Evaluating Six Sigma failure rate for inverse Gaussian cycle times

Six Sigma is a quality philosophy and methodology that aims to achieve operational excellence and delighted customers. The cost of poor quality depends on the sigma quality level and its corresponding failure rate. Six Sigma provides a well-defined target of 3.4 defects per million. This failure rate is commonly evaluated under the assumption that the process is normally distributed and its specifications are two-sided. However, these assumptions may lead to implementation of quality-improvement strategies that are based on inaccurate evaluations of quality costs and profits. This paper defines the relationship between failure rate and sigma quality level for inverse Gaussian processes. The inverse Gaussian distribution has considerable applications in describing cycle times, product life, employee service times, and so on. We show that for these processes attaining Six Sigma target failure rate requires higher quality efforts than for normal processes. A generic model is presented to characterise cycle times in manufacturing systems. In this model, the asymptotic production is described by a drifted Brownian motion, and the cycle time is evaluated by using the first passage time theory of a Wiener process to a boundary. The proposed method estimates the right efforts required to reach Six Sigma goals.     

Biofilm reduction,cell proliferation,anthelmintic and cytotoxicity effect of green synthesised silver nanoparticle using Artemisia vulgaris extract

Infectious diseases are caused by etiological agents. Nanotechnology has been used to minimise the effect of clinical pathogens which have resistance to antibiotics. In current research synthesis, characterisation and biological activities of green synthesised nanoparticles using Artemisia vulgaris extract have been done. The characterisation of AgNPs was carried out using Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, and scanning electron microscopy. Anti‐biofilm, cell viability, antibacterial, brine shrimp lethality, and deoxyribonucleic acid protection effects have been screened. UV‐Vis spectra showed the absorption peak of synthesised nanoparticles at 400 nm. FT‐IR indicated the involvement of the functional group in the preparation of AgNPs. SEM showed the spherical shape of AgNPs with 30 nm diameter. Biological screening results revealed the antibacterial effect against clinical bacterial pathogens. Biofilm reduction and cell viability assay also supported the antibacterial effect. Cytotoxicity effect was recorded as 100% at 200 μg/ml through brine shrimp lethality assay. Protein kinase inhibition zones recorded for AgNPs (16 mm bald) compared with A. vulgaris extract (11 mm bald). It has been concluded that green synthesised AgNPs are more effective against infectious pathogens and could be used as a potential source for therapeutic drugs.Inspec keywords: cellular biophysics, toxicology, silver, nanoparticles, nanomedicine, diseases, microorganisms, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, enzymes, molecular biophysicsOther keywords: biofilm reduction, cell proliferation, anthelmintic effect, cytotoxicity effect, green synthesised silver nanoparticle, Artemisia vulgaris extract, infectious diseases, aetiological agents, Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, scanning electron microscopy, SEM, antibiofilm, cell viability, brine shrimp lethality, deoxyribonucleic acid protection effects, AgNP, cytotoxicity, protein kinase inhibition zones, therapeutic drugs     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

Leveraging Transfer Learning for Spatio-Temporal Human Activity Recognition from Video Sequences

Human Activity Recognition (HAR) is an active research area due to its applications in pervasive computing, human-computer interaction, artificial intelligence, health care, and social sciences. Moreover, dynamic environments and anthropometric differences between individuals make it harder to recognize actions. This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications. It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network. Moreover, the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information. Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction. For temporal sequence, this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short Term Memory (BiLSTM) to capture long-term dependencies. Two state-of-the-art datasets, UCF101 and HMDB51, are used for evaluation purposes. In addition, seven state-of-the-art optimizers are used to fine-tune the proposed network parameters. Furthermore, this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network (CNN), where two streams use RGB data. In contrast, the other uses optical flow images. Finally, the proposed ensemble approach using max hard voting outperforms state-of-the-art methods with 96.30% and 90.07% accuracies on the UCF101 and HMDB51 datasets.     

Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region.     

A new method of reinforcing graphene nanoplatelets in glass/epoxy composites

This research aims to develop a method for the amalgamation of graphene nanoplatelets in glass/epoxy composites. The poor interface bonding between the fiber and matrix is critical and hinders the full performance of the composites. Glass fabric and epoxy were used as reinforcement and matrix in the composite, respectively. Graphene nanoplatelets were utilized as an additional nano-materials filler for the composites. Glass/graphene/epoxy and glass/epoxy composites were fabricated via vacuum infusion molding. The new method of applying graphene nanoplatelets as secondary reinforcement in the composite was developed based on proper functionalization in the sonication process. The physical, tensile, flexural, and short beam interlaminar properties of fabricated composites were examined to analyze the method's effectiveness. The results showed that density decreased by around 5 %; however, thickness increased by around 34 % after introducing graphene nanoplatelets into the composites. The tensile strength and modulus of the composites declined by approximately 19 %, on the other hand, flexural strength and modulus increased by around 63.3 % and 8.3 %, respectively, after the addition of graphene nanoplatelets into the composites. Moreover, interlaminar shear strength of the composite was enhanced by approximately 50 %.     

