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Copyright of Istanbul Commerce University Journal of Social Sciences / İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi is the property of Istanbul Commerce University Journal of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Efil Journal of Economic Research / Efil Ekonomi Araştırmaları Dergisi is the property of Efil Journal of Economic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agriculture & Nature / Kahramanmaraş Sütçü İmam Üniversitesi Tarım & Doğa Dergisi is the property of Kahramanmaras Sutcu Imam Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has caused unprecedented changes in the global economy and society, with many studies attempting to understand the impact of the virus on different countries and industries. This study focuses on the effects of COVID-19 on a consulting company that specializes in technology services. By analyzing the company's sales data for the five-year period before the pandemic, and using machine learning techniques via the KNIME platform, the study aims to predict the sales data for the COVID-19 period. Three different regression models - linear, gradient boosting, and random forest - were used to make these predictions, and the models were compared based on their coefficient of determination (R2) to determine which model performed best. The chosen model was then used to interpret the impact of COVID-19 on the company. The findings of the study provide insights into how COVID-19 has affected the consulting company. The chosen model showed that the pandemic had a significant negative impact on the company's sales, with a sharp decline in the second quarter of 2020. However, the company was able to recover some of its losses by the fourth quarter of the year. The study also highlights the importance of using machine learning techniques to predict future sales data during unpredictable events such as the COVID-19 pandemic. Overall, this study sheds light on the impact of COVID-19 on a technology consulting company and demonstrates the importance of using data analysis and machine learning techniques to make predictions and interpret the effects of significant events on business operations. [ABSTRACT FROM AUTHOR]

This study aims to estimate the driving times of drivers who prefer electric scooter vehicles. In general, e-scooters reduce the loss of time caused by traffic jams thanks to their smaller size and maneuverability, therefore, these vehicles provide rapid progress in urban journeys. E-scooters also offer an advantage in finding a parking space and easy parking thanks to their more compact structure. In this study, ML algorithms were used to predict the driving times of drivers who prefer e-scooter vehicles. The AB model performed well with a low Mean Square Error (MSE) value (0.005). The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values were also relatively low (0.069 and 0.039, respectively), indicating that the model's predictions were close to the actual values. Also, the high R-squared-Coefficient of Determination (R²) value (0.947) suggested that this model explained the data quite well, and its predictions approached the actual values with high accuracy. On the other hand, the GB algorithm performed poorly compared to different algorithms, with its high margin of error and low accuracy rate. These results provide an advantage in time management by estimating the travel time a driver will make with the e-scooter. As a result, e-scooters offer drivers the opportunity to save time and manage their daily mobility more effectively, driving these vehicles attractive for transportation. [ABSTRACT FROM AUTHOR]

Copyright of Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi is the property of Artvin Coruh University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkish Journal of Trauma & Emergency Surgery / Ulusal Travma ve Acil Cerrahi Dergisi is the property of KARE Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Econder International Academic Journal is the property of Econder International Academic Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkish Journal of Land Management / Türkiye Arazi Yönetimi Dergisi is the property of Mersin University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cancer disease is known as the second highest cause of death in the world. One of the most common types of this disease is skin cancer. As in almost all cancer diseases, early diagnosis and treatment of skin cancer is of great importance. In the process of diagnosing cancer, Machine Learning-based methods are also widely used in addition to traditional methods. The most important advantage of these methods is that they eliminate or minimize human errors that may arise during the cancer diagnosis process. In this study, skin cancer was diagnosed with K-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forest (RO), Logistic Regression (LR), and Artificial Neural Networks (ANN) methods using images taken from patients. In these algorithms, the training and testing process was run, the results were analyzed and models were created. With these models, benign and malignant lesions were compared and skin cancerous lesions were detected and success percentages were revealed. As a result, the best results were obtained using the ANN method, with a training percentage of 99.1% and a testing percentage of 98.6%. When different inputs were given to the created and proposed ANN model, it was observed that the model predicted skin cancer with a high accuracy rate. The results obtained revealed the success of the study and that machine learning methods are a usable method in the cancer diagnosis process. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Social Sciences / Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi is the property of Afyon Kocatepe University (AKU) Sosyal Bilimler Enstitusu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Being able to forecast events has always been important for humans. Humans did forecasting by inspecting movements of material and non-material objects in ancient times. However, thanks to the technological developments and the increasing amount of data in recent years, forecasting is now done by computers, especially by machine learning methods. One of the areas where these methods are used frequently is numerical weather forecasting. In this type of forecast, short, medium and long-term weather forecasts are made using historical data. However, predictions are inherently error-prone phenomena and should be stated which error range the predictions fall. In this study, numerical weather forecasting was done by combining Genetic Programming and Inductive Conformal Prediction method. The effect of 10 and 20 days of historical data on short (1-day), medium (3-days) and long-term (5-days) weather forecasts was examined. Results suggested that Genetic Programming has a good potential to be used in this area. However, when Genetic Programming was combined with the Inductive Conformal Prediction method, it was shown that forecasts gave meaningful results only in short-term; forecasts made for medium and long-term did not produce meaningful results.

