Lärande organisationer har sedan slutet av 80-talet kommit att få mycket plats inomorganisationsforskning och begreppet har blivit synonymt med hur företag anpassar sig efter denomvärld de verkar i. Forskningen har visat hur givande lärandet kan vara för företag både i termer avvinst och välmående hos medarbetarna, men empiriska undersökningar har samtidigt visat att lärandeorganisationer inte fått fäste i praktiken. Parallellt med detta finns vissa rön som stödjer det traditionellasynsättet att vissa individer är mer kapabla att fatta strategiska beslut, medan andra är mer lämpadesom enbart utförare, och därmed är i behov av styrning. Vårt syfte var att i detta arbete undersöka hurlärande inom en organisation kan förstås genom en grupp chefers upplevelser. Studien designades somen fallstudie inom ett företag och empirin inhämtades från sex kvalitativa intervjuer. Genomklassificering av resultaten i enlighet med Senges discipliner, visade studien att intervjupersonernasupplevelser till stor del sammanföll med det aktuella företagets profilering som lärande organisation,då arbetssättet beskrevs som adaptivt, både inom den egna organisationen och gentemot omvärlden. Imaterialet kunde vi skönja en bakomliggande människosyn, vilket kunde möjliggöra en diskussion påett aggregerat plan. Vilka implikationer detta har för lärandet kan inte identifieras utifrån studiensresultat, utan kräver vidare undersökningar, förslagsvis innehållande både observationer ochintervjuer med fler medarbetare. / Since the end of the 1980s, learning organizations have gained a lot of space in organizational researchand have become synonymous with how companies adapt to the world in which they operate. Researchhas shown how rewarding learning can be for companies both in terms of profit and well-being of theiremployees, but empirical studies also show that learning organizations have not gained foothold inactual practice. In parallel with this, some evidence supports the traditional view that some individualsare more capable of making strategic decisions, while others are more suited to following orders andtherefore need to be controlled. The present study was designed as a case study within a company andthe empirical evidence was obtained from six qualitative interviews. By categorizing the results inaccordance with Senge's disciplines, the results showed that the interviewees' experiences by and largewere consistent with the company's profile as a learning organization, as the work methods weredescribed as adaptive, both within the organization and vis-à-vis the outside world. The materialshowed an underlying attitude towards colleagues, which made a discussion on an aggregated levelpossible. The implications of this relationship for learning could not be identified on the basis of theresults of the present study, and would require further investigations, including both observations andinterviews with more employees. / <p>2021-06-07</p>

Indiana University-Purdue University Indianapolis (IUPUI) / For pedestrians and autonomous vehicles (AVs) to co-exist harmoniously and safely in the real-world, AVs will need to not only react to pedestrian actions, but also anticipate their intentions. In this thesis, we propose to use rich visual and pedestrian-environment interaction features to improve pedestrian crossing intention prediction from the ego-view.We do so by combining visual feature extraction, graph modeling of scene objects and their relationships, and feature encoding as comprehensive inputs for an LSTM encoder-decoder network. Pedestrians react and make decisions based on their surrounding environment, and the behaviors of other road users around them. The human-human social relationship has al-ready been explored for pedestrian trajectory prediction from the bird’s eye view in stationary cameras. However, context and pedestrian-environment relationships are often missing incurrent research into pedestrian trajectory, and intention prediction from the ego-view. To map the pedestrian’s relationship to its surrounding objects we use a star graph with the pedestrian in the center connected to all other road objects/agents in the scene. The pedestrian and road objects/agents are represented in the graph through visual features extracted using state of the art deep learning algorithms. We use graph convolutional networks, and graph autoencoders to encode the star graphs in a lower dimension. Using the graph en-codings, pedestrian bounding boxes, and human pose estimation, we propose a novel model that predicts pedestrian crossing intention using not only the pedestrian’s action behaviors(bounding box and pose estimation), but also their relationship to their environment. Through tuning hyperparameters, and experimenting with different graph convolutions for our graph autoencoder, we are able to improve on the state of the art results. Our context-driven method is able to outperform current state of the art results on benchmark datasetPedestrian Intention Estimation (PIE). The state of the art is able to predict pedestrian crossing intention with a balanced accuracy (to account for dataset imbalance) score of 0.61, while our best performing model has a balanced accuracy score of 0.79. Our model especially outperforms in no crossing intention scenarios with an F1 score of 0.56 compared to the state of the art’s score of 0.36. Additionally, we also experiment with training the state of the art model and our model to predict pedestrian crossing action, and intention jointly. While jointly predicting crossing action does not help improve crossing intention prediction, it is an important distinction to make between predicting crossing action versus intention.

The theme chosen for the dissertation is based on the research which deals with the question how to transform a real perception into abstraction. In a free way, I am inspired by my abstract paintings created through all my master's degree study. For a long time, the subject matter of my paintings has been the blending of structure, light and landscape. By gradual simplyfication and penetration of scenery motives with arquitecture and living nature, I try to create a complex piece of work. This abstract composition exposes always with particular impression of landscape's mood. It also stylizes a question,what we are focusing on and where we are looking, while being fascinated by the scenery - when staring into space.

