bluuebunny/crossref_metadata_2025_split huggingface dataset:
A network analysis-based framework to understand the representation dynamics of graph neural networks
Authors: Bonifazi Gianluca , Cauteruccio Francesco , Corradini Enrico , Marchetti Michele , Ursino Domenico , Virgili Luca 
Published: November 2023

AbstractIn this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.
Potential of computational models in personalized treatment of obstructive sleep apnea: a patient-specific partial 3D finite element study
Authors: Ayyalasomayajula Venkat , Moxness Mads , Skallerud Bjørn 
Published: November 2023

AbstractThe upper airway experiences mechanical loads during breathing. Obstructive sleep apnea is a very common sleep disorder, in which the normal function of the airway is compromised, enabling its collapse. Its treatment remains unsatisfactory with variable efficacy in the case of many surgeries. Finite element models of the upper airway to simulate the effects of various anatomic and physiologic manipulations on its mechanics could be helpful in predicting surgical success. Partial 3D finite element models based on patient-specific CT-scans were undertaken in a pilot study of 5 OSA patients. Upper airway soft tissues including the soft palate, hard palate, tongue, and pharyngeal wall were segmented around the midsagittal plane up to a width of 2.5 cm in the lateral direction. Simulations of surgical interventions such as Uvulopalatopharyngoplasty (UPPP), maxillo-mandibular advancement (MMA), palatal implants, and tongue implants have been performed. Our results showed that maxillo-mandibular advancement (MMA) surgery of 1 cm improved the critical closing pressure by at least 212.2%. Following MMA, the best improvement was seen via uvulopalatopharyngoplasty (UPPP), with an improvement of at least 19.12%. Palatal and tongue implants also offered a certain degree of improvement. Further, we observed possible interacting mechanisms that suggested simultaneous implementation of UPPP and tongue stiffening; and palatal and tongue stiffening could be beneficial. Our results suggest that computational modeling is a useful tool for analyzing the influence of anatomic and physiological manipulations on upper airway mechanics. The goal of personalized treatment in the case of OSA could be achieved with the use of computational modeling.
Anticancer effect of hUC-MSC-derived exosome-mediated delivery of PMO-miR-146b-5p in colorectal cancer
Authors: Yu Siming , Liao Ran , Bai Lu , Guo Madi , Zhang Yu , Zhang Yumin , Yang Qi , Song Yushuai , Li Zhiwei , Meng Qingwei , Wang Shubin , Huang Xiaoyi 
Published: November 2023

AbstractAntisense oligonucleotide (ASO) is a novel therapeutic platform for targeted cancer therapy. Previously, we have demonstrated that miR-146b-5p plays an important role in colorectal cancer progression. However, a safe and effective strategy for delivery of an ASO to its targeted RNA remains as a major hurdle in translational advances. Human umbilical cord mesenchymal cell (hUC-MSC)–derived exosomes were used as vehicles to deliver an anti-miR-146b-5p ASO (PMO-146b). PMO-146b was assembled onto the surface of exosomes (e) through covalent conjugation to an anchor peptide CP05 (P) that recognized an exosomal surface marker, CD63, forming a complex named ePPMO-146b. After ePPMO-146b treatment, cell proliferation, uptake ability, and migration assays were performed, and epithelial-mesenchymal transition progression was evaluated in vitro. A mouse xenograft model was used to determine the antitumor effect and distribution of ePPMO-146b in vivo. ePPMO-146b was taken up by SW620 cells and effectively inhibited cell proliferation and migration. The conjugate also exerted antitumor efficacy in a xenograft mouse model of colon cancer by systematic administration, where PPMO-146b was enriched in tumor tissue. Our study highlights the potential of hUC-MSC-derived exosomes anchored with PPMO-146b as a novel safe and effective approach for PMO backboned ASO delivery.
Graphical Abstract
Schematic illustration of the preparation of an exosomal anchor peptide (CP05)-PMO that conjugately binds to exosomes from hUC-MSCs (ePPMO-146b) and the antitumor effect of ePPMO-146b in CRC, which occurs through the inhibition of Smad signaling and epithelial–mesenchymal transition.
A novel twin branch network based on mutual training strategy for ship detection in SAR images
Authors: Lv Yilong , Li Min , He Yujie 
Published: November 2023

AbstractThere are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7–4.9% based on different backbone networks, and the detection performance is better than other advanced algorithms.
Instability of Gravitational and Electromagnetic Perturbations of Extremal Reissner–Nordström Spacetime
Authors: Apetroaie Marios Antonios 
Published: November 2023

