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In this study, the liquefied petroleum gas (LPG) accident in Wenling, Zhejiang Province, China, on June 13, 2020, was analyzed and simulated. An LPG tank truck overturned and collided with the concrete guardrail; the subsequent explosion of the tank released 25.36 t LPG. Shortly afterward, the LPG tank was shot into the air, and the gas cloud ignited, thereby triggering a fire and vapor cloud explosion (VCE). This accident killed 20 people, injured 175 people, and caused significant property loss. The accident timeline was established based on multiple accident images, and the accident process was discussed in detail. The distribution of tank debris, evaporation and spreading of the LPG pool, dispersion of the LPG gas cloud, and VCE were simulated with the EFFECTS and ALOHA software. The images of the accident scene helped to determine and analyze the accident process. In the particular case in which a tank is shot into the air with a continuous two-phase jet, the actual flight distance of the tank is much greater than the prediction of the debris distribution model. The gas cloud distribution simulated with the SLAB model approximately corresponds to a major part of the severely damaged zone. In this accident, the TNO multi-energy method and ALOHA provide relatively consistent predictions with the actual building damage distribution when models use a specific confined explosive mass.

Few-shot text classification aims to learn a classifier from very few labeled text data. Existing studies on this topic mainly adopt prototypical networks and focus on interactive information between support set and query instances to learn generalized class prototypes. However, in the process of encoding, these methods only pay attention to the matching information between support set and query instances, and ignore much useful information about intra-class similarity and inter-class dissimilarity between all support samples. Therefore, in this paper we propose a negative-supervised capsule graph neural network (NSCGNN) which explicitly takes use of the similarity and dissimilarity between samples to make the text representations of the same type closer with each other and the ones of different types farther away, leading to representative and discriminative class prototypes. We firstly construct a graph to obtain text representations in the form of node capsules, where both intra-cluster similarity and inter-cluster dissimilarity between all samples are explored with information aggregation and negative supervision. Then, in order to induce generalized class prototypes based on those node capsules obtained from graph neural network, the dynamic routing algorithm is utilized in our model. Experimental results demonstrate the effectiveness of our proposed NSCGNN model, which outperforms existing few-shot approaches on three benchmark datasets.

We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep networks, guided by an approach for class-conditional feature detection. The decomposition of the filter-n-gram interactions of a convolutional neural network (CNN) and a linear layer over a pre-trained deep network yields a strong binary sequence labeler, with flexibility in producing predictions at—and defining loss functions for—varying label granularities, from the fully supervised sequence labeling setting to the challenging zero-shot sequence labeling setting, in which we seek token-level predictions but only have document-level labels for training. From this sequence-labeling layer we derive dense representations of the input that can then be matched to instances from training, or a support set with known labels. Such introspection with inference-time decision rules provides a means, in some settings, of making local updates to the model by altering the labels or instances in the support set without re-training the full model. Finally, we construct a particular K-nearest neighbors (K-NN) model from matched exemplar representations that approximates the original model’s predictions and is at least as effective a predictor with respect to the ground-truth labels. This additionally yields interpretable heuristics at the token level for determining when predictions are less likely to be reliable, and for screening input dissimilar to the support set. In effect, we show that we can transform the deep network into a simple weighting over exemplars and associated labels, yielding an introspectable—and modestly updatable—version of the original model.

Few‐shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few‐shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self‐attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one‐hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL‐One and Wiki‐One datasets.

In august, the summer Olympics wrapped up after a year’s delay due to the pandemic. The games featured reminders that we live in interesting times. A shot cracked at the start of each event, while we in the audience focused on the competitors’ performance. In the background, we heard the muted cheers of attendees and gazed at eerie stadiums full of empty seats.

The article discusses the history of the Stealth aircraft creation. The destruction of the F117A stealth aircraft in Yugoslavia, carried out with the participation of Soviet specialists and technologies, is described for the first time. The article analyzes the capabilities of VHF radar stations for the stealthy targets reconnaissance and for the improvement of the air defence forward edge information and control subsystem.

