bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new fully projective O(lg N) line convex polygon intersection algorithm
Authors: Skala Vaclav 
Published: June 2024

Abstract
Intersecting algorithms, especially line clipping in

$${E^{2}}$$

E
2

and

$${E^{3}}$$

E
3

in computer graphics, have been studied for a long time. Many different algorithms have been developed. The simplest case is a line clipping by a convex polygon in

$${E^{2}}$$

E
2

with O(N) computational complexity and with known polygon edges orientation. This contribution presents a new algorithm for a line clipping by a convex polygon in E

$$^{2}$$

2

with O(lg N) complexity, which is based on the point-in-half plane test. The proposed algorithm does not require prior knowledge of the polygon edge orientation. The vertices of the convex polygon and the clipped line can be given in projective space using homogeneous coordinates. The algorithm uses vector–vector operations for efficient implementation with SSE or AVX vector–vector instructions or on GPUs. It is simple and robust.
Response of atmospheric pCO$$_2$$ to a strong AMOC weakening under low and high emission scenarios
Authors: Boot Amber A. , von der Heydt Anna S. , Dijkstra Henk A. 
Published: June 2024

AbstractThe Earth System is warming due to anthropogenic greenhouse gas emissions which increases the risk of passing a tipping point in the Earth System, such as a collapse of the Atlantic Meridional Overturning Circulation (AMOC). An AMOC weakening can have large climate impacts which influences the marine and terrestrial carbon cycle and hence atmospheric pCO$$_2$$

2

. However, the sign and mechanism of this response are subject to uncertainty. Here, we use a state-of-the-art Earth System Model, the Community Earth System Model v2 (CESM2), to study the atmospheric pCO$$_2$$

2

response to an AMOC weakening under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios over the years 2015–2100. A freshwater flux anomaly in the North Atlantic strongly weakens the AMOC, and we simulate a weak positive pCO$$_2$$

2

response of 0.45 and 1.3 ppm increase per AMOC decrease in Sv for SSP1-2.6 and SSP5-8.5, respectively. For SSP1-2.6 this response is driven by both the oceanic and terrestrial carbon cycles, whereas in SSP5-8.5 it is solely the ocean that drives the response. However, the spatial patterns of both the climate and carbon cycle response are similar in both emission scenarios over the course of the simulation period (2015–2100), showing that the response pattern is not dependent on cumulative CO$$_2$$

2

emissions up to 2100. Though the global atmospheric pCO$$_2$$

2

response might be small, locally large changes in both the carbon cycle and the climate system occur due to the AMOC weakening, which can have large detrimental effects on ecosystems and society.
Coherent solvus of disordered alkali feldspar: experiment, atom probe tomography and thermodynamic model
Authors: Heuser D. , Dubosq R. , Petrishcheva E. , Bian G. , Rentenberger C. , Lengauer C. L. , Gault B. , Habler G. , Abart R. 
Published: June 2024

AbstractAt temperatures above about 600 °C, alkali feldspar forms a continuous solid solution between the Na and K end members. Towards lower temperatures a miscibility gap opens, and alkali feldspar of intermediate composition exsolves, forming an intergrowth of relatively more Na-rich and K-rich lamellae. During exsolution, the crystal structure usually remains coherent across the lamellar interfaces, a feature that may be preserved over geological times. Due to the compositional dependence of the lattice parameters, coherent intergrowth requires that the lamellae are elastically strained. The associated elastic strain energy counteracts exsolution, and the solvus delimiting the misciblity gap for coherent intergrowth lies below the solvus for strain free phase equilibria. To determine the coherent solvus, homogeneous gem quality alkali feldspar of intermediate composition was annealed at conditions falling into the two-phase region of the phase diagram. Thereby a coherent intergrowth of approximately 10–20 nanometers wide lamellae was produced. Lamellar compositions were determined with atom probe tomography defining points on the coherent solvus. In parallel, the coherent solvus was calculated using a thermodynamic mixing model calibrated on the same alkali feldspar as used for the exsolution experiments and accounting for the elastic strain energy associated with coherent lamellar intergrwoth. The experimentally determined and the calculated coherent solvus are in excellent agreement indicating that phase equilibria in coherent lamellar intergrowth of alkali feldspar are adequately described, providing a sound basis for the interpretation of phase relations in coherently exsolved alkali feldspar.
Prognostic implications of TOR1B expression across cancer types: a focus on basal-like breast cancer and cellular adaptations to hypoxia
Authors: Zhang Yan , Cai Zhongfu , Chen Wen , Ye Lei , Wu Xinquan 
Published: June 2024

