Grayscale Research Articles

A novel colorimetric sensor through peroxymonosulfate activated by CoFe@CN nanozymes for on-site resorcinol detection

The presence of resorcinol (RS) in water contaminated with pollutants may engender environmental and ecosystem risks. In this study, we developed rapid and efficient colorimetric methods for the on-site quantitative determination of RS. Specifically, a synthetic nanozyme composed of nitrogen-doped carbon-coated cobalt-iron (CoFe@CN) was utilized to initiate the oxidation reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) with the activator of peroxymonosulfate (PMS). Compared to the activator of hydrogen peroxide (H2O2), PMS demonstrated a sixfold enhancement in catalytic efficacy, markedly elevating the detection performance. Density Functional Theory calculations indicate that RS can serve as a reducing agent for oxTMB, confirming the theoretical feasibility of this process. A linear relationship between inhibition rate (IR %) of RS concentration and absorbance was established and the colorimetric analysis revealed a linear relationship between RS concentration and gray scale values over the range of 0–45 μM, with a detection limit of 3.39 μM. Furthermore, smartphone-based analysis of color images allows for on-site quantitative analysis of RS concentration, leveraging portability and real-time detection capabilities. This study provides pioneering analytical support for on-site water quality monitoring within the ecological environment.

Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.

Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework.

The present study deals with image enhancement, which is a very common problem in image processing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in "real-time", which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.

A Novel Chaos-Based Encryption Technique with Parallel Processing Using CUDA for Mobile Powerful GPU Control Center

Chaotic systems possess unique properties that can be leveraged for cybersecurity due to their complex and unpredictable nature, making it challenging for systems to interpret them. When combined with CUDA, chaotic systems benefit from high-efficiency parallel processing capabilities, allowing for the rapid and secure handling of large data sets. In this study, a CUDA-supported chaos-based parallel processing encryption mechanism for mobile control centers is developed. The powerful GPU of the control center enables fast encryption and decryption of image data from multiple connected devices. The Logistic Map is used for random number generation, and XOR operations are applied to encrypt the R, G, B, and Gray Scale channels of images. Initially, an analysis of the numbers generated from the Logistic Map is conducted, followed by a detailed explanation of the encryption technique. The technique is then applied to image data, and image analyses are performed. Finally, the performance of the encryption technique is compared with other studies, and encryption speeds are examined. Results indicate that the new encryption technique provides significantly fast encryption and security levels comparable to other studies. The key discovery is that the mechanism is well-suited for parallel processing, allowing for rapid image encryption using the proposed method. For encrypting large IoT files, random number generation is performed first, followed by statistical tests, and then encryption using the developed algorithm. Security analyses are conducted, and the performance of the mechanism is compared with other studies. Results from image analysis and encryption performance demonstrate that the developed mechanism can be effectively used with high security for IoT applications.

Infrared Image Enhancement Based on Adaptive Guided Filter and Global–Local Mapping

Infrared image enhancement technology plays a crucial role in improving image quality, addressing issues like low contrast, lack of sharpness, and poor visual effects within the original images. However, existing decomposition-based algorithms struggle with balancing detail enhancement, noise suppression, and utilizing global and local information effectively. This paper proposes an innovative method for enhancing details in infrared images using adaptive guided filtering and global–local mapping. Initially, the original image is decomposed into its base layer and detail layer through the adaptive guided filter and difference of the Gaussian filter. Subsequently, the detail layer undergoes enhancement using a detail gain factor. Finally, the base layer and enhanced detail layer are merged and remapped to a lower gray level. Experimental results demonstrate significant improvements in global and local contrast, as well as sharpness, with an average gradient enhancement of nearly 3% across various scenes.

Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression

Classification of Heart Sounds Using Grey Level Co-occurrence Matrix and Logistic Regression

Detection of hidden bruises on kiwifruit using hyperspectral imaging combined with deep learning

SummaryDuring harvesting, transportation and storage of kiwifruit, the flesh is often bruised by collision or compression. However, the bruises in kiwifruit are extremely difficult to recognise by naked eyes and are called hidden bruises. Accordingly, a fast method for detecting hidden bruises in kiwifruit was developed in this study based on hyperspectral imaging (HSI) coupled with deep learning. The spectral range (924–1277 nm) and feature wavelengths (928.19, 1051.03 and 1190.47 nm) sensitive to hidden bruises in kiwifruit were selected using the principal component analysis (PCA). Subsequently, three‐channel images (Dataset 1), grayscale images (Dataset 2) and pseudo‐colour images (Dataset 3) were generated according to the images of feature wavelengths of the kiwifruit. The YOLOv5s model for detecting the hidden bruised areas of the kiwifruit was developed using these three datasets. The results showed that the YOLOv5s detection model performed best at Dataset 1, and the values of Precision, Recall, F1, mAP and FNR of this model were 98.25%, 97.50%, 97.87%, 99.12% and 2.50% respectively. The study showed that HSI technology combined with the YOLOv5s model can effectively detect hidden bruises in kiwifruit, providing references for detecting hidden bruises in other fruit.

