Effect of hydrothermal duration on synthesis of WO<Subscript>3</Subscript> nanorods

Among different type of transition metal oxides, tungsten trioxide (WO₃) is a suitable candidate for electronic device fabrication due to its n-type property and wide band gap. Herein, one-dimensional tungsten trioxide (WO₃) nanorods were achieved from an aqueous solution of sodium tungstate dihydrate (Na₂WO₄·2H₂O) and sodium chloride (NaCl) in an acidic media by a time-optimized hydrothermal synthesis in autoclave at 180°C or different synthesis durations. For studying morphology and size of obtained powder, X-ray diffraction (XRD), scanning electron microscope (SEM), and high resolution transmission electron microscope (HRTEM) were applied. Finally, WO₃ nanorods of about 2–3 μm in length and 100–200 nm in diameter were obtained during 3 h hydrothermal process.     

Optimization of optical absorption coefficient in double modified Pöschl–Teller quantum wells

In this work, the intersubband optical absorption coefficient in double quantum wells (QWs) represented by a modified Pöschl–Teller confining potential is optimized. This potential is well suited for such purposes as it can easily become asymmetrical by a correct choice of its parameters set. By using the particle swarm optimization algorithm and numerical solution of the Schrödinger equation, accompanied with the perturbation theory, we found the optimal structure that the optical absorption coefficient is maximum. Applying this algorithm to double modified Pöschl–Teller QWs structure shows that the total optical absorption coefficient for this structure is \(1.22\times 10^6\, \hbox {m}^{-1}\) . Then, the refractive index changes is investigated by using the density matrix method.     

Identification of volatile compounds in soymilk using solid-phase microextraction-gas chromatography

Headspace solid-phase microextraction (HS-SPME) gas chromatography was used to analyze volatile compounds in soymilk. The effect of incubation temperature (30–70 °C) and time (5–60 min), sample volume (0.5–5 ml), and type of SPME fiber (65 μm CWAX–DVB, 70 μm PDMS–DVB and 85 μm CAR–PDMS) were studied. All the factors markedly affected sensitivity and selectivity. Among the three fibers tested, the CAR–PDMS fiber had greater sensitivity to a more diverse range of volatile compounds, followed by PDMS–DVB and CWAX–DVB fibers using both soymilk and water with added volatiles as a matrix. SPME optimization conducted using a water matrix with added known soy volatiles, showed the following conditions to be optimal for selectivity and sensitivity: incubation temperature of 40 °C, incubation time of 20 min, and sample volume of 5 ml (for volatile compound concentration of ∼25 ppm). The selected conditions were used for the analysis of volatiles in six commercial soymilk samples. A total of 30 volatile compounds were identified. The results showed significant differences in the total volatiles of the soymilk products. The repeatability of measurements of total volatiles compounds of soymilk was ∼5.4% for four replicate analyses. Similar volatile compounds were present in all the samples analyzed but at different concentrations. The method proposed is simple and can be used to measure both hexanal and/or total volatiles in soymilk samples.     

Robust nonlinear MPC without terminal constraint for tracking changing setpoints in the presence of non-additive unknown disturbance

This paper develops a novel robust tracking model predictive control (MPC) without terminal constraint for discrete-time nonlinear systems capable to deal with changing setpoints and unknown non-additive bounded disturbances. The MPC scheme without terminal constraint avoids difficult computations for the terminal region and is thus simpler to design and implement. However, the existence of disturbances and/or sudden changes in a setpoint may lead to feasibility and stability issues in this method. In contrast to previous works that considered changing setpoints and/or additive slowly varying disturbance, the proposed method is able to deal with changing setpoints and non-additive non-slowly varying disturbance. The key idea is the addition of tightened input and state (tracking error) constraints as new constraints to the tracking MPC scheme without terminal constraints based on artificial references. In the proposed method, the optimal tracking error converges asymptotically to the invariant set for tracking, and the perturbed system tracking error remains in a variable size tube around the optimal tracking error. Closed-loop input-to-state stability and recursive feasibility of the optimization problem for any piece-wise constant setpoint and non-additive disturbance are guaranteed by tightening input and state constraints as well as weighting the terminal cost function by an appropriate stabilizing weighting factor. The simulation results of the satellite attitude control system are provided to demonstrate the efficiency of the proposed predictive controller.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Migration of Bisphenol-A into packaged food simulants: Development of a novel MAE-LDS-ME strategy for the investigation of storage time and temperature

In this research, migration of bisphenol A from epoxy resin into 64 food simulants including tuna fish cans, eggplant cans and cherries cans have been measured. The effective of storage time in the range 0–180 days and storage temperature at 25 and 45°C were investigated. Microwave-assisted extraction and low-density solvent based on microextraction followed by high-performance liquid chromatography has been applied to analyze migrated-BPA. The limit of detection 2.7 ng ml⁻¹ and limit of quantification 9.33 ng ml⁻¹ were obtained for the proposed method. The results confirmed that the migration of BPA has been increased with raising the storage time and temperature. Also, the migration of BPA in oily simulant was higher than acidic simulant. The migrated-BPA concentration range has been measured in the range 3.42–4165 ng ml⁻¹. The highest amount of migrated-BPA has been found for simulant of tuna fish cans at 45°C and the lowest amount of migrated-BPA has been detected for simulant of cherries cans at 25°C.     

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.     

Conflict management techniques for model merging: a systematic mapping review

Software and Systems Modeling - Model merging conflicts occur when different stakeholders aim to integrate their contradicting changes that are applied concurrently to update software models. We...