No need for the needle. A qualitative analysis of the antivax movement in Romania

The antivax movement is now a constant phenomenon with increasing social implications. This study explores how the antivax movement is articulated in Romania on the basis of qualitative analysis applied to interviews. Our pilot study focuses on the opinions of 100 persons who oppose vaccination interviewed between 2017 and 2020. We conducted both face-to-face and online semistructured interviews to trace the factors determining attitudes against vaccination. To the best of the authors’ knowledge, this is the first such extended study to target individuals rather than groups or media discourse. We strive to provide a multifaceted view on how the antivax phenomenon is taking shape. Responses varied in style and length, so we needed to systematize the narratives. We filtered the answers using the interpretive net described by Entman (1993), thereby grouping the main narratives into four sections. We then reconstructed the implicit frames used by individuals in interpreting their position. We consider content quality analysis to be a relevant method to reveal the facets and depth of the antivax phenomenon, thereby enabling more complex explanations. We compare the results of this study with rationales stemming from similar investigations conducted around the world and then highlight opinions specific to the Romanian public.

Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography

PurposeThe aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.Design/methodology/approachThis paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.FindingsAt the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.Originality/valueBCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

STRUCTURE OF THE DECISION SUPPORT INFORMATION SYSTEM BY THE NPP OPERATOR

Рассматривается структура системы информационной поддержки процессов принятия решений оператором АЭС в оперативных условиях. Анализируются функциональные возможности системы информационной поддержки оператора (СИПО) на примере Нововоронежской атомной электростанции (НВ АЭС). Данная система дает возможность оператору, управляющему распределенным комплексом технологических объектов АЭС, проводить качественный анализ и обработку больших объемов сложностpуктурированной информации и принимать своевременные адекватные решения в темпе реального времени. Кроме того, рассматривается объект управления и его структура, приводятся рекомендации, направленные на увеличение функциональных возможностей СИПО на базе искусственных нейронных сетей. Одной из многочисленных функций СИПО является прогнозирование состояния объекта управления на основе реализации программно-технологического комплекса модели энергоблока (ПТК МЭ). Однако существующая модель не способна учесть все факторы, влияющие на производственный процесс. Альтернативой здесь выступает искусственная нейронная сеть, которая в процессе обучения может сформировать искомые зависимости между большим числом параметров объекта управления и получить более полный и достоверный прогноз. Предложена структура искусственной нейронной сети на базе нечёткой системы вывода, которая реализует возможности нейронных сетей и нечеткой логики We considered the structure of the information support system for decision-making by the NPP operator in operational conditions. We analyzed the functional capabilities of the operator information support system (SIPO) using the example of the Novovoronezh nuclear power plant (NV NPP). This system provides the operator managing the distributed complex of NPP technological facilities to carry out high-quality analysis and processing of large volumes of complex structured information and make timely adequate decisions in real time. In addition, we considered the control object and its structure and made recommendations aimed at increasing the functionality of the SIPO based on artificial neural networks. One of the many functions of the SIPO is to predict the state of the control object based on the implementation of the software and technological complex of the power unit model. However, the existing model is not able to take into account all the factors influencing the production process. An alternative here is an artificial neural network, which in the learning process can form the required dependencies between a large number of parameters of the control object and get a more complete and reliable forecast. The proposed structure of an artificial neural network based on a fuzzy inference system, which implements the capabilities of neural networks and fuzzy logic

Computational studies on physico-chemical properties in the quality analysis of corn and peanut oil

Oils are commonly used in cooking as a frying medium which has been constantly subjected to different levels of heating. In this work, we have considered the most commonly used oils namely peanut oil and corn oil. Quality analyses of corn and peanut oils were made by relating macroscopic properties (ultrasonic velocity, viscosity, and density) to microscopic parameters (intermolecular free length, adiabatic compressibility etc.,) by subjecting them to six cycles of heating (190 ˚C). Variation in the mentioned property indexes, the degree of degradation and reusability for the next heating cycle that could be used in the food industry and processing were monitored. Using Newton-Laplace and Wood’s equation, the adiabatic compressibility, acoustic impedance, and intermolecular free length of the oil were estimated from the experimental data. Ultrasonic velocity was observed linearly as related to viscosity with the dependency factor (R2 = 0.932). With the aid of experiential data, the physical thermodynamic parameters, particularly particle size, packing factor, chemical potential, and L-J potential were computed. A high correlation factor was observed by fitting ultrasonic velocity, viscosity, and density to Parthasarathy and Bakshi, and Rodenbush equations. In the study, ultrasonic velocity, a macroscopic parameter, could be decoded to determine the microscopic variations in oil subjected to different temperatures in an industrial application.