Previous deep learning methods usually required large‐scale annotated data, which is computationally exhaustive and unrealistic in certain scenarios. Therefore, few‐shot learning, where only a few annotated training images are available for training, has attracted increasing attention these days, showing huge potential in practical applications, such as portable equipment or security inspection, and so on. However, current few‐shot learning methods usually neglect the valuable semantic correlations between samples, thereby failing in extracting discriminating relations to achieve accurate predictive results. In this work, extending on a recent state‐of‐the‐art few‐shot learning method, transductive relation‐propagation network (TRPN), which considers the correlations between training samples, a constrained relation‐propagation network is proposed to further regularise the distilled correlations and thus achieve favourable few‐shot classification performance. The proposed framework contains three main components, namely preprocess module, relational propagation module, and relation constraint module. First, sample features are extracted and a relation graph node is constructed by treating the relation of each support–query pair as a graph node in the preprocess module. After that, in the relation propagation module (RPM), the valuable information of support–query pairs is modelled and propagated to directly generate the relational representations for further prediction. Then, a relation constraint module is introduced to regularise the relational representations and make it consistent with the ground‐truth relations as much as possible. With the guidance of the effective RPM and relation constraint module, the relational representations of the support–query pairs are distinguishable and thus can achieve accurate predictive results. Comprehensive experiments conducted on widely used benchmarks validate the effectiveness of our method compared to state‐of‐the‐art few‐shot classification approaches.

PURPOSE The purpose of this study is to examine the goalkeeper’s area of defense and how the range varies depending on the relative position of the goal, goalkeeper, and ball in a 9-meter jump shot during handball matches.METHODS Data was collected from seven qualifying divisions in mens handball from the 99th National Sports Festival in Korea. A total of 231, 9-meter jump shots were analyzed with the goalkeeper‘s area of defense measured from the point the ball left the shooter’s hand and calculated based on the relative position of the goal, goalkeeper, and ball. Video analysis was conducted using the Kwon3D 3.01 program and three-dimensional coordinates calculated using the DLT method.RESULTS First, dimension of handball goals measure 3m wide and 2m high, however, results show that goalkeeper’s actual area of defense was narrower than the width of the goal posts, while vertically, area exceeded the height of the cross bar. Second, if the goalkeeper defended the striker’s shot from the side rather than from the front, the goalkeeper’s defense range was higher for the opposite side of the goal post than the near side of the goal post.CONCLUSIONS Key factors influencing goalkeeper’s area of defense include height of shot and position of goalkeeper. Results also indicate that vertical movements are more important than horizontal movements for goalkeepers in handball thus such implementation in training may lead to performance enhancement.

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).

When studying the space path of a cloud of pellets from a shotgun and evaluating its parameters, it is advisable to have a simulation model of the optical sensor signal when a shot sheaf crosses the light screen. To create such a model, you need to get the relationship between the scale parameter in the Rayleigh distribution and the time span of the shot sheaf. Studies of the temporal distribution density of the shot sheaf span are performed, and graphs of the distribution density of the span for near-real situations are constructed. A linear dependence of the mathematical expectation of the span distribution density on the scale parameter in the Rayleigh distribution is established. A simplified expression is obtained for calculating the mathematical expectation of the time span of the shot sheaf. Statistical modeling confirmed the possibility of the practical use of the proposed formulas, including with a large number of pellets (up to 1000). The dependence of the mathematical expectation of the span on the number of pellets is investigated, its approximation by various functions is carried out, and the approximation errors are estimated. The research conducted allows us to create a simulation model of the optical sensor signal when the shot sheaf crosses the light screen on the basis of empirical data on the real-signal duration (with averaged measurement data for several shots being helpful).

