bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facile synthesis of Samarium (Sm 3+ ) doped Cobalt-iron oxide nano ferrite as an advanced electrode material for supercapacitor applications
Authors: Khasim Syed , PASHA APSAR , BN Ramakrishna , B.S Prathibha , P.R Koushalya 
Published: April 2024

Abstract
Herein, we present the design and fabrication of samarium (Sm3+) doped cobalt-iron oxide ferrites nanocomposites for utilization as an efficient energy storage material. We have employed a simple, low cost and quick one step solution combustion method used to synthesize CoFe2 − xSmxO4 (x = 0.0, 0.050, 0.075 and 0.1) ferrites composites. The synthesized CoFe2 − xSmxO4 NPs undergo different analytical and spectroscopic characterizations methods like scanning electron microscope (SEM), energy dispersive X-ray spectroscopy (EDS), transmission electron microscope (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and ultraviolet visible (UV-visible) analytical and spectroscopic methods that used to confirm the morphological and structural properties of the synthesized NPs. The electrochemical properties synthesized ferrites composites were significantly improved after inclusion of rare earth (RE) metal such as samaniuim (Sm3+) nanoparticles (NPs) into the host cobalt-iron-oxide. It was notice that the creation of single phase in pure CoFe2 − xSmxO4 ferrite remains unaltered by the mechanism of doping even in the ferrites composite. Nevertheless, doping of RE metal significantly influences over the morphological and structural properties, further more enhancement in the electrochemical performance of samarium doped CoFe2 − xSmxO4 ferrite composite. The highest specific capacity about 850 F/g was achieved for CoFe2 − xSmxO4 (x = 0.1) composite electrode material, which shows more superior in compare to pure CoFe2 − xSmxO4 (x = 0) which is about 340 F/g. However, CoFe2 − xSmxO4 (x = 0.1) composite shows a superior capacitance retention of the order of 98% even after 5000 cycles of operation at a scan rate of 250 mV/s. The electrode material fabricated by using CoFe2 − xSmxO4 ferrite composites behave as positive electrode and at the same time activated nickel behave as negative electrode which is render an energy density of 30.16 Wh/kg at a power density of 400 Wh/kg. The results obtained in presented studies offer a hopeful way for the fabrication high-performance electrode material for supercapacitor which is more suitable for light weight electronic devices, electric vehicles, and forthcoming generation supercapacitor applications.
Livestock farmer-reported knowledge and attitudes regarding agroforestry planning and management
Authors: De-Sousa Karolini Tenffen , Wright Melanie , Cárdenas Laura , Deniz Matheus , Dittrich João Ricardo , Hötzel Maria José , Enriquez-Hidalgo Daniel 
Published: April 2024

Abstract

This study aimed to explore the knowledge and attitudes of livestock farmers from the United Kingdom regarding agroforestry planning and management issues. The farmers (n = 48) answered an online survey with demographic, open, closed and Likert scale questions. Almost half of the participants said they need more information to successfully plan and manage an agroforestry, and self-reported low knowledge on management practices related to trees. Participants stated they did not expect getting technical support from governmental agencies to maintain the agroforestry area. However, they would like to improve their knowledge trough field days, courses, and Internet sources. Benefits to the environment, animals and farm profitability were considered central to successful agroforestry systems. In conclusion, participants cannot successfully plan and manage agroforestry, but they are willing to improve their knowledge and skills.
Identification of Groundwater recharge zone in peri urban watershed with the help of MAUT and MEREC techniques
Authors: Begam Sadria , Majumder Mrinmoy 
Published: April 2024

