Study of Adsorption/Desorption Effect of 2-Mercaptobenzothiazole as Sweet Corrosion Inhibitor on API-5L X60 Steel

Protection of Metals and Physical Chemistry of Surfaces - In this study, the inhibitive performance of 2-mercaptobenzothiazole (2MBT) and 2-aminobenzothiazole (2ABT) were investigated on API-5L X60...     

Bayesian estimation based on vague lifetime data

In Classical Bayesian approach, estimation of lifetime data usually is dealing with precise information. However, in real world, some informations about an underlying system might be imprecise and represented in the form of vague quantities. In these situations, we need to generalize classical methods to vague environment for studying and analyzing the systems of interest. In this paper, we propose the Bayesian estimation of failure rate and mean time to failure based on vague set theory in the case of complete and censored data sets. To employ the Bayesian approach, model parameters are assumed to be vague random variables with vague prior distributions. This approach will be used to induce the vague Bayes estimate of failure rate and mean time to failure by introducing and applying a theorem called “Resolution Identity” for vague sets. In order to evaluate the membership degrees of vague Bayesian estimate for these quantities, a computational procedure is investigated. In the proposed method, the original problem is transformed into a nonlinear programming problem which is then divided into eight subproblems to simplifying computations.     

A potential trackable bone filler: Preparation and characterization

This study reported the synthesis of fluorescent hydroxyapatite/alginate/carbon quantum dots (HA/Alg/CQDs) nanocomposites via the co-precipitation technique. The N-doped CQDs as a new class of fluorescent materials were prepared by the citric acid pyrolysis method, with an average size around 4 nm. Physical, chemical, and optical properties of the synthesized nanocomposites were investigated by X-ray diffraction (XRD), Fourier-transformed infrared spectroscopy (FTIR), atomic force microscopy (AFM), field-emission scanning electron microscopy (FESEM), UV–visible spectroscopy, and photoluminescence (PL) spectroscopy, respectively. The PL spectroscopy data verified the favorable in vitro luminescent emission of the HA/Alg/CQDs nanocomposites in comparison with HA/Alg and HA samples. The XRD patterns of the prepared samples confirmed the formation of crystalline HA in all composites, possessing a Ca/P ratio around 1.5 as obtained by EDX elemental analysis. The FESEM analysis exhibited HA nanoplates that homogeneously distributed throughout the alginate matrix. Therefore, the synthesized nanocomposites could be regarded as potential trackable drug carriers for hard tissue engineering applications.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.     

A Novel Magnetic Hardening Mechanism for Nd-Fe-B Permanent Magnets Based on Solid-State Phase Transformation

Permanent magnets based on neodymium-iron-boron (Nd-Fe-B) alloys provide the highest performance and energy density, finding usage in many high-tech applications. Their magnetic performance relies on the intrinsic properties of the hard-magnetic Nd₂Fe₁₄B phase combined with control over the microstructure during production. In this study, a novel magnetic hardening mechanism is described in such materials based on a solid-state phase transformation. Using modified Nd-Fe-B alloys of the type Nd₁₆Fe_bal-x-y-zCo_xMo_yCu_zB₇ for the first time it is revealed how the microstructural transformation from the metastable Nd₂Fe₁₇B_x phase to the hard-magnetic Nd₂Fe₁₄B phase can be thermally controlled, leading to an astonishing increase in coercivity from ≈200 kAm⁻¹ to almost 700 kAm⁻¹. Furthermore, after thermally treating a quenched sample of Nd₁₆Fe₅₆Co₂₀Mo₂Cu₂B₇, the presence of Mo leads to the formation of fine FeMo₂B₂ precipitates, in the range from micrometers down to a few nanometers. These precipitates are responsible for the refinement of the Nd₂Fe₁₄B grains and so for the high coercivity. This mechanism can be incorporated into existing manufacturing processes and can prove to be applicable to novel fabrication routes for Nd-Fe-B magnets, such as additive manufacturing.     

Nanocrystalline Nd–Fe–B Anisotropic Magnets by Flash Spark Plasma Sintering

Flash spark plasma sintering (flash SPS) is an attractive method to obtain Nd–Fe–B magnets with anisotropic magnetic properties when starting from melt-spun powders. Compared to the benchmark processing route via hot pressing with subsequent die upsetting, flash SPS promises electroplasticity as an additional deformation mechanism and reduced tool wear, while maximizing magnetic properties by tailoring the microstructure—fully dense and high texture. A detailed parameter study is conducted to understand the influence of Flash SPS parameters on the densification and magnetic properties of commercial MQU-F powder. It is revealed that the presintering conditions and preheating temperature before applying the power pulse play a major role for tailoring grain size and texture in the case of hot deformation via Flash SPS. Detailed microstructure and magnetic domain evaluation disclose the texture enhancement with increasing flash SPS temperature at the expense of coercivity. The best compromise between remanence and coercivity (1.37 T and 1195 kA m⁻¹, respectively) is achieved through a combination of presintering at 500 °C for 120 s and preheating temperature of 600 °C, resulting in a magnet with energy product (BH)_max of 350 kJm⁻³. These findings show the potential of flash SPS to obtain fully dense anisotropic nanocrystalline magnets with high magnetic performance.     

Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.     

Effect of chemical environments on palladium phthalocyanine thin film sensors for humidity analysis

Chemiresistors based on palladium phthalocyanine (PdPc) thin films were investigated as humidity sensors. The samples were thermally evaporated onto gold electrodes with a thickness about 100 nm. Optical and electrical characteristics of PdPc thin films were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and electrical measurements. The SEM image demonstrates PdPc (30–60 nm) nanosized particles, and XRD pattern shows that thin films are in α-phase at room temperature. Electrical measurements also confirm that PdPc exhibit semiconducting and photoconducting behaviors, and thermal activation energies of thin films were calculated. After that, the sensitivity and reversibility of devices were investigated on exposure to 20–90% RH in various chemical environments at 293 and 323 K. The response time (35–45 s) and recovery time (75–105 s) of sensors were measured at 293 K with respect to different chemical environments. At last, the stability of devices versus different RH% and chemical environments were tested. The sensors show very good stability on exposure to RH for a period of 2 months but their stability has been reduced in ethanol, acetone, and ammonia environments.