Road dust should be considered as a secondary source of contamination in the environment, especially when re-suspended. In our study road dust samples were collected from 8 high-capacity urban roads in two districts of Kraków (Krowodrza and Nowa Huta). Total concentration of toxic elements, such as Cd, Cr, Cu, Mn, Zn, Co, Pb, Ni, Ba and Se were determined using ICP –MS ELAN 6100 Perkin Elmer. A fractionation study were performed using VI step sequential extraction, according to the modified method provided by Salomons and Fӧrstner. Appropriate quality control was ensured by using reagent blanks and analysing certified reference material BCR 723 and SRM 1848a. Concentration of metals in the road dust varied as follows [mg/kg]: Cd 1.02-1.78, Cr 34.4-90.3, Cu 65-224, Mn 232-760, Zn 261-365, Co 4.32-6.46, Pb 85.6-132, Ni 32.2-43.9, Ba 98.9-104 and Se 78.3-132. Degree of contamination of road dust from Nowa Huta was very high (Cdeg 54) and considerable for road dust from Krowodrza (Cdeg 25). Results revealed that road dust samples were heavily contaminated with Cd, Cu, Zn, Mn, Co, Pb, Ni, Ba and Se, in amounts exceeding multiple times geochemical background values. The chemical speciation study using VI step sequential extraction, followed by assessing risk assessment code (RAC) revealed that elements in road dust are mostly bound with mobile and easy bioavailable fractions such as carbonates and exchangeable cations, with the exception for Cr and Cu being mostly associated and fixed with residual and organic matter fraction.

The aim of the study was to assess the profile of EC (elemental carbon) and OC (organic carbon) temperature fractions in PM1 and PM2.5 samples and in wet deposition samples (material collected on a filter). The research was conducted at the urban background station in Zabrze (southern Poland) in the period of Oct 2020–Oct 2021. PM samples were collected with high-volume samplers; the automatic precipitation collector NSA 181 by Eigenbrodt was used to collect the deposition samples. Concentrations of EC and OC were determined using thermal-optical method (carbon analyzer from Sunset Laboratory Inc., “eusaar_2” protocol). Regardless of the type of research material, organic carbon constituted the dominant part of the carbonaceous matter, and this dominance was more visible in the non-heating season. The profile of temperature fractions of OC and EC was clearly different for dust washed out by precipitation. Noteworthy is a much lower content of pyrolytic carbon (PC) in OC, which can be explained by the fact that PC is most often combined with the water soluble organic carbon. In addition, a high proportion of the OC3 fraction was observed, followed by OC4, which may indicate that these fractions are of a more regional origin. With regard to the EC fractions, the differences are less visible and concern, in particular, the higher share of EC4 and the lower EC2. The obtained results may be a valuable source of information about the actual status of the carbonaceous matter and its transformation in the atmosphere.

This article focuses on discussing the adsorption process of phenol and its chloro-derivatives on the HDTMA-modified halloysite. Optimized chemical structures of phenol, 2-, 3-, 4-chlorophenol, 2,4-dichloro-, and 2,4,6-trichlorophenol were obtained with computational calculation (the Scigress program). Charge distributions and the hypothetical structure of the system HDTMA-modified halloysite are among their key features. The above-mentioned calculations are applied in order to explain adsorption mechanism details of chlorophenols on the HDTMA-modified halloysite in aqueous solutions. The results of electron density distribution and solvent accessible surface area calculations for phenol and chlorophenols molecules illustrate the impact of chlorine substitution position in a phenol molecule, both on the mechanism and the kinetics of their adsorption in aqueous solutions. Experimental adsorption data were sufficiently represented using the Langmuir multi-center adsorption model for all adsorbates. In addition, the relations between adsorption isotherm parameters and the adsorbate properties were discussed. This study also targets at explaining the role of meta position as a chlorine substituent for mono-chloro derivatives. Given the above findings, two possible mechanisms were utilized as regards chlorophenol adsorption on the HDTMA-modified halloysite, i.e., electrostatic and partition interactions when the chlorophenols exist in a molecular form.

