Hasil untuk "Costs"

R. Huang, H. Stoll

Thomas L. Fleischner

D. Bertsimas, A. Lo

E. Ziegel

Michael A. Jones, David L. Mothersbaugh, S. Beatty

D. Pimentel

N. Key, E. Sadoulet, A. Janvry

S. Snapp, S. Swinton, R. Labarta et al.

L. Kyne, M. Hamel, Rajashekhar Polavaram et al.

Ruth V. Aguilera, I. Filatotchev, H. Gospel et al.

M. Golosov, Robert E. Lucas, Jr.

Costas Arkolakis

M. Hurd, P. Martorell, Adeline Delavande et al.

Benjamin W. Mooneyham, J. Schooler

Robert B. Jackson, A. Vengosh, J. W. Carey et al.

C. Duffield, M. Roche, C. Homer et al.

Дмитро АНРДЄЄВ, Олексій ЛИГУН, Андрій ДРОЗД et al.

Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide. The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries. The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations. Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets. The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.

ABDULRASAQ MUSTAPHA

Kenya's exposure to climate risks and fiscal volatility has raised concerns about the pricing of its sovereign Eurobonds in global markets. This study investigates the impact of green finance announcements, ESG risk scores, and inflation on Kenya’s sovereign Eurobond yield spreads over U.S. Treasuries from 2015 to 2024. Employing a quantitative explanatory research design, the study analyzed secondary monthly data on yield spreads, macroeconomic indicators, and ESG metrics using multiple linear regression. Descriptive statistics and diagnostic tests confirmed data suitability, while correlation analysis revealed expected directional relationships. Findings show that green finance announcements significantly reduce Kenya’s sovereign risk premium, aligning with signaling theory that credible sustainability communication enhances investor confidence. ESG risk scores were also found to have a statistically significant negative effect on yield spreads, underscoring the importance of non-financial performance in sovereign debt pricing. Conversely, inflation had a significant positive effect, reflecting heightened risk aversion toward macroeconomic instability. The study concludes that climate and ESG signals now influence investor pricing behavior in African debt markets. It recommends that the Kenyan government institutionalize green finance disclosures, improve ESG reporting systems, and enforce effective inflation-targeting policies to reduce borrowing costs and enhance debt sustainability. The findings offer vital information for policymakers and investors in understanding the evolving dynamics of climate-adjusted sovereign risk.

Sercan Yalçın

Tire failures pose significant safety risks, necessitating advanced inspection techniques. This research investigates the application of magnetic sensors and deep learning for detecting defects in steel belts of the tires. It was aim to develop a robust and accurate fault detection system by measuring magnetic field variations caused by defects. In this study, the magnetic image sensor circuit had been designed and then the images obtained from it have been classified as none, crack, and delamination type steel belt errors. Various deep learning models and their hybrid architectures, were explored and compared. Experimental results demonstrate that all models exhibit strong performance, with the Transformer model achieving the highest accuracy of 96.12%. The developed system offers a potential solution for improving tire safety and reducing maintenance costs in industries.

C. Groenewald, B. Essner, D. Wright et al.