Effect of Volumetric Energy Density on Microstructure and Properties of Grade 300 Maraging Steel Fabricated by Laser Powder Bed Fusion

Metallurgical and Materials Transactions A - This study investigated the effect of laser powder bed fusion process parameters on the microstructure and properties of maraging steel. The results...     

Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

About 170 nations have been affected by the COvid VIrus Disease-19 (COVID-19) epidemic. On governing bodies across the globe, a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive, and they feel challenging to tackle this situation. Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations. In the previous works, Long Short-Term Memory (LSTM) was used to predict future COVID-19 cases. According to LSTM network data, the outbreak is expected to finish by June 2020. However, there is a chance of an over-fitting problem in LSTM and true positive; it may not produce the required results. The COVID-19 dataset has lower accuracy and a higher error rate in the existing system. The proposed method has been introduced to overcome the above-mentioned issues. For COVID-19 prediction, a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach is presented. In this suggested research study, the COVID-19 predicting dataset is employed as an input, and the min-max normalization approach is employed to normalize it. Optimum features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization (LDIWCSO) algorithm, enhancing the accuracy of classification. The Cat Swarm Optimization (CSO) algorithm’s convergence is enhanced using inertia weight in the LDIWCSO algorithm. It is used to select the essential features using the best fitness function values. For a specified time across India, death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network (HBDCNN) technique based on selected features. As demonstrated by empirical observations, the proposed system produces significant performance in terms of f-measure, recall, precision, and accuracy.     

A survey of perfluorooctane sulfonate and related perfluorinated organic compounds in water,fish, birds,and humans from Japan

Occurrence of perfluorooctane sulfonate (PFOS) in the tissues of humans and wildlife is well documented. In this study, concentrations and distribution of PFOS, perfluorohexane sulfonate (PFHS), and perfluorobutane sulfonate (PFBS) were determined in samples of surface water, fish and bird blood and livers, and human blood collected in Japan. Notable concentrations of PFOS were found in surface water and fish from Tokyo Bay. PFOS was found in all of the 78 samples of fish blood and liver analyzed. Based on the concentrations of PFOS in water and in fish livers, bioconcentration factors were calculated to range from 274 to 41 600. Concentrations of PFOS in the blood of Japanese human volunteers ranged from 2.4 to 14 ng/mL. PFHS was detected in 33% of the fishes analyzed, at concentrations severalfold less than those of PFOS.     

Contribution of known endocrine disrupting substances to the estrogenic activity in Tama River water samples from Japan using instrumental analysis and in vitro reporter gene assay

To quantitatively characterize the substances contributing to estrogenic activity in river water, in vitro bioassay using MVLN cells and instrumental analysis using liquid chromatograph–mass spectrometer (LC/MS) or liquid chromatograph–tandem mass spectrometer (LC/MS/MS) were applied to river water extracts taken from various locations in the Tama River, Japan. Tama River water samples were extracted using solid phase extraction and the crude extracts were fractionated by high-performance liquid chromatography (HPLC) into 10 fractions. The sixth fraction contained nonylphenol (NP) and octylphenol (OP) at concentrations in the range of 51.6–147 and 6.9–81.9 ng/L, respectively (concentrations corresponding to the original sample volumes). No estrogenic activity, expressed as 17β-estradiol equivalents (E2-EQ_B), however, was observed in this fraction (<0.6 ng-E2eq/L). Instrumentally determined estrogenic activity (E2-EQ_C), which is the concentrations of NP and OP multiplied by their corresponding relative potency, was below the detection limit of the MVLN cell bioassay. Estrogenic activities were detected only in HPLC fraction nos. 7, 8 and 9. Estrone (E1), estradiol (E2) and bisphenol A (BPA) were detected in these fractions. Estriol (E3) and ethynylestradiol (EE2) were not detected (<0.2 ng/L) in these fractions. The calculated E2-EQ_C for BPA was below the detection limit of bioassay. The E2-EQ_C for E1 and E2 were on the same order as the estrogenic activity determined by the bioassay (E2-EQ_B). The ratios of E2-EQ_C and E2-EQ_B for E1 and E2 in the three factions collectively (nos. 7–9) were 0.49–0.97 and 0.29–1.12, respectively. Above results indicated that the major causal substances to the estrogenic activity in the Tama River were E1 and E2.     

Multi-objective optimization of feature selection using hybrid cat swarm optimization

With the pervasive generation of information from a wide range of sensors and devices, there always exist a large number of input features in databases, thus complicating machine learning problem formulation. However, certain features are relatively impertinent to specific problems, which may degrade the performances of classifiers in terms of prediction accuracy, sensitivity,specificity, and recall rate. The main goal of a multi-objective optimization problem is to identify the subsets of the given features. To this end, a hybrid cat swarm optimization(HCSO) algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO) that incorporates guided and competitive inherent characteristics into the original CSO. The performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark datasets. The number of class labels for these datasets varies between 2 and 40. The time complexity analysis of both CSO and HCSO has also been evaluated. Moreover, the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art techniques. The performances obtained by HCSO have an average 2.68% improvement with a standard deviation of 2.91.The maximum performance improvement is up to 10.09% in prediction accuracy. Tested on the same datasets, CSO has yielded improvements within the range of-7.27% to 8.51% with an average improvement 0.9% and standard deviation 3.96. The statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.     

