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A physical unclonable function (PUF) is a technology designed to safeguard sensitive information and ensure data security. PUFs generate unique responses for each challenge by leveraging random deviations in the physical microstructures of integrated circuits (ICs), making it incredibly difficult to replicate them. However, traditional silicon PUFs are now susceptible to various attacks, such as modeling attacks using conventional machine learning techniques and reverse engineering strategies. As a result, PUFs based on new materials or methods are being developed to enhance their security. However, in the realm of survey papers, it has come to our attention that there is a notable scarcity of comprehensive summaries and introductions concerning these emerging PUFs. To fill this gap, this article surveys PUFs based on novel technologies in the literature. In particular, we first provide an insightful overview of four types of PUFs that are rooted in advanced technologies: bionic optical PUF, biological PUF, PUF based on printed electronics (PE), and PUF based on memristors. Based on the overview, we further discuss the evaluation results of their performance based on specific metrics and conduct a comparative analysis of their performance. Despite significant progress in areas such as limited entry and regional expertise, it is worth noting that these PUFs still have room for improvement. Therefore, we have identified their potential shortcomings and areas that require further development. Moreover, we outline various applications of PUFs and propose our own future prospects for this technology. To sum up, this article contributes to the understanding of PUFs based on novel technologies by providing an in-depth analysis of their characteristics, performance evaluation, and potential improvements. It also sheds light on the wide range of applications for PUFs and presents enticing prospects for future advancements in this field. [ABSTRACT FROM AUTHOR]

The local expansion method is a novel and promising community detection algorithm. Just based on part of network information, it can detect overlapping communities effectively, but some problems exist such as seed node aggregation, poor quality and inaccurate community coverage. Therefore, we propose a local expansion overlapping community detection algorithm based on dispersed seeds. There are four essential parts of this algorithm: 1) We firstly generate non-overlapping partitions of the network, and locate seed nodes with the largest influence in their own partition by using a new index of node influence, which combines the information centrality of nodes and the number of k-order neighbors. 2) Secondly, on the condition of the neighborhood overlap measure maximization, seed nodes merge unseeded nodes to generate a preliminary seed community; 3) Then based on the community conductance gain, the allocated nodes are screened and the free nodes are assigned to the seed community; 4) In the end, a node-community similarity based on common connection edge is proposed to re-allocate new free nodes and obtain the final community structure. This method can make the community distribution more proper and the coverage more reasonable. The experimental results on some artificial data and real network data show that the algorithm performs well on overlapping community indicators such as EQ and ONMI, while the community detection results are more stable.

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Methane (CH4), as the main component of natural gas and coal mine gas, is widely used in daily life and industrial processes and its leakage always causes undesirable misadventures. Thus, the rapid detection of low concentration methane is quite necessary. However, due to its robust chemical stability resulting from the strong tetrahedral-symmetry structure, the methane molecules are usually chemically inert to the sensing layers in detectors, making the rapid and efficient alert a big challenge. In this work, palladium nanoparticles (Pd NPs) embedded indium oxide porous hollow tubes (In2O3 PHTs) were successfully synthesized using Pd@MIL-68 (In) MOFs as precursors. All In2O3-based samples derived from Pd@MIL-68 (In) MOFs inherited the morphology of the precursors and exhibited the feature of hexagonal hollow tubes with porous architecture. The gas-sensing performances to 5000 ppm CH4 were evaluated and it was found that Pd@In2O3-2 gave the best response (Ra/Rg = 23.2) at 370 °C, which was 15.5 times higher than that of pristine-In2O3 sensors. In addition, the sensing materials also showed superior selectivity against interfering gases and a rather short response/recovery time of 7 s/5 s. The enhancement in sensing performances of Pd@In2O3-2 could be attributed to the large surface area, rich porosity, abundant oxygen vacancies and the catalytic function of Pd NPs. [ABSTRACT FROM AUTHOR]

LaFe11.6Si1.4/10wt%PrxCo7 (x = 1, 2, 3, 5) magnetocaloric composites were prepared by spark plasma sintering (SPS) and diffusion annealing. The phase composition, microstructure, magnetic, mechanical and thermal properties were studied. The addition of PrxCo7 binder can be used to tune the phase fraction and magnetic properties. Pr2Co7 binder promoted the formation of 1:13 phase. LaFe11.6Si1.4/10wt%Pr2Co7 composites possess the highest 1:13 content (~ 89 wt%). The Curie temperature (TC) can be tuned in the technologically useful range of 280–347 K. Relatively large maximum magnetic entropy change (− ∆SM)max (2 T) of 1.45−3.16 J/kg K and refrigeration capacity (RC) (2 T) of 117–137 J/kg were obtained. These composites exhibit superior compressive strength of 1005–1250 MPa and excellent thermal conductivity of 17.68–27.77 W/m K. Thus, LaFe11.6Si1.4/10wt%PrxCo7 magnetocaloric composites possess excellent mechanical and thermal properties, with adjustable magnetic properties and have the potential application for near room temperature magnetic refrigeration. [ABSTRACT FROM AUTHOR]

Recovering missing values in time series is critical when performing time series analysis. And the blackouts issue studied in this paper, described as losing all the data during a certain period, is among the most urgent issues due to its devastating impact on service quality, and is challenging because of the absence of coevolving data sequences for reference. As a result, many existing approaches that rely on data from other coevolving sequences for missing value recovery are infeasible in handling blackouts. To address the issue, this work proposes a novel Hankel matrix factorization approach, HKMF-T, to recover missing values during blackouts for tagged time series, where a tagged time series consists of a data sequence and a corresponding tag sequence. Motivated by real-world observations, HKMF-T decomposes the data sequence into two components: 1) an internal, slowly-varying smooth trend, and 2) external impacts indicated by the tag sequence. By transforming a partially observed data sequence into a corresponding Hankel matrix, we learn the above two components and estimate the missing values under a unified framework of Hankel matrix factorization. Extensive experiments are conducted to evaluate the practical performance of HKMF-T on real-world data sets. And the results suggest HKMF-T outperforms the baseline approaches for blackouts with long durations. [ABSTRACT FROM AUTHOR]