This document presents an algorithm for a non-obtrusive recognition of Sleep/Wake states using signals derived from ECG, respiration, and body movement captured while lying in a bed. As a core mathematical base of system data analytics, multinomial logistic regression techniques were chosen. Derived parameters of the three signals are used as the input for the proposed method. The overall achieved accuracy rate is 84% for Wake/Sleep stages, with Cohen’s kappa value 0.46. The presented algorithm should support experts in analyzing sleep quality in more detail. The results confirm the potential of this method and disclose several ways for its improvement.

The 16 articles in this special section examine both licensed and unlicensed spectrum for 5G/B5G wireless networks. The incredible increase in connected appliances and downloaded applications has pushed mobile operators to the limits of their licensed spectrum bands. This has triggered the idea of evolving the current radio access network to use the underutilized unlicensed spectrum to extend spectrum resources beyond current usage charts. This mode of cellular access has raised a lot of questions about use cases, enabling technologies, and fairness to other native unlicensed users, such as WiFi. Nevertheless, unlicensed access is being accepted as one of the most significant solutions to improve the resource availability and system scalability in future fifth generation (5G)/beyond 5G (B5G) networks.

Background: Preclinical ultrasound (US) and contrast-enhanced ultrasound (CEUS) imaging have long been used in oncology to noninvasively measure tumor volume and vascularity. While the value of preclinical US has been repeatedly demonstrated, these modalities are not without several key limitations that make them unattractive to cancer researchers, including: high user-variability, low throughput, and limited imaging field-of-view (FOV). Herein, we present a novel robotic preclinical US/CEUS system that addresses these limitations and demonstrates its use in evaluating tumors in 3D in a rodent model. Methods: The imaging system was designed to allow seamless whole-body 3D imaging, which requires rodents to be imaged without physical contact between the US transducer and the animal. To achieve this, a custom dual-element transducer was mounted on a robotic carriage, submerged in a hydrocarbon fluid, and the reservoir sealed with an acoustically transmissive top platform. Eight NOD/scid/gamma (NSG) female mice were injected subcutaneously in the flank with 8×109 786-O human clear-cell renal cell carcinoma (ccRCC) cells. Weekly imaging commenced after tumors reached a size of 150 mm3 and continued until tumors reached a maximum size of 1 cm3 (∼4-5 weeks). An additional six nude athymic female mice were injected subcutaneously in the flank with 7 × 105 SVR angiosarcoma cells to perform an inter-operator variability study. Imaging consisted of 3D B-mode (conventional ultrasound) of the whole abdomen ( Results: Wide-field US images reconstructed from 3D volumetric data showed superior FOV over conventional US. Several anatomical landmarks could be identified within each image surrounding the tumor, including the liver, small intestines, bladder, and inguinal lymph nodes. Tumor boundaries were clearly delineated in both B-mode and BVD images, with BVD images showing heterogeneous microvessel density at later timepoints suggesting tumor necrosis. Excellent agreement was measured for both inter-reader and inter-operator experiments, with alpha coefficients of 0.914 (95% CI: 0.824-0.948) and 0.959 (0.911-0.981), respectively. Conclusion: We have demonstrated a novel preclinical US imaging system that can accurately and consistently evaluate tumors in rodent models. The system leverages cost-effective robotic technology, and a new scanning paradigm that allows for easy and reproducible data acquisition to enable wide-field, 3D, multi-parametric ultrasound imaging. Note: This abstract was not presented at the meeting. Citation Format: Tomasz Czernuszewicz, Virginie Papadopoulou, Juan D. Rojas, Rajalekha Rajamahendiran, Jonathan Perdomo, James Butler, Max Harlacher, Graeme O9Connell, Dzenan Zukic, Paul A. Dayton, Stephen Aylward, Ryan C. Gessner. A preclinical ultrasound platform for widefield 3D imaging of rodent tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1955.

Abstract Motivation Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data. Results We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings. Availability and implementation The source code for Hintra is available at https://github.com/sahandk/HINTRA.

Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts’ judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.

This paper presents a method for distributed generation (DG) allocation in low voltage distribution network based on the total annual energy loss reduction and Artificial Neural Network (ANN). The proposed method is applied to the PV solar based DG allocation problem in the low voltage distribution network using realistic network data and measurements. This research is motivated by numerous realistic issues faced by the Distribution System Operator in the area of DG planning. The main objective of this work is to develop, test and validate a robust method for DG allocation which can be used in practical problems without the need for extensive system modelling and load flow analysis. The results confirm the importance of appropriate DG planning and show that the proposed method can be used as a promising tool for efficient and effective DG allocation in low voltage distribution network.

