Modelling of acrylic cut-pile carpet compression properties by means of expert systems based on carpet structural parameters

Abstract

Compression properties of carpet are a detrimental factor influencing carpet wear performance. In fact, carpet compression behavior determines carpet serviceability which in turn affects carpet appearance. Therefore, it is important to predict the carpet compression behavior in order to reduce the product cost. In this paper, a fuzzy logic model for prediction of compressional properties of acrylic cut-pile carpets is implemented in terms of the carpet structural parameters including carpet pile density, linear density of pile yarns and carpet pile height. In order to provide experimental data, six different acrylic cut-pile carpet samples fabricated by using six different acrylic spun pile yarns with yarn count of 18/3, 21/3, 27/3, 30/3, 33/3 and 36/3?Nm. Half of each provided carpet subjected to re-shearing process. All 12 carpet samples in two different cases (normal and reduced pile height) have been tested by standard carpet static and dynamic loading tests to measure thickness-loss as one of compression properties. Fuzzy logic model has been implemented and improved using genetic algorithm. Results show that correlation coefficients of model predictions with experimental values are 0.97, 0.98, 0.98 and 0.98 for carpet thickness-loss after low dynamic, high dynamic and static loading with short and long relaxation times, respectively. Also, linear regression trend between predicted and experimental values represented with a slope near to 1 and almost small bias (intercept). The result indicates that the developed fuzzy logic model is a reliable model predicting the acrylic cut-pile compression behavior.     

Possible Crosstalk of the Immune Cells within the Lung and Mediastinal Fat-Associated Lymphoid Clusters in the Acute Inflammatory Lung Asthma-Like Mouse Model

Recently, we clarified the function of mediastinal fat-associated lymphoid clusters (MFALCs) in the progression of several respiratory diseases. However, their role has not yet been identified in the lung asthmatic condition. Hence, we compared the immune cells in lung and MFALCs of C57BL/6N mice on days 3 and 7 following intranasal instillation of either papain (papain group “PG”) or phosphate buffer saline (PBS) (vehicle group “VG”). The PG showed significantly prominent MFALCs, numerous goblet cells (GCs), and higher index ratios of different immune cells (macrophages, natural helper cells (NHC), B- and T-lymphocytes) within the MFALCs and lung than in the VG on both days 3 and 7. Interestingly, a tendency of decreased size of MFALCs and a significant reduction in the number of GCs and immune cells were observed within the MFALCs and lung in the PG on day 7 than on day 3. Furthermore, the quantitative parameters of these immune cells in MFALCs were significantly and positively correlated with the size of MFALCs and immune cells in the lung. This suggested that the possible crosstalk between immune cells within MFALCs and the lung could play a critical role in the progression and recovery of the acute inflammatory lung asthma.     

Soft Computing Techniques for Classification of Voiced/Unvoiced Phonemes

A method that uses fuzzy logic to classify two simple speech features for the automatic classification of voiced and unvoiced phonemes is proposed. In addition, two variants, in which soft computing techniques are used to enhance the performance of fuzzy logic by tuning the parameters of the membership functions, are also presented. The three methods, manually constructed fuzzy logic (VUFL), fuzzy logic optimized with genetic algorithm (VUFL-GA), and fuzzy logic with optimized particle swarm optimization (VUFL-PSO), are implemented and then evaluated using the TIMIT speech corpus. Performance is evaluated using the TIMIT database in both clean and noisy environments. Four different noise types from the AURORA database—babble, white, restaurant, and car noise—at six different signal-to-noise ratios (SNRs) are used. In all cases, the optimized fuzzy logic methods (VUFLGA and VUFL-PSO) outperformed manual fuzzy logic (VUFL). The proposed method and variants are suitable for applications featuring the presence of highly noisy environments. In addition, classification accuracy by gender is also studied.     

PD-L1 Expression in Non-Small Cell Lung Cancer Specimens: Association with Clinicopathological Factors and Molecular Alterations

Immune checkpoint inhibitors (ICI) targeting programmed cell death-1 or its ligand (PD-L1) have improved outcomes in non-small cell lung cancer (NSCLC). High tumor PD-L1 expression, detected by immunohistochemistry (IHC) typically on formalin-fixed paraffin-embedded (FFPE) histological specimens, is linked to better response. Following our previous investigation on PD-L1 in cytological samples, the aim of this study was to further explore the potential impacts of various clinicopathological and molecular factors on PD-L1 expression. Two retrospective NSCLC cohorts of 1131 and 651 specimens, respectively, were investigated for PD-L1 expression (<1%/1–49%/≥50%), sample type, sample site, histological type, and oncogenic driver status. In both cohorts, PD-L1 was positive (≥1%) in 55% of the cases. Adenocarcinomas exhibited lower PD-L1 expression than squamous cell carcinomas (p < 0.0001), while there was no difference between sample types, tumor locations, or between the two cohorts in multivariate analysis (all p ≥ 0.28). Mutational status correlated significantly with PD-L1 expression (p < 0.0001), with the highest expression for KRAS-mutated cases, the lowest for EGFR-mutated, and the KRAS/EGFR wild-type cases in between. There was no difference in PD-L1 levels between different prevalent KRAS mutations (all p ≥ 0.44), while mucinous KRAS-mutated adenocarcinomas exhibited much lower PD-L1 expression than non-mucinous (p < 0.0001). Our data indicate that cytological and histological specimens are comparable for PD-L1 evaluation. Given the impact of KRAS mutations and the mucinous growth pattern on PD-L1 expression, these factors should be further investigated in studies on ICI response.     

