bluuebunny/crossref_metadata_2025_split huggingface dataset:
Visual Question Answering Models for Zero-Shot Pedestrian Attribute Recognition: A Comparative Study
Authors: Castrillón-Santana Modesto , Sánchez-Nielsen Elena , Freire-Obregón David , Santana Oliverio J. , Hernández-Sosa Daniel , Lorenzo-Navarro Javier 
Published: June 2024

AbstractPedestrian Attribute Recognition (PAR) poses a significant challenge in developing automatic systems that enhance visual surveillance and human interaction. In this study, we investigate using Visual Question Answering (VQA) models to address the zero-shot PAR problem. Inspired by the impressive results achieved by a zero-shot VQA strategy during the PAR Contest at the 20th International Conference on Computer Analysis of Images and Patterns in 2023, we conducted a comparative study across three state-of-the-art VQA models, two of them based on BLIP-2 and the third one based on the Plug-and-Play VQA framework. Our analysis focuses on performance, robustness, contextual question handling, processing time, and classification errors. Our findings demonstrate that both BLIP-2-based models are better suited for PAR, with nuances related to the adopted frozen Large Language Model. Specifically, the Open Pre-trained Transformers based model performs well in benchmark color estimation tasks, while FLANT5XL provides better results for the considered binary tasks. In summary, zero-shot PAR based on VQA models offers highly competitive results, with the advantage of avoiding training costs associated with multipurpose classifiers.
Piloting Systematic Implementation of Educational Technology in Swedish K-12 Schools – Two-Years-In Report
Authors: Nordmark Susanna , Augustsson Hanna , Davidsson Mattias , Andersson-Gidlund Tobias , Holmberg Kristina , Mohseni Zeynab , Rack John , Masiello Italo 
Published: June 2024

AbstractHalfway through a four-year research project supported by implementation science and the Active Implementation Frameworks (AIF), this article reports on the status of the initial two implementation stages. Our research investigates the impact of systematically preparing educators and educational institutions to integrate digital learning materials and learning analytics dashboards to enrich teaching practices and improve student performance outcomes.Furthermore, it seeks to establish a foundation for the use of innovative and validated educational technology (EdTech) through sustainable implementation strategies, evidence-based evaluation, and continuous redesign of digital learning materials. By adopting this comprehensive approach, we aim to enhance the knowledge base regarding effective digital innovation integration within educational environments.We argue that applying implementation science in educational settings facilitates the adoption of effective innovations, promotes evidence-based decision-making, and helps identify and address obstacles to change. Our ongoing research underscores the transformative impact of implementation science in education. Thus far, we have highlighted the crucial role of teacher perspectives and the necessity of co-designing technology aligned with teaching and learning objectives.This nuanced approach refutes the notion of a one-size-fits-all solution or a quick fix achievable in a single academic year. Instead, it advocates a dynamic, collaborative model that acknowledges the multifaceted nature of implementation. Our journey has reaffirmed the dedication of teachers, showcasing their readiness to invest time and effort when their professionalism is respected, and their input is genuinely valued and acted upon.
Judicial leadership matters (yet again): the association between judge and public trust for artificial intelligence in courts
Authors: Fine Anna , Marsh Shawn 
Published: June 2024

AbstractArtificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.
Simulating urban energy use under climate change scenarios and retrofit plans in coastal Texas
Authors: Zhu Chunwu , Ye Xinyue , Du Jiaxin , Hu Zhiheng , Shen Yang , Retchless David 
Published: June 2024

Abstract Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.
Beyond the surface: a scoping review of vision-based underwater experience technologies and user studies
Authors: Hagen Oksana , Aly Amir , Jones Ray , Varga Marius , Bazazian Dena 
Published: June 2024

AbstractExploring the underwater world presents significant physical and financial barriers. Telepresence technologies offer a potential solution by a providing a more accessible version of an underwater experience. Our research involved a scoping review to consolidate previous findings on vision-based technologies that aim to recreate underwater experiences and their user evaluations. We searched 5 academic databases for papers describing or evaluating technologies providing visual underwater experiences without actual submersion. We systematically searched YouTube to include immersive experiences not documented in academic publications. Our review included 45 academic papers and 23 YouTube videos classified by their level of ‘reality’, ‘degrees of freedom’, and presence of interactive elements. The technologies reviewed included virtual reality, 360$$^{\circ }$$

