Road Trials Research Articles

Associations of patient‐specific resting state variability with CSF and clinical biomarkers: Data from Alzheimer’s disease clinical trials

AbstractBackgroundChanges of brain functional connectivity assessed using MRI in patients with Alzheimer’s disease (AD) have been previously reported. However, factors contributing to the variability among patients have not been sufficiently investigated. Here, we investigated the association of patient‐specific resting state parameters with CSF markers and clinical measures.MethodWe analyzed the baseline rs‐fMRI of 158 patients (87 female, age 50‐89) with mild AD collected in the Marguerite RoAD trial of gantenerumab in mild AD (NCT02051608) and 132 cognitively unimpaired adults (88 female, age 56‐90) from ADNI. We investigated the within‐ and between‐subjects’ variability in five brain networks (Sensory Motor, Cerebellar, Visual, Default Mode and Cognitive Control), inferred from the two groups using a Bayesian probabilistic model (PROFUMO, Harrison‐Smith‐2015; Harrison‐Woolrich‐2020). We computed spatial maps, BOLD amplitude changes, and spatial (temporal) functional connectivity between‐ and within‐networks for each subject. Those metrics were compared between groups using T‐tests. In AD patients we used linear models to investigate associations of the extracted metrics with CSF markers (Amyloid Beta 42, p‐Tau217, pTAU/Abeta 1‐42 ratio and Neurogranin) and cognitive scores (ADAS‐Cog 13, MMSE and ADCS‐ADL), while controlling for age, sex and MRI‐scanner type. All results were corrected for multiplicity using FDR.ResultOn average the signal amplitude was lower in the Cognitive Control and the Sensory Motor (p < 0.0001) networks in AD patients compared to cognitively unimpaired adults. Interestingly, in AD patients the lower amplitude observed in the cognitive control network was associated with cognitive decline (β = 0.02, p = 0.01). Moreover, significant associations were observed in AD patients between lower signal amplitude and higher CSF levels of ABeta 42 (β = ‐0.02, p = 0.036) and cognitive decline (β = 0.02, p = 0.008) (Fig.2). AD group showed on average reduced functional connectivity compared to the healthy one. Furthermore, the within‐AD patients’ analysis showed weak between‐ and strong within‐networks connectivity (T‐test, q < 0.05, Fig.1).ConclusionIn patients with mild AD, the signal amplitude and connectivity in the cognitive control and DMN networks were associated with both CSF level of ABeta42 and cognitive scores.

U Bolt Failure Analysis

U bolt failures occur commonly in commercial vehicles. U bolt failures occur at various stages. 17 U bolts failures were analyzed to have a detailed understanding of the issues. Failures commonly occur during assembly, ORT (On Road Trial) validations and in field. In general, the failures can be classified as assembly failures and during service. Analysis revealed that assembly failures are torsional and ductile failures & field or ORT failures are fatigue failures.

Pavement Marking Incorporated With Binary Code for Accurate Localization of Autonomous Vehicles

Accurate localization is a critically important issue for autonomous vehicles as it is closely related to the safety and efficiency of autonomous driving. However, current technologies for autonomous vehicle localization face many challenges. To provide accurate and robust localization services to autonomous vehicles, we propose a novel solution by employing a newly designed pavement marking. This marking operates on color contrast, temperature contrast, and binary code with some special features. We also trained and customized an object detector based on a deep learning model: YOLOv5, and integrated it with the decoding algorithm. The localization system is capable of running at a steady frame rate of more than 50 FPS. Road trials up to 80 km/h were conducted, and satisfactory results confirmed the feasibility and robustness of the localization system. Specifically, with a common onboard camera, more than four continuous frames can be detected and decoded correctly when the speed is slower than 30 km/h. At least one frame can be detected and decoded correctly at a higher speed (i.e., 30– 50 km/h). With a high-speed camera, more than 18 frames can be detected and decoded even at 80 km/h. The findings suggest that the specially designed road marking and associated algorithms can provide a viable and economical option for accurate localization of autonomous vehicles. The performance of the system has potentials for further improvement by using better hardware such as faster CPUs, GPUs, and thermal imaging techniques.

