Search Results (87)

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article

(1) Background: Side-channel attacks (SCAs) exploit unintended information leakage to compromise cryptographic security. In cyber-physical systems (CPSs), embedded systems are inherently constrained by limited resources, restricting the implementation of complex countermeasures. Traditional countermeasures, such as hiding techniques, attempt to obscure power consumption patterns; however, their effectiveness has been increasingly challenged. This study evaluates the vulnerability of dummy power traces against deep learning-based SCAs (DL-SCAs). (2) Methods: A power trace dataset was generated using a simulation environment based on Quick Emulator (QEMU) and GNU Debugger (GDB), integrating dummy traces to obfuscate execution signatures. DL models, including a Recurrent Neural Network (RNN), a Bidirectional RNN (Bi-RNN), and a Multi-Layer Perceptron (MLP), were used to evaluate classification performance. (3) Results: The models trained with dummy traces achieved high classification accuracy, with the MLP model reaching 97.81% accuracy and an F1-score of 97.77%. Despite the added complexity, DL models effectively distinguished real and dummy traces, highlighting limitations in existing hiding techniques. (4) Conclusions: These findings highlight the need for adaptive countermeasures against DL-SCAs. Future research should explore dynamic obfuscation techniques, adversarial training, and comprehensive evaluations of broader cryptographic algorithms. This study underscores the urgency of evolving security paradigms to defend against artificial intelligence-powered attacks. Full article

This study addresses the issue of resistance plate deterioration in ultra-high voltage energy devices by proposing an improved symplectic geometric mode decomposition-wavelet packet (ISGMD-WP) algorithm that effectively extracts the component characteristics of leakage currents. The extracted features are subsequently input into the I-Informer network, allowing for the prediction of future trends and the provision of early short-term warnings. First, we enhance the symplectic geometric mode decomposition (SGMD) algorithm and introduce wavelet packet decomposition reconstruction before recombination, successfully isolating the prominent harmonics of leakage current. Second, we develop an advanced I-Informer prediction network featuring improvements in both the embedding and distillation layers to accurately forecast future changes in DC characteristics. Finally, leveraging the prediction results from multiple adjacent columns mitigates the impact of power grid fluctuations. By integrating these data with the deterioration interval, we can issue timely warnings regarding the condition of lightning arresters across each column. Experimental results demonstrate that the proposed ISGMD-WP effectively decomposes leakage current, achieving a decomposition ability evaluation index (EIDC) 1.95 under intense noise. Furthermore, in long-term prediction, the I-Informer network yields mean absolute error (MAE) and root mean square error (RMSE) indices of 0.02538 and 0.03175, respectively, enabling the accurate prediction of the energy device’s fault. Full article

In order to effectively monitor harmful gas leakage, this paper presents the design of an ultra-low-power IoT-based harmful gas monitoring system. The system is equipped with a custom-designed, low-power microcontroller motherboard, carefully selected low-power sensors, and high-efficiency, low-power communication modules. In addition, the system optimizes data acquisition and processing algorithms to segment gases of different concentrations. While ensuring real-time data acquisition and transmission, it achieves extremely low power consumption. By controlling the concentration of harmful gases and current for sensor performance testing, the experiment has shown that when the concentration of carbon monoxide reaches 500 ppm and methane reaches 2000 ppm, the system will trigger an alarm and upload relevant information; the sensor can detect and respond to the harmful gases within 60 s; and the system’s operating current fluctuation range remains within 0.5 mA, with an average power consumption much lower than that of other devices. Full article

Small and mid-sized water utilities face persistent challenges due to limited technical expertise and financial resources, impeding effective management and decision making. This study presents an enhanced version of the MACS Water Smart application, which integrates artificial intelligence and EPANET-based hydraulic modelling with GIS (geographical information system) functionalities to optimize water supply networks. The methodology was applied to the potable water system of Khelvachauri, Georgia, which experiences significant pressure deficits, particularly in its southern area during peak consumption time. By employing machine learning algorithms, the WS tool automates tasks such as pipe diameter optimization and pressure recovery, gradually eliminating the total need for expert intervention. The AI-powered optimization achieved pressure increases above 25 m, reduced flow velocities below 1.5 m/s, improved pumping efficiency by 15%, and lowered leakage rates by 8%. Additionally, computational time was reduced by 35% compared with traditional methods. These findings validate the performance of AI-based hydraulic simulation and its ability to replicate engineering decisions. Furthermore, the tool provides a scalable solution for planning future network expansions. This work highlights the practicality of combining AI and hydraulic modelling for sustainable water management in resource-constrained settings, emphasizing its cost-effectiveness and potential for widespread adoption in small and mid-sized utilities. Full article

