Sustainable Computing-Informatics & Systems最新文献

{"title":"Scalable and low-power reversible logic for future devices: QCA and IBM-based gate realization","authors":"Seyed-Sajad Ahmadpour ,&nbsp;Nima Jafari Navimipour ,&nbsp;Muhammad Zohaib ,&nbsp;Neeraj Kumar Misra ,&nbsp;Mahsa Rastegar Pour ,&nbsp;Hadi Rasmi ,&nbsp;Sankit Kassa ,&nbsp;Jadav Chandra Das","doi":"10.1016/j.suscom.2025.101182","DOIUrl":"10.1016/j.suscom.2025.101182","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101182"},"PeriodicalIF":5.7,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144916880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrate multiple energy sources of the microgrid: Enhancing performance and sustainability in multi-energy systems","authors":"Xiaolin Zhang,&nbsp;Zhi Liu","doi":"10.1016/j.suscom.2025.101181","DOIUrl":"10.1016/j.suscom.2025.101181","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101181"},"PeriodicalIF":5.7,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The early warning method for offshore wind turbine gearbox oil temperature based on FSTAE-ATT","authors":"Anping Wan ,&nbsp;Shuai Peng ,&nbsp;Khalil AL-Bukhaiti ,&nbsp;Yunsong Ji ,&nbsp;Shidong Ma","doi":"10.1016/j.suscom.2025.101180","DOIUrl":"10.1016/j.suscom.2025.101180","url":null,"abstract":"<div><div>Offshore wind turbine gearboxes often experience malfunctions due to harsh environmental conditions, resulting in significant downtime and financial losses. This study presents an innovative early warning system for monitoring gearbox oil temperature using a novel FSTAE-ATT model. The system leverages SCADA data and employs Feature Mode Decomposition (FMD) to enhance feature extraction from gearbox oil temperature measurements. The FSTAE-ATT model integrates Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependencies, augmented by a self-attention mechanism to highlight critical features. The model's reconstruction error serves as an early warning indicator for gearbox oil temperature anomalies. The effectiveness of the FSTAE-ATT model was validated using real-world data from an offshore wind farm in Yangjiang, Guangdong, China. Comparative analysis with other models, including STAE, STAE-ATT, AE, TAE, and SAE, demonstrated that the FSTAE-ATT model outperforms them with lower RMSE (e.g., 0.003452 for unit #40) and MAE (e.g., 0.002828 for unit #40) metrics. Additionally, significantly earlier warning times (e.g., up to 22 h and 36 min for unit #40), provide substantial lead time for preventative maintenance. This work contributes to advancing offshore wind turbine condition monitoring and fault detection, enhancing the sustainability and profitability of offshore wind energy systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101180"},"PeriodicalIF":5.7,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring artificial intelligence potential in solar energy production forecasting: Methodology based on modified PSO optimized attention augmented recurrent networks","authors":"Luka Jovanovic ,&nbsp;Nebojsa Bacanin ,&nbsp;Aleksandar Petrovic ,&nbsp;Miodrag Zivkovic ,&nbsp;Milos Antonijevic ,&nbsp;Vuk Gajic ,&nbsp;Mahmoud Mohamed Elsayed ,&nbsp;Mohamed Abouhawwash","doi":"10.1016/j.suscom.2025.101174","DOIUrl":"10.1016/j.suscom.2025.101174","url":null,"abstract":"<div><div>The use of renewable power sources is vital for reducing the world’s reliance on limited fossil fuels, reducing the impact on climate and mitigating the losses associated with power transmission. However, renewable sources such as solar power, often suffer from fluctuations in production due to their heavy reliance on weather conditions. This can have a significant impact on their reliability, as well as an impact on the power grid. Nevertheless, these issues could be mitigated by utilizing powerful and robust forecasting models, allowing for more efficient planning and fuller utilization of the produced power. This work explores the use of artificial intelligence (AI) in order to predict the yield of photovoltaic-generated energy. Different artificial neural network architectures are explored, including recurrent neural network (RNN), gated recurrent unit (GRU), and the long short-term memory (LSTM). Additionally, attention mechanism is integrated into the best-performing model to help further improve its performance. To ensure favorable outcomes, an adapted variant of the particle swarm optimization (PSO) is introduced to optimize hyper-parameter settings of each model. Simulations with real-world data showcased promising results while the rigorous statistical analysis confirmed that the observed improvements are statistically significant. The best-performing models were subjected to feature importance analysis to help future endeavors, as well as data collection efforts. The best performing models attained an impressive normalized mean square error (MSE) and coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.007240 and 0.894693, respectively, suggesting strong perspective for real world applications. Nonetheless, the introduction of attention mechanism did not provide further improvements to the best performing model. Lastly, this study confirmed that the modifications made to the baseline PSO strengthened the original approach, as it statistically significantly outperformed other metaheuristics.