Sulphur dioxide loadings over megacity Lahore (Pakistan) and adjoining region of Indo-Gangetic Basin

This article presents spatial and temporal variations of planetary boundary layer (PBL) sulphur dioxide (SO₂) over megacity Lahore and adjoining region, a typical representative area in the Indo-Gangetic Basin (IGB) largely influenced by transported volcanic SO₂ from Africa, Middle East, and southern Europe, by using data retrieved from satellite-based Ozone Monitoring Instrument (OMI) during October 2004–September 2015. We find a positive trend of 2.4% per year (slope 0.01 ± 0.005 with y-intercept 0.35 ± 0.03 Dobson Unit (DU), correlation coefficient r = 0.55 and 2-tailed p-value at 0.1) of OMI-SO₂ column with the average value of 0.4 ± 0.05 DU. Strong seasonality of OMI-SO₂ column is observed over the region linked with local meteorology, patterns of anthropogenic emissions, crop residue burning, and vegetation cover. There exists a seasonal high value in winter 0.56 ± 0.24 DU with a peak in December 0.67 ± 0.26 DU. The seasonal lowest value is observed to be 0.29 ± 0.11 DU in wet summer with minimum value in July 0.25 ± 0.06 DU. High growth rates of OMI-SO₂ column over the study region have been observed in January, June, October, and December ranging from 5.7% to 11.6% per year. Satellite data show elevated OMI-SO₂ columns in 2007, 2008, 2011, and 2012 largely contributed by trans-boundary volcanic SO₂. A detailed analysis of volcanic SO₂ transported from Africa and Middle East (Jabal Al-Tair, Dalaffilla, and Nabro volcanoes) over the study area is presented. Air mass trajectories suggest the presence of long-range transported volcanic SO₂ at high altitude levels over Lahore and IGB region during the volcanic episodes. The SO₂ enhancements in PBL during winter season are generally due to significant vertical downdraft of high-altitude volcanic SO₂. For the first time, we present significant influence of volcanic SO₂ from southern Europe (Mt. Etna volcano) reaching over the study area. Daily mean OMI-SO₂ levels up to 21.4, 10.0, 5.6, and 2.4 DU have been noticed due to the eruptions from Dalaffilla, Mt. Etna, Nabro, and Jabal Al-Tair volcanoes, respectively.     

Détermination du facteur de multiplication M pour un fonctionnement optimal des photodétecteurs à avalanche

The purpose of this paper is relative to the determination of the multiplication factor Mfor an optimum behaviour of the avalanche photodetector. Two types of avalanche photodetector s are compared: a silicon structure n ⁺ πpπp ⁺ Read diode and a GaAlAs heterostructure. Calculating the Noise Equivalent Power (nep)of the whole system, the law S_i(f) =2q I_phoM_x is used, where the parameter x is determined experimentally in both avalanche photodetectors. The exponent x (between 2 and 4) dominates in the nep expression and the ideal nep for the avalanche photodetector system is obtained when x equals 2.     

Three Dimensional Conceptualisation of Hydrogeological Environment to Underpin Groundwater Management in Irrigation Area

Australian irrigated agriculture utilises about 70?% of all water used in the country, 21?% of which is derived from groundwater. Sustainability for irrigated agriculture also depends on keeping the watertables at a safe level below the rootzone to avoid salinisation and reduction in crop yields. There is a vital need to understand groundwater and aquifer systems and their roles in the sustainability of irrigated agriculture in order to manage groundwater properly. This study builds on the previous hydrogeological and groundwater investigations of the Coleambally Irrigation Area (CIA) in New South Wales of Australia. It presents a new approach which systematically characterises regional hydrogeological environment using a three-dimensional (3-D) conceptual framework developed in ArcGIS. The 3-D hydrological conceptualisation of the CIA has integrated disparate sources of data into a coherent knowledge base for a better visualisation of hydrogeological characteristics and a comprehensive analysis of groundwater flow and aquifers. As an application example, the model was used to develop cross-sectional models of the area and to estimate regional-scale net recharge. The results have provided a basis for the numerical modelling and added values to procedures which underpin irrigation system management investment decisions through improving the understanding of hydrogeology underlying the area and creating an action-oriented dialogue among stakeholders.