Cluster Enterprise Comprehensive Risk Assessment: Methodology Based on the Functional-Target Approach

The effectiveness of the unification of enterprises in the cluster is also associated with high uncertainty and risks. Thus, the development of theoretical approaches and methodological instruments for efficient risk management of enterprises under the conditions of cluster association is an urgent scientific task. The methodology of a comprehensive risk assessment of the cluster enterprise is based on the use of the approach for building a functional-target model of a cluster enterprise, and is reduced to the search for a response to the question: can an event change the value of a providing indicator in such a way that this will lead to a deterioration in the resulting indicator in each enterprise subsystem? Based on the results of forecasting external risks, it was established that the group of state and global risks, in particular, political, territorial and financial, is characterized by significant threats for the next 5 years for the studied cluster enterprises. We proposed and tested a methodology for a comprehensive assessment of the risks of cluster enterprises, based on a functional-target approach, according to which a cluster enterprise as a socio-economic system is considered as a set of three basic subsystems: management, production and financial and economic.

Updating Training in the Medical Field

This chapter briefly reviews the literature that explores the training technique of deliberate practice and the related constructs, training outcomes of achievement goal orientation, self-efficacy, perceived instrumentality, and reflective practice. This work explains how educators can use and measure these variables to enhance current training methodologies. As part of creating more effective training, the TARGET model, developed by Ames, will be utilized to discuss potential ways to enhance training outcomes in a post-COVID-19 world. Specifically, suggestions are offered for enhancing online training using deliberate practice combined with the TARGET model within a medical setting where there are limited resources.

Applying Eigenstructure Assignment to Inner-Loop Flight Control Laws for a Multibody Aircraft

Unmanned aircraft used as high-altitude platform system has been studied in research and industry as alternative technologies to satellites. Regarding actual operation and flight performance of such systems, multibody aircraft seems to be a promising aircraft configuration. In terms of flight dynamics, this aircraft strongly differs from classical rigid-body and flexible aircraft, because a strong interference between flight mechanic and formation modes occurs. For unmanned operation in the stratosphere, flight control laws are required. While control theory generally provides a number of approaches, the specific flight physics characteristics can be only partially considered. This paper addresses a flight control law approach based on a physically exact target model of the multibody aircraft dynamics rather than conventionally considering the system dynamics only. In the target model, hypothetical spring and damping elements at the joints are included into the equations of motion to transfer the configuration of a highly flexible multibody aircraft into one similar to a classical rigid-body aircraft. The differences between both types of aircraft are reflected in the eigenvalues and eigenvectors. Using the eigenstructure assignment, the desired damping and stiffness are established by the inner-loop flight control law. In contrast to other methods, this procedure allows a straightforward control law design for a multibody aircraft based on a physical reference model.

Efficient Online Object Tracking Scheme for Challenging Scenarios

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

The Role of 5G Network Image Information Based on Deep Learning in UAV Prediction Target Trajectory Tracking

With the development of information technology in the network era and the popularization of the 5G era, UAV-related applications are becoming more and more widely used, which is one of the essential basic technologies. Therefore, the technology has great research value and practical significance, a multiobjective detector based on support vector machine (SVM) is designed based on directional gradient histogram (HOG), and the startup method used with cross-validation methods can improve detector performance. It makes the detector accuracy above 98% and has good resistance to the target scale. A real-time target tracker is designed with its rotation variation and with an improved average displacement algorithm. The algorithm must manually select the target model and suggest the target model to achieve automatic acquisition of the target model. Due to the ambiguity of the target tracking state, several judgment conditions are set to determine whether the tracking has failed and whether the tracker state is correctly verified, with several similar target tracking algorithms. When the system is started, the system detects targets frame by frame. And it will locate a possible target by color segmentation and specify the target to be tracked to recommend the relevant model during the tracking process and open the tracker to determine the target tracking state frame by frame and perform target detection at each frame. Then it will find possible goals and will follow them to achieve a balance of stable and real-time system performance, using the results of the TPD-KCF method. The percentage of correctly tracking images can reach 98%, and the efficiency is significantly improved.