Boar Semen Shipping for Artificial Insemination: Current Status and Analysis of Transport Conditions with a Major Focus on Vibration Emissions.

Simple SummaryLiquid preserved boar semen is a perishable product, and many environmental influences can affect sperm quality during transport from the boar station to the sow farm. Previous studies have shown that vibration emissions have a negative effect on spermatozoa, however, there has been no documentation of the vibrations spermatozoa are exposed to during transport. To answer this question, several breeding companies worldwide were interviewed about their delivery process. As it turns out, environmental influences are only rarely recorded, and vibrations are not monitored at all. To record and to evaluate vibrations that occur during transport, we have developed a measurement system and investigated vibration intensities in standardized road trials. We were able to show that both speed and road surface significantly influence the produced vibrations. Furthermore, the occurrence of the vibrations on a reference delivery from a German boar stud was studied. In the future, it is recommended that in order to avoid negative influences during transport, the transport of boar semen should be permanently monitored.In the modern pig reproduction system, artificial insemination (AI) doses are delivered from AI centers to sow farms via logistics vehicles. In this study, six breeding companies in three countries (Brazil, Germany, and the USA) were interviewed about their delivery process. It was found that there is currently no comprehensive monitoring system for the delivery of semen. The entire process “shipping of boar semen” was documented using Business Process Model and Notation (BPMN). Although it is not currently known which vibrations occur at all, it is suspected that vibration emissions affect the quality of boar semen. For this reason, a prototype of a measuring system was developed to calculate a displacement index (Di), representing vibration intensities. Vibrations were analyzed in standardized road trials (n = 120) on several road types (A: smooth asphalt pavement, B: rough asphalt pavement, C: cobblestone, and D: dirt road) with different speeds (30, 60, 90, 120, and 150 km/h). A two-way ANOVA showed significant differences in mean Di, depending on road surface and speed as well as an interaction of both factors (p < 0.001). A field study on a reference delivery from a German AI center to several sow farms indicated that 33% of the observed roads are in good quality and generate only a few vibrations (Di ≤ 1), while 40% are of a moderate quality with interrupted surfaces (Di = 1–1.5). However, 25% of the roads show markedly increased vibrations (Di ≥ 1.5), as a consequence of bad conditions on cobblestones or unpaved roads. Overall, more attention should be paid to factors affecting sperm quality during transport. In the future, an Internet of Things (IoT) based solution could enable complete monitoring of the entire transport process in real time, which could influence the courier’s driving behavior based on road conditions in order to maintain the quality of the transported AI doses.

A Two-Stage Optimization-Based Motion Planner for Safe Urban Driving

Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to pose safety requirements as planning constraints in nonlinear, nonconvex optimization problems of motion synthesis. However, many implementations of this approach are limited by uncertain convergence and local optimality of the solutions achieved, affecting overall robustness. To improve upon these issues, we propose a novel two-stage optimization framework: In the first stage, we find a solution to a mixed-integer linear programming (MILP) formulation of the motion synthesis problem, the output of which initializes a second nonlinear programming (NLP) stage. The MILP stage enforces hard constraints of safety and road rule compliance generating a solution in the right subspace, while the NLP stage refines the solution within the safety bounds for feasibility and smoothness. We demonstrate the effectiveness of our framework via simulated experiments of complex urban driving scenarios, outperforming a state-of-the-art baseline in metrics of convergence, comfort, and progress.

