Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information. Decoding functional magnetic resonance imaging (fMRI) based on visual stimulation is helpful in understanding the working mechanism of the brain visual function regions. Traditional brain decoding algorithms cannot accurately extract stimuli features from fMRI. To address these shortcomings, this paper proposed a brain decoding algorithm based on a graph convolution network (GCN). Firstly, 11 regions of interest (ROI) were selected according to the human brain visual function regions, which can avoid the noise interference of the non-visual regions of the human brain; then, a deep three-dimensional convolution neural network was specially designed to extract the features of these 11 regions; next, the GCN was used to extract the functional correlation features between the different human brain visual regions. Furthermore, to avoid the problem of gradient disappearance when there were too many layers of graph convolutional neural network, the residual connections were adopted in our algorithm, which helped to integrate different levels of features in order to improve the accuracy of the proposed GCN. The proposed algorithm was tested on the public dataset, and the recognition accuracy reached 98.67%. Compared with the other state-of-the-art algorithms, the proposed algorithm performed the best. As the center of the nervous system, the brain is the most complex and powerful tissue of the human organs ; it contBrain decoding is one of the hotspots in the research of the neuroscience field and involves computational models that describe the correspondence between brain activity patterns and external stimuli and predict external stimulus information from the observed brain response activity . Thus, tWhen humans see different types of images, they produce different stimuli in the visual functional area, and this difference will eventually be reflected in the fMRI images . The purWith the rapid development of neuroscience and technology, the methods of brain decoding are emerging ,10,11,12Notably, with regard to the visual brain decoding using fMRI, in 2017 Wen et al.  mapped vThe above DL algorithms have decoded brain fMRI data and achieved good results. However, there are still some shortcomings: (1) the fMRI has time-serial 4D data, and several volume images are acquired over a period of time, which means that the DL algorithms should focus on both spatial and temporal features of the brain activities that need to be analyzed and decoded. (2) The network layers of the graph convolutional network (GCN) cannot be too deep. The GCN can investigate the topological structure of the brain functional areas and then predict the cognitive state of the brain , but theOur work is inspired by the cascading processing properties of biological visual pathways. The human brain has evolved and learned over a long period of time to become the most effective biological intelligence system; however, the intelligence of computer vision still needs to be improved. Currently deep networks suffer from low robustness and lack of interpretability and constructing more brain-like deep network models is the key to the current artificial intelligence field. Our study tries to use biological vision mechanisms to build deep network computational models to accomplish a brain visual stimuli decoding task.The novelties of the proposed method are:(1)We do not design a graph convolutional neural network for the whole fMRI image but only for those functional areas that are responsive to visual stimuli for processing and analysis, which eliminates the non-visual stimuli areas by using template images and reduces the amount of data processing and avoids the interference of other irrelevant areas to the decoding process.(2)Through extensive experiments, we find the optimal functional connectivity matrix between each visual functional area of the brain, and this matrix , combined with the multidimensional features of each node obtained from the 3D convolutional neural network by using Formula (1), can well describe the decoding process of visual stimuli to achieve a good classification performance.The overall architecture of the proposed algorithm is shown in Our work aimed at decoding the fMRI stimulus from vision; therefore, we mainly focused on the visual functional regions of the human brain. Our work was based on the assumption that the human brain visual system is a multi-layer transmission perception system from the concrete to the abstract. The visual system first obtains some concrete features from the external visual stimulus and then obtains more abstract features from these concrete features, by which the human brain can accurately understand the images seen by the subject’s eyes.The visual function of the human brain is divided into the primary visual region, the intermediate visual function region, and the advanced visual function region. From primary to advanced, the visual features are more and more abstract and contain more and more semantic image information. Among the visual functional partitions of the human brain, the V1 and V2 regions are mainly responsible for encoding simple image information, such as local orientation and spatial frequency . The V3 To accurately segment the eleven ROIs in the fMRI images, we asked neurologists to manually label them and saved the labeling results as template image files. The voxel value of the eleven ROIs in the template image was 1, while the voxel value of other regions was 0.fMRI data are four-dimensional, and the commonly used the 2D CNN cannot process it. Therefore, this paper used the 3D CNN with multiple channels to extract its features. The resolution of the fMRI dataset used in this algorithm was 64 × 64 × 50 × 3, which meant that the resolution of the two-dimensional slice was 64 × 64, and each volume included 50 slices, and the fMRI scan included 3 volumes. There were eleven ROIs in the proposed algorithm; so, we used eleven 3D CNNs to extract the features of the eleven regions, respectively. The structures of these eleven 3D CNNs were the same, as is shown in C denotes the size of each dimension of the output feature map; I denotes the size of each dimension of the input data; and P denotes the number of filled pixel values at the edges of each dimension of the input. There is no filling in this equation; so, the value of P is 0; D denotes the spacing between elements in the convolution kernel or pooling kernel (the default is 1); K denotes the size of the convolution kernel or pooling kernel; and S denotes the step size, and the calculation results are rounded down.The size of each dimension of the feature map obtained after the convolution and pooling operations is calculated as In the 3D CNN, we use a cascade convolution of small convolution kernels followed by a pooling layer for every two convolutional layers because this method can deepen the network depth; cascade convolution can extend the perceptual field, and the feature extraction ability of a cascade convolution of multiple small convolution kernels is the same as that of a single large convolution kernel convolution . For example, using two 3 × 3 convolutional kernels instead of one 5 × 5 convolutional kernel can reduce the number of parameters and computations, thus increasing the training speed and processing speed of the network. In addition, the cascaded convolution of small convolutional kernels has more nonlinear activation layers, which increases the discriminative power and thus can improve the effectiveness of the network.The number of channels outputted by each convolution layer was 16, 16, 32, 64, 64, and 8, respectively. The output of one 3D CNN was a 1 × 8 vector, and each ROI was processed by one 3D CNN; therefore, we could obtain the 11 × 8 feature matrix while we combined all the eleven 3D CNN outputs. Next, this 11 × 8 feature matrix was used as one of the inputs to the GCN.A represented the adjacency matrix; l and l + 1 of the proposed GCN, respectively.Based on graph spectrum domain analysis, the proposed GCN’s interlayer propagation rules are shown in Formula (1):The network structure of the proposed GCN is shown in The dropout layer can effectively prevent overfitting, and the graph convolution layer makes accurate predictions by matrix multiplication of the input feature matrix, the adjacency matrix, and the parameters for training, so that the feature vectors of each region are linked to each other for fusion. Batch normalization allows the 3D fMRI data distribution to be converted to a normal distribution with mean 0 and variance 1, thus allowing the input of the activation function to fall in the sensitive regions and have the ability to avoid the problem of gradient disappearance. The problem of over-smoothing occurs when the number of layers in a convolutional neural network increases, i.e., the eigenvalues of each node converge to the same value. Therefore, we introduce the residual connection used in the convolutional neural network, which can effectively deepen the depth of the graph convolutional neural network. The final output is classified in the fully connected layer so as to predict the class of the object in the picture seen by the subject based on the fMRI image data.The proposed algorithm used the adjacency matrix to reflect the functional correlation between the eleven ROIs. V1, V2, and V3 mainly process the low-level visual features, while V4, LOC, FFA, PPA, and HVC mainly process the medium- and high-level visual features. Therefore, the proposed algorithm divided the eleven ROIs into two groups: V1d, V1v, V2d, V2v, V3d, and V3v, which were called low-level visual regions, and V4, LOC, FFA, PPA, and HVC, which were called medium- and high-level visual regions. We used a Pearson correlation coefficient between ROIs as the functional correlations, and we supposed that the functional correlations from the same group were stronger; therefore, we added larger weights to the coefficients if the ROIs were from the same groups; the adjacency matrix was defined as Formula (2) and is shown in P(ω in Formula (2), we made the value of ω equal to 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and tested the accuracy of the brain decoding by the network model in ω = 3. To further verify the validity of the value of We tried two other definitions of the adjacency matrix, such as setting a random 11 × 11 adjacency matrix or calculating the Pearson correlation coefficient between each two ROIs as the elements of the adjacency matrix. The experimental results showed that the prediction accuracy values of these two options were only 35.2% and 46%, respectively, which was far lower than the prediction accuracy of the adjacency matrix defined in According to the experimental results, we found that when the number of layers in the GCN increases it will cause gradient disappearance. Therefore, the number of layers in the traditional GCN is generally 2 or 3, and the deeper the number of layers, the worse the performance. To address this problem, we used the residual connection  in the phttps://openneuro.org/datasets/ds001246/versions/1.2.1 (accessed on 10 October 2022). Each scanning session consisted of functional (EPI) and anatomical (inplane T2) data. The functional EPI images covered the entire brain  and inplane T2-weighted anatomical images were acquired with the same slices used for the EPI . The dataset also includes a T1-weighted anatomical reference image for each subject . In the experiment, we used the dataset from reference , which cThe data were collected by 5 healthy subjects  with normal vision or vision corrected to normal, and all the subjects had experience and training in the fMRI experiment. Each subject conducted 24 rounds of separate collection; each round lasted for 8 min and 54 s, of which 39 s were in a resting state. There were 55 stimulation blocks in each run, including 50 blocks with different images and 5 randomly interspersed blocks. The whole-brain scan took 3 s at a time, and the duration of each block was 9 s.The stimulus images used in the experiment were collected from the online image database ImageNet . In thisThe software and hardware experimental environment in this paper is shown in The computing cost of the proposed method is 0.2 s for each fMRI scan. TP represented the true positive rate, FP represented the false positive rate, TN represented the true negative rate, FN represented the false negative rate, and β = 1.In the experiments, we used precision, recall, F-score, and accuracy as the quantitative evaluation metrics.The training parameters of the proposed algorithm were set as shown in p(x) represented the real value, and Q(x) represented the predicted value; the optimizer used in this paper was the adaptive moment estimation (Adam). Moreover, there were some important parameters to be selected for the proposed algorithm, such as the number of network layers of the 3D CNN, the number of elements of the feature vector obtained by the 3D CNN, and the number of network layers of the GCN.In this paper, the 3D CNN and the GCN were trained together, and the loss function of the proposed algorithm was the cross-entropy function.(1)the number of network layers of the 3D CNNThe number of network layers of the 3D CNN can affect the depth of the network, and we tested six layers , seven layers , eight layers , nine layers , ten layers , and eleven layers  under the same experimental environment. The results are shown in (2)the number of elements of the feature vectorThe output of the proposed 3D CNN was a feature matrix, which was composed of eleven feature vectors. Therefore, the number of elements of the feature vector was also an important parameter for the performance of the proposed algorithm. We tested the values from 2 to 12, and the experimental result is shown in (3)the number of network layers of GCN rSimilarly, we also tested the number of layers of the GCN, and the results are shown in To verify the effectiveness of the proposed algorithm, we tested the trained model on the testing dataset and comprehensively evaluated and analyzed the proposed algorithm by calculating the precision, recall, F-score, and accuracy of each class. At the same time, we compared the proposed algorithm with several other classical deep learning algorithms, including AlexNet-decoding , ResNet-In the present study, we proposed a brain fMRI decoding algorithm based on deep learning methods. With the aim of increasing the accuracy of the decoding results, we extracted the feature matrix using eleven 3D CNNs from the ROIs, delineated the functional connections between the visual brain functional regions, and used residual connections to deepen the proposed GCN. According to the experimental results, the proposed algorithm obtained a good performance.In the proposed method, we combined the CNN and the GCN, where the role of the CNN was to extract the abstract features of the functional MRI image and describe them in the form of an 8 × 1 feature vector, and the role of the GCN was to analyze the extracted features and brain functional network structure to decode and classify visual fMRI stimuli. Similar combinations of the CNN and the GCN can also be found in the references .Compared with other state-of-the-art algorithms, from There is another class of functional MRI decoding algorithms for graph convolutional neural networks; these are based on node-embedding algorithms  that mapThe input channel to the 3D CNN is set to three because each block of the original fMRI data has three fMRI scans, which means that during the fMRI data acquisition each whole-brain scan takes 3 s at a time, and the duration of each block is 9 s. Moreover, to verify whether increasing the number of channels would improve the accuracy of the visual decoding, we added an experiment using an interpolation algorithm between the original 3 channels to obtain 5 and 7 channels of data, respectively, but the experimental results showed that increasing the number of channels did not improve the accuracy of the classification but instead slightly diminished it.Inspired by references ,32, the The performance of the 3D CNN algorithm was the worst in the experimental results. The main reason was that the 3D CNN network input was the fMRI image data of the whole brain, while the visual functional regions only covered a small part of the brain. The information of the other non-visual function regions in the fMRI image was not helpful for the final prediction but may have caused interference. The second reason was that the whole network of the 3D CNN was composed of two convolution layers, a pooling layer and a full connection layer. The number of network layers was too few to extract the useful features. ResNet had a deeper network structure, with a total of 18 layers, including 17 convolution layers and a full connection layer, and the features of each layer were fused through residual connections. Therefore, ResNet can extract richer features and achieve better results than the 3D CNN. However, ResNet also inputted fMRI images of the whole brain and did not make use of the temporal information contained in the fMRI data. The C3d-LSTM extracted the spatial features in the fMRI images through a series of 3D CNNs and then inputted them into the LSTM to extract the temporal features. However, the C3d-LSTM did not take advantage of the functional correlations between the ROIs. The proposed algorithm combined all the advantages of the above algorithms. Firstly, eleven brain visual function regions were selected as the ROIs to eliminate the information interference of the other regions, and then, the 3D CNN was used to extract the features of the eleven ROIs. Furthermore, the fMRI images at multiple time points were inputted at the same time to ensure that the temporal features could also be extracted. Finally, the GCN was used to fuse these features based on the functional correlations between the ROIs, and the number of layers of the GCN was increased by using the residual connection. All the five algorithms performed best for ostrich, backpack, and horse because these types of images were quite different from the other types and had obvious characteristics. Therefore, when the subjects watched these types of images, the brain’s response to visual stimuli was quite different from that of the other kinds of images. For hummingbird, fighter, watermelon, hot air balloon, and light bulb, the errors of the prediction were relatively high; the main reason was that (1) the shape and appearance of the hummingbird and battleplane were somewhat similar, which may cause a similar response from the brain visual function regions; (2) some watermelons in the stimulus picture were cut off, while some watermelons were not; and (3) the shape of watermelon, hot air balloon, and bulb was oval, which may have led to misprediction among the three types. Therefore, when the shape, color, and background of the object in the stimulus images were quite different, the brain visual stimuli were different when the subjects watched the images, which may have led to the significantly different fMRIs, and the accuracy of the proposed algorithm’s prediction was higher. Conversely, if the subjects were watching two similar objects, the accuracy was reduced ,33,34.To illustrate the role of the proposed GCN, we removed the GCN from the proposed algorithm, and the features extracted by eleven 3D CNNs were directly inputted into the fully connected layers for classification without GCN processing, and all the other parameters were kept unchanged. Using the same training and testing dataset, the experimental results illustrated that the use of the GCN effectively improved the accuracy of the network, which increased from 86.62% to 98.67%. The main reasons were as follows: (1) the GCN multiplied the feature matrix (obtained from the 3D CNN) with the adjacency matrix  and added appropriate weights  to make this feature information fuse well and make full use of the characteristics of the functional correlations between the ROIs; (2) the GCN can increase the depth of the whole network so that the network can extract richer and deeper features.To illustrate the role of the residual connections in the proposed GCN, we built another graph convolution network without residual connections, which was named the GCN-noRC. We compared the proposed GCN with the GCN-noRC using the same experimental environment and the same dataset and found that the accuracy of the proposed GCN was 98.67% and that of the GCN-noRC was 85.21%, which indicated that residual connections can alleviate the phenomenon of gradient disappearance and effectively improve the accuracy of the graph convolution network.The limitations of the proposed algorithm are mainly reflected in the following three aspects: (1) the sample size is limited, and the same patients belong to the training and test set, which may weaken the generalizability; in the next step, we will collect more data from more subjects. (2) The number of classification types for brain decoding is only ten, and we will enlarge it in the future. (3) The lengths of the dataset are only three for each fMRI block, which restricts the accuracy of the classification, and the proposed algorithm cannot be compared with other datasets with different block lengths.Regarding future work, we will make the proposed algorithm have better transferability, i.e., the data collected between different groups of subjects, and among different sites using distinct scanning sequences, can be applied to the model proposed in this paper, which is inspired by reference  that useAiming at the challenges and shortcomings of brain decoding algorithms, we proposed a brain decoding algorithm of fMRI based on a graph convolution neural network. The main innovations of the proposed algorithm were (1) the extraction of the image features of fMRI using the 3D CNN; (2) the construction of the GCN according to the functional connections between the visual functional regions of the human brain; (3) the combination of the CNN and the GCN to deepen the network. The experimental results showed that the prediction accuracy of the proposed algorithm was 98.67% and outperformed the other state-of-the-art algorithms.