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video and imaging, augmented and mixed reality, head-mounted displays, mobile devices, cameras, sensors, remotely operated vehicles (ROVs), and games. Half of the selected papers featured user evaluations (with sample sizes ranging from 5 to 1006 participant); these methods included interviews, performance tracking, and questionnaires. We identified six main application areas for these technologies: (i) general environmental awareness, (ii) formal education about marine life, (iii) therapeutic interventions, (iv) access to underwater heritage sites, (v) ROV teleoperation and simulations, and (vi) entertainment. Immersive technologies, such as head-mounted displays and augmented reality, were prevalent across all application categories, though their usability varied. Cost considerations were also diverse, with costs ranging from expensive ROVs and simulated environments to cheaper 360$$^{\circ }$$

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videos. Our findings indicate a need for more robust user studies, including long-term research and comparisons among real-time, pre-recorded, and simulated experiences. A better understanding of entertainment-driven applications could benefit education, environmental conservation, and healthcare. The findings of the scoping review are discussed with respect to the technologies identified and the corresponding user studies.
Association of red cell distribution width (RDW) with positive blood culture of neonatal sepsis in Khartoum state, Sudan
Authors: Mohamed Osama Abdalla Abdelmagid , Seedahmed Sahar Mohammed , Idris Alkhair Abd Almahmoud 
Published: June 2024

Abstract
Background
The red cell distribution width (RDW) was higher among neonates with early onset neonatal sepsis. The study aimed to determine the red cell distribution width (RDW) with positive blood culture of early onset neonatal sepsis. This was a descriptive laboratory-based study, conducted during the period from January to September 2023, done on a total of 244 blood sample containers, tested for Complete Blood Count (CBC) tests and blood culture for who were diagnosed as early onset neonatal sepsis.

Results
In this study (244) neonates were diagnosed as blood culture proven neonatal sepsis, The study showed that the cultured organism was isolated as following: 95 were Staphylococcus aureus, 70 were Candida Spp, 28 were Pseudomonas ssp, then 27 were Klebsiella ssp, 16 were Escherichia coli, after that 6 were Enterococcus feacalis and finally 2 were Listeria monocytogenes, and the RDW in this study was significantly higher in neonatal sepsis with average range (20 ± 2.5%).

Conclusions
The study concluded that RDW at levels more than or equal to 17% was the most sensitive hematological marker to predict mortality of neonatal sepsis.
Changes in Soil Carbon and Nitrogen Along a 3‐m Vertical Profile and Environmental Regulation in Alpine Grassland on the Tibetan Plateau
Authors: Sun Ruojun , Sun Kuo , Liu Leren , Wu Wenjuan , Xu Zhenzhu 
Published: June 2024

AbstractMuch attention has been given to the distribution of soil organic carbon and nitrogen in alpine grasslands, but the important role of the deep soil layers has been understudied. In this study, the soil organic carbon and nitrogen contents in the shallow (0–30 cm), middle (30–100 cm) and deep (100–300 cm) layers were examined, and the effects of climatic, soil and vegetation factors were investigated along a climatic gradient on the Tibetan Plateau. We found that although soil organic carbon and nitrogen on the Tibetan Plateau declined logarithmically with depth, the total soil organic carbon and nitrogen in the middle and deep layers accounted for more than two‐thirds of the total carbon and nitrogen in the 3‐m depth soil profile. Carbon to nitrogen ratio increased with soil depth in 1 m soil, but it remained consistent in 1–3 m soil. The surface carbon and nitrogen contents were positively correlated with precipitation. The comprehensive research has revealed that soil carbon and nitrogen contents are mainly influenced by the local humid climate, vegetation productivity, and soil properties, which strongly depend on soil depth. Therefore, more attention should be given to the changes in carbon and nitrogen in deep soils in alpine regions.
Modifications to the CLASS Boundary Layer Model for Improved Interaction Between the Mixed Layer and Clouds
Authors: Ryu Kyoungho , Salvucci Guido 
Published: June 2024

