bluuebunny/crossref_metadata_2025_split huggingface dataset:
Embedded 3D printing of engineered lung cancer model for assisting fine-needle aspiration biopsy
Authors: Hua Weijian , Zhang Cheng , Raymond Lily , Mitchell Kellen , Xiao Kuo , Coulter Ryan , Bandala Erick , Bishwokarma Manish , Yang Ying , Zhao Danyang , Xiao Na , Jin Yifei 
Published: December 2024

Abstract
Lung cancer is a serious global health issue that requires the development of patient-specific, lung cancer model for surgical planning to train interventionalists and improve the accuracy of biopsies. Although the emergence of three-dimensional (3D) printing provides a promising solution to create customized models with complicated architectures, current 3D printing methods cannot accurately duplicate anatomical-level lung constructs with tumor(s) which are applicable for hands-on training and procedure planning. To address this issue, an embedded printing strategy is proposed to create respiratory bronchioles, blood vessels, and tumors in a photocurable yield-stress matrix bath. After crosslinking, a patient-specific lung cancer analogous model is produced, which has tunable transparency and mechanical properties to mimic lung parenchyma. This engineered model not only enables the practical training of fine-needle aspiration biopsy but also provides the necessary information, such as coordinates of aspiration, wound depth, and interference with surrounding tissues, for procedure optimization.
Influence of forging on the microstructure and tensile properties of Al–based vortex plate
Authors: Yang Yang , Zhang Zhixia 
Published: December 2024

Abstract
An AHS-2 alloy billet (∼80 mm in diameter), with the composition of Al-10.13 wt% Si-4.08 wt% Cu-0.7 wt% Mg-0.24 wt% Fe, was hot forged to a vortex plate (∼120 mm in diameter) product by hot die forging process (HDFP), through the back-pressure processing technology. The microstructure of both the billet and the product was investigated by optical microscope, x-ray diffraction, field emission scanning electron microscope, and electron backscatter diffraction. The main second phases in the alloy include Si, Al2Cu, Mg2Si and Al5FeSi. The phase particles were broken into smaller ones during the HDFP process, while the averaged grain size of α-Al matrix was slightly increased from 3.8 to 4.5 μm. The α-Al matrix of the billet has the texture close to 〈100〉 // ND (normal direction) and 〈111〉 // ND, while the product keeps the texture close to 〈100〉 // TeD (tensile direction) and 〈111〉 // TeD. The yield strength is increased from 142 ± 2 MPa for the billet to 430 ± 2 MPa for the product, while the ultimate tensile strength is simultaneously enhanced from 222 ± 3 MPa to 477 ± 2 MPa. The enhancement of yield strengthening is regarded mainly related to the texture difference of α-Al matrix, while the ultimate tensile strength difference has relationship with the variance of both matrix texture and size change of second phase particles. This study may be referred for material choosing and forging parameter designing in new energy automobile industries.
Development of a low-dose strategy for propagation-based imaging helical computed tomography (PBI-HCT): high image quality and reduced radiation dose
Authors: Duan Xiaoman , Ding Xiao Fan , Khoz Samira , Chen Xiongbiao , Zhu Ning 
Published: December 2024

Abstract

Background. Propagation-based imaging computed tomography (PBI-CT) has been recently emerging for visualizing low-density materials due to its excellent image contrast and high resolution. Based on this, PBI-CT with a helical acquisition mode (PBI-HCT) offers superior imaging quality (e.g., fewer ring artifacts) and dose uniformity, making it ideal for biomedical imaging applications. However, the excessive radiation dose associated with high-resolution PBI-HCT may potentially harm objects or hosts being imaged, especially in live animal imaging, raising a great need to reduce radiation dose.Methods. In this study, we strategically integrated Sparse2Noise (a deep learning approach) with PBI-HCT imaging to reduce radiation dose without compromising image quality. Sparse2Noise uses paired low-dose noisy images with different photon fluxes and projection numbers for high-quality reconstruction via a convolutional neural network (CNN). Then, we examined the imaging quality and radiation dose of PBI-HCT imaging using Sparse2Noise, as compared to when Sparse2Noise was used in low-dose PBI-CT imaging (circular scanning mode). Furthermore, we conducted a comparison study on the use of Sparse2Noise versus two other state-of-the-art low-dose imaging algorithms (i.e., Noise2Noise and Noise2Inverse) for imaging low-density materials using PBI-HCT at equivalent dose levels. Results. Sparse2Noise allowed for a 90% dose reduction in PBI-HCT imaging while maintaining high image quality. As compared to PBI-CT imaging, the use of Sparse2Noise in PBI-HCT imaging shows more effective by reducing additional radiation dose (30%–36%). Furthermore, helical scanning mode also enhances the performance of existing low-dose algorithms (Noise2Noise and Noise2Inverse); nevertheless, Sparse2Noise shows significantly higher signal-to-noise ratio (SNR) value compared to Noise2Noise and Noise2Inverse at the same radiation dose level. Conclusions and significance. Our proposed low-dose imaging strategy Sparse2Noise can be effectively applied to PBI-HCT imaging technique and requires lower dose for acceptable quality imaging. This would represent a significant advance imaging for low-density materials imaging and for future live animals imaging applications.
Validation of a rapid algorithm for repeated intensity modulated radiation therapy dose calculations
Authors: Shaffer Nathan , Snyder Jeffrey , St-Aubin Joel 
Published: December 2024

