Anisotropic noble metal nanoparticles supporting more than one localized surface plasmon resonance can be tailored for efficient dual-mode fluorescence enhancement by ensuring an adequate coupling to both absorption and emission bands of fluorophores. This approach is naturally extended to two-photon excitation fluorescence, where a molecule is excited by simultaneous nonlinear absorption of two photons. However, the relative impact of plasmon coupling to excitation and emission on the overall fluorescence enhancement can be very different in this case. Here, by using the finite-difference time-domain method, we study the two-photon excitation fluorescence of near-infrared fluorescent protein (NirFP) eqFP670, which is the most red-shifted NirFP to date, in proximity to a silver nanobar. By optimizing the length and aspect ratio of the particle, we reach a fluorescence enhancement factor of  In the past few decades, fluorescence microscopy has become one the most widely used techniques in biological imaging due to its ability to selectively reveal otherwise hidden objects of interest inside living cells and organisms . Along wNew variants of fluorescence microscopy are being continuously developed to improve spatial resolution, to increase sensitivity and to lower the detection limit of the method. One of the most successful approaches is based on two-photon excitation resulting from simultaneous absorption of two photons with lower energy . It provTwo-photon excitation fluorescence microscopy is particularly important for deep tissue imaging, since applications of fluorescent proteins are constrained by the opacity of tissues to excitation and emission light below 600 nm. Fluorescent proteins with excitation and emission wavelengths in the red and near-infrared range are therefore of high demand for whole body and multicolor imaging. The eqFP670 protein is currently the most red-shifted fluorescent protein, with excitation and emission peaks at 605 nm and 670 nm, respectively [/650 nm)  and near/650 nm) . HoweverFluorophore brightness can be significantly improved by its coupling to metal nanostructures, which support a localized surface plasmon resonance (LSPR) ,11. MetaThe capability of plasmonic nanoparticles to focus light in tiny volumes with manifold amplification of electric field is especially promising for enhancing efficiency of two-photon absorption (TPA) processes . In addiAchieving the highest possible fluorescence enhancement factors by simultaneous boosting of two-photon excitation and emission of a fluorophore depends on several factors: (i) strong enhancement of optical fields upon excitation of a low-energy LSPR mode in the near-infrared spectral region; (ii) existence of another LSPR mode in the visible range localized at the same hotspot; (iii) good spectral overlap of these modes with TPA and emission bands of a fluorophore; (iv) precise positioning of a fluorophore molecule at the hotspot. To design an efficiently coupled hybrid bionanosystem for fluorescence enhancement upon two-photon excitation, deep understanding of the involved processes and their relative contributions to the overall enhancement factor is essential.Here, by using finite-difference time-domain (FDTD) simulations we study the fluorescence enhancement efficiency of eqFP670 near silver nanobars (NB), crystalline nanoparticles with a shape of square cuboid. Experimentally, these nanoparticles can be synthesized by silver ions reduction via polyol route ,20,21. Tx axis, with edges and vertices rounded to 5 nm radius of curvature. The mesh step size of z, with linear polarization being either parallel or perpendicular to the axis x.To study fluorescence enhancement we used three-dimensional finite-difference time-domain method realized in the FDTD Solutions software package (Ansys). We employed a single emitter–single nanoparticle model. The nanobar is modeled by a block of silver oriented along the ure data . The eqFk, the excitation rate The fluorescence signal of an isolated fluorophore depends on the collection efficiency The fluorescence quantum yield is defined as a ratio between the radiative relaxation rate constant and the total relaxation rate constant, which accounts for nonradiative processes:The presence of a nanoparticle affects both the excitation rate constant and the quantum efficiency of the fluorophore. The fluorescence enhancement factor can be calculated as a ratio between the fluorescence signals of a coupled system and an isolated fluorophore. Since within a two-photon absorption regime the excitation rate is proportional to the fourth power of the electric field at the position of the fluorophore, The efficiency of a coupled emitter–antenna system can be found using the following relation :(4)QE=PrIn this work, the fluorescence enhancement factor is calculated in two steps using Equation . First, The efficiency of multi-modal two-photon excitation fluorescence enhancement by Ag NB is studied using the fluorescent eqFP670 protein.The LSPR spectrum of Ag NB exhibits two distinct dipole resonances, which can be excited by light with different polarization with respect to the long axis of the NB. For large enough particles, quadrupole (and higher order) modes become visible at shorter wavelengths. The normalized extinction spectra of Ag NB together with the calculated charge distributions are presented in l of the particle. Thus, by changing l from 180 to 250 nm, while keeping the aspect ratio equal to 1.55, the peak is shifted from 900 to 1200 nm.Spectral positions of LSPR modes can be tailored by tuning the size of the NB, as indicated in a at constant l , the position of the quadrupole peak can be independently adjusted to match the emission band of the fluorophore (see By changing the nanobar’s width hore see B.The highest amplification of the electric field is observed near vertices of the nanobar see . AccordiTo analyze the impact of excitation and emission enhancement on the overall fluorescence enhancement factor of eqFP670, we select particles with the length of 220 nm and various aspect ratios of 1.55, 2.50, and 3.50 for further studies. To address this question, we study how the excitation rate enhancement and the quantum efficiency change with the emitter–particle distance. We place the point dipole emitting light at The quantum efficiency of the hybrid system, on the other hand, exhibits the opposite behavior: QE tends to the value of an isolated emitter In the one-photon absorption regime, the interplay between the excitation enhancement and the quenching effect results in a nonmonotonic dependence of the fluorescent enhancement factor on the distance with a maximum, which is usually observed at 5–10 nm separation between the molecule and the plasmonic nanoparticle ,24. In cThe data obtained for eqFP670 placed at 3 nm distance to the corner of the nanobars with various aspect ratios are presented in Increasing the NB’s aspect ratio reduces the antenna quantum efficiency QE due to a blue shift of the quadrupole mode away from the eqFP760 emission band. At the same time, it leads to a stronger optical field at the TPA wavelength To summarize, for the hybrid system of the fluorescent eqFP670 protein placed close to the vertex of a silver NB with its dipole moment parallel to the long axis of the nanoparticle, the dual-mode coupling to both TPA and emission bands of the fluorophore is found to be less important than the local field amplification at the TPA wavelength. In this case, the efficiency of fluorescence enhancement is primarily defined by the fluorophore’s excitation rate, whereas coupling to the emission band has lower impact on the total enhancement factor. We expect that these findings are applicable to nanoparticles with other geometries provided that they exhibit reasonably strong localization of electric field at wavelengths coinciding with TPA bands of fluorophores. In the future studies, various shapes of nanoparticles are to be considered in order to gain simultaneous enhancement from both two-photon excitation and stimulated emission, while also keeping the near-field as high as possible.By using FDTD numerical simulations, we show that silver nanobars can be used for efficient fluorescence enhancement of near-infrared fluorescent proteins upon two-photon excitation. The amplified optical near field is localized close to vertices of the nanobar particles upon excitation of both dipole and quadrupole LSPR modes. The resonance frequencies of these modes can independently be tuned by changing width and length of the particles.We reveal that the higher NB’s aspect ratio leads to the stronger localized electric field upon excitation of the longitudinal dipole resonance mode. This allows us to reach enhancement factors of up to 10


The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time. Plant disease has become a serious threat towards the production as well as the provision of food security all over the world. For instance, it was reported that more than 800 million people globally lack sufficient food, about 10 percent of the world’s food supply is lost due to plant disease which significantly affects over 1.3 billion people who survive on less than $1 per day . It is wRice is amongst the widely consumed food in the world with the total consumption of 493.13 million metric tons in 2019–2020 and 486.62 in the year 2018–2019 . This haDisease prediction and forecasting of rice leaves are essential in order to preserve the quantity and quality of rice production since detection at the initial stage of the disease are useful in ensuring that timely intervention could be provided to convert the growth of the disease to facilitate the healthy growth of the plant for increasing the production as well as the supply of the rice . GeneralThe conventional means of rice diseases identification are often carried out manually and has shown to be unreliable, expensive as well as time-consuming. The mapping technique of rice disease detection is relatively clear and easier to perform; however, it is possible to misinterpret some extremely similar diseases that have negative impacts on the growth of the rice. The latest computer-based identification system is yet to be broadly implemented due to the large environmental effect, slow detection speed, and low accuracy. Hence, developing a rice disease detection technique that could provide quick and accurate decisions on rice diseases is of great significance. Rice diseases are localized in leaves, which can be directed by leaf diagnosis to guide growers on whether the crops should be sprayed. It is worth noting that to date, a substantial progress has been made in the detection of plant diseases through the leaf features .Many researchers have worked on the automatic diagnosis of rice diseases through conventional means such as pattern recognition techniques , supportSupport Vector Machine (SVM) technique was also utilized by The feature extraction from diseased and unaffected leaf images, the gray level co-occurrence matrix (GLCM) and the color moment of the leaf lesion region were implemented by Nonetheless, it is worth noting that researchers have also begun to move away from such techniques to deep learning models in an effort to detect diseases in various plants . The ConThese advanced techniques are used not only for the rice diseases diagnosis but also for some other crops including wheat , maize , pumpkinDisease spot identification is considered as the basis of recognition for rice leaf disease, as such the accuracy of spot identification directly impacts on the accuracy of recognition of rice leaf disease. Hence, when choosing the target detection algorithm, recognition accuracy should be employed as the key indicator. YOLO, SSD and Faster R-CNN are the mainstream algorithms for the detection of the deep learning target. Among them, the Faster R-CNN algorithm creatively proposes the RPN structure to generate candidate regions, making the target positioning very precise. In addition, Faster R-CNN also has strong advantages in detection accuracy compared to YOLO and SSD. The proposed study employed Faster R-CNN as the key research algorithm due to its efficacy in detecting the spot of the disease reliably.The