Performance of Recycled Materials in Pavement Subbase After 4 Years in a Hot Desert Climate

The paper presents a field investigation on the use of alternative subbase materials from excavation and demolition waste in subbase applications in a desert area. A road trial was constructed with three subbase sections, comprising excavation waste (EW) and crushed concrete aggregate (RCA) plus a control section of EW with 20% dune sand (control). Visual inspection after 4 years in service revealed excellent performance with no surface defects or obvious differences between the different sections. The subbase surface was exposed and field-testing using lightweight deflectometer indicated very similar values for the unbound subbases of EW and the control section. The RCA subbase exhibited excellent performance due to self-hardening, providing much higher stiffness than the unbound subbases. The extracted unbound subbase materials satisfied the physical and mechanical requirements of the Qatar Construction Specifications with the exception of liquid limit, plastic limit, and sand equivalent, which are of less significance where the material is placed above the water table. The paper demonstrates that recycled aggregate can perform as well as conventional subbase materials and promotes their wider use to support sustainable development. Key words: alternative aggregates, construction &amp; demolition waste, excavation waste, field investigation, natural resources

Thermal Analysis-Based Field Validation of the Deformation of a Recycled Base Course Made with Innovative Road Binder.

The deformation of the cold recycled mixture with foamed bitumen in a recycled base with an innovative three-component road binder and foamed bitumen is analysed. Numerical simulation results for the pavement constructed, based on laboratory test results, were verified against the data from the monitoring system installed on the road trial section. In addition, environmental effects, such as air temperature and humidity levels in the pavement structure layers, were considered. Thermal analyses were conducted to identify the thermal properties of the pavement materials under steady heat transfer rate. Determining temperature distribution in the road cross-section in combination with relaxation functions determined for individual pavement layers contributed to the high effectiveness of the numerical simulation of deformation and displacement in the recycled base and the entire pavement. The experimental method of identifying thermal properties allows a fast and satisfactory prediction of temperature distribution in the pavement cross-section.

300 GHz radar object recognition based on deep neural networks and transfer learning

For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.

Machine Learning-Based Detection for Cyber Security Attacks on Connected and Autonomous Vehicles

Connected and Autonomous Vehicle (CAV)-related initiatives have become some of the fastest expanding in recent years, and have started to affect the daily lives of people. More and more companies and research organizations have announced their initiatives, and some have started CAV road trials. Governments around the world have also introduced policies to support and accelerate the deployments of CAVs. Along these, issues such as CAV cyber security have become predominant, forming an essential part of the complications of CAV deployment. There is, however, no universally agreed upon or recognized framework for CAV cyber security. In this paper, following the UK CAV cyber security principles, we propose a UML (Unified Modeling Language)-based CAV cyber security framework, and based on which we classify the potential vulnerabilities of CAV systems. With this framework, a new CAV communication cyber-attack data set (named CAV-KDD) is generated based on the widely tested benchmark data set KDD99. This data set focuses on the communication-based CAV cyber-attacks. Two classification models are developed, using two machine learning algorithms, namely Decision Tree and Naive Bayes, based on the CAV-KDD training data set. The accuracy, precision and runtime of these two models when identifying each type of communication-based attacks are compared and analysed. It is found that the Decision Tree model requires a shorter runtime, and is more appropriate for CAV communication attack detection.

Evaluation of fuel efficiency of the car owing to change of engine cylinders dimensions under condition of breaking force stream in transmission

