Original Classification Research Articles

Saint Petersburg 3D: Creating a Large-Scale Hybrid Mobile LiDAR Point Cloud Dataset for Geospatial Applications

At the present time, many publicly available point cloud datasets exist, which are mainly focused on autonomous driving. The objective of this study is to develop a new large-scale mobile 3D LiDAR point cloud dataset for outdoor scene semantic segmentation tasks, which has a classification scheme suitable for geospatial applications. Our dataset (Saint Petersburg 3D) contains both real-world (34 million points) and synthetic (34 million points) subsets that were acquired using real and virtual sensors with the same characteristics. An original classification scheme is proposed that contains a set of 10 universal object categories into which any scene represented by dense outdoor mobile LiDAR point clouds can be divided. The evaluation procedure for semantic segmentation of point clouds for geospatial applications is described. An experiment with the Kernel Point Fully Convolution Neural Network model trained on the proposed dataset was carried out. We obtained an overall 92.56% mIoU, which demonstrates the high efficiency of using deep learning models for point cloud semantic segmentation for geospatial applications in accordance with the proposed classification scheme.

Classification urine leakage from urethrovesical anastomosis after prostatectomy

Background. Urine leakage from urethrovesical anastomosis (UVA) is a frequent and significant complication after prostatectomy.Aim. To determine frequency urine leakage from the anastomosis after prostatectomy, evaluate diagnostic methods, develop classification and management guidelines for patients with this complication.Materials and methods. A retrospective analysis of 1426 patients who underwent prostatectomy was performed. Results. In total, 97 (6.8 %) patients developed UVA failure in the early postoperative period. The complication was diagnosed on the 7th day after prostatectomy using retrograde cystography. An original classification urine leakage from the anastomosis after prostatectomy was proposed using numbers (0, I, II, III) and letters (A, B, C). In 50 (3.4 %) patients, grade I (A, B, C) UVA failure was diagnosed; 38 (2.7 %) patients had grade II (A, B, C) UVA failure; 9 (0.6 %) patients had grade III (A, B) UVA failure. The developed classification allows to accurately determine the severity of UVA failure and apply the appropriate methods for its correction.Conclusion. Urine leakage from UVA is a serious problem in surgical treatment of prostate cancer. Currently, it is necessary to systematize the available data and introduce unified classification and algorithm for correcting this complication into clinical practice.

It takes two to tango: A taxonomy of franchise success determinants

PurposeThe authors conduct a structured analysis of the literature on the determinants of economic and financial franchise performance and develop an integrative framework that unifies the literature from franchisor as well as franchisee perspectives.Design/methodology/approach53 relevant research articles on economic and financial franchise success published between 1976 and 2020 were identified. After providing an overview of the empirical methods and theoretical perspectives found in the literature, an original classification system of franchise success determinants was developed.FindingsMore than 50 different variables impacting economic or financial franchise success were identified and grouped into ten distinct categories. The most impactful categories are the franchise relationship, franchisee characteristics and capabilities, franchisor variables and the franchise environment. The study’s integrative framework illustrates not only the impact of these factors on franchise success, but also reveals which areas require more attention.Research limitations/implicationsAs the study focused on understanding the determinants of franchise success, any measures related to franchise failure were excluded. Also, the role of venture capital and stock market listing as growth strategies in the franchise sector were excluded.Practical implicationsThe study’s framework shows how the management of franchise success is complex and that franchise relationship, franchisee and franchisor variables as well as the franchise context need to be taken into account. It illustrates that there is a hierarchy with which these determinants should be prioritized.Originality/valueThe article proposes the first systematic review of the literature on the determinants of economic and financial franchise success. The contribution offers both, researchers and practitioners, new and useful insights for future knowledge development in the field.

Is the Robinson classification of clavicle fractures accurate enough within the setting of the Swedish Fracture Register?

