The concept of sustainable management in culture has been recognised in global strategic documents on sustainable development for more than a decade. It is also increasingly reflected in the cultural polices of particular states, and – very importantly –cultural managers responsible for shaping the cultural offer in cities are becoming more interested in this concept. Despite the increasing attention being paid to this topic among both practitioners and theoreticians of management, in none of these documents or other works can we find any content directly related to the possibility of applying this concept in a town which, due to political turmoil, has been divided by a national border. Hence, this gap was the direct impulse for taking up research in this field. In the article, by using different notions of the market, our own definition of a cross-border market for cultural services was developed, and the conditions for the functioning of this market were presented based on the example town of Cieszyn (Poland) and Český Těšín (Czech Republic). In the opinion of the authors of the article, the development and functioning of a cross-border market for cultural services is essential for the application of the concept of sustainable management of the cultural offer in a town divided by a border. For the purpose of the article, a survey and individual interviews with experts shaping the cultural offer in Cieszyn and Český Těšín were conducted. The results of the research prove that despite numerous cross-border Czech-Polish projects carried out by cultural institutions, there are still many barriers in the town which make it difficult for the residents to benefit from the cultural offer available on the other side of the border. These barriers limit the full implementation and application of the concept of sustainable management of the cultural offer.

The sub-economic dynamics studies the relationship between people's economic impulses and market behavior. Using Bourbaki structuralism as a philosophical consideration and quantum chromodynamics as a conceptual and modeling method, sub-economic dynamics theory is constructed. In this theory, we discuss the deep structure of the free market and analyze the errors in Mises' anthropological arguments. Sub-economic dynamics is the psychological basis of market dynamics. Guided by the preface, this paper is divided into six sections: (1) The Free Market and Impulse Flavor Charge, including topics such as the fate of the free market, the food chain and psychology, and the flavor charge of economic impulses; (2) The Nature of Economic Impulses, including topics such as the flavor and generation of impulses and counter-impulses, and the bound state of impulses and fractional market charges; (3) Freud's Color Charge Space and Spin, including topics such as the Pauli exclusion rule and color charge, and Freud's personality theory; (4) Consciousness and Gluons; (5) Gauge Transformations and SU(3) Symmetry Group; and (6) General Discussion.

The use of the un-indexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is an infamously dangerous place where all kinds of criminal activities take place, despite advances in web forensics techniques, tools, and methodologies, few studies have formally tackled the dark and deep web forensics and the technical differences in terms of investigative techniques and artefacts identification and extraction. This research proposes a novel and comprehensive protocol to guide and assist digital forensics professionals in investigating crimes committed on or via the deep and dark web, the protocol named D2WFP establishes a new sequential approach for performing tasks and subtasks to improve the accuracy and effectiveness of current tools' output. Quantitative and qualitative research has been conducted by testing the protocol following a comprehensive and rigorous process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting D2WFP. The second contribution of D2WFP is the artefacts correlation and cross-validation which enables Digital Forensics professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.

This study seeks to determine how economic policy uncertainty (EPU) influences investment decisions and the market value of the Pakistan Stock Exchange. The study examines investment and operational data from 249 energy and petroleum companies between 2015 and 2020, in addition to macroeconomic variables such as EPU. This study investigates the moderating effects of EPU on investments in fixed and intangible assets, as well as its effect on Tobin's Q and the market price per share. The outcomes demonstrate that EPU reduces the costs of both tangible and intangible assets for businesses. In addition, companies with a greater Tobin's Q and market price per share are more impacted by uncertain corporate investment policies. However, financial leverage is found to be negatively correlated with share price and positively correlated with earnings per share and earnings per unit. Tobin's Q is positively correlated with financial leverage, indicating that firms that raise capital through debt are more likely to create value for investors. The research indicates that market-dependent enterprises are more susceptible to the unpredictability of monetary policy. Eventually, the findings suggest that consistent and transparent economic policies may increase the efficiency of corporate investment.

Demographic change is leading to the aging of German society. As long as the baby boom co-horts are still of working age, the working population will also age - and decline as soon as this baby boom generation gradually reaches retirement age. At the same time, there has been a trend towards increasing absenteeism (times of inability to work) in companies since the zero years, with the number of days of absence increasing with age. We present a novel stochastic forecast approach that combines population forecasting with forecasts of labor force participation trends, considering epidemiological aspects. For this, we combine a stochastic Monte Carlo-based cohort-component forecast of the population with projections of labor force participation rates and morbidity rates. This article examines the purely demographic effect on the economic costs associated with such absenteeism due to the inability to work. Under expected future employment patterns and constant morbidity patterns, absenteeism is expected by close to 5 percent by 2050 relative to 2020, associated with increasing economic costs of almost 3 percent. Our results illustrate how strongly the pronounced baby boom/ baby bust phenomenon determines demographic development in Germany in the midterm.

