bluuebunny/crossref_metadata_2025_split huggingface dataset:
OffRoadSynth Open Dataset for Semantic Segmentation using Synthetic-Data-Based Weight Initialization for Autonomous UGV in Off-Road Environments
Authors: Małek Konrad , Dybała Jacek , Kordecki Andrzej , Hondra Piotr , Kijania Katarzyna 
Published: May 2024

AbstractThis article concerns the issue of image semantic segmentation for the machine vision system of an autonomous Unmanned Ground Vehicle (UGV) moving in an off-road environment. Determining the meaning (semantics) of the areas visible in the recorded image provides a complete understanding of the scene surrounding the autonomous vehicle. It is crucial for the correct determination of a passable route. Nowadays, semantic segmentation is generally solved using convolutional neural networks (CNN), which can take an image as input and output the segmented image. However, proper training of the neural network requires the use of large amounts of data, which becomes problematic in the situation of low availability of large, dedicated image data sets that consider various off-road situations - driving on various types of roads, surrounded by diverse vegetation and in various weather and light conditions. This study introduces a synthetic image dataset called “OffRoadSynth” to address the training data scarcity for off-road scenarios. It has been shown that pre-training the neural network on this synthetic dataset improves image segmentation accuracy compared to other methods, such as random network weight initialization or using larger, generic datasets. Results suggest that using a smaller but domain-dedicated set of synthetic images to initialize network weights for training on the target real-world dataset may be an effective approach to improving semantic segmentation results of images, including those from off-road environments.
Spatial Heterogeneity and Subjective Wellbeing: Exploring the Role of Social Capital in Metropolitan Areas Using Multilevel Modelling
Authors: Lignier Phil , Jarvis Diane , Grainger Daniel , Chaiechi Taha 
Published: May 2024

AbstractThe role of social capital, the social networks that influence human wellbeing has been explored by empirical research in the US and Europe, however no study so far has undertaken a systematic investigation of the impact of the various dimensions of social capital in metropolitan areas. Addressing this gap in knowledge can have practical and policy-oriented implications by contributing to more informed decision-making processes in metro areas, better targeted interventions and ultimately an improved quality of life for residents. This study adopts a multi-level modelling approach to investigate life satisfaction and social capital heterogeneity within metropolitan areas in Australia. Our dataset was collected by the Household Income and Labour Dynamics in Australia survey and includes almost 4,000 individual respondents. Our results show that social trust, social engagement and connection, and a psychological sense of community measured at an individual level have a strong positive influence on individual life satisfaction. Conversely negative individual perceptions about neighbourhood criminality and shabbiness are associated with a lower level of life satisfaction. The application of a model using random slope coefficients for social capital variables suggests that most of the spatial heterogeneity between census districts is explained by between-individual (compositional) variations, rather than contextual differences. Only social connection and engagement appeared to have a distinctive contextual influence. These findings confirm the importance of social inclusion in enhancing wellbeing for everyone and may inform social policy on how to promote social networks in urban areas by all levels of government.
Publication Trends and Hot Spots of ChatGPT’s Application in the Medicine
Authors: Li Zhi-qiang , Wang Xue-feng , Liu Jian-ping 
Published: May 2024

Abstract
This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which “digital health”was not only the largest in scale but also had the most citations. The study highlights ChatGPT’s cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT’s specific technical uses in medical practice.
AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation
Authors: Tenscher-Philipp Robin , Schanz Tim , Harlacher Fabian , Fautz Benedikt , Simon Martin 
Published: May 2024

AbstractTraining data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.
How Much Physiotherapy, Chiropractic, and Osteopathy Care Do Compensated Australian Workers with Low Back Pain Receive? A Retrospective Cohort Study
Authors: Di Donato Michael , Gray Shannon , Sheehan Luke R. , Buchbinder Rachelle , Iles Ross , Collie Alex 
Published: May 2024

Abstract
Objectives
To identify the prevalence and frequency of physiotherapy, chiropractic, and/or osteopathy care in Australians with workers’ compensation claims for low back pain (LBP).

Methods
We included workers with accepted workers’ compensation claims longer than 2 weeks from the Australian states of Victoria, Queensland, South Australia, and Western Australia. Workers were grouped by whether they attended physiotherapy, chiropractic, and/or osteopathy in the first 2 years of their claim. Descriptive statistics and logistic regression were used to describe differences between groups. Descriptive statistics and negative binomial regression were used to describe differences in the number of attendances in each group.

Results
Most workers had at least one physical therapy attendance during the period of their claim (n = 23,619, 82.0%). Worker state, socioeconomic status, and remoteness were the largest contributing factors to likelihood of physical therapy attendance. Most workers only attended physiotherapy (n = 21,035, 89.1%, median of 13 times). Far fewer only attended chiropractic (n = 528, 2.2%, median of 8 times) or only osteopathy (n = 296, 1.3%, median of 10 times), while 1,750 (7.5%) attended for care with more than one type of physical therapy (median of 31 times).

