Synthesis and Photocatalytic Activity of Ni–Fe Layered Double Hydroxide Modified Sulphur Doped Graphitic Carbon Nitride (SGCN/Ni–Fe LDH) Photocatalyst for 2,4-Dinitrophenol Degradation

In this work, visible light-active sulphur doped graphitic carbon nitride coupled with Ni–Fe layered double hydroxide (SGCN/Ni–Fe LDH) was prepared through co-precipitation procedure using commercially available thiourea, nickel nitrate, and ferric nitrate. The surface morphology characterization showed LDH crystallite growth onto the surface of SGCN, exploiting the delocalized π-electrons of graphitic structure to attain chemical stability. The synthesized photocatalyst exhibited 98% 2,4-dinitrophenol (DNP) photodegradation within 120 min of visible light irradiations, which was surprisingly high compared to 60 and 55% obtained for bare GCN and Ni–Fe LDH samples. This photo removal efficiency could be due to suitable bandgap energy, layered graphitic and brucite Ni–Fe layered structures, and sufficient pollutant adherence to active sites provided by incorporation of S dopant into bare GCN. The characterization results obtained by cyclic voltammetry graph photoluminescence and electrochemical impedance spectra indicated minimum charge carrier recombination due to the type-II charge transfer route along with an active generation of ?O₂^? and h⁺ as dominant reactive species participating in DNP mineralization into more unaffected inorganic ions. The photocatalytic activity enhanced in an acidic medium at optimized parameters, i.e., pH 4, photocatalyst dosage 50 mg in 50 mL solution, and DNP concentration 1.0?×?10^?4 mol/dm³, due to ionic interactions between negatively charged DNP and positive intercalated structure of Ni–Fe LDH. The as-prepared photocatalyst photodegradation ability was retained after 5 catalytic cycles, confirming its environmentally-compatible usage in water treatment.

     

Static expansion primary vacuum standard — Part 1: Determination of the volume ratio of the expansion stage

Measurement of volume ratio forms an essential part of the characterization of a static expansion system (SES) for the generation of pressures in the medium and high vacuum region. Because of the complexity of the vacuum chambers, it is not possible to determine the absolute volumes of the chambers involved. Hence an expansion process is resorted to, in which the gas enclosed in the small volume at a known pressure is allowed to expand, under isothermal conditions, into the large volume. The accuracy of the volume ratio thus determined depends on the stability of the vacuum gauges used for the measurement of initial and the final pressures and the ambient temperature. High accuracy resonant silicon gauges (RSGs) of two different ranges, one 130 kPa full scale and the other 1 kPa full scale for measurement of the initial and final pressures are found to be useful. This paper reports the results obtained with these RSGs for the measurement of the volume ratio of the National Physical Laboratory, India (NPLI) SES which is nominally 2820. With the use of the RSGs, and calibrated platinum resistance thermometers (PRTs) mounted inside the vacuum chambers, it has been possible to measure the volume ratio within the relative expanded uncertainty (k = 2) of 0.0014. The results of the volume ratio determination exercise are given in this paper while the Part 2 of this paper will discuss the procedure for the calibration of spinning rotor gauges on the Static expansion primary standard.     

Cryoprotective additives and cryostabilisation effects on muscle fillets of the freshwater teleost fish Rohu carp (Labeo rohita) during prolonged frozen storage

The effects of various cryoprotective additives separately and in combination were studied on the myofibrillar protein integrity, biochemical enzyme activity levels and muscle ultrastructure in the freshwater teleost fish Rohu carp (Labeo rohita). Fish muscle samples were divided into eight groups and immersed in different mixtures of cryoprotective additives (S1–S8), then frozen at ? 20 or ? 30 °C for 24 months. Electrophoretic studies revealed early (within 6 months) alteration of the myofibrillar proteins myosin light chain, α‐actinin and tropomyosin. Reduction of the storage temperature from ? 20 to ? 30 °C slowed down the degradative processes. Sodium dodecyl sulfate polyacrylamide gel electrophoresis indicated that fish muscle treated with cryoprotective mixture S8 (40 g L^?1 sorbitol/3 g L^?1 sodium tripolyphosphate/4 g L^?1 sodium alginate) showed minimal post mortem changes in myofibrillar proteins. Ultrastructural results also revealed post mortem damage to the muscle, seen earliest (within 6 months) in the sample frozen‐stored without additives (S2), as compared with the normal, unfrozen muscle (S1). The influence of cryoprotectants alone and in combination on fish muscle structural proteins, myosin and actin filaments (A and I bands), during prolonged frozen storage was investigated. After 12 months, samples frozen‐stored with various cryoprotective additives (S2‐S7), except S8, showed signs of myofibrillar disintegration. Beyond that time the degradative processes started showing up in all samples, with minimal muscle ultrastructural damage in sample S8. Again, reducing the storage temperature from ? 20 to ? 30 °C slowed down the degradative processes. Ultrastructural results correlated well with levels of biochemical enzymes (Ca²⁺ myofibrillar ATPase and succinic dehydrogenase) during frozen storage. This is the first report of the cryoprotective effects of these additives on this popular edible fish species. Of the various combinations of additives tested, cryoprotective mixture S8 was found to preserve the muscle structure longest under frozen storage conditions. However, even this mixture was only effective for 18 months at ? 30 °C. Beyond that time the myofibrillar degradative processes were apparent with correlative electrophoretic, biochemical and ultrastructural studies. Copyright © 2006 Society of Chemical Industry     