AbstractWe study the linear stability problem to gravitational and electromagnetic perturbations of the extremal, $$ |Q|=M, $$

|
Q
|
=
M
,

Reissner–Nordström spacetime, as a solution to the Einstein–Maxwell equations. Our work uses and extends the framework [28, 32] of Giorgi, and contrary to the subextremal case we prove that instability results hold for a set of gauge invariant quantities along the event horizon $$ {\mathcal {H}}^+ $$

H

+

. In particular, for associated quantities shown to satisfy generalized Regge–Wheeler equations we prove decay, non-decay, and polynomial blow-up estimates asymptotically along $$ {\mathcal {H}}^+ $$

H

+

, the exact behavior depending on the number of translation invariant derivatives that we take. As a consequence, we show that for generic initial data, solutions to the generalized Teukolsky system of positive and negative spin satisfy both stability and instability results. It is worth mentioning that the negative spin solutions are significantly more unstable, with the extreme curvature component $$ {\underline{\alpha }} $$

α
̲

not decaying asymptotically along the event horizon $$ {\mathcal {H}}^+, $$

H

+

,

a result previously unknown in the literature.
Standardisation efforts of ISO/TC 261 “additive manufacturing” 22nd plenary meeting of ISO/TC 261 “additive manufacturing”
Authors: Pei Eujin , Lakomiec Marius 
Published: November 2023

AbstractThe main objective of ISO/TC 261 is to standardize the processes of Additive Manufacturing, the process chains (Data, Materials, Processes, Hard- and Software, Applications), test procedures, quality parameters, supply agreements, environment, health and safety, fundamentals and vocabularies. This section provides readers with news regarding standardisation efforts of ISO/TC 261. Further up-to-date information regarding recently published documents, such as new standards, revised standards, the status of standards can be found in the ISO/TC261 webpages: https://www.iso.org/committee/629086.html and from the committee webpages: https://committee.iso.org/sites/tc261/home/news.html.
Using a Bayesian approach to reconstruct graph statistics after edge sampling
Authors: Arnold Naomi A. , Mondragón Raúl J. , Clegg Richard G. 
Published: November 2023

AbstractOften, due to prohibitively large size or to limits to data collecting APIs, it is not possible to work with a complete network dataset and sampling is required. A type of sampling which is consistent with Twitter API restrictions is uniform edge sampling. In this paper, we propose a methodology for the recovery of two fundamental network properties from an edge-sampled network: the degree distribution and the triangle count (we estimate the totals for the network and the counts associated with each edge). We use a Bayesian approach and show a range of methods for constructing a prior which does not require assumptions about the original network. Our approach is tested on two synthetic and three real datasets with diverse sizes, degree distributions, degree-degree correlations and triangle count distributions.
A Novel Equation of Motion to Predict Elastoplastic Deformation of 1-D Stochastic Bars
Authors: Desai Shivang 
Published: November 2023

AbstractBased on stochasticity in local and nonlocal deformation-gamuts, a stochastic nonlocal equation of motion to model elastoplastic deformation of 1-D bars made of stochastic materials is proposed in this study. Stochasticity in the energy-densities as well as energy-states across the spatial domain of given material and stochasticity in the deformation-gamuts parameters are considered, and their physical interpretations are discussed. Numerical simulations of the specimens of two distinct materials, subjected to monotonic as well as cyclic loadings, are carried out. Specimens are discretized using stochastic as well as uniform grids. Thirty realizations of each stochastic process are considered. The mean values of the results from all realizations are found to be in good agreement with deterministic values, theoretical estimations and experimental results published in open literature.
Hydrological risk assessment for Mangla Dam: compound effects of instant flow and precipitation peaks under climate change, using HEC-RAS and HEC-GeoRAS
Authors: Ahmad Izhar , Waseem Muhammad , Ashraf Ammar , Leta Megersa Kebede , Ahmad Sareer , Wahab Hira 
Published: November 2023