AbstractThe TOR1B gene is known to play a pivotal role in maintaining cellular homeostasis and responding to endoplasmic reticulum stress. However, its involvement in cancer remains relatively understudied. This study seeks to explore the prognostic implications of TOR1B across various cancers, with a specific focus on Basal-like Breast Cancer (BLBC) and its underlying cellular mechanisms. Through comprehensive analysis of data from TCGA, TARGET, GEO, and GTEx, we investigated TOR1B expression and its correlation with patient outcomes. Furthermore, in vitro experiments conducted on BLBC cell lines examined the impact of TOR1B modulation on cell viability, apoptosis, and metabolic activity under varying oxygen levels. Our statistical analysis encompassed differential expression analysis, survival analysis, and multivariate Cox regression. Our findings indicate that TOR1B is overexpressed in BLBC and other cancers, consistently correlating with poorer prognosis. Elevated TOR1B levels were significantly associated with reduced overall and disease-free survival in BLBC patients. In vitro experiments further revealed that TOR1B knockdown augmented apoptosis and influenced metabolic activity, particularly under hypoxic conditions, highlighting its potential role in cancer cell adaptation to stress. Overall, our study underscores the importance of TOR1B in cancer progression, particularly in BLBC, where it serves as a notable prognostic indicator. The interaction between TOR1B and metabolic pathways, as well as its regulation by HIF-1α, suggests its significance in adapting to hypoxia, thereby positioning TOR1B as a promising therapeutic target for aggressive breast cancer subtypes.
SRSF9 promotes cell proliferation and migration of glioblastoma through enhancing CDK1 expression
Authors: Luo Chunyuan , He Juan , Yang Yang , Wu Ke , Fu Xin , Cheng Jian , Ming Yue , Liu Wenrong , Peng Yong 
Published: June 2024

Abstract
Background
Glioblastoma (GBM) is a highly aggressive and prevalent brain tumor that poses significant challenges in treatment. SRSF9, an RNA-binding protein, is essential for cellular processes and implicated in cancer progression. Yet, its function and mechanism in GBM need clarification.

Methods
Bioinformatics analysis was performed to explore differential expression of SRSF9 in GBM and its prognostic relevance to glioma patients. SRSF9 and CDK1 expression in GBM cell lines and patients’ tissues were quantified by RT-qPCR, Western blot or immunofluorescence assay. The role of SRSF9 in GBM cell proliferation and migration was assessed by MTT, Transwell and colony formation assays. Additionally, transcriptional regulation of CDK1 by SRSF9 was investigated using ChIP-PCR and dual-luciferase assays.

Results
The elevated SRSF9 expression correlates to GBM stages and poor survival of glioma patients. Through gain-of-function and loss-of-function strategies, SRSF9 was demonstrated to promote proliferation and migration of GBM cells. Bioinformatics analysis showed that SRSF9 has an impact on cell growth pathways including cell cycle checkpoints and E2F targets. Mechanistically, SRSF9 appears to bind to the promoter of CDK1 gene and increase its transcription level, thus promoting GBM cell proliferation.

Conclusions
These findings uncover the cellular function of SRSF9 in GBM and highlight its therapeutic potential for GBM.
Assessment of volume status of pediatric hemodialysis patients
Authors: Fadel Fatina I. , Salah Doaa M. , Mawla Mohamed A. Abdel , Galal Eman , Sayed Shaimaa 
Published: June 2024

Abstract
Background
Accurate volume status assessment and dry weight achievement are the most challenging goals for a nephrologist. We aimed to evaluate the role of ultrasonographic parameters including lung ultrasound and inferior vena cava (IVC) measurements as practical methods of volume status assessment in children on hemodialysis by comparing them with established techniques, such as clinical evaluation and bioimpedance spectroscopy.

