Relation between working memory capacity and auditory stream segregation in children with auditory processing disorder

Background: This study assessed the relationship between working memory capacity and auditory stream segregation by using the concurrent minimum audible angle in children with a diagnosed auditory processing disorder (APD). Methods: The participants in this cross-sectional, comparative study were 20 typically developing children and 15 children with a diagnosed APD (age, 9-11years) according to the subtests of multiple-processing auditory assessment. Auditory stream segregation was investigated using the concurrent minimum audible angle. Working memory capacity was evaluated using the non-word repetition and forward and backward digit span tasks. Nonparametric statistics were utilized to compare the between- group differences. The Pearson correlation was employed to measure the degree of association between working memory capacity and the localization tests between the 2 groups. Results: The group with APD had significantly lower scores than did the typically developing subjects in auditory stream segregation and working memory capacity. There were significant negative correlations between working memory capacity and the concurrent minimum audible angle in the most frontal reference location (0° azimuth) and lower negative correlations in the most lateral reference location (60° azimuth) in the children with APD. Conclusion: The study revealed a relationship between working memory capacity and auditory stream segregation in children with APD. The research suggests that lower working memory capacity in children with APD may be the possible cause of the inability to segregate and group incoming information. © 2016, Shiraz University of Medical Sciences. All rights reserved

Neurometabolic Diagnosis in Children who referred as Neurodevelopmental Delay (A Practical Criteria, in Iranian Pediatric Patients)

How to Cite This Article: Karimzadeh P, Jafari N, Nejad Biglari H, Jabbehdari J, Khayat Zadeh S, Ahmad Abadi F, Lotfi A. Neurometabolic Diagnosis in Children who referred as Neurodevelopmental Delay (A Practical Criteria, in Iranian Pediatric Patients). Iran J Child Neurol. Summer 2016; 10(3):73-81. ObjectiveWe aimed to investigate the clinical and para clinical manifestations of neuro metabolic disorders, in patients who presented by neuro developmental delay in their neuro developmental milestones.Materials & MethodsThe patients diagnosed as neuro developmental delay and regression with or without seizure at the Neurology Department of Mofid Children Hospital in Tehran, Iran between 2004 and 2014 were included in our study. These patients diagnosed as neuro developmental delay by pediatric neurologists in view of diagnostic /screening neuro developmental assessment tests. The patients who completed our inclusion criteria as neuro metabolic disorders were evaluated in terms of metabolic and genetic study in referral lab.ResultsOverall, 213 patients with neurometabolic disorders were diagnosed. 54.3% of patients were male. The average age of patients was 41 +-46.1 months. 71.4% of parent’s patients had consanguinity of marriages. Eighty seven percent of patients had developmental delay (or/and) regression. 