Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Detection of microscopic glaucoma through fundus images using deep transfer learning approach

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.     

A comparative study between gravel and rubber drainage columns for mitigation of liquefaction hazards

Liquefaction is one of the most destructive natural hazards that cause damage to engineering structures during an earthquake. This study aims to examine the effect of rubber and gravel drainage columns on the reduction of liquefaction potential of saturated sandy soils using a shaking table. Experiments were carried out in various conditions such as construction materials, different arrangements and diameters of drainage columns. Effects of the relative density and the input motion on the base test were investigated as well. The results demonstrate that rubber drainage columns have slightly better performance compared to gravel drainage columns at high relative density and high input acceleration. Soil improvement using gravel drainage columns, which leads to reduction in liquefaction effects at moderate input acceleration and low relative density, is a more effective method than that using rubber drainage columns. By increasing the number and diameter of gravel and rubber drainage columns, deformations due to liquefaction are reduced. The drainage rate of gravel drains is higher than that of rubber drains after shaking. Totally, the outcomes indicate that densification is the most important factor controlling liquefaction.     

Process optimization for efficient biomediated PHA production from animal-based waste streams

Conventional polymers are made of crude oil components through chemical polymerization. The aim of the project ANIMPOL is to produce biopolymers by converting lipids into polyhydroxyalkanoates (PHA) in a novel process scheme to reduce dependence on crude oil and decrease greenhouse gas emissions. PHA constitutes a group of biobased and biodegradable polyesters that may substitute fossil-based polymers in a wide range of applications. Waste streams from slaughtering cattle are used as substrate material. Lipids from rendering are used in this process scheme for biodiesel production. Slaughtering waste streams may also be hydrolyzed to achieve higher lipid yield. Biodiesel then is separated into a high- and low-quality fraction. High-quality biodiesel meets requirements for sale as fuel and low quality is used for PHA production as carbon source. Selected offal material is used for acid hydrolysis and serves as a source of organic nitrogen as well as carbon source for PHA-free biomass with high production rate in fermentation process. Nitrogen is a limiting factor to control PHA production during the fermentation process. It is available for bacterial growth from hydrolyzed waste streams as well as added separately as NH₄OH solution. Selected microbial strains are used to produce PHA from this substrate. The focus of the paper is about an overview of the whole process with the main focus on hydrolysis, to look for the possibility of using offal hydrolysis as an organic nitrogen substitute. The process design is optimized by minimizing waste streams and energy losses through cleaner production. Ecological evaluation of the process design will be done through footprint calculation according to Sustainable Process Index methodology.     

Electrical diagnostics of laser ablated platinum plasma

The most significant parameters of laser-induced plasma are investigated here experimentally. Pure platinum targets of 4 N were irradiated both in air and under vacuum (∼10⁻³ Torr) by Q-switched Nd: YAG laser (1064 nm, 10 mJ, 9-14 ns, 1.1 MW). A self fabricated Langmuir probe was used as an electric diagnostic tool. Varying voltages were applied to the probe and the corresponding values of currents were recorded to draw current-voltage (I-V) characteristics curves. The electronic signals were recorded on 500 MHz DL1740, YOKOGAWA four channels digital storage oscilloscope. Different plasma characteristics like electron temperature, electron density, Debye's length, plasma frequency and numbers of particles in the “Debye's Sphere” were calculated both in air and under vacuum.     

A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

Many countries developed and increased greenery in their country sights to attract international tourists. This planning is now significantly contributing to their economy. The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’ arrivals are accurately predicted. But accurate prediction is not easy as empirical evidence shows that the tourists’ arrival data often contains linear, nonlinear, and seasonal patterns. The traditional model, like the seasonal autoregressive fractional integrated moving average (SARFIMA), handles seasonal trends with seasonality. In contrast, the artificial neural network (ANN) model deals better with nonlinear time series. To get a better forecasting result, this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model. Then, we have used the proposed model to predict the tourists’ arrival in New Zealand, Australia, and London. Empirical results showed that the proposed hybrid model outperforms in predicting tourists’ arrival compared to the traditional SARFIMA and ANN models. Moreover, these results can be generalized to predict tourists’ arrival in any country or region with a complicated data pattern.     

Processing of metallic fiber hybrid spun yarns for better electrical conductivity

This research was aimed at processing of metallic fiber hybrid spun yarns consisting of polyester/stainless steel and viscose/stainless steel staple fibers to achieve better electrical conductivity. Conventional ring spinning machine and ring twister machine were used to produce the single and plied yarns respectively. The linear electrical resistance of yarns was analyzed with reference to the three levels of twist multiplier (TM) for same yarn count, three levels of yarn fineness (Ne) at the same TM level, and number of plies for the same final yarn count. These results showed that by increasing twist, the electrical conductivity of yarn was increased. However, yarn fineness was in inverse relation with the electrical conductivity of yarns. The effect of yarn plying and twisting to produce the Ne 10s yarn was also found critical in governing the electrical properties. The electrical conductivity of viscose and stainless steel hybrid yarn was found more sensitive to increase with an increase in relative humidity contrary to that of polyester and stainless steel hybrid yarns. The findings of the study are significant to produce the hybrid spun conductive yarns for their potential applications in a variety of tailor-made functional, protective and smart textiles.     

Correlation among tardiness-based measures for scheduling using priority dispatching rules

One of the most commonly used methods to schedule manufacturing systems is to use priority dispatching rules (pdrs). It is frequently desired to distinguish the behaviour of pdrs with regard to tardiness-based performance measures. However, the relation among these performance measures is generally not obvious even for simple scheduling strategies such as pdrs. In this paper, we first focus on the maximum tardiness, which is a very interesting performance measure for the decision-maker on the shop floor. However, because of its singularity, it is not trivial to assess. We conducted a simulation study on a benchmark model of a dynamic job-shop system to evaluate the relative performance of a set of pdrs chosen either because they are used extensively or because they exhibit very good performance. Based upon the distribution of the maximum tardiness of these benchmark pdrs, we identify two sub-sets of pdrs. From this, we conducted experiments on the root-mean-square tardiness, which is used to distinguish a system with a few very tardy jobs from a system with a lot of less tardy jobs. The experiments show a positive correlation between maximum tardiness and root-mean-square tardiness. Because of the fact that the root-mean-square tardiness is an aggregate measure, it is much easier to assess than the maximum tardiness. This provides an opportunity to predict the relative performance of pdrs with regard to the maximum tardiness as well as the width of the tardiness by evaluating the root-mean-square tardiness only.     

Evaporated Thin Films of CdS and CdTe: Optimization for Photovoltaic Applications

Thin films of cadmium sulphide and cadmium telluride have been prepared by thermal evaporation under various conditions of deposition. These films have been characterized optically. electrically and for structure determination. The results of these characterizations along with the initial results of all thin film CdS/CdTe solar cells are presented in this paper     

Role of Co in formation of Ni-Ti clusters in maraging stainless steel

The effect of Co addition on the formation of Ni-Ti clusters in maraging stainless steel was studied by three dimensional atom probe (3DAP) and first-principles calculation. The cluster analysis based on the maximum separation approach showed an increase in size but a decrease in density of Ni-Ti clusters with increasing the Co content. The first-principles calculation indicated weaker Co-Ni (Co-Ti) interactions than Co-Ti (Fe-Ti) interactions, which should be the essential reason for the change of distribution characteristics of Ni-Ti clusters in bcc Fe caused by Co addition.