Lie algebra representations and 2-index 4-variable 1-parameter Hermite polynomials

This paper is an attempt to stress the usefulness of multi-variable special functions by expressing them in terms of the corresponding Lie algebra or Lie group. The problem of framing the 2-index 4-variable 1-parameter Hermite polynomials (2I4V1PHP) into the context of the irreducible representations $\uparrow _{\omega,\mu}$ of $\mathcal{G}(0,1)$ and $\uparrow^{'} _{\omega,\mu}$ of $\mathcal{K}_{5}$ is considered. Certain relations involving 2I4V1PHP $H_{m,n}(x,y,z,u;\rho)$ are obtained using the approach adopted by Miller. Certain examples involving other forms of Hermite polynomials are derived as special cases. Further, some properties of the 2I4V1PHP $H_{m,n}(x,y,z,u;\rho)$ are obtained by using a quadratic combination of four operators defined on a Lie algebra of endomorphisms of a vector space

Internet of Things (IoT) for Healthcare Application: Wearable Sleep Body Position Monitoring System Using IoT Platform

People with health conditions such as dementia often face problems sleeping, experience wake-rest routine changes and suffer from emotional disturbances amid sleep disorders. Caregivers, such as family members, of dementia sufferers face great challenge in taking care of such patients at night as it takes a toll on their own sleep quality resulting in sleep deprivation and other issues. The goal of this work, presented in this paper, is to develop a wearable body position monitor that can detect user's body position and keep online records during sleep; provide light when data shows the user is not asleep, aid user to fall asleep with audio assist feature, and if necessary, activate emergency alert call to caregivers when the patient remains seated or stands for longer durations (e.g. more than 20 minutes) at night. Main system components of the developed prototype include MySignals HW Complete Kit (e-health medical development platform), Arduino Uno microcontroller, LEDs, speakers, micro SD card, micro SD card reader, SPI interface and esp8266 module. Real-time transmission, data analysis and visualization and remote data storage has been realized. The plan for the next phase of this work will include application of sleep pattern recognition and machine learning techniques on large datasets and real biometric measurements

Internet of Things (IoT) Enabled Smart Indoor Air Quality Monitoring System

This article introduces development of a system that monitors indoor air quality by using Internet of Things (IoT) technology. The objective of this system is to monitor and improve indoor air quality automatically, i.e. with minimum human intervention. The system contains physical circuit and an interactive platform. Main components used in physical circuit are Arduino Leonardo, Dust Sensor, Temperature and Humidity Sensor, LCD Display and Fan. Interactive platforms involved are The Things Network and Ubidots. Principal parameters of interest are sensed by physical circuit and converted into Air Quality Index (AQI), which is then sent to an interactive platform via gateway. After estimating AQI, the Interactive platform triggers events based on certain predetermined conditions to improve air quality through SMS alerts and circuit actuators

Artificial Intelligence enabled Smart Refrigeration Management System using Internet of Things Framework

Design of an intelligent refrigeration management system using artificial intelligence and Internet of Things (IoT) technology is presented in this paper. This system collects the real-time temperature inside the refrigeration implement, record the information of products and enhance function of refrigerators through the application of Internet of Things technology to facilitate people in managing their refrigerated and frozen groceries smartly. The proposed system is divided into two parts, On-board sub-system and Internet based sub-system. An Arduino Leonardo board is used in onboard sub-system to control other components including low power machine vision OpenMV module, temperature & Humidity sensor, and GY-302 light intensity sensor. OpenMV camera module is used for recognizing types of food, reading barcodes and OCR (optical character recognition) through convolution neural network (CNN) algorithm and tesseract-ocr. The food type identification model is trained by the deep learning framework Caffe. GY-302 light intensity sensor works as a switch of camera module. DHT11 sensor is used to monitor the environmental information inside the freezer. The internet based sub-system works on the things network. It saves the information and uploads it from onboard sub-system and works as an interface to food suppliers. The system demonstrates that the combination of existing everyday utility systems and latest Artificial Intelligence (AI) and Internet of Things (IoT) technologies could help develop smarter applications and devices

Association Between Cytomegalovirus Infection and Bad Obstetric Outcomes in Women From Kirkuk

