Orchard Sports Injury and Illness Classification System (OSIICS) Version 15

Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness Classification System(OSIICS)is one of two sports medicine coding systems recommended by the International Olympic Committee.Regular updates of coding systems are required.Methods:For Version 15,updates for mental health conditions in athletes,sports cardiology,concussion sub-types,infectious diseases,and skin and eye conditions were considered particularly important.Results:Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes.Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes.Rugby union protocols on head injury assessment were used to create additional concussion codes.Conclusion:It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility.The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS.

Intelligent Garbage Recycling: Design and Implementation Exploration of Automatic Classification System

This paper introduces an intelligent waste recycling automatic classification system,which integrates sensors,image recognition,and robotic arms to achieve automatic identification and classification of waste.The system monitors the composition and properties of waste in real time through sensors,and uses image recognition technology for precise classification,and the robotic arm is responsible for grabbing and disposing.The design and implementation of the system have important practical significance and application value,and help promote the popularization and standardization of waste classification.This paper details the system s architecture,module division,sensors and recognition technology,robotic arm and grabbing technology,data processing and control system,and testing and optimization process.Experimental results show that the system has efficient waste recycling efficiency and accuracy in practical applications,bringing new development opportunities to the waste recycling industry.

An engineering classification system for the detached granite blocks distributed on slopes

Gentle slopes with large amounts of granite blocks are widespread in granitic areas with warm and humid climate.These blocks pose a potential risk to the existing and planned infrastructure.The instability type and geometry of these blocks will influence their motility and destructive power to some extent.This study aims at creating a classification system that can indicate both the shape and the instability type of these blocks and then developing a block removal scheme.The classification system was constructed based on the mechanical stability analysis of blocks on an inclined surface.This analysis identified key factors affecting stability,including block shape,block weathering roundness,the existing state of a block on a slope,and the friction between the block and the slope.The achieved work allowed the establishment of a direct correlation between block geometry and their instability types.The availability of this classification system was validated by field data and experimental data in the literature.The proposal to remove blocks identified as the toppling types,such as cylindrical-toppling types,cuboid-toppling types,cube-toppling types,was put forward to avoid the uneconomical problem of a complete clearance.Meanwhile,this classification provides a foundation for further research on the instability probability of each type of block and the development of a more refined blocks’removal scheme.The classification approach adopted in this paper can provide a reference for the classification of other lithological blocks under similar engineering geological conditions.

New classification system for radical rectal cancer surgery based on membrane anatomy

BACKGROUND Total mesorectal excision along the“holy plane”is the only radical surgery for rectal cancer,regardless of tumor size,localization or even tumor stage.However,according to the concept of membrane anatomy,multiple fascial spaces around the rectum could be used as the surgical plane to achieve radical resection.AIM To propose a new membrane anatomical and staging-oriented classification system for tailoring the radicality during rectal cancer surgery.METHODS A three-dimensional template of the member anatomy of the pelvis was established,and the existing anatomical nomenclatures were clarified by cadaveric dissection study and laparoscopic surgical observation.Then,we suggested a new and simple classification system for rectal cancer surgery.For simplification,the classification was based only on the lateral extent of resection.RESULTS The fascia propria of the rectum,urogenital fascia,vesicohypogastric fascia and parietal fascia lie side by side around the rectum and form three spaces(medial,middle and lateral),and blood vessels and nerves are precisely positioned in the fascia or space.Three types of radical surgery for rectal cancer are described,as are a few subtypes that consider nerve preservation.The surgical planes of the proposed radical surgeries(types A,B and C)correspond exactly to the medial,middle,and lateral spaces,respectively.CONCLUSION Three types of radical surgery can be precisely defined based on membrane anatomy,including nerve-sparing procedures.Our classification system may offer an optimal tool for tailoring rectal cancer surgery.

