فهرست مطالب mohsen hadizadeh

The economic conditions prevailing in the country, sanctions, rapid changes and transformations of the markets, and the complexity and disorder of the market environment, have made it necessary to pay attention to the anti-fragility approach in the industries. One of the most interesting industries is the textile industry. Hence, due to the strategic nature and having a large domestic consumption market, the textile industry must be anti-fragile in order to quickly adapt to new conditions and maintain and improve its competitive value in a safe and stable condition. This research was conducted with a review of the literature in order to achieve the anti-fragility of textile industries throughout the supply chain. We identified the anti-fragility components and weighed them using the best-worst method. Then, we reviewed the literature on the textile industry and interview the experts in this industry, to identify and evaluate the fragility factors. By using the CoCoSo method the most important factors that disrupt anti-fragility were ranked. Finally, the results of the research showed that the components: unexpectedness, Adequate supervisors, and Attraction are the most important components. Also, the results obtained from the CoCoSo method showed that the of ''Lack of investment in the (clothing) industry", "Saturation of the market with counterfeit brands due to the structural weakness of the distribution and smuggling network", and "Undeveloped distribution network" are the most important in the supply chain of the textile industry.

The rapid changes and transformations of the markets and the complexity and disorder of the market environment and the conditions of the economic situation in the country due to the sanctions have made it necessary to pay attention to anti- fragility approach in the industries. The textile industry is one of the strategic industries that should be considered and rewarded due to the large domestic consumption market. Various textile industries can quickly adapt to new conditions and maintain and even improve their competitive value in a safe and stable condition. The current research was conducted with a review of the literature in order to achieve anti-fragility of textile industries throughout the supply chain. In this way, the anti- fragility components were first identified and weighed using the updated best-worst model technique under conditions of uncertainty. Then, by reviewing the literature of the textile industry and interviewing the experts of this industry, stressful factors were identified and evaluated using the TOPSIS method in order to identify and rank the most important factors that disrupt anti-fragility. Finally, the results of the research showed that the components: unexpectedness, sufficient supervisors and redundancy are the most important components. Also, the results obtained from the TOPSIS method showed that the lack of access to foreign capital and the main export markets, the wear and tear of the machinery of the spinning chain industry, and the import of clothing from low-cost producing countries are the most important in the supply chain of the textile industry.

Fabric compressibility has a significant effect on the application, comfort, quality, and thermal properties of the fabric. Lateral compression affects the diameter of the yarns and fabric density, which causes a change in the cover factor (CF). The purpose of this paper is to investigate fabric compressibility, the cover factor, and the cover factor variation in compressed states. In this research, woven fabrics were produced with different weft density and different weft materials (polyester and viscose). The compression test was conducted at different speeds and the fabric was filmed at the same time. The density and the diameter of the warp and weft yarns, as well as the CF were extracted by image processing using video pictures. Then the fabric compressibility, CF, and, CF Variation (CFV) were modeled using the response surface method (RSM). The results obtained in the study clearly showed these models are acceptable with a high correlation. According to this model, with increasing pressure and decreasing density, fabric compressibility and CFV often increase. The compression variation range and CF were greater in polyester fabrics than in viscose fabrics by changing weft density.

Nonwoven needle-punched fabrics are the mostcommon textile structures used as geotextiles. In mostapplications, geotextiles are subjected to compressive forces.These forces cause the layers to deform and eventually createpuncture. The present study develops an intelligent model forthe evaluation of static puncture resistance and real elongationof nonwoven needle-punched polyester fabrics using fuzzylogic method. The fuzzy logic expert system, contrary tomany other mathematical methods, can considerably forecastthe behavior of nonlinear complex phenomena. Parametersof needle penetration depth, needle punch density, andfabric areal weight were considered as input variables of thedesigned model. The experimental results were conductedby a universal strength tester based on the well-known staticpuncture (CBR) test method. The fuzzy model showed thatpuncture resistance increases with the enhancement of fabricareal weight, but excessive increase of the needling parameterscauses the puncture resistance to decrease. Furthermore, theresults of the model demonstrated that the fabric puncturereal elongation decreases, while the input variables increase.It was also observed that the real and predicted values ofpuncture resistance and puncture real elongation of thefabrics were in good agreement with very low absolute error.

