Physics Students’ Conceptual Understanding of Electricity and Magnetism in Nine Years Basic Education in Rwanda

We investigate students’ misconceptions in electrostatics, direct current (DC) and magnetism which are important in electricity and magnetism. We developed and administered a multiple-choice questionnaire test to reveal students’ misconceptions related to charged bodies, lightning, electric fields, electric potential, forces, DC resistive electric circuits and magnets. This test aimed at obtaining quantitative information about misconceptions and was administered to 380 senior two students from Nine Year Basic Education (9YBE) Schools. The selected students have some experience with the new Rwandan secondary physics Competence Based Curriculum (CBC) that is currently under implementation. We find that senior two students have several common misconceptions related to these concepts. The data indicate that although students have some backgrounds on the subject matter, they still seem to believe that if the two charges are separated by a distance, a large-charged object exerts a greater force of attraction or repulsion on the small one. Considerable number of participated students held the misconception of considering current consumption in the resistor/bulb or the electrical devices in the circuits. They also believed that the battery was a continuous current source. The findings also revealed that students held a misconception that a bar magnet when broken into pieces, it is demagnetized. Moreover, a considerable number of participants hold the misconception that all metals are attracted by a magnet. Our study also revealed some of the statistically significant differences in terms of either gender or location of schools for some items.

Quantify pixel-level detection of dam surface crack using deep learning

An increasing number of detection methods based on computer vision are applied to detect cracks in water conservancy infrastructure. However, most studies directly use existing feature extraction networks to extract cracks information, which are proposed for open-source datasets. As the cracks distribution and pixel features are different from these data, the extracted cracks information is incomplete. In this paper, a deep learning-based network for dam surface crack detection is proposed, which mainly addresses the semantic segmentation of cracks on the dam surface. Particularly, we design a shallow encoding network to extract features of crack images based on the statistical analysis of cracks. Further, to enhance the relevance of contextual information, we introduce an attention module into the decoding network. During the training, we use the sum of Cross-Entropy and Dice Loss as the loss function to overcome data imbalance. The quantitative information of cracks is extracted by the imaging principle after using morphological algorithms to extract the morphological features of the predicted result. We built a manual annotation dataset containing 1577 images to verify the effectiveness of the proposed method. This method achieves the state-of-the-art performance on our dataset. Specifically, the precision, recall, IoU, F1_measure, and accuracy achieve 90.81%, 81.54%, 75.23%, 85.93%, 99.76%, respectively. And the quantization error of cracks is less than 4%.

Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning

The salinity level of the growing medium has diverse effects on the development of plants, including both physical and biochemical changes. To determine the salt stress level of a plant endures, one can measure these structural and chemical changes. Raman spectroscopy and biochemical analysis are some of the most common techniques in the literature. Here, we present a combination of machine learning and Raman spectroscopy with which we can both find out the biochemical change that occurs while the medium salt concentration changes and predict the level of salt stress a wheat sample experiences accurately using our trained regression models. In addition, by applying different machine learning algorithms, we compare the level of success for different algorithms and determine the best method to use in this application. Production units can take actions based on the quantitative information they get from the trained machine learning models related to salt stress, which can potentially increase efficiency and avoid the loss of crops.

Using Micro-CT Scanning to Quantitative Characterize Porosity in High Pressure Die Castings and Semi-Solid Castings

Porosity is one of the main defects that limits the performance of castings. Porosity in aluminum castings can originate from several sources, including the volumetric shrinkage occurring during solidification, the precipitation of dissolved hydrogen, and entrapment of gasses such as air, boiling water, vaporized lubricants, etc. Traditional methods of identifying and measuring porosity in castings include 2D x-rays, sectioning and polishing, and Archimedes density measurements, but none of these provide a satisfactory quantitative estimate of the size, total volume and distribution of the pores. X-ray CT scanning is a relatively new method that generates not only a 3-dimensional view of the size and distribution of the pores, but can also provide quantitative information of the volume, surface area, size, shape and position of each pore within a casting. Micro-CT scanning is a specialized sub-category of CT scanning, which provides excellent resolution of fine porosity (a resolution limit of 4 microns in one of the case-stores presented in this paper), but it should be noted that the resolution limit in CT scanning techniques is related to sample size. This paper describes results from micro-CT scanning studies of two high pressure die castings and a semi-solid casting, and provides quantitative data on the total porosity content, and the porosity distribution. The paper will also demonstrate the capabilities of the micro-CT scanning process to provide a quantitative comparison of the porosity content in these different types of aluminum castings.