Carbon Monoxide Emission and Air Quality Analysis Based on an Improved Double-Weight Fuzzy Comprehensive Evaluation

In this article, we adopt an improved double-weighted fuzzy comprehensive evaluation method to investigate the air condition of Hefei City from July 2016 to July 2021. We focus on the impact of the toxicity index, especially the impact of carbon monoxide, which is also considered in some other kinds of quality evaluation, such as water classification. Firstly, we found that with the increasing awareness of environmental protection and with the attention of the government to the quality of air in recent years, the air conditions have become better (the grades become lower). Secondly, the value of the factors, PM2.5, PM10, SO2, CO, NO2, and O3 periodically fluctuate from year to year; and the periodicity of O3 is reversed with the other factors. Finally, the monthly average analysis shows that the overall air quality is good; all the grades are I-II, except for December 2017 which has a grade III. Furthermore, the air quality in the winter (especially in December and January) is not always good.

Teaching Quality Analysis of College Ideological and Political Education Based on Deep Learning

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.

Subdaily meteorological measurements of temperature, direction of the movement of the clouds, and cloud cover in the Late Maunder Minimum by Louis Morin in Paris

We have digitized three meteorological variables (temperature, direction of the movement of the clouds, and cloud cover) from copies of Louis Morin’s original measurements (Source: Institute of History / Oeschger Centre for Climate Change Research, University of Bern) and subjected them to quality analysis to make these data available to the scientific community. Our available data cover the period 1665–1709 (temperature beginning in 1676). We compare the early instrumental temperature dataset with statistical methods and proxy data to validate the measurements in terms of inhomogeneities and claim that they are, apart from small inhomogeneities, reliable. The Late Maunder Minimum (LMM) is characterized by cold winters and autumns, and moderate springs and summers, with respect to the reference period of 1961–1990. Winter months show a significant lower frequency of westerly direction of movement of the clouds. This reduction of advection from the ocean leads to a cooling in Paris in winter. The influence of the advection becomes apparent when comparing the last decade of the 17th century (cold) and the first decade of the 18th century (warm). A lower frequency of westerly direction of movement of the clouds can also be seen in summer, but the influence is stronger in winter than in summer. Consequently, the unusually cold winters in the LMM can be attributed to a lower frequency of westerly direction of movement of the clouds. An impact analysis reveals that the winter of 1708/09 was a devastating one with respect of consecutive ice days, although other winters are more pronounced (e.g., the winters of 1676/77, 1678/79, 1683/84, 1692/93, 1694/95 and 1696/97) in terms of mean temperature, ice 15 days, cold days or consecutive cold days. An investigation of the cloud cover data revealed a high discrepancy in the seasons, where the winter season (DJF) (−13.2 %) and the spring season (MAM) (−12.6 %) show a negative anomaly of the total cloud cover (TCC), whereas summer (JJA) (−0.5 %) shows a moderate anomaly of TCC with respect to the 30 year mean of the Meteobluedata (1985–2014).

Chemical Pattern Recognition for Quality Analysis of Lonicerae Japonicae Flos and Lonicerae Flos Based on Ultra-High Performance Liquid Chromatography and Anti-SARS-CoV2 Main Protease Activity

Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.

What Is the Most Effective Technique for Bonding Brackets on Ceramic—A Systematic Review and Meta-Analysis

Background: There has been an increase in demand for orthodontic treatment within the adult population, who likely receive restorative treatments using ceramic structures. The current state of the art regarding the most effective method to achieve an appropriate bond strength of brackets on ceramic surfaces isn’t consensual. This systematic review aims to compare the available surface treatments to ceramics and determine the one that allows to obtain the best bond strength. Methods: This systematic review followed the PRISMA guidelines and the PICO methodology was used, with the question “What is the most effective technique for bonding brackets on ceramic crowns or veneers?”. The research was carried out in PubMed, Web of Science, Embase and Cochrane Library databases. In vitro and ex vivo studies were included. The methodological quality was evaluated using the guidelines for reporting of preclinical studies on dental materials by Faggion Jr. Results: A total of 655 articles searched in various databases were initially scrutinized. Sevety one articles were chosen for quality analysis. The risk of bias was considered medium to high in most studies. The use of hydrofluoric acid (HF), silane and laser afforded the overall best results. HF and HF plus laser achieved significantly highest bond strength scores in felsdphatic porcelain, while laser was the best treatment in lithium disilicate ceramics. Conclusions: The most effective technique for bonding brackets on ceramic is dependent on the type of ceramic.