Poly(-hydroxybutyrate), PHB is a bacterial polyester known for its excellent bone compatibility, however, the material lacks blood and tissue compatibility. Poly(ethylene glycol), PEG,-modulated fermentation of Alcaligenes latus and Azotobacter vinelandii UWD was employed to yield copolymers consisting of PHB and PEG that exhibit diminished cell-adhesion surface properties. PEGs with molecular weights of 3400, 2000, and 400 as well as diethylene glycol, DEG, and pentaerythritol ethoxylate, PEE, were used in a concentration of 2% (w/v) for amending the fermentation broths. This modulation of the fermentation conditions did not influence polymer yields. However, the resulting copolymers had drastically reduced molecular weights, 82% less for the DEG-amended fermentation of A. latus. The reduction in molecular weight was attributed to an end-capping reaction of the nascent PHB-chain with PEG and/or early chain termination by water facilitated by the presence of the highly hydrophilic PEG-molecules. The formation of a covalent linkage was proven unambiguously by H-NMR-spectroscopic methods only for the copolymers obtained in the DEG-modified fermentations of both strains. Cell growth experiments using SK-MEL 28 and MDA-MB 231 cells were used for the evaluation of polymer-cell interaction. Copolymer films obtained from PEG-modulated syntheses showed significantly less cell Downloaded from adhesion with reductions in cell adhesions; up to 74% less in the two-day experiments (MDA-MB 231 on the copolymer obtained in DEG-modified fermentation of A. latus) and 48% less in the seven-day experiments (SK-MEL 28 on the copolymer obtained in PEG 400-modified fermentation of A. vinelandii UWD). In the two-day experiments, no differences in the cellinteraction was observed between the polymers obtained from two different bacterial sources, the polymers differed in their long-term, seven-day, cell interaction with copolymers obtained from A. vinelandii UWD maintaining more effective cell repulsion.

<span lang="EN-US">Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.</span>

Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements.

Summary Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short‐term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi‐preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1‐score. Consequently, the MPIA can be integrated with real time recommender systems.