Problem. Application of repair dimensions method predetermines enlargement of cylinders liners diameters by means of boring, which causes increase of engine capacity. This will change not only the engine thrust, but also the indicators of its fuel efficiency, which requires further research. Goal. Computer simulation of car movement was conducted with different engine options, which took into account breaking of force stream while switching stepped gear box. For this typical riding cycles were used, which are regulated by current Ukrainian standards. Results. Computations of maximal acceleration while reverse gear is engaged are laid down into car raising simulation algorithm. The algorithm allows taking into account shifting of gear from lower to higher speed. The moment of gear shift is the point in time, when engine crankshaft rotation frequency reaches its maximum. In this case, there is a need to take into account breaking of force stream during gear switching with the help of the clutch. In this moment, the power is not transmitted to driving wheels and the speed is decreasing as a result of the act of running resistance force. Simulations results has shown that slight increase of cylinder diameter up to a repair size do not worsen indicators of car fuel efficiency (and even improve them up to 1,2%) because of increase of compression ratio. Originality. Algorithms of car movement simulation under conditions of typical riding cycles are implemented as software. Computer program, written in Delphi object–oriented language, provides high level of results accuracy, which was confirmed by road trials. Practical. Obtained results showed high efficiency of car engine repair by the means of repair dimensions method applied upon its cylinders. It was demonstrated, that such repair activities do not impair fuelefficiency indicators of the car and will lead to substantial cost savings on spare parts.

Implementation of a GIS-based pilot application for roads management applied to the conservation and maintenance of unpaved low traffic roads

The lack of updating and unreliability of information has become one of the main problems that occur in the area of management of unpaved roads in developing countries as Colombia. Therefore, a GIS-based application was developed which allows keeping updated all the information at the structural and functional level of the road, and to control the premature pavement deterioration favouring the support for making decisions in management activities of the unpaved road network. In the municipality of Urrao (Antioquia), a research proposal on “La Encarnación” road was proposed. The Road Trial Section (RTS) is part of a bidirectional 3rd level road, which has 240 m-length and a 4 m-width. The RTS was divided into eight segments or cells (30 m-length), where each of them was stabilized with different conventional and unconventional materials (cement, hydrated lime, coal ash, brick dust, sulfonated oil, polymer and sugarcane sludge). Based on the results gathered in six pavement performance follow-up and evaluation campaigns, a geospatial database (GDB) was designed. This GDB is the core of the application “SGVVT Urrao v1.0” which is able to represent graphically the behaviour of the tests of functionality and the bearing capacity carried out (International Roughness Index, slip resistance, surface macro texture, lightweight deflectometer and visual inspection). The application is able to forecast the deterioration values allowing the prioritization of rehabilitation activities for the most affected sections in a given time period which is possible to extend the useful life of the roads, reduce the operation cost of the vehicles, and guarantee the road safety and the comfort of the unpaved road network users.

Back analysis of settlement of Teven Road trial embankment using Bayesian updating

ABSTRACTA trial embankment constructed on soft clay has been back analysed using Bayesian updating of parameters in a one-dimensional geotechnical model. The purpose of the back analysis was to demonstrate that Bayesian updating can be used to fit the data and to predict the final settlement. There was a limited amount of laboratory test data and site investigation data available, as is normal commercial practice. The initial material parameters were deemed to be biased due to sample disturbance and its effect on laboratory test data. The monitoring data comprised surface settlement measurements at the centreline of the embankment. The embankment was constructed in two stages over a period of 7 years. Bayesian updating of parameters using settlement was able to fit the entire time-settlement history.

Performance based pavement design and construction

Pavement designs for roads have evolved from empirical approaches whose origins lie in AASHTO road trials in the 1950’s. These are embodied in AASHTO guidelines from USA, the British Road note 29 and 31 as well as Arahan Teknik JKR guidelines. This approach constrains the designer and the contractor to use “standard” materials which were assumed in the empirical approach since these were the materials used in the trials. Any materials that are “nonstandard” cannot be utilised in the empirical approach. This leads to expensive pavement designs. With the development of mechanistic pavement design principles along with equipment to measure performance criteria such as stiffness, roughness, surface texture, friction etc performance-based pavement design and construction has become possible. This paper outlines the mechanistic approach with a case history of such pavement along with other important factors to be considered such as drainage of pavements which are essential for better pavement performance. Based on past successful history of performance-based pavement designs and construction there is a strong case that pavement designs can be done by mechanistic designs and the performance criteria be defined in specifications by way of roughness, stiffness, friction, surface texture in all road works contracts.

