I n t h i s p a p e r, t h e p r o b l e m o f s i m u l t a n e o u s p r o d u c t i o n p l a n n i n g, i n v e n t o r y c o n t r o l, t r a n s p o r t a t i o n, a n d p r i c i n g o f p e r i s h a b l e g o o d s (w i t h l i m i t e d l i f e t i m e) i n a t w o-s t a g e s u p p l y c h a i n i s i n v e s t i g a t e d. E x t e n s i v e r e s e a r c h h a s e x a m i n e d e a c h o f t h e i m p o r t a n t s u p p l y c h a i n s u b-p r o b l e m s, i n c l u d i n g p r o d u c t i o n a n d i n v e n t o r y p l a n n i n g, d i s t r i b u t i o n a n d t r a n s p o r t a t i o n p l a n n i n g, a n d p r i c i n g, s e p a r a t e l y. O n t h e o t h e r h a n d, t h e g l o b a l o p t i m u m s o l u t i o n c a n b e a c h i e v e d w h e n t h e s e s u b-p r o b l e m s a r e s o l v e d s i m u l t a n e o u s l y a n d i n t h e f o r m o f a n i n t e g r a t e d m o d e l. H o w e v e r, l e s s r e s e a r c h h a s f o c u s e d o n i n t e g r a t i n g t h e s e d e c i s i o n s. T h e r e a r e a l s o m a n y r e s e a r c h p a p e r s t h a t a s s u m i n g i n v e n t o r y i t e m s c a n b e s t o r e d i n d e f i n i t e l y t o m e e t f u t u r e d e m a n d s. W h i l e t h e r e a r e c e r t a i n t y p e s o f p r o d u c t s t h a t e i t h e r d e c a y o r b e c o m e o b s o l e t e o v e r t i m e a n d, a s a r e s u l t, b e c o m e u n u s e d. P e r i s h a b l e g o o d s i n c l u d e f o o d, v e g e t a b l e s, h u m a n b l o o d, p h o t o g r a p h i c f i l m s, e t c. w h i c h h a v e a m a x i m u m s h e l f l i f e t o u s e. I f t h e p r o d u c t i s p e r i s h a b l e, t h e n t h e r e w i l l b e m o r e n e e d f o r i n t e g r a t e d d e c i s i o n-m a k i n g. A n o t h e r i m p o r t a n t i s s u e t o c o n s i d e r i s t h e u n c e r t a i n t y o f t h e a v a i l a b l e d a t a. I n o t h e r w o r d s, t h e p a r a m e t e r s i n f l u e n c i n g t h e s e d e c i s i o n s a r e n o t d e t e r m i n i s t i c a n d t h i s u n c e r t a i n t y m u s t b e c o n t r o l l e d t o m i n i m i z e t h e p o s s i b i l i t y o f l o s s e s a s s o c i a t e d w i t h t h e d e c i s i o n s. A n o n-d e t e r m i n i s t i c m u l t i-p e r i o d o p t i m i z a t i o n m o d e l, i n w h i c h d e m a n d u n c e r t a i n t y d e p e n d s o n t h e p r o d u c t p r i c e a n d t h e r e m a i n i n g p e r i o d s, i s p r o p o s e d t o s o l v e t h e p r o b l e m. I n t h e p r o p o s e d m o d e l, r o b u s t p o s s i b i l i t y p l a n n i n g i s u s e d t o d e a l w i t h u n c e r t a i n t y. T o v a l i d a t e t h e p r o p o s e d m o d e l a n d s o l u t i o n a p p r o a c h, d a t a f r o m a c a s e s t u d y (t a k e n f r o m P a t r o n C o m p a n y, w h i c h p r o d u c e s g r e e n m o r t a r a n d i s u s e d i n t h e s t e e l i n d u s t r y) w e r e u s e d. T h e r e s u l t s o f c o m p u t a t i o n a l e x p e r i m e n t s s h o w t h a t b y a p p l y i n g t h e p r o p o s e d a p p r o a c h w h i l e m a k i n g i n t e g r a t e d d e c i s i o n-m a k i n g, s u p p l y c h a i n c o s t s c a n b e r e d u c e d b y a n a v e r a g e o f 16%. A l s o, b y c o m p a r i n g t h e p r o p o s e d r o b u s t p o s s i b i l i t y a p p r o a c h w i t h t h e n o m i n a l a p p r o a c h i n u n c e r t a i n t y c o n t r o l, i t i s o b s e r v e d t h a t t h e m a x i m u m a n d a v e r a g e d e v i a t i o n s f r o m o p t i m a l i t y a r e r e d u c e d b y 46% a n d 11%, r e s p e c t i v e l y.