Conclusion
Most Australian workers with workers’ compensation time loss claims for LBP attend physiotherapy at least once during their claims. State of claim is the strongest predictor of which physical therapy profession they attend, possibly due to regional availability. Workers who see a physiotherapist have significantly more attendances. Future research should explore the relationship between these patterns of care and claimant outcomes, including work disability duration.
VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models
Authors: Bakır Halit 
Published: May 2024

AbstractIn this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments.
Investigating the impact of tropical deforestation on Indian monsoon hydro-climate: a novel study using a regional climate model
Authors: Lodh Abhishek , Haldar Stuti 
Published: May 2024

AbstractThis study uses a state-of-the-art regional climate model (RCM) to examine how tropical deforestation affects the meteorology of the Indian Summer Monsoon (ISM). Incorporating insights from existing research on deforestation by climate scientists, alongside evidence of environmental deterioration in semi-arid, hilly and tropical regions of Southeast Asia, this research seeks to elucidate the critical influence of anthropogenic reasons of climate change on the hydroclimate of ISM. Employing “tropical deforestation” design experiments with the ICTP-RegCMv4.4.5.10 RCM the study evaluates the effects on meteorological parameters including precipitation, circulation patterns and surface parameters. This experimental design entails substituting vegetation type in the land use map of RegCMv4.4.5.10 model, such as deciduous and evergreen trees in Southeast Asia with “short grass” to mimic tropical deforestation. Findings reveal that deforestation induces abnormal anti-cyclonic circulation over eastern India curtails moisture advection, diminishing latent heat flux and moisture transport, leads to a decrease in precipitation compared to control experiment scenario. Alterations in albedo and vegetation roughness length attributable to deforestation impact temperature, humidity, precipitation, consequently exacerbating drought and heatwave occurrences. Additionally, the study also explores deforestation-induced feedback on ISM precipitation variability. The study concludes that deforestation substantially alters land-surface characteristics, water and energy cycle, and atmospheric circulation, thereby influencing regional climate dynamics. These findings offer foundational insights into comprehending land-use and land-cover changes and their implications for climate change adaptation strategies.
Appearance of fluid content in Rathke’s cleft cyst is associated with clinical features and postoperative recurrence rates
Authors: Iwata Takamitsu , Oshino Satoru , Saitoh Youichi , Kinoshita Manabu , Onoda Yuji , Kijima Noriyuki , Mukai Kosuke , Otsuki Michio , Kishima Haruhiko 
Published: May 2024

Abstract
Purpose
The contents of Rathke’s cleft cysts (RCCs) vary from clear and slightly viscous to purulent. Surgical treatment of symptomatic RCCs involves removing the cyst contents, whereas additional cyst-wall opening to prevent reaccumulation is at the surgeon’s discretion. The macroscopic findings of the cyst content can reflect the nature of RCCs and would aid in surgical method selection.

Methods
We retrospectively reviewed the records of 42 patients with symptomatic RCCs who underwent transsphenoidal surgery at our institute between January 2010 and March 2022. According to the intraoperative findings, cyst contents were classified into type A (purulent), type B (turbid white with mixed semisolids), or type C (clear and slightly viscous). Clinical and imaging findings and early recurrence rate (within two years) were compared according to the cyst content type.

Results
There were 42 patients classified into three types. Patients with type C were the oldest (65.4 ± 10.4 years), and type A included more females (92.9%). For magnetic resonance imaging, type-A patients showed contrast-enhanced cyst wall (92.9%), type-B patients had intracystic nodules (57.1%), and all type-C patients showed low T1 and high T2 intensities with larger cyst volumes. Fewer asymptomatic patients had type C. Preoperative pituitary dysfunction was most common in type A (71.4%). Early recurrence was observed in types A and C, which were considered candidates for cyst-wall opening.

Conclusion
The clinical characteristics and surgical prognosis of RCCs depend on the nature of their contents.
A tale of government spending efficiency and trust in the state
Authors: Afonso António , Jalles João Tovar , Venâncio Ana 
Published: May 2024

AbstractThis paper empirically links the efficiency and performance assessment of the general government, proxied by efficiency scores, to the trust in government. Government spending efficiency scores are first computed via data envelopment analysis (DEA). Then, relying on panel data and instrumental variable approaches, we estimate the effect of public sector efficiency on citizens trust on national governments. The sample covers 36 OECD countries between 2007 and 2019. We find that the more efficient countries in terms of government spending are Australia, Chile, Ireland, New Zealand, South Korea, Switzerland. Secondly, our main finding is that better public sector spending efficiency is positively associated with citizens’ higher trust in governments. In general, political economy variables and the existence of fiscal rules do not seem to significantly affect our measure of trust. The results hold using alternative proxies for public sector efficiency, alternative measures for trust, specifications with different control variables and different empirical approaches (instrumental variables).
Firms’ borrowing costs and neighbors’ flood risk
Authors: Bassetti Thomas , Dal Maso Lorenzo , Pieroni Valentina 
Published: May 2024

