bluuebunny/crossref_metadata_2025_split huggingface dataset:
Segmental contribution to whole-body angular momentum during stepping in healthy young and old adults
Authors: Begue Jérémie , Peyrot Nicolas , Lesport Angélique , Turpin Nicolas A. , Watier Bruno , Dalleau Georges , Caderby Teddy 
Published: October 2021

AbstractRecent evidence suggests that during volitional stepping older adults control whole-body angular momentum (H) less effectively than younger adults, which may impose a greater challenge for balance control during this task in the elderly. This study investigated the influence of aging on the segment angular momenta and their contributions to H during stepping. Eighteen old and 15 young healthy adults were instructed to perform a series of stepping at two speed conditions: preferred and as fast as possible. Full-body kinematics were recorded to compute angular momenta of the trunk, arms and legs and their contributions to total absolute H on the entire stepping movement. Results indicated that older adults exhibited larger angular momenta of the trunk and legs in the sagittal plane, which contributed to a higher sagittal plane H range during stepping compared to young adults. Results also revealed that older adults had a greater trunk contribution and lower leg contribution to total absolute H in the sagittal plane compared to young adults, even though there was no difference in the other two planes. These results stress that age-related changes in H control during stepping arise as a result of changes in trunk and leg rotational dynamics.
Prediction of osteoporosis from simple hip radiography using deep learning algorithm
Authors: Jang Ryoungwoo , Choi Jae Ho , Kim Namkug , Chang Jae Suk , Yoon Pil Whan , Kim Chul-Ho 
Published: October 2021

AbstractDespite being the gold standard for diagnosis of osteoporosis, dual-energy X-ray absorptiometry (DXA) could not be widely used as a screening tool for osteoporosis. This study aimed to predict osteoporosis via simple hip radiography using deep learning algorithm. A total of 1001 datasets of proximal femur DXA with matched same-side cropped simple hip bone radiographic images of female patients aged ≥ 55 years were collected. Of these, 504 patients had osteoporosis (T-score ≤ − 2.5), and 497 patients did not have osteoporosis. The 1001 images were randomly divided into three sets: 800 images for the training, 100 images for the validation, and 101 images for the test. Based on VGG16 equipped with nonlocal neural network, we developed a deep neural network (DNN) model. We calculated the confusion matrix and evaluated the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). We drew the receiver operating characteristic (ROC) curve. A gradient-based class activation map (Grad-CAM) overlapping the original image was also used to visualize the model performance. Additionally, we performed external validation using 117 datasets. Our final DNN model showed an overall accuracy of 81.2%, sensitivity of 91.1%, and specificity of 68.9%. The PPV was 78.5%, and the NPV was 86.1%. The area under the ROC curve value was 0.867, indicating a reasonable performance for screening osteoporosis by simple hip radiography. The external validation set confirmed a model performance with an overall accuracy of 71.8% and an AUC value of 0.700. All Grad-CAM results from both internal and external validation sets appropriately matched the proximal femur cortex and trabecular patterns of the radiographs. The DNN model could be considered as one of the useful screening tools for easy prediction of osteoporosis in the real-world clinical setting.
The OpenDeID corpus for patient de-identification
Authors: Jonnagaddala Jitendra , Chen Aipeng , Batongbacal Sean , Nekkantti Chandini 
Published: October 2021

AbstractFor research purposes, protected health information is often redacted from unstructured electronic health records to preserve patient privacy and confidentiality. The OpenDeID corpus is designed to assist development of automatic methods to redact sensitive information from unstructured electronic health records. We retrieved 4548 unstructured surgical pathology reports from four urban Australian hospitals. The corpus was developed by two annotators under three different experimental settings. The quality of the annotations was evaluated for each setting. Specifically, we employed serial annotations, parallel annotations, and pre-annotations. Our results suggest that the pre-annotations approach is not reliable in terms of quality when compared to the serial annotations but can drastically reduce annotation time. The OpenDeID corpus comprises 2,100 pathology reports from 1,833 cancer patients with an average of 737.49 tokens and 7.35 protected health information entities annotated per report. The overall inter annotator agreement and deviation scores are 0.9464 and 0.9726, respectively. Realistic surrogates are also generated to make the corpus suitable for distribution to other researchers.
A multi‐agent reinforcement learning anti‐jamming method with partially overlapping channels
Authors: Zhang Yunpeng , Jia Luliang , Qi Nan , Xu Yifan , Chen Xueqiang 
Published: October 2021

