Sustainable Computing-Informatics & Systems最新文献_第9页

Efficient QCA‐Based Circuits for Low‐Power Medical IoT System 用于低功耗医疗物联网系统的高效QCA电路

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-09-04 DOI: 10.1016/j.suscom.2025.101203

D. Ajitha , Muhammad Zohaib , Firdous Ahmad , Khalid Zaman , S.M. Prabin

The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.

物联网（IoT）通过提供精确的诊断和治疗功能，在最近的医疗保健行业中发挥着至关重要的作用。人们对将医疗物联网纳入医疗保健系统的兴趣越来越大。所有医疗物联网的处理单元均采用互补金属氧化物半导体（CMOS）技术。然而，CMOS医疗物联网技术已经集成到纳米级生物医学硬件系统中。由于监管、道德和技术方面的挑战，包括处理速度慢、功耗高、开关频率慢，特别是在千兆赫（GHz）范围内。另一方面，与传统计算机相比，量子技术将使处理速度加快一个数量级，并影响所有人工和医疗（AI）以及医疗物联网处理应用。量子点元胞自动机（QCA）在医疗物联网中是一种很有前途的数字硬件系统。QCA技术为在医疗物联网应用中建立人工智能电路设计框架提供了最佳选择，在医疗物联网应用中，低成本、实时、节能性能至关重要。此外，加密和解密电路已用于医疗物联网操作，以保护正在传输和存储的敏感患者数据。在此背景下提出了基本算术和逻辑单元（ALU），它是纳米级设备医疗物联网系统处理和计算单元的基础。系统的方法涉及集成加法器、多路复用器、ALU和逻辑单元，以通过医疗物联网中的加密和解密增强处理器驱动和隐私。实验结果表明，推荐的设计在单元数上超过了15.48 %，在面积上超过了16.07 %。利用qcaddesigner - e2.0.3软件工具对设计进行了精确仿真。

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引用次数: 0

Sustainable transient frequency management in eco-industrial park microgrids considering e-shared mobility storage using efficient fractional-order computing 基于高效分数阶计算的生态工业园区微电网暂态频率管理

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-09-03 DOI: 10.1016/j.suscom.2025.101197

SeyedJalal SeyedShenava, Peyman Zare, Amir Mohammadian

The evolving architecture of rich-renewable Eco-Industrial Park Microgrids (EIP-MGs) introduces significant frequency stability challenges due to the intermittent nature and low inertia of integrated renewable energy sources. To address these limitations, advanced energy storage systems, comprising fixed and mobile electric energy storage systems, have been adopted. Among them, mobile EV energy storage, particularly in the context of e-shared mobility, offers a flexible and scalable solution for load frequency control in modern EIP-MGs. This study presents a novel framework for sustainable transient frequency management using a fractional-order computing-based hybrid cascade controller, TFOID–3DOF–TID (Tilted Fractional-Order Integral and Derivative with Three Degrees of Freedom), optimized via the Crested Porcupine Optimizer (CPO). The proposed control scheme is validated through six case studies under three industrial load disturbance scenarios, with emphasis on transient stability and real-world uncertainties. The evaluations are structured around frequency-domain design criteria based on integral error metrics, including squared and absolute formulationsaimed at analyzing efficiency, sensitivity, adaptability, robustness, stability, and computational burden. The proposed control scheme, featuring the TFOID and 3DOF-TID controllers, is evaluated in comparison with validated metaheuristic-based algorithms. Simulation results demonstrate that the CPO-based TFOID–3DOF–TID controller consistently outperforms other schemes, with improvements including a 22 %–48 % reduction in settling time, a 25 %–55 % decrease in undershoot, and a 30 %–60 % reduction in overshoot across varying scenarios. Additionally, Bode plot evaluations confirm superior phase margins and damping characteristics, while robustness margins improve by up to 60 %, affirming the controller’s resilience under non-ideal operational conditions. These findings provide practical insights for policymakers and engineers aiming to enhance the resilience and sustainability of future-ready industrial microgrids.