Methods
A prospective cross-sectional study compared pre- and post-dialysis volume status using bioimpedance spectroscopy (BIS) parameters and clinical data with ultrasonographic lung B-lines and IVC parameters in children on regular hemodialysis.

Results
A total 60 children (mean age 9.4 ± 2.8 years) were enrolled. Twenty patients (33.3%) were clinically overloaded to varying degrees (17 patients had mild to moderate signs of fluid overload and 3 patients had moderate to severe signs of fluid overload). All other patients (66.7%) were clinically euvolemic. Sonographic parameters were significantly lower post-dialysis than pre-dialysis, including lung B-line count and IVC diameter. IVC collapsibility index mean was significantly higher post-dialysis than pre-dialysis. There was a significant correlation between the lung B-line count, IVC parameters, and BIS-measured overhydration both before and after hemodialysis. Nine patients had ≥ 8 B-lines post-dialysis, only three of them were hypertensive.

Conclusions
Clinical criteria alone are not specific for determining accurate fluid status in pediatric hemodialysis patients. Lung B-line score, IVC parameters, and BIS may be complementary to each other and to clinical data. Lung B-lines outperform IVC measurements and BIS in subclinical volume overload detection in pediatric hemodialysis patients.

Graphical abstract
AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition
Authors: Hosney Reham , Talaat Fatma M. , El-Gendy Eman M. , Saafan Mahmoud M. 
Published: June 2024

AbstractAutism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential.
BIDGCN: boundary-informed dynamic graph convolutional network for adaptive spline fitting of scattered data
Authors: Giannelli Carlotta , Imperatore Sofia , Mantzaflaris Angelos , Scholz Felix 
Published: June 2024

AbstractSurface reconstruction from scattered point clouds is the process of generating surfaces from unstructured data configurations retrieved using an acquisition device such as a laser scanner. Smooth surfaces are possible with the use of spline representations, an established mathematical tool in computer-aided design and related application areas. One key step in the surface reconstruction process is the parameterization of the points, that is, the construction of a proper mapping of the 3D point cloud to a planar domain that preserves surface boundary and interior points. Despite achieving a remarkable progress, existing heuristics for generating a suitable parameterization face challenges related to the accuracy, the robustness with respect to noise, and the computational efficiency of the results. In this work, we propose a boundary-informed dynamic graph convolutional network (BIDGCN) characterized by a novel boundary-informed input layer, with special focus on applications related to adaptive spline approximation of scattered data. The newly introduced layer propagates given boundary information to the interior of the point cloud, in order to let the input data be suitably processed by successive graph convolutional network layers. We apply our BIDGCN model to the problem of parameterizing three-dimensional unstructured data sets over a planar domain. A selection of numerical examples shows the effectiveness of the proposed approach for adaptive spline fitting with (truncated) hierarchical B-spline constructions. In our experiments, improved accuracy is obtained, e.g., from 60% up to 80% for noisy data, while speedups ranging from 4 up to 180 times are observed with respect to classical algorithms. Moreover, our method automatically predicts the local neighborhood graph, leading to much more robust results without the need for delicate free parameter selection.
An interpretable Bayesian deep learning-based approach for sustainable clean energy
Authors: Ezzat Dalia , Ahmed Eman , Soliman Mona , Hassanien Aboul Ella 
Published: June 2024

AbstractSustainable Development Goal 7 is dedicated to ensuring access to clean and affordable energy that can be utilized in various applications. Solar panels (SP) are utilized to convert sunlight into electricity, acting as a renewable energy source. It is important to keep SP clean to obtain the required performance, as the accumulation of snow and dust on SP greatly affects the amount of electricity generated. On the other hand, excessive cleaning has some detrimental effects on the SP, therefore cleaning should only be done when necessary and not on a regular basis. Consequently, it is critical to determine whether the cleaning procedure is necessary by automatically detecting the presence of dust or snow on the panels while avoiding inaccurate predictions. Research efforts have been made to detect the presence of dust and snow on SP, but most of the proposed methods do not guarantee accurate detection results. This paper proposes an accurate, reliable, and interpretable approach called Solar-OBNet. The proposed Solar-OBNet can detect dusty SP and snow-covered SP very efficiently and be used in conjunction with the methods used to clean SP. The proposed Solar-OBNet is based on a Bayesian convolutional neural network, which enables it to express the amount of confidence in its predictions. Two measurements are used to estimate the uncertainty in the outcomes of the proposed Solar-OBNet, namely predictive entropy and standard deviation. The proposed Solar-OBNet can express confidence in the correct predictions by showing low values for predictive entropy and standard deviation. The proposed Solar-OBNet can also give an uncertainty warning in the case of erroneous predictions by showing high values of predictive entropy and standard deviation. The proposed Solar-OBNet’s efficacy was verified by interpreting its results using a method called Weighted Gradient-Directed Class Activation Mapping (Grad-CAM). The proposed Solar-OBNet has achieved a balanced accuracy of 94.07% and an average specificity 95.83%, outperforming other comparable methods.
Cultural heritage digital twin: modeling and representing the visual narrative in Leonardo Da Vinci’s Mona Lisa
Authors: Amelio Alessia , Zarri Gian Piero 
Published: June 2024

