The state-of-the-art deep learning-based object recognition YOLO algorithm and object tracking DeepSORT algorithm are combined to analyze digital images from fluid dynamic simulations of multi-core emulsions and soft flowing crystals and to track moving droplets within these complex flows. The YOLO network was trained to recognize the droplets with synthetically prepared data, thereby bypassing the labor-intensive data acquisition process. In both applications, the trained YOLO + DeepSORT procedure performs with high accuracy on the real data from the fluid simulations, with low error levels in the inferred trajectories of the droplets and independently computed ground truth. Moreover, using commonly used desktop GPUs, the developed application is capable of analyzing data at speeds that exceed the typical image acquisition rates of digital cameras (30 fps), opening the interesting prospect of realizing a low-cost and practical tool to study systems with many moving objects, mostly but not exclusively, biological ones. Besides its practical applications, the procedure presented here marks the first step towards the automatic extraction of effective equations of motion of many-body soft flowing systems. Over the last decade, machine learning has taken the scientific world by storm and even more so the commercial one. Even though it is not all old gold that glitters , the potDeep learning is a sub-field of machine learning specifically inspired by the structure and function of the human brain . Its corNowadays, digital cameras assisted by deep learning are used everywhere for traffic management, surveillance, crowd management, automated billing, and customer services –19. GiveIn recent years, multi-layer convolutional neural networks (CNN) have a surge of popularity for feature extraction to accomplish object identification, classification, and tracking –23. The For the first task of object detection, various deep learning-based models differ in their network architecture, such as the number of convolutional layers, number of pooling layers, filter sizes, and the base algorithm used to analyze the input. At present, Faster region-based convolutional neural networks (FRCNNs) , You OnlIn this work, we develop YOLO + DeepSORT based droplet recognition application to analyze the multi-core emulsions and soft granular media simulated via Lattice Boltzmann (LB) methods. We train the object detection network to identify droplets from digital images with a synthetically prepared dataset, thereby avoiding the labor-intensive training data gathering process. In principle, the developed application can also be used to analyze the data generated by the experimental setup of similar physical systems. The goal is to extract trajectories of the center of mass of individual droplets as they move within the flow and then use this information to analyze the dynamics of this complex many-body system. The application developed in this work can extract trajectories of the droplets in real-time by analyzing the video of the system.The paper is organized as follows. Before we dive into details of the YOLO + DeepSORT based droplet recognition application, in the next section, we provide a brief account of the Lattice Boltzmann method and relevant simulation details. In Sect. We employed two variants of the Lattice Boltzmann approach for the multicomponent flows simulations, namely the color-gradient approach with near-contact interactions and the free energy model for multicomponent fluids. In Appendices This system is studied using the color gradient LB. The simulation setup consists of a two-dimensional microfluidic channel made of an inlet reservoir followed by a thinner channel connected to a further downstream reservoir. The height of the chambers is radient) .The soft granular material is formed by droplets  at constant speed In Fig. entclass1pt{minimaus works , 34, it entclass1pt{minimaThis system approximately corresponds to a real multi-core emulsion with a shell of diameter The output of the simulations of the physical systems described above are saved as video files and later given as input to the deep learning-based application for automatic extraction of the trajectories of the moving droplets. In the supplementary material , see vidIn this work, we develop an application to extract trajectories of individual droplets simulated via LB methods by adapting two different algorithms, one for object detection and another one for object tracking. The first task consists of training an artificial neural network to analyze digital images and locate the objects of interest, which in this case are the droplets. Later, the second task is to track the located objects across multiple sequential frames to infer their trajectories. Figure You Only Look Once (YOLO) is a state-of-the-art algorithm that employs a single multi-layer network for object identification and classification. The YOLO algorithm has shown remarkable accuracy with high processing speed in identifying and classifying objects from the COCO dataset, which consists of 80 types (classes) of everyday objects .Fig. 4HiThe building structure of the YOLO network consists of two main components, a feature extractor and a detector. When an image is given as an input to the YOLO network, the extractor  produces feature representations at different scales see Fig. . Such reB bounding boxes with their confidence score for the object and C conditional class probabilities for the given object belonging to a specific class. This information is then combined to produce the final output as a single bounding box around the detected object along with the class of that object. The final output is then passed to the DeepSORT algorithm for tracking the droplets.The YOLO algorithm achieves high speed of image analysis due to its operating principle, namely by treating object detection as a regression instead of a classification task in determining class probabilities to the bounding boxes. The operating procedure of the YOLOv3 algorithm is sketched in Fig. DeepSORT is an algorithm that tracks detected objects between two successive frames . By analThe YOLO and the DeepSORT algorithms together accomplish droplet recognition and tracking from the simulated systems. In the next section, we outline the training data acquisition process employed in this work and later describe the training process for the YOLO network for droplet detection.Before training a network, acquiring training data is a crucial part of an object detection network. Training data consists of several images of the objects to be detected along with information about their location and dimensions. Practically, acquiring the training data is a labor-intensive task involving gathering the images and manually marking the positions and dimensions of the objects. For example, gathering thousands of images of street view cameras and marking the location of the cars to train a network to identify cars in a video feed. In addition, the prospect of manual marking of the objects makes the training data susceptible to human errors.We avoided the labor-intensive process by preparing a synthetic training data set. In this dataset, each image is a collage with few solid white circles, mimicking the droplets placed randomly on a dark uniform background. The solid white circles of different sizes and shapes are prepared with commonly used computer graphics software. A separate text file is generated to note the positions and dimensions of the randomly placed droplets. A Python script generates several thousand images and associated text files in a matter of few seconds. The script is provided in the supplementary material . We prepWe wish to emphasize that the training data, prepared synthetically, was intended to mimic the snapshots of the LB simulation, and it is not generated by a physical process associated with LB simulations. However, it is noteworthy that the synthetic data preparation method described above bypasses the labor-intensive data acquisition part and minimizes human errors, thus making the development process of the droplet tracking application much quicker and more precise. This method can potentially be used in several other object detection applications. In the next section, we describe the YOLO network’s training process to identify the droplets with the synthetic data.The training of the YOLO network is carried out through an iterative process. A subset of training data, called batch size, is passed through the network in each iteration, and then the network predicts the bounding boxes around the detected objects. A loss value is computed based on the difference between the output and the ground truth. The supplied label information is taken as the ground truth. The network parameters (weights and biases of the nodes) are updated to minimize the total loss and improve the accuracy of the network. For the YOLOv3 algorithm, the loss value, also called total loss, is the weighted sum of three separate contributions, regression loss, confidence loss, and classification loss . In the At regular intervals, i.e., after a few iterations, the accuracy of the network is assessed by computing mean average precision (mAP) , 38. TheWe trained two YOLO networks to identify the droplets from the LB simulations. We adapted the code for training the network from Ref. . The firFor training the YOLO-tiny network, we consolidated a total of 10000 images, consisting of an equal number of two different types of synthetic images as shown in Fig. The second network we trained is the full YOLO network, with Darknet-53 as a backbone. Ten thousand images similar to the image shown in Fig. By training the YOLO network, we accomplish the first task of droplet recognition from the digital images, while the DeepSORT algorithm handles the second task of droplet tracking. In the next section, we analyze two LB simulations as case studies to test and use the developed application.After the model is trained with the desired accuracy in terms of mean average precision, it is ready to analyze the real-world data. From simulations or experiments, the real word images can be given as input to the trained network, and individual droplets’ trajectories can be obtained. This process is also called inference.We run inference on the data generated with two LB simulations to test our trained network as described in Sect. Figure It is then straightforward to extract trajectories of the individual droplets across sequential frames since we have dimensions of all the bounding boxes in all the frames with their unique ID. The center of mass of the individual droplets is approximated as the geometric center of its bounding box. Figure In the second case, we run inference on LB simulation of soft granular media. This time, we employed the YOLO network to identify the droplets since the trained YOLO-tiny network makes many mistakes in droplet recognition. This is likely due to the increased complexity of the system, which presents a densely packed configuration of droplets moving through a narrow channel significantly deviating from its initial shape.The YOLO network, however, can locate the droplets with almost perfect accuracy. Here, we note again that the training of the YOLO network was carried out with the training data consisting of randomly placed dense clusters of solid white circles. As before, Fig. In Fig. During the training, validation data were used to estimate the accuracy of the object detector model. In our case, they were statistically identical to the training dataset and could provide a biased estimate of the accuracy. Unbiased accuracy of the trained model can be obtained by analyzing a test case , ideally from a real-world dataset with known droplet trajectories. From the output trajectories of the model and the known trajectories as ground truth, the accuracy of the trained model can be estimated for real-world