It is difficult to identify the ship images obtained by a synthetic aperture radar (SAR) due to the influence of dense ships, complex background and small target size, so a deep learning-based target detection algorithm was introduced to obtain better detection performance. However, in order to achieve excellent performance, most of the current target detection algorithms focus on building deep and high-width neural networks, resulting in bloated network structure and reduced detection speed, which is not conducive to the practical application of target detection algorithms. Thereby, an efficient lightweight network Efficient-YOLO for ship detection in complex situations is proposed in the present work. Firstly, a new regression loss function ECIOU is proposed to enhance the detection boxes localization accuracy and model convergence speed. Secondly, We propose the SCUPA module to enhance the multiplexing of picture feature information and the model generalization performance. Thirdly, The GCHE module is proposed to strengthen the network’s ability to extract feature information. At last, the effectiveness of our method is tested on the specialized ship dataset: SSDD and HRSID datasets. The results show that Efficient-YOLO outperforms other state-of-the-art algorithms in accuracy, recall and detection speed, with smaller model complexity and model size. Accurate object detection has great scientific and practical significance in ocean, forestry and traffic navigation, and has always been a research hotspot in the field of remote sensing. However, remote sensing images obtained from satellite sensors have different points of view compared with images directly acquired in real life (images directly observed or photos taken by humans). These remote sensing images contain diverse complex landscapes and are often more susceptible to atmospheric, background clutter, light differences and have less spatial details , which mIn SAR image ship target detection, the Constant False Alarm Rate (CFAR)  algorithGenerally speaking, the existing target detection methods can be divided into two-stage detection and single-stage detection. The two-stage detection algorithm divides the detection process into two stages. Firstly, the pre-selected boxes are generated by the region candidate network, and secondly, the classification and regression of the pre-selected boxes are realized by the detection network. Therefore, the algorithm has good positioning and target recognition accuracy, such as the R-CNN series ,9,10. HoInspired by these advanced methods, many remote sensing experts have tried to introduce deep learning into the field of SAR target detection ,18. In lThe detection accuracy and detection speed seem to be a pair of natural contradictions. Greatly improving the detection accuracy will bring the network structure of the deep learning-based target detection algorithm bloated, resulting in low real-time and extending the training time. However, in the pursuit of efficient speed, it is necessary to lightweight the network and streamlines the network structure, but this may lead to the network structure being too thin to fully extract the feature information and location information of the target to be detected, which will greatly reduce the detection performance of the network. For example, YOLOX-M has a simplified network structure and a small model size, but its detection performance is inferior to that of YOLOv4, which has a similar model architecture . YOLOv4-Therefore, in order to verify the above ideas, we propose an efficient and lightweight object detection algorithm Efficient-YOLO for ship detection in complex situations in this paper. Compared to other state-of-the-art methods, the detection framework proposed in this paper has less parameter amount and computational complexity and higher detection speed, while maintaining satisfactory accuracy. The main contributions of our work are summarized as follows.1. A new regression loss function Efficient Complete Intersection over Union (ECIoU) is proposed to improve network performance without increasing network complexity. Its formula complexity is low, the effect is robust, the convergence speed is fast, the detection frame positioning accuracy is high, and the practical application performance is excellent.2. Combined with Shuffle Depthwise separable convolution Based on ReLU6 (SDBR) module and Upsample-S module, a new feature fusion network SDBR-based style Channel Upsampling Path Aggregation (SCUPA) module is proposed. To put it simply, firstly, the SDBR convolution block is used to interactively fuse all channel information to enhance the reuse of image feature information and effectively improve the generalization performance of the model. Secondly, a new upsampling module (Upsample-S) is applied to further avoid the loss of image feature information caused by traditional upsampling operations.3. A Gate Channel Head Embedded (GCHE) module is proposed to reduce the interference information in the network structure and enhance the ability of the network prediction part to extract feature information.N indicates the number of examples in the mini-batch, C is the number of channels, H and W indicate spatial dimensions [N represents the number of samples, C represents the number of channels, and The SRM  is a simmensions ), we obtThe gating mechanism has been successfully applied in several recurrent neural network structures. The Long Short Term Memory Network (LSTM)  introducGCT  is a simFirstly, the Secondly, the channel normalization obtained by Finally, the obtained information is input into the tanh function to obtain the final output through gating adaptation.M and N represent the input and output channels, respectively. Depthwise separable convolution  is a conAlthough the current deep learning target detection algorithms have good performance, most of them have large size and computation, which is not conducive to the practical application of object detection algorithms.To further obtain higher accuracy and smaller computation, the backbone network of this paper is built according to a reasonable architecture based on the MobileNetv3 basic modules, where BneckX represents X MobileNetv3 basic modules, while choosing P3, P4 and P5 as three different feature layers. The SPP module and the SCUPA module proposed in the present paper are selected as the neck network to realize the fusion of low-resolution and semantic strong features with more efficient high-resolution features. The GCHE module proposed in this paper is chosen as the prediction network, the ECIoU loss function serves as the regression loss function, and the cross entropy loss function is used to detect the confidence error and classification error. To significantly reduce the network parameters while maintaining the network detection performance without great loss, all 3 × 3 convolutions in the Efficient-YOLO proposed here employ deeply separable convolution. Due to the superiority of the YOLOv4 network architecture, the network model proposed in this paper basically follows its architecture. Determine experimentally that the proposed Efficient-YOLO network model can greatly reduce the number of parameters, and achieve higher accuracy and faster detection speed. As shown in Currently, an increasing number of lightweight CNNs can significantly reduce the number of parameters and reduce the demand for computational resources, and gradually become the mainstream network in the field of deep learning.MobileNetv3 network  is a ligThe neck network consists mainly of SCUPA, Spatial Pyramid Pooling (SPP) , an SDBRThe channel shuffle mechanism  reorganiBased on the cubic and quintic convolution blocks in Path Aggregation Network (PANet) , we propThe path aggregation network (PANet) structure adds a top-down structure to the structure of the feature pyramid network (FPN) , which iHowever, PANet has two disadvantages, affecting the fusion of network feature information. Firstly, a large number of five convolution blocks are directly used to extract the picture feature information. Although the detection effect is good, it does not make full use of the feature information, and the network model parameters are too large. Secondly, PANet uses the nearest proximity interpolation method to upsample the extracted feature map, resulting in the loss of the feature map information, making it difficult for the network to distinguish the main information of the feature map, and will blur the expression of the target feature information to be detected.To address the above problems, the SCUPA module is presented in this paper, as shown in In 2015, the SPP module was proposed to convert feature maps of any size into fixed-size eigenvectors. The SPP module can reduce the loss and deformation of the image feature information, and enhance the recognition accuracy of the network.In this paper, the Spatial Pyramid Pooling (SPP) is applied to the last layer of the Mobilenetv3 backbone extraction network to pool the output features and produce a fixed-length output. It can slice the image into coarse levels of fine and then integrate local features, thus greatly increasing the receptive field and separating the most prominent contextual features. As shown in YOLOv4 follows the method of multiscale prediction, outputting three feature maps of different sizes to detect three target objects of large, medium and small, respectively. The detection head consists of two convolutional layers, with the final prediction obtained after using 3 × 3 and 1 × 1 convolutional layers . The numHowever, when YOLOv4 uses the convolutional features extracted by the network prediction part, it does not weigh the different positions and channels in the convolutional kernel. That is, we treat each region in the whole feature graph equally, considering that each region has the same contribution to the final detection. This leads to poor generalization performance of networks because the complex and rich contextual information around objects to be detected is often presented in real-life scenarios.To address the above problems, it is presented in the present work that the Gate Channel Head Embedded (GCHE) module combined with the gated mechanism (GCT) and the YOLOv4 predictive network.As shown in Because direct lightweight processing of target detection algorithm networks may lead to a decrease in detection performance, more and more scholars considered improving the part of the algorithm except for the model structure without increasing the network complexity. Because the target detection can be generally divided into two parts: positioning and detection, where the accuracy of positioning is mainly dominated by the regression loss function. Therefore, many new regression loss functions have been proposed ,36,37.By selecting the appropriate positive and negative samples, the Intersection over Union (IoU) plays the most popular indicator of the boundary box regression, whose function is to measure the similarity between the prediction box and the real box. To further obtain the optimal IoU metric, the IoU loss function  is propoc represents the diagonal distance at the minimal closure region capable of containing both the prediction box and the real box, v measures the consistency between the aspect ratio between the prediction box and the true box. w is the width of the prediction box and h is the height of the prediction box.In the above method, CIoU is currently the best-performing border regression loss function, which puts three important geometric factors into consideration: the overlap area, center point distance, and aspect ratio. The CIoU measures the overlap area of the target and real boxes with the IoU, the Euclidean distance, and the corresponding aspect ratio with the angle. The loss function is as follows.v of the CIoU loss function uses the arctangent function to form the penalty term of the rectangular aspect ratio with the following two problems, which affect the convergence speed and robustness of CIoU.However, the last term v robustness is weak, sensitive to the outliers, and is greatly affected by the outliers, resulting in large fluctuations in the value change of the loss function, and affecting the performance of the loss function.1. The 2. The value domain of the arctangent function is To solve the above problems, we propose a more efficient and direct regression loss function ECIoU based on the CIoU loss, which is defined as follows.v, and its function output value is The last term It can be clearly seen from To further explore the applicability of the penalty term This section demonstrates the performance of Efficient-YOLO through some experiments.We use SAR Ship Detection Dataset (SSDD)  and HighThe SSDD is the first publicly available satellite remote sensing dataset, specifically used for SAR image ships by using the PASCAL_VOC annotation format. Images were mainly obtained from the RadarSat-2, TerraSAR-X, and Sentinel-1 sensors, and were obtained by using HH , HV , VV , and VH  polarization methods. The dataset is divided into ship targets for many different scenarios, including simple scenarios with a clean background, complex scenarios with many disturbances, and scenes close to the shore. The SSDD has 1160 images, and 2456 ships, with an average of 2.12 ships per image. We randomly selected 930 as the training set and the remaining 230 as the test set.HRSID is a large-scale ship detection dataset with 5604 images of size 800 × 800 and 16,951 ships. The SAR ship imagery in HRSID comes from Sentinel-1 and TerraSAR-X and is capable of 0.1 m to 3 m resolution. Compared with SSDD, the HRSID dataset has higher resolution, more complex scenes and richer ship feature information. We divide the entire dataset into two parts: training set (4034) and test set (1570).To ensure the consistency of the comparative experimental environment and enhance the credibility of the experimental results, all the experimental results in this paper are based on the same dataset and run on the same device.The software and hardware platform parameters implemented by the algorithm in this paper are shown in To evaluate the performance of the model and demonstrate the effectiveness of the proposed Efficient-YOLO, the following metrics are selected for performance evaluation.N represents the number of all categories, AP is the mean of accuracy at different recall rates used to evaluate the detection accuracy of a certain class of samples in the dataset, here we give the formula.P is the accuracy of a certain class of samples refers to the ratio of the number of positive samples detected by the network model to the number of all samples detected. R represents recall which means the probability of all the number of positive samples correctly detected by the network, and the accuracy P and recall R are formulated as follows.TP represents the number of samples correctly divided into positive, namely the target categories detected consistent with the true ones. FP is the number of samples predicted to be positive but actually negative and FN is the number of samples predicted to be negative but actually positive.As the measure of detection accuracy in object detection, the formula for Mean Average Precision (mAP) isF1 score usually serves as the final measure of the multi-classification problem which is the harmonic average of precision and recall. The larger the F1 value, the better the model performance. The F1 scores in individual categories can be calculated asAs a measure of the classification problem, Frames Per Second (FPS) means the number of pictures that can be detected in the object detection network per second. FPS is referred as the index which is used to evaluate the detection speed of the object detection network. A larger FPS implies a faster network detection speed.The Log-Average Miss Rate (LAMR) indicates the omission of the test set in the dataset. Larger LAMR indicates more missed targets; smaller LAMR implies and less missed targets and better model performance.FLOPs is short for floating point of operations, the number of floating-point operations that can be used to measure algorithm or model complexity. The smaller the FLOPs, the lower the computational complexity of the network, the better the effect.In order to demonstrate the effectiveness of the Efficient-YOLO algorithm in the present paper, several different object detection algorithms were selected based on the same dataset and the same device, comparing various performance indicators and verifying the algorithm performance under various complex conditions to detect the performance of the algorithm.As can be learned from It can be clearly seen from In this section, different combinations of modules were set up to analyze the effects of the ECIoU regression loss function, SCUPA and GCHE modules on the performance of the algorithm separately. The benchmark network of this experiment is Orignal-YOLO, which takes Mobilenetv3 as the main stem extraction network, PANet and SPP are the neck network, the prediction network of YOLOv4 is the prediction network and the regression loss function is CIoU. The SSDD dataset was selected for the experiment, with an input image size of 512 × 512, a batch size of 4, and a training step of 100 epochs. The model hyperparameter initialization is shown in As shown in Therefore, here, we comprehensively consider the two aspects of network lightweight and performance efficiency, and give an incremental way of the network module combination order with the best overall effect: ECIOU loss function + SCUPA + GCHE. It is clearly seen from It can be clearly seen from In this section, we replace the ECIoU regression loss function in Efficient-YOLO with the DIoU and CIoU regression loss functions, respectively. Taking the performance of the DIoU loss function as the benchmark performance, the effectiveness of the ECIoU regression loss function proposed in this paper is analyzed in a variety of complex situations.As shown in It can be clearly seen from In a variety of complex scenarios, comparing the CIoU and DIoU loss functions, we can clearly find from In order to further test the generalization performance of Efficient-YOLO, we select the HRSID dataset with a larger scale and more complex environment to detect the performance effects of input image sizes of 640 × 640 and 800 × 800, respectively.As shown in To further explore the Efficient-YOLO network, we additionally select the HRSID dataset and the new network for comparative experiments. From As shown in Most current object detection algorithms mainly focus on a single aspect of detection accuracy and network real-time performance, and few methods can achieve a balance between them. For example, some scholars will continue to expand the network structure in order to obtain the ideal detection effect, resulting in poor network real-time performance, making it difficult to apply related algorithms to practice.This paper presents a novel and efficient lightweight network, Efficient-YOLO, for SAR ship target detection in complex environments. The network can quickly locate the ship’s target area, significantly reducing the computational amount and missed detection rate of ship detection. The SCUPA module in the network structure can effectively improve the transmission ability of the image feature information, ensure the integrity of the target information to be detected, and further reduce the false detection rate of the ship target detection. GCHE module can minimize the impact of interference information on image information, ensure as much as possible that the network obtains the target, and enhance the target detection capability of the network. The ECIoU loss function has a faster convergence speed and robustness, which can help the model achieve the ideal detection effect at the fastest speed in complex environments.Looking at the comparison of the above performance indicators and visualizations, it can be clearly seen that the performance of the Efficient-YOLO network is superior and can easily cope with various complex environments. This shows that the improved method proposed in this paper is effective and the cited architectural patterns are reasonable. However, we also noticed from The research carried out in this paper introduces an efficient and lightweight SAR image marine environment ship detection method based on deep learning. According to the above experimental results of ship detection in complex scenarios, this method achieves an ideal balance between accuracy and speed with a simplified network structure, better regression loss function, SCUPA and GCHE module. The overall effect is better than other advanced object detection algorithms. Summarized as follows.1. A new regression loss function ECIOU is proposed. Its formula is simpler, the matching degree between the detection boxes and the ground truth is higher, the convergence speed is faster, and the application effect is better.2. We propose the SCUPA module, while reducing the computational complexity of the network, it can strengthen the multiplexing of image feature information, and effectively improve the generalization performance and detection performance of the model.3. The GCHE module is proposed to effectively shield the interference information of the target to be detected and enhance the ability of the network to extract feature information.The network model Efficient-YOLO proposed in this paper has strong real-time performance, small model size and high detection accuracy, making it a broader application prospect in the field of ocean monitoring. Efficient-YOLO only performs experiments on SSDD and HRSID datasets in this paper, and in the future we will focus on performing performance validation on more ship datasets.


