bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Retrospective Pilot Study of Imagery Rehearsal Therapy Enhanced with Narrative Therapy Principles for the Treatment of Nightmares in US Military Veterans
Authors: Wagner Ann Marie , Richards Anne , Chiros Christine , Thuras Paul , Parsons Elizabeth C. , Oien Angela D. , Schenck Carlos H. , Irfan Muna 
Published: November 2023

Abstract
Introduction Chronic nightmares are a common and disabling feature of posttraumatic stress disorder (PTSD) for which broadly effective treatments are still lacking. While imagery rehearsal therapy (IRT) demonstrates benefits for patients with idiopathic nightmares and some patients with PTSD-related nightmares, research indicates it may be less beneficial for veterans. Narrative therapy (NT) is a form of psychotherapy which is client-centered and value-focused and has demonstrated benefits for PTSD patients. The application of NT principles to IRT may provide a valuable therapeutic approach for treatment in veterans.
Objective To perform a retrospective chart review of veteran clients participating in a novel, brief intervention developed by the first author consisting of IRT enhanced with NT principles (N-IRT) for the treatment of nightmares. The primary outcomes were nightmare frequency and intensity, and the secondary outcome was the impact of the intervention on nightmare distress and coping, subjective sleep quality, and overall PTSD symptoms.
Materials and Methods We conducted retrospective chart reviews for eight veterans referred to the first author for the treatment of nightmares, who completed N-IRT, including baseline and end-of-treatment measures. The protocol involved a single 60-minute NT-enhanced rescripting session and assigned homework to rehearse the revised dream script, and a follow-up evaluation 4 weeks later. The subjects completed a sleep and nightmare interview developed by the first author and the PTSD Checklist at baseline and after the intervention at the follow-up evaluation. Paired t-tests were conducted to test for pre-to-post differences.
Results In the statistical analysis, we observed a statistically significant and clinically meaningful reduction in the frequency (p = 0.04) and intensity of nightmares (p = 0.001) from pretreatment to the 1-month follow-up. Measures of nightmare-associated emotional distress, the ability to cope with nightmares, sleep duration and sleep efficiency, as well as overall PTSD symptoms also demonstrated significant improvements.
Conclusion These pilot data provide compelling preliminary evidence that a single-session IRT intervention modified with NT (N-IRT) is effective in reducing nightmare frequency and intensity, reducing nightmare distress, improving the act of coping with nightmares, and improving sleep quality and overall PTSD symptoms in veterans. Further investigation of this method with gold-standard clinical trial designs and larger sample sizes is indicated to confirm effectiveness and to better understand the possible mechanisms of treatment effect.
Ulnar Head Fracture with Volar Dislocation of the Distal Radioulnar Joint: A Case Series and Systematic Review
Authors: Samy David Ananth , Julian Harriet S. , Pe Eindere Zaw , Brown Daniel 
Published: November 2023

AbstractDislocations of the distal radioulnar joint associated with a fracture of the distal ulna articular surface without an associated radial fracture are extremely rare.We present a case of a fracture of the distal ulna with an associated dislocation of the distal radioulnar joint, where the articular head fragment was displaced anterior to the sigmoid notch.We also present the results of a formal systematic review of these injuries which confirmed this is the first such injury described, although there is a very similar fracture pattern described in the literature with dorsal dislocation.We describe the mechanism of injury and hypothesize about the etiology of the fracture pattern and suggest that it represents the end of a spectrum that also includes the isolated dislocations and those associated with an ulna styloid fracture.
Hepatocellular Carcinoma Presenting as Extension of Tumor Thrombus into the Right Atrium from the Portal Vein through Inferior Vena Cava Treated with Chemoembolization: A Rare Case Report
Authors: Khurana Kashish , Toshniwal Saket , Pantbalekundri Nikhil , Kumar Sunil , Acharya Sourya 
Published: November 2023

