Two dimensional transport characteristics of surface stabilized zero-valent iron nanoparticles in porous media

Zero-valent iron nanoparticles (INP) were synthesized and stabilized using poly acrylic acid (PAA) to yield stabilized INP (S-INP). A two-dimensional physical model was used to study the fate and transport of the INP and S-INP in porous media under saturated, steady-state flow conditions. Transport data for a nonreactive tracer, INP, and S-INP were collected under similar flow conditions. The results show that unstabilized INP cannot be transported into groundwater systems. On the other hand, the S-INP can be transported like a tracer without significant retardation. However, the S-INP plume migrated downward as it moved horizontally in the physical model, indicating that small density gradients have significant influence on two-dimensional transport. The variable-density groundwater flow model SEAWAT was used to model the observed density-driven transport patterns. This is the first time a two-dimensional transport data set is reported for demonstrating the multidimensional transport characteristics of nanoparticles. The data shows the importance of density effects, which cannot be fully discerned using one-dimensional, column experiments. Finally, we also demonstrate that the numerical model SEAWAT can be used to predict the density-driven transport characteristics of S-INP in groundwater aquifers.     

2-Aminobenzothiazoles Inhibit Virulence Gene Expression and Block Polymyxin Resistance in Salmonella enterica

One promising strategy to combat antibiotic-resistant bacteria is to develop compounds that block bacterial defenses against antibacterial conditions produced by the innate immune system. Salmonella enterica, which causes food-borne gastroenteritis and typhoid fever, requires histidine kinases (HKs) to resist innate immune defenses such as cationic antimicrobial peptides (CAMPs). Herein, we report that 2-aminobenzothiazoles block histidine kinase-dependent phenotypes in Salmonella enterica serotype Typhimurium. We found that 2-aminobenzothiazoles inhibited growth under low Mg²⁺, a stressful condition that requires histidine kinase-mediated responses, and decreased expression of the virulence genes pagC and pagK. Furthermore, we discovered that 2-aminobenzothiazoles weaken Salmonella’s resistance to polymyxin B and polymyxin E, which are last-line antibiotics and models for host defense CAMPs. These findings raise the possibilities that 2-aminobenzothiazoles can block HK-mediated bacterial defenses and can be used in combination with polymyxins to treat infections caused by Salmonella.     

Survey of Deep-Learning Techniques in Big-Data Analytics

Big-data research studies relying upon Deep-learning methods are revitalized the decision-making mechanism in the business sectors and the enterprise domains. The firms’ operational parameters also have the dependency of the Big-data analytics phase, their way of managing the data, and to evolve the outcomes of Big-data implementation by using the Deep-learning algorithms. Deep-learning approaches enhancements in Big-data applications facilitate the decision-making process such as the information-processing to the employees, analytical potentials augmentation, and in the transition of more innovative work. In this DL-approach, the robust-patterns of the data-predictions resulted from the unstructured information by conceptualizing the Decision-making methods. Hence this paper reviewed the impact of the Deep-learning process utilizing the Big-data in the enterprise and Business sectors. Also this study provides a comprehensive survey of all the Deep-learning techniques illustrating the efficiency of Big-Data processing and their impacts of operational parameters. Further it concentrating the data-dimensionality factors and the Big-data complications rectifying by utilizing the DL-algorithms, usage of Machine-learning or deep-learning process for the decision-making mechanism in the Enterprise sectors and business sectors. This research discussed the predictions of the Big-data analytics resulting to the decision parameters within the organisations, and in the management of larger scale of datasets in Big-data analytics processing by utilizing the Deep-learning implementations. The comparative analysis of the reviewed studies has also been described by comparing existing approaches of Deep-learning methodologies in employing Big-data analytics.

