bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of the Association of Pelvic Floor Muscles and Inspiratory Muscles Training in Women With Stress Urinary Incontinence Resulting From Chronic Obstructive Pulmonary Disease: Protocol Study
Authors: Dourado Tamires Santos Barbosa , Santos João Lucas Peres dos , de Gouveia Guilherme Pertinni Pertinni 
Published: October 2024

Abstract

Stress Urinary Incontinence is a prevalent issue among women with Chronic Obstructive Pulmonary Disease (COPD), primarily due to the increased intra-abdominal pressure from chronic coughing. This study proposes a novel protocol combining Pelvic Floor Muscle Training (PFMT) and Inspiratory Muscle Training (IMT) to address SUI in women with COPD. The research aims to evaluate the effectiveness of this combined intervention in reducing urinary leakage and dyspnea, thus improving the quality of life for these patients. The study is a randomized, double-blind clinical trial with 64 female participants, aged 18 to 65, who have both COPD and SUI. Participants are divided into an intervention group and a control group, with assessments conducted before and after a five-week treatment period. The primary outcome measured is the daily incidence of urine leakage episodes, while secondary outcomes include functional capacity tests, muscle strength measurements, and various clinical and sociodemographic profiles. Interventions include low to moderate intensity resistance exercises for inspiratory muscles and pelvic floor muscles, progressively increased based on patient progress. The intervention group undergoes IMT with the PowerBreathe device at 50% of their maximum inspiratory pressure, while the control group is set at 10% without modifications. The expected outcomes are a significant reduction in urinary leakage and improved respiratory function, highlighting the potential of this non-invasive and comfortable intervention. The study aims to fill a gap in the literature by providing evidence on the efficacy of combined PFMT and IMT in treating SUI in women with COPD, thereby offering new insights and tools for clinical practice and future research.
Modeling Regional Determinants of Early Motherhood in Colombia and its impact during COVID-19
Authors: Davila Fabian Antonio , Cala-Viteri Favio Ernesto 
Published: October 2024

Abstract

Introduction
Early motherhood, defined as occurring in women under 19, poses significant social challenges with immediate and long-term consequences. This study focuses on early fertility rates (early motherhood) in Colombia, examining their relationship with social and demographic factors.
Methods
Departmental-level data from Colombia's Social Protection Information System (RUAF) and the National Administrative Department of Statistics (DANE) were cleaned and aggregated from 2018 to 2023. This allowed the estimation of the overall fertility rate, infant fertility rate, and adolescent fertility rate by department and year. A model was conducted to estimate the influence of recognized risk factors such as low educational attainment (% of under-educated population), poverty level (% of population under subsidized health schemes, current GDP), employment rates (% of workforce participation), violence against women (% of female deaths, both general and violent), and infant mortality. The model was refined to improve performance by removing variables with low contributions to early motherhood.
Results
Fertility indicators focusing on early fertility in Colombia were described. Regions that should be prioritized for early fertility prevention strategies were identified because of their increase in the early pregnancy rate during the pandemic. Significant risk factors found included the percentage of the population under subsidized health schemes, GDP per capita, %violent deaths, % of female deaths, and % deaths among < 18 years of age.
Conclusions
we identified regions that should be prioritized for early fertility prevention strategies, highlighting areas particularly vulnerable that increased early fertility rates during the pandemic. These findings reveal the effects of relaxed public health measures alongside social and demographic factors needed for targeted interventions. Further research is necessary to quantify the impact of specific interventions on early fertility rates.
Obstetric outcomes of women undergoing office hysteroscopy in infertility conditions: a retrospective cohort study
Authors: Gawron Iwona , Zmaczynski Andrzej , Szwech Julia , Szmigiel-Soja Agata , Jach Robert 
Published: October 2024

Abstract

Purpose
To present and compare obstetric outcomes in women undergoing office hysteroscopy (OH) during the diagnostic evaluation and ineffective treatment of infertility due to various underlying factors.
Methods
The retrospective study included women experiencing infertility of diverse type and etiology. Obstetric outcomes, including rates of live births, clinical and biochemical pregnancies, and time to conception, were compared within four preoperative diagnosis groups (idiopathic infertility, uterine polyp, uterine factors other than polyps, and other conditions) and four postoperative diagnosis groups (uterine polyp, clinical features of chronic endometritis (CE), uterine factors, and other findings), taking into account the status of chronic endometritis.
Results
Among preoperative diagnoses, women undergoing hysteroscopy for recurrent miscarriages had the highest percentage of pregnancies, while those with polyps had the lowest (p = 0.012). With a positive CE status and empirical antibiotic treatment, the highest pregnancy rate was in women with idiopathic infertility, while the lowest was in those with polyps (p = 0.011). Regarding hysteroscopic findings, the highest percentage of achieved pregnancies occurred in women undergoing resection of a uterine septum or intrauterine adhesions, while the lowest was observed in those following polypectomy (p = 0.036). No differences in obstetric outcomes were noted concerning CE status. None of the hysteroscopic findings were independent prognostic factors for live birth.
Conclusion
In planning hysteroscopy for idiopathic infertility, verifying CE status is recommended to facilitate treatment and improve obstetric outcomes if positive. The removal of a uterine septum or intrauterine adhesions enhances the rate of achieved pregnancies, regardless CE status.
Contrasting Differences in the Utility of Green Space Accessibility of Major Large-Scale Cities
Authors: Xu Caixia , Zhao Jiawen , Zhang Yuxiang , Xiang Yutong , Feng Wei , Zhou Chan , Sun Liqun 
Published: October 2024