In this digital world, Video analysis is the most important and useful task. Currently, tremendous tasks have been done in video analysis like compressing the videos, video retrieval process and video database indexing, etc. For all these tasks, one common step is segmenting the video shots, which are referred to as Video Shots Segmentation (VSS). Video shots segmentation is used to segment the input videos into a number of frames sequentially where the scene changes occurred, i.e. called shots. In this article, segmenting the video shots follows a hybrid procedure. Here, we have introduced the moments of colors, distance metrics and threshold techniques. All the videos follow the above mentioned steps for segmenting the video shots. But, before that, the input video is converted into a specific color model i.e. YCbCr. Then, apply the color moments to extract the feature vectors of frames, which are differentiated based on the color features of frames. In every two frames of the video, distance metrics methods are applying to compute the similarity and dissimilarity of frames. And the dissimilarity of the frames can be computed by using the threshold technique to get the shots from the video. In this paper, we are using the adaptive threshold technique to segment the videos into various shots. In this step, we will get a true number of shots. By the experimental results, this proposed methodology can be evaluated with the sequence of videos based on the performance or evaluation metrics.

Basketball sport originated as a non-contact sport but recently it has evolved into physically more challenging in which contact is accepted and expected. This transition of the sports has increased the injury rates substantially. Basketball includes multiple movements such as jumps, turns and change of direction, running and deceleration in its various movements of jump shot, free throw and layup shot. The complexities of these movements imposes various demands on basketball players, which makes it more important to know the health oriented aspects of the movement. This review explains the movement pattern analysis of these basketball specific movements in detail which can help the physiotherapist to plan the injury prevention strategies for their athletes. KEY WORDS: Basketball, Basketball Movements, Jump Shot, Layup Shot, Basketball Free Throw.

Die cast alloys have to be poured at temperatures much higher than their liquidus in order to make the alloys castable. Pouring at a high temperature reduces the formation of dendrites in the shot sleeve which tend to choke the flow at the in-gate but causes increased damage to the shot tooling and dies. Forming globular grains in the shot sleeve is an effective way of breaking the limit of fluidity on reducing the pouring temperature in order to increase die life and to improve the integrity of die casting products. This study investigated the feasibility of producing globular grains in a shot sleeve using a water-cooled ultrasound trough for feeding molten metal into the shot sleeve. Globular grains were obtained using this unique trough. The temperature window within which globular grains can be produced for E380 alloy was determined. Without using the WCUST, the grains in the shot sleeve were fully dendritic and the grain size was about 300 µm. When the molten metal was poured over the WCUST, small globular grains were obtained for the entire pouring temperature window, from 610 °C (1130 °F) to 700 °C (1292 °F), tested in this study. Analysis was made on the conditions for obtaining globular grains. The analysis seems in agreement with experimental results. In addition to producing globular grains for die casting applications, the water-cooled ultrasound trough may be suitable for grain refinement of other gravity casting processes.

With the rapid development and application of computer technology, the application of computer science knowledge in basketball is also more and more extensive. Based on genetic algorithm and the background subtraction method, video analysis and 3D detection simulation model of shot jump action precision were constructed in this study. According to the genetic algorithm search method, jump shot precision was analyzed, and the problems encountered in the actual shooting process of basketball players were studied and solved. The results show that this study is necessary and feasible. RESUMO Com o rápido desenvolvimento e aplicação da tecnologia da computação, a aplicação do conhecimento da ciência da computação no basquete também vem crescendo cada vez mais. Com base no algoritmo genético e no método da subtração de fundo, construiu-se um modelo de análise de vídeo e simulação de detecção 3D para a precisão de arremesso. De acordo com o método de busca do algoritmo genético, analisou-se a precisão do arremesso, e os problemas encontrados no processo de arremesso dos jogadores de basquete foram estudados e resolvidos. Os resultados mostram que este estudo é necessário e viável. RESUMEN Con el rápido desarrollo y aplicación de la tecnología de la computación, la aplicación del conocimiento de la ciencia de la computación en el baloncesto también viene creciendo cada vez más. Basándose en el algoritmo genético y en el método de la sustracción de fondo, se construyó un modelo de análisis de video y simulación de detección 3D para la precisión de lanzamiento. De acuerdo con el método de búsqueda del algoritmo genético, se analizó la precisión del lanzamiento, y los problemas encontrados en el proceso de lanzamiento de los jugadores de baloncesto fueron estudiados y resueltos. Los resultados muestran que este estudio es necesario y viable.

