Compliance of Healthcare Workers with Hand Hygiene Practices in the Northeast of Iran: an Overt Observation

Hand hygiene (HH) is one of the most effective methods to prevent transmission and spread of microorganisms from one patient to another, also, it used to reduce the spread of pathogens in clinical settings and to help control outbreaks but compliance is usually poor. The purpose of this study was to analyze the compliance of hand hygiene and affecting factors among healthcare workers (HCWs) of northeast hospitals in Iran. This study was conducted based on observation method for the compliance of hand hygiene according to the World Health Organization (WHO) guidelines. HCWs were observed during routine patient care in different shifts, also the technique of hand hygiene was assessed through hand washing with alcohol-based disinfectant. Data were collected during 1 year, from June 2014 to July 2015 by the infection control teams in the northeast hospital of Iran. By direct observation, we evaluated a total of 92518 hand hygiene opportunities from 29 hospitals in the northeast of Iran during 1 year, with overall compliance rates in these hospitals were 43.42%. Compliance rates differed by role: nurses43%, doctors 19 % and other health workers 29%. In this observational study, we identified that adherence to hand hygiene practice and use of alcohol-based disinfectant was very low in this hospitals, so effective intervention programs to promote adherence to hand hygiene and use of disinfectants could be effective to increase compliance

An Energy Efficient Mac Layer Design for Wireless Sensor Network

Recent technological advances in sensors, low power integrated circuits, and wireless communications have enabled the design of low-cost, lightweight, and intelligent wireless sensor nodes. The IEEE 802.15.4 standard is a specific Wireless Personal Area Network (WPAN) standard designed for various wireless sensor applications. Idle listening, packet collision, control packet overhead and overhearing are considered as energy consuming resources in WSNs. As the idle listening and packet collision are two major power consuming parts, we considered two solutions for reducing both of them to achieve an energy efficient protocol. We concentrate on the MAC layer design to overcome the energy consumption by radio management procedure and the backoff exponent mechanism. In the radio management, we analyze the contention part of the active duration of the MAC IEEE 802.15.4 standard superframe and allow nodes to enter the sleep state regarding to their available data for transmission instead of staying awake for the entire active period. This method will be useful especially when sensors do not have any data to send. The proposed non-persistent Carrier Sense Multiple Access (np-CSMA) protocol employs backoff exponent management mechanism. This algorithm helps the network to be reliable under traffic changes and saves more energy by avoiding collision. It assigns different range of BE (backoff exponent) to each node with respect to node’s contribution in network traffic. In our scheme a coordinator can observe the network traffic due to the data information associated with devices. It can manage the Personal Area Networks (PANs) devices by the beacon packet to go to sleep mode when they do not have any packet to send. In this thesis, by using the sleep period together with backoff exponent management in our protocol design, the amount of energy consumption will be reduced. The proposed model has been compared to original 802.15.4 standard and the existing Adaptive Backoff Exponent (ABE) MAC protocol to illustrate the improvement. Moreover, the BE management algorithm derives better system performance such as end-to-end delay, throughput, packet delivery ratio and Link Quality Indicator (LQI). The proposed model has been designed in such a way that the introduction of extra sleep period inserted in superframe improves the energy efficiency while maintaining other system performance parameters. The proposed MAC protocol has improved the energy consumption around 60% as compared to ABE-MAC. The proposed MAC protocol with an extra radio management technique together with backoff management procedure can achieve 70% more energy saving than MAC IEEE 802.15.4 standard

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable. View Full-Tex

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework

Adaptive data collection algorithm for wireless sensor networks

Periodical Data collection from unreachable remote terrain and then transmit information to a base station is one of the targeted application of sensor networks. The energy restriction of battery powered sensor nodes is a big challenge for this network as it is difficult or in some cases not feasible to change the power supply of motes. Therefore, in order to keep the networks operating for long time, efficient utilization of energy is considered with highest priority. In this paper we propose TA-PDC-MAC protocol - a traffic adaptive periodic data collection MAC which is designed in a TDMA fashion. This design is efficient in the ways that it assigns the time slots for nodes’ activity due to their sampling rates in a collision avoidance manner. This ensures minimal consumption of network energy and makes a longer network lifetime, as well as it provides small end-to-end delay and packet loss ratio. Simulation results show that our protocol demonstrates up to 35% better performance than that of most recent protocol that proposed for this kind of application, in respect of energy consumption. Comparative analysis and simulation show that TA-PDC-MAC considerably gives a good compromise between energy efficiency and latency and packet loss rate

Trade-off between energy consumption and target delay for wireless sensor network

Wireless sensor networks (WSN) consists of unattended sensors with limited storage, energy (battery power) and computational and communication capabilities. Since battery power is the most crucial resource for sensor nodes and delay time is a critical metric for certain WSN applications, data diffusion between source sensors and sink should be done in an energy efficient and timely manner. We characterize the trade off between the energy consumption and source to sink delay in order to extend the operation of individual sensors and hence increase the lifetime of the WSN. To achieve this goal, the transmission range of sensors is first decomposes into certain ranges based on a minimal distance between consecutive forwarding sensors and then classifies these ranges due to Degree of Interest. It is also shown that the use of sensor nodes which lie on or closely to the shortest path between the source and the sink helps minimize these two metrics