Role of Shear Wave Elastography for Assessment of Renal-Allograft Fibrosis and its Correlation With Histopathology.

To investigate whether shear wave elastography (SWE) can accurately identify interstitial fibrosis and tubular atrophy (IFTA) in chronic renal allograft injury (CRAI) and whether it can differentiate between different grades of IFTA. Prospective observational study on renal transplant recipients who presented with CRAI. Patient selection was done on the basis of clinical presentation, serum creatinine, and eGFR levels. Biopsy and SWE were performed and SWE values were correlated with histopathological findings according to Banff schema. Receiver operating characteristic (ROC) was also analyzed to assess the diagnostic efficacy of SWE. Sxity-one patients were evaluated. Ten patients had no IFTA, 33 patients had mild IFTA, 16 patients had moderate IFTA, and 2 patients had severe IFTA. Mean parenchymal stiffness values in no IFTA, mild IFTA, moderate IFTA and severe IFTA were 39.86 ± 2.17 kPa (3.64 ± 0.09 m/s), 41.59 ± 3.36 kPa (3.71 ± 0.15 m/s), 47.59 ± 3.34 kPa (3.98 ± 0.14 m/s), and 53.83 ± 1.41 kPa (4.25 ± 0.03 m/s), respectively. SWE values of parenchymal stiffness reached statistical significance to differentiate between mild, moderate, and severe IFTA. ROC analysis revealed cut-off values of 45.09 kPa (3.89 m/s) to differentiate between mild IFTA and moderate IFTA, 52.06 kPa (4.18 m/s) to differentiate between moderate IFTA and severe IFTA with acceptable sensitivity and specificity. SWE is a non-invasive and cost-effective imaging tool to evaluate the disease status of renal allografts affected by CRAI. Thus, it can be of paramount importance if added to the regular follow-up imaging protocol of renal allograft along with grayscale and Doppler imaging.

Comparison of Scheimpflug Imaging (Pentacam HR) and Swept-Source Optical Coherence Tomography (CASIA2) in Eyes With Macular Corneal Dystrophy.

Assessment of tomographic characteristics and interdevice comparability between Scheimpflug imaging (Pentacam HR, Oculus Optikgeräte GmbH, Wetzlar, Germany) and swept-source optical coherence tomography (CASIA2, Tomey Corp., Nagoya, Japan) in eyes with macular corneal dystrophy (MCD). Eyes with MCD were examined by Pentacam HR and CASIA2. Interdevice comparison was performed using a Wilcoxon matched pairs test and Bland-Altman plots with 95% limit of agreement. A Spearman rank correlation coefficient was used for correlating indices of both devices. This retrospective study included 31 eyes of 18 patients (mean age: 32.1 ± 10.7 years). Eyes with MCD demonstrated a moderate astigmatism with a Cylinder anterior of 2.56 ± 1.50 D (Pentacam HR) and 2.52 ± 1.57 D (CASIA2) without a difference between both devices. CASIA2 (0.34 ± 0.14 D) measured lower values of Cylinder posterior compared with Pentacam HR (0.96 ± 0.66 D) (P < 0.0001). Comparison of pachymetry (Pentacam HR vs. CASIA2) showed higher values of the central corneal thickness (619 ± 227 μm vs. 445 ± 67 μm, P = 0.0001) and the thinnest corneal thickness (499 ± 165 μm vs. 430 ± 60 μm, P = 0.0167) for Pentacam HR. Corneal densitometry measurement revealed that increasing gray scale units caused a greater interdevice difference for pachymetry values, as Pentacam HR measured higher than CASIA2 for more opaque corneas. Eyes with MCD tend to have thinner corneas and a higher amount of corneal astigmatism than healthy eyes. In advanced MCD, Scheimpflug technology may mistakenly overestimate corneal thickness. The pachymetry measurement of the optical coherence tomography should be used when planning corneal surgery such as excimer laser-assisted phototherapeutic keratectomy to determine the ablation depth.

Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma

PurposeThe tumor microenvironment (TME) plays a crucial role in tumor progression and treatment response. Radiomics offers a non-invasive approach to studying the TME by extracting quantitative features from medical images. In this study, we present a novel approach to assess the stability and discriminative ability of radiomics features in the TME of vestibular schwannoma (VS). MethodsMagnetic Resonance Imaging (MRI) data from 242 VS patients were analyzed, including contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) sequences. Radiomics features were extracted from concentric peri-tumoral regions of varying sizes. The intraclass correlation coefficient (ICC) was used to assess feature stability and discriminative ability, establishing quantile thresholds for ICCmin and ICCmax. ResultsThe identified thresholds for ICCmin and ICCmax were 0.45 and 0.72, respectively. Features were classified into four categories: stable and discriminative (S-D), stable and non-discriminative (S-ND), unstable and discriminative (US-D), and unstable and non-discriminative (US-ND). Different feature groups exhibited varying proportions of S-D features across ceT1 and hrT2 sequences. The similarity of S-D features between ceT1 and hrT2 sequences was evaluated using Jaccard’s index, with a value of 0.78 for all feature groups which is ranging from 0.68 (intensity features) to 1.00 (Neighbouring Gray Tone Difference Matrix (NGTDM) features). ConclusionsThis study provides a framework for identifying stable and discriminative radiomics features in the TME, which could serve as potential biomarkers or predictors of patient outcomes, ultimately improving the management of VS patients.

Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively.

In-situ composition analysis during laser powder bed fusion of Nd-Fe-based feedstock using machine-integrated optical emission spectroscopy

Powder bed fusion of metals using a laser beam (PBF-LB/M) is a widely used additive manufacturing (AM) technique that enables the material-efficient fabrication of complex geometries in metallic parts. However, achieving high-quality parts with desired properties heavily depends on variances in the manufacturing process and powder composition, making quality control an indispensable aspect of almost all applications. Today, mainly grayscale imaging, photodiodes, or pyrometry are employed, whereas in-situ recording of chemical (compositional) information has rarely been done during PBF-LB/M. This pilot study uses Nd-Fe-B as a compositional sensitive model material to explore the feasibility of in-situ optical emission spectroscopy (OES) for elemental analysis of metallic samples during PBF-LB/M, validated by ex-situ analysis. Our results show that the Boltzmann-corrected local emissivity of the Fe and Nd lines in the plume is a reliable indicator for determining the temperature and elemental material concentration voxel-wise, which could enable a digital 3-D reconstruction of the material composition of a printed part in the future.

A novel CNN architecture for robust structural damage identification via strain measurements and its validation via full-scale experiments

In this study, an innovative two-dimensional Convolutional Neural Network (2D CNN) architecture is proposed and investigated for the classification of bridge damage. Employing unique strain time-history data transformed into grayscale images, the approach seamlessly combines feature extraction and classification, allowing for the precise identification and categorization of structural damage. The method’s effectiveness was validated through field experiments on a full-scale bridge mock-up sample subjected to several controlled damage states under nonstationary, commercial vehicle loads. A wide range of realistic damage conditions, from minor to severe structural damage states, was included in the experimental scenarios together with inherent operational uncertainties. The robustness of the 2D CNN model was rigorously tested against fluctuating loads and introduced noise. Demonstrating remarkable accuracy, the 2D CNN successfully classified different damage states with over 95% accuracy, effectively identifying a damage state that was visually undetectable. Furthermore, the architecture proved to be highly versatile, effectively handling variations in the number of sensors. uncertainties included in the experimental data, and elevated levels of measurement noise.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. The radiomics features were non-reproducible with significant variability in values among different CT techniques. Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Radiomic feature robustness in CT scans affected by fiducial marker induced streak artifacts for patients with hepatocellular carcinoma.

