bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional and Oncological Outcomes of Very Large Prostate Sizes Post Robotic Radical Prostatectomy: A Propensity Score‐Matched Analysis
Authors: Gamal Ahmed , Moschovas Marcio C. , Jaber Abdel R. , Saikali Shady , Reddy Sumeet , Kunta Avaneesh , Sandri Marco , Rogers Travis , Patel Vipul 
Published: December 2024

ABSTRACTBackgroundRobotic‐assisted radical prostatectomy (RARP) is widely used as the main surgical approach to treat prostate cancer in the United States. Prostate size is often described as a factor affecting the outcomes of RARP as shown by many studies. However, these studies are limited to a small number of patients.ObjectiveTo evaluate the functional and oncologic outcomes of RARP in very large prostate sizes.MethodsThree hundred and seventy‐five RARP patients were divided into two groups according to prostate size: Group 1 had prostates larger than 150 g and Group 2 smaller than 50 g. Perioperative variables were matched with propensity score matching 1:3 and postoperative variables were analyzed for significant differences in outcomes between groups. Variables analyzed included estimated blood loss (EBL), operative time, catheter time, hospitalization time, postoperative complications, pathological staging, positive surgical margins (PSM) rates, biochemical recurrence (BCR), potency, and continence rates.ResultsThe two groups exhibited similar preoperative characteristics. Patients with larger prostates (Group 1) were more likely to have higher blood loss (EBL), longer console time, and more days with catheter. However, we could not find significant difference in the overall postoperative complications (Clavien‐Dindo). Pathological outcomes were also statistically different as patients with larger prostates had (69.7%) more pT2 disease and (12.1%) lower rates of PSM. Finally, we could not find significant difference in the functional outcomes between the groups.ConclusionThe results demonstrate that prostate size impacts multiple outcomes. Larger prostates had lower‐grade disease, reduced EPE and PSM rates, with no significant differences in BCR or functional outcomes. Perioperative differences, such as increased blood loss and console time, were also observed.
Software Reliability Growth Model Considering Imperfect Debugging and Fault Removal Efficiency
Authors: Samal Umashankar 
Published: December 2024

ABSTRACTSoftware reliability is a critical metric for determining the readiness and quality of software products before release. In this study, a software reliability growth model (SRGM) is proposed that integrates imperfect debugging and fault removal efficiency to better reflect real‐world testing environments. The model accounts for the possibility of introducing new faults during debugging and adjusts the fault removal process based on the testing team's efficiency and learning progression of the testing team. Furthermore, an optimal software release time is derived by considering various costs, including testing, debugging, warranty, and reputation, with the required reliability levels. The proposed model is validated using two widely adopted datasets, demonstrating consistently superior performance compared to several existing models. This model offers a valuable tool for researchers in software reliability, providing a more accurate representation of debugging processes, and facilitating more informed decisions regarding optimal software release timing.
Certainty vs. Intelligence
Authors: Lee Edward A. 
Published: December 2024

AbstractMathematical models can yield certainty, as can probabilistic models where the probabilities degenerate. The field of formal methods emphasizes developing such certainty about engineering designs. In safety-critical systems, such certainty is highly valued and, in some cases, even required by regulatory bodies. But achieving reasonable performance for sufficiently complex environments appears to require the use of AI technologies, which resist such certainty. This paper suggests that certainty and intelligence may be fundamentally incompatible. First, Bayes Theorem shows, rather trivially, that certainty implies an inability to learn when presented with new data. A more subtle issue, however, is that logic and mathematics, necessary for certainty, may be a result of intelligence rather than the foundations of intelligence. This paper makes the case that intelligence is an evolved form of prediction, that logic and mathematics were not discovered but rather were invented because of their predictive value, and that the certainty they can give us cannot be about systems that exhibit intelligence.
Towards Verifying Robotic Systems Using Statistical Model Checking in STORM
Authors: Lampacrescia Marco , Klauck Michaela , Palmas Matteo 
Published: December 2024

