基于群体感应研究苦丁茶多酚对荧光假单胞菌腐败特性的抑制作用

为探究苦丁茶多酚对分离自卵形鲳鲹的腐败菌荧光假单胞菌（Pseudomonas fluorescens）的抑制作用,本文以报告菌株紫色杆菌(Chromobacterium violaceum) CV026 为检测模型,测定了苦丁茶多酚对细菌的群体感应抑制活性,以胞外酶活性、生物被膜形成量及形态、群集与泳动性为指标,测定了苦丁茶多酚对荧光假单胞菌腐败特性的抑制作用。结果表明:苦丁茶多酚对CV026、荧光假单胞菌的最小抑菌浓度分别为2.1、3.0 mg/mL;在亚抑菌浓度下,苦丁茶多酚可以显著降低紫色杆菌CV026 紫色菌素的产生量（P<0.05）;当浓度为2.0 mg/mL 时,对紫色菌素的抑制率达68.89%,且不影响CV026 菌株的生长;苦丁茶多酚能有效抑制荧光假单胞菌胞外酶活性、生物被膜形成、群集与泳动能力等相关腐败特性,其抑制作用呈剂量依赖性,苦丁茶多酚浓度为2.0 mg/mL时,对蛋白酶、脂肪酶活性、生物被膜形成、群集和泳动能力的抑制率分别达到80.68%、53.50%、44.90%、79.56%和90.12%;苦丁茶多酚具有较好的群体感应抑制活性,可以为新型天然群体感应抑制剂研发提供参考。     

Development of a method employing reversed‐phase thin‐layer chromatography for establishing milk fat purity with respect to adulteration with vegetable oils

A systematic study was conducted using reversed‐phase thin‐layer chromatography wherein unsaponifiable matter of samples namely, pure cow and buffalo milk fats, vegetable oils (groundnut oil, soya bean oil and sunflower oil) and milk fats adulterated with vegetable oils (≥1%) were run along with the reference standards using two new solvent systems to detect adulteration in milk fat. The results of the study revealed that adulteration at even a 1% level could easily be detected using this rapid, reliable and reproducible method based on the presence of β‐sitosterol as a marker and some additional spots ascribable to their occurrence in vegetable oils only.     

Low Complexity Preprocessing Approach for Wireless Physical Layer Secret Key Extraction Based on PCA

Achieving communication security, along with high computational efficiency, is one of the challenging issues in the advancement of modern resource constraint wireless networks. Wireless physical layer secure key extraction in conjunction with suitable preprocessing techniques may be a possible way out. Principal component analysis (PCA) is one of the dimensionality reduction techniques employed commonly in various domains for different applications. However, the physical layer secure key extraction employing PCA as dimensionality reduction is untouched so far. This paper presents a comprehensive study on PCA based wireless secret key extraction with real-time experimentation. In this work, we propose to apply PCA as a preprocessing technique to reduce the total number of numerical computations required in the key generation process, by cutting down the dimension of the input data set. We propose to select the extracted principal components to be processed further for key generation, based on their information content and cross-correlation. We analyzed the performance of the proposed in terms of bit disagreement rate, key randomness and pass ratio. The computational complexity of the proposed approach is derived and the effect of dimensionality reduction factor (\({\mathbf{R}}_{\mathbf{f}}\)) on the required numerical computations is analyzed. It is found that substantial improvement in bit disagreement performance is achieved along with a significant reduction in the required numerical computations. Remarkably, these outcomes are achieved by slightly modifying one of the blocks of the traditional key generation system. Furthermore, the practicability of the proposed technique is verified through real-time experimentation in different physical scenarios.

     

MoS2–HgTe Quantum Dot Hybrid Photodetectors beyond 2 µm

Mercury telluride (HgTe) colloidal quantum dots (CQDs) have been developed as promising materials for the short and mid‐wave infrared photodetection applications because of their low cost, solution processing, and size tunable absorption in the short wave and mid‐infrared spectrum. However, the low mobility and poor photogain have limited the responsivity of HgTe CQD‐based photodetectors to only tens of mA W^?1. Here, HgTe CQDs are integrated on a TiO₂ encapsulated MoS₂ transistor channel to form hybrid phototransistors with high responsivity of ≈10⁶ A W^?1, the highest reported to date for HgTe QDs. By operating the phototransistor in the depletion regime enabled by the gate modulated current of MoS₂, the noise current is significantly suppressed, leading to an experimentally measured specific detectivity D* of ≈10¹² Jones at a wavelength of 2 µm. This work demonstrates for the first time the potential of the hybrid 2D/QD detector technology in reaching out to wavelengths beyond 2 µm with compelling sensitivity.     

A Low-Current and Analog Memristor with Ru as Mobile Species

The switching parameters and device performance of memristors are predominately determined by their mobile species and matrix materials. Devices with oxygen or oxygen vacancies as the mobile species usually exhibit a great retention but also need a relatively high switching current (e.g., >30 µA), while devices with Ag or Cu as cation mobile species do not require a high switching current but usually show a poor retention. Here, Ru is studied as a new type of mobile species for memristors to achieve low switching current, fast speed, good reliability, scalability, and analog switching property simultaneously. An electrochemical metallization-like memristor with a stack of Pt/Ta₂O₅/Ru is developed. Migration of Ru ions is revealed by energy-dispersive X-ray spectroscopy mapping and in situ transmission electron microscopy within a sub-10 nm active device area before and after switching. The results open up a new avenue to engineer memristors for desired properties.     

Investigation of Spontaneous Polarization and Phase Transition Phenomena in KH2PO4-Type Crystals by Green’s Function Approach

Journal of Low Temperature Physics - In this paper, the spontaneous polarization and the phase transition of order–disorder type H-bonded KH2PO4-type crystals are theoretically investigated....     

A Probabilistic Model of Clock Offset Estimator (PMCOE) for Clock Synchronization in Wireless Sensor Network

Wireless Personal Communications - Synchronization of clock within a wireless sensor network epitomizes crucial problems in the efficient and reliable operation of the sensors. This paper discusses...     

Supervised learning and frequency domain averaging-based adaptive channel estimation scheme for filterbank multicarrier with offset quadrature amplitude modulation

Filterbank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is an attractive alternative to the orthogonal frequency division multiplexing (OFDM) modulation technique. In comparison with OFDM, the FBMC-OQAM signal has better spectral confinement and higher spectral efficiency and tolerance to synchronization errors, primarily due to per-subcarrier filtering using a frequency-time localized prototype filter. However, the filtering process introduces intrinsic interference among the symbols and complicates channel estimation (CE). An efficient way to improve the CE in FBMC-OQAM is using a technique known as windowed frequency domain averaging (FDA); however, it requires a priori knowledge of the window length parameter which is set based on the channel's frequency selectivity (FS). As the channel's FS is not fixed and not a priori known, we propose a k-nearest neighbor-based machine learning algorithm to classify the FS and decide on the FDA's window length. A comparative theoretical analysis of the mean-squared error (MSE) is performed to prove the proposed CE scheme's effectiveness, validated through extensive simulations. The adaptive CE scheme is shown to yield a reduction in CE-MSE and improved bit error rates compared with the popular preamble-based CE schemes for FBMC-OQAM, without a priori knowledge of channel's frequency selectivity.