A term systems of systems (SoS) refers to a setup in which a number of independent systems collaborate to create a value that each of them is unable to achieve independently. Complexity of a SoS structure is higher compared to its constitute systems that brings challenges in analyzing its critical properties such as security. An SoS can be seen as a set of connected systems or services that needs to be adequately protected. Communication between such systems or services can be considered as a service itself, and it is the paramount for establishment of a SoS as it enables connections, dependencies, and a cooperation. Given that reliable and predictable communication contributes directly to a correct functioning of an SoS, communication as a service is one of the main assets to consider. Protecting it from malicious adversaries should be one of the highest priorities within SoS design and operation. This study aims to investigate the attack propagation problem in terms of service-guarantees through the decomposition into sub-services enriched with preconditions and postconditions at the service levels. Such analysis is required as a prerequisite for an efficient SoS risk assessment at the design stage of the SoS development life cycle to protect it from possibly high impact attacks capable of affecting safety of systems and humans using the system.

Real-time adaptive systems are complex systems capable to adapt their behavior to changing conditions in the environment, and/or internal state changes. Highly dynamic and possibly unpredictable environments, and uncertain operating conditions call for new paradigms of software design, and run-time adaptation mechanisms, to overcome the lack of knowledge at design time. Main application areas include vehicles or robots that need to collaborate to achieve a common task, e.g., minimize fuel consumption, moving objects at a construction site, or performing a set of operations in a factory. Moreover, these vehicles or robots need to interact and possibly collaborate with humans in a safe way, e.g., avoiding accidents or collisions, and prevent hazardous situations that may harm humans and/or machines. % This paper proposes a framework for developing safe and secure adaptive collaborative systems, with run-time guarantees. To enable this, our focus is on requirement engineering and safety assurance techniques to capture the specific safety and security properties for the collaborative system, and to provide an assurance case guaranteeing that the system is sufficiently safe. Moreover, the paper proposes an architecture and behavioral models to analyze the requirements at run-time. Finally, we design a suitable deployment platform to perform the run-time analysis and planning while guaranteeing the real-time constraints.

Poor adherence to medical recommendations is a well-recognized catalyst for public health consequences worldwide. The literature highlights health consciousness as a likely antecedent to patient–physician trust, which in turn promotes medical adherence. Nevertheless, principles of patient-centered care suggest that patient perceptions of their doctor’s appraisal of their emotions may influence the path between trust and medical adherence. Accordingly, this study tested the mediating role of patient–physician trust in the relation between health consciousness and medical adherence and assessed whether patient ratings of their doctor’s appraisal of their own and their patients’ emotions moderated the mediated relation. Data were collected via self-report questionnaires from two culturally and economically diverse countries: Bosnia-Herzegovina (N = 262) and the United States (N = 314). Participants were young, healthy adults who visited their primary care physician in the past year. The study employed confirmatory factor analysis, mediation, and moderated mediation analyses. The results indicate that health consciousness positively related to patient–physician trust, which was in turn related to higher medical adherence and which mediated 28% of the total effect of health consciousness. Nevertheless, among patients who rated their physicians to have low appraisal for their patients’ emotions but high appraisal for their own emotions, the path from trust to adherence was not significant. These results highlight the importance of promoting health consciousness among young individuals, all while training practitioners to be attuned to their patients’ emotions and circumstances above their own. However, additional findings indicate that the interrelation between doctors’ emotional attributes and adherence is not necessarily one directional and warrants further investigation.

Resistive pressure sensors has become popular and used in different applications. The usage of the pressure sensors in designing wearable solutions requests flexible and light materials. However, these materials are exhibiting challenging behavior in respect to precision, sensitivity and repeatability of measurement. In this paper we present a set of experiments demonstrating typical problems, and also we discuss the causes and possible remedies. The experiments were conducted with pressure sensors implemented using the VelostatTM material and Arduino platform for acquisition of the measurements.

The class of even-hole-free graphs is very similar to the class of perfect graphs, and was indeed a cornerstone in the tools leading to the proof of the Strong Perfect Graph Theorem. However, the complexity of computing a maximum independent set (MIS) is a long-standing open question in even-hole-free graphs. From the hardness point of view, MIS is W[1]-hard in the class of graphs without induced 4-cycle (when parameterized by the solution size). Halfway of these, we show in this paper that MIS is FPT when parameterized by the solution size in the class of even-hole-free graphs. The main idea is to apply twice the well-known technique of augmenting graphs to extend some initial independent set.

Many companies own a significant number of vehicles. To ensure the undisturbed company workflow, all vehicles have to be tracked. The standard way of vehicle tracking is via a GPS device. Sometimes, GPS devices are sending fallacious data to the server. That data can cause significant errors in daily reports or in the vehicle route preview. This paper describes an efficient technique for finding different types of anomalies in GPS data. The paper describes a connection between finding a QRS complex in ECG signal and anomalies in GPS data. The algorithm is implemented and used as a part of the GPS tracking system that is used by distribution companies in Bosnia and Herzegovina.