applications.N is the total number of frames, To measure the accuracy of the trained model, we compute mean square errors (MSE) by a Euclidean metric:y-coordinates of the center of mass . In FigFor measuring the accuracy of the full YOLO + DeepSORT model, we prepared a synthetic test case where the center of mass of the droplets move along some predefined paths (see Appendix We emphasize that we measure the accuracy of the developed application, including the object recognition and the object tracking process. The error values are indicative of the total expected error in extracted trajectories from the real-world data. Apart from the model’s accuracy, the model’s analysis speed is an important factor to consider while developing an application for practical use. In the next section, we report the inference speed, i.e., the rate of image analysis by the developed application on different computer hardware configurations.We test the inference speed of the object detection and tracking model on a typical notebook computer and a commonly used GPU machine. For inference, we used Tensorflow implementation . The infThese data clearly show that there is room for improvement and optimization of the inference speed for both YOLO and DeepSORT, for instance, by coding them in other programming languages, such as C++. This is an interesting topic for future work.Having accurate trajectories of moving objects is the first step to study the system. In the next section, we present an analysis of the inferred trajectories to test a hypothesis.At first sight, the droplets seem to move like the agents in active matter systems, such as birds in a flock. Several statistical physics models have been proposed to explain the flocking behavior in animal groups –45, and i are defined as droplets within a circle of radius R, fixed on the center of the droplet i. We set the size of the neighborhood in such a way that few neighboring droplets are included (see Fig. Given the superficial similarity between a flock and the soft granular media system studied here, we perform a quick analysis to test a hypothesis that the droplets behave like agents in an active matter system. To this end, we calculate the average moving direction see Fig. a. FigureIn Fig. We conclude that individual droplets frequently deviate from the average direction of their neighbors than the individual birds do in a polarized flock. This is plausible since, besides some similarities, there are also several points of departure between the two systems. First, in soft granular media systems, the fluid flow carries the droplets along the flow direction, like a systematic wind and second, the wall boundaries impose a stringent constraint on the motion of the droplets. None of the two aspects above are generally considered in the modeling of active matter systems.In this analysis, we compared only the polarization between the system of droplets and the ordered flock. Numerous other observations characterize a flock , and a dWe adapted and combined deep learning-based object recognition algorithm YOLO and object tracking algorithm DeepSORT to analyze data generated by fluid simulations of two soft flowing systems, multicore emulsions, and soft granular media. In particular, the trajectories of the individual droplets moving within a microchannel flow were extracted from the digital images of the system.We determine the accuracy of the complete application by comparing the inferred trajectories with the ground truth trajectories computed by independent methods. Although we used LB simulation data as a case study; the developed application can also be applied data generated by two-dimensional experimental setups.With the use of commonly available GPUs, the YOLO + DeepSORT based application can analyze the images at a remarkable rate, above the image capture rate of typical cameras, thus offering a practical, low-cost application capable of analyzing real-time data as it is acquired.From the technical point of view, we showed that the synthetically prepared datasets could be used to train the object detection network with almost perfect accuracy, thus avoiding labor-intensive, time-consuming training data acquisition for object recognition network training.We combined two state-of-the-art algorithms to identify and track the droplets and tested the model on two physical systems. It would be interesting to see how the application works for other dense emulsion systems. Nevertheless, YOLO and DeepSORT have shown remarkable performance in object recognition and tracking in more complex environments. We hope and expect that further developments of the work presented here may pave the way to the automatic detection and tracking of moving agents in complex flows of scientific, engineering, and biological interest.


Background: pancreatic cancer is one of the most lethal malignancies and a leading cause of cancer-related death worldwide. The only chance to improve the long-term outcomes of patients with pancreatic cancer is surgery with radical intent. Methods: in the present paper, we aim to describe a case series of 9 patients submitted to radical surgery for borderline resectable pancreatic cancer. Results: in all cases, negative resection margins were achieved. The types of venous resection consisted of tangential portal vein resection in four cases, circumferential portal vein resection with direct reanastomosis in one case and circumferential resection with graft placement in another four cases; postoperatively, one patient developed a vascular surgery-related complication consisting of graft thrombosis and thus necessitated prolonged anticoagulant therapy. Conclusions: extended venous resections can be a safe and efficient way to maximize the benefits of radical surgery in locally advanced, borderline resectable pancreatic cancer. Pancreatic adenocarcinoma is a particularly aggressive malignancy which represents a leading cause of cancer-related death worldwide. Therefore, the lifespan in such cases remains extremely poor, usually being only a few months following the time of the initial diagnosis ,2. The oAfter obtaining the approval of the Ethics Committee of Fundeni Clinical Institute no. 191/2021, data of patients submitted to venous resections en bloc with pancreatic head resections were retrospectively reviewed; among the 12 identified cases, 3 patients needed arterial resections and were thus excluded from the study. Finally, nine cases were considered as eligible for this study. All patients had been submitted to surgery in Fundeni Clinical Institute between January 2020 and April 2021.The mean age at the time of surgery was of 53 years (range 43–62 years) while the sex ratio male/female was 7/2. In all cases, the serum values of CA19-9 were preoperatively measured and the mean value was 312 U/mL (range 23–528 U/mL); interestingly, in one case a non-secretant tumor was found, the serum level of CA 19-9 being 23 U/mL while the biopsy demonstrated the presence of a moderately differentiated pancreatic adenocarcinoma. All cases were preoperatively submitted to computed tomography, in two out of the nine cases, a venous invasion was not described; meanwhile, in all of the cases magnetic resonance imaging was performed in order to better evaluate the local status as well as of the possibility of association with distant metastases. Meanwhile, in all of the cases an endoscopic ultrasound was performed, and a biopsy was retrieved; at the time, a local venous invasion was objected in eight out of the nine cases. As for the result of the histopathological studies of the biopsies, in all of the cases the presence of pancreatic ductal adenocarcinoma was observed. The most significant associated comorbidities were diabetes mellitus in five cases, obesity in three cases, arterial hypertension in three cases and chronic pulmonary obstructive disease in two cases.After the final preoperative workup, all of the cases were submitted to surgery with curative intent. In all of the cases, pancreatoduodenectomy was associated with portal vein resection. Intraoperative details are shown in Postoperatively, a single patient developed a complication related to vascular resection and consisted of a partial graft thrombosis which necessitated prolonged anticoagulant treatment with low-molecular heparin for the next month, followed by oral anticoagulant for the next two months. At the third month, a follow-up of all of the cases showed a satisfactory venous blood flow at the level of the portal system.The subject of vascular resections as part of radical surgery for locally advanced pancreatic cancer has been widely debated in the last decades, and the most appropriate therapeutic strategy has seen permanent changes, being significantly influenced by the development in the field of surgical technique and perioperative management of such cases ,17,18,19However, the fact that the notion of portal vein resection is a very generic one which includes tangential, short circumferential or extended circumferential resections of the portal system should not be omitted. Therefore, according to the extent of the local invasion, several classification systems have been proposed. The most commonly used is that proposed by the International Study Group of Pancreatic Surgery in 2014; according to this system, tangential resections were classified as type I if per primam venous suture is possible or type II if graft is needed, respectively, type III and IV if circumferential resection is performed. Cases in which an end-to-end anastomosis was feasible were considered as type III resections while cases in which the length of the resected segment did not allow an end-to-end anastomosis and in which a graft was needed were classified as type IV resections . As expep = 0.001). Meanwhile, there were no significant differences between cases submitted to pancreatoduodenectomy and segmental portal vein resections and those submitted to total pancreatectomy and tangential venous resection. Furthermore, the multivariate analysis demonstrated that the necessity of total pancreatectomy as well as the necessity of performing a circumferential resection were the only independent poor prognostic factors affecting the overall survival of the patients. This fact was explained through the fact that cases necessitating segmental resection or total pancreatectomy most frequently presented larger tumors or more advanced disease and a higher risk of microscopically positive resection margins (defined as a R1 resection) [Meanwhile, another important subject which has been debated is related to the extent of the pancreatic resection whenever vascular resections are needed. Therefore, certain studies raised the question of whether total pancreatectomy should become a standard procedure whenever vascular resections are needed, in order to minimize the risk of perioperative complications and maximize the rates of negative resection margins . One of section) .Another very important issue which should be taken in consideration in cases where portal vein invasion is present is the location of the invaded segment. Therefore, Chaoyang et al. consider this the greatest inconvenience regarding the classification proposed by the International Study Group of Pancreatic Surgery in 2014 and therefore have proposed a new classification system . AccordiAlthough venous invasion in pancreatic head adenocarcinoma was initially considered as the formal contraindication for surgery, recently published results demonstrate that, in selected cases, extended resections of the pancreatic tumor en block with the invaded segment of the venous structure can be safely performed in selected cases with acceptable postoperative morbidity rates. Meanwhile, the long-term survival outcomes in such cases seem to be similar to the ones reported in cases submitted to standard pancreatic resections, once again demonstrating the efficacy of the method.


