bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extramedullary and extranodal manifestations in chronic lymphocytic leukemia – an update
Authors: Robak Tadeusz , Puła Anna , Braun Marcin , Robak Ewa 
Published: July 2024

AbstractChronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is a common leukemia characterized by clonal expansion of mature CD5+/CD23 + B cells in the blood, bone marrow (BM) and lymphoid tissues. CLL can undergo extramedullary and extranodal infiltration, with one study noting an incidence of only 0.3 per 100,000 people, and in 17.6% of CLL patients in another report. The most common extranodal sites of leukemic involvement are the skin and central nervous system; however, other organs, including liver, lungs, kidney, gastrointestinal tract, bone, prostate and heart, are occasionally involved. The prognostic significance of extra-medullary CLL is still under debate, but the prognosis in such patients seems to be better in the era of novel targeted drugs. Following a diagnosis of extranodal CLL, survival appears to depend on the site of infiltration. This review presents an overview of CLL in patients with extramedullary and extranodal leukemic lesions, focusing on its epidemiology, pathogenesis, prognosis, clinical characteristics and treatment results.
Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis
Authors: Behera Santi Kumari , Das Ashis , Sethy Prabira Kumar 
Published: July 2024

AbstractThis study presents a robust approach for the classification of ovarian cancer subtypes through the integration of deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages the powerful feature extraction capabilities of EfficientNet-B0, utilizing its deep features for subsequent fine-grained classification using the fine-KNN approach. The UBC-OCEAN dataset, encompassing histopathological images of five distinct ovarian cancer subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell ovarian carcinoma (CC), endometrioid carcinoma (EC), low-grade serous carcinoma (LGSC), and mucinous carcinoma (MC), served as the foundation for our investigation. With a dataset comprising 725 images, divided into 80% for training and 20% for testing, our model exhibits exceptional performance. Both the validation and testing phases achieved 100% accuracy, underscoring the efficacy of the proposed methodology. In addition, the area under the curve (AUC), a key metric for evaluating the model’s discriminative ability, demonstrated high performance across various subtypes, with AUC values of 0.94, 0.78, 0.69, 0.92, and 0.94 for MC. Furthermore, the positive likelihood ratios (LR+) were indicative of the model’s diagnostic utility, with notable values for each subtype: CC (27.294), EC (9.441), HGSC (12.588), LGSC (17.942), and MC (17.942). These findings demonstrate the effectiveness of the model in distinguishing between ovarian cancer subtypes, positioning it as a promising tool for diagnostic applications. The demonstrated accuracy, AUC values, and LR+ values underscore the potential of the model as a valuable diagnostic tool, contributing to the advancement of precision medicine in the field of ovarian cancer research.
The role of psychological safety in promoting software quality in agile teams
Authors: Alami Adam , Zahedi Mansooreh , Krancher Oliver 
Published: July 2024

AbstractPsychological safety continues to pique the interest of scholars in a variety of disciplines of study. Recent research indicates that psychological safety fosters knowledge sharing and norm clarity and complements agile values. Although software quality remains a concern in the software industry, academics have yet to investigate whether and how psychologically safe teams provide superior results. In this study, we explore how psychological safety influences agile teams’ quality-related behaviors aimed at enhancing software quality. To widen the empirical coverage and evaluate the results, we chose a two-phase mixed-methods research design with an exploratory qualitative phase (20 interviews) followed by a quantitative phase (survey study, N = 423). Our findings show that, when psychological safety is established in agile software teams, it induces enablers of a social nature that advance the teams’ ability to pursue software quality. For example, admitting mistakes and taking initiatives equally help teams learn and invest their learning in their future decisions related to software quality. Past mistakes become points of reference for avoiding them in the future. Individuals become more willing to take initiatives aimed at enhancing quality practices and mitigating software quality issues. We contribute to our endeavor to understand the circumstances that promote software quality. Psychological safety requires organizations, their management, agile teams, and individuals to maintain and propagate safety principles. Our results also suggest that technological tools and procedures can be utilized alongside social strategies to promote software quality.
The Independent Associations of Attachment Representations to Parents and Depressive Symptoms with Friendships and Romantic Relationships in Young Adults
Authors: Fadli Fatmawati , Moberly Nicholas J. , Psychogiou Lamprini 
Published: July 2024

AbstractThe formation of friendships and romantic relationships represents an important developmental task in young adulthood. However, little is known about the potential factors associated with the quality of these interpersonal relationships. This cross-sectional study aimed to examine whether attachment representations to parents and depressive symptoms were independently associated with the quality of friendships and romantic relationships. Using the Prolific platform, 196 young adults (M age = 22.01 years, SD = 1.62) participated in the study. Each participant reported their attachment representations to mother and father and depressive symptoms through the Inventory of Parent and Peer Attachment and Patient Health Questionnaire 8, respectively. Positive (companionship, intimate disclosure, emotional support, approval, and satisfaction) and negative (conflict, criticism, pressure, exclusion, and dominance) dimensions of relationship quality with a close friend and a romantic partner were assessed with the Network of Relationships Inventory-Relationship Quality Version. Results showed that secure attachment representations to mother (but not father) were significantly associated with increased emotional support, approval, and satisfaction in romantic relationships, even after controlling for gender and depressive symptoms. Increased depressive symptoms were significantly associated with more conflict, criticism, and pressure in friendships, above and beyond gender and attachment representations to parents. There was a significant interaction between gender and depressive symptoms in predicting friendship quality. Men with increased depressive symptoms reported more conflict and dominance. No significant effects were found for women. These findings underscore the importance of secure attachment representations to mother in predicting healthy romantic relationships and depressive symptoms in predicting problematic friendships, especially for men.
An assessment of existing wildfire danger indices in comparison to one-class machine learning models
Authors: Ismail Fathima Nuzla , Woodford Brendon J. , Licorish Sherlock A. , Miller Aubrey D. 
Published: July 2024

AbstractPredicting wildfires using Machine Learning models is relevant and essential to minimize wildfire threats to protect human lives and reduce significant property damage. Reliance on traditional wildfire indices for forecasting wildfires has failed to provide the expected prediction outcomes, resulting in limited application of these models. Thus, this research compares the outcome of wildfire forecasting using fire danger rating indices against Machine Learning model outcomes. Furthermore, the performance effectiveness of the fire danger rating indices and Machine Learning model outcomes are assessed using the same wildfire incidents. The One-class Machine Learning algorithms used are Support Vector Machine, Isolation Forest, Neural network-based Autoencoder and Variational Autoencoder models. The two global wildfire indices investigated were the US National Fire Danger Rating System for California and the McArthur Forest Fire Danger Index for Western Australia, using similar features. For the same data sets, the National Fire Danger Rating System and the McArthur Forest Fire Danger Index prediction outcomes were compared with Machine Learning model outcomes. Higher wildfire prediction accuracy was achieved by the One-class models, exceeding the performance of the two wildfire danger indices by at least 20%. The implications of our research findings have the potential to influence both these wildfire indices and state-of-the-art methods in wildfire prediction by proposing alternative ML methods to model the onset of wildfires.
High efficiency laser ablation of gold thin film by 2.8 GHz intraburst repetition rate pulses
Authors: Hasar Emre , Aşmanoğlu Selin , Ilday Fatih Ömer , Elahi Parviz 
Published: July 2024

AbstractThis work initially presents our progress in developing a 14 W all-polarization-maintaining (PM) Yb-doped fiber laser system. This system can generate bursts with a 2.8 GHz intra-burst repetition rate and offers flexibility in burst repetition rates, starting from 100 kHz. The laser produces pulse energies up to 210 nJ, which can be dechirped to approximately 650 fs. Subsequently, we utilized the developed laser in thin film gold processing. We sent up to 6.4 W and employed three different intraburst pulse configurations on a 100 nm-thick gold film coated on glass. We achieved a record gold removal efficiency of 125 $$\upmu \text{g}/\text {W.s}$$