AbstractThis study examines whether Italian firms exposed to physical climate risks incur additional borrowing costs due to spatial spillovers. Using a sample of 419,040 firm-year observations from 2016 to 2019, we find a positive relationship between a firm’s cost of debt and its neighborhood’s average exposure to climate risk. According to our findings, the costs associated with neighborhood climate risk are as relevant as those associated with a firm’s direct risk, with small businesses being the only ones affected by spillover effects. These results may be explained by small enterprises’ lack of financial diversification, poor bargaining power, and strong reliance on credit from financial intermediaries.
A Review on the Use of Large Language Models as Virtual Tutors
Authors: García-Méndez Silvia , de Arriba-Pérez Francisco , Somoza-López María del Carmen 
Published: May 2024

AbstractTransformer architectures contribute to managing long-term dependencies for natural language processing, representing one of the most recent changes in the field. These architectures are the basis of the innovative, cutting-edge large language models (LLMs) that have produced a huge buzz in several fields and industrial sectors, among the ones education stands out. Accordingly, these generative artificial intelligence-based solutions have directed the change in techniques and the evolution in educational methods and contents, along with network infrastructure, towards high-quality learning. Given the popularity of LLMs, this review seeks to provide a comprehensive overview of those solutions designed specifically to generate and evaluate educational materials and which involve students and teachers in their design or experimental plan. To the best of our knowledge, this is the first review of educational applications (e.g., student assessment) of LLMs. As expected, the most common role of these systems is as virtual tutors for automatic question generation. Moreover, the most popular models are GPT-3 and BERT. However, due to the continuous launch of new generative models, new works are expected to be published shortly.
Lineage Thinking in Evolutionary Biology: How to Improve the Teaching of Tree Thinking
Authors: Jenner Ronald A. 
Published: May 2024

AbstractIn 1988, Robert O’Hara coined the now ubiquitous phrase “tree thinking” to highlight the importance of cladistics for proper evolutionary reasoning. This accessible phrase has been taken up widely in the professional, popular, and educational literatures, and it has played an important role in helping spread phylogenetic thinking far beyond the disciplinary borders of systematics. However, the undeniable benefits of the spread of tree thinking have become marred by being widely linked to several misconceptions that were present in O’Hara’s original writings. O’Hara incorrectly considered clades to be the central subjects of evolutionary narratives. By failing to appreciate that clades contain independently evolving lineages, O’Hara has promoted the misleading view that evolution is irreducibly branched. In this paper, I show how an exclusive focus on the branching realm of taxa has created a cladistic blindfold that has caused a form of lineage blindness that has spread widely through the literature dedicated to the teaching of tree thinking. Its symptoms include the rejection of phenomena and concepts that are fundamental to the realm of evolving lineages, including linear evolutionary imagery and narratives, the concepts of anagenetic evolution and missing links, our evolutionary descent from monkeys and apes, and the promotion of the nonsensical concept of collateral ancestors. To avoid simplistic tree thinking, it is crucial to recognize that the realms of taxa and lineages have distinctive features that require different kinds of thinking. I close by suggesting that teaching can be improved by linking tree thinking explicitly to lineage thinking.
Lawscapes
Authors: Mancuso Salvatore 
Published: May 2024

AbstractComparative law is a subject always in motion. Scholarly discussion about its methodology is always vivid, in search of better tools to make the different possible comparative endeavors. The paper explores the concept of lawscape and its link with comparative legal methodology. The concept of lawscape will be linked with other relevant concepts in comparative law, like those of legal pluralism, legal transplants, legal formants and legal fluxes to make the necessary connections and find its space within the tools of comparative law.
Don't wait any longer, conceive in time: a systematic review and meta-analysis based on semen parameters after varicocelectomy
Authors: Mei Yangyang , Ji Nuo , Feng Xingliang , Xu Renfang , Xue Dong 
Published: May 2024

Abstract
Background
Varicocelectomy was considered to be beneficial to patients with varicocele-related infertility. However, there are only a few researchers who have explored the relationship between better timing and postoperative semen improvement in patients.

Methods
We conducted this meta-analysis by enrolling published prospective studies to find out the best waiting time after varicocelectomy to wait for better improvement of semen quality. An extensive search was conducted in PubMed, Web of Science, and Cochrane Library to identify eligible studies. The included studies were then analyzed comprehensively using STATA software and standardized mean differences (SMDs) and their corresponding 95% confidence intervals were calculated.

Results
Our comprehensive analysis showed that after varicocelectomy, follow-up results within 3 months or longer showed a significant improvement in semen parameters compared to the preoperative period. Notably, no further improvement in semen parameters was observed when the follow-up period reached six months or longer (semen volume: WMD: − 0.07 (− 0.29, 0.16); sperm concentration: WMD: − 1.33 (− 2.33, − 4.99); sperm motility: WMD: 2.31 (− 0.55, 5.18); sperm morphology: WMD: 1.29 (− 0.66, 3.24); sperm total motile count: WMD: 3.95 (− 6.28, 14.19)).

Conclusions
Three months after varicocelectomy may be the optimal time for semen parameters compared to six months or even longer, which means it is also the preferable time for conception. However, more well-designed prospective studies are needed in the future to validate our conclusion.