhipati provides an account of genuine intersubjectivity in which different mental continuums are recognized as subjects and engage in causal interactions among different spatio-temporal worlds. In Kuiji’s framing, this genuine intersubjectivity gives rise to the sense of one shared world.
Shared Sovereignties and Criminal Governances in the Context of the Pandemic in Colombia
Authors: Niño César , Guerrero-Sierra Hugo , Wilches Jaime 
Published: June 2024

AbstractThe Covid-19 pandemic led to a process of social isolation, but this does not appear to have applied to criminal organisations in Colombia. On the contrary, they had opportunities to strengthen and demonstrate capacities to coexist with the state. So, how were shared sovereignties and criminal governances configured during Covid-19 in Colombia? Through an ethnography of digital narratives and the construction of a model of governance types, we found that the political model of shared sovereignty and criminal governance was strengthened in the context of the pandemic, allowing legal and illegal actors to accept a mutually beneficial –but painful– draw, to control their areas of social influence and territorial control.
Exploring the relationship between personality and gaming disorder symptoms in a sample of Dota 2 players
Authors: Bäcklund Christian , Sörman Daniel Eriksson , Röhlcke Sebastian , Nyström Markus B. T. 
Published: June 2024

AbstractThis study explored the relationship between the big five personality traits and gaming disorder symptoms (GDS) in a sample of Dota 2 players. Recent research has indicated that the relationship between personality traits and GDS may depend on the video game genre investigated. However, the association between GDS and personality has yet to be investigated within a specific game, which may be even more relevant to explore as each game offers unique gameplay mechanics that can influence player behavior differently. Thus, the present study investigated the relationship between the big five personality traits and GDS in a sample of video game players from a specific game, DOTA 2 (n = 321, M = 23.25 years, SD = 4.51). Multiple linear regression was used to analyze GDS formatted as a composite score, and one-way Analysis of Variance (ANOVA) were further conducted for analyses in which gamers were classified into normal gamers (i.e., less than three criteria endorsed), moderate-risk gamers (i.e., four criteria endorsed), and high-risk gamers (i.e., all criteria endorsed). The analysis of the composite score showed a significant relationship between neuroticism, conscientiousness, agreeableness, and GDS. Analyses of the gaming classifications revealed that neuroticism was the most crucial factor concerning differentiating moderate-risk and high-risk from normal gamers. The findings and their practical implications are further discussed.
Should I Just Stick to the Wall? Evaluating the Social Acceptance and Preferred Driving Side of Wall Following
Authors: Robinson Nicole , Yeung Christopher Tsz-Hang , Cosgun Akansel 
Published: June 2024

AbstractThe need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective.
An Internet Snapshot Survey Assessing the sale of Synthetic Cannabinoid Receptor Agonists for use with Electronic Vaping Devices
Authors: Gould Allon , Dargan Paul I , Wood David M 
Published: June 2024

Abstract
Introduction
Synthetic cannabinoid receptor agonists (SCRAs) are associated with significant toxicity and are increasingly used in electronic vaping devices. We assessed the availability of SCRA vaping products to UK purchasers on the surface web.

Methods
An internet snapshot survey was performed between October 2022 and January 2023 on ‘google.com’ using the search terms “buy c-liquid vape”, “buy herbal incense vape liquid”, “buy cannabis vape liquid”, “buy hashish vape liquid”, “buy K2 vape liquid”.

Results
62 websites selling 128 SCRA vaping brands were identified. Most were purportedly based in the USA (41 websites, 66%) and most sold other controlled substances. Purchase incentives offered included discreet packaging (38, 61%), discounts for bulk purchase (34, 55%) and tracked delivery (30, 48%). Many websites stated SCRA products were: not for human consumption (41, 66%), for research purposes only (15, 24%), or legal (28, 45%). Websites sold a median (IQR) of 16 (7–25) SCRA vaping brands. Almost all were bottles of vaping liquid (1220/1225, 99.6%). The most common bottle size was 5mL (60%), the median (IQR) total volume of SCRA liquid per sale was 50mL (10–200mL). Median (IQR) price was £3.39/mL (£2.01/mL– £5.29/mL). Price decreased with increasing volume purchased (£6.58/mL for ≤ 5mL, £1.60/mL for > 200mL).