Facial Expression recognition is a computer vision problem that took relevant benefit from the research in deep learning. Recent deep neural networks achieved superior results, demonstrating the feasibility of recognizing the expression of a user from a single picture or a video recording the face dynamics. Research studies reveal that the most discriminating portions of the face surfaces that contribute to the recognition of facial expressions are located on the mouth and the eyes. The restrictions for COVID pandemic reasons have also revealed that state-of-the-art solutions for the analysis of the face can severely fail due to the occlusions of using the facial masks. This study explores to what extend expression recognition can deal with occluded faces in presence of masks. To a fairer comparison, the analysis is performed in different occluded scenarios to effectively assess if the facial masks can really imply a decrease in the recognition accuracy. The experiments performed on two public datasets show that some famous top deep classifiers expose a significant reduction in accuracy in presence of masks up to half of the accuracy achieved in non-occluded conditions. Moreover, a relevant decrease in performance is also reported also in the case of occluded eyes but the overall drop in performance is not as severe as in presence of the facial masks, thus confirming that, like happens for face biometric recognition, occluded faces by facial mask still represent a challenging limitation for computer vision solutions. Emotions understanding plays a key role in everyday people’s lives and interactions. Recognizing what a person is experiencing in a certain moment is part of the so-called emotional intelligence. Emotional intelligence is defined as the capacity of an individual to notice, discriminate, and identify other people emotions, as well as categorize them effectively. Similarly, for an automated system, being able to understand how the user feels while performing an interaction through a graphic user interface (GUI), for example, can allow the system itself to make the user experience more pleasant and improve the human-computer interaction quality. The most common way of recognizing emotions is through the interpretation of facial expressions. Normally, people show their emotional state, such as sadness, happiness, or anger, through facial expressions. The recognition of facial expressions is part of the AFEA  systems, which aim to automatically analyse and recognize facial expressions using visual information, movements, and changes in facial features . AffectiTypically, Facial Expression Recognition (FER) systems work on the features of the whole face for a person’s emotion identification. With the COVID-19 pandemic this assumption seems to be not obvious: the use of PPEs  and the enforcement of wearing a facial mask to protect people from the infection, raised a significant issue for all these systems that use the human face as a principal biometric trait for the analysis. The use of masks makes the emotion recognition problem even more challenging. Facial masks hide almost half of the human face, covering the area from the nose to the chin. In such a scenario the lower part of the face is completely unavailable for emotion understanding and a significant part of the information is lost. Nowadays the emotion recognition algorithms can efficiently identify seven basic emotions  achieving good recognition performance, but what happens if these algorithms are applied on masked faces?The motivations beyond this research can be found in the above question, posed in a particular historical period of Pandemic. Furthermore, a study conducted by  analyse 1Scenario 1: unmasked faces for training and masked faces for test.Scenario 2: both training and test sets with masks.Scenario 3: faces with occluded eyes for test and original face images for training.Scenario 4: both training and test sets are images with occluded eyes.In the next section, a brief description of the related works is reported. The rest of the paper is organised as follows: Section In this work, an analysis of the behaviour of the top FER methods existing in literature when the face analysed is covered by a mask is presented. Three top methods, with available code, are considered: ResidualMaskingNetwork , Facial-Ekman and Friesen  pointed Facial Expression Recognition systems can be classified through two main criteria: i) the use of videos or static images through machine learning solutions and ii) the use of deep learning methods versus the traditional approach based on handcrafted features.Static expression recognition is referred to the use of facial images, which show a specific expression in the picture, to extract a set of static features . The vidparticularly involved the field of computer vision and image understanding [In recent years, the trend of deep learning methods has standing . The maistanding . Moreovestanding , for exastanding  apply a standing  and the standing  models, standing  dataset.standing  propose standing . Residuastanding  results standing  with thestanding . In recestanding , to the standing , to the standing .Residual Masking Network Luan et al. Facial-Expression-Recognition-with-CNNs Nischal .Amending Representation Module (ARM) Shi and Zhu Residual Masking Network is presented in Luan et al. [n et al.  is a novA representation in brief of the Residual Masking Network is shown in Table 1Facial-Expression-Recognition-with-CNNs is a publicly available Convolutional Neural Network built in Keras and Tensorflow. The repository is under MIT license Nischal [ Nischal . The mod(i) Convolutional blocks (ConvBlock): four blocks of sequential 2D Convolutional, batch normalization, activation, maxpooling2D, and dropout layers transform the input shape from  to ; (ii) a flatten layer is applied and the data dimension is reduced from two dimensional to one dimensional data shape. (iii) Fully connected blocks (FullyConnBlock) are other two blocks of sequential, dense, batch normalization, activation, and dropout layers, applied to prepare the tensor for the last dense layer whose size produces outputs corresponding to the seven emotions. Table k and s represent the Kernel of Conv2D layers and MaxPooling filter size, respectively. The model is designed to recognize the seven basic emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral.Amending Representation Module is another novel Convolutional Neural Network Architecture proposed in Shi and Zhu [ and Zhu . It consThe weight with a larger degree of albino erosion is generally lower, and vice versa. The last SA block separates facial features into two parts to simplify representation learning. Experiments using publicly available benchmarks show that this model significantly improves FER’s performance. In fact, among the analyzed methods, the Amend Representation model is the one achieving the best accuracy.A set of three top deep learning-based algorithms for recognizing emotions in static images has been chosen among the systems currently presented in the literature. The selection of the methods involved both the performances and the availability of the code on the web. More specifically, the code for each method is available on GitHub in the following repository: Residual Masking Network and FER-with-CNNs are trained on to Facial Expression Recognition 2013 Database (FER2013) dataset, Amending Representation Module, instead, is trained on Real-world Affective Faces Database (RAF-DB). The aim of this work is to test the models on faces occluded by facial masks, for this reason, and for a fair comparison of the performance achieved by each method, different versions of the aforementioned datasets were used for benchmarking: FER2013 masked and FER2013 eye-occluded, RAF-DB masked and RAF-DB eye-occluded.Facial Expression Recognition 2013 Database (FER2013) Goodfellow et al.  is compoThe Real-world Affective Faces Database (RAF-DB) Li et al.  is a larA version of masked and eye-occluded datasets to perform experiments for the different Scenarios is presented in this subsection. For Scenarios 1 and 2 fake masks are applied to each face image in the datasets. The two datasets were synthetically masked using an algorithm that masks facial photos by overlaying masks on a detected face.The number of training and test examples for both datasets is unchanged. In Figs. Scenario 1: in this first setup, unmasked faces were used for training and masked faces for the test.Scenario 2: both training and test sets consist of masked faces.Scenario 3: mirroring the first Scenario, this configuration tests the model trained on the original dataset using eye-occluded images.Scenario 4: eye-occluded images are used for the training and the test phases.Starting from the best configuration of each model described in Section The results obtained applying each experimental configuration to the three top models are summarised in Table For Scenario 1, a drop in the performances is reported for all compared methods: Residual Masking Network goes from an accuracy of 74,14% to 35,65% and a similar decrease can be observed with the FER CNNs model too. The worst-case scenario, which displays the highest decline, is the Amend-Representation Module. Despite having the best performance, it shows a greater decrease than the first two methods, specifically it goes from an originally reported accuracy of 90,42% to 45,45% using a masked dataset for the test phase. This means that a method designed to recognize emotions from a non-occluded face significantly fails if it is tested on masked faces. Performance improves when models are trained on masked datasets. An expected result since the models have seen masked faces also in the training examples. Anyway, in this second Scenario, the accuracy also shows a non-negligible average decrease of 10%. In order to verify if the loss of the mouth was truly significant with respect to any other obstruction, in Scenarios 3 and 4 images with periocular occlusions were taken into consideration. In this case, the greatest decrease can be observed in the scenario where only the test set is eye-occluded (Scenario 3). Anyway, this loss in performance appears less marked than the one in Scenario 1. The average decrease, in fact, results to be 28% compared to the 40% observed in the first Scenario against the original accuracy. Although there is a significant difference in performance between Scenario 1 and Scenario 3, the accuracy scores for Scenario 2 and 4 are quite similar. The Scenario involving mouth occlusions (Scenario 2) has a higher drop, however, there is a difference of roughly 12% between the accuracy of Scenario 2 and Scenario 4. The latter scenario shows the smallest drop in accuracy which averages around 5% compared to the original results.The confusion matrix depicted in Figures from Figs. In particular, it can be observed that the best recognized emotion is the Happy one for almost every model in each Scenario. ResMasking and AR modules are able to better discriminate also the Neutral emotion. The only case in which Neutral and Surprise emotions present a better behavior than the Happy one is reported in Fig. Experimental results show an expected consideration about the reduction in terms of accuracy in all scenarios that introduce an occlusion compared to the level of performances achieved in full visible face. On the other hand, it can be observed that the occlusions of the periocular areas are not as severe as those due to facial masks. In presence of facial masks, the deep classification models register a decrease in accuracy during the testing stage up to half of the performance achieved in normal operative conditions. Conversely, a similar dramatic trend is not reported when the models are exposed to the difficulties of working with occlusions of the periocular area. In such a last case, the overall level of performance does not compare with ideal conditions but the failure cases as such that the mean accuracy states about -10%.In computer vision, emotion recognition is the process of identifying human emotions from images. Differently for other well-assessed approaches to analysis of the face like biometric recognition, the recognition of the facial expression is not a trivial task. People learn to classify the expressions since childhood and they can vary widely in the accuracy at recognizing the emotions of others according to the experience and nationality. The use of digital solutions for emotion recognition is a relatively emerging research area. Deep learning approaches have been demonstrated to achieve encouraging levels of accuracy in ideal conditions and even in-the-wild scenarios. On the other hand, COVID pandemic restrictions questioned all computer visions algorithms for face analysis for a wide range of purposes due to the presence of occluding facial masks. Even though research studies demonstrated that the periocular region can help the classification of some expressions, e.g., surprise, fear, disgust, and anger, many others are strongly dependent on the analysis of labial area.In this work, the attention is focused on the analysis of state-of-the-art solutions for expression recognition from still images in presence of facial masks. The objective is to discuss how feasibly the most performing algorithms can be retrained to take into account the presence of the occlusions introduced by the facial masks and the level of performance that can achieve in this challenging acquisition condition. For a fairer comparative analysis, the results obtained in presence of the facial masks are analysed against the occlusion of the periocular region. These considerations led to exploring different experimental scenarios where the occlusions are interleaved with normal/ideal conditions starting from two public datasets for expression recognition.The results reported in this study reveal that recent pandemic restrictions are significantly affecting all those application scenarios where the analysis of the human face is useful to enable advanced human computer interaction. For example, human-robot interaction is a typical case where the recognition of human emotion by the robot can improve the interaction among the parties. Also in public indoor areas, expression recognition can be helpful for marketing and advertisement. Since the COVID pandemic is demonstrating that our life has been significantly changed and that facial masks seem to be mandatory for a long time, longer than the current expectations, face analysis must take into consideration this higher level of complexity in the problem of extracting information from the human’s face. We believe that the results reported in our study are useful to point attention to the problem and to consider it as a fruitful emerging field of research over this time.


