EEG calmness index establishment using computational of Z-score

A lot of useful information can be obtained through observation of the electroencephalogram (EEG) signal such as the human psychophysiology. It has been proven that EEG is handy in human diagnosis and tools to observe the brain condition. The study aims to establish a calmness index, which can differentiate the calmness level of an individual. Alpha waves were selected as the data features and computed into asymmetry index. The data features were clustered using Fuzzy C-Means (FCM) and resulted in three clusters. Wilcoxon Signed Ranks test was applied to determine the significance of the data features clustered by FCM. The Z-score obtained successfully distinguish three level of calmness index from the lower index until the higher index. With the advancement of signal processing techniques, the feature extractions for calmness index establishment computation is achievable

Decomposition-assisted computational technique based on surrogate modeling for real-time simulations

The development of complex simulation systems is extremely costly as it requires high computational capability and expensive hardware. As cost is one of the main issues in developing simulation components, achieving real-time simulation is challenging and it often leads to intensive computational burdens. Overcoming the computational burden in a multidisciplinary simulation system that has several subsystems is essential in producing inexpensive real-time simulation. In this paper, a surrogate-based computational framework was proposed to reduce the computational cost in a high-dimensional model while maintaining accurate simulation results. Several well-known metamodeling techniques were used in creating a global surrogate model. Decomposition approaches were also used to simplify the complexities of the system and to guide the surrogate modeling processes. In addition, a case study was provided to validate the proposed approach. A surrogate-based vehicle dynamic model (SBVDM) was developed to reduce computational delay in a real-time driving simulator. The results showed that the developed surrogate-based model was able to significantly reduce the computing costs, unlike the expensive computational model. The response time in surrogate-based simulation was considerably faster than the conventional model. Therefore, the proposed framework can be used in developing low-cost simulation systems while yielding high fidelity and fast computational output. © 2017 Nariman Fouladinejad et al

Anatomical variations of median nerve formation, distribution and possible communication with other nerves in preserved human cadavers

Formation, distribution and possible communication of the median nerve are essential to know in treatment and surgeries of various conditions of injuries e.g. repair or reconstruction of the median nerve post traumatic accident. In the present study, 44 upper limbs were dissected. Root forming the median nerve, the median nerve in relation with the axillary artery and communication of the median nerve with other nerves were noted

Escherichia coli growth modeling using neural network

The assessment of water microbial quality is normally performed by verification ofEscherichia coli where the growth is in nonlinearity. NARX is computational tools that haveextensive utilization in solving nonlinear time series problems. It is well known as one of thetechnique that has the ability to predict with efficient and good performance. Using NARX, ahighly accurate model was developed to predict the growth of Escherichia coli (E. coli) basedon pH water parameter. The multiparameter portable sensor and spectrophotometer data wereused to build and train the neural network. The selection of neural network structure for pHand optical density modelling was optimized and also the training and validation wereanalyzed. The result exhibited that NARX modelling was able to predict the growth of E. colibased on pH water parameter with overall regression is 0.99956.Keywords: neural network; NARX; prediction; Escherichia coli; pH; optical density

Evaluation of electromagnetics radiation for stroke patients and non-stroke participants according to body segmentation

This research evaluates the electromagnetic radiation (EMR) for the stroke patients and non-stroke patients according to body segmentation. The human body is divided into three segments: top, middle and bottom. The frequency in hertz is collected at 23 points around the human body namely left side, right side and chakra points from 199 subjects undergoing post-stroke treatment and 100 non-stroke participants. The EMR is captured using frequency detector equipped with a dipole antenna. The data is collected by taking the reading of the frequency 5 times at each point at the same location; hence, the average value is calculated. The statistical analysis of the EMR are examined using SPSS software and Microsoft excel is used to calculate the average frequency of the data. In conclusion, the findings significantly shows that stroke patients has lower frequency value of EMR for both right side and left side but has higher frequency for chakra system. This is true for all the three segments of the body. Furthermore, it is also shown that there is no correlation between the left and the right side frequency for the stroke patients whereas the left-right correlation values are significantly high for the non-stroke participants. This observation justify that EMR from human body can contribute to early detection for stroke

Agarwood oil quality classifier using machine learning

Agarwood oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies and religious prayers. Its quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately. Keywords: agarwood oil; k-nearest neighbours; quality; machine learning

