Modelling the yield point phenomena during deformation at elevated temperatures: case study on Inconel 600

The discontinuous yield behaviour (DYB) of Inconel 600 was studied during hot compression tests at temperatures in range of 850–1150°C and strain rates of 0.001–1?s^?1. The yield point phenomena were observed in the temperature range of 850–1000°C and strain rates of 0.001–0.1 s^?1. The DYB was modelled by considering the evolution of dislocation density at the early stages of yielding. The opposite effects of dislocation multiplication, dislocation interaction (work hardening) and dynamic recovery (DRV) were considered. It was shown that the dislocation multiplication and DRV result in flow softening, while the dislocation interaction leads to work hardening. The model was established in a way to consider the effects of various microstructural evolutions on the σ(ε) function. The discontinuous flow curves were fitted by the developed model with acceptable precision. The variations of material constants with temperature and strain rate were found physically meaningful. The dislocation multiplication parameter was determined at various temperatures and strain rates. It was concluded that the rate of dislocation multiplication increases as temperature rises or strain rate declines. Accelerated dislocation multiplication leads to less drop in yield stress between the upper and lower yield points.     

Application of ionic liquid-based ultrasonic-assisted microextraction coupled with HPLC for determination of citalopram and nortriptyline in human plasma

In this paper, an effective and environmentally friendly method of ultrasound-assisted ionic liquid-based dispersive liquid–liquid microextraction (UA-IL-DLLME) combined with high-performance liquid chromatography (HPLC)–photodiode array detector was applied for extraction and determination of two antidepressant drugs citalopram hydrobromide and nortriptyline hydrochloride from human plasma samples. Several important parameters affect the steps and efficiency of extraction, some of which are sample solution’s pH, type and volume of ionic liquid, ultrasonic time, centrifuging time and rate, and the ionic strength of solution. Optimum conditions were obtained at pH?=?11, 1-octyl-3-methyl imidazolium hexafluorophosphate for ionic liquid, 55?µL for ionic liquid volume, 4?min for ultrasonic time, 5?min and 3,500?rpm for centrifuging time and rotation’s speed, due to ionic strength by the addition of NaCl 1%. Under optimized conditions, the linearity was obtained in the range of 0.02–2,000?µg/L, with correlation coefficients higher than 0.995. The limits of detection were 10?µg/L for citalopram and 6?µg/L for nortriptyline. Preconcentration factors were 920 for citalopram and 800 for nortriptyline. The present method of UA-IL-DLLME combined with HPLC was successfully used for the determination of citalopram and nortriptyline drugs in real samples of human plasma.     

Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

CRISPR/Cas9 is a powerful genome-editing technology that has been widely applied in targeted gene repair and gene expression regulation. One of the main challenges for the CRISPR/Cas9 system is the occurrence of unexpected cleavage at some sites (off-targets) and predicting them is necessary due to its relevance in gene editing research. Very few deep learning models have been developed so far to predict the off-target propensity of single guide RNA (sgRNA) at specific DNA fragments by using artificial feature extract operations and machine learning techniques; however, this is a convoluted process that is difficult to understand and implement for researchers. In this research work, we introduce a novel graph-based approach to predict off-target efficacy of sgRNA in the CRISPR/Cas9 system that is easy to understand and replicate for researchers. This is achieved by creating a graph with sequences as nodes and by using a link prediction method to predict the presence of links between sgRNA and off-target inducing target DNA sequences. Features for the sequences are extracted from within the sequences. We used HEK293 and K562 t datasets in our experiments. GCN predicted the off-target gene knockouts (using link prediction) by predicting the links between sgRNA and off-target sequences with an auROC value of 0.987.     

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia.     

Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-Based Recommendation in E-Commerce

Boosting the sales of e-commerce services is guaranteed once users find more items matching their interests in a short amount of time. Consequently, recommendation systems have become a crucial part of any successful e-commerce service. Although various recommendation techniques could be used in e-commerce, a considerable amount of attention has been drawn to session-based recommendation systems in recent years. This growing interest is due to security concerns over collecting personalized user behavior data, especially due to recent general data protection regulations. In this work, we present a comprehensive evaluation of the state-of-the-art deep learning approaches used in the session-based recommendation. In session-based recommendation, a recommendation system counts on the sequence of events made by a user within the same session to predict and endorse other items that are more likely to correlate with their preferences. Our extensive experiments investigate baseline techniques (e.g., nearest neighbors and pattern mining algorithms) and deep learning approaches (e.g., recurrent neural networks, graph neural networks, and attention-based networks). Our evaluations show that advanced neural-based models and session-based nearest neighbor algorithms outperform the baseline techniques in most scenarios. However, we found that these models suffer more in the case of long sessions when there exists drift in user interests, and when there are not enough data to correctly model different items during training. Our study suggests that using the hybrid models of different approaches combined with baseline algorithms could lead to substantial results in session-based recommendations based on dataset characteristics. We also discuss the drawbacks of current session-based recommendation algorithms and further open research directions in this field.     

Finding susceptible areas for a 50 MW solar thermal power plant using 4E analysis and multiobjective optimization

Journal of Thermal Analysis and Calorimetry - In the present study, a power plant design was first carried out using thermo flow software. Energy, exergy, economic, environmental, and economic (4E)...     

Amberlyst-15 as a recyclable heterogeneous catalyst for synthesis of N-tert-butylamides via Ritter-type reaction

Highly efficient method for the preparation of N-tert-butylamides by reaction of nitriles with tert-butylacetate is described using Amberlyst-15 as a recyclable heterogeneous catalyst.Selective amidation of benzonitrile in the presence of acetonitrile was also achieved.     

Chemical durability of lead silicate glass in HNO3, HCl and H2SO4 aqueous acid solutions

Acid dissolution of silicate glasses with different lead contents was rigorously investigated. Aqueous solutions containing 0.5, 1, and 2 N HNO₃, HCl and H₂SO₄ were used to measure the durability of the glass probes. Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), Inductively Coupled Plasma (ICP), X-ray Diffraction (XRD) and weight loss analyses were used to evaluate the morphological/compositional changes of the probes, the ash deposit, and the aqueous solutions produced due to the dissolution of the glass specimens. Empirical results showed that any increase in the lead content of the probes deteriorated the durability of the glasses by accelerating the hydrolysis of the silica network. ZrO₂ and TiO₂ additions had inverse effect and improved the chemical durability and the practical life-time of the lead glasses.