Video labelling is the assigning of meaningful information to raw videos. With the evolution of artificial intelligence and its intended incorporation into the operating room, video datasets can be invaluable tools for education and the training of intelligent surgical workflow systems through computer vision. However, the process of manual labelling of video datasets can prove costly and time-consuming for already busy practising surgeons. Twenty-five robot-assisted radical prostatectomy (RARP) procedures were recorded on Proximie, an augmented reality platform, anonymised and access given to a novice, who was trained to develop the knowledge and skills needed to accurately segment a full-length RARP procedure on a video labelling platform. A labelled video was subsequently randomly selected for assessment of accuracy by four practising urologists. Of the 25 videos allocated, 17 were deemed suitable for labelling, and 8 were excluded on the basis of procedure length and video quality. The labelled video selected for assessment was graded for accuracy of temporal labelling, with an average score of 93.1%, and a range of 85.6–100%. The self-training of a novice in the accurate segmentation of a surgical video to the standard of a practising urologist is feasible and practical for the RARP procedure. The assigning of temporal labels on a video labelling platform was also studied and proved feasible throughout the study period. Video labelling is the process through which different aspects of a video are assigned specific informative labels which a machine can utilise in machine learning and in the development of computer vision. Computer vision is the field of artificial intelligence (AI) which enables machines to develop a human-like understanding of different aspects of images and videos and enables the human-like learning of information from visual data. Machines to date can be taught to infer, analyse, and detect subtle data patterns and compute on their own through unsupervised machine learning, necessitating no explicit instruction .One application of video labelling in surgery is the automatic segmentation of surgical videos. This application could be employed in the education of surgical trainees with research supporting the utilisation of video-based educational interventions , 3. The Although automation in surgical procedures is likely to remain out of reach in the near future, there is potential in the synergy between computer vision in AI and automation of surgical video labelling. A difficulty in the training of machine learning algorithms for classifying features of visual data is the prerequisite for large quantities of pre-labelled data . Due to Research for crowd annotation with surgical video is scarce, and current evidence suggesting a strong corelation between crowd annotators and clinical experts remains inconclusive for tasks of higher complexity or the labelling of full-length surgical procedures . The estOver a period of 2 months, 25 RARP procedures performed on the Da Vinic Si HD dual console system were recorded on Proximie, a novel commercially available GDPR and HIPAA compliant augmented reality platform, approved by Guys and St. Thomas’ NHS trust (GSTT) IT department.The footage was captured in the endoscopic view, producing a library of two-dimensional (2D) full-length RARP videos which were deidentified and anonymised. This video set was accessible via a secure, online, cloud-based storage which only the study participants had access to.The student was trained on video labelling through self-directed learning, a review of the literature and reference to online video materials. Subsequently, a random video was selected from the dataset to be labelled on an online video labelling platform. This same video was then assessed according to an agreed pre-set checklist, on a 5-point Likert scale by four practising urologists experienced in performing RARP. The accuracy of video labelling was then calculated and documented.Patients were not involved in the design and conduction of this study. Prostatectomy patients at GSTT undergoing RARP completed a standardised consent form for the storage and usage of surgical video. Surgical videos were than obtained and accessed via Proximie, a GDPR and HIPAA compliant platform.The student was able to access the video through a secure online cloud-based storage with files protected by a 256-bit advanced encryption standard (AES). Patients had full knowledge of recording prior to the procedure and completed a consent form enabling the subsequent usage of the operative video. Videos were anonymised using an arbitrary numerical system deidentifying patients. A random number generator was utilised to select a video for assessment of video labelling accuracy .A comprehensive library of operative steps was outlined according to those previously defined in the literature, considering the steps which would be visible throughout operative video . The nine final steps assessed are outlined in Table Video labelling was performed on the VGG image annotator (VIA) platform  by the sFollowing the learning period, a video amongst the data set was randomly selected  to test See Table Of the 25 analysed videos, 8/25 (32%) videos were deemed incomplete/low quality, and these videos were not used for review or labelling by the student due to the operative video being less than 1 h in length (incomplete), missing significant steps or the video being pixelated and unclear to the viewer. Of the 17 videos which were deemed to be of sufficient quality for analysis, videos were time stamped by the student as part of the learning process (Fig. The annotated video then underwent review by the four participating urologists, who then quantified the accuracy of labelling of each procedural step, over a 5-point Likert scale. The mean accuracy of the labelled video across all steps was 93.06%, with a range from 85.6 to 100%. Refer to Table See Table This purpose of this pilot study is to investigate the feasibility of video labelling by a novice student, as an initial step towards the development of an AI algorithm, automating the process of video labelling in RARP as previously done for laparoscopic sleeve gastrectomy , laparosThe results of this study demonstrated the feasibility of a novice student to train in the labelling and accurate segmentation of operative video over a short-term period on a part-time basis; in this case, the robotic prostatectomy procedure performed primarily via a transperitoneal approach. A procedural video amongst the data set was then selected for review by four practising urologists. The results demonstrated an average of over 90% accuracy in the time stamping and video labelling of the procedural steps. The step which had the lowest accuracy score was step 5 (Bladder neck transection, see Table For the purposes of this study, the VIA annotation software  was not The fields of surgical education, robotic surgery, and innovation in surgical technology with artificial intelligence are constantly evolving in the light of new technological developments. The video labelling performed in this project has several applications. In the immediate context, the organisation of a video dataset into a cloud storage network and its segmentation into the constituting steps of the procedure can be used for: research purposes, increasing efficiency of post-operative review of surgeon performance, and educational purposes whereby clarity of video materials is possibly improved through the addition of time stamps . The eduLabelled video has multiple further applications, for example, the development of context aware operating rooms with surgical workflow analysis , 21–23, However, an inherent difficulty encountered with this process is the time and expertise investment due as part of the manual labelling process, often demanding the participation of expert surgeons or motivated trainees , 15, 24.Though this study has limitations. The results of this project may not be generalisable to all other endoscopic procedures. The results suggest promise in the ability of a student to learn to accurately identify the procedural steps within a surgical video, and subsequently label these. However, replicating this study for other procedures may demonstrate a difference in learning curves. Another significant feature of this study is that the data set was obtained from a single hospital, and the approach taken in performing RARP procedures in other hospitals may not be representative. It is known that there are several dissection techniques which may be done for RARP, with each differing in the anatomical structures encountered, the ability for labelling through different anatomical approaches was not tested in this study .The context in which this study was conducted was during the peak of the COVID-19 pandemic, where clinical responsibilities of the participant surgeons and contact restrictions limited the recruitment of a student cohort. Despite the limitations, this study serves as a pilot study for more comprehensive future video labelling research. A formalised educational process can be applied amongst a cohort of students, to determine interrater reliability for students who have undergone the same educational process.Advancements in AI and surgical technology have prompted further research studies. A barrier to the conduction of such studies can often be due to the requirement of high expertise and associated funding. This study has shown that a B.Sc. student, over the course of 2 months, was able to self-train in the understanding and accurate segmentation of the RARP procedure with no prior exposure to robotic surgery, having analysed and manually labelled an entire dataset. This could form the basis for an educational surgical video library, a reference point for assessors, however most applicable to this project, the use of video datasets in computer vision research and AI applications. Studies assessing the ability of students to perform such roles are scarce in the literature and may be of benefit to the scientific community due to the existent interest for research participation in students.