The North of Iran, especially the province of Mazandaran supplies a large portion of Iran's consumer rice. During the growing season of rice, yield components are affected by environmental conditions and the actual yield of a cultivar will depend on its genotype and response to environmental conditions. In this study to identify stable genotypes, promising lines of rice along with their parents (Deilamani, Sepidrood, Sange-Tarom and Neda) were planted in three different regions of Mazandaran Province, namely Sari Agricultural sciences and natural resources university, the Rice Research Institute of Mazandaran in Amol and the Rice Research Station of Chaparsar, Tonekabon. Based on AMMI analysis, environment, genotype and their interaction had significant effect on yield and explained., . and., of the total variation respectively. According to AMMI biplot analysis, Neda and Line had specific adaptability to Amol region, Line to Sari, and Sepidrood and Line were specifically adapted to Tonekabon. Based on the results, line with an average yield of t ha-was the most stable genotype that could be recommended for all of the studied regions.

Based on principal component analysis (PCA), GGE biplot analysis is an effective method to fully assess multi-environmental yield trials (METs). Additive main effects and multiplicative interaction (AMMI) is an alternative method for assessing phenotypic stability and adaptability. In this research, MET data of 14 maize inbred lines were used to perform AMMI and GGE biplot analyses. These genotypes were evaluated under diverse climatic conditions in five Iranian locations during two cropping cycles (2007 and 2008). A genotype (inbred line) by location table was used for performing the analyses. Based on both mean grain yield and yield stability, inbred lines K3615/2, K19/1, K166B and K18 proved to be superior and also had greater mean performance among the test inbred lines. Graphic analysis was used to identify the most suitable inbred lines for each test environment. Inbred lines K3615/2, K19/1, K166B, K18, K3653/2 and K3547/5 were identified as suitable in all locations. The AMMI and GGE biplot graphics revealed three separate groups of environments, i.e., three mega-environments. Group one included three sites, Karaj, Kabootar Abad and Zarghan, while group two included only Islam Abad-e-Gharb and group three only Miandoab. Islam Abad Gharb and Miandoab were more discriminative for genotypes. The inbred lines that were most responsive to the environment were K3615/2, K166B, K19/1 and K18; the least responsive line was K3547/5.

Retrofit actions are amongst the most commonly used measures for reducing the seismic vulnerability of buildings. For any given building, multiple seismic retrofit options are often available. Each option has specific requirements, cost, and performance. Estimating the cost of each candidate action is essential to the selection, planning, and implementation of seismic retrofit initiatives. Primary cost estimation plays a vital role in allocating budget for retrofit projects. Past studies used a variety of methods to develop cost estimation models. This research harnesses the capabilities of various regression models via modern machine learning methods for cost estimation. A dataset from 167 retrofit projects for masonry school buildings in Iran was used to develop models. Three main retrofit actions were implemented in the projects, namely Shotcrete, Steel belt, and Fiber reinforced polymer. Several regression methods including multiple linear regression, ridge regression, lasso regression, and also elastic net regression were applied to the dataset. The proposed framework comprised 12 models, which were attained by four regression methods on three retrofit actions. The cross-validation method was used for model evaluation in order to use all available data for training and testing. The model at the beginning of the development process contained all the probable effective parameters. Next, to increase the simplicity and accuracy of the models, a simple model reduction method was implemented. This model reduction method eliminated almost two-thirds of the parameters in the majority of basic models. Then, the candidate models were evaluated in terms of quantity and quality of prediction, heteroscedasticity, autocorrelation of residuals, and non-normality. This paper identifies the height of the building as the most influential parameter governing retrofit cost. Furthermore, lateral area of walls, footprint area, and added lateral strength are influential in the mentioned retrofit actions. This research contributes to enhancing the understanding of the factors, the effects, and the costs of the retrofit actions.