Elastic constants of Scots pine (Pinus sylvestris L.) wood grown in Turkey were investigated using non-destructive ultrasound tests. Elastic modulus in longitudinal and perpendicular directions (EL, ER, ET), shear modulus in principal planes (GLR, GLT, GRT) and Poisson ratios (ʋLR, ʋRL ʋLT, ʋLT, ʋRT, ʋTR) were calculated using cubic samples (20 mm) which were conditioned at 65 % relative humidity and 21 °C. Longitudinal and transverse ultrasonic sound velocities in fiber (L), radial (R) and tangential (T) directions were measured using 2.25 MHz and 1 MHz sensors, respectively. Transverse sound wave velocities at an angle of 45° to the L, R and T directions were also measured with a 1 MHz sensor in order to calculate the Poisson ratios. The predicted elastic modulus based on ultrasound in L, R, T directions were 10600, 1300 and 470 N/mm2, respectively. The predicted shear modulus based on ultrasound in LR, LT, RT planes were 1180, 1050 and 350 N/mm2, respectively. Poisson ratios varied between 0.04 to 0.95. Comparing the data available in the literature, elastic constants of scots pine determined using ultrasonic method were within the acceptable values.

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive Network Based Fuzzy Logic Inference System (ANFIS); has been developed as a prediction model by using the learning ability of artificial neural networks (ANN) and the decision-making mechanism of fuzzy logic approach. Daily average discharges at two stream gages located in the Kızılırmak River is tried to be predicting with two different ANFIS models in this study. Daily average discharge of the river observed between 2014-2021 and daily total precipitation data of two Weather Stations (AWS) representing the river basins are used in the models. ANFIS models have been formed with 2 input and 1 output approach for SG- 1 Stream Gage in the upstream, and with 3 input - 1 output approach for SG-2 Stream Gage which takes place at downstream. Total daily precipitation has two days lag time (t-2) and average daily discharge has one day lag time (t-1) taken as input data and (t) days as output. 75% of the data is used as training and 25% as test. While creating the rules, three different clusters have been made, and the membership function of the target value is obtained. Coefficient of determination (R²) and root mean square error (RMSE) metrics are used for the performance of the models. The best results for both SG-1and SG-2 are three clustered model with respectively, R² = 0.9578 and 0.976, RMSE = 1.49 and 2.20. As a result, it was observed that the ANFIS model predicted the daily average discharge with high success. [ABSTRACT FROM AUTHOR]

