Dynamic Scaling Research Articles

Human CSTF2 RNA Recognition Motif Domain Binds to a U-Rich RNA Sequence through a Multistep Binding Process.

The RNA recognition motif (RRM) is a conserved and ubiquitous RNA-binding domain that plays essential roles in mRNA splicing, polyadenylation, transport, and stability. RRM domains exhibit remarkable diversity in binding partners, interacting with various sequences of single- and double-stranded RNA, despite their small size and compact fold. During pre-mRNA cleavage and polyadenylation, the RRM domain from CSTF2 recognizes U- or G/U-rich RNA sequences downstream from the cleavage and polyadenylation site to regulate the process. Given the importance of alternative cleavage and polyadenylation in increasing the diversity of mRNAs, the exact mechanism of binding of RNA to the RRM of CSTF2 remains unclear, particularly in the absence of a structure of this RRM bound to a native RNA substrate. Here, we performed a series of NMR titration and spin relaxation experiments, which were complemented by paramagnetic relaxation enhancement measurements and rigid-body docking, to characterize the interactions of the CSTF2 RRM with a U-rich ligand. Our results reveal a multistep binding process involving differences in ps-ns time scale dynamics and potential structural changes, particularly in the C-terminalα-helix. These results provide insights into how the CSTF2 RRM domain binds to U-rich RNA ligands and offer a greater understanding for the molecular basis of the regulation of pre-mRNA cleavage and polyadenylation.

Characterizing Rotational Dynamics and Heterogeneity via Single-Molecule Intensity Measurements.

Dynamic heterogeneity in glassy systems has typically been characterized at the single-molecule level by extracting rotational relaxation time scales from linear dichroism (LD) collected via two orthogonally polarized channels. However, in such measurements, localization precision is diminished due to photons lost relative to collection in a single detection channel. This poses challenges in characterizing rotational and translational dynamics simultaneously, as translational measurements require high localization precision. In this paper, we present a method for extracting rotational dynamics of glassy systems at the single-molecule level from intensity fluctuations of fluorescent probe molecules in a wide-field configuration without the use of a polarizing optical component. Through numerical analysis, we show that LD and intensity measurements probing rotational dynamics report similar, approximately second-order rotational correlation decays even at low signal to noise. Thus, within the assumptions of small, isotropic rotations, LD and intensity autocorrelation analysis should provide identical information on the time scale and heterogeneity of rotational dynamics. We then present experimental results that validate this numerical result, with direct comparison of LD and intensity-based approaches across probe molecules in both polymeric and small-molecule glass formers as well as across optical configurations. Our results demonstrate moderate correlation on a per-probe basis between rotational time scales obtained from both approaches, with deviations consistent with those expected in a dynamically heterogeneous system. We envision that this easily accessible strategy will be of use across disciplines for characterizing single-molecule rotational dynamics in limited signal situations and/or when high-precision localization is required.

Энергоэффективность многоядерных процессоров: сравнительное исследование аппаратных технологий

Background: The constant increase in processing demand has prompted the development of multi-core processors, which are critical to obtaining high performance in a variety of computing devices. However, this growth in processing capacity comes at the expense of increased energy consumption, posing substantial problems for energy efficiency. Objective: The article will examine and compare several hardware solutions for improving energy efficiency in multi-core processors, offering a full overview of their effectiveness and application. Methods: We used a quantitative research methodology, simulating multi-core processing environments with various hardware optimization techniques such as Dynamic Voltage and Frequency Scaling (DVFS), Clock Gating, and Power Gating. Each technique was evaluated in a variety of operational circumstances to establish its effect on energy consumption and processing efficiency. Results: The findings show that DVFS provides significant energy savings with negligible performance trade-offs in cases with moderate workloads. In contrast, Clock Gating performed best in low workload settings, whereas Power Gating performed best in high-load conditions, dramatically reducing idle power consumption. Conclusion: The comparison research demonstrates that no single technique beats all others; rather, the choice of an effective energy efficiency strategy is significantly influenced by unique workload factors. A hybrid strategy, which combines both techniques depending on real-time workload demands, can greatly improve the energy efficiency of multi-core processors. This study advances our understanding of energy management in advanced computing systems, providing insights for future research and practical applications in the field of energy-efficient computing.

