bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scaffolds based on chitosan with phenolic acids for tissue engineering – in vivo assessment
Authors: Kaczmarek-Szczepańska Beata , Polkowska Izabela , Paździor-Czapula Katarzyna , Nowicka Beata , Otrocka-Domagała Iwona 
Published: July 2022

Abstract
Chitosan-based scaffolds modified by gallic acid, ferulic acid, and tannic acid were fabricated. The aim of the experiment was to study the compatibility of scaffolds by in vivo method. For such purpose materials were implanted into rabbit in the middle of the latissimus dorsi muscle length. Scaffold based on unmodified chitosan was implanted by the same method as a control. The results showed that all studied materials were safe and nontoxic. However, chitosan scaffolds modified by gallic acid and tannic acid were resorbed faster and as a result tissues were organized faster than those modified by ferulic acid or unmodified.
Analysis of Reaction Forces in Fixture Locating Points: An Analytical, Numerical, and Experimental Study
Authors: Parvaz Hadi , Hosseini Seyed Vahid 
Published: July 2022

Abstract

Reaction forces are one of the key parameters in the fixture design procedure. They are generated at the fixture locating points by application of the clamping forces and machining loads. They are used as input values in several stages of the fixture design procedure. In this paper, an analytical model has been developed based on the minimum norm principle to calculate these forces at the fixture locating points. Numerical simulations and experimental tests have been performed on a 3D polyhedral workpiece to validate the theoretical predictions. The simulations have been performed using Abaqus® software and experimental tests have been conducted using an experimental setup consisting of a fixture and a 3D polyhedral workpiece. An appropriate agreement was observed between the theoretical, numerical, and experimental results for the normal component of reaction forces. The maximum errors of 3.9% and 15% were observed between the theoretical predictions compared to the numerical and experimental results, respectively. The effects of the influential parameters, including coefficient of friction and clamping force, were also studied on the results. The agreement between the theoretical, numerical, and experimental results indicated the efficiency of the proposed model in the rapid calculation of reaction forces in fixturing 3D polyhedral workpieces.
Stateless and Rule-Based Verification For Compliance Checking Applications
Authors: Besharati Mohammad Reza , Izadi Mohammad , Asgari Ehsaneddin 
Published: July 2022

Abstract
Context: Underlying computational model has an important role in any computation. The state and transition (such as in automata) and rule and value (such as in Lisp and logic programming) are two comparable and counterpart computational models. Both of deductive and model checking verification techniques are relying on a notion of state and as a result, their underlying computational models are state dependent. Some verification problems (such as compliance checking by which an under compliance system is verified against some regulations and rules) have not a strong notion of state nor transition. Behalf of it, these systems have a strong notion of value symbols and declarative rules defined on them. Objective: SARV (Stateless And Rule-Based Verification) is a verification framework that designed to simplify the overall process of verification for stateless and rule-based verification problems (e.g. compliance checking).Method: In this paper, a formal logic-based framework for creating intelligent compliance checking systems is presented. We define and introduce this framework, report a case study and present results of an experiment on it. Results: The case study is about protocol compliance checking for smart cities. Using this solution, a Rescue Scenario use case and its compliance checking are sketched and modeled. An automation engine for and a compliance solution with SARV are introduced. Based on 300 data experiments, the SARV-based compliance solution outperforms famous machine learning methods on a 3125-records software quality dataset. Conclusions: The proposed SARV method could be beneficial for stateless verification in compliance checking applications.
Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants
Authors: Yengec-Tasdemir Sena Busra , Akay Ebru , Dogan Serkan , Yilmaz Bulent 
Published: July 2022

