Development of docetaxel nanocapsules for improving in vitro cytotoxicity and cellular uptake in MCF-7 cells

The aim of this study was to fabricate docetaxel loaded nanocapsules (DTX-NCs) with a high payload using Layer-by-Layer (LbL) technique by successive coating with alternate layers of oppositely charged polyelectrolytes. Developed nanocapsules (NCs) were characterized in terms of morphology, particle size distribution, zeta potential (ζ-potential), entrapment efficiency and in vitro release. The morphological characteristics of the NCs were assessed using transmission electron microscopy (TEM) that revealed coating of polyelectrolytes around the surface of particles. The developed NCs successfully attained a submicron particle size while the ζ-potential of optimized NCs alternated between (+) 34.64?±?1.5 mV to (?) 33.25?±?2.1 mV with each coating step. The non-hemolytic potential of the NCs indicated the suitability of the developed formulation for intravenous administration. A comparative study indicated that the cytotoxicity of positively charged NCs (F4) was significant higher (p?in vitro on MCF-7 cells. Furthermore, cell uptake studies evidenced a higher uptake of positive NCs (≥1.2 fold) in comparison to negative NCs. In conclusion, formulated NCs are an ideal vehicle for passive targeting of drugs to tumor cells that may result in improved efficacy and reduced toxicity of encapsulated drug moiety.     

Unit commitment ? a fuzzy mixed integer Linear Programming solution

Unit commitment (UC) of a large system is a complex puzzle with integer/continuous variables and numerous inter-temporal constraints. After deregulation, price offers submitted by GenCos are predominantly in the form of linear price quantity (PQ) pairs. A fuzzy UC formulation that uses price offers modeled as PQ pairs. This fuzzy linear optimisation formulation of UC is solved using a mixed integer linear programming (MILP) routine. In this formulation, start up cost is modelled using linear variables. The fuzzy formulation provides modeling flexibility, relaxation in constraint enforcement and allows the method to seek a practical solution. The use of MILP technique makes the proposed solution method rigorous and fast. The method is tested on a 24 h, 104-generator system demonstrating its speed and robustness gained by using the LP technique. A five-generator system is additionally used to create a see-through example demonstrating advantages of using the fuzzy optimisation model.     

Generation of an eight node brick element using Papcovitch-Neuber functions

Some conventional finite elements suffer from drawbacks, such as shear locking, membrane locking, etc. To overcome them researchers have developed various techniques, termed as tricks by some and variational crimes by others. Many attempts have been made, but satisfactory explanations for why some of these techniques work have not been obtained, especially in the case of solid elements. This paper attempts a simple non-conforming solid element using assumed displacement fields which satisfy the Navier equation exactly. Its behaviour under simple loadings like bending, torsion and tension is examined and comparisons are made with existing elements.     

Multi-stage classifier design

In many classification systems, sensing modalities have different acquisition costs. It is often unnecessary to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, measurements in a new modality can be acquired at each stage for an additional cost. We seek decision rules to reduce the average measurement acquisition cost. We formulate an empirical risk minimization problem (ERM) for a multi-stage reject classifier, wherein the stage k classifier either classifies a sample using only the measurements acquired so far or rejects it to the next stage where more attributes can be acquired for a cost. If we restrict ourselves to binary classification setting then, to solve the ERM problem, we show that the optimal reject classifier at each stage is a combination of two binary classifiers, one biased towards positive examples and the other biased towards negative examples. We use this parameterization to construct stage-by-stage global surrogate risk, develop an iterative algorithm in the boosting framework and present convergence and generalization results. We test our work on synthetic, medical and explosives detection datasets. Our results demonstrate that substantial cost reduction without a significant sacrifice in accuracy is achievable.     

Detection of cross-channel anomalies

The data deluge has created a great challenge for data mining applications wherein the rare topics of interest are often buried in the flood of major headlines. We identify and formulate a novel problem: cross-channel anomaly detection from multiple data channels. Cross-channel anomalies are common among the individual channel anomalies and are often portent of significant events. Central to this new problem is a development of theoretical foundation and methodology. Using the spectral approach, we propose a two-stage detection method: anomaly detection at a single-channel level, followed by the detection of cross-channel anomalies from the amalgamation of single-channel anomalies. We also derive the extension of the proposed detection method to an online settings, which automatically adapts to changes in the data over time at low computational complexity using incremental algorithms. Our mathematical analysis shows that our method is likely to reduce the false alarm rate by establishing theoretical results on the reduction of an impurity index. We demonstrate our method in two applications: document understanding with multiple text corpora and detection of repeated anomalies in large-scale video surveillance. The experimental results consistently demonstrate the superior performance of our method compared with related state-of-art methods, including the one-class SVM and principal component pursuit. In addition, our framework can be deployed in a decentralized manner, lending itself for large-scale data stream analysis.     

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.     

Analysis of laminated shells of revolution with laminated stiffeners using a doubly curved quadrilateral finite element

A finite element analysis of laminated shells of revolution reinforced with laminated stifieners is described here-in. A doubly curved quadrilateral laminated anisotropic shell of revolution finite element of 48 d.o.f. is used in conjunction with two stiffener elements of 16 d.o.f. namely: (i) A laminated anisotropic parallel circle stiffener element (PCSE); (ii) A laminated anisotropic meridional stiffener element (MSE).These stifiener elements are formulated under line member assumptions as degenerate cases of the quadrilateral shell element to achieve compatibility all along the shell-stifiener junction lines. The solutions to the problem of a stiffened cantilever cylindrical shell are used to check the correctness of the present program while it's capability is shown through the prediction of the behavior of an eccentrically stiffened laminated hyperboloidal shell.     

Design of artificial neuron controller for STATCOM in dSPACE environment

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt–ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.