μ-Squarato-κ2 O 1:O 2-bis­{[2-(2-amino­ethyl)pyridine-κ2 N,N′]aqua­nickel(II)} squarate 0.25-hydrate

The asymmetric unit of title compound, [Ni2(C4O4)(C7H10N2)4(H2O)2]C4O4·0.25H2O, contains one-half of a squarate ligand, one-half of an uncoordinated squarate dianion, two 2-(2-amino­ethyl)pyridine ligands and one aqua ligand, all coordinated to an NiII ion. The compound also contains 0.25 solvent water mol­ecules. The NiII ion has distorted octa­hedral geometry. The squarate ligand adopts a μ-1,2 coordination mode, the intra­dimer NiII⋯NiII separation being 7.1442 (7) Å, while the other squarate unit acts as a counter-anion. The crystal structure is stabilized by inter­molecular O—H⋯O and N—H⋯O hydrogen-bond inter­actions, forming a three-dimensional network

Aqua­(di-2-pyridyl­amine-κ2 N 2,N 2′)(pyridine-2,6-dicarboxyl­ato-κ3 O 2,N,O 6)zinc monohydrate

In the title compound, [Zn(C7H3NO4)(C10H9N3)(H2O)]·H2O, the ZnII atom has a distorted octa­hedral coordination geometry. One of the water mol­ecules is coordinated with the ZnII ion and this mol­ecule forms an O—H⋯O inter­action with the lattice water mol­ecule. The pyridine-2,6-dicarboxyl­ate ligand is almost planar (r.m.s. deviation = 0.0242 Å). In the crystal, C—H⋯O, C—H⋯N, O—H⋯O and N—H⋯O hydrogen bonds are present

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as "Turkey Tourism Year" in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities

Comparison of results of conservative method and plate fixation method for the treatment of Ruedi/allgower type 1 Pilon fractures

The aim of this study was to compare the results of cast application and plate fixation in the management of Ruedi/Allgower type I Pilon fracturesPatients and methods: Forty-two patients (24 females, 18 males) with Ruedi/Allgower type I Pilon fractures were retrospectively reviewed. Sixteen patients (Group 1) (10 females, 6 males; mean age 43 years; range 18-56) had been treated with cast application and 26 patients (Group 2) (14 females, 12 males; mean age 37.7 years; range 19-52) had been treated with plate fixation. All patients were diagnosed with X-ray. Radiologic examinations were made using Ovadia and Beal’s criteria. Long term functional results of foot and ankle were evaluated according to the Tenny and Wiss citeria. The mean follow-up period was 28 months (range 12-44) in Group 1 and 31 months (range 16-46) in Group 2.Results: Mean reduction quality score was 12 points in Group 1, and 10 points in Group 2. The difference between the groups was statistically significant (p<0.05). Mean long term functional results of foot and ankle scored 84 and 86 in Group 1, and Group 2, respectively (p>0.05). Time to union was not different between both groups (p>0.05).Conclusion: Although the quality of reduction obtained with cast application was not as satisfactory as plate fixation; good results could be obtained in both groups regarding long term functions

A New Method Based on Machine Learning for the Diagnosis of Parkinson’s Disease

Parkinson hastalığı (PH), dopamin üreten beyin hücrelerinin ölmesiyle ya da zarar görmesiyle ortaya çıkan bir beyin hastalığıdır. Böyle bir durumda, beyin normal fonksiyonlarını yerine getiremez. PH, konuşma, yürüme ve yazma gibi insan hareketlerini olumsuz olarak etkiler. Bu hastalığın teşhisinde detaylı tıbbi öykü, geçmiş tedavi öyküsü, fiziksel testler ve bazı kan testleri ile beyin filmleri istenilmektedir. Bu işlemler maliyetli ve meşakkatli olabildiği için daha az maliyetli ve daha kolay yapılabilen teşhis bu noktada önem kazanmaktadır. Bu çalışmada doktorun kararına destek olabilmesi için 252 bireyden alınan ses verileri ile PH’ın teşhis edilebilmesi amaçlanmıştır. Verilerden daha iyi sonuç alabilmek için bazı ön işlemler uygulanmıştır. Verilerde dengeleme işlemi yapılmış ve sistematik örnekleme metodu ile işleme alınacak veriler belirlenmiştir. Öznitelik seçme algoritması ile özniteliklerin etiket üzerindeki etki gücü hesaplanıp bazı veri grupları oluşturulmuştur. Sınıflandırma algoritmalarından Karar ağacı, Destek Vektör Makineleri ve K En Yakın Komşu Algoritması kullanılıp, performans değerlendirme kriterleri - bunlar; Doğruluk Oranı, Duyarlılık, Özgünlük, F-Ölçümü, Kappa, Auc - değerlendirilmiştir. En yüksek performans değerine sahip veri grubu ve kullanılan sınıflandırma algoritması belirlenip model oluşturulmuştur. Model en ilgiliden en ilgisize doğru sıralanmış veri setinin %45’inden ve Destek vektör makineleri algoritması kullanılarak oluşturulmuştur. Performans kriterlerinde %85 doğruluk oranı ve diğer kriterlerde de olumlu sonuçlar elde edilmiştir. Böylece PH olma ihtimali olan bireyin ses kayıtlarından oluşturulan veri seti ve uygulanan model yardımı ile doktora tıbbi karar destek sağlanacağı anlaşılmıştır.Parkinson’s disease (PD) is a brain disease caused by death or damage of dopamine producer brain cells. In such case, the brain can not perform its normal functions. PD negatively affects human movements such as speech, walking and writing. In the diagnosis of this disease, detailed medical history, history of treatment, physical tests and some blood tests and brain films are required. Because these operations can be costly and difficult, less costly and easier making of the diagnosis has such important in this subject. In this study, it was aimed to diagnose PD with voice data from 252 individuals to support the doctor’s decision. In order to get better results, some pre-treatments were applied. The datas were balanced and datas that taken to treatments with systematic sampling method were determined. With the feature selection algorithm, some data groups were created by calculating the effect of the attributes on the label. Of the classification algorithms; Decision tree, Support Vector Machines and K Nearest Neighbor Algorithm are used and Performance evaluation criteria such as Accuracy, Sensitivity, Specificity, F-Measurement, Kappa, Auc - were evaluated. The data group with the highest performance value and the used classification algorithm were determined and model was created by using support vector machines algorithm and from 45% of data set that was sorten from the most effective to the most ineffective. The performance criterias has an accuracy of 85% besides in other criterias possitive results were earned. Thus, it was understood that medical decision support to the doctor would be provided with the help of applied model and data set formed by sound recordings of the individuals who possibly been P

