A Spanning Tree for Enhanced Cluster Based Routing in Wireless Sensor Network

Wireless Sensor Network (WSN) clustering architecture enables features like network scalability, communication overhead reduction, and fault tolerance. After clustering, aggregated data is transferred to data sink and reducing unnecessary, redundant data transfer. It reduces nodes transmitting, and so saves energy consumption. Also, it allows scalability for many nodes, reduces communication overhead, and allows efficient use of WSN resources. Clustering based routing methods manage network energy consumption efficiently. Building spanning trees for data collection rooted at a sink node is a fundamental data aggregation method in sensor networks. The problem of determining Cluster Head (CH) optimal number is an NP-Hard problem. In this paper, we combine cluster based routing features for cluster formation and CH selection and use Minimum Spanning Tree (MST) for intra-cluster communication. The proposed method is based on optimizing MST using Simulated Annealing (SA). In this work, normalized values of mobility, delay, and remaining energy are considered for finding optimal MST. Simulation results demonstrate the effectiveness of the proposed method in improving the packet delivery ratio and reducing the end to end delay.

Machine Learning Approach for Identifying Dementia from MRI Images

This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.

Preparation of Heterogeneous Ferrite Catalysts and Their Application for Fenton-Like Oxidation of Radioactive Organic Wastewater

Fenton oxidation technology is the general strategy for the treatment of organic compounds-contained wastewater. However, a considerable amount of ferric sludge was produced during the Fenton process as secondary wastes, which were needed to be further removed from the effluent and treated. In this study, heterogeneous catalysts based on ferrite oxide (Cu-Fe-Ce-O) were synthesized and characterized, and their application for Fenton-like oxidation of simulated and actual radioactive organic wastewater was investigated. The results of TOC decomposition efficiency around 54% ~ 99% were obtained when the catalyst loading, H2O2 loading, pH, temperature, and reaction time were controlled. In this case, no secondary wastes formed and the given catalysts were able to be separated by magnetic devices and reused again.

Quartz Crystal Microbalance Holder Design for On-Line Sensing in Liquid Applications

In this paper, the design of a QCM sensor for liquid media measurements in vertical position is described. A rugged and low-cost proof holder has been designed, the cost of which is significantly lower than those of traditional commercial holders. The crystal is not replaceable but it can be easily cleaned. Its small volume permits to be used by dipping it in the liquid with the desired location and orientation. The developed design has been experimentally validated by measuring changes in the resonance frequency and resistance of the QCM sensor immersed vertically in different calibrated aqueous glycerol solutions. The obtained results show a great agreement with the Kanazawa theoretical expression. Consequently, the designed QCM sensor would be appropriate for sensing applications in liquids, and might take part of a future on-line multichannel low-cost QCM-based measurement system.

Active Islanding Detection Method Using Intelligent Controller

An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Development of a Complete Single Jet Common Rail Injection System Gas Dynamic Model for Hydrogen Fueled Engine with Port Injection Feeding System

Modeling of hydrogen fueled engine (H2ICE) injection system is a very important tool that can be used for explaining or predicting the effect of advanced injection strategies on combustion and emissions. In this paper, a common rail injection system (CRIS) is proposed for 4-strokes 4-cylinders hydrogen fueled engine with port injection feeding system (PIH2ICE). For this system, a numerical one-dimensional gas dynamic model is developed considering single injection event for each injector per a cycle. One-dimensional flow equations in conservation form are used to simulate wave propagation phenomenon throughout the CR (accumulator). Using this model, the effect of common rail on the injection system characteristics is clarified. These characteristics include: rail pressure, sound velocity, rail mass flow rate, injected mass flow rate and pressure drop across injectors. The interaction effects of operational conditions (engine speed and rail pressure) and geometrical features (injector hole diameter) are illustrated; and the required compromised solutions are highlighted. The CRIS is shown to be a promising enhancement for PIH2ICE.

