Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes four stages, such as pre-processing, segmentation, highlight extraction, and the order. From the outset, the Computerized Tomography (CT) image of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological factors for feature observation. By getting the features are analysed and the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumour, giving two classes in particular, knob or non-knob. The proposed system produce high performance as well compared to the other system. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity, precision, affectability, and the explicitness.     

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography–mass spectrometry–based untargeted metabolomics

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography–mass spectrometry from 30 Heterotrigona itama (H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares–discriminant analysis model from the discriminant analysis were validated using cross-validation (R²: 0.93; Q²: 0.83; accuracy: 0.97) and the permutation test (p < 0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP > 1.0) and the Kruskal–Wallis test (p < 0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.     

Scattering matrix elements of fractal-like soot agglomerates

The possibility of measuring scattering-matrix (Mueller matrix) elements of soot agglomerates with laser diagnostic techniques is explored. To show this, we calculated the scattering-matrix elements of arbitrary-shaped soot agglomerates. The sensitivity of scattering-matrix elements to optical and morphological characteristics of fractal-like soot agglomerates is discussed. Finally, possible measurement techniques are suggested to identify soot structures from scattering-matrix elements.     

A robust and high precision optimal explicit guidance scheme for solid motor propelled launch vehicles with thrust and drag uncertainty

A new nonlinear optimal and explicit guidance law is presented in this paper for launch vehicles propelled by solid motors. It can ensure very high terminal precision despite not having the exact knowledge of the thrust–time curve apriori. This was motivated from using it for a carrier launch vehicle in a hypersonic mission, which demands an extremely narrow terminal accuracy window for the launch vehicle for successful initiation of operation of the hypersonic vehicle. The proposed explicit guidance scheme, which computes the optimal guidance command online, ensures the required stringent final conditions with high precision at the injection point. A key feature of the proposed guidance law is an innovative extension of the recently developed model predictive static programming guidance with flexible final time. A penalty function approach is also followed to meet the input and output inequality constraints throughout the vehicle trajectory. In this paper, the guidance law has been successfully validated from nonlinear six degree-of-freedom simulation studies by designing an inner-loop autopilot as well, which enhances confidence of its usefulness significantly. In addition to excellent nominal results, the proposed guidance has been found to have good robustness for perturbed cases as well.     

Preparation of emulsifying wax/glyceryl monooleate nanoparticles and evaluation as a delivery system for repurposing simvastatin in bone regeneration

Simvastatin (Sim) is a widely known drug in the treatment of hyperlipidemia, which has attracted so much attention in bone regeneration due to its potential osteoanabolic effect. However, repurposing of Sim in bone regeneration will require suitable delivery systems that can negate undesirable off-target/side effects. In this study, we have investigated a new lipid nanoparticle (NP) platform that was fabricated using a binary blend of emulsifying wax (Ewax) and glyceryl monooleate (GMO). Using the binary matrix materials, NPs loaded with Sim (0–500?µg/mL) were prepared and showed an average particle size of about 150?nm. NP size stability was dependent on Sim concentration loaded in NPs. The suitability of NPs prepared with the binary matrix materials in Sim delivery for potential application in bone regeneration was supported by biocompatibility in pre-osteoclastic and pre-osteoblastic cells. Additional data demonstrated that biofunctional Sim was released from NPs that facilitated differentiation of osteoblasts (cells that form bones) while inhibiting differentiation of osteoclasts (cells that resorb bones). The overall work demonstrated the preparation of NPs from Ewax/GMO blends and characterization to ascertain potential suitability in Sim delivery for bone regeneration. Additional studies on osteoblast and osteoclast functions are warranted to fully evaluate the efficacy of Sim-loaded Ewax/GMO NPs using in-vitro and in-vivo approaches.     