Achieving sustainability often requires fostering changes in norms and behaviours through a combination of restrictions and aware-raising activities. The phasing out of lead in ammunition to reduce lead poisoning in waterfowl provides a case in point. Twenty years after the Parties to the African Eurasian Waterbird Agreement (AEWA) committed to phase out lead shot in wetlands, such bans have been implemented in a third of Europe only. Furthermore, while evidence is scant, compliance varies widely where bans have been introduced. As a result, waterfowl are still suffering from lead poisoning. Here, I survey the use of different types of shot while practicing and when taking game in different habitats in Sweden, one of the Parties to AEWA. Furthermore, I test the hypotheses that mandatory shooter education, or stakeholder communication, influence compliance with existing restrictions in Sweden, as well as the voluntary phasing out of lead shot where its use still is permitted. More than half of the shooters practiced using lead and a third used lead shot over what they considered as wetlands, in both cases in violation of the current legislation. Furthermore, what constitutes a wetland appears to have been unclear to many shooters, especially in alpine areas. Both mandatory education and stakeholder communication were positively related to compliance. Only 12–15 % used lead free alternatives in shooting over habitats where the use of lead shot is permitted. There was a weak positive relationship between stakeholder communication and the voluntary phasing out of lead shot, but no effect from education. I suggest that the planned and ongoing phasing out of lead in ammunition should rest on a combination of legislation, enforcement, education and stakeholder communication in order to be successful. The findings add to our general understanding of how to turn environmental policy into sustainable management actions in practice.

More than just a framework: In this final article in the series, we look at how to capture the child's learning as they engage with the curriculum, moving beyond capturing ‘snap-shot’ moments.

Penetrating abdominal injuries, especially from gun shot wounds, are a common occurrence in South African hospitals, and a vast majority of these patient receive surgical intervention for suspected intra-abdominal injuries. Not all bullets are found and removed however, and we detail the case of a gentleman who presented to our institution with obstructive jaundice secondary to an extraluminal compression of his biliary tree by a retained migrated bullet, that spontaneously resolved, without any surgical intervention.

Zero Shot learning (ZSL) aims to use the information of seen classes to recognize unseen classes, which is achieved by transferring knowledge of the seen classes from the semantic embeddings. Since the domains of the seen and unseen classes do not overlap, most ZSL algorithms often suffer from domain shift problem. In this paper, we propose a Dual Discriminative Auto-encoder Network (DDANet), in which visual features and semantic attributes are self-encoded by using the high dimensional latent space instead of the feature space or the low dimensional semantic space. In the embedded latent space, the features are projected to both preserve their original semantic meanings and have discriminative characteristics, which are realized by applying dual semantic auto-encoder and discriminative feature embedding strategy. Moreover, the cross modal reconstruction is applied to obtain interactive information. Extensive experiments are conducted on four popular datasets and the results demonstrate the superiority of this method.

Lifestyle choices around the time of a vaccination can affect your immune response to it. Helen Thomson explores how to give it the best shot at success

Netflix’s supernatural crime series Tidelands (2019) was the subscription video service’s first commissioned original series to be produced in Australia. Shot in tropical Queensland with a diverse ...