μ
g
/
W.s

using $$\sim$$
∼
5 nJ pulses. Moreover, we determined an ablation pulse energy threshold of approximately 1 nJ, representing the lowest pulse energy to the best of our knowledge.
Analyzing the effectiveness of quantum annealing with meta-learning
Authors: Pellini Riccardo , Ferrari Dacrema Maurizio 
Published: July 2024

AbstractThe field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, but there is not yet a clear understanding of which are the characteristics of a problem that make it difficult to solve with QA. In this work, we propose a new methodology to study the effectiveness of QA based on meta-learning models. To do so, we first build a dataset composed of more than five thousand instances of ten different optimization problems. We define a set of more than a hundred features to describe their characteristics and solve them with both QA and three classical solvers. We publish this dataset online for future research. Then, we train multiple meta-models to predict whether QA would solve that instance effectively and use them to probe which features with the strongest impact on the effectiveness of QA. Our results indicate that it is possible to accurately predict the effectiveness of QA, validating our methodology. Furthermore, we observe that the distribution of the problem coefficients representing the bias and coupling terms is very informative in identifying the probability of finding good solutions, while the density of these coefficients alone is not enough. The methodology we propose allows to open new research directions to further our understanding of the effectiveness of QA, by probing specific dimensions or by developing new QUBO formulations that are better suited for the particular nature of QA. Furthermore, the proposed methodology is flexible and can be extended or used to study other quantum or classical solvers.
Against digital constitutionalism
Authors: Terzis Petros 
Published: July 2024

AbstractDigital Constitutionalism makes sense. We can all see the problem it tries to solve. And like many other ‘constitutionalisms’, Digital Constitutionalism is a project fuelled by the institutional hope of striking a power equilibrium between competing actors. In seeking this equilibrium, however, important assumptions are made and serious questions are oversimplified.Firstly, Digital Constitutionalism ignores the production logics of digital technologies thereby mistaking ‘objects’ with ‘subjects’. Secondly, Digital Constitutionalism’s myopic lenses lead to narrations of ‘constitutional moments’ caused by digital powers and digital revolutions, separate from the histories of private corporations in the global stage. Finally, Digital Constitutionalism falsely implies the absence of law rather than its presence – or indeed irrelevance – as major causes of this ‘constitutional crisis’. On this account, it seeks to respond to the ‘digital revolution’ with a ‘legal revolution’.Against this background, this Article invites legal scholars to look back to think differently, and to engage critically with the long evolution of legal regulation that has enabled the emergence of the corporate form and the consolidation of economic and political power on the global stage. This historicisation may ultimately lead us to turn inward and reflect on the role of legal experts and professionals in the political economy of technology. It may even inspire us to break free from restraining mental maps and thinking patterns and to unlock novel intellectual and methodological pathways in the field of Law and Technology.And if it takes a Digital Constitutionalism to come to this, it will have been worth it.
Determination of Outer Radiation Belt MeV Electron Energization Rates and Delayed Response Times to Solar Wind Driving During Geomagnetic Storms
Authors: Srinivas P. , Spencer E. 
Published: July 2024

AbstractRadiation Belt Storm Probes (RBSP) data show that seed electrons generated by sub‐storm injections play a role in amplifying chorus waves in the magnetosphere. The wave‐particle interaction leads to rapid heating and acceleration of electrons from 10's of keV to 10's of MeV energies. In this work, we examined the changes in the radiation belt during geomagnetic storm events by studying the RBSP REPT, solar wind, AL, SML, and Dst data in conjunction with the WINDMI model of the magnetosphere. The field‐aligned current output from the model is integrated to generate a proxy E index for various energy bands. These E indices track electron energization from 40 KeV to 20 MeV in the radiation belts. The indices are compared to RBSP data and GOES data. Our proxy indices correspond well to the energization data for electron energy bands between 1.8 and 7.7 MeV. Each E index has a unique empirical loss rate term (τL), an empirical time delay term (τD), and a gain value, that are fit to the observations. These empirical parameters were adjusted to examine the delay and charging rates associated with different energy bands. We observed that the τL and τD values are clustered for each energy band. τL and τD consistently increase going from 1.8 to 7.7 MeV in electron energy flux Ee and the dropout interval increases with increasing energy level. The average trend of ΔτD/ΔEe was 4.1 hr/MeV and the average trend of ΔτL/ΔEe was 2.82 hr/MeV.
Probable Controls From the Lower Layers on Sporadic E Layer Over East Asia
Authors: Zhao Hai‐Sheng , Xu Zheng‐Wen , Xue Kun , Wu Jian , Liu Ya‐Xin , Feng Jie , Wang Cheng , Zhang Yu‐Sheng , Li Na 
Published: July 2024

