Laser-assisted synthesis of Au NPs on MgO/chitosan:Applications in electrochemical hydrogen storage

Hydrogen with high energy density is an environmental alternative to fossil fuels which can respond to the demand for energy considering environmental conditions.It can be stored on porous materials employing physical interaction(e.g.adsorption process).The H2storage capacity of materials can be evaluated through electrochemical methods.Therefore,a fast and straightforward approach was employed to fabricate magnesium oxide/chitosan/Au nanoparticles(Mg O/CS/Au)nanocomposites with porous structure for electrochemical hydrogen storage.Herein,laser ablation in water as a fast and green method was utilized to obtain Au nanoparticles(Au NPs).The obtained Au NPs were loaded on Mg O/CS nanocomposite through physical mixing.Structural and morphological investigation of nanocomposites display spherically shaped Au NPs with a diameter of 49–58 nm agglomerated on the Mg O/CS.Drop casting,the fast and cost-effective method was deployed to deposit the benign,and reusable Mg O/CS/Au-x(x is Au NPs weight percentage of 1,3 and 5 wt.%)nanocomposites on stainless steel mesh and their electrochemical hydrogen storage were measured by cyclic voltammetry(CV),indicating good stability and significant hydrogen storage capacity(28 C/g)after 300 CV scans for MgO/CS/Au-1 sample.

Effects of sampling frequency on node mobility prediction in dynamic networks: A spectral view

The field of mobility prediction has been widely investigated in the recent past,especially the reduction of the coverage radius of cellular networks,which led to an increase in hand-over events.Changing the cell coverage very frequently,for example,may lead to service disruptions if a predictive approach is not deployed in the system.Although several works examined mobility prediction in the new-generation mobile networks,all of these studies focused on studying the time features of mobility traces,and the spectral content of historical mobility patterns was not considered for prediction purposes as yet.In the present study,we propose a new approach to mobility prediction by analyzing the effects of a proper mobility sampling frequency.The proposed approach lies in the mobility analysis in the frequency domain,to extract hidden features of the mobility process.Thus,we proposed a new methodology to determine the spectral content of mobility traces(considered as signals)and,thus,the appropriate sampling frequency,which can provide numerous advantages.We considered several types of mobility models(e.g.pedestrian,urban,and vehicular),containing important details in the time and frequency domains.Several simulation campaigns were performed to observe and analyze the characteristics of mobility from real traces and to evaluate the effects of sampling frequency on the spectral content.

A new GIS-compatible methodology for visibility analysis in digital surface models of earth sites

As a GIS tool,visibility analysis is used in many areas to evaluate both visible and non-visible places.Visibility analysis builds on a digital surface model describing the terrain morphology,including the position and shapes of all objects that can sometimes act as visibility barriers.However,some barriers,for example vegetation,may be permeable to a certain degree.Despite extensive research and use of visibility analysis in different areas,standard GIS tools do not take permeability into account.This article presents a new method to calculate visibility through partly permeable obstacles.The method is based on a quasi-Monte Carlo simulation with 100 iterations of visibility calculation.Each iteration result represents 1%of vegetation permeability,which can thus range from 1%to 100%visibility behind vegetation obstacles.The main advantage of the method is greater accuracy of visibility results and easy implementation on any GIS software.The incorporation of the proposed method in GIS software would facilitate work in many fields,such as architecture,archaeology,radio communication,and the military.

Imperfect Channel State Information of AF and DF Energy Harvesting Cooperative Networks

Wireless information and powered transfer networks(WIPT) has recently been implemented in 5th generation wireless networks. In this paper, we consider half-duplex relaying system in which the energy constrained relay node collects energy via radio frequency(RF) signals from the surrounding resources. Regarding energy harvesting protocol, we propose power time switching-based relaying(PTSR) architecture for both amplify-and-forward(AF) and decode-and-forward(DF). Especially, we reveal the analytical expressions of achievable throughput, ergodic capacity and energy-efficient in case of imperfect channel state information(CSI) for both AF and DF network. Through numerical analysis, we analyse the throughput performance, energy-efficient and ergodic capacity for different parameters, including power splitting ratio and energy harvesting time. Moreover, we also depict the performance comparison between AF and DF network with perfect and imperfect CSI. The results in numerical analysis reveal that the result of AF relaying network is less significant than DF relaying network in the various scenarios.