Technical details associated with a novel relative motion sensor system are elaborated in the paper. By utilizing the Doppler effect, the optical sensor system estimates the relative motion rates between the sensor and the moving object equipped with modulating light sources and relatively inexpensive electrical components. A transimpedance amplifier (TIA) sensing circuit is employed to measure the Doppler shift exhibited by the amplitude modulated light sources on the moving platform. Implementation details associated with the amplitude modulation and photo-detection processes are discussed using representative hardware elements. A heterodyne mixing process with a reference signal is shown to improve the signal-to-noise ratios of the Doppler shift estimation processing pipeline. Benchtop prototype experiments are used to demonstrate the utility of the proposed technology for relative motion estimation applications. Safe proximity operations of a moving vehicle are dependent on accurate relative navigation to drive the guidance and control loops. To this end, several relative position and attitude estimation systems have been developed for the navigation of vehicles . The preWhile the signals and navigation measurements obtained from the integration of the GPS and INS sensors form an acceptable positioning, heading and attitude solution for low frequency (less than 5 Hz) motion platforms, it is not acceptable for high-frequency relative motion platforms. High-frequency motions are typically experienced by platforms that exhibit faster motions such as unmanned aerial systems (UAS). UAS platforms routinely employ challenging and aggressive maneuvers to negotiate complex mission scenarios. This capability is also desired in self-driving automobiles when collision avoidance and passenger safety operations stipulate rapid reaction times. To this end, a variety of sensing solutions are used to augment the GPS-INS system in localization and pose estimation of autonomous systems. Furthermore, the use of ancillary relative positioning and navigation systems aids in catastrophic failures in the absence of GPS signals. A plethora of radio frequency (RF) solutions are used to augment the GPS-INS localization sensing systems. Proximity sensors installed on a modern car typify the type and kind of RF solutions employed by the engineers to augment the localization services provided by the GPS-INS technologies. Radar systems are routinely used to maintain speed control and formations in automotive tracking systems. The modern automotive radar system is a chipset type sensor consisting of multiple transmit and receive channels that provide the angular information about the target by determining the phase difference between the signals. The automotive radar sensors are classified into short-range radar (SRR) (≈30 m) and long-range radar (LRR) (≈150 m) according to their capability of range detection. These sensors can provide the instantaneous radial velocity of a moving object. To accommodate wider bandwidth demands, a 77 GHz sensor is being used . In the Vision-based navigation systems form an important alternative to radio spectrum for relative navigation sensor systems. VisNav is an electro-optical sensing system that uses a set of active beacons mounted at known locations on the structure to image a position-sensitive photodetector (PSD) ,9. The PWhile the direct sensing of the relative pose between the structured light beacons is of great utility in guidance and control of vehicles executing six degrees of freedom (DOF) maneuvers, an estimate of the relative angular velocities and translational speeds is useful for more aggressive maneuvers in autonomous systems. While the rate information can be inferred if the relative pose can be estimated at sufficiently high update rates using a Kalman filter that makes use of a simple kinematic model for relative motion dynamics, the direct sensing of rates enables estimation of the rates with a much higher bandwidth and better signal-to-noise (SNR) characteristics. Inertial measurement units (IMUs) on the vehicles involved in the proximity operations provide sufficient signals for relative pose-rate estimation in the cooperative proximity operations. However, in non-cooperative applications, the inertial sensors do not have the ability to measure the relative translational rates between the bodies involved. This is because the rate gyroscopes only measure the angular velocity vector of the bodies involved with respect to an inertial frame. Complementary rate sensing technologies are useful to augment the effect of systematic drifts incurred by the inertial sensor systems. Direct estimation of the rate also enables accurate relative pose estimation by integration from the best estimated relative pose. The multi-sensor integration involved by combining different sensing modalities is an important element in the automation of vehicular proximity operations.Modulated electromagnetic signals modulated periodically experience a Doppler shift when the waveform emitter and receiver experience relative velocity with respect to each other . The amoThe Doppler effect is observed when a relative motion takes place between the vehicle and the observer. A Doppler radar acquires the velocity of the moving target along the line of sight vector by observing how the target shifts the radio frequency (RF) signal transmitted by the radar. Since the actual speed of a vehicle is incomparably smaller than the speed of light, the radar is required to modulate the transmitted RF signal. In the presence of a target within the radar’s operating range, the transmitting signal with modulating frequency The transmitted signal is a coherent sinusoidal wave with frequency of r is the radial distance to a target and c is the light speed.The received signal is delayed by The perceived frequency, d signal ,(1)fs=dThen, the frequency shift can be expressedIn this paper, the sensor holds that a radar is stationary on the ground and the target of interest has a constant velocity coming forward to the radar.ht angle ,20. A frTypical RF radars modulate the transmitting signals at a very high frequency (VHF) band (30–300 MHz) to super-high frequency (SHF) band (3–30 GHz). For example, to detect very slow motion, traffic speed guns use an extremely high frequency (EHF) band (30–300 GHz) that even record the speeds of pedestrians. While the principles of the Doppler radar are pretty commonly used in RF applications, other electromagnetic waves can also be used in interferometric applications. In this paper, light modulation is considered as the primary modality of interferometric energy transmitters for Doppler sensing.All incident light must be converted to electrical signals, transmitted, received and processed. Our navigation system utilizes light to deliver the Doppler range rate information of the aircraft to the sensor. With the use of light, the sensor can take advantage of prompt data transmission and function safely without the risk of electromagnetic interference on electronics.Light is an electromagnetic wave with a frequency of 430 THz far beyond the frequency resolution of photodetectors . TherefoFor data transmission, both optical sources and detectors should operate within the detector’s frequency resolution. There are two main types of optical signal sources; light-emitting diodes (LED) and light amplification by stimulated emission of radiation (laser) diodes. LEDs are the economical and lower-performance sources used in applications where low data rates and/or short distances are anticipated. Laser light sources operate at much higher speeds, dissipate greater power levels, and require temperature compensation or control to maintain specified performance levels. Laser light sources also support long distance propagation. Photodetectors are also categorized into two main categories—PIN photodiodes and avalanche photodiodes (APD). PIN photodiodes are more commonly used, especially in less constricted applications. On the other hand, APD has properties of very high sensitivity and gain although it requires a much higher operating voltage than a PIN photodiode. With a high specification in gain, APD can be utilized for low light level measurement at high speed. For prototyping the proposed Doppler sensor, LED and APD are chosen as the light transducer and photodetector, respectively. This is because the LED beam loses optical power in a way that is inversely proportional to the square of the range, and APD is more capable of detecting the attenuated light energy.Thermal noise (Johnson noise): This unavoidable noise is generated by random thermal motion of charge carriers. It is generated regardless of the voltage applied.Flicker noise: The flicker electronic noise has a PSD  of 1/(frequency). This is a low-frequency phenomenon because higher frequencies are overshadowed by white noise. Typically, this noise affects the DC components of the circuit.An advantage of LED modulation is that its direct modulation shows high linearity between an input voltage to LED and the modulated output . An anal the LED . For LED the LED . TypicalMeasuring the optical Doppler shift can be quite complicated. Unlike sound and radio waves, the light sources do not usually propagate in a uniform manner to constrain the noise level far below the true signal. The frequency shift of modulated light can be explained by the group Doppler effect ,27.n = 1) and the shifted side of frequencies isWhen an optical signal propagates, the frequency of the It means that the modulation frequency spectrum .fsm=fm±The modulated frequency within the frequency resolution may be far less than that of typical RF-based Doppler radars. Therefore, the speed of a target affects the signal-to-noise (SNR) ratio of the Doppler circuit. If a target moves with pedestrian speed, the Doppler sensing in the MHz range will not result in a measurable frequency shift. However, if the design range of the sensor system is to detect speeds between 5 and 200 m/s, the shift outcomes are able to avoid the flicker noise that hinders low-frequency measurements.R is the electrical resistance and The modulation bandwidth of the illumination source is determined as the point where the signal power e device . If R isThe ratio of the output power at the frequency The electrical 3-dB bandwidth takes place at the frequency point where P(0)=0.5 . The 3-dIn order to take advantage of high-linearity, low DC level (zero frequency component), high gain and wide bandwidth, the proposed sensor employs a trans-impedance amplifier (TIA) integrating an avalanche photodiode. As illustrated in The frequency response of the TIA shows where the signal power gain is maximized and noise gain is minimized. A greater value of quencies . This frncy (fz) .