Climate change, the economic crisis and the current geopolitical situation are the biggest challenges of today. They participate to a fundamental extent in the creation of international policies. Renewable energy sources are thus gaining worldwide popularity. The paper deals with the assessment of the impact of four selected stages of the life cycle of a NZEB building on the environment in 13 impact categories. The analysis is performed in accordance with the LCA method using the attributional modeling approach. The results show the partial and total shift of impacts on the environment of photovoltaic energy storage in comparison with photovoltaic energy export across the building life cycle. Along the climate change impact reduction as a positive effect on the environment, a substantial impact increase is observed on the depletion of abiotic resources. Results also show the total environmental impact of the building life cycle, considering the use of stored energy in a lithium-based battery as being beneficial in most categories despite the relatively high impact increment in the stage of replacement. Environmental awareness is increasing worldwide; sources of pollution are constantly being identified, and solutions are being sought to reduce them. International conventions are held, and new agreements are created, especially in the area of reducing the impacts of climate change ,2,3,4,5.2 emissions in 2020” and concurrently mentions reductions in emissions and energy intensity related to buildings by 17.2% to 48 kg CO2.m−2 and by 5.7% to 606 MJ.m−2 compared to 2015, respectively. In addition, the number of countries with the introduction of energy regulation of buildings increased by 30.6%. The COVID-19 pandemic has temporarily reduced global emissions, but it appears that we are still lagging behind the decarbonization of buildings, given the Paris Agreement commitments [The construction sector is one of the areas covered by these goals. The report of the United Nations Environment Program  states “−2.year−1, also referred to as nearly zero energy buildings [One of the options to reduce the negative impact of buildings on the environment is to increase energy efficiency. Currently, passive buildings represent a standard in the field of construction providing a satisfactory indoor environment in terms of thermal comfort and indoor air quality at the lowest possible energy costs. Passive building technology is based on a ventilation system with forced heat recovery, airtightness, improved thermal insulation and reduction in thermal bridges ,10. Accouildings . These cuildings  claimed uildings  proved tuildings ,16,17,18uildings  thoroughLife cycle assessment (LCA) provides a systematic assessment of the environmental aspects of the investigated system throughout its life cycle. ISO 14040 and 14044 establish the key principles, framework and procedures of the LCA method ,21. WithSeveral software have been developed to improve the efficiency and reproducibility of LCA. Currently, among the most popular ones are SimaPro and Gabi, integrating a vast number of general databases and environmental impact assessment methods . Yang et2 emissions contrary to wood-based buildings [2 emissions to the building production stage and 67% of these emissions as being caused by operational energy requirements. Based on the study of Zhao et al. [2 emissions point of view. On the other hand, they found mineral depletion to be alarmingly high.Current LCA research in the field of NZEB covers two areas—the impact of construction materials (the production stage) and the operational energy impact related to the increase in energy efficiency . Climate change is the most assessed category among the number of other impacts . From thuildings ,29,30. Auildings  also demuildings  assignedo et al. , photovoNZEB are generally regarded as environmentally friendly buildings in the context of reducing greenhouse gas (GHG) emissions during the use stage of a building. The purpose of this study is to highlight impact categories that significantly affect the total impact of a selected NZEB within certain life cycle stages through the comparison of photovoltaic energy export and storage. The analysis is performed according to LCA principles using attributional modeling based on the cut-off system approach of the ecoinvent v3.8 database. The results may serve as a reference document for policymakers and stakeholders.2 and gross internal area (GIA) of 159.6 m2 and is located in central Slovakia. The load-bearing structure consists of a finger-jointed solid timber insulated by rock wool. The foundations are designed with a concrete slab and foam glass filling. A flat roof structure is covered by external vegetation. Heating, ventilation and air-conditioning (HVAC) are performed via a heat-pump with controlled ventilation with recuperation, a wall-heating system and photovoltaic panels.The system under study refers to a balloon-frame timber structure designed as a NZEB with a life span of 50 years . The two−2.year−1. The estimated annual photovoltaic energy generation is calculated to 3956 kWh and exported to a grid. Both stages are modeled to reflect the listed energy and material consumption within 50 years of building usage.The B4 stage data comprise the necessary replacement of the HVAC worn parts based on a manufacturer’s recommendation. The data on operational energy are calculated according to the directive on the energy performance of buildings  and compTwo life cycles are modeled, the one considering an export of the energy produced by a roof-placed photovoltaic system  :The B6 stage in the alternative life cycle is assumed to utilize photovoltaic energy stored in a lithium-based battery (Equation (2)), and therefore, the amount of electricity needed from the grid is reduced.U), an efficiency factor (η) of 95%, a depth of discharge (hv) of 80% and losses (l) of 5% (Equations (3)–(5)):PV is energy produced by the photovoltaic system per year.In order to calculate input/output exchanges of the B6 life cycle stage related to the storage of the produced energy, the following battery properties are considered, namely, the energy from the photovoltaic system used in the building per year (EL) (Equation (6)) thus equal to 1099.8 kWh.year−1.Electricity losses (EG) is 1285.6 kWh.year−1 (Equation (7)).Subsequently, the need for electricity from the grid   softwareFirst, the environmental impact of the reference life cycle is set . All theSIC) when energy storage is applied (ISSIC), compared to the impact of energy export (ISEIC) (Equation (8)).Second, the results of the alternative life cycle are compared with the reference life cycle. The following charts  depict tThe biggest change in the impact of the product stage (A1–A3) is observed in minerals and metals use, accounting for 20.53% increment . The secThe B4 stage shift of impacts within categories is much higher compared to the product stage . An incrThe impact change caused by the transportation to the construction site (A4 stage) is negligible in nearly all categories . The higNotable impact changes occur in the B6 stage, of which the most remarkable is more than 663% decrease in minerals and metals use regarding the photovoltaic energy storage . In factNext, the impact within life cycle stages is summed and overall, the impact increment is set . The higSubsequently, statistical analysis is performed, showing the mean, median, maximum and minimum change of the environmental impact of photovoltaic energy storage compared to the impact originating from the photovoltaic energy export . The higThe reference building life cycle deals with a wood-based construction in the standard of NZEB and reflects environmental impact related to photovoltaic energy export, which is compared to the impact of the alternative life regarding the storage of produced electricity in a lithium-based battery. Impact change in comparison with the reference wooden building occurs in the product stage, transport to the construction site, replacement and operational energy use. The building end-of-life stage is not taken into account.2 emissions in the A1–A3, A4 and B4 stages and simultaneously decrease the impact of the B6 stage, resulting in overall impact reduction, except for biogenic carbon emissions. Due to the high quantity, fossil emissions mainly contribute to the reduction in the impact, despite the highest percentage impact decrease in LULUC emissions. Fossil emissions come from all fossil-burning processes over the course of the life cycle, with the energy sector being the major source [The climate change category sums the emissions of three sources—biogenic, fossil and LULUC. The storage of energy increases COr source ,36.Negative biogenic emissions reflect carbon capture, especially in the wood-based construction materials of the product stage ,29,30. BA negative impact change of emissions increase is detected in the A1–A3, A4 and B4 stages, followed by positive impact change, leading to emissions reduction in the B6 stage as well as the total emissions score. The lowest effect is connected with photochemical ozone formation. In contrast, percentage wise, the highest emission reduction occurs in freshwater eutrophication as the most positively affected category. Each category is connected with a supply of electricity from the grid , as it iEmissions causing ozone depletion and use of water tend to rise across all of the studied life cycle stages resulting in the third and the second highest impact contribution in the overall score. The reason lies in the demanding photovoltaic panel production ,40,41,42The impact of fossil resource use copies fossil climate change emissions within the life cycle. On the other hand, minerals and metals use is found to be the most negatively affected category in nearly all stages with a peak in total score of 156.84% increment. A change of nearly −664% in the B6 stage is derived from the actual negative impact of the reference life cycle −0.41 kg Sb eq), thus resulting in an impact increase of 2.73 kg Sb eq to 2.32 kg Sb eq. Lithium together with cobalt, nickel and manganese, essential elements of battery systems, are regarded as critical raw materials  and play kg Sb eqNZEBs are designed as “green buildings” due to involving RES simultaneously with high energy efficiency ,45. The Maierhofer et al.  studied On the other hand, the ratio of 1:18 in the reference life cycle regarding photovoltaic energy export compared to the alternative life cycle regarding ratio of 2:23 declares energy storage reduces differences between the product and the operational energy use stages. Therefore, energy storage can be seen as one of the means of mitigating the impacts of climate change.2 emissions compared to the fossil-based energy sources and at the same time escalate the water consumption. A mismatch is observed in the freshwater eutrophication category, where they found an increase in impact. This might be caused due to the different electricity grid mix [Energy storage reduces the amount of electricity needed from the grid, which in European conditions, comes mainly from fossil sources . An impagrid mix .High percentage increments sign a relatively low reference value as shown by Wu et al. . MineralResults of the A1–A3 stage relate to a specific wood-based building structure. Other construction systems may have different impact results, as the wood components mainly influence carbon emissions ,30,49,50Databases used as a source data in the A1–A3 stage do not consider the transportation of the goods, while this feature is a part of the transportation stage (A4) covering average transportation distances in the Slovak Republic. Stages of replacement (B4) and operational energy use (B6) are bound to default distances given by the ecoinvent database . Stages For the application and comparison purposes of the study results, the same conditions must be considered, such as the type of used databases, the calculation method, the cut-off attributional system, and the geographical location.The paper depicts the change in the impact of the building on the environment when storing photovoltaic energy in comparison with its export to the electricity grid in four stages of the building life cycle in the conditions of Slovakia. The reference building is designed according to the NZEB standard, using a balloon-frame structure and finger-jointed timber as the primary structural element.Impact of a vast majority of categories rises in the A1–A3, A4 and B4 stages when battery usage is applied;Positive effect of energy storage is observed in the B6 stage, where most of the categories reduce the impact;Adding up the impacts through the selected life cycle stages, the storage of photovoltaic energy in most categories reduces the overall impact on the environment, especially in the categories of climate change, fossil climate change, LULUC climate change, acidification, all three eutrophication categories, photochemical ozone creation and use of fossil resources.The most significant reduction in impact records freshwater eutrophication (−37.18%), fossils resource use (−30.30%), LULUC climate change (−27.06%) and climate change (−20.53%) categories, respectively.On the other hand, impact increase occurs in categories of biogenic climate change, ozone depletion, water use and use of minerals and metals. The last mentioned causes a significant burden of 156.84% impact increment, which can have a significant impact on the availability of these resources in the future and thus affect the economy. Therefore, it is important to take circular economy aspects into account, especially in connection with electrical devices usage.Both positive and negative impact changes are observed between the individual life cycle stages of the building within the selected categories. To sum up, the main outcomes of the study are as follows:The study showed that photovoltaic energy storage has several environmental benefits besides its climate change mitigation potential, underlining its justification in future applications. However, its massive mineral depletion potential pushes on the development of innovative circular economy strategies to avoid the eventual shortage of critical raw materials.