Zero shot learning (ZSL) is aim to identify objects whose label is unavailable during training. This learning paradigm makes classifier has the ability to distinguish unseen class. The traditional ZSL method only focuses on the image recognition problems that the objects only appear in the central part of images. But real-world applications are far from ideal, which images can contain various objects. Zero shot detection (ZSD) is proposed to simultaneously localizing and recognizing unseen objects belongs to novel categories. We propose a detailed survey about zero shot detection in this paper. First, we summarize the background of zero shot detection and give the definition of zero shot detection. Second, based on the combination of traditional detection framework and zero shot learning methods, we categorize existing zero shot detection methods into two different classes, and the representative methods under each category are introduced. Third, we discuss some possible application scenario of zero shot detection and we propose some future research directions of zero-shot detection.

The aim of the paper is to investigate the influence of compressive residual stresses on the fatigue initiation life (S-Nini) curve of AISI 1141 steel. The analysis has been performed by means of a micro-structurally-based fatigue crack initiation modelling approach. The focus is set on the local (surface) residual stresses typically imposed by a surface treatment technique known as shot -peening. By superimposing such stresses on stresses resulting from outer cyclic loading, it could be shown that an impact on the fatigue curve in the S-Nini diagram can be achieved.

Recently, the strength of the hull structure including fatigue strength has seen significant improvement because of application of IACS CSR (Common Structure rule), developed by IACS. Various methods which can increase the fatigue strength at welded joints are suggested, since the damage morphology on the recently welded structures was observed to be primarily fatigue failure. In Shipbuilding industry, smoothening of the welding bead toe by grinding is the method that is customarily adopted in order to increase fatigue strength. And recently, peening treatment at the welding bead has also been adopted. The peening treatment can reduce stress concentration by smoothening of the welding bead toe, and a compressive residual stress field generated by the impact of peening. In case of generating compressive residual stress by impact, the same effect could be expected by shot blasting, which is the steel surface treatment before painting. In this study, the condition of the residual stress generated at welding toe by shot blasting was investigated, and then from results of the fatigue test, the increase in fatigue strength due to residual stress is verified.

PURPOSE To present a case of retinal detachment caused by a Nerf-gun shot in a pediatric patient. METHODS Single case report. RESULTS A 16-year-old white man presented with a nasal visual field deficit and floaters in his left eye for 1 month after being shot with a foam-ball Nerf gun by a friend. Fundus examination revealed a midperipheral retinal dialysis extending from 1 to 4:30 clock hours with a macula-on retinal detachment and superficial proliferative vitreoretinopathy. He underwent successful 25-gauge pars plana vitrectomy with membrane peel, retinectomy, injection of perfluoron, endolaser, and use of 1,000 centistoke silicone oil for tamponade. CONCLUSION This is the first reported case of traumatic retinal detachment secondary to a Nerf-gun shot.

The article analyzes the design features of existing flamethrowers of former Soviet Union, Russian and foreign manufacture. It is determined that the infantry rocket flamethrower can perform only a single action. A flamethrower consists of a disposable container - launcher with an aiming device in which a thermobaric warhead and a jet engine are mounted, which are separated during the shot. The shot is carried out using a launching machine attached to the disposable container - launcher. The selection of an analogue product that meets the requirements for an advanced domestic flamethrower in terms of tactical and technical characteristics was made. Analysis of the design of existing systems showed that the closest to the conditions of the technical - development requirements in terms of technical and weight-and-dimensional characteristics is RPO-A "Shmel", thus it was chosen as a prototype during the development of a domestic infantry flamethrower. The main features of the damage effects are analyzed. The main adverse factors of thermobaric warheads are determined. They are: maximum pressure, shock-wave velocity, action time of excess pressure. In the process of designing a new rocket flamethrower in order to increase the adverse factors indicators, the following decisions were made: to use nitrate ester with a lower oxygen balance, incapable of detonation transformation - isoamyl nitrite; to use an explosive of bursting charge with a higher detonation rate - okfol. Experimental studies have confirmed that the developed infantry rocket flamethrower does not rank below the world analogues, and in terms of particular characteristics (damage effects) even exceeds those analogues.