(4)Zi(sFlicker noise The noise curve begins to increase from e of 1/β . AccordiThe proposed Doppler sensor consists of the light source (beacon) affixed to a moving vehicle (target) and photodetection sensor on a station along platform. The beacon emits amplitude modulated light with distinct modulation frequency There are two different photodiode configurations: photoconductive and photovoltaic mode. The photovoltaic detector is a zero bias voltage mode in which current is generated as a result of the absorption of photons of a voltage difference across a p–n junction. The photoconductive mode uses an external reverse bias voltage to a photodiode to increase electrical conductivity and expedite response time by increasing the width of the depletion layer when photons are absorbed . For theThe output current of the photodiode is transformed to voltage for use by subsequent sensor electronics by the TIA. Implementation details of the TIA used for light-based Doppler range rate sensing are now described. Note that the objective of this description is to provide a representative description of all the electronic considerations involved in the development of the prototype. For the Doppler sensor, TIA action is modeled as a linear relation of an output voltage (f and Cf . The useThe employed TIA includes the high voltage module and a single gate junction field-effect transistor (JFET) ‘OPA657’ as an OpAmp. The voltage module provides a 150 V bias voltage to the APD and the OpAmp is fed by a 1 MX7 multiplier used to transfer output frequencies to a band seven times higher. The multiplier board includes a Schottky diode and compatible filter circuit to achieve a low conversion loss as well as high rejection of irrelevant harmonics near its X7 output. To remove the unwarranted harmonics at high bandwidth, the active low pass filter is connected at the output of the TIA sensor. The low pass filter is designed to have a 3-dB attenuation at 500 MHz with very low signal loss.The frequency of the waveform observed at the output of the TIA is shifted from The output of the circuit in N is the number of input samples, and n and k are the index of time samples and index of frequency point, respectively. The calculation returns a two-sided spectrum in complex form and each output point has the real (R) and imaginary (I) part. The processor computes the power spectrum (P) of each output point in dBm by the following equation.In this project, we utilized the built-in ADC function in the digital oscilloscope. The employed digital oscilloscope is equipped with a 12-bit resolution ADC that returns the digitized data of sinusoidal waveform. The oscilloscope is connected to the output of the multiplier and records the digitized data while conducting the experiment. We used these data to extract the dominant frequencies and estimate the velocity of the relative motion as a post-processing. In the digital computer, fast Fourier transform (FFT) is carried out to calculate the dominant frequency of the stored data. Our FFT implementation is based on the discrete Fourier transform (DFT) of the frequency domain points, The dominant frequency is obtained by the index value of the greatest peak of the power spectrum in the FFT domain and its corresponding frequency value. The beat signal is generated as a sinusoidal format by the frequency difference between the local oscillation and the received signal. To extract the frequency of the beat signal, the processor utilizes FFT implementation and determines the dominant frequency of the beat signal. Since the output of the mixer directly denotes the Doppler shift proportional to the relative velocity of a target, the velocity is obtained by simple conversion. It is noted that a separately embedded system processing pipeline needs to be incorporated in a sensor to replace this process for real-time signal processing. Optical signals from the beacon collected by the sensor are subject to ADC and FFT processing to obtain the numerical value of the Doppler shift. Although the output of the mixer does not specify the sign of the Doppler shift, it can be accessed contextually. In embedded system applications, these signal processing operations are carried out by dedicated computing elements.To evaluate the functionality of TIA’s sensing ability, the measurement along the one-way travel of a signal is performed. A single LED operation is used in the experiment. It is assumed that the characterized single LED performance can be replicated to a multi-beacon operation. The proposed sensor requires substantial linear velocities to derive the generations of Doppler shift detectable in the laboratory. Therefore, a tangential velocity of circular motion is used to generate the required Doppler shift. A fan blade driven by a high revolution-per-minute (RPM) DC motor is set up and the LED transmitting a reference signal is attached at the end of the blade. The LED is powered by a signal generator built affixed to the blade, as shown in x, rpm measured by a tachometer, we find that the relative velocity is given byFor the length of blade (R) of 1.0 m and the angular velocity of The experiment is performed at nine different speeds to demonstrate the functionality of the sensor. Rates of 300, 350, 400, 450, 500, 550, 600, 650, 700 that correspond to tangential velocities (m/s) of 31.4, 36.7, 41.9, 47.1, 52.6, 57.6, 62.8, 68.1, 73.3, respectively, are used to demonstrate the prototype. Measurements at every 5-MHz increase in the input modulation frequency are used for the rate sensing.The TIA sensor placed at the bench processes the optical signal coming from beacons to the RF port of the frequency mixer. The schematic in From Equation , the freTheoretically, frequency shifts should be directly proportional to both the rpm and the modulation frequency. The measured empirical results are now discussed.To demonstrate the capability of the sensor, the prototype circuit board and an LED beacon powered by a signal generator are tested at different modulating frequencies by operating the rotary experiment at different RPMs.The results of the experiment reported in However, the frequency band of interest is narrowed down to 70 MHz. A comparison of measured frequency shifts with corresponding theoretical frequency shifts for modulation within 70 MHz is plotted in In this paper, the relative velocities between the target and the sensor are obtained by simple conversions from the frequency shift data, as shown in Equation .(9)VrelThe digital oscilloscope records the data of The bias is offset by the average relative velocity obtained, assuming an initial zero bias. In practice, the precision information of the sensor is required to characterize the sensor performance. At each RPM, the residuals of the sensor output are observed, as reported in after fp . If a cag the Cf ,31. The In comparison to a laser Doppler vibrometer that detects the displacement and velocity of a target using a laser beam as a source, the proposed sensor system employs low-cost and simpler design, which is designed for the part of a moving platform. For example, OptoMet’s HeNe single laser vibrometer  that shoo et al.  also meaThis paper presents a new method for estimating the velocity of a moving vehicle by combining the Doppler shift concept with optical signal modulation. A prototype opto-electronic sensor that implements proximity range rate measurements is devised. An LED light source, a photodetector, TIA circuit and a digital signal processor are used to build the prototype system. Experimental results demonstrate that the frequency shifts of the modulated light can be generated in proportion to a relative velocity between the sensor and light source. The accuracy of the proposed sensing system is found to be limited when the modulation is conducted beyond the bandwidth of the LED light source while the prototype bandwidth is significantly lower than that of a comparable system built using radio waves. The system shows promise reagarding the use of light in direct measurements of the Doppler range-rate of vehicles moving in relation to each other. A rotating experiment is developed to test the prototype Doppler range rate measurement system over a wide range of speeds. The frequency shift of the measured analog signals is examined by electronic instruments and a digital computer. Using the Doppler shift formula, the obtained frequency shift is converted to the relative velocity with acceptable accuracy. This result shows optimism regarding the use of a Doppler range-rate measurement system for relevant navigation applications.


The unbalanced resource utilization of physical machines (PMs) in cloud data centers could cause resource wasting, workload imbalance and even negatively impact quality of service (QoS). To address this problem, this paper proposes a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration. It uses Gaussian model to adaptively estimate the probability that the running PMs are in the multi-resource utilization balance status. Given the estimated probability of the multi-resource utilization balance state, we propose effective selection algorithms for live VM migration between the source hosts and destination hosts, including adaptive Gaussian model-based VMs placement (AGM-VMP) algorithm and VMs consolidation (AGM-VMC) method. Experimental results show that the AGM-VMC method can effectively achieve load balance and significantly improve resource utilization, reduce data center energy consumption while guaranteeing QoS. Cloud data centers have stringent requirements for their operating environment, and the maintenance of the normal operation of a cloud data center usually results in high power consumption. Previous research  indicateIn cloud data centers, live virtual machine (VM) migration technology allows VMs to be migrated from one host to another after a short downtime , therebyIn this paper, we propose a multi-resource collaborative optimization control (MCOC) approach to balance resource utilization. In our approach, PMs are first categorized into nine states according to the distribution of the utilization of multiple resources. A Gaussian model is used to estimate the probability that running PMs are in a balanced state of multi-resource utilization. Based on that, a set of algorithms are proposed for selecting the VMs to be migrated and their destination hosts, with the goals to minimize the migration time and maximize the probability of load balance state of the hosts. Finally, an adaptive Gaussian model-based VM consolidation (AGM-VMC) method that integrates these algorithms for VM consolidation is proposed. The experimental results of the proposed VM consolidation method on real data sets show that it effectively reduces the total number of VM migrations, decreases energy consumption and maintains the load balance of the cloud data center.VM migration has been exploited for load balancing and VMs consolidation, which aims to improve energy efficiency and quality of service (QoS) of data centers. VMs placement addresses the issue that where the migrated VMs come from and where to, thus is the key for VMs consolidation.The VM placement problem  is usualSeveral works proposed VM placement approaches with exploiting patterns of resource load or statistics-like schemes. VM placement is subject to multiple resource constraints and requires an optimal balance of multiple goals. It has been pointed out  that VM Compared with the heuristic and statistics-like methods, intelligent algorithms can usually obtain an approximate optimal solution, thus they are more effective in dealing with the complicated workload imbalance problem in VM placement. However, they are also greatly influenced by the optimization model. A suitable optimization model can help guide an algorithm’s evolution and improve its convergence speed. However, the algorithm needs to be iterated during the evolution process, which leads to long execution time and low efficiency.https://github.com/alibaba/clusterdata/blob/master/cluster-trace-v2018/trace_2018.md). The data center collects resource utilization data from the physical machine every 5 min, for a total of 288 data points obtained over a 24-h period. As VM consolidation cycle changes, the utilization of one type of resources differs greatly from that of another type, even within the same period. Moreover, the utilization of