The emergence of the Internet of Things (IoT) technologies has greatly enhanced the lives of individuals with disabilities by leveraging radio frequency identification (RFID) systems to improve autonomy and access to essential services. However, these advancements also pose significant security risks, particularly through side-channel attacks that exploit weaknesses in the design and operation of RFID tags and readers, potentially jeopardizing sensitive information. To combat these threats, several solutions have been proposed, including advanced cryptographic protocols built on cryptographic algorithms such as elliptic curve cryptography. While these protocols offer strong protection and help minimize data leakage, they often require substantial computational resources, making them impractical for low-cost RFID tags. Therefore, it is essential to focus on the efficient implementation of cryptographic algorithms, which are fundamental to most encryption systems. Cryptographic algorithms primarily depend on various finite field operations, including field multiplication, field inversion, and field division. Among these operations, field multiplication is especially crucial, as it forms the foundation for executing other field operations, making it vital for the overall performance and security of the cryptographic framework. The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. Therefore, this research aims to develop a novel unidirectional systolic array structure for the Dickson basis multiplier, which is anticipated to achieve lower space and power consumption, facilitating the efficient and secure implementation of computationally intensive cryptographic algorithms in RFID systems with limited resources. This advancement is crucial as RFID technology becomes increasingly integrated into various IoT applications for individuals with disabilities, including secure identification and access control. Full article

Ensuring operational reliability and efficiency in steam power plants requires advanced and generalized fault detection methodologies capable of addressing diverse fault scenarios in boiler and turbine systems. This study presents an autonomous fault detection framework that integrates deep feature extraction through Convolutional Autoencoders (CAEs) with the ensemble machine learning technique, Extreme Gradient Boosting (XGBoost). CAEs autonomously extract meaningful and nonlinear features from raw sensor data, eliminating the need for manual feature engineering. Principal Component Analysis (PCA) is employed for dimensionality reduction, enhancing computational efficiency while retaining critical fault-related information. The refined features are then classified using XGBoost, a robust ensemble learning algorithm, ensuring accurate fault detection. The proposed model is validated through real-world case studies on boiler waterwall tube leakage and motor-driven oil pump failure in steam turbines. Results demonstrate the framework’s ability to generalize across diverse fault types, detect anomalies at an early stage, and minimize operational downtime. This study highlights the transformative potential of combining deep feature extraction and ensemble machine learning for scalable, reliable, and efficient fault detection in power plant operations. Full article

The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets. Full article

Hydraulic motors have been widely used in large-scale machinery such as ground heavy equipment and heavy-duty vehicles, ships, and so on because of their high-power drive capability. However, the driving device is confronted with constraints related to its size and weight. Typically, the hydraulic axial piston motor is preferred for its simplicity and efficiency. However, the oil distributor in traditional hydraulic motors faces significant challenges, such as evident oil leakage and power loss from the mating surfaces of the fixed oil distributor and rotating cylinder block. To enhance the reliability and performance of hydraulic motors employed in paper driving applications, this paper introduces a digital radial hydraulic motor used for heavy-duty vehicle traction. The motor is powered by an on-board pump station from which several on/off valves can distribute the hydraulic oil. This design effectively mitigates the performance degradation issues associated with friction and wear in traditional hydraulic motor oil distributors. The drive characteristics of the motor can be flexibly adjusted through the combination of valves. Our investigation into the motor’s design principles and parameter analysis is poised to make an indirect yet significant contribution to the optimization of heavy-duty vehicle traction systems. This paper delineates the application conditions and operational principles of the digital hydraulic motor, thoroughly analyzes the intricate topological interrelationships of its parameters, and meticulously develops a detailed component-level model. Through comprehensive calculations, it reveals the impact of configuration and flow valve parameters on motor efficiency. A simulation model is established for the purpose of verification. Furthermore, the influence of the flow allocation method on efficiency and pressure pulsation is examined, leading to the proposal of a novel flow allocation strategy, the efficacy of which is substantiated through simulation. In conclusion, this paper formulates critical insights to inform the design and selection of components for digital hydraulic motors. These findings may provide a feasible solution for heavy-duty vehicle traction application scenarios. Full article

Background and Objectives: Vasopressin increases blood pressure through aquaporin-2-mediated water retention and is useful for managing hemodynamics after surgery. However, even after decades of study, clear clinical guidelines on doses and ideal use cases after cardiovascular surgery remain unclear. Here, the existing literature is synthesized on vasopressin use for cardiac surgeries and coupled with real-world clinical experience to outline a clearer clinical path for vasopressin use. Materials and Methods: Literature from 1966 to the present was searched, and information on surgical outcomes for cardiovascular surgery was extracted. Clinicians from the University of Tsukuba with extensive vasopressin experience in pediatric cardiovascular patients were consulted for general use guidelines. Results: Vasopressin response after cardiovascular surgery is multifaceted, and low-power trials, plus conflicting study reports, generally render it as a secondary choice behind norepinephrine. Clinical experience indicates that low doses of 0.2–0.3 mU/kg/min with constant blood pressure and oxygen monitoring for response are required. Although sole use is not recommended, vasopressin may aid in controlling hemodynamics when given with other volemic or osmolal drugs. Conclusions: Vasopressin may work in a select population of first-line non-responders, but relevant response factors remain unanalyzed and clear guidelines for use remain unestablished. Future, large-scale studies are needed to delineate temporal and demographic characteristics that affect response to vasopressin for the purpose of managing post-surgical capillary leakage and hemodynamics. Full article

