Laser-induced Breakdown Spectroscopy System Research Articles

Classification of e-waste using machine learning-assisted laser-induced breakdown spectroscopy.

Waste management and the economy are intertwined in various ways. Adopting sustainable waste management techniques can contribute to economic growth and resource conservation. Artificial intelligence (AI)-based classification is very crucial for rapid and contactless classification of metals in electronic waste (e-waste) management. In the present research work, five types of aluminium alloys, because of their extensive use in structural, electrical and thermotechnical functions in the electronics industry, were taken. Laser-induced breakdown spectroscopy (LIBS), a spectral identifier technique, was employed in conjunction with machine learning (ML) classification models of AI. Principal component analysis (PCA), an unsupervised ML classifier, was found incapable to differentiate LIBS data of alloys. Supervised ML classifier was then trained (for 10-fold cross-validation) on randomly selected 80% and tested on 20% spectral data of each alloy to assess classification capacity of each. In most of the tested variants of K nearest neighbour (kNN) the resulting accuracy was lower than 30% but kNN ensembled with random subspace method showed improved accuracy up to 98%. This study revealed that an AI-based LIBS system can classify e-waste alloys rather effectively in a non-contactless mode and could potentially be connected with robotic systems, hence, minimizing manual labour.

Laser‐Induced Breakdown Spectroscopy Online Quantitative Analysis for Laser Processing

Plasma fluctuations, the uncertainty of laser ablation, and bremsstrahlung limit the identification of online element analysis during laser processing and cause difficulty in achieving concentration results with sufficient accuracy and repeatability. A laser‐induced breakdown spectroscopy (LIBS) online monitoring system with plasma spatial filtering and spectral screening is proposed to solve this problem. In this system, the high‐frequency ablation noise component of the plasma is eliminated using a specially designed optical Fourier filtering structure, and a spectral screening system based on plasma time waveform monitoring is used to suppress the influence of plasma fluctuations. Without noise filters or algorithm optimizations and based only on the basic internal standard method, the calibration curves of all nine elements in the alloy sample exhibits a Pearson's R2 value ranged from 0.91 to 0.99, with a mean of 0.94. The relative standard deviations are all in the range of 3.5%–8.4% with a mean of 5.4%. The accuracy and repeatability are comparable to those of typical LIBS systems.

Study of spectral lines behaviour with a double pulse 1064–1064 nm laser-induced breakdown spectroscopy system at the direct analysis of algae on the filter

The nickel and zinc contamination of algae on a cellulose filter was studied with double pulse 1064–1064 nm laser-induced breakdown spectroscopy (LIBS). Samples of the green alga Desmodesmus subspicatus were contaminated with zinc and nickel. The surface concentrations on the filter calculated from the reference Inductively Coupled Plasma Mass Spectrometry analysis were 25 and 64 ng cm−2 for nickel and zinc respectively. The algae are resting on the filter after drying and a double adhesive tape was used to fix the sample on the microscopic glass. During optimization method a compromise of 23–30 mJ pulse energies were set for maintaining a detectable or highest signal intensity of the analyte and not destroying the filter. Best conditions for maximum analyte signal and minimum deviation between the spectral behaviour of the analyte and the compared line of Na I, K I, Mg I, Mg II, Mn I, Mn II, Ca I, Ca II, and C I was 700 ns gate delay and 0 s interpulse delay. The deviation was rather more influenced with the signal-to-noise ratio of the analyte line than the closeness of the upper-level energies of the compared lines. The filter without algae showed systematically lower electron number density and total emissivity by units of percents than the filter with algae.

A micro-gas column assisted in situ laser-induced breakdown spectroscopy system: Development and field testing

Laser-induced breakdown spectroscopy (LIBS) has attracted extensive attention as a new technique for in situ marine applications. In this paper, a high-sensitivity underwater micro-gas column assisted in situ LIBS system (MGC-LIBSea) was designed and implemented. The system creates a stable millimeter-scale gas column in water and a pulsed laser is focused on the gas–liquid interface of the micro-gas column to obtain enhanced LIBS signals. To improve its resistance in underwater complex conditions, a protective water flow layer is set on the outer surface of the gas colum. A pressure-balancing airbag was also designed to ensure a stable gas supply at all depths. Both the laboratory and nearshore testing results demonstrated the enhancement feasibility of the MGC-LIBSea system. Particularly, the ion lines of Mg and Ca exhibit remarkable enhancement compared to those without MGC. Moreover, clear signals of H and O could be excited by adoption of the MGC metho, making in situ calibration for LIBS more convenient. By using H as an internal standard, the RSD decreased from 5.21% to 2.95% in laboratory experiments, and a RSD of 4.58% was obtained in sea trial in Qingdao Jiaozhou Bay. A profiling sea trial was carried out at an 18 m depth, proving the potential of MGC-LIBSea for high-pressure environments.