different resources peaks at different times of the day. This observation demonstrates a real scenario in which wastage of physical resources occurs several times during a day while the objective of optimizing utilization of multiple types of resources is not met. Since VMs request multiple resources during VM consolidation, peak utilization of any resource in the physical machine would place the physical machine at a higher overload risking. Therefore, controlling the utilization of multiple resources within a desirable range would effectively reduce the resource wastage in the data center and mitigate host overloading risk.Modern cloud data centers generally contain a variety of computing resources such as CPU and memory. PMs can be classified into three states based on their CPU utilization: low CPU load, normal CPU load, and high CPU load. An excessively low CPU load is wastage of the resource, while an excessively high CPU load will likely result in an overloading of PMs when CPU resource requests from VMs suddenly increase. This will then affect the QoS of the data center. The possibility of such undesirable situation can be minimized to the greatest extent only when the CPU load is maintained in a normal state prior to the request. Similarly, other data center resources such as memory can be classified into underload, normal-load, and high-load states. As suggested above, load balance can be achieved in the data center to reduce resource wastage only when the different resources are simultaneously in a normal state of utilization. This study proposes a VM consolidation mechanism and algorithm for coordinated optimization and control of multiple resources in physical machines. It aims to reduce resource wastage arising from unbalanced resource utilization in physical machines and maintain the utilization of multiple resources at a reasonable level.Suppose a running physical machine has two resources, CPU and memory, which are denoted by e states . It meanThe load of a running physical machine is essentially the sum of all the resources allocated to VMs operating on the physical machine, with the load increasing or decreasing as the resources are requested or returned by the VMs. VM-requested resource utilization is derived from the task requests that are submitted by users to the data center, and these requests are random; this causes the resource utilization requests of VMs to be random. As analyzed in Section of “Workload imbalance between multiple resources”, when the total resource utilization of the physical machine is at the  studies  have shoLet the resource utilization of Further, the cumulative probability of i.e., the probability that the resource utilization of the physical machine is in a balanced state, is Consequently, the probability that the host If CPU resource utilization of the physical machine is relatively high at a time when the memory resource utilization is low, the CPU resource will be more likely to be overloaded than the memory resource at that time. It suggests that the CPU resource has a larger impact on the host load than the memory resource. Accordingly, As shown in To determine The key issue to resolve first in the VM placement process is how to efficiently and quickly identify the source and destination hosts involved in a VM migration. Let Overloading hosts are one of the main factors causing the QoS degradation of a data center. This section proposes a Gaussian model based multi-resource overloaded detection (GMMHOD) algorithm for detection of overloading hosts. The algorithm first develops a Gaussian model using When a physical machine in a data center is at high overloading risk, some VMs need to be migrated from the physical machine to avoid overload and consequent degradation of QoS. To maximize the probability that the host is in the peration  and therAs suggested by Each VM to be migrated should be placed in a suitable destination host. The first requirement for a suitable destination host is that it has sufficient resources. In a multi-resource constrained VM placement problem, if a VM requests a large amount of CPU but a small amount of memory, it is easy to deploy the VM in a physical machine with a small amount of available CPU and a large amount of available memory resource is prone to overload the CPU resource. In contrast, placing the VM in a physical machine with a large amount of available CPU and a small amount of available memory can not only effectively avoid host overloading but also improve memory resource utilization of the physical machine. In addition, it is desirable to select the physical machine that will have the highest probability of As suggested by An adaptive Gaussian model based VM consolidation (AGM-VMC) scheme is further proposed integrating the GMMHOD, GMMMT, and GMMOP algorithms. The working steps of the AGM-VMC method are as follows: (1) first, the scheme computes the overloading probability of running PMs using the GMMHOD algorithm, and includes the PMs at high risk of overload in the collection of overloaded PMs; (2) secondly, the method uses the GMMMT algorithm to perform live VM migration one by one from the overloaded PMs until the overload risk is eliminated, and adds the migrated VMs to the collection of outward migrated VMs; (3) thirdly, the method uses the GMMOP algorithm to find suitable destination hosts for the outward migrated VMs, namely those which satisfy the resource needs of the outward migrated VMs, and the low-overload-risk needs of PMs after VM placement; (4) finally, the method uses a greedy algorithm to iteratively select PMs with the lowest load, moves all of their VMs to suitable destination hosts, and turns off or hibernates them until the VMs on any running physical machine cannot be fully moved to any other running physical machine. Additionally, when there are many candidate destination hosts, multiple virtual machines are migrated through “one by one” scheme continuously to one or multiple different destination hosts during each cycle of VM consolidation.The pseudo code of AGM-VMC method is described as below.https://www.spec.org/power_ssj2008/results/). In experiments, CloudSim creates VMs based on the user requests and randomly deploy them to PMs. Empirically, the two thresholds including We use CloudSim to simulate a cloud data center that contains 800 heterogeneous PMs and three instances of VMs. https://github.com/alibaba/clusterdata/blob/master/cluster-trace-v2018/trace_2018.md), a real dataset published by Alibaba Data Center in 2018, which records actual operational data and is referred to as the Alibaba dataset in this study. The Alibaba dataset contains a huge volume of historical records of both CPU and memory resources. Samples of task requests submitted on June 6, July 26, and August 15, 2018 are randomly selected here as the test workloads, with the workload characteristics detailed in We use workload data from Alibaba Cluster Data V2018  for host overload detection and a random selection (RS) algorithm for VM selection. The UP-BFD method performs VM consolidation using the utilization prediction-aware best fit decreasing algorithm . The FFDTo compare the different VM consolidation manners, this section will analyze and evaluate the performance based on the proposed six evaluation indicators . Table 4The Service Level Agreement Violation (SLAV), also known as the violation rate of the service level agreement by a data center, reflects the data center’s QoS. Higher SLAV indicates a longer duration of service level agreement violation, namely a greater difficulty in guaranteeing QoS. Among the four methods above, the UP-BFD method has the highest SLAV, followed by the ND-RS method, the FFD-MMT method, and the AGM-VMC method in decreasing order. This is because the ND-RS, UP-BFD, and FFD-MMT methods are unable to predict workload trends when performing VM consolidation and thereby are more likely to cause host overloading and thus QoS degradation.EC represents the energy consumption of each algorithm. As shown in ESV, which is the product of EC and SLAV, is a comprehensive evaluation index of data center energy consumption and QoS, with smaller ESV representing better comprehensive performance of the data center in both aspects. The AGM-VMC method cause less SLAV and EC than the other three methods and thereby lower ESV. The other three methods are mutually compared as follows: (1) the FFD-MMT method causes higher EC but much lower SLAV than the other two methods, thereby leading to the lowest ESV of the three methods; (2) the UP-BFD method causes approximately the same EC as the ND-RS method but 2.25 times higher SLAV, thereby leading to higher ESV than the ND-RS method.VMM represents the number of VM migrations. Since a migration VM will suspend services, reducing VMM is an important measure to improve the QoS of a data center. As shown in i.e., the smaller the impact of VM migration on data center QoS. The AGM-VMC method comprehensively considers the factors that would minimize migration time and the factors that would maximize the probability of physical machines being in the PDM is affected by both the number and duration of VM migrations during VM consolidation. In general, the lower the number of VM migrations and the shorter the VM migration time, the lower the PDM, The longer duration that the resource requests of VMs cannot be fulfilled, the higher the SLATAH, and the greater the negative impact on QoS. The AGM-VMC method employs a strategy of maximizing the probability of PMs being in the The four VM consolidation methods are compared in terms of changes in the number of running PMs, changes in the number of VM migrations, and changes in the utilization of physical machine resources as follows.In summary, the AGM-VMC method effectively improves the energy consumption and QoS of a data center and reduces the number of VM migrations by balancing the utilization of various resources in PMs. As revealed by further analysis of host changes and VM changes during VM consolidation, the AGM-VMC method promotes load balancing among the PMs and reduces unnecessary VM migrations, thereby improving resource utilization and reducing resource wastage; this further helps reducing the energy consumption of a data center and in guaranteeing QoS.This paper proposes a coordinated optimization control mechanism for multi-resource utilization of PMs, and the AGM-VMP algorithm and AGM-VMC method based on this mechanism. The experiment results demonstrate their effectiveness in reducing the total number of VM migrations, decreasing energy consumption and maintaining the load balance of the cloud data center.etc.; second, since cloud data center is a stochastic environment, it is hard to define the equilibrium state of resource utilization; then, VM consolidation can be triggered periodically by the cloud service provider, or triggered by the service monitor during the light workload time. Here, the cost of VM consolidation frequency is very challenging and need further study; finally, the three states of the two resource utilization and their parameters are currently determined by experience, and it is worth to develop a method that learn and define them dynamically and adaptively.However, there are a few limitations that need to be further addressed in future works. First, the nine combination states of resource utilization only consider two kinds of resources. In fact, the constrained resources in the process of VM consolidation have others such as bandwidth, 10.7717/peerj-cs.852/supp-1Supplemental Information 1Click here for additional data file.