The human cytomegalovirus (CMV) is one of the common viral infections worldwide that represent a major causes of congenital infections. To determine the seroprevalence of CMV in women with bad obstetric history and sociodemographic characteristics that may influence the seropositivity, a case control descriptive prospective study was conducted in Kirkuk, Iraq. A 838 women with age range from 14 to 48 were included in the study. Of the total, 547 women were with bad obstetric history(BOH) and 291 women with normal previous pregnancy as control group. All the serum samples collected from the study and control groups were tested for CMV IgM and IgG antibodies by ELISA kits. CMV IgM seroprevalence was higher in women with BOH. CMV IgG seroprevalence was with no significant difference between BOH and control. CMV IgG seroprevalence significantly influenced by age, education, smoking, and family size. However, CMV IgM seroprevalence significantly associated with pregnancy, residence, and animal exposure. Odd ratio confirmed the association between CMV IgG and age, crowding index, residence, smoking, and number of abortion in women with BOH. In addition, current CMV infection significantly associated with residence in women with BOH

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design

Evaluate the Shear Bond Strength for Alkasite in Comparison with other Esthetic Restorative Materials

Aims: To assess and compare the shear bond strength of alkasite restoration, as well as, to compare the shear bond strength between alkasite with and without bonding. Materials and methods: Twenty-five permanent maxillary premolars were used in which, with diamond disks, their buccal surfaces were flattened until a clear superficial dentinal surface could be seen. Samples were randomly assigned to five groups (n=5). Group 1: alkasite without adhesive, Group 2: alkasite with adhesive, Group 3: Nanohybrid composite, Group 4: Glass ionomer cement, and Group 5: Resin modified glass inomer cement. Following the recommendations of the manufacturers, cylinders of the five restorative materials were bonded to the buccal surfaces. Following 24 hours storage at 37°C. The evaluation of shear bond strength was employed by the use of the universal testing machine. Under a stereomicroscope (×20), the fracture mode was determined. Data were statistically analyzed using a nonparametric independent sample Kruskal-Wallis test at the confidence level of 95%. Result: There were statistical differences among groups and there was a significant difference between the alkasite with and without bonding. Conclusion: Alkasite with bonding showed a higher shear bond strength in comparison with GIC and resin-modified GIC, but still lower than that of nanohybrid composite Moreover, the shear bond strength of alkasite highly improved with the use of bonding

Bone Histomorphometry Revisited

Bone histomorphometry is defined as a quantitative evaluation of bone micro architecture, remodelling and metabolism. Bone metabolic assessment is based on a dynamic process, which provides data on bone matrix formation rate by incorporating a tetracycline compound. In the static evaluation, samples are stained and a semi-automatic technique is applied in order to obtain bone microarchitectural parameters such as trabecular area, perimeter and width. These parameters are in 2D, but they can be extrapolated into 3D, applying a stereological formula. Histomorphometry can be applied to different areas; however, in recent decades it has been a relevant tool in monitoring the effect of drug administration in bone. The main challenge for the future will be the development of noninvasive methods that can give similar information. In the herein review paper we will discuss the general principles and main applications of bone histomorphometry

Evaluation of the knowledge and practices of pregnant Yemeni Women regarding teratogens

Purpose: To investigate the knowledge and practice of pregnant women with regards to teratogens.Methods: A month-long cross-sectional study was carried out among 150 pregnant women selected from four Motherhood and Child Healthcare Centers (MCHCs) in Mukalla District of Yemen. Data collection was conducted during face-to-face interviews using a questionnaire. Descriptive and simple regression analyses were used.Results: Of the 150 pregnant women who participated in the study, 95.3 % of the pregnant women were < 36 years old, 7.4 % had children with congenital malformations, 62 % indicated that they had heard about folic acid; however, only 16.6 % knew the significance of folic acid. Regarding toxoplasmosis, 94.7 % indicated that they had heard about toxoplasmosis, and 76 % knew about the serious consequences of the disease (congenital malformation and abortion) during pregnancy. Based on simple regression analysis, the results indicate that education and parity, irrespective of age or income level, were the major factors determining better knowledge and practices in pregnancy with regards to toxoplasmosis.Conclusion: Knowledge of folic acid deficiency among pregnant women in Mukalla District of Yemen is relatively low. Furthermore, preventive practices to avoid folic acid deficiency are minimal.Keywords: Knowledge, Practices, Teratogens, Pregnant Yemeni women, Folic acid deficienc

Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning

This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms in order to minimise power consumption. A reinforcement learning(RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with aXilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms