A modeling study with an artificial neural network: developing estimation models for the tomato plant leaf area

The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R2 ) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R2 = 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R2 = 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area.The leaf area measurement is an important parameter in understanding the growth and physiology of a plant. Therefore, this study aimed to develop the best leaf area estimation model for tomato plants grown in plastic greenhouse conditions. The artificial neural network (ANN) and regression analysis techniques were used in the formation of a leaf area estimation model by using the leaf width and leaf length measurements determined by the linear measurement method. The plant material for the study consisted of 420 leaf samples of the Typhoon F1 tomato type grown in plastic greenhouse conditions. In the comparison of the created models according to both methods, the criteria of selecting low values for the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE), and high value for the determination coefficient (R2 ) were taken into account, and the best estimation models were determined. In the comparison made according to these criteria, it was concluded that the error values of the ANN model [R2 = 0.96, RMSE = 3.30, MAE = 1.94, and MAPE = 0.05] were lower than those of the regression model [R2 = 0.92, RMSE = 4.71, MAE = 3.31, and MAPE = 0.08], and that the ANN method provided a better fit to the actual values; therefore, the ANN model can be used as an alternative method in estimating the leaf area

Evaluating logistics villages in Turkey using hybrid improved fuzzy SWARA (IMF SWARA) and fuzzy MABAC techniques

Positioning in the right location for organizing logistics activities is a determinative factor in the aspect of costs, effectivity, productivity, and performance of these operations carried out by logistics firms. The proper logistics village selection is a crucial, complicated, and time-consuming process for decision-makers who have to make the right and optimal decision on this issue. Decision-makers need a methodological frame with a practical algorithm that can be implemented quickly to solve these decision-making problems. Within this scope, the current paper aims to present an evaluation tool, which provides more reasonable and reliable results for decision-makers to solve the logistics village selection problem that is very complicated and has uncertain conditions based on fuzzy approaches. In this study, we propose the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA), a modified and extended version of the traditional fuzzy Step-Wise Weight Assessment Ratio Analysis (F-SWARA) to identify the criteria weights. Also, we suggest applying the fuzzy Multi-Attributive Border Approximation area Comparison (F-MABAC) technique to determine the preference ratings of the alternatives. This combination has many valuable contributions. For example, it proposes to use a more reliable and consistent evaluation scale based on fuzzy sets. Hence, decision-makers can perform more reliable and reasonable pairwise comparisons by considering this evaluation scale. Besides, it presents a multi-attribute evaluation system based on the identified criteria weights. From this perspective, the proposed model is implemented to evaluate eight different logistics village alternatives with respect to nine selection criteria. According to the analysis results, while A8 is the most appropriate option, C1 Gross National Product (GNP) is the most significant criterion. A comprehensive sensitivity analysis was performed to test the robustness and validation of the proposed model, and the results of the analysis approve the validity and applicability of the proposed model. As a result, the suggested integrated MCDM framework can be applied as a valuable and practical decision-making tool to develop new strategies and improve the logistics operations by decision-makers

Approaches of Turkish Dentists in Cases of Orthodontic Lingual Retainer Failures

Objective:This study aimed to investigate the approaches of Turkish dentists in cases of orthodontic lingual retainer failures.Methods:A self-administered questionnaire was used to quantify dentists’ approaches to lingual retainer failures. The first part of the study investigated the demographic characteristics. In the second part, dentists’ approaches to cases of failed retainers were assessed. The third part had questions related to the type of retainers bonded solely to the canines or to all the 6 anterior teeth. Descriptive statistics were done with Pearson’s χ2 test, and Mann-Whitney U test was used.Results:A total of 320 Turkish dentists participated in the survey. Experienced and public dentists preferred to advise the patients whose retainers had failed to contact their orthodontist more frequently (p<0.05). Regarding their approach to patients who requested removal of the bonded retainer, inexperienced dentists more frequently preferred to refer the patients to an orthodontist (p<0.05). With regard to factors affecting the choice to remove a bonded retainer, the most and the least importance were attributed to the orthodontist’s opinion and the patient’s demand, respectively.Conclusion:Turkish dentists prefer referring their patients to orthodontists rather than performing procedures in cases of failure associated with bonded retainers. Different demographic characteristics seem to have an impact on these approaches

EVALUATION OF THE TRANSITIONS POTENTIAL TO CYBER-PHYSICAL PRODUCTION SYSTEM OF HEAVY INDUSTRIES IN TURKEY WITH A NOVEL DECISION-MAKING APPROACH BASED ON BONFERRONI FUNCTION