The Use of LMS and RLS Adaptive Algorithms for an Adaptive Control Method of Active Power Filter

This paper deals with the adaptive control mechanism management meant for shunt active power filters (SAPF). Systems driven this way are designed to improve the quality of electric power (power quality) in industrial networks. The authors have focused on the implementation of two basic representatives of adaptive algorithms, first, the algorithm with a stochastic LMS (least mean square) gradient adaptation and then an algorithm with recursive RLS (recursive least square) optimal adaptation. The system examined by the authors can be used for non-linear loads for appliances with rapid fluctuations of the reactive and active power consumption. The proposed system adaptively reduces distortion, falls (dip) and changes in a supply voltage (flicker). Real signals for measurement were obtained at a sophisticated, three-phase experimental workplace. The results of executed experiments indicate that, with use of the certain adaptive algorithms, the examined AHC system shows very good dynamics, resulting in a much faster transition during the AHC connection-disconnection or during a change in harmonic load on the network. The actual experiments are evaluated from several points of view, mainly according to a time convergence (convergence time) and mistakes in a stable state error (steady state error) of the investigated adaptive algorithms and finally as a total harmonic distortion (THD). The article presents a comparison of the most frequently used adaptive algorithms.

WEIGHTED PSEUDO ALMOST-PERIODIC SOLUTIONS OF SHUNTING INHIBITORY CELLULAR NEURAL NETWORKS WITH MIXED DELAYS

In this paper, we prove the existence and the global exponential stability of the unique weighted pseudo almost-periodic solution of shunting inhibitory cellular neural networks with mixed time-varying delays comprising different discrete and distributed time delays. Some sufficient conditions are given for the existence and the global exponential stability of the weighted pseudo almost-periodic solution by employing fixed point theorem and differential inequality techniques. The results of this paper complement the previously known ones. Finally, an illustrative example is given to demonstrate the effectiveness of our results.

Prognosis of residual coal gas capacity made by the ‘Express' method

An easy, reliable, and inexpensive method, called ＇Express＇ method, was described to determine the residual gas capacity of deep mines using results from an air and gas balance. Air and gas balances are common elements of mine management and must be performed periodically. Using the process described here to obtain balance results, it is straightforward to obtain the residual gas capacity, which is an important parameter for decision-making in current mine operations. After a mine is closed, the residual gas capacity becomes a dominant factor used to select methods to protect against gas emissions from the closed underground area or perhaps to provide information for the use of gas reserves. The proposed ＇Express＇ method is a much simpler method to obtain the residual gas capacity than other procedures used for this purpose to date.

Enhancements of SDR-Based FPGA System for V2X-VLC Communications

This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization(FFSDE)methods of the least mean squares(LMS),normalized LMS(NLMS)and QR decomposition-based recursive least squares(QR-RLS)algorithms.Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results.Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals(USRP)from National Instruments.The transmitting/receiving elements used multistate quadrature amplitude modulation(M-QAM)implemented in LabVIEW programming environment.Experimental results were verified based on bit error ratio(BER),error vector magnitude(EVM)and modulation error ratio(MER).Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution(longer effective communication range,higher immunity to interference,deployment of higher state QAM modulation formats,higher transmission speeds etc.),as the adaptive equalization significantly improved BER,MER and EVM parameters.The best results were achieved using the QR-RLS algorithm.The results measured on deployed QR-RLS algorithm had significantly better Eb/N0(improved by approx.20 dB)and BER values(difference by up to two orders of magnitude).