Copyright of Acta Infologica is the property of Acta Infologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Balikesir Health Sciences Journal / Balıkesir Sağlık Bilimleri Dergisi is the property of Balikesir Health Sciences Journal (BAUN Health Sci J) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Turkish Journal of Remote Sensing / Türkiye Uzaktan Algılama Dergisi is the property of Turkish Journal of Remote Sensing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Manda yetiştiriciliğinin avantajları ve mandadan elde edilen ürünlerin insan sağlığındaki üstünlüklerine rağmen, dünyada manda popülasyonunun azalan bir trendle düşük olduğu ve nesli tehlikede olan türler arasında olduğu belirtilmektedir. Dünyada manda yetiştiriciliği yapılan ülkeler arasında manda sayısında en hızlı azalışın gerçekleştiği ülke konumunda olan Türkiye’de, Samsun ili manda popülasyonunda geçmişten günümüze birinci sırada yer almaktadır. Bu çalışmanın amacı Türkiye ve Samsun ilinde 2023-2030 yılları arasında manda popülasyonunun Çift Üstel Düzleştirme ve Holt-Winters yöntemleri ile geleceğe yönelik tahminlerini gerçekleştirmektir. Çalışmada Türkiye manda popülasyon serisi için 1929-2022 yılları arası, Samsun ili manda popülasyon serisi için ise 1991-2022 yılları arası verileri kullanılmıştır. Araştırmada serilerin durağan olmadığı belirlenmiş olup, birinci farkı alındıktan sonra seriler durağanlaştırılmıştır. Manda popülasyon tahmini için Holt-Winters modelinin veri setine en uygun model olduğuna karar verilmiştir. Bu modele göre 2030 yılında 2022 yılına göre Türkiye manda popülasyonunun %7,29 oranında artış yaşanacağı, Samsun ili manda popülasyonunun ise gelecek sekiz yıl içinde stabil kalacağı belirlenmiştir. Çalışmada, yakın gelecekte Türkiye'de manda popülasyonunun yok olma tehlikesi ile karşı karşıya kalacağı belirlenmiştir. Türkiye’de manda varlığının sürdürülebilirliği için hükümet tarafından uzun vadeli ve etkili politika araçları uygulamaya konularak verilen desteklemeler artırılmalı ve tüketicilerin de manda sütü ve etinin faydaları hakkında bilinçlendirilmesi sağlanarak talep artışı sağlanmalıdır.

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: The aim of this study is to build personbased prediction models for simulated and real datasets separately with the SHapley Additive exPlanations method, and to demonstrate whether the obtained person-based models are more valid and applicable than overall models. Material and Methods: Simulated datasets encompassed 13 independent and 1 dependent variable, across sample sizes of 250, 500, and 1,000, while the real dataset contained 826 patient records with 11 variables. "bindata", "shaper" and "RWeka" packages in the R (version 4.1.2) programming language were used. Extreme Gradient Boosting, Bagging, Random Forest, Support Vector Machine and Logistic Regression were used as classification methods. The assessment employed 10-fold crossvalidation, repetaed 1,000 times. Results: Accuracy values of the overall model in the datasets with 250, 500, and 1,000 samples were found to be 0.856, 0.886, and 0.891, respectively. In these samples, the person-based accuracy values were found to be 0.886, 0.964, and 0.962 for those with "yes" prediction results, and 0.930, 0.961, and 0.961 for those with "no" prediction results, respectively. In the real dataset, the accuracy of the overall model was found to be 0.736. The person-based accuracy values were found to be 0.783 in the patient who was predicted with stroke, and 0.868 in the patient who was predicted without stroke. Conclusion: Personbased predictions consistently outperformed model-based results across datasets due to real-life individual heterogeneity, emphasizing the need for attention. Considering this diversity, person-based modeling is expected to produce a more realistic and clinically applicable model. [ABSTRACT FROM AUTHOR]