Abstract

One of the Sustainable Development Goals is to provide universal access to green space for urban residents. Nevertheless, whether city dwellers can readily access green space remains unanswered physically and mentally. In this study, we introduce a novel concept, the utility of green space accessibility (GSAU), which integrates three existing indices: green space accessibility (GSA), green space accessibility inequality (GSAI), and travel aversion index (TAI). These indices assess the ease with which residents of 30 major Chinese cities can access green space, considering physical and mental terms. The study identifies contrasting differences in the GSAU between Chinese northern and southern zones. When the willingness of residents to travel is not considered, the cities in the eastern zone have higher GSA and greater GSAI compared to those in the western region. However, when considering the travel willingness of residents, these cities show a clear north-south geographical distribution the number of cities in the southern region with a GSAU higher than 0.6 (including ideal, annual, summer, and winter GSAU) is approximately twice that of northern cities. These findings highlight the need to consider residents' subjective travel willingness for green space based on environmental quality justice.
A novel initial weld point guidance and path planning method for robot welding based on structured light vision
Authors: Wang Tianqi , Li Qiaochu , He Junjie , Li Xiao 
Published: October 2024

Abstract

Aiming at the problems of low efficiency and poor adaptability of teaching-and-playback and offline programming. This paper proposed a method for positioning initial weld point (IWP) and planning welding trajectory based on structured light vision. Firstly, a camera is fixed at the end of the robot to capture images of the welding workpiece from two specific positions. An improved Yolov8 algorithm is employed to recognize IWP, achieving approximate positioning of IWP with "monocular vision dual-position shooting." Subsequently, the position of the camera relative to IWP is determined based on the structured light plane. Image processing is then conducted to extract the structured light feature points, and the feature points are fitted to obtain groove planes and weld seam line, thereby planning the overall welding pose. Experimental results demonstrate that this method can effectively complete the recognition of IWP, the extraction of weld seams, and trajectory planning tasks, which is of positive significance in improving the welding efficiency and adaptive ability of robots in various welding environments and workpiece conditions.
CNEKA: An Algorithm for SDN Controller Placement based on Graph Convolutional Networks
Authors: Rao Yirui , Chen Jue , Qiu Xihe , Wang Lizhi , Tao Hongyu 
Published: October 2024

Abstract

Software-Defined Networking (SDN) is an emerging network architecture characterized by the decoupling of the data plane from the control plane, as well as managing the whole network in a centralized logic. As the scale of networks continues to expand, the single-controller architecture is no longer able to meet the performance and reliability requirements of the network. Consequently, a logically centralized while physically distributed multi-controller architecture has been proposed, in which the number and locations of controllers must be determined, formulating the Controller Placement Problem (CPP). In order to solve the CPP and optimize the propagation latency, we propose a Convolutional Node Embedding and K-means Algorithm (CNEKA), which integrates Graph Convolutional Networks (GCN) with the K-means algorithm. More specifically, we adopt the principles of GCN to facilitate mutual information propagation among adjacent nodes, and then use the K-means algorithm to achieve graph segmentation through the low-dimensional embedding vectors calculated by GCN. The study demonstrates that the CNEKA algorithm significantly enhances performance in optimizing average and worst-case latency between controllers and switches, as well as overall network latency, particularly under high controller counts. The algorithm achieves a mere 1.10% deviation from the global optimum in average controller-switch latency, underscoring its high efficacy.
Financial Time Series Forecasting Based on Adversarial Training and Dynamic Weight Design
Authors: Liu Xiang , Ye Zheng , Ge Jun , Cheng Yu 
Published: October 2024