The use of this device could be helpful to perform shockwaves valvuloplasty as an option to retard TAVR/SAVR, or as a pretreatment to facilitate prosthesis implantation and minimize the occurrence of paravalvular leak.The increasing incidence of calcific aortic valve disease necessitates the elaboration of new strategies to retard the progression of the pathology with an innovative solution. While the increasing diffusion of the transcatheter aortic valve replacements (TAVRs) allows a mini-invasive approach to aortic valve substitution as an alternative to conventional surgical replacement (SAVR) in an always larger patient population, TAVR implantation still has contraindications for young patients. In addition, it is liable to undergo calcification with the consequent necessity of re-intervention with conventional valve surgery or repeated implantation in the so-called TAVR-in-TAVR procedure. Inspired by applications for non-cardiac pathologies or for vascular decalcification before stenting , in the present study, we show the feasibility of human valve treatment with a mini-invasive device tailored to deliver shockwaves to the calcific leaflets. We provide evidence of efficient calcium deposit ruptures in human calcified leaflets treated  CAVD and other aging-related disorders are therefore anticipated to have an epidemic diffusion in the next decades.Calcific aortic valve disease (CAVD) is one of the most prevalent pathologies in elderly people with a trend to increase worldwide –4, along2. By contrast, severe AVS is defined by a peak velocity greater than 4 m/s, a mean gradient of 40 mmHg or more and an aortic valve area less than 1 cm2. For several decades, the gold standard treatment of severe aortic stenosis has been surgical aortic valve replacement (SAVR) with mechanical or bio-prosthetic valves. Recently, a new trend has been established by the introduction of transcatheter aortic valve replacements (TAVRs) which has become an optimal solution for patients in the age range of 60–65 to ~80 years , giving rise to large calcific nodules. These nodules distort the leaflet geometry and make leaflets less pliable, preventing complete valve closure and giving rise to regurgitation and valve mechanical insufficiency. This progresses rapidly and can lead to death within 2–3 years after the beginning of the calcification process –10. For 80 years  or patie80 years . At the ex vivo without altering the vitality or affecting the structure and gross morphology of the leaflets, and of pericardium as the most represented material normally employed to manufacture bioprosthetic valves. We also show that the employment of the device is feasible in pigs. Based on our results, we propose TDD as an alternative treatment for young patients not eligible for TAVR. It may also be an alternative to the TAVR-in-TAVR procedure when restoring valve performance in patients with preexisting TAVRs affected by post-implant calcification.Inspired by the use of shockwaves to disintegrate kidney stones or gallstones , 14, whiin vivo trial was performed at an external facility authorized for the execution of cardiothoracic surgery trials. Animals were treated according to the ethical guidelines approved by the Italian Ministry of Health.Human stenotic aortic valves were obtained from a leftover of valve surgery replacement after approval from the Ethical Committee at Centro Cardiologico Monzino for the purpose of the present study. Porcine pericardia were collected from euthanized adult pigs in the experimental piggery facility and the slaughterhouse of the Department of Veterinary Medical Sciences of the University of Bologna. The pig The TDD used in the present study was an evolution of the device we preliminarily tested . The theImmediately after gentle isolation of the parietal pericardial membrane, the tissue was immersed in a solution of Phosphate-buffered saline (PBS)-containing antibiotics (penicillin/streptomycin) and stored or shipped at 4°C until the beginning of the experiments. Before the treatment with shockwaves, the remaining fat tissue was removed from the pericardial flap under sterile conditions. Subsequently, it was divided into stripes with a sterile knife and further cut into three samples/stripes. TDD treatment was localized at the center of the samples after marking with a surgical pen for discriminating the treated vs. the untreated portions in subsequent analyses.3. The system was mounted with the two piezoelectric transducers clamping the portion of the tissue to be treated before connecting to the system amplifier . No moderate to severe aortic regurgitation was ever detected by echocardiographic monitoring during and after TDD treatment. No pacing or rapid pacing was needed for the treatment.Human and pig valve samples were fixed in 4% paraformaldehyde (4°C overnight), dehydrated in alcoholic scale, and embedded in paraffin. Histological sections (5 μm in thickness) were cut, dewaxed, and stained. Von Kossa staining was performed to evaluate calcium deposits in untreated vs. treated portions of the specimens. Conventional staining  was performed to observe in greater detail the structure of the tissue. Images of the tissue sections were acquired using an AxioPlane optical microscope (Carl Zeiss) and analyzed with ZenBlue software.2 in the center of the transducer and 80 mj/mm2 at the edges . Volume reduction was calculated from the post-treatment vs. pre-treatment leaflet by CT scan . To this aim, we selected areas of the leaflets that were not characterized by the largest calcific deposits or affected by sclerosis, where, instead, calcium deposition is not involved  solutions were performed. The magnifications in each of the panels indicate areas with possible tissue damage due to the treatment. As expected, evident signs of mild/moderate ruptures on the aortic side of the right leaflet  planned in the next version of the in vivo device will help to improve the efficiency of calcium deposits debridement. Accordingly, suitable mechanical tests in a pulse duplicator system will be necessary on treated valves to confirm the variations in hemodynamics after treatment with TDD in its full power configuration.In the present study, we provide the first proof of concept demonstration of a new device specifically tailored to operate with a high level of safety and efficiency. It is a partial debridement of the calcific deposits affecting the aortic valve motion and hydrodynamic performance. Although innovative, this approach is not the first to be employed to this aim. In fact, in recent literature, there are emerging descriptions of lithotripsy application as a preventive strategy to improve the ellipticity index and limit the paravalvular leak of transcatheter aortic valve implantations (TAVIs) , 23, to in vivo, ideally using animal models, i.e., sheep . DV and MLB are supported by the local funding program RFO-UNIBO.EF, DB, MCP, and EP are collaborators of the equity-financed company AorticLab S.r.l. AorticLab S.r.l. is an equity-financed Company for development of the TDD as a novel minimally invasive calcium debridement procedure in human pathologic valves. GG and CR were employed by ASST Fatebenefratelli Sacco. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The literature has shown that there is no consensus regarding the best resin composite photoactivation protocol. This study evaluated the efficiency of the conventional, soft-start, pulse-delay and exponential protocols for photoactivation of resin composites in reducing the shrinkage stress and temperature variation during the photopolymerisation. The photoactivation processes were performed using a photocuring unit and a smartphone app developed to control the irradiance according each photoactivation protocol. These photoactivation methods were evaluated applying photoactivation energies recommended by the resins manufactures. Three brands of resin composites were analysed: Z-250, Charisma and Ultrafill. The cure effectiveness was evaluated through depth of cure experiments. All results were statistically evaluated using one-way and multi-factor analysis of variance (ANOVA). The use of exponential and pulse-delay methods resulted in a significant reduction of the shrinkage stress for all evaluated resins; however, the pulse-delay method required too long a photoactivation time. The increases on the temperature were lower when the exponential photoactivation was applied; however, the temperature variation for all photoactivation protocols was not enough to cause damage in the restoration area. The evaluation of the depth of cure showed that all photoactivation protocols resulted in cured resins with equivalent hardness, indicating that the choice of an alternative photoactivation protocol did not harm the polymerisation. In this way, the results showed the exponential protocol as the best photoactivation technique for practical applications. These2 . Nowaday2 [2 . However2 [2 ,10.Some studies have reported that unsatisfactory behaviour and properties of resin composites, such as excessive polymerisation shrinkage, can be induced by high levels of irradiance during the photoactivation process . The polRecent studies have demonstrated that control of irradiance during the polymerisation process can contribute to an improvement in the mechanical properties of the cured biomaterials, and one of the main goals in this research area is to identify an optimal protocol for photoactivation of resins composites . GuimarãThree models/brands of resin composites were selected for this study. Details regarding each resin composite are shown in Photoactivation processes were performed using a polymerisation device developed by our research group . This device is equipped with a dental blue LED . The curing device was connected to a smartphone via Bluetooth connection. An Android app also developed by our research group  was used to send the photoactivation parameters to the device, which allowed the control of the LED irradiance during the photoactivation process according to a mathematical function, or the use of the classical photoactivation protocols. Output irradiance was gauged using a radiometer . TOTALE) was obtained from the resin manufacturer’s recommendations. The other photoactivation parameters (as time of pulse and time of delay) were determined for each photoactivation technique  from the consensus of the literature, which showed that irradiances generally start with half the value of the total irradiance of the photoactivator in the pulse-delay and soft-start protocols. For the conventional, soft-start and pulse-delay protocols, the light-curing device emits constant irradiance as a function of time  and total photoactivation energy given by the product of irradiance and exposure time. The parameters for the exponential photoactivation method, however, were determined using the model of the exponential function proposed by Guimarães et al. [i) as a function of the time (t):a = 0.58309, b = 4.928 and i0 = 300. The last term in Equation (1) (i0) corresponds to the irradiance at the start point of the photoactivation process (300 mW/cm2). The total time of photoactivation for each technique was determined by simple integration of Equation (1) so that the activation energy reached the expected values, as presented in Equation (2):The total photoactivation energy  is the only integration variable for adjusting the function to the activation energies. All photoactivation parameters were summarized in In Equation (2), time (3 (6 mm × 2 mm × 1 mm). The device tip was positioned on the 6 mm side of the steel base. The distance for photopolymerisation was 2 mm.Monitoring of the polymerisation shrinkage