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The prediction of manoeuvring performance for safe navigation and effective design of ships increasingly depends on artificial intelligence (AI), mainly digital twin technology. This technology requires a digital model of the physical ship. The hydrodynamic coefficients and parameters of these models are commonly obtained through two experimental methods: the planar motion mechanism (PMM) and the circular motion test (CMT). These methods are time-consuming and expensive, which may not be feasible during the early stages of the design process. This study investigates a cost-effective alternative approach to these methods by implementing a grey box method on ships. For the first of these implementations, a full-scale tanker ship was applied with artificial training data of zigzag manoeuvres. A validation study was carried out by comparing the simulation and free-running model test results of the tanker. For the second of these implementations, a scale model of a car carrier was selected, and several numerical search methods were combined to obtain a more accurate digital model. The 3-degree-of-freedom (DOF) Manoeuvring Modelling Group (MMG) models identified through this combination were validated with simulations and compared with the free-running model test results for various manoeuvres. The contribution of this study lies in the accurate capture of the manoeuvring characteristics of the physical model, which is achieved through the use of the adjustment interval and the combination of various numerical search method of the grey box method. Consequently, the developed model can be used in future studies as a faster decision-making tool for determining the straight-line stability or instability of a ship in the ship design and in predicting the manoeuvring performance of the ship. [ABSTRACT FROM AUTHOR]

Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature. [ABSTRACT FROM AUTHOR]

• Two powerful datasets were created for the selection of the wart treatment method. • In the dataset pre-analysis, it was evaluated that it is very difficult to determine a treatment method without classification techniques. • As a result of the tests on the datasets, it was determined that the Ensemble Classification method was the most appropriate. • It has been observed that the estimations of the Ensemble model fully comply with the real data. • The performance values of the Ensemble method are higher than the literature models. Warts caused by Human Papilloma Virus (HPV) do not disappear spontaneously with the body's natural immunity. This is due to many features of HPV, such as its weak immune effect, not entering the bloodstream and causing an immune response, and permanent infection of skin cells. HPV requires urgent treatment due to these characteristics and the fact that warts are most common on the hands and feet and are painful and disturbing. Choosing the most appropriate Cryotherapy or Immunotherapy treatment methods according to the patient's physiological characteristics is important in order to carry out the treatment process quickly. This study focuses on helping experts choose the most appropriate treatment for the most common plantar and common warts. For the selection of treatment method machine learning have been used. Bagged Tree and Subspace K-Nearest Neighbors (KNN) Ensemble classification algorithms were developed and tested on two data sets. These data sets were used to develop classification algorithms with pre- and post-treatment information of 180 patients receiving cryotherapy and immunotherapy treatments. The Subspace KNN Ensemble algorithm correctly predicted the treatment results of 88 of 90 patients receiving cryotherapy treatment, reaching 97.80% accuracy, and the Bagged Tree Ensemble algorithm correctly predicted 78 out of 90 patients receiving immunotherapy, reaching 86.7% accuracy. It has been shown that algorithms can help experts in terms of both high accuracy values ​​and fast decision making. The algorithms proposed have not been used before, and it has also been seen that they have superior performances than similar studies. [ABSTRACT FROM AUTHOR]

Copyright of Cukurova Medical Journal / Çukurova Üniversitesi Tip Fakültesi Dergisi is the property of Cukurova University, Faculty of Medicine 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 International Social Research is the property of Journal of International Social 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.)

Summary Objective The aim of this study was to determine nodules using newly developed software with a computer-assisted visual process technique for the calculation of size. The effects of the ratios of nodule base and width were evaluated with voice acoustic analysis. Methods A total of 72 patients with pediatric vocal nodule were evaluated. Nodules were marked with the ImageJ News program on photographs obtained from the video recordings in the videostroboscopic examination and classified according to the Shah et al scale. Segmentation was applied automatically. The ratios were taken as base of nodule/width and base of nodule/vocal cord. In the voice acoustic analysis, basic frequencies (mean F0), jitter (local %), shimmer (local %), and harmonicity (mean harmonics-to-noise [mean HNR]) were evaluated. Results A statistically significant negative correlation was determined between the mean F0 value and the nodule base/width ratio (P = 0.042, r = −0.240). A negative statistically significant relationship was determined between jitter (%) and vocal nodule base/width (P = 0.009, r = −0.305). A statistically significant positive correlation was determined between mean HNR and vocal nodule base/width (P = 0.034, r = 0.324). In discriminant analysis, correct classification of the Shah et al scale degrees of the classifying variables was 73.6%. Conclusion Through collaboration with the biomedical engineering department, the results of this study determined new ratios in patients with pediatric vocal nodule. In voice acoustic analysis, the mean F0 was more affected by the width of the nodule, mean HNR was affected by the length of the base of the nodule, and jitter (%) was affected by the width of the nodule. [ABSTRACT FROM AUTHOR]

