Usage of fractional order [Formula: see text] controller as AQM algorithm.

The article describes the Usage of fractional order [Formula: see text] controller as AQM algorithm. The alternative, integer-based calculation process for [Formula: see text] controller is proposed and tested in numerical analysis, simulation environment, and Linux-based testbed environment with real-life devices. The FPGA design for the calculation process is presented. Experimental evaluation and setup process for AQM in the network is presented.

Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer.

One of the most challenging topics in robotics is simultaneous localization and mapping (SLAM) in the indoor environments. Due to the fact that Global Navigation Satellite Systems cannot be successfully used in such environments, different data sources are used for this purpose, among others light detection and ranging (LiDARs ), which have advanced from numerous other technologies. Other embedded sensors can be used along with LiDARs to improve SLAM accuracy, e.g. the ones available in the Inertial Measurement Units and wheel odometry sensors. Evaluation of different SLAM algorithms and possible hardware configurations in real environments is time consuming and expensive. In our study, we evaluate the accuracy of mapping and localization (based on Absolute Trajectory Error and Relative Pose Error). Our use case is a robot used for room decontamination. The results for a small room show that for our robot the best hardware configuration consists of three LiDARs 2D, IMU and wheel odometry sensors. On the other hand, for long hallways, a configuration with one LiDAR 3D sensor and IMU works better and more stable. We also described a general approach together with tools and procedures that can be used to find the best sensor setup in simulation.

Building a Real-Time Testing Platform for Unmanned Ground Vehicles with UDP Bridge.

Perception and vehicle control remain major challenges in the autonomous driving domain. To find a proper system configuration, thorough testing is needed. Recent advances in graphics and physics simulation allow researchers to build highly realistic simulations that can be used for testing in safety-critical domains and inaccessible environments. Despite the high complexity of urban environments, it is the non-urban areas that are more challenging. Nevertheless, the existing simulators focus mainly on urban driving. Therefore, in this work, we describe our approach to building a flexible real-time testing platform for unmanned ground vehicles for indoor and off-road environments. Our platform consists of our original simulator, robotic operating system (ROS), and a bridge between them. To enable compatibility and real-time communication with ROS, we generate data interchangeable with real-life readings and propose our original communication solution, UDP Bridge, that enables up to 9.5 times faster communication than the existing solution, ROS#. As a result, all of the autonomy algorithms can be run in real-time directly in ROS, which is how we obtained our experimental results. We provide detailed descriptions of the components used to build our integrated platform.

Healthcare-Acquired Infection Surveillance in Neurosurgery Patients, Incidence and Microbiology, Five Years of Experience in Two Polish Units.

Introduction: Patients in neurosurgical units are particularly susceptible to healthcare-associated infections (HAI) due to invasive interventions in the central nervous system. Materials and methods: The study was conducted between 2014 and 2019 in neurosurgery units in Poland. The aim of the study was to investigate the epidemiology and microbiology of HAIs and to assess the effectiveness of surveillance conducted in two hospital units. Both hospitals ran (since 2012) the unified prospective system, based on continuous surveillance of HAIs designed and recommended by the European Centre for Disease Prevention and Control (protocol version 4.3) in the Healthcare-Associated Infections Surveillance Network (HAI-Net). In study hospitals, HAIs were detected by the Infection Prevention Control Nurse (IPCN). The surveillance of healthcare infections in hospital A was based mainly on analysis of microbiological reports and telephone communication between the epidemiological nurse and the neurosurgery unit. HAI monitoring in hospital B was an outcome of daily personal communication between the infection prevention and control nurse and patients in the neurosurgery unit (HAI detection at the bedside) and assessment of their health status based on clinical symptoms presented by the patient, epidemiological definitions, microbiological and other diagnostic tests (e.g., imaging studies). In hospital A, HAI monitoring did not involve personal communication with the unit but was rather based on remote analysis of medical documentation found in the hospital database. Results: A total of 12,117 patients were hospitalized. There were 373 HAIs diagnosed, the general incidence rate was 3.1%. In hospital A, the incidence rate was 2.3%, and in hospital B: 4.8%. HAI types detected: pneumonia (PN) (n = 112, 0.9%), (urinary tract infection (UTI) (n = 108, 0.9%), surgical site infection (SSI) (n = 96, 0.8%), bloodstream infection (BSI) (n = 57, 0.5%), gastrointestinal system infection (GI) (n = 13, 0.1%), skin and soft tissue (SST) (n = 9, 0.1%). HAI with invasive devices: 44 ventilator-associated pneumonia (VAP) cases (45.9/1000 pds with ventilator); catheter-associated urinary tract infection (CA-UTI): 105 cases (2.7/1000 pds with catheter); central venous catheter (CVC-BSI): 18 cases (1.9/1000 pds with CVC). The greatest differences between studied units were in the incidence rate of PN (p < 0.001), UTI (p < 0.001), and SSI (p < 0.05). Conclusions: The way HAIs are diagnosed and qualified and the style of work of the infection control team may have a direct impact on the unit epidemiology with the application of epidemiological coefficients. Prospective surveillance run by the infection prevention and control nurse in hospital B could have been associated with better detection of infections expressed in morbidity, especially PN and UTI, and a lower risk of VAP. In hospital A, the lower incidence might have resulted from an inability to detect a UTI or BSI and less supervision of VAP. The present results require further profound research in this respect.