Abstract
In this work, we propose a computer-aided diagnosis system for automatic classification of adenomatous polyps from colonic histological images. The proposed method employs an ensemble of ConvNeXt variants, which is one of the most recent and prominent convolution based deep learning architectures. Comprehensive experiments show that classification performance of the proposed method outperforms the state-of-the-art Deep CNN models on our dataset, with a 95% accuracy. It achieves 91.1% and 90% classification accuracy respectively on EBHI and UniToPatho datasets, which shows that the model can generalize well for other datasets as well. Furthermore, we investigate the effect of stain normalization preprocessing on the classification accuracy. While the other methods are sensitive to the normalization, the proposed method retains its accuracy levels under the variations, which also indicates a high generalization ability. Lastly, we use the Grad-Cam method and show that the proposed model gives more attention to the regions where the cancer indicators potentially reside.
Financial technology and financial inclusion in Africa
Authors: ALHASSAN ABDUL LATIF , Yengeni Sandisiwe 
Published: July 2022

Abstract
This paper examines the effect of financial technology (FINTECH) on financial inclusion in 36 African countries for 2011, 2014 and 2017 in a two-stage analysis. In the first stage, data on access, usage and availability of financial services is employed to compute financial inclusion index (FII) using the Principal Component Analysis. In the second stage, a panel data regression model is estimated using the fixed and random effects techniques to examine the effect of proxies of financial technology of the FII. The findings suggests that the sample is characterized by low levels of financial inclusion. From the panel regression analysis, FINTECH is observed to have a positive effect on financial inclusion. Specifically, the use of mobile accounts is observed to have the greatest impact on financial inclusion compared to the use of digital payments from traditional bank accounts. The policy implications of the findings are discussed.
Horizon Scale tests of Quantum Gravity using the Event Horizon Telescope observations
Authors: Marongwe Stuart 
Published: July 2022

Abstract
We test the fundamental physics of the Nexus Paradigm of quantum gravity
using horizon scale images and shadows of the supermassive black holes
(SMBHs) Sagittarius (Sgr)A* and M87* as imaged by the Event Horizon Telescope
(EHT). We consider the angular diameter of the emission ring peak as
predicted by the Nexus Paradigm including the size of the central dark
depression and the base diameter of each black hole. The predicted dimensions
are then compared to observations from which we obtain an excellent agreement
between observations and prediction at the 99th percentile credibility
interval.
Rapid Optimization of Chlorella protothecoides Biomass Production from Low-Cost Biowaste Under Mixotrophic Conditions
Authors: Zhang Ning , Bridges William C. , Walker Terry H. 
Published: July 2022

Abstract
Mixotrophic cultivation of Chlorella protothecoides was carried out in this study with an artificial light source. The culturing medium was supplemented with crude glycerol by-product from a biodiesel facility as an organic carbon source, and the spent yeast from a local brewery was used as an organic nitrogen source. Experiments were performed based on a 3×3 factorial design, with algal biomass (g/L) as the response. Data was fitted into a response surface model to investigate the impact on biomass of 3 factors: light intensity, carbon concentration and nitrogen concentration. According to the model, a maximal biomass of 20.94 g/L could be reached at a light intensity of 100 µmol m− 2s− 1, carbon concentration of 24.1 g/L and nitrogen concentration of 0.7 g/L.
Quantitative Study of Exoplanets and Calculation of Occurrence Rate
Authors: Mistry Priyashkumar , Pathak Kamlesh , Sonvane Yogesh 
Published: July 2022

Abstract
We present occurrence rates and statistical research of the exoplanets and their host stars discovered by various space-based and ground-based observatories. The data for the same was collected from NASA Exoplanet Archive. This data suggests that exoplanets are more likely to be occurred around the stars of the spectral types F, G and K. We also check for nearby observable sky regions and the population of different stars to ensure that our analysed results are not due to satellite observational constraints but rather to the unique characteristics of different stars. The calculated frequency of occurrence based on the concept of joint probability of two dependent events suggests that for Kepler Planetary Occurrence Rate can be ranked as G > K > F, and for TESS it is K > G > F. We calculated μ ± 1.645σ interval for visual magnitudes of parent stars. For TESS satellite it is carried out to be 7.53 - 14.58, which is comparable to earlier prediction of 8.2 - 13.1. Results suggest that TESS planets typically orbit stars with 3.4568 ± 1.3516 magnitudes brighter than Kepler planets and 1.9801 ± 0.6495 magnitudes brighter than K2 planets. The study based on exoplanetary radii showed that most confirmed exoplanets are smaller than the 6R_E, in which many exoplanets have radii in the range of 1-3 R_E. The findings of this study offer a novel research opportunity for exoplanet detection scientists, particularly in terms of identifying potential candidates from large amounts of satellite data.
Analysis of the Change of Productive Matrix of Fossil Fuels by Biofuel in Ecuador
Authors: Nevárez Eddie Zambrano , Murillo Sandra Peña , Correa William 
Published: July 2022