Stereotactic body radiation therapy for hepatocellular carcinoma necessitates the implantation of gold fiducial markers in the liver, resulting in artifacts on computed tomography (CT) scans, which affect radiomic feature values. This report aims to assess the effect of these artifacts on radiomic features and how removing CT slices affects radiomic features extracted from 3D regions of interest (ROI). First, the range variation in 38 tumor contours unaffected by artifacts was assessed after sequentially and randomly removing 25%, 50%, 75% of slices. Subsequently, the agreement of feature values before and after removing ROI slices containing artifacts from 186 patients' CT scans was assessed with Lin's concordance correlation coefficient (CCC) and a Wilcoxon signed-rank test. In artifact-free tumor volumes, at least 71% of features remain robust with up to 50% of slices removed, while 56% remains robust with up to 75% of slices removed. When comparing contours before and after removing slices containing artifacts, around a third of features in the tumor contour and surrounding area remain robust (CCC>0.9), compared to 44% in the healthy liver. Concerning the tumor, 13% (Gray Level Size Zone Matrix) to 61% (first order) of the features remain robust (CCC>0.9). Over 90% of features differ significantly as assessed by Wilcoxon signed-rank test, however. This study demonstrates that removing slices containing artifacts is a feasible solution for the CT fiducial problem in this patient population and provides insight into which features are affected.

Deep image prior and weighted anisotropic-isotropic total variation regularization for solving linear inverse problems

Deep learning, particularly unsupervised techniques, has been widely used to solve linear inverse problems due to its flexibility. A notable unsupervised approach is the deep image prior (DIP), which employs a predetermined deep neural network to regularize inverse problems by imposing constraints on the generated image. This article introduces an optimization technique (DIP-AITV) by combining the DIP with the weighted anisotropic-isotropic total variation (AITV) regularization. Furthermore, we utilize the alternating direction method of multipliers (ADMM), a highly flexible optimization technique, to solve the DIP-AITV minimization problem effectively. To demonstrate the benefits of the proposed DIP-AITV method over the state-of-the-art DIP, DIP-TV, DIP-WTV and CS-DIP, we solve two linear inverse problems, i.e., image denoising and compressed sensing. Computation examples on the MSE and PSNR values show that our method outperforms the existing DIP-based methods in both synthetic and real grayscale and color images.

Printing and dyeing of halloysite nano clay hybrid with natural chlorophyll dye on cotton fabric

This work deals with the synthesis of hybrids formed by the natural dye chlorophyll and halloysite for their subsequent application in the textile field. Once the pigments have been synthesized, with 98 % adsorption, they are used for two textile colouring functions, namely printing and dyeing. In both cases, satisfactory and interesting results were obtained. The hybrids were characterized by FTIR, XRD, TGA, SEM-EDX, BET and all the appropriate colour measurements were carried out to obtain the coordinates in CIELAB space and also to know their total solar reflectance (TSR) 45.98 %. In addition, statistical techniques were used with the use of ANOVA to evaluate the results obtained. The dyeing was carried out by exhaustion and its use was compared with the use of nibbled and non-nibbled fabrics. The textiles were tested for fastness and the results are presented in instrumentally obtained grey scale values, achieving in some cases values of 4–5. Thus, the chlorophyll adsorption capacity of halloysite and its stabilisation for subsequent application in printing and dyeing processes on textile substrates has been demonstrated. This process could be scalable at an industrial level.

Phonon-mediated infrared plasmonic metamaterial emitters towards high-capacity multifunctional encoding and display

This study introduces what we believe is a novel approach to manipulating light in the mid-infrared spectrum through phonon-mediated metal-insulator-metal (MIM) cavities. Leveraging the unique interactions between resonantly excited electric and magnetic dipoles and phonons within silicon dioxide spacers, we have developed a technology different from traditional methods that rely on geometric modifications of nanostructures, offering a more versatile and effective means of tailoring light-matter interactions at the nanoscale. Our experimental results showcase the ability of these MIM cavities to perform multifunctional information encoding, display, and concealment with high precision. Notably, we encoded 13 distinct gray levels, surpassing previous capabilities in the long-wave infrared spectrum using metamaterial emitters. Furthermore, the incorporation of rotating nanorod structures enabled the encoding of grayscale patterns through polarization states, enhancing the potential for high-capacity information storage. The study also demonstrates the capability of these structures for subwavelength-resolution printing and near-diffraction-limit information encoding in the long-wave infrared band. We have successfully employed an innovative ink coating method, transparent in the long-wave infrared but opaque in the visible spectrum, to conceal encoded information, thereby adding a layer of security. In summary, the phonon-mediated infrared plasmonic metamaterial emitters presented in this work pave the way for future research in high-capacity information storage, anti-counterfeiting, and security technologies.

Research on detection method of photovoltaic cell surface dirt based on image processing technology

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.