Abstract Introduction: Investigation of the internal surface Roughness of fluid transmission systems pipes is very important in the amount of energy loss. Different concepts and methods have been used to examine surface Roughness. Some of these methods are based on Roughness measurement devices and equipment in this field. Significant researches are done in surface Roughness measuring in steel, copper, plastic, or coated pipes. Methods: In this study, the inner surface Roughness of fiberglass pipes, has been evaluated using laboratory methods and Roughness measuring devices in several diameters and two different types. Calibration and verification results of the surface Roughness tester machine on sandpaper and U-PVC pipe wall surfaces are evaluated. In addition, the effect of Roughness parameters and their calculated surface Roughness and time using of fiberglass pipe have been investigated. Findings: According to the results, the Roughness parameters Rz and Ra in the cut length of 0.8 and 2.5 respectively are suitable parameters to estimate the Roughness of the inner surface of the fiberglass tube. Also, the Roughness of the inner surface of biaxial tubes is lower than uniaxial tubes. In addition, in comparing the Roughness of newly produced and old fiberglass pipes, the surface Roughness parameters decrease due to the passage of time and the use in projects. Whereas, the Roughness parameters related to the type of pipe have not changed. Conclusion: Based on the results of the Surftest SJ-210 device has best results with accuracy of the Roughness height reported for fiberglass pipes is equal to 98.84%. In U-PVC, similar to fiberglass pipe, the average Roughness values has been estimated with high accuracy using the Ra with a cutting length of 2.5.

Stereolithography has the most portions through rapid prototyping techniques in injection molding and vacuum casting manufacturing because of simultaneously possessing dimensional accuracy and strength. However, low surface finish and appearance of stair-step phenomenon restrict extensive of use of this process. In this research, the influence of process parameters and part orientation on surface finish was studied. For this purpose parts were built in various conditions of surface angle, hatch space and post curing time. Surface Roughness was measured by contact and non-contact method. Surface angle was in 0-180 degree range by 2 degree step. Considered post curing time was 20, 50 and 80 minutes and hatch spacing was 50, 75, 100 and 125 micrometer. In non-contact method using digital microscope, surface profile was obtained and then surface Roughness calculated using MATLAB software. Finally, using Analysis of Variance (ANOVA) a mathematical model has been developed among parameters and responses. Results showed that surface finish has reverse relation with hatch space and is almost independent of post curing time. Surface Roughness in up- facing increases swiftly with surface angle enhancement and then drops. In down- facing surface Roughness increases slowly with surface angle enhancement and then falls. Comparison of real dates with estimated values showed that estimation average error is less than 14 percent.

Rough is an exceptional mathematical tool for effectively analyzing and addressing the complexities of vague action descriptions in decision problems. This paper explores the concept of an L-algebra, which leads to the introduction of lower and upper approximations. The properties of these approximations are also discussed and elucidated. Furthermore, it is proven that the lower and upper approximations serve as interior and closure operators, respectively. Additionally, by employing A-lower and A-upper approximations, this paper presents and examines conditions for a nonempty subset to be definable. Furthermore, we investigated the circumstances under which the A-lower and A-upper approximations can be rough ideals. Finally, we define an operation ‎" -->"‎ on the set of all upper approximations of L ‎and ‎prove ‎that ‎it ‎is ‎made ‎an ‎‎L-algebra.

Smartphones are equipped with sensors such as accelerometers, gyroscope and GPS in one cost-effective device with an acceptable level of accuracy. There have been some research studies carried out in terms of using smartphones to measure the pavement Roughness. However, a little attention has been paid to investigate the validity of the measured pavement Roughness by smartphones via other subjective methods such as the user opinion. This paper aims at calculating the pavement Roughness data with a smartphone using its embedded sensors and investigating its correlation with a user opinion about the ride quality. In addition, the applicability of using smartphones to assess the pavement surface distresses is examined. Furthermore, to validate the smartphone sensor outputs objectively, the Road Surface Profiler is applied. Finally, a good Roughness model is developed which demonstrates an acceptable level of correlation between the pavement Roughness measured by smartphones and the ride quality rated by users.

