Safeguarding the Unseen: a Study on Data Privacy in DeFi Protocols

The financial sector's adoption of technology-driven data analysis has enhanced operational efficiency and revenue generation by leveraging personal sensitive data. However, the inherent characteristics of blockchain hinder decentralized finance (DeFi) from accessing necessary sensitive user data. To address this challenge, we introduce a protocol that both safeguards user privacy and ensures data availability through the incorporation of homomorphic encryption and zero-knowledge-proof techniques in blockchain technology. This novel protocol helps mitigate privacy risks caused by sensitive data leaks while improving the capital efficiency of the DeFi market. Furthermore, we explore the applicability of these privacy-preserving methods in on-chain ecosystems and cross-border financial applications. Our solution contributes to secure, user-centric solutions for DeFi while upholding principles of decentralization and privacy protection

Quantifying efficiency of remote excitation for surface enhanced Raman spectroscopy in molecular junctions

Surface-enhanced Raman spectroscopy (SERS) is enabled by local surface plasmon resonances (LSPRs) in metallic nanogaps. When SERS is excited by direct illumination of the nanogap, the background heating of lattice and electrons can prevent further manipulation of the molecules. To overcome this issue, we report SERS in electromigrated gold molecular junctions excited remotely: surface plasmon polaritons (SPPs) are excited at nearby gratings, propagate to the junction, and couple to the local nanogap plasmon modes. Like direct excitation, remote excitation of the nanogap can generate both SERS emission and an open-circuit photovoltage (OCPV). We compare SERS intensity and OCPV in both direct and remote illumination configurations. SERS spectra obtained by remote excitation are much more stable than those obtained through direct excitation when photon count rates are comparable. By statistical analysis of 33 devices, coupling efficiency of remote excitation is calculated to be around 10%, consistent with the simulated energy flow.Comment: 20 pages, 4 figures, plus 19 pages, 11 figures supporting informatio

Experimental and theoretical study of microwave enhanced catalytic hydrodesulfurization of thiophene in a continuous-flow reactor

Hydrodesulfurization (HDS) of thiophene, as a gasoline model oil, over an industrial Ni-Mo/Al 2O 3 catalyst was investigated in a continuous system under microwave irradiation. The HDS efficiency was much higher (5%–14%) under microwave irradiation than conventional heating. It was proved that the reaction was enhanced by both microwave thermal and non-thermal effects. Microwave selective heating caused hot spots inside the catalyst, thus improved the reaction rate. From the analysis of the non-thermal effect, the molecular collisions were significantly increased under microwave irradiation. However, instead of being reduced, the apparent activation energy increased. This may be due to the microwave treatment hindering the adsorption though upright S-bind (η 1) and enhancing the parallel adsorption (η 5), both adsorptions were considered to favor to the direct desulfurization route and the hydrogenation route respectively. Therefore, the HDS process was considered to proceed along the hydrogenation route under microwave irradiation.[Figure not available: see fulltext.]

An Improved hybrid DC circuit breaker with self-adaptive fault current limiting capability

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe effective fault current limiting is very significant for the dc distribution system. However, the traditional dc fault current limiting method, i.e., directly installing dc reactor, may trigger negative impacts the system normal operation and fast isolation of the circuit breaker. Therefore, an improved hybrid dc circuit breaker with self-adaptive fault current limiting capability is proposed in this article. Not only can it realize fault current limitation in a quick and efficient manner, but also ensures the continuous operation of the converter and the fault ride-through of the healthy network after the dc fault. In this sense, the requirements on the protection and arrester capacity are reduced. Compared with other types of fault current limiting methods, the proposed topology has the merit of few negative effects on system stability and transient response. It can effectively perform fault current limiting and fault isolation, with low conduction loss and low implementation difficulty. The working principle and advantages of the proposed topology are verified by experimental tests and simulation cases.Peer ReviewedPostprint (author's final draft

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease

Introduction: Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). Methods: In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Results: Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. Discussion: The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD

Novel engineered high performance sugar beetroot 2D nanoplatelet-cementitious composites

In this paper, we show for the first time that environmentally friendly nanoplatelets synthesized from sugar beetroot waste with surface area and hydroxyl functional groups similar to those of graphene oxide (GO) can be used to significantly enhance the performance of cementitious composites. A comprehensive experimental and numerical simulation study was carried out to examine the performance of the bio waste-derived 2D nanoplatelets (BNP) in cementitious composites. The experimental results revealed that the addition of BNPs decreased the workability of the cement pastes due to their high surface area and dominant hydrophilic functional groups. The experimental results also revealed that the BNP sheets altered the morphology of the hydration phases of the cementitious composites. At 0.20-wt%, the BNP sheets increased the content of the C-S-H gels. At higher concentrations (i.e., 0.40-wt% and 0.60-wt%), however, the BNP sheets increased the content of the calcium hydroxide (Ca(OH)2) products and altered their sizes and morphologies. The flexural results demonstrated that the 0.20-wt% BNPs produced the highest flexural strength and modulus elasticity and they were increased by 75% and 200%, respectively. The numerical simulations were in good agreement with the fracture test results. Both results showed that the 0.20-wt% BNPs optimal concentration significantly enhanced the fracture properties of the cementitious composite and produced mixed mode crack propagation as a failure mode compared to Mode I crack propagation for the plain cementitious composite due to combined crack bridging and crack deflection toughening mechanisms. Because of this, the fracture energy and the fracture toughness were increased by about 88% and 106%, respectively

Research on the implied carbon measurement and structural decomposition technology of power grid engineering based on structural analysis method

At present, with the development of “double-carbon”, it has an important impact on the investment form of the power grid, and the emerging investment objects continue to expand, and the new investment management mode is constantly highlighted. In this context, in-depth research is carried out on the total factor carbon measurement of new power system construction projects, and the total factor measurement framework system and technical method are put forward, which is conducive to improving the efficiency and efficiency level of investment control of power grid enterprises. Therefore, this paper puts forward the implicit carbon measurement and structure decomposition technology of power grid engineering based on structural analysis method, which provides support and reference for project type division and scientific decision-making under the new situation of power grid enterprises