data set for rice leaf disease is designed to provide a significant guarantee of the proposed model’s generalization capability. Here, diseased rice leaf images with standardized and complex backgrounds are captured both in the lab and in real field conditions to improve the robustness of the Faster R-CNN model. In addition, natural-diseased rice leaf images are processed to produce sufficient training images through data augmentation technology in order to solve the complexity of insufficient diseased rice leaf images and to avoid overfitting of Faster R-CNN models in the training phase.A Faster R-CNN network is employed for the real-time detection of rice leaf diseases. With the proposed deep-learning method, the discriminatory features of diseased rice images will automatically be classified, and the three major types of rice leaf diseases are recognized with high accuracy. Furthermore, the proposed method could manage all the rice leaf images collected from the rice farmland in real conditions.The development of a technique for automatic identification of rice leaf disease is hitherto faced many challenges. It is noted that the diagnosis, as well as detection, involves processes that could render the specific area in which the symptoms manifest within the rice plant very difficult to segment correctly. The capture conditions are hard to handle, which can make it harder to predict images and make detection of the disease more difficult. Moreover, the symptoms caused in different diseases can be identical visually, and the approaches of discrimination could be based on very tiny variations. Another very common issue is the discrepancies in the distribution of the data features to train the model as well as the data that could be used to validate the model. This situation creates overfitting problem. This is very important when plant diseases are automatically detected because the symptoms can differ from the geographical position and fall into the overfitting problem. It has also been observed that many of the suggested rice leaf disease diagnostic architectures are off-line, and only a few experiments have been carried out in real-time. Usually, the image resolution is enhanced in real-time by which the computational complexity should also be enhanced. In addition, the difficulty of real-time operations increases with a large variety of disease features, complex backgrounds and obscure boundaries of the disease symptoms. In order to address these challenges, the current study endeavors to employ the latest deep learning approach based on Faster R-CNN to conduct real-time detection of rice leaf diseases. The present investigation is sought to mitigate the lingering problems in the process of developing a system of diagnosing rice disease. The key contributions of the research are summed up as follows:The present manuscript is structured as follows: rice leaf diseases dataset (RLDD) generation techniques are implemented in the Materials and Methodology section. A detailed description of the development of the model for the detection for the rice leaf diseases is described in this section. Experimental outcomes to determine the accuracy of the proposed solution are described in the Results section, and the Discussion section exhibits a discussion on the comparison of the proposed model with other related studies along with limitations with prospective solutions for rice leaf disease detection approaches, followed by the Conclusion, which draws the outcome of the present study.Due to the absence of adequate data for real-time rice leaf disease, some of our authors and material resources were committed at the start of our study to collect diseased rice leaves. The patterns of diseases of rice leaves are varied over the season and other factors including moisture, temperature, different insects and illuminance. For example, most conspicuous symptoms of brown leaf spot disease occur on leaves and glumes of maturing plants. In real-time operation, the data collection process is very important since the inappropriate information in a dataset may hamper the experimental result. Hence, during the data collection process, the standard rule should be introduced and maintained.In this study, the rice images have been captured from the rice farmland and a different condition of the leaves were collected and brought to the lab. The rice leaf datasets were also collected from From the dataset, 600 images of rice blast, 650 images of brown spot, 500 images of hispa and 650 images of healthy rice leaf have been collected. A total number of 2,400 images were collected. The total number of images collected from each database (Kaggle and own dataset) are summarized in Data augmentation is the process of broadening the dataset to enhance the model’s performance by generating different forms of images. It also serves useful in mitigating the overfitting problem in the model during the training stage. The overfitting problem occurs when there is the presence of random noise or errors, rather than the underlying relationship. With the help of data augmentation, more image was generated from each image to train the model since some irrelevant patterns may occur during the training process of the model. For data augmentation operations, several techniques were used namely, rotation transformations, horizontal and vertical flips, as well as intensity disturbance which includes disturbances of brightness. A Gaussian noise processing scheme is employed in which the natural sources like thermal are responsible for the Gaussian noise. It is worth noting that in digital images, Gaussian noise interrupts the gray values. To train the model with training data set, Gaussian noise images were used for better results. With the above approaches, 7 new images are generated from each image as shown in Image annotation plays a key role in labeling the positions and classes of object spots in the disease and healthy images for multiclass object detection. In computer vision, Pascal VOC is the method which stores annotation in the XML file and the separate annotation files are saved for each image. The LabelIMG is the graphical image tool used for this process in VOC format which is developed in python. The Pascal VOC provides standardized image data sets for object detection. We constructed a file for every image of the dataset in the Pascal VOC. The XML file created includes information such as the bounding box coordinate values and the disease classes. For training purposes, 400 images were annotated for each class  from the dataset and the rest of the images for testing our model performance. Although the whole process is very challenging owing to the fact that the disease area seems to be tiny and difficult to detect a times, nonetheless, it is worth highlighting that a high detection performance in our model was observed. The A new effort Faster R-CNN  was launRPNs for candidate regionsFeature extractionThe main concept of RPN is to produce regions utilizing CNN that are explicitly suggested. The shared convolution network is fed by the rice leaf diseased images where feature map is achieved that is used as RPN input. The convolutional feature map points are the original image positions . The comThe processed RPN image is sent to the layer of RoI Pooling, which pools the areas of rice diseases. By further enhancing the SPP-Net algorithm, the Faster R-CNN algorithm suggests a region of interest (RoI Pooling). The RoI Pooling layer enables a number of dimensions to be transformed into a fixed size in line with the needs of the next fully connected network. Every rice disease candidate’s area is equally divided by the ROI pooling layer in M × N blocks and performs maximum pooling per block . On the Classification, regression and location refinementThe diseases are classified, and the position is refined by taking into account the pictures of rice diseases. The classification steps shall be: first, the classification of objects or non-objects for each of the two regions corresponding to the Anchor Box, k then models of regression . The formula  for the The training processes and loss functionThe rice disease location is determined by the size of the overlap region. The inaccuracy of the candidate’s frame and the slight overlap are often the main reasons for unreliable test results. Therefore, a judicious identification of the location is non-trivial towards attaining encouraging results. The eigenvectors achieved in the classification are determined by a complete connection and Softmax, and a species is generated with a probability of a certain rice disease species. The anchor box regression is used to compensate the region from its actual GT position, hence closer to the real position of the rice disease detection frame.The Caffe deep learning approach is used to carry out the experiment. The training set of rice diseases was sent randomly to the neural network for training. The model was tested, and the test results were analyzed after the completion of the training process. The following Faster R-CNN is optimized for a multi-task loss function . The muli is the index of an anchor, Ncls and Nreg respectively that are weighted by where, R is represented as a robust loss function.where, The complete architecture of a Faster R-CNN is presented in x, y, w, and h. The variables x, y, w and h also) are for the predicted box, anchor box, and groundtruth box respectively.where the center coordinates, width and height of the box are represented by Owing to the limited explanatory nature of CNN, visualization methods are often used to further understand the CNN features maps in order to decide how CNN’s can learn features of the different class evaluated. This experiment is carried out to comprehend better the variations between the feature maps extracted from different diseased rice leaf images. The visualization outcomes are shown in The visualization outcome for the healthy leaf is shown in The outcomes for the identification of rice leaf disease are shown in X-axis and corresponding Y-axis shows the training accuracy. The comparison of test accuracies of different pre-networks  are defined in terms of accuracy curve, as shown in Object detection algorithms like Single Shot Detector (SSD), Deconvolutional Single Shot Detector (DSSD) and Rainbow Single Shot Detector (R-SSD) essentially consist of two components. The first element is the pre-network model used to extract the basic features. The other is an auxiliary structure that utilizes multi-scale detection of feature maps. Various deep convolution networks including ResNet-101, ResNet-50, and VGGNet-16 , and MobWhen dealing with multiple classes of similar shape, classifiers may be confused. Infected rice leaf images on different levels or backgrounds can cause high complexity which leads to lower performance for the patterns displayed in the same class. The classification accuracy of a model can be visually tested using a confusion matrix. The entire dataset of our study is split into a training set and a testing set randomly in order to train and test the model. To evaluate the proposed model, the 50% dataset is used to train and the remaining 50% dataset is used to test. Total 8,400 observations are utilized for training the model, whereas another 8,400 observations are utilized for testing the model. The study shows that for the above three diseases and healthy leaf. Brown spot and hispa diseases are significantly differentiated from other diseases by their features and by their identification rates with 98.85% and 99.17%, respectively. In the healthy leaf study, the accuracy is achieved by 99.25%. According to the confusion matrix, it is apparent that the detection model is more prone to confusion in distinguishing rice blast and brown spot compared with other classes. Among 2,100 images in the testing set of rice blast spot, 31 images have been detected incorrectly as brown spot. On the other hand, among 2,275 images in the testing set of brown spots 20 images have been detected incorrectly as rice blast spots. This misclassification may be caused by the geometrical feature similarities between the two diseases. However, other classes are well distinguished. The