Zhodnocení výkonnosti nemovitostních akcií v Číně

This thesis is focused on real estate investment in China. After reform and opening up, the government has taken growth of GDP as an important aim. When the natural growth rate of the economy is lower than the declared target growth rate, the huge political inertia will force the government to intervene in the market, at the expense of resource mismatch to push GDP to achieve the goal. Especially in the thirty years after the marketization of real estate, the real estate industry in China has greatly improved the housing, consumption and urban landscapes of the huge population in China. It brought up about 60 industrial developed and have a significant impact on the Chinese economy and caused the high growth speed of houses’ price. With the competition is becoming more fierce, it is necessary to evaluate the current condition of real estate equity investment in China and if the property bubble will break in the future. The objective of the work is examining the changes in real estate equity investment performance across fifty selected real estate companies in China over the period 2000-2017. This thesis attempts to investigate how stocks of companies in real estate performed before financial crisis (2000-2008) and after crisis (2008-2017).Tato práce je zaměřena na investice do nemovitostí v Číně. Po reformě vláda považuje růst HDP za důležitý cíl. Když je přirozená míra růstu ekonomiky nižší než deklarovaná cílová míra růstu, politická setrvačnost přiměje vládu k intervenci na trhu, a to na úkor nesouladu zdrojů s cílem posunout HDP k dosažení tohoto cíle. Zejména v třiceti letech v souvislosti se změnami na trhu nemovitostí se v Číně výrazně zlepšilo bydlení. Vyvinulo se přibližně 60 průmyslových průmyslových odvětví, což významně ovlivnilo čínskou ekonomiku a způsobilo vysokou rychlost růstu cen nemovitostí. Vzhledem k tomu, že se konkurence stává stále silnější, je třeba zhodnotit současný stav investic do nemovitostí do nemovitostí v Číně a v případě, že bublina nemovitostí v budoucnu naruší. Cílem práce je prozkoumat změny v investiční výkonnosti nemovitostí ve více než padesáti vybraných realitních společnostech v Číně v období 2000-2017. Tato práce se pokouší prověřit, jak se akcie společností v oblasti nemovitostí prováděly před finanční krizí (2000-2008) a po krizi (2008-2017).154 - Katedra financídobř

Finanční analýza společnosti Daimler

Import 02/11/2016This thesis focus on how to evaluate financial condition of Daimler Company by using financial analysis. Financial analysis formulates the assessment of the company’s present and future financial position,which is based on financial statement and accounting data. Financial analysis is used to analyze and evaluate profitability, operation capacity and solvency of enterprises’ financial activities, investment activities and operating activities by adopting a series of specific financial analysis methodology. For managers, they evaluate the company’s financial conditions and operating results by doing financial analysis. It helps managers make important operating decisions, like whether the company should finance, invest or switch to other production. For investors, they decide whether they should invest more capital, transfer shares by analyzing condition of production and management. Creditors make judgment whether the company is worth loaning. The aim of this thesis is evaluating financial condition of Daimler company from 2011 to 2015 by using common-size analysis, financial ratios and pyramidal decomposition. Daimler company is one of the biggest manufacturers of automobile all over the world. But the financial condition of it was not good during 2011 and 2015. It mainly due to the great competition between other company in automobile industry. Daimler should to innovate and pay attention to product’s details of design. On the other way, the awareness of environmental protection of people is greater and greater. It might be another reason why the financial position weaker.This thesis focus on how to evaluate financial condition of Daimler Company by using financial analysis. Financial analysis formulates the assessment of the company’s present and future financial position,which is based on financial statement and accounting data. Financial analysis is used to analyze and evaluate profitability, operation capacity and solvency of enterprises’ financial activities, investment activities and operating activities by adopting a series of specific financial analysis methodology. For managers, they evaluate the company’s financial conditions and operating results by doing financial analysis. It helps managers make important operating decisions, like whether the company should finance, invest or switch to other production. For investors, they decide whether they should invest more capital, transfer shares by analyzing condition of production and management. Creditors make judgment whether the company is worth loaning. The aim of this thesis is evaluating financial condition of Daimler company from 2011 to 2015 by using common-size analysis, financial ratios and pyramidal decomposition. Daimler company is one of the biggest manufacturers of automobile all over the world. But the financial condition of it was not good during 2011 and 2015. It mainly due to the great competition between other company in automobile industry. Daimler should to innovate and pay attention to product’s details of design. On the other way, the awareness of environmental protection of people is greater and greater. It might be another reason why the financial position weaker.154 - Katedra financívelmi dobř

A Study of the Interaction between Cucurbit[7]uril and Alkyl Substituted 4-Pyrrolidinopyridinium Salts

The interaction between cucurbit[7]uril (Q[7]) and a series of 4-pyrrolidinopyridinium salts bearing aliphatic substituents at the pyridinium nitrogen, namely 4-(C4H8N)C5H5NRBr, where R = H (C0), Et (C2), n-butyl (C4), n-hexyl (C6), has been studied in aqueous solution by 1H NMR spectroscopy, electronic absorption spectroscopy, and mass spectrometry

