Sustainable Computing-Informatics & Systems最新文献_第7页

An efficient multi-objective task scheduling for Green cloud computing using hybrid GSCOA algorithm 基于混合GSCOA算法的绿色云计算多目标任务调度

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-16 DOI: 10.1016/j.suscom.2025.101209

Kata Vijay Kumar, Ganesh Reddy Karri

With the expansion of data centres in recent years, energy-related challenges have become worse. Green cloud computing (GCC) is a new computing paradigm designed to address cloud data centre energy consumption. Even with the advancements in GCC, large-scale green cloud data centres (GCDCs) continue to confront significant obstacles in lowering carbon emissions and energy consumption, particularly in the area of task scheduling. Ineffective task distribution can result in underutilized servers and overworked servers, wasting energy. Workload fluctuations make it difficult to manage resources effectively, which frequently results in energy spikes during periods of high demand. These dynamic demands are frequently not adequately satisfied by the current scheduling techniques since they might not take into consideration changing workload patterns. Therefore, in this work, an effective hybrid Greylag Sand Cat Swarm Optimization Algorithm (GSCOA) is introduced to schedule the task effectively in GCDC. This hybrid approach makes use of the Sand Cat Swarm Optimization Algorithm's (SCSOA) exploitation skills and the Greylag Goose Optimization algorithm's (GGOA) exploring capabilities. This combination makes it possible to schedule cloud user requirements to the cloud server efficiently by minimizing energy consumption. It helps the cloud server system emit less carbon dioxide, allowing for a more environmentally friendly atmosphere. Simulation results on two real-world workloads-NASA-IPSC and HPC2N, indicate that the proposed approach significantly outperforms existing scheduling methods by reducing energy consumption and improving overall system performance.

随着近年来数据中心的扩张，与能源相关的挑战变得更加严重。绿色云计算（GCC）是一种新的计算范式，旨在解决云数据中心的能源消耗问题。尽管海湾合作委员会取得了进展，但大型绿色云数据中心在降低碳排放和能源消耗方面继续面临重大障碍，特别是在任务调度领域。无效的任务分配可能导致服务器利用率不足和服务器过度工作，浪费能源。工作负载的波动使得难以有效地管理资源，这经常导致在高需求期间出现能量峰值。当前的调度技术往往不能充分满足这些动态需求，因为它们可能没有考虑到不断变化的工作负载模式。为此，本文引入了一种有效的混合灰沙猫群优化算法（GSCOA）来有效地调度GCDC中的任务。这种混合方法利用了Sand Cat Swarm Optimization Algorithm （SCSOA）的开发技能和Greylag Goose Optimization Algorithm （GGOA）的探索能力。这种组合可以通过最小化能耗来有效地将云用户需求安排到云服务器。它有助于云服务器系统排放更少的二氧化碳，从而营造更环保的氛围。在nasa - ipsc和HPC2N两个实际工作负载上的仿真结果表明，该方法通过降低能耗和提高系统整体性能，显著优于现有的调度方法。

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引用次数: 0

Enhanced energy-efficient load prediction in smart grids using bidirectional LSTM and gated recurrent unit networks 基于双向LSTM和门控循环单元网络的智能电网节能负荷预测

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-10-14 DOI: 10.1016/j.suscom.2025.101230

Elango Kannan , Ramesh Jayaraman , Cherukupalli Kumar , Gandhi Raj Rajamani

In the ever-evolving landscape of smart grids, the importance of accurate real-time load forecasting cannot be overstated. This paradigm shifting study presents a revolutionary methodology of combined Bidirectional Long Short-Term Memory Networks (Bi-LSTM) and Gated Recurrent Unit (GRU) to capture complex temporal relationships typical for energy consumption data. The importance of this concept is based on its ability to improve the functioning of smart grids and, therefore, help utilities to make the correct choices. The proposed hybrid model attains, in average, an overall forecasting prediction accuracy of 95 %; this exceeds the state-of-art. This accomplishment brings into focus how accurate load forecasting is, in essence to the proper functioning of smart grid systems. The detailed calculation and overall evaluation based on the performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) with the result of MAE= 1.8 %, RMSE= 2.1 %, and R-squared = 0.92 provide not only the proof of the effectiveness of the proposed approach but also the possible significance for improving the predictability and stability of the power grid. Beyond its significance for improving the accuracy of forecasts, this research establishes Bi-LSTM and GRU networks as central to the search for the most suitable approaches to energy management in the new era of the smart grid.