Abstract
The search for energy for humanity’s various activities led to the discovery of fossil fuels such as coal, oil, and natural gas. Today, fossil fuels provide 85% of the energy used worldwide. The use of fossil fuels causes environmental damage that has become more noticeable in recent decades. For this purpose, information is compiled through a bibliographic review of reliable sources such as Springer, Scielo, and other documentary sources, highlighting the advantages and disadvantages of a change in the productive matrix from fossil fuels to biofuels in Latin America. It is concluded that a significant investment must be made to make a difference in the abundant matrix; however, there is a great variety of raw materials in the Latin American area to obtain biofuels, and the necessary technology to process them is not available.
Association of obesity, diabetes, and hypertension with arsenic in drinking water: a case-control study in the CERHA region in north-central Mexico
Authors: Sánchez-Rodríguez B.L. , Castillo-Maldonado I. , Pedroza-Escobar D. , Delgadillo-Guzmán D. , Soto-Jimenez Martin Federico 
Published: July 2022

Abstract
Chronic endemic regional hydroarsenicism (CERHA) occurs worldwide, with millions of people exposed to arsenic (As) in drinking water. CERHA is present in north-central Mexico, where As typically exceeds the WHO guideline (10 µg L− 1) and Mexican limits (25 µg L− 1). Biochemical alterations related to the human As metabolism may increase the risk of obesity, type 2 diabetes (T2D), and hypertension (AHT). We conducted a case-control study to address the role of As in drinking water as a risk factor for those metabolic diseases in exposed populations with low (Lerdo) and high (San Pedro) exposition and in people inhabiting CERHA and non-CERHA municipalities. In drinking water and human urine, arsenic was analyzed by high-resolution inductively coupled plasma mass spectrometry—a significant correlation between As in drinking water and urine evidenced the As exposure in the population. Arsenic in drinking water (16.8 ± 9.2 vs. 39 ± 27.9 µg L− 1) and urine (6.20 ± 5.62 vs. 18.48 ± 21.56 µg L− 1) were lower in Lerdo than in San Pedro muncipality. Adjusted odds ratios evidenced higher (1.7–1.8) chances to be diagnosed with T2D and AHT in San Pedro than in Lerdo people. Also, a higher risk of obesity (1.3–1.4), T2D (1.5 to 3.3), and AHT (1.6 to 2.4) occurs in CERHA than in non-CERHA towns.
Smart Bacterial Foraging considering Evolutionary and Heuristic Strategies for Optimization practices
Authors: Samare-Hashemi M. , Daryabeigi E. 
Published: July 2022

Abstract
In this paper, a smart bacterial foraging optimization method considering evolutionary and heuristic strategies (SBFEH) is developed, which enjoys an individual intelligence (how to achieve a goal) and social intelligence (how to collaborate or compete with others for resources). The algorithm inspires some evolutionary and physiological heuristic strategies, which provide a higher performance in terms of efficiency and effectiveness. In other words, the exploration and exploitation of the proposed algorithm are improved. The proposed algorithm is applied on different benchmark functions including some CEC05/13 functions and two well-known engineering design optimization problems, the results obtained show that the SBFEHS algorithm is either more accurate and scalable or competitive than those algorithms available in the literature. In addition, a practical water management problem-based optimization is deal with the proposed algorithm in comparison with other state of art solutions.
Analysis of Volatile Compounds in Ficus Carica based on Regions, Cultivars, and Drying Treatments using GC-IMS
Authors: liu xinyu , Zhou Jiangwei , Jia Ming , Sun Rui , Sun Lei 
Published: July 2022

