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In this study, an innovative rugate filter configuration porous silicon (PSi) with enhanced photoluminescence intensity was fabricated. The fabricated PSi exhibited dual optical properties with both sharp optical reflectivity and sharp photoluminescence (PL), and it was developed for use in organic vapor sensing. When the wavelength of the resonance peak from the rugate PSi filters is engineered to overlap with the emission band of the PL from the PSi quantum dots, the PL intensity is amplified, thus reducing the full width at half maximum (FWHM) of the PL band from 154 nm to 22 nm. The rugate PSi filters samples were fabricated by electrochemical etching of highly doped n-type silicon under illumination. The etching solution consisted of a 1:1 volume mixture of 48% hydrofluoric acid and absolute ethanol and photoluminescent rugate PSi filter was fabricated by etching while using a periodic sinusoidal wave current with 10 cycles. The obtained samples were characterized by scanning electron microscopy (SEM), and both reflection redshift and PL quenching were measured under exposure to organic vapors. The reflection redshift and PL quenching were both affected by the vapor pressure and dipole moment of the organic species.

In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcrafted rules or conventional machine learning models falter when the data scale and linguistic variety grow. GraphRAG, a retrieval-augmented generation (RAG) framework that splits documents into fixed-length chunks and then retrieves the most relevant ones for generation, offers a scalable alternative yet still suffers from fragmentation and semantic gaps that erode graph integrity. To resolve these issues, this paper proposes SC-LKM, a cybersecurity knowledge-graph construction method that couples the GraphRAG backbone with hierarchical semantic chunking. SC-LKM applies semantic chunking to build a cybersecurity knowledge graph that avoids the fragmentation and inconsistency seen in prior work. The semantic chunking method first respects the native document hierarchy and then refines boundaries with topic similarity and named-entity continuity, maintaining logical coherence while limiting information loss during the fine-grained processing of unstructured text. SC-LKM further integrates the semantic comprehension capacity of Qwen2.5-14B-Instruct, markedly boosting extraction accuracy and reasoning quality. Experimental results show that SC-LKM surpasses baseline systems in entity-recognition coverage, topology density, and semantic consistency.

This paper overviews the recent advances in flux-adjustable permanent magnet (PM) machines for traction applications. The flux-adjustable PM machines benefit from the synergies of the high torque density and high efficiency in conventional PM machines as well as the controllable air-gap field in wound-field machines, which are attractive for the traction applications requiring enhanced capabilities of speed regulation and uncontrolled voltage mitigation. In general, three solutions have been presented, namely the hybrid excited (HE), the mechanically regulated (MR), and the variable flux memory (VFM) machines. Numerous innovations were proposed on these topics during the last two decades, while each machine topology has its own merits and demerits. The purpose of this paper is to review the development history and trend of the flux-adjustable PM machines, with particular reference to their topologies, working mechanism, and electromagnetic performance.

An amplitude modulator plays an essential role in the implementation of continuous-variable quantum key distribution (CVQKD), whereas it may bring about a potential security loophole in the practical system. The high-frequency modulation of the actual transmitter usually results in the high rate of the system. However, an imperfect amplitude modulator (AM) can give birth to a potential information leakage from the modulation of the transmitter. To reveal a potential security loophole from the high-frequency AM embedded in the transmitter, we demonstrate an influence on the practical security of the system in terms of the secret key rate and maximal transmission distance. The results indicate the risk of this security loophole in the imperfect AM-embedded transmitter. Fortunately, the legal participants can trace back the potential information leakage that has been produced from the imperfect transmitter at high frequencies, which can be used for defeating the leakage attack in CVQKD. We find the limitations of the imperfect AM-embedded transmitter of the high-frequency quantum system, and hence, we have to trade off the practical security and the modulation frequency of the AM-embedded transmitter while considering its implementation in a practical environment.

Infections caused by snakehead vesiculovirus (SHVV) have seen frequent outbreaks in recent years, inflicting significant losses on the snakehead aquaculture industry. Early detection is therefore essential for effective prevention and control of pathogenic infections and reduction of economic losses caused by infections. There is an urgent need for a simple, rapid, specific, sensitive, and intuitive method to monitor snakehead infected with SHVV. The aim of the present study was to develop and evaluate a loop-mediated isothermal amplification (LAMP) assay for the rapid visual detection of SHVV in snakehead. Three pairs of primers were designed according to the conserved region of phosphoprotein (P) gene sequences of SHVV and were applied for the detection of SHVV from fish samples. Time and temperature conditions for the amplification of SHVV were optimized at 65 °C and 55 min. The LAMP assay demonstrated high specificity, with no cross-reactivity with seven other viruses. Amplification results were visualized by a color change after the addition of hydroxynaphthol blue (HNB) dye. Sensitivity test results showed that the minimum detection volume with this method was 1.76 × 102 copies/μL, which was 100 times more sensitive than RT-PCR assay. We used the established LAMP system to test 50 clinical samples and detected 32 positive responses, whereas 22 positive samples out of 50 samples were detected by RT-PCR. The establishment of a visual LAMP assay further shortens the virus detection process and allows visual reading of positive responses through color changes; it is suitable for use in quarantine and field detection. Therefore, this proposed method provides a sensitive, specific, and user-friendly method for the rapid diagnosis of SHVV in snakehead farming.