AbstractRobust autonomy and interaction of robots with their environment, even in rare or new situations, is an ultimate goal of robotics research. We settle on Statistical Model Checking (SMC) for the benefit of robustness of robot deliberation and base our implementation on STORM, one of the most performant and comprehensive open-source model checkers, so far lacking an SMC extension. The SMC extension introduced in this paper offers various statistical methods, from which the user can choose to find the best trade-off between accuracy of the result and runtime. We demonstrate the efficiency of our SMC implementation by comparing it to other state-of-the-art SMC tools on well-established benchmarks and on a robotics-related example. The results indicate that our implementation, which will be continuously extended in the future to improve support for robotics use cases, is performant enough to bridge the gap between robotic systems and model checking in industry.
Team Design Patterns for Meaningful Human Control in Responsible Military Artificial Intelligence
Authors: van Diggelen Jurriaan , Boshuijzen-van Burken Christine , Abbass Hussein 
Published: December 2024

AbstractA key requirement for responsible AI in the military is to ensure meaningful human control (MHC) in operational settings. However, MHC is not a binary concept; it could vary from one operational setting to another depending on context and the competencies of available humans and autonomous systems. We propose and discuss five patterns of MHC and highlight some of their key characteristics. We argue that control is only meaningful when humans have sufficient cognitive resources to perform control functions and when the behavior of autonomous systems in their operational context is sufficiently predictable. By anchoring the MHC patterns in human’ s cognition and situation uncertainty, we propose initial requirements for interaction design, testing and validation, and identify gaps in existing scientific understanding.
The ENCIC-CT Model for the Development of Critical Thinking
Authors: Franco-Mariscal Antonio Joaquín , Cano-Iglesias María José , España-Ramos Enrique , Blanco-López Ángel 
Published: December 2024

AbstractThe development of critical thinking among citizens is crucial for the advancement of humanity in a dynamic and ever-changing world. Therefore, it is essential to promote a critical understanding of the challenges we face as a society. Initially, this chapter addresses how various scholars have discussed and developed the construct of critical thinking from philosophical and cognitive psychology perspectives, with a particular emphasis on its integration into science education. Next, the ENCIC-CT model to critical thinking development is introduced. This framework was formulated during the research project that underpins this book, and guides its constituent chapters. This model proposes developing critical thinking in science education through daily life problems and includes three key domains of action: knowledge, skills, and dispositions. Each domain comprises different dimensions that can be addressed through scientific practices such as argumentation, inquiry, or modeling, using various teaching strategies including gamification/role-playing, micro-debates, augmented reality, controversy mapping, and multimedia resources such as digital storytelling, among others. This model can also be applied to the analysis and design of educational proposals in teacher training.
Behaviour of solutions for a thermoelastic Cosserat medium with temperature gradients
Authors: Marin Marin , Vlase Sorin , Öchsner Andreas , Hapenciuc O. M. 
Published: December 2024

Abstract
Our study falls within the linear theory of thermoelasticity of Cosserat media. Unlike other works that fall into similar contexts and that use the entropy balance low, our approach is based on an entropy production inequality. The entropy flux tensor is introduced and thermoelastic media are considered for which the stress tensors are dependent on the temperature gradients. In this way, a fourth-order differential equation satisfied by temperature is obtained. In this context the mixed initial-boundary value problem is formulated for which an uniqueness result regarding the solution of this problem is proven. Also, a continuous dependence result is deduced for the solution of the mixed formulated problem with regard to the charges and the initial values.
Pulmonary exposure to renewable diesel exhaust particles alters protein expression and toxicity profiles in bronchoalveolar lavage fluid and plasma of mice
Authors: McCarrick Sarah , Malmborg Vilhelm , Gren Louise , Danielsen Pernille Høgh , Tunér Martin , Palmberg Lena , Broberg Karin , Pagels Joakim , Vogel Ulla , Gliga Anda R. 
Published: December 2024