55.5% of them had different type of seizures. Overall, 213 patients with 34 different neurometabolic disorders were diagnosed and classified in the 7 sub classes, consisting of:1- organic acidemia and aminoacidopathy (122 patients), 2-storage disease (37 patients) 3- eukodystrophy (27 patients), other classes consisted: lipid oxidation disorders, urea cycle disorders, progressive myoclonic epilepsy; and peroxizomal disorders (27 patients).ConclusionIn patients with developmental delay or regression, with or without seizure, abnormal neurologic exam along with positive family history of similar disorder or relative parents, abnormal brain imaging with specific patterns, neurometabolic disorders should be considered as one of the important treatable diseases. ReferencesBrismar J. CT and MRI of the brain in inherited neurometabolic disorders. J Child Neurol 1992 Apr;7 Suppl:S112-31.Barkovich AJ. An approach to MRI of metabolic disorders in children. J Neuroradiol 2007; 34(2):75-88.Barkovich AJ. A magnetic resonance approach to metabolic disorders in childhood. Rev Neurol 2006 10;43 Suppl 1:S5-16.Zimmerman RA. Neuroimaging of inherited metabolic disorders producing seizures. Brain Dev 2011;33(9):734-44.Cakir B, Teksam M, Kosehan D, Akin K, Koktener A.Inborn errors of metabolism presenting in childhood. J Neuroimaging 2011;21(2):e117-33.Burton BK. Inborn errors of metabolism in infancy: a guide to diagnosis. Pediatrics 1998;102(6):E69.Iman G. Mahmoud M, Marwa M, Miral R, Marian G, Nevin W, Ameera E. Clinical, Neuroimaging, and Genetic Characteristics of Megalencephalic Leukoencephalopathy With Subcortical Cysts in Egyptian Patients. Pediatr Neurol 2014;140e-148.Sarar M, Ebtessam M, Melegy B, Iman T, Amany H, Khaled K. Neurometabolic Disorders-Related Early Childhood Epilepsy: A Single-Center Experience in Saudi Arabia. Pediatr Neurol 2015; xx, 1e9.Talebian A, Jahangiri M, Rabiee M, Masoudi N, Akbari H, Sadat Z. The Etiology and Clinical Evaluations of Neonatal Seizures in Kashan, IRAN. Iran J Child Neurol 2015;9(2):29-35.Vigevano F, Bartuli A. Infantile epileptic syndromes and metabolic etiologies. J Child Neurol 2002;17:3S9e13.Karimzadeh P. Approach to Neurometabolic Diseases from a Pediatric Neurological Point of View. Iran J Child Neurol 2015;9(1): 1-16.Hee Eun S, Houn Hahn S. Metabolic evaluation of children with global developmental delay. Korean J Pediatr 2015;58(4):117-122.Kroll R, Pagel M, Roman S, Barkovich A, D’Agostino A, Neuwelt E.White matter changes associated with feline GM2 gangliosidosis (Sandhoff disease): correlation of MR findings with pathologic and ultrastructural abnormalities. Am J Neuroradiol 1995;16(6):1219-26.Karimzadeh P, Jafari N, Nejad Biglari H, Jabbeh Dari S, Ahmad Abadi F, Alaee M,. GM2-Gangliosidosis (Sandhoff and Tay Sachs disease): Diagnosis and Neuroimaging Findings (An Iranian Pediatric Case Series). Iran J Child Neurol 2014;8(3):55-60.Wang Q, Ding Y, Liu Y, Li X, Wu T, Song J, et al.(Clinical and laboratory studies on 28 patients with glutaric aciduria type 1). Zhonghua Er Ke Za Zhi 2014 Jun;52(6):415-9.Karimzadeh P, Pirzadeh Z, Ahmadabadi F, Jafari N, Jabbehdari S, Nemati H,. Glutaric aciduria type 1: diagnosis and neuroimaging findings of this neurometabolic disorder in an Iranian pediatric case series. Int J Develop Dis 2014; 60(3): 1–6Weller S, Rosewich H, Gärtner J. Cerebral MRI as a valuable diagnostic tool in Zellweger spectrum patients. J Inherit Metab Dis 2008;31(2):270-80.Groeschel S, Kehrer C, Engel C, I Dali C, Bley A, Steinfeld R, et al. Metachromatic leukodystrophy; natural course of cerebral MRI changes in relation to clinical course. J Inherit Metab Dis 2011;34(5):1095-102.Klee D, Thimm E, Wittsack HJ, Schubert D, Primke R, Pentang G,et al. Structural white matter changes in adolescents and young adults with maple syrup urine disease. J Inherit Metab Dis 2013;36(6):945-53.Karimzadeh P. Approach to