Self-Sovereign Identity (SSI) is widely positioned as the decentralized future of digital identity. However, although traditional SSI literature emphasizes architecture and privacy, it largely overlooks a systematic analysis of how trust is formed, propagated, and managed. Trust is fundamental because, without it, the actions performed by key actors, including credential issuers, lose much of their practical value. In this landscape, trust is the ground on which SSI systems stand; when that ground is absent or unstable, any structure built on it crumbles. Against this backdrop, we chart the landscape of trust through a comprehensive survey of Trust Models (TMs) and Trust Management Systems (TMSs) in SSI. At the abstract level, we analyze TMs by examining the types of trust relationships among SSI actors and how trust is defined and represented. At the implementation level, we discuss TMSs in terms of how trust is represented, stored, and protected in the systems proposed in the literature. We analyzed 26 sources (24 peer-reviewed papers plus the Sovrin whitepaper and governance framework document), consolidated into 24 approaches, benchmarking 16 features across these dimensions. Based on this analysis, we identify five open challenges: enabling cross-chain trust portability; designing a hybrid TM that blends Web of Trust and hierarchies; supporting heterogeneous actors such as IoT devices and humans; implementing privacy-preserving feedback mechanisms; and TM and TMS interoperability in emerging settings, including delegated AI agents. Our analysis offers a compass to guide researchers and developers in selecting trust models and advancing SSI systems.
Open-set recognition (OSR) of low-probability-of-interception (LPI) radar signals is challenging in non-cooperative environments. Due to parameter variations and low signal-to-noise ratios (SNRs), LPI radar features often exhibit multimodal structures. This limits conventional single-center representations, leading to loose acceptance regions and increased false acceptance of unknown samples. To address this issue, this paper devises a feature-space multi-center framework for OSR of LPI radar signals. It represents each known class with multiple local prototypes and a shared within-class covariance matrix to capture complex intra-class distributions. A consistency-driven filtering mechanism calibrates the rejection boundary using validation data, while a class-level prototype configuration strategy adapts model complexity to heterogeneous class structures. Experiments on a simulated LPI radar signal dataset across a wide SNR range show that our solution outperforms several representative baseline approaches in terms of the area under the receiver operating characteristic curve (AUROC) and the false positive rate at a 95% true positive rate (FPR@95%TPR). At an SNR of −6 dB, the proposed method achieves an FPR@95%TPR of 19.09% and an AUROC of 92.11%, indicating effective discrimination under noise-dominated conditions. These results indicate the potential of the proposed method for handling complex intra-class structures in OSR of LPI radar signals under simulated non-cooperative conditions.
Growing interest in sustainable bio-lubricants as alternatives to conventional mineral oils has increased the demand for renewable feedstocks that do not compete with agricultural land use. In this study, waste cooking oil (WCO) was chemically modified through transesterification/partial hydrogenation (H-FAME), partial hydrogenation (H-WCO_l and H-WCO), estolide formation (E-WCO), and epoxidation (EOs) followed by ethanol ringopening (POs) to produce potential bio-lubricants. The thermal, physicochemical, and rheological properties of the resulting products were evaluated and compared with a commercial mineral lubricant (ISO VG 46), representative of oils used in industrial hydraulic systems. Tribological tests were performed as a preliminary comparative screening to determine friction and wear behaviour under selected pin-on-disc conditions. The WCO-derived products exhibited a broad range of properties depending on the applied chemical modification. Most samples showed thermal stability in air equal to or greater than ISO VG 46. Rheological analysis over 25-100 degrees C revealed Newtonian behaviour for H-FAME and POs, while E-WCO and EOs exhibited shearthinning behaviour, indicating fluid-like and grease-like structures, respectively. Kinematic viscosity at 40 degrees C ranged from 3.61 to 286 cSt, with polyols (POs) displaying viscosity indices between 111 and 133, exceeding that of ISO VG 46 (107). Friction coefficients were similar across all samples, although WCO, H-WCO_l, and H-WCO showed slightly lower values. Wear testing demonstrated comparable or reduced wear for E-WCO and almost fully epoxidized oil (EO_100) relative to ISO VG 46. These results highlight the potential of WCO-derived products as sustainable bio-lubricant candidates with tuneable properties, while further tribological testing under varied load, speed, and temperature conditions will be required to assess their application-specific performance.
In this paper, we study infinite dimensional holomorphic vector fields on sequence spaces, having a fixed point at 0. Under suitable hypotheses we prove the existence of germs of analytic invariant submanifolds passing through the fixed point. The restricted dynamics is analytically conjugate to the linear one under some Diophantine-like condition.
Melt pyrolysis of mixed polyolefin waste is limited by slow, non-uniform heating of viscous, low-conductivity melts and coke formation on overheated or poorly wetted walls. Scraped falling-film reactors can alleviate these limitations by renewing thin melt films, but their design remains challenging because scraping simultaneously enhances heat transfer, alters residence time, and affects film stability. Here, we develop a physics-informed CFD–surrogate–optimization framework for a vertical scraped falling-film pyrolysis reactor. A two-phase volume-of-fluid CFD model, benchmarked against TG and batch pyrolysis data, couples endothermic cracking kinetics with a Danckwerts-type surface-renewal closure using a dimensionless scraping number, avoiding moving-mesh resolution of blade motion. The model reproduces batch oil yields at 440 and 480 °C with absolute deviations within 1.3 wt%. Mechanistic maps show that stronger scraping monotonically increases heat-transfer enhancement, whereas conversion enhancement peaks at intermediate intensity, revealing a non-monotonic balance between surface renewal and effective residence time. To accelerate design-space interrogation, a physics-informed neural-network surrogate constrained by energy and reaction-progress residuals is trained on CFD data; on 60 held-out cases, it achieves R2 = 0.83–0.98 for key scalar and field outputs. Coupling the surrogate with NSGA-III identifies Pareto trade-offs among oil yield, specific energy input, and coking risk. Balanced candidates cluster at 460–471 °C and scraping numbers of 11–14; within this region, B3–B4, corresponding to 466–469 °C and scraping numbers of 13–14, are predicted to deliver 80.9–81.4 wt% oil, defining a prioritized operating window within the design space.