     

A stochastic approximation approach to active queue management

Recently, a dynamic adaptive queue management with random dropping (AQMRD) scheme has been developed to capture the time-dependent variation of average queue size by incorporating the rate of change of average queue size as a parameter. A major issue with AQMRD is the choice of parameters. In this paper, a novel online stochastic approximation based optimization scheme is proposed to dynamically tune the parameters of AQMRD and which is also applicable for other active queue management (AQM) algorithms. Our optimization scheme significantly improves the throughput, average queue size, and loss-rate in relation to other AQM schemes.     

Long‐Term Electrical and Mechanical Function Monitoring of a Human‐on‐a‐Chip System

The goal of human‐on‐a‐chip systems is to capture multiorgan complexity and predict the human response to compounds within physiologically relevant platforms. The generation and characterization of such systems is currently a focal point of research given the long‐standing inadequacies of conventional techniques for predicting human outcome. Functional systems can measure and quantify key cellular mechanisms that correlate with the physiological status of a tissue, and can be used to evaluate therapeutic challenges utilizing many of the same endpoints used in animal experiments or clinical trials. Culturing multiple organ compartments in a platform creates a more physiologic environment (organ–organ communication). Here is reported a human 4‐organ system composed of heart, liver, skeletal muscle, and nervous system modules that maintains cellular viability and function over 28 days in serum‐free conditions using a pumpless system. The integration of noninvasive electrical evaluation of neurons and cardiac cells and mechanical determination of cardiac and skeletal muscle contraction allows the monitoring of cellular function, especially for chronic toxicity studies in vitro. The 28‐day period is the minimum timeframe for animal studies to evaluate repeat dose toxicity. This technology can be a relevant alternative to animal testing by monitoring multiorgan function upon long‐term chemical exposure.     

Waterproof,Breathable, and Antibacterial Self‐Powered e‐Textiles Based on Omniphobic Triboelectric Nanogenerators

Multifunctional electronic textiles (e‐textiles) incorporating miniaturized electronic devices will pave the way toward a new generation of wearable devices and human–machine interfaces. Unfortunately, the development of e‐textiles is subject to critical challenges, such as battery dependence, breathability, satisfactory washability, and compatibility with mass production techniques. This work describes a simple and cost‐effective method to transform conventional garments and textiles into waterproof, breathable, and antibacterial e‐textiles for self‐powered human–machine interfacing. Combining embroidery with the spray‐based deposition of fluoroalkylated organosilanes and highly networked nanoflakes, omniphobic triboelectric nanogenerators (R^F‐TENGs) can be incorporated into any fiber‐based textile to power wearable devices using energy harvested from human motion. R^F‐TENGs are thin, flexible, breathable (air permeability 90.5 mm s^?1), inexpensive to fabricate (<0.04$ cm^?2), and capable of producing a high power density (600 µW cm^?2). E‐textiles based on R^F‐TENGs repel water, stains, and bacterial growth, and show excellent stability under mechanical deformations and remarkable washing durability under standard machine‐washing tests. Moreover, e‐textiles based on R^F‐TENGs are compatible with large‐scale production processes and exhibit high sensitivity to touch, enabling the cost‐effective manufacturing of wearable human–machine interfaces.     

Touch‐Interactive Flexible Sustainable Energy Harvester and Self‐Powered Smart Card

Sustainable and safe energy sources combined with cost effectiveness are major goals for society when considering the current scenario of mass production of portable and Internet of Things (IoT) devices along with the huge amount of inevitable e‐waste. The conceptual design of a self‐powered “eco‐energy” smart card based on paper promotes green and clean energy, which will bring the zero e‐waste challenge one step closer to fruition. A commercial raw filter paper is modified through a fast in situ functionalization method, resulting in a conductive cellulose fiber/polyaniline composite, which is then applied as an energy harvester based on a mechano‐responsive charge transfer mechanism through a metal/conducting polymer interface. Different electrodes are studied to optimize charge transfer based on contact energy level differences. The highest power density and current density obtained from such a paper‐based “eco‐energy” smart card device are 1.75 W m^?2 and 33.5 mA m^?2 respectively. This self‐powered smart energy card is also able to light up several commercial light‐emitting diodes, power on electronic devices, and charge capacitors.