AbstractThe sporadic E‐layer (Es) exhibits unique opportunity for exploring the coupling from lower to upper atmosphere. It was found that the East Asia is with the highest intensity and occurrence probability of Es. By using the long‐term data of 21 ionosonde stations in China and Japan over the past 60 yrs, this paper explores the probable control on the Es layer from the lower layers. It is found that the intensity of the Es layer is strongly correlated with the surface atmospheric temperature, terrain, and land‐sea boundary. The correlation coefficient of the intensity of Es with surface temperature is as high as 0.8204, while that with the terrain and land‐sea boundary is up to 0.6668. Based on the coupling between the lower and upper atmosphere, this paper reveals the probable controls from lower layers on the intensity of the Es in East Asia.
Physically‐Informed Super‐Resolution Downscaling of Antarctic Surface Melt
Authors: de Roda Husman Sophie , Hu Zhongyang , van Tiggelen Maurice , Dell Rebecca , Bolibar Jordi , Lhermitte Stef , Wouters Bert , Munneke Peter Kuipers 
Published: July 2024

AbstractBecause Antarctic surface melt is mostly driven by local processes, its simulation necessitates high‐resolution regional climate models (RCMs). However, the current horizontal resolution of RCMs (≈25–30 km) is inadequate for capturing small‐scale melt processes. To address this limitation, we present SUPREME (SUPer‐REsolution‐based Melt Estimation over Antarctica), a deep learning method to downscale surface melt to 5.5 km resolution using a physically‐informed super‐resolution model. The physical information integrated into the model originates from observations tied to surface melt, specifically remote sensing‐derived albedo and elevation. These remote sensing data, in addition to a Regional Atmospheric Climate Model (RACMO) run at 27 km resolution, account for the diverse drivers of surface melt across Antarctica, facilitating effective generalization beyond the training region of the Antarctic Peninsula. A comparison of SUPREME with a dynamically downscaled RACMO run at 5.5 km over the Antarctic Peninsula shows high accuracy, with average yearly RMSE and bias of 5.5 mm w.e. yr−1 and 4.5 mm w.e. yr−1, respectively. Validation at five automatic weather stations reveals SUPREME's marked improvement with substantially lower average RMSE (81 mm w.e.) compared to RACMO 27 km (129 mm w.e.). Beyond the training region, SUPREME aligns more closely with remote sensing products associated with surface melt than super‐resolution models lacking physical constraints. While further validation of SUPREME is needed, our study highlights the potential of super‐resolution techniques with physical constraints for high‐resolution surface melt monitoring in Antarctica, providing insights into the impacts of localized melting on processes affecting ice shelf integrity such as hydrofracturing.
Partially Saturated Fracture‐Matrix Infiltration in Experiments and Theory
Authors: Rüdiger Florian , Dentz Marco , Kordilla Jannes 
Published: July 2024