Detection of Al, Mg, Ca, and Zn in copper slag by LIBS combined with calibration curve and PLSR methods

The precise measurement of Al, Mg, Ca, and Zn composition in copper slag is crucial for effective process control of copper pyrometallurgy. In this study, a remote laser-induced breakdown spectroscopy (LIBS) system was utilized for the spectral analysis of copper slag samples at a distance of 2.5 m. The composition of copper slag was then analyzed using both the calibration curve (CC) method and the partial least squares regression (PLSR) analysis method based on the characteristic spectral intensity ratio. The performance of the two analysis methods was gauged through the determination coefficient (R 2), average relative error (ARE), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP). The results demonstrate that the PLSR method significantly improved both R 2 for the calibration and test sets while reducing ARE, RMSEC, and RMSEP by 50% compared to the CC method. The results suggest that the combination of LIBS and PLSR is a viable approach for effectively detecting the elemental concentration in copper slag and holds potential for online detection of the elemental composition of high-temperature molten copper slag.

Effects of feature engineering on the robustness of laser-induced breakdown spectroscopy for industrial steel classification

Steel is a widely used material. Since steel has different properties and uses depending on the steel type, it is essential to accurately classify the steel types. LIBS is a promising technique for real-time classification due to its advantages such as rapid analysis and feasibility of measurement in air. However, enhancing the robustness of classification models is still challenging in industrial applications. This study investigates the effects of feature engineering on the robustness of laser-induced breakdown spectroscopy (LIBS) for industrial steel classification. To make LIBS applicable to the steel industry, a remote LIBS system was developed. The LIBS system was utilized to classify the six representative types of steel. To evaluate the performance of the LIBS system, the measurements were reproduced, with the same sample, at seven different laser energies. The robustness of the LIBS system was investigated by conducting experiments using various feature-engineering and learning-based algorithms on the LIBS data, with test datasets having laser energies different from those of the training datasets. The results indicate that the LIBS signal intensity ratio as the input data leads to a more robust classification model than using principal components or random forest-based datasets. In addition, the intensity ratios with similar upper-state energies were found to be more suitable input data for steel classification. These findings demonstrate the potential of the LIBS system for the highly accurate and robust classification of industrial-grade steel.

The mechanism of sample composition variation in the selective laser melting process based on the laser-induced breakdown spectroscopy and Raman system detection

Laser induced-breakdown spectroscopy (LIBS) and Raman spectroscopy are utilized to characterize the changing patterns of elements and molecules before and after additive manufacturing.

Development of a radiation tolerant laser-induced breakdown spectroscopy system using a single crystal micro-chip laser for remote elemental analysis

ABSTRACT For the development of the remote elemental analysis method in a radiation environment based on the laser-induced breakdown spectroscopy (LIBS), the radiation effects on the laser oscillation properties of the single crystal (SC) Nd:YAG microchip laser (MCL) were investigated and compared with those of ceramics Nd:YAG MCL. The laser oscillation properties were measured under gamma ray irradiation as a function of dose rate. The effects on the SC MCL properties were found to be very small compared to those on the ceramics, indicating minimal radiation effects on the LIBS signal when using SC MCL. Pulse energy and oscillating build-up time (BUT) were measured for cumulative dose exceeding 1400 kGy. The pulse energy remained stable, and the laser continued to oscillate under irradiation. The BUT also remained stable, demonstrating negligible optical loss accumulation that could affect laser properties even at the demonstrated cumulative dose. The results indicate that the effects of dose rate and cumulative dose on SC MCL laser properties were minimal. The SC MCL was then integrated into the LIBS system, and the gadolinium signal of composite oxides, simulating fuel debris, was successfully measured at the dose rate of 5 kGy/hr. These findings highlight the radiation tolerance of SC MCL as a laser medium for remote LIBS applications in harsh radiation environments.

