Application of artificial neural network (ANN)–self-organizing map (SOM) for the categorization of water,soil and sediment quality in petrochemical regions

The utilization of mathematical and computational tools for pollutant assessment frameworks has become increasingly valuable due to the capability to interpret integrated variable measurements. Artificial neural networks (ANNs) are considered as dependable and inexpensive techniques for data interpretation and prediction. The self-organizing map (SOM) is an unsupervised ANN used for data training to classify and effectively recognize patterns embedded in the input data space. Application of SOM–ANN is useful for recognizing spatial patterns in contaminated zones by integrating chemical, physical, ecotoxicological and toxicokinetic variables in the identification of pollution sources and similarities in the quality of the samples. Water (n = 11), soil (n = 38) and sediment (n = 54) samples from four areas in the Niger Delta (Nigeria) were classified based on their chemical, toxicological and physical variables applying the SOM. The results obtained in this study provided valuable assessment using the SOM visualization capabilities and highlighted zones of priority that might require additional investigations and also provide productive pathway for effective decision making and remedial actions.     

Integration of growing self-organizing map and continuous genetic algorithm for grading lithium-ion battery cells

This study attempts to employ growing self-organizing map (GSOM) algorithm and continuous genetic algorithm (CGA)-based SOM (CGASOM) to improve the performance of SOM neural network (SOMnn). The proposed GSOM + CGASOM approach for SOMnn is consisted of two stages. The first stage determines the SOMnn topology using GSOM algorithm while the weights are fine-tuned by using CGASOM algorithm in the second stage. The proposed CGASOM algorithm is compared with other two clustering algorithms using four benchmark data sets, Iris, Wine, Vowel, and Glass. The simulation results indicate that CGASOM algorithm is able to find the better solution. Additionally, the proposed approach has been also employed to grade Lithium-ion cells and characterize the quality inspection rules. The results can assist the battery manufacturers to improve the quality and decrease the costs of battery design and manufacturing.     

Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. The Simplified Acute Physiology System 2nd version (SAPS II) is one of the most popular mortality scoring systems currently available. This study retrospectively collected data on 496 patients admitted to intensive care units from year 2000 to 2001. The average patient age was 59.96 ± 1.83 years old and 23.8% of patients died before discharge. We used these data as training data and constructed an exponential Bayesian mortality prediction model by combining BSM (Bayesian statistical model) and GA (genetic algorithm). The optimal weights and the parameters were determined with GA. Furthermore, we prospectively collected data on 142 patients for testing the new model. The average patient age for this group was 57.80 ± 3.33 years old and 21.8% patients died before discharge. The mortality prediction power of the new model was better than SAPS II (p < 0.001). The new model combining BSM and GA can manage both binary data and continuous data. The mortality rate is predicted to be high if the patient’s Glasgow coma score is less than 5.     

Using artificial neural networks to invert 2D DC resistivity imaging data for high resistivity contrast regions: A MATLAB application

MATLAB is a high-level matrix/array language with control flow statements and functions. MATLAB has several useful toolboxes to solve complex problems in various fields of science, such as geophysics. In geophysics, the inversion of 2D DC resistivity imaging data is complex due to its non-linearity, especially for high resistivity contrast regions. In this paper, we investigate the applicability of MATLAB to design, train and test a newly developed artificial neural network in inverting 2D DC resistivity imaging data. We used resilient propagation to train the network. The model used to produce synthetic data is a homogeneous medium of 100 Ω m resistivity with an embedded anomalous body of 1000 Ω m. The location of the anomalous body was moved to different positions within the homogeneous model mesh elements. The synthetic data were generated using a finite element forward modeling code by means of the RES2DMOD. The network was trained using 21 datasets and tested on another 16 synthetic datasets, as well as on real field data. In field data acquisition, the cable covers 120 m between the first and the last take-out, with a 3 m x-spacing. Three different electrode spacings were measured, which gave a dataset of 330 data points. The interpreted result shows that the trained network was able to invert 2D electrical resistivity imaging data obtained by a Wenner–Schlumberger configuration rapidly and accurately.     

How does customer self-construal moderate CRM value creation chain?

