Preschool Teachers’ Perspectives on the Importance of STEM Education in Greek Preschool Education

Given the rapid development of technology and therefore of society in recent years, the need for education systems worldwide to modernize the methods and means used is a matter of necessity. In the present study, this modernization translates into the application of STEM education and deepens into the research of preschool teachers’ perspectives on the importance of STEM in Greek preschool education. Investigating teachers' perceptions is a complex process, through which we are examining the pedagogical methods they use in their classrooms, their knowledge of STEM approaches and the understanding of the usefulness of STEM education. It is well known that teachers’ perceptions can influence instructional choices. Therefore, it is necessary to investigate their perceptions of STEM education, in order to take action timely and explore the prospects for the school of future. In this dissertation, we are thoroughly investigating preschool teachers’ perspectives on their role in the classroom, but also on the importance of integrating the instruction of STEM disciplines in early years. The research findings show high percentage agreement in relation to the perspectives on the usefulness of STEM, but when it comes to analyzing responses regarding the implementations of STEM, teachers are hesitating to respond with absolute certainty. The presentation of an ideal situation however, is far from reality, as STEM education also requires a proper preparation and training for the kindergarten teachers. However, their positive perspectives on STEM can be the first step in integrating the element of innovation into pre-school education. Keywords: STEM, innovation, teaching methods, education curricula, teachers, perspectives. DOI: 10.7176/JEP/11-14-01 Publication date:May 31st 202

Visualisation of urban airborne laser scanning data with occlusion images

Airborne Laser Scanning (ALS) was introduced to provide rapid, high resolution scans of landforms for computational processing. More recently, ALS has been adapted for scanning urban areas. The greater complexity of urban scenes necessitates the development of novel methods to exploit urban ALS to best advantage. This paper presents occlusion images: a novel technique that exploits the geometric complexity of the urban environment to improve visualisation of small details for better feature recognition. The algorithm is based on an inversion of traditional occlusion techniques

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000–2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of −0.76 and −0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and synthetic aperture radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra moderate resolution imaging spectroradiometer (MODIS), and Suomi-NPP visible infrared imaging radiometer suite (VIIRS). The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding, i.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of 91% [respectively, mean per-class Intersection-over-Union (mIoU) scores >60%] and generalizes well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Recent Ice Trends in Swiss Mountain Lakes: 20-year Analysis of MODIS Imagery

Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000-2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolved maps of lake ice for these Alpine lakes with supervised machine learning. To train the classifier we rely on reference data annotated manually based on webcam images. From the ice maps, we derive long-term LIP trends. Since the webcam data are only available for two winters, we cross-check our results against the operational MODIS and VIIRS snow products. We find a change in complete freeze duration of -0.76 and -0.89 days per annum for lakes Sils and Silvaplana, respectively. Furthermore, we observe plausible correlations of the LIP trends with climate data measured at nearby meteorological stations. We notice that mean winter air temperature has a negative correlation with the freeze duration and break-up events and a positive correlation with the freeze-up events. Additionally, we observe a strong negative correlation of sunshine during the winter months with the freeze duration and break-up events.Comment: accepted for PFG Journal of Photogrammetry, Remote Sensing and Geoinformation Scienc

Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement

Mechanical properties of short doughs and their corresponding biscuits

The mechanical properties of short doughs of various composition were determined in small amplitude oscillatory experiments and in uniaxial compression. Regardless of composition, the linear region was very limited; beyond that, pronounced yielding and flow occurred. Conductimetry was also used to evaluate the effect of fat type and fat content on dough structure. Short doughs showed large differences in mechanical spectra, conductivity and apparent biaxial extensional viscosity, according to fat and sucrose contents, fat type and mixing time. It is concluded that short doughs are bicontinuous systems; reducing the fat content or changing the rheological properties of the fat relative to those of the non-fat phase results in fat-dispersed systems. The rheological properties of the non-fat phase are largely determined by intact flour particles present in a concentrated sucrose syrup. Sucrose delays, if not inhibits, gluten development through its effect on solvent quality and facilitates formation of a non-fat continuous phase via its effect on solvent quantity. Mixing promotes formation of a continuous fat phase.Mechanical properties of short-dough biscuits of various composition were determined in three-point bending tests. Increasing fat content or omitting sucrose from the recipe decreased the modulus and the fracture stress of the biscuits. The effect of fat content, however, was dependent on fat type. Temperature during dough preparation, dough water content and temperature during bending tests affected the mechanical properties of biscuits to an extent which depended on fat content. Diffusion of Sudan III into the biscuits indicated that low-fat biscuits are fat-dispersed systems and high-fat biscuits are bicontinuous. Differential scanning calorimetry showed that, irrespective of composition, starch gelatinisation was slight, if not absent, presumably due to the limited water content coupled with the low baking temperature. Under certain storage conditions, biscuits are in a glassy state. Upon water uptake, the matrix undergoes a glass-rubber transition. It is concluded that the mechanical properties of biscuits are mainly determined by air volume fraction, fat continuity, size of inhomogeneities, and physical state of the non-fat phase