Issues of medical examination and selection of citizens for military service under a contract in Armed Forces of the Russian Federation

In the context of the reform of the Armed Forces of the Russian Federation and a significant increase in the proportion of military personnel doing military service under contract, the issues of manning the troops with healthy, physically developed citizens with high moral and business qualities are of paramount importance. Of particular importance in the selection of candidates for military service under the contract is the conduct of laboratory and instrumental studies, professional and psychological selection, determination of the level of citizens’ physical fitness. The Federal Law «On Military Duty and Military Service» defines a two-stage system for medical examination of citizens entering military service under contract, which is necessary as a barrier in order to prevent citizenswho do not meet the necessary requirements for military personnel from entering the Russian Armed Forces. At the first stage (preliminary examination), the military and medical examination of citizens was carried out by specialist doctors working in medical organizations of the outpatient-polyclinic link of municipalities at the place of citizens’ permanent residence. Medical specialists of the regular military medical commission of the military commissariat of the constituent entity of the Russian Federation participated in the second stage (final examination) of the military medical examination. During the five-year period under study, 5,133 citizens (72.9 %) were selected out of 7,043 candidates for military service under contract, who fully met all the criteria for defenders of the Fatherland.

An End-to-End Efficient Lucene-Based Framework of Document/Information Retrieval

In the context of big data and the 4.0 industrial revolution era, enhancing document/information retrieval frameworks efficiency to handle the ever‐growing volume of text data in an ever more digital world is a must. This article describes a double-stage system of document/information retrieval. First, a Lucene-based document retrieval tool is implemented, and a couple of query expansion techniques using a comparable corpus (Wikipedia) and word embeddings are proposed and tested. Second, a retention-fidelity summarization protocol is performed on top of the retrieved documents to create a short, accurate, and fluent extract of a longer retrieved single document (or a set of top retrieved documents). Obtained results show that using word embeddings is an excellent way to achieve higher precision rates and retrieve more accurate documents. Also, obtained summaries satisfy the retention and fidelity criteria of relevant summaries.

Tertiary wavelet model based automatic epilepsy classification system

PurposeThis work proposes a tertiary wavelet model based automatic epilepsy classification system using electroencephalogram (EEG) signals.Design/methodology/approachIn this paper, a three-stage system has been proposed for automated classification of epilepsy signals. In the first stage, a tertiary wavelet model uses the orthonormal M-band wavelet transform. This model decomposes EEG signals into three bands of different frequencies. In the second stage, the decomposed EEG signals are analyzed to find novel statistical features. The statistical values of the features are demonstrated using multi-parameters graph comparing normal and epileptic signals. In the last stage, the features are inputted to different conventional classifiers that classify pre-ictal, inter-ictal (epileptic with seizure-free interval) and ictal (seizure) EEG segments.FindingsFor the proposed system the performance of five different classifiers, namely, KNN, DT, XGBoost, SVM and RF is evaluated for the University of BONN data set using different performance parameters. It is observed that RF classifier gives the best performance among the above said classifiers, with an average accuracy of 99.47%.Originality/valueEpilepsy is a neurological condition in which two or more spontaneous seizures occur repeatedly. EEG signals are widely used and it is an important method for detecting epilepsy. EEG signals contain information about the brain's electrical activity. Clinicians manually examine the EEG waveforms to detect epileptic anomalies, which is a time-consuming and error-prone process. An automated epilepsy classification system is proposed in this paper based on combination of signal processing (tertiary wavelet model) and novel features-based classification using the EEG signals.

YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach

Wearing safety rope while working at the loft and over the side of a ship is an effective means to protect seafarers from accidents. However, there are no active and effective monitoring methods on ships to control this issue. In this article, a one-stage system is proposed to automatically monitor whether the crew is wearing safety ropes. When the system detects that a crew enters the work area without a safety rope, it will warn the supervisor. In this regard, a safety rope wearing detection dataset is established. Then a data augmentation algorithm and a boundary loss function are designed to improve the training effect and the convergence speed. Furthermore, features from different scales are extracted to get the final detection results. The obtained results demonstrate that the proposed approach YOLO-SD is effective at different on-site conditions and can achieve high precision (97.4%), recall rate (91.4%), and mAP (91.5%) while ensuring real-time performance (38.31 FPS on average).

