A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for ?stanbul Atatürk Airport.     

Towards quality requirements via animated formal specifications

Assuring a high quality requirements specification document involves both an early validation process and an increased level of participation. An approach and its supporting environment which combines the benefits of a formal system specification and its subsequent execution via a rapid prototype is reported. The environment assists in the construction, clarification, validation and visualisation of a formal specification. An illustrative case study demonstrates the consequences of assertions about system properties at this early stage of software development. Our approach involves the pragmatic combination of technical benefits of formal systems engineering based techniques with the context‐sensitive notions of increased participation of both developer and user stakeholders to move us closer towards a quality requirements specification document.     

Variable structure control of a class of uncertain systems

This brief paper proposes a method for tuning the parameters of a variable structure controller. The approach presented extracts the error at the output of the controller and applies a nonlinear tuning law using this error measure. The adaptation mechanism drives the state tracking error vector to the sliding hypersurface and maintains the sliding mode. In the simulations, the approach presented has been tested on the control of Duffing oscillator and the analytical claims have been justified under the existence of measurement noise, uncertainty and large nonzero initial errors.     

Attribute weighted Naive Bayes classifier using a local optimization

The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.     

Competitive Sperm-Marked Beetles for Monitoring Approaches in Genetic Biocontrol and Studies in Reproductive Biology

Sperm marking provides a key tool for reproductive biology studies, but it also represents a valuable monitoring tool for genetic pest control strategies such as the sterile insect technique. Sperm-marked lines can be generated by introducing transgenes that mediate the expression of fluorescent proteins during spermatogenesis. The homozygous lines established by transgenesis approaches are going through a genetic bottleneck that can lead to reduced fitness. Transgenic SIT approaches have mostly focused on Dipteran and Lepidopteran pests so far. With this study, we provide sperm-marked lines for the Coleopteran pest model organism, the red flour beetle Tribolium castaneum, based on the β2-tubulin promoter/enhancer driving red (DsRed) or green (EGFP) fluorescence. The obtained lines are reasonably competitive and were thus used for our studies on reproductive biology, confirming the phenomenon of ‘last-male sperm precedence’ and that the spermathecae are deployed for long-term sperm storage, enabling the use of sperm from first mating events even after secondary mating events for a long period of time. The homozygosity and competitiveness of the lines will enable future studies to analyze the controlled process of sperm movement into the long-term storage organ as part of a post-mating cryptic female choice mechanism of this extremely promiscuous species.     

Realization of reconfigurable filtering horn antennas using active frequency selective surfaces for GSM and LTE signal filtering

Herein, it is aimed to design an Active Frequency Selective Surface (AFSS) based high performance reconfigurable filtering antenna (Filtenna). The proposed AFSS unit element is consist of a single PIN diode with a simple microstrip patch design which performance characteristics are varies with the state of the diodes. The proposed unit elements have been used to form an array that is placed at the aperture of a double ridge horn antenna. With the variation in state of diodes, the antenna acts as a Filtenna module to filtering the incoming electromagnetic waves in frequency band of 1.8 to 2.8 GHz. From the experimental results, it is shown that the proposed Filtenna achieves a signal attenuation of 7.3 dBi at 2.1 GHz.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Sodium and ultrafiltration profiling in hemodialysis: A long-forgotten issue revisited

Sodium and ultrafiltration profiling are method of dialysis in which dialysate sodium concentration and ultrafiltration rate are altered during the course of the dialysis session. Sodium and ultrafiltration profiling have been used, commonly simultaneously, to improve hemodynamic stability during hemodialysis. Sodium profiling is particularly effective in decreasing the incidence of intradialytic hypotension, while ultrafiltration profiling is suggested to decrease subclinical repeated end organ ischemia during dialysis. However, complications such as increased interdialytic weight gain and thirst due to sodium excess have prevented widespread use of sodium profiling. Evidence suggest that different sodium profiling techniques may lead to different clinical results, and preferring sodium balance neutral sodium profiling may mitigate adverse effects related to sodium overload. However, evidence is lacking on the long-term clinical outcomes of different sodium profiling methods. Optimal method of sodium profiling as well as the utility of sodium/ultrafiltration profiling in routine practice await further clinical investigation.     

Synthesis,Thermal and Morphological Properties of Polyurethanes Containing Azomethine Linkage

This paper presents synthesis, photophysical, electrochemical, thermal and morphological properties of Schiff bases containing various side-group substitutions and polyurethanes (PUs) containing azomethine linkage. Morphological properties of PUs containing azomethine bonding were investigated by scanning electron microscopy (SEM). SEM images showed that PU containing azomethine consist of semi-crystalline particles. Thermal transitions in PUs containing azomethine units were studied using DSC. The obtained DSC curves showed that PUs containing azomethine are semi-crystalline materials due to they contain both crystallization and melting peaks. Electrochemical properties also investigated by using cyclic voltammetry (CV). According to the cyclic voltommagrams and CV data, PUs containing azomethine have below 2.0 eV electrochemical band gap.     

Preparation and characterization of conductive polypyrrole/kaolinite composites

Conductive polypyrrole (PPy)/kaolinite clay composites were prepared by in situ chemical polymerization of pyrrole in the presence of kaolinite using FeCl₃ as oxidant. The PPy content and conductivity of the composites reached 32.8% and 8.3×10^?2 S/cm at HCl concentrations of 1.5 M and 0.5 M, respectively. The microhardness of the composites containing different amounts of PPy was higher than that of the PPy and kaolinite components. The highest microhardness observed was 30.17 kg/mm² for the composite containing 9.6% PPy. The electrical resistance of the composites was monitored during heating–cooling cycles over the range 5–120 °C. The change in resistance with temperature was more repeatable for the composite than for PPy. The composites were characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The humidity-sensing properties were also examined.