Conclusion
SCRA vaping products are easily obtainable online, in both small and bulk quantities. Information provided to purchasers on safety and legality is lacking or misleading. Further studies are needed to confirm the chemistry of these products. Policymakers should consider steps to limit the potential harm caused by the purchase and use of these products.
Drug delivery to and through the skin
Authors: Guy Richard H. 
Published: June 2024

AbstractDrug delivery technology has advanced significantly over >50 years, and has produced remarkable innovation, countless publications and conferences, and generations of talented and creative scientists. However, a critical review of the current state-of-the-art reveals that the translation of clever and sophisticated drug delivery technologies into products, which satisfy important, unmet medical needs and have been approved by the regulatory agencies, has - given the investment made in terms of time and money - been relatively limited. Here, this point of view is illustrated using a case study of technology for drug delivery into and through the skin and aims: to examine the historical development of this field and the current state-of-the-art; to understand why the translation of drug delivery technologies into products that improve clinical outcomes has been quite slow and inefficient; and to suggest how the impact of technology may be increased and the process of concept to approved product accelerated.
Graphical Abstract
Exploring the feasibility of prestressed anchor cables as an alternative to temporary support in the excavation of super-large-span tunnel
Authors: Zhou Shunhua , Jin Yuyin , Tian Zhiyao , Zou Chunhua , Zhao Heming , Miao Zengrun 
Published: June 2024

AbstractExcavating super-large-span tunnels in soft rock masses presents significant challenges. To ensure safety, the sequential excavation method is commonly adopted. It utilizes internal temporary supports to spatially partition the tunnel face and divide the excavation into multiple stages. However, these internal supports generally impose spatial constraints, limiting the use of large-scale excavation equipment and reducing construction efficiency. To address this constraint, this study adopts the “Shed-frame” principle to explore the feasibility of an innovative support system, which aims to replace internal supports with prestressed anchor cables and thus provide a more spacious working space with fewer internal obstructions. To evaluate its effectiveness, a field case involving the excavation of a 24-m span tunnel in soft rock is presented, and an analysis of extensive field data is conducted to study the deformation characteristics of the surrounding rock and the mechanical behavior of the support system. The results revealed that prestressed anchor cables integrated the initial support with the shed, creating an effective “shed-frame” system, which successively maintained tunnel deformation and frame stress levels within safe regulatory bounds. Moreover, the prestressed anchor cables bolstered the surrounding rock effectively and reduced the excavation-induced disturbance zone significantly. In summary, the proposed support system balances construction efficiency and safety. These field experiences may offer valuable insights into the popularization and further development of prestressed anchor cable support systems.
First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation
Authors: Lukomski Leandra , Pisula Juan , Wagner Tristan , Sabov Andrii , Große Hokamp Nils , Bozek Katarzyna , Popp Felix , Kann Martin , Kurschat Christine , Becker Jan Ulrich , Bruns Christiane , Thomas Michael , Stippel Dirk 
Published: June 2024

Abstract
Background
Living kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML) to predict the progression of kidney function deterioration over time using the estimated GFR (eGFR) slope as the target variable.

Methods
We included 238 living kidney donors who underwent donor nephrectomy. We divided the dataset based on the eGFR slope in the third follow-up year, resulting in 185 donors with an average eGFR slope and 53 donors with an accelerated declining eGFR-slope. We trained three Machine Learning-models (Random Forest [RF], Extreme Gradient Boosting [XG], Support Vector Machine [SVM]) and Logistic Regression (LR) for predictions. Predefined data subsets served for training to explore whether parameters of an ESKD risk score alone suffice or additional clinical and time-zero biopsy parameters enhance predictions. Machine learning-driven feature selection identified the best predictive parameters.

Results
None of the four models classified the eGFR slope with an AUC greater than 0.6 or an F1 score surpassing 0.41 despite training on different data subsets. Following machine learning-driven feature selection and subsequent retraining on these selected features, random forest and extreme gradient boosting outperformed other models, achieving an AUC of 0.66 and an F1 score of 0.44. After feature selection, two predictive donor attributes consistently appeared in all models: smoking-related features and glomerulitis of the Banff Lesion Score.

Conclusions
Training machine learning-models with distinct predefined data subsets yielded unsatisfactory results. However, the efficacy of random forest and extreme gradient boosting improved when trained exclusively with machine learning-driven selected features, suggesting that the quality, rather than the quantity, of features is crucial for machine learning-model performance. This study offers insights into the application of emerging machine learning-techniques for the screening of living kidney donors.

Graphical abstract