IntroductionIn the Swedish Fracture Register (SFR) clavicle fractures are classified according to the Robinson classification. This study aimed to evaluate the accuracy of the classification of clavicle fractures in the SFR. A secondary aim was to assess inter- and intraobserver agreement. Materials and MethodsClavicle fractures (n = 132) were randomly selected from the SFR and radiographs were requested for each patient from their treating departments. Not all radiographs could be acquired, and after exclusion, 115 fractures were independently classified by three expert raters blinded to patient information. The 115 fractures were classified on two occasions, 3 months apart. The raters’ consensus classification was used as a gold standard that was compared to the classification registered in the SFR. The accuracy, defined as the degree of agreement between the gold standard and SFR classifications, was reported, as was the inter- and intraobserver agreement for the expert raters. ResultsAgreement between the classification in the SFR and the gold standard classification was fair (kappa = 0.35). Fractures with only partial displacement were often incorrectly classified as fully displaced in the SFR (n = 31 of 78 displaced fractures in the SFR). The inter- and intraobserver agreement among the expert raters was almost perfect (interobserver kappa = 0.81–0.87, intraobserver kappa = 0.84–0.94). ConclusionsThe accuracy of the classification of clavicle fractures in the SFR was only fair, whereas the inter- and intraobserver agreement among the expert raters was almost perfect. Accuracy in the SFR may be improved if the classification instructions in the SFR are updated by incorporating the original classification displacement criteria, both in text and in illustrated form.

Max–min distance embedding for unsupervised hyperspectral image classification in the satellite Internet of Things system

In modern society, hyperspectral images have widely been used in many applications. In the earth observation system, hyperspectral images are acquired by hyperspectral sensors and transmitted to the cloud computing center for analysis, which are the part of satellite Internet of Things(IoT) to build a large scale land-cover surveillance system. Thus, during recent years, researchers throughout academia and industry have been advancing the classification of hyperspectral images. Luckily for swamped remote sensing researchers, new technology is allowing brands to extract useful knowledge using artificial intelligence to enhance supervised hyperspectral image classification technology. However, conventional methods that label and process hyperspectral images manually are labor and time-consuming. Hence, in this paper, we proposed an unsupervised hyperspectral image classification framework. The paper differs from the existing methods by three aspects: (1) We adopt multi-scale spatial features to enhance the discriminability of data. (2) We propose deep autoencoder with max–min distance embedding to extract features and reduce dimensions of data, which is more suitable for large-scale data clustering. (3) We apply k-means clustering method to get original classification, then optimize the results by the guided filter. Experimental results on two widely used hyperspectral data, Salinas and Pavia University, show the competitive performance obtained by the proposed framework compared with other approaches.

Histological Features of IgA Nephropathy in Pediatrics and the Magnitude of the Disease in Saudi Children.

This review addresses the microscopic features of immunoglobulin A nephropathy (IgA nephropathy), its prognostic variables in children, and measures to which extent these features and variables differ from adults. Furthermore, it describes the extent of this disease process among children in Saudi Arabia and the rest of the Arab countries and compares it with the data from the West and the Far East. All the original work described the histological features of pediatric IgA nephropathy, and studies involved in developing the prognostic classification of IgA nephropathy, Oxford Classification, were reviewed. Moreover, the studies describing the crescent prevalence and outcome in pediatric IgA nephropathy in addition to thrombotic microangiopathy association were studied. National studies describing the prevalence of pediatric IgA nephropathy and pediatric crescentic glomerulonephritis were tracked with an overview of the regional data from the rest of the Arab world. IgA nephropathy in children showed more glomerular proliferative changes and less glomerular vascular and tubule-interstitial chronic injury compared to adults. The reference study that described the association between thrombotic microangiopathy and IgA nephropathy did not include the pediatric age group. Moreover, it was found that the data from the Middle East was not encountered in developing the original and updated IgA nephropathy Oxford Classification. Furthermore, the prevalence of IgA nephropathy in children is described in the regional literature, but its histological features were not well detailed. Finally, the percentage of crescentic glomerulonephritis (GN) due to IgA nephropathy is less in our country compared to the West and concords with the Far East findings. A well-designed regional study addressing IgA nephropathy in Middle East children with a focus on histological features, association with crescent, and thrombotic microangiopathy and challenging the validity of the updated IgA nephropathy Oxford Classification is recommended.

