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In this paper, a wheel structured Zeonex-based hexagonal packing photonic crystal fiber (PCF) sensor has been proposed for sensing camel milk with a refractive index of 1.3423 and cow milk with a refractive index of 1.3459. This sensor has been investigated for porosities of 85%, 90%, and 98% within a terahertz (THz) region ranging from 0.2 to 2.0 THz. At an operating frequency of 2 THz, this sensor has shown a maximum sensitivity of 81.16% and 81.32% for camel and cow milk, respectively. EML of 0.033013 cm-1 and 0.03284 cm-1 has been found for camel and cow milk, respectively, at the same operating conditions with negligible confinement losses of 8.675  ×  10-18 cm-1 1.435  ×  10-18 cm-1. Several other parameters, such as the effective area, flattened dispersion, and numerical aperture, have also been obtained during the investigation. Since considerable attention has not been given yet in detecting various types of dairy products using PCF terahertz sensors, this design will pave a whole new path in further implementing THz sensing in the dairy industry.Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till now, but seldom those have been used to forecast future long-term pollution trends. Forecasting long-term pollution trends into the future is highly important for government bodies around the globe as they help in the framing of efficient environmental policies. This paper presents a comparative study of various statistical and deep learning methods to forecast long-term pollution trends for the two most important categories of particulate matter (PM) which are PM2.5 and PM10. The study is based on Kolkata, a major city on the eastern side of India. The historical pollution data collected from government set-up monitoring stations in Kolkata are used to analyse the underlying patterns with the help of various time-series analysis techniques, which is then used to produce a forecast for the next two years using different statistical and deep learning methods. buy AZD0156 The findings reflect that statistical methods such as auto-regressive (AR), seasonal auto-regressive integrated moving average (SARIMA) and Holt-Winters outperform deep learning methods such as stacked, bi-directional, auto-encoder and convolution long short-term memory networks based on the limited data available.Photo-CIDNP (photo-chemically induced dynamic nuclear polarization) refers to nuclear polarization created by the spin-chemical evolution of spin-correlated radical pairs (SCRPs). This phenomenon occurs in gases, liquids and solids. Based on the solid-state photo-CIDNP effect observed under magic-angle spinning (MAS), photo-CIDNP MAS NMR has been developed as analytical method. Here we report the origin, the theory and the state of the art of this method.In this paper, we present the results of a multi-analytical characterization of a glaucophane sample collected in the Piedmont region of northwestern Italy. Investigation methods included optical microscopy, powder X-ray diffraction, Fourier-transform infrared spectroscopy, μ-Raman spectroscopy, Mössbauer spectroscopy, electron probe microanalysis, environmental scanning electron microscopy and energy-dispersive X-ray spectroscopy, and scanning/transmission electron microscopy combined with energy-dispersive X-ray spectroscopy and electron energy-loss spectroscopy. In addition to the crystal-chemical characterization of the sample from the mesoscale to the near-atomic scale, we have also conducted an extended study on the morphology and dimensions of the mineral particles. The main finding is that studying the same particle population at different magnifications yields different results for mineral habit, dimensions, and dimensional distributions. As glaucophane may occur as an elongate mineral particle (e.g., asbestiform glaucophane occurrences in California and Nevada), the observed discrepancies therefore need to be considered when assessing potential breathability of such particles, with implications for future regulations on elongate mineral particles. While the sample preparation and particle counting methods are not directly investigated in this work, our findings suggest that different magnifications should be used when characterizing an elongate mineral particle population, irrespective of whether or not it contains asbestiform material. These results further reveal the need for developing improved regulation for elongate mineral particles. We thus propose a simple methodology to merge the datasets collected at different magnifications to provide a more complete description and a better risk evaluation of the studied particle population.Internet of Things (IoT) is a network of internet connected devices that generates huge amount of data every day. The usage of IoT devices such as smart wearables, smart phones, smart cities are increasing in the linear scale. Health care is one of the primary applications today that uses IoT devices. Data generated in this application may need computation, storage and data analytics operations which requires resourceful environment for remote patient health monitoring. The data related with health care applications are primarily private and should be readily available to the users. Enforcing these two constraints in cloud environment is a hard task. Fog computing is an emergent architecture for providing computation, storage, control and network services within user's proximity. To handle private data, the processing elements should be trustable entities in Fog environment. In this paper we propose novel Trust Enforced computation ofFLoading technique for trust worthy applications using fOg computiNg (TEFLON). The proposed system comprises of two algorithms namely optimal service offloader and trust assessment for addressing security and trust issues with reduced response time. And the simulation results show that proposed TEFLON framework improves success rate of fog collaboration with reduced average latency for delay sensitive applications and ensures trust for trustworthy applications.
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