Global output feedback control for uncertain nonlinear systems with input quantisation and sensor gain perturbation

A new quantised control method via output feedback is proposed by establishing two pairs of time-varying coupled matrix inequalities. Compared to relevant quantised control results, our control method has the novelty: (i) the sensor gain perturbation is allowed to be large and nondifferentiable; (ii) the growth constraint of system nonlinearities is relaxed to depend on unmeasured states and x 1 -polynomial function; (iii) a dynamic scaling technique, instead of backstepping design, is introduced to reduce the computational complexity of controller design. In particular, by using a symbolic function (or hyperbolic tangent function), global asymptotic stability is achieved under any hysteresis quantiser.

Composite dark matter with forbidden annihilation

A dark matter model based on QCD-like SU(Nc) gauge theory with electroweakly interacting dark quarks is discussed. Assuming the dark quark mass m is smaller than the dynamical scale Λd ~ 4πfd, the main component of the dark matter is the lightest G-parity odd dark pion associated with chiral symmetry breaking in the dark sector. We show that nonzero dark quark mass induces the universal mass contribution to both G-parity odd and even pions, and their masses tend to be degenerate. As a result, dark pion annihilation into heavier G-parity even dark pion also affects the dark matter relic abundance. Thus, our setup naturally accommodates forbidden dark matter scenario and realizes heavy dark matter whose mass is O\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{O} $$\\end{document}(1–100) TeV, which is different from conventional electroweakly interacting dark matter such as minimal dark matter. We also discuss CP-violation from the θ-term in the dark gauge sector and find that the predicted size of the electron electric dipole moment can be as large as ∼ 10−32e cm.

Efficient resource allocation in cloud computing environments using AI-driven predictive analytics

This paper proposes an innovative AI-driven approach for efficient resource allocation in cloud computing environments using predictive analytics. The study addresses the critical challenge of optimizing resource utilization while maintaining high quality of service in dynamic cloud infrastructures. A hybrid predictive model combining XGBoost and LSTM networks is developed to forecast workload patterns across various time horizons. The model leverages historical data from a large-scale cloud environment, encompassing 1000 servers and over 52 million data points. A dynamic resource scaling algorithm is introduced, which integrates the predictive model outputs with real-time system state information to make proactive allocation decisions. The proposed framework incorporates advanced techniques such as workload consolidation, resource oversubscription, and elastic resource pools to maximize utilization efficiency. Experimental results demonstrate significant improvements in key performance indicators, including increasing resource utilization from 65% to 83%, reducing SLA violation rates from 2.5% to 0.8%, and enhancing energy efficiency, with PUE improving from 1.4 to 1.18. Comparative analysis shows that the proposed model outperforms existing prediction accuracy and resource allocation efficiency methods. The study contributes to the field by presenting a comprehensive, AI-driven solution that addresses the complexities of modern cloud environments and paves the way for more intelligent and autonomous cloud resource management systems.

Tuning of optical, thermodynamic, and thermoelectric properties of Cs2CuBiX6 (X = Cl, Br, I) halide perovskites for solar cells and energy harvesting applications

