bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical and Radiological Results of Minimally Invasive Two-Hole Dynamic Hip Screw Fixation of Stable Intertrochanteric Fractures: A Prospective Study of 50 Patients
Authors: Dar Fiaz Ahmad , Iqbal Zaffar , Lone Ansarul Haq , Ali Nadeem 
Published: April 2023

Objectives
Intertrochanteric fractures are fractures of elderly population and are known for morbidity and mortality. Early stabilization with minimal surgical trauma is important for early mobilization to prevent recumbence-related complications.

Material and Methods
This prospective study was conducted on 50 stable intertrochanteric fractures that were managed by fixation with a small two-hole dynamic hip screw –(DHS) side plate using a minimally invasive technique. The surgical time, intraoperative blood loss, length of the skin incision, duration of hospital stay, time to full weight bearing and fracture union, and complications were recorded.

Results
The mean age of the patients was 60.2 years with 64% belonging to male sex. The mean operative time, average blood loss, and mean incision length were 35.6 minutes, 68.4 mL, and 3.7 cm, respectively. Time to bone healing ranged from 10 to 16 weeks (mean 13 weeks). Two patients had hip screw backout but that did not interfere with failure of fixation.

Conclusion
Stabilization of stable intertrochanteric fractures with two-hole DHS using minimally invasive approach is a viable option with negligible complication rate related to failure of small DHS side plate.
Iatrogenic Injuries Related to Thermal Ablation of the Liver and Pancreas
Authors: HonShideler Curtis , Lin Liz , HonShideler Kelsey , Guez David , Bader Mohammad 
Published: April 2023

AbstractThermal ablation is a widely utilized treatment option for tumors of the liver and pancreas; procedures may be performed intraoperatively or percutaneously, under image guidance (more recently, endoscopy-guided ablation of pancreatic tumors has also been introduced). Through differing mechanisms, radiofrequency ablation, microwave ablation, and cryoablation aim to precisely eradicate tumor, with adequate margins and minimal injury to normal surrounding tissue. Knowledge of thermal ablative risks and complications is crucial for both therapeutic planning and postprocedural management to optimize outcomes and avoid significant patient injury. In this review, we discuss iatrogenic injuries related to thermal ablation of hepatic and pancreatic malignancies.
The Emerging Spectrum of Respiratory Diseases in the U.S. Cannabis Industry
Authors: Sack Coralynn , Simpson Christopher , Pacheco Karin 
Published: April 2023

AbstractWhile the cannabis industry is one of the fastest growing job markets in the United States and globally, relatively little is known about the occupational hazards that cannabis production workers face. Based on the closely related hemp industry and preliminary studies from recreational cannabis grow facilities, there is concern for significant respiratory exposures to bioaerosols containing microbial and plant allergens, chemicals such as pesticides, volatile organic compounds, and other irritant gases. Components of the cannabis plant have also recently been identified as allergenic and capable of inducing an immunoglobulin E-mediated response. Accumulating evidence indicates a spectrum of work-related respiratory diseases, particularly asthma and other allergic diseases. Disentangling causal relationships is difficult given the heterogeneity of mixed exposures, diagnostic challenges, and confounding by personal cannabis use. Despite and because of these uncertainties, better regulatory guidance and exposure controls need to be defined in order to reduce the risk of work-related disease.
The Bush Doctrine redux: changes and continuities in American grand strategy since ‘9/11’
Authors: Löfflmann Georg 
Published: April 2023

AbstractThis article examines the ideational dimension and political performance of American grand strategy since ‘9/11’ and explores to what extent the strategic assumptions, ideological dispositions, and security practices of the Bush Doctrine have endured since the 2001 terrorist attacks. It advances a constructivist understanding of American grand strategy as a nexus of national identity discourses and security practices. The article will first explore the significance of American exceptionalism in the ideational dimension of the Bush Doctrine and its practical impact in the pursuit of US national security. The article will then focus on the Obama Doctrine, arguing that its embrace of cooperative engagement and multilateralism represented a limited strategic course correction within the paradigm of liberal hegemony. Finally, the article will contrast grand strategy discourses under Bush and Donald Trump, exploring their shared foundations in Jacksonian nationalism and unilateralism that demonstrate the continued relevance of Bush’s strategic vision of US primacy.
Optimal inference of molecular interaction dynamics in FRET microscopy
Authors: Kamino Keita , Kadakia Nirag , Avgidis Fotios , Liu Zhe-Xuan , Aoki Kazuhiro , Shimizu Thomas S. , Emonet Thierry 
Published: April 2023

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.
ANN for design and fabrication of ENi-P-nano TiO2 coatings on AH36 steel
Authors: Anthoni Sagaya Selvan R. , Thakur Dinesh G. , Salunkhe Sachin 
Published: April 2023