Generation control of interconnected power systems using computational intelligence techniques

Basic power systems are steadily growing and have become large and more complex with interconnections to neighbouring systems. A novel approach is presented for steady-state analysis of generation control, incorporating load and generation characteristics, which gives nearly exact practical solution for large interconnected power systems during normal operating conditions and under disturbance. It also assists planning and operation engineers to observe the steady-state behaviour of a network disturbances, such as loss of generation and loss of load. Adopting decoupled Newton-Raphson method, steady-state frequency deviation (DeltaF) and tie-line error (DeltaP_tie)are estimated for the disturbance. In the paper, the knowledge of frequency deviation and tie-line error are handled to produce the required change in generation using both a fuzzy-logic controller and an artificial-neural-network controller separately.     

Chaotic map models of soot fluctuations in turbulent diffusion flames

In this paper, we introduce a methodology to characterize time-dependent soot volume fraction fluctuations in turbulent diffusion flames via chaotic maps. The approach is based on the hypothesis that fluctuations of properties in turbulent flames are deterministic in nature, rather than statistical. Our objective is to develop models of these fluctuations to be used in comprehensive algorithms to study the nature of turbulent flames and the interaction of turbulence with radiation. To this end we measured the time series of soot scattering coefficient in an ethylene diffusion flame from light scattering experiments and fit these data to linear combinations of chaotic maps of the unit interval. Both time series and power spectra can be modeled with reasonable accuracy in this way. © 1998 Elsevier Science Ltd. All rights reserved.     

Evaluation of the impact of the surrounding urban morphology on building energy consumption

Empirical models of minimum (T_min), average (T_avg) and maximum (T_max) air temperature for Singapore estate have been developed and validated based on a long-tem field measurement. There are three major urban elements, which influence the urban temperature at the local scale. Essentially, they are buildings, greenery and pavement. Other related parameters identified for the study, such as green plot ratio (GnPR), sky view factor (SVF), surrounding building density, the wall surface area, pavement area, albedo are also evaluated to give a better understanding on the likely impact of the modified urban morphology on energy consumption.The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the building energy consumption in tropical climate of Singapore. In order to achieve this goal, a series of numerical calculation and building simulation are utilized. A total of 32 cases, considering different urban morphologies, are identified and evaluated to give better a understanding on the implication of urban forms, with the reference to the effect of varying density, height and greenery density. The results show that GnPR, which related to the present of greenery, have the most significant impact on the energy consumption by reducing the temperature by up to 2 °C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively.     

Light-scattering and dispersion behavior of multiwalled carbon nanotubes

Elliptically polarized light-scattering measurements were performed to investigate the dispersion behavior of multiwalled carbon nanotubes (MWNT). Xylene- and pyridine-derived MWNT powders were dispersed in water and ethanol in separate optic cells and allowed to sit undisturbed over a two-week time period after probe sonication. Continuous light-scattering measurements taken between scattering angles of 10-170 deg and repeated over several days showed that the nanotubes formed fractal-like networks. The pyridine-derived MWNTs showed greater dispersion variation over time, tending to aggregate and clump much faster than the xylene-derived tubes. The water suspensions appeared much more stable than the ethanol suspensions, which transformed into nonfractal morphology after a few hours. We relate the dispersion stability to size and fringe patterns on the outer surface of the nanotubes. Measured values of fractal dimension were distinctly lower than those in previous studies of single-walled carbon nanotubes. Profiles of both diagonal and off-diagonal scattering matrix elements are presented.     

Human-vision-based real-time stereopsis

The progression in the field of stereoscopic imaging has resulted in impressive 3D videos. This technology is now used for commercial and entertainment purposes and sometimes even for medical applications. Currently, it is impossible to produce quality anaglyph video using a single camera under different moving and atmospheric conditions with the corresponding depth, local colour, and structural information. The proposed study challenges the previous researches by introducing single camera based method for anaglyph reconstruction and it mainly concentrates on human visual perception, where as the previous methods used dual camera, depth sensor, multi view, which demand not only long duration they also suffer from photometric distortion due to variation in angular alignment. This study also contributes clear individual image without any occlusion with another image. We use an approach based on human vision to determine the corresponding depth information. The source frames are shifted slightly in opposite directions as the distance between the pupils increases. We integrate the colour components of the shifted frames to generate contrasting colours for each one of the marginally shifted frames. The colour component images are then reconstructed as a cyclopean image. We show the results of our method by applying it to quickly varying video sequences and compare its performance to other existing methods.