bluuebunny/crossref_metadata_2025_split huggingface dataset:
Attention-based CNN–LSTM deep learning hybrid model for the prediction of atmospheric ozone concentration
Authors: JIANG YUAN , HUA DENGXIN , WANG YUFENG , YANG XUETING , DI HUIGE , YAN QING 
Published: April 2024

Abstract
Considering that ozone is essential to understanding air quality and climate change, this study introduces a deep learning method for predicting atmospheric ozone concentrations. The method combines an attention mechanism with a convolutional neural network (CNN) and long short-term memory (LSTM) to address the nonlinear nature of multivariate time-series data. It employs CNN and LSTM to extract features from short series, enhanced by the attention mechanism for improved short-term prediction accuracy. The model uses eight meteorological and environmental parameters from 16,806 records (2018–2019) as input, selected through principal component analysis (PCA). It features a hybrid attention-CNN-LSTM model with specific settings: a time step of 5, a batch size of 25, 15 units in the LSTM layer, the Relu activation function, 25 epoch iterations, and an overfitting avoidance strategy at 0.15. Experimental results demonstrate that this hybrid model outperforms independent models and the CNN-LSTM model, especially in forward prediction with a multi-hour time lag. The model exhibits a high prediction determination coefficient (R2 = 0.971) and a root mean square error of 3.59 for a 1-hour time lag. It also shows consistent accuracy across different seasons, highlighting its robustness and superior time-series prediction capabilities for ozone concentration.
Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach
Authors: Aman Nishit , Panyametheekul Sirima , Sudhibrabha Sumridh , Pawarmart Ittipol , Xian Di , Gao Ling , Tian Lin , Manomaiphiboon Kasemsan , Wang Yangjun 
Published: April 2024

Abstract

In this study, a range of machine learning (ML) models including random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting, cat boosting, and a stacked ensemble model, were employed to predict visibility at Bangkok airport. Furthermore, the impact of influential factors was examined using the Shapley method, an interpretable ML technique inspired by the game theory-based approach. Air pollutant data from seven Pollution Control Department monitoring stations, visibility, and meteorological data from the Thai Meteorological Department's Weather station at Bangkok Airport, ERA5_LAND, and ERA5 datasets, and time-related dummy variables were considered. Daytime visibility ((here, 8–17 local time) was screened for rainfall, and ML models were developed for visibility prediction during the dry season (November – April). The light gradient boosting model is identified as the most effective individual ML model with superior performance in three out of four evaluation metrics (i.e., highest ρ, zero MB, second lowest ME, and lowest RMSE). However, the SEM outperformed all the individual models in visibility prediction at both hourly and daily time scales. The seasonal mean and standard deviation of normalized meteorological visibility are lower than those of the original visibility, indicating more influence of meteorology than emission reduction on visibility improvement. The Shapley analysis identified RH, PM2.5, PM10, day of the season year, and O3 as the five most important variables. At low relative humidity (RH), there is no notable impact on visibility. Nevertheless, beyond this threshold, negative correlation between RH and visibility. An inverse correlation between visibility and both PM2.5 and PM10 was identified. Visibility is negatively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The day of the season year (i.e., Julian day) (JD) exhibits an initial negative and later positive association with visibility, suggesting a periodic effect. The dependence of the Shapley values of PM2.5 and PM10 on RH, and the equal step size method to understand RH effects, suggest the effect of hygroscopic growth of aerosol on visibility. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and comprehending the factors influencing its fluctuations. Based on the above findings, certain policy–related implications, and future work have been suggested.
GF-CSTNet: A Method for Pan-Sharpening Remote Sensing Images by Integrating CSPNet and Transformer
Authors: Chen Yingxia , Liu Huiqi , Fang Faming 
Published: April 2024

Abstract
To tackle challenges like limited receptive fields and inadequate feature extraction encountered in conventional ConvolutionalNeural Networks (CNNs), this paper introduces a remote sensing image sharpening approach named GF-CSTNet. Themethod draws inspiration from the FusionNet architecture and combines CSPNet with Transformer for improved performance.Firstly, this method utilizes guided filtering to enhance the acquired remote sensing image data. The CSPNet and Transformerstructures are then combined to further enhance fusion performance by leveraging their respective advantages. Subsequently, aRep-Conv2Former method is designed to streamline attention and extract diverse receptive field features through a multi-scaleconvolution modulator block. Simultaneously, a reparameterization module is constructed to integrate the multiple branchesgenerated during training into a unified branch during inference, thereby optimizing the model’s inference speed. Furthermore,a residual learning module incorporating attention has been devised to augment the modeling and feature extraction capabilitiesof images. Finally, to tackle the overfitting issue that emerges during training, weight decay has been incorporated into the lossfunction. Experimental results obtained from the GaoFen-2 and WorldView-3 datasets demonstrate the effectiveness of theproposed GF-CSTNet approach. It effectively extracts detailed information from images while avoiding the problem of spectraldistortion.
Development of a Random Forest Climate Model Correction Algorithm
Authors: Holthuijsen Tim 
Published: April 2024

Abstract

In this paper, a variety of machine learning models for reducing climate model inaccuracy are developed and critically examined. The most effective model at mitigating climate model inaccuracy is a random forest regressor, which reduces temperature Root Mean Square Error (RMSE) from 2.90 to 0.44 in the Global Ensemble Forecast System (GEFS). Multiple linear models, neural networks, and random forest regressor correction models are trained on a large climate model inaccuracy dataset. This inaccuracy dataset is created by comparing the results of a climate reanalysis with the results of a climate reforecast, assuming that the reanalysis is more accurate at representing real climate values than the reforecast. This assumption is successfully validated by comparing both datasets to an observational validation set. The random forest correction model performs significantly better than the other correction approaches, for which possible explanations are discussed. Finally, this method of climate model correction is applied to a generalized setting, creating a program that can automatically generate a tailor-made random forest correction model for any climate model output.
Maternal Concentrations of Vitamin D Metabolites in Response to High-Dose Oral Vitamin D During First Trimester Pregnancy: A Randomized Controlled Trial
Authors: Syafitri Inayah , Irwinda Rima , Saroyo Yudianto Budi , Purwosunu Yuditiya , Wibowo Noroyono 
Published: April 2024

Abstract
Vitamin D deficiency has become a common problem globally. The present study aimed to evaluate the effects of different vitamin D3 regimens on maternal concentrations of vitamin D metabolites during pregnancy. This randomized controlled trial was registered at ClinicalTrials.gov (NCT06054919) on 22nd September 2023. Subjects were ≤ 14 weeks gestation pregnant women with vitamin D deficient or insufficient (25(OH)D < 30 ng/ml]. Two intervention groups were randomly assigned: 5,000 IU of vitamin D3 daily or 50,000 IU weekly of vitamin D3. Maternal blood samples were collected before and after four weeks of intervention to assess changes in serum concentrations of 25-hydroxyvitamin D (25(OH)D), 1,25-dihydroxyvitamin D (1,25(OH)2D), vitamin D binding protein (VDBP), and 24,25-dihydroxyvitamin D (24,25(OH)2D). Sixty subjects were randomized into two groups, and eight subjects were dropped out. There were no differences in the baseline demographics or baseline levels of any of the vitamin D metabolites between the two groups. In the 50,000 group, the 25(OH)D levels increased from 15.3 ± 4.7 ng/mL to 26.9 ± 6.1 ng/mL (p < 0.001) and 34.6% of the subjects achieved vitamin D sufficiency. While in the 5,000 group, the 25(OH)D levels increased from 14.5 ± 4.3 ng/mL to 27.9 ± 9.3 ng/mL (p < 0.001) and 23.1% of the subjects achieved vitamin D sufficiency. Both groups showed an increasing trend in the total levels of 25(OH)D, 1,25(OH)2D, VDBP, and 24,25(OH)2D. However, the increment of all vitamin D metabolites were not significantly different between two groups. Vitamin D3 50,000 IU weekly is equally effective and safe as 5,000 IU daily in increasing vitamin D levels in pregnant women with vitamin D deficient or insufficient.
Quadruplet therapy for newly diagnosed myeloma: Comparative analysis of sequential cohorts with triplet therapy lenalidomide, bortezomib and dexamethasone (RVd) vs daratumamab with RVD (DRVd) in transplant-eligible patients
Authors: Joseph Nisha , Kaufman Jonathan , Gupta Vikas , Hofmeister Craig , Madhav Dhodapkar , Boise Lawrence , Roberts Danielle , DiCamillo Sara , Nooka Ajay , Lonial Sagar 
Published: April 2024

Abstract

Lenalidomide, bortezomib and dexamethasone (RVd) has previously been established as standard of care induction therapy for newly diagnosed multiple myeloma (NDMM). More recently, randomized phase 3 data have demonstrated the benefit of the addition of daratumumab (Dara-RVd) to the RVd backbone in terms of improved both depth of response and long-term survival benefit as measured by progression free survival (PFS). Our group has previously published on a historical cohort of 1000 NDMM patients uniformly treated with RVd induction with impressive both PFS and overall survival. Here, we present a comparative analysis of our RVd cohort with a contemporary cohort of 326 patients induced with Dara-RVd at our institution with intent to transplant. This analysis demonstrates the utility of this regimen in real-world clinical practice, and provides additional insights into D-RVd performance in patient subsets often underrepresented in clinical trials, as well as the impact of daratumumab in maintenance for all NDMM patients.
A Case of Acute Tubulointerstitial Nephritis Following the Use of Chenopodium album L
Authors: Demiray¹ Alparslan , yılmaz tuğba , Demiray Sevil , Akgün Hülya , Koçyiğit İsmail 
Published: April 2024

Abstract

Introduction:
Chenopodium album, commonly known as "lambsquarters," is a plant consumed as food and used in traditional medicine. Its popularity is increasing due to the belief that it has fewer side effects compared to synthetic drugs. However, its use can lead to acute or chronic poisoning. The growing interest in herbal remedies, along with uncontrolled usage and disregard for expert recommendations, contributes to adverse effects.
Case Presentation
: A 68-year-old female patient presented to the emergency department with nausea, vomiting, and flank pain following the use of lambsquarters. Impaired kidney function was detected in the patient. A biopsy performed after Chenopodium album usage led to the diagnosis of tubulointerstitial nephritis (TIN). The patient responded positively to corticosteroid and hemodialysis treatment.
Discussion
Caution is necessary in the use of herbal medicines and traditional treatments. A thorough evaluation of factors such as patients' nutritional status, herbal product usage, medication history, and genetic background is crucial. Chenopodium album can cause tubulointerstitial nephritis, resulting in kidney damage. Similarly, heavy metal poisoning through herbal products can lead to kidney damage. Adopting a multidisciplinary approach in the diagnosis and treatment process can contribute to better patient management.
Conclusion
This case presents a rare instance of tubulointerstitial nephritis developed due to the use of herbal products. Physicians should inquire about patients' history of exogenous substance use and conduct a comprehensive assessment, keeping such situations in mind. Conscious use of herbal medicines and traditional treatments can help prevent serious complications like kidney damage.
Application of digital image processing in determination of soil consolidation coefficient
Authors: paknejad Kamal , Ejlali Reza goli , Fattahi Saeyyed Javad Saeyyed , Javaheri Hossain 
Published: April 2024

Abstract
In geotechnical engineering, in order to perform analyzes related to consolidation settlement, appropriate values ​​of consolidation coefficient should be available. One-dimensional consolidation test with Casagrande odometer is a conventional method to determine the value of consolidation coefficient. Due to the recent significant developments in digital image technology in the field of geotechnical engineering and due to the reasonable costs and high accuracy of measurement, various researchers have used this technique in the analysis of soil deformation characteristics. In this research, the digital image processing (DIP) technique has been used to determine the deformation of the consolidation sample during the consolidation process. Due to the limitations of the conventional consolidation machine in terms of sample dimensions and also the lack of visibility of the sample and as a result the inability to take pictures, a new large-scale consolidation machine has been made. In order to ensure the proper functioning of the new device, 3 series of consolidation tests have been performed on Tabriz saturated clay samples with the new consolidation device as well as the conventional consolidation device. The comparison of the results shows that the consolidation parameters measured by the new device are comparable to the conventional odometer device. Then, by performing 4 series of consolidation tests with a new consolidation device and taking pictures of the sample during the consolidation process, the information related to the deformation of the sample was recorded and with the help of the DIP technique, the deformation of the consolidation sample, and as a result, the load-deformation information was measured and the consolidation coefficient of the samples has been determined based on them. The results show the accuracy and efficiency of the DIP technique in determining soil consolidation parameters.
DNA Barcoding and Diversity of Mammals in an Agricultural Landscape in the Northwestern Andes
Authors: Martínez-Cerón Juan M. , Porras Susan Saavedra , Diaz-Nieto Juan Fernando 
Published: April 2024

Abstract
The evolutionary history of species has become relevant to understanding and explaining the composition and structure of biological communities; however, we need to identify species clearly and have a phylogenetic framework to consider such a historical perspective. This study seeks to understand the community-level patterns of mammals in Andean highland forest remnants associated to agricultural landscapes. Our methods included fieldwork to survey small terrestrial mammals, bats, and medium to large species during two sampling periods in avocado plantations in the Western Cordillera of Colombia. We implemented three approaches to identify mammal species: traditional morphological identification, DNA barcoding, and phylogenetic analyses. We also evaluated the Phylogenetic Diversity of the mammal community of this study with other assemblages in montane forests. Our fieldwork recorded 738 records of 37 mammal species included in 13 families. Our study generated sequences for 18 mammal species of Colombia and ten new DNA barcodes, highlighting the importance of producing genetic libraries for Neotropical mammals. Our phylogenetic diversity analyses show that although our study area is more species-rich than other Andean localities, it has lower phylogenetic diversity values because many mammalian lineages are absent in these transformed ecosystems. We propose expanding the use of DNA-based species identification and Phylogenetic Diversity analyses to provide an objective characterization of the communities rather than simplistic and misleading parameters such as species richness.
Skeletal Muscle Mass, Strength, and Physical Performance Gains Are Similar Between Healthy Postmenopausal Women and Postmenopausal Breast Cancer Survivors After 12 Weeks of Resistance Exercise Training
Authors: Artigas Macarena , Alegría-Molina Andrea , Vidal-Seguel Nicolás , Muñoz-Cofre Rodrigo , Carranza-Leiva Juan , Sepúlveda-Lara Alexis , Vitzel Kaio Fernando , Huard Nolberto , Sapunar Jorge , Salazar Luis A. , Curi Rui , Marzuca-Nassr Gabriel Nasri 
Published: April 2024