The double perovskites are outstanding materials for solar cells and transport applications to clean harvest energy. Therefore, the Cs2CuBiX6 (X = Cl, Br, I) are discussed comprehensively for energy harvesting by modified Becke and Johnson (mBJ) potential. The studied DPs fit the structural, mechanical, and dynamic stability scale by tolerance factor, Born–Huang criteria, and phonon dispersion band structures. The band gaps (1.20, 1.0, 0.70) eV for (Cl, Br, I) based DPs ensure the Cs2CuBiCl6 has an absorption band in the visible region while Cs2CuBiBr6 and Cs2CuBiI6 has an absorption band in the infrared region. Heavy elements’ spin–orbit coupling effect (Cs, Bi) reduces the band gap to 0.08 eV. Thermoelectric behavior regarding the merit scale against dopant carriers and temperature has been elaborated. The ultralow lattice thermal conductivity, large Debye temperature, hardness, and melting temperature increase their implication for thermoelectric and other thermodynamic applications. The variation in band gap makes them important for diverse optoelectric and thermoelectric applications. The Cs2CuBiCl6 with a band gap of 1.20 eV is suitable for solar cells, while Cs2CuBiBr6 and Cs2CuBiI6 with band gaps of 1.0 eV and 0.70 eV are significant for thermoelectric generators.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

Application of Proximal Policy Optimization for Resource Orchestration in Serverless Edge Computing

Serverless computing is a new cloud computing model suitable for providing services in both large cloud and edge clusters. In edge clusters, the autoscaling functions play a key role on serverless platforms as the dynamic scaling of function instances can lead to reduced latency and efficient resource usage, both typical requirements of edge-hosted services. However, a badly configured scaling function can introduce unexpected latency due to so-called “cold start” events or service request losses. In this work, we focus on the optimization of resource-based autoscaling on OpenFaaS, the most-adopted open-source Kubernetes-based serverless platform, leveraging real-world serverless traffic traces. We resort to the reinforcement learning algorithm named Proximal Policy Optimization to dynamically configure the value of the Kubernetes Horizontal Pod Autoscaler, trained on real traffic. This was accomplished via a state space model able to take into account resource consumption, performance values, and time of day. In addition, the reward function definition promotes Service-Level Agreement (SLA) compliance. We evaluate the proposed agent, comparing its performance in terms of average latency, CPU usage, memory usage, and loss percentage with respect to the baseline system. The experimental results show the benefits provided by the proposed agent, obtaining a service time within the SLA while limiting resource consumption and service loss.

KPZ scaling from the Krylov space

Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang (KPZ) scaling in late-time correlators and autocorrelators of certain interacting many-body systems, such as Heisenberg spin chains, has been reported. Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis. We focus on the Heisenberg time scale, which approximately corresponds to the ramp–plateau transition for the Krylov complexity in systems with a large but finite number degrees of freedom. Two frameworks are under consideration: i) the system with growing Lanczos coefficients and an artificial cut-off, and ii) the system with the finite Hilbert space. In both cases via numerical analysis, we observe the transition from Gaussian to KPZ-like scaling, or equivalently, the diffusion-superdiffusion transition at the critical Euclidean time tE∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {t}_E^{\\ast } $$\\end{document} = ccrK, for the Krylov chain of finite length K, and ccr = O(1). In particular, we find a scaling ~ K1/3 for fluctuations in the one-point correlation function and a dynamical scaling ~ K−2/3 associated with the return probability (Loschmidt echo) corresponding to autocorrelators in physical space. In the first case, the transition is of the 3rd order and can be considered as an example of dynamical quantum phase transition (DQPT), while in the second, it is a crossover. For case ii), utilizing the relationship between the spectrum of tridiagonal matrices at the spectral edge and the spectrum of the stochastic Airy operator, we demonstrate analytically the origin of the KPZ scaling for the particular Krylov chain using the results of the probability theory. We argue that there is interesting outcome of our study for the double scaling limit of matrix models. For the case of topological gravity, the white noise term of order O (1/N) is identified, which should be taken into account in the controversial issue of ensemble averaging in 2D/1D holography.

