A Reliable Communication Model Based on IEEE802.15.4 for WSANs in Smart Grids

Creating cyber-physical systems (CPSs) based on wireless sensor and actuator networks (WSANs) has great potential to improve the performance of Smart Grid. In addition, IEEE802.15.4 has widely been regarded as an appropriate standard for WSANs, due to some striking and unique features. WSANs require provisioning strict quality of service (QoS) due to noisy harsh environments in Smart Grid applications. Although analytical models have been studied in the literature, they have not provided a full-fledged model for Smart Grid. In this paper, we have added a MAC-level buffer, and a novel Markov chain model has been also proposed. By comparison with previous studies, retransmission confines, acknowledgment, packet length variation, saturated traffic, and degenerate distribution of packet generation are accounted for. The algorithm has been experimentally implemented and appraised on a platform with self-designed WSAN. The analytical model predicts well our exhaustive experiments. Further, Monte Carlo simulations validate mathematical results

Determination of Heavy Metals through Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) in Iranian Cheese and Their Potential Health Risks to the Adult Consumers

In Iran, cheese is one of the dairy products that widely consumed as a main diet for breakfast. Moreover, trace metals in dairy products have recently gained considerable attention. Iranian cheese samples were collected from Tehran, Iran (February to May 2013). Trace metals including Pb, Cd, Ni, Fe, Sn, Zn, Cr, and Cu were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) after dry ashing. All the tested metals were detected in the cheese samples. The mean concentration of metals in cheese showed the following decreasing order Zn > Fe > Cu > Ni > Sn > Cr > Pb > Cd, with values of 12.98, 7.95, 1.96, 0.83, 0.46, 0.37, 0.34, and 0.01 mg/kg, respectively. There were no significant differences between types of cheese samples in terms of content of trace metals (p>0.05). All the samples had Pb contents of greater than Codex limit (0.02 mg/kg). According to the measured values of the metals in this study, the intake of all the studied elements through the common consumption of cheese in Iran was below the dangerous level according to permissible intake value for each metal. Also, levels of correlations between the element pairs were analyzed

Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Background and Aims A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. Objectives This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. Methodology A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. Results By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. Conclusion This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning

Ocular dimensions by three-dimensional magnetic resonance imaging in emmetropic versus myopic school children

Background: Magnetic resonance imaging (MRI) has been used to investigate eye shapes; however, reports involving children are scarce. This study aimed to determine ocular dimensions, and their correlations with refractive error, using three-dimensional MRI in emmetropic versus myopic children. Methods: Healthy school children aged < 10 years were invited to take part in this cross-sectional study. Refraction and best-corrected distance visual acuity (BCDVA) were determined using cycloplegic refraction and a logarithm of the minimum angle of resolution (logMAR) chart, respectively. All children underwent MRI using a 3-Tesla whole-body scanner. Quantitative eyeball measurements included the longitudinal axial length (LAL), horizontal width (HW), and vertical height (VH) along the cardinal axes. Correlation analysis was used to determine the association between the level of refractive error and the eyeball dimensions. Results: A total of 70 eyes from 70 children (35 male, 35 female) with a mean (standard deviation [SD]) age of 8.38 (0.49) years were included and analyzed. Mean (SD) refraction (spherical equivalent, SEQ) and BCDVA were -2.55 (1.45) D and -0.01 (0.06) logMAR, respectively. Ocular dimensions were greater in myopes than in emmetropes (all P < 0.05), with no significant differences according to sex. Mean (SD) ocular dimensions were LAL 24.07 (0.91) mm, HW 23.41 (0.82) mm, and VH 23.70 (0.88) mm for myopes, and LAL 22.69 (0.55) mm, HW 22.65 (0.63) mm, and VH 22.94 (0.69) mm for emmetropes. Significant correlations were noted between SEQ and ocular dimensions, with a greater change in LAL (0.46 mm/D, P < 0.001) than in VH (0.27 mm/D, P < 0.001) and HW (0.22 mm/D, P = 0.001). Conclusions: Myopic eyeballs are larger than those with emmetropia. The eyeball elongates as myopia increases, with the greatest change in LAL, the least in HW, and an intermediate change in VH. These changes manifest in both sexes at a young age and low level of myopia. These data may serve as a reference for monitoring the development of refractive error in young Malaysian children of Chinese origin

Pterygium classification using deep patch region-based anterior segment photographed images

Early pterygium screening is crucial to avoid blurred vision caused by cornea and pupil encroachment. However, medical assessment and conventional screening could be laborious and time-consuming to be implemented. This constraint seeks an advanced yet efficient automated pterygium screening to assist the current diagnostic method. Patch region-based anterior segment photographed images (ASPIs) focus the feature on a particular region of the pterygium growth. This work addresses the data limitation on deep neural network (DNN) processing with large-scale data requirements. It presents an automated pterygium classification of patch region-based ASPI using our previous re-establish network, “VggNet16-wbn”, the VggNet16, with the addition of batch normalisation layer after each convolutional layer. During an image pre-processing step, the pterygium and nonpterygium tissue are extracted from ASPI, followed by the generation of a single and three-by-three image patch region-based on the size of the 85×85 dataset. Data preparation with 10-fold cross-validation has been conducted to ensure the data are well generalised to minimise the probability of underfitting and overfitting problems. The proposed experimental work has successfully classified the pterygium tissue with more than 99% accuracy, sensitivity, specificity, and precision using appropriate hyperparameters values. This work could be used as a baseline framework for pterygium classification using limited data processing

Deep learning for an automated image-based stem cell classification

Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research

Mechanisms of nano powder Production by means of sol-gel route

In this work production of nano powders by means of sol-gel route were reviewed. Proposed mechanisms, required materials such as solvents or solutes that used for production of industrial nano powders and the effects of them on the characteristics of products were evaluated. Various processes that used for production of nano metal fluorides such as AlF3 and MgF2, nano composites such as Al2O3-ZrO2 and nano metal oxides such as TiO2 and TiO2 adapted to Mg2+ and Br2+ were also reviewed

Determination of Heavy Metals through Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) in Iranian Cheese and Their Potential Health Risks to the Adult Consumers

In Iran, cheese is one of the dairy products that widely consumed as a main diet for breakfast. Moreover, trace metals in dairy products have recently gained considerable attention. Iranian cheese samples were collected from Tehran, Iran (February to May 2013). Trace metals including Pb, Cd, Ni, Fe, Sn, Zn, Cr, and Cu were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) after dry ashing. All the tested metals were detected in the cheese samples. The mean concentration of metals in cheese showed the following decreasing order Zn > Fe > Cu > Ni > Sn > Cr > Pb > Cd, with values of 12.98, 7.95, 1.96, 0.83, 0.46, 0.37, 0.34, and 0.01 mg/kg, respectively. There were no significant differences between types of cheese samples in terms of content of trace metals (p>0.05). All the samples had Pb contents of greater than Codex limit (0.02 mg/kg). According to the measured values of the metals in this study, the intake of all the studied elements through the common consumption of cheese in Iran was below the dangerous level according to permissible intake value for each metal. Also, levels of correlations between the element pairs were analyzed