在不断发展的智能电网中，准确的实时负荷预测的重要性再怎么强调也不为过。这项范式转换研究提出了一种结合双向长短期记忆网络（Bi-LSTM）和门控循环单元（GRU）的革命性方法，以捕获能源消耗数据中典型的复杂时间关系。这一概念的重要性在于它能够改善智能电网的功能，从而帮助公用事业公司做出正确的选择。所提出的混合模型总体预测精度平均为95% %；这超过了技术水平。这一成就使人们关注到负荷预测的准确性，本质上是智能电网系统的正常运行。基于平均绝对误差（MAE）、均方根误差（RMSE）等性能指标的详细计算和综合评价结果表明，MAE= 1.8 %，RMSE= 2.1 %,r²= 0.92，不仅证明了所提出方法的有效性，而且对提高电网的可预测性和稳定性可能具有重要意义。除了对提高预测准确性的重要性之外，本研究还将Bi-LSTM和GRU网络确立为寻找智能电网新时代最合适的能源管理方法的核心。

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引用次数: 0

Automated deep learning and Internet of Things framework for building energy management: A university case study 自动化深度学习和物联网框架的建筑能源管理：一个大学案例研究

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-10 DOI: 10.1016/j.suscom.2025.101198

Deepshikha Shrivastava , Prerna Goswami

Monitoring energy consumption in buildings presents significant opportunities, especially in developing economies like India. However, current solutions often overlook cost-effective, small-scale, accurate, and open-source data-driven methodologies. Research in this area is often hindered by concerns related to security and privacy, high investment costs, and unpredictable returns. To address these challenges, we developed an automated hybrid deep learning and Internet of Things (DL-IoT) building energy management system (BEMS) aimed at conserving energy. The DL-IoT combines deep learning techniques with fuzzy logic to effectively manage uncertainty and noise in electrical properties. Our DL-IoT regression model demonstrated low mean absolute error and mean squared error, achieving a coefficient of determination of 0.99 for out-of-sample energy consumption predictions. We extracted twenty-seven electricity usage variables from raw data to train the model. Experimental results revealed a linear relationship between these characteristics and energy use. The proposed model successfully predicted features that could contribute to energy savings, such as Power Factor and Power in the Y Phase. Specifically, it estimated that a one-unit increase in Power in the Y Phase and Power Factor would result in a reduction in energy consumption. The findings of the experiment indicated that the model captured the variability of the data better than other models. The results demonstrated the superiority of the proposed model over other mainstream existing models. Through the results of this paper, a more efficient energy data management and consumption plan can be established.

监测建筑能耗带来了巨大的机遇，尤其是在印度这样的发展中经济体。然而，当前的解决方案往往忽略了成本效益高、规模小、准确和开源的数据驱动方法。这一领域的研究经常受到安全和隐私、高投资成本和不可预测回报等问题的阻碍。为了应对这些挑战，我们开发了一种自动化的混合深度学习和物联网（DL-IoT）建筑能源管理系统（BEMS），旨在节约能源。DL-IoT将深度学习技术与模糊逻辑相结合，有效管理电性能中的不确定性和噪声。我们的DL-IoT回归模型显示出较低的平均绝对误差和均方误差，样本外能耗预测的决定系数为0.99。我们从原始数据中提取了27个用电量变量来训练模型。实验结果表明，这些特征与能源使用之间存在线性关系。所提出的模型成功地预测了有助于节能的特征，如功率因数和Y阶段的功率。具体来说，它估计在Y相和功率因数中增加一个单位的功率将导致能耗的减少。实验结果表明，该模型比其他模型更好地捕捉了数据的可变性。结果表明，该模型优于其他主流模型。通过本文的研究结果，可以建立一个更有效的能源数据管理和消费计划。

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引用次数: 0

Integrating blockchain and iot with advanced predictive modeling for energy efficient urban transportation systems 将区块链和物联网与高效节能城市交通系统的先进预测建模相结合

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.suscom.2025.101208

Jyotsnarani Tripathy , M. Kaliappan , Gnana Kousalya Chellathevar , J. Relin Francis Raj , Ravivarman Shanmugasundaram , Manjunathan Alagarsamy , S.Patricia Nancy , Ali Algahtani

In a world that is rapidly urbanising and EV-dependent, the energy efficiency and sustainability of transportation infrastructures is a daunting challenge. It introduces the Blockchain-Based IoT Urban Transport Optimizer (BIUTO), a new approach that combines IOT, blockchain and predictive modeling for traffic management, EV charging efficiency, and building energy consumption. The framework aims to circumvent major drawbacks of current centralized architectures, such as data breaches, scaling, and inability to dynamically manage nested urban systems. The technology leverages IoT for real-time collection, Machine Learning Models (LSTM, Gradient Boosted Decision Trees, or GBDT) for predictive analytics, and blockchain for secure and decentralized data storage. The traffic subsystem helped reduce peak congestion by 23 % via real-time traffic flow prediction, and the EV charging subsystem increased energy efficiency by 15 %. The building energy efficiency subsystem reported high RMSE values for heating and cooling loads. The blockchain layer made data secure and transparent, alleviating issues with centralized system malfunctions. This work brings to the table a single, scaling approach to sustainable city transport based on energy efficiency, which will also serve as part of the sustainability agenda worldwide. The flexibility of BIUTO to integrate multiple urban subsystems represents an enormous step forward towards smart, low-carbon cities. Future efforts will include scaling the blockchain latency and scaling up the model to include renewable energy.

在一个快速城市化和依赖电动汽车的世界，交通基础设施的能源效率和可持续性是一项艰巨的挑战。它引入了基于区块链的物联网城市交通优化器（BIUTO），这是一种将物联网，区块链和交通管理，电动汽车充电效率和建筑能耗预测建模相结合的新方法。该框架旨在规避当前集中式架构的主要缺点，例如数据泄露、扩展以及无法动态管理嵌套城市系统。该技术利用物联网进行实时收集，利用机器学习模型（LSTM、梯度增强决策树或GBDT）进行预测分析，利用区块链进行安全和分散的数据存储。交通子系统通过实时交通流预测，帮助高峰拥堵减少了23% %，电动汽车充电子系统提高了15% %的能源效率。建筑能效子系统报告了加热和冷却负荷的高RMSE值。区块链层使数据安全和透明，减轻了集中式系统故障的问题。这项工作为基于能源效率的可持续城市交通提供了一种单一的、可扩展的方法，这也将成为全球可持续发展议程的一部分。BIUTO集成多个城市子系统的灵活性代表着向智能低碳城市迈进了一大步。未来的努力将包括扩大区块链延迟和扩大模型以包括可再生能源。

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引用次数: 0

Secured and effective task scheduling in cloud computing using Levy Flight - Secretary Bird Optimization and Hash-based Message Authentication Code – Secure Hash Authentication 256 在云计算中使用Levy飞行-秘书鸟优化和基于哈希的消息认证代码-安全哈希认证256安全有效的任务调度

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-09-21 DOI: 10.1016/j.suscom.2025.101211

Nida Kousar Gouse, Gopala Krishnan Chandra Sekaran

Dynamic computing resources are accessible through Cloud Computing (CC), which has gained popularity as a computing technology. Effective Task Scheduling (TS) is an essential aspect of CC, crucial in optimizing task distribution over available resources for high performance. Assigning tasks in cloud environments is a complex process influenced by multiple factors such as network bandwidth availability, makespan and cost considerations. This study proposes a Hash-based Message Authentication Code – Secure Hash Authentication 256 (HMAC-SHA256) and Advanced Encryption Standard (AES) to ensure enhanced security in the task scheduling process within the CC environment. The HMAC-SHA256 algorithm is utilized for key generation, providing integrity verification and data authentication. The AES algorithm is employed to encrypt task data, then the Levy Flight - Secretary Bird Optimization (LF-SBO) algorithm is implemented to schedule optimal tasks in the cloud. The proposed HMAC-SHA256 – AES and LF-SBO algorithms demand lower energy requirements of 121.6 J for 10 tasks, 180.48 J for 25 tasks, 310.21 J for 50 tasks, 400.15 J for 75 tasks, and 520.34 J for 100 tasks, outperforming existing Particle Swarm Optimization (PSO).

