Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO₂, CO, PM₁₀, NO₂ temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field.

Objective: To determine the demographic and biochemical features of childhood and juvenile thyrotoxicosis and treatment outcome. Methods: We reviewed the records of children from 22 centers in Turkey who were diagnosed with thyrotoxicosis between 2007 to 2017. Results: A total of 503 children had been diagnosed with thyrotoxicosis at the centers during the study period. Of these, 375 (74.6%) had been diagnosed with Graves' disease (GD), 75 (14.9%) with hashitoxicosis and 53 (10.5%) with other less common causes of thyrotoxicosis. The most common presenting features in children with GD or hashitoxicosis were tachycardia and/or palpitations, weight loss and excessive sweating. The cumulative remission rate was 17.6% in 370 patients with GD who had received anti-thyroid drugs (ATDs) for initial treatment. The median (range) treatment period was 22.8 (0.3-127) months. No variables predictive of achieving remission were identified. Twenty-seven received second-line treatment because of poor disease control and/or adverse events associated with ATDs. Total thyroidectomy was performed in 17 patients with no recurrence of thyrotoxicosis and all became hypothyroid. Ten patients received radioiodine and six became hypothyroid, one remained hyperthyroid and restarted ATDs and one patient achieved remission. TWo patients were lost to follow up. Conclusion: This study has demonstrated that using ATDs is the generally accepted first-line approach and there seems to be low remission rate with ATDs in pediatric GD patients in Turkey.

Muestras de las aleaciones 6082 A1 y 7075 A1 se sometieron a un tratamiento térmico en horno de inducción de ultraalta frecuencia (UHFIHS) a 580 ºC durante 1 min y suministro de agua al final del proceso. El envejecimiento artificial se llevó a cabo a 190 °C durante 2, 4, 6 y 8 min. En ambas aplicacio-nes, el calentamiento se llevó a cabo utilizando un sistema de inducción con una frecuencia de 900 kHz y una potencia de 2,8 kW. Para estas series y diseños de aluminio, se calcularon los costos del tratamiento térmico. Adicionalmente, se compararon los valores de dureza de las muestras de 6082 A1 y 7075 A1 envejecidas artificialmente a 190 °C durante 10 h con los métodos convencionales 540 °C durante 5 h. Los resultados de dureza de la muestra 6082 Al obtenidos en 10 h mediante métodos convencionales, tardaron sólo 8 min mediante el envejecimiento artificial con el sistema de inducción.

İnternet çağını çok hızlı yaşadığımız günümüzde, sürekli olarak artan Nesnelerin İnterneti (IoT) uygulamaları hayatımızı kolaylaştırmaktadır. İnsan makine etkileşimli IoT tabanlı uygulamaların en popüleri şüphesiz akıllı ev uygulamalarıdır. Akıllı ev uygulamalarında cihazlar birbiri ile haberleşerek insan müdahalesi olmadan çalışabilirler. Buda bize zaman ve enerjiden tasarruf etmemizi sağlar. Bu çalışmada; 32-bit Dual Core ESP32 modül ve Blynk IOS/Android arayüz geliştiricisi kullanarak gerçek zamanlı veri izleme ve kontrol uygulaması gerçekleştirilmiştir. Blynk platformuyla tasarlanan Android tabanlı uzaktan izleme ve kontrol arayüzü sayesinde bir akıllı eve ait sıcaklık ve nem verileri kullanılarak ısıtma sisteminin kontrolü sağlanmıştır. Mobil cihaza kurulan android arayüz, ısıtma sisteminin çalışma saatlerini belirleyen haftalık programlama özelliğine sahiptir. Mobil cihazdan elde edilen konum bilgisi kullanılarak, ESP32 modül ile mobil cihaz arasındaki mesafe anlık olarak hesaplanır. Hesaplanan bu mesafeye göre ısıtma sistemi otomatik olarak çalışır veya durur. Blynk uygulamasının sunduğu bulut hizmeti, bize sistem ile ilgili sayısal verilerin depolama imkanını sunmaktadır. Önerilen IoT tabanlı bu akıllı ısıtma sistemi sağladığı enerji tasarrufunun yanında kullanıcıların konforunu da arttırmaktadır.

Studies on the PLL (phase locked loop) based interharmonic measurements have been considerably increased recently. The method proposed in this study is a hybrid method based on the frequency analysis. The proposed method presents solutions for the leading problems about the interharmonic measurement, such as fundamental frequency shifting, spectral leakage, and picket‐fence effect, using digital PLL and adaptive notch filter. In addition, a new approach is presented to solve the multizero crossing problem encountered in the analysis of the harmonic‐interharmonic. Both simulative and field data tests have been performed to verify the effectiveness of the proposed method.