Abstract

A catchment area that is neither rural nor urbanised is referred to as a peri-urban watershed. These basins have unique hydrological and geophysical features that interact directly with the local water demand. In addition to becoming scarce in terms of useable surface water, ground water is gradually but steadily getting less in many parts of the world. As a consequence, many heavily urbanised metropolises are experiencing a severe water scarcity, while many rural areas are rapidly becoming desert due to unchecked resource extraction. The issue pertaining to peri-urban watersheds is distinct in that the population that depends on them is neither very huge nor small, resulting in a moderate water scarcity. Their interactions are becoming more and more significant as a result of the little urban colonies that are sprouting up in response to the fierce competition for the existing resources that is seen in metropolitan settings. As a result, many rural regions are being transformed into periurban areas. Now, in order to meet the water needs of the periurban population, it is crucial to determine the ground water recharge zone. However, because of the physical makeup of the watersheds, each element plays a more significant role in determining the probable ground water recharge zones. In order to anticipate the significance of each element and, with the aid of the results, identify the probable GWR zones, the current work attempts to utilize novel MCDM techniques like MAUT and MEREC. Previous research has attempted to detect GWR using MCDM and GIS approaches; however, the focus of these studies was not on periurban watersheds, but rather on micro, meso, or macro level basins. Furthermore, since the application of MEREC and MAUT for these objectives has never been tried, the current study is novel and can assist engineers and urban planners in developing plans for sustainable development that will guarantee that residents in periurban watersheds experience no water stress.
A case-control study based on the National Health and Nutrition Examination Survey to evaluate the effects of human papilloma virus on bone health in women
Authors: Li Xiang , Jiao Guangjun , Chen Yunzhen 
Published: April 2024

Abstract

Background
Human papillomavirus (HPV) infection and osteoporosis (OP) are global health concerns, with higher prevalence observed in women compared to men. However, the impact of HPV infection on bone health remains uncertain.
Methods
This case-control study utilized data from the National Health and Nutrition Examination Survey (NHANES). Comparable datasets were created using nearest neighbor propensity score matching (PSM) at a ratio of 1:1. The association between HPV infection and bone mineral density (BMD) was analyzed using the Welch two-sample t-test. Furthermore, linear mixed models were employed for validation purposes. Restricted cubic spline (RCS) analysis and Kendall's tau-b tests were performed to explore the effect of different types of HPV infection on BMD.
Results
Individuals with HPV infection had lower BMD in the lower limbs and lumbar spine compared to uninfected individuals. RCS analysis revealed that an increasing number of cooccurring HPV types in women was associated with lower BMD. Specifically, four HPV types were negatively associated with lower limb BMD, while 14 HPV types were negatively associated with lumbar spine BMD. Additionally, HPV types 53, 59, and 89 exhibited effects on both lower limb and lumbar spine BMD.
Conclusions
HPV infection is associated with a decrease in BMD, and co-infection with multiple types of HPV implies even lower BMD. Appropriately designed trials are needed to determine if interventions targeted at preventing HPV infection can have a protective effect on BMD.
Fast No-reference Deep Image Dehazing
Authors: Qin Hongyi , Belyaev Alexander 
Published: April 2024

Abstract

This paper presents a deep learning method for image dehazing and clarification.The main advantages of the method are high computational speed and usingupaired image data for training. The method adapts the Zero-DCE approach for the image dehazing problem and uses high-order curves to adjust the dynamicrange of images and achieve dehazing. Training thhe proposed dehazing neuralnetwork does not require paired hazy and clear datasets but instead utilizes a setof loss functions, assessing the quality of dehazed images to drive the trainingprocess. Experiments on a large number of real world hazy images demonstratethat our proposed network effectively removes haze while preserving details andenhancing brightness. Furthermore, on an affordable GPU-equipped laptop, theprocessing speed can reach more than 1000 FPS for images with 2K resolution,making it highly suitable for real-time dehazing applications.
Data analysis and Forecasting of Tuberculosis and HIV co-infection: Exploring models from classical statistics to machine learning
Authors: Abade Andre , Porto Lucas Faria , Scholze Alessandro Rolim , Kuntath Daniely , Barros Nathan da Silva , Berra Thaís Zamboni , Ramos Antonio Carlos Vieira , Arcêncio Ricardo Alexandre , Alves Josilene Dália 
Published: April 2024