Novel Calibration Approach for Monitoring Aerosol Hydrogen Isotopes Using Laser-Induced Breakdown Spectroscopy for Molten Salt Reactor Off-Gas Streams.

Online monitoring is a key challenge for the continued development of molten salt reactor (MSR) technology. Laser-induced breakdown spectroscopy (LIBS) has previously been demonstrated to be a viable tool for monitoring aerosolized species and noble gases in real time, but the ability to discern varying isotopes in these streams has not yet been investigated for MSR applications. Tritium will form in MSRs from ternary fission and from (n,α)-reactions occurring in lithium-containing salts. This study compares three spectrometers of varying resolutions and types for measuring hydrogen isotope shifts in LIBS spectra of wetted filter paper. For each spectrometer, multivariate models were built (i.e., principal component regression, partial least squares regression, and multivariate curve resolution) to quantify the isotope ratio. The top models were then modified and corrected to apply the models to aerosol samples with varying isotope ratios. This novel calibration strategy offers an 82% reduction in volume of the calibration samples needed and is a more viable pathway for calibrating deployable LIBS systems. Lastly, this calibration model was compared with an all-aerosol trained model for monitoring hydrogen isotopes during a real-time test where the protium/deuterium ratio, along with representative salt species (i.e., lithium, sodium, and potassium) were adjusted dynamically. Results of this test validated the predictive capabilities of the transferred model and highlighted the capabilities of LIBS for real-time monitoring of MSR effluent streams.

Applying laser induced breakdown spectroscopy (LIBS) and elemental imaging on marine shells for archaeological and environmental research.

Using LIBS for the analysis of archaeological and geological marine mollusc shells is a growing research area that relies on customised instrumentation and specific workflows that can accommodate the variety and precision of the required sampling parameters. However, the increased efficiency offered by LIBS, which enables the study of a larger quantity of shell samples for temperature variation, ecological parameters, and human consumption practices, outweighs the initial efforts required to develop customised instrumentation and workflows. In this work, we present detailed specifications and parameters for the development of a LIBS system capable of generating Mg/Ca images on marine shells that directly correlate with seasonal sea temperatures. Our main objective was to develop specifications that enable easy adaptation of LIBS systems to existing laboratories for studying hard-tissue samples. These specifications were used to develop a customised micro-LIBS system and apply it to a real-world example of an archaeological study to better understand its efficiency on the marine mollusc shells and demonstrate its potential for broader applications in interdisciplinary research. In total 101 shell specimens have been analysed within a time frame of approximately 71h of machine time, producing 234 images (100µm resolution: 100 images, 30µm resolution: 134 images). SEM analysis of the irradiated sections of the shell revealed a primary ablated area of 10-15µm in diameter, while a secondary affected area of the shell's crystal fabric extended to 30-50µm after repeated shots. Overall, this new customised system reliably and efficiently analysed marine mollusc specimens without major destructive effects, enabling additional analyses for other proxies to be carried out. This study highlights the potential of the LIBS method for interdisciplinary research, encompassing applications in paleoclimatology, marine ecology, and archaeology.

Assessment of gunshot residue detection on a large variety of surfaces by portable LIBS system for crime scene application

The application of Laser-Induced Breakdown Spectroscopy (LIBS) in forensic science has garnered increasing attention. The ability to perform real-time, on-site analysis of Gunshot Residue (GSR) particles and potential elements originating from bullets or projectile cores on various surfaces holds the potential to assist in resolving firearms-related cases. This includes facilitating trajectory determination by locating distinct impact points and identifying the types of ammunition used. This study evaluates the utilization of a portable LIBS device for ballistic forensic purposes. Additionally, it focuses on the assessment of potential false positives and false negatives arising from the different materials where the shots have been fired. Since the system performs laser ablation of both surface particles and the substrate, it emphasizes the importance of conducting preliminary screening in an area with the same composition as the impact zone to minimize potential false positives during direct surface analysis. Furthermore, the results demonstrate the capability to detect the constituent elements of characteristic gunshot residue particles (GSR particles): lead (Pb), antimony (Sb), and barium (Ba) adhering to bullets, as well as the principal elements composing the jacket or core of the projectile: lead (Pb), copper (Cu), and zinc (Zn) through direct analysis, without the need for a sampling kit, on different surfaces such as walls, furniture, or fabrics. Analyses conducted a month after the shots were fired indicate the potential for finding residues in the vicinity of the bullet hole. Analyses conducted a month after the shots were fired indicate the possibility of finding residues in the area around the bullet hole.