Estimation of ship self-propulsion is important for the selection of the propulsion system and the main engine so that the ship can move forward with the required speed. Resistance characteristics of the vessel or the open-water performance of a propeller only are not usually enough to assess the working conditions of the ship. Both in numerical simulations and in experiments; there is a need to treat the propulsion system and the hull as a whole for a better estimation of the self-propulsion parameters. In this study, the self-propulsion points of one submarine (DARPA Suboff) and two surface piercing vessels (KCS and DTC) were obtained with methods based on computational fluid dynamics (CFD) approach. The self-propulsion points were also calculated by a classical engineering approach that makes use of the empirical relations that may be found in the literature. The results were evaluated with respect to the experiments and numerical results generated by other researchers in this field. It was found that the self-propulsion points of traditional ship forms can be very closely approximated with a classical engineering approach, given the basic geometric and the hydrostatic properties of the hull and the propeller. [ABSTRACT FROM AUTHOR]

Recent developments in Computational Fluid Dynamics (CFD) enabled common access for researchers and thus offers solutions to various complex problems in many different fields. With this motivation, in this study, two kinds of numerical methods were employed to investigate the vertical motions in variable regular waves. While the potential method is commonly known as linear "strip theory", the viscous approach is (the state of the art) named as URANS (Unsteady Reynolds Averaged Navier Stokes) solver which has a fully non-linear base. The DTMB 5415 ship model form was selected for a series of computational work. A numerical study was carried out to understand the seakeeping behaviour of the displacement hull for the stationary case Fn=0 and a high speed case Fn=0.41. The RAO (Response Amplitude Operator) graphs for the coupled heave - pitch motions and the ship's vertical accelerations were generated for five encounter frequencies. The numerical results obtained were validated with the existing experimental data and comparisons were made between the two numerical approaches with the help of RAO graphs. The obtained results showed that the limitations of the strip theory pose a handicap as the assumptions involved in the theory narrow down its application. The nonlinear viscous URANS approach tends to be a better option returning closer results to experiments in a wide Froude number range but on the other hand it does not possess the practicality of the strip theory. [ABSTRACT FROM AUTHOR]

Data science for engineers is the most recent research area which suggests to analyse large data sets in order to find data analytics and use them for better designing and modelling. Ship design practice reveals that conceptual ship design is critically important for a successful basic design. Conceptual ship design needs to identify the true set of design variables influencing vessel performance and costs to define the best possible basic design by the use of performance prediction model. This model can be constructed by design engineers. The main idea of this paper comes from this crucial idea to determine relational classification of a set of small vessels using their hull form parameters and performance characteristics defined by transfer functions of heave and pitch motions and of absolute vertical acceleration, by our in-house software application based on K-Means algorithm from data mining. This application is implemented in the C# programming language on Microsoft SQL Server database. We also use the Elbow method to estimate the true number of clusters for K-Means algorithm. The computational results show that the considered set of small vessels can be clustered in three categories according to their functional relations of their hull form parameters and transfer functions considering all cases of three loading conditions, seven ship speeds as non-dimensional Froude numbers (Fn) and nine wave-length to ship-length values (λ/L). [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi 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.)

In this paper, we are proposing a computer-based system which makes the automatic elimination of ship design parameters based on data analysis for seakeeping performance. Usually engineers do not have enough time to analyse the data. In this case it can be better to use less parameters in the data analysis. But if the investment has the high commercial worth then the engineers must consider and analyse all variables and their effects in the concept design to minimise the risks of further stages of the design. We are mainly focused on ship motions to identify their most influential parameters. By the use of statistics, the backward elimination method is constructed in a software based on the SQL Server Database. The system contains two modules named as "Identification" and "Elimination". Identification module is used to find out the weakest parameters by the method and then the elimination module avoids these parameters from the final model. In fact the most engineering areas concern with the problem of different parameters and physical issues to construct metamodels to calculate the closest prediction to real values. [ABSTRACT FROM AUTHOR]