Patient-reported outcome measures and their association to the original Strickland classification after flexor tendon repair.

The aim of this study was to examine the relationship between patient-reported outcome measures (PROMs) and the original Strickland classification after flexor tendon injuries in Zones 1 and 2. Data were collected from the Swedish national health care registry for hand surgery (HAKIR). The studied PROMs were the Quick Disabilities of Arm, Shoulder and Hand (QuickDASH) and the patient questionnaire from the HAKIR (HQ-8). Complete data of both range of motion (ROM) and PROMs were available for 215 patients at 3 months after surgery, and for 150 patients at 12 months after surgery. We found that QuickDASH values were low and similar between all groups as classified by the Strickland system at 12 months. A statistically significant difference between PROM values (for stiffness and satisfaction) was found only between the Strickland groups Fair and Good, but not between Poor and Fair or Good and Excellent. This suggests that further categorization according to the Strickland classification is less important to the patients as long as they regain 70% of their ROM.Level of evidence: III.

Holoprosencephaly: Review of Embryology, Clinical Phenotypes, Etiology and Management.

Holoprosencephaly (HPE) is the most common malformation of the prosencephalon in humans. It is characterized by a continuum of structural brain anomalies resulting from the failure of midline cleavage of the prosencephalon. The three classic subtypes of HPE are alobar, semilobar and lobar, although a few additional categories have been added to this original classification. The severity of the clinical phenotype is broad and usually mirrors the radiologic and associated facial features. The etiology of HPE includes both environmental and genetic factors. Disruption of sonic hedgehog (SHH) signaling is the main pathophysiologic mechanism underlying HPE. Aneuploidies, chromosomal copy number variants and monogenic disorders are identified in a large proportion of HPE patients. Despite the high postnatal mortality and the invariable presence of developmental delay, recent advances in diagnostic methods and improvements in patient management over the years have helped to increase survival rates. In this review, we provide an overview of the current knowledge related to HPE, and discuss the classification, clinical features, genetic and environmental etiologies and management.

Multimodal imaging in Best Vitelliform Macular Dystrophy: Literature review and novel insights.

Best Vitelliform Macular Dystrophy (BVMD) is a dominantly inherited retinal disease caused by dominant variants in the BEST1 gene. The original classification of BVMD is based on biomicroscopy and color fundus photography (CFP); however, advancements in retinal imaging provided unique structural, vascular, and functional data and novel insights on disease pathogenesis. Quantitative fundus autofluorescence studies informed us that lipofuscin accumulation, the hallmark of BVMD, is unlikely to be a primary effect of the genetic defect. It could be due to a lack of apposition between photoreceptors and retinal pigment epithelium in the macula with subsequent accumulation of shed outer segments over time. Optical Coherence Tomography (OCT) and adaptive optics imaging revealed that vitelliform lesions are characterized by progressive changes in the cone mosaic corresponding to a thinning of the outer nuclear layer and then disruption of the ellipsoid zone, which are associated with a decreased sensitivity and visual acuity. Therefore, an OCT staging system based on lesion composition, thus reflecting disease evolution, has been recently developed. Lastly, the emerging role of OCT Angiography proved a greater prevalence of macular neovascularization, the majority of which are non-exudative and develop in late disease stages. In conclusion, effective diagnosis, staging, and clinical management of BVMD will likely require a deep understanding of the multimodal imaging features of this disease.