Most of the existing literature on CRM value chain creation has focused on the effect of customer satisfaction and customer loyalty on customer profitability. In contrast, little has been studied about the CRM value creation chain at individual customer level and the role of self-construal (i.e., independent self-construal and interdependent self-construal) in such a chain. This research aims to construct the chain from customer value to organization value (i.e., customer satisfaction → customer loyalty → patronage behavior) and investigate the moderating effect of self-construal. To test the hypotheses suggested by our conceptual framework, we collected 846 data points from China in the context of mobile data services. The results show that customer’s self-construal can moderate the relationship chain from customer satisfaction to customer loyalty to relationship maintenance and development. This implies firms should tailor their customer strategies based on different self-construal features.     

Predicting guild commitment in World of Warcraft with the investment model of commitment

Many MMORPG offer players the possibility to become a member of a guild, a hierarchical organization of characters with common objectives. Guild membership can be beneficial to game progress, and offer opportunities for social interaction. In the current study we focus on the MMORPG World of Warcraft (WoW), with the main aim to examine whether guild commitment and players’ intention to remain in their guild can be predicted by players’ satisfaction, investments, and perceptions of alternatives to their guild. To this end, 165 WoW players completed an online questionnaire and answered questions related to their guild membership. They also completed the investment model scale which was reworded so all questions pertained to their guild and their fellow guild members. Results show that satisfaction level, quality of alternatives, and investment size significantly predict commitment level (p’s < .001), which in turn predicted likelihood of participants’ staying with their current guild (p < .001) and the number of guilds they had been a member of in the past (p < .001). Moreover, high levels of guild commitment were indicative of better mental health, whereas weekly hours of game play was negatively related to mental health. In the discussion, we conclude that interdependence theory and the investment model of commitment are applicable to online gaming environments, and we argue that commitment to one’s guild is one factor that could prevent the risks associated with online game play (i.e. problematic use).     

Experimental blood glucose interval identification of patients with type 1 diabetes

Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty.The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each.Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.     

Effect of single crystalline MgO powder treatment of phosphor surface on discharge property of high-Xe AC plasma display panels

This paper presents a method of surface treatment of phosphor using a single crystalline MgO powder, and investigates effect of the surface treatment on discharge property of a plasma display panel (PDP). The discharge gas of the PDP was a 88% Ne–12% Xe gas mixture at a pressure of 400 Torr. For the surface treatment, a single crystalline MgO powder was mixed in methanol, sprayed on the phosphor surface, and annealed at 400 °C. This treatment suppressed glow discharge, which was observed very often in the weak discharges for reset of PDP if the phosphor works as a cathode, and provided a favorable condition for the PDP reset operation. When an experimental PDP fabricated using the surface treatment was driven by the twin reset waveform, the measured time delay for 99.9% cumulative probability of address discharge was 830 ns, which meets the timing requirement for addressing a Full HD (1920 × 1080 resolution) PDP with the 10 sub-field single scan method.     

A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

A neural network combined to a neural classifier is used in a real time forecasting of hourly maximum ozone in the centre of France, in an urban atmosphere. This neural model is based on the MultiLayer Perceptron (MLP) structure. The inputs of the statistical network are model output statistics of the weather predictions from the French National Weather Service. These predicted meteorological parameters are very easily available through an air quality network. The lead time used in this forecasting is (t + 24) h. Efforts are related to a regularisation method which is based on a Bayesian Information Criterion-like and to the determination of a confidence interval of forecasting. We offer a statistical validation between various statistical models and a deterministic chemistry-transport model. In this experiment, with the final neural network, the ozone peaks are fairly well predicted (in terms of global fit), with an Agreement Index = 92%, the Mean Absolute Error = the Root Mean Square Error = 15 μg m⁻³ and the Mean Bias Error = 5 μg m⁻³, where the European threshold of the hourly ozone is 180 μg m⁻³.To improve the performance of this exceedance forecasting, instead of the previous model, we use a neural classifier with a sigmoid function in the output layer. The output of the network ranges from [0,1] and can be interpreted as the probability of exceedance of the threshold. This model is compared to a classical logistic regression. With this neural classifier, the Success Index of forecasting is 78% whereas it is from 65% to 72% with the classical MLPs. During the validation phase, in the Summer of 2003, six ozone peaks above the threshold were detected. They actually were seven.Finally, the model called NEUROZONE is now used in real time. New data will be introduced in the training data each year, at the end of September. The network will be re-trained and new regression parameters estimated. So, one of the main difficulties in the training phase – namely the low frequency of ozone peaks above the threshold in this region – will be solved.     