Five-Year Analysis of Surgical Site Infections in Three Orthopaedics and Trauma Wards under HAI-Net from the South of Poland in 2014-2018 Considering the Standardized Infection Ratio.

Background and Objectives: Surgical site infections (SSIs) are the most common healthcare-associated infections (HAIs) in surgical wards. The highest risk of developing SSI is carried by operations involving implants, such as: hip prosthesis (HPRO), knee prosthesis (KPRO), open reduction of fracture (FX), and closed reduction of fracture with internal fixation (CR). Objectives. The objective of the study was to assess the incidence of SSI in patients subjected to HPRO, KPRO, FX, and CR procedures in orthopaedics and trauma wards in 2014-2018 considering risk factors included in the SIR index. Materials and Methods: The study included 6261 patients who were subjected to orthopaedic surgery in 2014-2018. The investigation covered three hospitals with orthopaedics and trauma wards. The research was conducted in the framework of the national HAI surveillance programme according to the methodology of the HAI-Net, ECDC. Results: A total of 6261 surgeries were investigated, of which 111 cases of SSI were detected. The incidence was 1.8%; HPRO (incidence 2.1%, median (Me) surgery duration 90 min, and standardized infection ratio (SIR) above 1 in all units tested); KPRO (incidence 2.0%, Me 103 min, and SIR above 1 for all units tested); FX (incidence 1.9%, Me 70 min, and SIR above 1 for two units tested and below 1 in one unit); CR (incidence 1.0%, Me 55 min, and SIR-not calculated). The etiological agents that were most frequently isolated from patients with SSI were Staphylococcus aureus, coagulase-negative Staphylococcus, and Klebsiella pneumoniae. Conclusions: HPRO, KPRO, and FX operations performed in the studied wards carried a higher risk of developing SSI than that predicted by SIR. SSIs accounted for a significant percentage of the overall infection pool in CR surgeries. Actions should be undertaken to reduce the incidence of SSI in these surgeries. There should be a hospital network which facilitates cooperation in order to better monitor and analyse the incidence of SSI.

Ecosystem-Service Scaling Techniques to Evaluate the Benefits of Marine Debris Removal.

While knowledge of the ecological impacts of marine debris is continually advancing, methods to evaluate the comparative scale of these impacts are less well developed. In the case of costly environmental restoration in marine and coastal environments, quantifying and comparing the ecological impacts of diverse forms of ecosystem injuries can facilitate a more efficient selection of restoration projects. This article proposes evaluating marine debris removal projects in an ecological service equivalency analysis framework that can be used to compare marine debris removal to other types of environmental restoration. Drawing on existing spatial and temporal data with respect to marine debris impacts on habitats and resources, we demonstrate how resource managers and organizations involved in marine debris removal can quantify the ecological service benefits of a removal project and use it to comparatively select between projects based on present value ecological benefits. This valuation can be useful in natural resource damage assessment restoration selection, and for directing limited funds to marine debris removal projects which produce the greatest gains in ecological services. This ecological scaling framework is applied to a seagrass injury case study to demonstrate its application for scaling marine debris removal as compensatory restoration.

