Search Results (87)

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

By assuming that both travel and departure time are normally distributed variables, a multi-objective stochastic optimization model for the multi-airport flight cooperative scheduling problem (MAFCSP) with CvaR of travel and departure time is firstly proposed. Herein, conflicts of flights from different airports at the same waypoint can be avoided by simultaneously assigning an optimal route to each flight between the airport and waypoint and determining its practical departure time. Furthermore, several real-world constraints, including the safe interval between any two aircraft at the same waypoint and the maximum allowable delay for each flight, have been incorporated into the proposed model. The primary objective is minimization of both total carbon emissions and delay times for all flights across all airports. A feasible set of non-dominated solutions were obtained using a two-stage heuristic approach-based NSGA-II. Finally, we present a case study of four airports and three waypoints in the Beijing–Tianjin–Hebei region of China to test our study. Full article

In this study, the Gumbel distribution is utilized to construct exact analytical representations for two pivotal measures in financial risk evaluation: Value at Risk (VaR) and Conditional Value at Risk (CVaR). These refined formulations are developed with the intention of offering resilient and practically implementable tools to address the complexities inherent in economic risk analysis. Moreover, the newly established expressions are seamlessly integrated into the GARCH modeling framework, thereby enriching its predictive capabilities. In order to verify both the practical relevance and theoretical soundness of the presented methodology, it is systematically employed regarding the daily return series of a varied portfolio of stocks. The outcomes of the numerical experiments provide compelling evidence of the approach’s reliability and effectiveness, emphasizing its suitability for advancing contemporary risk management strategies in financial environments. Full article

Despite advancements in direct sensing technologies, accurately capturing complex wave–structure interactions remain a significant challenge in ship and ocean engineering. Ensuring the safety and reliability of floating structures requires precise monitoring of dynamic water interactions, particularly in extreme sea conditions. Recent developments in computer vision and artificial intelligence have enabled advanced image-based sensing techniques that complement traditional measurement methods. This study investigates the application of Computerized Video Analysis (CVA) for water surface tracking in maritime experimental tests, marking the first exploration of digitalized experimental video analysis at the Australian Maritime College (AMC). The objective is to integrate CVA into laboratory data acquisition systems, enhancing the accuracy and robustness of wave interaction measurements. A novel algorithm was developed to track water surfaces near floating structures, with its effectiveness assessed through a Wave Energy Converter (WEC) experiment. The method successfully captured wave runup interactions with the hull form, operating alongside traditional sensors to evaluate spectral responses at a wave height of 0.4 m. Moreover, its application in irregular wave conditions demonstrated the algorithm’s capability to reliably detect the waterline across varying wave heights and periods. The findings highlight CVA as a reliable and scalable approach for improving safety assessments in maritime structures. Beyond controlled laboratory environments, this method holds potential for real-world applications in offshore wind turbines, floating platforms, and ship stability monitoring, contributing to enhanced structural reliability under operational and extreme sea states. Full article

Background/Objectives: The present study aimed to investigate the relationship between forward head posture (FHP) and the mechanical properties of muscles as well as the influence of FHP on them. Methods: To define participants with FHP, craniovertebral angle (CVA) was measured. All participants were divided into two groups in accordance with their CVA: the experimental group (FHP) consisting of participants with a CVA below 50°, and the control group (CON) consisting of participants with a CVA above 50°. The tone, stiffness, and elasticity of the upper trapezius muscle (UT), the middle trapezius muscle (MT), the lower trapezius muscle (LT), the sternocleidomastoid muscle (SCM), the splenius capitis muscle (SC), the pectoralis major muscle (PM), and the serratus anterior muscle (SA) were measured using MyotonPro (Myoton AS, Tallinn, Estonia). Results: Both tone and stiffness in the UT were statistically significant (p < 0.05). In addition, stiffness in the LT was statistically significant (p < 0.05). No significant differences were found in tone, stiffness, and elasticity of the MT, SCM, SC, PM, and SA muscles (p > 0.05). A significant correlation was found between FHP and both tone and stiffness in the UT (r = −0.731, p = 0.000; r = −0.749, p = 0.000, respectively). No significant correlation was found between FHP and tone, stiffness, and elasticity of the MT, LT, SCM, SC, PM, and SA muscles. Conclusions: Since the UT was the muscle in which changes in mechanical properties were first induced by FHP, an approach targeting UT is necessary as a priority when treating patients with FHP. Full article

The strategic planning of open-pit mining projects is highly influenced by geological variability, economic fluctuations, and operational uncertainties. Traditional deterministic optimization models often fail to account for these uncertainties, leading to potentially misleading economic evaluations. This paper proposes a hybrid model that integrates deterministic and stochastic optimization methods, following the principles of the ISO 31000 risk management framework, to comprehensively quantify uncertainty through key parameters affecting strategic mine planning. Monte Carlo simulations were applied to generate probability distributions of input parameters, including metal prices, mining and processing costs, and ore grade variability, allowing for a more robust financial assessment. The results demonstrate that while the deterministic approach estimates an NPV of USD 130.8 million, the stochastic model yields an average NPV of USD 155.5 million with a standard deviation of USD 76.5 million, highlighting the significant variability in financial outcomes. Risk assessment using Value at Risk (VaR) and Conditional Value at Risk (CVaR) further quantifies potential financial losses, revealing a 3% probability of project unprofitability. The developed methodology provides a structured approach to integrating uncertainty into mine planning, enabling more reliable economic evaluations and improving decision-making in strategic mining operations. Full article