Designing optimal ENi-P-nano TiO2 with higher surface hardness is time-consuming and expensive. Thus, this study is aims to develop the optimal optimiser-based ANN model to forcast Vickers hardness for newer process parametric combinations and find the ideal combination before manufacture. Consequently, three ANN model were devised using ADAM, AdaMax, and RMSprop optimisers with identical data set for training and testing. mean Squared Error (MSE) and R2 values were used to compare and assess their performance. Rectified Linear Unit (ReLU) is used to provide nonlinearity to the neural network's output since the activation function affects ANN model performance. The study also highlights hyperparameter setting's impact on optimiser performance. The ADAM-based ANN model was determined as optimal design through comparing the predicted and experimental Vickers hardness for the unknown combinations using Taguchi DOE. In addition, FESEM and XRD micrographs confirmed the optimal coating's morphology and phases. This study results shown the compatibility of utilising ANN-based prediction models for ENi-P-nanoTiO2 coating design and prediction, however future research much incoporates more process parameters and their level ranges.
CCQM-K68.2019, nitrous oxide (N2O) in air, ambient level, final report
Authors: Viallon J , Choteau T , Flores E , Idrees F , Moussay P , Wielgosz R I , Lim J S , Lee Jeongsoon , Lee Jinbok , Moon Dongmin , Wijk J I T van , Persijn S , Veen A M H van der , Efremova O V , Konopelko L , Kustikov Y , Kolobova A , Klimov A , Bi Z , Cecelski C , Carney J , Toman B , Possolo A , Hall B , Brewer P , Worton D , Saarnio K , Aaltonen H , Tshilongo J , Mogale D Mphara , Mphaphuli G , Marebane P Mohweledi 
Published: April 2023

Main text
The key comparison CCQM-K68.2019 was aimed at evaluating the level of comparability of laboratories' capabilities for preparing nitrous oxide in air primary reference mixtures at ambient amount fractions, in the range 320 nmol mol−1 to 350 nmol mol−1.
The comparison was coordinated by the BIPM and the KRISS. It consisted in the simultaneous comparison of a suite of 2n primary gas standards, two standards to be prepared by each of the n participating laboratories. Two independent analytical methods were used by the BIPM to analyse the amount fraction of N2O in air, namely Gas Chromatography with an Electron Capture Detector (GC−ECD) and Quantum Cascade Laser Absorption Spectroscopy (QCLAS).
Since the circulation of the Draft A report in April 2021, four meetings took place with participants to discuss the mathematical treatment of the comparisons results, and several models were proposed. The model chosen by participants is the Bayesian Errors−In−Variables regression with shades of dark uncertainty. In this final report, the Key Comparison Reference Values were obtained with this model, with calculations performed by B Toman and A Possolo.
The key comparison CCQM-K68.2019 is considered to present an analytical challenge and therefore classified as Track C comparison in the CCQM nomenclature.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Inverse problems of damped wave equations with Robin boundary conditions: an application to blood perfusion
Authors: Fang Yan-Long , Lesnic Daniel , Alosaimi Moataz 
Published: April 2023

Abstract
Knowledge of the properties of biological tissues is essential in monitoring any abnormalities that may be forming and have a major impact on organs malfunctioning. Therefore, these disorders must be detected and treated early to save lives and improve the general health. Within the framework of thermal therapies, e.g. hyperthermia or cryoablation, the knowledge of the tissue temperature and of the blood perfusion rate are of utmost importance. Therefore, motivated by such a significant biomedical application, this paper investigates, for the first time, the uniqueness and stable reconstruction of the space-dependent (heterogeneous) perfusion coefficient in the thermal-wave hyperbolic model of bio-heat transfer from Cauchy boundary data using the powerful technique of Carleman estimates. Additional novelties consist in the consideration of Robin boundary conditions, as well as developing a mathematical analysis that leads to stronger stability estimates valid over a shorter time interval than usually reported in the literature of coefficient identification problems for hyperbolic partial differential equations. Numerically, the inverse coefficient problem is recast as a nonlinear least-squares minimization that is solved using the conjugate gradient method (CGM). Both exact and noisy data are inverted. To achieve stability, the CGM is stopped according to the discrepancy principle. Numerical results for a physical example are presented and discussed, showing the convergence, accuracy and stability of the inversion procedure.
Evaluation of the transdermal permeation and anticancer effects of plasma-activated Aristoflex AVC hydrogel in an artificial skin model
Authors: Jing Xixi , Wu Tong , Liu Dingxin , Zhang Hao , Xu Shengduo , Zhang Jishen , Wang Zifeng , Zhou Renwu , Xu Dehui , Xu Han , Wang Xiaohua , Rong Mingzhe , Zhao Yan 
Published: April 2023