Abstract

Considering the complexity and severity of TB/HIV coinfection, accuracy in forecasting future trends is crucial for the efficient allocation of public health resources and the development of intervention strategies. This study explores the application of predictive models, ranging from classical statistical approaches to machine learning techniques, to analyze the time series of TB/HIV coinfection case notifications stratified for men, women, and the general population. Traditional models using Exponential Smoothing and Autoregressive Integrated Moving Average (ARIMA) methods were initially applied, providing a basis for understanding the overall temporal trend and the presence of seasonality. Subsequently, machine learning models, including Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Deep Neural Networks such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), were tested to find the best model to capture the complex dynamics of TB/HIV coinfection and the inherent non-linearities in the data. Performance evaluation, based on error metrics such as MSE, MAE, and sMAPE, revealed that Deep Learning models, especially Bidirectional LSTM and CNN combined with LSTM, significantly outperformed the classical statistical methods, demonstrating the effectiveness of these techniques for modeling TB/HIV coinfection time series and, consequently, providing more accurate forecast models.
Association between prolactin and history of stress fracture in elite sprinters and jumpers
Authors: Tsukahara Yuka , Torii Suguru , Taniguchi Yukiko , Kusakabe Torao , Murakami Hideki , Yamasawa Fumihiro , Akama Takao 
Published: April 2024

Abstract
Purpose Research related to stress fractures and the female athlete triad is abundant. However, since there is not enough evidence to support the risk factors for stress fractures is not sufficient. The aim of this study is to investigate the risk factor for stress fractures and the characteristics of the laboratory data of them in eumenorrheic female track and field athletes competing at an elite levelMethods Eumenorrheic sprinters and jumpers competing at a national level were recruited. Bone mineral density, bone metabolism markers, 25-OH Vitamin D, and hormones including prolactin and free testosterone were measured.Results A total of 84 high school and collegiate athletes were enrolled in the study, and among them, 33 athletes had a history of stress fracture after high school. Their personal background, bone mineral density, bone metabolism markers, 25-OH Vitamin D, and free testosterone were not different between the group with a history of stress fracture and without a history of stress fracture. Prolactin was higher in those with stress fractures compared to those without stress fractures, and it was statistically different between the two groups (15.70 ± 10.59 ng/mL and 11.59 ± 5.06 ng/mL, respectively, p = 0.019). Logistic regression analysis revealed that it was also a significant predictive factor for stress fractures (odds ratio: 1.10; 95% CI: 1.01–1.20; P = 0.023).Conclusion Prolactin may be linked to stress fractures in eumenorrheic sprinters and jumpers.
Research on textile Defect detection Algorithm for Deep Learning
Authors: Si Yachao , Gao Jiajie , Zhao Xingxuan 
Published: April 2024

Abstract
Textile defect detection is a crucial aspect of the textile industry. However, the detection accuracy is often low due to the small size of textile defects and the influence of fabric texture background. To address these issues, we propose a textile defect detection algorithm based on YOLOv8.The reparameterized structure was introduced to the original YOLOv8 to achieve hardware efficiency with the RepGhost module. The head section incorporates a new GD Mechanism and includes the Wasserstein Distance loss function.This method accurately identifies the location and type of defects in textiles with high precision under complex backgrounds, meeting the needs of industrial textile production. We conducted training and comparison experiments on different models using the AITEX public dataset and Roboflow open source dataset. We also designed ablation experiments to verify the considerable improvement of each enhancement on the evaluation index. The experimental results indicate that the detection accuracy of the enhanced YOLOv8 model in the task of detecting textile defects has increased by 18.9\% to 93.8\% on the public dataset. The number of parameters has increased by 10.94MB, and the FLOPs have increased from 8.2G to 11.3G. The improved algorithm has significantly enhanced the accuracy. It can effectively handle large amounts of data and recognize defects even under complex backgrounds, meeting the fast, complex, and high-precision requirements of the textile industry.
Establishment and validation of a nomogram model for predicting failed conversion of epidural labor analgesia to epidural surgical anesthesia in parturients undergoing intrapartum cesarean delivery
Authors: LI Zhiyue , Xinlu Fu , Dan Lu 
Published: April 2024