The increasing penetration of distributed renewable energy sources introduces challenges in maintaining balance within power systems. Civic energy initiatives offer a promising solution by decentralizing balancing responsibilities to local areas, with energy clusters serving as an example of such communities. This article proposes a novel mixed-integer linear programming (MILP) model for optimizing the energy mix within a cluster, addressing both planned balancing (day-ahead) and unplanned real-time adjustments. The proposed approach focuses on mid-term decision-making, including the integration of additional wind energy sources into the cluster and the procurement of new demand-side response (DSR) contracts, that allow for short-term planned and unplanned balancing. While increased wind energy enhances the system’s renewable capacity, it also raises operational stiffness, whereas DSR contracts provide the flexibility necessary for effective system balancing. The model incorporates risk aversion by employing Conditional Value at Risk (CVaR) as a risk measure, enabling a nuanced evaluation of trade-offs between cost and risk. The interactive framework allows decision-makers to tailor solutions by adjusting confidence levels and assigning weights to cost and risk metrics. A representative numerical example, based on a typical energy cluster in Poland, illustrates the model’s applicability. This case study demonstrates that the model responds intuitively to varying decision-maker preferences and can be efficiently solved for practical problem sizes. Full article

To enhance the utilization of emerging energy sources, the application of battery energy storage systems (BESSs) was increasingly explored by investors. However, the immature development of BESS technologies introduced supply–demand imbalances, complicating the establishment of standardized cost analysis frameworks for potential investments. To address this challenge, a hybrid optimization model for a user-side BESS was developed to maximize total net returns over the system’s entire life cycle. The model accounted for factors such as energy storage arbitrage revenue, government tariff subsidies, reductions in electricity transmission fees, delays in grid upgrades, and overall life cycle costs. Conditional value-at-risk (CVaR) was employed as a risk assessment metric to provide investment allocation recommendations across various risk scenarios. An example analysis was conducted to allocate and evaluate the net returns of different battery types. The results demonstrated that the model identified optimal investment strategies aligned with investors’ risk preferences, enabling informed decision-making that balanced returns with operational stability. This approach enhanced the resilience and economic viability of user-side energy storage configurations. Full article

In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, this work introduces a risk management strategy based on a combination of the well-known risk measures of the Value at Risk (VaR) and Conditional Value at Risk (CVaR). The goal is to optimize electricity procurement by forecasting hourly prices over a certain horizon and allocating a fixed budget using the aforementioned measures to minimize the financial risk. To generate price predictions, a Gaussian process regression model is used. The aim of this hybrid approach is to design a model that is easily understandable but allows for a comprehensive evaluation of potential financial exposure. It enables consumers to adjust their consumption patterns or market traders to invest and allows more cost-effective and risk-aware decision-making. The potential of our approach is shown in a case study based on the German market. Moreover, by discussing the political and economical implications, we show how the implementation of our method can contribute to the realization of a sustainable, flexible, and efficient energy market, as outlined in Germany’s Renewable Energy Act. Full article

This paper introduces a mean profit- conditional value-at-risk (CVaR) model for purchasing electricity on the day-ahead market (DA) by electric vehicles fleet aggregator (EVA). EVA controls electric vehicles (EVs) during their workplace parking, enabling smart charging and cost savings by accessing market prices that are potentially lower than flat rates available during home charging. The proposed stochastic linear programming model leverages market price scenarios to optimize aggregated charging schedules, which serve as templates for constructing effective DA bidding curves. It integrates an aspiration/reservation-based formulation of the mean profit-risk criteria, specifically Conditional Value at Risk (CVaR) to address the EVA’s risk aversion. By incorporating interactive analysis, the framework ensures adaptive and robust charging schedules and bids tailored to the aggregator’s risk preferences. Its ability to balance profitability with risk is validated in case studies. This approach provides a practical and computationally efficient tool for EV aggregators of global companies that can benefit from the workplace charging their fleets thanks to buying energy in the DA market. Full article