Abstract
Plasma-activated hydrogels have promising application prospects in the field of transdermal penetration therapy, such as subcutaneous tumours, due to their long-lasting, slow-release properties and ability to be easily applied to skin. In this study, surface air plasma was used to prepare plasma-activated Aristoflex AVC hydrogel (PAH) and plasma-activated water (PAW) by irradiation. The characteristics of PAH and PAW were further studied in terms of reactive oxygen species (ROS) storage, transdermal permeation, and in vitro anticancer effects in an artificial skin model. The experimental results showed that PAH had a prolonged storage time of ROS and more transdermal permeation of ROS through artificial skin in Franz diffusion cell after 30 h compared to PAW, which means PAH could be superior to PAW for long-term applications on the skin surface. The in vitro anticancer effects of PAH in an artificial skin model were also better than those of PAW, reducing the viability of A375 cells without causing damage to HaCaT cells. Our study shows that PAH is superior to PAW in body-surface coating applications and can be used for subcutaneous anticancer applications.
A novel non-contact, magnetic-based stress inspection technology and its application to stress concentration zone diagnosis in pipelines
Authors: He Tengjiao , Liao Kexi , Leng Jihui , He Guoxi , Zhu Hongdong , Zhao Shuai 
Published: April 2023

Abstract
During their construction and long-term operation, stress concentration zones (SCZs) will inevitably occur in buried pipelines. This may cause pipe leakage or fracture failures. Therefore, it is essential to inspect and identify the precise position and stress damage degree of SCZs. Non-contact pipeline magnetic-based stress inspection (PMSI) is an important topic in the field of oil and gas pipeline stress inspection. In order to determine the response of a non-contact magnetic gradient signal to the stress damage of SCZs, we establish a PMSI model to analyze the three-dimensional magnetic signals of SCZs. The effects of changes to the axial stress value, the stress concentration length, the detection height and the gradiometer jitter are also studied. In addition, the PMSI is carried out on a 10.3 km liquefied natural gas buried pipeline, which is validated by the excavation inspection results. The results indicate that the x component of the magnetic gradient signal, Gx
, shows sinusoidal fluctuation near a SCZ, and the y and z components, Gy
and Gz,
each reach a peak value. The G
max value, the maximal magnetic gradient modulus, increases with the severity and length of the SCZs. As a result, two level-II SCZs and two level-III SCZs are identified.
Real-time measurement of minimum cross-sectional area and radius of curvature of miniature plate specimens using a high-speed laser profiler for calculating true stress-true strain curves
Authors: Kato Taichiro , Ruan Xiaoyong , Nozawa Takashi 
Published: April 2023

Abstract
True stress-true strain curves are fundamental for designing and analyzing structures such as fusion reactors. These curves are typically obtained by conducting tensile tests on round bar specimens. However, due to material dimension limitations, plate specimens are sometimes used instead of round bar specimens. Obtaining true stress-true strain curves experimentally from plate specimens can be challenging. To address this challenge, this study aims to obtain true stress-true strain curves of miniature plate specimens using both analytical and experimental methods. The analytical method involved inverse finite element method (FEM), while the experimental method utilized real-time measurement of the minimum cross-sectional area and radius of curvature of a miniature plate specimen with a high-speed laser profiler. Comparing the true stresses obtained from the analytical and experimental methods, we found that the difference was typically within 5%. These findings suggest that inverse FEM and laser profilometry are effective methods for determining the true stress-true strain of miniature plate specimens.
Intelligent indoor localization based on CSI via radio images and deep learning
Authors: Zhang Yunwei , Fan Mochan , Xu Chendong 
Published: April 2023

Abstract
The universality of intelligent indoor localization based on WIFI has increasingly emerged as a multifold challenge. However, in practical applications, its indoor localization accuracy is limited by noises, diffractions, and multipath effects. To overcome these drawbacks, we design a new intelligent indoor localization system based on channel state information (CSI) of the wireless signal from multiple-input multiple-output (MIMO): IILC. In IILC, the initial amplitude information is first processed in the measured CSI data, which can effectively suppress the impact from noise and other interferences. Next, we devised a method to construct radio images. It can fully use space-frequency and time-frequency information from CSI-MIMO to obtain more location information. Next, we design a new deep learning network to determine the optimal effectiveness of radio image classification. Subsequently, a mixed norm is proposed to impose sparsity penalty and overfit constraint on the loss function, making the valuable feature units active and the others inactive. The experimental results verify that the IILC system exhibits excellent performance. The overall localization accuracy of the IILC in the office scene can reach 97.10%, and the probability of localization error within 1.2 m is 86.21%.
Measurement of bearing reaction force by distributed strain variation
Authors: Wang Rui , Kong Chengcheng , Zhang Yudong , Yu Wennian , Huang Wenbin , Wang Liming 
Published: April 2023

