Graphene oxide‐ellagic acid nanocomposite as effective anticancer and antimicrobial agent

In this study, ellagic acid (ELA), a skin anticancer drug, is capped on the surface(s) of functionalised graphene oxide (GO) nano‐sheets through electrostatic and π–π staking interactions. The prepared ELA‐GO nanocomposite have been thoroughly characterised by using eight techniques: Fourier‐transform infrared spectroscopy (FTIR), zeta potential, X‐ray diffraction (XRD), thermogravimetric analysis (TGA), Raman spectroscopy, atomic force microscopy (AFM) topographic imaging, transmission electron microscopy (TEM), and surface morphology via scanning electron microscopy (SEM). Furthermore, ELA drug loading and release behaviours from ELA‐GO nanocomposite were studied. The ELA‐GO nanocomposite has a uniform size distribution averaging 88 nm and high drug loading capacity of 30 wt.%. The in vitro drug release behaviour of ELA from the nanocomposite was investigated by UV–Vis spectrometry at a wavelength of λ _max 257 nm. The data confirmed prolonged ELA release over 5000 min at physiological pH (7.4). Finally, the IC ₅₀ of this ELA‐GO nanocomposite was found to be 6.16 µg/ml against B16 cell line; ELA and GO did not show any cytotoxic effects up to 50 µg/ml on the same cell lines.     

A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps

A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods.     

Novel manufacturing process for improving mechanical properties of flax/PP composites

Many attempts have been made in the recent past to improve the mechanical properties of flax/polypropylene (PP) thermoplastic composites. Most of these attempts have not been translated at industrial level due to additional chemical treatment process involved. In the present work, needle-punched nonwoven preforms which can be readily used for composite manufacturing was prepared. To improve the interfacial bonding of the composites, a new modified route for composite preparation is proposed. The modified processing strategy implemented to produce flax with polypropylene composite using compression molding machine. The nonwoven preforms were heated to elevated temperatures followed by quenching. The composite samples were then hot consolidated and their mechanical properties were studied and compared with the samples produced by the conventional method. 154, 75, 37, and 9.6% of improvement have been observed for peel strength, compressive strength, hardness, and impact strength, respectively. The reasons for the improvement are discussed in detail using various analytical tools.     

Face Attribute Convolutional Neural Network System for Data Security with Improved Crypto Biometrics

Due to the enormous usage of the internet for transmission of data over a network, security and authenticity become major risks. Major challenges encountered in biometric system are the misuse of enrolled biometric templates stored in database server. To describe these issues various algorithms are implemented to deliver better protection to biometric traits such as physical (Face, fingerprint, Ear etc.) and behavioural (Gesture, Voice, tying etc.) by means of matching and verification process. In this work, biometric security system with fuzzy extractor and convolutional neural networks using face attribute is proposed which provides different choices for supporting cryptographic processes to the confidential data. The proposed system not only offers security but also enhances the system execution by discrepancy conservation of binary templates. Here Face Attribute Convolutional Neural Network (FACNN) is used to generate binary codes from nodal points which act as a key to encrypt and decrypt the entire data for further processing. Implementing Artificial Intelligence (AI) into the proposed system, automatically upgrades and replaces the previously stored biometric template after certain time period to reduce the risk of ageing difference while processing. Binary codes generated from face templates are used not only for cryptographic approach is also used for biometric process of enrolment and verification. Three main face data sets are taken into the evaluation to attain system performance by improving the efficiency of matching performance to verify authenticity. This system enhances the system performance by 8% matching and verification and minimizes the False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER) by 6 times and increases the data privacy through the biometric cryptosystem by 98.2% while compared to other work.     

Taguchi-based grey relational analysis for modeling and optimizing machining parameters through dry turning of Incoloy 800H

The present research focused on the optimization of machining parameters and their effects by dry-turning an incoloy 800H on the basis of Taguchi-based grey relational analysis. Surface roughness (Ra, Rq and Rz), cutting force (Fz), and cutting power (P) were minimized, whereas Material removal rate (MRR) was maximized. An L ₂₇ orthogonal array was used in the experiments, which were conducted in a computerized and numerical-controlled turning machine. Cutting speed, feed rate, and cut depth were set as controllable machining variables, and analysis of variance was performed to determine the contribution of each variable. We then developed regression models, which ultimately conformed to investigational and predicted values. The combinational parameters for the multiperformance optimization were V = 35 m/min, f = 0.06 mm/rev and a = 1 mm, which altogether correspond to approximately 48.98 % of the improvement. The chip morphology of the incoloy 800H was also studied and reported.     

Semi‐distributed dynamic inter‐cell interference coordination scheme for interference avoidance in heterogeneous networks

Inter‐cell interference (ICI) is a major problem in heterogeneous networks, such as two‐tier femtocell (FC) networks, because it leads to poor cell‐edge throughput and system capacity. Dynamic ICI coordination (ICIC) schemes, which do not require prior frequency planning, must be employed for interference avoidance in such networks. In contrast to existing dynamic ICIC schemes that focus on homogeneous network scenarios, we propose a novel semi‐distributed dynamic ICIC scheme to mitigate interference in heterogeneous network scenarios. With the goal of maximizing the utility of individual users, two separate algorithms, namely the FC base station (FBS)‐level algorithm and FC management system (FMS)‐level algorithm, are employed to restrict resource usage by dominant interference‐creating cells. The distributed functionality of the FBS‐level algorithm and low computational complexity of the FMS‐level algorithm are the main advantages of the proposed scheme. Simulation results demonstrate improvement in cell‐edge performance with no impact on system capacity or user fairness, which confirms the effectiveness of the proposed scheme compared to static and semi‐static ICIC schemes.     

