Classification of outdoor areas to environmental zones in terms of obtrusive light

Import 05/08/2014Tato diplomová práce je zaměřena na problematiku při zatříďování venkovních prostorů do environmentálních zón z pohledu rušivého světla. Energetická vyspělost společnosti dnes umožňuje pro bezpečnost obyvatel během celých nocí osvětlovat silnice či pěší zóny, ale osvětlují se také památky a místa, u kterých by stálo za to se zamyslet, zda je opravdu potřeba na ně během nocí takto plýtvat elektrickou energií. Nejde však pouze o zbytečně spotřebovanou elektrickou energii. Jde především o případné negativní účinky na lidi a celý ekosystém, které venkovní světelné zdroje během nocí způsobují. Hlavním cílem praktické části této diplomové práci je řešit otázku nejrůznějších úskalí a kompromisů při přiřazování odpovídající environmentální zóny městu Studénce. Toto město skýtá několik zajímavých překážek pro správnou volbu environmentální zóny, které souvisí především s jeho polohou. Po přezkoumání všech kritérií a konečném zařazení města Studénky do odpovídající environmentální zóny práce pokračuje vytvořením návrhu nové osvětlovací soustavy pro fotbalový stadion v tomto městě, a to za předpokladu dodržení normativních požadavků podle předcházejícího zatřídění Studénky.The focus of this thesis deals with the proposal of environmental zones in outdoor areas as it relates to lighting. In these areas where public safety is not an issue, an energy conservation program could be considered. Not only should the unnecessary consumption of electricity be accounted for when deciding illumination requirements, but also the possible negative affects on humans and the entire ecosystem at night time, outdoor lighting may cause. From this perspective, an important aspect of this work is the different normative requirements for the correct classification of the sites in defined environmental zones. Knowledge and data is applied when recommending a specific environmental zone classification, while keeping in sight the isssues from the perspective of the Czech Republic and the concerns of the municipal significance for the city or town. The main goal of the practical part of this thesis is to address the issues of various difficulties and compromises in assigning the appropriate environmental zones of Studenka city. Mainly due to its location, this city offers some interesting obstacles when deciding the most reasonable selection of environmental zones. After reviewing all of the criteria and assigning final classifications for the zones in Studenka city, corresponding environmental work continues by creating a proposal for a new lighting system for the football stadium in the city while continuing compliance with the standards of the previous classification of Studenka.410 - Katedra elektroenergetikyvýborn

Network analysis of exploratory behaviors of mice in a spatial learning and memory task

<div><p>The Barnes maze is one of the main behavioral tasks used to study spatial learning and memory. The Barnes maze is a task conducted on “dry land” in which animals try to escape from a brightly lit exposed circular open arena to a small dark escape box located under one of several holes at the periphery of the arena. In comparison with another classical spatial learning and memory task, the Morris water maze, the negative reinforcements that motivate animals in the Barnes maze are less severe and less stressful. Furthermore, the Barnes maze is more compatible with recently developed cutting-edge techniques in neural circuit research, such as the miniature brain endoscope or optogenetics. For this study, we developed a lift-type task start system and equipped the Barnes maze with it. The subject mouse is raised up by the lift and released into the maze automatically so that it can start navigating the maze smoothly from exactly the same start position across repeated trials. We believe that a Barnes maze test with a lift-type task start system may be useful for behavioral experiments when combined with head-mounted or wire-connected devices for online imaging and intervention in neural circuits. Furthermore, we introduced a network analysis method for the analysis of the Barnes maze data. Each animal’s exploratory behavior in the maze was visualized as a network of nodes and their links, and spatial learning in the maze is described by systematic changes in network structures of search behavior. Network analysis was capable of visualizing and quantitatively analyzing subtle but significant differences in an animal’s exploratory behavior in the maze.</p></div

Processing flows of the conventional, search strategy, and network analyses.

<p>The processing flows of the conventional (A), strategy (B), and network (C) analyses are illustrated. The analysis started from the input of point sequences, represented by the parallelogram symbol, and then executed along the arrows. The diamond and rectangle symbols represent conditional branch and particular processing, respectively. Similar processing steps are included in the larger rectangles with the processing labels on the top. The filled symbols represent outcomes. The balloons indicate representative images of processing definitions or outputs. For instance, the images in the balloons in the strategy analysis (B) indicate representative trajectories of each strategy depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#pone.0180789.g003" target="_blank">Fig 3A</a>. Similarly, the bottom image in the network analysis (C) indicates the static zones and nodes shown in Fig B in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#pone.0180789.s002" target="_blank">S2 File</a>. The top image indicates an example of the local network, and the middle image is a sample of the global network shown (see the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#sec002" target="_blank">Materials & Methods</a>).</p

Design of the Barnes circular maze with a start lift.

<p>(A–D) Four views of the Barnes circular maze apparatus (overview, top view, side view before task start in the LIFT entry [see main text], and side view at the task start). The scales are proportional to the actual apparatuses except for the recording camera and the camera arm. Both items were enlarged for visibility.</p

Changes in search strategies accompanied by spatial learning.