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101174"},"PeriodicalIF":5.7,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phoeni6: A systematic approach for evaluating the energy consumption of neural networks","authors":"Antônio Oliveira-Filho ,&nbsp;Wellington Silva-de-Souza ,&nbsp;Carlos Alberto Valderrama Sakuyama ,&nbsp;Samuel Xavier-de-Souza","doi":"10.1016/j.suscom.2025.101172","DOIUrl":"10.1016/j.suscom.2025.101172","url":null,"abstract":"<div><div>This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology automates energy evaluations through containerized tools, robust database management, and versatile data models. In the first case study, the energy consumption of AlexNet and MobileNet was compared using raw and resized images. Results showed that MobileNet is up to 6.25% more energy-efficient for raw images and 2.32% for resized datasets, while maintaining competitive accuracy levels. In the second study, the impact of image file formats on energy consumption was evaluated. BMP images reduced energy usage by up to 30% compared to PNG, highlighting the influence of file formats on energy efficiency. These findings emphasize the importance of Phoeni6 in optimizing energy consumption for diverse neural network applications and establishing sustainable artificial intelligence practices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101172"},"PeriodicalIF":5.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT and XAI-driven data aggregation framework for intelligent decision-making in smart healthcare systems","authors":"Azath Mubarakali ,&nbsp;Asma AlJarullah","doi":"10.1016/j.suscom.2025.101179","DOIUrl":"10.1016/j.suscom.2025.101179","url":null,"abstract":"<div><div>The Internet of Things (IoT) is used in healthcare to monitor patients via wearable sensors to measure different physiological parameters. Smart healthcare IoT-enabled sensors and medical device data collaborate with other smart devices to transfer collected sensitive healthcare data to the central server in a secure manner. However, this collected data suffers from noise, imbalance, privacy concerns, and challenges in real-time analysis. Thus, this work is to develop a novel IoT and Explainable Artificial Intelligence (XAI) based data aggregation framework in smart healthcare systems to enable accurate patient health status and decision-making in real-time. Initially, body-integrated wearable sensors and devices collect physiological data, forming a comprehensive dataset. After that, this data is preprocessed and encrypted using Fully Homomorphic Encryption for secure transmission to the centralized servers. Meaningful features are extracted from the preprocessed data using Autoencoders, which perform effective dimensionality reduction while preserving critical information. Finally, Tabular Network (TabNet) classifies health status and risks with high precision. TabNet is a deep learning model specifically designed for structured data, which efficiently handles tabular data using attention mechanisms for feature selection and decision-making. The framework integrates XAI methods to provide interpretable predictions and actionable insights, ensuring transparency for healthcare providers. As a result, TabNet demonstrates a remarkable accuracy rate of 99.57 %, making it possible for doctors to provide consultations at any time, thereby improving the efficiency of traditional medical systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101179"},"PeriodicalIF":5.7,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing intrusion detection and Kerberos attack prevention with an integrated blockchain and AI-based approach","authors":"Nisha Rajpal ,&nbsp;Dinesh Rai","doi":"10.1016/j.suscom.2025.101178","DOIUrl":"10.1016/j.suscom.2025.101178","url":null,"abstract":"<div><div>In today's linked digital world, securing computer networks as well as systems is critical. The increasing complexity as well as the regularity of network attacks demand creative and effective intrusion detection solutions to protect against possible threats. Kerberos, an established token-based authentication technology, is notable for its cryptographic method, privacy assurance, and data protection whenever identifying eligible users. At the same time, it fails to offer proper channel protection for transmitting user credentials between the client and server pathways. This study presents an integrated approach for detecting Kerberos attacks and intrusions within computing systems and networks. It combines artificial intelligence and Blockchain technology with a proxy re-encryption scheme to enhance security measures. After pre-processing, the input data is recorded on the blockchain, subjected to proxy re-encryption, and stripped of noise. The utilization of threshold proxy re-encryption in the consensus process eliminates dependence on third-party central service providers. As proxy service nodes, a number of consensus nodes within the blockchain network re-encrypt data and combine translated ciphertext. Throughout the process, no personal information is revealed. In this study, the methods of Principal Component Analysis and Chi-square Test are used to reduce the dimension of the main components with the greatest variation and to discover and pick the most relevant features from the target variable. To detect the normal and attack systems, all selected important features have been categorized utilizing the KNN classifier. Throughout the investigation, the proposed approach was used to evaluate the openly available dataset KDD-99.