(1) As the craft beer industry continues to expand in Canada, understanding the regional differences between the Eastern and Western markets becomes pivotal for brewing ingredient companies seeking to penetrate or expand within these regions. This study aims to provide a comprehensive review of these two distinctive markets, focusing on economic, logistical, ingredient, and cultural differences. (2) The study primarily relies on secondary data from government, industry reports, and scholarly publications, providing a holistic view of the Canadian craft beer landscape. It further highlights the need to consider market distinctions between Canada and the United States. (3) Findings reveal that Western Canadian breweries, unlike their Eastern counterparts, operate under constrained storage space, prefer locally-sourced ingredients, and offer a distinct variety of beer styles catering to local tastes. The study further uncovers the regulatory and financial complexities of the Canadian market compared to the United States. (4) Based on these findings, this study provides strategic recommendations for brewing ingredient companies, emphasizing domestic logistics, local sourcing, product adaptation, educational support, and regulatory navigation, fostering success in the Canadian craft beer landscape.

In the evolving landscape of Portfolio Management (PM), the fusion of advanced machine 1 learning techniques with traditional financial methodologies has opened new avenues for innovation. 2 Our study introduces a cutting-edge model combining Deep Reinforcement Learning (DRL) with 3 a Non-stationary Transformer architecture. This model is specifically designed to decode complex 4 patterns in financial time series data, enhancing portfolio management strategies with deeper insights 5 and robustness. It effectively tackles the challenges of data heterogeneity and market uncertainty, 6 key obstacles in PM. Our approach integrates key macroeconomic indicators and targeted news 7 sentiment analysis into its framework, capturing a comprehensive picture of market dynamics. This 8 amalgamation of varied data types addresses the multifaceted nature of financial markets, enhancing 9 the model’s ability to navigate through the complexities of asset management. Rigorous testing 10 demonstrates the model’s efficacy, highlighting the benefits of blending diverse data sources and 11 sophisticated algorithmic approaches in mastering the nuances of PM.12

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an on-line adaptive manner in pre-crisis segments. The on-line sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint to the importance of the U.S. housing bubble as trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states and could work as a comparative impact rating of specific economic events.

This paper explores the application of machine learning in financial time series analysis, focusing on predicting trends in financial enterprise stocks and economic data. It begins by distinguishing stocks from stocks and elucidates risk management strategies in the stock market. Traditional statistical methods such as ARIMA and exponential smoothing are discussed in terms of their advantages and limitations in economic forecasting. Subsequently, the effectiveness of machine learning techniques, particularly LSTM and CNN-BiLSTM hybrid models, in financial market prediction is detailed, highlighting their capability to capture nonlinear patterns in dynamic markets. The study demonstrates the advancements in predictive accuracy and robustness achieved by deep learning methods through empirical analysis and model validation. The findings contribute significantly to academic discourse and offer practical insights for investors, financial analysts, and policymakers navigating market volatility and optimizing investment strategies. Finally, the paper outlines prospects for machine learning in financial forecasting, laying a theoretical foundation and methodological framework for achieving more precise and reliable economic predictions.

Since the Paris Agreement in 2016, the goals of limiting climate change and moving toward climate resilience stand. With a share of about \SI{80}{\percent} of global \ce{CO2} emissions, the energy sector is an essential driver for these goals. A shift to low-carbon energy production and a decentralized system for more efficient energy transmission distribution is necessary. In this paper, we present our work on Modeling of Power Exchanges, Algorithms for \ac{LEM}, Competitiveness of \ac{CHP} and Energy Feedback Devices. The study was conducted considering technical, economic, social and regulatory framework. For easy integration into energy simulations or a \ac{DEMS}, a model for power exchanges was created that allows flexible input or deterministic price patterns. The algorithm handles the clearing of an \ac{LEM} by a district aggregator using limit orders with the goal of increasing the share of locally consumed electricity using economic incentives. An investigation was conducted into the operation of flexible \acp{CHP} in low-carbon power systems to balance the volatility of renewable energy. An energy signal light was developed as an energy feedback device, which is integrated into the \ac{DEMS} in a living lab and allows individual configuration. In summary, the results presented should be compared with those of other research approaches in the future and require qualitative and quantitative evaluation.

This study investigates the latest advancements in utilizing machine learning techniques for predicting financial market movements. Financial markets are complex systems influenced by numerous factors, making accurate predictions challenging but highly valuable. The research explores various machine learning approaches, including supervised learning, unsupervised learning, reinforcement learning, and deep learning, to enhance the accuracy and reliability of financial market forecasts. The study provides a comprehensive review of current methodologies, highlighting key innovations and their applications in financial market prediction. Techniques such as neural networks, support vector machines, random forests, and ensemble methods are examined in the context of their ability to analyze vast amounts of financial data, identify patterns, and generate actionable insights. Additionally, the research delves into the integration of alternative data sources, such as social media sentiment, news articles, and macroeconomic indicators, to improve prediction models. Through empirical analysis and case studies, the study demonstrates the effectiveness of advanced machine learning models in predicting stock prices, currency exchange rates, and commodity prices. It also discusses the challenges associated with these techniques, including overfitting, data quality issues, and the need for interpretability in model outputs. Findings suggest that machine learning techniques can significantly enhance the predictive power of financial models, offering more accurate and timely insights for investors and financial analysts. The study concludes with recommendations for future research directions, emphasizing the importance of model transparency, the incorporation of diverse data sets, and the continuous adaptation of machine learning algorithms to evolving market conditions.