Adaptive Hurst-Sensitive Active Queue Management.

An Active Queue Management (AQM) mechanism, recommended by the Internet Engineering Task Force (IETF), increases the efficiency of network transmission. An example of this type of algorithm can be the Random Early Detection (RED) algorithm. The behavior of the RED algorithm strictly depends on the correct selection of its parameters. This selection may be performed automatically depending on the network conditions. The mechanisms that adjust their parameters to the network conditions are called the adaptive ones. The example can be the Adaptive RED (ARED) mechanism, which adjusts its parameters taking into consideration the traffic intensity. In our paper, we propose to use an additional traffic parameter to adjust the AQM parameters-degree of self-similarity-expressed using the Hurst parameter. In our study, we propose the modifications of the well-known AQM algorithms: ARED and fractional order PIαDß and the algorithms based on neural networks that are used to automatically adjust the AQM parameters using the traffic intensity and its degree of self-similarity. We use the Fluid Flow approximation and the discrete event simulation to evaluate the behavior of queues controlled by the proposed adaptive AQM mechanisms and compare the results with those obtained with their basic counterparts. In our experiments, we analyzed the average queue occupancies and packet delays in the communication node. The obtained results show that considering the degree of self-similarity of network traffic in the process of AQM parameters determination enabled us to decrease the average queue occupancy and the number of rejected packets, as well as to reduce the transmission latency.

Is it up to business, governments, or individuals to tackle the marine plastic problem? A hybrid mixed logit approach.

Marine plastic pollution is one of the most talked about environmental issues of our time. While marine plastic pollution generally originates from mismanaged waste from land, waste from ships and fishing gear produce a unique threat to the global seas. Using a choice experiment, we explore preference for a marine debris removal and prevention programme focusing on derelict fishing gear. Additionally, we explore preferences for increasing removal efforts of debris in the North Western Hawaiian Islands. We find overwhelming support for these interventions; however, we find evidence that change, and therefore subsequent action, is strongest for individuals who believe that governments hold the majority of the responsibility for reducing and cleaning plastic pollution in marine environments.

Supervised Learning of Neural Networks for Active Queue Management in the Internet.

The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router's queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.

LiDAR Point Cloud Generation for SLAM Algorithm Evaluation.

With the emerging interest in the autonomous driving level at 4 and 5 comes a necessity to provide accurate and versatile frameworks to evaluate the algorithms used in autonomous vehicles. There is a clear gap in the field of autonomous driving simulators. It covers testing and parameter tuning of a key component of autonomous driving systems, SLAM, frameworks targeting off-road and safety-critical environments. It also includes taking into consideration the non-idealistic nature of the real-life sensors, associated phenomena and measurement errors. We created a LiDAR simulator that delivers accurate 3D point clouds in real time. The point clouds are generated based on the sensor placement and the LiDAR type that can be set using configurable parameters. We evaluate our solution based on comparison of the results using an actual device, Velodyne VLP-16, on real-life tracks and the corresponding simulations. We measure the error values obtained using Google Cartographer SLAM algorithm and the distance between the simulated and real point clouds to verify their accuracy. The results show that our simulation (which incorporates measurement errors and the rolling shutter effect) produces data that can successfully imitate the real-life point clouds. Due to dedicated mechanisms, it is compatible with the Robotic Operating System (ROS) and can be used interchangeably with data from actual sensors, which enables easy testing, SLAM algorithm parameter tuning and deployment.

Diffusion Model of a Non-Integer Order PIγ Controller with TCP/UDP Streams.