Phase evolution and crystallography of precipitates during decomposition of new “tungsten-free” Co(Ni)–Mo–Al–Nb γ–γ′ superalloys at elevated temperatures

This article reports the microstructural stability and consequent phase decomposition including the appearance of topologically close-packed (TCP) phases at high temperature of recently discovered tungsten-free γ–γ′ alloys of base composition Co–10Al–5Mo–2Nb with or without the addition of Ni and Ti. On prolonged aging at 800 °C of the Co–10Al–5Mo–2Nb alloy, needle-shaped DO₁₉-ordered precipitates with stoichiometry of Co₃(Mo, Nb) start appearing in the microstructure. In addition, growth of cellular domains from the grain boundaries featuring a three-phase composite lamellar structure could be observed. These phases are fcc γ-Co with composition different from the original matrix, CoAl with B2 ordering and Co₃(Mo, Nb) with DO₁₉ ordering. All the phases exhibit well-defined crystallographic orientation relationships. The decomposition of the alloys depends on the solvus temperature of the γ′ phase. The Ni-containing alloy exhibits no phase decomposition until 100 h of aging at 800 °C without any significant effect on γ′ volume fraction (76 %). However, at 950 °C, the alloy decomposes leading to the appearance of four different phases including TCP phases: a Cr₃Si-type cubic phase, a hexagonal Laves phase, rhombohedral μ phase, and solid solution of Co phase. The γ–γ′ microstructure in the Co–10Al–5Mo–2Nb and Co–30Ni–10Al–5Mo–2Ta alloys is not stable at 800 and 950 °C, respectively, on long-term aging. This shows that the measured solvus temperatures (i.e., 866 and 990 °C) are metastable solvus temperatures. We also report that the Ti-containing alloy exhibits superior stability with no evidence of either TCP phase formation or any other decomposition of γ′ precipitates, even after aging at 950 °C for 100 h.     

Thermal performance of diesel engine exhaust heat recovery system using concentric tube heat exchanger inserts with different porous materials

This paper aims to analyse and improve the thermal performance of a concentric tube heat exchanger using porous material inserts. In this method, two different types of porous materials such as cast iron and mild steel were introduced on the outer surface of the inner tube wall of a concentric tube exchanger. The porous particle improves the convective heat transfer co-efficient between the tube wall and fluid flow surface. Experimentally, it was found that the inserted porous material enhances the heat exchanger effectiveness by 76% for cast iron and 77% for mild steel. The effect of inserting porous particles on the waste heat recovered from the engine exhaust was also investigated in the present study.     

Prediction of tool wear using regression and ANN models in end-milling operation

Tool wear prediction plays an important role in industry for higher productivity and product quality. Flank wear of cutting tools is often selected as the tool life criterion as it determines the diametric accuracy of machining, its stability and reliability. This paper focuses on two different models, namely, regression mathematical and artificial neural network (ANN) models for predicting tool wear. In the present work, flank wear is taken as the response (output) variable measured during milling, while cutting speed, feed and depth of cut are taken as input parameters. The Design of Experiments (DOE) technique is developed for three factors at five levels to conduct experiments. Experiments have been conducted for measuring tool wear based on the DOE technique in a universal milling machine on AISI 1020 steel using a carbide cutter. The experimental values are used in Six Sigma software for finding the coefficients to develop the regression model. The experimentally measured values are also used to train the feed forward back propagation artificial neural network (ANN) for prediction of tool wear. Predicted values of response by both models, i.e. regression and ANN are compared with the experimental values. The predictive neural network model was found to be capable of better predictions of tool flank wear within the trained range.     

Phenanthroline derivatives with improved selectivity as DNA-targeting anticancer or antimicrobial drugs

Phenanthroline derivatives are of interest due to their potential activity against cancer, and viral, bacterial, and fungal infections. In a search for highly specific antitumor and antibacterial compounds, we report the activities of 1,10-phenanthroline-5,6-dione (phendione or L(1)), dipyrido[3,2-a:2',3'-c]phenazine (dppz or L(2)), and their corresponding platinum complexes ([PtL(1)Cl2] and [PtL(2)Cl2]), and provide the solid-state 3D structure for [PtL(1)Cl2]. It is generally known that a toxic metal ion coordinated to an active organic moiety leads to a synergistic effect; however, we report herein that the platinum complexes [PtL(1)Cl2] and [PtL(2)Cl2] have weaker activities relative to those of the free ligands, especially against bacteria. Testing these agents against a variety of human cancer cell lines revealed that L(1) and [PtL(1)Cl2] were at least as active as cisplatin against several of the cell lines (including a cisplatin-resistant cell line). The absence of antibacterial activity of [PtL(1)Cl2] removes the detrimental effect of phenanthrolines toward intestinal flora, suggesting a highly promising new strategy for the development of anticancer drugs with reduced side effects.