“Professionalization” or “Proletarianization”: Which Concept Defines the Changes in Teachers’ Work?

AbstractIn many parts of the world, particularly starting from 1980 a set of transformations is being experienced in the field of education. With these transformations while the meaning and content of the education changes, the teachers who are the most basic actors of education field are expected to keep pace with this change process and to be even active agents of this process. Therefore, it is possible to talk about the fact that some significant changes in education, employment and working conditions of teachers have been taking place in recent times. The discussions in literature in regards to efforts to understand and explain the change and transformations experienced in the profession of teaching are usually based on two main approaches. While in the first approach it is claimed that teachers are being “professionalized” over time, in the latter approach it is claimed that teachers are not professionals no longer, on the contrary, with the transformation process they are increasingly getting deskilled and hence are being “proletarianized”. The aim of this study is that within the axis of profession of teaching and by focusing on theoretical discussions regarding “professionalization” and “proletarianization”, to establish a theoretical framework to understand the changes experienced in teachers’ work

Analysis The Influencing Factors of Urban Traffic Flows by Using Emerging Urban Big Data

This research applies spatial Durbin model to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. The results show that the overall built environment within a buffer area has more significant impact on urban traffic flow compared to the nearby location within a few meters. Areas with more young and white dwellers are associated with more traffic flows. With the influence of COVID-19, residents prefer to spend their daily life in their local neighborhood rather than having long distance travel. The initial findings from this research provide evidence of developing 20-minute city via active travel for achieving net-zero carbon target

Equalizing urban agriculture access in Glasgow: a spatial optimization approach

Glasgow, Scotland, United Kingdom, has long-term issues with inequalities in health and food security, as well as large areas of vacant and derelict land. Urban agriculture projects can increase access to fresh food, improve mental health and nutrition, and empower and bring communities together. We investigated the distribution of urban agriculture in Glasgow and found that the current configuration of urban agriculture projects is mostly located centrally in the city, covering 36 % of the total population (approximately 635,000) within 10-minute walking distance. We also found a positive correlation (r = 0.13, p = 0.0003) between the walking travel time to the nearest urban agriculture project and the food desert status. To increase urban agriculture access across the city, we used the Maximal Covering Location Problem (MCLP) model to optimally situate new urban agriculture projects on vacant and derelict land to maximize the covered population. We identified that a minimum of 15 new urban agriculture projects could increase the population coverage to 49 % and equalize the access disparity to a statistically non-significant level. This research shows that converting vacant and derelict land in Glasgow into urban agriculture projects could both help with the city’s problem of vacant and derelict land and bring many potential benefits to local communities

A Transformer-Based Model for Effective Representation of Geospatial Data and Context

Machine learning (ML) and Artificial Intelligence (AI) models have been increasingly adopted for geospatial tasks. However, geospatial data (such as points and raster cells) are often influenced by underlying spatial effects, and current model designs often lack adequate consideration of these effects. Determining the efficient model structure for representing geospatial data and capturing the underlying complex spatial and contextual effects still needs to be explored. To address this gap, we propose a Transformer-like encoder-decoder architecture to first represent geospatial data with respect to their corresponding geospatial context, and then decode the representation for task-specific inferences. The encoder consists of embedding layers that transform the input location and attributes of geospatial data into meaningful embedding vectors. The decoder comprises task-specific neural network layers that map the encoder outputs to the final output. Spatial contextual effects are measured using explainable artificial intelligence (XAI) methods. We evaluate and compare the performance of our model with other model structures on both synthetic and real-world datasets for spatial regression and interpolation tasks. This work proposes a generalizable approach to better modeling and measuring complex spatial contextual effects, potentially contribute to efficient and reliable urban analytic applications that require geo-context information

9-(1,1-Dimethyl-3-oxobut­yl)adenine

The title compound, C11H15N5O, crystallizes with two independent mol­ecules in the asymmetric unit, both of which contain essentially planar imidazole and pyrimidine rings [maximum deviations = 0.002 (2) and 0.026 (2) Å, respectively, for the first mol­ecule, and 0.001 (2) and 0.025 (2) Å for the second]; the dihedral angles between the rings are 2.1 (2) and 1.7 (2)° in the two mol­ecules. The crystal structure is stabilized by inter­molecular N—H⋯N hydrogen bonds, defining chains along a, which are further linked by weak inter­molecular π–π contacts [centroid centroid distance = 3.7989 (16) Å] into planes parallel to (01)

Production, purification and characterization of polyclonal antibody against the truncated gK of the duck enteritis virus

Duck virus enteritis (DVE) is an acute, contagious herpesvirus infection of ducks, geese, and swans, which has produced significant economic losses in domestic and wild waterfowl. With the purpose of decreasing economic losses in the commercial duck industry, studying the unknown glycoprotein K (gK) of DEV may be a new method for preferably preventing and curing this disease. So this is the first time to product and purify the rabbit anti-tgK polyclonal antibody. Through the western blot and ELISA assay, the truncated glycoprotein K (tgK) has good antigenicity, also the antibody possesses high specificity and affinity. Meanwhile the rabbit anti-tgK polyclonal antibody has the potential to produce subunit vaccines and the functions of neutralizing DEV and anti-DEV infection because of its neutralization titer. Indirect immunofluorescent microscopy using the purified rabbit anti-tgK polyclonal antibody as diagnostic antibody was susceptive to detect a small quantity of antigen in tissues or cells. This approach also provides effective experimental technology for epidemiological investigation and retrospective diagnose of the preservative paraffin blocks