neurometabolic diseases from a pediatric neurological point of view. Iran J Child Neurol 2015;9(1):1-16.Leuzzi V1, Tosetti M, Montanaro D, Carducci C, Artiola C, Carducci C,et al. The pathogenesis of the white matter abnormalities in phenylketonuria. A multimodal 3.0 tesla MRI and magnetic resonance spectroscopy (1H MRS) study. J Inherit Metab Dis 2007;30(2):209-16. Epub 2007 Jan 23.Karimzadeh P, Ahmadabadi F, Jafari N, Shariatmadari F, Nemati H, Ahadi A, Karimi Dardashti S, Mirzarahimi M, Dastborhan Z, Zare Noghabi J. Study on MRI changes in phenylketonuria in patients referred to mofid hospital/ iran. Iran J Child Neurol 2014 ;8(2):53-6.Abdelhalim AN, Alberico RA, Barczykowski AL, Duffner PK. Patterns of magnetic resonance imaging abnormalities in symptomatic patients with Krabbe disease correspond to phenotype. Pediatr Neurol 2014;50(2):127-34.Santosh Rai PV, Suresh BV, Bhat IG, Sekhar M, Chakraborti S.Childhood adrenoleukodystrophy - Classic and variant - Review of clinical manifestations and magnetic resonance imaging. J Pediatr Neurosci 2013;8(3):192-7.George U, Varte N, Rathore S, Jain V, Goyal S. “Split thalamus”: Internal medullary involvement in Wilson’s disease. Neurol India 2010;58:680Oder W, Prayer L, Grimm G, Spatt J, Ferenci P, Kollegger H, et al. Wilson’s disease: evidence of subgroups derived from clinical findings and brain lesions. Neurology 1993;43:120-4.Bickel H.(Brain atrophy and disorders of the amino acid metabolism). Monatsschr Kinderheilkd 1967;115(4):254-8.Karimzadeh P, Jafari N, Alai M, Jabbehdari S, Nejad Biglari H. Homocystinuria: Diagnosis and Neuroimaging Findings of Iranian Pediatric patients. Iran J Child Neurol 2015;9(1):94-8.Sreenivasan P, Purushothaman KK.Radiological clue to diagnosis of Canavan disease. Indian J Pediatr 2013;80(1):75-7.Karimzadeh P, Jafari N, Nejad Biglari H, Rahimian E, Ahmadabadi F, Nemati H, Nasehi MM, Ghofrani M, Mollamohammadi M. The Clinical Features and Diagnosis of Canavan’s Disease: A Case Series of Iranian Patients. Iran J Child Neurol 2014 ;8(4):66-71.Nguyen HV, Ishak GE. Canavan disease - unusual imaging features in a child with mild clinical presentation. Pediatr Radiol 2014 Aug 9.Rogers T, al-Rayess M, O’Shea P, Ambler MW.Dysplasia of the corpus callosum in identical twins with nonketotic hyperglycinemia. Pediatr Pathol 1991;11(6):897-902.Johnson JA, Le KL, Palacios E.Propionic acidemia: case report and review of neurologic sequelae. Pediatr Neurol 2009;40(4):317-20.Karimzadeh P, Jafari N, Ahmad Abadi F, Jabbedari S, Taghdiri MM, Alaee MR, Ghofrani M, Tonekaboni SH, Nejad Biglari H. Propionic acidemia: diagnosis and neuroimaging findings of this neurometabolic disorder. Iran J Child Neurol 2014;8(1):58-61.35.Desai S, Ganesan K, Hegde A.Biotinidase deficiency: a reversible metabolic encephalopathy. Neuroimaging and MR spectroscopic findings in a series of four patients. Pediatr Radiol 2008;38(8):848-56.Karimzadeh P, Ahmadabadi F, Jafari N, Jabbehdari S, Alaee MR, Ghofrani M, Taghdiri MM, Tonekaboni SH. Biotinidase deficiency: a reversible neurometabolic disorder (an Iranian pediatric case series). Iran J Child Neurol 2013;7(4):47-52.Brismar J, Ozand PT.CT and MR of the brain in disorders of the propionate and methylmalonate metabolism. Am J Neuroradiol 1994;15(8):1459-73.Karimzadeh P, Jafari N, Ahmad Abadi F, Jabbedari S, Taghdiri MM, Nemati H, Saket S, Shariatmadari SF, Alaee MR, Ghofrani M, Tonekaboni SH. Methylmalonic acidemia: diagnosis and neuroimaging findings of this neurometabolic disorder (an Iranian pediatric case series). Iran J Child Neurol 2013;7(3):63-6.

