ALBERT

Description: The numerical weather forecast model Weather Research and Forecasting (WRF) has a range of applications because it offers multiple physical options, enabling the users to optimizing WRF for specific scales, geographical locations and applications. Summer rainfall cannot be predicted well in North West of Iran (NWI). Most of them are convective. Sometimes rainfall is heavy, so that it causes flash flood. In this research, some configurations of WRF were tested with four summer rainfall events in NWI to find the best configuration. Five cumulus, four planetary boundary layers (PBL) and two microphysical schemes were combined. Twenty-six different configurations (models) were implemented at two resolutions of 5 and 15 km for duration of 48 h. Four events, with over 20 mm convective daily rainfall total, were selected at NWI during summer season between 2010 and 2015. These events were tested by developing 26 unique models. Results were verified using several methods. The aim was to find the best results during the first 24 h. Although no single configuration can be introduced for all times, thresholds, and atmospheric system to provide reliable and accurate forecast, the best configuration for WRF can be identified. Kain-Fritsch (new Eta), Betts-Miller-Janjic, Modified Kain-Fritsch, Multi-scale Kain-Fritsch and newer Tiedtke cumulus schemes and Mellor-Yamada-Janjic, Shin-Hong ‘scale-aware’, Medium Range Forecast (MRF) and Yonsei University (YSU) Planetary Boundary Layer schemes and Kessler, WRF Single Moment 3 class simple ice (WSM3) microphysics schemes were selected. The result show that Cumulus schemes are the most sensitive and Microphysics schemes are the less sensitive. The comparison of 15 km and 5 km resolution simulations do not show obvious advantages in downscaling the results. Configuration with newer Tiedtke cumulus, Mellor-Yamada-Janjic PBL, WSM3 and Kessler microphysics schemes give the best results for the 5 and 15 km resolutions. The output image of models and statistical methods verification indexes show that WRF could not accurately simulate convective rainfall in the NWI in summer.

Description: Accurate built-up area extraction is one of the most critical issues in land-cover classification. In previous studies, various techniques have been developed for built-up area extraction using Landsat images. However, the efficiency of these techniques under different technical and geographical conditions, especially for bare and sandy areas, is not optimal. One of the main challenges of built-up area extraction techniques is to determine an optimum and stable threshold with the highest possible accuracy. In many of these techniques, the optimum threshold value fluctuates substantially in different parts of the image scene. The purpose of this study is to provide a new index to improve built-up area extraction with a stable optimum threshold for different environments. In this study, the developed Automated Built-up Extraction Index (ABEI) is presented to improve the classification accuracy in areas containing bare and sandy surfaces. To develop and evaluate the accuracy of the new method for built-up area extraction with Landsat 8 OLI reflective bands, five test sites located in the Iranian cities (Babol, Naqadeh, Kashmar, Bam and Masjed Soleyman), eleven European cities (Athens, Brussels, Bucharest, Budapest, Ciechanow, Hamburg, Lyon, Madrid, Riga, Rome and Porto) and high resolution layer imperviousness (HRLI) data were used. Each site has varying environmental and complex surface coverage conditions. To determine the optimal weights for each of the Landsat 8 OLI reflective bands, the pure pixel sets for different classes and the improved gravitational search algorithm (IGSA) optimization were used. The Kappa coefficient and overall error were calculated to evaluate the accuracy of the built-up extraction map. Additionally, the ABEI performance was compared with the urban index (UI) and normalized difference built-up index (NDBI) performances. In each of the five test sites and eleven cities, the extraction accuracy of the built-up areas using the ABEI was higher than that using the UI, and NDBI (P-value of 0.01). The relative standard deviations of the optimal threshold values for the ABEI and UI were 27 and 155% (at five test sites) and were 16 and 37% (at eleven European cities), respectively, which indicates the stability of the ABEI threshold value when the location and environmental conditions change. The results of this study demonstrated that the ABEI can be used to extract built-up areas from other land covers. This index is effective even in bare soil and sandy areas, where other indices experience major challenges.