Abstract

To address the challenges of financial market instability, high data correlation , anomalies, and noise, traditional adversarial training methods enhance model robustness and nonlinear modeling capabilities by introducing adversarial samples. However, current research primarily relies on single models and singular adversarial methods, which limits the diversity and adaptability of these approaches. Additionally, perturbing only the output layer overlooks the dynamic characteristics present in various layers of the model. To overcome these shortcomings , this paper proposes a strategy that integrates multiple adversarial methods into different deep learning models, combined with a two-stage training and dynamic weight adjustment. In the first stage, the model identifies fundamental patterns by training on the original dataset. In the second stage, adversarial training is introduced to enhance the model’s ability to cope with complex market conditions. Furthermore, the introduction of dynamic weight parameters allows the model to flexibly adjust the weights of the two stages, thereby improving its adaptability to the characteristics of financial data. This paper also introduces two novel adversarial methods: the Hessian perturbation method and the return volatility perturbation method. These methods not only strengthen the model’s nonlinear modeling capabilities but also enhance its adaptability and generalization ability. Experimental results demonstrate that, on the CSI100 dataset, our proposed comprehensive strategy significantly outperforms baseline models in terms of annualized return, information ratio, and maximum drawdown.This achievement not only validates the effectiveness of adversarial methods in predicting financial time series but also illustrates the contribution of the two-stage training framework in enhancing adversarial training, thereby improving the model's adaptability under complex market conditions. Additionally, the introduction of dynamic weight parameters enables the model to flexibly adjust the focus of each stage, allowing for a more effective response to the dynamic characteristics of financial data.
A comparative analysis of DETR and Conditional DETR for identifying smoking scenes
Authors: Utama Ferzha Putra , Vatresia Arie , Mahendra Robby 
Published: October 2024

Abstract

Smoking remains a significant global public health concern, leading to over 8 million deaths annually and significantly increasing the risk of various diseases. Despite the effectiveness of policies like banning smoking scenes in media and implementing public smoking bans in controlling smoking behaviors, the proliferation of digital media presents new challenges in managing exposure to smoking-related content. In response to these challenges, this study introduces an automated solution that leverages advanced computer vision techniques, specifically utilizing DETR and Conditional DETR models with ResNet-50 backbones, to detect and apply Gaussian blur to smoking scenes in videos. The performance of these models was rigorously evaluated using mean Average Precision (mAP) metrics across datasets containing 4,000, 3,000, and 2,000 images. The results demonstrated that the DETR model achieved the highest AP of 58.5 with the 4000-image dataset, surpassing the Conditional DETR model, which achieved an AP of 55.0 with the 3000-image dataset. Furthermore, in video processing tasks, the DETR model exhibited superior efficiency, with an average processing time of 56.8 seconds, compared to Conditional DETR's 59.2 seconds. The implementation of Gaussian blur effectively obscured smoking activities in the video content, preserving the overall context while significantly reducing the visibility of harmful content. These findings underscore the advantages of the DETR model in terms of both detection speed and accuracy, particularly when applied to larger datasets. This study highlights the potential of DETR in enhancing automated content moderation tools, contributing to the ongoing efforts to mitigate the public health risks associated with smoking in the digital age.
Atmospheric dispersion downstream a two-dimensional obstacle: experimental evaluation of turbulence closure models
Authors: Salizzoni Pietro , Fellini Sofia , Gamel Hervé , Marro Massimo , Soulhac Lionel 
Published: October 2024

Abstract

This study investigates the turbulent dispersion of pollutants in the wake of a two-dimensional square obstacle, focusing on assessing the reliability of turbulence closure models used in atmospheric pollutant dispersion. Utilizing Laser Doppler Anemometry (LDA) and Particle Image Velocimetry (PIV), we characterized the flow dynamics, identifying a recirculation zone with an average length of 6H downstream of the obstacle. This zone, characterized by high shear and increased turbulent viscosity, plays a crucial role in turbulent momentum exchange. We evaluated the turbulent kinetic energy (t.k.e.) budget, estimating its dissipation rate ($\varepsilon$), and found traditional isotropy and Taylor hypothesis methods inadequate within the wake region.
Furthermore, we explored pollutant dispersion from a linear source located downstream. Analysis of mean concentration and variance revealed that the log-normal distribution is most effective for modeling concentrations within the recirculating region, while the Gamma distribution suits areas outside it. Testing various closure models for turbulent mass fluxes highlighted the limitations of the Simplified Gradient Diffusion Hypothesis model, favoring more complex closure models for longitudinal trends, though these still faced challenges with intensity estimation. The Simplified Gradient Diffusion Hypothesis model proved robust for vertical mass fluxes, with satisfactory results in turbulent diffusivity and turbulent Schmidt number calculations.
Our findings underscore the necessity for improved spatial resolution in measurements near pollutant sources, particularly between 0.5H and 2H downstream. The experimental results allowed us to assess the reliability of a wide variety of parameterizations adopted in Reynolds-Averaged Navier-Stokes (RANS) formulations and within pollutant dispersion models. This database serves as a benchmark to test the accuracy of more sophisticated modeling approaches such as Large Eddy Simulations (LES) and Direct Numerical Simulations (DNS).
Public Sector Job Guarantees and Family Planning Choices in Rural India
Authors: Kishore Siddharth , Manning Dale T. 
Published: October 2024

Abstract

We exploit the staggered timing of an employment guarantee program in rural India to investigate how providing work opportunities affects married women’s use of family planning methods. Using survey data from rural India, we employ a difference-in-differences strategy and inverse-probability-of-treatment weighting to control for confounding factors influencing family planning decisions. Results suggest an increase of 1.8 percentage points (a 3.2% increase from the variable’s sample mean) in the use of modern family planning methods among married women after the introduction of an employment guarantee program. Results are robust to the choice of controls, regression weights, and matching method. We also demonstrate that program impacts increase with the amount of time a workfare program has been in place. These results provide empirical evidence that employment guarantees that include women can affect family planning choices, with important implications for economic development outcomes.
Small Traffic Sign Detection Algorithm Based on Multi-Scale Feature Fusion
Authors: Zhang Jing , Du Xiaoyu , Zhou Ye , Tan Yajun 
Published: October 2024

Abstract

In autonomous driving, traffic sign detection is easily affected by environmental lighting, changes in target size, and complex backgrounds. To address these issues, this paper proposes a multi-scale traffic sign detection algorithm, SS-YOLO (Small Sign). This method enhances multi-scale feature fusion by generating additional high-resolution feature layers and optimizes small object detection by removing the large-object detection head. At the same time, the C3 module is replaced with the more efficient C3-DS module, and the EMA attention mechanism is introduced to improve the ability to capture channel information. Additionally, the DySample upsampling operator is used in the feature fusion layer to increase the receptive field and optimize feature fusion. Experimental results show that SS-YOLO achieves an average precision (mAP) of 86.8% on the CCTSDB 2021 dataset, a 10.5% improvement over YOLOv5s, while reducing the number of parameters by 23.23%, achieving both fewer parameters and higher detection accuracy.
Optimized 3-D Chaotic Map for Compressed Image Encryption for Secure Communication
Authors: Tiwari Akshat , Diwan Prachi , Diwan Tarun Dhar 
Published: October 2024

Abstract

In today’s digital world, there is a significant increase in data transfer online especially in multimedia form. This online transmission faces many cyber threats such as denial-of-service attacks, phishing, man-in-the-middle attacks, etc. These threats result in potential security risks and raise the need for secure communication. In this paper, a chaotic map-based encryption technique is presented. The proposed algorithm presents optimized parameter-based 3D chaotic maps for image encryption. The method integrates three primary components: stacked autoencoder (SAE), optimized parameter-based chaotic mapping, and encryption/decryption module, to ensure robust and secure encryption of images. The result evaluated the proposed secure image encryption algorithm with a randomness test, pixel adjacency correlation test, and differential analysis. The mean entropy was approx. 7.9 and the mean number of pixels changing rate (NPCR) was approx. 99.8, unified average changing intensity (UACI) was approx. 33.46. Moreover, the proposed algorithm also investigated the result under noise attacks and shows better cryptanalysis results as compared to comparative state-of-art models.
Causal roles of gut microbiota in OS etiology suggested by genetic study
Authors: Wei Wei , Zhou Zihuan , Li Xiaolin , Wu Zhipeng , Xiao Jianru , Wei Haifeng 
Published: October 2024

Abstract

Objectives
Osteosarcoma (OS) represents a prevalent primary malignant bone tumor with limited treatment options at present. The relationship between gut microbiota and OS remains ambiguous, with uncertainty surrounding whether this association is causal or influenced by bias. To investigate the potential link between gut microbiota and the development of OS, we conducted a two-sample Mendelian randomization (MR) analysis.
Methods
We employed a two-sample MR study design to elucidate the causal association between gut microbiota and OS. Our analysis encompassed a total of 196 bacterial features across five taxonomic levels: Phylum, Class, Order, Family, and Genus. The MR analysis incorporated the inverse-variance-weighted (IVW), weighted median, MR-Egger, weighted mode, and simple median methods. The primary analysis was conducted using the IVW method, and the MR results were validated through heterogeneity tests, sensitivity analysis, and pleiotropy analysis.
Results
Our findings revealed significant associations between nine distinct bacterial traits and OS using the IVW method. Specifically, we identified that genus. Odoribacter.id.952 (OR: 15.19; 95% CI: 2.08, 111.18; p = 0.007) and genus.Clostridiumsensustricto1.id.1873 (OR: 10.78; 95% CI: 1.78, 65.32; p = 0.009) were associated with a significantly increased risk of OS. Conversely, genus.LachnospiraceaeUCG001.id.11321 (OR: 0.21; 95% CI: 0.06, 0.70; p = 0.011) was found to significantly decrease the risk of OS.
Conclusions
Research suggests specific gut microbiota affect OS risk, highlighting their role in disease development and paving the way for new OS treatment strategies.