A Scanning Electron Microscope Study on the Effect of an Experimental Irrigation Solution on Smear Layer Removal

Introduction: The aim of this in vitro study was to evaluate the effect of an experimental irrigation solution, containing two different concentrations of papain, Tween 80, 2% chlorhexidine and EDTA, on removal of the smear layer. Methods and Materials: Thirty-six single-rooted teeth were divided into two experimental groups (n=12) and two positive and negative control groups of six. The canals were prepared with BioRaCe instruments up to BR7 (60/0.02). In group 1, canals were irrigated with a combination of 1% papain, 17% EDTA, Tween 80 and 2% CHX; in group 2, canals were irrigated with a combination of 0.1% papain, 17% EDTA, Tween 80 and 2% CHX. In group 3 (the negative control), the canal was irrigated with 2.5% NaOCl during instrumentation and at the end of preparation with 1 mL of 17% EDTA was used; in group 4 (positive control), normal saline was used for irrigation. The amount of the remaining smear layer was quantified according to Hulsmann method using scanning electron microscopy (SEM). Data was analyzed by the Kruskal-Wallis and Mann-Whitney tests. Results: Two-by-two comparisons of the groups revealed no significant differences in terms of smear layer removal at different canal sections between the negative control group (standard regiment for smear layer removal) and 1% papain groups (P<0.05). Conclusion: Under the limitations of the present study, combination of 1% papain, EDTA, 2% chlorhexidine and Tween 80 can effectively remove smear layer from canal walls

On generalization based on Bi et al. Iterative methods with eighth-order convergence for solving nonlinear equations

The primary goal of this work is to provide a general optimal three-step class of iterative methods based on the schemes designed by Bi et al. (2009). Accordingly, it requires four functional evaluations per iteration with eighth-order convergence. Consequently, it satisfies Kung and Traub's conjecture relevant to construction optimal methods without memory. Moreover, some concrete methods of this class are shown and implemented numerically, showing their applicability and efficiency.The authors thank the anonymous referees for their valuable comments and for the suggestions to improve the readability of the paper. This research was supported by Islamic Azad University, Hamedan Branch, and Ministerio de Ciencia y Tecnologia MTM2011-28636-C02-02.Lotfi, T.; Cordero Barbero, A.; Torregrosa Sánchez, JR.; Abadi, MA.; Zadeh, MM. (2014). On generalization based on Bi et al. Iterative methods with eighth-order convergence for solving nonlinear equations. The Scientific World Journal. 2014. https://doi.org/10.1155/2014/272949S2014Behl, R., Kanwar, V., & Sharma, K. K. (2012). Another Simple Way of Deriving Several Iterative Functions to Solve Nonlinear Equations. Journal of Applied Mathematics, 2012, 1-22. doi:10.1155/2012/294086Fernández-Torres, G., & Vásquez-Aquino, J. (2013). Three New Optimal Fourth-Order Iterative Methods to Solve Nonlinear Equations. Advances in Numerical Analysis, 2013, 1-8. doi:10.1155/2013/957496Kang, S. M., Rafiq, A., & Kwun, Y. C. (2013). A New Second-Order Iteration Method for Solving Nonlinear Equations. Abstract and Applied Analysis, 2013, 1-4. doi:10.1155/2013/487062Soleimani, F., Soleymani, F., & Shateyi, S. (2013). Some Iterative Methods Free from Derivatives and Their Basins of Attraction for Nonlinear Equations. Discrete Dynamics in Nature and Society, 2013, 1-10. doi:10.1155/2013/301718Bi, W., Ren, H., & Wu, Q. (2009). Three-step iterative methods with eighth-order convergence for solving nonlinear equations. Journal of Computational and Applied Mathematics, 225(1), 105-112. doi:10.1016/j.cam.2008.07.004Bi, W., Wu, Q., & Ren, H. (2009). A new family of eighth-order iterative methods for solving