This study mainly focused on the current situation of antibiotic pollution in coastal wetlands by screening for four common antibiotics (norfloxacin - NOR, ofloxacin - OFL, azithromycin - AZM, and roxithromycin - RXM) and two coastal wetland plants (Suaeda and Nelumbo nucifera), to determine the removal of antibiotic pollution by phytoremediation technology and its mechanism. We aimed to provide ideas for the remediation of antibiotics in coastal wetlands and their mechanisms of action in the context of intensive farming. The results showed that both plants had remediation effects on all four antibiotics, the phytoremediation of NOR and OFL was particularly significant, and the remediation effect of N. nucifera was better than that of Suaeda . The removal rates of the four antibiotics by Suaeda and N. nucifera at low antibiotic concentrations (10–25 μg/L) reached 48.9%–100% and 77.3%–100%, respectively. The removal rates of the four antibiotics at high antibiotic concentrations (50–200 μg/L) reached 7.5%–73.2% and 22%–84.6%, respectively. Moreover, AZM was only detected in trace amounts in the roots of N. nucifera, and RXM was not detected in either plant body.

In recent years, there has been a marked increase in the amount of municipal waste generated in Poland. In 2020, 21.6% of all municipal waste was subjected to a thermal treatment process. Consequently, the amount of ashes generated is significant. Due to their properties, it is difficult to utilize this type of waste within concrete production technology. One of the waste utilization methods is to add it to hardening slurries used in, among others, cut-off walls. The article assesses the possibility of using ashes from municipal waste incineration as an additive to hardening slurries. It also discusses the technological properties of hardening slurries with the addition of the ashes in question. The experiment showed that it is possible to compose a hardening slurry based on tested ashes with technological properties suitable for use as a cut-off wall. Further research directions were proposed.

This study describes the creation of a low-cost silica material using a silicate extract as a precursor. This precursor is made from inexpensive palm frond waste ash through a simple calcination process at 500°C and a green extraction with water. Nitrogen adsorption-desorption, FTIR analyses, and transmission electron microscopy were used to characterize the samples. The surface area of the obtained mesoporous silica ash material was 282 m2/g1, and the pore size was 5.7 nm. For the adsorption of copper ions, an excellent adsorbent was obtained. The maximum copper ion adsorption capacity of this inexpensive silica ash-based adsorbent for removing heavy metal ions Cu(II) from aqueous solutions was 20 mg/g, and the effect of pH, temperature, and time on its adsorption capacity were also investigated. In addition, the adsorption isotherms were fi tted using Langmuir and Freundlich models, and the adsorption kinetics were evaluated using pseudo-fi rst-order and pseudo-secondorder models. The results demonstrated that the synthesized adsorbent could effectively remove heavy metal ions from aqueous solutions at pH levels ranging from 2 to 5. The adsorption isotherms followed the Langmuir model, and the kinetic data fi t the pseudo-second-order mode well. The thermodynamic results Negative values of G° indicate that the adsorption process was spontaneous, and negative values of entropy S° indicate that the state of the adsorbate at the solid/solution interface became less random during the adsorption process. According to the findings, prepared silica from palm waste ash has a high potential for removing heavy contaminating metal ions Cu (II) from aqueous solutions as a low-cost alternative to commercial adsorbents.

Biological treatment in wastewater treatment plants appears to be one of the most crucial factors in water quality management and planning. Though, measuring this important factor is challenging, and obtaining reliable results requires signifi can`t effort. However, the use of artificial neural network (ANN) modeling can help to more reliably and cost-effectively monitor the pollutant characteristics of wastewater treatment plants and regulate the processing of these pollutants. To create an artificial neural network model, a study of the Samsun Eastern Advanced Biological WWTP was carried out. It provides a laboratory simulation and prediction option for flexible treatment process simulations. The models were created to forecast influent features that would affect effluent quality metrics. For ANN models, the correlation coefficients RTRAINING and RALL are more than 0.8080. The MSE, RMSE, and MAPE were less than 0.8704. The model’s results showed compliance with the permitted wastewater quality standards set forth in the Turkish water pollution control law for the environment where the treated wastewater is discharged. This is a useful tool for plant management to enhance the quality of the treatment while enhancing the facility’s dependability and efficiency.