Abstract
As adaptive radiotherapy workflows and deep learning model training rise in popularity, the need for repeated applications of a rapid dose calculation algorithm increases. In this work we evaluate the feasibility of a simple algorithm that can calculate dose directly from MLC positions in near real-time. Given the necessary machine parameters, the intensity modulated radiation therapy (IMRT) doses are calculated and can be used in optimization, deep learning model training, or other cases where fast repeated segment dose calculations are needed. The algorithm uses normalized beamlets to modify a pre-calculated patient specific open field into any MLC segment shape. This algorithm was validated on 91 prostate IMRT plans as well as 20 lung IMRT plans generated for the Elekta Unity MR-Linac. IMRT plans calculated using the proposed method were found to match reference Monte Carlo calculated dose within

98.02
±
0.84
%

and

96.57
±
2.41
%

for prostate and lung patients respectively with a 3%/2 mm gamma criterion. After the patient-specific open field calculation, the algorithm can calculate the dose of a 9-field IMRT plan in 1.016 ± 0.284 s for a single patient or 0.264 ms per patient for a parallelized batch of 24 patients relevant for deep learning training. The presented algorithm demonstrates an alternative rapid IMRT dose calculator that does not rely on training a deep learning model while still being competitive in terms of speed and accuracy making it a compelling choice in cases where repetitive dose calculation is desired.
Expressive quantum perceptrons for quantum neuromorphic computing
Authors: Bravo Rodrigo Araiza , Patti Taylor L , Najafi Khadijeh , Gao Xun , Yelin Susanne F 
Published: December 2024

Abstract
Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy quantum hardware and is poised to realize challenging algorithms in the near term. One key issue in QNC is the characterization of the requisite dynamics for ensuring expressive quantum neuromorphic computation. We address this issue by adapting previous proposals of quantum perceptrons (QPs), a quantum version of a simplistic model for neural computation, to the QNC setting. Our QPs compute based on the analog dynamics of interacting qubits with tunable coupling constants. We show that QPs are, with restricted resources, a quantum equivalent to the classical perceptron, a simple mathematical model for a neuron that is the building block of various machine learning architectures. Moreover, we show that QPs are theoretically capable of producing any unitary operation. Thus, QPs are computationally more expressive than their classical counterparts. As a result, QNC architectures built using our QPs are, theoretically, universal. We introduce a technique for mitigating barren plateaus in QPs called entanglement thinning. We demonstrate QPs’ effectiveness by applying them to numerous QML problems, including calculating the inner products between quantum states, energy measurements, and time reversal. Finally, we discuss potential implementations of QPs and how they can be used to build more complex QNC architectures such as quantum reservoir computers.
Quantum distance approximation for persistence diagrams
Authors: Ameneyro Bernardo , Herrman Rebekah , Siopsis George , Maroulas Vasileios 
Published: December 2024

Abstract
Topological data analysis (TDA) methods can be useful for classification and clustering tasks in many different fields as they can provide two dimensional persistence diagrams that summarize important information about the shape of potentially complex and high dimensional data sets. The space of persistence diagrams can be endowed with various metrics, which admit a statistical structure and allow to use these summaries for machine learning algorithms, e.g. the Wasserstein distance. However, computing the distance between two persistence diagrams involves finding an optimal way to match the points of the two diagrams and may not always be an easy task for classical computers. Recently, quantum algorithms have shown the potential to speedup the process of obtaining the persistence information displayed on persistence diagrams by estimating the spectra of persistent Dirac operators. So, in this work we explore the potential of quantum computers to estimate the distance between persistence diagrams as the next step in the design of a fully quantum framework for TDA. In particular we propose variational quantum algorithms for the Wasserstein distance as well as the

d

p

c

distance. Our implementation is a weighted version of the quantum approximate optimization Algorithm that relies on control clauses to encode the constraints of the optimization problem.
Parameter uncertainties for imperfect surrogate models in the low-noise regime
Authors: Swinburne Thomas D , Perez Danny 
Published: December 2024

