Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems

A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and out-of-control average run length (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005) of the proposed method are compared with the ones from the well-known Multivariate Cumulative Sum (MCUSUM), Multivariate Exponentially Weighted Moving Average (MEWMA) and Hotelling T ² methods in different scenarios of mean shifts. The results of the simulation study show that the proposed methodology performs better than other methods for all shifts of the process mean. Additionally, the estimated probabilities of making correct classifications by the proposed approach are encouraging.     

Hard- and soft-modeling approaches for resolution of complex formation of Co2+ and Ni2+ with glycine

Principal component analysis (PCA) was used to obtain information about the number of components in the complex formation equilibria of Co(2+) and Ni(2+) with glycine (Gly). In order to obtain a clearer insight into these complex formation systems, multivariate curve resolution-alternating least squares (MCR-ALS) was used. Using MCR-ALS as a soft-modeling method, well-defined concentration and spectral profiles were obtained under unimodality, non-negativity, and closure constraints. Based on the obtained results, an equilibrium model was proposed and subsequently, a hard-modeling method was used to resolve the complex formation equilibria completely. Due to the presence of multiple equilibria, the resolution of such systems is very difficult. The Co-Gly system was best described by a model consisting of M(GlyH), M(Gly), M(Gly)(2), M(Gly)(2)H, and M(Gly)(3) (M = Co(2+)) with the overall stability constants determined to be 7.10 ± 0.011, 5.14 ± 0.006, 9.28 ± 0.009, 13.75 ± 0.016, and 11.10 ± 0.010, respectively. On the other hand, the system of Ni-Gly was best fitted by a model containing M(GlyH), M(Gly), M(Gly)(2), M(Gly)(3), and M(Gly)(2)(OH) (M = Ni(2+)) with overall stability constants of 10.95 ± 0.04, 6.41 ± 0.03, 11.31 ± 0.03, 15.39 ± 0.06, and 14.32 ± 0.02, respectively.     

Analytical studies on woven fabrics’ bagging performance affected by the material,yarn, and fabric parameters

The present work focuses on investigating the bagging behavior of woven fabrics produced from viscose and polyester/viscose yarns. In this paper, the bagging deformation – in terms of different bagging parameters (bagging resistance, bagging fatigue, bagging hysteresis, and residual bagging hysteresis) – has been interpreted by fabric’s physical and mechanical properties which were obtained from FAST system. The statistical analysis revealed that there is a significant correlation between the bagging parameters. Also, it was concluded that the samples’ physical and mechanical properties correlate significantly with bagging parameters. Additionally, the effects of four different variables such as material type, spinning system, weave pattern, and weft density on bagging parameters have been studied. Considering the high correlations between all the bagging parameters, it was decided to analyze only the parameter bagging fatigue in this paper. The results showed that all the fabric variables except the parameter weft density have significant effects on bagging fatigue performance.     

Improvement of optical properties and thermal stability of poly vinyl alcohol using salicylic acid confined in nanohybrid material

This study aims to benefit from the confinement of salicylic acid in the nanoscale to convert poly vinyl alcohol to be UV absorber. Therefore, Zn–Al LDH was prepared and used as a host. It intercalated with salicylic acid which used as a guest to form nanohybrid material. This nanohybrid material was inserted inside poly vinyl alcohol to build nanocomposite material. X-ray diffraction results and electron microscopy images showed that the interlayer spacing of Zn–Al LDH increased from 0.77 to 1.55 nm after intercalation reaction with salicylic acid. The thermal analyses revealed the presence of a complex system of supra-molecular host–guest interaction. The optical properties of Zn–Al–salicylate nanohybrid material showed new absorption bands in the UV region comparing with the absorption bands of salicylic acid sodium salt. Nanocomposite based on both Zn–Al–salicylate nanohybrid and PVA were characterized by X-ray diffraction, Scanning electron microscopy, UV–Visible absorption spectra, and thermal analyses. The experimental results suggested that the nanocomposite was formed via exfoliated structure and the nanohybrid layers were completely and uniformly dispersed in a continuous polymer matrix. By comparing with the thermal and optical properties of the original PVA, the PVA nanocomposite became more stable and UV absorber.     

Dual biomarkers of anaerobic hydrocarbon degradation in historically contaminated groundwater