nonlinear equations. Applied Mathematics and Computation, 214(1), 236-245. doi:10.1016/j.amc.2009.03.077Kung, H. T., & Traub, J. F. (1974). Optimal Order of One-Point and Multipoint Iteration. Journal of the ACM, 21(4), 643-651. doi:10.1145/321850.321860Cordero, A., Hueso, J. L., Martínez, E., & Torregrosa, J. R. (2010). New modifications of Potra–Pták’s method with optimal fourth and eighth orders of convergence. Journal of Computational and Applied Mathematics, 234(10), 2969-2976. doi:10.1016/j.cam.2010.04.009Cordero, A., & Torregrosa, J. R. (2011). A class of Steffensen type methods with optimal order of convergence. Applied Mathematics and Computation, 217(19), 7653-7659. doi:10.1016/j.amc.2011.02.067Cordero, A., Torregrosa, J. R., & Vassileva, M. P. (2011). Three-step iterative methods with optimal eighth-order convergence. Journal of Computational and Applied Mathematics, 235(10), 3189-3194. doi:10.1016/j.cam.2011.01.004Džunić, J., & Petković, M. S. (2012). A Family of Three-Point Methods of Ostrowski’s Type for Solving Nonlinear Equations. Journal of Applied Mathematics, 2012, 1-9. doi:10.1155/2012/425867Džunić, J., Petković, M. S., & Petković, L. D. (2011). A family of optimal three-point methods for solving nonlinear equations using two parametric functions. Applied Mathematics and Computation, 217(19), 7612-7619. doi:10.1016/j.amc.2011.02.055Heydari, M., Hosseini, S. M., & Loghmani, G. B. (2011). On two new families of iterative methods for solving nonlinear equations with optimal order. Applicable Analysis and Discrete Mathematics, 5(1), 93-109. doi:10.2298/aadm110228012hGeum, Y. H., & Kim, Y. I. (2010). A multi-parameter family of three-step eighth-order iterative methods locating a simple root. Applied Mathematics and Computation, 215(9), 3375-3382. doi:10.1016/j.amc.2009.10.030Geum, Y. H., & Kim, Y. I. (2011). A uniparametric family of three-step eighth-order multipoint iterative methods for simple roots. Applied Mathematics Letters, 24(6), 929-935. doi:10.1016/j.aml.2011.01.002Geum, Y. H., & Kim, Y. I. (2011). A biparametric family of eighth-order methods with their third-step weighting function decomposed into a one-variable linear fraction and a two-variable generic function. Computers & Mathematics with Applications, 61(3), 708-714. doi:10.1016/j.camwa.2010.12.020Kou, J., Wang, X., & Li, Y. (2010). Some eighth-order root-finding three-step methods. Communications in Nonlinear Science and Numerical Simulation, 15(3), 536-544. doi:10.1016/j.cnsns.2009.04.013Liu, L., & Wang, X. (2010). Eighth-order methods with high efficiency index for solving nonlinear equations. Applied Mathematics and Computation, 215(9), 3449-3454. doi:10.1016/j.amc.2009.10.040Wang, X., & Liu, L. (2010). New eighth-order iterative methods for solving nonlinear equations. Journal of Computational and Applied Mathematics, 234(5), 1611-1620. doi:10.1016/j.cam.2010.03.002Wang, X., & Liu, L. (2010). Modified Ostrowski’s method with eighth-order convergence and high efficiency index. Applied Mathematics Letters, 23(5), 549-554. doi:10.1016/j.aml.2010.01.009Sharma, J. R., & Sharma, R. (2009). A new family of modified Ostrowski’s methods with accelerated eighth order convergence. Numerical Algorithms, 54(4), 445-458. doi:10.1007/s11075-009-9345-5Soleymani, F. (2011). Novel Computational Iterative Methods with Optimal Order for Nonlinear Equations. Advances in Numerical Analysis, 2011, 1-10. doi:10.1155/2011/270903Soleymani, F., Sharifi, M., & Somayeh Mousavi, B. (2011). An Improvement of Ostrowski’s and King’s Techniques with Optimal Convergence Order Eight. Journal of Optimization Theory and Applications, 153(1), 225-236. doi:10.1007/s10957-011-9929-9Soleymani, F., Karimi Vanani, S., & Afghani, A. (2011). A General Three-Step Class of Optimal Iterations for Nonlinear Equations. Mathematical Problems in Engineering, 2011, 