249 research outputs found
Zhodnocení finanční pozice vybrané společnosti
Financial analysis should be based on value, and the range of value is very wide. Value is based on the business. The foothold is the good and bad of the business, to judge whether the current business development is good or bad, and how to maintain or improve it in the future. The results of the analysis can make investors know more clearly whether this company is worth investing in. The goal of the thesis is to analyze and evaluate Evergrande Group's financial performance, based on data from Evergrande Group's financial statements from 2014 to 2018. This thesis is divided into five chapters. The first and the last chapter is introduction and conclusion of the thesis. The second chapter is devoted to financial analysis methodology, we will describe meaning and goal of financial analysis and financial statement as a basic tool for analysis. The third chapter includes the overview of Evergrande Group, social responsibility of Evergrande Group and Evergrande Group's main competitors. In the fourth chapter we will calculate and analyze the results of financial analysis.Financial analysis should be based on value, and the range of value is very wide. Value is based on the business. The foothold is the good and bad of the business, to judge whether the current business development is good or bad, and how to maintain or improve it in the future. The results of the analysis can make investors know more clearly whether this company is worth investing in. The goal of the thesis is to analyze and evaluate Evergrande Group's financial performance, based on data from Evergrande Group's financial statements from 2014 to 2018. This thesis is divided into five chapters. The first and the last chapter is introduction and conclusion of the thesis. The second chapter is devoted to financial analysis methodology, we will describe meaning and goal of financial analysis and financial statement as a basic tool for analysis. The third chapter includes the overview of Evergrande Group, social responsibility of Evergrande Group and Evergrande Group's main competitors. In the fourth chapter we will calculate and analyze the results of financial analysis.154 - Katedra financívelmi dobř
Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG)
because it reveals the relations among diseases and thus can be utilized to
guide the generation process. However, constructing a comprehensive KG is
labor-intensive and its applications on the MRG process are under-explored. In
this study, we establish a complete KG on chest X-ray imaging that includes 137
types of diseases and abnormalities. Based on this KG, we find that the current
MRG data sets exhibit a long-tailed problem in disease distribution. To
mitigate this problem, we introduce a novel augmentation strategy that enhances
the representation of disease types in the tail-end of the distribution. We
further design a two-stage MRG approach, where a classifier is first trained to
detect whether the input images exhibit any abnormalities. The classified
images are then independently fed into two transformer-based generators,
namely, ``disease-specific generator" and ``disease-free generator" to generate
the corresponding reports. To enhance the clinical evaluation of whether the
generated reports correctly describe the diseases appearing in the input image,
we propose diverse sensitivity (DS), a new metric that checks whether generated
diseases match ground truth and measures the diversity of all generated
diseases. Results show that the proposed two-stage generation framework and
augmentation strategies improve DS by a considerable margin, indicating a
notable reduction in the long-tailed problem associated with under-represented
diseases
Analýza EaR aplikací metodologie CorporateMetrics
The main goal is to use the CorporateMetrics methodology to predict the Sinopec's operating profit in 2021 under market risk.
This thesis includes five chapters. The first chapter is a general introduction to the thesis. The second chapter deals with a theoretical description. The third chapter focuses on Sinopec. The fourth chapter applies the CorporateMetrics methodology to the analysis of the selected company. The fifth chapter is a conclusion of the whole thesis.
In chapter 2, the theoretical introduction to CorporateMetrics is divided into three main parts. These are definition of CorporateMetrics, framework components and the comparison to other risk management tools. The framework components include the metric specification, the exposure mapping, the scenario generation, valuation and the risk computation.
In chapter 3, the Sinopec will be introduced in more detail. It will start with a general introduction to Sinopec, followed by the history of the company. A brief overview of Sinopec's main competitors will also be presented and compared. Finally, a SWOT analysis of Sinopec will be presented.
In chapter 4, the part of CorporateMetrics that will be used to measure risk, mainly using the Earning-at-Risk analysis. The 1000 scenarios of crude oil prices and market rates will be modelled. Then, the parameters that will be used to calculate the operating profit will then be calculated. Finally, a valuation of the operating profit can be obtained. The results are expressed as frequency and probability distributions and are also presented graphicallyThe main goal is to use the CorporateMetrics methodology to predict the Sinopec's operating profit in 2021 under market risk.