AbstractFractures provide pathways for preferential flow, whereas porous rock acts as storage that delays fluid propagation through matrix imbibition. These dual‐porosity mechanisms are investigated in laboratory experiments of partially saturated fracture infiltration. We analyze flow dynamics in terms of the fluid penetration depth in the fracture and delineate fracture‐ and matrix‐dominated flow regimes at different flow rates. We compare wetting front propagation in fracture and matrix and examine the interference of matrix‐wetting fronts with the lateral system boundary. The experimental data are interpreted using the analytical model of Nitao (1991), which accounts for the impact of fracture‐matrix interactions on fluid propagation in the fracture. We find that matrix imbibition affects the observed discontinuous, partially saturated fracture flow to behave, on average, like plug flow. Consequently, and within the range of applied flow rates above a critical threshold, the model’s plug flow assumption is not a relevant precondition for its applicability. Fluid propagation in the fracture exhibits three characteristic scaling regimes (FP1‐3) corresponding to the matrix imbibition state. Only two scaling regimes are established for flow rates below a critical threshold, hence required to recover bulk infiltration for the chosen geometry. Furthermore, wetting fronts switch from fracture‐to matrix‐dominated at moderate to high flow rates, indicating a flow‐rate‐dependent limitation of fracture‐dominated infiltration depth. While the scaling regimes agree with experiments for applied flow rates above the critical threshold, the model underestimates the initial penetration depth below. Here, we observe the direct onset of flow regime FP2 and the delayed transition into FP3.
Carbon Export in the Subantarctic Zone Revealed by Multi‐Year Observations From Biogeochemical‐Argo Floats and Sediment Traps
Authors: Yang Xiang , Wynn‐Edwards Cathryn A. , Strutton Peter G. , Shadwick Elizabeth H. 
Published: July 2024

AbstractThe biological gravitational pump (BGP) and particle injection pumps (PIPs) are significant export pathways for particulate organic carbon from the surface ocean to the interior. Part of this exported carbon fuels remineralization in the mesopelagic ocean and part is sequestered in the deep ocean. Using observations from Biogeochemical‐Argo, we characterized the seasonality and magnitude of the BGP and two PIPs: the mixed layer pump (MLP) and eddy subduction pump (ESP), in the Australian sector of the Subantarctic Zone (SAZ sector). For the first time, float‐based estimates were rigorously combined with sediment trap flux (F1000) observations from the Southern Ocean Time Series (SOTS), to investigate these pumps' relative and cumulative contributions to carbon export. The BGP exports about 28.6 g C m−2 year−1, mostly during the productive season and dominates the F1000 seasonality. The MLP exports about 7.6 g C m−2 year−1, mostly while the mixing layer seasonally shoals; the ESP sporadically exports up to 100 mg C m−2 day−1, such that these two PIPs have a short but intense impact on the F1000. The carbon transfer efficiency is 3.6% in the SOTS region. An oxygen‐based annual net community production estimate (∼50 g C m−2 year−1) further strengthens this study, and suggests the BGP and MLP make the dominant contribution to the mesopelagic carbon budget. This is representative of the broader SAZ sector in terms of the magnitude and seasonality of carbon export, the consumption of organic material in the mesopelagic, and the organic carbon sequestration in the deep sea.
Summer Westerly Wind Intensification Weakens Southern Ocean Seasonal Cycle Under Global Warming
Authors: Zhang Yiwen , Chen Changlin , Hu Shineng , Wang Guihua , McMonigal Kay , Larson Sarah M. 
Published: July 2024

AbstractSince the 1950s, observations and climate models show an amplification of sea surface temperature (SST) seasonal cycle in response to global warming over most of the global oceans except for the Southern Ocean (SO), however the cause remains poorly understood. In this study, we analyzed observations, ocean reanalysis, and a set of historical and abruptly quadrupled CO2 simulations from the Coupled Model Intercomparison Project Phase 6 archive and found that the weakened SST seasonal cycle over the SO could be mainly attributed to the intensification of summertime westerly winds. Under the historical warming, the intensification of summertime westerly winds over the SO effectively deepens ocean mixed layer and damps surface warming, but this effect is considerably weaker in winter, thus weakening the SST seasonal cycle. This wind‐driven mechanism is further supported by our targeted coupled model experiments with the wind intensification effects being removed.