Smooth and safe tram journeys: tram driver perspectives and opportunities using a haptic master controller in a virtual reality environment.

Tram drivers operate a master controller to control the acceleration and braking of the tram. Operation should ensure passenger comfort and safety through smooth tram motion and the avoidance of jerkiness that may cause passengers to fall in the carriage. This work investigates current driver practices and strategies for tram driving in normal operations through interviews and the capacity of a new haptic master controller to support drivers in achieving smooth and safe tram journeys. A haptic feedback algorithm based on viscosity was implemented on the master controller to provide drivers with feedback on the rate at which they were accelerating and braking the tram. This aspect was tested in a virtual tram within a simulated inner city virtual reality environment. Results indicate that the haptic master controller and coupled viscosity feedback algorithm did not increase smoothness of driving during the simulated experiences. Despite this, the drivers indicated a preference for the provision of further haptic information to support driving tasks and the overall journey safety and smoothness. Practitioner Summary: This research comprises two studies. The first investigates strategies currently used by drivers to operate a tram smoothly in order to elicit design requirements for a haptic tram master controller. The second study evaluates the impact of a novel haptic master controller on journey smoothness within a virtual environment.

New Service Virtualisation Approach to Generate the Categorical Fields in the Service Response.

Software services communicate with different requisite services over the computer network to accomplish their tasks. The requisite services may not be readily available to test a specific service. Thus, service virtualisation has been proposed as an industry solution to ensure availability of the interactive behaviour of the requisite services. However, the existing techniques of virtualisation cannot satisfy the required accuracy or time constraints to keep up with the competitive business world. These constraints sacrifices quality and testing coverage, thereby delaying the delivery of software. We proposed a novel technique to improve the accuracy of the existing service virtualisation solutions without sacrificing time. This method generates the service response and predicts categorical fields in virtualised responses, extending existing research with lower complexity and higher accuracy. The proposed service virtualisation approach uses conditional entropy to identify the fields that can be used to drive the value of each categorical field based on the historical messages. Then, it uses joint probability distribution to find the best values for the categorical fields. The experimental evaluation illustrates that the proposed approach can generate responses with the required fields and accurate values for categorical fields over four data sets with stateful nature.

Cognitive Service Virtualisation: A New Machine Learning-Based Virtualisation to Generate Numeric Values.

Continuous delivery has gained increased popularity in industry as a development approach to develop, test, and deploy enhancements to software components in short development cycles. In order for continuous delivery to be effectively adopted, the services that a component depends upon must be readily available to software engineers in order to systematically apply quality assurance techniques. However, this may not always be possible as (i) these requisite services may have limited access and (ii) defects that are introduced in a component under development may cause ripple effects in real deployment environments. Service virtualisation (SV) has been introduced as an approach to address these challenges, but existing approaches to SV still fall short of delivering the required accuracy and/or ease-of-use to virtualise services for adoption in continuous delivery. In this work, we propose a novel machine learning based approach to predict numeric fields in virtualised responses, extending existing research that has provided a way to produce values for categorical fields. The SV approach introduced here uses machine learning techniques to derive values of numeric fields that are based on a variable number of pertinent historic messages. Our empirical evaluation demonstrates that the Cognitive SV approach can produce responses with the appropriate fields and accurately predict values of numeric fields across three data sets, some of them based on stateful protocols.

Building a Relationship between Robot Characteristics and Teleoperation User Interfaces.

The Robot Operating System (ROS) provides roboticists with a standardized and distributed framework for real-time communication between robotic systems using a microkernel environment. This paper looks at how ROS metadata, Unified Robot Description Format (URDF), Semantic Robot Description Format (SRDF), and its message description language, can be used to identify key robot characteristics to inform User Interface (UI) design for the teleoperation of heterogeneous robot teams. Logical relationships between UI components and robot characteristics are defined by a set of relationship rules created using relevant and available information including developer expertise and ROS metadata. This provides a significant opportunity to move towards a rule-driven approach for generating the designs of teleoperation UIs; in particular the reduction of the number of different UI configurations required to teleoperate each individual robot within a heterogeneous robot team. This approach is based on using an underlying rule set identifying robots that can be teleoperated using the same UI configuration due to having the same or similar robot characteristics. Aside from reducing the number of different UI configurations an operator needs to be familiar with, this approach also supports consistency in UI configurations when a teleoperator is periodically switching between different robots. To achieve this aim, a Matlab toolbox is developed providing users with the ability to define rules specifying the relationship between robot characteristics and UI components. Once rules are defined, selections that best describe the characteristics of the robot type within a particular heterogeneous robot team can be made. A main advantage of this approach is that rather than specifying discrete robots comprising the team, the user can specify characteristics of the team more generally allowing the system to deal with slight variations that may occur in the future. In fact, by using the defined relationship rules and characteristic selections, the toolbox can automatically identify a reduced set of UI configurations required to control possible robot team configurations, as opposed to the traditional ad-hoc approach to teleoperation UI design. In the results section, three test cases are presented to demonstrate how the selection of different robot characteristics builds a number of robot characteristic combinations, and how the relationship rules are used to determine a reduced set of required UI configurations needed to control each individual robot in the robot team.