This study focuses on mapping the groundwater’s vulnerability to pollution in the region of Ouargla, located in the North-East of the northern Sahara, exposed to potential risks of alteration. By applying the methods (GOD, DRASTIC, and SINTACS), coupled with a Geographic Information System (GIS), we were able to identify a medium to high vulnerability trend. In light of the results recorded, the DRASTIC and SINTACS methods prove to be more suitable for our study region. This makes it possible to highlight the recharge zones and land use as being the most vulnerable in the territory studied. The GOD method presents a strong vulnerability trend over 77.02% of the study area. Such a result is directly related to the depth of the water table. It can therefore be argued that this method is far from being representative of the reality on the ground because of these very heterogeneous characteristics.

The article shows the effect of the supply pressure of fog nozzles on the process of ammonia sorption. In the tests, the nozzles flow characteristics Q=f(p) and the dependence of NH3 concentration as a function of the water stream feeding in time at different supply pressures were determined. For the TF 6 NN, TF 6 V, NF 15, CW 50 nozzles, measurements were carried out at the following supply pressures: 0.1 MPa; 0.2MPa; 0.3MPa; 0.4MPa; 0.5MPa. It was observed that the greatest effect of nozzle feed pressure on ammonia sorption efficiency may be expected at lower pressure values. At higher values, the sorption rate becomes stabilized and even starts to decrease. The decreases in the sorption rate constant observed for higher pressures may be due to a reduction contact time of the droplet and the achievement of the critical mixing rate of ammonia vapors in the air intensively saturated with water streams. This is due to diffusion rate limitations. The measurements show that the use of supply pressures for fog nozzles above 0.4 MPa is not justified. It should be noted that varying the feed pressure of nozzles of various designs can affect their ammonia sorption efficiency differently. The type of nozzle and supply pressure affects the distribution of droplets in space. The angle of dispersion and the shape of the generated jet have a critical influence on the efficiency of the sorption process. Complete filling of the space and a large spray angle assure relatively high sorption efficiency.

In the context of resource utilization, the applications of waste biomass have attracted increasing attention.Previous studies have shown that forming biochar by heat treatment of sludge could replace the traditional sludge disposal methods, and sludge biochar is proved to be efficient in wastewater treatment. In this work, the pyrolysis, hydrothermal carbonization and microwave pyrolysis methods for preparing sludge biochar were reviewed, and the effects of different modification methods on the performance of sludge biochar in the synthesis process were comprehensively analyzed. This review also summarized the risk control of heavy metal leaching in sludge biochar, increasing the pyrolysis temperature and use of the fractional pyrolysis or co-pyrolysis were usually effectively meathods to reduce the leaching risk of heavy metal in the system, which is crucial for the wide application of sludge biochar in sewage treatment. At the same time, the adsorption mechanism of sludge biochar and the catalytic mechanism as the catalytic material in AOPs reaction, the process of radical and non-radical pathway and the possible impacts in the sludge biochar catalytic process were also analyzed in this paper

The temperature dependence of photoluminescence spectra has been studied for the HgCdTe epilayer. At low temperatures, the signal has plenty of band-tail states and shallow/deep defects which makes it difficult to evaluate the material bandgap. In most of the published reports, the photoluminescence spectrum containing multiple peaks is analyzed using a Gaussian fit to a particular peak. However, the determination of the peak position deviates from the energy gap value. Consequently, it may seem that a blue shift with increasing temperature becomes apparent. In our approach, the main peak was fitted with the expression proportional to the product of the joint density of states and the Boltzmann distribution function. The energy gap determined on this basis coincides in the entire temperature range with the theoretical Hansen dependence for the assumed Cd molar composition of the active layer. In addition, the result coincides well with the bandgap energy determined on the basis of the cut-off wavelength at which the detector response drops to 50% of the peak value.