<p>(A) Examples of representative trajectories of the random, serial, and spatial strategies. The random, serial, and spatial strategies are shown in violet, green, and orange trajectories, respectively. The trajectory of one mouse per day of trials is shown; all trajectories were transformed so that the goal is located at the right top hole, noted as “Target”. The number of trajectories displayed in each panel was adjusted so that the total distance of trajectories is comparable between the three strategies. (B) The normalized stacked bar graphs indicating the ratios of strategy usage each day for the MANUAL entry (left panel) and the LIFT entry (right panel). The vertical and horizontal axes indicate the proportion of the strategy usage and the training days, respectively. Each bar represents the ratio of the strategy in a particular training day and is normalized so that the sum of each strategy ratio is 1. Temporal changes in usage within each strategy across training trials seemed to be more clearly observable for the LIFT than for the MANUAL entry group (see also Tables H–K in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#pone.0180789.s001" target="_blank">S1 File</a>).</p

The probe test for spatial reference memory in the Barnes maze with MANUAL and LIFT entries.

<p>(A) The occupancy rates in each block of the field are shown on the contour map. The results of the MANUAL and LIFT entry groups are shown. The definitions of the color code and field markers are the same as those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#pone.0180789.g003" target="_blank">Fig 3A</a>. (B) Prolonged searching time near the target hole in the LIFT entry. The horizontal axis indicates the locations of the holes expressed as angle differences from the target. The vertical axis indicates the search time for the individual hole. The blue and red squares represent the mean results of the MANUAL and LIFT entries, respectively. The value of and change in each individual mouse’s data are shown as dots and light lines. Mice in both entry groups spent significantly more time near the target hole compared with the other holes. The LIFT group mice spent slightly, but significantly, more time near the target hole compared with the MANUAL group mice (see also Table E in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180789#pone.0180789.s001" target="_blank">S1 File</a>), indicating that more precise spatial reference memory was generated in the LIFT entry than in the MANUAL entry.</p

Network features generated by the dynamic node generation method were comparable between the MANUAL and LIFT entries in the probe test.

<p>The results of the MANUAL and LIFT entries of the probe tests on a log scale are shown in <i>blue</i> and <i>red</i>, respectively. Dots and squares indicate the raw and median values, respectively.</p

Summary and abbreviations for behavioral features.

<p>Summary and abbreviations for behavioral features.</p

Visualization of temporal changes in the search networks during spatial learning in the MANUAL and LIFT entries.

<p>(A) Exmples of local networks from an individual mouse on Day 1 of the LIFT entry. Local networks of mouse #1 to mouse #n generated by the dynamic node generation method are shown in a three-dimentional coordinate system. One local network was generated following a single trial of a particular mouse. The x- and y-axes indicate the plane coordinates, including the arena. The z-axis indicates the sample numbers. The small dots and light gray lines indicate local nodes and links, respectively. One of four colors is assigned to each local node in accordance with a global node associated with the local node (see below). (B) Temporal changes in the global networks in the MANUAL and LIFT entries during spatial learning. Network structures of exploratory behaviors formed by the dynamic node generation method in the MANUAL and LIFT entries are plotted in the upper and lower rows, respectively. Each column corresponds to a single training day. Small colored dots and light gray lines represent nodes and links in the local networks. The local nodes are represented by any one of four colors. Note that this color-coding simply distinguishes geometrically adjacent local nodes belonging to different global nodes using different colors. To describe the global network structure, all local networks of the same entry group on a single training day, such as Fig 6A, were projected on plane coordinates. Dark gray circles and dark gray lines are global nodes and links in global networks, respectively. The magnitude of each global node was determined using logarithmic transformation of the number of local nodes assigned to each global node. A similar procedure was performed to determine global links from sets of local links. The network structure simplified as spatial learning was established. (C) Topological expression of global networks. The layout of rows and columns are the same as in Fig 6B. Global nodes were sorted by rank-order of degree (<i>M</i>_<i>i</i>) and plotted on polar coodinates, so that the node with the highest degree was located at 0 degrees while the lowest one was located at 360 degrees. The locations of the global nodes that are most densely linked are highlighted by red arrows. Circles and lines represent global nodes and links, respectively. Each sorted global node is represented by any one of five colors by the magnitude of degree; <i>white</i> (<i>M</i>_<i>i</i> ≥ 12), <i>yellow</i> (12 > <i>M</i>_<i>i</i> ≥ 9), <i>orange</i> (9 > <i>M</i>_<i>i</i> ≥ 6), <i>red</i> (6 > <i>M</i>_<i>i</i> ≥ 3), <i>brown</i> (3 > <i>M</i>_<i>i</i>).</p

Learning-dependent changes in network features during training.

<p>Network features generated by the dynamic node generation method during spatial learning are shown. The results of each network feature on a log scale are described on each individual panel. The vertical and horizontal axes indicate the values of each network feature and training day, respectively. The results of the MANUAL and LIFT entries are shown in <i>blue</i> and <i>red</i>, respectively. Bold lines represent the changes in the median values in each entry group. Red or blue dots indicate the actual values of an individual mouse in a single day, and the changes across days are depicted by light lines. Simplifying processes of network structures were quantified with temporal changes in network features, indicating that the values of network features changed more apparently in the LIFT entry than in the MANUAL entry.</p