Parkinson Hastalığı Teşhisi İçin Makine Öğrenmesi Tabanlı Yeni Bir Yöntem

Parkinson hastalığı (PH), dopamin üreten beyin hücrelerinin ölmesiyle yada zarar görmesiyle ortaya çıkan bir beyin hastalığıdır. Böyle bir durumda, beyin normal fonksiyonlarını yerine getiremez. PH, konuşma, yürüme ve yazma gibi insan hareketlerini olumsuz olarak etkiler. Bu hastalığın teşhisinde detaylı tıbbi öykü, geçmiş tedavi öyküsü, fiziksel testler ve bazı kan testleri ile beyin filmleri istenilmektedir. Bu işlemler maliyetli ve meşakkatli olabildiği için daha az maliyetli ve daha kolay yapılabilen teşhis bu noktada önem kazanmaktadır. Bu çalışmada doktorun kararına destek olabilmesi için 252 bireyden alınan ses verileri ile PH’ın teşhis edilebilmesi amaçlanmıştır. Verilerden daha iyi sonuç alabilmek için bazı ön işlemler uygulanmıştır. Verilerde dengeleme işlemi yapılmış ve sistematik örnekleme metodu ile işleme alınacak veriler belirlenmiştir. Özellik seçme algoritması ile niteliklerin etiket üzerindeki etki gücü hesaplanıp bazı veri grupları oluşturulmuştur. Sınıflandırma algoritmalarından Karar ağacı, Destek Vektör Makineleri ve K En Yakın Komşu Algoritması kullanılıp, performans değerlendirme kriterleri - bunlar; Doğruluk Oranı, Duyarlılık, Özgünlük, F-Ölçümü, Kappa, Auc - değerlendirilmiştir. En yüksek performans değerine sahip veri grubu ve kullanılan sınıflandırma algoritması belirlenip model oluşturulmuştur. Model en ilgiliden en ilgisize doğru sıralanmış veri setinin %45’inden ve Destek vektör makineleri algoritması kullanılarak oluşturulmuştur. Performans kriterlerinde %85 doğruluk oranı ve diğer kriterlerde de olumlu sonuçlar elde edilmiştir. Böylece PH olma ihtimali olan bireyin ses kayıtlarından oluşturulan veri seti ve uygulanan model yardımı ile doktora tıbbi karar destek sağlanacağı anlaşılmıştır

Diaqua­bis(ethyl­enediamine-κ2 N,N′)copper(II) 2,2′-dithio­dinicotinate sesquihydrate

In the title compound, [Cu(C2H8N2)2](C12H6N2O4S2)·1.5H2O, there are two half-molecules of the cationic complex in the asymmetric unit. The Cu2+ ions lie on inversion centres and are octa­hedrally coordinated by two ethyl­enediamine (en) and two aqua ligands in a typical Jahn–Teller distorted environment with the water O atoms in the axial positions. Two 2-mercaptonicotinate units (mnic) are linked by a disulfide bridge. All the ethyl­enediamine N—H and O—H groups form inter­molecular hydrogen bonds with acceptor O and N atoms, giving rise to a three-dimensional network. One of the uncoordinated water molecules has a site occupation factor of 0.5