As the Internet of Things technology is developing rapidly, companies have an ability to observe the health of engine components and constructed systems through collecting signals from sensors. According to output of IoT sensors, companies can build systems to predict the conditions of components. Practically the components are required to be maintained or replaced before the end of life in performing their assigned task. Predicting the life condition of a component is so crucial for industries that have intent to grow in a fast paced technological environment. Recent studies on predictive maintenance help industries to create an alert before the components are corrupted. Thanks to prediction of component failures, companies have a chance to sustain their operations efficiently while reducing their maintenance cost by repairing components in advance. Since maintenance affects production capacity and the service quality directly, optimized maintenance is the key factor for organizations to have more revenue and stay competitive in developing industrialized world. With the aid of well-designed prediction system for understanding current situation of an engine, components could be taken out of active service before malfunction occurs. With the help of inspection, effective maintenance extends component life, improves equipment availability and keeps components in a proper condition while reducing costs. Real time data collected from sensors is a great source to model component deteriorations. Markov Chain models, Survival Analysis, Optimization algorithms and several machine learning approaches have been implemented in order to model predictive maintenance. In this paper Long Short Term Memory (LSTM) networks has been performed to predict the current situation of an engine. LSTM model deals with a sequential input data. Training process of LSTM networks has been performed on large-scale data processing engine with high performance. Since huge amount of data is flowing into the predictive model, Apache Spark which is offering a distributed clustering environment has been used. The output of the LSTM network is deciding the current life condition of components and offering the alerts for components before the end of their life. The proposed model also trained and tested on an open source data that is about an engine degradation simulation provided by the Prognostics CoE at NASA Ames.

Wind energy (WE), which is one of the renewable energy (RE) sources for generating electricity, has been making a significant contribution to obtaining clean and green energy in recent years. Fitting an appropriate statistical distribution to the wind speed (WS) data is crucial in analyzing and estimating WE potential. Once the best suitable statistical distribution for WS data is determined, WE potential and potential yield could be estimated with high accuracy. The main objective of this paper is to propose a novel approach for calculating wind energy potential. For this purpose, the Efficient Global Optimization (EGO) technique was proposed for fitting a statistical distribution to WS data and the performance of the technique was compared with genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE). Performance metrics showed that EGO is providing better estimations compared with GA, SA, and DE. Based on Weibull parameters obtained by using EGO, potential WE and potential annual revenue were estimated for Gdańsk, which is the capital of Pomerania Voivodeship in Poland, in the case of having city-type wind turbines in the city center. Estimations for Gdańsk showed that city-type wind turbines might be helpful for producing electricity from WE in the city without being limited by constraints such as having a long distance between wind turbines and buildings. If such wind turbines were erected on the roofs of residential buildings, malls, or office buildings, there is a possibility that part of the electric energy needed for such buildings could be generated using WE. However, this topic should be further investigated from technical and financial perspectives.

Dimensionally stable material design is an important issue for space structures such as space laser communication systems, telescopes, and satellites. Suitably designed composite materials for this purpose can meet the functional and structural requirements. In this paper, it is aimed to design the dimensionally stable laminated composites by using efficient global optimization method. For this purpose, the composite plate optimization problems have been solved for high stiffness and low coefficients of thermal and moisture expansion. Some of the results based on efficient global optimization solution have been verified by genetic algorithm, simulated annealing, and generalized pattern search solutions from the previous studies. The proposed optimization algorithm is also validated experimentally. After completing the design and optimization process, failure analysis of the optimized composites has been performed based on Tsai–Hill, Tsai–Wu, Hoffman, and Hashin–Rotem criteria.

Third molars have the highest prevalence of impaction in teeth and can cause pathological damage on the adjacent second molars. This study aims to evaluate the effects of factors related to impacted third molars on external root resorption (ERR) in adjacent second molars using cone-beam computed tomography (CBCT). In CBCTs, the effect of impacted third molars on the root surface of adjacent second molars was investigated. Inclusion criteria for subjects were being older than 16 and younger than 55, presence of at least one impacted third molar and adjacent second molar. Exclusion criteria were pathology, a follicle gap greater than 5 mm, crowned second molar, severe decay, an artifact on a radiologic image, and previous surgery on the second or third molars. The investigations were made based on age range, gender, tooth inclination, Pell-Gregory classification, retention type, contact area, root formation, pericoronal width, and tooth absence on the same quadrant for potential risk factors. The collected data were statistically analyzed with R software. The Chi-Square test was used to find out any significant difference. Logistic regression analyses were done for potential risk factors for ERR. A total of 437 impacted third molars and adjacent second molars were investigated using CBCT. Of these, 381 met the inclusion criteria. Mesioangular and horizontal inclination, Pell-Gregory Class B-C, contact area, and retention type were found the statistically potential risk factors for ERR. The impacted third molar with horizontal or mesioangular position, and osseous retention, with Pell and Gregory Class B and C, are more likely to cause external root resorption in adjacent second molars.

Predictive maintenance (PdM) is a type of approach for maintenance processes, allowing maintenance actions to be managed depending on the machine's current condition. Maintenance is therefore carried out before failures occur. The approach doesn’t only help avoid abrupt failures but also helps lower maintenance cost and provides possibilities to manufacturers to manage maintenance budgets in a more efficient way. A new deep neural network (DNN) architecture proposed in this study intends to bring a different approach to the predictive maintenance domain. There is an input layer in this architecture, a Long-Short term memory (LSTM) layer, a dropout layer (DO) followed by an LSTM layer, a hidden layer, and an output layer. The number of epochs used in the architecture and the batch size was determined using the Genetic Algorithm (GA). The activation function used after the output layer, DO ratio, and optimization algorithm optimizes loss function determined by using grid search (GS). This approach brings a different perspective to the literature for finding optimum parameters of LSTM. The neural network and hyperparameter optimization approach proposed in this study performs much better than existent studies regarding LSTM network usage for predictive maintenance purposes.