AbstractThe impact of clouds on the mixed layer (ML) is critical for understanding the evolution of boundary layer humidity and temperature over the course of a day. We found that accounting for moistening of the cloud layer (CL) by humidity originating in the ML dramatically alters the interaction between the ML and the CL in a one‐dimensional cloud‐topped boundary layer model: Chemistry Land‐surface Atmosphere Soil Slab (CLASS) (Vilà‐Guerau de Arellano et al., 2015, https://doi.org/10.1017/CBO9781316117422). We demonstrate that enabling CLASS to moisten the lower CL improves the prediction of humidity (and the flux of humidity) both above and below the ML top (h). To account for this moistening, we propose a length scale, L, above h, over which mixing of mass fluxes into the environment occurs. The mass fluxes are assumed to decrease linearly from h to a height L meters above h, analogous to a convective plume detraining into the environment at a height‐independent rate. Accounting for this process is accomplished by modifying the differential equations representing the growth of the jumps (sharp changes in humidity and temperature) at h. From analysis of a large number of diurnal Large Eddy simulations (covering approximately 11,000 different early morning initial conditions), we provide a regression model for parameterizing L from early morning weather variables. With the regression‐based estimate of L, the modified model (CLASS‐L) accounts for moistening the lower CL, and as a result, yields improved humidity dynamics, humidity flux profiles, and cloud growth under a broad range of conditions.
Biocrusts Critical Regulation of Soil Water Vapor Transport (Diffusion, Sorption, and Late‐Stage Evaporation) in Drylands
Authors: Sun Fuhai , Xiao Bo , Kidron Giora J. , Heitman Joshua 
Published: June 2024

AbstractSoil surface cover is one of the most critical factors affecting soil water vapor transport, especially in drylands where water is limited, and the water movement occurs predominantly in the form of vapor instead of liquid. Biocrusts are an important living ground cover of dryland soils and play a vital role in modifying near‐surface soil properties and maintaining soil structure. The role of biocrusts in mediating soil water vapor transport during daytime water evaporation and nighttime condensation remains unclear. We investigated the differences in vapor diffusion properties, vapor adsorption capacity, and water evaporation between bare soil and three types of biocrusts (cyanobacterial, cyanobacterial‐moss mixed, and moss crusts) in the Chinese Loess Plateau. Our results showed that the three types of biocrusts had 5%–39% higher vapor diffusivity than bare soil. At the same level of ambient relative humidity and temperature, the initial vapor adsorption rates and cumulative adsorption amounts of the biocrusts were 10%–70% and 11%–85% higher than those of bare soil, respectively. Additionally, the late‐stage evaporation rate of cyanobacterial‐, cyanobacterial‐moss mixed‐, and moss‐biocrusts were 31%–217%, 79%–492%, and 146%–775% higher than that of bare soil, respectively. The effect of biocrusts on increasing vapor transport properties was attributed to the higher soil porosity, clay content, and specific surface area induced by the biocrust layer. All of these modifications caused by biocrusts on surface soil vapor transport properties suggest that biocrusts play a vital role in reshaping surface soil water and energy balance in drylands.
Satellite‐Derived Bathymetry in Dynamic Coastal Geomorphological Environments Through Machine Learning Algorithms
Authors: Ashphaq Mohammad , Srivastava Pankaj K. , Mitra D. 
Published: June 2024

AbstractIn the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite‐derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high‐resolution satellite imagery and in‐situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground‐truth data and imagery from Aster, Landsat‐8, and Sentinel‐2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K‐Neighbors Regressor, k‐fold cross‐validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat‐8 data set with the GPR algorithm achieves R2 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel‐2 data set with GPR and RF algorithms attains R2 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.
Antarctic Polar Stratospheric Cloud Analysis of ACE‐FTS Data From 2005 to 2023
Authors: Lecours M. , Boone C. D. , Bernath P. F. 
Published: June 2024

AbstractWe present an analysis of Antarctic polar winters from 2005 to 2023 as observed by the Atmospheric Chemistry Experiment (ACE). The unique broad band infrared spectral features in ACE “residual” spectra are used to classify the spectra of polar aerosols by composition into polar stratospheric clouds (PSCs) and sulfate aerosols. The spectra of PSCs are further classified into nitric acid trihydrate, supercooled ternary solutions, supercooled nitric acid, ice‐mix, and mixtures of PSCs. A breakdown of PSC composition is presented for each year. Antarctic winter seasons with unusual compositions are: 2011, in which volcanic ash mixed with PSCs was observed from July to August; 2019, which experienced a stratospheric warming event; 2020, the PSC season following the Australian Black Summer pyrocumulonimbus event; and 2023, which had unusually large sulfate aerosols following the Honga‐Tonga Honga Ha'apai eruption of 2022.
Freezing‐Thawing Hysteretic Behavior of Soils
Authors: Teng Jidong , Dong Antai , Zhang Sheng , Zhang Xiong , Sheng Daichao 
Published: June 2024