1-10. doi:10.1155/2011/469512Soleymani, F., Vanani, S. K., Khan, M., & Sharifi, M. (2012). Some modifications of King’s family with optimal eighth order of convergence. Mathematical and Computer Modelling, 55(3-4), 1373-1380. doi:10.1016/j.mcm.2011.10.016Soleymani, F., Karimi Vanani, S., & Jamali Paghaleh, M. (2012). A Class of Three-Step Derivative-Free Root Solvers with Optimal Convergence Order. Journal of Applied Mathematics, 2012, 1-15. doi:10.1155/2012/568740Thukral, R. (2010). A new eighth-order iterative method for solving nonlinear equations. Applied Mathematics and Computation, 217(1), 222-229. doi:10.1016/j.amc.2010.05.048Thukral, R. (2011). Eighth-Order Iterative Methods without Derivatives for Solving Nonlinear Equations. ISRN Applied Mathematics, 2011, 1-12. doi:10.5402/2011/693787Thukral, R. (2012). New Eighth-Order Derivative-Free Methods for Solving Nonlinear Equations. International Journal of Mathematics and Mathematical Sciences, 2012, 1-12. doi:10.1155/2012/493456Thukral, R., & Petković, M. S. (2010). A family of three-point methods of optimal order for solving nonlinear equations. Journal of Computational and Applied Mathematics, 233(9), 2278-2284. doi:10.1016/j.cam.2009.10.012Wang, J. (2013). He’s Max-Min Approach for Coupled Cubic Nonlinear Equations Arising in Packaging System. Mathematical Problems in Engineering, 2013, 1-4. doi:10.1155/2013/382509Babajee, D. K. R., Cordero, A., Soleymani, F., & Torregrosa, J. R. (2012). On a Novel Fourth-Order Algorithm for Solving Systems of Nonlinear Equations. Journal of Applied Mathematics, 2012, 1-12. doi:10.1155/2012/165452Montazeri, H., Soleymani, F., Shateyi, S., & Motsa, S. S. (2012). On a New Method for Computing the Numerical Solution of Systems of Nonlinear Equations. Journal of Applied Mathematics, 2012, 1-15. doi:10.1155/2012/751975Soleymani, F. (2012). A Rapid Numerical Algorithm to Compute Matrix Inversion. International Journal of Mathematics and Mathematical Sciences, 2012, 1-11. doi:10.1155/2012/134653Soleymani, F. (2013). A new method for solving ill-conditioned linear systems. Opuscula Mathematica, 33(2), 337. doi:10.7494/opmath.2013.33.2.337Thukral, R. (2012). Further Development of Jarratt Method for Solving Nonlinear Equations. Advances in Numerical Analysis, 2012, 1-9. doi:10.1155/2012/493707Cordero, A., & Torregrosa, J. R. (2007). Variants of Newton’s Method using fifth-order quadrature formulas. Applied Mathematics and Computation, 190(1), 686-698. doi:10.1016/j.amc.2007.01.06

Formal Introduction to Fuzzy Implications

SummaryIn the article we present in the Mizar system the catalogue of nine basic fuzzy implications, used especially in the theory of fuzzy sets. This work is a continuation of the development of fuzzy sets in Mizar; it could be used to give a variety of more general operations, and also it could be a good starting point towards the formalization of fuzzy logic (together with t-norms and t-conorms, formalized previously).Institute of Informatics, University of Białystok, PolandMichał Baczyński and Balasubramaniam Jayaram. Fuzzy Implications. Springer Publishing Company, Incorporated, 2008. doi:10.1007/978-3-540-69082-5.Adam Grabowski. Basic formal properties of triangular norms and conorms. Formalized Mathematics, 25(2):93–100, 2017. doi:10.1515/forma-2017-0009.Adam Grabowski. The formal construction of fuzzy numbers. Formalized Mathematics, 22(4):321–327, 2014. doi:10.2478/forma-2014-0032.Adam Grabowski. On the computer certification of fuzzy numbers. In M. Ganzha, L. Maciaszek, and M. Paprzycki, editors, 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Federated Conference on Computer Science and Information Systems, pages 51–54, 2013.Adam Grabowski. Lattice theory for rough sets – a case study with Mizar. Fundamenta Informaticae, 147(2–3):223–240, 2016. doi:10.3233/FI-2016-1406.Adam Grabowski and Magdalena Jastrzębska. Rough set theory from a math-assistant perspective. In Rough Sets and Intelligent Systems Paradigms, International Conference, RSEISP 2007, Warsaw, Poland, June 28–30, 2007, Proceedings, pages 152–161, 2007. doi:10.1007/978-3-540-73451-2_17.Adam Grabowski and Takashi Mitsuishi. Extending Formal Fuzzy Sets with Triangular Norms and Conorms, volume 642: Advances in Intelligent Systems and Computing, pages 176–187. Springer International Publishing, Cham, 2018. doi:10.1007/978-3-319-66824-6_16.Adam Grabowski and Takashi Mitsuishi. Initial comparison of formal approaches to fuzzy and rough sets. In Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, and Jacek M. Zurada, editors, Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I, volume 9119 of Lecture Notes in Computer Science, pages 160–171. Springer, 2015. doi:10.1007/978-3-319-19324-3_15.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.Takashi Mitsuishi, Noboru Endou, and Yasunari Shidama. The concept of fuzzy set and membership function and basic properties of fuzzy set operation. Formalized Mathematics, 9(2):351–356, 2001.Zdzisław Pawlak. Rough sets. International Journal of Parallel Programming, 11:341–356, 1982. doi:10.1007/BF01001956.Lotfi Zadeh. Fuzzy sets. Information and Control, 8(3):338–353, 1965.25324124

Various indicators for the assessment of hospitals' performance status: Differences and similarities

Background: Hospitals are the most costly operational and really important units of health system because they consume about 50-89 of total health resources. Therefore efficient use of resources could help in saving and reallocating the financial and physical resources. Objectives: The aim of this study was to obtain an overview of hospitals' performance status by applying different techniques, to compare similarities and differences between these methods and suggest the most comprehensive and practical method of appraisal for managers and policy makers. Patients and Methods: This is a cross sectional study conducted in all hospitals of Ahvaz (eight hospitals affiliated with Jundishapur University of Medical Sciences and eight non-affiliated hospitals) during 2007 to 2011. Two kinds of data were collected through separate special checklists. Excel 2007 and Windeap 2.1 software were applied for data analysis. Results: The present findings show that the average of bed occupancy rate (BOR) in the studied hospitals was about 65.91 ± 1.16. The maximum number of inefficient hospitals in the present study happened in the years 2007, 2008 and 2010 (four hospitals) but there were two hospitals in the third part of the present graph which had maximum level of efficiency and optimal level of productivity in the years 2007 and 2009. Data Envelopment Analysis (DEA) showed that the mean score of technical efficiency for the studied hospitals is 0.924 ± 0.105 with the minimum of 0.585 ± 0.905 for hospital number 1. Furthermore It shows that only five hospitals (31.25) reach complete technical efficiency (TE) scores across all five years of 2007-11 (TE = 1). Conclusions: Results of the present and similar studies should be considered for the future planning and resource allocation of Iranian public hospitals. At the same time it is very important to consider need assessment results for each region according to its potentials, population under the coverage and other geographical and cultural indices. Furthermore because of potential limitations of each of the above models it is highly recommended to apply different methods of performance evaluation to reach a complete and real status view of the hospitals for future planning. © 2014, Iranian Red Crescent Medical Journal; Published by Kowsar Corp

Basic Formal Properties of Triangular Norms and Conorms