Molecules several errors in our paper [A reader recently brought to the attention of the Editor-in-Chief and the Editorial Office of ur paper , with an1) Lane C without a loaded sample in  Lane C w1, Re, Rb1, Rb2, Rc and Rd in ginseng roots  were obtained using the HPLC gradient reported in Section 3.4 Quantification of Ginsenoside from Root Extracts, when in fact those conditions were used for the separation and quantification of the five ginsenosides Rg1, Re, Rb1, Rb2, and Rd, but not for ginsenoside Rc. As a result of a peer reviewer’s comment that the amount of Rc seemed unduly high, the original HPLC traces were re-examined in 2014 and it was noted that in several chromatograms the Rc peak was not resolved, resulting in erroneously high integration values in the thesis. The experiments were re-run using a modified solvent gradient that isolated the ginsenoside Rc peaks and gave lower values consistent with the literature and those results are the ones reported in [(2) It was claimed that the HPLC ginsenoside quantification results in  did not (3) All co-authors of the article have read and agree with the content of this Correction, except for Dr A. J. Chen, who in communications with the corresponding author and the Editorial Office has insisted for personal reasons on a retraction of the article rather than a correction. However, having examined the provided copies of HPLC traces and laboratory notebooks and the explanations provided by the authors, the Editor in Chief is satisfied that a correction without Dr Chen’s agreement and not a retraction is warranted for this paper.The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.


The effect of water cooling on lengthening the unit campaign life of the trough was investigated using industrial tests, numerical simulations, and theoretical analysis. Results showed that by using water cooling, the throughput of the trough in a unit campaign was increased by 40,000 tons, and its unit campaign life was increased by 34 days. During a unit campaign cycle, the influence of the water cooling device on the temperature distribution in refractory materials gradually developed from the low-temperature zone to the high-temperature zone, and the expansion of the high-temperature zone was suppressed. Therefore, the water cooling device inhibited the dissolution of refractory materials and retarded the chemical erosion from the molten slag and the atmosphere.A long campaign life of the trough of the blast furnace (BF) is important for improving the BF operation efficiency, reducing the cost of blast furnace ironmaking, and bettering the working conditions of the casting yard. In this paper, a water cooling device was designed for the trough of a BF with a volume of 630 m In blast furnace (BF) ironmaking, the iron runner system is a device consisting of the main trough, an iron runner, a slag runner, and a skimmer. During BF tapping, the main trough plays the function of transporting and separating iron from slag after its extraction from the taphole. A long service of the trough is important to BF ironmaking as it can improve BF operation efficiency, reduce hot metal cost, and better the working environment of the tapping yard.2O3-iC-C castables can now fulfill many requirements of modern-day BF ironmaking practices [A typical campaign cycle of the trough could be described as follows: complete installation, operation, and demolition. During the campaign, frequent repairs are needed to maintain a satisfying performance of the trough. The repair of the trough owes to the degradation and wear of the lining refractory. The life of the trough depends on several factors including the BF operation conditions ,2, the gractices . Howeverractices . By decrractices ,14. In tractices ,16 and tractices  are emplractices ,19, and,ractices ,21. As aIn this research, a water cooling device for the BF trough was designed. Industrial tests were then carried out to examine its influence on the trough life. Thereafter, the influence of water cooling on the temperature field in the trough was numerically investigated and the mechanism of water cooling for protecting the trough was analyzed.3, producing 2400 tHM/day. The sinter used in the BF was of low grade, resulting in a large slag rate of approximately 400 kg/tHM. The temperature of HM and slag fluctuated around 1753 K. The composition of the slag is shown in 2O3-SiC-C castables. Chemical compositions of castables in the working layer and the permanent layer are listed in The object for the present study was the No. 1 BF of the Xinchuang iron and steel company in Yunnan Province (China). As the BF operating parameters exert significant influences on the trough life, the employed BF was introduced briefly first. The BF had an effective volume of 630 mA water cooling device was designed for the trough. The water cooling device was composed of several water cooling plates. The structure of a water cooling plate is shown in The industrial tests were conducted on the trough with no water cooling first. In a unit campaign cycle, the top thicknesses of both embankments ; T is the temperature, K; x, y are the two-dimensional coordinate directions, m.Ignoring the internal heat sources, the heat conduction equation for heterogeneous isotropic materials in Ω is Equation (1).1) is assumed to be 1.0 W/(m·k). In the permanent layer (Ω2), since the castable in the permanent layer is insignificantly eroded and is continuously pressed by molten iron and slag, it becomes dense. Therefore, its thermal conductivity is increased and is assumed to be 3.0 W/(m·k). The thickness of the steel frame (Ω3) is 15 mm, and its thermal conductivity is taken as 54 W/(m·k).The thermal conductivity of the material in each subdomain is determined according to the following method. Due to the oxidation of carbon particles in the working layer of the trough, porosity and pore size in the castable become large, resulting in a sharp decrease in its thermal conductivity. Therefore, the thermal conductivity of the working layer (Ωh is the heat transfer coefficient and is 5.0 W/(m2·K) [Tair is the temperature of the atmosphere and is 313 K.The boundary conditions of Case I are the following. On the symmetry (W/(m2·K) , and TaiFheat is the heat flux, J/(m2·s); inT and outT are the temperatures of the inlet and outlet water of the water cooling plate, K, respectively; Cp is the specific heat capacity of water, J/(kg·k); Q is the water flow rate, kg/s; and A is the contacting area between the cooling plate and the steel frame, m2.The boundary conditions of Case II are similar to those of Case I except for that on 2, and the boundary conditions are considered as source items in the discrete equation of Equation (1). The criterion for convergence is Equation (4).newT is the new iteration temperature of the grid, and oldT is the old iteration temperature of the grid.In the computation domain, the above model was numerically solved using an in-house code developed using the C programming language. In the numerical simulations, the finite volume method (FVM) and structure grid method  were use1, Ω2, and Ω3), and the boundary includes eight sub-boundaries , the cooling intensity of the water cooling device on the trough was 24,000 W/mT > 1373 K) to the steel frame.Simulation results of Case III and Case IV are shown in The simulation results disclosed that the water cooling device had an insignificant influence on the area of the high-temperature zone of the trough in the initial stage of a unit campaign cycle. However, it suppressed the expansion of the high-temperature zone during the service of the trough.2O3, SiC, C, and metallic silicon, and the corundum aggregate occupies a large portion. In addition, the pitch is mixed in as an additive. In the baking stage of the trough, the pitch is carbonized and then the graphite carbon is precipitated, providing a binding force for the particles in the castable. During the BF casting, the periodic damages to the lining are dissolution into the slag and chemical attack from atmosphere, molten slag, and molten iron.As can be seen from 2 and CaO in the slag react with Al2O3 in the lining, forming some low-melting-point compounds including anorthite (CAS2) and gehlenite (C2AS) under the reactions given by Equations (5) and (6). The temperature range of slag solidification is from 1373 K to 1673 K [2O3 particles leave the lining with the flowing slag. After the small Al2O3 particles in the lining are depleted, the dissolution of slag shifts to corundum aggregate. Once the corundum-aggregate-based structure is destroyed, the SiC particles begin to loosen and are flushed into the slag.The iron–slag jet from the taphole enters in the front of the trough, scouring both the hot side and the bottom of the trough. In the region between the slag line and the metal line, dissolution of the castable occurs after it contacts the slag. However, this erosion is negligible below the metal line. The mechanism of this erosion is that the molten slag penetrates the hot side through pores. SiOo 1673 K . The calo 1673 K  showed tT > 1373 K) in the refractory is continuously decreased. The viscosity of the slag increases with the decrease in temperature [2O3 particles through pores to generate the low-melting compounds, the temperature in the refractory material is lower than their melting points. Thus, these low-melting compounds cannot be taken away by the slag in a molten state. The above analysis shows that the water cooling device can weaken the flushing effect of high-temperature slag on the Al2O3-based matrix of the trough.From perature . Therefo2 in the atmosphere following the reaction given by Equation (7). SiC in the lining then reacts with FeO following the reactions given by Equation (8). O2 in the atmosphere reacts with the carbon particles according to the reaction given by Equation (9), and SiO2 and FeO in the slag react with carbon particles following the reactions given by Equations (10) and (11), respectively. From the Gibbs free energies of the reactions given by Equations (7)–(10) in the standard state [2 in the atmosphere diffuses through the pores of the lining to react with carbon forming many channels in the lining. These channels facilitate the permeation of slag into the lining leading to a decarburized layer in the vicinity of the slag/lining interface. As the binging force in the decarburized layer is weak, the lining is gradually spalled and damaged. A small ratio of some antioxidants  is added to the castable in the working layer to prevent carbon oxidation. Under high temperatures, they can preferentially react with oxygen forming high-temperature ceramic bonds to inhibit carbon oxidation to some extent. However, due to the long service time of the trough, the occurrence of the reaction (7)–(11) is still inevitable after the main trough is used for a certain period.The chemical erosions of the main trough are solely from the atmosphere and the molten slag. These erosions occur above the slag line of the trough. During the cessation of cast operation, part of the slag/lining interface is exposed to the atmosphere, and during cast operation, some air is entrained in the iron–slag jet. The molten iron is oxidized by Ord state , it is k2 in the slag is thus enhanced.The oxidation of the carbon particles (reaction given by Equation (9)) is active above 1073 K but is negligible below 1073 K . From Fi3. The effect of water cooling on lengthening the campaign life of the trough was investigated using industrial tests, numerical simulations, and theoretical analysis. The following conclusions were drawn.A water cooling device has been designed for a BF with a volume of 630 m(1)The industrial tests showed that, after installing the water cooling device, the throughput of the main trough increased from 80,000 tons to 120,000 tons, and its unit campaign life increased from 66 days to 100 days.(2)The simulation results disclosed that the influence of the water cooling device on the temperature distribution in the refractory material of the trough gradually develops from the low-temperature zone to the high-temperature zone in a unit campaign cycle, inhibiting the expansion of the high-temperature zone in the working layer.(3)Theoretical analysis indicated that the water cooling device inhibited the dissolution of the castable and retarded the chemical erosion from molten slag and the atmosphere.


