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- PublicationEfficient Voltage Regulation: An RW-ARO Optimized Cascaded Controller Approach(2024)This work introduces novel advancements in automatic voltage regulator (AVR) control, addressing key challenges and delivering innovative contributions. The primary motivation lies in enhancing AVR performance to ensure stable and reliable voltage output. A crucial innovation in this work is the introduction of the random walk aided artificial rabbits optimizer (RW-ARO). This novel optimization strategy incorporates a random walk approach, enhancing the efficiency of AVR control schemes. The proposed cascaded RPIDD2-PI controller, fine-tuned using the RW-ARO, stands out as a pioneering approach in the AVR domain. It demonstrates superior stability, faster response times, enhanced robustness, and improved efficiency compared to existing methods. Comparative analyses with established controller approaches reaffirm the exceptional performance of the proposed method. The new approach results in shorter rise times, quicker settling times, and minimal overshoot, highlighting its effectiveness and speed in achieving desired system responses. Moreover, the novel approach attains higher phase and gain margins, showcasing its superior performance in the frequency domain. The disturbance rejection and harmonic analysis are performed in order to demonstrate the efficacy of the proposed approach for potential real-world applications. The latter analyses further cement the superior capability of the proposed approach for the automatic voltage regulation.
7 1 - PublicationGIJA:Enhanced geyser‐inspired Jaya algorithm for task scheduling optimization in cloud computing(2024)Task scheduling optimization plays a pivotal role in enhancing the efficiency and performance of cloud computing systems. In this article, we introduce GIJA (Geyser‐inspired Jaya Algorithm), a novel optimization approach tailored for task scheduling in cloud computing environments. GIJA integrates the principles of the Geyser‐inspired algorithm with the Jaya algorithm, augmented by a Levy Flight mechanism, to address the complexities of task scheduling optimization. The motivation for this research stems from the increasing demand for efficient resource utilization and task management in cloud computing, driven by the proliferation of Internet of Things (IoT) devices and the growing reliance on cloud‐based services. Traditional task scheduling algorithms often face challenges in handling dynamic workloads, heterogeneous resources, and varying performance objectives, necessitating innovative optimization techniques. GIJA leverages the eruptive dynamics of geysers, inspired by nature's efficiency in channeling resources, to guide task scheduling decisions. By combining this Geyser‐inspired approach with the simplicity and effectiveness of the Jaya algorithm, GIJA offers a robust optimization framework capable of adapting to diverse cloud computing environments. Additionally, the integration of the Levy Flight mechanism introduces stochasticity into the optimization process, enabling the exploration of solution spaces and accelerating convergence. To evaluate the efficacy of GIJA, extensive experiments are conducted using synthetic and real‐world datasets representative of cloud computing workloads. Comparative analyses against existing task scheduling algorithms, including AOA, RSA, DMOA, PDOA, LPO, SCO, GIA, and GIAA, demonstrate the superior performance of GIJA in terms of solution quality, convergence rate, diversity, and robustness. The findings of GIJA provide a promising solution quality for addressing the complexities of task scheduling in cloud environments (95%), with implications for enhancing system performance, scalability, and resource utilization.
6 - PublicationWhen geoscience meets generative AI and large language models: Foundations, trends, and future challenges(2024)Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.
8 1 - PublicationFerroelastic Control of the Multicolor Emission from a Triply Doped Organic Crystal(2024)Emission from crystalline organic solids is often quenched by nonemissive energy-transfer deexcitation processes. While dispersion of fluorophores in polymers or other hosts has been used to enhance the emission intensity, this strategy results in randomization of guest orientation and optical losses at grain boundaries. Here, we report the doping of inherently nonemissive single crystals of anilinium bromide with three fluorescent organic molecules. The doping process equips the crystal with emission characteristics that tune from blue to deep orange. The emission intensity can be reversibly modulated by ferroelastic twinning, which causes the material to function as a multiemissive force sensor. This approach opens up new pathways in the manipulation of emissive properties in organic crystals and may have substantial implications for optoelectronic devices and sensors.
8 1 - PublicationStrategies to Diversification of the Mechanical Properties of Organic Crystals(2024)Structurally ordered soft materials that respond to complementary stimuli are susceptible to control over their spatial and temporal morphostructural configurations by intersectional or combined effects such as gating, feedback, shape-memory, or programming. In the absence of general and robust design and prediction strategies for their mechanical properties, at present, combined chemical and crystal engineering approaches could provide useful guidelines to identify effectors that determine both the magnitude and time of their response. Here, we capitalize on the purported ability of soft intermolecular interactions to instigate mechanical compliance by using halogenation to elicit both mechanical and photochemical activity of organic crystals. Starting from (E)-1,4-diphenylbut-2-ene-1,4-dione, whose crystals are brittle and photoinert, we use double and quadruple halogenation to introduce halogen-bonded planes that become interfaces for molecular gliding, rendering the material mechanically and photochemically plastic. Fluorination diversifies the mechanical effects further, and crystals of the tetrafluoro derivative are not only elastic but also motile, displaying the rare photosalient effect.
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