Some New Heterocyclic Sulphonates with potential antimicrobial activity

4-(Phenylhydrazono-4′-benzenesulphonato)-3-methyl-2-pyrazolin-5-one ( 2 ) was prepared by treating 4-(4′-hydroxyphenylhydrazono)-3-methyl-2-pyrazolin-5-one ( 1 ) with benzenesulphonyl chloride. Heating 2 with acetic anhydride, chloroacetyl chloride and benzenesulphonyl chloride afforded the N-acetyl ( 3 ), N-chloroacetyl ( 4 ) and O-benzenesulphonyl ( 5 ) derivatives, respectively. 2 reacted with benzenesulphonyl chloride under cooling conditions to give the N-benzenesulphonyl derivative ( 6 ). 2 undergoes Mannich reaction to give the N-Mannich base ( 7 ). Reaction of 2 with formaldehyde in methanol afforded the N-hydroxymethyl derivative ( 8 ) which undergoes condensation with ethyl acetoacetate, ethyl cyanoacetate and malononitrile to give compounds 9 , 10 and 11 respectively. The antimicrobial activity of these products against gram-positive (+ve), gram-negative (−ve) bacteria and fungi was screened. They showed potential bactericidal activity and some of them exhibited high fungicidal activity.     

Reactivity of 4-acetyl-3-chloro-5,6-diphenylpyridazine towards some nucleophilic reagents,Synthesis of Some Fused Pyridazine Derivatives

4-Acetyl-3-chloro-5,6-diphenylpyridazine ( 1 ), prepared by the action of phosphorus oxychloride on 4-acetyl-5,6-diphenylpyridazin-3(2H)-one ( 2 ), reacts with hydrazine hydrate and phenylhydrazine to give the pyrazolinopyridazine derivatives ( 3a , b ) respectively. Reaction of 1 with hydroxylamine hydrochloride gave the isoxazolopyridazine derivative ( 5 ), while its reaction with sodium azide in dimethyl formamide gave the tetrazolopyridazine derivative ( 8 ). Primary amines react with 1 to give either 9 or 10 depending upon the reaction conditions. Treatment of 1 with piperidine or morpholine gave the products 11a , b , respectivly. 4-Acetyl-5,6-diphenylpyridazin-3(2H)-thione ( 13 ) was readily obtained by the action of thiourea on ethanolic solution of 1 . The reactions of 1 with phenols were also investigated.     

Studies on 1,2,4-triazine derivatives. III. The Behaviour of some Derivatives of 1,2,4-Triazine toward diazomethane,amines, ethyl monobromoacetate and tetra-O-acetyl-α-D-glucopyranosyl bromide

The reaction of diazomethane with 6-phenyl-3-thioxo-5-oxo-2,3,4,5-tetrahydro-1,2,4-triazine 2a , and with 6-phenyl-3-methylmercapto-5-oxo-4,5-dihydro-1,2-triazine 3a effected N-methylation to 2b and 3b , respectively. Compound 3a and 3-methyl-mercapto-5, 6-diphenyl-1,2,4,-triazine 4a react with amines to give 3-N-substituted-amino-triazines 5a–h and 6a–h , respectively. 3-Mercapto-5,6-diphenyl-1,2,4-triazine 4b underwent S-alkylation with ethyl monobromoacetate and with tetra-O-acetyl-α-D-glucopyraosyl bromide.     

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis (CAD) models exploit artificial intelligence (AI) for chest X-ray (CXR) examination to identify the presence of tuberculosis (TB) and can improve the feasibility and performance of CXR for TB screening and triage. At the same time, CXR interpretation is a time-consuming and subjective process. Furthermore, high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis. Therefore, computer-aided diagnosis (CAD) models using machine learning (ML) and deep learning (DL) can be designed for screening TB accurately. With this motivation, this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification (WSODTL-TBC) model on Chest X-rays (CXR). The presented WSODTL-TBC model aims to detect and classify TB on CXR images. Primarily, the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation. Besides, a pre-trained residual network with a two-dimensional convolutional neural network (2D-CNN) model is applied to extract feature vectors. In addition, the WSO algorithm with long short-term memory (LSTM) model was employed for identifying and classifying TB, where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology, showing the novelty of the work. The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset, and the outcomes were investigated in many aspects. The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.     

Spectroscopic Study of Eu3+-Doped Magnesium Lanthanum Phosphate (MLPO) Films on SiO2 Substrate

Silicon - The distinctive optical band gap and photoluminescence of sol-gel Eu3+-doped magnesium lanthanum phosphate thin films on quartz (SiO2) substrate were successfully investigated. Effects of...