Hybrid Encrypted Watermarking Algorithm for Medical Images Based on DCT and Improved DarkNet53

To solve the problem of robustness of encrypted medical image watermarking algorithms, a zero watermarking algorithm based on the discrete cosine transform (DCT) and an improved DarkNet53 convolutional neural network is proposed. The algorithm targets medical images in the encrypted domain. In this algorithm, DCT is performed on the encrypted medical image to extract 32-bit features as feature 1. DarkNet53, a pre-trained network, was chosen for migration learning for the network model. The network uses a fully connected layer and a regression layer instead of the original Softmax layer and classification layer, changing the original classification network into a regression network with an output of 128. With these transformations, 128-bit features can be extracted from encrypted medical images by this network, and then DCT is performed to extract 32-bit features as feature 2. The fusion of features 1 and 2 can effectively improve the robustness of the algorithm. The experimental results show that the algorithm can accurately distinguish different encrypted medical images and can effectively restore the original information from the encrypted watermarked information under traditional and geometric attacks. Compared with other algorithms, the proposed method demonstrates better robustness and invisibility.

A unifying method-based classification of robot swarm spatial self-organisation behaviours

Self-organisation in robot swarms can produce collective behaviours, particularly through spatial self-organisation. For example, it can be used to ensure that the robots in a swarm move collectively. However, from a designer’s point of view, understanding precisely what happens in a swarm that allows these behaviours to emerge at the macroscopic level remains a difficult task. The same behaviour can come from multiple different controllers (ie the control algorithm of a robot) and a single controller can give rise to multiple different behaviours, sometimes caused by slight changes in self-organisation. To grasp the causes of these differences, it is necessary to investigate the relationships between the many methods of self-organisation that exist and the various behaviours that can be obtained. The work presented here addresses self-organisation in robot swarms by focusing on the main behaviours that lead to spatial self-organisation of the robots. First, we propose a unified definition of the different behaviours and present an original classification system highlighting ten self-organisation methods that each allow one or more behaviours to be performed. An analysis, based on this classification system, links the identified mechanisms with behaviours that could be considered as obtainable or not. Finally, we discuss some perspectives on this work, notably from the point of view of an operator or designer.

Benchmarking Evaluation of Building Energy Consumption Based on Data Mining

University building energy consumption is an important proportion of the total energy consumption of society. In order to work out the problem of poor practicability of the existing benchmarking management method of campus building energy consumption, this study proposes an evaluation model of campus building energy consumption benchmarking management. By analyzing several types of feature data of buildings, this study uses random forest method to determine the building features that have outstanding contributions to building energy consumption intensity and building classification, and uses the K-means method to reclassify buildings based on the building features obtained after screening, to obtain a building category that is more in line with the actual use situation and to solve the problem that the existing building classification is not in line with the reality. Compared with the original classification method, the new classification method showed significant improvement in many indexes, among which DBI decreased by 60.8% and CH increased by 3.73 times. Finally, the quart lines of buildings in the category of new buildings are calculated to obtain the low energy consumption line, medium energy consumption line and high energy consumption line of buildings, so as to improve the accuracy and practicability of energy consumption line classification.

A joint training network for learning more distinguishable relation features in relation classification

Relation classification is an important task in natural language processing, which aims to predict the semantic relation between a given entity pair in a sentence. There are datasets, like TACRED, that contain a large number of “no_relation” type samples. Most existing methods treat “no_relation” and normal relation types equally, and directly apply the softmax function over all relation types. In this paper, we propose a novel joint training network to learn more distinguishable relation features for relation classification. Specially, we convert the original multi-class classification problem into two joint optimized modules, binary classification of whether a relation is “no_relation” and multi-class classification of normal relation types. To further differentiate between similar normal relation types, we introduce a self-supervised contrastive learning method to learn more distinguishable features for them. We jointly optimize the above modules. Experimental results agree well with our design intention and demonstrate that our joint training network not only achieves superior performance against existing competitive models, but also is robust to “no_relation” problem.