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101178"},"PeriodicalIF":5.7,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing microgrid energy management with hybrid energy storage systems using reinforcement learning methods","authors":"Lejia Li","doi":"10.1016/j.suscom.2025.101177","DOIUrl":"10.1016/j.suscom.2025.101177","url":null,"abstract":"<div><div>With the growth of global energy demand and the pursuit of sustainable energy, microgrids, as an emerging energy supply system, are becoming increasingly important. However, the energy management of microgrid hybrid energy storage systems face numerous challenges, including significant energy waste and poor power supply stability. This study aims to optimize the energy management of microgrid hybrid energy storage systems using reinforcement learning methods. By constructing a reinforcement learning model architecture based on the Markov decision process, the state space, action space, and reward function are systematically designed. The improved proximal policy optimization (PPO) algorithm is then used for implementation. Historical microgrid operation data spanning one year was preprocessed to normalize critical variables, and a simulation was run in a Python environment using OpenAI Gym and proprietary energy system dynamics. The experiment utilizes the operational data of a regional microgrid for one year to compare the traditional model, based on fixed-priority energy allocation rules, with the neural network model. The results show that the reinforcement learning model has an average annual energy management efficiency of 84.5 %, which is significantly improved compared with the 54.25 % of the traditional model and 70 % of the neural network model; the energy loss rate is only 8 %, which is much lower than the 25 % of the traditional model and 18 % of the neural network model; the comprehensive index of power supply stability is 0.92, which is also better than other models. This study provides an efficient and adaptable solution for microgrid energy management, which is expected to promote the healthy development of the microgrid industry.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101177"},"PeriodicalIF":5.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based workload prediction and resource provisioning for mobile edge-cloud computing in healthcare applications","authors":"Durga S ,&nbsp;Esther Daniel ,&nbsp;Deepakanmani S ,&nbsp;Reshma V.K","doi":"10.1016/j.suscom.2025.101176","DOIUrl":"10.1016/j.suscom.2025.101176","url":null,"abstract":"<div><div>Edge computing has been greatly assisted by the quick development of cloud computing and mobile communications. Even though there has been a lot of interest in edge computing technologies, the majority of research has been application-specific and did not consider cloud providers' control perspective, which offers general-purpose edge services. Thus, a new model called Parallel Convolutional MobileNet (PConvM-Net) is presented for resource provisioning and workload prediction. First, Multi-Access Edge Computing (MEC) for resource provision is considered, and here resource provisioning manager includes two main components, like workload estimation and monitoring. In the prediction module, the workload prediction is performed by employing a Gated Recurrent Unit (GRU). In the decision module, the threshold scale-up process is executed. Moreover, in order to choose the number of resources in the scale-down and scale-up process, a Parallel Convolutional MobileNet (PConvM-Net) is utilized. Further, the decision is considered based on parameters such as bandwidth, Central Processing Unit (CPU), memory usage, energy, and execution time. Here, PConvM-Net is formulated by the amalgamation of MobileNet and Parallel Convolutional Neural Network (PCNN). The simulation outcomes of PConvM-Net calculated a minimum execution time, energy consumption, CPU utilization, Task Response Time, SLA Violation, and Availability of 8.616 sec, 39.876 J, 83.877 %, 7.644 sec, 2.877 %, and 91.876 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101176"},"PeriodicalIF":5.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does faster mean greener? Runtime and energy trade-offs in iOS applications with compiler optimizations","authors":"José Miguel Aragón-Jurado ,&nbsp;Abdul Ali Bangash ,&nbsp;Bernabé Dorronsoro ,&nbsp;Karim Ali ,&nbsp;Abram Hindle ,&nbsp;Patricia Ruiz","doi":"10.1016/j.suscom.2025.101166","DOIUrl":"10.1016/j.suscom.2025.101166","url":null,"abstract":"<div><div>Smartphones outnumber people nowadays, requiring efficient energy management. High application energy use leads to faster battery drain and frequent recharging, negatively impacting both battery life and the environment. This cycle also contributes to rising electronic and chemical waste due to discarded mobile phone batteries. Compiler optimization flags may play a crucial role in mitigating these issues by optimizing software performance. However, there has been little research on examining how compiler optimization flags impact the energy consumption of smartphone applications. This work presents an empirical study on the effect of the most aggressive iOS compiler optimizations on runtime, power consumption, and energy consumption across six different iOS applications. For each application, we developed a benchmark focused on the specified category we aimed to study. Our results show that reducing application runtime does not always directly correlate with improved energy consumption. In fact, we observed that optimizations aimed at enhancing runtime performance often come at an energy cost in the applications studied, highlighting a trade-off between runtime and energy consumption. For example, we found that using <span>-Ounchecked</span> in Swift, combined with <span>-Oz</span> from LLVM in the GhostRun video game, increases energy consumption by 34%, despite improving runtime performance by 9%.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"47 ","pages":"Article 101166"},"PeriodicalIF":3.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}