动态计算资源可以通过云计算（CC）访问，云计算作为一种计算技术已经得到了普及。有效任务调度（TS）是CC的一个重要方面，对于优化可用资源上的任务分配以获得高性能至关重要。在云环境中分配任务是一个复杂的过程，受到多种因素的影响，如网络带宽可用性、完工时间和成本考虑。本研究提出一种基于哈希的讯息验证码-安全哈希验证256 （HMAC-SHA256）和高级加密标准（AES），以确保CC环境下任务调度过程的安全性。使用HMAC-SHA256算法生成密钥，提供完整性验证和数据认证。采用AES算法对任务数据进行加密，然后采用Levy Flight - Secretary Bird Optimization （LF-SBO）算法在云中调度最优任务。提出的HMAC-SHA256 - AES和LF-SBO算法的能量需求较低，10个任务121.6 J， 25个任务180.48 J， 50个任务310.21 J， 75个任务400.15 J， 100个任务520.34 J，优于现有的粒子群优化（PSO）算法。

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引用次数: 0

Soft nanocomputing with QCA: Multipurpose sequential circuit realizations of D-latch, SRAM, flip-flop, and down counter 基于QCA的软纳米计算：d锁存器、SRAM、触发器和下行计数器的多用途顺序电路实现

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-12 DOI: 10.1016/j.suscom.2025.101253

Jitendra Kumar , Angshuman Khan , Rajeev Arya

This article presents the design and performance optimization of fundamental sequential circuits, a latch, SRAM, flip-flops, and a two-bit asynchronous down counter, within the Quantum-dot Cellular Automata (QCA) paradigm. As an effective alternative to conventional microelectronics, QCA utilizes quantum dots to encode binary information, promising ultra-low power consumption, higher speed, and superior circuit density while overcoming inherent scaling limitations. A major contribution is that all proposed circuits are realized as multiplexer-based single-layer designs, enhancing their structural simplicity and integrability. These designs, developed using coplanar crossover techniques, were simulated in QCADesigner 2.0.3. The D-latch achieved a 22 % reduction in cell count and a 95 % lower QCA-specific cost. The D flip-flop reduced cell count by 16 % and majority gates by 33 %, while the J K flip-flop cut majority gates by 50 %. The T flip-flop showed significant improvements in area, latency, and cost metrics. The two-bit counter also reduced gate and inverter counts. Energy dissipation analysis with QCADesigner-E confirms these layouts as very efficient, scalable, and high-performance solutions for advanced nanocomputing.

本文介绍了量子点元胞自动机（QCA）范例中的基本顺序电路、锁存器、SRAM、触发器和两位异步下行计数器的设计和性能优化。作为传统微电子技术的有效替代方案，QCA利用量子点对二进制信息进行编码，在克服固有的缩放限制的同时，有望实现超低功耗、更高速度和优越的电路密度。一个主要的贡献是，所有提出的电路都实现了基于多路复用器的单层设计，提高了它们的结构简单性和可积性。这些设计采用共面交叉技术开发，并在qcaddesigner 2.0.3中进行了仿真。D-latch使细胞计数减少了22% %，qca特异性成本降低了95% %。D触发器使细胞计数减少16 %，多数门减少33 %，而J K触发器使多数门减少50 %。T触发器在面积、延迟和成本指标上都有显著的改进。两位计数器还减少了门和逆变器计数。qcaddesigner - e的能量耗散分析证实了这些布局是非常高效、可扩展和高性能的先进纳米计算解决方案。

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引用次数: 0

Cooperative computing synergistic in hydrogen-based city energy community complementary clusters considering dual sustainable transportation and stakeholder social welfare 考虑双可持续交通和利益相关者社会福利的氢基城市能源社区互补集群协同计算

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-01 DOI: 10.1016/j.suscom.2025.101237