Abstract
Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, this loss ignores model form error, or misspecification, meaning parameter uncertainties are significantly underestimated and vanish in the large data limit. As misspecification is the main source of uncertainty for surrogate models of low-noise calculations, such as those arising in atomistic simulation, predictive uncertainties are systematically underestimated. We analyze the true generalization error of misspecified, near-deterministic surrogate models, a regime of broad relevance in science and engineering. We show that posterior parameter distributions must cover every training point to avoid a divergence in the generalization error and design a compatible ansatz which incurs minimal overhead for linear models. The approach is demonstrated on model problems before application to thousand-dimensional datasets in atomistic machine learning. Our efficient misspecification-aware scheme gives accurate prediction and bounding of test errors in terms of parameter uncertainties, allowing this important source of uncertainty to be incorporated in multi-scale computational workflows.
Amplifying exploration of regional climate risks: clustering future projections on regionally relevant impact drivers instead of emission scenarios
Authors: Buskop Franciscus Eduard , Sperna Weiland Frederiek , van den Hurk Bart 
Published: December 2024

Abstract
Climate impacts will continue to evolve over the coming decades, requiring regions worldwide to obtain actionable climate information. Global climate models (GCMs) are often used to explore future conditions, but the variability of projections among GCMs complicates regional climate risk assessments. This variability in future projections is only partly explained by the often-used emission scenarios. Model uncertainty and internal variability play a major role in the outcomes of projected meteorological conditions, especially for local precipitation patterns. As precipitation is a key driver for hazards such as floods, droughts, and wildfires, local assessment of resulting risks using emission-based multi-model means probably leads to limited impact exploration. This study proposes a method to select more impact-relevant scenarios by determining regionally relevant climatic impact drivers and clustering GCMs on their projected changes in these drivers. We quantify the effectiveness of our approach by comparing future impacts covered by multi-model means per emission scenario with our approach, expressed as an ‘exploratory amplification’ factor. We illustrate the method for flood risk in the Latvian Lielupe basin and find the novel method has an exploratory amplification up to a factor of eight for the mid-century. We conclude that our method results in locally relevant climate scenarios that significantly improve regional exploration of future climate impacts. Such scenarios provide targeted risk information that can be used in adaptation planning.
Spatial variability of cold waves over Southern Africa and their potential physical mechanisms
Authors: Meque Arlindo O , Pinto Izidine S , Chuwah Clifford 
Published: December 2024

Abstract
Despite significant evidence of global warming, cold waves still happen occasionally and are of great concern in regions such as Southern Africa as they are associated with negative impacts such as loss of lives, destruction of infrastructure, loss of crops and livestock and high peak demand of electricity consumption. In this study, we examine the cold wave characteristics over the southern African region for the extended austral winter (May to September) over the 1979–2021 period, using minimum temperature from the European Centre for Medium-Range Weather Forecasts and Berkeley Earth Surface Temperature reanalysis products. Cold waves are calculated using the excess cold factor. Principal component analysis was performed on the cold waves to extract their dominant modes. Linear regression, Spearman rank correlation as well as lagged correlations were employed to find the links between the cold wave characteristics and different climate drives. Results from the study reveal that the lowest nighttime temperatures are found in interior South Africa and Lesotho. There is a decreasing trend in terms of the number of cold wave events over southern Africa, especially north of latitude 15˚S. Local and remote seasonal sea surface temperatures (SST) have the potential to change the likelihood of cold wave characteristics over Southern Africa. Also, seasonal mean circulation results contain seasonal mean ridge/trough structures, which suggests the role of these synoptic features and cold fronts in modulating cold waves over Southern Africa. A future study is needed to analyze the impact of these climate drivers at daily and intraseasonal timescales. There is moderate to high lead correlation between El Ninõ Southern Oscillation (ENSO) and some of the cold wave characteristics, hence ENSO, SST surrounding Southern Africa are good indicators for predicting cold waves over the region. Findings from this study can be useful in improving weather and climate forecasting over the region. Also, these results can support local authorities and communities at risk in developing early actions for tackling cold waves.
Development of climate indices relevant for agriculture in Africa under different climate change scenarios based on GCM and RCM ensembles
Authors: Ziegler Katrin , Abel Daniel , Weber Torsten , Paeth Heiko 
Published: December 2024