AbstractPatients with tumor thrombosis in the portal vein due to hepatocellular carcinoma (HCC) are uncommon, and the majority of these cases is thought to be in an advanced stage with a bad prognosis. The right atrium and the inferior vena cava may be impacted by the intravascular tumor thrombus, with the latter having a bad prognosis. Portal vein involvement is one of the most serious consequences of HCC. For both systemic and locoregional therapy, targeting many pathways in the HCC cascade using a mix of medications and additional modalities like transarterial chemoembolization, transarterial radioembolization, radiation, and hepatic arterial infusion chemotherapy appears to be helpful.Portal vein tumor thrombosis, which affects 35 to 50% of patients, is a strong predictor of poor prognosis due to the higher likelihood of tumor migration into the bloodstream and the subsequent increased risk of recurrence. This case report highlights about a 60-year-old female who presented with HCC with portal vein tumor thrombosis not willing for chemo- and radiotherapy successfully treated with transarterial chemoembolization as a palliative management.
Clinical and Radiographic Outcomes of a Monoblock Fluted Titanium Tapered Stem Following Complex and Revision Total Hip Arthroplasty
Authors: Oakley Christian T. , Passano Brandon , Lutes William B. , Incavo Stephen , Park Kwan J. , Schwarzkopf Ran 
Published: November 2023

AbstractAchieving femoral stem stability in complex primary and revision total hip arthroplasty (rTHA) cases is a challenging goal that monoblock fluted titanium tapered stems (FTTS) were designed to address. However, as more modern FTTS are developed, there are still questions regarding outcomes for these stems. The purpose of this study is to determine the radiographic and clinical outcomes of a modern monoblock FTTS. A multicenter retrospective observational study examined the hip databases of three institutions to obtain all monoblock FTTS with up to 5 years of radiographic follow-up. Stem subsidence and osteointegration were assessed on the most recent radiograph. All-cause re-revisions and survival of the stem at latest follow-up were recorded. Descriptive statistics were calculated for demographics and outcomes. Ninety-six monoblock FTTS were examined. Median clinical follow-up was 28.5 months (range: 18.0–57.8 months). Median subsidence was 1.4 mm (range: 0–15.0 mm). Sixteen (16.7%) and 3 (3.1%) stems had subsidence greater than 5 and 10 mm, respectively. Overall, thirteen hips (13.5%) required reoperations, of which 5 stems were revised (5.2%). Four stems were revised for periprosthetic joint infection (PJI), and one stem was explanted for exposure during acetabular revision. Kaplan–Meier analysis yielded an all-cause stem survivorship of 95.8% at 2 years and 88.5% at 4 years. Stem survivorship excluding septic causes for revision was 99.0% at both 2 and 4 years. Monoblock FTTS in complex primary and rTHA demonstrate encouraging clinical and radiographic results at up to 5 years of follow-up.
Comparison of Inpatient Charges between Primary and Revision Total Hip Arthroplasties: A Study of Trends within the New York's Statewide Planning and Research Cooperative System
Authors: Passano Brandon , Simcox Trevor , Anil Utkarsh , Lin Charles , Barzideh Omid , Schwarzkopf Ran 
Published: November 2023

AbstractThe U.S. health care system continues to absorb greater costs for total hip arthroplasty (THA). The purpose of this study is to quantify inpatient charges for primary THA (pTHA) and revision THA (rTHA) and to review trends over the past decade. A New York statewide database was utilized to identify all inpatient elective pTHA and rTHA from 2010 to 2020. Nonelective indications were excluded. The primary outcome measure was total inpatient charges per encounter. Secondary outcomes included: accommodation charges, ancillary charges, and hospital THA volume. Subgroup analyses were performed based on rTHA indication and hospital volume. Total 256,879 pTHAs and 29,852 rTHAs were identified; 6,616 rTHAs were performed for instability, 5,976 for aseptic loosening, 5,155 for mechanical complications, 4,343 for infection, 3,014 for periprosthetic fracture, 1,427 for polyethylene wear, and 3,321 for other indications. Compared with pTHA, there were higher total, accommodation, and ancillary charges in the rTHA cohort (p < 0.001). High-volume centers achieved lower total charges for rTHA, lower accommodation charges for both cohorts, but higher ancillary charges for pTHA (p < 0.001). Between 2010 and 2020, total charges increased 46.6 and 76.2% for pTHA and rTHA, respectively. Inpatient costs of rTHA were significantly higher than that of pTHA. For rTHA, charges were highest for periprosthetic fracture and infection. Total inpatient charges of THA have greatly outpaced inflation over the past decade.
Sustainable agricultural practices in South Asia: A comprehensive review
Authors: Panchami L.R. , Gudi Nachiket , Patil Divya Sussana 
Published: November 2023

