85 research outputs found
Introduction to unified mechanics theory with applications
No full textThis text describes the mathematical formulation and proof of the unified mechanics theory (UMT) which is based on the unification of Newton’s laws and the laws of thermodynamics. It also presents formulations and experimental verifications of the theory for thermal, mechanical, electrical, corrosion, chemical and fatigue loads, and it discusses why the original universal laws of motion proposed by Isaac Newton in 1687 are incomplete. The author provides concrete examples, such as how Newton’s second law, F = ma, gives the initial acceleration of a soccer ball kicked by a player, but does not tell us how and when the ball would come to a stop. Over the course of Introduction to Unified Mechanics Theory, Dr. Basaran illustrates that Newtonian mechanics does not account for the thermodynamic changes happening in a system over its usable lifetime. And in this context, this book explains how to design a system to perform its intended functions safely over its usable life time and predicts the expected lifetime of the system without using empirical models, a process currently done using Newtonian mechanics and empirical degradation/failure/fatigue models which are curve-fit to test data. Written as a textbook suitable for upper-level undergraduate mechanics courses, as well as first year graduate level courses, this book is the result of over 25 years of scientific activity with the contribution of dozens of scientists from around the world including USA, Russia, Ukraine, Belarus, Spain, China, India and U.K. Presents engineering mechanics through explanation of the unified mechanics theory with extensive experimental validation and finite element implementation using real world examples Draws the connections to the thermodynamics of degradation in solids from mathematical and microstructural perspective Discusses shortcomings and incompleteness of Newton’s universal laws of motion Posits why the space-time coordinate system is insufficient to describe organic and inorganic systems and modifies Newtonian space-time with introduction of an additional axis (Thermodynamic State Index axis)
Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods
No full textSkin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and different feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%
Development of new control algorithms for high performance autopilots
No full textThe project simulates dynamics of an aircraft subjected to an external environment that has a direct impact on the stability and behaviour of the aircraft. The Eurofighter Typhoon 2000 was chosen as the reference aircraft for the course of the project. The aircraft was subjected to open loop testing using MATLAB/Simulink on the nonlinear system of equations formulated by using the general six degree of freedom set of equations for an aircraft moving through space. Stability derivatives for these equations were obtained from the TORNADO Toolbox using the general geometry of the wing and simulating it as a flat plate. Using a reference input signal, such as a general sinusoid, the PID Controllers with predefined control algorithms are defined and their working is visually depicted using an open source flight simulator software such as FlightGear. A trajectory is then defined for the aircraft to follow and the results are visualized using the six degree of freedom simulation platform. The main purpose of the project is to optimize controller performance for a given trajectory and to be able to predict and control the response of an aircraft to external stimuli from the environment. The adaptability of these controllers to stimuli is an area of further study in this field and provides significant opportunities for future research. Aircrafts are constantly under the influence of external unbalanced forces depending upon the ever changing environment around them (such as weather) or depending on the manoeuvres that need to be executed at various stages of the mission profile of the aircraft. Thus it is important to gain insight into and be able to gauge and counteract these external influences on the aircraft.Bachelor of Engineering (Aerospace Engineering
Generation of Induced Pluripotent Stem Cells from Patients with Multiple Myeloma
No full textObjective: Patient-specific induced pluripotent stem cells (iPSCs) have potential in human disease modeling and regenerative medicine. The in vitro phenotype of disease-specific iPSC-derived cells can be used to bridge the knowledge gap between clinical phenotype and molecular or cellular pathophysiology and to understand the pathology of diseases, along with further applications, such as creating new strategies for drug screening or developing novel therapeutic agents. The aim of our study was to generate iPSCs from multiple myeloma (MM) patients. Materials and Methods: Mesenchymal stem cells (MSCs) isolated from MM patients were induced for pluripotency via the Sendai virus. Fibroblasts were used as a control. Microscopic analysis was performed daily. For colony selection, live staining was done using alkaline phosphatase staining. Reprogramming experiments were confirmed by flow cytometry, immunofluorescence (IF) staining, and gene expression analyses. To confirm the spontaneous differentiation potential, an in vitro embryonic body (EB) formation assay was performed. Results: Fibroblasts and MSCs obtained from MM patients were reprogrammed using the Sendai virus, which contains reprogramming vectors with the four Yamanaka factors, Oct3/4, Sox2, Klf4, and c-Myc. Microscopic analysis revealed that the generated iPSCs possessed classical embryonic stem cell-like morphological characteristics. Reprogramming experiments further showed that both cell lines can be reprogrammed up to the pluripotent stage, which was confirmed by flow cytometry, IF staining, and gene expression analyses. Spontaneous differentiation potential was confirmed by in vitro EB formation assays. Conclusion: iPSCs have been successfully obtained from MM patients for the first time. These cells could clarify the molecular mechanisms behind this disease
FORTUNA, VIRTÙ Y GLORIA CONSIDERACIONES SOBRE LA MORAL REPUBLICANA DE MAQUIAVELO