AbstractThis paper investigates the problem of multi‐user anti‐jamming channel access with partially overlapping channels (POC). Compared with traditional anti‐jamming systems that use non‐overlapping channels, POC improve the spectral efficiency. However, the partial overlap of channels also brings more serious interference. For this, physical distance and channel separation on the interference intensity under partially overlapping channels are first considered and the malicious jamming and interference among users are formulated as a hierarchical binary model. Secondly, to cope with multi‐user decisions under dynamic jamming conditions, the Markov game framework is adopted to analyse the problem. Thirdly, a multi‐user collaborative anti‐jamming channel selection algorithm based on reinforcement learning is proposed as well as the optimal anti‐jamming strategy can be obtained. Finally, the simulation results validate that the proposed algorithm helps users cope with jamming and eliminate mutual interference. Compared with the non‐overlapping channel access scheme, the POC access scheme achieves higher network throughput.
Fault ride‐through strategy for hybrid HVDC with cascaded multi‐infeed MMC inverters
Authors: Zhao Jingbo , Wang Xiangfei , Li Baohong , Jiang Qin , Liu Tianqi 
Published: October 2021

AbstractThe hybrid HVDC with cascaded multi‐infeed MMC inverters suffers from the fragile characteristic of its MMC inverters and is easy to be over current and over voltage. To solve such a problem, this letter proposes a fault ride‐through strategy based on the fault current limiter with a novel topology. With the proposed limiter, the hybrid HVDC system can ride through both the DC side pole‐to‐ground fault and the commutation failure caused by the AC side fault. When the commutation failure happens, the current‐limiting resistor of the fault current limiter will be put on to decrease the current into the safety boundary. When the DC side pole‐to‐ground fault happens, the MMC capacitor discharge path will be cut off automatically by the topology of the fault current limiter. The simulations finally validate the analysis.
A new image processing strategy for surface crack identification in building structures under non‐uniform illumination
Authors: Parrany Ahmad Mahdian , Mirzaei Mohsen 
Published: October 2021

AbstractCrack detection in building structures is an important approach to evaluating the safety of these structures. However, in some cases under special circumstances, it is very difficult to identify all features of cracks by visual inspection. To tackle this problem and offer a higher potential for practical implementations, developing an automatic crack detection method seems essential. This paper establishes a novel algorithm for surface crack detection in building walls based on image processing techniques to enhance crack visibility. One of the most prevalent issues in surface crack detection from digital crack images is non‐uniform illumination of background which makes narrow and tiny cracks indistinguishable. In this regard, an integrated approach, combining adaptive image threshold using local first‐order statistics, contrast‐limited adaptive histogram equalization, and noise filtering using non‐linear diffusion filtering, is proposed to extract the whole skeleton of the crack from digital crack images with non‐uniform illumination. Several realistic crack images, suffering from noise and uneven lighting of background, are processed through the proposed algorithm to demonstrate the robustness and high accuracy of the method.
Multiplicity of Galactic luminous blue variable stars
Authors: Mahy L. , Lanthermann C. , Hutsemékers D. , Kluska J. , Lobel A. , Manick R. , Miszalski B. , Reggiani M. , Sana H. , Gosset E. 
Published: October 2021

