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A Mobility-as-a-Service (MaaS) concept, UbiGo, was implemented in Gothenburg, Sweden, and used for a 6-month period by 195 individuals in 83 households. Four participant subgroups were identified: Car shedders, Car accessors, Simplifiers, and Economizers. A qualitative analysis revealed that the subgroups had different reasons to join the service and different expectations of the change that would occur on the basis of the altered preconditions offered by the service. Previous car users reduced their use of private car and increased their use of public transport and active modes. Participants who did not have access to a privately-owned car but thought they needed one discovered that they managed well without. Other participants were reinforced in their existing behaviors but in ways they did not envisage, depending on which goals they had at the outset of the trial. Overall, the participants were also satisfied with the service, as well as with stated changes and non-changes, even if this in some cases meant more planning. Based on the empirical findings it could be argued that a service approach, such as UbiGo, has the potential to reduce the need for private car ownership, and enable people to change their mode choices and travel patterns. The potential relies however on a number of specific features of the service of which flexibility and a need- rather than a mode-based approach are key features.

As timely information about a project’s state is key for management, we developed a data toolchain to support the monitoring of a project’s progress. By extending the Measurify framework, which is dedicated to efficiently building measurement-rich applications on MongoDB, we were able to make the process of setting up the reporting tool just a matter of editing a couple of .json configuration files that specify the names and data format of the project’s progress/performance indicators. Since the quantity of data to be provided at each reporting period is potentially overwhelming, some level of automation in the extraction of the indicator values is essential. To this end, it is important to make sure that most, if not all, of the quantities to be reported can be automatically extracted from the experiment data files actually used in the project. The originating use case for the toolchain is a collaborative research project on driving automation. As data representing the project’s state, 330+ numerical indicators were identified. According to the project’s pre-test experience, the tool is effective in supporting the preparation of periodic progress reports that extensively exploit the actual project data (i.e., obtained from the sensors—real or virtual—deployed for the project). While the presented use case concerns the automotive industry, we have taken care that the design choices (particularly, the definition of the resources exposed by the Application Programming Interfaces, APIs) abstract the requirements, with an aim to guarantee effectiveness in virtually any application context.

The research problem concerning oils used for lubricating piston combustion engines is still very current and important. The proper selection of oil and its properties have a significant impact on engine reliability and durability, their efficiency, effective operating parameters, fuel consumption, environmental impact, and the proper operation of the turbocharger and exhaust system. The work concerned determining the effect of temperature and operating time on the dynamic viscosity of oils: mineral, semi-synthetic, and synthetic, used in compression-ignition engines (diesel engines). Dynamic viscosity tests were conducted for new oils, after a mileage of seven thousand kilometers, and after a mileage of fifteen thousand kilometers. The range of temperature measurement conditions used was from 0 to 50 °C and the shear transmission rate was 1000 s−1. This range allows the oil to be preserved at low and medium temperatures, which are crucial for engine operation during start-up and short operating cycles. As the conducted studies showed, both temperature and operating time have a very large influence on the dynamic viscosity of oils. It was demonstrated that as the operating time of the oils in the engine increased, their dynamic viscosity decreased, and increasing the viscosity measurement temperature results in smaller absolute changes in it.

Tribological tests in real conditions enable obtaining full data on the life of interacting machine parts. This article presents the results of operational tests on the elements of the support ring guidance system in a vertical ball-race mill. The guide and active armour operate under abrasive wear conditions with moderate-impact loads. The wear resistance of elements with overlay welding layers deposited with flux cored wire with a structure of high-alloy chrome cast iron and with a coating flame-sprayed with nickel-based powder was compared. The wear intensity of the overlay weld deposits was much lower than that of the sprayed coatings. The scope of this study also included the analysis of the chemical and phase composition, macro- and microscopic metallographic examinations, and the measurement of the hardness of the deposited layers and coatings.

The unmanned aerial vehicle (UAV) swarm plays an increasingly important role in the modern battlefield, and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm. Due to the high cost and long duration of operational tests, it is essential to plan the test in advance. To solve the problem of planning UAV swarm operational test, this study considers the multi-stage feature of a UAV swarm mission, composed of launch, flight and combat stages, and proposes a method to find test plans that can maximize mission reliability. Therefore, a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission. A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans. This study first constructs a mission reliability model for the UAV swarm in the combat stage. Then, the launch stage and flight stage are integrated to develop a complete PMS (Phased Mission Systems) reliability model. Finally, the Binary Decision Diagrams (BDD) and Multi Objective Quantum Particle Swarm Optimization (MOQPSO) methods are proposed to solve the model. The optimal plans considering both reliability and cost are obtained. The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

Tests for autonomous vehicles (AVs) and vehicle components are conducted in various ways in a laboratory, a virtual world, and a proving ground. However, since the aforementioned evaluation has limitations for reproducing scenarios of specific edge cases and unintended situations, Field Operational Test (FOT) for autonomous vehicles on the actual road is still necessary. Due to the higher automation levels of autonomous vehicles should be able to drive in more diverse environments without the intervention of the driver, FOT is particularly essential. In this study, we built D-Live, an autonomous vehicle FOT platform in Daegu metropolitan city, to evaluate self-driving in urban environments. We also developed an analysis system to evaluate the performance of autonomous vehicles and automotive sensors. The D-Live platform can evaluate autonomous vehicles by reflecting real-time driving environment changes, weather, and road conditions. In addition, the D-Live platform can quantitatively evaluate the FOT result by processing the data collected from autonomous vehicles and roadside sensors. The processed data is analyzed using the predefined Operational Design Domain (ODD), Object and Event Detection and Response (OEDR), and use cases. This paper introduces the D-Live platform to evaluate autonomous vehicles in urban areas and FOT evaluation methods.

New polymer thermal interface materials (TIM) with high thermal conductivity values are very interesting for high-end microelectronics. Nevertheless, most polymer composite materials do not have desirable properties due to the isolating distribution of filler particles in the matrix. It is assumed that thermal greases with hybrid fillers can form a densely packed percolate structure, leading to considerable thermal conductivity enhancement. The results demonstrate that the addition of a second filler allows the thermal conductivity of the specimen and the volume fraction to increase, which provides the formation of various packaging types within composite material production. The highest thermal conductivity value, 3.02 W/(m K), is achieved for the grease AlN:C (1:2 mass ratio). The simulation and operational bench test of a CPU under thermal loads show a considerable working temperature decrease for AlN and AlN:Al (4:1 mass ratio) greases to 46°C and 47°C, respectively, 29.2% lower than for greases with a graphite or silicon carbide filler.

As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles.

Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources.

In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed-integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure-time distribution is established. The joint posterior distribution of regression coefficients is derived using Bayesian theory, which are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments.