bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data Collection and Analysis Applied to Intelligent Transportation Systems A Case Study on Public Transportation
Authors: Oliveira Gabriel Gomes , Iano Yuzo , Vaz Gabriel Caumo , Suriyan Kannadhasan 
Published: September 2022

Abstract
The big data concept has been gaining strength over the last years. With the arise and dissemination of social media and high access easiness to information through applications, there is a necessity for all kinds of service providers to collect and analyze data, improving the quality of their services and products. In this regard, the relevance and coverage of this niche of study is notorious. It is not a coincidence that governments, supported by companies and startups, are investing in platforms to collect and analyze data, aiming at the better efficiency of the services provided to the citizens. Considering the aforementioned aspects, this work makes a contextualization of the Big Data and ITS (Intelligent Transportation System) concepts by gathering recently published articles, from 2017 to 2021, taking survey and case studies into consideration with the objective of demonstrating the importance of those themes in current days. Within the scope of big data applied to ITS, this study proposes a database for the public transportation in the city of Campinas (Brazil), enabling its improvement according to the population demands. Finally, this study tries to present clearly and objectively the methodology employed with the maximum number of characteristics, applying statistical analyses (box-and-whisker diagrams and Pearson correlation), highlighting the limitations, and expanding the studied concepts to describe the application of an Advanced Traveler Information System (ATIS), a branch of Intelligent Transportation System (ITS), in a real situation. Therefore, besides the survey of the applied concepts, this work develops a specific case study, highlighting the identified deficiencies and proposing solutions. Future works are also contemplated with the objective of expand this study and improve the accuracy of the achieved results.
Accelerated univariate sequential clustering
Authors: Enayati Elham , Mortazavi Reza , Basiri Abdolali 
Published: September 2022

Abstract
Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the sequential clustering problem. For instance, analyzing the moving pattern of an object and detecting community structure in a complex network are related to sequential clustering. The constraint of the continuous region prevents previous clustering algorithms from being directly applied to the problem. A dynamic programming algorithm was proposed to address the issue, which returns the optimal sequential clustering. However, it is not scalable. This paper addresses the issue via a greedy stopping condition that prevents the algorithm from continuing to search when it's likely that the best solution has been found. Experimental results on multiple datasets show that the algorithm is much faster than its original solution while the optimality gap is negligible.
A New Surgical Scheme for Determining Hepatectomy to Hepatocellular Carcinoma Patients with Clinically Significant Portal Hypertension
Authors: Lu Hua-ze , Mai Rong-yun , Wang Xiao-bo , Liang Rong , Lin Yan , Chen Jie , Wu Fei-xiang , Xiang Bang-de , Cheng Shu-qun , Li Le-qun , Guo Wei-xing , Ye Jia-zhou 
Published: September 2022

Abstract
Objective
To establish a new surgical scheme defining risk classes of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among hepatocellular carcinoma (HCC) patients combined with clinically significant portal hypertension (CSPH).
Backgrounds:
Hepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC.
Methods
Multivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram.
Results
The analysis included 924 patients, of whom 721(78.0%) were without CSPH, 137(14.8%) with mild-CSPH, and 66(7.1%) with severe-CSPH. The nomogram incorporated preoperative prolonged prothrombin time (PT), total bilirubin (T-Bil), indocyanine green retention rate at 15 min (ICG-R15), CSPH grade, and standard future liver remnant (sFLR) volume, and achieved good prediction performance in the training (C index = 0.891, 95%CI: 0.855–0.920), internal validation (C index = 0.850, 95%CI: 0.786–0.901), and external validation (C index = 0.872, 95%CI: 0.835–0.904) cohorts, with well-fitted calibration curves. Calculations of total points of diagnostic errors with 95%CI were concentrated in 110.5(range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR.
Conclusion
This new surgical scheme is practical to stratify risk classes in severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.
SELF: A Stacked-based Ensemble Learning Framework for Breast Cancer Classification
Authors: Jakhar Amit Kumar , Singh Mrityunjay , Gupta Aman 
Published: September 2022

Abstract
Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, about 2.3 million cases and 685000 deaths have been reported worldwide. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machine-based smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stacked-based ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computer-aided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and SELF selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and the logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on the BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.
Dietary threonine above the requirement for protein synthesis examined at the molecular level stimulates gill mucus cell production in juvenile Nile tilapia
Authors: Dalagnol Jakceline Marcante , Furuya Valéria Rossetto Barriviera , Cruz Thais Pereira , Almeida Fernanda Losi Alves , Dal-Pai Maeli , Paula Tassiana Gutierrez , Boscolo Wilson Rogério , Furuya Wilson Massamitu 
Published: September 2022