The study aims to critically assess the safe-haven properties of Bitcoin and a diverse set of commodities in mitigating stock market risks during periods of extreme financial turbulence. Specifically, this research seeks to evaluate the effectiveness of these assets as hedging tools or diversifiers in the portfolios of both OPEC and non-OPEC countries, focusing on their behavior during the COVID-19 pandemic. We employ a wavelet coherence approach to analyze the dynamic relationships between the variables. Portfolio optimization is conducted using CVaR to assess the effectiveness of these assets as safe havens, hedges, or diversification tools in mitigating financial risks during periods of heightened market volatility. The diversification benefits of commodities and Bitcoin in OPEC and non-OPEC stock portfolios decrease over time as their co-movement with stock markets increases. During the COVID-19 period, BTC did not act as a safe haven. However, gold served as a hedge for non-OPEC countries. Using CVaR, we found that BTC provides stronger diversification benefits than commodities, followed by gold. We examine the safe-haven role of Bitcoin and various commodities, specifically within the context of both OPEC and non-OPEC countries. Our study offers a more comprehensive analysis of how BTC and commodities function as portfolio assets during financial stress, providing valuable insights for investors and policymakers. Full article

Counterparty risk, which combines market and credit risks, gained prominence after the 2008 financial crisis due to its complexity and systemic implications. Traditional management methods, such as netting and collateralization, have become computationally demanding under frameworks like the Fundamental Review of the Trading Book (FRTB). This paper explores the combined application of Gaussian process regression (GPR) and Bayesian quadrature (BQ) to enhance the efficiency and accuracy of counterparty risk metrics, particularly credit valuation adjustment (CVA). This approach balances excellent precision with significant computational performance gains. Focusing on fixed-income derivatives portfolios, such as interest rate swaps and swaptions, within the One-Factor Linear Gaussian Markov (LGM-1F) model framework, we highlight three key contributions. First, we approximate swaption prices using Bachelier’s formula, showing that forward-starting swap rates can be modeled as Gaussian dynamics, enabling efficient CVA computations. Second, we demonstrate the practical relevance of an analytical approximation for the CVA of an interest rate swap portfolio. Finally, the combined use of Gaussian processes and Bayesian quadrature underscores a powerful synergy between precision and computational efficiency, making it a valuable tool for credit risk management. Full article

In this study, we examine the critical question of whether global equity and bond assets (both green and non-green) offer effective hedging and safe haven properties against stock market risks in South Asia, with a focus on Bangladesh, India, Pakistan, and Sri Lanka. The increasing integration of global financial markets and the volatility experienced during recent economic crises raise important questions regarding the resilience of South Asian markets and the potential protective role of global assets. Drawing on methods like VaR and CVaR tail risk estimators, the DCC-GJR-GARCH time-varying connectedness approach, and cost-effectiveness tools for hedging, we analyze data spanning from 2014 to 2022 to assess these relationships comprehensively. Our findings demonstrate that stock markets in Bangladesh experience lower levels of downside risk in each quantile; however, safe haven properties from the global financial markets are effective for Bangladeshi, Indian, and Pakistani stock markets during the crisis period. Meanwhile, the Sri Lankan stock market neither receives hedging usefulness nor safe haven benefits from the same marketplaces. Additionally, global green assets, specifically green bond assets, are more reliable sources to ensure the safest investment for South Asian investors. Finally, the portfolio implications suggest that while traditional global equity assets offer ideal portfolio weights for South Asian investors, global equity and bond assets (both green and non-green) are the cheapest hedgers for equity investors, particularly in the Bangladeshi, Pakistani, and Sri Lankan stock markets. Moreover, these results hold significant implications for investors seeking to optimize portfolios and manage risk, as well as for policymakers aiming to strengthen regional market resilience. By clarifying the protective capacities of global assets, particularly green ones, our study contributes to a nuanced understanding of portfolio diversification and financial stability strategies within emerging markets in South Asia. Full article

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance. Full article

Pancreatic cancer is one of the deadliest cancers globally, with limited success from existing therapies, including chemotherapies and immunotherapies like checkpoint inhibitors for patients with advanced pancreatic ductal adenocarcinoma (PDAC). A promising new approach is the use of oncolytic viruses (OV), a form of immunotherapy that has been demonstrated clinical effectiveness in various cancers. Here we investigated the potential of the oncolytic coxsackievirus B3 strain (CVB3) PD-H as a new treatment for pancreatic cancer. In vitro, PD-H exhibited robust replication, as measured by plaque assays, and potent lytic activity, as assessed by XTT assays, in most pancreatic tumor cell lines, outperforming two other coxsackievirus strains tested, H3N-375/1TS and CVA21. Thus, H3N-375/1TS showed efficient replication and lytic efficiency in distinctly fewer tumor cell lines, while most tumor cells were resistant to CVA21. The oncolytic efficiency of the three OV largely correlated with mRNA expression levels of viral receptors and their ability to induce apoptosis, as measured by cleaved caspase 3/7 activity in the tumor cells. In a syngeneic mouse model with subcutaneous pancreatic tumors, intratumoral administration of PD-H significantly inhibited tumor growth but did not completely stop tumor progression. Importantly, no virus-related side effects were observed. Although pancreatic tumors respond to PD-H treatment, its therapeutic efficacy is limited. Combining PD-H with other treatments, such as those aiming at reducing the desmoplastic stroma which impedes viral infection and spread within the tumor, may enhance its efficacy. Full article