This master thesis deals with an architecture Model-View-Controller. It explains the principle, model, usage and benefits of an architecture. Futhure more, it presents the best known web frameworks on Java platform which derive benefit from this architecture. There are frameworks like Apache Struts, Tapestry and Spring. In my thesis, the principles of framworks and the work with them are described. Concerning another part of my thesis, there are simple applications which use this frameworks. The work gives instructions to make these applications. It describes and compares the latest versions of these tools. It deals more with framework Spring, describes its elementary principles and modules with enhanced sight to Web MVC of Spring.

The building of the town hall Brno Kohoutovice is situated on the site next to the local hospital. Currently, there is a widespread paved area, which has become the place for markets and other cultural activities. Town hall completed urban structure, built on the most visible plot, and closes the plaza on the east side. From the south, the square is open to Kohoutovická valley. It is closed and opened at the same time by a colonnade. Functional composition is expressed in both - size and material. On a solid concrete base, which holds cultural and social events, lie two light steel boxes dedicated to the town hall. The middle part is cantilivered into the square, which symbolizes the proximity of town hall offices to the citizens. It is dedicated to the departements, which are in daily contact with the public. The upper part with large terrace has representative function. There are the internal departements and a ceremonial hall.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第20085号 / 社医博第76号 / 新制||社医||9(附属図書館) / 33201 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 福原 俊一, 教授 佐藤 俊哉, 教授 古川 壽亮 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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This dissertation uses the logic embodied in Strategic Fit Theory, the Natural Resource-Based View of the Firm (NRBV), strategic human resource management, and other relevant literature streams to empirically demonstrate how the environmental orientation of a firm's strategy impacts their environmental performance and resultant financial performance through the firm's Information Technology hiring practices. Specifically, it was hypothesized that firms with a strong relationship between the environmental orientation of their strategy and their green computing hiring practices will achieve higher environmental performance, and, as a result, higher levels of financial performance than firms lacking such fit. The organization's environmental orientation was measured via content analysis of the annual report texts (ARTs). Environmental performance was measured using KLD's award-winning environmental performance metrics. I triangulated across efficiency, effectiveness, and market-based metrics to capture a more holistic measure of the firm's financial performance using data from Compustat/Research Insight. The firm's green computing hiring practices were measured utilizing a web content data mining application that pulled job ads for computing graduates and then extracted the environmentally-oriented skills identified in such ads using content analytic techniques. Various control variables were employed to eliminate possible alternative explanations of my research findings. A number of statistical and analytical techniques were used to assess the nature and strength of the relationships in my theoretical model as are articulated in the proposed hypotheses. The sample size of firms is fairly large, thus increasing the statistical power of the empirical tests. Previous empirical testing of the relationship between environmental strategy and financial performance is still in the developmental stages and has produced mixed results, partly because important intervening mechanisms, such as green computing hiring practices, has not received adequate attention in the empirical literature. The combination of using a large sample of real world firms, a powerful combination of qualitative and quantitative methodological techniques to tap into key trace evidence not available through other methodological techniques, and leveraging an award-winning environmental data set has enhanced the robustness of the empirical findings in addressing this important gap in the literature. The results of the analyses show that there is a strong relationship between an organization's environmental posturing and its environmental performance. Additionally, this effect is mediated by the organization's environmental hiring practices, indicating that implementing the organization's environmental strategy through its hiring practices is important in achieving improved environmental performance. The current research also shows that there is a strong and positive relationship between an organization's environmental performance and financial performance. Surprisingly, these relationships are not significantly impacted by the organization's industry affiliation, which broadens the generalizability of the results of this study.

This study aimed to investigate the relationship between AI patents owned by companies, company earnings, industry type, and company size. The research question guiding the study was: How do AI patents owned by companies affect their earnings, and do these effects differ across industries and company sizes? Three hypotheses were developed to explore this question: 1. AI patents owned by companies have a positive effect on company earnings. 2. Patenting contributes more to a company’s earnings in high-tech industries than in low- tech industries. 3. The effect of owned patents on earnings is less pronounced the larger a company is. Using a quantitative approach, the researchers employed multiple linear regression analysis on a sample of companies across various industries and sizes. Data was collected from public databases, including patent records and financial statements. The analysis led to the following conclusions regarding the three initial hypotheses: Hypothesis 1: The findings indicate a positive but statistically insignificant relationship between AI patents and company earnings, suggesting that there may be a positive effect, but the analysis could not establish this relationship with statistical certainty. Hypothesis 2: The study did not find enough evidence to support or reject the hypothesis that patenting contributes more to earnings in high-tech industries than in low-tech industries. This may be due to limitations in the dataset or the analysis approach employed. Hypothesis 3: The influence of company size on the relationship between patents and earnings remains inconclusive. Although the results showed a positive relationship between the number of employees and earnings, the analysis did not provide sufficient evidence to determine the interaction effect between company size and patent ownership. These inconclusive findings suggest that further research is necessary to better understand the relationship between AI patents, company earnings, industry type, and company size. Future studies could address the limitations of this study by incorporating more granular data on different industries, conducting industry-specific analyses, and employing alternative statistical methods or longitudinal data. This would help to enhance our understanding of the complex relationships between these factors and provide more actionable insights for businesses, investors, academics, and policymakers.