Abstract
Overpopulation is a root cause that impedes food production and supply to the continuously growing population. Since agriculture is the primary source of food production, producers are inclined towards various unsustainable agriculture practices contributing to climate change. Therefore, sustainable agricultural practices have gained the spotlight to reduce the risk of climate change and increase crop yields. Specifically, South Asian countries take a significant hit with respect to agriculture and climate change because much of the population is earning their livelihood through agriculture. We conducted a comprehensive review to map different sustainable agriculture practices and understand how these practices impact climate change, the livelihood of farmers, and food security in South Asia. A systematic search carried out on PubMed (NCBI) and Scopus (Elsevier), resulted in the inclusion of 42 articles for analysis. The review identified agroforestry and organic farming as the most commonly used practices in the South Asian region, followed by zero tillage practices, use of hybrid seeds, residue and integrated pest management, and cropping systems. These sustainable agriculture practices ensure that sufficient food can be produced to feed the rising population with efficient use of natural resources. In addition, sustainable agriculture gains economic profitability for farmers and improved employment opportunities for women. Since sustainable agricultural practices are the best way forward, we recommend encouraging it by incentivizing the farmers. Due to contextual variations, future research can focus on identifying best practices for specific contexts.
UAV Swarm Formation Control Based on Disturbance Observer and Backstepping Controller
Authors: Zhao Hongchao , Zhao Jianzhong , Dong Xuexia 
Published: November 2023

Abstract
The formation control problem was researched for a fixed-wing unmanned aerial vehicle (UAV) swarm. The kinematic model was built for a UAV considering the lumped disturbances. The disturbance observers were designed to estimate the lumped disturbances in finite time. The desired UAV swarm formation was represented by a virtual structure. The backstepping controller was designed for every UAV to complete the formation maintaining a task. The sigmoid tracking differentiator (STD) was added to the backstepping controller, in order to settle the problem of “explosion of complexity”. The numerical simulation was executed to show the formation maintaining procedure of the UAV swarm. The simulation results demonstrate that the disturbance observer has good disturbance rejection capability and the backstepping controller based on the STD has good formation maintaining performance.
Machine Learning-based Investigation of the Influencing Factors on the Hardness of Steel Materials
Authors: Wu Shaoxiong , Wang Shiyu , Xie Daoxin , Zhu Shun-an , Li Junjie 
Published: November 2023

Abstract
The hardness of steel is often regarded as a crucial indicator of its production and application. The influences of chemical composition and process parameters on the hardness of steel exhibit strong non-linearity, making it challenging to accurately predict hardness through traditional multivariate regression or orthogonal experiments. Although machine learning has achieved distinguished success in diverse applications, its use in studying Metal materials has emerged only recently. Inspired by Sunčana et al.’s work, we select the Jominy distance as an input variable for the chemical composition, and instead of his subtly finding the best artificial neural networks, we simply use Generalized Regression Neural Network (GRNN) and due to the heterogeneity of the data, a second-order clustering method is employed as a data pre-processing step. The predicted hardness values were obtained using leave-one-out cross-validation, and the optimal smoothing factor (spread) was selected based on the Root Mean Square Error (RMSE) criterion. The optimal prediction results showed that in Configuration I, the RMSEs of the two types of data were 62.41 HV and 20.51 HV, respectively, while in Configuration II, they were 66.38 HV and 29.51 HV. Results showed Configuration I was more successful than Configuration II. This research presents a novel approach for predicting the hardness of metal materials using GRNN which is designed to address the need for quick adaptation to changes in design methods and the increasing demand for high-quality manufactured products.
Ballet Form Training Based on MediaPipe Body Posture Monitoring
Authors: Lei Miaowen , Wang Zuxuan , Chen Fang 
Published: November 2023