Ramification of Oil Prices on Renewable Energy Deployment

This paper contributes to the literature by updating the analysis of the impact of the recent oil prices fall on the renewable energy (RE) industry and deployment. The research analysis uses the Renewable Energy Industrial Index (RENIXX), which tracks the world’s 30 largest publicly traded companies and oil prices daily data from January 2003 to March 2016. RENIXX represents RE industries developing solar, wind, geothermal, bioenergy, hydropower and fuel cells technologies. This paper tests the hypothesis that claims high oil prices encourage the substitution of alternate energy sources for conventional energy sources. Furthermore, it discusses RENIXX performance behavior with respect to the governments’ policies factor that investors should take into account. Moreover, the paper proposes a theoretical model that relates RE industry progress with oil prices and policies through the fuzzy logic system.

A Practical and Efficient Evaluation Function for 3D Model Based Vehicle Matching

3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Towards Developing a Self-Explanatory Scheduling System Based on a Hybrid Approach

In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Induction Melting as a Fabrication Route for Aluminum-Carbon Nanotubes Nanocomposite

Increasing demands of contemporary applications for high strength and lightweight materials prompted the development of metal-matrix composites (MMCs). After the discovery of carbon nanotubes (CNTs) in 1991 (revealing an excellent set of mechanical properties) became one of the most promising strengthening materials for MMC applications. Additionally, the relatively low density of the nanotubes imparted high specific strengths, making them perfect strengthening material to reinforce MMCs. In the present study, aluminum-multiwalled carbon nanotubes (Al-MWCNTs) composite was prepared in an air induction furnace. The dispersion of the nanotubes in molten aluminum was assisted by inherent string action of induction heating at 790°C. During the fabrication process, multifunctional fluxes were used to avoid oxidation of the nanotubes and molten aluminum. Subsequently, the melt was cast in to a copper mold and cold rolled to 0.5 mm thickness. During metallographic examination using a scanning electron microscope, it was observed that the nanotubes were effectively dispersed in the matrix. The mechanical properties of the composite were significantly increased as compared to pure aluminum specimen i.e. the yield strength from 65 to 115 MPa, the tensile strength from 82 to 125 MPa and hardness from 27 to 30 HV for pure aluminum and Al-CNTs composite, respectively. To recognize the associated strengthening mechanisms in the nanocomposites, three foremost strengthening models i.e. shear lag model, Orowan looping and Hall-Petch have been critically analyzed; experimental data were found to be closely satisfying the shear lag model.

Mechanical Simulation with Electrical and Dimensional Tests for AISHa Containment Chamber

At Istituto Nazionale di Fisica Nucleare – Laboratorio Nazionale del Sud (INFN-LNS), a broad experience in the design, construction and commissioning of ECR and microwave ion sources is available. The AISHa ion source has been designed by taking into account the typical requirements of hospital-based facilities, where the minimization of the mean time between failures (MTBF) is a key point together with the maintenance operations, which should be fast and easy. It is intended to be a multipurpose device, operating at 18 GHz, in order to achieve higher plasma densities. It should provide enough versatility for future needs of the hadron therapy, including the ability to run at larger microwave power to produce different species and highly charged ion beams. The source is potentially interesting for any hadron therapy facility using heavy ions. In this paper, we analyzed the dimensional test and electrical test about an innovative solution for the containment chamber that allows us to solve our isolation and structural problems.

A ZVT-ZCT-PWM DC-DC Boost Converter with Direct Power Transfer

This paper presents a zero voltage transition-zero current transition (ZVT-ZCT)-PWM DC-DC boost converter with direct power transfer. In this converter, the main switch turns on with ZVT and turns off with ZCT. The auxiliary switch turns on and off with zero current switching (ZCS). The main diode turns on with ZVS and turns off with ZCS. Besides, the additional current or voltage stress does not occur on the main device. The converter has features as simple structure, fast dynamic response and easy control. Also, the proposed converter has direct power transfer feature as well as excellent soft switching techniques. In this study, the operating principle of the converter is presented and its operation is verified for 1 kW and 100 kHz model.