The article reviews the results of experimental tests assessing the impact of process parameters of additive manufacturing technologies on the geometric structure of free-form surfaces. The tests covered surfaces manufactured with the Selective Laser Melting additive technology, using titanium-powder-based material (Ti6Al4V) and Selective Laser Sintering from polyamide PA2200. The evaluation of the resulting surfaces was conducted employing modern multiscale analysis, i.e., wavelet transformation. Comparative studies using selected forms of the mother wavelet enabled determining the character of irregularities, size of morphological features and the indications of manufacturing process errors. The tests provide guidelines and allow to better understand the potential in manufacturing elements with complex, irregular shapes.

A device with a frequency-modulated output signal has been developed to increase the sensitivity and accuracy of measuring moisture content in crude oil and petroleum products in the range of 0~20%. The main element of the device is a self-oscillator transducer based on a transistor structure with negative differential resistance. A capacitive sensor in the form of a capacitive cylindrical structure with cylindrical electrodes was used to determine moisture content in crude oil and petroleum products. Electric permittivity of a two-component mixture of oil and water was estimated and the capacitance of the humidity-sensitive capacitive cylindrical structure with cylindrical electrodes was calculated. An electrical diagram of the device for measuring and controlling the humidity of crude oil and petroleum products has been developed. The relative error of converting the humidity of oil and petroleum products into capacitance which was caused by the change in oil temperature, was determined to be 0.225%. Values of relative errors of the device for measuring the humidity of oil and petroleum products are as follows: 1.355 · 10 ^-5% is caused by instability of the oscillator frequency, 0.01% is caused by fluctuations in the supply voltage of the self-oscillator transducer, 0.05% is caused by a change in ambient temperature by 1°C. For the developed device, which used errors of the first and second type, the reliability of humidity control of oil and petroleum products has been determined to be 0.9591.

A contactless laser hygrometer based on light absorption by H2O molecules at 1392.5 nm is described. However, measurement results can be affected by optical noise when applied to an atmospheric tunnel or glass cuvette. The noises (occurring in the form of periodic fringes in the recorded spectrum) come from unexpected interference of the light beams reflected from surfaces of the windows or other optical elements. The method of their suppression is described in this article. It is based on wavelength modulation and signal averaging over the fringes period. Also, an experiment confirming the usefulness of this method is described here.

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.

The most crucial transmission components utilized in rotating machinery are gears and bearings. In a gearbox, the bearings support the force acting on the gears. Compound Faults in both the gears and bearings may cause heavy vibration and lead to early failure of components. Despite their importance, these compound faults are rarely studied since the vibration signals of the compound fault system are strongly dominated by noise. This work proposes an intelligent approach to fault identification of a compound gear-bearing system using a novel Bessel kernel-based Time-Frequency Distribution (TFD) called the Bessel transform. The Time-frequency images extracted using the Bessel transform are used as an input to the Convolutional Neural Network (CNN), which classifies the faults. The effectiveness of the proposed approach is validated with a case study, and a testing efficiency of 94% is achieved. Further, the proposed method is compared with the other TFDs and found to be effective.

The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images.

To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.

The MEMS inclinometer integrates a tri-axis accelerometer and a tri-axis gyroscope to solve the perceived dynamic inclinations through a complex data fusion algorithm, which has been widely used in the fields of industrial, aerospace, and monitoring. In order to ensure the validity of the measurement results of MEMS inclinometers, it is necessary to determine their dynamic performance parameters. This study proposes a conical motion-based MEMS inclinometer dynamic testing method, and the motion includes the classical conical motion, the attitude conical motion, and the dual-frequency conical motion. Both the frequency response and drift angle of MEMS inclinometers can be determined. Experimental results show that the conical motions can accelerate the angle drift of MEMS inclinometers, which makes them suitable for dynamic testing ofMEMSinclinometers. Additionally, the tilt sensitivity deviation of theMEMS inclinometer by the proposed method and the turntable-based method is less than 0.26 dB.We further provide the research for angle drift and provide discussion.