Abstract
People are increasingly turning to the cloud in the context of “healthy China” to engage in online exercise. The use of artificial intelligence technology to address broad population health-related challenges has become increasingly important as information technology has matured. The MediaPipe artificial intelligence framework, which Google recently released, is used in this article to optimize video feedback and support the “cloud movement” of widespread home ballet instruction in order to examine the effects of digital technology-enabled ballet training on the general improvement of physical health. Based on the experiment’s findings, trainers can use MediaPipe video feedback as an additional method of “cloud movement” training in public homes. This allows trainers to visually reflect on any issues that arise during the exercise process and to promptly modify training according to motion monitoring in order to reduce the risk of physical injury brought on by improper motion training. It is possible to envision a day in the future when video feedback built on the MediaPipe architecture would combine artificial intelligence with exercise training to achieve training objectives that enhance the accuracy of trainers’ motions and enhance physical balance.
A novel quantum genetic algorithm with the application in LS-SVR
Authors: Xiang Hao 
Published: November 2023

Abstract
The characteristics of LS-SVR are analyzed. LS-SVR is fitted for modeling small samples and high dimensional data, but the performance of LS-SVR is related to the specific data distribution, the kind of kernel function, its related kernel parameter, and the penalty coefficient. In this paper, the radial basis function is applied as the kernel function of LS-SVR, and the real double-chain coding target gradient quantum genetic algorithm (DCQGA) is applied to optimize the kernel parameter and penalty item coefficient of LS-SVR, then the regression prediction model DCQGALSSVR is proposed. It is of great significance to build an accurate and reliable fault prediction model for the health monitoring and fault diagnosis of liquid rocket engines. The thrust of a liquid rocket engine is an important factor in its health monitoring. By predicting the thrust change value and comparing the predicted value with the engine thrust threshold, it can be predicted whether the engine will fail at a certain time. In this paper, the proposed DCQGALSSVR model is used to model the thrust of a liquid rocket engine. The simulation results show that the average relative error is 0.37% using LSSVR for modeling on 12 test samples, and is 0.3186% using DCQGALSSVR on the same samples. It can be seen that DCQGALSSVR is effective for the health monitoring of liquid rocket engines, so it has a certain promotion significance.
DualGGAN: A New Facial Style Transfer Network
Authors: Zhao Yu , Peng Chunyan , Zhang Xiaojuan 
Published: November 2023

Abstract
In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encoding is proposed. The network is trained using the least squares generative adversarial network, and the neural network is used as the image feature extractor to generate feature maps to obtain facial image features with attention weights from the feature maps, utilizing relative position encoding and mask loss to jointly constrain feature region style transfer. The experimental results show that the DualGGAN network effectively reduces artifact generation when implementing facial style transfer, maintains good background consistency, and has good generalization ability. Experiments have shown that the FID and KID indicators on the cat2dog and older2adult datasets are significantly improved compared to other algorithms.
Fast-DecoupledNet: An Improved Multi-branch Edge Enhanced Semantic Segmentation Network
Authors: Xue Junyu , Zhang Zhiyuan 
Published: November 2023

Abstract
There are existing semantic segmentation methods that incorporate the idea of edge detection by using multi-branch networks to focus on edges and subjects separately, but there are a large number of unfocused aspects and limited improvement. In this paper, we suggest the Fast-DecoupledNet, a semantic segmentation network. We design Edge Feature Extractor to extract the target’s edge features more accurately, and the global features obtained by joint downsampling are computed to obtain the subject features and the final features. In addition, we employ a shallower ResNet as the backbone network to reduce computational complexity while ensuring computational accuracy. Our proposed methods achieve the state-of-the-art 72.59 F-score and 77.64% mIoU on the Deepglobe Land Cover Classification dataset.
Separability Measure Supervised Network for Radar Target Recognition
Authors: Xue Lingyan , Zhang Xinyu , Qiu Xiangfeng , Jiang Weidong 
Published: November 2023