Context.Luminous blue variables (LBVs) are characterised by strong photometric and spectroscopic variability. They are thought to be in a transitory phase between O-type stars on the main sequence and the Wolf-Rayet stage. Recent studies also evoked the possibility that they might be formed through binary interaction. Only a few are known in binary systems so far, but their multiplicity fraction is still uncertain.Aims.We derive the binary fraction of the Galactic LBV population. We combine multi-epoch spectroscopy and long-baseline interferometry to probe separations from 0.1 to 120 mas around confirmed and candidate LBVs.Methods.We used a cross-correlation technique to measure the radial velocities of these objects. We identified spectroscopic binaries through significant radial velocity variability with an amplitude larger than 35 km s−1. We also investigated the observational biases to take them into account when we established the intrinsic binary fraction. We usedCANDIDto detect interferometric companions, derive their flux fractions, and their positions on the sky.Results.From the multi-epoch spectroscopy, we derive an observed spectroscopic binary fraction of 26−10+16%. Considering period and mass ratio ranges from log(Porb) = 0 − 3 (i.e. from 1 to 1000 days),q = 0.1 − 1.0, and a representative set of orbital parameter distributions, we find a bias-corrected binary fraction of 62−24+38%. Based on data of the interferometric campaign, we detect a binary fraction of 70 ± 9% at projected separations between 1 and 120 mas. Based on the derived primary diameters and considering the distances of these objects, we measure for the first time the exact radii of Galactic LBVs to be between 100 and 650 R⊙. This means that it is unlikely that short-period systems are included among LBV-like stars.Conclusions.This analysis shows for the first time that the binary fraction in the Galactic LBV population is large. If they form through single-star evolution, their orbit must be large initially. If they form through a binary channel, the implication is that either massive stars in short binary systems must undergo a phase of fully non-conservative mass transfer to be able to sufficiently widen the orbit to form an LBV, or that LBVs form through merging in initially binary or triple systems. Interferometric follow-up would provide the distributions of orbital parameters at more advanced stages and would serve to quantitatively test the binary evolution in massive stars.
Artificial Intelligence (AI) Framework for Multi-Modal Learning and Decision Making towards Autonomous and Electric Vehicles
Authors: Ramesh G. , Praveen J. 
Published: October 2021

An electric vehicle with autonomous driving is a possibility provided technology innovations in multi-disciplinary approach. Electric vehicles leverage environmental conditions and are much desired in the contemporary world. Another great possibility is to strive for making the vehicle to drive itself (autonomous driving) provided instructions. When the two are combined, it leads to a different dimension of environmental safety and technology driven driving that has many pros and cons as well. It is still in its infancy and there is much research to be carried out. In this context, this paper is aimed at building an Artificial Intelligence (AI) framework that has dual goal of “monitoring and regulating power usage” and facilitating autonomous driving with technology-driven and real time knowledge required. A methodology is proposed with multiple deep learning methods. For instance, deep learning is used for localization of vehicle, path planning at high level and path planning for low level. Apart from this, there is reinforcement learning and transfer learning to speed up the process of gaining real time business intelligence. To facilitate real time knowledge discovery from given scenarios, both edge and cloud resources are appropriately exploited to benefit the vehicle as driving safety is given paramount importance. There is power management module where modular Recurrent Neural Network is used. Another module known as speed control is used to have real time control over the speed of the vehicle. The usage of AI framework makes the electronic and autonomous vehicles realize unprecedented possibilities in power management and safe autonomous driving.Key words:Artificial IntelligenceAutonomous DrivingRecurrent Neural NetworkTransfer Learning
Current Approach to Planning Angiography and MAA Administration
Authors: Commander Clayton W. , Mauro David M. 
Published: October 2021

AbstractTransarterial radioembolization of primary and secondary hepatic malignancies utilizing yttrium-90 microspheres is a commonly performed treatment by interventional radiologists. Traditionally performed as a two-part procedure, a diagnostic angiography is performed 1 to 3 weeks prior to treatment with the injection of technetium-99m-macroaggregated albumin followed by planar scintigraphy in the nuclear medicine department. Careful attention must be paid to the details during the diagnostic angiography to ensure the delivery of a safe and optimal dose to the diseased liver and to minimize radiation-induced damage to both unaffected liver and adjacent structures. In this article, we will review the steps and considerations that must be made during the angiography planning and discuss current and future areas of research.
Recognizing and Managing Adverse Events in Y-90 Radioembolization
Authors: Laidlaw Grace L. , Johnson Guy E. 
Published: October 2021

