“Poly (sulfur/oil) impregnated cotton: A newly developed material for effective oil removal from contaminated water”

In this work, a unique material has been developed by carrying out in situ co-polymerization of sulfur and triglycerides (edible oil) within the cotton fibers to yield poly (sulfur/oil) impregnated cotton (PSOIC) composite material. The adsorbent was characterized by Fourier transform infrared, X-ray diffraction, and SEM analysis. The equilibrium oil uptake studies were carried out in batch mode experiments and the sorption data were analyzed by various sorption isotherm models. The Langmuir model was found to be best fitted with maximum oil sorption capacity Q_o of 9.52 g/g polymer. On the contrary, the conventional poly (sulfur/oil) adsorbent showed a Q_o value of 2.35 g/g polymer, thus indicating the suitability and superiority of PSOIC over PSO material. The newly developed PSOIC material could be pressed in the form of sheets and was successful in removing oil/grease layer from the oil/water mixture. The adsorbent showed fair re-generation capacity, thus minimizing the cost effectiveness to employ in large-scale removal processes.     

Chemokine-Cytokine Networks in the Head and Neck Tumor Microenvironment

Head and neck squamous cell carcinomas (HNSCCs) are aggressive diseases with a dismal patient prognosis. Despite significant advances in treatment modalities, the five-year survival rate in patients with HNSCC has improved marginally and therefore warrants a comprehensive understanding of the HNSCC biology. Alterations in the cellular and non-cellular components of the HNSCC tumor micro-environment (TME) play a critical role in regulating many hallmarks of cancer development including evasion of apoptosis, activation of invasion, metastasis, angiogenesis, response to therapy, immune escape mechanisms, deregulation of energetics, and therefore the development of an overall aggressive HNSCC phenotype. Cytokines and chemokines are small secretory proteins produced by neoplastic or stromal cells, controlling complex and dynamic cell–cell interactions in the TME to regulate many cancer hallmarks. This review summarizes the current understanding of the complex cytokine/chemokine networks in the HNSCC TME, their role in activating diverse signaling pathways and promoting tumor progression, metastasis, and therapeutic resistance development.     

Biomineralized and chemically synthesized magnetic nanoparticles: A contrast

Magnetic nanoparticles (MNPs) have widely been synthesized through chemical processes for biomedical applications over the past few decades. Recently, a new class of MNPs, known as bacterial magnetosomes, has been isolated from magnetotactic bacteria, a natural source. These magnetosomes are magnetite or greigite nanocrystals which are biomineralized in the bacterial cell and provide magnet-like properties to it. Contrary to MNPs, bacterial magnetosomes are biocompatible, lower in toxicity, and can be easily cleared from the body due to the presence of a phospholipid bilayer around them. They also do not demonstrate aggregation, which makes them highly advantageous. In this review, we have provided an in-depth comparative account of bacterial magnetosomes and chemically synthesized MNPs in terms of their synthesis, properties, and biomedical applications. In addition, we have also provided a contrast on how magnetosomes might have the potential to successfully substitute synthetic MNPs in therapeutic and imaging applications.     

Uncertain multi-objective multi-product solid transportation problems

The solid transportation problem is an important generalization of the classical transportation problem as it also considers the conveyance constraints along with the source and destination constraints. The problem can be made more effective by incorporating some other factors, which make it useful in real life situations. In this paper, we consider a fully fuzzy multi-objective multi-item solid transportation problem and present a method to find its fuzzy optimal-compromise solution using the fuzzy programming technique. To take into account the imprecision in finding the exact values of parameters, all the parameters are taken as trapezoidal fuzzy numbers. A numerical example is solved to illustrate the methodology.     

Evaluating Extraction Conditions of Glucosinolate Hydrolytic Products from Seeds of Eruca sativa (Mill.) Thell. Using GC‐MS

Glucosinolates and their hydrolytic products form an important class of plant secondary metabolites involved in various plant defense‐linked mechanisms. The successful isolation of particular glucosinolate hydrolytic products is limited by a number of factors like understanding the parent glucosinolate moiety, solubility, and stability under different drying conditions. The extraction protocols currently available were modified to achieve both an increased yield as well as an increased number of hydrolytic products. Eruca sativa (Mill.) Thell. (called arugula in the U.S.A.), a rich source of varied glucosinolates, was used for the standardization of different extraction protocols. We exploited the volatile nature of the glucosinolates and developed a method that not only enhanced the yield of glucosinolate hydrolytic products, but also reduced undesired compounds. Among all the tested protocols, hydrodistillation using Clevenger apparatus was judged as the best protocol, which was evident from an enhanced yield as well as an increased number of hydrolytic products when compared to the other methods as monitored by gas chromatography‐mass spectrometry.     

Failure resistant topology optimization of structures using nonlocal elastoplastic-damage model

For energy absorbing structures made up of ductile materials, the plastic strain accumulation often leads to early material damage and failure, which can deteriorate the overall structural performance. The goal of this work is to limit this damage in elastoplastic designs using the density-based topology optimization framework such that the optimized structures can absorb energy in a more controllable manner. To this end, an implicit nonlocal coupled elastoplastic damage model is considered for simulating the material damage and softening behavior. The nonlocal effect from the void elements is removed by introducing a scaling scheme for the nonlocal parameters. Path-dependent sensitivity is derived analytically using an adjoint method whose accuracy is further verified by the central difference method. The effectiveness of the proposed method is demonstrated through several numerical examples. It is shown that the load-carrying capacity, ductility, as well as ultimate plastic work dissipation capacity of the optimized design, can be considerably improved by the proposed method.     

An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran

Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings.In this paper,an attempt has been made to present an application of artificial neural network(ANN)to predict the blast-induced ground vibration of the Gol-E-Gohar(GEG)iron mine,Iran.A four-layer feed-forward back propagation multi-layer perceptron(MLP)was used and trained with Levenberg–Marquardt algorithm.To construct ANN models,the maximum charge per delay,distance from blasting face to monitoring point,stemming and hole depth were taken as inputs,whereas peak particle velocity(PPV)was considered as an output parameter.A database consisting of69data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models.Coefficient of determination(R2)and mean square error(MSE)were chosen as the indicators of the performance of the networks.A network with architecture4-11-5-1and R2of0.957and MSE of0.000722was found to be optimum.To demonstrate the supremacy of ANN approach,the same69data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression(MLR)analysis.The results revealed that the proposed ANN approach performs better than empirical and MLR models.     

Two-unit redundant system with random shocks and inspection

The present paper deals with a two unit cold standby system, in which the failure of an operating unit is subject to random shock and internal stress. After each repair, a unit is sent for inspection to decide whether the repaired unit is perfect or imperfect. If the repaired unit is found to be imperfect, then it goes for post repair. Using regenerative point technique several effective measures of reliability are obtained.