Pengaruh Laverage Maturity dan Size Perusahaan Terhadap Yield Obligasi Dengan Peringkat Obligasi Sebagai Variabel Intervening

This study aims to get empirical evidence on the effect of leverage, maturity, and size of the company against the results with intervening variables. The population in this study is all companies listed in Indonesia Stock Exchange (BEI) during 2011-2015 which has bonds inperiod 2012-2016.These bonds were assessed by rating agents of PT. Pefindo. Testing is done by using the path analysis, to determine the effect of leverage, maturity, and size of the company to the bond rating. Second path is for leverage, maturity, size, and ranking of results. Sobel test to process the mediation / intervening. The results show that leverage does not affect the rank, maturity and size of the company signed to the rating of bonds. The results of the road trials show that leverage and size have no effect on yield, maturity and assessment have a significant effect on results. From the Sobel, Results to test the test capable of mediating leverage variables, and the size of the bond yield, while the maturity of the results cannot be mediated by the bond rating.

Synthesis and Optimization of Steering Mechanism of Extra-Long Semi-Trailer Vehicle

The ongoing research work deals with the characterization and optimization of the steering system of an extra-long semi-trailer vehicle used for remote control station operations. Such a vehicle requires good manoeuvrability as the vehicle needs to be moved in unprepared narrow terrains with sharp turns. It was observed that the steering errors for the existing system is high i.e. around 80 which leads to shearing off of the tie rod pins at higher articulation angles between tractor and trailer. Genetic algorithm is used to synthesize the mechanism and to optimize the steering errors within the limitations of the hydro-mechanical steering system. Optimization is carried out in MATLAB, for which the mechanism including the optimization function was modelled mathematically and codes were written accordingly. For the purpose of characterization of current design, kinematic analysis of using MSC-ADAMS was carried out. Post optimization, it was found that the magnitude of errors in the angles of the wheels are less than 50. The magnitude of error in the tie rod (Translational displacement) was brought down from 30.4 mm to less than 1.05 mm. This error can be adjusted in tolerances of the joints thereby eliminating the additional load acting on pin during sharp turns due to over constraint steering system. The optimised errors are within acceptable range and bending/shearing of tie rod pin has not been observed after extensive road trials with simulated loads.

Transmission of lateral vibrations to children during car traveling and possibilities for their reduction

The publication addresses the need for technical solutions to reduce the transmission of lateral vibrations to passengers, especially children traveling by car in a child seat. A special design of a pendulum type tilting child seat has been proposed. Results of road trial conducted on a specific mountain road section are shown. The differences in the values of lateral accelerations acting on a child traveling in a conventional and in a tilting seat are given. The power spectral densities of lateral accelerations for both types of child seats are presented and compared. The root mean square values of lateral acceleration are given. Results for the values of the seat tilt angle are presented.

Design and optimization of a rubber-bitumen blend in preparation for a rubberized-asphalt road trial in the state of Kuwait

In this paper, test results are presented from laboratory trials of blending discarded tire derived crumb rubber with 60/70pen grade Kuwaiti bitumen. The virgin bitumen, in addition to 15% and 18% crumb rubber blends were tested for PG grading and multiple stress creep recovery at temperatures in the range from 40°C to 70°C. Elastic recovery and nonrecoverable creep compliance showed optimum performance at 18% rubber content. The optimized rubberized-bitumen blend was next converted into a pelletized form which was then used as a partial substitute to the conventional bitumen in the manufacture of continuously graded hot mix asphalts at various binder contents. The rubberized-bitumen pellet contents trialed were 2.5%, 3.0% and 3.5% by mass of mix. The Marshall method of mix design was used to assess the various mixes and the overall volumetric, stability and flow results, though satisfactory, was not entirely sufficient to aid in the selection of the best performing formulation.