Well-being and problematic Facebook use

Social networking sites are getting more widespread online communication forms all over the world. Especially adolescents and emerging adults use these sites to make connection, and share something. Although, Facebook is one of the most popular sites, research is still in nascent phase on the excessive use of Facebook. Hence, it is fundamental to determine potential predictors of overuse of Facebook. The present study investigated the relationship between well-being and problematic Facebook use. Participants were 311 university students [179_(58%) female, 132_(42%) male, M_age = 20.86 years, SD = 1.61] who completed the questionnaire packet including the Bergen Facebook Addiction Scale (Andreassen, Torsheim, Brunborg, & Pallesen, 2012), the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), the Subjective Vitality Scale (Ryan & Frederick, 1997), the Flourishing Scale (Diener et al., 2010), and the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999). A stepwise regression analyses was used with four independent variables (life satisfaction, subjective vitality, flourishing, and subjective happiness) to explain variance in problematic Facebook use. The results showed that life satisfaction, subjective vitality, flourishing, and subjective happiness were significant negative predictors of problematic Facebook use. The significance and limitations of the results are discussed.     

Performance assessment and tuning for exchange of clinical documents cross healthcare enterprises

BackgroundTo integrate electronic health records (EHRs) from diverse document sources across healthcare providers, facilities, or medical institutions, the IHE XDS.b profile can be considered as one of the solutions. In this research, we have developed an EHR/OpenXDS system which adopted the OpenXDS, an open source software that complied with the IHE XDS.b profile, and which achieved the EHR interoperability.ObjectiveWe conducted performance testing to investigate the performance and limitations of this EHR/OpenXDS system.MethodologyThe performance testing was conducted for three use cases, EHR submission, query, and retrieval, based on the IHE XDS.b profile for EHR sharing. In addition, we also monitored the depletion of hardware resources (including the CPU usage, memory usage, and network usage) during the test cases execution to detect more details of the EHR/OpenXDS system's limitations.ResultsIn this EHR/OpenXDS system, the maximum affordable workload of the EHR submissions were 400 EHR submissions per hour, the DSA CPU usage was 20%, memory usage was 1380 MB, the network usages were 0.286 KB input and 7.58 KB output per minute; the DPA CPU usage was 1%, memory usage was 1770 MB, the network usages were 7.75 KB input and 1.54 KB output per minute; the DGA CPU usage was 24%, memory usage was 2130 MB, the network usages were 1.3 KB input and 0.174 KB output per minute. The maximum affordable workload of the EHR queries were 600 EHR queries per hour, the DCA CPU usage was 66%, the memory usage was 1660 MB, the network usages were 0.230 KB input and 0.251 KB output per minute; the DGA CPU usage was 1%, the memory usage was 1890 MB, the network usages were 0.273 KB input and 0.22 KB output per minute. The maximum affordable workload of the EHR retrievals were 2000 EHR retrievals, the DCA CPU usage was 79%, the memory usage was 1730 MB, the network usages were 19.55 KB input and 1.12 KB output per minute; the DPA CPU usage was 3.75%, the memory usage was 2310 MB, and the network usages were 0.956 KB input and 19.57 KB output per minute.Discussion and conclusionFrom the research results, we suggest that future implementers who deployed the EHR/OpenXDS system should consider the following aspects. First, to ensure how many service volumes would be provided in the environment and then to adjust the hardware resources. Second, the IHE XDS.b profile is adopted by the SOAP (Simple Object Access Protocol) web service, it might then move onto the Restful (representational state transfer) web service which is more efficient than the SOAP web service. Third, the concurrency process ability should be added in the OpenXDS source code to improve the hardware usage more efficiently while processing the ITI-42, ITI-18, and ITI-43 transactions. Four, this research suggests that the work should continue on adjusting the memory usage for the modules of the OpenXDS thereby using the memory resource more efficiently, e.g., the memory configuration of the JVM (Java Virtual Machine), Apache Tomcat, and Apache Axis2. Fifth, to consider if the hardware monitoring would be required in the implementing environment. These research results provided some test figures to refer to, and it also gave some tuning suggestions and future works to continue improving the performance of the OpenXDS.     