AbstractTransarterial radioembolization using yttrium-90 (Y-90) microspheres is an important therapy in the management of unresectable primary liver tumors or hepatic metastases. While radioembolization is generally well-tolerated, it is not free from adverse events, and familiarity with the prevention and treatment of radioembolization-specific complications is an important component of patient care. This article aims to review radioembolization-specific toxicities stratified by hepatic, extrahepatic, and systemic effects, with a focus on preventing and mitigating radioembolization-induced morbidity.
Rare Case of Spontaneous Pneumorrhachis in a Young Male with COVID-19
Authors: Reddy Vikas , Jajodia Ankush , Pasricha Sunil , Goyal Sumit , Puri Sunil 
Published: October 2021

AbstractHere we present a rare case of spontaneous pneumomediastinum complicated with pneumorrhachis (PR) in a young man who is a known case of carcinoma rectosigmoid region. Our young male was diagnosed with novel coronavirus disease 2019 (COVID-19) and remained asymptomatic for any respiratory complaints. Though an association of pneumomediastinum has been reported in COVID 19 patients, to the best of our knowledge, this is the first report of PR in a COVID-19 oncological case. The role of a radiologist is to identify this condition as cases reported earlier may have serious neurological consequences. Pursuing the cause can be a diagnostic challenge but it reaps the clinical benefit in the appropriate management.
Health Intelligence Atlas: A Core Tool for Public Health Intelligence
Authors: Wilson Gabriela M. , Ball Marion J. , Szczesny Peter , Haymann Samuel , Polyak Mark , Holmes Talmage , Silva John S. 
Published: October 2021

Abstract
Background The dramatic increase in complexity and volume of health data has challenged traditional health systems to deliver useful information to their users. The novel coronavirus disease 2019 (COVID-19) pandemic has further exacerbated this problem and demonstrated the critical need for the 21st century approach. This approach needs to ingest relevant, diverse data sources, analyze them, and generate appropriate health intelligence products that enable users to take more effective and efficient actions for their specific challenges.
Objectives This article characterizes the Health Intelligence Atlas (HI-Atlas) development and implementation to produce Public Health Intelligence (PHI) that supports identifying and prioritizing high-risk communities by public health authorities. The HI-Atlas moves from post hoc observations to a proactive model-based approach for preplanning COVID-19 vaccine preparedness, distribution, and assessing the effectiveness of those plans.
Results Details are presented on how the HI-Atlas merged traditional surveillance data with social intelligence multidimensional data streams to produce the next level of health intelligence. Two-model use cases in a large county demonstrate how the HI-Atlas produced relevant PHI to inform public health decision makers to (1) support identification and prioritization of vulnerable communities at risk for COVID-19 spread and vaccine hesitancy, and (2) support the implementation of a generic model for planning equitable COVID-19 vaccine preparedness and distribution.
Conclusion The scalable models of data sources, analyses, and smart hybrid data layer visualizations implemented in the HI-Atlas are the Health Intelligence tools designed to support real-time proactive planning and monitoring for COVID-19 vaccine preparedness and distribution in counties and states.
Consequences of Rapid Telehealth Expansion in Nursing Homes: Promise and Pitfalls
Authors: Powell Kimberly R. , Alexander Gregory L. 
Published: October 2021