The accurate and quantitative description of gravel bed Roughness is essential in hydraulic studies. Traditional approach for characterization of random bed Roughness relies on grain size distribution curve. However, in recent years, as an alternative approach, gravel bed is considered as a random field of bed elevations which varies point by point in longitudinal and transversal directions. In fact, in new approach, a more comprehensive description of the gravel bed is provided by the statistical analysis (e.g. correlations, spectra, structure functions) of bed elevations. In present laboratory study, the results of employing these two approaches to an artificial gravel bed are reported. Existing methods for estimation of grain size distribution curve (sieve analysis, image analysis and caliper measurements) are reviewed and their results are compared. The comparison shows that grain size measurement using caliper is the most suitable method. Moreover, comparison between characteristics of artificial gravel bed in present study and natural water-worked gravel bed in rivers, obtained from statistical analysis, shows that despite the simplicity of the employed method for creating our laboratory gravel bed, most of their characteristics are different. This observation highlights the importance of statistical characterization of Roughness. Therefore, in laboratory study of gravel bed, it is recommended to precisely determine Roughness characteristics. This will quantify the difference of natural river beds and artificial rough bed in experimental flumes.

The objective of this research work is to evaluate the surface Roughness of polyacrylonitrile (PAN) nanowebs. For this purpose the nanowebs have been prepared in different concentrations of PAN solution from 11 to 15% (by wt). Surface Roughness of nanowebs was evaluated by entropy algorithm (ENT) as well as atomic force microscopy (AFM) and then the results of two methods have been compared. To evaluate surface Roughness using AFM, four Roughness parameters such as maximum height, ten point height, arithmetic mean of Roughness (AMR), and root mean square were measured and AMR parameter was used as surface Roughness. Based on the results obtained, the increase in concentration of PAN solution from 11 to 15% (by wt) would increase nanofiber diameter from 195 to 524 nm. The results obtained from two methods show that increasing the fiber diameters of nanowebs lead to the enhancement of surface Roughness of samples. The correlation coefficient of surface Roughness obtained from these two methods and nanofibers diameter is more than 0.90. Statistical analysis shows that there is a good agreement of surface Roughness between the two methods.

One leading question in discussion of "Roughness Theories" is:Do the human beings naturally rough?In this paper, the relationship between nature and Roughness has been briefly tried to clarifyfrom the viewpoint of Nahjolbalaghe.1- Nahjolbalaghe knows that the nature of human being is brimful of monotheism 2-ImamAli (peacebeuponhim) dissimulates not only Roughness in nature but also refrains using for in government and sovereignty.3- Government and sovereignty must have completely concentration on retaliation, killing, harshness and briefly incisiveness against the violations and infringements of the people"s rights by the condition of consideration and attention for justice.

Surface quality is a technical prerequisite in the field of manufacturing industries and can be treated as a quality index for machined parts. Attainment of appropriate surface finish plays a key role during functional performance of machined part. It is typically influenced by the machining parameters. Consequently, enumerating the good relation between surface Roughness (Ra) and machining parameters is a highly focused task. In the current work, response surface methodology (RSM) based regression models and flower pollination algorithm (FPA) based sparse data model were developed to predict the minimum value of surface Roughness in hard turning of AISI 4340 steel (35 HRC) using a single nanolayer of TiSiN-TiAlN PVD-coated cutting insert. The results obtained from this approach had good harmony with experimental results, as the standard deviation of the estimated values was simply 0.0804 (for whole) and 0.0289 (for below 1 µm Ra). When compared with RSM models, the proposed FPA based model showed the least percentage of mean absolute error. The model obtained the strongest correlation coefficient value of 99.75% among the other models values. The behavior of machining parameters and its interaction against surface Roughness in the developed models were discussed with Pareto chart. It was observed that the feed rate was highly significant parameter in swaying machining surface Roughness. In inference, the FPA sparse data model is a better choice over the RSM based regression models for prognosis of surface Roughness in hard turning of AISI 4340 steel (35 HRC). The model developed using FPA based sparse data for surface Roughness during hard turning operation in the current work is not reported to the best of author’s knowledge. This model disclosed a more dependable estimation over the multiple regression models.