由于集成可再生能源的间歇性和低惯性，丰富可再生生态工业园区微电网（eip - mg）架构的不断发展带来了显著的频率稳定性挑战。为了解决这些限制，采用了先进的储能系统，包括固定和移动电力储能系统。其中，移动电动汽车储能，特别是在电动共享出行的背景下，为现代eip - mg的负载频率控制提供了灵活和可扩展的解决方案。本研究提出了一种新的可持续瞬态频率管理框架，该框架使用基于分数阶计算的混合级联控制器TFOID-3DOF-TID（倾斜分数阶积分和三自由度导数），通过冠状Porcupine Optimizer （CPO）进行优化。通过三种工业负荷扰动情景下的六个案例研究验证了所提出的控制方案，重点研究了暂态稳定性和现实世界的不确定性。评估是围绕基于积分误差度量的频域设计标准构建的，包括平方和绝对公式，旨在分析效率、灵敏度、适应性、鲁棒性、稳定性和计算负担。采用TFOID和3DOF-TID控制器的控制方案与经过验证的元启发式算法进行了比较。仿真结果表明，基于cpo的TFOID-3DOF-TID控制器始终优于其他方案，在不同场景下，其改进包括沉降时间减少22 % -48 %，过冲减少25 % -55 %，超调减少30 % -60 %。此外，波德图评估确认了优越的相位裕度和阻尼特性，而鲁棒性裕度提高了60% %，确认了控制器在非理想操作条件下的弹性。这些发现为旨在增强面向未来的工业微电网的弹性和可持续性的政策制定者和工程师提供了实用的见解。

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引用次数: 0

Task scheduling in cloud computing system by improved honey badger optimization algorithm with two dimensional and three dimensional fractals 基于改进的二维和三维分形蜜獾优化算法的云计算系统任务调度

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-09-03 DOI: 10.1016/j.suscom.2025.101201

Yu-Feng Sun, Si-Wen Zhang, Jie-Sheng Wang, Shi-Hui Zhang, Yu-Cai Wang, Xiao-Fei Sui

Cloud computing task scheduling is not only the foundation for ensuring the efficient operation of the cloud platform, but also an important means of improving service quality and reducing costs. With the continuous development of cloud computing technology, the requirements for intelligent and automated task scheduling are also increasing. To address the demand for more efficient and flexible computations, an enhanced honey badger algorithm (HBA) utilizing two dimensional and three dimensional fractals is introduced. The digging phase of the honey badger's foraging strategy is improved by using the mathematical expressions of two dimensional and three dimensional fractals in rectangular and polar coordinates, which enhances the algorithm's performance while speeding up its convergence. The optimal solution HBACBKS-Z was selected by verification on the benchmark functions. The optimization problem of task scheduling in cloud computing systems is divided into large-scale task scheduling and small-scale task scheduling. Experiments were conducted in these two cases by using HBACBKS-Z and other traditional swarm intelligence optimization algorithms. It has been proved that HBACBKS-Z has significant advantages in terms of total cost, time cost, load cost and price cost, and can effectively solve the task scheduling optimization problem of cloud computing systems of various sizes.

云计算任务调度是保证云平台高效运行的基础，也是提高服务质量、降低成本的重要手段。随着云计算技术的不断发展，对任务调度的智能化、自动化的要求也越来越高。为了满足更高效和灵活的计算需求，介绍了一种利用二维和三维分形的增强型蜜獾算法（HBA）。利用矩形和极坐标下二维和三维分形的数学表达式对蜜獾觅食策略的挖掘阶段进行改进，提高了算法的性能，加快了算法的收敛速度。通过对基准函数的验证，选择了最优解HBACBKS-Z。云计算系统中的任务调度优化问题分为大规模任务调度和小规模任务调度。采用HBACBKS-Z等传统群体智能优化算法对这两种情况进行了实验。实践证明，HBACBKS-Z在总成本、时间成本、负载成本和价格成本方面具有显著优势，能够有效解决各种规模云计算系统的任务调度优化问题。

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引用次数: 0

Power management for smart grids integrating renewable energy sources using Greylag goose optimization and anti-interference dynamic integral neural network 基于灰雁优化和抗干扰动态积分神经网络的可再生能源集成智能电网电源管理

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-09-02 DOI: 10.1016/j.suscom.2025.101199

G.K. Jabash Samuel , P. Rajendran , Papana Venkata Prasad , Chinthalacheruvu Venkata Krishna Reddy

