Grease lubrication is a complex mixture of science and engineering, requiring an interdisciplinary approach, and is applied to the majority of bearings worldwide. Grease can be more than a lubricant; it is often expected to perform as a seal, corrosion inhibitor in electrical joints, electrical insulator and electrical connection improver. This work is concerned, therefore, with the comparative study between prepared electrical grease and the imported one. Physicochemical and electrical properties for the prepared grease and the imported one were evaluated. The results of dropping point, penetration, dynamic viscosity, corrosion inhibition, evaporation loss, total acid number and oil separation for the prepared grease under investigation are mostly the same compared with the imported grease. In addition, the results interpreted in terms of the physical and chemical properties of both greases revealed that no remarkable differences. In this respect, calorimetric study shows that the prepared grease, like the imported one, is thermally stable up to about 200 °C then decomposition and degradation started slightly faster and higher than that of the imported one. The electrical and dielectric parameters are very close at and around room temperature then the increase of charge carriers' mobility at higher temperatures explains the deviation from stability in case of the prepared grease. One can conclude that the prepared electrical grease could replace efficiently the imported electrical grease especially in isothermal application at and around room temperature. 相似文献
This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the signals in the Hilbert domain. The operation in the Hilbert domain guarantees working on the low-pass spectra of EEG signal segments to avoid artifacts. Signal attributes in the Hilbert domain including amplitude, derivative, local mean, local variance, and median are analyzed statistically to perform the channel selection and seizure prediction tasks. Pre-defined prediction and false-alarm probabilities are set to select the channels, the attributes, and bins of probability density functions (PDFs) that can be useful for seizure prediction. Due to the multi-channel nature of this process, there is a need for a majority voting strategy to take a decision for each signal segment. Simulation results reveal an average prediction rate of 96.46%, an average false-alarm rate of 0.028077/h and an average prediction time of 60.1595 min for a 90-min prediction horizon.
We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.
The volume-time criterion is discussed, and a modification is proposed with the aim of predicting breakdown voltages in SF6 under positive impulse stresses without introducing an empiricism in the analysis. The electrons initiating the breakdown are those produced by cosmic rays that penetrate to the gas volume. The results obtained, including the breakdown voltage and the time-to-breakdown, as influenced by the steepness of the applied impulse, are discussed and compared with those measured experimentally 相似文献
Zinc oxide/polyvinylpyrrolidone (ZnO/PVP) nanocomposite fibers with enhanced structural, morphological and optical properties were purposefully tailored using electrospinning technique. Meanwhile, ZnO nanoparticles (NPs),with particle size of ~50 nm, were synthesized using a co-precipitation method. The nanocomposite fibers were prepared by an electrospun solution of PVP containing ZnO NPs of 2, 4, 6 and 8 wt%. Evidently, the morphological, thermal and optical properties of the ZnO/PVP nanocomposite fibers were enhanced by dispersing ZnO NPs into PVP fibers. Typically, controlling the ZnO NPs content and their dispersibility (0–8 wt%) into PVP fibers result in improved the thermal stability (an increase of onset decomposition temperature by ~120 °C above pure PVP fibers) as well as the UV–Vis protection (reduction in UV transmission by 70%) and the photoluminescence properties (a sharp UV emission around 380 nm) Overall, based on the enhanced properties, the PVP/ZnO nanocomposite fibers can be considered a promise material in optoelectronic sensors and UV photoconductor. 相似文献
A series of (50 ? x) P2O5–20B2O3–20CaO–10Na2O (x?=?0–15 mol% MoO3) glass composition was prepared. Glass structure was analyzed using infrared absorption, UV–visible spectroscopy, electron spin resonance, density, and molar volume calculations. FTIR confirmed that Mo ions are contributed as MoO6 octahedral units in the glassy matrix, resulting in an increase in the pyrophosphate and BO3 groups at the expense of metaphosphate and BO4 units. UV–visible and ESR spectra detected Mo3+ and Mo5+ ions as species in the host glass due to the increase in MoO3 content. Broadband dielectric spectroscopy investigation on a broad range of frequencies and at different temperatures indicated that the enhancement of electrical conductivity of the prepared glasses due to molybdenum doping was prevented using confinement effect at the wells, causing demobilization of the charge carriers. Hence the dielectric spectra were caused by the mobility of charge carriers rather than the dynamics at the molecular scale. There is a clear correlation between the transport mechanism and dynamics at the interface of the charge carriers. Presently, the challenge is to understand if optimizing the accumulation of charges at the interfaces and electrodes is the origin of electrical storage energy.