This thesis includes five chapters. The first chapter is a general introduction to the thesis. The second chapter deals with a theoretical description. The third chapter focuses on Sinopec. The fourth chapter applies the CorporateMetrics methodology to the analysis of the selected company. The fifth chapter is a conclusion of the whole thesis.
In chapter 2, the theoretical introduction to CorporateMetrics is divided into three main parts. These are definition of CorporateMetrics, framework components and the comparison to other risk management tools. The framework components include the metric specification, the exposure mapping, the scenario generation, valuation and the risk computation.
In chapter 3, the Sinopec will be introduced in more detail. It will start with a general introduction to Sinopec, followed by the history of the company. A brief overview of Sinopec's main competitors will also be presented and compared. Finally, a SWOT analysis of Sinopec will be presented.
In chapter 4, the part of CorporateMetrics that will be used to measure risk, mainly using the Earning-at-Risk analysis. The 1000 scenarios of crude oil prices and market rates will be modelled. Then, the parameters that will be used to calculate the operating profit will then be calculated. Finally, a valuation of the operating profit can be obtained. The results are expressed as frequency and probability distributions and are also presented graphically154 - Katedra financídobř
Research on the Status Quo and Satisfaction of ' Internet + ' Home Care Model in Hangzhou in the Post-Epidemic Era
This paper takes Hangzhou as an example to study the current situation of the "Internet +" home care model in Hangzhou in the post-epidemic era and the satisfaction of citizens with this new pension model. We established a structural equation model of citizens' satisfaction with the "Internet +" home care model, and calculated the satisfaction index by combining the CSI satisfaction index
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
Protein (receptor)--ligand interaction prediction is a critical component in
computer-aided drug design, significantly influencing molecular docking and
virtual screening processes. Despite the development of numerous scoring
functions in recent years, particularly those employing machine learning,
accurately and efficiently predicting binding affinities for protein--ligand
complexes remains a formidable challenge. Most contemporary methods are
tailored for specific tasks, such as binding affinity prediction, binding pose
prediction, or virtual screening, often failing to encompass all aspects. In
this study, we put forward DeepRLI, a novel protein--ligand interaction
prediction architecture. It encodes each protein--ligand complex into a fully
connected graph, retaining the integrity of the topological and spatial
structure, and leverages the improved graph transformer layers with cosine
envelope as the central module of the neural network, thus exhibiting superior
scoring power. In order to equip the model to generalize to conformations
beyond the confines of crystal structures and to adapt to molecular docking and
virtual screening tasks, we propose a multi-objective strategy, that is, the
model outputs three scores for scoring and ranking, docking, and screening, and
the training process optimizes these three objectives simultaneously. For the
latter two objectives, we augment the dataset through a docking procedure,
incorporate suitable physics-informed blocks and employ an effective
contrastive learning approach. Eventually, our model manifests a balanced
performance across scoring, ranking, docking, and screening, thereby
demonstrating its ability to handle a range of tasks. Overall, this research
contributes a multi-objective framework for universal protein--ligand
interaction prediction, augmenting the landscape of structure-based drug
design
RandChain: A Scalable and Fair Decentralised Randomness Beacon
We propose RANDCHAIN, a Decentralised Randomness Beacon (DRB) that is the first to achieve both scalability (i.e., a large number of participants can join) and fairness (i.e., each participant controls comparable power on deciding random outputs). Unlike existing DRBs where participants are collaborative, i.e., aggregating their local entropy into a single output, participants in RANDCHAIN are competitive, i.e., competing with each other to generate the next output. The competitive design reduces the communication complexity from at least O(n2) to O(n) without trusted party, breaking the scalability limit in existing DRBs.
To build RANDCHAIN, we introduce Sequential Proof-of-Work (SeqPoW), a cryptographic puzzle that takes a random and unpredictable number of sequential steps to solve. We implement RANDCHAIN and evaluate its performance on up to 1024 nodes, demonstrating its superiority (1.3 seconds per output with a constant bandwidth of 200KB/s per node) compared to state-of-the-art DRBs RandHerd (S&P’18) and HydRand (S&P’20)
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