Modified symbiotic organisms search for structural optimization

The structural dynamic response predominantly depends upon natural frequencies which fabricate these as a controlling parameter for dynamic response of the truss. However, truss optimization problems subjected to multiple fundamental frequency constraints with shape and size variables are more arduous due to its characteristics like non-convexity, non-linearity, and implicit with respect to design variables. In addition, mass minimization with frequency constraints are conflicting in nature which intricate optimization problem. Using meta-heuristic for such kind of problem requires harmony between exploration and exploitation to regulate the performance of the algorithm. This paper proposes a modification of a nature inspired Symbiotic Organisms Search (SOS) algorithm called a Modified SOS (MSOS) algorithm to enhance its efficacy of accuracy in search (exploitation) together with exploration by introducing an adaptive benefit factor and modified parasitism vector. These modifications improved search efficiency of the algorithm with a good balance between exploration and exploitation, which has been partially investigated so far. The feasibility and effectiveness of proposed algorithm is studied with six truss design problems. The results of benchmark planar/space trusses are compared with other meta-heuristics. Complementarily the feasibility and effectiveness of the proposed algorithms are investigated by three unimodal functions, thirteen multimodal functions, and six hybrid functions of the CEC2014 test suit. The experimental results show that MSOS is more reliable and efficient as compared to the basis SOS algorithm and other state-of-the-art algorithms. Moreover, the MSOS algorithm provides competitive results compared to the existing meta-heuristics in the literature.

     

Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization

In this study, an improved version of the teaching–learning-based optimization (TLBO) algorithm is proposed for truss topology optimization (TTO), with static and dynamic constraints on planar and space trusses. The basic TLBO algorithm is improved to enhance its exploration and exploitation abilities by considering various factors such as the number of teachers, adaptive teaching, tutorial learning and self-motivated learning. The TTO problems are considered with multiple load conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria and kinematic stability conditions. TTO is achieved with the removal of superfluous elements and nodes from the ground structure, and results in a mass saving. In this method, difficulties arise owing to singular solution and unnecessary analysis; therefore, the finite element model is reformed to resolve these issues. A single-stage optimization approach is used, in which size and topology optimization are considered simultaneously. The results obtained are compared with the best solutions obtained by the algorithm. The results reveal that the modified subpopulation teaching–learning-based optimization (MS-TLBO) algorithm is more effective than other state-of-the-art algorithms.     

Multi-objective heat transfer search algorithm for truss optimization

Engineering with Computers - In the real world, we often come across conditions like optimization of more than one objective functions concurrently which are of conflicting nature and that makes...     

Analysis of rejection outcomes and implications--a report of the North American Pediatric Renal Transplant Cooperative Study

Two months after initiation of chemotherapy, a totally implantable vascular access system was used for the continuing administration of chemotherapeutic agents to a ferret with multicentric lymphoma. The subcutaneously located injection port minimized the need for restraint and facilitated repeated IV administration of drugs. Treatment with a combination of L-asparaginase, vincristine sulfate, doxorubicin, cyclophosphamide, methotrexate, chlorambucil, and prednisone failed to induce complete remission. Survival time (interval from histologic diagnosis to euthanasia) was 10 months. The vascular access system remained patent and did not cause infection in the surrounding subcutaneous tissues during the 5-month period that it was in position.     

A comparison of rapid amniotic fluid markers in the prediction of microbial invasion of the uterine cavity and preterm delivery

OBJECTIVE: The purpose of this study was to evaluate amniotic fluid lactate dehydrogenase level in comparison with other rapid markers in prediction of microbial invasion of the uterine cavity and preterm delivery < or = 36 hours after amniocentesis. STUDY DESIGN: One hundred thirty-one women in preterm labor with intact membranes underwent transabdominal amniocentesis. Amniotic fluid was analyzed for leukocyte count, glucose level, lactate dehydrogenase level, and Gram stain. Cultures for aerobes, anaerobes, and Mycoplasma sp. were performed. Amniocentesis-to-delivery interval was calculated. The study group was divided and the findings compared according to amniotic fluid culture results and according to amniocentesis-to-delivery interval. Sensitivity, specificity, and positive and negative predictive value were calculated for lactate dehydrogenase, leukocyte count, glucose, and Gram stain in the prediction of positive amniotic fluid culture and preterm delivery < or = 36 hours after amniocentesis. Receiver-operator characteristic curve analysis, logistic regression analysis, t tests, and nonparametric tests were used. RESULTS: The prevalence of positive amniotic fluid cultures was 12% (16 of 131). The median lactate dehydrogenase level (1084 U/L) was significantly greater for women with a positive amniotic fluid culture than for those with a negative culture (median lactate dehydrogenase level 194 U/L; p < 0.0002). The critical values calculated for optimal performance in prediction of a positive amniotic fluid culture were a lactate dehydrogenase level > or = 419 U/L, leukocyte count > or = 50 cells/mm3 (50 x 10(6)/L) and glucose < or = 17 mg/dl (0.94 mmol/L). Lactate dehydrogenase, leukocyte count, glucose, and Gram stain were equally sensitive and specific in prediction of a positive amniotic fluid culture. Thirty-nine women (29.8%) gave birth < or = 36 hours after amniocentesis. The median lactate dehydrogenase level (414 U/L) was significantly greater among women giving birth < or = 36 hours after amniocentesis than among women giving birth > 36 hours after amniocentesis (median lactate dehydrogenase, 173 U/L; p < 0.001). Critical values of lactate dehydrogenase > or = 225 U/L, leukocyte count > or = 10 cells/mm3 (10 x 10(6)/L) and glucose < or = 34 mg/dl (1.9 mmol/L) were selected for optimal performance in prediction of amniocentesis-to-delivery interval < or = 36 hours. Lactate dehydrogenase level had the best sensitivity (74%) in prediction of delivery < or = 36 hours after amniocentesis in contrast to leukocyte count (49%), glucose (62%), and positive Gram stain (26%). Amniotic fluid lactate dehydrogenase values > or = 225 U/L were associated with a fivefold greater risk for delivery < or = 36 hours after amniocentesis (odds ratio 5.46, 95% confidence interval 2.00 to 14.87; p = 0.0006). CONCLUSION: Amniotic fluid lactate dehydrogenase level has diagnostic value in prediction of a positive amniotic fluid culture and delivery < or = 36 hours after amniocentesis. Lactate dehydrogenase is a readily available, inexpensive, rapid amniotic fluid marker that can be measured in any hospital laboratory.