Optimizing Strategic Planning With Long-term Sequential Decision Making Under Uncertainty: A Decomposition Approach

The operations research literature has seen decision-making methods at both strategic and operational levels, where high-level strategic plans are first devised, followed by long-term policies that guide future day-to-day operations under uncertainties. Current literature studies such problems on a case-by-case basis, without a unified approach. In this study, we investigate the joint optimization of strategic and operational decisions from a methodological perspective, by proposing a generic two-stage long-term strategic stochastic decision-making (LSSD) framework, in which the first stage models strategic decisions with linear programming (LP), and the second stage models operational decisions with Markov decision processes (MDP). The joint optimization model is formulated as a nonlinear programming (NLP) model, which is then reduced to an integer model through discretization. As expected, the LSSD framework is computationally expensive. Thus, we develop a novel solution algorithm for MDP, which exploit the Benders decomposition with the ``divide-and-conquer\u27\u27 strategy. We further prove mathematical properties to show that the proposed multi-cut L-shaped (MCLD) algorithm is an exact algorithm for MDP. We extend the MCLD algorithm to solve the LSSD framework by developing a two-step backward decomposition (TSBD) method. To evaluate algorithm performances, we adopt four benchmarking problems from the literature. Numerical experiments show that the MCLD algorithm and the TSBD method outperform conventional benchmarks by up to over 90\% and 80\% in algorithm runtime, respectively. The practicality of the LSSD framework is further validated on a real-world critical infrastructure systems (CISs) defense problem. In the past decades, ``attacks\u27\u27 on CIS facilities from deliberate attempts or natural disasters have caused disastrous consequences all over the globe. In this study, we strategically design CIS interconnections and allocate defense resources, to protect the CIS network from sequential, stochastic attacks. The LSSD framework is utilized to model the problem as an NLP model with an alternate integer formulation. We estimate model parameters using real-world CIS data collected from a middle-sized city in the U.S. Previously established algorithms are used to solve the problem with over 45% improvements in algorithm runtime. Sensitivity analyses are conducted to investigate model behaviors and provide insights to practitioners

Posouzení finanční situace vybrané společnosti

This bachelor thesis mainly focus on the financial situation assessment of BNP Paribas. Generally, this thesis includes the information about: the basic information of EU financial market, the brief history and company information of BNP Paribas, theory of financial methodologies and the use of them with the financial statements of BNP Paribas and the final conclusion of the whole study. In chapter 4, the common financial analysis methods include common-size analysis, financial ratio analysis, DuPont analysis and influence quantification will be used to assess the financial situation of the company. At last there will be a conclusion of all the study and prediction of the company's future.This bachelor thesis mainly focus on the financial situation assessment of BNP Paribas. Generally, this thesis includes the information about: the basic information of EU financial market, the brief history and company information of BNP Paribas, theory of financial methodologies and the use of them with the financial statements of BNP Paribas and the final conclusion of the whole study. In chapter 4, the common financial analysis methods include common-size analysis, financial ratio analysis, DuPont analysis and influence quantification will be used to assess the financial situation of the company. At last there will be a conclusion of all the study and prediction of the company's future.154 - Katedra financívelmi dobř

Signature of Scramblon Effective Field Theory in Random Spin Models

Information scrambling refers to the propagation of information throughout a quantum system. Its study not only contributes to our understanding of thermalization but also has wide implications in quantum information and black hole physics. Recent studies suggest that information scrambling is mediated by collective modes called scramblons. However, a criterion for the validity of scramblon theory in a specific model is still missing. In this work, we address this issue by investigating the signature of the scramblon effective theory in random spin models with all-to-all interactions. We demonstrate that, in scenarios where the scramblon description holds, the late-time operator size distribution can be predicted from its early-time value, requiring no free parameters. As an illustration, we examine whether Brownian circuits exhibit a scramblon description and obtain a positive confirmation both analytically and numerically. We also discuss the prediction of multiple-quantum coherence when the scramblon description is valid. Our findings provide a concrete experimental framework for unraveling the scramblon field theory in random spin models using quantum simulators.Comment: 6 pages, 3 figures + supplemental materia