Description: The effect of laser irradiation with different numbers of laser shots on the microstructure, the surface, and the hardness of gun metal alloy was studied by a KrF pulsed excimer laser system, X-ray diffraction, Raman spectroscopy, scanning electron microscopy, and Vickers hardness test. The influence of 100–500 laser shots was irradiated on the surface hardness profile and on the microstructure of gunmetal alloy. XRD results showed the maximum 2θ shift, the maximum full width of half maximum FWHM, the maximum dislocation density, and the minimum crystallite size for the sample irradiated with 300 laser shots. The hardness was measured in three different regions at the laser irradiated spot, and it was found that maximum hardness was present at the heat affected zone for all samples. The hardness value of the un-irradiated sample of gun metal was 180, and the value increased up to 237 by raising the number of laser shots up to 300. The peak value of surface hardness of the laser treated sample was 32% higher than the un-irradiated sample. The Raman shift of the un-exposed sample was 605 cm−1 and shifted to a higher value of wave number at 635 cm−1 at 300 laser shots. The hardness value was decreased by further increasing the number of laser shots up to 500. The samples irradiated with 400 and 500 laser shots exhibited smaller hardness and dislocation defect density, which was assigned to possible annealing caused by irradiation.

Description: Purpose: Patient satisfaction can identify specific areas of improvement in community pharmacy services. Currently in Pakistan, no evidence exists in this regard. This study was conducted to determine the needs of patients and the current standards of pharmacies. Methods: A cross-sectional study was conducted between October 2016 and June 2017. A pilot tested questionnaire was used to collected the data from 1088 patients of 544 community pharmacies. Likert scale and one way ANOVA was used to analyze the data. Results: The response rate of community pharmacies was 80% and that of purchasers was 68.1%. The mean age of participants was 35.2 years. The mean overall satisfaction score of participants was 2.78/5.00. Many patients were dissatisfied (1.65/5.00) with parking facilities provided by pharmacies. Pharmacy service time fulfilled the requirements of most patients (4.16/5.00). The counseling person’s good attitude (3.99/5.00) was credited by purchasers. Level of patient satisfaction with the availability of medicines (3.19/5.00), safe storage of medicines in pharmacy stores (3.66/5.00), and quality of medicines (3.41/5.00) were almost moderate. Many patients were very satisfied (4.35/5.00) with readable instructions for their medications. Approximately half of the patients were dissatisfied with the waiting time. Many patients were also dissatisfied (2.28/5.00) with the knowledge of the counseling person. Patients perceived that staff interest in patient recovery (2.24/5.00) was low. No significant difference in level of satisfaction with regard to participant’s characteristics was found. Conclusions: The current study demonstrated a low level of patient satisfaction with regard to community pharmacy services in Pakistan. These services need improvement.

Description: This paper deals with sea salt transportation and deposition mechanisms and discusses the serious issue of degradation of outdoor insulators resulting from various environmental stresses and severe saline contaminant accumulation near the shoreline. The deterioration rate of outdoor insulators near the shoreline depends on the concentration of saline in the atmosphere, the influence of wind speed on the production of saline water droplets, moisture diffusion and saline penetration on the insulator surface. This paper consists of three parts: first a model of saline transportation and deposition, as well as saline penetration and moisture diffusion on outdoor insulators, is presented; second, dry-band initiation and formation modelling and characterization under various types of contamination distribution are proposed; finally, modelling of dry-band arcing validated by experimental investigation was carried out. The tests were performed on a rectangular surface of silicone rubber specimens (12 cm × 4 cm × 8 cm). The visualization of the dry-band formation and arcing was performed by an infrared camera. The experimental results show that the surface strength and arc length mainly depend upon the leakage distance and contamination distribution. Therefore, the model can be used to investigate insulator flashover near coastal areas and for mitigating saline flashover incidents.