Due to the size of the indentation and the size of the initial cracks, it is necessary to use Scanning Electron Microscopy (SEM) to observe and measure the indentations made and the generated cracks. For evaluation of the fracture toughness in mode I, the Laugier model was chosen experimentally. The fracture toughness analysis showed that doping with concentrations of 10% W and 10% Cr causes an increase in the fracture toughness for KIc = 4.98 for TiBW (10%) and KIc = 6.23 for TiBCr (10%).Nanoindentation is one of the methods that allows for determining the fracture properties of brittle materials. In this article, the authors present the possibility of the fracture toughness coefficient calculation of ceramic-based coatings doped by metal  by using the nanoindentation method with the Berkovich diamond indenter. The mechanical properties of selected coatings, such as hardness and Young’s modulus, were investigated from nanohardness experiments. We analyzed the brittle fracture, which includes changes in hardness (H), Young’s modulus (E), plasticity index H/E and resistance to plastic deformation H The indentation method consists of pressing hard indenters into the test sample and determining their response. These techniques are used more often because they are relatively easy to perform and do not require complicated preparation of the sample and cause only small damage to the surface. First, it was used to determine the hardness, which characterized the resistance of the plastic material to penetration. The study of the mechanical properties led to the development of nanoindentation devices that allow for the application of loads as low as a few mN and provide more information about material behavior than the traditional optical measurement methods .Ic. This parameter can be interpreted as a critical load, where the initiated process of cracking occurs in the way out of control. The form of the mathematical model used is modified depending on the nature of the cracks. The procedures for determining the experimental values of the KIc coefficient of solid materials use destructive (impact) methods. It has been described in the applicable standards [Ic value for PVD coatings is very difficult. The mechanical characterization of micro-volume systems such as thin films and coatings [Ic), including thin coatings, using the nanoindentation method. The fracture toughness is one of the most important parameters of technical materials, which often determines their application. In the case of solid materials, the value of fracture toughness is most often described by specifying the critical value of the stress concentration factor, defined as the material constant Ktandards  and constandards . Howevercoatings ,5 is a kIc coefficient value, which defined the fracture toughness of materials [Ic) for materials, where the initiation and generation of a fracture process is a result of penetration with a Vickers indenter [Ic = 0.74 MPa/m1/2–12 MPa/m1/2 [Ic parameter for coatings. This limitation results from the great difficulties in interpreting the obtained conclusion, which is a consequence of the appreciable and deliberate heterogeneity of the coatings, including, for example, multilayers and differentiation of phases. This applies to both solid materials and thin coatings, especially coatings used in the environment of cyclically changing external loads. One of the basic technological goals is achieving high fracture toughness for this type of coating, which consequently is the most important criterion for their suitability.Nanoindentation is a penetration technique that allows for the characterization of the micro-volume of the material by pressing an indenter into the tested material in the direction perpendicular to the surface. Then, the resulting impression is analyzed and the damage of the material accompanying the pressing process, including for example generated cracks, and the analysis of the penetration characteristics, i.e., mutual dependencies of the force acting on the indenter and depth of its penetration. Evans and Charles, in the 1970s, proposed nanoindentation as a method for diagnosing the Katerials . They deindenter ,11,12,13MPa/m1/2 ,15,16 wiMPa/m1/2 , Dukino MPa/m1/2 , and Ouc2) doped with different concentrations of tungsten (W) [There is no systematization of methodological principle regarding this research, including: The hardness range of the coating, which can use of the method is justified, the possibility of comparing coatings with different parameters , or the influence of the substrate on the obtained effect. The mechanical characteristics of the coatings, such as the fracture toughness, are very important in the process of improvement and better evolution of material solutions for coatings with high tribological effectiveness. Our previous investigation shows that it can be possible to use the Laugier model to assess the fracture toughness of the ceramic coating (TiBsten (W) ,21.Ic coefficient of thin PVD coatings treated using the magnetron sputtering method in the Direct Current (DC) system and examining which load is critical  for generating measurable cracks.The aim of the study was to propose a methodology for determining the KIc), which can be possible by applying the nanoindentation method. For a better understanding of the methodology, two coatings based on TiB2 ceramic with different concentrations of tungsten (TiBW) and chromium (TiBCr) doping were investigated. This study is focused on nanomechanical characterizations of thin coatings and the determination of their hardness (H), Young modulus (E) and fracture toughness coefficient in mode I  and pure-W (99.95%purity) and pure-Cr (99.95% purity), as reported in the papers [2 dopants with W and Cr coatings were produced in an atmosphere of pure argon (Ar 100%). The series of TiBW coatings were deposited on high-speed steel SW7M and the series TiBCr coatings were deposited on the hot work steel W320. In both cases, the samples have a diameter of 25 mm and a thickness 6 mm. Before the deposition process, the samples were polished by Struers Tegramin-25 . The samples were ion-etched in the Ar+ plasma prior to being situated in the process chamber. The substrates were heated up to 300 °C using resistance heaters to obtain better adhesion between the substrate and the coatings. The TiBW coatings were deposited by changes in the source power in the range 25–75 W, but in the case of TiBCr in the range of 70–165 W, respectively. The particular parameters for TiB2 coatings with dopants of W and Cr were listed in 2 (2000 W) target in the case of the TiBCr coatings deposition can be explained by the difference in deposition rates of the chromium target compared to the power of the tungsten target. The power of magnetron source was chosen experimentally.The TiB 10 at.%  were pree papers ,21,22. TThe samples from a series of TiBW and TiBCr coatings with different concentrations of dopant elements were examined for hardness testing and Young’s modulus. Evaluating the basic mechanical properties of the tested coatings were carried out using the CSM-TTX/NH2 Nano-Hardness Tester (NHT) by Anton Paar equipped with a Berkovich diamond tip. In agreement with the procedure, the maximum penetration depth was determined by the thickness of 10% of the total thickness of the coatings. To obtain better statistics, 15 measurements of hardness and Young’s modulus were carried out for each of the tested samples. P and the length crack (l)).Ic—the fracture toughness coefficient, vX—indenter geometry factor (for Laugier Equation vX = 0.016); E—Young modulus (GPa); P—the applied load (mN); a—the distance between the corner and the center of indentation (µm); l1,2,3—the average crack length, l = (l1 + l2 + l3)/3, c = l + a—the sum of a and l. For the nanoindentation experiments, the CSM-TTX/NH2 Nano-Hardness Tester (NHT) by Anton Paar, with a diamond Berkovich indenter, was used. The nanoindentation method requires flat, smooth surfaces and indentation equipment. The principle of this method is based on the application of the indenter under a given load and to measure the length of the corresponding cracks generated at the ends of the indentations. The calculation of the fracture toughness is based on the Laugier model, taking into account the two parameters ). The observation of the crack length measurement in the area of the indentations was carried out by using a scanning electron microscopy SEM Hitachi TM3000 . The analysis of the brittle cracking was carried out in two steps by using a nanohardness tester Anton Paar with a Berkovich diamond indenter. During the testing of TiBW and TiBCr coatings, the critical indenter load was selected, which generated the radial cracks. Indentation tests were performed with different applied loads 50, 100, 200, 300 and 500 mN for each of the tested coatings. The aim of the experiment was also to measure the indenter load, which generated visible cracks for all selected coatings. For each indentation, individual crack length measurements and 2 and TiBW and TiBCr coatings are shown in In this section, the results of the investigation are presented in detail and compared between two series of coatings with dopants of W and Cr. The concentration and thickness of the brittle fracture cross-section of all coatings obtained by the magnetron sputtering method after deposition were analyzed by scanning electron microscopy. The surface of all tested coatings is characterized by high smoothness and good coherence, which are free of cracks and defects. The thickness of the investigated coatings was in the range of 1.00–1.79 µm. The nanohardness measurements were performed according to the rule of 10% of the coating thickness i.e., max 179 nm. The mechanical properties for the tested TiBPcritical) which generated visible cracks during the selection of the applied loads (from 50 mN–500mN with a step every 50 mN). After the investigation by the Nano-Hardness Tester (NHT), scanning electron microscopy (SEM-Hitachi TM3000) was used to image all the generated cracks. The SEM analysis of indentations for different loads showed, that the indenter load of 200 mN , TiBW (6%). In the case of TiBW (10%), where the doping of tungsten is the highest, the cracks were visible and well-measurable only with the indenter load of 400 mN , TiBW (3%), TiBW (6%) coating with the selected indenter load , which was 200 mN. Only for the TiBW (10%) coating was the indenter load criticalP = 400 mN. The penetration depth of our TiBW coating was, respectively, h200mN = 1000 nm and h400mN = 1600 nm. The edge lengths for the indentations and the crack lengths ln1, ln2 and ln3 were measured (where n-number of indentations). The average values of l and a were determined according to Equation ln = (ln1 + ln2 + ln3)/3.According to the adopted methodology, 20 indentations were made for TiBl and a were calculated for the whole series of 20 indentations. The comparison between l). For TiB2 (1), the l value is 5.32 µm, for TiBW (3%)the l value is 2.12 µm, for TiBW (6%) the l value is 2.10 µm and for TiBW (10%) the value is 1.20 µm. The crack lengths for TiBW (10%) are around 7.5 times smaller than for pure TiB2 (1). The second observation is that increasing the tungsten concentration influences the length of the a value (the distance between the corner to the center of the indentation). The highest average value of a was measured for TiBW (10%). 