A grid-forming approach utilizing DC bus dynamics for low inertia power systems with HVDC applications.

While power electronic converters, such as voltage source converters (VSCs), are crucial for the operation of converter-dominated renewables and their integration with the electricity grid, their reliance on VSCs can pose a challenge. The limited inertia of these sources can lead to a deterioration of the rate of change of frequency, potentially causing power system stability issues. A grid-forming approach utilizing dc-link dynamics is one of the attractive alternatives to achieve grid synchronization and support grid frequency. Existing grid-forming control schemes, which assume a constant or virtually constant dc source, rely on a fixed physical dc-link capacitor. Nonetheless, the inertia support from such a capacitor is brief, owing to its limited energy storage capability. Consequently, enhancing inertia becomes imperative; otherwise, it may result in an increased rate of change of voltage on the dc side, potentially leading to issues with protection, undesirable interactions, and system instability. This paper proposes a new grid-forming control strategy that considers a virtual capacitor to achieve grid synchronization while simultaneously providing the network with inertia response services during power imbalances. Moreover, including a virtual resistor in the controller effectively attenuates power and dc voltage oscillations. Simulations using Simulink and small signal stability analysis are conducted to validate the efficacy of the proposed controller.

Parameter extraction and selection for a scalable N-type SiC MOSFETs model and characteristic verification along with conventional dc-dc buck converter integration.

Most silicon carbide (SiC) MOSFET models are application-specific. These are already defined by the manufacturers and their parameters are mostly partially accessible due to restrictions. The desired characteristic of any SiC model becomes highly important if an individual wants to visualize the impact of changing intrinsic parameters as well. Also, it requires a model prior knowledge to vary these parameters accordingly. This paper proposes the parameter extraction and its selection for Silicon Carbide (SiC) power N-MOSFET model in a unique way. The extracted parameters are verified through practical implementation with a small-scale high power DC-DC 5 to 2.5 output voltage buck converter using both hardware and software emphasis. The parameters extracted using the proposed method are also tested to verify the static and dynamic characteristics of SiC MOSFET. These parameters include intrinsic, junction and overlapping capacitance. The parameters thus extracted for the SiC MOSFET are analyzed by device performance. This includes input, output transfer characteristics and transient delays under different temperature conditions and loading capabilities. The simulation and experimental results show that the parameters are highly accurate. With its development, researchers will be able to simulate and test any change in intrinsic parameters along with circuit emphasis.

Novel and accurate 3D-Printed surgical guide for mandibular reconstruction with integrated dental implants.

PURPOSE: Patients with mandibular defects due to trauma or infiltrated disease are in a need of functional mandibular implants that will completely restore the function of their lower jaw. One of the most important roles of well-functioning jaw is mastication, a complex mechanism. A conventional approach used in oral and maxillofacial surgery accomplish this aim via two major surgeries- mandibular reconstruction and surgical placement of dental implants. Little work has been done on combining the two surgeries into with using Additive Manufacturing (AM) and digital planning. MATERIAL AND METHODS: This case study offers a mandibular implant design solution with pre-positioned dental implants that can reduce the requirement to only one surgery. Mandibular implant was designed using 3-Matic software (Materialise, Belgium). Positions for dental implants were restoratively-driven and planned on the designed mandibular implant in Blue Sky Plan 4 software (Blue Sky Bio, USA) and placed prior to mandibular reconstruction using a 3D-printed surgical guide. Finite Element Analysis (FEA) was used to evaluate the mechanical behaviour of the 3D-printed surgical guide during dental implant placement. RESULTS: The surgical guide was fabricated using SLA and stress distribution was evaluated in ANSYS Workbench FEM software (Ansys Inc Swanson, Houston, USA). Results showed that the designed surgical guide can withstand the forces occurring during the surgery. CONCLUSION: The proposed method substantially reduces the surgical procedure and recovery time, increases the accuracy, and allows for a predictable restorative solution that can be visualised from the beginning.

Review on the cooling potential of green roofs in different climates.