Abstract

This study investigates the hypothesis that dietary supplementation of threonine above the current recommendation for protein synthesis stimulates gill mucus cell production in juvenile Nile tilapia, Oreochromis niloticus. During our experiment, six isonitrogenous and isocaloric extruded diets were elaborated with graded threonine levels [8 (control), 12, 16, 20, 24, and 28 g kg−1]. Fish (n = 216; 10.78 ± 0.03 g) were randomly assigned to triplicate groups of 12 fish and hand-fed six times a day until apparent satiety. Maximum body weight gain and feed efficiency ratio occurred in fish fed up to 17.71 and 13.73 g kg−1 diet of threonine, respectively. However, gill mucus cell counts were significantly higher in fish fed up to 20 g kg−1 diet of threonine, than that fed diet control (8 g kg−1 diet threonine). The relative expressions of MyoD and MyoG genes were upregulated in fish fed threonine at 16 g kg−1. Second-order polynomial regression analysis of protein retention efficiency gain against dietary threonine levels estimates that the dietary threonine requirement for juvenile Nile tilapia is 16.30 g kg−1 diet (4.89 g 100 g−1 protein). In contrast, a higher threonine level of 20 g kg−1 diet (6.00 g100 g−1 protein) optimizes gill mucus cell counts. This study provides insight into threonine as functional amino acid in tilapia aquaculture.
Multiple regression approach to fracture energy determination of normal strength concrete using experimental evidence
Authors: R SUDHIR M , M BEULAH 
Published: September 2022

Abstract
The tensile stiffness of concrete, an important parameter for crack initiation, can be systematically determined by the principles of fracture mechanics. It is a widely accepted research conclusion that the stress-strain behaviour and the failure mode of concrete subjected to loads are regulated by the microcrack initiation and propagation. A heterogeneous material like concrete will have cracks even before the experimental investigations. Therefore, fundamental awareness of the fracture mechanism is a requisite to comprehend concrete behaviour. A key parameter in understanding the fracture behaviour of concrete is the fracture energy (GF). There is consensus among researchers that the fracture energy GF alone does not suffice to characterize the ductility/brittleness as well as the size dependency of concrete. Therefore, it is felt necessary to evaluate the fracture energy (GF) and the characteristic length Lch for a realistic estimation of the fracture behaviour of concrete. In this effort, this study examines firstly, the fracture energy of concrete by experimentation, and then the fracture energy proposed by various models in the literature. Further, the characteristic length proposed by Hillerborg which depicts both the material influence and that of the size effect is computed. Based on the RILEM – 50 FM recommendations, 18 specimens with varying grades of concrete and notch depths have been tested and the fracture energy parameters have been evaluated. Also, two multiple regression models with key fracture parameters as variables for two notch ratios, have been formulated for the concrete fracture energy. The arguments have been supported by experimental evidence.
Electromagnetohydrodynamic Effects with Single- Walled Carbon Nanotubes Particles in a Corrugated Microchannel
Authors: Ahmed Shaaban I. , Mekheimer Khaled. S. , Sayed Ahmed Y. , Abdel-wahed Mohamed S. 
Published: September 2022

Abstract
One of the advanced products of nanotechnology having notable mechanical and physical properties are the Single-Walled Carbon Nanotubes (SWCNTs). This motivated us to investigate the effect of single-walled carbon nanotubes (SWCNTs) suspended in a microchannel with corrugated walls under the effect of electromagnetic hydrodynamic flow (EMHD). The corrugation of the wavy walls are described by periodic sinusoidal waves of small amplitudes either in phase or out of phase. The problem simulated with a system of governing equations such as potential, momentum, and heat equations were solved analytically using the perturbation method. The behavior of nanofluid velocity, temperature, volumetric flow rate, and average velocity investigated under the multi-conditions of using three models for the sizes and shapes of carbon nanoparticles tubes. By addition of SWCNTs, the fluid velocity is reduced at the center of the channel by providing resistance to the fluid motion. The size and concentration of SWCNTs influence enhance the rate of heat transfer. Also, the Xue's model has the highest heat transfer rate when compared to the Maxwell and Hamilton-Crosser's (H-C) models. Finally, the obtained results for the flow rate were compared with previously published data and appeared in good agreement.
Assessments of classical dry-blending/moulding and solution casting PVC plasticization effectiveness – a comparative study
Authors: Czogała Joanna , Pankalla Ewa , Turczyn Roman 
Published: September 2022