An Improved Fully Automated Breast Cancer Detection and Classification System

More than 500,000 patients are diagnosed with breast cancer annually.Authorities worldwide reported a death rate of 11.6%in 2018.Breast tumors are considered a fatal disease and primarily affect middle-aged women.Various approaches to identify and classify the disease using different technologies,such as deep learning and image segmentation,have been developed.Some of these methods reach 99%accuracy.However,boosting accuracy remains highly important as patients’lives depend on early diagnosis and specified treatment plans.This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two deep-learning models and a classifier on different datasets.This method specifically uses ResNet50 and AlexNet,convolutional neural networks(CNNs),for deep learning and a K-Nearest-Neighbor(KNN)algorithm to classify data.Various experiments have been conducted on five datasets:the Mammographic Image Analysis Society(MIAS),Breast Cancer Histopathological Annotation and Diagnosis(BreCaHAD),King Abdulaziz University Breast Cancer Mammogram Dataset(KAU-BCMD),Breast Histopathology Images(BHI),and Breast Cancer Histopathological Image Classification(BreakHis).These datasets were used to train,validate,and test the presented method.The obtained results achieved an average of 99.38%accuracy,surpassing other models.Essential performance quantities,including precision,recall,specificity,and F-score,reached 99.71%,99.46%,98.08%,and 99.67%,respectively.These outcomes indicate that the presented method offers essential aid to pathologists diagnosing breast cancer.This study suggests using the implemented algorithm to support physicians in analyzing breast cancer correctly.

Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

Subaxial cervical spine injury classification system: is it most appropriate for classifying cervical injury?

The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.

Influence of data analysis when exploiting DFN model representation in the application of rock mass classification systems

Discrete fracture network(DFN) models have been proved to be effective tools for the characterisation of rock masses by using statistical distributions to generate realistic three-dimensional(3 D) representations of a natural fracture network. The quality of DFN modelling relies on the quality of the field data and their interpretation. In this context, advancements in remote data acquisition have now made it possible to acquire high-quality data potentially not accessible by conventional scanline and window mapping. This paper presents a comparison between aggregate and disaggregate approaches to define fracture sets, and their role with respect to the definition of key input parameters required to generate DFN models. The focal point of the discussion is the characterisation of in situ block size distribution(IBSD) using DFN methods. An application of IBSD is the assessment of rock mass quality through rock mass classification systems such as geological strength index(GSI). As DFN models are becoming an almost integral part of many geotechnical and mining engineering problems, the authors present a method whereby realistic representation of 3 D fracture networks and block size analysis are used to estimate GSI ratings, with emphasis on the limitations that exist in rock engineering design when assigning a unique GSI value to spatially variable rock masses.

Combination of a biopharmaceutic classification system and physiologically based pharmacokinetic models to predict absorption properties of baicalein in vitro and in vivo

Objective: To determine the in vitro and in vivo absorption properties of active ingredients of the Chinese medicine, baicalein, to enrich mechanistic understanding of oral drug absorption.Methods: The Biopharmaceutic Classification System(BCS) category was determined using equilibrium solubility, intrinsic dissolution rate, and intestinal permeability to evaluate intestinal absorption mechanisms of baicalein in rats in vitro. Physiologically based pharmacokinetic(PBPK) model commercial software GastroPlus~(TM) was used to predict oral absorption of baicalein in vivo.Results: Based on equilibrium solubility, intrinsic dissolution rate, and permeability values of main absorptive segments in the duodenum, jejunum, and ileum, baicalein was classified as a drug with low solubility and high permeability. Intestinal perfusion with venous sampling(IPVS) revealed that baicalein was extensively metabolized in the body, which corresponded to the low bioavailability predicted by the PBPK model. Further, the PBPK model predicted the key indicators of BCS, leading to reclassification as BCS-II. Predicted values of peak plasma concentration of the drug(C_(max)) and area under the curve(AUC)fell within two times of the error of the measured results, highlighting the superior prediction of absorption of baicalein in rats, beagles, and humans. The PBPK model supported in vitro and in vivo evidence and provided excellent prediction for this BCS class II drug.Conclusion: BCS and PBPK are complementary methods that enable comprehensive research of BCS parameters, intestinal absorption rate, metabolism, prediction of human absorption fraction and bioavailability, simulation of PK, and drug absorption in various intestinal segments across species. This combined approach may facilitate a more comprehensive and accurate analysis of the absorption characteristics of active ingredients of Chinese medicine from in vitro and in vivo perspectives.

