Rational Design of All Resistive Multifunctional Sensors with Stimulus Discriminability

A rational approach is proposed to design soft multifunctional sensors capable of detection and discrimination of different physical stimuli. Herein, a flexible multifunctional sensor concurrently detecting and distinguishing minute temperature and pressure stimuli in real time is developed using electrospun carbon nanofiber (CNF) films as the sole sensing material and electrical resistance as the only output signal. The stimuli sensitivity and discriminability are coordinated by tailoring the atomic- and device-level structures of CNF films to deliver outstanding pressure and temperature sensitivities of ? 0.96 kPa^?1 and ? 2.44%  ° C^?1, respectively, enabling mutually exclusive sensing performance without signal cross-interference. The CNF multifunctional sensor is considered the first of its kind to accomplish the stimulus discriminability using only the electrical resistance as the output signal, which is most convenient to monitor and process for device applications. As such, it has distinct advantages over other reported sensors in its simple, cost-effective fabrication and readout system. It also possesses other invaluable traits, including good bending stability, fast response time, and long-term durability. Importantly, the ability to simultaneously detect and decouple temperature and pressure stimuli is demonstrated through novel applications as a skin-mountable device and a flexible game controller.     

Vanadium pentoxide film for microsecond pulse generation in 1.5-μm region

Q-switched erbium doped fiber laser (EDFL) was passively realized using vanadium pentoxide (V2O5) embedded into polyethylene glycol (PEG) film as saturable absorber (SA). The laser could successfully generate stable self-starting pulses when the V2O5 film was placed in an EDFL cavity. It operated at 1 562.4 nm wavelength. The repetition rate can be varied from 91.7 kHz to 128.2 kHz while the pulse width shrank from 10.90 μs to 7.81 μs with rising pump power from 110.9 mW to 166.5 mW. The pulse energy recorded was 3.2 nJ at pump power of 166.5 mW. The results indicate that the saturable absorption of V2O5 has promising nonlinear photonic applications especially in fiber laser development for 1.5-μm region.     

Dual-wavelength Q-switched erbium-doped fiber laser using an SMF-MMF-SMF structure and graphene oxide

We have successfully demonstrated a stable dual-wavelength Q-switched erbium-doped fiber laser (EDFL) using a single mode fiber-multimode fiber-single mode fiber (SMF-MMF-SMF) structure-based filter. Using a graphene oxide (GO) saturable absorber (SA) to modulate the cavity loss, passive Q-switching of the dual-wavelength laser is achieved at 1 549.6 nm and 1 558.6 nm. The laser recorded the shortest pulse width of about 2.9 μs, the maximum pulse repetition rate of 65.27 kHz and the maximum average output power of 0.99 mW at pump power of 225.1 mW. The present laser has the maximum pulse energy of 15.17 nJ. A 2 SMF-MMF-SMF structure has been experimentally confirmed to be very promising as a wavelength filter.     

The effect of surface modification on the cathodic disbondment rate of epoxy and alkyd coatings

Surface modification of carbon steel substrates using appropriate functionalised silanes was carried out to investigate their effect on the dry and wet adhesion strength, and the cathodic disbondment rate, of coating binders based on epoxy and alkyd chemistries. Results show that pre-treatment of the steel substrate with 3-glycidoxypropyltrimethoxy silane (3-GPS) enhanced the dry and wet adhesion of an epoxy-based coating. Similarly, pre-treatment with 3-aminopropyltriethoxy silane (3-APS) enhanced the dry and wet adhesion of alkyd-based systems. However, although pre-treatment with 3-GPS reduced the cathodic disbondment rate for epoxy by a factor of 3, no effect on the disbondment rate for alkyd-based binders on substrates pre-treated with 3-APS was found. This strongly suggests that cathodic disbondment of epoxy proceeds by disruption of interfacial bonds (i.e. at the binder/substrate interface) but that disbondment of alkyds proceeds by direct degradation of the binder and that the interface plays little part in the process.     

A two-stage feature selection method for text categorization by using information gain,principal component analysis and genetic algorithm

Text categorization is widely used when organizing documents in a digital form. Due to the increasing number of documents in digital form, automated text categorization has become more promising in the last ten years. A major problem of text categorization is its large number of features. Most of those are irrelevant noise that can mislead the classifier. Therefore, feature selection is often used in text categorization to reduce the dimensionality of the feature space and to improve performance. In this study, two-stage feature selection and feature extraction is used to improve the performance of text categorization. In the first stage, each term within the document is ranked depending on their importance for classification using the information gain (IG) method. In the second stage, genetic algorithm (GA) and principal component analysis (PCA) feature selection and feature extraction methods are applied separately to the terms which are ranked in decreasing order of importance, and a dimension reduction is carried out. Thereby, during text categorization, terms of less importance are ignored, and feature selection and extraction methods are applied to the terms of highest importance; thus, the computational time and complexity of categorization is reduced. To evaluate the effectiveness of dimension reduction methods on our purposed model, experiments are conducted using the k-nearest neighbour (KNN) and C4.5 decision tree algorithm on Reuters-21,578 and Classic3 datasets collection for text categorization. The experimental results show that the proposed model is able to achieve high categorization effectiveness as measured by precision, recall and F-measure.     

An Application of Genetic Algorithm Search Techniques to the Future Total Exergy Input/Output Estimation

Since 1975, there has been a great deal of interest, particularly during the past decade, in the promising genetic algorithm (GA) and its application to various disciplines from medicine to cogeneration. However, the studies performed on energy-related GA modeling are relatively low in numbers. The main objective of the present study is to develop the exergy input/output estimation equations in order to estimate the future projections based on the GA notion. In this regard, the GA Future Total EXergy Input/Output Estimation Models (GAFTEXIEM/GAFTEXOEM) are used to estimate total exergy input/output demand of Turkey, which is selected as an application country, based on the economic and social indicators of gross domestic product (GDP), population, import, export and house production figures. The future prediction of Turkey's total exergy input/output values are projected between 2003 and 2023. It may be concluded that the models proposed here can be used as an alternative solution and estimation techniques to available estimation techniques. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.     

Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input–output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export–import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.