Effects of captions and time-compressed video on learner performance and satisfaction

Digital video is becoming increasingly popular in higher education with faculty digitally recording and broadcasting lectures for students to learn-on-demand, such as iTunes University or YouTube. Students have discovered accelerated playback features in popular computer software and use it to reduce the amount of time watching video-enhanced instruction. In the current study, 147 undergraduate students were randomly assigned to one of six video treatments based on a 3 Video Speed (1.0 = Normal vs. 1.25 = Fast vs. 1.50 = Very Fast) × 2 Captions (Captions Present vs. Captions Absent) × 2 Trial (Trial 1 vs. Trial 2) design. Results show no significant difference on learner performance across treatments based on Video Speed. Captions were found to have a significant negative effect on learner performance. A significant difference was found on learner satisfaction in favor of a normal Video Speed. The findings suggest that learners might be able to accelerate Video Speeds up to 1.5 times the normal speed, but are generally less satisfied with the learning experience.     

Construction of semantic pool and acquisition of semantic categories for automobile styling stance: A domain knowledge perspective

In recent years, the paradigm of “Art thinking” in automobile design has been based on automobiles having both high-tech and high-emotional attributes, relying on car design domain knowledge. The research in car design is indispensable for further developing proficiency in the automobile styling field, and semantic representation is an essential means of realization. In this paper, through online data collection, experts questionnaire from leading O.E.M.s and emerging E.V. companies, and word clustering surveys from transportation design students, 85 semantic attributes were identified, and six groups were obtained. Based on the research results, the semantic category patterns (S.C., D.C., V.T.C., E.C., B.C., G.C.), representative semantics (31 semantic words), and essential cognitive characters (dynamic, powerful, and aggressive) of automobile styling are proposed. The concept of automobile styling stance and its contents and categories are defined. Our findings offer reliable and reusable automobile styling domain knowledge and have implications for redefining cars’ body shapes and aesthetic styles.     

A survey on wireless sensor network infrastructure for agriculture

The hybrid wireless sensor network is a promising application of wireless sensor networking techniques. The main difference between a hybrid WSN and a terrestrial wireless sensor network is the wireless underground sensor network, which communicates in the soil. In this paper, a hybrid wireless sensor network architecture is introduced. The framework to deploy and operate a hybrid WSN is developed. Experiments were conducted using a soil that was 50% sand, 35% silt, and 15% clay; it had a bulk density of 1.5 g/cm³ and a specific density of 2.6 cm^? 3. The experiment was conducted for several soil moistures (5, 10, 15, 20 and 25%) and three signal frequencies (433, 868 and 915 MHz). The results show that the radio signal path loss is smallest for low frequency signals and low moisture soils. Furthermore, the node deployment depth affected signal attenuation for the 433 MHz signal. The best node deployment depth for effective transmission in a wireless underground sensor network was determined.     

On design and performance evaluation of an integrated SIP and HCoP-B architecture for nested network mobility

This paper proposes a fully-integrated SIP + HCoP-B architecture to provide efficient mobility management of the nested mobile network. It achieves the following merits, which are rare in the literature. First, it reduces network deployment costs by only equipping an integrated SIP mobile server. Second, it supports both SIP-based and non-SIP-based applications. Third, by adopting the analytical model proposed in Mohanty and Akyildiz (2007) [19], mathematical analyses are provided to investigate six performance metrics of SIP + HCoP-B and the other four well-known SIP's over NEMO schemes over the error-prone wireless link. Finally, it is shown that SIP + HCoP-B outperforms these four traditional schemes through intensive simulations.     

ANN based simulation and experimental verification of analytical four- and five-parameters models of PV modules

In this article, artificial neural network (ANN) is adopted to predict photovoltaic (PV) panel behaviors under realistic weather conditions. ANN results are compared with analytical four and five parameter models of PV module. The inputs of the models are the daily total irradiation, air temperature and module voltage, while the outputs are the current and power generated by the panel. Analytical models of PV modules, based on the manufacturer datasheet values, are simulated through Matlab/Simulink environment. Multilayer perceptron is used to predict the operating current and power of the PV module. The best network configuration to predict panel current had a 3–7–4–1 topology. So, this two hidden layer topology was selected as the best model for predicting panel current with similar conditions. Results obtained from the PV module simulation and the optimal ANN model has been validated experimentally. Results showed that ANN model provide a better prediction of the current and power of the PV module than the analytical models. The coefficient of determination (R²), mean square error (MSE) and the mean absolute percentage error (MAPE) values for the optimal ANN model were 0.971, 0.002 and 0.107, respectively. A comparative study among ANN and analytical models was also carried out. Among the analytical models, the five-parameter model, with MAPE = 0.112, MSE = 0.0026 and R² = 0.919, gave better prediction than the four-parameter model (with MAPE = 0.152, MSE = 0.0052 and R² = 0.905). Overall, the 3–7–4–1 ANN model outperformed four-parameter model, and was marginally better than the five-parameter model.     