This paper proposes a hybrid power management strategy for smart grids (SGs) that integrates renewable energy sources (RESs), such as battery energy storage systems (BESS), fuel cells (FCs), wind turbines (WT), and solar photovoltaic (PV). The GGO-AIDINN approach integrates Greylag Goose Optimization (GGO) and an Anti-Interference Dynamic Integral Neural Network (AIDINN) to address high emissions during low renewable energy (RE) availability and rising operational costs from advanced infrastructure. The GGO optimizes resource allocation and energy distribution, maximizing the use of available RE. Meanwhile, AIDINN predicts energy consumption patterns based on weather conditions, improving overall system performance. The proposed GGO-AIDINN model is implemented on MATLAB and evaluated against several existing methods, including Fuzzy Logic Control (FLC), Non-dominated Sorting Genetic Algorithm (NSGA-II), and others. Results show the hybrid method achieves significant improvements, with an operational cost of $1328 per MW, emissions of 13.76 kg per MW, and an efficiency of 98.7 %. These outcomes demonstrate that GGO-AIDINN outperforms traditional techniques, offering lower costs, reduced emissions, and enhanced system efficiency. This makes it a superior solution for sustainable power management in SGs incorporating RESs and BESS.

本文提出了一种集成可再生能源（RESs）的智能电网（SGs）混合电源管理策略，如电池储能系统（BESS）、燃料电池（fc）、风力涡轮机（WT）和太阳能光伏（PV）。GGO-AIDINN方法集成了灰雁优化（GGO）和抗干扰动态积分神经网络（AIDINN），以解决低可再生能源（RE）可用性和先进基础设施运营成本上升时的高排放问题。GGO优化资源分配和能源分配，最大限度地利用可用的可再生能源。同时，AIDINN根据天气状况预测能源消耗模式，提高整体系统性能。在MATLAB上实现了所提出的go - aidinn模型，并对几种现有方法进行了评估，包括模糊逻辑控制（FLC）、非支配排序遗传算法（NSGA-II）等。结果表明，混合方法取得了显著的改进，运行成本为1328美元/兆瓦，排放量为13.76 kg /兆瓦，效率为98.7 %。这些结果表明，go - aidinn技术优于传统技术，成本更低，排放更少，系统效率更高。这使得它成为SGs整合RESs和BESS的可持续电源管理的卓越解决方案。

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引用次数: 0

Integrating IoT and fuzzy logic for intelligent irrigation in sustainable agriculture for improving water scarcity: Benefits and challenges 将物联网和模糊逻辑集成到可持续农业智能灌溉中，改善水资源短缺：收益与挑战

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-29 DOI: 10.1016/j.suscom.2025.101191

Abdennabi Morchid , Ishaq G. Muhammad Alblushi , Haris M. Khalid , Hassan Qjidaa , Rachid El Alami

Modern agriculture faces significant challenges related to water scarcity and the impacts of climate change. To ensure crop sustainability and food security, irrigation systems must be optimized. Fuzzy logic and the Internet of Things (IoT) are two cutting-edge approaches to intelligent irrigation management that adjust water delivery to plants' real needs. Conventional irrigation techniques are wasteful and ineffective. Fuzzy logic and the IoT have exciting opportunities, but integrating them presents difficulties, especially (1) concerning implementation, (2) cost, and (3) data security. In light of water shortage, food security, and sustainable development issues, this proposed article examines how IoT and fuzzy logic might be used to create smart irrigation systems. It evaluates contemporary methods for optimizing water management using fuzzy logic and the IoT, as well as the effects of climate change on irrigation. While addressing the challenges of installation costs, implementation complexity, communication reliability, and data security, the proposed review highlights the benefits of these technologies, including reduced water consumption, increased agricultural yields, automation, and environmental adaptability. The main topics of this review's final section, including the integration of new, cutting-edge technology, enhanced decision-making models, and the adoption of sustainable solutions for more resilient and effective agriculture, also address potential directions for future research. importance of the research. Due to water constraints and climate change, this study highlights the importance of intelligent irrigation systems. It showcases creative methods to maximize water management and raise agricultural productivity by fusing IoT with fuzzy logic.

现代农业面临着与水资源短缺和气候变化影响有关的重大挑战。为了确保作物的可持续性和粮食安全，必须优化灌溉系统。模糊逻辑和物联网（IoT）是智能灌溉管理的两种前沿方法，可以根据植物的实际需求调整供水。传统的灌溉技术既浪费又无效。模糊逻辑和物联网有令人兴奋的机会，但整合它们存在困难，特别是(1)关于实施，(2)成本和(3)数据安全。鉴于水资源短缺、粮食安全和可持续发展问题，本文探讨了如何使用物联网和模糊逻辑来创建智能灌溉系统。它评估了利用模糊逻辑和物联网优化水管理的当代方法，以及气候变化对灌溉的影响。在解决安装成本、实施复杂性、通信可靠性和数据安全性等挑战的同时，拟议的审查强调了这些技术的好处，包括减少用水量、提高农业产量、自动化和环境适应性。本综述最后一部分的主要主题，包括整合新的尖端技术，增强决策模型，以及采用可持续解决方案以提高农业的抗灾能力和效率，也讨论了未来研究的潜在方向。研究的重要性。由于水资源限制和气候变化，本研究强调了智能灌溉系统的重要性。通过物联网和模糊逻辑的融合，展示了最大化水资源管理和提高农业生产力的创新方法。

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引用次数: 0

Flexibility regulation-based economic energy scheduling in multi-microgrids with renewable/non-renewable resource and stationary storage systems considering sustainable computing by hybrid metaheuristic algorithm 混合元启发式算法考虑可持续计算的可再生/不可再生资源和固定存储系统多微电网柔性调节经济能源调度

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-26 DOI: 10.1016/j.suscom.2025.101196

Ahad Faraji Naghibi , Ehsan Akbari , Saeid Shahmoradi , Mehdi Veisi , Sasan Pirouzi

This plan presents energy scheduling in a distribution grid with multi-microgrid according to estimation of environmental, economic, flexibility, operation, and security indicators in microgrids. Microgrid has a multi-bus structure, which includes renewable solar, wind and bio-waste devices, non-renewable resources, compressed air and hydrogen storage. Study contains the three objectives optimization. The objective functions are the minimization of operation cost of microgrids and resources, the environmental pollution of microgrids and voltage deviation function. The constraints of the problem include the optimal power flow formulation of microgrids based on the flexibility and voltage security limits, the performance model of renewable/non-renewable units, and storage devices. Study has parameters of price of energy, load, and renewable phenomena as uncertainty. For their modeling, the point estimation approach is used to according to low computational time and accurately model flexibility. The ε-constraint method is used to extract the single-objective model, and fuzzy decision-making technique is used to achieve the compromise solution. This scheme has a non-convex nonlinear formulation. To access a reliable response considering low deviation for last point, a combination of red panda optimization and ant-lion optimization is used. Funding indicate the ability of plan for improve the technical, environmental, and economic conditions of microgrids. Thus, energy scheduling of the aforementioned units and storages can improve operational, economic, environmental, and voltage stability conditions of microgrids by about 59.2 %, 44.2 %, 24.5 %-75 % and 17.3 %-27.4 %, respectively. In these conditions, study achieves 100 % flexibility for microgrids. Solution approach achieves the sustainable computing conditions, such that it has the most optimal solution at low computational time and a standard deviation of 0.97 % in the final response.

该方案通过对微网环境、经济、灵活性、运行和安全指标的估计，提出了多微网配电网的能源调度方案。微电网采用多总线结构，包括可再生太阳能、风能和生物废物装置、不可再生资源、压缩空气和氢气储存。研究包含三个优化目标。目标函数为微电网运行成本和资源成本最小化、微电网环境污染最小化和电压偏差函数。该问题的约束条件包括基于柔性和电压安全限制的微电网最优潮流公式、可再生/不可再生机组性能模型和存储设备。研究的参数有能源价格、负荷和可再生现象等不确定性。在对其建模时，采用点估计方法，计算时间短，模型灵活准确。采用ε约束方法提取单目标模型，采用模糊决策技术实现折中解。该格式具有非凸非线性形式。为了在考虑最后一点偏差小的情况下获得可靠的响应，采用了小熊猫优化和蚁狮优化相结合的方法。资金表明计划改善微电网的技术、环境和经济条件的能力。因此，上述机组和储能系统的能源调度可分别改善微电网运行、经济、环境和电压稳定条件，分别改善59.2% %、44.2% %、24.5% %- 75% %和17.3% %-27.4 %。在这些条件下，研究实现了100% %的微电网灵活性。求解方法实现了可持续的计算条件，使其在较低的计算时间下具有最优解，最终响应的标准差为0.97 %。

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引用次数: 0

Memetic salp swarm algorithm optimized control for operational resilience in grid-tied microgrid 模因藻群算法优化并网微电网运行弹性控制

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-24 DOI: 10.1016/j.suscom.2025.101195

Ravita Saraswat, Sathans Suhag

To ensure reliable & resilient operation of a microgrid, efficient voltage and power regulation strategies have to be in place. The instant study proposes the memetic salp swarm algorithm (MSSA) tuned fractional order proportional-integral-derivative (FOPID) control strategy towards improving operational resilience of the grid-connected microgrid, comprising solar panels, wind turbine, battery bank, and AC load, in the backdrop of solar, wind, and load uncertainties besides the eventuality of grid isolation. MATLAB® simulation results, both qualitative and quantitative, ideate effectiveness of recommended control strategy whose novelty lies in synergetic use of MSSA and FOPID, with the tuning competency of MSSA established against grey wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms.