Theory of correlated insulating behaviour and spin-triplet superconductivity in twisted double bilayer graphene

Two monolayers of graphene twisted by a small `magic' angle exhibit nearly flat bands leading to correlated electronic states and superconductivity, whose precise nature including possible broken symmetries, remain under debate. Here we theoretically study a related but different system with reduced symmetry - twisted {\em double} bilayer graphene (TDBLG), consisting of {\em two} Bernal stacked bilayer graphene sheets, twisted with respect to one another. Unlike the monolayer case, we show that isolated flat bands only appear on application of a vertical displacement field $D$. We construct a phase diagram as a function of twist angle and $D$, incorporating interactions via a Hartree-Fock approximation. At half filling, ferromagnetic insulators are stabilized, typically with valley Chern number $C_v=2$. Ferromagnetic fluctuations in the metallic state are argued to lead to spin triplet superconductivity from pairing between electrons in opposite valleys. Response of these states to a magnetic field applied either perpendicular or parallel to the graphene sheets is obtained, and found to compare favorably with a recent experiment. We highlight a novel orbital effect arising from in-plane fields that can exceed the Zeeman effect and plays an important role in interpreting experiments.Comment: main 15 pages, appendix 11 page

Improved Approximation Ratios of Fixed-Price Mechanisms in Bilateral Trades

We continue the study of the performance for fixed-price mechanisms in the bilateral trade problem, and improve approximation ratios of welfare-optimal mechanisms in several settings. Specifically, in the case where only the buyer distribution is known, we prove that there exists a distribution over different fixed-price mechanisms, such that the approximation ratio lies within the interval of [0.71, 0.7381]. Furthermore, we show that the same approximation ratio holds for the optimal fixed-price mechanism, when both buyer and seller distributions are known. As a result, the previously best-known (1 - 1/e+0.0001)-approximation can be improved to $0.71$. Additionally, we examine randomized fixed-price mechanisms when we receive just one single sample from the seller distribution, for both symmetric and asymmetric settings. Our findings reveal that posting the single sample as the price remains optimal among all randomized fixed-price mechanisms

An Empirical Study on the Influencing Factors of Customers\u27 Acceptance Intention towards Online Behavioral Advertising

Big data mining and analysis technology greatly influence the development of the advertising industry. In order to capture large information on consumers\u27 online behaviour, cookie files and Hadoop are widely adopted by advertisers to reach targeted consumers, which leads to online behavioural advertising. Based on an empirical study, this research mainly analyzes the factors influencing customers\u27 acceptance intention towards OBA from developing a conceptual framework. By collecting data through questionnaires and using SPSS and AMOS for data analysis, the result indicates that the factors of performance expectancy, effort expectancy, social influence and facilitating conditions have a positive relationship with customer acceptance intention. Moreover, performance expectancy, effort expectancy, and facilitating conditions have a positive relationship with attitudes towards OBA. However, attitudes do not positively impact customer acceptance intention and social influence has no significant relationship with attitudes, which could attribute to privacy concern and the rising of personality consciousness respectively. The result of this study is of great significance to the way of improving advertising effectiveness

