Sensing mechanism of Ag/α-MoO3 nanobelts for H2S gas sensor

Sensing mechanism is still a big problem in the field of gas sensor.In-depth study of the sensing mecha-nism can provide better ideas for the design of sensing ...     

Preparation and properties of carbon nanotube/polypropylene nanocomposite bipolar plates for polymer electrolyte membrane fuel cells

This study aims at the fabrication of lightweight and high performance nanocomposite bipolar plates for the application in polymer electrode membrane fuel cells (PEMFCs). The thin nanocomposite bipolar plates (the thickness <1.2 mm) consisting of multiwalled carbon nanotubes (MWCNTs), graphite powder and PP were fabricated by means of compression molding. Three types of polypropylene (PP) with different crystallinities including high crystallinity PP (HC-PP), medium crystallinity PP (MC-PP), low crystallinity PP (LC-PP) were prepared to investigate the influence of crystallinity on the dispersion of MWCNTs in PP matrix. The optimum composition of original composite bipolar plates was determined at 80 wt.% graphite content and 20 wt.% PP content based on the measurements of electrical and mechanical properties with various graphite contents. Results also indicate that MWCNTs was dispersed better in LC-PP than other PP owing to enough dispersed regions in nanocomposite bipolar plates. This good MWCNT dispersion of LC-PP would cause better bulk electrical conductivity, mechanical properties and thermal stability of MWCNTs/PP nanocomposite bipolar plates. In the MWCNTs/LC-PP system, the bulk electrical conductivities with various MWCNT contents all exceed 100 S cm⁻¹. The flexural strength of the MWCNTs/LC-PP nanocomposite bipolar plate with 8 phr of MWCNTs was approximately 37% higher than that of the original nanocomposite bipolar plate and the unnotched Izod impact strength of MWCNTs/LC-PP nanocomposite bipolar plates was also increased from 68.32 J m⁻¹ (0 phr) to 81.40 J m⁻¹ (8 phr), increasing 19%. In addition, the coefficient of thermal expansion of MWCNTs/LC-PP nanocomposite bipolar plate was decreased from 32.91 μm m⁻¹ °C⁻¹ (0 phr) to 25.79 μm m⁻¹ °C⁻¹ (8 phr) with the increasing of MWCNT content. The polarization curve of MWCNTs/LC-PP nanocomposite bipolar plate compared with graphite bipolar plate was also evaluated. These results confirm that the addition of MWCNTs in LC-PP leads to a significant improvement on the cell performance of the nanocomposite bipolar plate.     

Integrating PSONN and Boltzmann function for feature selection and classification of lymph nodes in ultrasound images

Lymph nodes (LNs), part of the lymphatic system, are important in the proper functioning of the immune system. LN metastasis is an important index for staging malignant tumors. The present study proposes a system that classifies lymph nodes according to pathological change from ultrasound (US) images. Features are selected and extracted from the US images. A feature selection method that integrates the particle swarm optimization neural network (PSONN) with the Boltzmann function is proposed to select significant features. A multi-class support vector machine (SVM) is adopted to classify diseases of the LN in the region of interests (ROIs) of US images into six categories. The experimental results show that the proposed approach decreases the number of selected features and that its classification is highly accurate.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.     

Wafer defect inspection by neural analysis of region features

Wafer defect inspection is an important process that is performed before die packaging. Conventional wafer inspections are usually performed using human visual judgment. A large number of people visually inspect wafers and hand-mark the defective regions. This requires considerable personnel resources and misjudgment may be introduced due to human fatigue. In order to overcome these shortcomings, this study develops an automatic inspection system that can recognize defective LED dies. An artificial neural network is adopted in the inspection. Actual data obtained from a semiconductor manufacturing company in Taiwan were used in the experiments. The results show that the proposed approach successfully identified the defective dies on LED wafers. Personnel costs and misjudgment due to human fatigue can be reduced using the proposed approach.     