为了确保微电网的可靠和弹性运行，必须制定有效的电压和功率调节策略。针对太阳能、风能和负荷不确定性以及电网隔离的可能性，提出了memetic salp swarm算法（MSSA）调谐分数阶比例积分导数（FOPID）控制策略，以提高并网微电网（包括太阳能电池板、风力发电机组、蓄电池组和交流负荷）的运行弹性。MATLAB®的定性和定量仿真结果表明，推荐的控制策略的有效性，其新颖之处在于MSSA和FOPID的协同使用，MSSA针对灰狼优化器（GWO）和粒子群优化算法（PSO）建立了调谐能力。

引用次数: 0

Research on optimization of distributed network security framework based on blockchain under green computing framework 绿色计算框架下基于区块链的分布式网络安全框架优化研究

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-20 DOI: 10.1016/j.suscom.2025.101183

Ling Liu, Jianbo Xu, Junwen Fang, Guoli Sun

In the current fast-changing digital world, distributed networks are under severe threat in terms of security and efficiency. Their decentralized nature and expanding amount of data raise system complexity and vulnerability. At the same time, sustainable computing demands energy-efficient solutions for network operations. This research proposes a Distributed Network Security Framework Based on Blockchain within a Green Computing Framework. It introduces a Dynamic Whale Optimized Adjustable Graph Neural Network (DWO-AGNN) to assess network security. The model leverages blockchain’s decentralized and tamper-proof features, using smart contracts to enhance resilience against cyberattacks. The framework also focuses on reducing the energy footprint of security operations. Key performance metrics include security effectiveness, energy consumption, and throughput. Results show strong performance: availability at 99.0 %, integrity at 96.8 %, and confidentiality at 95.2 %. The system achieves 95.7 Megabits per Second (Mbps) throughput, reduces energy usage from 1.20 to 0.85, and cuts energy costs from $500 to $375. This research demonstrates that blockchain-based models can deliver high security while supporting environmentally responsible computing. The DWO-AGNN offers a practical solution for resilient, energy-efficient distributed networks.

在当前快速变化的数字世界中，分布式网络在安全性和效率方面受到严重威胁。它们的分散性和不断扩大的数据量增加了系统的复杂性和脆弱性。同时，可持续计算需要高效节能的网络运营解决方案。本研究提出了一个绿色计算框架下基于区块链的分布式网络安全框架。引入动态鲸鱼优化可调图神经网络（DWO-AGNN）来评估网络的安全性。该模型利用区块链的去中心化和防篡改特性，使用智能合约来增强抵御网络攻击的能力。该框架还侧重于减少安全行动的能源足迹。关键性能指标包括安全性有效性、能耗和吞吐量。结果显示了强大的性能：可用性为99.0 %，完整性为96.8% %，保密性为95.2% %。该系统实现了每秒95.7兆比特（Mbps）的吞吐量，将能耗从1.20降低到0.85，将能源成本从500美元降低到375美元。这项研究表明，基于区块链的模型可以提供高安全性，同时支持对环境负责的计算。DWO-AGNN为弹性、节能的分布式网络提供了一种实用的解决方案。

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引用次数: 0

Scalable and low-power reversible logic for future devices: QCA and IBM-based gate realization 未来器件的可扩展和低功耗可逆逻辑：QCA和基于ibm的栅极实现

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-16 DOI: 10.1016/j.suscom.2025.101182

Seyed-Sajad Ahmadpour , Nima Jafari Navimipour , Muhammad Zohaib , Neeraj Kumar Misra , Mahsa Rastegar Pour , Hadi Rasmi , Sankit Kassa , Jadav Chandra Das