The Effects of Air Flow in the Wake of a Large Vehicle on Trailing a Passenger Car

Road driving condition under drafting is known to have an influence on the aerodynamic forces of the vehicle. Large vehicles such as busses and trucks traveling at high speeds give results in the formation of a large turbulent flow in the wake region. This turbulent flow is very unsteady in nature hence its influence on the air flow within its vicinity will also be unsteady. This paper investigates the relative values of drag and lift forces acting on a passenger car trailing a large vehicle (drafting) under unsteady conditions. The simulation is conducted using Computational Fluid Dynamics software, FLUENT for a two-dimensional flow domain at Re 3.18x106 for a trailing distance of 0 to 30 meters. The unsteady effect is studied at 15 time intervals for each time step. Turbulence is simulated using the Reynolds-Average Navier Stokes (RANS) k-ε model. Results show that aerodynamically, the critical drafting distance is between three to five meters where the lowest drag is found to occur at three meters. The results show the suitable distance for drafting which may serve as useful information for vehicle fuel economy and stability

A review on plants and biomass wastes as organic green corrosion inhibitors for mild steel in acidic environment

Acid corrosion is a problem pertaining to corrosion that involves an acid solution. It is important to treat metal to preserve its integrity. Thus, acids are utilized to clean and treat metal surfaces. In return, this may lead to over‐etching and metal degradation. Corrosion inhibitors were introduced as a solution for the issue. However, there are some problems associated with the usage of conventional corrosion inhibitors. Traces of nitrites and chromates that are present in the inhibi-tors may lead to serious health and environmental issues. As a solution, organic green corrosion inhibitors have been studied to replace the conventional corrosion inhibitors. These inhibitor molecules form a protective layer on top of the metal surface to suppress metal dissolution when added to the acid solution. This process prevents direct contact between the metal surfaces and the acid environment. This study explores the usage of natural resources and biomass wastes as the basis for organic green corrosion inhibitors. This study also provides some suggestions for new biomass wastes that can be studied as new organic corrosion inhibitors, and it is aimed at opening the per-spective of researchers on exploring new organic inhibitors by using natural resources and biomass wastes

CHEMICAL STABILIZATION OF SARAWAK CLAY SOIL WITH CLASS F FLY ASH

Chemical stabilization of Sarawak clay soil was studied via Fly Ash (FA) due to their potential benefit. FA is a by-product produced from thermal power plant and disposal of FA causing an environmental hazard. Investigation on the feasibility of FA as a potential stabilizer to stabilize the Sarawak clay soils was performed via Unconfined Compression Strength (UCS) and Triaxial Consolidated Isotropic Undrained (CIU). From the compaction results, the Maximum Dry Density (MDD) and the Optimum Moisture Content (OMC) for all mixtures increased and decreased respectively compared to natural soil. Based on the UCS test, the addition of 20% FA and 40% FA achieved a significant improvement in compressive strength and recommended as optimum stabilizer amount. The plasticity index and linear shrinkage for the FA stabilized soil decreased compared to the natural soil. The triaxial test was performed for the optimum amount of stabilizer and obtained significant improvement in effective cohesion and effective internal friction angle compared to natural soil. The deviator stress for FA stabilized soil also increased compared to the natural soil corresponding to the confining pressure. The morphology of stabilized soil shows the existence of cementitious product, which contributed to strength increased as observed via Scanning Electron Microscopy (SEM)

Electrode-Less Photo-Assisted Etching Of P-Type And N-Type GaN

Poor light extraction due to total internal reflection (TIR) phenomenon has become a major problem in InGaN/GaN light-emitting diode (LED) [1-2]. Surface texturing by photoelectrochemical (PEC) etching gives access to more than 70% improvement in light extraction as reported in [3]. In this work, an electrode-less photo-assisted etching is demonstrated on Ga-polar face of n-type and p-type GaN layers. Two type of etchant solutions, H3PO4 with KOH and H3PO4 with HNO3 were used and the surface morphology of all samples were measured using scanning electron microscopy (SEM). Hexagonal pit on the surface of all samples were observed. Interestingly, the pits were formed in various uniformity, density and size depending on the type of solution. Surface roughness of etch samples is improved after etching as measured using atomic force microscopy (AFM)