Abstract
Exposure to diesel exhaust is associated with increased risk of cardiovascular and lung disease. Substituting petroleum diesel with renewable diesel can alter emission properties but the potential health effects remain unclear. This study aimed to explore toxicity and underlying mechanisms of diesel exhaust from renewable fuels. Using proximity extension assay (Olink), 92 proteins linked to inflammation, cardiovascular function, and cancer were analyzed in bronchoalveolar lavage fluid (BALF) and plasma in mice 1 day after pulmonary exposure to exhaust particles at doses of 6, 18, and 54 µg/mouse. Particles were generated from combustion of renewable (rapeseed methyl ester, RME13, hydrogen-treated vegetable oil, HVO13; both at 13% O2 engine intake) and petroleum diesel (MK1 ultra-low-sulfur diesel at 13% and 17% O2 intake; DEP13 and DEP17). We identified positive dose–response relationships between exposure and proteins in BALF using linear models: 33 proteins for HVO13, 24 for DEP17, 22 for DEP13, and 12 for RME13 (p value < 0.05). In BALF, 11 proteins indicating cytokine signaling and inflammation (CCL2, CXCL1, CCL3L3, CSF2, IL1A, CCL20, TPP1, GDNF, LGMN, ITGB6, PDGFB) were common for all exposures. Several proteins in BALF (e.g., CCL2, CXCL1, CCL3L3, CSF2, IL1A) correlated (r
s ≥ 0.5) with neutrophil cell count and DNA damage in BAL cells. Interestingly, plasma protein profiles were only affected by RME13 and, to lesser extent, by DEP13. Overall, we identified inflammation-related changes in the BALF as a common toxic mechanism for the combustion particles. Our protein-based approach enables sensitive detection of inflammatory protein changes across different matrices enhancing understanding of exhaust particle toxicity.
Emotion, attention and stress regulation as markers of resilience in male and female Israeli soldiers during the Israel–Hamas war
Authors: Cohen Rotem , Punski-Hoogervorst Janne L. , Maoz Inon , Engel-Yeger Batya , Tatsa-Laor Lucian , Avital Avi 
Published: December 2024

AbstractPsychological resilience is a key factor for societal and military stability when faced with terror attacks and/or war. The research presents physiological findings—obtained with the electrodermal activity (EDA) and Auditory Sustained Attention Test (ASAT)—on stress responses, attentional and emotion regulation abilities in 57 Israel Defense Force male and female combat soldiers during the ongoing Israel–Hamas war. In addition, it shows self-reported resilience scores and post traumatic symptomatology measured by questionnaires and explores the relationship between the subjective and objective data. Compared to male soldiers, female soldiers showed significantly higher hyperarousal symptoms yet showed a tendency to a significantly lower specific skin conductance response (on the EDA) to the first startle sound. Furthermore, the self-reported acute stress symptoms positively and significantly correlated with the physiological emotion regulation measured by startle responses, and negatively correlated with attentional regulation measured by the ASAT. The lack of gender differences in stress level, resilience and self-regulation abilities emphasizes the high capabilities of women combat soldiers, especially due to gender-related risks in combat. Relatively high scores of acute stress symptomology in the population of combat soldiers invite later screening and assessment for the prevention of post traumatic disorders in vulnerable individuals. The combination of physiological measures and questionnaires highlights possible report biases, and thus underscores the importance of combining these objective/subjective measures for adequate assessment of resilience and post traumatic symptomology.
Investment casting of Cr–Si alloys with liquidus temperatures up to 1900 °C
Authors: Sandner Kilian , Völkl Rainer , Dickes Daniel , Kuo Jyun-Ting , Yeh An-Chou , Glatzel Uwe 
Published: December 2024