Purpose: This study examines the potential of production systems of the heavy industry branches with the help of cyber-physical systems. Sources of public and private sectors may not be sufficient to transform and develop all heavy industry branches simultaneously. Because of that, policymakers can determine priority industries for development and growth, which are sustainable and balanced in a country. Methodology: In current study, the proposed approach uses the LMAW (Logarithm Methodology of Additive Weights) technique to identify priority sectors. The LMAW is a novel MCDM (Multi-Criteria Decision Making) technique providing an opportunity to evaluate both objective and subjective criteria; in addition, it uses the Bonferroni functions to transform the subjective evaluations of decision-makers to the group decision. Findings: It has been observed that the most significant criterion is overall equipment effectiveness (OEE), and the most prior branch of heavy industry is the aerospace industry. Originality: This paper examines the transformation process of the heavy industry branches to the cyber-physical systems by using a new MCDM approach

A data mining application in animal breeding: Determination of some factors in Japanese quail eggs affecting fertility

Bu çalışmanın amacı, Japon bıldırcını yumurtalarının döllülük üzerine etkisi olan mevsim, seleksiyon ve yerleşim sıklığı faktörlerine göre veri madenciliği yöntemi ile sınıfandırılması ve bu faktörlerin etkisinin belirlenmesidir. Çalışmada seleksiyon yapılmış bir hattan ve rastgele çiftleştirilmiş bir kontrol hattından 3 farklı mevsimde (Yaz, Kış ve Sonbahar) elde edilen 180 dişi bıldırcın kullanılmıştır. İki farklı tip kafeste barındırılan (160-240 cm2/bıldırcın) bıldırcınlardan 12 haftalık yaşta bir hafta boyunca toplanan 1141 kuluçkalık yumurta çalışmanın materyalini oluşturmuştur. Araştırmada kullanılan sınıfandırma algoritmaları sırasıyla YSA, RBF Network, Naive Bayes, KStar, ve Ridor algoritmalarıdır. Söz konusu bu algoritmalara göre oluşturulan modellerin karşılaştırmasında Kappa istatistiği, Ortalama Mutlak Hata (OMH), Ortalama Hata Karekök (OHK), Göreli Mutlak Hata (GMH) ve Göreli Hata Karekök (GHK) performans kriterleri kullanılmıştır. Analizler sonucunda, yapılan karşılaştırmada performans kriter değerleri sırasıyla OMH: 0.002, OHK: 0.05, GMH: %1.07, GHK: %14.50 ve Kappa: 0.98 olan Ridor algoritmasına göre oluşturulan modelin en az hata ile sınıfandırma yaptığı görülmüştür. Yapılan bu çalışma ile %99.73 doğru sınıfandırma başarısı ile bıldırcın yumurtalarının genel olarak %85inin döllü, %15nin ise üreme kapasitelerinin düşük olduğu tespit edilmiştir.The purpose of this study, classification with data mining methods according to the factors of season, selection, and frequency of settlement which have an efect on fertility in Japanese quail eggs, and is to determine the efect of these factors. In this study, 180 female quails in three diferent seasons (summer, winter and autumn) which were obtained from a selection line and a control line were used. 1141 hatching eggs collected from quails which were hosted on two diferent types of cages (160-240 cm2/quail) during a week at 12 weeks of age have formed the material of study. Classification algorithms used in the study are YSA, RBF Network, Naive Bayes, KStar, and Ridor algorythms, respectively. In the comparison of the models formed according to these algorithms, Kappa statistic, Mean Absolute Deviation (MAD), Mean Square Root Error (MSE), Relative Absolute Error (RAE), Relative Square Root Error (RSE) performance criteria were used. As a result of analysis, it has been seen in the comparison made that the model formed according to Ridor algorithm that has MAD: 0.002, MSE: 0.05, RAE: 1.07%, RSE: 14.50% and Kappa: 0.98 performance criteria values, respectively, has made the classification with minimum error. With this study conducted, it was determined that 85% of the quail eggs fertile and 15% of them has low reproduction capacity with the accurate classification success of 99.73%

Spin-state studies with XES and RIXS: From static to ultrafast

We report on extending hard X-ray emission spectroscopy (XES) along with resonant inelastic X-ray scattering (RIXS) to study ultrafast phenomena in a pump-probe scheme at MHz repetition rates. The investigated systems include low-spin (LS) Fe-II complex compounds, where optical pulses induce a spin-state transition to their (sub)nanosecond-lived high-spin (HS) state. Time-resolved XES clearly reflects the spin-state variations with very high signal-to-noise ratio, in agreement with HS-LS difference spectra measured at thermal spin crossover, and reference HS-LS systems in static experiments, next to multiplet calculations. The 1s2p RIXS, measured at the Fe Is pre-edge region, shows variations after laser excitation, which are consistent with the formation of the HS state. Our results demonstrate that X-ray spectroscopy experiments with overall rather weak signals, such as RIXS, can now be reliably exploited to study chemical and physical transformations on ultrafast time scales. (C) 2012 Elsevier B.V. All rights reserved