Frequency Diversity Gain of a Wideband Radar Signal

Wideband radar has high-range directional resolution, which can effectively reduce the fluctuation of echo and improve the detection probability of a target under the same detection probability requirement. In this paper, a unified wideband radar χ2 distribution target model with more practical significance is innovatively established, on which the probability density function and detection probability function of Swerling 0, Swerling II and Swerling IV targets are analyzed, respectively. A generalized “frequency diversity gain” of wideband radar is proposed and defined based on the contradiction between suppression of fluctuation and accumulation loss, which represents the ratio of Signal-to-Noise Ratio (SNR) gain between broadband signal and reference bandwidth signal under the same condition (when the reference bandwidth is used, the radar target has only one range unit), and the mathematical relation equation of the target detection performance and signal bandwidth (equivalent to the number of distinguishable range elements of the target) is given. A Monte Carlo simulation experiment is designed. Based on the target model established in this paper, the optimal number of target range units corresponding to different detection probability requirements is obtained, which verifies the correctness of the concept proposed in this paper.

Investigation of Increasing the Tooth Surface Quality of Curvilinear Involute Gears

It is not possible to increase the surface quality of non-standard gears with curved involute tooth profile produced by 5-axis CNC milling method with standard gear grinding methods due to curved tooth profiles. In this study, the possibilities of increasing the surface quality of non-standard gears with curved involute tooth profile were investigated in order to expand their use in the industry. For this purpose, the target model was produced by using the mathematical parametric equations of the involute curve that forms the profile form of a tooth in the CAD environment. By using the target model, manufacturing codes were derived in the CAM environment, and gears with curved involute tooth profile were produced on a 5-axis CNC machine. Then these gears; The possibilities of increasing the tooth profile surface quality were investigated by applying four different methods, such as precision finishing, co-running in oil, co-running in oil with SiC added, and grinding on a 5-axis CNC machine with a finger grinding tip specially produced for gears. In the oil co-run method, considering the running-in stage for each method, the gears were tested with the help of the designed gearbox, at a revolution speed of 670 rpm, by turning a total of 150,000 turns in 6 periods of 25,000 turns. In the co-start method, the gears at the beginning of the test and at the end of each period; thermal records, noise analysis, photographic records, surface roughness values (Ra, Rz) were measured. In the co-operation methods applied to increase the surface quality, the tooth surface of the gears at the beginning of the test and at the end of each period; photographic records, surface roughness values (Ra, Rz) were measured, noise levels were measured during the test and thermal records were taken. Similarly, in precision machining and grinding methods, photographic recordings of the tooth surface were taken, surface roughness (Ra, Rz) was measured, micrographs were taken in SEM and analyzed after the process. As a result of the tests and analyzes carried out; It has been observed that the surface quality is the best in the process performed using a specially produced finger grinding tip and a CNC grinding machine.

Defining of the required parameters of the main fans on the ventilation shaft for the target model of the hard coal mine Y in connection with its decommissioning

The article presents the results of the analysis and calculations of the ventilation network of the decommissioned hard coal mine Y for the model of its target operation. After the end of hard coal mining and the liquidation of most of the mining excavations, the former Y hard coal mine will become an element of the drainage system and a protection for other, still active mining plants. In order for the Y excavation to be transformed into a mine water pumping station, its ventilation system should be rebuilt, and new parameters of the main fan should be determined for a smaller network of mining excavations. For this purpose, using the AERO-2016D program by POK “Zachód” Spółka z o.o., the parameters of the ventilation network were simulated in the target model of mine Y after the liquidation of the “Southern” shaft and mining excavations at levels 530m and 660m. The results of the simulation made it possible to select the optimal main fans for the target model of the transformed mine Y.

Conceptual considerations of the modernization of the main fan stations of mine Y and their economic aspect

In connection with the liquidation of the hard coal mine Y and the change its functionality, it is necessary to modernize the main fan station installed at the “Northern” shaft of mine Y. The article presents concepts of changing the efficiency of WPK 5.0 fans installed in the “Northern” shaft and the methods of air flow regulation for target model of a mine with a reduced network of mining excavations. It discusses four alternative variants of technical solutions. The most effective of them are illustrated with a preliminary economic analysis, the results of which enable the right investment decision to be made.