SummaryIn the article we present in the Mizar system [1], [8] the catalogue of triangular norms and conorms, used especially in the theory of fuzzy sets [13]. The name triangular emphasizes the fact that in the framework of probabilistic metric spaces they generalize triangle inequality [2].After defining corresponding Mizar mode using four attributes, we introduced the following t-norms: minimum t-norm minnorm (Def. 6),product t-norm prodnorm (Def. 8),Łukasiewicz t-norm Lukasiewicz_norm (Def. 10),drastic t-norm drastic_norm (Def. 11),nilpotent minimum nilmin_norm (Def. 12),Hamacher product Hamacher_norm (Def. 13), and corresponding t-conorms: maximum t-conorm maxnorm (Def. 7),probabilistic sum probsum_conorm (Def. 9),bounded sum BoundedSum_conorm (Def. 19),drastic t-conorm drastic_conorm (Def. 14),nilpotent maximum nilmax_conorm (Def. 18),Hamacher t-conorm Hamacher_conorm (Def. 17). Their basic properties and duality are shown; we also proved the predicate of the ordering of norms [10], [9]. It was proven formally that drastic-norm is the pointwise smallest t-norm and minnorm is the pointwise largest t-norm (maxnorm is the pointwise smallest t-conorm and drastic-conorm is the pointwise largest t-conorm). This work is a continuation of the development of fuzzy sets in Mizar [6] started in [11] and [3]; it could be used to give a variety of more general operations on fuzzy sets. Our formalization is much closer to the set theory used within the Mizar Mathematical Library than the development of rough sets [4], the approach which was chosen allows however for merging both theories [5], [7].Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pąk, and Josef Urban. Mizar: State-of-the-art and beyond. In Manfred Kerber, Jacques Carette, Cezary Kaliszyk, Florian Rabe, and Volker Sorge, editors, Intelligent Computer Mathematics, volume 9150 of Lecture Notes in Computer Science, pages 261–279. Springer International Publishing, 2015. ISBN 978-3-319-20614-1. doi:10.1007/978-3-319-20615-817.Didier Dubois and Henri Prade. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, 1980.Adam Grabowski. The formal construction of fuzzy numbers. Formalized Mathematics, 22(4):321–327, 2014. doi: 10.2478/forma-2014-0032.Adam Grabowski. On the computer-assisted reasoning about rough sets. In B. Dunin-Kȩplicz, A. Jankowski, A. Skowron, and M. Szczuka, editors, International Workshop on Monitoring, Security, and Rescue Techniques in Multiagent Systems Location, volume 28 of Advances in Soft Computing, pages 215–226, Berlin, Heidelberg, 2005. Springer-Verlag. doi: 10.1007/3-540-32370-815.Adam Grabowski. Efficient rough set theory merging. Fundamenta Informaticae, 135(4): 371–385, 2014. doi: 10.3233/FI-2014-1129.Adam Grabowski. On the computer certification of fuzzy numbers. In M. Ganzha, L. Maciaszek, and M. Paprzycki, editors, 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Federated Conference on Computer Science and Information Systems, pages 51–54, 2013.Adam Grabowski and Takashi Mitsuishi. Initial comparison of formal approaches to fuzzy and rough sets. In Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, and Jacek M. Zurada, editors, Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I, volume 9119 of Lecture Notes in Computer Science, pages 160–171. Springer, 2015. doi: 10.1007/978-3-319-19324-315.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi: 10.1007/s10817-015-9345-1.Petr Hájek. Metamathematics of Fuzzy Logic. Dordrecht: Kluwer, 1998.Erich Peter Klement, Radko Mesiar, and Endre Pap. Triangular Norms. Dordrecht: Kluwer, 2000.Takashi Mitsuishi, Noboru Endou, and Yasunari Shidama. The concept of fuzzy set and membership function and basic properties of fuzzy set operation. Formalized Mathematics, 9(2):351–356, 2001.Takashi Mitsuishi, Katsumi Wasaki, and Yasunari Shidama. Basic properties of fuzzy set operation and membership function. Formalized Mathematics, 9(2):357–362, 2001.Lotfi Zadeh. Fuzzy sets. Information and Control, 8(3):338–353, 1965.2529310