Multimodal feedback systems for smart laser osteotomy: Depth control and tissue differentiation.

The study aimed to improve the safety and accuracy of laser osteotomy (bone surgery) by integrating optical feedback systems with an Er:YAG laser. Optical feedback consists of a real-time visual feedback system that monitors and controls the depth of laser-induced cuts and a tissue sensor differentiating tissue types based on their chemical composition. The developed multimodal feedback systems demonstrated the potential to enhance the safety and accuracy of laser surgery. The proposed method utilizes a laser-induced breakdown spectroscopy (LIBS) system and long-range Bessel-like beam optical coherence tomography (OCT) for tissue-specific laser surgery. The LIBS system detects tissue types by analyzing the plasma generated on the tissue by a nanosecond Nd:YAG laser, while OCT provides real-time monitoring and control of the laser-induced cut depth. The OCT system operates at a wavelength of 1288 ± 30 nm and has an A-scan rate of 104.17 kHz, enabling accurate depth control. Optical shutters are used to facilitate the integration of these multimodal feedback systems. The proposed system was tested on five specimens of pig femur bone to evaluate its functionality. Tissue differentiation and visual depth feedback were used to achieve high precision both on the surface and in-depth. The results showed successful real-time tissue differentiation and visualization without any visible thermal damage or carbonization. The accuracy of the tissue differentiation was evaluated, with a mean absolute error of 330.4 μm and a standard deviation of ±248.9 μm, indicating that bone ablation was typically stopped before reaching the bone marrow. The depth control of the laser cut had a mean accuracy of 65.9 μm with a standard deviation of ±45 μm, demonstrating the system's ability to achieve the pre-planned cutting depth. The integrated approach of combining an ablative laser, visual feedback (OCT), and tissue sensor (LIBS) has significant potential for enhancing minimally invasive surgery and warrants further investigation and development.

Development of a portable laser-induced breakdown spectroscopy device for in situ and rapid characterization of wall composition on the first wall of EAST tokamak

During the long-pulse operation of nuclear fusion devices, plasma-material interaction processes such as material erosion, transportation of eroded materials, and impurity redeposition can deteriorate the performance of plasma-facing components (PFCs). Accurately identifying impurities and their spatial distributions on the first wall is crucial to comprehensively and deep understanding these processes. Laser-induced breakdown spectroscopy (LIBS) is a powerful spectral analytical technique that offers rapid, in situ, multi-elemental, and portable instrumentation advantages over the other techniques. Due to its unique and distinctive features, LIBS is a practical technique for composition and profilometry analysis in nuclear fusion devices. In this study, we report the development of a novel, compact, and portable LIBS device for in situ, real-time measurement of impurity distributions in nuclear fusion devices. This device is equipped with a mini-sized laser that can deliver 10 mJ per pulse, a main unit enclosing a power supply module, spectrometer modules, and customized software. The system was employed to characterize impurities on the first wall of the EAST tokamak under atmospheric conditions after the 2021 EAST spring-summer campaign. The successful in situ measurement demonstrates the feasibility of using the portable LIBS system integrated into a robotic arm in nuclear fusion device. The implement of this portable device can facilitate the operation of the nuclear fusion device by enabling real-time and continuous monitoring of impurities as well as the fuel retention.

Analysis of Cu and Zn contents in aluminum alloys by femtosecond laser-ablation spark-induced breakdown spectroscopy