One-Variable Attack on the Industrial Fault Classification System and Its Defense

Recently developed fault classification methods for industrial processes are mainly data-driven.Notably,models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data patterns.However,these data-driven models are vulnerable to adversarial attacks;thus,small perturbations on the samples can cause the models to provide incorrect fault predictions.Several recent studies have demonstrated the vulnerability of machine learning methods and the existence of adversarial samples.This paper proposes a black-box attack method with an extreme constraint for a safe-critical industrial fault classification system:Only one variable can be perturbed to craft adversarial samples.Moreover,to hide the adversarial samples in the visualization space,a Jacobian matrix is used to guide the perturbed variable selection,making the adversarial samples in the dimensional reduction space invisible to the human eye.Using the one-variable attack(OVA)method,we explore the vulnerability of industrial variables and fault types,which can help understand the geometric characteristics of fault classification systems.Based on the attack method,a corresponding adversarial training defense method is also proposed,which efficiently defends against an OVA and improves the prediction accuracy of the classifiers.In experiments,the proposed method was tested on two datasets from the Tennessee–Eastman process(TEP)and steel plates(SP).We explore the vulnerability and correlation within variables and faults and verify the effectiveness of OVAs and defenses for various classifiers and datasets.For industrial fault classification systems,the attack success rate of our method is close to(on TEP)or even higher than(on SP)the current most effective first-order white-box attack method,which requires perturbation of all variables.

Classification system and structure analysis of marine cultural landscapes of China

Marine cultural landscapes are an important part of cultural landscapes,as a new kind of heritage.With reference to the classification of cultural landscapes,a three-level classification system,composed of the marine cultural landscape type,the marine cultural landscape subtype and the marine cultural landscape model was constructed in this study,based on the spatial and regional characteristics of marine history and culture.By the analysis of the effects of natural and humanity factors on the formation of marine cultural landscapes,the system structure of the marine cultural landscapes was divided into three major parts:the physical foundation,the marked material and the recessive material.

A Preliminary Study on the Problems and Improvement of the Latest Land Use Classification System in China

The establishment of a unified land use classification system is the basis for realizing the unified management of land and sea,urban and rural areas,and aboveground and underground space.In November 2020,the Ministry of Natural Resources of the People's Republic of China issued the Classification Guide for Land and Space Survey,Planning and Use Control of Land and Sea(for Trial Implementation),which aims to establish a national unified land and sea use classification system,lay an important foundation for scientific planning and unified management of natural resources,rational use and protection of natural resources,and speed up the construction of a new pattern of land and space development and protection.However,there are still some obvious shortcomings in the Classification Guide.This paper analyzes some problems existing in this classification standard from three aspects of logicality,rigorousness and comprehensiveness,and puts forward some suggestions for further improvement.This has important practical significance to better guiding the practice of land use and land resources management,and then to achieving the goal of unified management of natural resources.

Pathohistological classification systems in gastric cancer:Diagnostic relevance and prognostic value

Several pathohistological classification systems exist for the diagnosis of gastric cancer. Many studies have investigated the correlation between the pathohistological characteristics in gastric cancer and patient characteristics, disease specific criteria and overall outcome. It is still controversial as to which classification system imparts the most reliable information, and therefore, the choice of system may vary in clinical routine. In addition to the most common classification systems, such as the Laurén and the World Health Organization (WHO) classifications, other authors have tried to characterize and classify gastric cancer based on the microscopic morphology and in reference to the clinical outcome of the patients. In more than 50 years of systematic classification of the pathohistological characteristics of gastric cancer, there is no sole classification system that is consistently used worldwide in diagnostics and research. However, several national guidelines for the treatment of gastric cancer refer to the Laurén or the WHO classifications regarding therapeutic decision-making, which underlines the importance of a reliable classification system for gastric cancer. The latest results from gastric cancer studies indicate that it might be useful to integrate DNA- and RNA-based features of gastric cancer into the classification systems to establish prognostic relevance. This article reviews the diagnostic relevance and the prognostic value of different pathohistological classification systems in gastric cancer.

A-BQ,a classification system for anisotropic rock mass based on China National Standard

The rock mass in nature is in most cases anisotropic,while the existing classifications are mostly developed with the assumption of isotropic conditions that not always meet the engineering requirements.In this study,an anisotropic system based on China National Standard of BQ,named as A-BQ,is developed to address the classification of anisotropic rock mass incorporating the anisotropy degree as well as the quality of rock mass.Two series of basic rating factors are incorporated including inherent anisotropy and structure anisotropy.The anisotropy degree of rock mass is characterized by the ratio of maximum to minimum quality score and adjusted by the confining stress.The quality score of rock mass is determined by the key factors of anisotropic structure occurrence and the correction factors of stress state and groundwater condition.The quality of rock mass is characterized by a quality score and classified in five grades.The assessment of stability status and probable failure modes are also suggested for tunnel and slope engineering for different quality grades.Finally,two cases of tunnel and slope are presented to illustrate the application of the developed classification system into the rock masses under varied stress state.

