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Perinteisillä ruotsinkielisillä kielisaarekkeilla työskentelevien hotellivirkailijoiden ruotsin kielen taito ja asenteet ruotsin kielen käyttöä kohtaan
No full textPrediction and mapping of boreal forest fire fuel loads using high-resolution satellite stereo imagery
No full textThe aim of this study is to evaluate the suitability of very high-resolution satellite stereo-imagery data for creating forest fire-related fuel load maps in the boreal region. We acquired stereo imagery from the GeoEye-1 (GE-1) satellite, which has a ground sampling distance of 50 cm. The images were acquired in August 2021 and 2023 (hence leaf-on). Our study area was centred around the Hiidenportti national park in central Finland, dominated by natural boreal forests. The ground reference was a field dataset consisting of measurements from 33 forested plots, each of 15 m radius. The dominant height (m), foliage biomass (t ha-1) and canopy base height (m) were predicted using multivariate linear regression models, while the understory presence (categorical; present/absent) was predicted using logistic regression analysis. Prediction models using area-based metrics based on airborne laser scanning (ALS) data had the smallest associated root mean square error (RMSE) (between 2.6% and 23.9%). Meanwhile, similar type of area-based metrics of stereo satellite data combined with an ALS-based digital terrain model (DTM) resulted in RMSEs of 6.6–30.3%. We also formulated models suitable for the case when only satellite data is available (i.e. high-quality DTM is absent), such as in remote locations of the boreal forest region. In this case, the models involved several canopy texture metrics and point cloud height and colour intensity-based metrics as predictors. The associated relative RMSEs were in the range of 11–30%. Dominant height, an important global vegetation metric, was predicted with an RMSE of 2.6 m, which compares well with other model predictions under similar circumstances. Our findings suggests that very high-resolution stereo satellite image data is promising for the generation and updating of wall-to-wall boreal forest fuel load maps, including remote areas lacking high resolution DTM data
Geometric phase and wave-particle duality of the photon
No full textThe concepts of geometric phase and wave-particle duality are interlinked to several fundamental phenomena in quantum physics, but their mutual relationship has remained a largely uncharted topic. Here we address this subject by studying the geometric phase of a photon in double-slit interference. In particular, we discover a general complementarity relation for the photon that connects the geometric phase it exhibits in the observation plane and the which-path information it encases at the two slits. The relation can be seen as quantifying wave-particle duality of the photon via the geometric phase, thus corroborating an important link between two ubiquitous notions in quantum physics
The agency of lecturers' widows in the late seventeenth-century diocese of Vyborg
No full textThis chapter provides a case study of the agency of two seventeenth-century widows, a mother and daughter, whose husbands worked as lecturers at the gymnasium of Vyborg, an eastern border town in the Swedish kingdom. Using letters of petition, lower court records, and biographical registers as sources, Kuha analyses the widows' agency within the appointment processes of their male relatives and studies the ways in which they strived to promote the future success and well-being of their households. To interpret the agency of widows, Kuha utilises Allyson M. Poska's concept of agentic gender norms. The chapter suggests that the widows of lecturers and clergymen were expected to promote the family strategy after their husbands died. The analysis further indicates that for wealthy widows, remarriage was not necessary, as they were able to survive and prosper independently after losing their husbands
“We all speak more of American English” : Investigating the Americanisation of Nigerian English
No full text"We're on the Same Side!" Teachers' Experiences of Co-Teaching and Collaborative Teaching in Worldview Education
No full textThe effect of pupil size on data quality in head-mounted eye trackers
No full textChanges in pupil size can lead to apparent gaze shifts in data recorded with video-based eye trackers in the absence of physical eye rotation. This is known as the pupil-size artifact (PSA). While the PSA is widely reported in desktop eye trackers, it is unknown whether and to what extent it occurs in head-mounted eye trackers. In this paper, we examined the effects of pupil size variations on eye-tracking data quality in four head-mounted eye trackers: the Pupil Core, the Pupil Neon, the SMI ETG 2w, and the Tobii Pro Glasses 2, in addition to a widely used desktop eye tracker, the SR Research EyeLink 1000 Plus. Participants viewed a central target on a monitor while we systematically varied the screen brightness to induce controlled pupil size changes. All head-mounted eye trackers exhibited PSA, with apparent gaze shifts ranging from 0.94 for the Pupil Neon to 3.46 for the Pupil Core. Except for the Pupil Neon, all eye trackers exhibited a significant change in accuracy due to pupil size variations. Precision measures showed device-specific effects of pupil size changes, with some eye trackers performing better in the bright condition and others in the dark condition. These findings demonstrated that, just like desktop eye trackers, head-mounted video-based eye trackers exhibited PSA
BMSC-Net: Bio-multisensory-collaboration inspired network for blood glucose forecasting
No full textAccurate forecasting of blood glucose (BG) is an indispensable part to provide an effective reference for determining the medication and diet of diabetics, while it is a difficult task because the series is generated through intricate physiological mechanisms and has too stochastic and nonlinear fluctuations. Additionally, some existing deep networks present effective fitting via integrating some tricks but lack reasonable design ideas, inheriting unclear flowcharts and layer functions. Given that biological systems outperform deep networks in many situations, this paper develops a bio-multisensory-collaboration inspired network, namely BMSC-Net, for BG forecasting, including the following three components. First, inspired by the fact that the multiple senses independently capture external stimulus into electrical impulses, the multi-sensory extraction block captures diverse features and shifts the learning process from raw data to feature space, further suppressing the data volatility. Second, inspired by the fact that the left and right brains integrate and analyze captured electrical impulses, the brain analysis block collaborates with one-dimensional convolutional and gated operations in parallel to extract time-related dependencies, learning inherent evolutionary rules of BG series. Third, inspired by the fact that higher cerebral cortex achieves external environment information based on the above features, the forecasting block integrates both linear and nonlinear features to generate final forecasts, not only reducing the corresponding feature loss but also coinciding with the idea of “divide and conquer”. The synergistic integration of these blocks enables faithful emulation of biological multisensory processing in BMSC-Net’s architecture, and further improves design rationality in the multi-level handling flowchart and layer function. Experiments of one-step, multi-step, and interval forecasting under two type-1 BG datasets illustrate that the BMSC-Net is superior to 12 benchmarks, being a supplement for BG forecasting