In this article, a way to employ the diffusion approximation to model interplay between TCP and UDP flows is presented. In order to control traffic congestion, an environment of IP routers applying AQM (Active Queue Management) algorithms has been introduced. Furthermore, the impact of the fractional controller PIγ and its parameters on the transport protocols is investigated. The controller has been elaborated in accordance with the control theory. The TCP and UDP flows are transmitted simultaneously and are mutually independent. Only the TCP is controlled by the AQM algorithm. Our diffusion model allows a single TCP or UDP flow to start or end at any time, which distinguishes it from those previously described in the literature.

The Incidence of Healthcare-Associated Infections, Their Clinical Forms, and Microbiological Agents in Intensive Care Units in Southern Poland in a Multicentre Study from 2016 to 2019.

INTRODUCTION: Healthcare-associated infections (HAIs) are a serious problem of modern medicine. Patients hospitalized in intensive care units (ICUs) develop HAI significantly more often than patients in other hospital units. MATERIALS AND METHODS: Analysis involved HAIs from three ICUs in southern Poland. The study was conducted in 2016-2019 on the basis of methodology recommended by the Healthcare-Associated Infections Surveillance Network (HAI-Net) and European Centre for Disease Prevention and Control (ECDC). The objective was to analyse HAIs, their clinical forms, and microbiological agents. RESULTS: The study included 3028 patients hospitalized for 26,558 person-days (pds) in ICU. A total of 540 HAIs were detected; incidence per 100 hospitalizations was 17.8%, incidence density per 1000 pds was 20.3. The mortality of patients with HAI was 16%, and in Clostridioidesdifficile infection (CDI), the mortality was 28%. The most common clinical form of HAI was bloodstream infection (BSI): 209 cases (incidence rate 6.9%), followed by pneumonia (PN): 131 (incidence rate 4.3%), and urinary tract infection (UTI): 110 cases (incidence rate 3.6%). The most frequently isolated bacteria were Klebsiella pneumoniae 16.4%, Acinetobacter baumannii 14.4%, Staphylococcus aureus 11.8%, and Escherichia coli 11.4%. CONCLUSIONS: A two-fold higher incidence rate of BSI was detected compared to the average incidence in European countries. BSI of unknown source (BSI-UNK) was predominant. K. pneumoniae and A. baumannii bacteria were the most often isolated microorganisms causing HAI. Infection control based on incidence rate for each type of infection is necessary in ICU to assess the epidemiological situation.

Long-Range Dependent Traffic Classification with Convolutional Neural Networks Based on Hurst Exponent Analysis.

The paper examines the ability of neural networks to classify Internet traffic data in terms of self-similarity expressed by the Hurst exponent. Fractional Gaussian noise is used for the generation of synthetic data for modeling the genuine ones. It is presented that the trained model is capable of classifying the synthetic data obtained from the Pareto distribution and the real traffic data. We present the results of training for different optimizers of the cost function and a different number of convolutional layers in the neural network.

Restoration Scaling Approaches to Addressing Ecological Injury: The Habitat-Based Resource Equivalency Method.

Natural resource trustee agencies must determine how much, and what type of environmental restoration will compensate for injuries to natural resources that result from releases of hazardous substances or oil spills. To fulfill this need, trustees, and other natural resource damage assessment (NRDA) practitioners have relied on a variety of approaches, including habitat equivalency analysis (HEA) and resource equivalency analysis (REA). The purpose of this paper is to introduce the Habitat-Based Resource Equivalency Method (HaBREM), which integrates REA's reproducible injury metrics and population modeling with HEA's comprehensive habitat approach to restoration. HaBREM is intended to evaluate injury and restoration using organisms that use the habitat to represent ecological habitat functions. This paper seeks to expand and refine the use of organism-based metrics (biomass-based REA), providing an opportunity to integrate sublethal injuries to multiple species, as well as the potential to include error rates for injury and restoration parameters. Applied by NRDA practitioners in the appropriate context, this methodology can establish the relationship between benefits of compensatory restoration projects and injuries to plant or animal species within an affected habitat. HaBREM may be most effective where there are appropriate data supporting the linkage between habitat and species gains (particularly regionally specific habitat information), as well as species-specific monitoring data and predictions on the growth, density, productivity (i.e., rate of generation of biomass or individuals), and age distributions of indicator species.