Forest fire is a disaster that destroys all living and non-living beings in the unity of life in the forest by burning and has economic and ecological damages. In recent years, temperatures and droughts that have been above the seasonal norms due to global warming have increased the risk of forest fires. In order to minimize the damage caused by forest fires, early warning, fast and effective intervention is very important in firefighting. Machine learning methods are used in early warning systems today. In this study, it is aimed to control and reduce the effects of fires by predicting possible forest fires in order to fight forest fires. The dataset for the wildfire prediction model was developed from the official website of NASA's Oak Ridge National Laboratory (ORNL) Center for Distributed Active Archives (DAAC). A forest fire prediction model was created by processing these data with machine learning methods. The data set was adapted to the classification model by applying various preprocessing steps. With the feature selection techniques, the least number of feature subsets were selected by providing the highest level of data integrity without using the entire data set. By choosing the most important and useful features in finding the target variable, a model was created with 6 different classification algorithms, namely Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbor and Naive Bayes. Validation process was performed to evaluate model performance and hyperparameter optimization was performed for best parameter selection. Among the classification algorithms used in this study, an accuracy rate of 97% was obtained with Random Forest and 96% with Naive Bayes, which is one of the two most successful algorithms with the validation process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences Institute / Sosyal Bilimler Enstitüsü Dergisi is the property of Bingol University / Rectorate and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cumhuriyet International Journal of Education is the property of Cumhuriyet University, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a new probabilistic technique (a variant of Multiple Model Particle Filter-MMPF) will be used to predict time-series datasets. At first, the reliable performance of our method is proved using a virtual random scenario containing sixty successive days; a large difference between the predicted states and the real corresponding values arises on the second, third, and fourth day. The predicted states that are determined by using our method converge rapidly towards the real values while a classical linear model exhibits a large amount of divergence if used alone here. Then, the performance of our approach is compared with some other techniques that were already applied to the same time-series datasets: IEX (Istanbul Stock Exchange Index), TAIEX (Taiwan Stock Exchange), and ABC (The Australian Beer Consumption). The performance evaluation metrics that are utilized here are the correlation coefficient, the mean absolute percentage error, and the root mean squared error.

Various pandemics have been recorded in world history until today. The Covid-19 outbreak, which emerged at the end of 2019, has recently been a hot topic in the literature. In this work, extreme learning algorithms are presented as a comparative study for predicting the positive rate for the countries: India, Turkey, Italy, USA and UK. The features to be used in the learning phase are determined with the F-test feature selection method. For each extreme learning approach, results are obtained for each country with the root mean square error evaluation criteria. Accordingly, the radial basis kernel function produces the best estimation results, while the linear kernel function has the highest RMSE. Accordingly, the lowest RMSE value has been obtained for India as 4.17E-03 with the radial basis kernel function based ELM. Also, since Turkey's data contains too many outliers, it has the highest RMSE value (0.015 - 0.035) in linear kernel method among the countries.

Airspeed data is so important for an aircraft operation. This study is focused on the estimation of the airspeed data without any additional measurement source such as hardware redundancy. The flight data obtained from a commercial aircraft is processed with a deep learning algorithm, particularly LSTM recurrent neural networks that are developed based on Matrix Laboratory (MATLAB). Correlation analysis is carried out for related data according to a 95% confidence interval for each coefficient in the study to show strong predictor candidates. Data related to the airspeed are processed using Holdout Cross-Validation Technique. According to the results, the designed model achieved an R-squared of 0.9999, a root-mean-squared error of 0.8303 knots, and a standard error of 0.0092 knots. These results show that it is possible to accurately estimate aircraft airspeed data using LSTM recurrent neural network in case of the airspeed data cannot be provided to the flight crew. [ABSTRACT FROM AUTHOR]