Abstract To enhance the sensitivity of detecting Cu and Zn in aluminum alloys, we incorporated a spark discharge unit into the femtosecond laser-induced breakdown spectroscopy (fs-LIBS) system, forming a femtosecond laser ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) system. We utilized both fs-LA-SIBS and fs-LIBS techniques to detect Cu and Zn in various aluminum alloy samples with different contents. Our results reveal that the spark discharge extends the duration of laser-induced plasma atomic radiation and amplifies the peak intensity of atomic radiation, thereby significantly increasing the time-integrated signal intensity of laser-induced plasma. Under the present experimental conditions, the limits of detection (LoDs) of Cu and Zn in aluminum alloy by fs-LA-SIBS are 16 and 12 ppm, respectively. In contrast, the LoDs of Cu and Zn by fs-LIBS technique under the same laser pulse energy are 96 and 84 ppm, respectively. Hence, for Cu, the LoDs by the fs-LA-SIBS technique are 1/6 of those by the fs-LIBS technique, and for Zn, the LoDs by the fs-LA-SIBS technique are 1/7 of those by the fs-LIBS technique. Our findings demonstrate that the fs-LA-SIBS technique is more sensitive than the fs-LIBS technique in the quantitative analysis of elements and may be a practical approach for elemental analysis in alloys.

The effect of ablation crater on geomaterials caused by laser shot accumulation on the laser-induced plasma and shock wave

The stability of laser-induced breakdown spectroscopy (LIBS) signal is vital for its application in geomaterials, which is challenged by spectral fluctuations caused by variable ablation craters. To investigate the influence of the ablation crater caused by laser shot accumulation, the shot-to-shot variation of plasma was analyzed from the aspects of shock wave propagation, ablation crater morphology, plasma morphology, and spectral evolution, which was conducted based on a fiber-optic LIBS (FO-LIBS) system. A pressure sensor was applied to monitor the shock wave signal due to the shock wave propagation characteristics related to the number of shots and types of binding materials. The evolution of the ablation crater profile and depth was tracked, and a linear correlation model between crater depth and shock wave energy was established. It was found that the spectral intensity increases first rapidly and gradually decreases, which can be attributed to the competition between the enhancement of spatial confinement and the plasma cooling phase. Strong self-reversal was observed due to the laser pulse accumulation, with an increase in the plasma expanding distance. This study illustrates the interaction of laser pulses, ablation, and plasma from different aspects, and provides a simple method to track the ablation process.

Investigating the Soil Unconfined Compressive Strength Based on Laser-Induced Breakdown Spectroscopy Emission Intensities and Machine Learning Techniques.

Laser-induced breakdown spectroscopy (LIBS) is a remarkable elemental identification and quantification technique used in multiple sectors, including science, engineering, and medicine. Machine learning techniques have recently sparked widespread interest in the development of calibration-free LIBS due to their ability to generate a defined pattern for complex systems. In geotechnical engineering, understanding soil mechanics in relation to the applications is of paramount importance. The knowledge of soil unconfined compressive strength (UCS) enables engineers to identify the behaviors of a particular soil and propose effective solutions to given geotechnical problems. However, the experimental techniques involved in the measurements of soil UCS are incredibly expensive and time-consuming. In this work, we develop a pioneering technique to estimate the soil unconfined compressive strength using artificial intelligent methods based on the spectra obtained from the LIBS system. Decision tree regression (DTR) and support vector regression learners were initially employed, and consequently, the adaptive boosting method was applied to improve the performance of the two single learners. The prediction power of the established models was determined using the standard performance evaluation metrics such as the root-mean-square error, CC between the predicted and actual soil UCS values, mean absolute error, and R2 score. Our results revealed that the boosted DTR exhibited the highest coefficient of correlation of 99.52% and an R2 value of 99.03% during the testing phase. To validate the models, the UCS values of soils stabilized with lime and cement were predicted with an optimum degree of accuracy, confirming the models' suitability and generalization strength for soil UCS investigations.

Detection of Off-Gassed Products From Molten Salts Using Laser-Induced Breakdown Spectroscopy.

The detection of off-gassed sodium from molten sodium nitrate (NaNO3) at temperatures between 330 °C and 505 °C and off-gassed calcium from molten lithium chloride-potassium chloride eutectic (LKE) mixtures at 510 °C with laser-induced breakdown spectroscopy (LIBS) was demonstrated. NaNO3 and LKE samples were melted in a custom-built crucible that promoted the generation of off-gassed products from the molten sample. The off-gassed products were analyzed with a LIBS system designed to probe the high-temperature environment. Na D emission lines, Na(I)588.99 nm and Na(I) 589.59 nm, were detected from the NaNO3 samples after reaching a temperature threshold, which indicated the occurrence of phase change. In LKE mixtures, the detection of Ca impurities at a concentration of 78 mg/kg was possible using the emission lines Ca(II) 393.66 nm and Ca(II) 395.85 nm. This work demonstrates the real-time monitoring capabilities of LIBS in high-temperature environments that simulate the conditions of molten salt reactors.