The National Basketball Association (NBA) is the main basketball league around the globe with estimated revenue of approximately US$ 49.5 billion. A key change in NBA rules was the implementation of the three-point (3PT) line in the 1979–1980 season. Recently a popular conception claims that NBA is passing through a 3PT revolution; however, few works have discussed the evolution of shot patterns in the NBA. Thus, in this article NBA shot data per 100 possessions for each player from all seasons since implementation of the 3PT line were gathered to compare 2PT and 3PT shot statistics, aiming to understand how the shot pattern evolved throughout the NBA history. The results show that the main differences in shot patterns happened due to modifications in the 3PT line distance. The only exception was the statistically significant increase of 3PT attempts (3PTA) in the 1986–1987 season, a likely result of the success of the first 3PT contest in the previous season, which probably led most players to attempt this kind of shot. Also, the results did not show any season-by-season statistically significant change in shot patterns after the 3PT line distance was returned to its original distance in the 1997–1998 season. Thus, the actual popularity and numbers of 3PTA gradually increased throughout the seasons. Electronic supplementary material The online version of this article (10.1007/s12662-020-00690-7) contains supplementary material, which is available to authorized users.

Losses caused by unreliable machines in a production line will affect the total cost losses from a manufacturing company's production process. Based on historical data of damage that has been obtained from the maintenance department of XYZ companies, the MACH MWJ 9/10 Shot Blast Machine is the machine that has the highest-level frequency of damage. This machine is useful for cleaning sand or residual production dirt that sticks to workpieces that have been cast in the casting process, especially for E-Clips components. The research's purpose is to determine the value of cost losses due to machine unreliability using the Cost of Unreliability (COUR) method, with Business Consequence analysis (BC) analysis. The cost effects of these costs include Corrective COUR and Downtime COUR. The final calculation of COUR shows that the total cost of COUR Downtime caused by the unreliability of the machine is greater than the total Corrective COUR. After calculating the COUR, an analysis of the business consequences resulting from the machine's unreliability is carried out using a risk matrix. The analysis results show that the shot blast machine's critical components are in the red or high-risk category and have a very high Probability of Failure (PoF). The results of COUR analysis with business consequence analysis will be an input for the company to make a machine maintenance system policy, especially for the MACH MWJ 9/10 Shot Blast machine's critical components. In general, this research's novelty is to combine the application of the Cost of Unreliability method with an analysis of the effects of the Business Consequence caused by the machine's selected critical components.

Introduction: This study investigated the relationship between physical fitness, anthropometric characteristics, visual acuity of athletes with visual impairments, and their right shot performance. Method: The participants of the study involved 34 male athletes with different visual acuities who participated in the 2015 Visually Impaired Schools Goalball Championship in Alanya, Turkey. The means of the participants’ age, body weight, height, and sport age were 14.95 ± 1.28 years, 52.06 ± 8.89 kilograms, 163.58 ± 10.39 centimeters, 02.39 ± 1.50 years, respectively. The participants’ physical fitness, body fat percentage, handgrip (right–left), and spine flexibility were measured. Upper and lower extremity lengths were obtained through anthropometric measurements, and the athletes’ right shot performance was calculated. To analyze the data, the Shapiro–Wilk test was used, and one-way analysis of variance and independent sample t-test were performed, because the data showed a normal distribution ( p = .106). In addition, multivariate regression model analysis was utilized to investigate the relationship between all parameters and the athletes’ right shot performance. Results: A significant difference was found among the groups’ right shot performance ( p = .03), specifically on behalf of the athletes with low vision. When the participants’ anthropometric features, physical fitness values, and their right shot performance were examined, a positive relationship was observed between their dominant (right) handgrip strength and right shot performance (stepwise method; p = .03, R2 = 0.14). However, no significant difference emerged between their anthropometric characteristics and right shot performance. Discussion: It is considered that visual acuity and upper extremity muscle strength are of vital importance in goalball. Implications for practitioners: Whereas the International Blind Sports Federation applies rules according to visual acuity to offer equal conditions for some areas of sport, in goalball such a rule does not exist. It is important to make a change in the regulations for competition groupings according to visual acuities in goalball like in other sports for individuals with visual impairments. More frequent workout oriented to upper extremity muscle strength during right shot performance is suggested in the training sessions.