Babak Mohamadi , Abdolmajid Dejamkhooy , Hossein Shayeghi , Peyman Zare , Amir Mohammadian

Hydrogen-based complementary clusters represent a transformative paradigm for shaping sustainable cities and communities in the global shift toward low-carbon urban ecosystems. Central to this transition is sustainable computing with stakeholder social welfare, enabling energy-aware, power-optimized management strategies across interconnected infrastructures. This study proposes an advanced computing framework for synergistic operation of community clusters, supporting dual sustainable transportation that integrates electric and hydrogen mobility. The framework applies x-to-x energy conversion, including power-to-hydrogen, hydrogen-to-power, and combined heat and power modules, to ensure interoperability across electric, thermal, and hydrogen networks. A hybrid uncertainty management strategy is developed by combining scenario-based stochastic programming with robust optimization via information gap decision theory. Unlike traditional single-method approaches, this strategy achieves cost-effectiveness under normal conditions and ensures reliability under extreme deviations in market prices, renewables, or demand variability. Nonlinear dynamics are managed using piecewise linear approximation and McCormick envelope relaxation, yielding a scalable mixed integer linear programming model. A ten-dimensional evaluation, covering economic performance, energy management, emissions, synergy, adaptability, robustness, scalability, and welfare, was conducted. Results from a representative case of four interconnected microgrids demonstrate significant benefits: over 15 % cost reduction, more than 10 % decrease in electricity imports, and above 20 % increase in local hydrogen production. Enhanced demand response further improves balancing and resilience under uncertainty. Overall, findings highlight the potential of sustainable computing and green welfare informatics to advance decentralized, hydrogen-integrated ecosystems and provide actionable insights for policymakers, planners, and energy stakeholders.

氢基互补集群代表了在全球向低碳城市生态系统转变的过程中塑造可持续城市和社区的变革范式。这种转变的核心是可持续计算与利益相关者的社会福利，实现能源意识，跨互联基础设施的电力优化管理策略。本研究提出了一个先进的计算框架，用于社区集群的协同运行，支持集成电动和氢交通的双重可持续交通。该框架应用x-to-x能量转换，包括电转氢、氢转电和热电联产模块，以确保电力、热力和氢网络的互操作性。将基于场景的随机规划与基于信息缺口决策理论的鲁棒优化相结合，提出了一种混合不确定性管理策略。与传统的单一方法不同，该策略在正常条件下实现成本效益，并确保在市场价格、可再生能源或需求变化的极端偏差下的可靠性。非线性动力学采用分段线性逼近和麦考密克包络松弛进行管理，得到一个可扩展的混合整数线性规划模型。对经济绩效、能源管理、排放、协同、适应性、鲁棒性、可扩展性和福利等十维度进行了评估。四个互联微电网的代表性案例的结果显示了显著的效益：成本降低15% %以上，电力进口减少10% %以上，当地氢气产量增加20% %以上。增强的需求响应进一步提高了不确定性下的平衡和弹性。总体而言，研究结果强调了可持续计算和绿色福利信息学在推进分散式氢集成生态系统方面的潜力，并为政策制定者、规划者和能源利益相关者提供了可行的见解。

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引用次数: 0

MediCloudX: A scalable and secure cloud-based big data analytics framework for smart healthcare applications MediCloudX：用于智能医疗保健应用程序的可扩展且安全的基于云的大数据分析框架

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1016/j.suscom.2025.101233

Pingliang Ding , Qijun Du

The growing complexity and volume of data in the Chinese critical care setting necessitates the need to predict mortalities with intelligent and scalable and explainable systems. Conventional approaches, like rule-based models and independent machine learning models, are largely ineffective at combining the multimodal characteristics of ICU data in Chinese hospitals, especially when only structured clinical variables or time-series vital data are considered. To resolve them, Medi CloudX presents a hybrid Deep Learning (DL) model based on TabNet when working with structured electronic health records (EHRs) and Informer when dealing with long-term time-series data on ICUs. This is a combination of the two which enables a higher accuracy of prediction by selecting interpretable features among structured data and extracting long-term dependencies in ICU signals. The Reptile Search Algorithm (RSA) search hyperparameter optimization improves the performance of models with minimum human intervention. MediCloudX on a dataset of Chinese ICU scored an accuracy of 98.0 %, sensitivity of 100, specificity of 96.0, and F1-score of 98.04, surpassing state-of-the-art models such as CatBoost (AUC = 0.889), and LSTM-augmented scoring systems (AUC ≈ 0.898). The cloud-native structure of MediCloudX guarantees scale elasticity, minimal inference latency, and safe data handling, which are suitable to real-time applications in the ICU in China. This smart and high-achieving system is explainable and efficient in resource utilization, and it has great prospects of implementation in intelligent hospitals.

