Life‐threatening spontaneous kidney rupture in a rare case with systemic lupus erythematosus: Prompt diagnosis with computed tomography

Nontraumatic, spontaneous parenchymal kidney rupture is a rare clinical entity that may cause extensive hemorrhage, hypovolemic shock, and death. Spontaneous nontraumatic kidney rupture is extremely rare in systemic lupus erythematosus (SLE) patients. Because of the high morbidity and mortality rates, an immediate establishment of the diagnosis and treatment are necessary. We present the case of a 30‐year‐old female with spontaneous parenchymal rupture of the right kidney who had renal failure due to SLE and presented with atraumatic sudden right flank pain during hemodialysis treatment. To our knowledge, this case is the second report of SLE manifesting as spontaneous kidney rupture in the literature.     

A New Multiplicative Seasonal Neural Network Model Based on Particle Swarm Optimization

In recent years, artificial neural networks (ANNs) have been commonly used for time series forecasting by researchers from various fields. There are some types of ANNs and feed forward neural networks model is one of them. This type has been used to forecast various types of time series in many implementations. In this study, a novel multiplicative seasonal ANN model is proposed to improve forecasting accuracy when time series with both trend and seasonal patterns is forecasted. This neural networks model suggested in this study is the first model proposed in the literature to model time series which contain both trend and seasonal variations. In the proposed approach, the defined neural network model is trained by particle swarm optimization. In the training process, local minimum traps are avoided by using this population based heuristic optimization method. The performance of the proposed approach is examined by using two real seasonal time series. The forecasts obtained from the proposed method are compared to those obtained from other forecasting techniques available in the literature. It is seen that the proposed forecasting model provides high forecasting accuracy.     

Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural networks and genetic algorithms have been used in fuzzy time series method to improve the method. While fuzzy clustering and genetic algorithms are being used for fuzzification, artificial neural networks method is being preferred for using in defining fuzzy relationships. In this study, a hybrid fuzzy time series approach is proposed to reach more accurate forecasts. In the proposed hybrid approach, fuzzy c-means clustering method and artificial neural networks are employed for fuzzification and defining fuzzy relationships, respectively. The enrollment data of University of Alabama is forecasted by using both the proposed method and the other fuzzy time series approaches. As a result of comparison, it is seen that the most accurate forecasts are obtained when the proposed hybrid fuzzy time series approach is used.     

A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks

Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification, defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Another study for increasing forecasting accuracy was made by Cheng et al. [6]. In their study, they employ adaptive expectation model to adopt forecasts obtained from first order fuzzy time series forecasting model. In this study, we propose a novel high order fuzzy time series method in order to obtain more accurate forecasts. In the proposed method, fuzzy relationships are defined by feed forward neural networks and adaptive expectation model is used for adjusting forecasted values. Unlike the papers of Cheng et al. [6] and Liu et al. [14], forecast adjusting is done by using constraint optimization for weighted parameter. The proposed method is applied to the enrollments of the University of Alabama and the obtained forecasting results compared to those obtained from other approaches are available in the literature. As a result of comparison, it is clearly seen that the proposed method significantly increases the forecasting accuracy.     

A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model

In the literature, there have been many studies using fuzzy time series for the purpose of forecasting. The most studied model is the first order fuzzy time series model. In this model, an observation of fuzzy time series is obtained by using the previous observation. In other words, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, this model can not be sufficient for some time series such as seasonal time series which is an important class in time series models. Besides, the time series encountered in real life have not only autoregressive (AR) structure but also moving average (MA) structure. The fuzzy time series models available in the literature are AR structured and are not appropriate for MA structured time series. In this paper, a hybrid approach is proposed in order to analyze seasonal fuzzy time series. The proposed hybrid approach is based on partial high order bivariate fuzzy time series forecasting model which is first introduced in this paper. The order of this model is determined by utilizing Box-Jenkins method. In order to show the efficiency of the proposed hybrid method, real time series are analyzed with this method. The results obtained from the proposed method are compared with the other methods. As a result, it is observed that more accurate results are obtained from the proposed hybrid method.     

Investigation of ride comfort limits on urban asphalt concrete pavements

It is well known that pavement distress negatively affects the drivers and passengers of vehicles. Many studies report that foremost among these negative effects is the vibrations that form within the vehicle. Ride comfort depends on the human response to vibration and vehicle response to the road. The goal of this study was to investigate the effect of pavement condition index on ride comfort and to determine the threshold comfort limits for passenger cars on urban asphalt concrete pavements. The pavement condition index (PCI) was determined for pavement sections subject to different surface distress using the PAVER system. Ride (driving) speeds of 20, 30, 40 and 50 km/h were assessed on the same pavement sections to measure vibrational effects inside the vehicle and on the passenger seat. These measurements were then evaluated using the ISO 2631-1 standard in order to determine the a _wz values. Using the logistic regression technique, predictive model that took into account linguistic concepts for estimating ride comfort levels based on PCI values was developed. With the aid of this mathematical model, comfort threshold values were determined for each driving speed within an interval of 0–100 PCI. The study results indicated that increasing driving speed was generally associated with higher PCI comfort thresholds.     

A new approach for determining the length of intervals for fuzzy time series

In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series.     

Model-based color halftoning using direct binary search.

In this paper, we develop a model-based color halftoning method using the direct binary search (DBS) algorithm. Our method strives to minimize the perceived error between the continuous tone original color image and the color halftone image. We exploit the differences in how the human viewers respond to luminance and chrominance information and use the total squared error in a luminance/chrominance based space as our metric. Starting with an initial halftone, we minimize this error metric using the DBS algorithm. Our method also incorporates a measurement based color printer dot interaction model to prevent the artifacts due to dot overlap and to improve color texture quality. We calibrate our halftoning algorithm to ensure accurate colorant distributions in resulting halftones. We present the color halftones which demonstrate the efficacy of our method.     

Comparison of physicochemical characteristics and photofermentative hydrogen production potential of wastewaters produced from different olive oil mills in Western-Anatolia,Turkey

Olive oil extraction produces a dark-colored wastewater that contains nutrients that can be further processed using biotechnology, in parallel with treatment for disposal. For instance, olive mill wastewater (OMW) can be used as a substrate for photofermentative hydrogen production by purple bacteria. A comparative study was investigated with several OMW samples from different olive oil mills in Western-Anatolia, Turkey. The composition of OMW varies significantly for each mill; thus, a detailed physicochemical analysis of each sample has been carried out. Subsequently, samples were assessed for their functioning in anaerobic photofermentative hydrogen production by Rhodobacter sphaeroides O.U.001. The highest hydrogen production potential (19.9 m³ m^?3) was obtained by the OMW sample with the highest organic content (mainly acetic acid, 9.71 kg m^?3) and the highest carbon-to-nitrogen (C/N) molar ratio (73.8 M M^?1). The organic content was found to be composed of primarily acetic, aspartic, and glutamic acids. There was a linear relationship between C/N ratio and hydrogen production potential across the different OMW samples. This study is unique due to the wide range of analyses of OMW samples and the comparison of many parameters for hydrogen production from wastewater. The results obtained throughout this study can aid in the design of systems using wastewater for biohydrogen production. Particularly, the C/N ratio was found to be the best parameter for choosing a proper substrate.