was performed using an Emic DL 3000 universal testing machine . Two steel bases were coupled to the equipment arms and adjusted for insertion and polymerisation of the resin . The gapDuring the polymerisation process, the controller software traced the curve of the stress force as a function of the polymerisation time. Although photoactivation is usually performed within 50 s, the monitoring time was 300 s, as the resin shrinkage occurs even after activation. The assays were performed in triplicate for each resin model/brand and photoactivation method.Healthy molar human teeth extracted by orthodontists’ indication were used to monitor/evaluate the temperature variation during the resin photoactivation processes. All patients who provided teeth for the present study completed a written consent form. The study protocol was approved by the Ethical Research Committee of the Medical School of Botucatu of São Paulo State University-UNESP. (CAAE: 56085916.0.0000.5411/Report: 1.621.713). Cavities with dimensions similar to the specimens used for the shrinkage experiments (6 mm × 2 mm × 1 mm) were made in each tooth. The cavities’ preparation was performed using water-cooled diamond burs . The resins were packed in the tooth cavity in a single increment, and then photoactivated using one of the photoactivation technique described in this study. The monitoring of the resin’s temperature variation during photoactivation was performed using a non-contact temperature sensor . A microcontroller was used to acquire real-time temperature from the samples. The temperature sensor was focused on and positioned at a distance of 1.5 cm from the resin surface. The temperature monitoring total time was the same used for the polymerisation shrinkage experiments, i.e., 300 s. The experiments were performed in triplicate.Experiments to determine the depth of cure were performed according to the methodology described by Alrahlah et al. . MatriceOne-way analysis of variance (or ANOVA) and Tukey post hoc test were applied to evaluate the effects of the 4 photoactivation protocols (independent variable): 1-conventional, 2-soft-start, 3-pulse-delay, and 4-exponential on the shrinkage stress (dependent variable) during the polymerisation of the 3 different resin composites . One-way ANOVA and Tukey test were also used to analyse the significant differences of the shrinkage stress (dependent variable) between different materials (independent variable). In addition, two-way ANOVA was used to analyse the interactions between the variables photoactivation protocol and resin composite, by considering the shrinkage stress as the dependent variable. The 30 maximum values of shrinkage stress for each resin composite and photoactivation protocol were selected to statistically compare the shrinkage stress results. The same procedure was used to statistically compare the results of resin/tooth temperature variation.Subsequently, one-way ANOVA and Tukey post hoc test were also applied to evaluate the effects of the 4 photoactivation protocols (independent variable) on the hardness of the cured resins in function of the depth on the resin (dependent variable). One-way ANOVA and Tukey test were also used to evaluate the significant differences of the hardness (dependent variable) between different resins (independent variable). Multi-factor ANOVA was used to analyse the interactions between the variables photoactivation protocol, resin composite and depth, by considering the hardness as the dependent variable. These analyses were performed considering all hardness values obtained from the experimental replicates.All statical analyses were performed using the R software considering a confidence level of 95% .p-value from one-way ANOVA  revealed that shrinkage stresses were dependent on the photoactivation protocol applied for the polymerization of all resin composites evaluated in this study. Statistical analyses also showed that the maximum values of shrinkage stress were statistically equivalent for pulse-delay and exponential photoactivation protocols. In addition, the p-value from two-way ANOVA showed that the interaction between the variables photoactivation protocol and resin composite was statistically significant (p < 0.001).The results showed consistent patterns in the variation of the shrinkage stress as a function of time for all resin composites. The conventional photoactivation method generated the highest values of stress throughout the monitoring process. The soft-start photoactivation method was able to significantly reduce the stress when compared to the conventional method but was less efficient in minimising the shrinkage stress when compared to the pulse-delay and exponential methods. The comparison of the shrinkage stress obtained by the use of the pulse-delay and exponential methods showed that the stress variation patterns were quite different in the time domain; however, the final and highest values of stress were equivalent for these two photoactivation techniques see . It should also be noted that the Ultrafill and Charisma resins presented higher values of shrinkage stress when submitted to the conventional photoactivation process. However, when the exponential technique was applied on these composites, the final stresses were comparable with the results obtained for Z-250 resin. In this sense, the variation of the shrinkage stress for the Ultrafill and Charisma were more sensitive to the photoactivation method.p-value from one-way ANOVA  revealed that maximum values of temperature variation were dependent on the photoactivation protocol for all resin composites evaluated in this study. In addition, the p-value from two-way ANOVA showed that the interaction between the variables photoactivation protocol and resin composite was statistically significant (p < 0.001).In general, the results showed that the use of the exponential photoactivation method resulted in lower temperature variation when compared to the others photoactivation methods. This result can be rationalised by the photoactivation energy being delivered in a controlled and optimised way in this method. Another relevant observation was that the pulse-delay photoactivation method was not able to minimise the effects of temperature variation (as was observed in the results of shrinkage stress). For this photoactivation technique, the graphs show that the temperature at the final step of the photoactivation process undergoes an increment similar to that observed for the soft-start and pulse-delay methods. Finally, of all the evaluated composites, the photoactivation of the Charisma resin resulted in the lowest increase in temperature during photoactivation  but depends on depth  for all brands/models of resins evaluated in this study. The p-value from multi-factor ANOVA showed that the interaction between the variables photoactivation protocol, resin composite and depth was statistically significant (p < 0.001). The results in One-way ANOVA summary  showed tTemperature variation results showed that there was an abrupt increase in temperature during the photoactivation process using the conventional, soft-start, and pulse-delay techniques, and it occurred precisely at the moment that the light irradiance undergoes the maximum values. This result reveals that the resin composites have a low thermal capacity, as the temperature variations are almost instantaneous following the irradiance variation. Although the results showed that the photoactivation process causes an increase in temperature on the set tooth/resin, the highest temperature variation value among all resins observed in this study was approximately 3.5 °C . This increment in the temperature is not enough to cause damage in the restoration area. Studies have shown that damage can be caused when the temperature in the tooth/resin exceeds a variation of 6 °C ,24. ThusHowever, the results of stress revealed that the polymerisation shrinkage can be considered a problem. Studies have shown that shrinkage stress with the same magnitude of the values observed in our results is sufficient to cause adhesion failure at the tooth/resin interface ,25. Our When the exponential method was compared to the pulse-delay method, the obtained results of shrinkage stress were statistically equivalent. However, the two methods differ in their application, because the pulse-delay photoactivation time is excessively long (251 s) and not feasible for practical applications. The exponential method can be performed in less than 1 min, being a favourable time for the routine in dental offices. In addition, if we compare all results from the tests performed using the exponential technique, a standardisation of the shrinkage stress for the different resin brands was observed. This observation can be considered favourable, indicating that the parameters used for the exponential photoactivation technique resulted in a mathematical function close to optimal.Another relevant result is that the Z-250 composite presented the lowest values for shrinkage stress, even for the conventional photoactivation technique. Composite resin manufacturers have focused on the improvement of dental materials by changing the organic matrices, and by modifying the morphological properties of inorganic charge . For theAn evaluation of the polymerisation efficiency could be performed by analysing the composite hardness at different depths, as the depth of cure is considered a physical property associated with the polymerisation effectiveness . The depAll these results can be used to subsidise the standardisation of the photoactivation process. Reported studies have proposed photoactivation processes using parameters with large variances, particularly for activation energy  ,40,41,42On the other hand, this study had some limitations. Studies have reported that storage conditions of the specimens after the photoactivation can affect their mechanical properties and conversion degree ,44,45. OThe results obtained in this study demonstrated that controlling the irradiance during the photoactivation process has a direct influence on the dynamic behaviour of the shrinkage stress and temperature variation during the resin composite polymerisation. The use of exponential and pulse-delay photoactivation techniques promoted better results than the use of conventional and soft-start methods and favoured a significant reduction in the polymerisation shrinkage stress. However, the pulse-delay method requires a relatively long photoactivation time, which impairs its application and adoption by professionals in the dental restoration area.The increments in the temperature were lower when the exponential photoactivation was applied. However, the temperature variation in the tooth/resin set during the photoactivation process were not sufficient to cause damage in the region of restoration for all photoactivation protocols. Thus, it can be concluded that if the activation energy recommended by the manufacturer is used, the temperature variation is not characterised as a problem for the patient when using one of the three resins evaluated in this study.The evaluation of the polymerisation efficiency by analysing the composite surface hardness showed that all photoactivation protocols resulted in resins cured with equivalent hardness, indicating that the choice of alternative protocol will not harm the polymerisation.All the results indicate that the exponential photoactivation method is the best alternative for resin composite photoactivation processes. This method can be adjusted for each type/brand of resin composite, promoting an operational standardisation of the photoactivation process, which has not been previously reported.


Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform. After an earthquake, it is necessary to understand its impact to provide relief and improved mitigation strategies. The post-disaster period is divided into four post-disaster phases according to the United Nations Development Programme (UNDP): emergency phase, early recovery, recovery, and development phase. During the relief or emergency response phase, the priority is to save lives by deploying search and rescue (SAR) task forces  sponsored data collection; however, this approach is time-consuming, expensive and often limited   and social sensors (witness reports)  for damage detection and assessment of earthquakes , 675 Tweets with the hashtags: #Albania, #AlbanianEarthquake and #albanianearthquake from the 26th November 2019 to the 3rd February 2020 were collected by a third-party vendor. The SM department of Newcastle University provided us with 1001 tweets with the hashtag #Albania collected from the 31st January to the 2nd February 2020. We obtained the text data from both sources in a report in excel. We integrated these two datasets and discarded those unrelated tweets, resulting in a database of 695 tweets. Tweets related to this event were written in English, Estonian, modern Greek, Icelandic, French, German, Catalan, Spanish, Norwegian, and other languages that could not be identified. We processed the data, which involved eliminating Twitter handles and hyperlinks, translating the tweets to English, and removing repeated tweets from the database, leaving us with a dataset of 255 original tweets .This dataset of original tweets was used to test the accuracy of the classification algorithm developed by MonkeyLearn. We used the MonkeyLearn algorithm to identify polarity in the text data collected from Twitter and related to the earthquake's emergency response and early recovery phase in Albania. This algorithm-based classification, also known as unsupervised or automatic classification, will be referred to as 'predicted' for the rest of this paper. The main result of running the tweets through the algorithm is classifying the tweets into a specific polarity with a degree of confidence . At the same time, through combining the experience in emergency management of the first author and monitoring post-disaster recovery processes from the first two authors, the rules to classify the polarity of the tweets were defined Alterman  and are The text data in the dataset of original tweets were also classified according to these rules and used as reference data to test the SA model's accuracy. The result of this supervised (rule-based) classification will be named:' truth'. The classification outcome from the pre-trained SA model was compared with the outcome from rule-based classification to determine the machine learnings platform's performance. The comparison of the classification outcomes is listed in Table The comparisons of the general results of the algorithm and rule-based polarity classification are listed in Table To determine the accuracy of the unsupervised classification, we calculate the overall accuracy (ACC):otal HSU . The recThis research aims to test the accuracy of a pre-trained SA classification model. This model is based on the no-code machine learning platform: MonkeyLearn. We selected this platform among other no code machine learning tools Wolff,  because The total number of tweets related to his case study is very low compared to the tweets collected for the cases of the 2020 Zagreb Earthquake   than those classified as TPt, TNg and TNt (0.71–0.72).One possible reason for the high positive polarity could be related to the Independence day of Albania. Seven tweets (3%) included explicit references to the Independence day of Albania, and while this is a small proportion, the pride in the population celebrating their independence could explain the high percentage of positive polarity in the text data, despite the earthquake. In this paper, we assess the accuracy of tweets, so we have only considered original tweets. To quantify an assessment of how positive or negative the sentiments of the community is regarding the emergency response and post-disaster recovery assessment, it is necessary to consider all tweets as this is a better reflection of the sentiment of the entire community  however this is beyond the scope of this paper.The most reliable positive tweets were mainly related to the immediate response. Tweets thanking, solidarity, encouraging and condolence messages, recovery wishes, and references to the Albanian Independence Day were identified as TPt. Tweets about international support for mapping damages activated through the European Union (EU) Civil Protection Mechanism were classified as TPt. Tweets about search and rescue (SAR) actions undertaken from the Israel Defense Forces (IDF), Kosovo, Serbia, Macedonia, Montenegro, Greece, Italy and Romania and Los Angeles County Fire Department (LACOFD), the last one activated through the Office of Foreign Disaster Assistance (OFDA) by the United States Agency for International Development (USAID) were also classified as TPt. The increase in the number of volunteers and humanitarian action from Red Cross was identified as TPt. The collection of humanitarian aid by citizens and activities to collect funds and donations from citizens to support early recovery were also classified as TPt.Reliable Tweets that expressed negative sentiments were related to the lack of accountability for casualties caused by the buildings that collapsed in Albania and tweets reporting injured people (both classified as TNg). Reports of casualties and injured people were also classified as TNg. Tweets commenting about impacts of aftershocks, destroyed infrastructure and bad construction practices were identified as TNg. Tweets reporting restrictions to inform about the impact of the earthquake were also classified as TNg. Tweets complaining about the lack of proper coverage of the impact of the Albanian earthquake 2019 by the press were identified as TNg. Problems with the management of assistance for the survivors were also identified as TNg. Expression of anxiety was classified as TNg. Tweets complaining about the low quality of the building damage survey undertaken after the earthquake on the 21 September 2019  unrelated tweets. The presence of unrelated tweets happens because some Twitter users included hashtags related to the earthquake to promote football games for example. The machine learning platform classified these unrelated tweets in the pre-trained SA model developed by MonkyeLearn as neutral, and we accepted as TNt.According to the rules defined by the authors, five tweets with negative polarity were misclassified as positive (FPtNg). In three cases, this misclassification is explained because the analysis is done at the tweet level, and tweets mix sentences with different polarities. One case is a tweet written with sarcasm, which usually includes a negative polarity never recognised by algorithms because of the lack of context Liu , b, c. TAlso, according to the rules defined by the authors, 11 tweets were misclassified as negative when they are positive (FNgPt). Cristiano Ronaldo and Gianluigi Buffon, forward and goalkeeper from the Juventus football club at that time, met young survivors. At the same time, the American singer Bebe Rexha and the Kosovo-Albanian rapper Capital, Tweeted about the disaster. The visit from prominent people contributes to improving the mood among survivors, and their tweets to the awareness of the situation potentially could attract more humanitarian support. Our rules consider volunteer initiatives to be positive. Tweets contain text data related to early recovery activities and donations, and fundraising activities are also considered positive. We currently cannot explain why the algorithm misclassifies some of these tweets, e.g. 'While the death toll raised to 31, solidarity with those affected by the Albanian Earthquake has been huge. Tonight in Durrës nobody is going to sleep outside in tents after families and hotels in the area were able to accommodate hundreds of those in need'.In the same way, we also classify expressions of condolence and solidarity as positive; however, the algorithm is less reliable as these often include expressions of sadness or sorrow. Another tweet containing seismic information was misclassified as negative when we classify as neutral (FNg-Nt), e.g. 'Previous internet detection in Albania 10 min ago is confirmed by seismic signal'. Fifty-two tweets were misclassified as neutral when they are positive (FNtPt). The delivery of humanitarian assistance and the presence of volunteers we classify as positive because they are oriented to meet the needs of the affected population. The Government visit to the SAR team is considered positive. Humanitarian actions such as sheltering, fundraising and donations we also classified as positive. This is due to the tweets being written in a matter-of-fact way and therefore are classified as neutral, e.g.:’I leave the house for shelter for our compatriots after that natural disaster. The house has an accommodation capacity for two families (10 people) and is located in the village of Medvec respectively 3 km away from Prishtina Airport. Contact No. + ***********. Albania.Albanian Earthquake’.Twenty-five tweets were misclassified as neutral when they are negative (FNtNg). Damages in buildings were wrongly reported as neutral (which is an important issue for response and recovery). Others reflect the lack of preparedness in Albania to face the challenges imposed by the earthquake. Another tweet detected as neutral should have been classified as negative because it reports the lack of coherence from the government regarding the management of humanitarian aid. Other tweets reported the end of SAR efforts, and others contained xenophobic messages.The precision metric indicates the probability that a polarity predicted by the algorithm is in agreement with the rule-based or supervised classification. This is referred to as reliability. In our sample of tweets related to the emergency and early recovery phase after the 2019 Albanian earthquake, the predicted polarity with the highest level of precision in its classification is the neutral polarity (96%), followed by the negative (58%) and the positive (54%). We consider these numbers acceptable for a no-code machine learning tool that has been trained with text data not related to earthquake reconnaissance. In further research, we will classify this data based on a customised model trained with the same text data to be classified and check if precision and accuracy in the classification increase. The recall, also known as sensitivity or probability of detection, indicates the probability that a specific polarity is correctly classified as it is. In this case, the polarity with the highest probability of detection is positive, followed by neutral and negative. These differences could be explained by using words that cannot be associated with a specific polarity. According to the ACC results, there is an acceptable probability that a tweet will be classified in the correct polarity using the predefined SA model developed by MonkeyLearn.In this paper, we have looked at the accuracy of an unsupervised classification algorithm to obtain polarities of tweeted text. We performed an unsupervised classification and compared the obtained polarities to those obtained using supervised classification. Considering the overall accuracy metric (ACC = 0.63) we can conclude that the pre-trained SA model developed by MonkeyLearn is acceptable for a quick estimation of the polarity of text data collected during the emergency and early recovery phase after an earthquake. The SA classification machine learning platform generally recognises positive and negative polarities, but neutral tweets are overclassified.MonkeyLearn machine learning platform has a confidence metric indicating how confident the platform has made a correct classification. Taking the average of the confidence metric as a simple indicator of the confidence of the classification, the correctly classified Tweets  have a score above 0.7. In contrast, the misclassified tweets  have a score between 0.5 and 0.67 . While the difference between correctly and incorrectly classified tweets is relatively small, it is consistent.For a large number of tweets (which is the case for a disaster), the unsupervised classification will save a great deal of effort over supervised. We also argue that another advantage of using a pre-trained SA classification model based on a machine learning platform is that it has the potential to improve the consistency of the polarity classification of the entire dataset of text .Looking at SM can provide a general background of the situation after the earthquake, related to seismic information , the number of injured and casualties, reports of damages in buildings, critical infrastructure (CI) and problems with construction practices, and geotechnical effects of the earthquake, preparedness level of the affected area, solidarity actions (donations and fundraising activities) and messages, SAR efforts, preliminary PDNA's, emergency management successes and failures, political aspects and prominent people, institutions and NGO's involved in the emergency response actions and early recovery initiatives.We suggest that we could improve the classification accuracy by making the SA analysis at the sentence level rather than at the tweet level. A tweet can contain different sentences with different polarities. Most tweets are classified as neutral by the machine learning platform because they are not customised to detect positive and negative aspects that contribute to or diminish emergency response and early recovery efforts. This problem may be overcome if a topic classification was first performed  Avoid selecting hashtags, which include only the name of the affected area, e.g. #Albania, (2) Eliminate the unrelated tweets from the dataset, and or (3) customise a SA model including the category of 'unrelated'. The usefulness of tweets is very variable. So, we believe there is a case for training and empowering citizens as sensors (Cervone and Hultquist