Correction to: Power and Resource Allocation in Wireless Communication Network

Wireless Personal Communications - Relying on the fact that, cascaded $$\alpha$$ – $$\mu$$ distribution provide simplified and excellent fitting to the measurement data compared to the other...     

Structural,magnetic and optical investigations of Fe and Ni co-doped TiO2 dilute magnetic semiconductors

A series of Ti_0.9Fe_0.1-xNi_xO₂ (x?=?0.0, 0.02, 0.04, 0.06, 0.08, 0.1) has been synthesized through the solid state reaction method at annealing temperature of 1100?°C for two hours. The synthesized samples have been analyzed by X-Ray diffractometer (XRD), Raman spectroscopy, Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), Vibrating sample magnetometer (VSM) and ultraviolet visible (UV–VIS) spectroscopy for structural investigations, defects measurements, surface morphology, magnetic and optical properties respectively. Structural analysis has revealed the dominant structure of rutile. An intensity loss and peak broadening of TiO₂ (rutile) vibration lines have been observed from Raman analysis, which is an indication of presence of oxygen vacancies. Magnetic analysis has revealed ferromagnetic behavior at room temperature. Optical characterization has shown the blue shift in band gap energy with increase of Ni concentration.     

Swell Drying: Coupling Instant Controlled Pressure Drop DIC to Standard Convection Drying Processes to Intensify Transfer Phenomena and Improve Quality—An Overview

Increasing globalization is driving agricultural production in developing countries toward better recovery levels and high additional value. A great opportunity is thus offered to emerging economies. By adopting appropriate and improved methods that allow better preservation, transport, and storage and that conform to the rigorous requirements and standards of organoleptic and nutritional quality as well as stringent hygienic criteria, emerging economies can derive significant benefits.

Drying of biological materials usually results in shrinkage. The impact of shrinkage is often highly prejudicial for both the function and use of these materials. On the one hand, shrinkage results in a sharp decrease in water diffusivity through the porous structure (for dehydration and rehydration processes). On the other hand, the compact structure of dried biological material is usually unsuitable in terms of organoleptic quality and is inadequate for grinding. Thus, it is becoming very important to include retexturing and structure-expanding processes in standard drying processes. To this end, the instant controlled pressure drop (DIC) technology has been proposed and successfully tested as a texturing process for partially dried materials, which should normally intensify the whole operation. The quality of the final product as well as the performance of the drying process can also be dramatically improved. Finally, for a large category of dried fruits and vegetables, and even freeze-dried materials, microbiological contamination and the presence of insects and larvae are serious problems. DIC technology can be used as an ultra-high-temperature treatment for a controlled decontamination. Such dried, expanded, and completely decontaminated products can be used as highly nutritional snacks. They can easily be ground to produce expanded granulated powders with excellent sensory, nutritional, functional, and textural properties.     

Adsorption of anionic surfactants onto sepiolite

Anionic surfactants constitute the main ingredients of detergents and a number of surfactant formulations used in a spectrum of diverse industries. The aim of this study is to examine the amenability of natural sepiolite to the adsorption of anionic surfactants, sodium dodecylsulfate (SDS) and sodium dodecylbenzenesulfonate (SDBS). Adsorption isotherms exhibit three regions with distinctly different slopes. The first region is characterized by the complexation of anionic surfactants with Mg2+ ions at the octahedral sheet or hydrogen bonding between the oxygen groups of anionic head groups of surfactant and H+ of the bound or zeolitic water. The Mg2+ ions released from sepiolite leads to the precipitation of magnesium salt of surfactant in the second region. The third region marks both the beginning of plateau region and micellar dissolution of the precipitate. The effect of temperature on surfactant adsorption for SDS/sepiolite system was utilized to calculate such thermodynamic parameters as the free energy of adsorption (DeltaG(ads)(degrees)) and the heat of adsorption (DeltaH(ads)(degrees)). The low value of DeltaH(ads)(degrees) (1.87 kJ/mol) is an evidence for the physical adsorption of anionic surfactants onto sepiolite. The relatively large value of entropic contribution (-TDeltaS(ads)(degrees)) indicates that the adsorption of anionic surfactants onto sepiolite is entropically governed.     

Factorial design–machine learning approach for predicting incident durations

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best-performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.