Efficacy of Pseudomonas chlororaphis subsp. aureofaciens SH2 and Pseudomonas fluorescens RH43 isolates against root-knot nematodes (Meloidogyne spp.) in kiwifruit

The Root-knot nematodes, Meloidogyne spp., are parasites of many crops and orchards, including kiwifruit trees. The Islamic Republic of Iran is among the leading kiwifruit producers in the world and M. incognita has been found as the dominant species responsible for severe loss of this crop. In order to evaluate the eff ectiveness of antagonistic bacteria on larval mortality, number of galls per plant and egg masses of nematode reduction, fifty local bacterial strains were isolated from root surrounding soils of kiwifruit plants in the northern production areas in Iran. Bacterial antagonists were characterized by morphological, physiological, biochemical and molecular methods. Two representative strains, showing the best nematicidal activity, were identif ed as Pseudomonas chlororaphis subsp. aureofaciens (isolate Sh2) and Pseudomonas fluorescens (isolate Rh43). They increased the percentage of larval mortality to 56:38% and 54:28% respectively in assays in vitro and showed excellent performance also in vivo with consistent reduction of number of galls (67:31% and 55:63%, respectively) and egg mass (86:46% and 84:29%, respectively) in plants. This study indicates that Pseudomonas chlororaphis subsp. aureofaciens isolate Sh2 and Pseudomonas fluorescens isolate Rh43 are good potential biocontrol agents for containing root-knot nematodes in kiwifruit trees.peer-reviewe

Simultaneous robust estimation of multi-response surfaces in the presence of outliers

A robust approach should be considered when estimating regression coefficients in multi-response problems. Many models are derived from the least squares method. Because the presence of outlier data is unavoidable in most real cases and because the least squares method is sensitive to these types of points, robust regression approaches appear to be a more reliable and suitable method for addressing this problem. Additionally, in many problems, more than one response must be analyzed; thus, multi-response problems have more applications. The robust regression approach used in this paper is based on M-estimator methods. One of the most widely used weighting functions used in regression estimation is Huber's function. In multi-response surfaces, an individual estimation of each response can cause a problem in future deductions because of separate outlier detection schemes. To address this obstacle, a simultaneous independent multi-response iterative reweighting (SIMIR) approach is suggested. By presenting a coincident outlier index (COI) criterion while considering a realistic number of outliers in a multi-response problem, the performance of the proposed method is illustrated. Two well-known cases are presented as numerical examples from the literature. The results show that the proposed approach performs better than the classic estimation, and the proposed index shows efficiency of the proposed approach

A neuro-data envelopment analysis approach for optimization of uncorrelated multiple response problems with smaller the better type controllable factors

In this paper, a new method is proposed to optimize a multi-response optimization problem based on the Taguchi method for the processes where controllable factors are the smaller-the-better (STB)-type variables and the analyzer desires to find an optimal solution with smaller amount of controllable factors. In such processes, the overall output quality of the product should be maximized while the usage of the process inputs, the controllable factors, should be minimized. Since all possible combinations of factors' levels, are not considered in the Taguchi method, the response values of the possible unpracticed treatments are estimated using the artificial neural network (ANN). The neural network is tuned by the central composite design (CCD) and the genetic algorithm (GA). Then data envelopment analysis (DEA) is applied for determining the efficiency of each treatment. Although the important issue for implementation of DEA is its philosophy, which is maximization of outputs versus minimization of inputs, this important issue has been neglected in previous similar studies in multi-response problems. Finally, the most efficient treatment is determined using the maximin weight model approach. The performance of the proposed method is verified in a plastic molding process. Moreover a sensitivity analysis has been done by an efficiency estimator neural network. The results show efficiency of the proposed approach

The effect of endurance and circuit resistance training on serum brain-derived neurotrophic factor and cortisol in inactive male students: A randomized clinical trial

زمینه و هدف: عامل رشد عصبی مشتق از مغز نقش مهمی در رشد و تکامل دستگاه عصبی دارد. تحقیقات حیوانی نشان داده اند که سطوح سرمی این فاکتور تحت تأثیر فعالیت ورزشی قرار می گیرد. هدف از انجام تحقیق حاضر تعیین تأثیر تمرین استقامتی و مقاومتی دایره ای بر عامل رشد عصبی مشتق شده از مغز و کورتیزول سرمی در مردان غیر فعال بود. روش بررسی: در این مطالعه کارآزمایی بالینی، 30 دانشجوی پسر غیر فعال به طور تصادفی به سه گروه تمرین استقامتی، تمرین مقاومتی و کنترل تقسیم شدند. آزمودنی های گروه استقامتی برنامه تمرینی استقامتی شامل 45-30 دقیقه دوی تناوبی با شدت 75-60 درصد ضربان قلب بیشینه را به مدت چهار هفته اجرا کردند. آزمودنی های گروه های تمرین مقاومتی نیز سه جلسه در هفته، به مدت چهار هفته تمرین مقاومتی دایره ای با شدت 75-60 درصد یک تکرار بیشینه را انجام دادند. قبل و 48 ساعت بعد از دوره‌ی تحقیق، نمونه گیری خونی برای سنجش مقادیر سرمی عامل رشد عصبی مشتق شده از مغز و کورتیزول از آزمودنی ها به عمل آمد. یافته ها: تمرین استقامتی و مقاومتی دایره ای غلظت سرمی عامل رشد عصبی مشتق شده از مغز را به طور معنی داری افزایش داد. در بررسی نتایج پس آزمون تفاوتی بین گروه های تمرینی مشاهده نشد؛ ولی بین دو گروه تمرین استقامتی و گروه کنترل تفاوت معنی دار بود. تمرین استقامتی و مقاومتی تأثیر معنی داری بر سطوح کورتیزول سرمی نداشت. نتیجه گیری: بر اساس یافته های این مطالعه، تمرین استقامتی و مقاومتی دایره ای باعث افزایش فاکتورهای نروتروفیک می شود که ممکن است بدین طریق باعث ایجاد سازگاری های ساختاری و عملکردی در دستگاه عصبی شود

Effective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and particle swarm optimization as meta-heuristic methods for the QAP. This research is dedicated to compare the relative percentage deviation of these solution qualities from the best known solution which is introduced in QAPLIB. Furthermore, a tuning method is applied for meta-heuristic parameters. Results indicate that TS is the best in 31%of QAPs, and the IFLS method, which is in the literature, is the best in 58 % of QAPs; these two methods are the same in 11 % of test problems. Also, TS has a better computational time among heuristic and meta-heuristic methods