This study reports that ongoing in situ anaerobic hydrocarbon biodegradation at a manufactured gas plant impacted site is occurring, 9 years after the initial investigation. Groundwater samples from the site monitoring wells (MW) were analyzed for biomarkers by GC-MS, end-point PCR, and quantitative PCR (qPCR). Metabolic biomarkers included specific intermediates of anaerobic naphthalene and/or 2-methylnaphthalene degradation: 2-naphthoic acid (2-NA); 5,6,7,8-tetrahydro-2-NA (TH-2-NA); hexahydro-2-NA (HH-2-NA); and carboxylated-2-methylnaphthalene (MNA). The analogues of gene bssA, encoding alpha subunit of enzyme benzylsuccinate synthase, were used as a genetic biomarker. Results indicate 1-2 orders of magnitude higher abundance of total bacteria in the impacted wells than in the unimpacted wells. End-point PCR analysis of bssA gene, with degenerate primers, indicated the presence of hydrocarbon degrading bacteria within the plume. In qPCR analysis, using primers based on toluene-degrading denitrifying or sulfate-reducing/methanogenic bacteria, bssA genes were detected only in MW-24, located downstream from the source. Metabolic biomarkers were detected in multiple wells. The highest abundance of 2-NA (6.7 μg/L), TH-2-NA (2.6 μg/L), HH-2-NA, and MNA was also detected in MW-24. The distribution of two independent biomarkers indicates that the site is enriched for anaerobic hydrocarbon biodegradation and provides strong evidence in support of natural attenuation.     

Fundamental examination of nanoparticle heating kinetics upon near infrared (NIR) irradiation

Near infrared (NIR) light, which spans wavelengths from ~700-1100 nm holds particular promise in bionanotechnology-enabled applications because both NIR light and nanoparticles (NPs) have the potential for remote activation leading to exquisite localization and targeting scenarios. In this study, aqueous solutions of carbon and metal-based NPs (carbon black, single-walled carbon nanotubes, silver nanoparticles and copper nanoparticles) were exposed to continuous NIR laser (λ = 1064 nm) irradiation at powers of 2.2W and 4.5W. The differential heating of bulk aqueous suspension of NPs with varying physicochemical properties revealed maximum temperatures of 67 °C with visible evidence of condensation and bubble formation. The basis of the NP heating is due to the strong intrinsic optical absorbance in the NIR spectral window and the transduction of this NIR photon energy into thermal energy. In this regard, UV-vis measurements can accurately predict NP heating kinetics prior to NIR irradiation. Further, a uniform thermodynamic heating model demonstrates close agreement with the experimental data for the low NIR-absorbing NPs. However, the uniform thermodynamic heating model used in this study does not accurately portray the energy release upon localized NP heating because of bubble formation for the highly absorbing NPs. Therefore, this study reveals the differential heating kinetics of NPs excited with NIR with implications in the development of novel NIR-NP-based systems.     

Scanning tunneling microscopy of cauliflower-like DNA nanostructures synthesised by loop-mediated isothermal amplification

DNA nanotechnology is a novel approach for synthesis of DNA-based nanostructures. Stem-loops, nanojunctions, sticky-ends and periodic lengths of DNA are the most essential nanostructures in DNA nanofabrications. Loop-mediated isothermal amplification (LAMP) is a powerful technology for repetitive synthesis of double-stranded and cauliflower-like DNAs. The process leads to long and repetitive sequences of DNAs, which are fabricated via loop primers. The authors demonstrate here scanning tunneling micrographs of LAMP-synthesised DNAs deposited on highly ordered pyrolytic graphite. The scans are compared with natural DNAs. Scanning tunneling microscopy (STM) images indicated the creation of periodic long DNAs, stem-looped DNAs and three-way DNA nanojunctions. It is also suggested that such nanomaterials could be promising candidates for use in DNA-based nanodevices.     

Investigation into the curling behavior of single jersey weft-knitted fabrics and its prediction using neural network model

This research investigates the effect of fiber, yarn, and fabric parameters on curling phenomenon of single jersey weft-knitted fabrics which is interpreted to have curling surface in both course and wale direction. Taguchi’s experimental design is used to estimate the optimum process conditions and to examine the individual effects of all controllable factors on curling one by one. The controllable factors are blending ratio of polyester to cotton fiber, yarn twist and count, fabric structure, knit density, and relaxation time. Results show that fabric structure and knit density have the most dominant effect on the fabric curling. The optimum conditions of minimum curling values were also determined. Finally, the curling surface in course and wale direction as a two features of curling phenomenon was predicted using artificial neural network which selects scale conjugate gradient learning algorithm based on process parameters of single jersey weft-knitted fabrics. Our findings confirm the good capability of artificial neural network algorithm to predict these features.     

Structure and properties of nylon-6/amino acid modified nanoclay composite fibers

Biodegradable amino acid modified nanocaly was applied to produce nylon-6/nanoclay composite fibers using melt blending and melt spinning processes. Field emission scanning electron microscopy images showed that the surface of composite fibers was uniform and free from defects. Layer spacing of modified nanoclay was increased due to the penetration of polymer molecules into clay layers. Crystallinity, γ crystalline percentage and total molecular orientation of composite fibers were higher in comparison to neat nylon-6 fibers, as revealed by WAXD and birefringence measurements. Tensile strength of composite fibers was lower when compared to neat nylon-6 fibers. This may be attributed to some aggregation of nanoclay and its weakening effect. Melting and glass transition temperature of composite fiber was decreased due to the addition of modified nanoclay, indicating the formation of γ crystals and also breaking of some hydrogen bonds between the polymer molecules and the formation of new hydrogen bonds between the modified clay and the polymer molecules.