Coping with information in social media: The effects of network structure and knowledge on perception of information value

The explosive growth of social media has intrigued many scholars to inquire into why people willingly share information with others. However, relatively little attention has been devoted to how people determine which information they share in the networked environment. In this study, a 2 (network density – dense vs. sparse) × 2 (knowledge – expert vs. novice) × 3 (information valence – negative vs. neutral vs. positive) online experiment was performed to examine how the three factors interact and cross over in shaping individuals’ perceptions of the value of information for themselves and for others in the network. Results show that individuals’ perceptions of information value are influenced not just by their level of knowledge, but also by how the network environment is structured. Implications for the findings are discussed.     

Human lower extremity joint moment prediction: A wavelet neural network approach

Joint moment is one of the most important factors in human gait analysis. It can be calculated using multi body dynamics but might not be straight forward. This study had two main purposes; firstly, to develop a generic multi-dimensional wavelet neural network (WNN) as a real-time surrogate model to calculate lower extremity joint moments and compare with those determined by multi body dynamics approach, secondly, to compare the calculation accuracy of WNN with feed forward artificial neural network (FFANN) as a traditional intelligent predictive structure in biomechanics.To aim these purposes, data of four patients walked with three different conditions were obtained from the literature. A total of 10 inputs including eight electromyography (EMG) signals and two ground reaction force (GRF) components were determined as the most informative inputs for the WNN based on the mutual information technique. Prediction ability of the network was tested at two different levels of inter-subject generalization. The WNN predictions were validated against outputs from multi body dynamics method in terms of normalized root mean square error (NRMSE (%)) and cross correlation coefficient (ρ).Results showed that WNN can predict joint moments to a high level of accuracy (NRMSE < 10%, ρ > 0.94) compared to FFANN (NRMSE < 16%, ρ > 0.89). A generic WNN could also calculate joint moments much faster and easier than multi body dynamics approach based on GRFs and EMG signals which released the necessity of motion capture. It is therefore indicated that the WNN can be a surrogate model for real-time gait biomechanics evaluation.     

Design of embedded TCAM based longest prefix match search engine

The content addressable memory (CAM) based solutions are very useful in network applications due to its high speed parallel search mechanism. This paper presents a novel Ternary CAM (TCAM) based NAND Pseudo CMOS–Longest Prefix Match (NPC–LPM) search engine. The proposed system provides a simple hardware based solution using novel 11T TCAM cell structures and NPC word line technique, for network routers. The experiments were performed on 256 × 128 NPC–LPM system under 0.13 μm technology. The simulation result shows that the proposed design provides low power dissipation of 5.78 mW and high search speed of 315 MSearches/s under 1.3 V supply voltage. The presented NPC–LPM system meets the speed requirement of Optical Carrier (OC) 3072 with line-rate of 160 Gb/s in Ethernet networking and IPv6 protocol. The experimental results also show that the proposed system improves power-performance by 65%.     

Guided local search as a network planning algorithm that incorporates uncertain traffic demands

A search based heuristic for the optimisation of communication networks where traffic forecasts are uncertain and the problem is NP-complete is presented. While algorithms such as genetic algorithms (GA) and simulated annealing (SA) are often used for this class of problem, this work applies a combination of newer optimisation techniques specifically: fast local search (FLS) as an improved hill climbing method and guided local search (GLS) to allow escape from local minima. The GLS + FLS combination is compared with an optimised GA and SA approaches. It is found that in terms of implementation, the parameterisation of the GLS + FLS technique is significantly simpler than that for a GA and SA. Also, the self-regularisation feature of the GLS + FLS approach provides a distinctive advantage over the other techniques which require manual parameterisation. To compare numerical performance, the three techniques were tested over a number of network sets varying in size, number of switch circuit demands (network bandwidth demands) and levels of uncertainties on the switch circuit demands. The results show that the GLS + FLS outperforms the GA and SA techniques in terms of both solution quality and optimisation speed but even more importantly GLS + FLS has significantly reduced parameterisation time.