Abstract
Objective
The emergency conversion of epidural labor analgesia to intrapartum cesarean section anesthesia can lead to serious maternal and neonatal complication. This study aimed to establish a clinical predictive model to identify the risk of failed epidural conversion (FEC).
Methods
Clinical characteristics of 286 parturients who underwent conversion from epidural labor analgesia (ELA) in the Clinical Medical College of Yangzhou University were retrospectively collected. Univariate analysis and multivariate logistic regression were used to identify FEC risk factors. Risk factors were used to develop a predictive nomogram model. Area under the receiver-operating characteristic curve (AUC) calibration plots, and decision curve analysis (DCA) were used to assess the performance of the nomogram model.
Results
Independent predictors for FEC risk included ELA duration, non-obstetric anesthesiologist, visual analogue scores (VAS) within 2 h preceding cesarean section and size of the cervical orifice. The clinical prediction model was established based on the above four risk factors and showed superior predictive power both in training cohort (AUC = 0.876) and validation cohort (AUC = 0.839). The nomogram was well-calibrated. The decision curve analysis displayed that the FEC risk nomogram was clinically applicable.
Conclusions
The nomogram model can be used as a reliable and simple predictive tool for the identification of FEC, which will provide practical information for individualized treatment decisions.
High Speed Steel tool performance through hybrid texturing followed by shock peening towards sustainable machining
Authors: Prasad K. Nagendra , Ismail Syed , Satish M 
Published: April 2024

Abstract

Surface texturing is a promising sustainable technique to get better the machining performance of cutting tools. Laser surface texturing and micro-EDM are the most commonly used fabrication techniques and the textured tools exhibit better tool performance as compared to conventional tool. However, these processes involve re-solidification of material surface which makes the surface softening and reduction in the surface hardness. In the present work, cutting performance of the High-Speed Steel (HSS) tool is studied by fabricating hybrid (micro-grooves with dimples) textures on its rake face using both laser ablation and micro-EDM methods followed by laser shock peening without ablative coating (LSPWAC) process. Hybrid textured with shock peened HSS tools are used in orthogonal dry turning of 316 L stainless steel. It has been observed that hybrid textures with shock peened HSS tool exhibits higher tool life of up to 255% and 85% than the conventional and the hybrid textured (HT) tools respectively. The results shown that surface texturing followed by shock peening is an innovative method to improve the sustainability in machining process than only surface texturing method. Moreover, analysis of chip is studied by finite element analysis.
Accuracy assessment of old large-scale maps and reducing positional error in land use change analyses
Authors: Kratochvílová Darina , Cajthaml Jiří 
Published: April 2024

Abstract
Context Old large-scale maps are one of the main data sources on historic landscapes and form the basis of many landscape studies. However, few studies have addressed the issue of assessing the accuracy of map sources and the impact of this accuracy on the results of spatiotemporal analyses of landscape evolution.Objectives The purpose of this study was to verify the positional accuracy of large-scale maps used in landscape analyses and to test the possibility of eliminating the influence of mutual positional inconsistency of map sources on the results of this analysis. Narrow residual polygons, referred to as sliver polygons, arising during overlay operations because of positional errors in old maps can affect the results of the analysis, so it is appropriate to determine to what extent this happens, whether and when it is necessary to eliminate their influence and by what methods.Methods The positional accuracy of the vector models derived from old maps was verified in three model areas around the Vltava River by quantifying the mean positional error of a set of control points. Different methods for removing sliver polygons were proposed and tested for the selected test area within the model area by comparing the selected results of the spatiotemporal analysis.Results The achieved values of the mean positional errors for the historical data models from the mid-19th and mid-20th centuries are in the range of three to four metres for the model areas, which is highly accurate considering the scale values of the old maps used, confirming the suitability of these maps for landscape studies. The reverse vectorization of the time series of the maps eliminated the residual polygons due to positional error and thus reduced the false change areas, which was most evident in the change maps. The change maps after using this procedure better reflected the true changes. A method of identifying them based on their position within a buffer of a given width and then eliminating them by joining them to a neighbouring polygon was proposed as the most appropriate method for removing sliver polygons in overlay analyses.Conclusions Old large-scale maps are a very valuable source of historical data and have a place in landscape studies, especially when researching smaller areas, such as municipalities or cadastres, where they allow work at the level of land parcels. It has been confirmed that the positional inconsistency of map sources can be eliminated to a certain extent by the chosen time series vectorization procedure. Considering the type of study, the type of spatial data used, and the type of results that characterise the change in the area, it is advisable to choose an adequate method for refining the results.
Integration of objective weighting methods for criteria and MCDM methods: application in material selection
Authors: Dua Tran Van , Duc Duong Van , Bao Nguyen Chi , Trung Do Duc 
Published: April 2024

Determining weights for criteria is an extremely crucial step in the process of selecting an option based on multiple criteria, also known as Multi-Criteria Decision Making (MCDM). This article presents the combination of five objective weighting methods for criteria with three MCDM methods in the context of material selection. The five objective weighting methods considered are Entropy, MEREC (Method based on the Removal Effects of Criteria), LOPCOW (Logarithmic Percentage Change-driven Objective Weighting), CRITIC (Criteria Importance Through Intercriteria Correlation), and MEAN. The three MCDM methods employed are MARA (Magnitude of the Area for the Ranking of Alternatives), RAM (Root Assessment Method), and PIV (Proximity Indexed Value). Material selection investigations were conducted in three different cases, including lubricant selection for two-stroke engines, material selection for manufacturing screw shafts, and material selection for manufacturing gears. The Spearman's rank correlation coefficient was calculated to assess the stability of ranking the alternatives using different MCDM methods. The combinations of objective weighting methods and MCDM methods were evaluated based on factors such as consistency in identifying the best material type, range, average value, and median of each set of Spearman's rank correlation coefficients. Two significant findings were identified. First, the weights of criteria calculated using LOPCOW method appear to be inversely related to those calculated using the Entropy method. Second, among the three MCDM methods used, MARA was identified as the most suiTable for lubricant selection for two-stroke engines, RAM was found to be the most suiTable for material selection for screw shafts and gears. The best material type in each case was also determined
Experimental investigation of turbulent flow through a square-sectioned duct and 90ᵒ square elbow by using circular turbulator
Authors: Putra Randi Purnama , Sutardi  , Widodo Wawan Aries , Yuvenda Dori , Lapisa Remon , K. Arwizet , Krismadinata  , Harmanto Dani 
Published: April 2024

The ducting system is made up of 90° elbows and a few other fittings and accessories in addition to straight ducts. The friction loss, separation loss, and secondary flow loss are the main causes of the pressure decrease in an elbow, and they all increase with the presence of elbows. Utilizing too much energy to propel the flow is a cost associated with pressure loss. The aim of this research is to investigate turbulent flow via 90° square elbows and a square-sectioned duct through experimental methods by adding a circular turbulator (CT) close to the elbow wall's inner radius. The Reynolds numbers (ReDh) used in this research are 1.6×104, 4.8×104, and 9.5×104, with average flow velocities of 2 m/s, 6 m/s, and 12 m/s. Circular turbulators are added to the inner walls with angular positions (α) of 5°, 10°, 15°, and 20°. The results showed that the turbulence intensity increased toward the inner radius wall of the elbow in ducting with CT. For this study, the flow within the ducting with the CT is generally more turbulent than the flow inside the ducting without the CT. By including CT, it was possible to reduce the overall pressure loss in ducting with an elbow. The pressure reduction at two ReDh values (1.6×104 and 4.8×104) was only lessened by CT positioned at α=10°, 15°, and 20° out of the four CT placements. Conversely, CT positioned at α=5° can only effectively mitigate pressure decrease at ReDh=1.6×104. CT installation cannot lessen the pressure drop that happens in the ducting at a value of ReDh=9.5×104. CT placement at the three α values often helps to lower the pressure drop in the ducting. Using the CT, positioned at α=15°, is the greatest strategy to reduce the overall pressure decrease