在中国的重症监护环境中，日益增长的复杂性和数据量使得需要用智能、可扩展和可解释的系统来预测死亡率。传统的方法，如基于规则的模型和独立的机器学习模型，在结合中国医院ICU数据的多模态特征方面基本上是无效的，特别是当只考虑结构化临床变量或时间序列生命数据时。为了解决这些问题，Medi CloudX在处理结构化电子健康记录（EHRs）时提出了基于TabNet的混合深度学习（DL）模型，在处理icu的长期时间序列数据时提出了Informer模型。这是两者的结合，通过在结构化数据中选择可解释的特征和提取ICU信号中的长期依赖关系，可以实现更高的预测精度。爬行动物搜索算法（Reptile Search Algorithm， RSA）搜索超参数优化在最小人为干预的情况下提高了模型的性能。MediCloudX在中国ICU数据集上的准确率为98.0 %，灵敏度为100，特异性为96.0，f1评分为98.04，超过了CatBoost （AUC = 0.889）和lstm增强评分系统（AUC≈0.898）等最先进的模型。MediCloudX的云原生结构保证了规模弹性、最小的推理延迟和安全的数据处理，适合中国ICU的实时应用。该系统具有可解释性强、资源利用效率高的特点，在智能医院中具有广阔的应用前景。

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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 : 2025-12-01 Epub 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

The application of bim technology in green building, design bim技术在绿色建筑设计中的应用

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

Sustainable Computing-Informatics & Systems

Pub Date : 2025-12-01 Epub Date: 2025-11-03 DOI: 10.1016/j.suscom.2025.101244

Fang Liu

The construction sector faces growing pressure to reduce energy use and carbon emissions while meeting urban development needs. Green building practices have emerged as a response, yet their effectiveness is often limited by fragmented workflows and weak integration of sustainability principles. This study aims to evaluate BIM’s role in green building design by analyzing energy performance, resource allocation, and lifecycle sustainability metrics.

A case study methodology was adopted, focusing on certified green building projects with documented BIM adoption. Quantitative indicators—including energy saving rate, carbon footprint reduction, and operational cost savings—were used to assess performance. Results show that BIM-based design achieves 20–32 % energy savings, 15–22 % operational cost reductions, and 18–30 % decreases in carbon footprint compared to conventional approaches.

The findings confirm that BIM extends beyond design coordination to function as a comprehensive tool for sustainability evaluation. The study’s novelty lies in integrating BIM across the entire building lifecycle with measurable sustainability indices, offering practical insights for architects, engineers, and policymakers.

建筑行业在满足城市发展需求的同时，面临着越来越大的减少能源使用和碳排放的压力。绿色建筑实践作为一种回应而出现，但它们的有效性往往受到支离破碎的工作流程和可持续性原则整合不力的限制。本研究旨在通过分析能源性能、资源分配和生命周期可持续性指标来评估BIM在绿色建筑设计中的作用。采用了案例研究方法，重点关注认证的绿色建筑项目，并记录了BIM的采用情况。量化指标——包括节能率、碳足迹减少和运营成本节约——被用来评估绩效。结果表明，与传统方法相比，基于bim的设计可节省20 - 32% %的能源，降低15 - 22% %的运营成本，减少18 - 30% %的碳足迹。研究结果证实，BIM超越了设计协调，成为可持续性评估的综合工具。该研究的新颖之处在于将BIM与可衡量的可持续性指标整合到整个建筑生命周期中，为建筑师、工程师和政策制定者提供了实用的见解。

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引用次数: 0