Improving the Energy Efficiency of an Autonomous Source of Electric Energy by Regulating the Gas Distribution of an Internal Combustion Engine

On the example of an autonomous source of electric energy with an internal combustion engine, the structure of a control circuit with reduced energy consumption is substantiated. The possibility of increasing the energy efficiency of the electric power generation system was evaluated

PHOTOGRAMMETRIC MODEL OPTIMIZATION IN DIGITALIZATION OF ARCHITECTURAL HERITAGE: YEDIKULE FORTRESS

The idea of "digitalization of architectural heritage" has recently gained prominence to represent architectural and historical assets. With all these potentials, this study aims to create optimized models that can be used in serious gaming environments by presenting a method of photogrammetry. As a case study, Yedikule Fortress and its surroundings, which have a multi-layered structure that includes many cultural aspects such as Byzantine, Ottoman, and Republican periods in the historical process, have been studied within the scope of digitizing the architectural heritage to create an optimized model for gaming environments. The study was methodologically constructed in three phases: Photogrammetry, polygon modeling, and low poly/high poly baking process. The fortress and its surroundings are modeled using a high-detail point cloud and a high-poly mesh using aerial photogrammetry. The high-poly model was taken as a reference and transferred into a low-poly model as a mesh map, texture, and light characteristics. This allowed the high poly model to operate more efficiently and effectively in game engines. As a result, the study created a detailed and optimized model for the game engines to produce serious games specific to light and texture data, to be used on devices that support mixed reality (MR) technologies

Advances in Electronic-Nose Technologies Developed for Biomedical Applications

The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

Sığ göllerde su seviyesi değişimi ve balık beslenmesinin makroomurgasız toplulukları ve perifiton büyümesi üzerindeki etkileri - mezokozm yaklaşımı

A mesocosm experiment was conducted in Lake Eymir between June – September 2009 in order to elucidate the effects of water level changes and fish predation on periphyton growth and macroinvertebrates in semi-arid shallow lakes. Twenty four cylindrical enclosures, each with 1.2 m diameter, open to lake bottom and atmosphere, were placed at three different depths, i.e. 0.8 m (low water level, LW), 1.6 m (high water level, HW) and 2.3 m (however, data regarding the enclosures at 2.3 m were excluded in this study due to complications after fifth sampling) to simulate water level fluctuations. At each water level, four replicates were stocked with omnivorous–planktivorous fish (Tinca tinca and Alburnus escherichii) and the other four replicates were left fishless to observe the effect of fish predation. Ten shoots of submerged macrophytes (Potamogeton pectinatus) were planted and six polyethylene strips were hung in the water column in each enclosure to monitor macrophyte and periphyton growth. The mesocosms were sampled for physical, chemical and biological parameters weekly in the first month and fortnightly thereafter. Benthic macroinvertebrate samples were taken before the start, in the middle and at the end of the experiment with Kajak corer. Macrophytes were harvested after the last sampling for determination of dry weight, epiphyton, and the associated macroinvertebrates. All macroinvertebrate samples were sieved through 212 μm mesh size before identification and counting. Over the course of the experiment, an average of 0.46 ± 0.03 m water level decrease in the mesocosms triggered submerged macrophyte growth in all LW enclosures, overriding the negative effects of fish predation. The results indicate that while fish predation pressure had negative influences on macroinvertebrate communities in terms of both abundance and richness, structural complexity created by dense vegetation in the LW mesocosms weakened the top-down effect of fish on macroinvertebrates by acting as a refuge in this semi-arid shallow lake.M.S. - Master of Scienc

Quantitative classification of HbA1C and blood glucose level for diabetes diagnosis using neural networks

In this study, artificial neural network structures were used for the quantitative classification of Haemoglobin A1C and blood glucose level for diabetes diagnosis as a non-invasive measurement technique. The neural network structures make inferences from the relationship between the palm perspiration and blood data values. For this purpose, feed forward multilayer, Elman, and radial basis neural network structures were used. The quartz crystal microbalance type and humidity sensors were used for the detection of palm perspiration rates. Total 297 volunteer's data is used in this study. Three quarters of the data was used to train the neural networks. The remaining data were used as test data. The best results for the quantitative classification were obtained from the feed forward NN structure for the detection of the glucose and HbA1C level quantities. And, the performances of all neural networks for the HbA1C value were better than the performances of these neural networks for the glucose level