Chiari Malformations: A Review of the Current Literature

Chiari malformations (CM) are a group of malformations involving the posterior fossa and rhombencephalic structures that may have other associated intra- or extracranial defects. Initially, the 4 classic varieties were described by the Austrian pathologist Hans Chiari towards the end of the 19th century; years later, the Swiss pathologist Julius Arnold described a case of type II CM with associated myelomeningocele. years later, two of his students used the term "Arnold-Chiari Malformation", this term is often misused for all types. Currently it is estimated that the incidence and prevalence data are underestimated due to the asymptomatic presentation of the pathology in many cases and the limited access to health services of different populations at a global level, for this reason the epidemiological data vary between the literature. The original classification has been modified by contributions from various authors in order to more precisely describe the variety of morphological findings; currently the classification includes CM types 0, I, 1.5, II, III and IV. The symptoms and associated pathological conditions vary for each type and type III and IV are the most severe forms. Diagnosis requires that secondary causes of cerebellar tonsillar ectopy (main finding of CM) be ruled out, along with a physical examination, neurological evaluation, and complementary tests; however, diagnosis requires resonance neuroimaging to demonstrate the findings. The management basically consists of performing a decompression of the posterior fossa and whose main indication is to reduce the symptoms, however, it should be noted that there is no global agreement among specialists on the subject of surgical management, for which each patient must be individualized by your treating physician and the decision made is subject to individual criteria.

Classification of CMT Additive Manufacturing Defects based on Statistical Features

Aiming at the problem of the CMT(cold metal transfer) additive manufacturing process defect monitoring method being susceptible to environmental influence, this paper proposes a classification method for additive manufacturing process defects with current as the research object. By setting up three sets of experiments, experimenting and collecting the current parameters of each group, using the statistical feature method to pretreat the current, and then classifying the pretreated data into random forests, to highlight the superiority of statistical characteristics plus random forests, two sets of control groups are also set up for comparison. It was concluded that the principal component analysis method could not classify the non-defective group and the non-protective gas group, while the use of random forest could achieve two by two classifications of the three working conditions; After the statistical feature method treatment, the random forest classification is compared with the original current direct random forest classification, and the accuracy of the training set, verification set, and test set has been improved.

Guide devices in the rehabilitation of interrupting mandibular bone defects: a proposal for a new classification.

Interrupting mandibular surgical resections may lead to mandibular deviation toward the resected side. This further complicates future surgical and prosthodontics reconstructions. The rehabilitation of this mandibular deviation by the specialist in maxillofacial prosthodontics is often done by guide devices. This helps to prevent or reduce postsurgical mandibular deviation. The current literature lacks data on a standard classification of these devices despite the various clinical situations that require different rehabilitation techniques. After a deep appraisal of the current literature on this topic and many years of experience, the authors propose an overview of the indications, the types, and an original classification of these prosthetic devices. We believe this new classification will facilitate communication among practitioners, the development of treatment plans, and the comparison of outcomes among different expert centers.

Composition analysis and identification of ancient glass objects based on LightGBM

In this paper, for a batch of existing data related to ancient glass objects in China, by analyzing the relationship between the basic information of cultural relics and the information of chemical composition detected by cultural relics, a prediction model that can predict the chemical composition data before weathering by the chemical composition data after weathering is established, a classification model based on the existing data is built to simulate the original classification law, and the model is used to identify the type of unclassified cultural relics, and then by The importance of the feature selects barium oxide as a criterion for subcategory classification. The proportion of chemical composition of the classified glass artifacts was determined by feature engineering to determine whether the 0 in the detection data was filled in artificially, and it was used as a new feature to reduce the impact of extreme data 0 on the model. The data were then used for model training to obtain the LightGBM classification model. The mean value of the chemical components with the highest feature importance in the model was selected as the criterion for subclass classification, and the values in each class were divided into 2 subclasses. The chemical components with the top 3 feature importance were subjected to sensitivity analysis, and sensitivity judgments were made by the indicators of the model. After first performing feature engineering on the data information of the unknown category of artifacts, the type identification output results were performed using the classification model built in Problem 2. The top 3 chemical components of importance in the model were selected to let their values fluctuate up and down by 5%, and the model indicator curves were plotted for sensitivity analysis.