Abstract
In the radar automatic target recognition (RATR) field, radar high-resolution range profiles (HRRPs) have garnered significant attention. While traditional methods focus on extracting features having physical explanations, including power spectra, FFT magnitudes, etc, the effectiveness of these features relies heavily on personal experience and skills. In contrast, deep learning networks have shown strong competence in extracting discriminative features of HRRPs. However, the deep learning networks’ feature extraction procedure is solely based on the targets’ label information, which has almost no correlation with the feature separability. As a result, this approach can lead to poor convergence and limited recognition performance. To address this issue, we propose a Separability Measure Supervised Network (SMSN), which integrates a separability measure based on the rate-distortion function into the loss function to direct the training of the network. Comparative experiments on the airplane electromagnetic simulation HRRP dataset demonstrate that SMSN achieves higher recognition accuracy compared to the backbone networks, with significantly improved feature separability.
Resource allocation algorithm in Heterogeneous Network Based on Non-orthogonal Multiple Access
Authors: Wu Guowei , Chen Guifen , Zhu Tong 
Published: November 2023

Abstract
The proposed resource allocation algorithm with joint power control and user pairing addresses the problem of degraded uplink user fairness due to cross-cell interference in NOMA uplink and improves user fairness. In the power control section, by decoupling power control from user pairing, the WOA algorithm is proposed in the statistical channel state scenario to solve the nonconvex optimization problem of intra-user pair fairness; in the user pairing section, the PTS algorithm is proposed to introduce randomness into the algorithm to improve the algorithm’s global search performance, solve the local optimum problem of integer programming, and improve inter-user pair fairness. Simulation results show that relative to the α-fairness comparison algorithm, the algorithm in this paper can improve fairness by about 5% under different interfering user densities, cell coverage radius, and tiny area transmit power.
Myocardial Segmentation Algorithm of U-Net Network Based on Cardiac Ultrasound Images
Authors: Yang Wang , Li Xin , Li Yan , Hou Haoxiong , Chen Zuxing 
Published: November 2023

Abstract
The myocardial state is always regarded as an important basis for identifying cardiac diseases. In order to assist physicians in diagnosis in an accurate manner, this paper proposes myocardial segmentation using a U-Net network based on cardiac ultrasound images. Firstly, we collected a large amount of clinical data and employed professional cardiac ultrasound imaging physicians to mark the myocardial regions as the gold standard. Then, we built an optimized U-Net network to establish the relationship between images and semantics to extract original image features. Finally, a newly fused loss function for training the network is created. According to the experiments, it shows that the accuracy, precision, and recall rate of U-Net indexes proposed in this paper reaches more than 96%, and MIOU more than 94%, which can effectively assist doctors in diagnosis in an accurate manner.
A Novel Channel-attention-based Dense Network for Gas Recognition in Complex Airflow Environments
Authors: Li Fei , Li Yiran , Zhao Wenlong , Peng Xiaoyan 
Published: November 2023

Abstract
An electronic nose is a commonly used technology for gas detection. However, due to the complex diffusion mechanism of gaseous chemical analytes in the natural environment, the gas often exhibits irregular turbulent flow patterns. This variability results in different responses to the same gas in the e-nose, which presents significant challenges in the gas identification tasks. In this study, we propose an efficient method for gas recognition by combining a Dense Convolutional Network (DenseNet) with an Efficient Channel Attention Network (ECANet), which uses one-dimensional convolutional neural networks to improve the capability of extracting sequence signals. We evaluate the proposed method using an open-source dataset and observe that it outperforms the best current methods available, including the ResNet, Long Short-term Memory Network (LSTM) networks, and Gate Recurrent Unit (GRU) networks, with a classification accuracy of 99.8%.