Wireless sensor networks consist of sensor nodes with sensing and communication capabilities. We focus on data aggregation problems in energy constrained sensor networks. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we present a survey of data aggregation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency and data accuracy. We conclude with possible future research directions.

The excluded volume effect (EVE) rules all life processes. It is created by macromolecules that occupy a given volume thereby confining other molecules to the remaining space with large consequences on reaction kinetics and molecular assembly. Implementing EVE in fibroblast culture accelerated conversion of procollagen to collagen by procollagen C-proteinase (PCP/BMP-1) and proteolytic modification of its allosteric regulator, PCOLCE1. This led to a 20-30-and 3-6-fold increased collagen deposition in two-and three-dimensional cultures, respectively, and creation of crosslinked collagen footprints beneath cells. Important parameters correlating with accelerated deposition were hydrodynamic radius of macromolecules and their negative charge density.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

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Emulsion-based crystallization to produce spherical crystalline agglomerates (SAs) is an attractive route to control crystal size during downstream processing of active pharmaceutical ingredients (APIs). However, conventional methods of emulsification in stirred vessels pose several problems that limit the utility of emulsion-based crystallization. In this paper, we use capillary microfluidics to generate monodisperse water-in-oil emulsions. Capillary microfluidics, in conjunction with evaporative crystallization on a flat heated surface, enables controllable production of uniformly sized SAs of glycine in the 35–150 μm size range. We report detailed characterization of particle size, size distribution, structure, and polymorphic form. Further, online high-speed stereomicroscopic observations reveal several clearly demarcated stages in the dynamics of glycine crystallization from emulsion droplets. Rapid droplet shrinkage is followed by crystal nucleation within individual droplets. Once a nucleus is formed within a droplet, crystal growth is very rapid (<0.1 s) and occurs linearly along radially advancing fronts at speeds of up to 1 mm/s, similar to spherulitic crystal growth from impure melts. The spherulitic aggregate thus formed ages to yield the final SA morphology. Overall crystallization times are on the order of minutes, as compared to hours in conventional batch processes. We discuss these phenomena and their implications for the development of more generalized processes applicable to a variety of drug molecules. This work paves the way for microfluidics-enabled continuous spherical crystallization processes.

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Collagen networks, the main structural/mechanical elements in biological tissues, increasingly serve as biomimetic scaffolds for cell behavioral studies, assays, and tissue engineering, and yet their full spectrum of nonlinear behavior remains unclear. Here, with self-assembled type-I collagen as model, we use metrics beyond those in standard single-harmonic analysis of rheological measurements to reveal strain-softening and strain-stiffening of collagen networks both in instantaneous responses and at steady state. The results show how different deformation mechanisms, such as deformation-induced increase in the elastically active fibrils, nonlinear extension of individual fibrils, and slips in the physical cross-links in the network, can lead to the observed complex nonlinearity. We demonstrate how comprehensive rheological analyses can uncover the rich mechanical properties of biopolymer networks, including the above-mentioned softening as well as an early strain-stiffening, which are important for understanding physiological response of biological materials to mechanical loading.