Description: The properties of small- and medium-sized enterprises (SMEs) make them one of the most important categories of enterprises for the economics of challenging world. SMEs, in most countries, are still enterprises with marketing and financial challenges. In addition, most of these challenges are related to their production and product characteristics. On the other hand, SMEs should fulfil the costumer’s demands. In order to reach these goals, SMEs must reach the highest level of production quality and quantity and successfully sustain them. Consequently, various manufacturing paradigms have been offered by an Industry 4.0 concept, which offers a variety of solutions to increase the productivity and enhance the performance of SMEs. It should be noted that implementation of these manufacturing paradigms for SMEs is quite difficult and sometimes risky for several reasons. Still, amidst all these difficulties and challenges, the benefits and idealism of the Industry 4.0 paradigms prevail. From productivity to market, it is difficult to deny that SMEs are frightened by the challenges they face and fleeing from the potential of overcoming them. This paper is an extended version of the research by Ghafoorpoor Yazdi et al. (2018) and conducts a hybrid methodology to satisfy the SMEs by validating and verifying any optimization idea before implementing the Industry 4.0 concept. To reach the study goals, an intelligent Material Handling System (MHS) with agent-based control architecture has been developed. The developed MHS has been utilized for auto parts distribution. The system performance has been evaluated, and some solutions have been provided to optimize the performance of system. To evaluate the target system’s performance, an analytical time study method has been utilized. The time study has an Overall Equipment Effectiveness (OEE) standard approach to identifying the matters that need to be resolved and optimized to increase system performance. The other part of the methodology is generating a simulation model of the real system by use of ARENA® software to evaluate the system’s performance before implementing the optimization idea and modifying the real system. Furthermore, as the sustainability strategies create many synergistic effects for SMEs, after evaluating the effects of the optimization ideas on OEE percentage, the influence of the OEE changes on manufacturing sustainability has been investigated. The results show that optimizing the OEE in SMEs with sustainability approaches can create competitive advantages, rather than simply focusing on reducing unsustainability.

Description: Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

Description: Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R2 〉 0.71 and NSE 〉 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 〈 R2 〈 0.72 and−0.06 〈 NSE 〈 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

Description: In this paper, a fuzzy general linear method of order three for solving fuzzy Volterra integro-differential equations of second kind is proposed. The general linear method is operated using the both internal stages of Runge-Kutta method and multivalues of a multisteps method. The derivation of general linear method is based on the theory of B-series and rooted trees. Here, the fuzzy general linear method using the approach of generalized Hukuhara differentiability and combination of composite Simpson’s rules together with Lagrange interpolation polynomial is constructed for numerical solution of fuzzy volterra integro-differential equations. To illustrate the performance of the method, the numerical results are compared with some existing numerical methods.

Description: The main objective of this study is to examine trend and homogeneity through the analysis of rainfall variability patterns in Iran. The study presents a review on the application of homogeneity and seasonal time series analysis methods for forecasting rainfall variations. Trend and homogeneity methods are applied in the time series analysis from collecting rainfall data to evaluating results in climate studies. For the homogeneity analysis of monthly, seasonal and annual rainfall, homogeneity tests were used in 140 stations in the 1975–2014 period. The homogeneity of the monthly and annual rainfall at each station was studied using the autocorrelation (ACF), and the von Neumann (VN) tests at a significance level of 0.05. In addition, the nature of the monthly and seasonal rainfall series in Iran was studied using the Kruskal-Wallis (KW) test, the Thumb test (TT), and the least squares regression (LSR) test at a significance level of 0.05. The present results indicate that the seasonal patterns of rainfall exhibit considerable diversity across Iran. Rainfall seasonality is generally less spatially coherent than temporal patterns in Iran. The seasonal variations of rainfall decreased significantly throughout eastern and central Iran, but they increased in the west and north of Iran during the studied interval. The present study comparisons among variations of patterns with the seasonal rainfall series reveal that the variability of rainfall can be predicted by the non-trended and trended patterns.