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.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

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.     

An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

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.     

Martensitic transformations in Ti-(16–26 at%) Nb alloys

Martensitic phase transformations in the solution-treated and water-quenched binary Ti-Nb alloys in the range of 16–26 at% Nb, were examined. An ordered, base-centred orthorhombic martensite was observed for alloys containing up to 23.4 at% Nb. The substructure of this martensite was generally composed of twins and stacking faults, the presence of antiphase boundaries observed in the plates indicating that the martensite underwent ordering during quenching. Both order-disorder and M _s temperatures were observed to be affected by total interstitial content, higher contents increasing both temperatures. Increasing the niobium content to above 23.4% resulted in retention of the phase, this phase containing either athermal or diffuse depending upon niobium and total interstitial concentration. Finally, the microhardness of the Ti-Nb alloys examined was observed to decrease with increase in niobium and decrease in total interstitial content.     

Dynamic storage and access load balancing for answering range queries in peer-to-peer networks

Recently, many applications have used Peer-to-Peer (P2P) systems to overcome the current problems with client/server systems such as non-scalability, high bandwidth requirement and single point of failure. In this paper, we propose an efficient scheme to support efficient range query processing over structured P2P systems, while balancing both the storage load and access load. The paper proposes a rotating token scheme to balance the storage load by placing joining nodes in appropriate locations in the identifier space to share loads with already overloaded nodes. Then, to support range queries, we utilize an order-preserving mapping function to map keys to nodes in order preserving way and without hashing. This may result in an access load imbalance due to non-uniform distribution of keys in the identifier space. Thus, we propose an adaptive replication scheme to relieve overloaded nodes by shedding some load on other nodes to balance the access load. We derive a formula for estimating the overhead of the proposed adaptive replication scheme. In this study, we carry simulation experiments with synthetic data to measure the performance of the proposed schemes. Our simulation experiments show significant gains in both storage load balancing and access load balancing.