Bony Remodeling of Adolescent Displaced Clavicle Fractures: A FACTS Study

Background: Bony remodeling of displaced clavicle fractures in adolescents remains poorly understood. Purpose: To evaluate and quantify clavicle remodeling in a large population of adolescents with completely displaced fractures, which were treated nonoperatively, to better understand the factors that may influence this process. Study Design: Case series; Level of evidence, 4. Methods: Patients were identified from the databases of a multicenter study group investigating the functional outcomes of adolescent clavicle fractures. Patients between the ages of 10 and 19 years with completely displaced middiaphyseal clavicle fractures that were treated nonoperatively and who had further radiographic imaging of the affected clavicle at a minimum of 9 months from initial injury were included. Fracture shortening, superior displacement, and angulation were measured on the injury and final follow-up radiographs using previously validated techniques. Furthermore, fracture remodeling was classified as complete/near complete, moderate, or minimal, using an original classification system found to have good to excellent reliability (interobserver reliability = 0.78, intraobserver reliability = 0.90). Classifications were subsequently analyzed quantitatively and qualitatively to determine the factors associated with deformity correction. Results: Ninety-eight patients (mean age, 14.4 ± 2.0 years) were analyzed at a mean radiographic follow-up of 3.4 ± 2.3 years. Fracture shortening, superior displacement, and angulation significantly improved during the follow-up period by 61%, 61%, and 31%, respectively (P < .001). Furthermore, while 41% of the population had initial fracture shortening >20 mm at final follow-up, only 3% of the cohort had residual shortening >20 mm. Fracture remodeling was found to be associated with follow-up time; those with longer follow-up time demonstrated more remodeling (P = .001). Eighty-five percent of patients aged <14 years and 54% of patients aged ≥14 years at time of injury with a minimum follow-up of 4 years underwent complete/near-complete remodeling. Conclusion: Significant bony remodeling occurs in adolescent patients with completely displaced clavicle fractures, including older adolescents, and appears to continue over longer time intervals, even beyond the adolescent years. This finding may help explain the low rate of symptomatic malunions in adolescents, even in severely displaced fractures, and particularly when compared with rates reported in adult studies.

Modeling the Automating Process of the Creation, Checking and Updating Texts of Contracts

Purpose of research. Is to increase the degree of automation of contractual relations legal support of the parties as in the case of publishing activities through the development of model-algorithmic control and software for the automated creation and updating of texts of publisher-author license agreements.Methods. When analyzing the processes of automating legal activities in relation to the creation, development and updating texts of contracts, Russian and foreign technologies and software that allow automating the processes of contractual activities have been considered. Conceptual and set-theoretics models of the process of automated building texts of contracts have been proposed.Results. An original classification of tasks and ways of automating the legal activities including a list of promising information technologies have been proposed. Conceptual and set-theoretics models of the process of automated building texts of contracts have been developed; they differ in the use of a set of public acts, information resources, and expert information available as sources of information to create the necessary document in the selected subject area based on the algorithm of data verification, generation, visualization, contract updating, which provide contract creation and updating in an automated mode. An algorithmic model for automated processing of contract parameters which differs in the distribution of tasks between experts (a lawyer, a programmer) and software tools at has been developed. The main stages of the document life cycle involve creating, updating, deleting a contract, which, based on the event model, checks for updates in public acts and automatically corrects the text values of the parameter of the contract. The proposed solutions have been tested in case of a license agreement between an author of the article and the publishing house issuing Informatics and Automation journal.Conclusion. The development of model-algorithmic control and software for processing text information available from public acts, information resources provided by representatives of subject areas necessary for the automated generation, verification, and visualization of individual acts contributes to an increase in the degree of automation, reduces the time of preparation of legal documents that meet the requirements of existing public acts. Means of automating contractual activities should be built on the principle of openness, be dynamic, and also be externally controlled. The software services of the St. Petersburg Federal Research Center of the Russian Academy of Sciences for the automation of legal activities developed within the framework of this study are available at http://legaltech.viwo.ru/.

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV, are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, as well as path-planning approaches, and outlines open research questions together with future directions.