2 (1) and TiBW (6%).In accordance with relation to Equations (2) and (3), the average values of criticalP = 400 mN. For TiBCr (6%) coating, the critical load was found at criticalP = 350 mN and for TiBCr (10%) the value of criticalP = 500 mN. For comparison, in the case of the TiB2 (2) reference sample, the critical load, when the cracks were generated, was observed in the load of criticalP = 250 mN. The required critical load for generated visible cracks in the case of TiB2 (2) is two times smaller than the critical load for TiBCr (10%).The procedure of the measurement for TiBCr coating was similar to the TiBW coating presented in Smolik et al. . First, Ic values. The value of penetration depth for TiBCr (3%) coatings was respectively h400mN = 1500 nm, for TiBCr (6%) the value of h350mN is 1400 mN and for TiBCr (10%) the value of h500mN is 1700 nm compared with the value of h250mN for TiB2 (2) which is 1200 nm. According to the Laugier model, 20 indentations were induced for each coating with critical applied load (P) and then calculated the Kl for TiB2 (2) is 1.83 µm, for TiBCr (3%)the value of l is 1.92 µm, for TiBCr (6%) the value of l is 3.04 µm and for TiBCr (10%)the value of l is 0.71 µm. The crack lengths for TiBCr (10%) are around 2.5 times smaller than for pure TiB2. An additional observation is that the crack length for the coatings from TiBCr series is longer than for coatings from TiBW series. The highest average value of a was calculated for TiBCr (10%). Another important observation is that for TiBW coatings, stable loads are needed for the generation of cracks but in the case of TiBCr coatings, the different critical loads were used for the generation of measurable cracks. The example of indentations made for TiB2 (2) and TiBCr (10%) coatings was presented in The comparison between Ic. It was significantly visible that the fracture toughness increased with increasing concentrations of tungsten and chromium. One of the explanations for this phenomenon is the changing power magnetron during the process. As the authors showed previously in [The presented analysis using the Laugier model showed that the doping of the tungsten (W) or chromium (Cr) in different concentrations affects the change of the value of Kously in , changin2 coatings with the doping of chromium and tungsten. Doping the TiB2 coating with 3% of chromium presents the increasing plasticity index of coatings H/E and resistance to plastic deformation of coatings H3/E2 and the fracture toughness coefficient present value of KIc = 2.55 MPa·m1/2 in the critical load criticalP = 400 mN. The fracture toughness for TiBCr (6%) coatings achieves the value KIc = 1.60 MPa·m1/2 in the critical load criticalP = 350 mN. For TiBCr (10%) coatings, the fracture toughness is KIc = 6.23 MPa·m1/2 in the critical load criticalP = 500 mN. This value is around three times higher than for pure TiB2 (2) (2.05 MPa·m1/2) and 1.25 higher than for TiBW (10%). The very important observation is that, when the chromium and tungsten concentration increase, the critical load for generated visible cracks and the value of Kic coefficient also increase. In this article, the authors showed the comparison of TiBIc coefficient for each of the coatings, which shows a characteristic threshold, indicating the value of the load acting on the indenter, when the cracking process is initiated. After exceeding this characteristic threshold, the value of the KIc coefficient stabilizes. The changes, which are determined for cracks obtained at higher indenter load values depend on the accuracy of the analytical model, the precision of the impressions and the accuracy of microscopic observations and crack length measurements. If the changes in the KIc coefficient value for the loads acting on the indenter greater than the threshold load responsible for the generation of cracks do not exceed the value of measurement error, it proves a correctly selected measurement methodology. The value of the KIc coefficient determined for cracks generated at the lowest value of the load acting on the indenter should be considered as the value of fracture toughness.2 coatings is increasing with the increasing of dopants tungsten and chromium. The analysis shows that the crack initiation in the group of TiB2, TiBCr (3%), TiBCr (6%) and TiBCr (10%) coatings occur when the critical load acting on the indenter  is exceeded, which changes for different coatings and different substrate parameters. Based on this, the range of the load acting on the indenter was selected, i.e., criticalP = 200–500 mN. The nanoindentation method, especially the Laugier model, is very convenient for assessing the fracture toughness of coatings based on ceramic. The tungsten and chromium doping increases the value of the KIc coefficient. The study demonstrated that the brittleness of thin TiBcriticalP), which is also dependent on the parameters of the coating and the parameters of the substrate. This article presents the basis of a methodology for testing the fracture toughness of thin coatings with the use of the penetration method formulated. As a measure of the fracture toughness of the coatings, the KIc coefficient is interpreted as the critical load, where the initiated fracture process proceeds in an uncontrolled manner as indicated. The correct selection of the analytical model and the simultaneous correct selection of the parameters of the penetration test, including: The geometry of the indenter and the load acting on the indenter, in combination with the information base for various substrate-PVD coating systems, create a very interesting measuring tool enabling quick comparisons to fracture toughness of various substrate-coating systems.Our test results showed the complex influence of many factors on the process of indentation and generation of cracks in PVD coatings by pressing a Berkovich indenter. The generation of measurable cracks in the tested coatings is strictly dependent on not only the properties of the coating itself, including the microstructure and phase structure, but also on the properties of the entire substrate-coating system, including its ability to transfer external mechanical loads, which are determined by the property’s elastically—plastic substrates  and the thickness of the coating. Based on the conducted analysis, it was shown that the crack initiation in the coating takes place after exceeding the critical load (


Research with deuterium-depleted water (DDW) in the last two decades proved that the deuterium/hydrogen ratio has a key role in cell cycle regulation and cellular metabolism. The present study aimed to investigate the possible effect of deuterium-depleted yolk (DDyolk) alone and in combination with DDW on cancer growth in two in vivo mouse models. To produce DDyolk, the drinking water of laying hens was replaced with DDW (25 ppm) for 6 weeks, resulting in a 60 ppm D level in dried egg yolk that was used as a deuterium-depleted food additive. In one model, 4T1, a cell line with a high metastatic capacity to the lung was inoculated in the mice’s mammary pad. After three weeks of treatment with DDW and/or DDyolk, the tumor volume in the lungs was smaller in all treated groups vs. controls with natural D levels. Tumor growth and survival in mice transplanted with an MCF-7 breast cancer cell line showed that the anticancer effect of DDW was enhanced by food containing the deuterium-depleted yolk. The study confirmed the importance of the D/H ratio in consumed water and in metabolic water produced by the mitochondria while oxidizing nutrient molecules. This is in line with the concept that the initiation of cell growth requires the cells to generate a higher D/H ratio, but DDW, DDyolk, or the naturally low-D lipids in a ketogenic diet, have a significant effect on tumor growth by preventing the cells from raising the D/H ratio to the threshold. Caenorhabditis elegans [After the discovery of the heavy isotope of hydrogen, deuterium (D), in the early 1930s, its possible role in living organisms was not investigated for 60 years ,3. The f elegans . Regardi elegans .Further studies confirmed the role of D in cancer development, in prevention , and proIn the experiments revealing the role of deuterium in cell growth, cell metabolism, and physiological changes, the D level was manipulated by the application of DDW.In the meantime, research confirmed that the D content of organic molecules, such as carbohydrates or lipids, showed significant differences, suggesting that the synthesis pathways of these molecules strongly influence D content. In organic compounds of plants, the D/H ratio may also substantially differ from that of the environment. The explanation for that lies in the metabolic processes of various plants. In plants using the so-called C3 or C4 photosynthetic pathways to fix carbon from the atmosphere, D concentration in the glucose molecules will decrease  to different extents. In plants using the C3 carbon