Photosensitized free radical polymerization using pyridinium salts

Summary The polymerization of methyl methacrylate (MMA), ethyl acrylate (EA), styrene (St) and 2-vinyl pyridine (VP) is initiated upon irradiation at >350 nm of dichloromethane solutions containing N-ethoxy-2-methylpyridinium hexafluorophosphate (EMP⁺PF₆ ^-) and anthracene or thioxanthone. Initiation mechanisms involving the formation of ethoxyl radicals during the decomposition of EMP⁺ ions via electron transfer are proposed.     

On the design considerations of solid-state power amplifiers for satellite communications: A systems perspective

Conventional solid-state power amplifier (SSPA) design approach isolates radio frequency (RF) design from communication theory. In this paper, a unified SSPA design approach is proposed, which optimizes SSPA parameters (bias voltage and input RF signal power) to minimize total DC power consumption while satisfying received SNR constraint specified by the link budget. The effect of SSPA nonlinearity is quantified by the error vector magnitude measured at its output and the corresponding received SNR degradation is analyzed. Using the quantitative metrics for received SNR, it is possible to evaluate highly nonlinear SSPA classes such as Class-B or deep-Class AB, which are normally not considered in conventional SSPA design approach to be used in satellite communication applications.     

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.     

Characterisation of sol–gel method synthesised MgZnFe2 O4 nanoparticles and its cytotoxic effects on breast cancer cell line,MDA MB‐231 in vitro

In this study, nanocrystalline magnesium zinc ferrite nanoparticles were successfully prepared by a simple sol–gel method using copper nitrate and ferric nitrate as raw materials. The calcined samples were characterised by differential thermal analysis/thermogravimetric analysis, Fourier transform infrared spectroscopy and X‐ray diffraction. Transmission electron microscopy revealed that the average particle size of the calcined sample was in a range of 17–41 nm with an average of 29 nm and has spherical size. A cytotoxicity test was performed on human breast cancer cells (MDA MB‐231) and (MCF‐7) at various concentrations starting from (0 µg/ml) to (800 µg/ml). The sample possessed a mild toxic effect toward MDA MB‐231 and MCF‐7 after being examined with MTT (3‐[4, 5‐dimethylthiazol‐2‐yl]‐2, 5 diphenyltetrazolium bromide) assay for up to 72 h of incubation. Higher reduction of cells viability was observed as the concentration of sample was increased in MDA MB‐231 cell line than in MCF‐7. Therefore, further cytotoxicity tests were performed on MDA MB‐231 cell line.Inspec keywords: sol‐gel processing, nanoparticles, nanofabrication, magnesium compounds, zinc compounds, toxicology, biological organs, cancer, cellular biophysics, nanomedicine, calcination, differential thermal analysis, Fourier transform infrared spectra, X‐ray diffraction, transmission electron microscopy, particle size, organic compoundsOther keywords: sol‐gel method, cytotoxic effects, breast cancer cell line, MDA MB‐231 in vitro, nanocrystalline magnesium zinc ferrite nanoparticles, copper nitrate, ferric nitrate, raw materials, calcined samples, differential thermal analysis, thermogravimetric analysis, Fourier transform infrared spectroscopy, X‐ray diffraction, transmission electron microscopy, average particle size, cytotoxicity testing, human breast cancer cells, mild toxic effect, 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5 diphenyltetrazolium bromide) assay, cell viability, MCF‐7, MDA MB‐231 cell line, size 17 nm to 41 nm     

Integrated Risk of Progress-Based Costs and Schedule Delays in Construction Projects

This article aims to explore the impact of the integration of risk factors into delayed milestones for construction projects. A simulation model was developed to determine the impact of schedule variability on cost estimation. To generate random scenarios a Monte Carlo Simulation (MCS) technique was applied. The developed model computes the cost impact of delayed milestone in the expected budget. Using a risk integration approach revealed the critical time frame that may lead to a budget deficit for a project. As a result, a number of cost-sensitive risk factors and schedule delays were identified for the critical time period where the risk of budget deficit increases. The method of integration proposed in this article highlights the priority of risk factors and schedule delays for construction contracts involving Payments at Event Occurrences (PEO). Consequently, the developed method can be useful for practitioners in anticipation of potential increase of costs, hence, prevention of failure due to budget deficit.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.     

Effect of magnetic field and temperature on the voltage-current characteristics of YBCO superconductor

Voltage-current characteristics of YBCO superconductor was studied under magnetic field up to 0.4 T at different temperatures below T_c. The critical temperature decreases and the transition width broadens under magnetic field. V-I data below T_c were fitted to a power law expression V I^(T,B) in which (T,B) is found to decrease with increase of magnetic field and temperature, gradually approaching unity as T approaches T_c, being independent of magnetic field. Similarly, (T,B) approaches unity as magnetic field increases being independent of temperature.     

Preserving spatial information and overcoming variations in appearance for face recognition

Local feature-based approaches mainly aim to achieve robustness to variations in facial images by assuming that only some parts of the facial images may be affected. However, such approaches may lose spatial information. In this study, a compromise feature extraction scheme is studied which extracts local features while preserving spatial information. The proposed scheme exploits an ensemble of classifiers where each member is constructed using randomly selected design parameters including the size, number and location of sub-images for local feature extraction. Experiments conducted on FERET and ORL databases have shown that proposed scheme surpasses the local feature-based reference systems which focus on either local information or preserving spatial information.