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish a comprehensive framework to reverse engineer the TLB configuration, a critical aspect of side-channel analysis attacks that have previously succeeded in extracting sensitive data. The methodology involves detailed reverse engineering efforts on Intel CPUs, complemented by analytical tools to support TLB reverse engineering. This study successfully reverse-engineered the TLB configurations for Intel CPUs and introduced visual tools for further analysis. These results can be used to explore TLB vulnerabilities in greater depth. However, when attempting to apply the same methodology to the IBM Power9, it became clear that the methodology was not transferable, as mapping functions and performance counters vary across different vendors. Full article

With the development of modernization, traditional fossil energy reserves are decreasing, and the power industry, as one of the main energy consumption forces, has begun to pay attention to increasing the proportion of clean energy generation. With the deepening of electrification, the peak-valley difference of residential electricity consumption increases, but photovoltaic and wind power generation have fluctuations and are manifested as reverse peak regulation. Thermal power plants as the main force of peak regulation gradually reduce the market share, making nuclear power plants bear the heavy responsibility of participating in peak regulation. The traditional method of adjusting operating power by inserting and removing control rods has great safety risks and wastes resources. Therefore, this paper proposes a new energy storage system that can keep the nuclear power plant running at full power and produce hydrogen to synthesize ammonia from excess power. A comprehensive evaluation model of energy storage based on z-score data standardization and objective parameter assignment AHP (analytic hierarchy process) analysis method was established to evaluate energy storage systems according to a multi-index system. With an AP1000 daily load tracking curve as the input model, the simulation model built by Aspen Plus V14 was used to calculate the operating conditions of the system. In order to provide a construction basis for practical engineering use, Haiyang Nuclear Power Plant in Shandong Province is taken as an example. The system layout scheme is proposed according to the local environmental conditions. The accident tree analysis method is combined with ALOHA 5.4.1.2 (Areal Locations of Hazardous Atmospheres) hazardous chemical analysis software and MARPLOT 5.1.1 geographic information technology. A qualitative and quantitative assessment of risk factors and the consequences of leakage, fire, and explosion accidents caused by hydrogen and ammonia storage processes is carried out to provide guidance for accident prevention and emergency rescue. The design of an “Electric-Hydrogen-Ammonia” energy storage system proposed in this paper provides a new idea for zero-carbon energy storage for the peak shaving of nuclear power plants and has a certain role in promoting the development of clean energy. Full article

To protect important data and files, people often use air gap isolation, also known as air gap separation, to block external threats. However, internal networks may still introduce pollution due to supply chain contamination, human error, or social engineering. Although internal devices cannot directly communicate with the outside world. This paper proposes a new technology called Bit Sufi-Dance that utilizes electricity meters and optical devices to detect exfiltrated data. Most electricity meters have power indicator mechanical turntables or LED lights which can be indirectly controlled by the device’s power consumption oscillation. This allows for information encoding and the extraction of data from the air-gapped computer. It is important to note that this exfiltration channel does not require any hardware or firmware modifications and cannot be detected by existing Data Leakage Prevention (DLP) systems. The article discusses its design and implementation issues while evaluating it using different types of electricity meters. Our experiment demonstrates that data can be exfiltrated from the air-gap isolated computer through an electricity meter at a bit rate of 101 b/h. Finally, we assess this security threat and discuss defense mechanisms and preventive measures. Full article

Side-channel attacks (SCAs) are powerful techniques used to recover keys from electronic devices by exploiting various physical leakages, such as power, timing, and heat. Although heat is one of the less frequently analyzed channels due to the high noise associated with thermal traces, it poses a significant and growing threat to the security of very large-scale integrated (VLSI) microsystems, particularly system in package (SiP) technologies. Thermal side-channel attacks (TSCAs) exploit temperature variations, risking not only hardware damage from excessive heat dissipation but also enabling the extraction of sensitive data, like cryptographic keys, by observing thermal patterns. This dual threat underscores the need for a synergistic approach to thermal management and security in designing integrated microsystems. In response, this paper presents a novel approach that improves the early detection of abnormal thermal fluctuations in SiP designs, preventing cybercriminals from exploiting such anomalies to extract sensitive information for malicious purposes. Our approach employs a new concept called Thermal Digital Twin (TDT), which integrates two previously separate methods and techniques, resulting in successful outcomes. It combines the gradient direction sensor scan (GDSSCAN) to capture thermal data from the physical field programmable gate array (FPGA), which guarantees rapid thermal scan with a measurement period that could be close to $10 \mathsf{\mu }\mathrm{s},$ a resolution of ${0.5 }^{\circ }$C, and a temperature range from −${40 }^{\circ }$C to ${140 }^{\circ }$C; once the data are transmitted in real time to a Digital Twin created in COMSOL Multiphysics^® 6.0 for simulation using the Finite Element Method (FEM), the real time required by the CPU to perform all the necessary calculations can extend to several seconds or minutes. This integration allows for a detailed analysis of thermal transfer within the SiP model of our FPGA. Implementation and simulations demonstrate that the Thermal Digital Twin (TDT) approach could reduce the risks associated with TSCA by a significant percentage, thereby enhancing the security of FPGA systems against thermal threats. Full article