Trading strategies are commonly utilized to identify optimal trading signals and mitigate trading volatility. Enforcing a trading strategy to include technical indicators from different categories results in resilient and robust performance. To that end, we take the Diverse Technical Indicator Pool (DTIP) into consideration. This paper proposes a memetic-based optimization algorithm for finding a more diverse technical indicator-based strategy, which enables robust and adaptable performance across varying market regimes. The DTIP ensures that selected technical indicators are drawn from distinct categories: volatility, momentum, volume, and trend, thereby providing resilient signals that resist overfitting. Building on previous research, this study conducts a rigorous comparative analysis of three local search algorithms (FA, PSO, and SA) within the memetic framework. Empirical results from a walk-forward validation on the US, Taiwan, and Cryptocurrency markets demonstrate that SA consistently yields the highest stability and risk-adjusted returns. Furthermore, the proposed framework is shown to significantly outperform a machine learning baseline, particularly in high-volatility assets like Bitcoin, where it reduced volatility while maintaining superior profitability.
Emergency response decision-making in high-uncertainty socio-technical systems is often hindered by incomplete information, reporting bias, and the trade-off between rapid intervention and unnecessary deployment. In the present study, we propose a novel Bayesian-Stackelberg methodology to support emergency response decision-making under uncertainty by formalizing the strategic interaction between information providers and responders. In this framework, the operator acts as the strategic leader and transmits a signal regarding the severity of an incident; an additional sensor is treated as a non-strategic evidence source; and the responder acts as the follower, deciding whether to engage or wait based on prior information and a posterior-risk threshold. The methodology integrates prior incident probabilities that can be derived from quantitative risk assessment, historical data, or expert elicitation, together with reporting-credibility parameters, and consequence-based cost structures into a closed-form deployment threshold. It further incorporates incentive-design mechanisms to evaluate truthful reporting under a Perfect Bayesian Stackelberg Equilibrium. A hydrogen refueling station is used as a scenario-based operational case study to demonstrate the practical applicability of the proposed approach. Scenario-based and sensitivity analyses are conducted to demonstrate model behavior and examine the influence of key parameters on response decisions. The study provides a transparent, data-informed, and calibration-ready decision-support framework for emergency response under uncertainty, with potential to improve communication credibility, response consistency, and cost-consequence reasoning in complex industrial environments.
The growing digitalization of chemical-process-industries (CPIs) has brought benefits in efficiency and product quality, but it has simultaneously introduced new vulnerabilities. Cyber-threats represent new risks to CPIs, and compromised measurements can lead to hazardous situations. This study examines cybersecurity in CPIs, emphasizing the importance of process-domain knowledge in identifying and mitigating cyber-threats. While current detection strategies, such as anomaly detectors, network-centric intrusion systems, and secure-control methods, provide valuable protection, many suffer from key limitations, including limited adaptability and inadequate integration of heterogeneous cyber–physical information sources. These shortcomings reduce their practicality in large-scale, nonlinear, safety-critical CPIs. This work highlights important knowledge gaps, including a lack of scalable validation platforms, realistic cyber–physical datasets, adaptive control structures, and detection methods. To move beyond these limitations, this paper puts forward an interdisciplinary cybersecurity-framework that draws on expertise from chemical engineering, industrial communication engineering, data science, and computer science. This approach enables the development of cyber–physical co-simulation environments, multi-source-hybrid detection architectures, standardized interfaces, and human-in-the-loop decision tools. The study concludes that active engagement of chemical and process safety engineers, together with coordinated cross-domain collaboration, is vital for creating resilient, scalable, and forward-thinking cybersecurity solutions for CPIs safety and security.
Microbial platforms are now recognized as sustainable sources of long-chain polyunsaturated fatty acids (PUFAs). This study presents a novel bioprocessing approach that integrates abiotic stress with exogenous phytohormones, indole-3-acetic acid (IAA), salicylic acid (SA), and abscisic acid (ABA) to enhance the biosynthesis of PUFAs in marine Thraustochytrium sp. BM2. While individual stress strategies are known to either enhance lipid accumulation or modulate oxidative responses. The combined effect of phytohormones and multiple treatments of cold stress conditions (4 °C) enhanced the PUFAs fraction (including DHA, EPA, DPA, and ARA) and lowered the SFAs fraction. IAA combined with cold stress marginally enhanced lipid yield by 73.3
This study investigates the influence of financial derivatives on bank credit risk, accounting for heterogeneity across derivative types, risk measures, and banking structures. The analysis is based on a panel of 68 large banks from 21 countries over the period from 2010 to 2024 and employs the two-stage instrumental variable (IV) approach. Both accounting-based (Z-score) and market-based (Distance to Default) indicators are measured to capture different dimensions of credit risk. The findings reveal that the effects of derivatives are specific to each instrument on credit risk. Interest rate, equity, and commodity derivatives consistently mitigate credit risk, reinforcing their role as effective hedging tools. Conversely, foreign exchange and credit derivatives display ambiguous or risk-increasing effects, particularly when assessed using market-based measures. Additional evidence suggests that the use of derivatives contributes to higher credit risk in bank holding companies compared to commercial banks, which reflects differences in institutional complexity and risk-taking behavior. These results underscore that derivatives are not inherently stabilizing or destabilizing, highlighting the necessity for regulation that is specific to each instrument and the implementation of comprehensive risk assessment frameworks.
