Transformer winding turn-to-turn fault is the prominent cause of transformer total failure, so detecting the winding fault in real time to stop the failure development in advance is imperative. However, existing techniques entailing periodic offline inspections fail to continuously monitor transformer winding states while causing extra costs due to the outage during inspections. This has driven researchers to consider effective continuous online monitoring methods from several technical perspectives, including typically port voltage current analysis, online frequency response analysis, and vibration analysis. Since these methods are conventionally evaluated with qualitative comparisons focusing only on feasibility, quantitative assessments indispensable for the targeted improvement of the methods and the most suitable method decision in specific scenarios are still missing. To this end, we conduct a comprehensive evaluation on the three methods by leveraging both experiment and theoretical analysis. Specifically, a customized experiment platform has been designed to support data acquisition under different operating conditions. As conventional feature mining algorithms cannot process the monitoring data produced by different methods in a uniform manner, a feature extraction algorithm leveraging image mining is proposed to extract data features after mapping the test data into a high-dimensional image. This novel algorithm allows us to fully assess several fundamental aspects (i.e., sensitivity, repeatability, and anti-interference capability) of these monitoring methods.
User authentication is a critical module to achieve security and privacy protections, especially for pervasive Internet of Things (IoT) deployments. However, existing methods on IoT devices are significantly short of implementability thanks to the lack of device uniformity and protocol openness. For instance, password becomes useless for devices void of text entry interfaces. Biometrics may not scale well as they require both non-trivial sensors and cumbersome user involvement. Proximity-based methods exploiting shared ambient contexts are vulnerable to co-located malicious attacks. Therefore, a low-cost authentication scheme widely implementable on heterogeneous IoT devices is urgently demanded. To this end, we propose MagSign that leverages two fundamental capabilities owned by common IoT devices: the ubiquity of magnetic induction sensors and the power of screens to change magnetic field. Essentially, MagSign controls screen contents of an authorized device (possessed by a user) to generate specific currents in its electronic components that in turn induce a magnetic signature. This signature, sensed by a nearby device, allows the user to be authenticated and hence to unlock that device. In designing MagSign, we explore critical parameters employable to magnetic signature generation by analyzing electronic components' workflow. Moreover, we innovatively encode binary sequences into magnetic intensity transitions, so that a sequence issued from a trusted server can be converted into a magnetic signature. Different from existing proximity-based approaches relying on shared static environment information, magnetic signature is directly derived from a server-issued sequence, allowing for dynamic signature generation that effectively thwarts potential attacks. The comprehensive experiments show MagSign has a false acceptance rate (FAR) of 0.38% and a false rejection rate (FRR) of 3.13%.
Door lock is regarded as a critical line of defending the privacy and security of personal areas. However, for inner doors in environments like factories, existing locking mechanisms can be poor in user-friendliness and high in cost. For instance, mechanical locks require carrying keys that inevitably compromise user experiences, while smart locks always require non-trivial sensors. Therefore, inner doors urgently require a lightweight unlocking scheme that can properly balance user-friendliness, cost, and security. To this end, we propose HandKey as a keyless unlocking scheme to supplement existing lock systems. HandKey relies on two principles: the simplicity of hand knocking doors and the uniqueness of vibration triggered by the knocking force. In other words, a door and a hand knocking it jointly form a unique physical system that generates hand-dependent and user-specific vibration signatures uniquely representing a user identity. In designing HandKey, we first analyze the vibration mechanism behind it and the impacts of gestures and door materials on vibration signatures. Then we innovatively construct a signal processing and deep learning-based pipeline to extract signatures robust to variable knocking behaviors for representing user identity. Finally, we implement a HandKey prototype and use extensive evaluation to demonstrate its security and effectiveness.
The current collector is a key component of most light rail transit (LRT) trains. It draws electric power from the catenary or the power rail for each moving train. Due to wear, tear, and harsh operating environments, the current collector will malfunction, which can lead to unexpected power interruptions for the train and directly affect service reliability. This paper presents an inductive coupling method for current collector health monitoring of LRT trains powered by the three-phase ac power rail. The monitoring system developed using the proposed method can be easily fitted on a train so that it doubles up as a train-borne health monitoring system (TBHMS) during revenue hours. The TBHMS monitors the electrical contact quality (ECQ) between the current collector shoe (CCS) and the power rail in a non-contact and safe manner. By doing so, it eliminates the need of inspection during engineering hours, which is both labor-intensive and time-consuming. The current collector of such trains comprises a current collector assembly (CCA) that holds the CCSs. Two performance indexes, the Contact Quality Index (CQI) and the Contact Loss Index (CLI), are defined to quantify the health conditions of CCS and CCA, respectively.
Positioning is an essential service for various applications and is expected to be integrated with existing communication infrastructures in 5G and 6G. Though current Wi-Fi and cellular base stations (BSs) can be used to support this integration, the resulting precision is unsatisfactory due to the lack of precise control of the wireless signals. Recently, BSs adopting reconfigurable holographic surfaces (RHSs) have been advocated for positioning as RHSs’ large number of antenna elements enable generation of arbitrary and highly-focused signal beam patterns. However, existing designs face two major challenges: i) RHSs only have limited operating bandwidth, and ii) the positioning methods cannot adapt to the diverse environments encountered in practice. To overcome these challenges, we present HoloFed, a system providing high-precision environment-adaptive user positioning services by exploiting multi-band (MB)-RHS and federated learning (FL). For improving the positioning performance, a lower bound on the error variance is obtained and utilized for guiding MB-RHS’s digital and analog beamforming design. For better adaptability while preserving privacy, an FL framework is proposed for users to collaboratively train a position estimator, where we exploit the transfer learning technique to handle the lack of position labels of the users. Moreover, a scheduling algorithm for the BS to select which users train the position estimator is designed, jointly considering the convergence and efficiency of FL. Our performance evaluation based on simulations confirms that HoloFed achieves a 57%lower positioning error variance compared to a beam-scanning baseline and can effectively adapt to diverse environments.
Living cells display dynamic and complex behaviors. To understand their response and to infer novel insights not possible with traditional reductionist approaches, over the last few decades various computational modelling methodologies have been developed. In this chapter, we focus on modelling the dynamic metabolic response, using linear and nonlinear ordinary differential equations, of an engineered Escherichia coli MG1655 strain with plasmid pJBEI-6409 that produces limonene. We show the systems biology steps involved from collecting time-series data of living cells, to dynamic model creation and fitting the model with experimental responses using COPASI software.
Coulomb interactions among electrons and holes in two‐dimensional (2D) semimetals with overlapping valence and conduction bands can give rise to a correlated insulating ground state via exciton formation and condensation. One candidate material in which such excitonic state uniquely combines with non‐trivial band topology are atomic monolayers of tungsten ditelluride (WTe 2 ), in which a 2D topological excitonic insulator (2D TEI) forms. However, the detailed mechanism of the 2D bulk gap formation in WTe 2 , in particular with regard to the role of Coulomb interactions, has remained a subject of ongoing debate. Here, we show that WTe 2 is susceptible to a gate‐tunable quantum phase transition, evident from an abrupt collapse of its 2D bulk energy gap upon ambipolar field‐effect doping. Such gate tunability of a 2D TEI, into either n ‐ and p ‐type semimetals, promises novel handles of control over non‐trivial 2D superconductivity with excitonic pairing. This article is protected by copyright. All rights reserved
Vitrimers are an innovative class of polymers that boast a remarkable fusion of mechanical and dynamic features, complemented by the added benefit of end‐of‐life recyclability. This extraordinary blend of properties makes them highly attractive for a variety of applications, such as the automotive sector, soft robotics, and the aerospace industry. At their core, vitrimer materials consist of crosslinked covalent networks that have the ability to dynamically reorganize in response to external factors, including temperature changes, pressure variations, or shifts in pH levels. In this review, the aim is to delve into the latest advancements in the theoretical understanding and computational design of vitrimers. The review begins by offering an overview of the fundamental principles that underlie the behavior of these materials, encompassing their structures, dynamic behavior, and reaction mechanisms. Subsequently, recent progress in the computational design of vitrimers is explored, with a focus on the employment of molecular dynamics (MD)/Monte Carlo (MC) simulations and density functional theory (DFT) calculations. Last, the existing challenges and prospective directions for this field are critically analyzed, emphasizing the necessity for additional theoretical and computational advancements, coupled with experimental validation.
Plain English Summary Co-innovation increases with intercultural proximity – and even more so when countries are socially fragmented. International joint innovation allows knowledge-intensive businesses to synergistically draw upon the ideas, expertise, and experience of innovators from their respective cultures (countries). However, such collaborations are often hampered by the uncertainty of partner exploitation and free-riding especially in the absence of formal institutions such as strong intellectual property rights (IPR) protection. In such cases, while intercultural proximity (e.g., along the dimensions of religion, ethnicity, and language) can promote cooperation through the informal institution of trust, social fragmentation can induce mistrust and in turn hamper collaboration. This is puzzling in light of evidence that fragmentation also promotes innovation. Thus, we empirically show that the positive effect of intercultural proximity operates through trust especially when IPR protection is weak. The positive effect of fragmentation on international patent cooperation operates through tolerance and acceptance especially when IPR protection is strong. This implies that nurturing tolerance and acceptance while strengthening IPR and developing the intellectual property ecosystem in fragmented societies, building intercultural trust, and increasing diversity in countries, alliances, or firms can promote co-patenting success.
Tosyl cyanide is a commonly used reagent for cyanation and sulfonylation in organic synthesis and pharmaceutical chemistry. The photocatalytic transformations of tosyl cyanide are generally conducted under mild conditions with...
Background Previous research indicated outcomes among refractory out-of-hospital cardiac arrest (OHCA) patients with initial shockable rhythm were different in Singapore and Osaka, Japan, possibly due to the differences in access to extracorporeal cardiopulmonary resuscitation. However, this previous study had a risk of selection bias. To address this concern, this study aimed to evaluate the outcomes between Singapore and Osaka for OHCA patients with initial shockable rhythm using only population-based databases. Methods This was a secondary analysis of two OHCA population-based databases in Osaka and Singapore, including adult OHCA patients with initial shockable rhythm. A machine-learning-based prediction model was derived from the Osaka data ( n = 3088) and applied to the PAROS-SG data ( n = 2905). We calculated the observed-expected ratio (OE ratio) for good neurological outcomes observed in Singapore and the expected derived from the data in Osaka by dividing subgroups with or without prehospital ROSC. Results The one-month good neurological outcomes in Osaka and Singapore among patients with prehospital ROSC were 70% (791/1,125) and 57% (440/773), and among patients without prehospital ROSC were 10% (196/1963) and 2.8% (60/2,132). After adjusting patient characteristics, the outcome in Singapore was slightly better than expected from Osaka in patients with ROSC (OE ratio, 1.067 [95%CI 1.012 to 1.125]), conversely, it was worse than expected in patients without prehospital ROSC (OE ratio, 0.238 [95%CI 0.173 to 0.294]). Conclusion This study showed the outcomes of OHCA patients without prehospital ROSC in Singapore were worse than expected derived from Osaka data even using population-based databases. (249/250 words).
Flexible piezoelectric sensors have been spotlighted as an essential human–machine interface (HMI) by obtaining high‐quality data from omnipresent biomechanical inputs. Because human voice is the most intuitive bio‐signal among them, flexible piezoelectric acoustic sensors (f‐PAS) have a potential to shift the paradigm of HMI technologies. Despite the reported outstanding performance such as high sensitivity and speaker recognition accuracy, the theoretical investigation of f‐PAS has been insufficient to realize future customized development, because sensing principles are fundamentally different from commercialized microphones. Here, a theoretical framework of self‐powered f‐PAS by using mechanical and electrical physics is introduced. First of all, the basic theory of f‐PAS is compared with the auditory system of human cochlear. Based on the biomimetic trapezoidal shape, the resonant frequencies are analyzed with various structural and material conditions. In addition, the piezoelectricity of f‐PAS is derived to predict the sensitivity and SNR prior to experiments. To investigate sensor properties under the medium condition that is similar to human ear, the acoustic responses depending on the states of matter are theoretically compared. Finally, the distance limit of f‐PAS is studied with the correlations between piezoelectricity and sound pressure, which would provide novel strategies of functional material design for future applications of f‐PAS.
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