This study utilized financial and non-financial data from 233 companies listed in the Borsa Istanbul BIST SINAI Index from 2010 to 2020. The XGBOOST machine learning algorithm was employed to predict whether these companies would encounter financial distress. The machine was trained using supervised learning, with 80% of the data used for training and 20% for testing purposes. Financial ratios were utilized as independent variables in predicting financial distress. The 25 financial ratios can be categorized into four main headings: Liquidity, Financial Structure, Activity, and Profitability Ratios. Furthermore, the model allowed for individual analysis of each company. In predicting whether companies would experience financial distress, the maximum F1 score (85.1%), recall (84.5%), precision (85.7%), and accuracy (91.6%) were achieved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Statistical Research / İstatistik Araştırma Dergisi is the property of Turkish Statistical Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Düzce University Journal of Forestry / Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi is the property of Duzce University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Studies on academic performance prediction, a sub-branch of Educational Data Mining, have increased in recent years. Educational datasets in real environments often have class imbalanced and multi-class target variables. However, studies with these datasets are very few. In this context, in this study, with the ethical no decision of 23.05.2022-286783, using the data set of Marmara University (MU) Faculty of Technology (TF) students, a student graduation status estimation was made with the multiclass imbalanced educational dataset to identify the students at risk. 1394 samples and 11 features were obtained through data preprocessing and feature selection (FS) stages. 153 students belonging to 2016 were used for robustness control. 3 different datasets containing 11, 7 and 5 features obtained with 7 different FS were created. Using 9 different sampling methods and 16 different machine learning algorithms, 750 different models were created. Models were checked for robustness. F1 Score and Repeated Stratified 5*5 fold-CV were used as success criteria. Hyperparameter settings were made with GridSearchCV. As a result, although ROS+RF was the most successful algorithm with an F1 Score of 0.9935, the most successful and most consistent models were the 7-featured None+ET, None+MLP, None+Bagging_DT and None+RF models. With these models, the decision support system web application was developed and presented to MU TF faculty members. [ABSTRACT FROM AUTHOR]

Extraordinary periods experienced since the beginning of human history have caused the formation of specific patterns. The current coronavirus disease 2019 pandemic we are experiencing has provided critical viewpoint on the use and supply of preventive consumable materials like masks, gowns, and disinfectant. These are used as hygienic items to protect against infectious diseases and are assumed not to be very significant and easily managed in hospitals during normal periods. This study first assessed the supply, stock, and consumption processes for these protective and preventive items considering data from 2019, considered a normal period in hospital operation. In the second part of the study, the differences in supply and use of these items were modeled based on data during the development of the pandemic. To estimate the use of consumption of the protective equipment, number of doctors, healthcare workers, administrative personnel, patients, and surgeries were chosen as independent variables. Multivariate linear regression analysis was applied to examine the changes in the independent variables on protective consumables. It has been observed that different variables are effective in estimating the consumption of each protective consumable. N95 mask, tie band surgical mask, and medical face mask consumptions were explained by the number of coronavirus disease patients and healthcare workers. Hand disinfectant and examination glove consumption were predicted with the number of doctor and coronavirus disease patients. Surgical glove prediction was estimated by using the number of surgeries. In this study, multivariate regression models are proposed to help predict the consumption of protective consumables in hospitals. [ABSTRACT FROM AUTHOR]