Heterogeneous Environmental Regulations And Green Economic Efficiency In China: The Mediating Role of Industrial Structure

As the largest emerging economy, China has been experiencing significant environmental problems. To sustain green economic efficiency and modernize industrial structure, China has devised several environmental regulations. While, previous studies have obtained insightful conclusions on this subject, others have presented the reverse arguments, thereby leaving gaps in the literature. This study therefore analyzes the effects of heterogeneous environmental regulations on green economic efficiency in China while taking industrial structure as a mediator over the period 2003-2017. The green economic efficiency was estimated through slacks-based direction distance function (SBM-DDF) model which considers slacks and avoids overestimation. The results of dynamic panel two-stage system generalized method of moments (GMM) show that control-and-command regulation, market-based regulation and voluntary regulation are conducive to China’s green economic efficiency. Regarding spatial heterogeneity, control-and-command and voluntary regulations significantly promote green economic efficiency of inland provinces while the effect is insignificant in coastal provinces. Additionally, market-based regulation promotes green economic efficiency by advanced and rationalized industrial structure. Control-and-command regulation accelerates green economic efficiency only through advanced industrial structure. Voluntary regulation yields positive effect on green economic efficiency through advanced industrial structure and negative effect through rationalized industrial structure. Finally, a number of policy implications are provided.

Space Thermoacoustic Radioisotopic Power System, SpaceTRIPS: The Magnetohydrodynamic Generator

Electricity production is a major problem for deep space exploration. The possibility of using radioisotope elements with a very long life as an energy source was investigated in the framework of an EU project “SpaceTRIPS”. For this, a two-stage system was tested, the first in which thermal energy is converted into mechanical energy by means of a thermoacoustic process, and the second where mechanical energy is converted into electrical energy by means of a magnetohydrodynamic generator (MHD). The aim of the present study is to develop an analytical model of the MHD generator. A one-dimensional model is developed and presented that allows us to evaluate the behavior of the device as regards both electromagnetic and fluid-dynamic aspects, and consequently to determine the characteristic values of efficiency and power.

Cauda equina syndrome—a practical guide to definition and classification

Purpose International uniformity of definition and classification are crucial for diagnosis and management of cauda equina syndrome (CES). They are also useful for clinicians when discussing CES with patients and relatives, and for medicolegal purposes. Methods We reviewed published literature using PubMed on definition and classification of cauda equina syndrome since 2000 (21 years). Using the search terms ‘cauda equina’ and ‘definition’ or ‘classification’, we found and reviewed 212 papers. Results There were 17 different definitions of CES used in the literature. There were three well-defined methods of classification of CES. The two-stage system of incomplete CES (CESI) versus CES with retention (CESR) is the most commonly used classification, and has prognostic value although the details of this continue to be debated. Conclusion We used the existing literature to propose a clear definition of CES. We also drew on peer-reviewed published literature that has helped to amplify and expand the CESI/CESR dichotomy, adding categories that are both less severe than CESI, and more severe than CESR, and we propose clear definitions in a table form to assist current and future discussion and management of CES.

Comparison of Four Lymph Node Stage Methods for Predicting the Prognosis of Distal Cholangiocarcinoma Patients After Surgery