Presenting an engineering classification system for coal spontaneous combustion potential

The phenomenon of coal spontaneous combustion is one of the common hazards in coal mines and also one of the important reasons for the loss of coal in piles and mines. Based on previous researches, different types of coals have different spontaneous combustion characteristics. For coal loss prevention, a measure is necessary for prediction of coal spontaneous combustion. In this study, a new engineering classification system called ＂Coal Spontaneous Combustion Potential Index （CSCPI）＂ is presented based on the Fuzzy Delphi Analytic Hierarchy Process （FDAHP） approach. CSCPI classifies coals based on their spontaneous combustion capability. After recognition of the roles of the effective parameters influencing the initiation of a spontaneous combustion, a series of intrinsic, geological, and mining characteristics of coal seams are investigated. Then, the main stages of the implementation of the FDAHP method are studied and the weight of each parameter involved is calculated. A classification list of each parameter is formed, the CSCPI system is described, and the engineering classifying system is subsequently presented. In the CSCPI system, each coal seam can be rated by a number from 0 to 100; a higher number implies a greater ease for the coal spontaneous combustion capability. Based on the CSCPI system, the propensity of spontaneous combustion of coal can be classified into three potential levels： low, medium, and high. Finally, using the events of coal spontaneous combustion occurring in one of the Iranian coal mines, Eastern Alborz Coal Mines, an initial validation of the mentioned systematic approach is conducted. Comparison of the results obtained in this study illustrate a relatively good agreement.

Application of rock mass classification systems to rock slope stability assessment:A case study

The stability of rock slopes is considered crucial to public safety in highways passing through rock cuts, as well as to personnel and equipment safety in open pit mines. Slope instability and failures occur due to many factors such as adverse slope geometries, geological discontinuities, weak or weathered slope materials as well as severe weather conditions. External loads like heavy precipitation and seismicity could play a significant role in slope failure. In this paper, several rock mass classification systems developed for rock slope stability assessment are evaluated against known rock slope conditions in a region of Saudi Arabia, where slopes located in rugged terrains with complex geometry serve as highway road cuts. Selected empirical methods have been applied to 22 rock cuts that are selected based on their failure mechanisms and slope materials. The stability conditions are identified, and the results of each rock slope classification system are compared. The paper also highlights the limitations of the empirical classification methods used in the study and proposes future research directions.

A classification system for tableting behaviors of binary powder mixtures

The ability to predict tableting properties of a powder mixture from individual components is of both fundamental and practical importance to the efficient formulation development of tablet products. A common tableting classification system(TCS) of binary powder mixtures facilitates the systematic development of new knowledge in this direction. Based on the dependence of tablet tensile strength on weight fraction in a binary mixture,three main types of tableting behavior are identified. Each type is further divided to arrive at a total of 15 sub-classes. The proposed classification system lays a framework for a better understanding of powder interactions during compaction. Potential applications and limitations of this classification system are discussed.

An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique.

Signal classification system using global-local feature extraction algorithm

A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine(SVM)is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is1.3x10_5under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3dB,which is24times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is13times lower than the one using the global feature extraction method and24times lower than the one using the local feature extraction method.

Applicable conditions for a classification system of aquifer-protective mining in hallow coal seams

Based on the conclusions of domestic and foreign research, we have analyzed the collapse-fall characteristics of overlying strata and the mechanism of aquifer-protective mining in shallow coal seam working faces at the Shendong Mine. We have selected the height of the water-conducting fracture zone in overlying strata as a composite index and established the applicable conditions of aquifer-protective mining in shallow coal seams with a multi-factor synthetic-index classification method. From our calculations and analyses of variance, we used factors such as the overlying strata strength, mining disturbing factors and rock integrity as related factors of the composite index. We have classified the applicable conditions of aquifer-protective mining in shallow coal seams into seven types by comparing the result of the height of water-conducting fractured zones of long-wall and short-wall working faces with the thickness of the bedrock, the thickness of the weathered zone and the size of safety coal-rock pillars. As a result, we propose the preliminary classification system of aquifer-protective mining in shallow coal seams. It can provide a theoretical guidance for safe applications of aquifer-protective mining technology in shallow coal seams under similar conditions.