confusion matrix describes the low inaccuracies in the identification of different classes in the present investigation.The research seeks to mitigate the loss of function, thereby reducing errors in the model. In doing so, every machine learning algorithm repeats calculations several times, until the loss is plateaued. The learning rate plays a significant role in order to minimize the loss function. In the proposed study, the learning rate is set to 0.0002. TensorBoard is a fantastic tool for viewing these metrics and finding possible problems. TensorBoard frequently upgrades the measurements and provides the outcomes to the user. In this purpose, the model trained with 50,965 iterations with the help of a training dataset. The comparison of the proposed model with existing related studies is represented in Most of the studies listed in The network looks at the whole image, and not in just one go but sequentially concentrates on part of the image. Thus, the algorithm requires many passes to extract all objects through a single image which is time-consuming. To address this issue, a network should be recommended which can extract objects of an image in a single pass.Since several processes have been conducted one after the other, the performance of the further system depends on how the previous system performed. Thus, a model should be trained carefully with appropriate datasets to achieve the desired performance.The misclassification issues could occur as a result of the geometrical feature similarities between the diseases. To overcome this obstacle, it should be required to train the network with more datasets which have similar geometrical features. It also recommended addressing more efficient deep learning algorithm which can classify the diseases containing small dissimilarities in features.In a real-time approach, the rice leaves conditions vary with the season having different humidity, temperature, and illuminance. Hence, some overfitting problem may emanate when random noise or errors occurs rather than the underlying relationship, as previously described . During Although the proposed model outperforms state-of-art rice leaf diseases detection methods, some of the drawbacks are also identified. Some limitations of this study with the prospective solution to address these challenges are as follows:The signs of infection appear in various sections of the plant, and leaves are widely used to diagnose the plant disease. The advanced computer vision technology encourages researchers around the world to carry out extensive experiments on plant disease recognition using leaf image analysis techniques. In the past few years, deep learning methods have notably been utilized to recognize plant leaf infection. This paper proposes a real-time rice leaf disease diagnosis framework based on the Faster R-CNN technique. The rice leaf infected image database consists of healthy leaf and three diseases, including rice blast, brown spot, and hispa. In order to enhance the robustness of the proposed system, our own captured rice leaf image is combined with a publicly available online database. Moreover, we have used several image augmentations schemes to enrich the dataset, which familiarizes the model with the different possible conditions of the image. This strategy also enhances the model’s performance and generalization capability. The obtained results of the proposed study are very encouraging to diagnose healthily and the different types of infected leaves in both laboratory-based images and real-field images. However, an additional study should be carried out to make segmented the infected portions of the leaf image by minimizing the surrounding interference. The existing rice leaf disease diagnosis systems are designed using laboratory-based captured images. Although we have implemented real-time disease recognition architecture using real field rice leaf images, the proposed system is still not fully automated. Therefore, further study should be carried out to implement a dynamic and automatic system to recognize large-scale rice leaf diseases. This system could be made up of a mobile terminal processor and agricultural Internet of Things that may be favorable to modernize the agricultural industry.10.7717/peerj-cs.432/supp-1Supplemental Information 1Click here for additional data file.


This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets  are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods—namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)—are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500. Triggered by the recent rapid rise in the price of cryptocurrencies such as Bitcoin, Ethereum, and Litecoin, a lively interest in research on these explosive bubbles has emerged as to whether the COVID-19 pandemic stimulated risky behaviors among financial investors. The major concern of this debate is that the digital asset markets have neither intrinsic value nor offer dividends. In the last couple of years, the cryptocurrency markets have launched an exponential increase in the demand for those assets, and thereby have witnessed a huge growth in the degree of market capitalization  model. Second, the DDC model was applied, which allows conditional correlations to vary over time Engle . This moWhile this paper focuses on three different but integrated methods, there are also many studies that use different techniques to show that the volatility spillovers in the cryptocurrency markets are still prevalent. These methods can be classified as follows: rolling sample analysis , central bankers , and investors , most of the major cryptocurrencies are speculative bubbles. For instance, Patterson  argues tOne of the core differences between cryptocurrency markets and other financial markets is the limit on the number of transactions. While the supply of traditional currencies is under the control of monetary authorities, the number of digital assets in circulation is processed independently. In other words, this non-authorized peer-to-peer payment network is used in a decentralized manner, and therefore, no single person or group has control of the blockchain system, which is assumed to be the digital ledger of transactions. However, the lack of authority in the supply of digital assets and the increasing rates of their returns have raised questions about the safety and stability of cryptocurrency markets. In the case of its potential to lead to financial instability, one of the most common ways to reduce the fast-growing cryptocurrency market is government-issued notices. The other most influential feature of the legislation is the issue of taxation Marian . HoweverRegarding volatility spillovers in the cryptocurrency market, the empirical results provided by the majority of studies indicate that information inefficiency is of primary importance  framework and dynamic conditional correlation (DCC) models  accounting and value formation  from November 17, 2019  to January 25, 2021, during which the prices of top cryptocurrencies started rising to unexpected levels. Therefore, the potential differences in cryptocurrency markets in terms of time-led variations in investors’ behavior caused is to move away from the time series approach adopted by Kumar and Ajaz , Ethereum (ETH), Stellar (XLM), Ripple (XRP), Tether (USDT), Cardano (ADA), Litecoin (LTC), and Eos (EOS), p, q, r) is obtained aslog of the conditional variance. This means that the leverage effect is exponential rather than quadratic. In this vein, the forecasting of conditional variance ensures that the estimates are non-negative. Moreover, As a primary concern of this section, the three different methods, EGARCH, DCC-GARCH, and wavelet-based models will be explained in their theoretical context to understand the degree of volatility persistence, which may not have been directly observable during the COVID-19 pandemic. First, we start with the EGARCH model Nelson  used to t-distribution. The covariance matrix is expressed as follows:The DCC-GARCH model Engle  was usedn in Eq. :3\documed in Eq. :4\documeThe standardized residuals, which is In this regard, the DCC model can be estimated by the unconditional covariance of the standardized distribution The dynamic conditional correlations are then given by:According to Engle , the estt-DCC-GARCH model that uses the devolatized digital asset returns represented as In addition to the Gaussian distribution of the DCC-GARCH model, Pesaran and Pesaran  also sugd in Eq. :10\documFinally, the empirical specification is based on multiscale correlation techniques using wavelet power spectrum, wavelet coherence, and wavelet cross-spectrum analyses. The wavelet models have a technical advantage for examining the relationship among various digital assets, both at different time horizons and frequency bands. Therefore, these models consider investors’ operations at different time scales. They capture the low- and high-scale effects of financial shocks that may occur within and across cryptocurrency markets. Theoretically, the wavelet approach is used to decompose the time series into different frequency components without confronting a loss in the time dimension. Therefore, the implementation of wavelet methods enables us to capture volatility spillovers in cryptocurrency markets for different time scales. A wavelet  is the highest among the selected digital assets. The average return of XLM (0.089) is the lowest return relative to the others. The standard deviation shows the risk or volatility of returns in cryptocurrency markets. The standard deviation of the BTC market returns is 6973, which is the highest, whereas the standard deviation of the USDT market returns is 0.002, which is the lowest among the selected cryptocurrencies. The skewness of seven out of eight of the return series is positive, which indicates that the overall performance of cryptocurrency markets is favorable and symmetric in a given return series; it is highly correlated with the overall behavior of investors during the COVID-19 outbreak. The kurtosis values are above 3, which means that the data are not normally distributed  test, and ARCH effect. The results are shown in Table 2 statistics and ARCH-LM statistics) along with different lag structures. The results show that all the series have ARCH effects. In addition, the series have a serial correlation in the case of both Q and Q2 statistics. Therefore, the residual diagnostic tests reveal that the GARCH family models can provide statistically reliable results compared to traditional ARCH models, which leads us to test the EGARCH and DCC-GARCH models empirically.Further, Table Table The parameter estimation results of the EGARCH  model are presented in Table ηj term implies that the size of the shock has a significant effect on the volatility of returns. The signs of the terms are positive for all cryptocurrencies, which show a positive relationship between the past variance and the current variance, implying that the larger the magnitude of the shock to the variances of series, the higher the volatilities over the markets.The results presented in Table Second, the leverage effect term (γ) gives us insight into how the sign of the shock impacts the future volatility of cryptocurrency returns. Except for the USDT market, the signs of the leverage effect terms are positive with statistically significant coefficients, which indicates that good news will increase volatility more than bad news of the same size. This is evidence of the leverage effect for those cryptocurrencies.Finally, the GARCH term (λ) provides us with insight into the persistence of past volatility and how past volatility helps predict future volatility. The signs of the GARCH terms differ for each cryptocurrency. Therefore, the significant feature of the coefficients shows that past volatility has a persistent effect on future volatility and its predictions. However, the problem for the EGARCH model to capture volatility spillovers across markets is its inability to provide co-volatility, as the model cannot predict volatility for two or more time series. The assumption is that the current volatility of one series is influenced not only by its own past process but also by