Abstract
Background Expectations regarding use and potential benefits of telehealth (TH) in nursing homes (NHs) are high; however, unplanned and unexpected consequences can occur as a result of major policy and technological changes.
Objectives The goal of this study was to elicit stakeholder perspectives of consequences of rapid TH expansion in NHs.
Methods Using a qualitative descriptive design, we drew a sample based on findings from a national study examining trends in NH information and technology (IT) maturity, including TH use. We used maximum variation sampling to purposively select participants who (1) participated in our IT maturity survey for two consecutive years, (2) completed year 1 of the IT maturity survey prior to TH expansion (before March 6, 2020) and year 2 after TH expansion (after March 6, 2020), (3) represented a broad range of facility characteristics, and (4) were identified as an end user of TH or responsible for TH implementation. Using six questions from the IT maturity survey, we created a total TH score for each facility and selected participants representing a range of scores.
Results Interviews were conducted with (n = 21) NH administrators and clinicians from 16 facilities. We found similarities and differences in perceptions of TH expansion according to facility TH score, NH location, and participant role. Desirable consequences included four subthemes as follows: (1) benefits of avoiding travel for the NH resident, (2) TH saving organizational resources, (3) improved access to care, and (4) enhanced communication. Undesirable consequences include the following five subthemes: (1) preference for in-person encounters, (2) worsening social isolation, (3) difficulty for residents with cognitive impairment, (4) workflow and tech usability challenges, and (5) increased burden on NH staff/infrastructure. Participants from rural NHs perceived lack of training, poor video/sound quality, and internet/connectivity issues to be potential pitfalls.
Conclusion Clinicians and NH administrators should consider leveraging the desirable consequences of rapid TH expansion and implement mitigation strategies to address the undesirable/unanticipated consequences.
From the theater to the hippodrome: A critique of Jeffrey Green’s theory of plebiscitary democracy and an alternative
Authors: Illés Gábor , Körösényi András 
Published: October 2021

AbstractThe article argues that the theory of plebiscitary leader democracy (PLD), originally developed by Max Weber, is in its somewhat rejuvenated version a helpful framework in interpreting longer-term and more recent empirical trends in contemporary democracies, such as the growing personalization of politics, the emergence of populist leaders, rising levels of polarization, and the growing importance of social media. However, to realize the potential of the theory, it should be detached from Jeffrey Green’s most original, yet insufficiently realistic elaboration of plebiscitary democracy that he made a decade ago. The article argues that instead of a passive and unifiable entity, the citizenry should be thought of as reactive and deeply divided, a setting which can be characterized by the metaphor of the infamous Byzantine chariot races rather than that of the theater, implicit in Green’s theory. Plebiscitary democracy should be thought of as representational, where popular control is manifested as the veto power of the popular voice. Additionally, despite its realist minimalism, the theory we propose may still have some critical potential, because it adopts the refurbished ideal of competition. The article closes by identifying further avenues of theoretization leading towards a more elaborate view of PLD.
Optimal resonant frequency analysis for underground wireless power transfer with metal tube interference
Authors: Xu Yu , Chen Qili , Zhang Yongquan , Li Bo , Tang Huiming 
Published: October 2021

Magnetic resonant wireless power transfer (MR-WPT) has several advantages over conventional wired underground power supply methods. However, MR-WPT inevitably encounters metal conductors, which reduce the system efficiency through the eddy loss induced in the conductor by high-frequency electromagnetic waves. In this paper, the effect of resonant frequency variations caused by metal tube interference with the aim of maximizing the system efficiency is studied. According to the variation in the resonant frequency, the system efficiency is analytically derived by an equivalent circuit model. Electromagnetic simulations are carried out to further analyze the metal tube interference on the system. The results demonstrate the existence of an optimal resonant frequency that maximizes the system efficiency. Aluminum tube interference produces a lower optimal resonant frequency and higher efficiency than a 304 stainless-steel tube. When the metal tube is slotted, the optimal slot width and number of slots enhance the maximum efficiency and reduce the optimal resonant frequency and frequency drift. With slot widths of 2 and 8 mm, the system efficiency reaches ∼67% at 40.1 kHz and 55% at 48 kHz, respectively. Finally, different types of slotted tubes are fabricated, and the theoretical results are experimentally verified.