A multi-view vision-based hand motion capturing system

Vision-based hand motion capturing approaches play a critical role in human computer interface owing to its non-invasiveness, cost effectiveness, and user friendliness. This work presents a multi-view vision-based method to capture hand motion. A 3-D hand model with structural and kinematical constraints is developed to ensure that the proposed hand model behaves similar to an ordinary human hand. Human hand motion in a high degree of freedom space is estimated by developing a separable state based particle filtering (SSBPF) method to track the finger motion. By integrating different features, including silhouette, Chamfer distance, and depth map in different view angles, the proposed motion tracking system can capture the hand motion parameter effectively and solve the self-occlusion problem of the finger motion. Experimental results indicate that the hand joint angle estimation generates an average error of 11°.     

Microwave-Assisted Pulse-Spouted Vacuum Drying of Apple Cubes

Microwave-assisted pulse-spouted vacuum drying (MPSVD) of apple cubes was examined in a laboratory-scale apparatus. Aside from the drying time, structural and textural properties of the dried cube were measured. Results are compared with alternative drying techniques developed earlier in our laboratory. These include microwave-spouted bed drying (MSBD), microwave vacuum drying (MVD), and conventional vacuum drying (VD). Comparison is made in terms of the key quality parameters, viz. color, texture, apparent density, rehydration property, and sensory evaluation. Over the range of operating conditions tested, MPSVD apple cubes had the best color and significantly highest sensory evaluation score.     

SFFS–SVM based prostate carcinoma diagnosis in DCE-MRI via ACM segmentation

The prostate carcinoma is amongst the most commonly occurring cancers in Taiwanese males. Moreover, it is one of the chief reasons for cancer deaths among Taiwanese men, and early diagnosis of prostate cancer is vital for effective treatment. In this work, a diagnosis model for identifying the prostate carcinoma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. The urologists utilize the DCE-MRI as a support mechanism for better diagnosis of the carcinoma development in the prostate. Gadolinium is utilized as the contrast agent for the DCE-MRI data, and it was injected once and the time series data were captured at distinct time intervals of 0, 20, 60, and 100 s correspondingly. Primarily, after pre-processing the DCE-MRI information, the prostate data is segmented by employing the active contour model. Subsequently, 136 features are extracted from the segmented prostrate expanse of the DCE-MRI data, and the relative intensity change curve is computed. Afterward, Fisher’s discriminant ratio and sequential forward floating selection is deployed for choosing ten highly discriminative features. Lastly, the segmented prostate regions are classified into two groups, namely: tumor and normal classes by employing the support vector machine classifier. The experimental results elucidate that the proposed system is superior on the subject of accuracy, sensitivity, and specificity when compared with specific existing methods. Additionally, the proposed system also demonstrates a 94.75% accuracy. Moreover, this signifies the fact that the proposed method for analyzing the DCE data has shown prodigious prospects in the prostate carcinoma diagnosis.

     

Sulfonated poly(propylene oxide) oligomers/Nafion acid-base blend membranes for DMFC

A novel functional poly(propylene oxide)-backboned diamine of M_w 400 (abbreviated as D400) was grafted with sulfonic acid (abbreviated as D400-PS) to improve the performance of Nafion^® membranes in direct methanol fuel cells (DMFCs). The interaction of the D400-PS with Nafion^® was studied by Fourier transform infrared spectroscopy (FT-IR) and differential scanning calorimetry (DSC). The performance of the blend Nafion^®/D400-PS membranes was evaluated in terms of methanol permeability, proton conductivity and cell performance. The proton conductivity of the blend membrane was slightly reduced by rendering proton conductivity to D400 by functionalized with an organic sulfonic acid. The methanol permeability of the blend membrane decreased with increasing of D400-PS content. The methanol permeability of the blend Nafion^®/D400-PS with the composition 3/1 (–SO₃H/–NH₂) was 1.02 × 10⁻⁶ cm² S⁻¹, which was reduced 50% compared to that of pristine Nafion^®. The current densities that were measured with Nafion^®/D400-PS blend membranes in the ratio 1/0 and 5/1 (–SO₃H/–NH₂), were 51 and 72 mA cm⁻², respectively, at a potential of 0.2 V. Consequently, the blend Nafion^®/D400-PS membranes critically improved the single-cell performance of DMFC.