past processes of volatilities of other series. Therefore, the next issue is to predict multivariate GARCH models using the DCC-GARCH approach.1 and λ2) and the correlation decay parameters (δ1 and δ2) of the Gaussian DCC-GARCH models for the cryptocurrency price series.The time-varying correlations and volatilities are significant in identifying the gains and returns of cryptocurrencies. The DCC-GARCH model with a Gaussian distribution was used to detect these points based on the maximum likelihood values. Table 1 and λ2 gives us insights for the case in which the conditional volatilities of the series are mean-reverting with gradual decay of volatility because the total values are less than 1 for each series, which indirectly indicates that univariate volatility estimations are reliable to account for only one time series.As shown in Table 1 = 0.95349 and δ2 = 0.02237, is equal to δ1 + δ2 = 0.97586, indicating that the conditional correlations are also mean-reverting since the total value is less than 1 for each series. This means that the markets slowly equilibrate their returns, and indicates that the shocks slowly decayed. Therefore, the results of the volatility and correlation decay parameters indicate that investors in these markets are less likely to be confronted with an immediate loss as a consequence of shocks.Furthermore, the sum of the correlation decay parameters, where δThe next step was to represent the unconditional volatilities through the diagonal elements of the covariance matrix. The results imply that the value of unconditional volatility in all markets is far from unity, which is considered to have low volatility in returns. Table indirect influence of risk-free assets on the risky asset may be more effective relative to the indirect influence between two risky assets. This also means that a low level of correlation indicates that the investors do not invest in only one cryptocurrency. Rather, they diversify their financial resources by buying different cryptocurrencies.Table Figure Figure Figure The second useful investigation was the wavelet coherence, defined as the square of the cross-spectrum, normalized by the individual power spectra. It shows how much the linear information of one asset is explained by the other, and thus, it can be used to estimate causality among the selected assets. Figures Furthermore, this study uses cross-spectrum analysis to investigate the nature of volatility spillover across different time horizons. In particular, it allows for an estimation of the coherence between different assets, indicating how much linear information is transferred from one to another at each frequency. Meanwhile, the estimation of the interaction strength between the selected cryptocurrencies is provided using wavelet cross-spectrum. From the plots represented in Figs. Results from the three different but integrated models imply that financial investors tended to move to assets where the prices of those assets were biased toward an increase in line with leading a surge in volatility transmission among the cryptocurrencies. Therefore, the economic implication is that the financial investors were substantially exposed to herding behavior during the COVID-19 pandemic. This can be considered as a critical way to understand why and how financial investors follow the same kind of behavior at the time of economic problems that arise from events such as the COVID-19 pandemic or global recessions. As a result of investors’ expectations of potential future earnings during abnormal times, many investors move away from markets where severe financial distress can occur toward different markets such as cryptocurrency markets to compensate for their financial losses. However, the main problem is the potential surge in risky behaviors in these markets, in line with an increase in herding behavior. Since many investors follow the same behavior in these markets, they also initiate an increase in the volatility rate for such assets, in which their prices rose rapidly over a short time.value-at-risk (VaR) and (2) the conditional value-at-risk (CVaR). The technical reason to use those methods is to measure the level of financial risk within cryptocurrencies over the COVID-19 pandemic, and thereby, to formulate an idea about potential explosive behavior. Further, the analysis also integrates Shanghai Composite and S&P 500 indices to compare the weight of different financial instruments in the presence of a speculative bubble. In other words, the potential for loss in different markets is evaluated through the implementation of VaR—which represents a worst-case loss associated with a probability and a time horizon—and CVaR—which is the expected loss if the worst-case threshold is ever crossed—methods, and thus, the possibility of occurrence for defined loss is determined to quantify the level of financial risk. Moreover, these methods control the level of risk exposure in which a surge in risk may presuade some giant investors to invest in that market, which may then result in an increase in the asset price and may tear down the financial markets in what is sometimes referred to as a “crash” or a “bubble burst”.Given that the volatility spillover was binding for BTC, ETH, and LTC from November 17, 2019, to January 25, 2021, the final step covers the analysis of whether the risky behavior for the crypto assets was valid at the time of the COVID-19 pandemic. Two integrated methods were implemented to identify any possible speculative bubble in selected digital currencies: (1) the The first step was to illustrate the safe haven features of eight selected crypto assets in a qualitative manner by analyzing the price dynamics and adding the price changes in the Shanghai Stock Exchange (SSE) and S&P 500 Index. The economic reason for detecting price movement over the selected time horizon was to provide an initial understanding of the safe haven characteristics of these digital currencies. During the decline in the SSE and S&P 500 at the first and second lockdowns, the selected digital assets substantially moved in lockstep with the market, decreasing by more than the SSE and S&P 500 over the two periods as shown in Fig. Table The VaR and CVaR estimations at the 1% and 5% confidence levels are also shown in Table In this study, we examined the return and volatility spillover across eight core cryptocurrencies with the help of the EGARCH model, DCC-GARCH model, and wavelet-based methods. Daily closing prices of those selected cryptocurrencies, namely Bitcoin (BTC), Ethereum (ETH), Stellar (XLM), Ripple (XRP), Tether (USDT), Cardano (ADA), Litecoin (LTC), and Eos (EOS), from November 17, 2019, to January 25, 2021, were used for the analysis. First, we identified Bitcoin as the core market in terms of the demand scale. The EGARCH model was utilized to detect the conditional variance of the closing prices of selected assets and to capture the leverage effects of shocks in terms of the relationship between shocks to variance and shocks to returns. We found that positive shocks had a greater impact on volatility than negative shocks  of the same magnitude , while it indicates the possibility of moderate and close to low volatility spillover for the rest of the return pairs. The maximum likelihood estimates of the Gaussian DCC model of cryptocurrencies show that volatilities could be mainly explained by their fluctuations. Further, the correlation structure between the selected asset pairs strengthened during the moment of shocks, especially for Bitcoin, Ethereum, and Litecoin prices, implying investor panic. This means that the COVID-19 pandemic has led to more integrated cryptocurrency markets, and thus has also stimulated herding behavior among financial investors.The final step was to apply the multiscale correlation technique using wavelet methods, which capture information across different frequencies without losing information from the given time horizon. Using these methods provided a way to investigate the relationship between various assets at different time scales and frequency bands by capturing the low- and high-scale effects of any shock that occurred within and across financial markets. We examined three types of wavelet methods, namely wavelet power spectrum, wavelet coherence, and wavelet cross-spectrum. The common point of these measures is that the possibility of the selected cryptocurrency markets to a large extent is significant in the short run, especially as depicted in the wavelet coherence analysis. In addition, from the wavelet cross-spectrum, it is found that volatility spillover is relatively high for three major cryptocurrency markets, namely Bitcoin, Ethereum, and Litecoin, which was exacerbated after the second lockdown effect in November 2020, but persisted up to 128 days. However, the others reflected a moderate spillover in terms of their volatility and existed only in the short run, up to 8–16 days.The empirical findings imply that cryptocurrency markets are largely assumed to be one of the core financial platforms where financial investors increasingly participated for higher returns during the COVID-19 outbreak. However, it led to three major outcomes: (1) increased levels of risky investments, (2) a greater level of herding in financial markets, and (3) exacerbating the nature of volatility spillover. While a moderate level of volatility offers a certain number of advantages to financial investors to diversify their assets, it poses some problems because the cryptocurrencies have no intrinsic value, and they do not offer any dividends or a specific returns. Therefore, these markets face notable concerns over circumstances such as legal position, safety, and transparency. Considering the apprehension about the ongoing increase in the prices of such cryptocurrencies together with unfettered demand, the increasing uncertainty and the level of risk may cause the dynamics of volatility spillover to change over time. In conclusion, increased unfettered demand for some major cryptocurrencies may lead to a serious loss of returns for many financial investors, resulting in a sudden unexpected decrease in prices.Although the empirical findings confirm that the volatility spillover in cryptocurrency markets is a widespread issue, there are some limitations to this study. Primarily, the methodological framework restricts the implementation of additional factors in empirical analysis. Therefore, following the general trend in the existing literature, empirical estimations are limited to some degree. In addition, although the presence of volatility spillover for the three major cryptocurrencies Bitcoin, Ethereum, and Litecoin are validated under the implementation of current methods, the reasons behind the volatility spillover can be estimated based on observations. This fact can also be generalized for other cryptocurrency markets used in the empirical analysis. Another critical limitation concerns the existing literature in which the studies are not extensive in terms of their role of exploring explosive behaviors on the volatility of digital asset markets during the COVID-19 pandemic. This restricts us from comparing the empirical results found in this study with those of others. Finally, the data selection process did not proceed with rule-bound data because the series were selected based on a comparison technique by looking at alternative data. However, even though the data selection process did not adhere to the rules, the empirical findings pursuant to the use of prices of selected cryptocurrencies that are derived from the alternative datasets validate the current results in the context of robustness checks. In light of these limitations and drawbacks, potential future directions for research are based on two things: first, future studies will be expanded using alternative methodologies, considering the presence of speculative-led behaviors and volatility spillovers in cryptocurrency markets during the COVID-19 pandemic. Second, future studies will use alternative models in which other volatility-based potential factors can be integrated into the analysis to detect reasons for a surge in speculative motives among investors and thereby volatility spillover.


