Decay Rate Kinetics of Ozone Gas in Rice Grains

The present study was carried out to evaluate the effects of ozone on rice grains for the following three conditions: saturation time, decay rate, and half-life of ozone. Experiments were performed in different bed thicknesses (5 and 10 cm) and moisture content (11.4 and 14.2% wb) at atmospheric conditions. The lowest saturation time of ozone was 119 min, with the concentration of 516 ppm for rice grains ozonated at 5-cm bed thickness with 11.4% (wb) moisture content. The decay rate kinetics of ozone obtained were consistent with a first-order model. Regarding the half-life of ozone, the lowest value obtained was 6.78 min for rice grains ozonated at 10-cm bed thickness with 14.2% (wb) moisture content.     

Energy management for commercial servers

Servers: high-end, multiprocessor systems running commercial workloads, have typically included extensive cooling systems and resided in custom-built rooms for high-power delivery. Recently, as transistor density and demand for computing resources have rapidly increased, even these high-end systems face energy-use constraints. Commercial-server energy management now focuses on conserving power in the memory and microprocessor subsystems. Because their workloads are typically structured as multiple application programs, system-wide approaches are more applicable to multiprocessor environments in commercial servers than techniques that primarily apply to single-application environments, such as those based on compiler optimizations.     

Bacteria-Responsive Self-Assembly of Antimicrobial Peptide Nanonets for Trap-and-Kill of Antibiotic-Resistant Strains

Bacterial trapping using nanonets is a ubiquitous immune defense mechanism against infectious microbes. These nanonets can entrap microbial cells, effectively arresting their dissemination and rendering them more vulnerable to locally secreted microbicides. Inspired by this evolutionarily conserved anti-infective strategy, a series of 15 to 16 residue-long synthetic β-hairpin peptides is herein constructed with the ability to self-assemble into nanonets in response to the presence of bacteria, enabling spatiotemporal control over microbial killing. Using amyloid-specific K114 assay and confocal microscopy, the membrane components lipoteichoic acid and lipopolysaccharide are shown to play a major role in determining the amyloid-nucleating capacity as triggered by Gram-positive and Gram-negative bacteria respectively. These nanonets displayed both trapping and killing functionalities, hence offering a direct improvement from the trap-only biomimetics in literature. By substituting a single turn residue of the non-amyloidogenic BTT1 peptide, the nanonet-forming BTT1-3A analog is produced with comparable antimicrobial potency. With the same sequence manipulation approach, BTT2-4A analog modified from BTT2 peptide showed improved antimicrobial potency against colistin-resistant clinical isolates. The peptide nanonets also demonstrated robust stability against proteolytic degradation, and promising in vivo efficacy and biosafety profile. Overall, these bacteria-responsive peptide nanonets are promising clinical anti-infective alternatives for circumventing antibiotic resistance.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier

For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process.     

An Efficient Lossless Telemetry Data Compression and Fault Analysis System Using 2SMLZ and CMOW-DLNN

Wireless Personal Communications - In this paper, the effect of perfect electric conductor (PEC) as a ground plane on antenna array is investigated. Vertical electric dipole which is of...     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

Robust observer‐based stabilization of Lipschitz nonlinear uncertain systems via LMIs ‐ discussions and new design procedure

This paper presents a new observer‐based controller design method for Lipschitz nonlinear systems with uncertain parameters and ‐bounded disturbance inputs. In the presence of uncertain parameters, the separation principle is not applicable even in the case of linear time invariant systems. A state of the art review for uncertain linear systems is first presented to describe the shortcomings and conservatism of existing results for this problem. Then a new LMI‐based design technique is developed to solve the problem for both linear and Lipschitz nonlinear systems. The features of the new technique are the use of a new matrix decomposition, the allowance of additional degrees of freedom in design of the observer and controller feedback gains, the elimination of any need to use equality constraints, the allowance of uncertainty in the input matrix and the encompassing of all previous results under one framework. An extensive portfolio of numerical case studies is presented to illustrate the superiority of the developed design technique to existing results for linear systems from literature and to illustrate application to Lipschitz nonlinear systems. Copyright © 2016 John Wiley & Sons, Ltd.     

Closed loop identification and control loop reconfiguration: an industrial case study

The results of a joint university–industry collaborative project for control loop reconfiguration using closed loop experimental data from a fuel gas pressure control system are described in this paper. The fuel gas pressure was being regulated using a butane stream. For economic reasons, it was necessary to switch control to the ethane stream. Previous attempts at effecting this changeover had proved unsuccessful. In this study, a powerful system identification technique namely Canonical Variate Analysis (CVA) was employed to obtain the empirical plant models. A PI controller was then designed using the direct synthesis method. Acceptable closed loop behavior was obtained with little online tuning.     

On spacing policies for highway vehicle automation

This paper develops a framework for the design and evaluation of spacing policies for adaptive cruise control. Spacing policies are evaluated from the point of view of string stability, traffic flow stability and traffic flow capacity. The standard constant time-gap spacing policy can guarantee string stability but is shown to suffer from poor traffic capacity and traffic flow instability. An "ideal" spacing policy is proposed that evolves naturally from the evaluation framework. The proposed spacing policy is a nonlinear function of speed. It provides string stability and traffic flow stability as well as a higher traffic flow capacity compared to the standard time-gap controller. An associated result proved in the paper is that traffic flow stability implies string stability for flow volumes up to a described maximum value.