Computer‐aided diagnosis software for vulvovaginal candidiasis detection from Pap smear images

Vulvovaginal candidiasis (VVC) is a common gynecologic infection and it occurs when there is overgrowth of the yeast called Candida. VVC diagnosis is usually done by observing a Pap smear sample under a microscope and searching for the conidium and mycelium components of Candida. This manual method is time consuming, subjective and tedious. Any diagnosis tools that detect VVC, semi‐ or full‐automatically, can be very helpful to pathologists. This article presents a computer aided diagnosis (CAD) software to improve human diagnosis of VVC from Pap smear samples. The proposed software is designed based on phenotypic and morphology features of the Candida in Pap smear sample images. This software provide a user‐friendly interface which consists of a set of image processing tools and analytical results that helps to detect Candida and determine severity of illness. The software was evaluated on 200 Pap smear sample images and obtained specificity of 91.04% and sensitivity of 92.48% to detect VVC. As a result, the use of the proposed software reduces diagnostic time and can be employed as a second objective opinion for pathologists.     

Unsupervised identification of arbitrarily-damped structures using time-scale independent component analysis: Part I

In this study, a new method is proposed to identify the dynamic parameters of structures with higher accuracy compared to current methods. First, the wavelet-transformed representation of system responses is extracted from measured responses, and then the independent component analysis is used to achieve the modal characteristics. The simulation results of a multi-degree-of-freedom system illustrate that this method is capable of accurately identifying the modal information of lightly- and highly-damped structures. It is represented that continuous wavelet transform, due to its adaptive time-frequency resolution, is more efficient to be incorporated into independent component analysis compared to Short time Fourier transform (STFT). The latter is unable to accurately determine the modal response, especially at higher frequencies, while the proposed method can identify the system with marked accuracy. The efficiency of proposed method is also investigated under additive noise. Results shown that for highly- and lightly- damped system, the proposed method is able to capture the modal parameters especially in higher frequencies of vibration, along with the modal assurance criterion values with satisfactory accuracy, which indicates the robustness of the procedure compared to other available methodologies.     

Studio in Transformation: Transformation in Studio

To explore the potential of a holistic pedagogical model that incorporates motion studies into existing curricular structures, the Pedagogy of Motion provides a methodical series of steps regarding the best methods for teaching the principles of transformable architecture. The development of this pedagogy could recast the architectural design process, transmuting the landscape of how we do architecture. As vehicles for examining the opportunities of this approach, two design studios¹¹ transLAB was offered at Virginia Tech University, and transSTUDIO was taught at Texas A&M University.View all notes in two schools of architecture at Virginia Tech and Texas A&M University were offered, occasioning theoretical overviews and practical methods for designing morphological changes in architecture.     

Dynamic DTI (dDTI) shows differing temporal activation patterns in post-exercise skeletal muscles

Object

To assess post-exercise recovery of human calf muscles using dynamic diffusion tensor imaging (dDTI).

Materials and methods

DTI data (6 directions, b = 0 and 400 s/mm²) were acquired every 35 s from seven healthy men using a 3T MRI, prior to (4 volumes) and immediately following exercise (13 volumes, ~7.5 min). Exercise consisted of 5-min in-bore repetitive dorsiflexion-eversion foot motion with 0.78 kg resistance. Diffusion tensors calculated at each time point produced maps of mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and signal at b = 0 s/mm² (S₀). Region-of-interest (ROI) analysis was performed on five calf muscles: tibialis anterior (ATIB), extensor digitorum longus (EDL) peroneus longus (PER), soleus (SOL), and lateral gastrocnemius (LG).

Results

Active muscles (ATIB, EDL, PER) showed significantly elevated initial MD post-exercise, while predicted inactive muscles (SOL, LG) did not (p < 0.0001). The EDL showed a greater initial increase in MD (1.90 × 10^?4mm²/s) than ATIB (1.03 × 10^?4mm²/s) or PER (8.79 × 10^?5 mm²/s) (p = 7.40 × 10^?4), and remained significantly elevated across more time points than ATIB or PER. Significant increases were observed in post-exercise EDL S₀ relative to other muscles across the majority of time points (p < 0.01 to p < 0.001).

Conclusions

dDTI can be used to differentiate exercise-induced changes between muscles. These differences are suggested to be related to differences in fiber composition.

     

Viscoelastic fatigue resistance of asphalt binders modified with crumb rubber and styrene butadiene polymer

In the present research study, the fatigue behavior of modified asphalt with crumb rubber (CR) and styrene-butadiene-styrene (SBS) are investigated. Each of these additives was mixed with a 85–100 neat bitumen at two different dosages. The time sweep test was used to investigate the viscoelastic fatigue properties of asphalt binders in terms of energy dissipation. Linear amplitude sweep tests were conducted to study the fatigue life of asphalt binders under varying loads based on the concepts of viscoelastic continuum damage mechanics. Results showed that adding CR and SBS can enhance the fatigue resistance of modified asphalts based on the energy approach and viscoelastic continuum damage analysis.     

Evaluation of numerical schemes for capturing shock waves in modeling proppant transport in fractures

In petroleum engineering, the transport phenomenon of proppants in a fracture caused by hydraulic fracturing is captured by hyperbolic partial differential equations (PDEs). The solution of this kind of PDEs may encounter smooth transitions, or there can be large gradients of the field variables. The numerical challenge posed in a shock situation is that high-order finite difference schemes lead to significant oscillations in the vicinity of shocks despite that such schemes result in higher accuracy in smooth regions. On the other hand, first-order methods provide monotonic solution convergences near the shocks, while giving poorer accuracy in the smooth regions. Accurate numerical simulation of such systems is a challenging task using conventional numerical methods. In this paper, we investigate several shock-capturing schemes. The competency of each scheme was tested against onedimensional benchmark problems as well as published numerical experiments. The numerical results have shown good performance of high-resolution finite volume methods in capturing shocks by resolving discontinuities while maintaining accuracy in the smooth regions. These methods along with Godunov splitting are applied to model proppant transport in fractures. It is concluded that the proposed scheme produces non-oscillatory and accurate results in obtaining a solution for proppant transport problems.     

Hybrid composite using recycled polycarbonate/waste silk fibers and wood flour

In this work, hybrid composite materials were made from the combination of waste silk fibers (WSFs) and poplar wood flour (PWF) as reinforcement, recycled polycarbonate (RPC) as polymer matrix, and silane as coupling agent. RPC was obtained from waste compact discs. The effects of fiber type and loading on the mechanical and physical properties of the composites were studied. Experimentally, it was found that the weight content of PWF is a key parameter that would substantially influence the mechanical properties of the samples. The obtained results showed that tensile and flexural strengths and moduli of the composites were significantly enhanced with the addition of biofibers in both types (fiber and flour), as compared with neat RPC. However, the increase in WSFs and PWF contents substantially improved the notched Izod impact strength, but reduced the thermal stability. The significant improvements in mechanical properties of the composites with the incorporation of WSF and PWF were further supported by scanning electron microscopy micrographs. Composites containing more fraction of WSF exhibited higher water absorption (WA) compared with PWF‐filled composites. In addition, composite with higher WSF and PWF (30 wt%) loading showed maximum WA during the whole duration of immersion. POLYM. COMPOS., 37:1667–1673, 2016. © 2014 Society of Plastics Engineers     

Microgrid dynamic responses enhancement using artificial neural network‐genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds

The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN‐GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.     

Hybrid classifier for fault location in active distribution networks

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.