Abstract
Bearing is a crucial transmission component of aero-engines under high-speed and heavy-load conditions. To design a roller bearing with high loading capacity and reliability, it is essential to focus on the relationship between the external load state (reaction force) and the internal load distribution (load distribution). Therefore, a measurement method for bearing reaction force using load distribution and radial basis function neural network is presented in this study. Unlike conventional static reaction force measurement methods, both the direction and magnitude of the reaction force are considered in the proposed method without modifications to bearing. First, an experimental system is designed to investigate the load distribution in a roller bearing under different reaction forces using strain variation measurements. Then, a finite element analysis is conducted, and simulation results of the strain variations at three interested points match well with the experimental measurements. Finally, a radial basis function neural network with strong nonlinear fitting ability is applied to construct the mapping relationship between strain variation and reaction force. The results demonstrate that the proposed method can predict the reaction force with high accuracy based on the strain variation at three measuring points.
Calculating the Sun radius using a projection tube, a smartphone and a geometry software
Authors: Arias Maritza , Caerols Hugo , Asenjo Felipe A 
Published: April 2023

Abstract
We revisit the known experiment of using the solar projection along a tube in order to calculate the Sun’s radius. However, we propose several features that greatly improve the precision in the measurements. We discuss how the shape of the performation hole affect the results, and what is the best way to construct it. We also discuss how the examination of the measurements can be performed through photographs taken with smartphones, and their analysis using different free softwares. We also propose how a general theory can be formulated to determine the radius of the Sun with only measurements made with this kind of experiment, without any external parameter to the apparatus. Its usefulness and applicability is also considered. With all these additions to this known experience, this work is focused in taking advantage of the strength of smartphones and software technologies to get powerful and precise astronomical results as an educational tool.
2D medical image synthesis using transformer-based denoising diffusion probabilistic model
Authors: Pan Shaoyan , Wang Tonghe , Qiu Richard L J , Axente Marian , Chang Chih-Wei , Peng Junbo , Patel Ashish B , Shelton Joseph , Patel Sagar A , Roper Justin , Yang Xiaofeng 
Published: April 2023

Abstract

Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models. Approach. The proposed 2D image synthesis framework is based on a diffusion model using a Swin-transformer-based network. This model consists of a forward Gaussian noise process and a reverse process using the transformer-based diffusion model for denoising. Training data includes four image datasets: chest x-rays, heart MRI, pelvic CT, and abdomen CT. We evaluated the authenticity, quality, and diversity of the synthetic images using visual Turing assessments conducted by three medical physicists, and four quantitative evaluations: the Inception score (IS), Fréchet Inception Distance score (FID), feature similarity and diversity score (DS, indicating diversity similarity) between the synthetic and true images. To leverage the framework value for training AI models, we conducted COVID-19 classification tasks using real images, synthetic images, and mixtures of both images. Main results. Visual Turing assessments showed an average accuracy of 0.64 (accuracy converging to

50
%

indicates a better realistic visual appearance of the synthetic images), sensitivity of 0.79, and specificity of 0.50. Average quantitative accuracy obtained from all datasets were IS = 2.28, FID = 37.27, FDS = 0.20, and DS = 0.86. For the COVID-19 classification task, the baseline network obtained an accuracy of 0.88 using a pure real dataset, 0.89 using a pure synthetic dataset, and 0.93 using a dataset mixed of real and synthetic data. Significance. A image synthesis framework was demonstrated for medical image synthesis, which can generate high-quality medical images of different imaging modalities with the purpose of supplementing existing training sets for AI model deployment. This method has potential applications in many data-driven medical imaging research.
Assessment of relevant pinch magnitudes of plasma focus devices based on non-uniform internal distributions of density and temperature
Authors: Giovachini R , Barbaglia M , Abraham-Ekeroth R M , Clausse A 
Published: April 2023

Abstract
A novel model of the dense z-pinch of a plasma focus (PF) discharge is presented, assuming Gaussian radial distributions of density and temperature inside the pinch. The model is used to assess the temperature, size and lifetime of the focus conflating with available experimental data gathered from discharges in Deuterium performed in the PF PACO with three cathode configurations. The resulting estimations were mean pinch temperatures between 1 and 12 keV, and pinch durations between 10 and 120 ns, each magnitude decreasing and increasing with the filling pressure, respectively. Moreover, the relation found between the mean pinch radius and temperature,

⟨

r

p

⟩

and

⟨

T

p

⟩

, is consistent with an adiabatic compression given by

⟨

r

p

⟩

⟨

T

p

⟩

3

/

4

≅
1.2
−
1.6

mm keV3/4. The estimated width of the Gaussian profile is in agreement with spectroscopic images obtained in another small device.