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Bu çalışmada günlük hayatta maruziyetinden sakınılamayan farklı frekanslardaki cihazların oluşturduğu elektromanyetik yayılım için kas eşdeğer modelleri oluşturulmuştur. Günümüzde elektromanyetik alan yayan cihazların kullanımının giderek yaygınlaşması maruz kalınan frekans ve güç seviyelerinin çeşitliliğini artırmaktadır. Elektromanyetik dalgalar farklı güçlerde ve farklı frekanslarda, insan vücudunda değişik etkilerde bulunmaktadır. Bu etkiler vücutta dokuları ısıtmakta veya kimyasal yoldan değişimlere yol açmaktadır. Bu çalışmada, 900 MHz ve 1800 MHz frekanslarındaki cep telefonu, 2.45 GHz frekansındaki kablosuz haberleşme cihazlarının ve ayrıca 27.12 MHz frekansındaki tıbbi fizyoterapi tedavi cihazlarının (kısa dalga diatermi) insan kas dokusu üzerindeki etkilerini belirlemek amaçlı kas eşdeğer sıvıları oluşturulmuştur. Bu oluşturulan fantom model üzerinde elektromanyetik maruziyetin neden olduğu ısıl değişimler ölçülmüştür.

With the development of technology, analysis methods have also increased and, as with many products, the properties of essential oils can be studied using methods based on electrical properties. In this study, the electrical properties of lavender essential oil mixed with various fixed oils were to be investigated to determine the adulteration rate more quickly and easily. Corn oil, olive oil, and sunflower oil were selected as fixed oils to obtain the oil samples with different adulteration rates. In this context, the real and imaginary parts of the permittivity, the loss tangents, the electrical conductivities, the amplitudes, and the phases of the impedances of the oil samples were measured. The measurements were also used to create an equivalent Debye circuit model for the lavender essential oil in order to analyze its electrical properties. The results showed that the lavender essential oil and the oil samples mixed at different rates showed different results in terms of electrical properties at different frequencies. According to the measurements, electrical conductivity was a critical parameter for a mixture of pure lavender essential oil and corn oil. The loss tangent was investigated for the mixture of pure lavender essential oil and olive oil, and the mixture of pure lavender essential oil and sunflower oil. It was observed that the electrical conductivity in the studied frequency band increased when the adulteration rate in the mixed corn oil samples increased. In addition, the loss tangent of the olive oil mixture and the sunflower oil mixture increased as the adulteration rate of said oil samples increased. By measuring the electrical properties, it may be possible to determine whether there is an adulteration and dilution process for particularly valuable essential oils, and if so, in what proportion.

Over the last decade, advances in artificial intelligence have begun to transform educational practices. With the development of OpenAI's ChatGPT, it has become clear that AI chatbots' capabilities—such as generating human-like text, facilitating automated conversations, and answering questions—will have widespread impact across various sectors, including education and healthcare. Consequently, the use of chatbots in various applications has accelerated. Today's most popular chatbots, OpenAI ChatGPT and large language models (LLMs) like Google Gemini and Bard, are making significant progress with applications in various fields, including electrical and electronics education. In this study, the performance of ChatGPT (GPT-3.5), ChatGPT (GPT-5.1), Bard and Gemini was analyzed in answering 240 carefully prepared questions in Turkish language using clear, specific and plain text formats to determine knowledge levels, covering 12 sub-specialties in the field of electrical and electronics. Two experts analyzed the accuracy metric in the performance analysis, and the results are presented comparatively. The statistical significance of the findings was also determined, and it was observed that there were significant differences for the exam evaluations of some courses.

Agaricus bisporus , known as the white-capped mushroom, is among the cultivated mushrooms widely grown worldwide due to its high nutritional value and economic yield. However, this species is threatened by various disease agents including fungal, bacterial, viral and abiotic (non-microbial) agents under intensive production conditions. Early diagnosis of the symptoms caused by these diseases is of great importance in terms of preventing yield losses and minimizing the economic losses associated with them. In this study, it was aimed to classify five different diseases and healthy samples seen in A. bisporus based on images and to evaluate the effects of different color spaces on the classification accuracy. In this context, classification experiments were conducted on image datasets converted into different color space representations, with the SqueezeNet convolutional neural network (CNN) applied separately to each representation. The findings revealed that the XYZ, RGB and YCbCr color space provided higher classification accuracy compared to other formats in the detection of diseases.