Abstract
Fig is a commonly consumed fruit worldwide, and its volatile compounds are influenced by many factors. To study the aroma changes in fresh and dried figs of China, the volatile compounds of figs from various regions and cultivars and subjected to different drying treatments were analyzed using gas-ion migration technique, and statistical analysis was conducted using fingerprints combined with principal component analysis. The results showed that the main aromatic compounds in Chinese figs were ethyl acetate (31.5–43.0%), benzaldehyde (0.8–12.7%), (E)-2-hexenal (1.9–56.8%), hexanal (0.8–12.7%), ethyl 2-methylbutyrate (2.6–8.8%) and butanoic acid methyl ester (6.6–10.7%). The peel was the main source of volatile compounds; however, there were significant differences in the amount volatile compounds based on regions, cultivars, and drying methods. We revealed that aldehydes, such as furfural and nonanal, in fresh fruits are important markers to distinguish the three cultivars, Bojihong, Branswick, Banane. We also found that after drying, figs produce four main aldehydes (3-methylbutanal, pentanal, butanal, and 2-methylpropanal) and a small amount of ketones, alcohols, and ethers, and that most esters are lost completely. Our findings provide a comprehensive explanation of how fig volatile compounds are influenced by different factors and a scientific basis for future studies on fig selection, fresh fruit consumption, and processing.
Using Delphi method to develop Chinese women’s cervical cancer screening intention scale based on planned behavior theory
Authors: Xin Tingting , Ding Xian , Gao Han , Li Chunting , Jiang Yuting , Chen Xiao 
Published: July 2022

Abstract
Background
Cervical cancer is the most common malignant tumor in women with a high mortality rate. However, the awareness and participation of women in cervical cancer screening are not high, and rare attention was paid to cervical cancer screening. The extensive promotion and execution of cervical cancer screening in China are still facing difficulties. In order to fully comprehend and evaluate the barriers and promote factors of cervical cancer screening in women, the objective of this study is to develop a scientifically sound and clinically useful Chinese cervical cancer screening intention scale. This study will allow for the development of targeted interventions which may contribute to the increase of individual participation in cervical cancer screening going forward.
Methods
This study used the Delphi method to construct a Chinese cervical cancer screening intention scale based on the theory of planned behavior (TPB) and evaluate its validity. The study was based on the overview of the TPB questionnaire proposed by Ajzen, and was conducted through the literature search and two rounds of the Delphi expert consultation. According to the literature search published from 2012 to 2022, the scale item pool was established and a questionnaire was designed. A survey of 16 experts from 6 different provinces, cities and regions in China was conducted, and the Delphi technique was used to collect and analyze expert opinions data.
Results
The final scale consisted of 4 dimensions and 23 items. The response rates in two rounds of expert consultation were 80% and 93.75%, respectively, with authority coefficients of 0.928 and 0.930. Variation coefficients varied from 0.07 to 0.21. Dimensions included “behavioral attitude”, “subjective norm”, “perceived behavioral control” and “behavioral intention”.
Conclusions
Women’s cervical cancer screening intentions could be assessed with the scale, since it had high validity and reliability, as well as high authority and coordination, meanwhile affording explanations and improving the efficiency of interventions.
Hybrid manufacturing process planning with predictive approach: a comprehensive literature review
Authors: Velázquez Daniel René Tasé , Simon Alexandre Tadeu , Helleno André Luís 
Published: July 2022