Incongruent gestures slow the processing of facial expressions in university students with social anxiety

In recent years, an increasing number of studies have examined the mechanisms underlying nonverbal emotional information processing in people with high social anxiety (HSA). However, most of these studies have focused on the processing of facial expressions, and there has been scarce research on gesture or even face-gesture combined processing in HSA individuals. The present study explored the processing characteristics and mechanism of the interaction between gestures and facial expressions in people with HSA and low social anxiety (LSA). The present study recruited university students as participants and used the Liebowitz Social Anxiety Scale scores to distinguish the HSA and LSA groups. We used a 2 (group: HSA and LSA) × 2 (emotion valence: positive, negative) × 2 (task: face, gesture) multifactor mixed design, and videos of a single face or gesture and combined face-gesture cues were used as stimuli. We found that (1) there is a distinction in the processing of faces and gestures, with individuals recognizing gestures faster than faces; (2) there is an attentional enhancement in the processing of gestures, particularly for negative gestures; and (3) when the emotional valence of faces and gestures align, it facilitates the recognition of both. However, incongruent gestures have a stronger impact on the processing of facial expressions compared to facial expressions themselves, suggesting that the processing of facial emotions is more influenced by environmental cues provided by gestures. These findings indicated that gestures played an important role in emotional processing, and facial emotional processing was more dependent on the environmental cues derived from gestures, which helps to clarify the reasons for biases in the interpretation of emotional information in people with HSA

DANAA: Towards transferable attacks with double adversarial neuron attribution

While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which targets the learnt features in the hidden layers to improve its transferability across different models. Yet it is observed that the transferability has been largely impacted by the neuron importance estimation results. In this paper, a double adversarial neuron attribution attack method, termed `DANAA', is proposed to obtain more accurate feature importance estimation. In our method, the model outputs are attributed to the middle layer based on an adversarial non-linear path. The goal is to measure the weight of individual neurons and retain the features that are more important towards transferability. We have conducted extensive experiments on the benchmark datasets to demonstrate the state-of-the-art performance of our method. Our code is available at: https://github.com/Davidjinzb/DANAAComment: Accepted by 19th International Conference on Advanced Data Mining and Applications. (ADMA 2023

The influence of responsible leadership on teachers’ green behavior: The mediating role of psychological capital

This research aimed to explore the impact of responsible leadership on teachers’ green behavior in Chinese university, and applied psychological capital as a mediator variable to establish a research model. A questionnaire was conducted with 303 teachers using convenience sampling. SPSS version 19 was used to analyze the data and Sobel was used to test the mediating relationships. The results show that responsible leadership has a positive yet significant effect on teachers’ green behavior. It also shows positive impact on psychological capital. Furthermore, psychological capital is shown to positively impact teachers’ green behavior, while having a mediating effect between responsible leadership and teachers’ green behavior. This study enriches the research of teachers’ green behavior and fill the gap in previous education management research. The research conclusions enable managers to better understand teachers’ green behavior and provides them with theoretical guidance for promoting psychological capital and improving teachers’ green behavior

A Fast Near-Infrared Image Colorization Deep Learning Mode

Near-infrared(NIR) image colorization is the main research content in the field of current near-infrared image application. It has a wide range of application value. For the problem of image colorization, such as diffuse color and even color error, and can not be automated, A fast near-infrared image colorization model consisting of a lightweight image recognition network module and an image colorization CNN module with a fusion layer, firstly using a lightweight image recognition network for image recognition of near-infrared images, and then selecting from the IamgeNet image library The image of the same class as the scene is used as the training set of the colorized network. After training with the colored CNN module with the fusion layer, the near-infrared image is input as the testing set for colorization. The experimental results show that the color is colored by the algorithm. The image details are clear, the color transfer effect is good and the running speed is fast

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

Safe Reinforcement Learning (RL) aims to find a policy that achieves high rewards while satisfying cost constraints. When learning from scratch, safe RL agents tend to be overly conservative, which impedes exploration and restrains the overall performance. In many realistic tasks, e.g. autonomous driving, large-scale expert demonstration data are available. We argue that extracting expert policy from offline data to guide online exploration is a promising solution to mitigate the conserveness issue. Large-capacity models, e.g. decision transformers (DT), have been proven to be competent in offline policy learning. However, data collected in real-world scenarios rarely contain dangerous cases (e.g., collisions), which makes it prohibitive for the policies to learn safety concepts. Besides, these bulk policy networks cannot meet the computation speed requirements at inference time on real-world tasks such as autonomous driving. To this end, we propose Guided Online Distillation (GOLD), an offline-to-online safe RL framework. GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms. Experiments in both benchmark safe RL tasks and real-world driving tasks based on the Waymo Open Motion Dataset (WOMD) demonstrate that GOLD can successfully distill lightweight policies and solve decision-making problems in challenging safety-critical scenarios