A Extensive Review of Recent Technologies and Trends in Low-Power VLSI Design

The demand for energy-efficient electronic devices has driven significant advancements in low-power Very-Large-Scale Integration (VLSI) design. This paper presents a Extensive review of the recent technologies and trends that have emerged to address the challenges associated with power utilization in VLSI circuits. The review covers a broad spectrum of innovations, including advanced transistor technologies such as FinFETs, Gate-All-Around (GAA) FETs, and Silicon-on-Insulator (SOI), which offer improved power efficiency. It also explores cutting-edge power enhance techniques like Dynamic Voltage and Frequency Scaling (DVFS), power gating, and Multi-Threshold CMOS (MTCMOS), highlighting their impact on reducing both dynamic and leakage power. Further, the paper examines energy-efficient architectures, including Near-Threshold Computing (NTC), approximate computing, and asynchronous circuits, which promise substantial power savings. The fusing of machine learning and AI in power optimization and the development of advanced Electronic Design Automation tools are also discussed as emerging trends. Finally, the paper considers future directions, including the potential of 3D Integrated Circuits (3D ICs), quantum and neuromorphic computing, and post-CMOS technologies, to revolutionize low-power VLSI design. This review provides a critical exploration of the actual state of the art and offers comprehensions into the future trajectory of low-power VLSI, making it a valuable resource for researchers and practitioners in the field.

Patterns of non‐migratory fish metacommunity from Neotropical floodplains: Local mechanisms, regional scale and hydrological dynamics

AbstractThe spatial distribution of biological communities may be regulated by environmental and spatial processes, and the intensity of these processes depend on the species characteristics, spatial heterogeneity and spatial extent. We investigated the relationship between the non‐migratory fish metacommunity with environmental and spatial variables using the variance partitioning procedure. We used data from lakes of four Neotropical floodplains: Amazon, Araguaia, Pantanal and Paraná. We hypothesised that the processes that drive the spatial distribution of non‐migratory fish in floodplain systems depend on connectivity driven by flood pulse. We predict that spatial variables are less important to explaining the fish community structures during rainy periods when there is no dispersal limitation. In contrast, environmental variables influence these structures during rainy and dry periods. During dry periods, metacommunities depend on species' dispersal abilities, but during rainy and dry periods, spatial distribution depends on the environmental filters selected by each species. For most of the environmental variables, it was possible to observe more similarity in the rainy period. Our results showed different patterns of fish communities for the lakes of the four floodplains systems, which led us to reject our hypothesis. Non‐migratory fish remain in their natal environment and perform only small lateral movements. In this scenario, we found a little influence from spatial variables in both periods and a great contribution of environmental variables for Amazon (16%) and Pantanal (32%) floodplains in structuring fishes' distribution in the rainy period.

Sampled-data control for Markovian switching neural networks with output quantization and packet dropouts

This paper explores sampled-data control for Markovian switching neural networks (MSNNs) with dynamic output quantization and packet dropouts. The primary goal is to construct a multi-mode, quantized sampled-data controller that ensures stochastic stability and H∞ disturbance-reduction performance of the closed-loop MSNN. A Bernoulli-distributed random variable with uncertain probability is introduced to characterize the incidence of packet dropouts. To describe potential mode inconsistencies that may occur between the MSNN and controller, an exponential hidden Markov model is employed. Furthermore, the quantizer’s dynamic scaling factor is intentionally built as a piecewise function to avoid the potential division-by-zero problem. A sufficient condition for stochastic stability and H∞ disturbance-reduction performance is proposed, utilizing a mode- and time-dependent Lyapunov-type functional and several stochastic analysis tools. Then, through decoupling nonlinearities, a numerically efficient approach for determining the desired controller gains and parameter range associated with the dynamic scaling factor is developed. In order to facilitate comparisons, the situation with no uncertainty in the probability of packet dropouts is studied, and both analysis and design approaches are offered. Finally, two simulation examples are provided to validate the effectiveness and applicability of the developed approaches.