Abstract
Background: Resistance exercise training (RET) has been shown to be effective in increasing skeletal muscle mass and strength in healthy postmenopausal women. In postmenopausal breast cancer survivors, however, the effects of RET on these parameters are reported as either controversial or limited.
Aim: To compare the effects of a 12-week progressive whole-body RET program, on skeletal muscle mass, muscle strength and physical performance in healthy postmenopausal women versus postmenopausal women who are breast cancer survivors.
Methods: Healthy (HEA, 54±3 years, BMI 26.6±2.7 kg·m2, n=13) and breast cancer survivors (BCS, 52±5 years, BMI 26.8±2.1 kg·m2, n=11) postmenopausal women were subjected to 12 weeks of progressive whole-body RET, three times per week. quadriceps muscle thickness ultrasound; one-repetition maximum (1RM) of leg extension, leg press, chest press, horizontal row, and elbow extension; grip strength and physical performance (6-minute walk test, dynamic balance test, and chair stand-up test) were assessed before and after the RET program.
Results: Quadriceps muscle thickness increased from 3.6±0.6 to 3.9±0.7 cm2 (5±7%) and from 3.5±0.50 to 3.7±0.5 cm2 (7±4%), in HEA and BCS, respectively (Time, P<0.001; η2=0.44) without differences between groups. 1RM leg extension strength increased from 57±10 to 80±15 kg (41±12%) and from 54±13 to 77±18 kg (43±16 %) (Time, P<0.001; η2=0.88) in the HEA and BCS groups, respectively. Similar improvements were observed for 1RM leg press, 1RM chest press, 1RM horizontal row, and 1RM elbow extension (Time effect: all P<0.001; η2≥0.85), and grip strength (Time effect: P=0.035; η2=0.19) in the HEA and BCS groups. No differences in responses to RET were observed between groups (Time×Group, all P>0.185; all η2 ≤0.09) for all strength outcomes. Physical performance showed significant improvement (all P<0.001; η2 ≥0.45), with no differences between groups (Time×Group, P≥0.05; η2≤0.05).
Conclusion: Twelve weeks of RET significantly increase skeletal muscle mass, strength, and physical performance in postmenopausal women. No differences were observed between healthy postmenopausal women and postmenopausal breast cancer survivors. These findings point out that this study's RET promotes marked skeletal muscle mass, strength, and performance gains regardless of breast cancer.
Trial registration: This trial is registered on ClinicalTrials.gov: NCT05690295 as of December 22, 2022
Development and Evaluation of a Cardio-Cerebrovascular Disease Prevention Education Program for Middle-Aged Women in the Community Using Self-Determination Theory: An Intervention Study
Authors: Lee Yein , Kim Yunhee 
Published: April 2024

Abstract
Background
This study aimed to develop and evaluate the effectiveness of a cardio-cerebrovascular disease (CVD) prevention program using the self-determination theory model of health behavior changes in middle-aged women in the community.
Methods
The effectiveness of the program was tested using a non-equivalent, control group, pre-post design. We recruited 59 middle-aged women who visited two community healthcare centers in City B, South Korea. The experimental group (n = 26) received a CVD prevention program, while the control group (n = 33) received only CVD prevention education materials and one general phone consultation. The program’s strategy was to meet basic psychological needs. The 10 educational sessions included eight face-to-face and two online education sessions, and there were four telephone counseling sessions. All data were analyzed using SPSS version 26.0, independent t-test, and Mann-Whitney’s U test.
Results
Findings showed a significant difference between the experimental and control groups in autonomy (p < .001), competence (p < .001), relatedness (p = .004), awareness of myocardial infarction warning symptoms (p = .001), awareness of stroke warning symptoms (p < .001), health behavior practice (p < .001), loneliness and social isolation (p < .001), aging anxiety (p = .001), and resilient coping (p < .001).
Conclusions
The implementation of a CVD prevention educational intervention based on the self-determination theory model of health behavior changes significantly increased the physical and mental health and basic psychological need fulfilment scores of middle-aged women living in the community.
Robot-assisted fracture reduction system combined with TiRobot for the treatment of unstable pelvic fractures
Authors: Dai Yonghong , Wu Zhengjie 
Published: April 2024

Abstract
Objective
The reduction of pelvic fractures represents a global challenge. We have applied a robot-assisted fracture reduction (RAFR) system in clinical practice, addressing the limitation of the TiRobot's lack of fracture reduction capabilities.The purpose of this study is to discuss the early efficacy and Safety of the RAFR system combined with TiRobot in the treatment of unstable pelvic fractures.
Methods
A retrospective evaluation of 20 patients with unstable pelvic fractures treated with the RAFR system combined with TiRobot at Foshan Hospital of Traditional Chinese Medicine between May 2022 and August 2023 was conducted.
Results
With the joint intervention of the the RAFR system and TiRobot, all 20 patients successfully completed minimally invasive closed reduction and internal fixation of pelvic fractures. With the exception of one patient who was not followed up because he could not be contacted, the remaining patients were followed up for 6 to 19 months. All 19 patients had their fractures healed and their pelvic function was well recovered.
Conclusion
In our study, the intelligent robot-assisted pelvic fracture reduction system combined with TiRobot could complete intelligent, safe, accurate, minimally invasive, and homogeneous closed reduction and internal fixation for most patients with unstable pelvic fractures, and could achieve good early efficacy.