Combined in situ XRF–LIBS analyses as a novel method to determine the provenance of central Mediterranean obsidians

This work presents a new calibration method for determining the provenance of obsidian artefacts based on the combined use of XRF and laser-induced breakdown spectroscopy (LIBS). At first, obsidian samples collected from the main Mediterranean sources were characterized using portable XRF and LIBS systems. After data treatment, elemental information was used to carry out principal component analysis (PCA) for each technique. Rb, Sr, Zr, Y and Fe elements, detected by using XRF, were found to be the key parameters enabling obsidians discrimination. Likewise, LIBS data helped differentiating the analysed patterns by the intensity of their main elemental components (Ca, Al, Mg and K). After selecting the key parameters detected by each technique, a new data matrix combining XRF and LIBS data was finally built. According to PCA results, the discrimination of Mediterranean sources based on combined XRF–LIBS data ensured a higher reliability over mono-analytical models, by increasing the Euclidean distance between sources projections over three-dimensional principal components plots. Knowing that the representativeness of elemental data could be compromised by the presence of superficial degradation products or deposition patinas, a shot-to-shot comparison of in-depth LIBS analyses is finally proposed as a method to disclose whether the spot under analysis was superficially contaminated or altered. Thus, the proposed strategy based on the combined use of portable XRF and LIBS spectrometers could be particularly useful for the in situ analysis of obsidian artefacts that underwent superficial alteration or could be covered by patina products.Graphical abstract

Double-enhanced LIBS system with N2 atmosphere and cylindrical cavity confinement for quantitative analysis of Sr element in soil

To effectively improve the quality of laser-induced plasma spectra and the detection accuracy of laser-induced breakdown spectroscopy (LIBS) simultaneously for Sr element in soil, the dual mechanism of N2 atmosphere combined with cylindrical cavity confinement for laser-induced Sr plasma emission enhancement was proposed and investigated in this paper, and a multi-spectral fusion internal standard analysis model was established. Optimum enhancement effect can be achieved under the conditions of 2 mm diameter and 6 mm height of the confinement cavity in N2 atmosphere, where the enhancement factor was about 3.25, the signal-to-noise ratio reached 710.28, and the LIBS spectrum quality was the best; the relative standard deviation value (2.64%) was the smallest, and the LIBS signal reproducibility was the best. For the samples in this study, the limit of detection of the sample Sr elements under the dual enhancement mechanism was 34.60 mg kg−1, which was 40.43% lower than the limit of detection (LOD) without enhancement mechanism (58.08 mg kg−1), and the R 2 of the multispectral fusion internal standard model was 1.23% higher and the relative error was 3.41% lower than that of the internal standard method. The results showed that the dual enhancement mechanism combining N2 atmosphere and cylindrical cavity confinement improved the spectral quality, signal reproducibility, and detection sensitivity.

Quantification of alloying elements in steel targets: The LIBS 2022 regression contest

We present the results of the regression contest organized for the LIBS 2022 conference. While the motivation and design of the contest are briefly presented, the work focuses on the methodologies of the three best-performing teams. The employed spectral preprocessing strategies, choice of regression models and its optimization are detailed for each team separately. The aim of the contest reflects the long-term challenges faced by quantitative laser-induced breakdown spectroscopy (LIBS) analysis. Thus, the contest was designed with the purpose of providing a transparent platform for comparing and evaluating the large range of data processing tools available in the LIBS literature. Namely, the contest consisted of the quantification of two major (Cr, Ni) and two minor (Mn, Mo) elements in 15 steel targets. For constructing an appropriate regression model, spectra of 42 targets were provided. The spectra were collected using a commercially available laboratory-based LIBS system and made publicly available. The contest lasted 53 days during which the teams did not receive feedback. In total, 21 teams participated out of which the three best-performing methodologies are presented here. A single linear partial least squares model and two artificial neural network regression models are presented. The corresponding feature selection strategies included emission line selection, spectral range selection, and automatized wavelength selection. Various spectral normalization strategies and data augmentation strategies are also presented.