Study of Magnetic Properties of Fe-Doped CuO: Monte Carlo Simulations

Using Monte Carlo simulations, we are studying the magnetic properties of Fe-doped CuO thin films. The total magnetizations and the susceptibilities are studied as a function of the effect doping, external magnetic field, and exchange coupling. The critical temperature is discussed as a function of the effect of iron concentration. On the other hand, we investigate the effect of increasing temperatures on the coercive field for a constant value of exchange coupling and a fixed concentration. The coercive magnetic field is found to decrease with increasing temperature values until reaching its null value. The effect of increasing the exchange coupling amount on the saturation magnetic field H _s is illustrated. A linear growth of the saturation magnetic field is found as a function of the exchange coupling interaction. To complete this study, we presented and discussed the magnetic hysteresis cycle loops.     

On dual-phase-lag thermoelasticity theory with memory-dependent derivative

A new mathematical model of generalized thermoelasticity with memory-dependent derivatives for the dual-phase-lag heat conduction law is constructed. The governing equations of the new model are applied to a half-space subjected to ramp-type heating. Laplace transforms technique is used. The solution is obtained for different types of functions representing the thermal shock and for different values of the parameter of the time fraction derivative of the model. The effects of time-delay and arbitrary kernel function on elastic material are studied and represented graphically. The predictions of the theory are discussed and compared with dynamic classical coupled theory.     

Effect of initial composition on phase selection in Ni–Si powder blends processed by mechanical alloying

The effect of initial powder blend composition on the synthesis and formation mechanism of nickel silicide phases was investigated by mechanical alloying in Ni-60 and Ni-66.7?at.% Si powder blends. It was noted that the equilibrium NiSi phase started to form in the early stages of milling and that the amount of the NiSi phase in the milled powder increased with increasing milling time. Even though, under equilibrium conditions, a mixture of both the NiSi and NiSi₂ phases was expected to be present in the Ni-60?at.% Si composition and the stoichiometric NiSi₂ phase in the Ni-66.7?at.% Si composition, the NiSi phase was present in both the compositions investigated. However, while only the NiSi phase was present homogeneously in the Ni-60?at.% Si powder blend, both the NiSi phase and a very small amount of unreacted Si were present in the powder blend of Ni-66.7?at.% Si composition. This unexpected phase constitution in the milled powders was attributed to a partial loss of Si during mechanical alloying of the powder blends, confirmed by energy dispersive X-ray spectrometer analyses, and explained on a thermodynamic basis.     

Assessing abrasion performance of self-consolidating concrete containing synthetic fibers using acoustic emission analysis

The key objective of this investigation was to evaluate the abrasion resistance of self-consolidating concrete (SCC) with and without synthetic fibers (SynFs). The abrasion resistance of normal concrete was also investigated in this study for comparison. The abrasion test was performed on concrete specimens according to the rotating-cutter method along with continuous monitoring of acoustic emission (AE) using attached AE sensors. The effects of changing concrete type and incorporating various types (flexible and semi-rigid) and lengths of SynFs on the abrasion behaviour were investigated with the aid of AE analysis. AE signal characteristics such as amplitude, signal strength, number of hits, and duration were gathered during testing. Furthermore, the collected AE data was used to complete b-value analysis as well as intensity analysis resulting in three additional parameters: b-value, severity (S_r), and historic index (H(t)). The results showed that the AE parameters were directly correlated with the abrasion damage in all tested mixtures. Adding SynFs to all SCC mixtures enhanced their abrasion resistance. The flexible fibers variety exhibited better abrasion performance on average than the semi-rigid fibers. Meanwhile, longer fibers showed lower abrasion resistance than the shorter ones with the same type. The results also indicated that AE intensity analysis was able to determine the ranges for H(t) and S_r that identify the extent of damage due to abrasion of SynF-reinforced SCC.     

Effect of Mn-Bearing Dispersoids on Tensile Properties and Recrystallization Resistance of Al-3Mg-0.8Mn Rolled Alloy

Herein, the precipitation behavior of Mn-bearing dispersoids in Al-3Mg-0.8Mn (AA5454) alloy subjected to different heat treatments is investigated. The effects of Mn-bearing dispersoids on the tensile properties and recrystallization resistance of the abovementioned alloy during hot/cold rolling and postrolling annealing are evaluated. The results show that a low-temperature three-step heat treatment (275 °C/12 h + 375 °C/48 h + 425 °C/12 h) generates a higher number density of Mn-bearing dispersoids with finer sizes compared with the typical high-temperature heat treatment (430 °C/2 h + 480 °C/2 h + 525 °C/2 h), thus resulting in significantly improved tensile strengths of hot/cold-rolled sheets. After annealing at 300 °C, the yield strength (YS) of the alloy reached 196 MPa after hot rolling and 237 MPa after cold rolling, showing an improvement of 30%–32% over samples subjected to high-temperature heat treatment. In addition, the low-temperature heat treatment provides a higher recrystallization resistance after hot and cold rolling owing to the higher number density of Mn-bearing dispersoids and the lower fraction of dispersoid-free zones. The YS contributions of various strengthening components after hot and cold rolling are discussed based on constitutive models. The predicted yield strengths agree well with the experimental values.     

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.     

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

Infrared Spectroscopy-Based Chemometric Analysis for Lard Differentiation in Meat Samples

One of the most pressing concerns for the consumer market is the detection of adulteration in meat products due to their preciousness. The rapid and accurate identification mechanism for lard adulteration in meat products is highly necessary, for developing a mechanism trusted by consumers and that can be used to make a definitive diagnosis. Fourier Transform Infrared Spectroscopy (FTIR) is used in this work to identify lard adulteration in cow, lamb, and chicken samples. A simplified extraction method was implied to obtain the lipids from pure and adulterated meat. Adulterated samples were obtained by mixing lard with chicken, lamb, and beef with different concentrations (10%–50% v/v). Principal component analysis (PCA) and partial least square (PLS) were used to develop a calibration model at 800–3500 cm⁻¹. Three-dimension PCA was successfully used by dividing the spectrum in three regions to classify lard meat adulteration in chicken, lamb, and beef samples. The corresponding FTIR peaks for the lard have been observed at 1159.6, 1743.4, 2853.1, and 2922.5 cm⁻¹, which differentiate chicken, lamb, and beef samples. The wavenumbers offer the highest determination coefficient R² value of 0.846 and lowest root mean square error of calibration (RMSEC) and root mean square error prediction (RMSEP) with an accuracy of 84.6%. Even the tiniest fat adulteration up to 10% can be reliably discovered using this methodology.