Optic fiber interferometers are highly sensitive ultrasonic sensors for partial discharge detection. However, low-frequency vibration and environmental noise will disturb the sensors in the field, and cause a phase fading suppression effect that reduces sensitivity. This paper analyzed the problems existing in the phase feedback control system based on PZT, and an improved scheme incorporating a high-frequency carrier phase demodulation is proposed. Based on an acousto-optic modulator, the proposed phase feedback control system overcomes the phase fading suppression effect. A test is carried out on an ultrasonic calibration platform and a transformer oil discharge platform. The test results show that the stability of the improved phase demodulation system has been significantly improved, and meets the requirements of field applications. Compared with the signal-to-noise ratio at the time of phase fading of the system before the improvement, the signal-to-noise ratio of the improved system is improved by 69 dB. Partial discharge detection is an effective means to find insulation defects in power equipment, and is of great significance in terms of improving the operational reliability of power equipment ,2. At prThe traditional acoustic measurement uses piezoelectric transducers (PZT) to sense the ultrasonic wave generated by partial discharges in power equipment. However, PZT has a relatively weak sensitivity and is difficult to multiplex, and as such, it struggles to meet the needs of partial discharge detection in power equipment . A varieIn 1996, scholars at the University of New South Wales first developed an optical fiber ultrasonic sensor with a 150 m long optical fiber, and successfully detected the ultrasonic wave produced by transformer partial discharge based on Michelson interference sensing system . In 20152), and the frequency spectrum is distributed within 1 kHz. The mechanical vibration of the shell caused by knife switching is larger, and the frequency spectrum distribution is about 100~1 kHz. Although the vibration frequency is much lower than that of ultrasonic signals generated by partial discharge, the modulation effect of these vibrations is much stronger. These low-frequency interference signals will lead to random changes in the initial phase of the interferometric ultrasonic sensing system, which makes it difficult to work stably , the open-loop ascending hysteresis curve (dotted line) and open-loop falling hysteresis curve (solid line) do not coincide. A hysteresis loop (shadow) surrounded by an uncertain region is formed. In the uncertain region, when the input voltage is arbitrary, the rising curve and falling curve corresponding to the input voltage are the upper and lower limits of the piezoelectric ceramic displacement [The main material of PZT is piezoelectric ceramic, and the hysteresis characteristic is the inherent property of piezoelectric ceramics. The hysteresis nonlinear characteristics of electronic ceramic materials are shown in lacement .Usually, there are two kinds of piezoelectric ceramic controllers: open-loop control and closed-loop control. In the open-loop control, the reference displacement is directly inputted into the controller as voltage, which is amplified by the driving module and then pressurized to the piezoelectric ceramic. This control method is simple in structure and convenient to control, but the hysteresis characteristics will greatly affect the accuracy of the system. The closed-loop control frame uses a displacement sensor to realize the closed-loop negative feedback, which has a strong anti-disturbing ability. The gap between the actual output and the expected displacement output corresponding to the reference input is calculated, and then the feedback loop is used to modulate the input. It can achieve high precision control, but greatly reduces the response ability of piezoelectric ceramics.Because the interferometric ultrasonic sensing system requires high response ability, the open-loop control mode based on PZT is usually used to adjust the piezoelectric ceramics. However, PZT introduces the contradiction between response ability and control precision into the system. The hysteresis nonlinearity of the piezoelectric ceramics will affect the ultrasonic measurement system. The analysis is as follows:When a sinusoidal disturbance signal is applied to the piezoelectric ceramic, the modulation curve, as shown in f1 = 200 MHz is the fixed frequency shift introduced by the AOM.An acousto-optic modulator (AOM) is introduced into the reference fiber of the system as a high frequency carrier phase demodulator. As shown in I’ is a high frequency vibration signal with frequency f1, and the phase shift f1, but has no effect on the result of phase demodulation. Therefor the acoustic pressure amplitude of the external ultrasonic signal can be obtained by the phase shift at the f1, and there is no more phase fading.It can be seen from (2) that the interference light intensity In order to demodulate hm (DCM) . 1 and U2 as follows:Take Equation (2) as the input U, and set the expressions of the orthogonal reference signals UCalculated by the DCM algorithm which shown in In order to test the phase fading suppression effect of the proposed system, the test is carried out both on an ultrasonic calibration platform and on a transformer oil discharge platform.The objects of comparison are an ultrasonic sensing system based on an AOM high frequency carrier phase demodulator (Sensing System A) and an ultrasonic sensing system with phase feedback control loop (Sensing System B).The calibration platform of the ultrasonic sensor is established with reference to the national standard GB/T 19801-2005, as shown in On the ultrasonic sensor calibration platform shown in The acoustic source PZT continuously emits ultrasonic signals of a fixed amplitude and frequency. The measurement results of the two systems are shown in In order to verify the improved optical fiber ultrasonic sensing system, a test platform for partial discharge detection in transformer oil is built, as shown in The 25# transformer oil is loaded into the steel transformer oil tank and the suspension discharge model is preset in the oil. Two cylindrical mandrel fiber optic ultrasonic sensors are installed on the outer wall of the fuel tank using the same torque through the magnet adsorption device. The two sensors are connected to the above two different sensing systems respectively.The operation of the step-up transformer gradually increases the power frequency test voltage, resulting in suspension discharge in the transformer oil tank.During the experiment, the step was 2 kV and the voltage was kept at 1 min after each step. The signals detected by the two sensors at each voltage level were recorded until the partial discharge was detected. When the applied voltage gradually increases to a peak of 63.5 kV, the time domain signal is detected, as shown in t = 0.67 s, the partial discharge signal measured by system A is 15 times of the amplitude of system BIt can be seen in (1)The influence mechanism of environmental noise on the sensitivity of interferometric optical fiber ultrasonic sensing system is revealed and the problems in the existing phase control methods are pointed out. We discovered that the hysteresis characteristics of PZT could lead to the slow shift of the system working point, eventually causing phase fading.(2)An improved scheme of high frequency carrier phase demodulation based on an AOM is proposed. An acousto-optic modulator is introduced into the reference fiber of the system as a high frequency carrier phase demodulator, and the phase signal is demodulated by a differential cross multiplication algorithm, which effectively suppresses phase fading.(3)The calibration platform of the ultrasonic sensor is established with reference to the national standard, and calibration experiments were then conducted. Compared with the signal-to-noise ratio at the time of phase fading of the system before the improvement, the signal-to-noise ratio of the improved system is improved by 69 dB.(4)A test platform for partial discharge detection in transformer oil is built. The measurement results show that the improved system measured more discharge times, while the system based on PZT phase feedback control lost part of the partial discharge signal due to phase fading. The improved system is more suitable for actual engineering measurements.An optical fiber sensing system for partial discharge ultrasonic detection with phase stability control is developed in this paper. The main conclusions are as follows:


X-ray fluorescence spectroscopy is a non-destructive technique employed for elemental analysis of a wide range of materials. Its advantages are especially valued in archaeometry, where portable instruments are available. Considering ancient glass, such instruments allow for the detection of some major, minor, and trace elements linked to the deliberate addition of specific components or to impurities in the raw materials of the glass batch. Besides some undoubted advantages, portable XRF (p-XRF) has some limitations that are addressed in this study. The performance assessment of four different p-XRF units and the reconciling of their output were conducted. The results show the limitations in cross-referencing the data obtained from each unit and suggest procedures to overcome the issues. The p-XRF units were tested on the set of Corning reference glasses and on a small set of archaeological glasses with known composition. The compatibility of the output was assessed using multivariate statistical tools. Such a workflow allows us to consider data from multiple sources in the same frame of reference. XRF analyses of archaeological materials conducted with portable instruments (p-XRF) are increasingly popular within the archaeometry community and have a history of development that spans decades ,2,3,4,5.Unfortunately, p-XRF has several drawbacks for the investigation of ancient glass. The most evident is a significant limitation in providing accurate information for “light” elements (Z < 14), which may constitute a large part of the total mass fraction in the glass matrix. This may inhibit a characterization of the fluxing agents used in glass making, which is one of the first research questions to be faced when studying archaeological glass. To overcome the issue, the p-XRF units can be equipped with helium-flow setups or with small chambers for vacuum analyses to reduce the absorption of X-rays by Ar from the atmosphere and enhance the possibility of detecting the weak XRF signals from Na and Mg ,12,13.The above-mentioned issues combine with those inherent to the general XRF analytical approach. Characteristic fluorescence lines of different elements may overlap, with typical examples relevant for glass studies being the interference of Fe Kβ and Co Kα, or that of Pb Lα and As Kα. These phenomena limit the accuracy of the quantification and may prevent elements from being detected if they are present in minor quantities . MoreoveAdjustment of the acquisition parameters can improve the quality of the data and might expand the number of applications for p-XRF as well as mark their limits ,16,17.Under the p-XRF label, we can find many types of spectrometers, with a variety of features . MoreoveSome users explore the spectra in a purely qualitative way: as long as energies are associated with elements and the position and height of the peaks correspond to the respective element’s presence/concentration, the comparison of the relative heights of the peaks provides information on the qualitative composition of the examined samples , though The so-called “semi-quantitative” approach is based on different workflows. This approach does not allow direct comparison of the results with external data. Therefore, results obtained with different instruments cannot be directly compared. Nevertheless, it enables the possibility of distinguishing groups of artifacts within the obtained dataset. The basic assumption in this