The objective of this study was to determine genotype × environment (GE) interaction and yield stability of ten cotton genotypes. The trials were conducted at randomized complete block design (RCBD) with four replications at six locations during-cropping seasons. Combined analysis of variance (ANOVA) revealed significant difference among genotypes (G), environment (E) and genotype × environment interactions (GE) for yield, boll weight, boll number, earliness index and monopod branches number. The K performed high yielding genotype ( kgh-) followed by NSK, SKSH-, BC-and GKTB-. According to yield, stability parameters and adaptability analysis, the NSK, SKSH-and GKTBwere ideal genotypes with broad adaptability. Specific adaptation genotypes (e. g. K ) are ideal for maximizing yield and yield stability in stress conditions. Stability parameters similarity for genotype selection ranged from to percent. Significant relationships were found between yield and stability parameters (except for regression coefficient, bi). Finally, our results recommend the NSK-for release as commercial cultivar followed by GKTB-and SKSH-in cotton breeding program of Iran.

P a t t e r n r e c o g n i t i o n i s a b r a n c h o f m a c h i n e l e a r n i n g t h a t r e c o g n i z e s t h e p a t t e r n s a n d r e g u l a r i t i e s i n a s e t o f d a t a, a n d d i g i t r e c o g n i t i o n i s c o n s i d e r e d o n e o f t h e p a t t e r n r e c o g n i t i o n c a t e g o r i e s. D u e t o t h e s i m i l a r i t i e s b e t w e e n s o m e d i g i t s i n e a c h l a n g u a g e, e s p e c i a l l y i n P e r s i a n, d i f f e r e n t a l g o r i t h m s h a v e b e e n d e v e l o p e d t o r e c o g n i z e t h e h a n d w r i t i n g d i g i t s w i t h t h e l e a s t e r r o r a n d i n t h e s h o r t e s t t i m e c o m p l e x i t y. O n e o f t h e m o s t c o m m o n l y u s e d m e t h o d s i n d a t a c l a s s i f i c a t i o n i s t h e n e u r a l n e t w o r k a l g o r i t h m. W h i l e n e u r a l n e t w o r k s h a v e b e e n u s e d i n t h e l i t e r a t u r e f o r h a n d w r i t i n g d i g i t s r e c o g n i t i o n, t h e c o m b i n a t i o n o f c l u s t e r i n g a p p r o a c h e s a n d n e u r a l n e t w o r k c l a s s i f i e r s h a s n o t b e e n c o n s i d e r e d f o r t h i s p r o b l e m. A c c o r d i n g l y, t h i s p a p e r p r o p o s e s a n a l g o r i t h m b a s e d o n t h e c o m b i n a t i o n o f c l u s t e r i n g a p p r o a c h e s a n d n e u r a l n e t w o r k c l a s s i f i e r s t o r e c o g n i z e t h e P e r s i a n h a n d w r i t t e n d i g i t s a c c u r a t e l y. T h i s a l g o r i t h m p e r f o r m s p a t t e r n t r a i n i n g a n d r e c o g n i t i o n b a s e d o n P r o b a b i l i s t i c N e u r a l N e t w o r k s (P N N) a n d m u l t i l a y e r p e r c e p t r o n (M L P) n e u r a l n e t w o r k s. I n t h i s r e g a r d, a f t e r e x t r a c t i n g t h e c h a r a c t e r i s t i c l o c i f e a t u r e a n d z o n i n g f r o m e a c h i m a g e i n t h e t r a i n i n g d a t a b a s e, t h e d a t a o f e a c h o f t h e t e n c l a s s e s h a s b e e n c l u s t e r e d u s i n g l i n k a g e, P a r t i t i o n A r o u n d M e d o i d s (P A M), a n d F u z z y C-M e a n s (F C M) m e t h o d s b a s e d o n t h e e x t r a c t e d f e a t u r e s. T h e n, t h e n e w t e n c l a s s e s r e s u l t i n g f r o m t h e c l u s t e r i n g a l g o r i t h m a r e t a u g h t b y o n e o f t h e t w o c l a s s i f i e r s, i n c l u d i n g M L P a n d P N N. I n o r d e r t o d e t e r m i n e t h e o p t i m a l n u m b e r o f c l u s t e r s i n e a c h c l a s s, t h e T a b u s e a r c h o p t i m i z a t i o n a l g o r i t h m, o n e o f t h e m o s t a c c u r a t e m e t a-h e u r i s t i c o p t i m i z a t i o n a l g o r i t h m s, i s u s e d. T h e p e r f o r m a n c e o f t h e p r o p o s e d a l g o r i t h m s i s e v a l u a t e d a n d c o m p a r e d w i t h e x i s t i n g a l g o r i t h m s b a s e d o n t h e H O D A d a t a s e t. B a s e d o n t h e r e s u l t s, t h e p r o p o s e d a l g o r i t h m a c c u r a t e l y r e c o g n i z e s t h e P e r s i a n h a n d w r i t t e n d i g i t s. I n a d d i t i o n, t h e p r o p o s e d m e t h o d p e r f o r m s m o r e a c c u r a t e l y a n d m u c h f a s t e r t h a n m o s t c o m p e t i n g a l g o r i t h m s.