BackgroundThe metastatic status of regional lymph nodes is an effective risk factor for the prognosis of distal cholangiocarcinoma (dCCA). But existing lymph node staging is not accurate enough and is susceptible to interference. This study aims to explore the predictive ability of the log odds of positive lymph nodes (LODDS) staging system of dCCA compared with existing lymph node staging systems.MethodsA total of 928 dCCA patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort, and 207 dCCA patients from West China Hospital who underwent surgery were reviewed as the validation cohort. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were conducted to identify the most meaningful factors relevant to prognosis. The performance of four lymph node stage systems was compared by a model-based approach.ResultAge at diagnosis, pathological grade, American Joint Committee on Cancer (AJCC) tumor 7th T stage, tumor size, radiotherapy, chemotherapy, and lymph node stage system were independent prognostic factors. The model with the LODDS system had a better model fit with the highest C-index (0.679) and 1-/3-/5- area under the receiver operating characteristic curve (AUC) (0.739/0.671/0.658) as well as the lowest Akaike information criterion (AIC) (5,020.52). External validation results from 207 dCCA patients showed a C-index of 0.647 and 1-/3-/5-AUC of 0.740/0.683/0.589. Compared with the lymph node ratio (LNR), AJCC 8th N system, and 7th N system, the 5-year net reclassification improvement (NRI) of the LODDS system was 0.030 (95% CI: −0.079 to 0.147), 0.042 (95% CI: −0.062 to 0.139), and 0.040 (95% CI: −0.057 to 0.146), respectively. The integrated discrimination improvement (IDI) of LODDS improved compared with the LNR model (0.016; 95% CI: −0.001 to 0.036), AJCC 8th N system (0.020; 95% CI: 0.003–0.037), and AJCC 7th N system (0.019; 95% CI: 0.002–0.036). Decision curve analysis (DCA) also shows a greater net benefit of LODDS. In lymph node-negative patients, LODDS reveals a positive linear relationship with the hazard ratio (HR). The stage capacity of LODDS in a subgroup analysis stratified by examined lymph node number (ELNN) was consistent.ConclusionsThe LODDS lymph node stage system has superior predictive performance as compared with the LNR, AJCC 7th, and 8th lymph node stage systems. Meanwhile, LODDS has a more detailed staging ability and good stability.

Dynamic Nomogram Predicts Overall Survival in Patients With Occult Breast Cancer: a Population-based Analysis

Background: Due the rarity of occult breast cancer (OBC), no precise prognostic instruments were available to assess the overall survival (OS) in patients with OBC. The aim of this study is to construct a nomogram for predicting the OS probability in patients with OBC. Methods: Patients who were enrolled in the Surveillance, Epidemiology, and End Results database between 2004 and 2015 were regarded as subjects and studied. We constructed a dynamic nomogram that can predict prognosis in patients with OBC based on crucial independent factors by using univariate and multivariate Cox regression analyses. C-index and calibration plots were chosen for validation. Net reclassification index (NRI), integrated discrimination improvement (IDI) and DCA (Ddecision Curve Analysis) were used to evaluate the nomogram’s clinical pragmatism. Results: Totally, 693 patients with OBC were included in this study. The nomogram integrated six independent prognostic factors through multivariate Cox regression analysis, such as surgical method, radiotherapy status, chemotherapy status, ER status, AJCC-stage and age. The prediction model exhibited robustness with the C-index 0.75 (95%CI: 0.72-0.77) in training cohort and 0.79 (95%CI: 0.76-0.82) in validation group. Moreover, the calibration curves presented favorably. The NRI values of 0.61 (95%CI: 0.28-0.99) for 5-year,0.53 (95%CI: 0.23-0.77) for 8-year OS prediction in the training cohort,0.75 (95%CI: 0.36-1.23) for 5-year and 0.6 (95%CI: 0.15-1.2) for 8-year OS prediction in the validation cohort,and the IDI values of 0.1 (95%CI: 0.04-0.17) for 5-year and 0.11 (95%CI: 0.03-0.19) for 8-year OS prediction in the training cohort, 0.21 (95%CI: 0.09-0.3) for 5-year and 0.22 (95%CI: 0.08-0.32) for 8-year OS prediction in the validation cohort,, indicated that the established nomogram performed significantly better than the AJCC stage system alone. Furthermore, DCA showed that the nomogram in our study was clinically useful and had better discriminative ability than the AJCC stage system. Conclusions: A nomogram was developed and validated to accurately predict the individualized probability OS for patients with occult breast cancer (OBC) and is expected to offer guidance for strategic decision.