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Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory–artificial neural network (DWT-ANN), discrete wavelet theory–adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm–artificial neural network (GA-ANN) and genetic algorithm–adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

Negatively charged carboxylate-functionalized mixed monolayer protected clusters (MMPCs) effectively bind and inhibit alpha-chymotrypsin based on complementary electrostatic surface recognition. We demonstrate that this binding can be disrupted by varying the ionic strength of the medium. Enzyme activity in the presence of MMPCs increases from 5% to 97% of native activity as salt concentration is increased from 0 to 1.5 M. Variation of ionic strengths after complete binding over 13 h results only in a modest restoration of enzymatic activity (< 35%). The conformation of chymotrypsin was characterized using circular dichroism and fluorescence spectroscopy, correlating structure with enzymatic activity. This work provides a useful insight of the electrostatic influence on protein--MMPC interactions and can be applied toward MMPC-based controlled release of proteins in vivo.

The interaction of nanomaterials with cells and lipid bilayers is critical in many applications such as phototherapy, imaging, and drug/gene delivery. These applications require a firm control over nanoparticle-cell interactions, which are mainly dictated by surface properties of nanoparticles. This critical Review presents an understanding of how synthetic and natural chemical moieties on the nanoparticle surface (in addition to nanoparticle shape and size) impact their interaction with lipid bilayers and cells. Challenges for undertaking a systematic study to elucidate nanoparticle-cell interactions are also discussed.

Intracellular trafficking of nanoscale objects is regulated by cell membranes through interactions that are under intense investigation. These materials are typically internalized by cells into membrane-bounded endosomes and never gain access to the cell machinery located in the cytosol. While some bio-macromolecules can penetrate or fuse with cell membranes without overt membrane disruption, no synthetic material of comparable size has shown this property yet. Cationic nanoscale objects (mostly assisted by some degree of hydrophobicity) pass through cell membranes by generating transient holes, a process associated with cytotoxicity. To date, studies aimed at generating cell-penetrating nanomaterials have focused on the effect of size, shape, and composition. Here we compare membrane penetration by two nanoparticle ‘isomers’ with the same composition (same hydrophobic content), one coated with sub-nanometer striations of alternating anionic and hydrophobic groups, and the other coated with the same moieties but in a random distribution. We show that the former particles penetrate the plasma membrane without bilayer disruption while the latter were instead mostly trapped in endosomes. Our results highlight a new design rule for materials that interact with cells and offer a paradigm for analyzing the fundamental problem of cell membrane penetrating bio- and macro-molecules. We anticipate consequences for designing materials for biomedical applications and for understanding potential safety issues associated with synthetic nanomaterials.