Abstract
Plasticizers are the most popular and indispensable plastic additives, facilitating their processability and providing suitable flexibility. Plasticizing efficiency is a term that is used to compare between different types of plasticizers. Plasticizing efficiency can be assessed using specimen fabricated through either a three-stage method consisting of mixing, extruding and pelletizing, or a solution casting method. In this paper, we aim to compare the results of plasticization efficiency assessed for specimen fabricated through both methods, to reveal their applicability and possible limitations. As an experimental model, we used poly(vinyl chloride) plasticized with three common plasticizers, namely bis(2-ethylhexyl) terephthalate, bis(2-ethylhexyl) phthalate and tris(2-ethylhexyl) trimellitate. Plasticization efficiency assessment was based on mechanical, thermal and migrability properties, including elongation at break, tensile strength, exudation and leaching in n-hexane, thermal stability, and glass transition temperature. The results revealed inconsistencies in the assessment of plasticization efficiency depending on plasticization method. For instance, the migration resistance studies showed that plasticizer release was much faster from solution casted specimens than from the moulded ones. On the other hand, higher tensile strength and elongation at break values were obtained for samples prepared by a solution casting method. The dependency of the results on the method of specimens’ plasticization highlights it as a factor that overestimates/underestimates plasticizer efficiency, and could affect the process of selection of plasticizers for industrial practice.
Expert Review of Global Real-World Data on COVID-19 Vaccine Booster Effectiveness &amp; Safety During the Omicron-dominant Phase of the Pandemic
Authors: Solante Rontgene , Alvarez-Moreno Carlos , Burhan Erlina , Chariyalertsak Suwat , Chiu Nan-Chang , Chuenkitmongkol Sunate , Dung Do Van , Hwang Kao-Pin , Ibarra Javier Ortiz , Kiertiburanakul Sasisopin , Kulkarni Prasad S. , Lee Christopher , Lee Ping-Ing , Lobo Rommel Crisenio , Macias Alejandro , Nghia Cao Huu , Ong-Lim Anna , Rodriguez-Morales Alfonso J. , Richtmann Rosana , Safadi Marco Aurélio Palazzi , Satari Hindra Irawan , Thwaites Guy 
Published: September 2022

Abstract

Introduction
COVID-19 vaccines have been highly effective in reducing morbidity and mortality during the pandemic. However, the emergence of the Omicron (B.1.1.529) variant and subvariants as the globally dominant strains have raised doubts about the effectiveness of currently-available vaccines and prompted debate about potential future vaccination strategies.
Areas covered
Using the publicly available IVAC VIEW-hub platform, we reviewed 52 studies on vaccine effectiveness (VE) after booster vaccinations. VE data were reported for SARS-CoV-2 symptomatic infection, severe disease and death and stratified by vaccine schedule and age. In addition, a non-systematic literature review of safety was performed to identify single or multi-country studies investigating adverse event rates for at least two of the currently-available COVID-19 vaccines.
Expert opinion
Booster shots of the current COVID-19 vaccines provide consistently high protection against Omicron-related severe disease and death. Additionally, this protection appears to be conserved for at least 3 months, with a small but significant waning after that time. The positive risk-benefit ratio of these vaccines is well established, thus increasing confidence in administering additional doses as required. Future vaccination strategies will likely include a combination of schedules based on a person’s risk profile, as overly frequent boosting may be neither beneficial nor sustainable for the general population.
Is stroke the cause of vascular dementia? A two-sample Mendelian Randomization analysis
Authors: Zhang Ke-Jia , Chen Lan-Lan , Zhang Peng , Sun Xin , Qu Yang , Yi Yang , Guo Zhen-Ni 
Published: September 2022

Abstract
Background
Vascular dementia, the second most common cause of dementia, is closely related to stroke. This study aimed to determine whether stroke is causally associated with vascular dementia.
Methods
A two-sample Mendelian randomization (MR) was performed. Genetic instruments were acquired with a genome-wide significant threshold (p < 5x10− 8) from the MRC Integrative Epidemiology Unit (IEU) open genome-wide association studies (GWAS) project. Methods including inverse variance-weighted (IVW), MR-Egger, weighted-median methods, and MR pleiotropy residual sum and outlier (MR-PRESSO) were performed to evaluate the effects and to assess heterogeneity and horizontal pleiotropy. Controversies were resolved with a meta-analysis. A leave-one-out analysis was also performed.
Results
A causal association was observed between total stroke and vascular dementia, with an odds ratio (OR) of 1.81 (95% CI, 1.81 to 2.34) and a P-value of 0.026 for IVW, and an OR of 1.81 (95% CI, 1.32 to 2.31) and a P-value of 0.047 for MR-PRESSO. Ischemic stroke was also causally associated with vascular dementia, with an OR of 1.87 (95% CI, 1.39 to 2.36), a P-value of 0.011 for IVW. MR-PRESSO had an OR of 1.87 (95% CI, 1.35 to 2.40) and a P-value of 0.048. Body mass index (BMI) was also associated with vascular dementia, with an OR of 1.29 (95% CI, 1.13 to 1.48) and P-value < 0.01 according to the meta-analysis. No significant association was observed between stroke subtypes, other stroke risk factors, or stroke-related medications.
Discussion
An estimation of two-sample MR found that stroke and BMI were the causes of vascular dementia.
Theoretical and experimental analysis of the temperature rise of a ball screw
Authors: Qiu yalan , Zhou Chang-Guang , Ou Yi , Feng Fu-Tian 
Published: September 2022