No full textIn this paper some key concepts, such as fortune, virtù, and glory,are considered in order to seek clarification regarding the problemof the relation between morality and politics in Machiavelli. TheMachiavellian virtù certainly includes a good deal of energy andtalent, but does not completely lack moral components. From theconcept of fortune we can derive a number of criteria for politicalaction, which form a part of the Machiavellian virtù, which, thoughdistant from the Christian morals of his time, places him within arepublican conception of morality. Starting from an inquiry into the concept of glory, we discover how glory is a reward for virtù, but itis not awarded to every politician who is successful in his ventures,but only to those who are able to save their country by benign mean
Estimation of the FRP-concrete bond strength with code formulations and machine learning algorithms
No full textThe present study pertains to the bond strength and development length of FRP bars embedded in concrete. The experimental results in the literature were compared to the analytical estimates from the equations of different international codes and machine learning techniques, i.e. Gaussian Process Regression, Artificial Neural Networks, Support Vector Machines Regression, Regression Tree and Multiple Linear Regression. The comparison was realized for four different experimental methods, i.e. hinged beam, beam-end, spliced beam and pullout, to specify the analytical equation or method with the highest agreement with the test results for each method. GPR method was found to provide the highest accuracy with a mean value of 0.95 and a standard deviation of 0.14 for the predicted-to-experimental bond strength ratio. Based on coefficient of determination, Root Mean Square Error and Mean Absolute Percentage Error statistical criteria, GRP method was followed by ANN, MLR and SVMR based on the agreement with the experimental results. Among the code equations, the bond strength equation of the ACI 440.1R-15 code resulted in highest agreement with experimental results, but the predicted values remained on the over-conservative side. The other code formulations were established to yield to estimates, nearly constant for varying test parameters and highly conservative
A novel automatic UAV launcher design by using bluetooth low energy integrated electromagnetic releasing system
No full textThis paper presents a novel state of the art catapult launcher system designed for small size unmanned aerial vehicles (UAVs) by using a bluetooth integrated electromagnetic feedback system and provides in depth know-how on the design, fabrication, structural test and software implementation. Whereas the launcher uses a spring as the primary energy source, its carriage uses the electromagnetic energy to hold and release the UAV. First, the novel design and its implementation are briefly explained. Second, the working principle of the electromagnetic releasing system as well as data acquisition of the launcher by using ultra sonic and pressure sensors are given. The real time test results show the efficiency of the novel and cost effective design.</p
Intermittent Therapy with Terbinafine for Dermatophyte Toe-Onychomycosis: A New Approach
No full textClassification of walnut varieties obtained from walnut leaf images by the recommended residual block based CNN model
No full textWalnuts are widely used, although they come in a variety of types and qualities. It is essential to choose the correct walnut variety with the necessary ecological characteristics to continue the production of walnut fruit, which has positive benefits on human health. Because planting a walnut garden is expensive and the harvesting process takes a while. However, since the colour and feel of walnut leaves are so similar, it can be challenging to tell them apart. Experts must devote a significant amount of time to differentiating walnut kinds, and morphological tests should be conducted. There are different studies in the literature for walnut variety differentiation. Nevertheless, those are studies conducted with the classification of a small number of walnut varieties or laboratory experiments. With the advancement of technology, deep learning techniques based on computers are now routinely utilized for leaf recognition. These technologies enable significant reductions in error rates, time saves, and cost. With a total of 1751 leaf pictures collected from 18 species of walnuts, a special walnut dataset was constructed for this study in order to identify walnut types from walnut leaves. To automatically classify the provided dataset, images are trained with residual block-based convolutional neural network architectures. Following the discovery of each image's deep features, the Atom Search Optimization algorithm was used to choose the most distinctive characteristics. Support vector machines (SVM) were used to classify walnut species with the new feature set created. The experimental studies of the proposed model based on Residual block and Atom Search optimization successfully categorised the walnut dataset with an accuracy rating of 87.42%
Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images
No full textOtitis media (OM), known as inflammation of the middle ear, is a condition especially seen in children. To carry out a definitive diagnosis of the discomfort that manifests itself with various symptoms such as pain in the ear, fever, and discharge, the eardrum in the middle ear should be examined by a specialist. In this study, a convolution neural network was used for feature extraction from middle ear otoscope images to diagnose different types of OM. These features were extracted using AlexNet, VGG-16, GoogLeNet, ResNet-50 models. The deep features extracted from these models were combined into a new deep feature vector. This feature vector consisting of 4000 deep features was examined, and the most relevant 222 deep features were selected from this large feature set by using the neighbourhood component analysis. In this case, the number of features was decreased and a more effective feature set was obtained. In the next stage of this experimental study, this new feature set was applied as the input to the support vector machine. As a result of the experimental study, an accuracy rate of 79.02% was achieved. The results point out that the use of deep features in detecting OM provides efficient results, and the proposed approach is beneficial in reducing the number of deep features as well as achieving better classification results
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