The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples  with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images. In many cases, the estimation of particle size distribution of a nanoparticle population remains a major challenge for the industrial development of nanomaterials. Today, scanning electron microscopy (SEM) is widely used in laboratories and manufacturing industries and is considered in metrology as a reference technique capable of reliably determining the size, size distribution and shape of nanoparticles. It is a so-called direct technique because it is based on direct observations and the measurement result is directly traceable to the SI unit of length, the meter . The basImage segmentation is a long time challenge for the computer vision community and many different approaches were developed. These problematics are essential in the field of nanometrology where the studies of particles are based on microscopic imaging such as Scanning Electron Microscopy imaging (SEM) or Transmission Electron Microscopy imaging (TEM). We will focus in this section on the various strategies of automation of image segmentation.A first approach of image segmentation is based on classic image processing techniques, among which we can find watershed methods and its variants and adaptations ,4,5, hisA second approach is based on software tools such as ImageJ , IlastikA third approach is based on the development of image processing workflows, mixing general image processing methods and machine learning methods such as the EM algorithm  or the KThe final category of methods is based on deep-learning algorithms which we can separate on two distinct sub-categories: semantic segmentation and instance segmentation methods. Semantic segmentation methods are mainly using auto-encoders network such as UNet . These aTherefore, instance segmentation methods seem to be adapted to our case study and the specificity of the acquired TiOThe main algorithm used for instance segmentation is an algorithm developed by the Facebook Research Team (FAIR) called “Mask-RCNN” . It has The Mask-RCNN has become a standard in the deep learning community, being both generic and efficient. It can be seen as a two-stage algorithm: the first stage generates candidate object bounding boxes and the second stage predicts the class, bounding box, and binary mask for each region of interest (ROI). The convolutional backbone performs feature extraction over the whole input image and the head part ensuring both bounding-box recognition (classification and regression) and the prediction of the binary masks. A specific network called the Region Proposal Network (RPN), as its name indicates, computes the region proposals by using a sliding window  technique directly on the feature maps (backbone outputs) to generate the bounding boxes. The proposed bounding boxes, having different sizes, are mapped to fixed spatial resolution using standard bi-linear interpolation to produce processed feature maps. After this, a more common pipeline is setup so that, for each ROI, fully connected layers (FC) serve to predict the class and to refine the associated bounding box, and, simultaneously, a fully convolutional network (FCN) produces binary masks. Residual learning network (ResNet)  is the sFor more insights on the algorithm, please refer to the original article .The key step in any development using statistical learning is the creation of a function-specific database from which the network will learn how to make predictions on new data, never processed by the network, hence the systematic breakdown of entries into a learning, testing and validation database. If the Mask-RCNN algorithm is very powerful for computer vision tasks, it is nonetheless resource-intensive, in other words, its training requires a very large image database . In our case, it is simply unthinkable to manually segment so many images. W and L correspond to the width and length in number of pixels of each SEM image , C corresponds to the number of channels of the image  and finally N represents the number of available samples (N = 77). The annotations for each image in the reference database are stored in a JSON file which allows for representing a class instance (here a particle in the image) by an identifier and a binary mask (corresponding to the reference segmentation produced by each operator). The database built to perform the segmentation is currently composed of 77 images manually segmented by four operators trained by nanometrology experts representing 5947 particles of TiOData augmentation is very common in statistical learning, especially when a small database is available. This step should improve the robustness of the algorithm and artificially enrich the initial database by applying mathematical transformations on the input images such as rotation, translation, cutting,… The data enhancement proposed here is application specific. The main objective is to create “fake” data images as close as possible to real images without requiring any post processing to obtain the reference segmentation. We also want to avoid introducing any underlying logic in the generated images.Starting from the reference segmented images, each agglomerate of each segmented images is extracted to build a library of agglomerates . For exaIt is important to note that, during the phase of placement, no superposition among agglomerates should occur. While superposing agglomerates can create a totally new configuration of TiO the SEM .Thus, after inserting the first agglomerate in the frame, eight different sub-frames are “created” where the following agglomerate will be placed randomly . The proThe proposed data augmentation procedure is coupled with more common data augmentation strategy such as randomly applying one or multiple transformations among horizontal flip, vertical flip, Gaussian blurring, contrast normalization, additive Gaussian noise, and pixel value multiplication. While the work of creating the database is tedious, the choice of network hyper-parameters remains a matter for experts. Among the most common hyper-parameters in computer vision, one can list the learning rate, the optimization solver (Stochastic Gradient Descent (SGD), ADAM , MomentuOne of the first adaptations of the algorithm was therefore made to the Region Proposal Network (RPN). The objective of the RPN is to scan (by means of anchors) over all the feature maps extracted by the backbone network and scores the presence of object and estimates refining deltas for a better fitting of the anchor to the detected object.Anchors allow the network to detect multiple object of different sizes and scales. It is then necessary to adapt the sizes and aspect ratios of anchors to the objects we want to detect. We can also specify the number of trained anchors which must be of the same magnitude as the number of objects to detect and the anchor stride which specifies the number of pixel between two anchor positions.The sizes of anchors were modified according to the minimum and maximum size of TiOThe number of trained anchors was modified according to the maximum number of particle in one image: 1024;The stride between two consecutive anchors was set to 1 due to the agglomeration phenomenon.Thus, we adapt these hyper-parameters to the case study:The second part of adjustments was driven by the wish to improve the segmentation accuracy produced by the network. Indeed, this is the most critical aspect for our specific task. In order to do so, we modified the FPN Mask Graph, the network responsible to produce the final predicted mask. We simply augmented the “resolution” of the predicted mask by adding an additional transposed convolutional layer. This modification allowed to improve slightly the segmentation performances (improvement of 0.5 over the dice coefficient).Finally, we adjust several hyper-parameters of the algorithm to fit the specificities of our segmentation task, such as the number of maximum ground truth instance (corresponding to the maximum instance to detect in one image), the number of trained ROIs (corresponding to the number of ROIs to generate and to feed the head networks), and the use of mini-mask , the size of mini-mask roughly corresponding to the maximum size of a single particle  and, finally, the mean pixel value for image normalization . Other hyper-parameters linked with training strategy are detailed in Transfer learning is a deep learning technique allowing one to avoid training its network from scratch. Indeed, transfer learning consists of initializing the network weights from weights of another network (having the same architecture) but trained for another task. Then, it is only needed to fine-tune these weights according to the specific task. This technique is possible due to the fact that, during training, neural networks “learn” how to extract low-level features on shallow layers, while task specific features are extracted thanks to more deeper layers.The most common approach in transfer learning is to start the training by focusing only on the network heads (transferred weights in the firsts layers are kept fixed at this stage), and, then, after a chosen number of epochs, to train the whole network for the specific task (the whole weights are updated), in this case the detection of titanium dioxide particles. The weights transferred are from the algorithm trained on the MS COCO database . This daOur strategy of transfer learning in our application follows the common approach previously explained. Details about the training strategy are available in This section details the training strategy of the Mask R-CNN and the software and hardware specifications used during the training process.Our train set was constituted of 699 images, from which 77 were “real” images and 622 were images produced via the data augmentation strategy.As explained previously, the network heads were trained during 38 epochs with a learning rate of 0.001, four epochs with a learning rate of 0.0001. Then, we trained the all network during 28 epochs with a learning rate of 0.001 and finally seven epochs with a learning rate of 0.0001.Each epoch is made up of 698 steps, where each step uses two images per GPU during the head training phase and one image per GPU during the entire network training phase.We used a stochastic gradient descent optimizer with a momentum of 0.9 and with a gradient norm clipping of 5.0. We used Lht decay  of 0.000For more details about data augmentation strategy and transfer learning, please refer to the respective section.Our network was trained on a GPU NVIDIA GeForce RTX 2080 with 8 GB of memory (with the 26.21.14.4575 version of the driver).We used the 2.1.6 version of Keras with the 1.8.0 version of Tensorflow GPU library. We used the 7.6.5 version of Cudnn and the 9.0 version of Cudatoolkit.The test set is made of 19 images representing 3741 particles of TiOIsolated: the particle is completely imaged and located outside of an agglomerate,Complete: the particle is completely imaged and located in or near an agglomerate,Touch complete: the particle is completely imaged but interlocked with another particle (between the complete state and the masked state),Masked: the particle is partly hidden by an other particle,Unusable: the particle is masked by an other particle with a very small visible area The different images also show a great diversity of particle layout :IsolatedThe following results will be displayed accordingly to these configurations.In order to evaluate the detection performance of the network, the mAP metric from the COCOApi was used. Then, the Sørensen–Dice coefficient  between As specified previously, our test set consists of 3741 particles of TiOGPU NVIDIA GeForce RTX 2080 with 8 GB of memoryCPU Intel Core i9 3.60 GHzRAM 32GoThe test evaluation was performed using the same hardware as for the training:The segmentation task over the entire test set (19 images) was performed in 110 s, equivalent to 5.79 s per image or 0.035 s per detected particle. To give some points of comparison, a segmentation performed manually takes about 15 to 30 s per particle. If extended to the entire test set, it takes at least 15 × 3741 = 56,115 s (~15.6 h).Visually, we can note several interesting points. First, almost all particles were detected over the images, except for one particle in We can also underline the fact that the segmentation mask shows saw-tooth borders. This phenomenon is due to the rescaling step applied to the inferred segmentation masks in order to retrieve the original size.Overall, the Mask R-CNN algorithm was able to detect around 84 percent of particles. This value reaches 97 percent when we only keep useful particles . The detection performance is piloted by the hyper-parameter controlling the detection threshold and thus can be modified. The threshold value is set in order to detect almost all useful particle instances without generating false positives .To complete the evaluation of the detection performance of our network, we calculate the mean Average Precision  (mAP). mPreviously, we detailed the detection performance of the algorithm thanks to the detection scores and the mAP metric. Now, we want to detail the segmentation performance of the algorithm by measuring the Dice coefficient over the detected particles. These results are shown below and are expressed according to the type of the detected particle.Once again, the global performance of the network is satisfactory with a global dice coefficient of 0.936. The coefficient reaches 0.95 over useful particles. The dice coefficient drops to 0.90 when we are looking at “unusable” particles. This can be explained by looking at the definition of particles classified as “unusable”. We recall that they are masked particles, whose visible area is very small (less than 40 percent of the particle area), which makes them more difficult to segment.Finally, one can wonder how the dice coefficient is translated to the final measurands which are the area, the equivalent surface diameter, the Feret min diameter , the FerThe distribution shows a mean of 0.56 and a standard deviation of 2.48. Moreover, more than 96 percent of measurements show an error of less than 5 percent and 51 percent of measurements show an error of less than 1 percent .The distribution shows a mean of 1.06 and a standard deviation of 4.93.Bias: the residual distributions are globally centered on 0, the bias could come from the network itself, or from a bias in the reference annotations.Variance: the variance of the residual distribution is higher over the area measurand than for the Feret min diameter; in fact, a small error over the radius has a squared influence over the calculated area,Two observations can be made over these two distributions:We should highlight that the hardware is currently the main performance limitation. Stronger architectural modifications of the Mask R-CNN network will allow a sensitive performance improvement but need more GPU memory to process it.Thus, through this work, we successfully showed that the instance segmentation deep learning algorithm Mask R-CNN shows robustness and high performance when applied to SEM images of TiOThis work aims to fully automate the particle size characterization chain in scanning electron microscopy. Coupled with the work of  to complFinally, in this article, we did not tackle the post processing techniques in order to improve segmentation results. Several approaches exist in order to post process the optimal border location . It will


This paper presents a low-cost architecture that allows for beamforming with antenna arrays installed onto unmanned aerial vehicles (UAVs). Beam switching is proposed to improve the antenna gain towards the ground station with two three-element arrays installed below the wings of the UAV. The electromagnetic modeling of the complete structure (UAV and integrated antennas) was performed with commercial electromagnetic simulator Ansys HFSS. The radiation patterns were synthesized with particle swarm optimization (PSO). By employing lumped surface-mount device (SMD) components and switches, the design of the feeder to deliver proper excitation coefficients to the antennas is presented, and its performance was assessed by simulations. The proposed approach is demonstrated to be very effective with low-cost production. Unmanned aerial vehicles (UAVs) are an emerging technology. There are several applications and conventional technologies with lower costs where UAVs can perform. One example is in agriculture, where UAVs are being used for aerial spraying. Besides the lower costs involved, UAVs can fly safely at lower heights than ordinary aircraft can, and this is an important trait to minimize the drift of the poison or fertilizer being sprayed. Other important applications are environmental monitoring or disaster management. In these cases, large areas can be surveyed without exposing human beings to hazardous environments.In order to improve safety, sophisticated systems are integrated into aircraft for communication and navigation purposes . In 2,33,4,5,6, According to civil aviation regulations, it is mandatory to assess the radiation properties of antennas installed onto airplanes. A classical way to assess the installed performance of antennas is by inflight measurements . The maiAnother way to assess the radiation properties of antennas installed onto aircraft is by means of electromagnetic simulations. In , a compaIn the above references, analyses considered that airframes are made of perfect electric conductors. This assumption is appropriate for most commercial aircraft. However, UAVs are built with parts made of plastic, wood, and other nonconductive materials. Therefore, another approach for simulations needs to be taken into account. Analysis of a UAV composed of dielectric materials was presented in , where aIn this paper, a new architecture to use switchable beams is proposed. Although the same UAV is considered as in , operatiThe paper is organized as follows: For aerial vehicles, smaller antennas have less impact on aerodynamics, so higher frequencies may be preferred, especially for the integration of antennas onto UAVs. Therefore, the present study was carried out at 2.4 GHz, which is also an ISM frequency and unlicensed in many countries.x axis  format. Attenuators were designed using standard enuation . Phase sse shift . The resIn order to verify the performance of each monopole, a preliminary simulation was run in Ansys HFSS electromagnetic simulator. Computations were set up using a combination of the finite-element and the boundary integral (FEBI) methods. After the mesh optimization process, the complete structure was modeled with 410,913 tetrahedra. For this analysis, only one antenna was fed at a time while terminating the remaining monopoles with matched loads. The resulting radiation patterns are shown in The effect of impedance matching and mutual coupling was also assessed by analyzing the scattering parameters (S parameters) of the arrays. Curves as a function of frequency are shown in n-th particle can be represented mathematically byi-th excitation coefficient, respectively, to be imposed at the terminals of the i-th antenna.Radiation pattern synthesis was widely reported in the literature. In simplified formulations, isotropic antennas are considered ,22. In tThe ability of a particle to solve the optimization problem is measured by a fitness function that is based on the error between the desired and the calculated radiation pattern according to . A symmeOptimization was performed considering sidelobe level Case 1: Case 2: Case 3: Swarm size: 30 particles;Maximal number of iterations: 35;Inertia  were considered, where each case stands for one or two directions where the main beam (or main beams) are to be indicated. The PSO was used to optimize the excitation coefficients so as to point the main beam to the following directions:Case 1: the main beam should be pointed towards the UAV front part or Case 2: the main beam should be pointed to Case 3: the main beam should be pointed towards the right wing or For array 1:Case 1: the main beam should be pointed towards the UAV front part or Case 2: the main beam should be pointed to Case 3: the main beam should be pointed towards the left wing or For array 2:After running PSO, the optimized excitation coefficients are listed in The radiation patterns for Array 2 are shown in To impose the beamforming coefficients at each antenna terminal, the proper hardware needs to be designed to suit the beamforming strategy. In the present case, the goal was to produce switched beams with low-cost hardware. One of the possibilities was to digitally produce the beamforming coefficients with a digital-processing unit, which demands the use of digital-to-analog converters. The drawback of such an approach is the need to generate baseband signals with proper magnitudes and phases, which need to be upconverted to the RF operating frequency. This demands electronic circuitry along with a power divider to deliver the local-oscillator signal to the upconverting mixers. Hence, the needed hardware may be complex for integration into a UAV. Another strategy is to use digitally controlled variable gain amplifiers and phase shifters. The main drawback is the cost of these components.The two approaches described above allow for beamforming with large flexibility. However, in the proposed architecture, for the synthesis of only three beamforming conditions, a much simpler feeder can be designed. The attenuators and phase-shifting units can be produced of passive components, yielding a cost-effective solution. As shown in s and p denote series or shunt connection, respectively.The attenuator topology was composed of three SMD resistors with 0402 dimensions in