Abstract

The temperature rise of a ball screw is the main factor affecting machining accuracy. The traditional methods of calculation and analysis carry out coefficient fitting of known experimental data or using the formula for a friction heat source in bearing theory. However, the screw raceway parameters are ignored in the model. The variation in friction torque with temperature is not considered in the model, so it cannot be effectively applied to engineering. In this paper, the friction heat equation of the ball screw is established theoretically with the screw raceway parameter, and the friction torque is considered with the effect of temperature. A mathematical model of steady temperature and stable time is established for the effective heat source stroke section of the screw, and the temperature rise curve of the screw is fitted. The friction is divided into Coulomb friction torque and viscous friction torque. The viscosity affects viscous friction torque at low temperatures, so the variation in friction torque should be taken into account when calculating the temperature rise. When a specific temperature is reached, the viscous friction torque has little effect on the overall friction torque; then the temperature rise among different velocities is exponential with velocity ratio. The screw stability time decreases with increasing rotational speed. Twelve groups of tests are conducted to verify the theoretical analysis using two samples, two lubricating media, and three speeds. The experimental results agree well with the theory. This proves that the approach can be applied to engineering practice.
Hydrochemical characteristics and distribution of uranium in groundwater of the Geum River Basin, South Korea
Authors: Choi Hanna , Koh Dong Chan , Yoon Yoon Yeol 
Published: September 2022

Abstract
In the Geum River basin in Korea, local groundwater contamination has been occurring due to the complex influence of urbanization and agricultural activities. For proper utilization as a water resource, this study identified major influencing factors of groundwater hydrochemistry in the bedrock aquifer by statistical analysis and regional uranium (U) distribution as a redox-sensitive contaminant. The H-3 concentration of the groundwater was high in the plains and low in the mountain areas; thus, it was deemed to be affected by the residence time of groundwater after precipitation recharge. The hydrochemical properties and factor loading values of the principal components indicated that the major factors were water‒rock interactions and residence time, but a positive correlation of K-NO3 and Mg-Cl showed the influence of agricultural activities. Cl− increased as it moved downstream, while NO2− was found to decrease from upstream to midstream, and NO3− showed no regularity. Thus, the main groundwater pollutants upstream and downstream in the Geum River basin were likely to be contributed by agricultural activities and seawater infiltration, respectively. The U in groundwater existed in the UO2(CO3)22− (namely, uranyl ion), and the highest concentration was observed at neutral pH. It had a positive correlation with HCO3, pH, and Ca and a weak negative correlation with NO3.
Comprehensive Environmental Assessment Index of Ecological Footprint
Authors: Khezri Mohsen , Razzaghi Somayeh , Mamghaderi Mahnaz , Heshmati Almas 
Published: September 2022

Abstract
This paper aims to contribute to the growing body of research literature on assessing environmental efficiency by introducing a new key performance indicator (KPIs) in more complete and dependable aspects of ecological footprint indices. For this purpose, the DEA model considering three inputs (energy consumption, labor force, and capital stock), one desirable output (GDP), and different undesirable outputs (CO2 emissions, ecological footprint indicators) are applied to 27 OECD countries from 2000 to 2017. According to the results, Norway, Luxemburg, and United Kingdom are the most environmentally efficient countries in both terms of environmental efficiency and ecological footprint efficiency. On the other hand, the lowest environmental and ecological footprint efficiencies were in countries like Lithuania, Slovak, Czech, Estonia, and the USA. In addition, these nations fare poorly regarding their carbon footprint and farmland efficiency. In further detail, Lithuania, South Korea, Portugal, and Spain have a critical status in fishing ground efficiency, while Estonia, Latvia, Lithuania, and the forest area efficiency is very acute in Estonia, Latvia, Lithuania, and Czech.JEL Classification Codes: F64; O44; O47; Q56; Q57