Periodontal diseases are immune inflammatory responses induced by dental plaque in which microorganisms harboured within a susceptible periodontium contributes to tissue destruction, bone loss and eventually tooth loss. The etiopathogenesis of periodontal disease is multifactorial which includes host associated factors, genetic factors, immune system dysfunction and environmental factors. Existing treatment modalities have resulted only in arresting the disease progression but have not cured the disease completely, nor do they avert the recurrence. Hence there is a need for therapeutic modalities which may include vaccines targeting periodontal pathogens. Vaccination is induction of immunity by injecting a dead or attenuated form of pathogen. Till date, no pre-emptive modality exists for periodontal disease, the availability of periodontal vaccine would prevent the progression of periodontal diseases. The aim of this review article is to confer the various approaches associated with periodontal vaccine.

Background:: In the internet era, there is a prime need to access and manage the huge volume of multimedia data in an effective manner. Shot is a sequence of frames captured by a single camera in an uninterrupted space and time. Shot detection is suitable for various applications such that video browsing, video indexing, content based video retrieval and video summarization. Objective:: To detect the shot transitions in the video within a short duration. It compares the visual features of frames like correlation, histogram and texture features only in the candidate region frames instead of comparing the full frames in the video file. Methods: This paper analyses candidate frames by searching the values of frame features which matches with the abrupt detector followed by the correct cut transition frame with in the datacube recursively until it detects the correct transition frame. If they are matched with the gradual detector, then it will give the gradual transition ranges, otherwise the algorithm will compare the frames within the next datacube to detect shot transition. Results:: The total average detection rates of all transitions computed in the proposed Data-cube Search Based Shot Boundary Detection technique are 92.06 for precision, 96.92 for recall and 93.94 for f1 measure and the maximum accurate detection rate. Conclusion:: Proposed method for shot transitions uses correlation value for searching procedure with less computation time than the existing methods which compares every single frame and uses multi features such as color, edge, motion and texture features in wavelet domain.

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

Objectives: To develop a rapid and accurate 4D deformable image registration (DIR) approach for online adaptive radiotherapy. Methods: We propose a deep learning (DL)-based few-shot registration network (FR-Net) to generate deformation vector fields from each respiratory phase to an implicit reference image, thereby mitigating the bias introduced by the selection of reference images. The proposed FR-Net is pretrained with limited unlabeled 4D data and further optimized by maximizing the intensity similarity of one specific four-dimensional computed tomography (4DCT) scan. Because of the learning ability of DL models, the few-shot learning strategy facilitates the generalization of the model to other 4D data sets and the acceleration of the optimization process. Results: The proposed FR-Net is evaluated for 4D groupwise and 3D pairwise registration on thoracic 4DCT data sets DIR-Lab and POPI. FR-Net displays an averaged target registration error of 1.48 mm and 1.16 mm between the maximum inhalation and exhalation phases in the 4DCT of DIR-Lab and POPI, respectively, with approximately 2 min required to optimize one 4DCT. Overall, FR-Net outperforms state-of-the-art methods in terms of registration accuracy and exhibits a low computational time. Conclusion: We develop a few-shot groupwise DIR algorithm for 4DCT images. The promising registration performance and computational efficiency demonstrate the prospective applications of this approach in registration tasks for online adaptive radiotherapy. Advances in knowledge: This work exploits DL models to solve the optimization problem in registering 4DCT scans while combining groupwise registration and few-shot learning strategy to solve the problem of consuming computational time and inferior registration accuracy.