Enhancing Students’ Performance in Basic Science and Technology in Nigeria Using Moodle LMS

One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P

Modeling of Steady State Creep in Thick-Walled Cylinders under Internal Pressure

The present study focused on carrying out the creep analysis in an isotropic thick-walled composite cylindrical pressure vessel composed of aluminum matrix reinforced with silicon-carbide in particulate form. The creep behavior of the composite material has been described by the threshold stress based creep law. The values of stress exponent appearing in the creep law were selected as 3, 5 and 8. The constitutive equations were developed using well known von-Mises yield criteria. Models were developed to find out the distributions of creep stress and strain rate in thick-walled composite cylindrical pressure vessels under internal pressure. In order to obtain the stress distributions in the cylinder, the equilibrium equation of the continuum mechanics and the constitutive equations are solved together. It was observed that the radial stress, tangential stress and axial stress increases along with the radial distance. The cross-over was also obtained almost at the middle region of cylindrical vessel for tangential and axial stress for different values of stress exponent. The strain rates were also decreasing in nature along the entire radius.

Probabilistic Damage Tolerance Methodology for Solid Fan Blades and Discs

Solid fan blades and discs in aero engines are subjected to high combined low and high cycle fatigue loads especially around the contact areas between blade and disc. Therefore, special coatings (e.g. dry film lubricant) and surface treatments (e.g. shot peening or laser shock peening) are applied to increase the strength with respect to combined cyclic fatigue and fretting fatigue, but also to improve damage tolerance capability. The traditional deterministic damage tolerance assessment based on fracture mechanics analysis, which treats service damage as an initial crack, often gives overly conservative results especially in the presence of vibratory stresses. A probabilistic damage tolerance methodology using crack initiation data has been developed for fan discs exposed to relatively high vibratory stresses in cross- and tail-wind conditions at certain resonance speeds for limited time periods. This Monte-Carlo based method uses a damage databank from similar designs, measured vibration levels at typical aircraft operations and wind conditions and experimental crack initiation data derived from testing of artificially damaged specimens with representative surface treatment under combined fatigue conditions. The proposed methodology leads to a more realistic prediction of the minimum damage tolerance life for the most critical locations applicable to modern fan disc designs.

Tensile and Fracture Properties of Cast and Forged Composite Synthesized by Addition of in-situ Generated Al3Ti-Al2O3 Particles to Magnesium

TiO2 particles have been added in molten aluminium to result in aluminium based cast Al/Al3Ti-Al2O3 composite, which has been added then to molten magnesium to synthesize magnesium based cast Mg-Al/Al3Ti-Al2O3 composite. The nominal compositions in terms of Mg, Al, and TiO2 contents in the magnesium based composites are Mg-9Al-0.6TiO2, Mg-9Al-0.8TiO2, Mg-9Al-1.0TiO2 and Mg-9Al-1.2TiO2 designated respectively as MA6T, MA8T, MA10T and MA12T. The microstructure of the cast magnesium based composite shows grayish rods of intermetallics Al3Ti, inherited from aluminium based composite but these rods, on hot forging, breaks into smaller lengths decreasing the average aspect ratio (length to diameter) from 7.5 to 3.0. There are also cavities in between the broken segments of rods. β-phase in cast microstructure, Mg17Al12, dissolves during heating prior to forging and re-precipitates as relatively finer particles on cooling. The amount of β-phase also decreases on forging as segregation is removed. In both the cast and forged composite, the Brinell hardness increases rapidly with increasing addition of TiO2 but the hardness is higher in forged composites by about 80 BHN. With addition of higher level of TiO2 in magnesium based cast composite, yield strength decreases progressively but there is marginal increase in yield strength over that of the cast Mg-9 wt. pct. Al, designated as MA alloy. But the ultimate tensile strength (UTS) in the cast composites decreases with the increasing particle content indicating possibly an early initiation of crack in the brittle inter-dendritic region and their easy propagation through the interfaces of the particles. In forged composites, there is a significant improvement in both yield strength and UTS with increasing TiO2 addition and also, over those observed in their cast counterpart, but at higher addition it decreases. It may also be noted that as in forged MA alloy, incomplete recovery of forging strain increases the strength of the matrix in the composites and the ductility decreases both in the forged alloy and the composites. Initiation fracture toughness, JIC, decreases drastically in cast composites compared to that in MA alloy due to the presence of intermetallic Al3Ti and Al2O3 particles in the composite. There is drastic reduction of JIC on forging both in the alloy and the composites, possibly due to incomplete recovery of forging strain in both as well as breaking of Al3Ti rods and the voids between the broken segments of Al3Ti rods in composites. The ratio of tearing modulus to elastic modulus in cast composites show higher ratio, which increases with the increasing TiO2 addition. The ratio decreases comparatively more on forging of cast MA alloy than those in forged composites.