Self-confidence attitude of novice primary teachers reflection on teaching mathematics

The teacher’s attitude towards mathematics teaching is seen as an essential factor in forming students’ attitudes towards mathematics. However, no one has extensively described the reflection of teachers’ self-confidence in teaching mathematics, especially for novice primary teachers. Therefore, the purpose of this study sought to describe a reflection of the self-confidence attitude of novice primary teachers in teaching mathematics. A questionnaire based on novice primary teachers’ teaching experience was administered to a total of 28 novice primary teachers (N = 22 males, N = 6 females) conveniently selected to participate in the study reported in this article. The semi-structured interviews data explored novice primary teachers’ reflections on the given questionnaire scale items. The qualitative data obtained from semi-structured interviews informed the quantitative information extracted from the questionnaires. The results showed that the reflection of the self-confidence attitude of novice primary teachers in low, moderate, and high participants on the scale of confidence in teaching mathematics raises three essential findings, specifically (1) ability on content knowledge, (2) ability to explain, and (3) ability in classroom management. The resulting reflection in low, moderate, and high participants on the scale was an attitude toward success in teaching mathematics, namely, the appraisal of others, and on the scale, the usefulness of mathematics teaching, namely the ability to understand the usefulness of mathematics.

Hygroscopicity of Polycatechol and Polyguaiacol Secondary Organic Aerosol in sub- and supersaturated water vapor environments

Polycatechol and polyguaiacol are light-absorbing and water-insoluble particles that efficiently form from iron-catalyzed reactions with aromatic compounds from biomass burning emissions. Little quantitative information is known about their water uptake...

Dimensional analysis of partial discharge initiated by a metallic particle adhering to the spacer surface in a gas-insulated system

Partial discharges (PDs) constitute important phenomena in a Gas-Insulated System (GIS) that warrant recognition (and, subsequently, mitigation) as they are obvious symptoms of system degradation. This paper proposes the application of dimensional analysis, based on Buckingham pi theorem, for characterizing PDs provoked by the presence of metallic particles adhering to the spacer surface in a GIS employing SF6 (Sulphur hexafluoride). The ultimate goal of the analysis is to formulate the relationships that express three PD indicator quantities, namely current, charge, and energy, in terms of six independent quantities that collectively influence these indicators. These six quantities (henceforth referred to as the influencing, determining or affecting variables) include the level of applied voltage, the SF6 pressure, the length and position of the particle on the spacer, the duration of voltage application, and the gap between electrodes. To compute the pertinent dimensionless products, we implement three computational methods based on matrix operations. These three methods produce exactly the same dimensionless products, which are subsequently used for constructing the models depicting the relationships between each of the three PD dependent quantities and the common six determining variables. The models derived provide partial quantitative information and facilitate qualitative reasoning about the considered phenomenon.

Diatom response to environmental gradients in the high mountain lakes of the Colombia’s Eastern Range

A survey of 60 high mountain lakes of Colombia’s Eastern Range was performed to evaluate the response of surface-sediment diatoms to environmental variables. In each one of these lakes, water samples were taken for physical and chemical characterization, and diatoms were collected from the superficial bottom sediment at the deepest part. Multivariate statistical analyses were made to determine the relationships between environmental and biological data, specifically which environmental variables explain the diatom distribution. For each of these significant environmental variables, optima and ecological tolerances were calculated using the weighted-average method, which allowed for the classification of the species according to their environmental preferences. The lakes showed a wide range of environmental gradients in variables such as pH, alkalinity, and nutrients. In addition, the depth of the lakes was a direct determinant of the light environment of the water column. A total of 339 diatom taxa were identified belonging mainly to the genera Eunotia and Pinnularia. Variables related to pH-alkalinity gradient, trophic condition (nitrates and phosphorus), and physical factors (radiation at the bottom) had a significant effect on diatom composition. Despite the fact that the total organic carbon environmental range was high, the effect of this variable on diatom species composition was not significant. In conclusion, the diatoms of the studied lakes showed a significant ecological relationship with environmental variables which are potentially important in environmental reconstruction. Diatoms in the study sites can provide useful and independent quantitative information to investigate the recent impacts of global change on tropical high mountain ecosystems.

Qualitative Modeling for Bridging Expert-Knowledge and Social-Ecological Dynamics of an East African Savanna

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modeling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modeling frameworks have proved useful for explaining ecosystem responses to perturbations, while only requiring qualitative information about social-ecological interactions and events and providing more general predictions due to their validity for wide ranges of parameter values. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modeling framework based on “if-then” rules generating non-deterministic dynamics. Based on expert knowledge, observations, and literature, we use EDEN to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups have highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modeling.

Impact of Recruitment and Selection Practices on Teaching Faculties Satisfaction at Community Colleges of Nepal

The process of attracting, evaluating, and hiring individuals for an organization is known as recruitment. Selection is the process of identifying an individual from a pool of job applicants with the requisite qualifications and competencies to fill jobs in the organization. The purpose of this paper is to explore the effects of recruitment and selection practices on teaching faculty satisfaction in community colleges. The researcher adopted a Qual-Quan approach with a descriptive and cross-sectional research design. A structured questionnaire was applied for quantitative information collection from 49 respondents, and an FGD was conducted to collect qualitative information. Stratified and random sampling techniques were used to select the sample from the targeted population, and data processing was done using SPSS version 26. In order to reach a conclusion, ANOVA, Chi-square and frequency statistical tools were used for data analysis. The analyses showed there was a significant impact of recruitment and selection practices on teaching faculty satisfaction in community colleges of Nepal.