DEVELOPING A THINKING CULTURE IN THE CLASSROOM: A PARTICIPATORY ACTION RESEARCH

In this study, cooperating with the classroom teacher, the researcher aimed to improve the thinking skills of students through teaching activities prepared based on the dimensions of thinking cultures. The research is designed using the qualitative paradigm in the participatory action research model. A total of six cycles were carried out during the thirty-three-week application process in line with the ‘cultures of thinking dimensions’ along the research process. The implementation process focusing on the cooperation of the teacher and researcher as a weekly cycle in the form of problem detection, literature review and seminar, preparation, implementation, monitoring and evaluation of the action plan. The action research reveals that developing a culture of thinking increases the diversity and frequency of utilization of the thinking skills by students. The results indicate, that although practices related to the culture of thinking approach contribute to the development of students’ thinking skills, this development is slow and the teacher’s motivation in this regard is an important variable.

Athleticism in the system of physical education of students of a higher education institution

The article, based on the analysis and generalization of data from literature sources and practical experience, identifies the main features of the organization and planning of athletics classes with student youth. Emphasis is placed on the fact that the nature and length of training cycles in athletics depends on the method of training, the nature of the means of recovery and patterns of development of sports form. It is emphasized that the effectiveness of training is determined by the weight of the load, the intensity of work, the number of approaches and repetitions, the sequence of individual exercises in training, the number of exercises in a weekly cycle and rest time between approaches. It is noted that the structure of a separate training session in athletics is characterized by general principles of construction, regardless of its content or form, and consists of three parts: preparatory, basic and final. The comparative analysis of the training equipment of various design is carried out. It is shown that simulators by dosed physical activity and targeted impact on certain muscle groups can selectively affect the musculoskeletal system, cardiovascular, respiratory and nervous systems. It is optimal to conduct athletics classes with student youth three times a week in the form of optional classes. This number is due to the fact that most students have a low level of physical fitness and health, namely the strength training is facilitated by the technique, which provides for three classes during the week cycle. One of the conditions for improving the efficiency of this process is to build an algorithm for the annual cycle of athletics, taking into account the peculiarities of the training schedule. This allows you to ensure the optimal dynamics of strength training depending on the periods of the semester, exam session, practice, winter and summer vacations. This algorithm allows to achieve correspondence between the factors of pedagogical influence and restorative measures, rational use of various tools and methods of force development, which allows to systematically and gradually increase the load in the process of training. During the first semester, students' strength abilities are improved. The first month (September) should be aimed at gradually preparing students for further strength training with a higher intensity and volume of strength. The intensity of the load this month is relatively small, but the volume sometimes reaches significant values.

Extreme Aerosol Events at Mesa Verde, Colorado: Implications for Air Quality Management

A significant concern for public health and visibility is airborne particulate matter, especially during extreme events. Of most relevance for health, air quality, and climate is the role of fine aerosol particles, specifically particulate matter with aerodynamic diameters less than or equal to 2.5 micrometers (PM2.5). The purpose of this study was to examine PM2.5 extreme events between 1989 and 2018 at Mesa Verde, Colorado using Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring data. Extreme events were identified as those with PM2.5 on a given day exceeding the 90th percentile value for that given month. We examine the weekly, monthly, and interannual trends in the number of extreme events at Mesa Verde, in addition to identifying the sources of the extreme events with the aid of the Navy Aerosol Analysis and Prediction (NAAPS) aerosol model. Four sources were used in the classification scheme: Asian dust, non-Asian dust, smoke, and “other”. Our results show that extreme PM2.5 events in the spring are driven mostly by the dust categories, whereas summertime events are influenced largely by smoke. The colder winter months have more influence from “other” sources that are thought to be largely anthropogenic in nature. No weekly cycle was observed for the number of events due to each source; however, interannual analysis shows that the relative amount of dust and smoke events compared to “other” events have increased in the last decade, especially smoke since 2008. The results of this work indicate that, to minimize and mitigate the effects of extreme PM2.5 events in the southwestern Colorado area, it is important to focus mainly on smoke and dust forecasting in the spring and summer months. Wintertime extreme events may be easier to regulate as they derive more from anthropogenic pollutants accumulating in shallow boundary layers in stagnant conditions.