A phase III randomized trial of gantenerumab in prodromal Alzheimer\u2019s disease

BackgroundGantenerumab is a fully human monoclonal antibody that binds aggregated amyloid-β (Aβ) and removes Aβ plaques by Fc receptor-mediated phagocytosis. In the SCarlet RoAD trial, we assessed the efficacy and safety of gantenerumab in prodromal Alzheimer’s disease (AD).MethodsIn this randomized, double-blind, placebo-controlled phase III study, we investigated gantenerumab over 2 years. Patients were randomized to gantenerumab 105 mg or 225 mg or placebo every 4 weeks by subcutaneous injection. The primary endpoint was the change from baseline to week 104 in Clinical Dementia Rating Sum of Boxes (CDR-SB) score. We evaluated treatment effects on cerebrospinal fluid biomarkers (all patients) and amyloid positron emission tomography (substudy). A futility analysis was performed once 50% of patients completed 2 years of treatment. Safety was assessed in patients who received at least one dose.ResultsOf the 3089 patients screened, 797 were randomized. The study was halted early for futility; dosing was discontinued; and the study was unblinded. No differences between groups in the primary (least squares mean [95% CI] CDR-SB change from baseline 1.60 [1.28, 1.91], 1.69 [1.37, 2.01], and 1.73 [1.42, 2.04] for placebo, gantenerumab 105 mg, and gantenerumab 225 mg, respectively) or secondary clinical endpoints were observed. The incidence of generally asymptomatic amyloid-related imaging abnormalities increased in a dose- and APOE ε4 genotype-dependent manner. Exploratory analyses suggested a dose-dependent drug effect on clinical and biomarker endpoints.ConclusionsThe study was stopped early for futility, but dose-dependent effects observed in exploratory analyses on select clinical and biomarker endpoints suggest that higher dosing with gantenerumab may be necessary to achieve clinical efficacy.Trial registrationClinicalTrials.gov, NCT01224106. Registered on October 14, 2010.

Predicting the Wear of High Friction Surfacing Aggregate

High friction surfacing (HFS) is a specialist type of road coating with very high skid resistance. It is used in the UK at locations where there is significant risk of serious or fatal accidents. This paper considers the aggregate used in HFS. Calcined bauxite is the only aggregate that provides the highest levels of skid resistance over the longest period. No naturally occurring aggregate has been found to give a comparable level of in-service performance. This paper reviews the historical development of HFS in the UK relating to aggregate. In-service performance is predicted in the laboratory using the Wear test which subjects test specimens to an estimated 5–8 years simulated trafficking. Examples are given of Wear test data. They illustrate why calcined bauxite performs better than natural aggregate. They show how the amount of calcined bauxite can be reduced by blending with high skid resistant natural aggregates. Data from the Wear test can be related to every HFS laboratory experiment and road trial carried out in the UK for over the last 50 years. Anyone considering the prediction of HFS performance needs to carefully consider the data given in this paper with any other test method currently being considered or used to investigate HFS.

Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection

The massive growth of data produced in the automotive industry by acquiring data during production and test of vehicles requires effective and intelligent ways of analysing these recordings. In order to detect potential faults, data from the in-vehicle network interconnecting vehicle subsystems is recorded during road trials. The complexity and volume of this data keeps increasing since the degree of interconnection between the vehicle subsystems and the amount of data transmitted over the in-vehicle network is augmented with each functionality added to modern vehicles. In this paper, an anomaly detection approach is proposed that (a) is capable of detecting faults of known and previously unknown fault types, (b) functions as an out-of-the-box approach not requiring the setting of expert-parameters and (c) is robust against different driving scenarios and fault types. To achieve this, an ensemble classifier is used consisting of two-class and one-class classifiers. Without modelling effort and user parameterisation the approach reports anomalies in the multivariate time series which point the expert to potential faults. The approach is validated on recordings from road trials and it could be shown that the ensemble-anomaly detector is robust against different driving scenarios and fault types.