[Display omitted] • 1432 images in the dataset used in the study were labeled as Periodontal bone loss and Non-periodontal bone loss. • Image features were extracted using AlexNet and SqueezeNet, also images are directly classified using the EfficientNetB5. • AlexNet and SqueezeNet based image features are classified using the five different classifiers. • To save time and reduce the workload of experts, the user-friendly GUI application was designed. As artificial intelligence in medical imaging is used to diagnose many diseases, it can also be employed to diagnose whether a person has periodontal bone loss or not. Accurate and early diagnosis performs a vital task in the treatment of the patient's dental disorder. Therefore, such medical images are known to be an important clinical adjunct. In this manuscript, whether the patient has periodontal bone loss or non-periodontal bone loss is diagnosed employing hybrid artificial intelligence-based systems. Herein, after tagging a total of 1432 images by an expert, we extract 1000 deep image features for each image using AlexNet and SqueezeNet deep learning architectures. On the other hand, we classify these images directly without extracting the image features using the EfficientNetB5 deep learning architecture. First, we categorize AlexNet-based deep image features using the Coarse Tree, Weighted K-Nearest Neighbor (KNN), Gaussian Naïve Bayes, RUSBoosted Trees Ensemble, and Linear Support Vector Machine (SVM) classifiers. Afterward, we classify SqueezeNet-based deep image features using Medium Tree, Gaussian Naïve Bayes, Boosted Trees Ensemble, Coarse KNN, and Medium Gaussian SVM classifiers. With the help of the ten classifiers employed in this study, we also design a user-friendly Graphical User Interface (GUI) application. Thanks to this application, we aim to reduce the workload of experts, save time and help to diagnose dental disorders early. The results show that the best classifiers for AlexNet-based, SqueezeNet-based, and Direct-Convolutional Neural Network (CNN) are Linear SVM, Medium Gaussian SVM, and EfficientNetB5, respectively. Among these classifiers, the best classifier is Linear SVM, and its accuracy, error, sensitivity, specificity, precision, and F 1 score values are 81.49%, 18.51%, 84.57%, 79.14%, 75.68%, and 79.88%, respectively. [ABSTRACT FROM AUTHOR]

The article reports on an extensive ancient tunnel system constructed during the reigns of Roman emperors Vespasian and Titus to supply water to the extinct Turkish coastal city of Seleukis Pieria, near the village of Çevlik, Turkey. The report focuses on investigations of the Titus Tunnel there and the traces of test tunnel probes that have enabled experts to decipher and reconstruct the planning and technology of the engineered stone waterways. The difficulty of tunnel building and the complexity of exacting measurements in joining tunnels is described.

Discusses the determination of optimum heat transfer area for vacuum evaporators in ships. Computation of the condenser heat transfer area; Heat balance between the hot and cold fluid in the heat exchanger; Case study for a diesel ship plant.

Copyright of Teknoloji is the property of Engineering Science & Technology, an International 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.)

Microaneurysms are lesions in the shape of small circular dilations which result from thinning in peripheral retinal blood vessels due to diabetes and increasing intra-retinal blood pressure. Because it is considered as the most important clinical finding in the diagnosis of diabetic retinopathy, accurate detection of these lesions bear utmost importance in the early diagnosis of diabetic retinopathy. The present study aims to accurately, effectively and automatically detect microaneurysms which are difficult to detect in color fundus images in early stage. To this aim, ant colony algorithm, which is an important optimization method, was used instead of conventional image processing techniques. First, retinal vascular structure was extracted from color fundus images in Messidor and DiaretDB1 data sets. Afterwards, the segmentation of microaneurysms was effectively carried out using ant colony algorithm. The same procedure was also applied to five different image processing and clustering algorithms (watershed, random walker, k-means, maximum entropy and region growing) in order to compare the performance of the proposed method with other methods. Microaneurysm images manually detected by a specialist eye doctor were used to measure the performances of above-mentioned methods. The similarities among microaneurysms which were automatically and manually segmented were tested using Dice and Jaccard similarity index values. Dice index values obtained from the study vary between 0.52 and 0.98 in maximum entropy, 0.55 and 0.88 in watershed, 0.75 and 0.86 in region growing, 0.55 and 0.78 in k-means, and 0.66 and 0.83 in random walker, and 0.81 and 0.9 in ant colony. Similar performance values were also obtained in Jaccard index. The results show that different performances were observed in the conventional segmentation of microaneurysms depending on the image quality. On the other hand, the ant colony based method proposed in this paper displays a more stabilized and higher performance irrespective of image contrast. Therefore, it is evident that the proposed method successfully detects microaneurysms even in low quality images, thus helping specialists diagnose them in an easier way. [ABSTRACT FROM AUTHOR]