Simultaneous accommodation of Dy3+ at the lattice site of β-Ca3(PO4)2 and t-ZrO2 mixtures: Structural stability,mechanical, optical,and magnetic features

Structurally stable β-Ca₃(PO₄)₂/t-ZrO₂ composite mixtures with the aid of Dy³⁺ stabilizer were accomplished at 1500°C. The precursors comprising Ca²⁺, P⁵⁺, Zr⁴⁺, and Dy³⁺ have been varied to obtain five different combinations. The results revealed the fact that complete phase transformation of calcium-deficient apatite to β-Ca₃(PO₄)₂ occurred only at 1300°C, whereas the evidence of t-ZrO₂ crystallization is obvious at 900°C. The dual occupancy of Dy³⁺ at β-Ca₃(PO₄)₂ and t-ZrO₂ structures was evident; however, Dy³⁺ initially prefers to occupy β-Ca₃(PO₄)₂ lattice until its saturation limit and thereafter accommodates at the lattice site of ZrO₂. The typical absorption and emission behavior of Dy³⁺ were noticed in all the systems and, moreover, the surrounding symmetry of Dy³⁺ domains has been determined from the luminescence study. All the systems ensured paramagnetic response that is generally contributed by the presence of Dy³⁺. A gradual increment in the phase content of t-ZrO₂ in the composite mixtures ensured a significant improvement in the hardness and Young's modulus of the investigated compositions.     

Experimental Investigation and Simulation of Magnetic Flux Leakage from Metal Loss Defects

Journal of Failure Analysis and Prevention - Magnetic flux leakage (MFL) principle is widely used in detecting defects in cross-country pipelines. The tools based on the MFL techniques termed as...     

Identification of Inverse Bainite in Fe-0.84C-1Cr-1Mn Hypereutectoid Low Alloy Steel

A unique dilatation trend is observed for isothermal bainite transformation in Fe-0.84 pct C-1 pct Cr-1 pct Mn steel. The dilatation is found to occur in two stages with volumetric contraction dominating the first stage, followed by volumetric expansion dominating the second stage. Through electron microscopic characterization, bainitic microstructure is identified as inverse bainite with cementite (Fe₃C) nucleating first from supersaturated austenite followed by the transformation of ferrite and secondary carbides (Fe₃C, Fe₂C, and Fe₅C₂) from carbon-depleted austenite.     

The iterative contribution and relevance of modelling to UK energy policy

This paper discusses the iterative provision of modelling insights on long-term decarbonisation scenarios for UK energy policy makers. A multi-year model construction process of the UK MARKAL-Macro-hybrid energy-economic model, and four subsequent major policy analyses illustrates the scope of this interaction. The initial set of modelling runs focused on the technical feasibility of long-term 60% carbon dioxide (CO₂) reduction scenarios, the role of key technologies, and the underlying uncertainties. Furthermore subsequent modelling studies were aimed to generate insights on more stringent targets, and on issues and uncertainties that may make targets harder to achieve. Hence, this paper analyses the large number of long-term UK CO₂ reduction scenarios through a clustering approach on target stringency and barriers to implementation. Robust findings and key uncertainties are highlighted, including the critical role of the power sector, trade-offs between resources, sectors, key energy technologies and behavioural responses, and the increasing level and spread of CO₂ marginal prices and GDP impacts. The relevance and use of modelling insights to the UK energy policy process is shown in the continuation of the energy modelling–policy interface. This constitutes both ongoing model development, and nuanced scenario analysis designed to further explore key uncertainties in evolving policy issues.     

Anorexic contribution to increased linoleate mobilization and oxidation in lymphoma-bearing mice

To test for a regulatory defect in adipose triacylglycerol (essential) fatty acid mobilization in lymphoma-bearing mice, free [1-¹⁴C]linoleic acid/mouse serum albumin was injectediv into lymphoma-bearing and control mice, adapted to a reversed light cycle, and studied in three dietary states in the dark period. Mean daily food intake decreased in mice with small and large tumor burdens. Plasma free fatty acid (FFA) oxidation rates, which approximate FFA mobilization rates, were estimated by multicompartmental analysis (CONSAM). Oxidation of linoleate to CO₂ was reduced significantly (85%) inad libitum fed as compared to briefly fasted control mice but not in fedvs. fasted mice with large or small tumor burdens. However, plasma FFA oxidation rates to CO₂ did not differ in briefly fasted tumor-bearing and pairfed control mice. When re-fed a 250-mg test meal, briefly fasted mice with small tumors suppressed plasma free linoleic acid oxidation, as did controls. During simulated night, mildly anorexic, tumor-bearing mice with small tumor burdens mobilized essential fatty acids much faster than controls. This could explain body fat loss. The abnormally rapid rates of FFA (free linoleic acid) mobilization at night probably result from anorexia rather than from inability of food to suppress fat mobilization.