Ink-jet printing technology is one of the most promising printing techniques enabling fabrication of conductive patterns in a one-step and direct process. In this study, a microwave transmission line is fabricated on RO4003C substrate using water-based reactive inks and ink-jet printing technique. The fabricated transmission line structure includes an ink-jet printed silver line, a dielectric layer, and a continuous metallic ground plate. The conductivity of the printed line is measured using Four-point probe method. The electromagnetic wave absorption rate of the printed transmission line is simulated according to the measured conductivity, which proves a good agreement with the measured absorption rate at S band (2-4 GHz frequency range).

 Vaginal reconstruction is the choice medical approach in many congenital abnormalities, injuries, or cancers. Reconstructive techniques applying non-vaginal tissues can be associated with more complications. In special situations such as dilatation failure or large defects, surgical vaginoplasty is recommended. In this regard the main challenge is the lack of sufficient native tissue to reconstruct the organ.

 In this study, nanofibrous scaffolds of elastomeric polyurethane (PU) were fabricated by electrospinning. The physical and chemical properties of the scaffolds were characterized. The toxicity and biocompatibility of each scaffold was evaluated by the MTT assay using human vaginal epithelial cells (HVECs). Cell homing, proliferation and cytokeratine expression was evaluated on the scaffolds by scanning electron microscopy (SEM), Hematoxylin & Eosin (H&E) and fluorescent staining, respectively.

Polyurethane nanofibers diameter and porosity of scaffold were 322±176 nm and 80%±2%, respectively. After 120 days of degradation, the average weight loss of scaffold was 2.3%. The SEM image also preserves the structural integrity of the fibers but shows small white dots on the nanofibers. SEM microscopy images, MTT assay and H&E staining showed adhesion, viability and prolifration of epithelial cells on the scaffold. Finally, the cytokeratine expression of these cells was confirmed by fluorescent staining on the polyurethane nanofibers scaffold.

The results showed that PC-3575A polyurethane nanofibers scaffold has good potential for vaginal tissue engineering.

Fabric bending rigidity evaluation plays a very important role in determining end-use quality of products. This property has a non-linear behavior. Many techniques, such as mathematical, multiple regression, artificial neural network model, etc., have been used to predict mechanical properties of fabrics. This paper presents a method to model the bending rigidity of plain-woven fabrics using fuzzy logic. The input variables are yarn count, yarn diameter, yarn spacing, yarn bending rigidity and yarn length. The output variable is fabric bending rigidity. These results revealed the efficiency of fuzzy model to predict bending rigidity based on the mentioned parameters. Then the prediction accuracy of fuzzy logic model in comparison with three modeling methodologies based on mathematical, empirical and artificial neural network was evaluated. The comparison of the prediction performance showed that the fuzzy model is more powerful than the other models.

Conventional methods of hair dyeing involve the use of chemical materials that result in unpleasant side effects, such as breakage of hair and cancer. A need was felt to formulate a dye containing plant products which is safe for use and does not have the problems of hypersensitive reactions. In this study, formulations containing natural hair colorants with madder dye and additives like herbal oils (almond, olive and coconut), ammonia and lemon juice were prepared. Response surface method was used to analyze color strength of hair colorant as dependent variable. Dye concentration, bath temperature and additives have been selected as independent variables. Color strength predictive model of natural hair colorants with madder dye is provided and this model has a high coefficient of determination. Also, it was indicated that, bath temperature has a great effect on enhancing the color strength of natural hair colorant. The optimization conditions of dyeing process parameters for polyhedral hair colorant production using response surface methodology were identified.

There is always a probability of recurring failures in a manufacturing system. These failures disturb the output of a system quantitatively and qualitatively, and on the whole they are menace to the system's productivity. This paper introduces a technique called "failure-mode-and-effects analysis (FMEA)" to recognize or monitor and reduce harmful effects of the most critical errors in a carpet manufacturing system. This method requires a team of inspecting members to discuss the type of failures and their potential risk by classifying each failure under a grade called "risk priority number (RPN)". The failures with highest RPN are the most critical type of failures for which corrective actions must be preferentially taken to increase the quality of the production. In this research work, FMEA technique is adopted in a carpet manufacturing system to evaluate and monitor the type and quality of the failures, while presenting solutions to eliminate them most effectively.

Extracting appropriate information from infinite number of data requires modern methods. Data mining is one of these tools and approaches that may help in the area of knowledge management in knowledge discovery from the databases. Self-organizing neural network is undoubtedly the most powerful neural networks for data mining. This paper presents a size chart that has been developed based on measurements taken from 2000 women of Yazd. The important input variables have been defined as the chest، waist and hip measurements. In this study، the different size clusters have been investigated through self-organizing neural network model. The results of using the self-organizing neural network indicate that there are four effective size groups that are categorized based on the lowest mean error.