fixation pathway , the glucose D concentration is 135–137 ppm, and in plants using the C4 pathway  it is 141–143 ppm ,16. In cIt is obvious that, in the case of a normal diet, the water content of the food will represent the D concentration of the local environment. But that is not the only factor influencing the final D concentration of the body, since the organic molecules of the nutrients also contain both hydrogen and deuterium which appear in the metabolic water after oxidation by the mitochondria.So, the final D concentration of the cells depends, on the one hand, on the D concentration of the fluid intake, including the water content of the food, but, on the other hand, the D concentration of nutrient molecules also has an effect via the D level of the metabolic water produced in the mitochondria in the oxidative utilization of fats, carbohydrates, and proteins.Recently, a ketogenic diet has been used as complementary cancer therapy with high efficacy . Based oThe study presented here aimed to investigate the effect of the D concentration of organic nutrient compounds on tumor development. For this purpose, foods with artificially modified depleted D content were produced, and their effect was tested in two in vivo mouse model systems to evaluate the role of altered D/H ratio of organic molecules and their effect on tumor growth.Mouse and rat studies indicated that replacing normal drinking water with heavy water increased the D content of organic molecules within a short time . Based oThe D concentration reached equilibrium 6−7 weeks after the DDW consumption had started. After 7 weeks of DDW treatment of hens, the eggs were collected and used for the experiments. The albumen and yolk were separated, freeze-dried , and the yolk was used as a food component for mice.per os by gavage 400 µL of the solution daily in the first two weeks of the four-week-long experiment. The mice in the groups receiving the yolk-containing food lost vitality, attributable to the avidin content of the yolk causing biotin deficiency. So, in the second two weeks, the yolk solution was treated at 80 °C for 10 min and the adverse effects disappeared.Two different in vivo studies using mice were performed  at 7%. The mix was re-tableted and sterilized for feeding the mice.2 and 100% humidity in RPMI-1640 Medium  containing penicillin (50 IU/mL) , streptomycin (50 mg/mL) , and 10% fetal bovine serum .A metastasis-specific mouse mammary carcinoma cell line 4T1 with high metastatic capacity to the lung  was grown at 37 °C under 5% of CO2 and 100% humidity in Eagle’s Minimum Essential Medium (EMEM)  containing penicillin (50 IU/mL) , streptomycin (50 mg/mL) , and 10% fetal bovine serum .An MCF-7 human breast cell line (ATCC) was grown at 37 °C under 5% of CO5 (4T1) or 5 × 106 (MCF7) cells were counted and resuspend in appropriate serum-free medium and injected into the mammary pad in 50 µL volume.Of both cell lines, the third and fourth passages were used for inoculation. Before inoculation, the cells were trypsinized, washed, and resuspended in sterile PBS. A total of 10n = 10, 5 males and 5 females)  were used. The mice (20 ± 4 g weight) were group-housed (5/cage), fed VRF1 commercial diet ad libitum, and housed in an animal facility under a 12 h light/dark cycle at constant temperature (22 °C) and humidity. The mice were randomly divided into six groups  . Water wOn day 0, the cells  were inoculated into the mice’s mammary pad, and DDW treatment was started. The administration of the yolk solution started on day 1.Since feeding by gavage with the 400 µL solution of the dried yolk was stressful for the mice, an additional control group was set up  to compare the data of the other two treated groups receiving DDW and/or yolk. DU283, a compound with documented anticancer effect, was administered  as a positive control . The gron = 10, 5 males and 5 females) were created. The control group consumed normal water ad libitum and dried VRF1-based food supplemented with 7% normal dried yolk .All animals were treated moribund and were euthanized at the observation of the first sign of torment. All operative procedures and animal care conformed strictly to the Hungarian Council on Animal Care guidelines. After treatment, the animals were randomly divided into different groups and kept under standard conditions conforming to ARRIVE  guidelines  and the It was supposed that the replacement of the laying hens’ normal drinking water with DDW of 25 ppm D concentration would influence the D content of the molecules synthesized in the developing eggs. The time course of D levels in water and dry substance of egg white and yolk is shown in The D concentration of the water extracted from the yolk and egg white was equal, suggesting an equilibrium of D within the liquid phase. There was a sharp 50 ppm decrease in the first 9 days, and a further 30 ppm decrease during the next 12 days, but only a few ppm decrease in the subsequent 3 weeks. The time course of the change of D concentration in egg white and yolk was quite different. The D level of the albumen was higher already before the hens started drinking DDW, and it decreased from 160 ppm to 110 ppm by the forty-second day of the study. The D concentration of the yolk started to decrease rapidly right after the hens started consuming DDW and decreased much faster than the D concentration of the water content of the yolk, reaching equilibrium after only two weeks. By the time of collecting the eggs for the experiments, there was a 50 ppm difference between the D concentration of the egg white (110 ppm) and the yolk (60 ppm).The 4T1 model system was chosen as a cell line with high metastatic capacity to the lung. Therefore, the size of the primary tumor and the weight of the lung metastases were followed.3, 30.6 mm3, 47.0 mm3 vs. 15.7 mm3, 33.4 mm3 50.3 mm3, respectively), but the difference was not significant. In the group consuming DDW and DDyolk, the average primary tumor size was smaller vs. control on days 6, 8, and 10 (9.9 mmp = 0.0354). These differences may be explained with earlier findings showing that DDW inhibits the migration of tumor cells [The average weight of the metastases in the lungs and the primary data are shown in or cells .p = 0.0623, Gehan-Breslow-Wilcoxon test: p = 0.0689).The average tumor volume data and survival data are shown in These results confirm the anticancer effect of DDW and our assumption that by administration of nutrients as deuterium-depleted organic compounds (DDyolk) to normal food, the anticancer effect of deuterium depletion can be boosted. Increasing the amount of DDyolk in the food may enhance antitumor efficacy.The consumed food has an undoubtedly significant impact on cell metabolism and physiological processes in living organisms. The effects are typically attributed to the composition of the macronutrients  in foods, whereas the possible role of heavy isotope of hydrogen, deuterium, has not been investigated. To preserve health, the most common dietary approach is to reduce fats and increase carbohydrates within the total caloric intake, arguing that the burden of cardiovascular diseases can be reduced this way.Our study aimed to investigate the impact of the varying D/H ratio in food on tumor growth. To obtain deuterium-depleted nutrients, the drinking water of laying hens was replaced with DDW and the decrease of D concentration in the eggs was followed. Interestingly, the kinetics of the D level change in yolk and egg white was different which suggested that the distribution of D is strongly determined by biochemical pathways.The mice studies presented here clearly showed the distinct antitumor effect of DDyolk. The significant weight decreases of lung metastases generated by the 4T1 cancer cells in the first study indicated that deuterium depletion may have inhibited the migration. Inhibition of lung cancer cell migration by DDW in vitro has been described earlier . Tumor gThe application of a ketogenic (very low in carbs and high in fats) diet was tested in cancer patients with convincing clinical evidence of the anticancer effect of this type of nutrition. The changes in the metabolic parameters in patients during such a diet, and the beneficial antitumor effects, are well-documented ,30,31. OA carbohydrate-rich diet will result in the production of metabolic water with a D concentration close to the Standard Mean Ocean Water (SMOW) value of 155.75 ppm. However, the more complex the molecules are, such as lipids, the lower their D content, because the biochemical processes in the synthesis of these molecules show a preference to the lighter isotope of hydrogen due to the isotopic effect ,3,18. CoThe data in A high-carbohydrate and low-fat diet alone results in a higher average D concentration of the body. Consumed carbohydrates are converted into glucose for immediate energy, and into glycogen and fat as stored energy. Oxidation of glucose, immediately or after storage as glycogen, yields metabolic water with higher D level, whereas lipids become deuterium-depleted during synthesis and yield lower D in metabolic water. The D atoms not included into the lipids will be enriched in other molecules, preferably molecules with hydrogen in the exchangeable position, such as amino- and carboxyl groups of amino acids. This may explain the dissimilar D level in egg yolk and white of the DDW-treated hens .Malignancies are not the only chronic disease positively influenced by reduced D levels. A recent animal study proved that the optimal D concentration of blood for reducing blood sugar levels in rats was between 125 and 140 ppm, stimulating the translocation of GLUT4 from the cytosol to the cell membrane . The benTo reduce the incidence of chronic diseases, including malignant tumors, in the human population is a paramount aim. The data presented here prove that the isotopic composition of foods has a major impact on cancer cell growth. These observations raise the need to carry out further preclinical studies and human clinical trials to reveal the mechanism of deuterium depletion and to optimize the D concentration of nutrients to reduce the average D level in the blood from 145–150 ppm to 125–140 ppm.