approach is that the collected data are similar but may be biased from an accepted nominal value. Using the definitions given in ISO 5725-1 , we shal2 values to compensate for the difference in total composition to 100% [Some operators use face values of factory calibration provided by the p-XRF unit . They ca to 100% . Regardi to 100%  or estim to 100% . Again, Accurate concentrations can be obtained when standard or reference materials are used to calibrate the spectra. This approach yields the highest quality results. The output of the process should in principle be comparable with results obtained from other p-XRF units or other types of analytical equipment. Some producers of p-XRF units prepare built-in calibrations that can be used when analysing archaeological glass or prepare them by request, using the Fundamental Parameters (FP) method or the Compton normalization ,33. The In general, the above-mentioned examples demonstrate that p-XRF is widely used in the analysis of archaeological glass, although most of the matrix remains undetected. Despite extensive applications to ancient glass, many researchers rely on other methods for quantitative compositional information, reducing p-XRF to a tool for preliminary analysis and using the p-XRF values in a strictly qualitative or semi-quantitative way.Recently, the need for implementing integrated approaches between chemistry and archaeology has significantly grown within the entities that are responsible for studying and protecting cultural heritage. As a consequence, many relevant archaeological museums and research institutions own analytical equipment, with p-XRF units being among the most popular, as there are many research questions or purposes that can be answered or fulfilled by this technique. Consequently, an extended archaeometric investigation may face the issue of having different p-XRF units available for analysing multitudes of archaeological objects in different institutions. As the collected data needs to be compared to produce a new knowledge on the investigated collections, we must exclude any bias introduced by the use of different equipment.There have been several papers reporting a comparison of the results obtained with different p-XRF units. They are mostly related to environmental samples and metals, whereas, to the best of the authors’ knowledge, none deals specifically with archaeological glass. Some of these papers highlight specific issues that cannot be overlooked while assessing the reproducibility of data from different instruments. In particular, it has been demonstrated how the data treatment can influence the final output  and thatThe present work aims at highlighting the main limitations in reproducibility of p-XRF analyses performed with units of different configurations , thus shedding light on the actual possibility to face the main archaeological questions for glass with a p-XRF approach if different instruments are employed. The study is preliminary to an analytical campaign that will be conducted in situ on large collections of Iron Age glasses held in several archaeological museums in Italy. The focus is set on quantification using FP combined with calibration using the reference glass approach, which would allow results to be exploited throughout a specific research project and beyond.Certified Reference Materials (CRM) for glass that are offered on the market provide a wide selection of elements with certified or informational concentration values. Yet, the resemblance of the compositional range of ancient glasses with these (modern) reference materials remains poor. Therefore, for this study, we selected a set of three reference glasses provided by the Corning Museum of Glass (CMOG). The CMOG reference glasses were developed to represent different compositional groups of historic glass, and they feature the elements that play a role in glass (de)coloring and opacification, as well as some common impurities that enter the glass batch with the silica source or with other glass components. Each glass piece is named with letters for different glass types. CMOG A and CMOG B are soda-lime-silica glasses that represent typical soda plant ash (A) and natural evaporites (B) varieties of Mediterranean production glasses from the Bronze Age to the Medieval times . CMOG D The compositions of CMOG glasses have been established through an inter-laboratory study, and since the 1970s, the glasses have been tested and their concentration revised according to the new results ,47. The In addition to the CMOG reference glasses, five pieces of archaeological glass  were also included in the investigated glass set and their composition is reported in 2 area and 2–3 mm thick). In addition, they were polished using diamond pastes of 6 and 1 µm grain size.In this work, VA08 and VA27 were analysed as chunks of glass, while VA70 and the CB samples were analysed as cross-sections. This allows us to evaluate the role of the shape of the surface on the quantification. Successful quantification of irregularly shaped samples will substantiate the quantification on future analyses to be performed non-invasively on archaeological glasses. The CMOG reference glasses underwent polishing to ensure the correct geometry of the acquisitions. Grinding paper of 500, 1200, and 2400 grit was used on flat pieces (about 1 cmXGLab ELIO (hereafter ELIO). The commercial unit is produced by XGLab S.R.L. ;Unisantis XMF-104 (hereafter Unisantis). The commercial unit is equipped with polycapillary optics and produced by Unistantis . Regarding the size and configuration of the instrument, this is the only one with limited sample size analysis due to the dimensions of the sample chamber and can therefore be included in the category of transportable X-ray spectrometers ;Frankie (hereafter Frankie). An ad hoc unit with policapillary optics, developed by the Italian National Institute of Nuclear Physics-National Laboratories of Frascati ((INFN-LNF) Italy);NITON XL3T-900 GOLDD (hereafter NITON). The commercial unit is by Thermo Fisher Scientific . This instrument acquires a series of spectra in different energy ranges to enhance the signal in specific parts of the spectrum.Four p-XRF units were included in this study:The most relevant technical features of the equipment and the acquisition parameters used in this study are reported in On each CMOG reference glass, 10 spectra (ELIO and NITON units) or 3 spectra (Unisantis and Frankie units) on different spots were acquired. For the archaeological references, 3 measures on different spots were acquired for each p-XRF unit, except for Frankie, for which only one acquisition was performed on each archaeological reference.The obtained spectra were converted to .spc, .spe, or .mca before fitting with the PyMCA software . The conThe Limit of Quantification (LOQ)—the lowest amount of analyte that can be quantified with a given uncertainty—for each element’s concentration was estimated by using the value of the gross area B under the relative peak . The LOQ was set to be equal to B + 10σB. This corresponds to an uncertainty of ±30% in the measured value at the 99% confidence level . This vaThe relative standard deviation (RSD) of tri- and decaplicates was used to calculate measurement precision. To check the accuracy, the values were converted to elemental weight % and compared to the nominal concentration . In ordeIn order to ensure that all the data points are a real representation of the concentration in each of the references, the Limits of Quantification (LOQs) were calculated. The automated fitting of the spectra assigned values to the selected elements even when there were no pronounced peaks in the spectra. Therefore, LOQs are needed to filter out the data that were not significant for the quantification process. According to this procedure, the data that were equal or below the LOQ were removed before any precision and accuracy checks.There are several ways of establishing LOQs ,56,57. ALOQs in XRF analyses usually depend on the matrix composition and the element’s atomic number. It is lower for elements with Z ranging between 25 and 40, and higher for lighter ones. The features of the source of radiation are also important to determine LOQ, as source peaks due to Compton and Rayleigh scattering might interfere with the characteristic energy peaks of the analysed elements, raising the LOQs. The same effect is observed when the characteristic energies of the elements in the analysed sample overlap in the detected spectrum.Unfortunately, for many minor elements, the number of data is equal to or less than five. Therefore, calculated uncertainty may not reflect the true value for these elements. Larger datasets are available for K, Ca, Mn, Fe, Cu, Sr, and Pb. Some elements  may have only one data point considered by a single instrument (there might be more data for another instrument). Data on fewer represented elements will reflect poorer precision, and this will be considered in the further discussion of the data. Furthermore, because of their heteroscedastic nature, quantitative data near the LOQ should be handled with caution .The variability among the repeated measures was estimated as the Relative Standard Deviation (RSD). Mean RSD values per unit of equipment, together with their respective maximum values, are shown in It can be noticed that, generally, the relative dispersion of the data is less than 20% for most of the elements across all the XRF units. The polished cross-sections of CMOG glasses do not differ significantly from the sections and the chunks of archaeological references. The only exceptions are some of the Unisantis acquisitions . One of the reasons for high RSD values might be the proximity of the measured values to the respective LOQ (as in the case of ELIO CB65 Sn (68%RSD) and CMOG B Co (36%RSD) values) since the integration of smaller peaks yields a greater error on the fitted peak area estimation. The Frankie unit data shows the tendency to increased RSDs for Sn Sb, Ba and Pb. This might be due to the lower sensitivity of the spectrum after the peaks of the W anode . The precision found in our set of data is normally higher than the instrumental precision obtained with other spectroscopic techniques . NeverthThe FP method is a well-established quantification algorithm that can provide accurate quantitative results, especially when used in conjunction with matrix-matching standards ,36. ValuThe 2σ error values for all the XRF units  are reported for each element. These values were calculated as two standard deviations of the absolute values of relative error for each data point. Many of the data points are within ±20% of the nominal values, but a significant number of them fall within ±50% of error. This quite broad range renders this data unacceptable for many applications. The accuracy values of each XRF unit differ for each element. It is remarkable that the Frankie unit values for the heaviest elements exceed the 100% limit. It should be kept in mind that Frankie unit values had lower precision for Sn, Sb, Ba, and Pb . The UniIt was decided to further look into the data in order to find the best way of improving the FP quantification of the datasets. By observing the data, it was noticed that some of the elements’ values were systematically under or overestimated within a single instrument’s dataset. This points out the limits of the FP model implemented through PyMCA: it accounts for a large number of variables, making the task of their optimization ever more laborious when approaching perfection. Instead, it was attempted to offset these variations by using the average ratios of nominal to acquired values of CMOG reference glasses to obtain the coefficients for normalization of all the values in the dataset. The formula for this type of correction is the following:The division of CMOG and archaeological reference groups was done to check the usefulness of such a correction. Since the coefficients were calculated on the CMOG set of three glasses, it was expected that the average recovery for the