Is this article simulation of statistical measurements is performed on the basis of which the analysis of the standard deviation of the obtained results is carried out. It is shown that the standard deviation is minimum and independent from measurement duration while an object is in the state of equilibrium. For objects in a stationary non-equilibrium state the standard deviation depends on the duration measurements and the parameters of the state. The influence of these factors on the standard deviation is assessed with equation which includes the relaxation time. The value of the relaxation time is determined by approximating the energy spectrum of the studied signals. The analysis of energy spectra showed that the spectrum of white noise is inherent in objects in equilibrium; the flicker component of the spectrum occurs when the state of the object deviates from equilibrium.

Due to high performance demands of grid-connected pulse-width modulation (PWM) converters in power applications, backstepping control (BSC) has drawn wide research interest for its advantages, including high robustness against parametric variations and external disturbances. In order to guarantee these advantages while providing high static and dynamic responses, in this work, a robust BSC (RBSC) with consideration of grid-connected PWM converter parameter uncertainties is proposed for three-phase grid-connected four-leg voltage source rectifiers (GC-FLVSR). The proposed RBSC for GC-FLVSR is composed of four independent controllers based on the Lyabonov theory that control DC bus voltage and input currents simultaneously. As a result, unit power factor, stable DC-bus voltage, sinusoidal four-leg rectifier input currents with lower harmonics and zero-sequence (ZS), and natural currents can be accurately achieved. Furthermore, the stability and robustness against load, DC capacitor, and filter inductance variations can be tested. The effectiveness and superiority of the proposed RBSC compared to the PI control (PIC) have been validated by processor-inthe- loop (PIL) co-simulation using the STM32F407 discovery-development-board as an experimental study.

The sequential multilateration principle is often adopted in geometric error measurement of CNC machine tools. To identify the geometric errors, a single laser tracker is placed at different positions to measure the length between the target point and the laser tracker. However, the measurement of each laser tracker position is not simultaneous and measurement accuracy is mainly subject to positioning repeatability of the machine tool. This paper attempts to evaluate the measurement uncertainty of geometric errors caused by the positioning repeatability of the machine tool and the laser tracker spatial length measurement error based on the Monte Carlo method. Firstly, a direct identification method for geometric errors of CNC machine tools based on geometric error evaluation constraints is introduced, combined with the geometric error model of a three-axis machine tool. Moreover, uncertainty contributors caused by the repeatability of positioning of numerically controlled axes of the machine tool and the laser length measurement error are analyzed. The measurement uncertainty of the geometric error and the volumetric positioning error is evaluated with the Monte Carlo method. Finally, geometric error measurement and verification experiments are conducted. The results show that the maximum volumetric positioning error of the machine tool is 84.1 μm and the expanded uncertainty is 5.8 μm (�� = 2). The correctness of the geometric error measurement and uncertainty evaluation method proposed in this paper is verified compared with the direct geometric error measurement methods.

The Kibble balance experiment is used to redefine the kilogram as a unit of mass based on the Planck constant. To demonstrate and understand the basic principle of the Kibble balance, the National Institute of Standards (NIS)-Egypt has constructed a prototype Kibble balance that can measure gram-level masses with 0.01% relative uncertainty. Through the construction of this prototype, the challenges can be studied and addressed to overcome the weaknesses of NIS’s prototype. This study presents the design and construction of the prototype Kibble balance. It also focuses on the design and performance of the magnetic system, which is a crucial element of the Kibble balance. Analytical modeling and finite element analysis were used to evaluate and improve the magnet system. Several other aspects were also discussed, including the yoke’s material and enhancing the magnetic profile within the air gap of the magnet system. Over a vertical distance of 30 mm inside the air gap, the magnetic flux density was found to be 0.3 T, and the uniformity was found to be 8 x 10 ^-5.