AbstractIn previous studies, chromium–silicon-based alloys have shown promising material properties for high-temperature applications. For chromium–silicon alloys to be processed on a large scale, primary shaping processes must be adapted to the requirements of these alloys. In this work, binary chromium–silicon alloys are primarily shaped using an investment casting process. Ceramic shell molds made of hafnium oxide stabilized zirconium oxide are developed to withstand the thermal, mechanical and chemical stresses resulting from pouring melts with liquidus temperatures of up to 1900 °C. The layered construction of the shell molds leads to a sufficient thermal shock stability. Energy-dispersive X-ray spectroscopy shows that chemical interactions between the shell mold and the melt do not occur, nor oxides are formed to a detectable extent. Microstructure investigations reveal that casting binary chromium–silicon alloys lead to a coarse grain structure with grain diameters above 300 µm. Strength-increasing precipitations of the intermetallic phase Cr3Si (A15 phase) can be detected from a silicon content of seven-atom percent in the as-cast state.
Parallactic delay for geodetic VLBI and non-orthogonality of the fundamental axes
Authors: Titov Oleg , Osetrova Angelina 
Published: December 2024

Abstract
The Gaia optical astrometric mission has measured the precise positions of millions of objects in the sky, including extragalactic sources also observed by Very Long Baseline Interferometry (VLBI). In the recent Gaia EDR3 release, an effect of negative parallax with a magnitude of approximately

$-17$

$\mu$

as was reported, presumably due to technical reasons related to the relativistic delay model. A recent analysis of a 30-yr set of geodetic VLBI data (1993–2023) revealed a similar negative parallax with an amplitude of

$-15.8 \pm 0.5$

$\mu$

as. Since both astrometric techniques, optical and radio, provide consistent estimates of this negative parallax, it is necessary to investigate the potential origin of this effect.
We developed the extended group relativistic delay model to incorporate the additional parallactic effect for radio sources at distances less than 1 Mpc and found that the apparent annual signal might appear due the non-orthogonality of the fundamental axes, which are defined by the positions of the reference radio sources themselves. Unlike the conventional parallactic ellipse, the apparent annual effect in this case appears as a circular motion for all objects independently of their ecliptic latitude. The measured amplitude of this circular effect is within a range of 10–15

$\mu$

as that is consistent with the ICRF3 stability of the fundamental axis. This annual circular effect could also arise if a Gödel-type cosmological metric were applied, suggesting that, in the future, this phenomenon could be used to indicate global cosmic rotation.
A Multiplex High-Resolution Melting (HRM) assay to differentiate Fusarium graminearum chemotypes
Authors: Singh Lovepreet , Drott Milton T. , Kim Hye-Seon , Proctor Robert H. , McCormick Susan P. , Elmore J. Mitch 
Published: December 2024

AbstractFusarium graminearum is a primary cause of Fusarium head blight (FHB) on wheat and barley. The fungus produces trichothecene mycotoxins that render grain unsuitable for food, feed, or malt. Isolates of F. graminearum can differ in trichothecene production phenotypes (chemotypes), with individuals producing predominantly one of four toxins: 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol, nivalenol, or NX-2. Molecular tools to diagnose chemotypes remain inefficient. This study aimed to develop a single-tube, multiplex molecular assay that can predict the four F. graminearum chemotypes. Conserved functional regions of three trichothecene biosynthetic genes (TRI1, TRI8, and TRI13) were targeted to develop a high-resolution melting (HRM) assay. Multiplex HRM analysis produced unique melting profiles for each chemotype, and was validated on a panel of 80 isolates. We applied machine learning-based linear discriminant analysis (LDA) to automate the classification of chemotypes from the HRM data, achieving a prediction accuracy of over 99%. The assay is sensitive, with a limit of detection below 0.02 ng of fungal DNA. The HRM analysis also differentiated chemotypes from a small sample of F. gerlachii, F. asiaticum, and F. vorosii isolates. Together, our results demonstrate that this simple, rapid, and accurate assay can be applied to F. graminearum molecular diagnostics and population surveillance programs.
DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction
Authors: Tawfiq Rund , Niu Kexin , Hoehndorf Robert , Kulmanov Maxat 
Published: December 2024

AbstractAnalyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated on or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is applied to diverse microbial datasets to demonstrate its use for gaining biological insights. Data and code are available at https://github.com/bio-ontology-research-group/deepgometa
Deep terrestrial indigenous microbial community dominated by Candidatus Frackibacter
Authors: Ford Sian E. , Slater Greg F. , Engel Katja , Warr Oliver , Lollar Garnet S. , Brady Allyson , Neufeld Josh D. , Lollar Barbara Sherwood 
Published: December 2024

AbstractCharacterizing deep subsurface microbial communities informs our understanding of Earth’s biogeochemistry as well as the search for life beyond the Earth. Here we characterized microbial communities within the Kidd Creek Observatory subsurface fracture water system with mean residence times of hundreds of millions to over one billion years. 16S rRNA analysis revealed that biosamplers well isolated from the mine environment were dominated by a putatively anaerobic and halophilic bacterial species from the Halobacteroidaceae family, Candidatus Frackibacter. Contrastingly, biosamplers and biofilms exposed to the mine environment contained aerobic Sphingomonas taxa. δ13C values of phospholipid fatty acids and putative functional predictions derived from 16S rRNA gene profiles, imply Candidatus Frackibacter may use carbon derived from ancient carbon-rich layers common in these systems. These results indicate that Candidatus Frackibacter is not unique to hydraulically fracked sedimentary basins but rather may be indigenous to a wide range of deep, saline groundwaters hosted in carbon-rich rocks.
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model
Authors: Li Rui , della Maggiora Gabriel , Andriasyan Vardan , Petkidis Anthony , Yushkevich Artsemi , Deshpande Nikita , Kudryashev Mikhail , Yakimovich Artur 
Published: December 2024

AbstractLight microscopy is a practical tool for advancing biomedical research and diagnostics, offering invaluable insights into the cellular and subcellular structures of living organisms. However, diffraction and optical imperfections actively hinder the attainment of high-quality images. In recent years, there has been a growing interest in applying deep learning techniques to overcome these challenges in light microscopy imaging. Nonetheless, the resulting reconstructions often suffer from undesirable artefacts and hallucinations. Here, we introduce a deep learning-based approach that incorporates the fundamental physics of light propagation in microscopy into the loss function. This model employs a conditioned diffusion model in a physics-informed architecture. To mitigate the issue of limited available data, we utilise synthetic datasets for training purposes. Our results demonstrate consistent enhancements in image quality and substantial reductions in artefacts when compared to state-of-the-art methods. The presented technique is intuitively accessible and allows obtaining higher quality microscopy images for biomedical studies.
HMSFU: A hierarchical multi‐scale fusion unit for video prediction and beyond
Authors: Zhu Hongchang , Fang Faming 
Published: December 2024

AbstractVideo prediction is the process of learning necessary information from historical frames to predict future video frames. Learning features from historical frames is a crucial step in this process. However, most current methods have a relatively single‐scale learning approach, even if they learn features at different scales, they cannot fully integrate and utilise them, resulting in unsatisfactory prediction results. To address this issue, a hierarchical multi‐scale fusion unit (HMSFU) is proposed. By using a hierarchical multi‐scale architecture, each layer predicts future frames at different granularities using different convolutional scales. The abstract features from different layers can be fused, enabling the model not only to capture rich contextual information but also to expand the model's receptive field, enhance its expressive power, and improve its applicability to complex prediction scenarios. To fully utilise the expanded receptive field, HMSFU incorporates three fusion modules. The first module is the single‐layer historical attention fusion module, which uses an attention mechanism to fuse the features from historical frames into the current frame at each layer. The second module is the single‐layer spatiotemporal fusion module, which fuses complementary temporal and spatial features at each layer. The third module is the multi‐layer spatiotemporal fusion module, which fuses spatiotemporal features from different layers. Additionally, the authors not only focus on the frame‐level error using mean squared error loss, but also introduce the novel use of Kullback–Leibler (KL) divergence to consider inter‐frame variations. Experimental results demonstrate that our proposed HMSFU model achieves the best performance on popular video prediction datasets, showcasing its remarkable competitiveness in the field.