Copyright of Ege Üniversitesi Ziraat Fakültesi Dergisi is the property of Ege Universitesi, Ziraat Fakultesi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Except for continuous positive airway pressure (CPAP), which is the first choice in the standard treatment of Obstructive Sleep Apnea Syndrome (OSAS), it is recommended to continue treatment with bilevel PAP (BPAP) in patients, who cannot tolerate constant pressure or no success was achieved with this treatment. However, there is a group of patients without complicated OSAS and do not have BPAP indication at the first hospitalization, but for which adequate titration cannot be performed with CPAP. The purpose of this study was to investigate which characteristics of these patients or which data in their polysomnography (PSG) may be indicative of BPAP need. Materials and Methods: Comorbid diseases [diabetes mellitus (DM), cardiac diseases, pulmonary diseases], body mass index, neck/core/hip circumference measurements the patients evaluated with total sleep time, apnea-hypopnea index (AHI), hourly obstructive/central apnea and hypopnea numbers, mean desaturation index (ODI), rapid eye movement (REM) sleep latency, REM time, AHI in REM and non-REM (REM/non-REM index), average overnight saturation (SaO2), lowest saturation value (min O2%), time when saturation is below 90% overnight (T90), a position dependency in PSG. Results: Presence of DM, hypertension and cardiac disease, elevation of neck/core/hip circumference measurements, ODI, REM index, T90 values and ODI/SaO2 ratios were found to be statistically significant in the BPAP group, elevation in min O2% and SaO2 levels were found to be statistically significant in the CPAP group (p<0.05). It was determined that the probability of BPAP increased with the presence of DM 0.214 times, the presence of heart disease 0.205 times, a one-unit increase in the REM index 1.018 times, and a one-unit increase in T90 1.030 times. The REM index and T90, which were found to be significant in the receiver operating characteristic analysis, were determined as 70.850 and 56.150 cut-off values, respectively. Conclusion: In this study; it was determined that the probability of CPAP being insufficient and switching to BPAP increases with the presence of DM and heart disease; and that T90 and REM index, and their cutoff values can be used for this purpose. It was also thought that regional adiposity may affect the type of PAP to be used. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sea surface temperature (SST) is a critical parameter in understanding the dynamics of oceanic and atmospheric systems and predicting future climate trends. In this study, we use data obtained from the European Space Agency Climate Change Initiative, specifically from the Sea Surface Temperature Climate Change Initiative (SST CCI) project, to model SST in the Zonguldak and Bartın provinces using the autoregressive integrated moving average (ARIMA) method. The dataset covers 40 years from 1981 to 2022 (longitude 31.25 and latitude 40.95) and includes an assessment of FMS trends and seasonal variations. The results show a slight but consistent increase in SST over the study period, with a mean squared error of 0.07.The changing SST trends in the Zonguldak and Bartın provinces have important implications for several industries and sectors, including fisheries and tourism. The results of this study can help inform decision- making in these areas as well as policy decisions pertaining to climate change adaptation and mitigation strategies. Our findings also provide valuable insights into the effectiveness of the ARIMA methodfor modeling SST data and the potential limitations of the data obtained from the SST CCI project. [ABSTRACT FROM AUTHOR]

The weather reanalysis datasets are very advantageous data types worldwide that fill the gaps of missed measuring data and are alternatives that compensate for the scarcity of observed climate data. The main purpose of this study was to evaluate the effect of horizontal distance, altitude, and climatic regions compared to sea level on NASA POWER reanalysis data for daily temperature variables, relative humidity, and wind speed observed in meteorology stations in the Mediterranean and Continental regions of Turkey. For this purpose, three different meteorology stations (Antalya airport, Elmalı, Teffenni) from the Mediterranean region with different distances and elevations compared to sea level and one station (Ankara) far from the Mediterranean region with continental climate were selected. The statistical approach used to compare observed and estimated values in this study was determination coefficient (R2), Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and Mean Bias Error (MBE). The results showed a high relation between the POWER reanalysis dataset and observed data for all parameters except wind speed. For daily maximum, minimum and mean temperature, the R2 and NSE achieved higher than 0.91 and 0.88 respectively, while the mean bias error MBE ranged between -3 °C up to +2 °C and the RMSE was less than 4 °C in all stations. Additionally, POWER estimated data correlation accuracy for temperature variables increased toward higher altitudes in the study area. Similarly, this performance was followed by relative humidity, increasing relation accuracy toward higher elevated regions. The R2 was higher than 0.69 in higher altitudes and less than 0.4 in lower elevations. The MBE for relative humidity ranges -2% in Antalya to +9% in Ankara, and the RMSE attained less than 13.81% in all regions. The POWER daily wind speed did not show relation with observed data without adjusting for elevation and seasonal bias correction. Overall, it was concluded that the NASA POWER dataset could predict temperature and relative humidity over study area and give a promising result if used in research, water, and agricultural decision-making where observation data are not available. [ABSTRACT FROM AUTHOR]

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