corrected values would be close to 100%. In such a case, one should look at the improvement of the data of the archaeological reference group. Indeed, corrected CMOG glasses’ recoveries for all instruments tend to align very near the 100% mark (red circles). As for the corrected archaeological reference glasses group, the resulting values mostly improve when compared to the values of initial quantification, but it is not always the case. For the Elio instrument, for example, an improvement in Cu recovery is “compensated” by a larger bias for Zn. However, most of the corrected values lie in the 20% interval from the nominal values (black circles) that was arbitrarily decided to be an acceptable deviation from the true values. Some corrected values are less than 10% off the nominal ones. Some elements do not have representation in the archaeological reference group of some XRF units . For such elements, it is impossible to assess the effect of correction. Hence, the data on these elements should not be considered as quantitative, yet the rest of the elements that are represented in the archaeological reference group do obtain better values after this type of correction, so it can be considered a useful transformation on the way to improving the overall accuracy.Another type of empirical coefficient correction that was tested in this study is the application of linear regression coefficients. This approach was described elsewhere . Here weIn order to build the final dataset, we adopted a mixed approach: with less than four data points for one XRF unit, data were corrected by the application of ratio coefficients; when four (or more) data points were available, we used linear regression correction. Concentration values for elements that were obtained by the application of ratio coefficients lack adequate uncertainty values. Those elements are presented in concentrations approaching the LOQ, therefore high uncertainty is expected. It is worth noting that no systematic bias from the nominal values emerged for VA08 and VA07, which were analysed as chunks. Therefore, no significant bias determined by surface morphology or glass alteration emerged from the data.In order to check if the obtained reproducibility among the different types of equipment supports the purpose of investigating a large set of archaeological glass with different p-XRF units, we employed multivariate analyses. Principal Component Analyses (PCA) and Hierarchical Cluster Analyses (HCA) were used to check if the compositions of the same set of samples obtained from different p-XRF units will result in a proper representation (PCA) or classification (HCA) of the samples. In archaeometry, the compositions of glass are normally used to highlight similarities or differences among archaeological samples, therefore it is fundamental to assess if the use of different p-XRF units will prevent the possibility to draw any conclusions of archaeological relevance based on the dataset.PCA and HCA are widely used tools in data exploration. PCA allows for reduction of dimensionality with a subsequent representation of all the data in a single space with two or three coordinates, whereas HCA organizes samples into groups based on how closely associated their compositions are and represents them in one single graph that highlights the relationships within the dataset ,60.In this particular instance, we employed both PCA and HCA to check if the data were from the same material, but different XRF units will be clustered together. Such group arrangements will support evidence of reproducibility for these datasets. On the other hand, distinguishing the reference samples will demonstrate the ability of quantitative p-XRF data to serve as a basis for archaeometric discussion on glass technology and, perhaps, even provenance. In order to perform the algorithm, the data corrected by linear regression or ratio coefficients . The scores and loadings plots of PC1 against PC2 and PC3 are reported in Clusters were calculated using Euclidean distances on SNV pre-treated data and were based on at least three valid measures. It was decided to present eight clusters . Both The investigation highlights some limitations and pitfalls of the quantitative p-XRF method for archaeological glass analyses, which remains a challenging task not only in terms of quantification strategy for adaptation to a specific matrix in p-XRF analyses but also due to the intrinsic complexity of the interpretation of compositional data for ancient glass that results from the great variety of components in the glass batch, and therefore in a large quantitative variability.On the other hand, by focusing on the analytical part, we demonstrated that, despite their hardware differences, four p-XRF units produced quite similar results provided that strictly controlled data handling is performed. Unfortunately, the set of certified reference materials available for elemental analyses of archaeological glass is very limited, and therefore it has not been possible to gather a large number of data points across all the elements of interest. Nevertheless, the results we presented in this work demonstrate that it is possible to achieve good-for-purpose reproducibility between different p-XRF units by following fairly practical procedures: ratio coefficients correction can be used on FP data when a low number of reference standards are available (less than four), whereas the data can be corrected by linear regression correction when four (or more) reference data points are available. This procedure, besides increasing the potential pool of quantitative compositional data on ancient glass, allows us to state that the compositions of glass determined through different p-XRF equipment can be actually used to highlight similarities or differences among archaeological samples , and that the data obtained through different p-XRF units will allow us to draw conclusions of archaeological relevance based on the glass composition determined in situ.


The formation, growth, division and proliferation of protocells containing RNA strands is an important step in ensuring the viability of a mixed RNA–lipid world. Experiments and computer simulations indicate that RNA encapsulated inside protocells can favor the protocell, promoting its growth while protecting the system from being over-run by selfish RNA sequences. Recent work has also shown that the rolling-circle replication mechanism can be harnessed to ensure the rapid growth of RNA strands and the probabilistic emergence and proliferation of protocells with functionally diverse ribozymes. Despite these advances in our understanding of a primordial RNA–lipid world, key questions remain about the ideal environment for the formation of protocells and its role in regulating the proliferation of functionally complex protocells. The hot spring hypothesis suggests that mineral-rich regions near hot springs, subject to dry–wet cycles, provide an ideal environment for the origin of primitive protocells. We develop a computational model to study protocellular evolution in such environments that are distinguished by the occurrence of three distinct phases, a wet phase, followed by a gel phase, and subsequently by a dry phase. We determine the conditions under which protocells containing multiple types of ribozymes can evolve and proliferate in such regions. We find that diffusion in the gel phase can inhibit the proliferation of complex protocells with the extent of inhibition being most significant when a small fraction of protocells is eliminated during environmental cycling. Our work clarifies how the environment can shape the evolution and proliferation of complex protocells. The RNA world hypothesis, according to which an RNA-based life preceded the DNA and protein-based life on prebiotic earth, has been an important hypothesis regarding the origin of life. The abiotic synthesis of ribonucleotides ,2,3 in sComputer simulations have shown that the evolution of functional RNA sequences within protocells offers several advantages ,26,27,28J) as There are two major hypotheses regarding a suitable environment for life’s origins: the hydrothermal vent hypothesis  and the pol/Khyd . Howeverpol/Khyd . It causs Lavg∝J . Therefos Lavg∝J . Neverths Lavg∝J . Therefos Lavg∝J ,43. In as Lavg∝J ,45, the s Lavg∝J ,46. Lipis Lavg∝J . During s Lavg∝J ,48. An is Lavg∝J . Figure Along with laboratory-based experiments, recently, few experiments conducted near hot springs have provided confirmation of the spontaneous assembly of vesicles and formation of long RNA polymers in such locations ,49,50. AIn our previous work , we consWe found that in the presence of dry and wet phase only, the initial population of protocells can evolve toward a state where protocells containing multiple types of ribozymes dominate. However, when the process of long polymer diffusion in the gel phase is taken into account, there is a significant slow down in the evolution of the protocell population, suggesting an inhibitory effect of gel phase diffusion. This effect is most pronounced when we consider degradation of entire protocells in the wet phase with small probabilities. The long polymer diffusion process in the gel phase then becomes the primary reason for the gradual decay and ultimately elimination of RNA strands, and as a result, the protocell population fails to evolve. On the other hand, the dry and wet phase alone or a dry, wet and gel phase that does not facilitate long polymer diffusion can sustain the evolution even in the presence of protocell degradation processes. Finally, in the presence of the dry and wet phase and absence of diffusion in the gel phase, we also observed a spatial expansion of the protocell population when initially one or very few of the sites contain circular templates, even when protocells are allowed to degrade in the wet phase. The growth of RNA strands inside those protocells followed by their subsequent division and occupation of neighboring sites by daughter protocells eventually lead to the outward spread of the protocell population with multiple ribozymes until it covers almost the entire lattice. Our work provides quantitative support for the viability of the hot spring environment in creating and sustaining an evolving population of protocells characterized by increasingly complex functionality.s) template of length 200 nt [According to the hot spring hypothesis ,43, lipih 200 nt  at each K was determined from sequence-based simulations by fully extending a primer using the experimental primer extension rates [L) of the template and the relative amount of mismatch  by attaching with a small complementary primer (∼8 nt) followed by non-enzymatic, template-directed extension of the primer. We assume the lipid membrane of the lamellae to be permeable to monomers and short oligomers  (≤8 nt) on rates ,52,58 onl) are diverse in terms of secondary structures [r), cyclase (c), nucleotide synthase (n) and peptidyl transferase (p). Recent work by Dingle et al. [The newly created, open-ended single strands , whemeasured , for extb is the number of monomers in units of 200 mers that a single nucleotide synthase can create . These reactions form the basis of our coarse-grained model and can be represented as the following set of equations D=PhoThe diffusion of molecules from one site to another also depends on the difference in the number of strands between them. The molecules from the central site are more likely to diffuse to the neighbor that is relatively empty. The size of the lattice site is et phase ,63. AlthWe first simulated the evolution of RNA strands and protocells in the dry and wet phase after inactivating the gel phase in order to understand the dynamics in the absence of long-strand diffusion. Such a scenario also provides a benchmark for evaluating the impact of diffusion in the gel phase when it is subsequently switched on. We find that the strands in a few sites which happen to contain faster replicating circular templates initially increase and become encapsulated in vesicles