Aims The G-quadruplex structural motifs of DNA are considered a novel target for drug discovery. As potential compounds, small molecules that selectively target the G-quadruplex structures may be used for therapeutic purposes. The aim of this study was the thermodynamic investigation of copper porphyrazines and phthalocyanine interaction with human telomeric G-quadruplex DNA. Materials & Methods In the present experimental study, interaction of an anionic watersoluble phthalocyanine Cu(PcTs) and two cationic water-soluble tetrapyridinoporphyrazines, including [Cu(2, 3-tmtppa)]4+ and [Cu(3, 4-tmtppa)]4+ complexes with human telomeric G-quadruplex DNA was thermodynamically investigated in different concentrations of Na+ and K+ cations, using fluorescence spectroscopy. The data were analyzed via the Stern-Volmer plot and the van’ t Hoff plot. Findings Fluorescent intercalator displacement indicated the displacement ability of the complexes with thiazole orange. Stern-Volmer plots of the porphyrazines exhibited a slight positive deviation from a straight line, suggesting both static and dynamic quenching. In addition, the quenching effect of the two porphyrazines was noticeably higher than the phthalocyanine, implying binding of Cu(PcTs) to both forms of the quadruplex was weaker compared to Cu(2, 3-tmtppa) and Cu(3, 4-tmtppa). Gibbs free energy (Δ G) for binding was negative, implying that the interaction between the complexes and the G-quadruplex DNA was favorable thermodynamically. Conclusion The binding of copper porphyrazines to G-quadruplex DNA is stronger than copper phthalocyanine, and their binding is favorable thermodynamically. Porphyrazines have potential to be used as anti-cancer compounds and are suitable complexes for pharmaceutical studies.

Column shear failure remains one of the most important causes of damage and collapse of reinforced concrete buildings during recent earthquakes, which should be avoided due to its low ductility and brittle failure mechanism. In previous studies, various parameters have been introduced as effective factors in short column mechanism and in each case, the effect of some of them has been studied. However, there is no comprehensive study that investigates the importance of all of these parameters. In this paper, using Monte Carlo algorithm and considering the normal distribution for 10 effective parameters in short column formation, including column cross-section size, column longitudinal reinforcement ratio, column transverse reinforcement ratio, effective column length, concrete compressive strength, reinforcement yield strength, beam length, axial force ratio, infill wall to column height ratio, and wall thickness, a database consisting of 200,000 samples is created. OpenSees software is used to model the concrete moment frame by considering the flexural and shear behavior of the column, and the model is verified by comparison with experimental studies. Then, by using push-over analysis, the type of failure mechanism of the column in a moment frame with infill and opening is determined to be flexural or shear failure. The importance of each parameter is investigated using machine learning methods including Principal Component Analysis (PCA), Decision Tree (DT), and F-Test (FT) as well as Pearson and Spearman correlation methods. DT and FT machine learning methods as well as both Pearson and Spearman correlation methods are well able to identify the importance of each parameter in the formation of the short column. By summarizing the results of all methods, the parameters of the percentage of column shear reinforcement as well as the ratio of wall to column height have been determined as the most important and effective parameters. Also, the least important parameters are fy, L-Beam and axial load ratio. The result of this paper will be useful for designer of RC building and also to develop models and criteria for rapid short column identification in seismic evaluation of existing buildings.