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

Thermostability of montmorillonitic clays

Bentonite is one of the most widespread used clays connected with various applications. In the case of foundry technology, bentonite is primarily used as a binder for mold manufacture. Thermal stability of bentonites is a natural property of clay minerals and it depends on the genesis, source and chemical composition of the clay. This property is also closely connected to bentonite structure. According to DTA analysis if only one peak of dehydroxylation is observed(about 600 oC), the cis- isomerism of bentonite is expected, while two peaks of de-hydroxylation(about 550 and 850 oC) are expected in the trans- one. In this overview, the bentonite structure, the water – bentonite interaction and the swelling behavior of bentonite in connection with the general technological properties of bentonite molding mixture are summarized. Further, various types of methods for determination of bentonite thermostability are discussed, including instrumental analytical methods as well as methods that employ evaluation of various technological properties of bentonite binders and/or bentonite molding mixtures.

Harmonic Currents of Semiconductor Pulse Switching Converters

Due to the operation of power semiconductor switching converters, the content of harmonic currents, which these switching converters take from the feed array, is still increasing. One of the possible ways of minimizing these currents is the use of pulse switching converters. On one hand, the original, characteristic harmonic ones are minimized, but, on the other hand, new frequencies caused by the modulation frequency appear in the current spectrum. The level of the currents of these frequencies is small and is scarcely dependent on the load of the converter. It may happen that the proportional value of the monitored harmonic one is high, although the absolute value is low. In the article presented, there is a description of the activity of the pulse voltage rectifier and an analysis of the current taken. The other part contains the results of the harmonic analysis of the stated current, including both the absolute and proportional values according to the load. In the conclusion, there are results of measurements of pulsed switching converters taken from the real measurement.

High Temperature Property Development of Bauxite Based Bricks

Physical properties and microstructural evolution on firing of 3 types of bauxite based refractory bricks were carried out using techniques such as porosimetry, XRD and SEM. The groups differed from each other by progressive refining of the matrix or bond phase. It is revealed that such a refinement of the matrix can improve the high temperature properties of the bricks made from poor grade materials .

Specific Power Consumption as Factor Disintegration Process Control of Digging-Wheel Excavators

The determination of digging resistance is of decisive importance when evaluating the mining process of digging-wheel excavators. Methodic of measuring and determinant relations （formulae） are different in various countries and by the comparison of them, underlying data for complex and objective evaluation using the knowledge from the field of rock disintegration can be obtained. In virtue of analysis of many measurements in situ, rather high specific power consumption with a wheel of excavator has been proved. What was the criterion for evaluation was the power consumption related to the volume of a rock disintegrated per unit time. From theoretical analyses, possibilities of eliminating a negative influence of the human factor upon the mining process and thus possibilities of optimum utilizing the power input of a wheel drive follow.

Comparison of the Palaeomagnetic Parameters of Non-Marine Jurassic-Cretaceous Boundary Sediments in Dorset(SW England),Hebei and Liaoning(NE China)—A Preliminary Study

The Purbeck beds in Dorset, the Tuchengzi Formation in western Liaoning Liaoning Province or the Houcheng Formation in northern Hebei Province are non-marine Jurassic/Cretaceous (J/K) boundary sequences. A Czech-China Inter-Governmental S&T Cooperation Project has been carried out to search for the non-marine J/K boundary in northern China and making international correlation with the Purbeck beds in southern England. The combination of palaeomagnetism and biostratigraphy in northern China and southern England localities proves that these distant places had similar climatic conditions and the same fauna during the Late Jurassic and Early Cretaceous. A preliminary joint research has shown a fruitful result in searching for the non-marine J/K boundary in northern China.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Noise Reduction in Industry Based on Virtual Instrumentation

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction.In the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G deployment.This study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a dataset.The experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft.We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency.Using virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested algorithms.Laser welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+ICA.Press workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.