The Obstetrics Gynecology and Children’s Hospital Emergency Room waiting time before hospitalization

Background: One of the most substantial factors affecting patient satisfaction in the Obstetrics–Gynecology and Children’s Hospital is the wait time in the emergency room.Objective: We retrospectively studied the waiting periods of patients visiting the emergency room patients in Bolu Izzet Baysal Obstetrics-Gynecology and Children’s hospital.Method: Using an automated documentation system for each patient that recorded the season in which the patients consulted the emergency room, the month, day, time, examination time, hospitalization decision time, the hospitalization clinic following the decision to hospitalize, and the time to hospitalization, we retrospectively studied the waiting periods of emergency room patients in Bolu Ýzzet Baysal Obstetrics–Gynecology and Children’s Hospital.Results: A total of 15,004 patients who consulted the hospital emergency room between November 24, 2009, and August 25, 2011, and who were hospitalized in a clinic were included in this study. The highest frequency of emergency room patient visits occurred during the summer season (28.1%), in the month of July (10.2%), on Mondays (16.1%), and between 8 and 11 AM (22.1%; p < 0.05). The emergency room wait time of patients consulting the pediatric clinic was (55 ± 67 min), which was significantly shorter than the wait time of patients consulting other clinics (p < 0.05).Conclusion: The majority of patients who were hospitalized in any clinic through the emergency room consulted the hospital during the daytime hours. The time to hospitalization for the admitted patients was within an acceptable time frame. We believe that conducting comprehensive research to determine whether it is possible to reduce wait times even further to increase patient satisfaction will be instructive.Keywords: Emergency Room, acceptance-waiting time, emergency-waiting time, hospitalization-waiting timeAfrican Health Sciences 2013; 13(4): 1162 - 116

Subclinical immune reactions to viral infections may correlate with child and adolescent diagnosis of attention-deficit/hyperactivity disorder: A preliminary study from Turkey

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common neuro-developmental disorders of childhood and  adolescence. Studies focusing on the relationship of infectious agents and ADHD are scarce. It is also known that cerebellar injury may lead to  hyperactive behavior. This study aimed to evaluate the relationship  between viral agents of cerebellitis and the diagnosis of ADHD.Methods: The study group was formed of 60 consecutive ADHD patients and 30 healthy children. IgG levels for VZV; HSV-1, CMV, Measles, Mumps, Rubella and EBV were evaluated.Results: Males were significantly higher among patients with ADHD (65% vs. 40%, p=0.025). Patients with ADHD displayed significantly higher positivity for measles IgG (80% vs. 60%, p=0.044). When patients with ADHD were classified according to their pubertal status, adolescents with ADHD displayed higher positivity for mumps (100% vs. 74.4%, p=0.043). Most of the patients were diagnosed with ADHD-Combined or  Hyperactive/Impulsive Subtypes (56.6%) while 43.3% were diagnosed with ADHD-predominantly inattentive type. When patients with subtypes of ADHD were compared in terms of seropositivity, it was found that patients with ADHD-Combined/ Hyperactive-Impulsive subtypes had significantly elevated reactions for Rubella (100% vs. 88.5%, p=0.044).Conclusion: Although limited to a single center and may be prone to sampling biases, our results may support the notion that immune reactions may be related with ADHD among children and adolescents. Further,  prospective studies from multiple centers are needed to support our  findings and establish causality.Key words: ADHD, infection, immunology, measles, rubella, mumps, Ig

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

Metaheuristic optimization of reinforced concrete footings

The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings

Social Algorithms

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.Comment: Encyclopedia of Complexity and Systems Science, 201

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering

In today’s developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing.Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.Используемые программы Adobe AcrobatВ современном развивающемся мире отрасли промышленности постоянно нуждаются в совершенствовании и продвижении вперед. Внедряются новые подходы для определения оптимальных значений и решений для таких моделей, как искусственный интеллект и машинное обучение. Исследования необходимы для определения того, как эти новейшие методы применяются в области инженерии и какие эффективные решения они предоставляют. Приложения искусственного интеллекта и машинного обучения в гражданском, механическом и промышленном проектировании - это сборник инновационных исследований по методам и внедрению машинного обучения и искусственного интеллекта в различных областях инженерии. Освещая такие темы, как устройства управления, геотехнологии и искусственные нейронные сети, эта книга идеально предназначена для инженеров, академиков, исследователей, практиков и студентов, ищущих актуальные исследования по решению инженерных задач с использованием интеллектуальных технологий