Switching Studies on Ge15In5Te56Ag24 Thin Films

Germanium Telluride based quaternary thin film switching devices with composition Ge15In5Te56Ag24, have been deposited in sandwich geometry on glass substrate with aluminum as top and bottom electrodes. The bulk glassy form of the said composition is prepared by melt quenching technique. In this technique, appropriate quantity of elements with high purity are taken in a quartz ampoule and sealed under a vacuum of 10-5 mbar. Then, it is allowed to rotate in a horizontal rotary furnace for 36 hours to ensure homogeneity of the melt. After that, the ampoule is quenched into a mixture of ice - water and NaOH to get the bulk ingot of the sample. The sample is then coated on a glass substrate using flash evaporation technique at a vacuum level of 10-6 mbar. The XRD report reveals the amorphous nature of the thin film sample and Energy - Dispersive X-ray Analysis (EDAX) confirms that the film retains the same chemical composition as that of the base sample. Electrical switching behavior of the device is studied with the help of Keithley (2410c) source-measure unit interfaced with Lab VIEW 7 (National Instruments). Switching studies, mainly SET (changing the state of the material from amorphous to crystalline) operation is conducted on the thin film form of the sample. This device is found to manifest memory switching as the device remains 'ON' even after the removal of the electric field. Also it is found that amorphous Ge15In5Te56Ag24 thin film unveils clean memory type of electrical switching behavior which can be justified by the absence of fluctuation in the I-V characteristics. The I-V characteristic also reveals that the switching is faster in this sample as no data points could be seen in the negative resistance region during the transition to on state and this leads to the conclusion of fast phase change during SET process. Scanning Electron Microscopy (SEM) studies are performed on the chosen sample to study the structural changes at the time of switching. SEM studies on the switched Ge15In5Te56Ag24 sample has shown some morphological changes at the place of switching wherein it can be explained that a conducting crystalline channel is formed in the device when the device switches from high resistance to low resistance state. From these studies it can be concluded that the material may find its application in fast switching Non-Volatile Phase Change Memory (PCM) Devices.

Lean Healthcare: Barriers and Enablers in the Colombian Context

Lean philosophy has evolved over time and has been implemented both in manufacturing and services, more recently lean has been integrated in the companies of the health sector. Currently it is important to understand the successful way to implement this philosophy and try to identify barriers and enablers to the sustainability of lean healthcare. The main purpose of this research is to identify the barriers and enablers in the implementation of Lean Healthcare based on case studies of Colombian healthcare centers. In order to do so, we conducted semi-structured interviews based on a maturity model. The main results indicate that the success of Lean implementation depends on its adaptation to contextual factors. In addition, in the Colombian context were identified new factors such as organizational culture, management models, integration of the care and administrative departments and triple helix relationship.

Management Control Systems in Post-Incubation: An Investigation of Closed Down High-Technology Start-Ups

Insufficient informal communication systems can lead to the first crisis (‘Crisis of Leadership’) for start-ups. Management Control Systems (MCS) are one way for high-technology start-ups to successfully overcome these problems. So far the literature has investigated the incubation of a start-up, but focused less on the post-incubation stage. This paper focuses on the use of MCS in post-incubation and, if failed start-ups agree, on how MCS are used. We conducted 14 semi-structured interviews for this purpose, to obtain our results. The overall conclusion is that the majority of the companies were closed down due to a combination of strategic, operative and financial reasons.

Measuring Enterprise Growth: Pitfalls and Implications

Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.