An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.

In this work, a broadband and broad-angle polarization-independent random coding metasurface structure is proposed for radar cross section (RCS) reduction. An efficient genetic algorithm is utilized to obtain the optimal layout of the unit cells of the metasurface to get a uniform backscattering under normal incidence. Excellent agreement between the simulation and experimental results show that the proposed metasurface structure can significantly reduce the radar cross section more than 10 dB from 17 GHz to 42 GHz when the angle of incident waves varies from 10° to 50°. The proposed coding metasurface provides an efficient scheme to reduce the scattering of the electromagnetic waves.

With the continuous development of the information age, data sharing and exchange are gradually increasing. The Internet and big data technology provide a guarantee for data sharing and transmission. At present, as the amount of data increases rapidly, how to realize data sharing has become a huge challenge. To solve this problem, this paper proposes a data sharing platform based on the combination of blockchain and interplanetary file system (IPFS) technology to solve the data sharing and storage. Firstly, by constructing the alliance blockchain, the consensus mechanism of computing power competition is used to maintain the data written into the blockchain, and the IPFS data storage system is established to store data using distributed storage, file splitting, and splicing technologies. Secondly, a data sharing platform composed of blockchain module, IPFS module, encryption and decryption module, and fast retrieval module is built. Data encryption is processed by encryption and decryption module, and the processed data is uploaded to THE IPFS module; the abstract and other information are finally written into the blockchain through the blockchain module. The fast retrieval module can quickly locate the required data according to the retrieval conditions in the mass blockchain data; finally, the security and storage of data sharing platform are guaranteed through security and performance evaluation. The research results solve the problem of large amount of data sharing, realize the data decentralization, and ensure the data storage security.

Piroplasmosis and anaplasmosis are important zoonotic diseases of animal origin, which can be transmitted by ticks to infect animals. However, there is limited information on the infection of piroplasmosis and anaplasmosis in Tan sheep in Ningxia, China. In order to understand the prevalence of piroplasmosis and anaplasmosis in Tan sheep in Ningxia, 150 blood samples of Tan sheep from farms in five urban areas of Ningxia were detected by PCR, and some positive samples were sequenced to establish a phylogenetic tree. PCR revealed that the prevalence of Anaplasma spp. in Tan sheep in Ningxia was 28.0%. The overall prevalence of Piroplasmids was 33.3%, of which Theileria spp. and Babesia spp. were 20.7% and 12.7%, respectively. Among the samples of different ages, the highest detection rates of Piroplasmids and Anaplasma spp. were found in Tan sheep aged 20–30 months, and the detection rate of Theileria spp., Babesia spp., and Anaplasma spp. were 25.4%, 23.6%, and 36.3%, respectively. In this study, one Theileria species was identified as Theileria uilenbergi, two Babesia species were identified as Babesia molasi and Babesia ovis, and two Anaplasma species were identified as Anaplasma ovis and Anaplasma phagocytophilum, and the dominant species were A. ovis and T. uilenbergi. To the best of our knowledge, this is the first report detailing the infection rate and genotype of Piroplasmids and Anaplasma spp. in Tan sheep in Ningxia, China. The results of this study provide valuable data for understanding the epidemiology of tick-borne disease in Tan sheep in Ningxia, China, and lay a theoretical foundation for the prevention and control of piroplasmosis and anaplasmosis in Tan sheep in Ningxia, northwest China.

Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications. The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve. Although simple, its efficiency is hindered by the step-by-step coordinate shifting and bitwise operations. To tackle this problem, we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd, which adopt two optimization methods to boost the algorithm’s efficiency: 1) we design efficient lookup tables (LT) that convert encoding and decoding operations into table-lookup operations; 2) we design a bit detection mechanism that skips partial order of a coordinate or a Z-value with consecutive 0s in the front, avoiding unnecessary iterative computations. We propose order-parallel and point-parallel OpenMP-based algorithms to exploit the modern multi-core hardware. Experimental results on discrete, skewed, and real datasets indicate that our point-parallel algorithms can be up to 12.6× faster than the existing algorithms.