A Strong Authentication Scheme with User Privacy for Wireless Sensor Networks

Wireless sensor networks (WSNs) are used for many real‐time applications. User authentication is an important security service for WSNs to ensure only legitimate users can access the sensor data within the network. In 2012, Yoo and others proposed a security‐performance‐balanced user authentication scheme for WSNs, which is an enhancement of existing schemes. In this paper, we show that Yoo and others' scheme has security flaws, and it is not efficient for real WSNs. In addition, this paper proposes a new strong authentication scheme with user privacy for WSNs. The proposed scheme not only achieves end‐party mutual authentication (that is, between the user and the sensor node) but also establishes a dynamic session key. The proposed scheme preserves the security features of Yoo and others' scheme and other existing schemes and provides more practical security services. Additionally, the efficiency of the proposed scheme is more appropriate for real‐world WSNs applications.     

Joining of SiC,alumina, and mullite by the Refractory Metal—Wrap pressure-less process

The aim of this work was to discuss the suitability of the joining process called “RM-Wrap” (RM = Refractory Metals, ie, Mo, Nb, Ta, Zr) as a pressure-less and tailorable technique to join several different ceramics such as SiC, alumina, and mullite (3Al₂O₃.2SiO₂). In the RM-Wrap joining technique the refractory metal foil is used as a wrap containing one or more silicon foils. It is performed at 1450°C, under flowing argon, and the resulting joining materials are in situ formed composites made of refractory metal disilicides (MoSi₂, NbSi₂, TaSi₂, or ZrSi₂) embedded in a silicon-rich matrix; their coefficient of thermal expansion has been calculated and the Laser Flash Method was used to measure the thermal diffusivity of one of them (MoSi₂/Si) in 25°C-1000°C range, then to calculate its thermal conductivity. All the obtained joints are uniform, continuous, and crack free. Some preliminary oxidation tests were carried out on all joints at 1100°C, 6 hours in air, giving unchanged morphology of the interface and the joining materials itself; the joint strength of RM-Wrap joined SiC was measured at room temperature using three different mechanical tests: (a) single lap (SL), (b) single lap off-set (SLO) and (c) torsion on hourglass-shaped samples (THG) (on Mo-wrap joined SiC).     

CT image denoising using NLM and its method noise thresholding

Computed Tomography (CT) is one of the major tools to identify diagnose in medical science. The quality of CT images is dependent of X-ray amount. If X-ray dose is higher, the quality of CT image is better but it may generate bed impact to the patients. Low dose CT images are noisy due to some major reasons such as statistical uncertainty in all physical measurements. If noise can be reduced or removed from low dose CT images, then quality of low dose CT images can be improved without increasing dose. Hence in this paper, a method is proposed in which Non-local means (NLM) filter and wavelet packet based thresholding are processed. For better edge preservation and noise reduction, method noise concept is used. The results of proposed method is analyzed and also compared with some existing methods. From comparative result analysis, it was observed that performance of the proposed scheme is superior to the existing methods in terms of visual quality, Image Quality Index (IQI), PSNR and Entropy Difference (ED).

     

A comprehensive review and analysis of solar forecasting techniques

In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.     

Integrating big data driven sentiments polarity and ABC-optimized LSTM for time series forecasting

Stock market is a dynamic and volatile market that is considered as time series data. The growth of financial data exposed the computational efficiency of the conventional systems. This paper proposed a hybrid deep learning model based on Long Short- Term Memory (LSTM) and Artificial Bee Colony (ABC) algorithm. ABC is best fit for hyper parameter selection for deep LSTM models and maintains the equilibrium of exploitation and exploration issues. Handling a large volume of multidimensional reviews from social media is a major challenge. This paper evolves the multiple aspects of market sentiments and uses the reliable Big data platform Hadoop ecosystem and its services to compute sentiment polarity index. The ABC-LSTM hybrid model is validated with other core and hybrid models with evolutionary algorithms as Differential Evolution (DE) and Genetic Algorithm (GA). For the experiments, 10 years of historical datasets and social media reviews of IT sector funds Apple Inc. (AAPL), Microsoft corporation (MSFT) and Intel corporation (INTL) from NASDAQ GS, an American stock exchange are considered to validate hybrid forecasting models. Proposed algorithm ABC-LSTM is used to tune the hyperparameters (window size, LSTM units, dropout probability, epochs, batch size and learning rate) and evaluated through Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as loss function. Performance analysis proves that with sentiment polarity, ABC optimized LSTM obtains improved forecasting accuracy over its counterpart models.