Hospital Greenspaces and the Impacts on Wayfinding and Spatial Experience: An Explorative Experiment Through Immersive Virtual Environment (IVE) Techniques

Background: Navigating large hospitals can be very challenging due to the functional complexity as well as the evolving changes and expansions of such facilities. Hospital wayfinding issues could lead to stress, negative mood, and poor healthcare experience among patients, staff, and family members. Objectives: A survey-embedded experiment was conducted using immersive virtual environment (IVE) techniques to explore people’s wayfinding performance and their mood and spatial experience in hospital circulation spaces with or without visible greenspaces. Methods: Seventy-four participants were randomly assigned to either group to complete wayfinding tasks in a timed session. Participants’ wayfinding performances were interpreted using several indicators, including task completion, duration, walking distance, stop, sign-viewing, and route selection. Participants’ mood states and perceived environmental attractiveness and atmosphere were surveyed; their perceived levels of presence in the IVE hospitals were also reported. Results: The results revealed that participants performed better on high complexity wayfinding tasks in the IVE hospital with visible greenspaces, as indicated by less time consumed and shorter walking distance to find the correct destination, less frequent stops and sign viewing, and more efficient route selection. Participants also experienced enhanced mood states and favorable spatial experience and perceived aesthetics in the IVE hospital with visible greenspaces than the same environment without window views. IVE techniques could be an efficient tool to supplement environment-behavior studies with certain conditions noted. Conclusions: Hospital greenspaces located at key decision points could serve as landmarks that positively attract people’s attention, aid wayfinding, and improve their navigational experience.

ROUTE SELECTION METHOD FOR HIGH-DENSITY WIRELESS IOT NETWORK

Приводятся результаты исследования зависимости скорости передачи данных (ПД) от параметров маршрута в беспроводной сети интернета вещей. Предложен метод выбора количества и позиций узлов для организации маршрута в сети. Метод учитывает влияние расстояния между узлами маршрута и их количества на задержку доставки данных и скорость ПД по маршруту. Метод позволяет получить выигрыш в достижимой скорости ПД по сравнению с методами выбора маршрута по критерию количества транзитных узлов. The article presents theresults ofthe studyof thedependence of the data transfer rate on the route parameters in wireless Internet of Things (loT) networks. A method for choosing the number and positions of nodes for organizing a route in the loT network is proposed. The method takes into account the influence of the distance between the nodes of the route and their number on the delay in data delivery and the speed of data transmission along the route. The method allows one to obtain a gain in the attainable data transfer rate in comparison with methods of choosing a route based on the criterion of the number of transit nodes.

Innate heuristics and fast learning support escape route selection in mice

When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to innately threatening stimuli in less than 250 milliseconds [1] and, in simple environments, use spatial memory to quickly escape to shelter [2,3]. Natural habitats, however, often offer multiple routes to safety which animals must rapidly identify and choose from to maximize the chances of survival [4]. This is challenging because while rodents can learn to navigate complex mazes to obtain rewards [5,6], learning the value of different routes through trial-and-error during escape from threat would likely be deadly. Here we have investigated how mice learn to choose between different escape routes to shelter. By using environments with paths to shelter of varying length and geometry we find that mice prefer options that minimize both path distance and path angle relative to the shelter. This choice strategy is already present during the first threat encounter and after only ~10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic is used to assign threat survival value to alternative paths after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modelling of different classes of reinforcement learning agents shows that the observed behavior can be replicated by model-based agents acting in an environment where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. They further suggest that integrating priors acquired through evolution with knowledge learned from experience supports adaptation to changing environments while minimizing the need for trial-and-error when the errors are very costly.

MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication

With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes.

From Coarse to Fine: An Augmented Reality Based Dynamic Inspection Method for Visualized Railway Routing of Freight Cars

It is a challenge for the dynamic inspection of railway route for freight car transporting cargo that out-of-gauge. One possible way is using the inspection frame installed in the inspection train to simulate the whole procedure for cargo transportation, which costs a lot of manpower and material resources as well as time. To overcome the above problem, this paper proposes an augmented reality (AR) based dynamic inspection method for visualized railway routing of freight car with out-of-gauge. First, the envelope model of the dynamic moving train with out-of-gauge cargo is generated by using the orbital spectrum of the railway, and the envelope model is matched with a piece of homemade calibration equipment located on the position of the railway that needs to be inspected. Then, the structure from motion (SFM) algorithm is used to reconstruct the environment where the virtual envelope model occludes the buildings or equipment along the railway. Finally, the distance function is adopted to calculate the distance between the obstacle and the envelope of the freight car with out-of-gauge, determining whether the freight car can pass a certain line. The experimental results show that the proposed method performs well for the route selection of out-of-gauge cargo transportation with low cost, high precision, and high efficiency. Moreover, the digital data of the environments along the railway and the envelope of the freight car can be reused, which will increase the digitalization and intelligence for route selection of out-of-gauge cargo transportation.