AbstractDams play a pivotal role in water resource management by storing and supplying water for a multitude of purposes. However, the looming threat of dam breach floods necessitates meticulous research and the simulation of potential failure scenarios. These endeavors are essential not only for comprehending the gravity of dam break floods but also for identifying vulnerable regions and informing emergency response strategies and land-use planning initiatives. This study employs a two-dimensional hydraulic model within the HEC-RAS (Hydrologic Engineering Center and River Analysis System) software to conduct an extensive dam breach analysis specifically focusing on the Mangla Dam located in Azad Kashmir, Pakistan. The analysis encompasses the prediction of various breach parameters, including the hydrograph of the breach flood, peak flow rates, arrival times of the flood, and the creation of inundation maps. Of primary concern is the Probable Maximum Floo, which drives the dam collapse model under unsteady flow conditions, accounting for both piping and overtopping failure scenarios. This study discerns the breach outflow hydrograph through the utility of HEC-RAS tools and evaluates hydraulic conditions at critical downstream locations. To dynamically route flood waves, the breach outflow hydrographs are harnessed. Furthermore, the HEC-RAS model is executed with breach parameters derived from five distinct empirical approaches, with ensuing outcomes subjected to rigorous comparative analysis. A comprehensive sensitivity study pertaining to breach parameters is also carried out to ascertain the sensitivity of peak flow and maximum stage. The results reveal peak flow rates of 174,850 m3/s and 177,850 m3/s in the downstream vicinity adjacent to the dam, translating into corresponding flooded areas of 379 km2 and 394 km2 attributable to piping and overtopping failures, respectively. The analysis of Land Use Land Cover data demonstrates that in the event of piping failure, 217 km2 of agricultural land and 56 km2 of urban areas would be completely submerged. Conversely, overtopping failure would inundate 220 km2 of agricultural land and 59 km2 of urban areas. The utilization of advanced remote sensing data, combined with flood modeling insights, equips engineers and stakeholders with invaluable knowledge. This knowledge, in turn, underpins strategic planning and well-informed decision-making processes, essential for addressing the potential global repercussions of similar catastrophes.
Novel deep learning models for yoga pose estimator
Authors: Talaat Amira Samy 
Published: November 2023

AbstractYoga pose recognition and correction are artificial intelligent techniques to provide standardized and appropriate yoga poses. Incorrect yoga poses can cause serious injuries and long-term complications. Analyzing human posture can identify and rectify abnormal positions, improving well-being at home. A posture estimator extracts yoga asana attributes from properly represented images. These extracted features are then utilized directly as inputs for various neural networks and machine learning models. These models serve the purpose of evaluating and predicting the accuracy of specific yoga poses. The objective of this research is to explore multiple methods for classifying yoga poses. The LGDeep model is introduced, which combines a novel residual convolutional neural network with three deep learning approaches: Xception, VGGNet, and SqueezeNet. Additionally, the LGDeep model incorporates feature extraction methods such as LDA and GDA. Experimental results demonstrate that the LGDeep classifier outperforms other approaches and achieves the highest classification accuracy ratio.
Mechatronic design of a composite vibration isolation system
Authors: Ghoneam Sobhy M. , Hamada Ahmed A. , Elkholy Ahmed M. 
Published: November 2023

AbstractComposite materials have attracted researchers in vibration and noise control applications due to their significant dynamic characteristics such as high strength and high damping level. In this paper, a Glass Fiber Reinforced Composite material (GFRC) is presented as a vibration isolation system to control vibration levels in industry. In addition, the impact of integration of a mechatronic control system to improve the machining process and increase the control of vibration nature. A prototype of an industrial cam–follower machine is motorized, and the Frequency Response Function (FRF) is recorded using a B&K data acquisition analyzer at five rotational speeds. The transmitted vibrations to the machine foundation are estimated without any isolation system. Then, two optimized GFRC plates of optimum stacking sequences are used as an isolation system to reduce the transmitted vibration. The displacement transmissibility is calculated theoretically and experimentally. The results show that the use of GFRC plates as an isolator reduces the vibration level of the system by 98.46% and 98.5% for [90/90/90/0/0]s and [90/ ± 45/ ± 35/90/ ± 35]s GFRC configurations respectively.
Gibt es eine Zweite-Person-Perspektive als eigenständigen epistemischen Zugang zur Fremdpsyche?
Authors: Disse Jörg 
Published: November 2023

ZusammenfassungIn der Intersubjektivitätsforschung kursiert die Idee einer Zweite-Person-Perspektive als eigenständiger epistemischer Zugang zu psychischen Phänomenen. In Auseinandersetzung mit den Ansätzen von Michael Pauen und Thomas Fuchs möchte ich aufweisen, dass es nicht sinnvoll ist, neben den etablierten Erste- und Dritte-Person-Perspektiven eine Zweite-Person-Perspektive einzuführen. Ausgehend von der Annahme, dass Perspektiven sich ausschließlich durch die Mittel, durch die unterschiedlichen Erfahrungskanäle und nicht durch die Beziehung auf unterschiedliche Gegenstandsbereiche sinnvoll voneinander unterscheiden, wird gezeigt, dass sowohl Pauen als auch Fuchs die Zweite-Person-Perspektive letztlich als die Beziehung zu einem bestimmten Gegenstandsbereich verstehen, und dass zugleich von einem eigenständigen Erfahrungskanal neben denen der Ersten- und Dritten-Person-Perspektive nicht sinnvoll die Rede sein kann.
Reduced grey matter volume in adolescents with conduct disorder: a region-of-interest analysis using multivariate generalized linear modeling
Authors: Zhang Ru , Blair R. James R. , Blair Karina S. , Dobbertin Matthew , Elowsky Jaimie , Bashford-Largo Johannah , Dominguez Ahria J. , Hatch Melissa , Bajaj Sahil 
Published: November 2023

Abstract
Background
Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV).