Microcephalic and normocephalic children with prenatal exposure to ZIKV presented with the mandatory components of the CAEPs, regardless of changes in the CNS, suggesting that this population has, to some extent, the cortical ability to process sound stimuli preserved. Prenatal exposure to ZIKV can cause neurologic and auditory damage. The electrophysiological responses obtained by Cortical Auditory Evoked Potentials (CAEP) may provide an objective method to investigate the function of cortical auditory pathways in children exposed to ZIKV. This case series analyzed the findings of CAEP in prenatal-period ZIKV-exposed children with and without microcephaly. The CAEP was performed in a total of 24 children. Five magnetic resonance imaging (MRI) images of the inner ear and brain of microcephalic children were analyzed and compared with CAEP measurements. Ventriculomegaly (80%), cortical/subcortical calcification (80%), and brain reduction (60%) were the most common alterations in the MRI. The P1-N1-P2 complex of the CAEP was observed in all children evaluated. The peak N2 was absent in two children. In the comparison of the CAEP measurements between the groups, children with microcephaly presented a higher amplitude of P2 ( Flaviviridae family, which was first isolated from humans in 1952. Between 2015 and 2017, a ZIKV epidemic occurred in Brazil, during which cases of congenital abnormalities were observed among neonates whose mothers were infected with the virus during pregnancy [Zika virus (ZIKV) is an arthropod-borne virus (arbovirus) of the regnancy .Today, Congenital Zika Syndrome (CZS) is recognized as a spectrum of clinical manifestations including both structural anomalies in different areas of the CNS, with microcephaly being one of the most prominent ,4,5,6,7,Notably, there is some evidence to suggest that prenatally ZIKV-exposed normocephalic children, without evident changes at birth, can also present with long-term neurodevelopmental sequels, including delays in cognitive and language development ,11,12. HSince 2019, the Joint Committee on Infant Hearing has added ZIKV to the list of prenatal infections that may increase risks of hearing loss and provided guidance recommending neonatal screening, preferably through brainstem auditory evoked potential (AEP), as well as the longer-term auditory follow-up of exposed children . NeverthIt is well established that the integrity of the auditory system is fundamental for the development of auditory abilities that occur in the first years of life. Cortical malformations and neurodevelopmental disorders can influence the maturation of the auditory pathways, impacting the processing of auditory information, language, and communication ,18,19. CIn this context, the aim of this study was to analyze the findings of CAEP in prenatally ZIKV-exposed children with and without microcephaly. Studying electrophysiological responses obtained through CAEP can provide an objective method for investigating the function of cortical auditory pathways in children exposed to ZIKV.This case series study with comparisons between children with and without microcephaly is linked to two major research projects entitled “Audiological Evaluation of Children with Suspected Congenital Zika Virus Infection” and the “Microcephaly Epidemic Research Group Pediatric Cohort (MERG–PC)” . The resEthical approval for the research project was provided by the Research Ethics Committees of the Hospital Agamenon Magalhães (CAAE 3.072.730) and the Faculdade de Ciências Médicas da Universidade de Pernambuco (CAAE 52803316.8.0000.5192). The parents and guardians responsible for the participants were informed about the study objectives and procedures and signed free and informed consent forms prior to participation. In this study, we defined confirmed cases as children and mothers who tested positive for ZIKV. The samples were tested for Zika virus-specific IgM antibodies using a capture ELISA based on the US Centers for Disease Control and Prevention (CDC) Emergency Use Authorization protocol, with reagents from the CDC  . The preWe defined probable cases as children with no laboratory evidence ZIKV testing results based on neonatal samples whose mothers presented rash and/or fever during pregnancy  but with laboratory evidence ) of ZIKV infection during pregnancy. This case series included children who presented with no evidence of peripheral hearing alterations, as indicated by the presence of wave V in the newborn hearing screening test and during auditory monitoring in the prior year using BAEP with click stimulus at the intensity of 35 dB nHL. This study excluded children with (i) congenital infections with other viruses, (ii) other congenital syndromes, (iii) known exposure to risk factors for hearing alterations according to the 2019 Joint Committee on Infant Hearing, and/or (iv) signs of alteration in peripheral auditory pathways .The participants were categorized in two groups based on head circumference at the time of birth. The microcephalic group included children with a head circumference Z-score of >2 standard deviations below the mean for age and sex, according to InterGrowth-21st reference curves . The norAmong the children with microcephaly, five underwent magnetic resonance imaging (MRI) of the inner ear and brain based on clinical indication. The MRI analysis was performed independently by two examiners, an experienced neuroradiologist and an experienced otorhinolaryngologist, and blinded in relation to the clinical presentation of the participants. Cases of disagreement were discussed together and decided by consensus. Imaging results were evaluated for abnormalities in the inferior and temporal frontal cerebral parenchyma, ventricles, brainstem, thalamus, callous body, cerebellum, cochlea, labyrinth, and cochlear and vestibular nerves . ClinicaOn the day of the auditory evaluation, an audiological screening was performed with otoscopy and imitanciometry , to exclude external and middle ear alteration. The normality pattern adopted was type A curve (admittance peak 0.3 to 1.6 mL and peak pressure −100 to +100 daPa) and presence of ipsi and contralateral stapedial reflexes. Participants were then evaluated for the CAEP examination, which was performed using Intelligent Hearing Systems (IHS) equipment. Four copper electrodes were used. The ground electrode was positioned in Fpz, the non-inverting electrode in Fz, and two inverting electrodes in the right (A1) and left (A2) lobes.During the CAEP examination, the children were positioned in a reclining armchair and remained awake in a state of stillness watching a video on an electronic tablet device without sound. Children lacking postural stability were positioned in the lap of their responsible parent or guardian.Pure tones of 1000 Hz and 4000 Hz to 80 dB HL via insertion phones (ER-3) were presented in a binaural and random way, with the proportions including 20% rare stimuli (4000 Hz) and 80% frequent stimuli (1000 Hz). The acquisition parameters were: impedance maintained at 1 kOhms, recording window of 510, filtering passes band of 1–30 Hz, alternating polarity, stimulation rate of 1.1 stimulus per second, and total of 500 sweeps.The CAEP were recorded and analyzed by two trained evaluators with experience in electrophysiology. The identification of the waves of the examination was performed in the tracing formed by the rare stimulus. For analysis, the presence of components P1, N1, P2, and N2, as well as their latencies and amplitudes, were identified. Any disagreements were resolved by consensus.Statistical Package for the Social Sciences (SPSS), version 21.1, software , and the significance level adopted was 5%. Data normality was verified with the Shapiro–Wilk test. Depending on the distribution of the data, the CAEP latency and amplitude measurements were compared between groups using independent t-tests and Mann–Whitney U tests. The point-biserial correlation coefficient was applied to verify the existence of correlation between the measurements of the CAEP and the findings of the MRI results in the children with microcephaly.All statistical analyses were performed using In total, this study evaluated 24 children and analyzed the data from 21 children, of whom 11 were microcephalic and 10 were normocephalic. Three children were excluded from the final sample because their tests were considered to contain artefacts, and it was not possible to interpret their results as reliable electrophysiological responses.Among the children with microcephaly included in the study, six were female and five were male, with a mean age of 45.6 months (standard deviation of 2.2 months). Four of the participants had severe microcephaly, with a head circumference of >3 standard deviations below the mean for age and sex, according to the InterGrowth-21st reference curves . In the In the CAEP measurements, a single outlier finding was observed in one of the children with microcephaly (p = 0.017).The frequencies, means, standard deviation, minimum and maximum interval, and significance value of the components of the CAEP in children exposed to ZIKV with and without microcephaly are described in p = 0.31). We performed the Mann–Whitney test to confirm our hypothesis and observed again that the presence of microcephaly had an effect on the means of the groups, so that the groups were statistically different . Therefore, when comparing the results with and without the presence of the outlier by means of statistical tests appropriate to the variance of the data, the statistical difference between the groups was maintained.In comparing the amplitude findings between the groups, a statistical analysis was performed without the previously described outlier finding. It was observed that the means of the groups are different and, on average, children with microcephaly had a higher mean amplitude of P2 than children without microcephaly (t(18) = 2.34; In this study, the P1-N1-P2 complex of the CAEP was observed in all children evaluated, regardless of the presence of microcephaly; however, peak N2 was absent in two children . In the comparison of the CAEP measurements between the groups, children with microcephaly presented a higher amplitude of P2. There was also some evidence to suggest an association between the measures of the CAEP and the MRI findings. The evaluation of peripheral auditory pathways in children diagnosed with confirmed or probable congenital ZIKV infection, as well as the language and auditory behavior reported by parents, have been described in the literature ,26,27,28The current evidence base in the published literature remains insufficient to fully understand the impacts caused by ZIKV on the central auditory system . The larThe P1-N1-P2 complex of the CAEP was observed in all children evaluated. This complex has been present since childhood and indicates neural response in the auditory cortex in the face of auditory stimulation ,31. FromIn this sense, the neurological damage associated with exposure to ZIKV during fetal development did not appear to interfere with the occurrence of CAEP waves. The main explanation stems from the fact that the P1-N1-P2 complex primarily reflects the characteristics of the acoustic stimulus, although it depends on the integrity of the afferent cortical auditory pathways. Therefore, it cannot be ruled out that children exposed to ZIKV perceive the spectral and time characteristics, and also passively discriminate the auditory stimulus.Peak N2 was absent in only two children, one with microcephaly and the other normocephalic. It is a mixed component that originates in multiple s-generated responses along the brain stem, cortical, and thalamic auditory regions ,33, whicIn the comparison of the measures of the CAEP between the groups, children with microcephaly presented with greater amplitude findings for P2 than their normocephalic peers. Amplitude measurements are directly related to the magnitude of neural response and the amount of nerve fibers recruited during the passage of the stimulus in the auditory . The P2 The measurements of latency and amplitude of P1-N1-P2-N2 found in the children of both groups of this study are similar to those reported in another study that evaluated children from a similar age group who were born at term and presented with no auditory complaints or developmental alterations . As prevIt is noteworthy that, while crude latency and amplitude values are an indication for the evaluation of central auditory pathways, they are not able to demonstrate the morphological differences in the electrical activity pattern of the children evaluated.Another factor that draws attention refers to the association between the measurements of the CAEP with the findings of imaging tests in which it was observed that the presence of trunk and thalamus hypoplasia was associated with a greater amplitude of the N2 wave compared to the group without hypoplasia. It is known that this component is related to the ability to identify and care for the rare stimulus, being correlated to the difficulty level of the task . Thus, tIn addition, the influence of individual variations intrinsic to the development of each individual cannot be ruled out, as well as exposure to different acoustic stimuli in the first moments of life. For example, a significant reduction in N2 amplitude and latency is reported for individuals with mild to moderately severe sensorineural hearing loss, a fact associated with sensory input problem ,40 deficAnother important factor is that the structural alterations of the CNS present in children with microcephaly in this study may be associated with alterations in multiple cognitive domains, such as memory, attention, orientation, perception, language, executive functions, or information processing ,41, whicThe presence of the mandatory components of the CAEP in this study suggests no change in the functioning of the central auditory pathway to the region of the generating sites of the N2 component. Due to the sample size and methodological clippings used, it is suggested that research with different designs can be carried out to better elucidate the efficiency of cortical decoding of sound stimuli in children prenatally exposed to ZIKV.In this case series, we observed that microcephalic and normocephalic children with prenatal exposure to ZIKV presented with the mandatory components (P1-N1-P2-N2) of the CAEPs, regardless of changes in the CNS, suggesting that this population has, to some extent, the cortical ability to process sound stimuli preserved. However, the presence of microcephaly seems to interfere with the amplitude of peak P2, a component closely related to attention capacity and auditory discrimination. The presence of trunk and thalamus hypoplasia was associated with a greater amplitude of the N2 wave compared to the group without hypoplasia.Future studies with larger samples, longitudinal follow-up, and use of verbal stimuli in the CAEP will provide more accurate information regarding the development and performance of the central auditory system for speech sounds at different times in development.