a The performance of each RF channel was verified by means of simulations with Ansys HFSS. An electrical simulation without considering the geometrical details of the implementation is not enough to obtain the desired performance, as demonstrated in , due to The transmission coefficient between inputs and outputs of the feeder for Antennas 1 and 4 is shown in The performance of the feeder for Antennas 2 and 5 is demonstrated in The feeder for Antennas 3 and 6 was also analyzed with the electromagnetic simulator. The transmission coefficients are shown in The connection of the RF switches with the attenuators and phase shifters was modeled in cosimulation with Ansys Designer. The switch itself was considered as an S-parameter block, which was set up with the S-parameter data provided by the manufacturer. The chosen model for the present application was HSWA4-63DR+, produced by Mini-Circuits. The performance of the resulting circuits is shown in After the complete electromagnetic modeling of the feeder channels, the UAV along with the integrated antennas was simulated again. The excitation coefficients were set to be the values presented in This paper presented an architecture for the beamforming of antenna arrays installed on an unmanned aerial vehicle (UAV). Analysis included extracting the radiation pattern of individual antennas (embedded patterns) in the presence of the UAV structure. As verified in the literature, only few papers dealt with this topic in a systematic approach.The beamforming of antennas taking their interaction with the UAV airframe is a complicated electromagnetic environment that can only be dealt with an optimization technique, since classical array theory cannot be successfully applied. The application of particle swarm optimization (PSO) allowed for obtaining excitation coefficients that resulted in radiation patterns with beams pointed to specific directions, hence allowing for beam switching. By considering only three beamforming cases, an angular range of nearly 180Simple feeders composed of passive lumped SMD components and RF switches were designed to yield a practical approach for beam switching. A thorough electromagnetic analysis of the feeder was presented, whereby the desired attenuation and phase-shift values had been obtained. Lastly, with excitation coefficients produced with the designed circuitry, the final radiation patterns were very close to the originally synthesized patterns with PSO, with only small deviations visible for beamforming Case 2. These deviations appeared because the phase-shifting sections also introduced additional insertion loss, and the obtained coefficients thereby deviated slightly from the desired ones listed in


The COVID-19 pandemic is one of the most devastating public health emergencies in history. In late 2020 and after almost a year from the initial outbreak of the novel coronavirus (SARS-CoV-2), several vaccines were approved and administered in most countries. Saudi Arabia has established COVID-19 vaccination centers in all regions. Various facilities were selected to set up these vaccination centers, including conference and exhibition centers, old airport terminals, pre-existing medical facilities, and primary healthcare centers. Deciding the number and locations of these facilities is a fundamental objective for successful epidemic responses to ensure the delivery of vaccines and other health services to the entire population. This study analyzed the spatial distribution of COVID-19 vaccination centers in Jeddah, a major city in Saudi Arabia, by using GIS tools and methods to provide insight on the effectiveness of the selection and distribution of the COVID-19 vaccination centers in terms of accessibility and coverage. Based on a spatial analysis of vaccine centers’ coverage in 2020 and 2021 in Jeddah presented in this study, coverage deficiency would have been addressed earlier if the applied GIS analysis methods had been used by authorities while gradually increasing the number of vaccination centers. This study recommends that the Ministry of Health in Saudi Arabia evaluated the assigned vaccination centers to include the less-populated regions and to ensure equity and fairness in vaccine distribution. Adding more vaccine centers or reallocating some existing centers in the denser districts to increase the coverage in the uncovered sparse regions in Jeddah is also recommended. The methods applied in this study could be part of a strategic vaccination administration program for future public health emergencies and other vaccination campaigns. Due to the rapid spread of the novel coronavirus disease COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), governments worldwide started to take action to contain this pandemic. Within a year from the start of the pandemic in March 2020 [9, causedWithin the first few months of the spread of the novel coronavirus disease, several test types and testing protocols for COVID-19 were approved and implemented . Hence, Fortunately, after almost two years from the initial COVID-19 outbreak in December 2019 and with international vaccine-development efforts, the approval for the emergency use of the first mRNA COVID-19 vaccine, Pfizer–BioNTech, was issued by the U.S. Food and Drug Administration (FDA) on 11 December 2020 . Since tAccording to the vaccine supply and the size of the population, public health authorities in most countries prioritize the distribution of the vaccine by targeting high-risk groups, such as healthcare workers, the elderly (over the age of 65 years), and individuals with medical conditions who are more likely to die or suffer severely from COVID-19. Additionally, as recommended by WHO , high-riDuring health emergencies and disease outbreaks, such as the COVID-19 pandemic, it is important to ensure a timely and equitable administration of medical care and health services for all individuals in the affected community. A successful emergency response requires identifying and managing locations or facilities accessible to the entire population to deliver basic needs and health services at these points. Spatial analysis and geographic information systems (GIS) technology can be successfully applied to assess the equitable allocation of sites for PODs (points of dispensing), where medications, vaccinations, or other health services can be provided for a large number of people during public health emergencies.Since the discovery of COVID-19 and its rapid spread across the world, spatial analysis has played a significant role in the study and mapping of the spread of the disease ,11. SeveIn the case of mass COVID-19 vaccinations and the scale of the expected coverage, GIS tools can help in planning the allocation of vaccine sites based on a set of potential candidate locations that can serve as vaccination hubs. GIS technology can map the vulnerable populations who might have problems reaching vaccination centers, such as people in rural or remote areas ,21. AddiThe first confirmed case of COVID-19 in Saudi Arabia was reported on 2 March 2020 . In JeddSaudi Arabia was one of the first countries to establish COVID-19 vaccination administration on 17 December 2020 using Pfizer–BioNTech, AstraZeneca, and, more recently, Moderna, with a target to vaccinate 70% of the adult population, including citizens and residents in the country . Close tThe goal of this study was to analyze the spatial distribution of the COVID-19 vaccination centers in Jeddah. In particular, it aimed to examine vaccination centers in Jeddah using GIS tools and methods to provide insight into the effectiveness of the current locations of the COVID-19 vaccination centers in terms of accessibility and coverage. An evaluation of the spatial distribution of the COVID-19 vaccination sites will facilitate the decision-making process for adjusting the number and locations of these designated centers to fulfill the expected vaccination rates for the entire population in Jeddah.The rest of the paper is organized as follows: Jeddah is one of the major cities in the region of Makkah, which is one of the 13 administrative provinces of Saudi Arabia. The city of Jeddah is located in the western region of the Kingdom ; its cenea level . The geotilities .The annual population growth rate has risen to 3.5%, mainly due to job opportunities in the city as well as its proximity to the busiest international airport, the largest seaport in the region, and the Holy City of Makkah . The aveIn addition to being one of the major cities in Saudi Arabia, Jeddah was chosen for this study mainly due to the availability of the required data and the significant population expansion over the last decade. The data acquired from the Jeddah Municipality and used in this study include population and district data. The numbers and locations of the COVID-19 vaccination centers in Jeddah were obtained from the published data provided by the Saudi Ministry of Health (MoH) in 2020. These data for the vaccination centers were updated for the additional vaccination centers established in 2021. All collected data were first imported into the ArcGIS platform and then projected to the Universal Transverse Mercator (UTM) 37 N coordinate system for further investigation. The data were preprocessed using a GIS environment to remove outliers, yielding a new weighted point feature class, used as input for the subsequent spatial-analysis phase. The methodology of this research work consists of five steps. First, spatial data were first imported into ArcGIS  for data processing. Then, in the second step, kernel density estimation (KDE) was employed to determine the hotspot locations of the vaccination centers established in 2020 in Jeddah. KDE is a powerful GIS approach used to comprehend and potentially indicate the patterns of a spatial event . In the Kernel density estimation is a statistical method that is classified as a technically advanced approach in point–pattern analysis. KDE is generally used to map and evaluate hotspots, which helps in the visualization of results. The main purpose of KDE is to comprehend and potentially indicate the patterns of a spatial event . By impls, n is the k is the kernel algorithm, which is a function of the bandwidth and distance [The KDE technique calculates the density for each zone or district by changing the values within the search radius for that reference point. The density is the greatest at the reference point location, reduces with distance, and converges to zero at the specific radius when the distance decay effect is taken into consideration . The foldistance . Variousdistance ,35,36.Regardless of the nature of the kernel function, the selection of the suitable grid size and bandwidth is significant for calculating accurate results ,37. An uThe bandwidth and cell size are the two most important elements that influence KDE analysis. The KDE’s output is often shown in a raster dataset, which consists of a grid of pixels. To simulate a cluster that is observed in reality, the cell size must be appropriately determined. In addition, the size of the cluster depends on the computational time, sample size, and amount of data to store. Larger grid cell sizes speed up processing; however, information is more likely to be averaged over a larger area, which may lead to oversmoothing and the loss of valuable hotspot information. On the other hand, selecting a small grid size may make it time consuming, rendering the analysis process burdensome.To estimate the spatial autocorrelation, first, a default minimum distance is chosen. The ISA technique then evaluates the distance between features to ensure that each segment has a neighbor. In this research, 2 km (kilometers) was set as the default distance for the ISA technique. The spatial autocorrelation values are then computed by increasing the distance incrementally.The ideal threshold distance is the one with the highest Z-score. The threshold distance in this investigation was determined to be 2 km. This determined distance was utilized in the spatial mechanisms to generate clustering in the data.The spatial connection model used in this study to estimate  I value . The Z-si and adjacent locations j. n refers to the numbers of vaccine locations, and i and nearby locations j. Moran’s I index is denoted as follows :(2)I=∑i=The threshold distance acquired was then used in the hotspot technique (Getis–Ord) to determine the statistical significance of the hotspots detected by KDE.Getis and Ord (1992) developed the tigation . The foltatistic .