Methods
The whole‐brain structural MRI data were collected from 96 adolescents with CD (mean age = $$16.188\pm 1.259$$

16.188
±
1.259

years; mean IQ = $$104.292\pm 8.107$$

104.292
±
8.107

; 63 males) and 90 TD individuals (mean age = $$15.956\pm 1.506$$

15.956
±
1.506

years; mean IQ = $$106.622\pm 9.076$$

106.622
±
9.076

; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate.

Results
A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F(1, 180) = 5.460–10.317, p < 0.05, partial eta squared = 0.029–0.054). The CD participants had smaller GMV in these regions than the TD participants (MCD–MTD = [− 614.898] mm3–[− 53.461] mm3).

Conclusions
Altered GMV within specific regions may serve as a biomarker for the development of CD in adolescents. Clinical work can potentially target these biomarkers to treat adolescents with CD.
Who Gets Credit? Citizen Responses to Local Public Goods
Authors: McKiernan Katherine 
Published: November 2023

ABSTRACTIn decentralized systems, citizens struggle to identify which level of government provides local goods. This problem is particularly salient in weakly institutionalized party environments, where politicians at different levels of government are less likely to benefit from partisan coattail effects. This article asks how citizens attribute credit for local public goods. I argue that citizens have a strong tendency to attribute credit to local politicians. As a result, citizens will respond differently to credit-claiming behavior by local and national politicians. Local politicians experience a ceiling effect, in which credit claiming has no effect on how citizens attribute credit. However, national politicians have no such ceiling and can claim credit to increase the likelihood that citizens will attribute credit to them. As a result, both political actors can receive credit for the same local goods. The article tests and supports these theoretical predictions using a vignette survey experiment in Colombia.
Informed consent in balloon Eustachian tuboplasty: a systematic review of possible complications and preventive measures
Authors: Hussain Syed Zohaib Maroof , Hashmi Salman , Qayyum Asad 
Published: November 2023

AbstractObjectiveTo systematically identify the complications associated with balloon Eustachian tuboplasty and their frequency of occurrence. This study will also highlight the measures that can be employed to avoid these complications and perform this procedure more safely.MethodsSystematically reviewed relevant papers published until January 2023. Each reference was checked and evaluated for any potential manuscripts. There was no registered protocol; the Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used.ResultsSixty-nine publications were found, from which 14 publications met our inclusion criteria: 2 randomised clinical trials, 5 retrospective studies, 2 systematic reviews, 2 case series and 3 case reports. Studies with balloon Eustachian tuboplasty procedure only were included, regardless of ethnicity, gender and age. All studies were excluded in which more than one procedure was performed.ConclusionBalloon Eustachian tuboplasty is a relatively safe procedure with an overall complication risk of 1.66 per cent. Major complication rate was 0.43 per cent. Surgical emphysema was the most common, around 0.40 per cent.
Fusion computed tomography–magnetic resonance imaging scans for pre-operative staging of congenital middle-ear cholesteatoma
Authors: Maccarrone Francesco , Cantaffa Carla , Genovese Maurilio , Tassi Sauro , Negri Maurizio 
Published: November 2023

AbstractObjectiveTo evaluate if fusion computed tomography–diffusion-weighted magnetic resonance imaging may have a role in the pre-operative assessment of congenital middle-ear cholesteatoma.MethodsA retrospective chart review of surgically treated congenital middle-ear cholesteatoma patients over a 2-year timespan was conducted. Pre-operative staging was performed on computed tomography and fusion computed tomography–diffusion-weighted magnetic resonance imaging based on extension of the disease according to the ChOLE classification system and the Potsic classification system. Intra-operative staging was compared to imaging findings to evaluate accuracy of the two imaging modalities in predicting congenital middle-ear cholesteatoma extent.ResultsComputed tomography was able to correctly predict congenital middle-ear cholesteatoma extent in three out of six cases according to the ChOLE classification system, all of which were staged as Ch1a and Ch1b on pre-operative computed tomography. Cases in which computed tomography was not able correctly to determine congenital middle-ear cholesteatoma extent were staged as Ch3 on pre-operative computed tomography. Fusion scans correctly determined congenital middle-ear cholesteatoma extent in all cases according to the ChOLE classification.ConclusionsFusion computed tomography–diffusion-weighted magnetic resonance imaging may be helpful in cases of congenital middle-ear cholesteatoma where pre-operative computed tomography shows mastoid and antrum opacification, in which computed tomography alone may overestimate cholesteatoma extension beyond the level of the lateral semi-circular canal.