The combined trends of urban heat island (UHI) intensification and global warming led to an increased tendency towards on the greening of cities as a tool for UHI mitigation. Our study examines the range of research approaches and findings regarding the role of green roofs in mitigating urban heat and enhancing human comfort. This review provides an overview of 89 studies conducted in three main climate types (hot-humid, temperate, and dry), from 2000 till 2020. All of the reviewed studies confirm the cooling effect of green roofs and its contribution to reduced heat island intensity regardless of the background climatic condition. However, dry climate has the highest (3 °C) median cooling effect of green roofs among all the climates investigated. Hot-humid climate presents the lowest cooling potential (median = 1 °C) of green roofs among all the climate types. Moreover, green roofs contribute a median surface temperature reduction of 30 °C in hot-humid cities. This value is relatively low for temperate climates (28 °C). Notably, no study has examined the impact of green roofs on surface temperature reduction in dry climates. This review can benefit urban planners and various stakeholders.

Waste to energy conversion for a sustainable future.

Air pollution, climate change, and plastic waste are three contemporary global concerns. Air pollutants affect the lungs, green gases trap heat radiation, and plastic waste contaminates the marine food chain. Two-thirds of climate change and air pollution drivers are emitted in the process of burning fossil fuels. Pollutants settle in months, green gases take centuries, and plastics take thousands of years. The most polluted regions on the planet are also the ones that are greatly affected by climate change. Air pollutants grow in most climate-change affected areas, contributing to the greenhouse effect. Smog affects local and regional transboundary countries. The biggest greenhouse gas (GHG) emitters may not be the worst-hit victims because wind and water flow distribute green gases and plastic waste worldwide. The major polluters are often rich and developed countries, and the worst affected countries are the underdeveloped poor communities. Technologically advanced countries may help the developing countries in research into removing particulate matter, green gases, and plastic waste. Intergovernmental Panel on Climate Change (IPCC) and Paris Accord have emphasized on immeasurable efforts to encourage the conversion of pollution, green gases, and plastic waste into energy. Conversion of CO2 into petrol, GHG gases into chemicals, biowaste into biofuels, plastic waste into building bricks, and concrete waste into construction materials fosters a circular economy. This work reviews existing waste to power, energy, and value-added product conversion technologies.

Design and Fabrication of Implants for Mandibular and Craniofacial Defects Using Different Medical-Additive Manufacturing Technologies: A Review.

Mandibular and craniofacial bone defects can be caused by trauma, inflammatory disease, and benign or malignant tumors. Patients with bone defects suffer from problems with aesthetics, speech, and mastication, resulting in the need for implants. Conventional methods do not always provide satisfactory results. Most of the techniques proposed by researchers in the field of biomedical engineering use reverse engineering, computer-aided design (CAD), and additive manufacturing (AM), whose implementation can improve the outcomes of reconstructive surgeries. Several literature reviews on this particular topic have been conducted. However, they provide mostly overviews of AM technologies for general biomedical devices. This paper summarizes the use of existing medical AM techniques for the design and fabrication of mandibular and craniofacial implants, and then discusses their advantages and disadvantages in terms of accuracy, costs, energy consumption, and production rate. The aim of this study is to present a comparative review of the most commonly used AM technologies to aid researchers in selecting the best possible AM technologies for medical use. Studies included in this review contain CAD designs of mandibular or cranial implants, as well as their fabrication using AM technologies. Special attention is paid to PolyJet technology, because of its high accuracy, and economical efficiency.

Verification of a bioclimatic modeling system in a growing suburb in Melbourne.

Urban climate knowledge has been increasingly integrated into urban design and planning practices. Numerical modeling systems, such as climatic and bioclimatic tools, are currently more popular than onsite field measurements. This higher popularity is mainly due to the complicated interactions in 3D urban environments and the spatial distribution of various climatic parameters that cannot be captured thoroughly via on-site measurements alone. Such modeling systems also offer better solutions to overcome the nonlinearity of urban climate in forecasting different "what if scenarios." This paper provides an overview of different types of climatic and bioclimatic modeling systems and presents their main benefits and shortcomings. In the second part of this study, one of the most commonly used tools in urban climate studies, namely, ENVI-met, was selected, and its reliability in different contexts was investigated by reviewing past researches. The applicability of ENVI-met in accurately simulating the influence of future urban growth on one of the fastest growing suburbs in Melbourne, was tested by conducting a sensitivity analysis on inputs and control parameters, backed up with a series of field measurements in selected points. RMSE value was calculated for different runs of the initial ENVI-met model with adjusted control parameters (e.g., factor of short-wave adjustment, initial air temperature, relative humidity, roughness length, wind speed, albedo of walls, and albedo of roofs). The model achieved the optimum performance by altering the short-wave adjustment factor from 0.5 to 1; therefore, ENVI-met was considered a reliable tool for relative comparison of urban dynamics. The findings of this study not only help planners select the most practical modeling systems that address project objectives but also educate them on limitations associated with using ENVI-met.

Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability.

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.