Abstract
The hybrid manufacturing has attracted industrial community interest due to its ability to produce functional parts with varied geometries, using different types of materials in the most varied applications. As with most manufacturing processes, before executing the hybrid process for the manufacture of a certain geometry, it is important to make a proper process planning that allows having an integral and proactive view of the operation. This work aims to synthesize through a systematic literature review (SLR) the state of the art on hybrid manufacturing process planning (HMPP) models with predictive approach. Three SLRs were developed to extract the articles of interest (2012-2022) from Science Direct, Springer Link, and Web of Science databases. PRISMA method was used to gather the papers and oriented the qualitative and quantitative analysis. Snowballing technique was used to gather articles of interest out of the period defined. Literature review showed that there is still no consensus between existing HMPP models, regarding all the activities necessary to ensure proper operation. In addition, the developed models have a limitation regarding the inclusion of a process simulation step. The simulation step on the existing models, does not allow to proactively check whether the experimental process conditions are adequate to ensure the manufacture of the modeled geometry before running the hybrid manufacturing process. An efficient simulation step would ensure a predictive approach to operation performance and would imply the reduction of geometric deviations without the need for excessive experimentation, reducing material consumption, manufacturing time, and resulting in a better deposition strategy. Using computational and statistical methods as support for decision-making in the process will allow the processing and analysis of data to assertively direct the structuring of a robust model for HMPP with predictive approach. A differentiated, more complete, and comprehensive process planning model is a relevant study opportunity for future research. This paper contributes to the theory development since it identifies and analyzes the existing HMPP models, highlights important gaps on the subject, and proposes insights for future development.
Gene expression profiling-based identification of different influenza patient molecular subgroups
Authors: Chen Liang , Hua Jie , He Xiaopu 
Published: July 2022

Abstract
Background
Influenza infections routinely circulate throughout the world, resulting in extensive patient morbidity and mortality. Even so, the molecular mechanisms underlying patient responses to influenza remain poorly understood. The present study was thus developed with the goal of classifying influenza patients into different molecular subgroups based on gene expression profiling data and assessing the relationship between these subgroups and disease severity.
Methods
The GSE101702 and GSE21802 microarray datasets were downloaded from the Gene Expression Omnibus, and the influenza cases in these datasets were classified via consensus clustering into differential gene expression profile-based subgroups. Transcriptomic differences among these subgroups were assessed via weighted gene co-expression network analysis (WGCNA), and enrichment analyses for individual WGCNA modules were then performed.
Results
In total, 143 influenza patients were separated into two distinct molecular subgroups through a consensus clustering approach. Patients in subgroup II exhibited increased influenza disease severity independent of age. In total, 8 subgroup-specific WCGNA modules were defined, and functional enrichment analyses revealed that relative to patients in subgroup I, those in subgroup II exhibited significant upregulation of immune response and inflammation-related pathways together with ribosome biogenesis pathway downregulation.
Conclusions
The transcriptomic classification of influenza patients performed herein was linked to disease severity, offering insight into the molecular pathogenesis of severe influenza infections.
Verteporfin Imparts Cell Cycle Arrest and Induces Apoptotic Machinery in TNBC Cells Through Sabotaging YAP Transactivation
Authors: Parambil Sulfath Thottungal , Antony Gisha Rose , Littleflower Ajeesh Babu , S Lakshmi 
Published: July 2022

Abstract
Triple-negative breast cancer (TNBC) subtype endows distinctive biological features including aberrant proliferation, deducing the molecular mechanism harmonizing the TNBC characteristics is crucial for the greater understanding and prognosis of the disease. The aim of the present study was to analyze the anti-tumorigenic effects of Verteporfin (VP) in TNBC in vitro. We evaluated the tumorigenic properties of TNBC cells upon verteporfin treatment to assess the cell viability, apoptosis, cell cycle, cell survival, and protein/mRNA expressions in MDA-MB-231 and SUM-159 breast cancer cells. Transient silencing of YAP was performed to validate the data. TNBC cells exhibited a comparatively higher active YAP downstream signaling and VP resulted in the nuclear exclusion of YAP to a significant extent. Verteporfin inhibits the proliferation of TNBC cells by altering cyclin-dependent kinase inhibitors, thereby rendering cellular arrest at the G0/G1 phase. In a dose-dependent manner, verteporfin induces the apoptotic machinery in TNBC cells. Moreover, transient silencing of YAP in TNBC cells exhibited a similar pattern of anti-proliferative effects. Elevated YAP nuclear activity and downstream signaling in TNBC is associated with sustaining the proliferative capacity of the cells by inhibiting the cellular apoptosis. Verteporfin induces anti-proliferative effects on TNBC via cytoplasmic retention of YAP. Consequently, cells rewire the course of the cell cycle and stimulate cellular death. We suggest YAP signaling as a prerequisite for the TNBC cell progression and verteporfin without light activation exerts anti-tumorigenic effects on TNBC cells.