Generalized Einstein relation for aging processes

Physical aging appears in many systems ranging from glassy/granular materials, blinking quantum dots to laser-cooled atoms. Aging is a process with three fingerprints: (i) slow, non-exponential relaxation, (ii) breaking of time-translation-invariance, and (iii) dynamical scaling. Here, we show that all these features are present in our minimal Langevin model for aging. A natural extension of the Einstein relation, which was expected to be true in an equilibrium state, is conjectured to hold in aging processes where both the damping and the temperature decrease with time in power-law forms. The generalized Einstein relation can be used to tackle the difficult problem of determining non-ergodic behaviours. The model shows a power-law-type diffusion away from the critical point and a logarithmic Sinai-type ultra-slow diffusion at the critical point. Application to granular gases is also discussed.

ESTUDIO DE DIFERENTES ALTERNATIVAS DE FUNCIONAMIENTO DE LOS GRUPOS EN LAS CENTRALES ELÉCTRICAS DE CANARIAS

Island electricity systems aim to adapt to the environmental expectations established by the European framework. For this reason, it is necessary to increase efforts to increase the penetration of renewables and optimize energy production. An extensive preliminary research study has been carried out on the current production system, its variables and its conditioning factors, with which a series of operating alternatives have been obtained that will optimize the energy production system through combustion technology (non-renewables) and combine them with the production of energy through renewable sources. renewables, complying both in dynamic response, security, scaling and integration with such systems, in terms of efficiency and power. The reduction of greenhouse gas emissions and the penetration of these renewable energies in the island's electricity systems will be increased. As has been proven in the research background, the usually established combination of power producing equipment is not the best to reduce pollution, and there are other possible combinations with better results. The study methodology followed leads to take measures based on: a) changing the type of fuel in the equipment that allows it, b) using the combination of less pollutant producing equipment with response capacity and c) considering the integration and incorporation of the pumped hydroelectric electric storage plant (PHES) "Chira-Soria" into Gran Canaria's electrical system. The different measures contribute to the improvement in the reduction of greenhouse gases (GHG) in a convincing way. Keywords: Isolated power system, Generation expansion planning, Energy transition, Insular electrical systems (IES), Energy policy, Canary Islands, Renewable energy, Pumped hydroelectric storage plant (PHES).

Advancing Oil Production Efficiency with Next-Generation Nano emulsion Capsules: Harnessing Doped MgZnO2 for Superior Inhibition of Calcite Scale Formation

Abstract This work presents doped MgZnO2 nano emulsion capsules as a novel approach for reducing calcite scale formation in oil production. The nano emulsion capsules are synthesized via ultrasonication and precipitation methods, with morphology and size distribution analysed by scanning electron microscopy (SEM), transmission electron microscope [TEM] and dynamic light scattering (DLS).` with an average particle size of 50 nm. Static scale inhibition tests showed over 90% inhibition efficiency at a Nano emulsion concentration of 100 mg/L. Rigorous static and dynamic scale inhibition testing precipitation and showcases remarkable efficiency exceeding 80% in preventing calcite deposition under simulated conditions. Gravimetric validates cation and online pressure monitoring validate superior performance compared to conventional inhibitors, with up to 30% enhancement in mitigation. The controlled release properties of the Nano emulsion capsule allow for persistent scale inhibition even after 72 hours of continuous operation. Furthermore, ecotoxicity evaluations through bioassays affirm the eco-friendly characteristics of the Nano emulsion technology. As a whole, this research shows how highly promising doped MgZnO2 Nano emulsion capsules are as a long-term, high-performing solution to scale-related problems in the oil sector.