AbstractIn this paper, Artificial Intelligence/Knowledge Representation methods are used for the digital modeling of cultural heritage elements. Accordingly, the new concept of digital cultural heritage twin is presented as composed of a physical component and an immaterial component of the cultural entity. The former concerns the physical aspects, i.e. style, name of the artist, execution time, dimension, etc. The latter represents the emotional and intangible aspects transmitted by the entity, i.e. emotions, thoughts, opinions. In order to digitally model the physical and immaterial components of the twin, the Narrative Knowledge Representation Language has been formally introduced and described. It is particularly suitable for representing the immaterial aspects of the cultural entity, as it is capable of modeling in a simple but rigorous and efficient way complex situations and events, behaviours, attitudes, etc. As an experiment, NKRL has been adopted for representing some of the most relevant intangible items of the visual narrative underlying the hidden painting that lies beneath the Mona Lisa (La Gioconda) image painted by Leonardo Da Vinci on the same poplar panel. Real-time application of the resulting knowledge base opens up novel possibilities for the development of virtual objects, chatbots and expert systems, as well as the definition of semantic search platforms related to cultural heritage.
Editorial to the special issue on JCDL 2022
Authors: Mayr Philipp , Hinze Annika , Schaer Philipp 
Published: June 2024

AbstractThis special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was “Bridging Worlds” and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue.
Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative
Authors: Paz Alexander , Lavikainen Jere , Turunen Mikael J. , García José J. , Korhonen Rami K. , Mononen Mika E. 
Published: June 2024

AbstractPhysics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.
Reinforcement learning based autonomous multi-rotor landing on moving platforms
Authors: Goldschmid Pascal , Ahmad Aamir 
Published: June 2024

AbstractMulti-rotor UAVs suffer from a restricted range and flight duration due to limited battery capacity. Autonomous landing on a 2D moving platform offers the possibility to replenish batteries and offload data, thus increasing the utility of the vehicle. Classical approaches rely on accurate, complex and difficult-to-derive models of the vehicle and the environment. Reinforcement learning (RL) provides an attractive alternative due to its ability to learn a suitable control policy exclusively from data during a training procedure. However, current methods require several hours to train, have limited success rates and depend on hyperparameters that need to be tuned by trial-and-error. We address all these issues in this work. First, we decompose the landing procedure into a sequence of simpler, but similar learning tasks. This is enabled by applying two instances of the same RL based controller trained for 1D motion for controlling the multi-rotor’s movement in both the longitudinal and the lateral directions. Second, we introduce a powerful state space discretization technique that is based on i) kinematic modeling of the moving platform to derive information about the state space topology and ii) structuring the training as a sequential curriculum using transfer learning. Third, we leverage the kinematics model of the moving platform to also derive interpretable hyperparameters for the training process that ensure sufficient maneuverability of the multi-rotor vehicle. The training is performed using the tabular RL method Double Q-Learning. Through extensive simulations we show that the presented method significantly increases the rate of successful landings, while requiring less training time compared to other deep RL approaches. Furthermore, for two comparison scenarios it achieves comparable performance than a cascaded PI controller. Finally, we deploy and demonstrate our algorithm on real hardware. For all evaluation scenarios we provide statistics on the agent’s performance. Source code is openly available at https://github.com/robot-perception-group/rl_multi_rotor_landing.