Real-time control of geometry and stiffness in adaptive structures

Adaptive structures are those which can adjust their geometry, stiffness and damping on demand to] meet the changes in their loading environment. With the advents in the piezo-electric device and composite material technologies, such structures are already being considered seriously in aero-space industry. The success of these structures depends largely on the effective control of the structure through the actuators, and the sensors embedded in their load carrying members. This work outlines the basic theory for the geometry, stiffness and damping control. Necessary and sufficient conditions for stress free geometry control in statically determinate and indeterminate adaptive discrete structures are given. In particular, for the subset of discrete adaptive structures viz. adaptive truss structures the equations similar to the forward and inverse kinematic equations of open loop mechanical manipulators are derived. These equations describe the large geometry control under show motion (i.e., no inertia forces) assumption. Two criteria for choosing the optimum control from among the possible ones are established. A fast algorithm based on variable order variable step multistep method is given that can compute the controls for a large maneuver in real-time. Numerical results from the algorithm are presented. As an example of damping and stiffness alteration on demand, the vibration control in adaptive trusses by means of elongations and elongation-rates of the active elements is also given.     

Reliable and secure data transfer in IoT networks

With the rapid technological improvements in mobile devices and their inclusion in Internet of Things (IoT), secure key management becomes mandatory to ensure security of information exchange. For instance, IoT applications, such as smart health-care and smart homes, provide automated services to the users with less or no user intervention. As these application use user-sensitive data, ensuring their security and privacy should be paramount, especially during the key management process. However, traditional approaches for key management will not suit well in IoT environment because of the inherent resource constraint property of IoT devices. In this paper, we propose a novel distributed key management scheme for IoT ecosystem. The proposed scheme efficiently provides security to IoT devices by delegating most of the resource consuming cryptographic processing to a local entity. This entity coordinates with other peer entities to provide a distributed key as well as an authentication mechanism to network devices. In particular, the proposed scheme exploits the advantages of mobile agents by deploying them in different subnetworks as and when required: (1) to process the cryptography work for the IoT devices, and (2) to act as an local authenticated entity to perform fast authentication process. To verify the effectiveness and correctness of our proposed scheme, we have simulated it in a large IoT scenario and evaluated against relevant metrics that includes user mobility, certification generation time, and communication overhead.

     

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.     

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 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.     

Polycarbonate Heat Molding for Soft Lithography

Soft lithography enables rapid microfabrication of many types of microsystems by replica molding elastomers into master molds. However, master molds can be very costly, hard to fabricate, vulnerable to damage, and have limited casting life. Here, an approach for the multiplication of master molds into monolithic thermoplastic sheets for further soft lithographic fabrication is introduced. The technique is tested with master molds fabricated through photolithography, mechanical micromilling as well as 3D printing, and the results are demonstrated. Microstructures with submicron feature sizes and high aspect ratios are successfully copied. The copying fidelity of the technique is quantitatively characterized and the microfluidic devices fabricated through this technique are functionally tested. This approach is also used to combine different master molds with up to 19 unique geometries into a single monolithic copy mold in a single step displaying the effectiveness of the copying technique over a large footprint area to scale up the microfabrication. This microfabrication technique can be performed outside the cleanroom without using any sophisticated equipment, suggesting a simple way for high‐throughput rigid monolithic mold fabrication that can be used in analytical chemistry studies, biomedical research, and microelectromechanical systems.     

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 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.     

Synergistic removal of Cu(II) and nitrazine yellow dye using an eco‐friendly chitosan‐montmorillonite hydrogel: Optimization by response surface methodology

A comprehensive feasibility study on adsorption of Cu(II) and a water‐soluble nitrazine yellow (NY) dye by chitosan‐montmorillonite (CS‐MMT) hydrogel as the biosorbent was investigated as a function of biosorbent dosage, initial concentration, pH, temperature, and the presence of salts. Box–Behnken methodology was applied to optimize the adsorption experiments. Maximum adsorption values were determined as 132.74 mg/g and 144.41 mg/g at pH = 5.0, for Cu(II) and NY dye, respectively. Equilibrium isotherms of Langmuir and Freundlich were analyzed by the non‐linear regression model. The equilibrium data were well described by Freundlich model and the adsorption process well fitted pseudo‐second order kinetics. The enthalpy change of adsorption (ΔH°) were calculated as ?3.78 kJ/mol and ?5.75 kJ/mol for Cu(II) and NY dye, respectively, indicating that the adsorption processes were exothermic. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43664.