Abstract
Africa is facing significant changes in different aspects of life caused by climate change. Our study analyses the temporal and spatial changes of several thermal, hygric, and agricultural indices in Africa and eleven subregions. The agricultural indices strongly depend on the rainy season’s onset and crop specific parameters for which maize acts as an example crop. We use ensembles of General Circulation Models (GCM_ens) from Coupled Model Intercomparison Project and Regional Climate Models (RCM_ens) driven by the respective GCMs within Coordinated Regional Downscaling Experiment-Coordinated Output for Regional Evaluation under the representative concentration pathways 2.6 (rcp26) and 8.5 (rcp85) until the end of the century. The results show complex changes in precipitation-related indices depending on the subregion, model family, scenario, and period. Focusing on the far-future (2071–2100), RCM_ens rcp85 simulates the largest changes compared to rcp26 and both GCM ensembles for most precipitation indices. A later onset of the first rainy season is depicted for most subregions. Regarding heat and drought, all indices project a strong change toward hotter and drier conditions for all subregions and models with large discrepancies between the emission scenarios. Combining both precipitation and temperature changes and further being sensitive to the temporal occurrence within a year, the used agricultural indices show mixed signals. However, most subregions are going to face an increasing crop water need but no clear trend of irrigation requirement or water availability as exemplified for maize. Overall, the RCMs project larger future climate changes than the GCMs under rcp85.
Counted Looping and Waveform Plots
Authors: Essick John 
Published: December 2024

Abstract
The properties of a digitized waveform are studied as readers write a sine-wave plotting program. Plotting of the digitized sine-wave data is facilitated by Python’s Matplotlib library, while the digitized data are generated by three counted-looping methods—for loop, list comprehension, and vectorization. The latter method is realized through the array-based mathematics of the NumPy library. Python’s iterable data structures called list and string are discussed, along with the various ways prewritten software can be imported into a Python program. Finally, the procedure for placing a Matplotlib plot on a GUI is implemented. In a Do It Yourself project, readers create a for-loop-based simulation that explores the statistics of tossed coins and a Use It! example implements a for loop to sum a sequence of numeric values.
Conditional Looping and Real-Time Plots
Authors: Essick John 
Published: December 2024

Abstract
Readers learn to use Python’s conditional looping structure—the while loop—to create a real-time plot of a sine wave. The advantage of using Matplotlib’s animation module, along with its blitting feature, for real-time plots is explored and the method for placing an animation on a GUI is executed. The tuple data structure is defined, while the procedure for indexing and slicing iterable data structures (such as strings, lists, NumPy arrays, and tuples) is explained. A Do It Yourself project directs readers in writing a while-loop-based program that executes until it finds the solution that satisfies a given mathematical criterion, and a Use It! example demonstrates how to implement the break command to terminate program looping.
Web Browser Engineering
Authors: Panchekha Pavel , Harrelson Chris 
Published: December 2024

Abstract
The web is at the center of modern computing. Every year the web expands its reach to more and more of what we do with computers. It now goes far beyond its original use for document-based information sharing: many people now spend their entire day in a browser, not using a single other application! So given this centrality, it's worth knowing how the web works. And in particular, it's worth focusing on the browser, which is the user agent and the mediator of the web's interactions, which ultimately is what makes the web's principles real. But web browsers are still opaque, not just to students but to faculty and industry programmers. This book dissipates this mystery by systematically explaining all the major components of a web browser.
External quality assessment in African clinical laboratories: A systematic review and meta-analysis
Authors: Cherie Negesse , Chane Elias , Angelo Abiy Ayele , Teketelew Bisrat Birke , Tamir Mebratu , Berta Dereje Mengesha 
Published: December 2024

Abstract

Objectives
External quality assessment (EQA) helps evaluate and improve the quality of laboratory testing by providing unbiased reviews. The study aimed to synthesize pooled EQA performance of clinical laboratories across the African region.

Methods
The review was registered in PROSPERO (CRD42024562987) and reported based on the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. An extensive search was employed using the PubMed, Scopus, Cochrane, and Embase databases as well as gray literature. After duplicates were removed, the remaining articles were evaluated based on title, abstract, and full text. Methodologic quality was assessed using the JBI critical appraisal checklist. Random effects meta-analysis was used to estimate the pooled performance of EQA at 95% CIs. In addition, the I2 statistic was used to assess heterogeneity, and a funnel plot along with Egger regression were employed to evaluate publication bias. Trim and fill analysis was also performed to adjust the publication bias.

Results
From an electronic database search, a total of 622 articles were retrieved. Of these, 17 articles met the inclusion criteria, encompassing a total sample size of 4509 participating laboratories. In this study, the overall pooled performance of EQA in the African region was 71.25% (95% CI, 63.12-79.39). The result indicated statistically significant heterogeneity (I2 = 96.36). The funnel plot displayed an asymmetrical distribution of the studies, and Egger regression revealed significant publication bias (P = .002).

Conclusions
The findings revealed that the pooled EQA performance of laboratories in Africa did not meet the typical standard of 80%, indicating a need for continuous improvement. We suggest conducting further studies to gain better insights.