(4)Gi* might be at play, affecting the production activities at different groups of sites. An alternate approach utilized in this study is the quantile approach, which classifies the predicted values into distinct groups. In this approach, groups of zones are generated and divided evenly across the classifications, resulting in an equal number of grid cells in each zone, as shown in The weighted overlay uses numerous layers to solve the multicriteria issues using a standard measurement scale according to importance. After preparing input layers, including the population distribution map, KDE map, and hotspot distribution map, all layers were then reclassified in the same scale. The population map in The locations of the vaccination centers in 2020 were spatially digitized using the ArcGIS platform, as shown in KDE analysis b was conOverall, experimenting with fixed intervals to determine which findings make the most sense is recommended . PreviouA total of 12 vaccination centers were observed in our study area of the Jeddah districts as shown in Overlay analysis was performed using the KDE output according to the locations of the vaccination centers in Jeddah in 2020 with the population and district distribution. The results show that most populated regions were not covered by vaccination centers, as shown in When KDE was overlaid with Jeddah districts, it was observed that, out of 90 districts, only 50 districts were covered or had access to vaccination centers, as shown in Kernel density analysis could show where the clusters in our data were located. However, we could not determine whether these clusters were formed randomly or as a consequence of an underlying spatial mechanism. Therefore, we conducted a subsequent analysis to both discover patterns and assess their significance. This subsequent analysis enabled us to discover underlying patterns, assess their significance, and ensure that conclusions were drawn based on reliable resources other than human visual perception.The Getis–Ord Gi approach is one of the analytical tools used to determine spatial dispersion. This statistical tool mainly relies on hotspot and coldspot analyses to determine the spatial distribution of data. To check for the hotspots and coldspots in the coverage of the vaccination centers, a hotspot-analysis algorithm was used. We used hotspot analysis to determine the groups of low and high points in the data. With the p- and z-values obtained, we then assessed how statistically significant these clusters were, with 99, 95, or 90 percent confidence.The main feature (vaccination center location) in the entire dataset was considered in hotspot analysis. This feature is assigned a value when the components are aggregated together. The value represents their count inside the aggregation region. A neighborhood is a collection of elements that surround a feature, including the feature itself. Although a high-value trait is intriguing, it may or may not be statistically significant. A component with a high value must be surrounded by another high-valued feature to be statistically significant. A statistically substantial z-score is obtained when a feature’s sum and neighbors are proportionally associated with the total number of features.A statistically significant z-score is calculated when the local sum differs considerably from the expected local sum. Such a significant difference is considered to be too high to be caused by randomness. When the data have more clustering of high values, the z-score for statistically significant positive z-scores becomes high. The denser the clustering of low values, the smaller the z-score for the statistically significant negative z-scores (coldspot) as shown in z-score is calculated when the local sum differs significantly from the expected local sum. The difference is too high to be a consequence of random choice. When the data have increased clustering of high values, the z-score for statistically significant positive z-scores becomes high. The denser the clustering of low values, the smaller the z-score for statistically significant negative z-scores (coldspot). Due to our data containing points, we utilized hexagonal bins to aggregate them.A statistically considerable The main objective of hotspot analysis is to determine if values are high or low. The results of the Getis–Ord Gi approach show that the red area in Hotspot density analysis is a geographical analysis and mapping approach that focuses on detecting the grouping of spatial phenomena. These spatial phenomena are represented on a map as points that correspond to the locations of events or objects. The regions of nominal values suggest that the lowest densities are dark blue and light blue. The areas with high-density values, i.e., hotspot zones, are red and light red.In 2021, the Saudi Ministry of Health established 30 new COVID-19 vaccination centers in the city of Jeddah. The locations of these new vaccinati using KDE and Getis–Ord Gi approaches was recommended to enhance the usefulness of the results. However, in this study, we only performed KDE analysis for the new vaccination centers in 2021, mainly because the spatial analysis of the vaccination centers in 2020 showed 95% similarity for both KDE and hotspot analysis. The KDE analysis for the vaccination centers in 2021 indicates that the blue areas are within a radius of 2 km , which have full access to vaccines and vaccination centers. The coverage of the vaccination centers was notable and shows enhancements compared to the 2020 coverage of vaccination centers. However, it was observed that a few areas  from our study area were still left uncovered by vaccination centers. We note that the vaccination locations are mainly concentrated in the vicinity of the city center, as shown in zone A in The KDE analysis of the vaccination centers for the year 2021 was compared with the population, which showed that 42 vaccination centers covered almost all of the highly populated areas. Still, low-to-medium-populated areas remained uncovered. The maximum population coverage is attained by these 42 vaccination centers, as shown in Based on the spatial analysis of the vaccination centers’ coverage in 2020 and 2021, coverage deficiency would have been addressed earlier if the GIS analysis methods presented in this study had been used by authorities while increasing the number of vaccination centers gradually . Of course, the current approaches do not consider other factors in determining the optimal locations of the vaccination centers, such as time constraints and cost. However, the spatial analysis applied in this study would show the missed opportunity between the initial 12 vaccination sites in 2020, the intermediate 22 or 27 sites, and the current 42 vaccination locations in 2021.The COVID-19 pandemic negatively impacted many aspects of life, including travel, education, the healthcare system, and the livelihoods of many people. Vaccination is the most effective approach to rapidly eradicating the pandemic’s effects. Due to the rapid spread of the disease, it is necessary to establish vaccination facilities outside hospitals to maximize vaccine coverage. Saudi Arabia, similarly to many countries, developed a COVID-19 vaccination plan to establish vaccination centers across the entire country. In this research work, we examined the significance of the locations of COVID-19 vaccination centers in the city of Jeddah.KDE analysis was conducted for the vaccination center data for 2020 and 2021 using district and population data for the city of Jeddah. Then, a hotspot analysis was conducted to identify the significant hotspots and coldspots and to determine clustering information and validate KDE analysis. The results revealed a lack of coverage based on the vaccination centers established in 2020. A significant improvement was observed upon repeating the analysis with the additional vaccination centers established in 2021.This study aimed to inform policymakers and other stakeholders about the regions in Jeddah that are in need of new or additional vaccination facilities. However, other factors, such as time constraints and cost, also need to be considered to optimally determine the locations of vaccination centers. Based on the results of this study, we recommend that the Ministry of Health in Saudi Arabia evaluate the assigned vaccination centers to include less-populated regions to ensure equity and fairness in the vaccine distribution. It is also recommended to add more vaccine centers or reallocate some existing centers in the denser districts to increase coverage in the uncovered sparse regions of Jeddah.The methods applied in this study could be used in the implementation of a strategic vaccination administration program. The various evaluation maps created using these methods could be used to determine the requirements for establishing vaccination centers. The scientific evaluation of vaccination locations using GIS technologies is crucial, especially during the spread of highly contagious diseases such as COVID-19. The work presented in this study could be a starting point for further research to apply location-allocation algorithms to suggest optimal or nearly optimal sites for vaccine centers in Jeddah to ensure the coverage of the entire population.