Diurnal periodicity of activity in the spawning perch P. fluviatilis L. [Translation from: Kalamies 1972(7) 3, 1972]

Diurnal periodicity of spawning in the perch so far are rather meagre and found to be partly contrary to experiences of perch anglers. Therefore a study was made on the spawning during a 5-day period in the spring of 1971 in the Kuusamo area. Observations were made during the main spawning season, between 4- 9 June 1971. The perch were often measured, weighed and then released back into the water. The differences between spawning and non-spawning perch were studied as well as the time of roe discharge in a 24 hour period. Activity and environmental factors such as light intensity were also taken into consideration

Seasonal variation in the diurnal periodicity of activity of the perch, Perca fluviatilis L. [Translation from: Kalamies 1973(3) 3.]

The most common catch of the amateur angler is the perch and it is the diurnal periodicity of activity (& catchability) which is examined in this study based on earlier articles and manuscripts by the authors. Of all environmental factors, variation in light and temperature are the chief reasons in establishing the times of activity periods. Winter, summer and autumn activity was studied. The spawning perch was found to be more active than the non-spawning perch. The time of day in which the fish may be active is dependant on its ability to sense changes in the external environment. Its adaptation to light is the reason for day-activity in the winter, and also accounts for the fact that hardly any activity occurs between sunset and sunrise when this period exceeds 6 hours

Gully Formation at the Haughton Impact Structure (Arctic Canada) Through the Melting of Snow and Ground Ice, with Implications for Gully Formation on Mars

The formation of gullies on Mars has been the topic of active debate and scientific study since their first discovery by Malin and Edgett in 2000. Several mechanisms have been proposed to account for gully formation on Mars, from dry mass movement processes, release of water or brine from subsurface aquifers, and the melting of near-surface ground ice or snowpacks. In their global documentation of martian gullies, report that gullies are confined to ~2783S and ~2872N latitudes and span all longitudes. Gullies on Mars have been documented on impact crater walls and central uplifts, isolated massifs, and on canyon walls, with crater walls being the most common situation. In order to better understand gully formation on Mars, we have been conducting field studies in the Canadian High Arctic over the past several summers, most recently in summer 2018 and 2019 under the auspices of the Canadian Space Agency-funded Icy Mars Analogue Program. It is notable that the majority of previous studies in the Arctic and Antarctica, including our recent work on Devon Island, have focused on gullies formed on slopes generated by regular endogenic geological processes and in regular bedrock. How-ever, as noted above, meteorite impact craters are the most dominant setting for gullies on Mars. Impact craters provide an environment with diverse lithologies including impact-generated and impact-modified rocks and slope angle, and thus greatly variable hill slope processes could occur within a localized area. Here, we investigate the formation of gullies within the Haughton impact structure and compare them to gullies formed in unimpacted target rock in the nearby Thomas Lee Inle

Power line mapping technique using all-terrain mobile laser scanning

Power line mapping using remote sensing can automate the traditionally labor-intensive power line corridor inspection. Land-based mobile laser scanning (MLS) can be a good choice for the power line mapping if an aerial inspection is impossible, too costly or slow, unsafe, prohibited by regulations, or if more detailed information on the power line corridor is needed. The mapping of the power lines using MLS was studied in a rural environment outside the road network for the first time. An automatic power line extraction algorithm was developed. The algorithm first found power line candidate points based on the shape and orientation of the local neighborhood of a point using principal component analysis. Power lines were retrieved from the candidates using random sample consensus (Ransac) and a new power line labeling method, which takes into account the three-dimensional shape of the power lines. The new labeling method was able to find the power lines and remove false detections, which were found, for example, from the forest. The algorithm was tested in forested and open field (arable land) areas, outside the road environment using two different platforms of MLS, namely, personal backpack and all-terrain vehicle. The recall and precision of the power line extraction were 93.3% and 93.6%, respectively, using 10 cm as a distance criterion for a successful detection. Drifting of the positioning solution of the scanner was the largest error source, being the (contributory) cause for 60–70% of the errors. The platform did not have a significant effect on the power line extraction accuracy. The accuracy was higher in the open field compared to the forest, because the one-dimensional point density along the power line was inhomogeneous and GNSS (global navigation satellite system) signal was weak in the forest. The results suggest that the power lines can be mapped accurately enough for inspection purposes using MLS in a rural environment outside the road network.</p

Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data

Tracking and Changes in Daily Step Counts among Finnish Adults

Purpose This study aimed to investigate the tracking and changes of steps per day in adults and their determinants over 13 yr. Methods A total of 2195 subjects (1236 women) 30-45 yr of age were randomly recruited from the ongoing Cardiovascular Risk in Young Finns Study in 2007 and were followed up in 2020. Steps per day, including both total and aerobic steps per day, were monitored for seven consecutive days with a pedometer in 2007-2008 and 2011-2012 and with an accelerometer in 2018-2020. Tracking was analyzed using Spearman's correlation. Stability and changes of steps per day over time in both low-active and high-active groups (based on median values) were described by percentage agreements, kappa statistics, and logistic regression. Associations of sex, age, and body mass index with the initial number and changes in steps per day were analyzed using linear growth curve modeling. Results Tracking correlations of total steps per day at 4-, 9-, and 13-yr intervals were 0.45-0.66, 0.33-0.70, and 0.29-0.60, while corresponding correlations for aerobic steps per day were 0.28-0.55, 0.23-0.52, and 0.08-0.55, respectively. Percentage agreements were higher than 54%, and kappa statistics ranged from slight to fair over time. Compared with the low-active group, the high-active group at baseline had a higher probability of being active later in adulthood. Female sex and higher age were associated directly with the initial number of steps per day and inversely with changes in the number of steps per day. Body mass index was inversely associated with the initial number of steps per day and changes in the number of total steps per day. Conclusion The 13-yr tracking of steps per day in adulthood was found to be low to moderately high. Daily ambulatory activity is essential to maintaining an active lifestyle throughout adulthood. Changes in the amount of adult steps per day vary by sex, age, and BMI

Accurate derivation of stem curve and volume using backpack mobile laser scanning

Forest inventories rely on field plots, the measurement of which is costly and time consuming by manual means. Thus, there is a need to automate plot-level field data collection. Mobile laser scanning has yet to be demonstrated for deriving stem curve and volume from standing trees with sufficient accuracy for supporting forest inventory needs. We tested a new approach based on pulse-based backpack mobile laser scanner (Riegl VUX-1HA) combined with in-house developed SLAM (Simultaneous Localization and Mapping), and a novel post-processing algorithm chain that allows one to extract stem curves from scan-line arcs corresponding to individual standing trees. The post-processing step included, among others, an algorithm for scan-line arc extraction, a stem inclination angle correction and an arc matching algorithm correcting for the drifts that are still present in the stem points after applying the SLAM algorithm. By using the stem curves defined by the detected arcs and tree heights provided by the pulse-based scanner, stem volume estimates for standing trees in easy (n = 40) and medium (n = 37) difficult boreal forest were calculated. In the easy and medium plots, 100% of pine and birch stems were correctly detected. The total RMSE of the extracted stem curves was 1.2 cm (5.1%) and 1.7 cm (6.7%) for the easy and medium plots, respectively. The RMSE were 1.8 m (8.7%) and 1.1 m (4.9%) for the estimated tree heights, and 9.7% and 10.9% for the stem volumes for the easy and medium plots, correspondingly. Thus, our processing chain provided stem volume estimates with a better accuracy than previous methods based on mobile laser scanning data. Importantly, the accuracy of stem volume estimation was comparable to that provided by terrestrial laser scanning approaches in similar forest conditions. To further demonstrate the performance of the proposed method, we compared our results against stem volumes calculated using the standard Finnish allometric volume model, and found that our method provided more accurate volume estimates for the two test sites. The findings are important steps towards future individual-tree-based airborne laser scanning inventories which currently lack cost-efficient and accurate field reference data collection techniques. The tree geometry defined by the stem curve is also an important input parameter for deriving quality-related information from trees. Forest management decision making will benefit from improvements to the efficiency and quality of individual tree reference information.</p

Neighborhood level risk factors for type 1 diabetes in youth: the SEARCH case-control study

<p>Abstract</p> <p>Background</p> <p>European ecologic studies suggest higher socioeconomic status is associated with higher incidence of type 1 diabetes. Using data from a case-control study of diabetes among racially/ethnically diverse youth in the United States (U.S.), we aimed to evaluate the independent impact of neighborhood characteristics on type 1 diabetes risk. Data were available for 507 youth with type 1 diabetes and 208 healthy controls aged 10-22 years recruited in South Carolina and Colorado in 2003-2006. Home addresses were used to identify Census tracts of residence. Neighborhood-level variables were obtained from 2000 U.S. Census. Multivariate generalized linear mixed models were applied.</p> <p>Results</p> <p>Controlling for individual risk factors (age, gender, race/ethnicity, infant feeding, birth weight, maternal age, number of household residents, parental education, income, state), higher neighborhood household income (p = 0.005), proportion of population in managerial jobs (p = 0.02), with at least high school education (p = 0.005), working outside the county (p = 0.04) and vehicle ownership (p = 0.03) were each independently associated with increased odds of type 1 diabetes. Conversely, higher percent minority population (p = 0.0003), income from social security (p = 0.002), proportion of crowded households (0.0497) and poverty (p = 0.008) were associated with a decreased odds.</p> <p>Conclusions</p> <p>Our study suggests that neighborhood characteristics related to greater affluence, occupation, and education are associated with higher type 1 diabetes risk. Further research is needed to understand mechanisms underlying the influence of neighborhood context.</p