Research on the Influence of Music

As for question 1, based on the directed relationship between influencers and followers, we building a network of musicians based on influential relationships. A Music Influence Evaluation Model (MIEM) was also established, and the model formula is shown in the text. We then select the top 200 artists in the “music influence” ranking to build a subnet. The larger the subnet node, the more lines are extended. Indicating that the node represents the musician’s influence is large and extensive. From the graph, we can see that Bob Dylan is influential, but the breadth of influence is not enough; Miles Davis influenced a wide range of music factions.As for question 2, we have developed a Music Similarity Evaluation Model (MSEM) to calculate the contribution parameters of fifteen different music metrics. Using fully connected neural networks combined with triple loss to solve the answer. According to the characteristics of Triple Loss, we can make the similar nodes in the space closer together and the dissimilar nodes further apart. After training, our neural network is able to distinguish artists very well. The results were obtained: artists within genres are far more similar than artists between genres, and a classification image of musicians from different genres was produced.As for question 3, a comparative plot of characteristics revealed that music genres also have their own particular musical characteristics. The comprehensive analysis concludes that the difference between genres is mainly reflected by the six features of valence, tempo, mode, key, acousticness, and instrumentalness, and this result is verified by k-means clustering. By plotting the percentage of influence as well as the change of musical characteristics, it was concluded that the influence of genres changes over time; some musical characteristics in genres also change over time. Finally, the similarity between each faction is calculated and plotted as a heat map, and the genres with high similarity must have interrelated relationships with each other.As for question 4, we have developed a Music Influence T-test Model (MITM). We hypothesized that “influencers” would not influence followers to create music, and a t-test using SPSS rejected the original hypothesis and concluded that “influencers” would influence followers to create music. Additionally, Contagious Evaluation Model(CEM) was also be created. We established the “contagious” index and calculated the Pearson correlation coefficients between “contagious” and 15 musical characteristics, and obtained the results: energy, loudness, and acousticness are more “contagious” than other characteristics. Results: energy, loudness and acousticness are more “contagious” than other features.As for question 5, a time series plot of the variation for each musical characteristic with year was plotted and the analysis yielded the following conclusion: There are characteristics that signify revolutions in musical evolution from these data. For example, the music after 1960s showed changes characterized by higher rhythmicity, faster tempo, and fewer spoken words. Based on these musical evolutionary changes, combined with the “musical influence” we calculated earlier, we select five musical change-makers: The Beatles, Bob Dylan, The Rolling Stones, Miles Davis and Jimi Hendrix.As for question 6, we combined musical influences to identify the most influential musicians in each genre in each era as dynamic influencers to represent the music of the genre in that period. Creating images of their musical characteristics over time and analyzing them in relation to the history of musical development led to the conclusion that an artist’s musical identity changes with technology, social development, and changes in genre representation?As for question 7, a Network Connectivity Evaluation Model(NCEM) was developed to measure which artists in the music network were heavily influenced by external factors during the time period. The first and middle of the 20th century were found to be highly connected online, and this period coincided with a period of social upheaval, with the Cold War, World War II, the Industrial Revolution, and the rapid development of the Internet having a great impact on music, from which many new musical styles were born

Systematic Study of Class I HDAC Inhibitor Largazole

Studying the mechanism of action for a small molecule is always challenging, not only because of the complex molecular interactions involved, but also due to the intricacies of intracellular processes, the diverse response of different cell types, and the evolving nature of biological systems under the influence of these compounds. Despite these challenges, it remains an indispensable task as it provides fundamental insights into cellular function, drug action, and can pave the way for the development of more effective therapeutic strategies. This thesis uses largazole, a class I HDACs inhibitor, as an example to show how to study the mechanism of action for a small molecule from scratch using various modern techniques and tools.&nbsp; In Chapter 1, the background knowledge of histone deacetylase (HDAC), HDAC inhibitors (HDACi) and largazole are overviewed. In Chapter 2, we identify the targets of largazole using CRISPRa-HTP, validating these targets through parallel and orthogonal methods. Subsequently, we delve deeper into the relationship between largazole&rsquo;s HDACi property and PRDX6, one of the identified targets. In Chapter 3, mechanism of action for PRDX6 knockdown induced sensitization of largazole was studied with the combination of ATAC-seq and ChIP-seq. And adaptive resistance of largazole was investigated using scRNA-seq. In Chapter 4, direct interaction between largazole and PRDX6, SP1 was studied using microscale thermophoresis, intact mass spectrometry and functional assays.&nbsp; In Chapter 5, cell type specificity and drug specificity were studied using various cell types and HDACi. Differential action was observed between largazole and paragazole. In Chapter 6, mechanism of action was studied in an animal model and PRDX6 as a biomarker for cancer aggressiveness was proposed In Chapter 7, conclusions and summaries of the current works are presented. In addition, perspectives and future directions are displayed.</p