The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

This study uses the annual financial data of Chinese A-share listed firms from 2010 to 2020 to investigate the relationship between multiple large shareholders (MLS) and earnings management (EM). After analyzing the samples using the Ordinary Least Squares (OLS) model and endogenous switching regression (ESR) model, the empirical results show that the presence of MLS can increase corporate EM activities and the MLS have a significantly positive effect on EM in both the treatment and control groups. In addition, this conclusion still holds after conducting multiple robustness tests. The cross-section analysis shows that the external audit supervision quality, institutional shareholders, and the uncertainty of the external economic environment have significant impacts on the baseline model results. Lastly, mediation effect analysis shows that the presence of MLS increases the corporate operating risk through EM activities. The conclusions of this paper are critical for policymakers to supervise China’s capital market, improve the level of corporate governance of China’s listed firms, and further promote reform of ownership structure.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

Relying on the D-Vine copula model, this paper delves into the hedging capabilities of Brent crude oil against the exchange rate of oil-exporting and oil-importing nations. The results affirm Brent crude oil’s role as a safeguard and a refuge against the fluctuations of major currencies. Furthermore, we reaffirm that oil retains its robust hedging and safe-haven attributes during times of crisis, with currency co-movements across all countries exhibiting greater correlation than during the entire dataset. Additionally, our empirical findings highlight an unusually positive correlation between Brent crude oil and the Russian exchange rate during the Russia-Ukraine conflict, demonstrating that oil functions as a less effective hedge and a less dependable refuge for the Russian exchange rate in such geopolitical turbulence.

In order to further alleviate the problems of large assessment deviations, low efficiency of trading organisation and difficulties in system optimisation in medium- and long-term market trading, the article proposes an optimisation model for continuous intra-month bidding trading in the electricity market that takes into account risk hedging. Firstly, the current situation of market players’ participation in medium and long-term trading is analysed; secondly, the impact of contract trading on reducing operational risks is analysed based on the application of hedging theory in the primary and secondary markets; finally, the continuous bidding trading mechanism is designed and its optimisation effect is verified. The proposed model helps to improve the efficiency of contract trading in the secondary market, maintain the stability of market players’ returns and accelerate the formation of a unified, open, competitive and well-governed electricity market system.

Urban areas are increasingly vulnerable to fire disasters due to high population density, sprawling infrastructure, and often inadequate safety measures. This study aims to analyze the capacity of the DKI Jakarta government in terms of human resource capabilities, asset readiness, and budget planning capabilities. Furthermore, it measures the government’s success as evidenced by the public response to the achievement of firefighter performance. This study uses qualitative analysis with a content analysis approach. Data sources come from annual performance report documents and the content of the DKI Jakarta Fire Department website containing city disaster information. Performance report and website data are analyzed and used as research data to support qualitative analysis. This research shows that command decisions are essential in the organizational structure of the fire brigade. Both laboratory services are carried out optimally as a concrete effort to map fire potential. The laboratory tests the safety and suitability of firefighting equipment. Available budgetary support provides broad operational powers for the fire service. The government’s strength in minimizing or overcoming fire problems has received a positive response from the public. The operational achievements of firefighting continue to be consistent and increase. Ultimately, this research provides scientific insight into disaster mitigation and reducing the fire risk in cities.