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SESSION TITLE: Clinical Prediction and Diagnosis of OSA

AbstractThe autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

SESSION TITLE: Clinical Prediction and Diagnosis of OSA

The goal of the ambient intelligence system is not only to enhance the way people communicate with the surrounding environment but also to advance safety measures and enrich human lives. In this paper, we introduce an integrated ambient intelligence system (IAmIS) to perceive the presence of people, identify them, determine their locations, and provide suitable interaction with them. The proposed framework can be applied in various application domains such as a smart house, authorisation, surveillance, crime prevention, and many others. The proposed system has five components: body detection and tracking, face recognition, controller, monitor system, and interaction modules. The system deploys RGB cameras and Kinect depth sensors to monitor human activity. The developed system is designed to be fast and reliable for indoor environments. The proposed IAmIS can interact directly with the environment or communicate with humans acting on the environment. Thus, the system behaves as an intelligent agent. The system has been deployed in our research lab and can recognise lab members and guests to the lab as well as track their movements and have interactions with them depending upon their identity and location within the lab.

Police social workers are crucial components of police departments when individuals or communities experience crises. They perform essential tasks, including well-being checks, crisis intervention, de-escalation, mediation, safety planning, referrals to community services, and other preventative measures to stabilize clients in crisis. The chapter will define police social work and give the reader insight into the stabilization approaches used by police social workers to prepare clients for their next level of care. The chapter begins with a brief history of the evolution of police social workers within the context of public policy and their most recent call to action to address mental health crises. The authors utilize a multi-tier approach to highlight stabilization approaches used by police social workers with a focus on empowering individuals, families, and communities to collaborate on solutions. The chapter uses case scenarios drawn from the experiences of police social workers and interns to demonstrate stabilization approaches. A racial equity, culturally responsive, and trauma-informed lens informs the approach to stabilizing client systems in law enforcement settings. © 2023, IGI Global. All rights reserved.

Background In 2016, the Centers for Disease Control found that more than 1.5 million people develop sepsis each year and about 250,000 Americans die from it. Early identification and treatment of sepsis can decrease mortality and morbidity, yet studies have shown student nurses are not prepared to rescue deteriorating patients. Method The purpose of this pilot study was to create and test a response to rescue simulation for use with undergraduate nursing students. The simulation depicted a patient deteriorating from sepsis. The Martinez Sepsis Competency Evaluation Tool (MSCET) developed to rate student behaviors during the simulation. Promoting Excellence and Reflective Learning in Simulation (PEARLS) debriefing model was used post simulation. Results The overall content validity of the MSCET was 0.88. Each item that scored a I-CVI of 0.78 or less were revised. The total percentage of behaviors met was 68 %. The inter-rater reliability of the MSCET conciseness was 0.47 (X = 67.508, df = 48, p ≤ .05). Conclusion The results indicate the simulation based experience was effective in preparing students to care for patients with early signs of sepsis. Students were complimentary about the experience, and preliminary data on the MSCET psychometrics were positive. Limitations of the study and recommendations for further revision of the simulation were made.

There is still an urgent need of finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via developing a mathematical relationship between model variables is urgently needed. In this paper, we explore the use of GP to develop a software cost estimation model utilizing the effect of both the developed line of code and the used methodology during the development. An application of estimating the effort for some NASA software projects is introduced. The performance of the developed Genetic Programming (GP) based model was tested and compared to known models in the literature. The developed GP model was able to provide good estimation capabilities compared to other models.

Greater informational masking is observed when the target and masker speech are more perceptually similar. Fundamental frequency (f0) contour, or the dynamic movement of f0, is thought to provide cues for segregating target speech presented in a speech masker. Most of the data demonstrating this effect have been collected using digitally modified stimuli. Less work has been done exploring the role of f0 contour for speech-in-speech recognition when all of the stimuli have been produced naturally. The goal of this project was to explore the importance of target and masker f0 contour similarity by manipulating the speaking style of talkers producing the target and masker speech streams. Sentence recognition thresholds were evaluated for target and masker speech that was produced with either flat, normal, or exaggerated speaking styles; performance was also measured in speech spectrum shaped noise and for conditions in which the stimuli were processed through an ideal-binary mask. Results confirmed that similarities in f0 contour depth elevated speech-in-speech recognition thresholds; however, when the target and masker had similar contour depths, targets with normal f0 contours were more resistant to masking than targets with flat or exaggerated contours. Differences in energetic masking across stimuli cannot account for these results.