One such revolutionary approach to changing the nano-electronic landscape is integrating reversible logic with quantum dot technology that will replace the conventional complementary metal-oxide semiconductors (CMOS) circuits for ultra-high speed, low density, and energy-efficient digital designs. The implementation of the reversible structure under the most inflexible conditions, as executed by quantum laws, is a highly challenging task. Furthermore, the enormous occupying areas seriously compromise the accuracy of the output in quantum dot circuits. Because of this challenge, quantum circuits can be employed as fundamental building blocks in high-performance digital systems since their implementation has a key impact on overall system performance. This study discusses a paradigm shift in nanoscale digital design by using a 4 × 4 reversible gate that redefines the basis of efficiency and precision. This reversible gate is elaborately used in a reversible full-adder circuit, fully symbolizing the core of minimum area, ultra-low energy consumption, and perfect output accuracy. The proposed reversible circuits have been fully realized using quantum-dot cellular automata technology (QCA), simulated, and verified by the highly reliable tool such as Qiskit IBM and QCADesigner 2.0.3. Furthermore, simulations results demonstrated the superiority of the QCA-based proposed adder, which reduced occupied area by 7.14 %, and cell count by 11.57 %, respectively. This work resolves some problems and opens new boundaries toward the future of digital circuits by addressing the main challenges of stability and pushing the boundaries of reversible logic design.

一种革命性的方法是将可逆逻辑与量子点技术相结合，以取代传统的互补金属氧化物半导体（CMOS）电路，实现超高速、低密度和节能的数字设计。根据量子定律，在最不灵活的条件下实现可逆结构是一项极具挑战性的任务。此外，巨大的占位面积严重影响了量子点电路输出的精度。由于这一挑战，量子电路可以作为高性能数字系统的基本构建模块，因为它们的实现对整个系统性能有关键影响。本研究通过使用4 × 4可逆栅极，讨论了纳米级数字设计的范式转变，重新定义了效率和精度的基础。该可逆栅极被精心应用于可逆全加法器电路中，充分体现了最小面积、超低能耗、完美输出精度的核心。采用量子点元胞自动机技术（quantum-dot cellular automata technology， QCA）完全实现了所提出的可逆电路，并通过Qiskit IBM和qcaddesigner 2.0.3等高可靠性工具进行了仿真和验证。此外，仿真结果证明了基于qca的加法器的优越性，其占用面积和细胞计数分别减少了7.14 %和11.57 %。这项工作解决了一些问题，并通过解决稳定性的主要挑战和推动可逆逻辑设计的边界，为数字电路的未来开辟了新的边界。

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引用次数: 0

Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems 整合微电网的多种能源：提高多能源系统的性能和可持续性

IF 5.7 3区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Sustainable Computing-Informatics & Systems

Pub Date : 2025-08-14 DOI: 10.1016/j.suscom.2025.101181

Xiaolin Zhang, Zhi Liu

This paper introduces a novel hybrid optimization framework for Multi-Energy Systems that jointly addresses cost efficiency, uncertainty, and demand-side flexibility. The proposed model uniquely integrates electric and thermal Load Response Plans within a unified structure and incorporates a Negative Risk Limit to explicitly control downside financial exposure under volatile conditions. A key innovation lies in the combination of scenario-based stochastic modeling and robust optimization to manage uncertainties in renewable generation, market prices, and consumer demand. The Flower Pollination Algorithm, a nature-inspired metaheuristic, is employed to efficiently solve the resulting high-dimensional problem. A residential-scale case study, involving photovoltaic panels, wind turbines, combined heat and power, boilers, electric vehicles, thermal storage, and heat pumps, demonstrates the framework’s applicability. Four simulation scenarios assess the individual and combined effects of Load Response Plans and risk constraints. Results indicate that energy purchases from upstream networks are reduced with coordinated load shifting, lowering peak hour procurement by 15–30 % compared to baseline operation. Electric vehicles exhibit active charge/discharge behavior in up to 75 % of daily time slots under joint Load Response Plan and Negative Risk Limit conditions, enhancing flexibility.

本文介绍了一种新型的多能源系统混合优化框架，该框架共同解决了成本效率、不确定性和需求侧灵活性问题。所提出的模型独特地将电力和热负荷响应计划集成在统一的结构中，并包含负风险限制，以明确控制不稳定条件下的下行财务风险。一个关键的创新在于将基于场景的随机建模和鲁棒优化相结合，以管理可再生能源发电、市场价格和消费者需求的不确定性。采用自然启发的元启发式算法——花授粉算法，有效地解决了由此产生的高维问题。住宅规模的案例研究，包括光伏板、风力涡轮机、热电联产、锅炉、电动汽车、储热和热泵，展示了该框架的适用性。四个模拟场景评估负载响应计划和风险约束的单独和综合影响。结果表明，与基线运行相比，通过协调负荷转移减少了上游网络的能源采购，高峰时间采购减少了15 - 30% %。在联合负荷响应计划和负风险限制条件下，电动汽车在高达75% %的每日时隙中表现出主动充放电行为，增强了灵活性。

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