Attention based multi-component spatiotemporal cross-domain neural network model for wireless cellular network traffic prediction

Wireless cellular traffic prediction is a critical issue for researchers and practitioners in the 5G/B5G field. However, it is very challenging since the wireless cellular traffic usually shows high nonlinearities and complex patterns. Most existing wireless cellular traffic prediction methods lack the abilities of modeling the dynamic spatial–temporal correlations of wireless cellular traffic data, thus cannot yield satisfactory prediction results. In order to improve the accuracy of 5G/B5G cellular network traffic prediction, an attention-based multi-component spatiotemporal cross-domain neural network model (att-MCSTCNet) is proposed, which uses Conv-LSTM or Conv-GRU for neighbor data, daily cycle data, and weekly cycle data modeling, and then assigns different weights to the three kinds of feature data through the attention layer, improves their feature extraction ability, and suppresses the feature information that interferes with the prediction time. Finally, the model is combined with timestamp feature embedding, multiple cross-domain data fusion, and jointly with other models to assist the model in traffic prediction. Experimental results show that compared with the existing models, the prediction performance of the proposed model is better. Among them, the RMSE performance of the att-MCSTCNet (Conv-LSTM) model on Sms, Call, and Internet datasets is improved by 13.70 ~ 54.96%, 10.50 ~ 28.15%, and 35.85 ~ 100.23%, respectively, compared with other existing models. The RMSE performance of the att-MCSTCNet (Conv-GRU) model on Sms, Call, and Internet datasets is about 14.56 ~ 55.82%, 12.24 ~ 29.89%, and 38.79 ~ 103.17% higher than other existing models, respectively.

Dietary Macronutrient and Micronutrient Intake over a 7-Day Period in Female Varsity Ice Hockey Players

This study examined the energy, macronutrient, and micronutrient intakes of female ice hockey players over a 7-d period including game, practice, and rest days. Twenty-three female varsity players (19.0 ± 1.1 yr, 167.1 ± 6.5 cm, 67.0 ± 8.0 kg) volunteered for the study. Average total daily energy expenditure (TDEE) was estimated over the 7-day period. Average 7-day energy intake (EI) and TDEE were 2354 ± 353 and 2304 ± 204 kcal. The majority (n = 19) of athletes had an EI ≥ 90% of their estimated TDEE. Macronutrient intake was 52% carbohydrate (CHO), 32% fat, and 16% protein of total EI, although CHO intake was slightly below recommendations (5 g/kg BM/d) on game and practice (4.8 ± 1.4 and 4.5 g/kg BM/d) days. Game day EI was greater than practice and rest days. Recommended micronutrient intakes were not met by most athletes for iron, calcium, vitamin D, and potassium, and intakes were similar between game, practice, and rest days. In summary, the average EI for female varsity ice hockey players appeared adequate to meet their energy needs over a weekly cycle of game, practice, and rest days. However, these female athletes would benefit from increasing CHO intake on game and practice days and selecting foods that are rich in vitamins and minerals.

Evaluation of TROPOMI/Sentinel-5 Precursor NO2 product against ground-based observations in Helsinki and first applications to Finnish society

<p>We evaluate the satellite-based TROPOMI (TROPOspheric Monitoring Instrument) NO2 products against ground-based observations in Helsinki (Finland). TROPOMI NO2 total (summed) columns are compared with the measurements performed by the Pandora spectrometer during April–September 2018. The mean relative and absolute bias between the TROPOMI and Pandora NO2 total columns is about 10% and 0.12 × 10<sup>15</sup> molec. cm<sup>-2</sup> respectively.<span> </span></p><p>We find high correlation (r = 0.68) between satellite- and ground-based data, but also that TROPOMI total columns underestimate ground-based observations for relatively large Pandora NO2 total columns, corresponding to episodes of relatively elevated pollution. This is expected because of the relatively large size of the TROPOMI ground pixel (3.5 × 7 km) and the a priori used in the retrieval compared to the relatively small field-of-view of the Pandora instrument. On the other hand, TROPOMI slightly overestimates relatively small NO2 total columns. Replacing the coarse a priori NO2 profiles with high-resolution profiles from the CAMS chemical transport model improves the agreement between TROPOMI and Pandora total columns for episodes of NO2 enhancement, but the overall bias remains the same (within the uncertainties).</p><p>In order to evaluate the capability of TROPOMI observations for monitoring urban air quality, we also analyse the consistency between satellite-based data and NO2 surface concentrations from the Kumpula air quality station in Helsinki. We find similar day-to-day variability between TROPOMI and in situ measurements, with NO2 enhancements observed during the same days. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities (as expected in urban sites).</p><p>Several applications have been already carried on to support informed decision making and Finnish society in general. We developed a simple web platform to inform environmental authorities at municipal level about the use of satellite observations for air quality monitoring. We assisted the Finnish authorities during the first period of the COVID-19 pandemic in assessing the effect of the lockdown on air quality. We supported the Finnish Ministry of Environment in compiling the periodic national air pollution assessment report to the EU. We participated in several international cooperation projects for assessing the major air pollution sources and the available air quality monitoring systems over several developing countries and for providing recommendations on strengthening air quality monitoring. We collaborated with the department of Social Science at the Univ. of Helsinki for the assessment of the environmental impacts of the energy and extracting sector in Yakutia (Russia).</p><p>Reference: Ialongo, I., Virta, H., Eskes, H., Hovila, J., and Douros, J.: Comparison of TROPOMI/Sentinel-5 Precursor NO<sub>2</sub> observations with ground-based measurements in Helsinki, Atmos. Meas. Tech., 13, 205–218, https://doi.org/10.5194/amt-13-205-2020, 2020.</p>

Weekly cycle of NOx emissions as laboratory of atmospheric chemistry

<p>Satellite observations provide unique information on the amount and spatial distribution of tropospheric NO2. Several studies use such datasets for deriving NOx emissions. However, due to nonlinearities in the NOx chemistry (i.e., the dependency of the OH concentration and thus the NO2 lifetime on the NO2 concentration), the observed column densities of NO2 are not directly proportional to the underlying NOx emissions. Consequently, a certain reduction in NOx emissions could result in disproportionate reduction of the corresponding NO2 columns, which could be stronger or weaker depending on the chemical state (O3, NOx and VOC levels) and conditions like temperature, humidity and acitinic flux. This effect complicates the quantification of NOx emissions from satellite measurements of NO2, and e.g. biases the emission reduction as derived from the reduction of NO2 column densities observed during recent lockdowns.  </p><p>Here we quantify the nonlinearity of the NOx system for different cities as well as power plants by investigating the effect of reduced NOx emissions on days of rest, i.e. Fridays/Sundays in Muslim/Christian culture, respectively. The reduction of NOx emissions is thereby quantified based on the continuity equation by calculating the divergence of the mean NO2 flux. This method has been proven to be sensitive for localized sources, where the uncertainties due to NO2 lifetime are small (Beirle et al., Sci. Adv., 2019). This reduction in emissions is then set in relation to the corresponding reduction of NO2 columns integrated around the source, which strongly depend on the NO2 lifetime.</p>

Ground-Based MAX-DOAS Observations of Tropospheric NO2 and HCHO During COVID-19 Lockdown and Spring Festival Over Shanghai, China

Reduced mobility and less anthropogenic activity under special case circumstances over various parts of the world have pronounced effects on air quality. The objective of this study is to investigate the impact of reduced anthropogenic activity on air quality in the mega city of Shanghai, China. Observations from the highly sophisticated multi-axis differential optical absorption spectroscope (MAX-DOAS) instrument were used for nitrogen dioxide (NO2) and formaldehyde (HCHO) column densities. In situ measurements for NO2, ozone (O3), particulate matter (PM2.5) and the air quality index (AQI) were also used. The concentration of trace gases in the atmosphere reduces significantly during annual Spring Festival holidays, whereby mobility is reduced and anthropogenic activities come to a halt. The COVID-19 lockdown during 2020 resulted in a considerable drop in vertical column densities (VCDs) of HCHO and NO2 during lockdown Level-1, which refers to strict lockdown, i.e., strict measures taken to reduce mobility (43% for NO2; 24% for HCHO), and lockdown Level-2, which refers to relaxed lockdown, i.e., when the mobility restrictions were relaxed somehow (20% for NO2; 22% for HCHO), compared with pre-lockdown days, as measured by the MAX-DOAS instrument. However, for 2019, a reduction in VCDs was found only during Level-1 (24% for NO2; 6.62% for HCHO), when the Spring Festival happened. The weekly cycle for NO2 and HCHO depicts no significant effect of weekends on the lockdown. After the start of the Spring Festival, the VCDs of NO2 and HCHO showed a decline for 2019 as well as 2020. Backward trajectories calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model indicated more air masses coming from the sea after the Spring Festival for 2019 and 2020, implying that a low pollutant load was carried by them. No impact of anthropogenic activity was found on O3 concentration. The results indicate that the ratio of HCHO to NO2 (RFN) fell in the volatile organic compound (VOC)-limited regime.

Analysis of the station’s track development load (according to the archives of railway switches and signal centralized systems)

The development of equipment and technology of train operation, railway automation systems and new methods of train driving open the way for further intensification of train operation, reducing the time spent on standard operation procedures. Virtual coupling allows you to reduce a train-to-train interval. Automated schedules of the executed movement record performance of train operation, automate the formation of a train-dispatcher’s reports and delivery of orders to station attendants, allow timely detection of the causes of train delays and take appropriate measures. The points of growth in traffic intensity and volume could be in enhancing the level of intelligence of automation and telemechanics systems. Modern dispatch and electrical centralized systems contain archiving subsystems that store prompt train operation conditions and operator orders. But such data is used only when analyzing emergency situations - there is no systematic analysis of archive information to find hidden patterns and extract knowledge useful for optimizing the transportation process. In this paper, we propose a method for estimating the current train operation load of a railway station, and provide relative criteria for evaluating the actual load and algorithms for determining their numerical values. The analysis of the time series of the proposed criteria obtained for a single station equipped with a relay-processor centralized system is carried out. The analysis indicated a weekly cycle of train operation load of a station with good repeatability. The fact of reducing the speed of trains before the input signal in the through-pass routes with a preset route was revealed. Smoothing out the identified irregularities can positively affect the carrying capacity of a railway section as a whole.