Scale‐sensitive marine law and policy design: Towards ecosystem‐based management across spatial and temporal scales

AbstractEcosystem‐based management (EBM) is promoted in scholarly literature and international policy as a way to improve marine ecosystem outcomes, through the integration of marine law and policies across sectors, communities, and scales. A legal and policy framework that supports EBM at appropriate temporal and spatial scales is fundamental to the successful deployment of EBM. However, marine management is typically fragmented and misaligned, with a range of laws, policies, governance institutions, and sectoral frameworks applying to different marine spaces and working on varying timescales. In this focus article, we draw on our comprehensive study of Aotearoa New Zealand's marine law and policy to analyze the challenges scale mismatches present for marine law and policy implementation, focusing on key policy areas across fisheries, environment, conservation, and Indigenous rights and interests. We identify key opportunities to better align marine law and policy to ecosystem scale dynamics, revealing critical lessons for the imperative of scale‐sensitive marine law and policy design, globally.This article is categorized under: Policy and Economics > Research and Development Policy and Economics > Governance and Regulation Climate and Environment > Ecosystem Services

A Hybrid Model for Short-Term Energy Load Prediction Based on Transfer Learning with LightGBM for Smart Grids in Smart Energy Systems

ABSTRACT Electricity load prediction plays a vital role in energy management. The necessity of combining scattered power generation channels of sustainable energy and other substitute resources into distribution grid networks is increasing because of the 4.1 percent rise in world electricity since 2021. The utilization of probability power generators and energy storage is maximized by short-term load prediction methods, which make it possible to anticipate potential energy usage. To facilitate the growth of energy systems in quite a sustainable way, urban energy planners utilize a range of methods and technological advances, including simulation, probability optimization, comprehensive improvement, and modelling techniques for predicting power requirements. Predicting energy consumption at granular time scales (e.g., hourly or sub-hourly intervals) is common practice for short-term energy load prediction. For similar time-series forecasting challenges, the authors have decided to use transfer learning (TL) with light gradient-boosting machine (LightGBM).1 This model can benefit from models trained on larger data sets through TL, which might boost prediction accuracy. Energy prediction and effective resource utilization largely support the accurate transition of energy systems. In this paper, the authors propose a hybrid model for short-term energy load prediction. Based on TL to optimize LightGBM (OLGBM) for smart grids, The proposed model first applies data pre-processing using an abnormal supplement strategy with an immediate deviation selection technique. This phase eliminates the missing values and extracts the required features. The second phase uses a TL-OLGBM with hyperparameters. The TL method helps to learn the dynamic time scale and complex data patterns, and the hyperparameters are tuned via the Bayesian optimization technique, which addresses the challenge of short-term load forecasting (STLF) and increases forecasting accuracy. Finally, an optimized LightGBM model is applied, combining the time and energy features to predict effective energy forecasting. The proposed model gained a 2.834589 mean absolute percentage error (MAPE) value and a 0.9706495 GINI index after execution on the prescribed data set. We compare the performance of the proposed model with other comparable models, and the simulation outcomes show that the model can generate the best results for MAPE, accuracy, and root-mean-square deviation (RMSE).

Recovery of synchronized oscillations on multiplex networks by tuning dynamical time scales

The heterogeneity among interacting dynamical systems or variations in the pattern of their interactions occur naturally in many real complex systems. Often they lead to partially synchronized states like chimeras or oscillation suppressed states like in-homogeneous or homogeneous steady states. In such cases, it is a challenge to get synchronized oscillations in spite of prevailing heterogeneity. In this study, we present a formalism for controlling multi layer, multi timescale systems and show how synchronized oscillations can be restored by tuning the dynamical time scales between the layers. Specifically, we use the model of a multiplex network, where the first layer of coupled oscillators is multiplexed with an environment layer, that can generate various types of chimera states and suppressed states. We show that by tuning the time scale mismatch between the layers, we can revive the synchronized oscillations. We analyse the nature of the transition of the system to synchronization from various dynamical states and the role of time scale mismatch and strength of inter layer coupling in this scenario. We also consider a three-layer multiplex system, where two system layers interact with the common environment layer. In this case, we observe anti synchronization and in-homogeneous steady states on the system layers and by tuning their time scale difference with the environment layer, they undergo transition to synchronized oscillations.

An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.