during the subsequent wet phase. However, at most other sites, the initial circular templates have low–medium replication rates as a result of which new RNA strands including ribozymes are not produced at a rate that is faster compared to the degradation process. As a consequence, those sites become devoid of RNA strands. This results in the concentration of a large number of strands at a few sites, while most of the remaining sites become empty. Eventually, as the vesicles originating from those few sites start to divide when the threshold volume is attained, the newly created daughter vesicles occupy the empty nearest neighbor sites. Through this vesicle division process, the RNA molecules gradually spread over the entire lattice, and we find that ∼90% sites (and subsequent protocells) contain all four types of ribozymes at equilibrium , D=0. Thibozymes . The SupD = 0 case, the time at which the curve starts rising steeply from 0 occurs at different times for different trial runs even for the same diffusion coefficient. Nevertheless, we observed that the time evolution for non-zero D values is still much slower than the D = 0 case across all trials. We therefore show the curves for only one trial for each diffusion coefficient in When the gel phase is switched on, allowing for the diffusion of long RNA polymers in that phase, the evolution toward equilibrium is much slower. The plots for the percentage of sites with four types of ribozymes vs. time for two diffusion coefficients are shown in absence of the relocation, a few initial vesicles with faster replicating strands cross the threshold volume, leading to vesicle division, which spreads some of their contents encapsulated in newly formed daughter protocells to neighboring sites. This process happens from a few locations on the lattice, and as the process continues, it leads to the formation and outward growth of clusters of vesicles containing all four ribozymes. This happens because dividing vesicles located on the edge of the cluster are more likely to contain larger numbers of ribozymes with one of the daughter protocells more likely to eliminate vesicles neighboring the boundary of the cluster (that have relatively fewer ribozymes) than those in the interior or edge of the cluster (that have a relatively larger number of ribozymes). Eventually, the spread of functionally complex protocells occurs through the merging of these expanding clusters. This is evident from As mentioned in the methods section, the presence of water in the wet phase will cause the vesicles to undergo rapid Brownian motion. Therefore, a vesicle which originates from a site on the lattice will not deposit on the same site after the wet phase. The Brownian motion of vesicles during each wet phase will effectively result in random relocation of the vesicles on the lattice. To account for this phenomenon, we randomly shuffle the vesicle positions on the lattice at the beginning of each wet phase. Protocell division occurs after reshuffling in those protocells where the number of RNA strands has crossed the threshold volume. So far, we have assumed that the protocells remain intact during the wet phase, but in reality, some protocells will degrade in every wet phase. To account for this effect, we include the process of protocell destruction in our simulation at the beginning of every wet phase. This is likely to inhibit the spread of protocells of increasing complexity in a manner that is dependent on the protocell killing probability. Each protocell can degrade along with its contents with a probability D = 0 and We also plot the total number of strands vs. time of a random site and its eight nearest neighbors for the case of ill=0.01 . As evidill=0.01 , which dill=0.01  or the pill=0.01 , both ofing site C becomesone non-empty site, even starting with 50 templates is inadequate to ensure the proliferation of protocells when strand diffusion is present. Therefore, strand diffusion in the gel phase can prevent the gradual spatial expansion of a functionally complex protocellular population. However, when the protocell relocation process in the wet phase was allowed to occur instead (with strand diffusion turned off), 2% of the initial sites containing one template each were able to evolve and expand, indicating that this process does not hamper spatial expansion. Finally, to check if initially starting with templates in fewer number of sites can lead to the evolution and expansion of the protocell population even when protocells can degrade in the wet phase, we varied both the fraction of non-empty sites and the number of initial templates in them. We found that expansion is possible when there are at least 10% non-empty sites with at least five random templates in each of them, and such expansion is possible even in the presence of the protocell relocation process.Given how a protocell can divide and its daughters can occupy a nearest neighbor site, we wanted to ascertain if the protocell population can proliferate across the entire lattice, starting from an initial state where only a few sites contain the initial templates. We first checked for the case with strand diffusion in the gel phase (Any compelling model of protocell evolution in a primordial RNA world must account for plausible environmental conditions in which such processes occurred and address the constraints imposed on the evolutionary processes by such conditions. The hot spring hypothesis of the origin of life provides an interesting test-bed to study evolutionary processes and assess the viability of such processes in aiding the formation, growth, division and eventual proliferation of protocells of increasing functional complexity. The ecological niche around hot springs when subject to dry–wet cycling provides suitable conditions for the formation of long RNA polymers which can then be encapsulated into vesicles. The encapsulation of RNA sequences by protocells and the replication of RNA via the rolling circle mechanism can lead to rapid growth of number of RNA strands and also increase the likelihood of the creation of ribozymes of diverse functionality . In thisThe hot spring hypothesis emphasizes the importance of the gel phase when protocells compartments can exchange molecules with each other by the strand diffusion process through their fused membranes. A question that can be raised in the light of our analysis is whether a gel phase characterized by the diffusion of long RNA sequences is essential for the spatial proliferation of functionally complex protocells containing multiple ribozymes. It has been argued  that theExperimental studies ,48,51 ha


Small cell neuroendocrine carcinoma (NEC) that arises from the tonsil is a particularly rare head and neck carcinoma. This kind of neoplasm mainly originated from the bronchopulmonary area; however, there were reported cases of extrapulmonary areas. The prognosis is poor as the tumour is an aggressive tumour and have a high risk of metastasis.We experienced a patient presented with painless right neck swelling and hard tonsillar hypertrophy for past six month. Computer tomography showed the tumour extended to the parapharyngeal space and metastasized to the thoracolumbar vertebras. The intraoral biopsy of the tonsil confirmed primary small cell neuroendocrine carcinoma of the tonsil. The clinical presentation, radiological imaging, histopathological investigations, and methods of treatment are discussed.Due to the rarity of this disease, there is no definitive treatment yet for this disease. The physicians must thoroughly understand the nature and characteristic of the disease to find the best treatment. The latest discoveries in chemotherapy drugs and radiotherapy may improve the treatment modalities in the future.  A small cell neuroendocrine carcinoma (NEC) of the tonsil is a scarce form of head and neck cancer . The comThe incidence of extrapulmonary account for 0.1% to 0.4%. Small cell NEC is a highly aggressive cancer. It carries a poor prognosis as it has a high mortality rate, and the treatment for this cancer is not yet fully established. Here, we described a scarce case of primary small cell NEC originated from the tonsil, its clinical features, histopathological and radiological findings and its treatment. A 52-year-old man was referred to the otorhinolaryngology department of the Hospital Raja Perempuan Zainab II for insidious and painless right neck swelling persisting for the previous six months. Physical examination revealed a non-tender right neck swelling measuring 8 cm x 5 cm and with a hard inconsistency at the lateral neck levels II and III. Intraoral examination showed bilateral tonsil grade III with a normal mucosa appearance. However, on palpation, the consistency of the right tonsil was found to be hard relative to normal tonsillar consistency. Nasoendoscopy revealed no abnormalities. A computer tomography (CT) scan of the neck found a lobulated, heterogeneously enhancing lesion with evidence of necrosis in the right cervical region measuring 3 cm x 6 cm x 7 cm and with an enlarged bilateral palatine tonsil on the right side. The plane of demarcation between the right lateral oropharyngeal wall and the parapharyngeal fat was obliterated . There wGlobally, the number of reported cases of primary small cell NEC in the head and neck is low. Three cases were initially reported in 1972 . The disThe main symptom of the NEC is a painless neck mass with or without throat pain and odynophagia. Physical examination may demonstrate asymmetrical tonsillar swelling or ulcerating tonsillar mucosa. However, some patients may present with a regular size and appearance of tonsillar mucosa. Thus, abnormal findings can be missed without proper palpation of the tonsil or radiological evaluation. Diagnostic tonsillectomy plays a significant role in the diagnostic workup of suspected malignancy of the head and neck . HoweverCT and MRI can be used to determine the size and infiltration of the tumour as well as to confirm that the tumour is not metastasis of another distant primary, especially from the lung. Positron emission tomography (PET) is the gold standard for determining the origin and epicentre of small cell neuroendocrine carcinoma in the head and neck . However2 area (Necrosis is common and widespread. Mitotic rates are high, averaging 80 mitoses per 2 mm2 area . In immuThe nuclei were finely dispersed with a salt-and-pepper chromatin pattern and the absence of nucleoli. Nuclear moulding was easily identified . The tumTreatment for small cell NEC in the head and neck is not well established due to its rarity. However, various treatment modalities based on treatment for small cell NEC of the larynx and lung have been suggested, including radiotherapy, chemotherapy, surgical resection and combined modality approaches. A previous study reported that extrapulmonary small cell carcinoma responded well to both chemotherapy and chemoradiotherapy, similar to small cell carcinoma of the lung. However, a poor prognosis remains due to its strong metastatic potential . Thus, pA recent study has proved that CPT-11 and CDDP may be used together to be an effective treatment for extrapulmonary small cell carcinoma of the oropharynx , while aBesides the local regional spreading to cervical lymph nodes, metastasizes of the tumour mostly to the liver, lungs, bones, brain, and skin. The concurrent radiotherapy and chemotherapy that our patient received managed to control the disease's progression. However, the immunocompromised state and the advanced disease spreading had led to a secondary infection that worsens his condition. Prognosis may be better if the patient was diagnosed earlier and at the early stage of the disease.In conclusion, small cell NEC of the tonsil is a rare head and neck carcinoma, which poses a significant challenge for the physicians. The prognosis is poor as the carcinoma is highly aggressive and can spread further to distant organs. The latest discoveries in chemotherapy drugs and radiotherapy may improve the treatment modalities in the future.