AbstractThe soil freezing characteristic curve (SFCC) plays a crucial role in investigating the soil freezing‐thawing process. Due to the challenges associated with measuring the SFCC, there is a shortage of high‐quality or rigorous test results with sufficient metadata to be effectively used for applications. Current researchers typically conduct freezing tests to measure the SFCC and assume a singular SFCC when studying the freezing‐thawing process of soils, although limited studies indicated that there is a hysteresis during the freezing and thawing process. In this paper, a series of freezing‐thawing tests were performed to assess the SFCC, utilizing a precise nuclear magnetic resonance apparatus. The test results reveal a hysteresis between the SFCC obtained from the freezing process and that from the thawing process. Through analyzing the test results, the hysteresis mechanism of the SFCC is attributed to supercooling. Supercooling inhibits initial pore ice formation during freezing, causing a drastic liquid water‐ice phase change once supercooling ends. Despite being considered closely related, the hysteresis of the SFCC differs from the soil water characteristic curve (SWCC), and the models used to simulate the hysteresis of SWCC cannot directly be used. To address the impact of supercooling on soil freezing‐thawing hysteresis, a novel theoretical model is proposed. Comparisons between the measured and predicted results affirm the validity of the proposed model.
Humanin activates integrin αV–TGFβ axis and leads to glioblastoma progression
Authors: Ha Cuong P. , Hua Tuyen N. M. , Vo Vu. T. A. , Om Jiyeon , Han Sangwon , Cha Seung-Kuy , Park Kyu-Sang , Jeong Yangsik 
Published: June 2024

AbstractThe role of mitochondria peptides in the spreading of glioblastoma remains poorly understood. In this study, we investigated the mechanism underlying intracranial glioblastoma progression. Our findings demonstrate that the mitochondria-derived peptide, humanin, plays a significant role in enhancing glioblastoma progression through the intratumoral activation of the integrin alpha V (ITGAV)–TGF beta (TGFβ) signaling axis. In glioblastoma tissues, humanin showed a significant upregulation in the tumor area compared to the corresponding normal region. Utilizing multiple in vitro pharmacological and genetic approaches, we observed that humanin activates the ITGAV pathway, leading to cellular attachment and filopodia formation. This process aids the subsequent migration and invasion of attached glioblastoma cells through intracellular TGFβR signaling activation. In addition, our in vivo orthotopic glioblastoma model provides further support for the pro-tumoral function of humanin. We observed a correlation between poor survival and aggressive invasiveness in the humanin-treated group, with noticeable tumor protrusions and induced angiogenesis compared to the control. Intriguingly, the in vivo effect of humanin on glioblastoma was significantly reduced by the treatment of TGFBR1 inhibitor. To strengthen these findings, public database analysis revealed a significant association between genes in the ITGAV–TGFβR axis and poor prognosis in glioblastoma patients. These results collectively highlight humanin as a pro-tumoral factor, making it a promising biological target for treating glioblastoma.
S100A8/A9hi neutrophils induce mitochondrial dysfunction and PANoptosis in endothelial cells via mitochondrial complex I deficiency during sepsis
Authors: Wang Yanghanzhao , Shi Yuxin , Shao Yuwen , Lu Xihua , Zhang Hao , Miao Changhong 
Published: June 2024

AbstractS100a8/a9, largely released by polymorphonuclear neutrophils (PMNs), belongs to the S100 family of calcium-binding proteins and plays a role in a variety of inflammatory diseases. Although S100a8/a9 has been reported to trigger endothelial cell apoptosis, the mechanisms of S100a8/a9-induced endothelial dysfunction during sepsis require in-depth research. We demonstrate that high expression levels of S100a8/a9 suppress Ndufa3 expression in mitochondrial complex I via downregulation of Nrf1 expression. Mitochondrial complex I deficiency contributes to NAD+-dependent Sirt1 suppression, which induces mitochondrial disorders, including excessive fission and blocked mitophagy, and mtDNA released from damaged mitochondria ultimately activates ZBP1-mediated PANoptosis in endothelial cells. Moreover, based on comprehensive scRNA-seq and bulk RNA-seq analyses, S100A8/A9hi neutrophils are closely associated with the circulating endothelial cell count (a useful marker of endothelial damage), and S100A8 is an independent risk factor for poor prognosis in sepsis patients.