Reclaimed asphalt test specimen preparation assisted by image analysis

This paper presents a laboratory investigation aimed at establishing a protocol for the production of homogeneous asphalt mixtures test specimens, incorporating reclaimed asphalt by using a gyratory compactor with coring and trimming works. Stone mastic asphalt specimens were compacted at the previously identified target densities with the final aim of obtaining specimens with a fixed and homogeneous air void distribution. A microstructural study was conducted to characterize the homogeneity in the air void distribution using X-ray computed tomography (CT) combined with image analysis techniques. The study concluded that the gyratory compactor is suitable for producing homogeneous test specimens for the specified mixtures and a set of detailed procedures has been proposed for the production of the compacted specimens and to perform the microstructural study

A spatial analysis of public transit routes in Amman, Jordan

Public transport is a basic service that must be provided to any society. An effective public transport system is the system that can provide adequate service coverage to all community groups, especially the low-income group, the elderly and the disabled. For performance evaluation, performance metrics are widely implemented; for this purpose transit accessibility is used in this study as it reflects the ease of reaching transit service and its convenience as a mode choice. This study aims to assess transit accessibility in Wadi Al-Seer, one of the main districts in Amman. Accessibility was measured on the basis of the percentage of service coverage area and served population. The analytical framework included the use of geographic information systems (GIS) software, through the creation of buffer areas representing the limits of pedestrians walking distance to public transit stations. The results shows that the overall accessibility is significant, but concentrated in the center of the district, while the outskirt is not properly served, in addition to a high percentage of overlapping routes. Thus improvements on the route distribution and increasing its numbers in low access areas are required

Mechanical and structural assessment of laboratory- and field-compacted asphalt mixtures

Compaction forms an integral part in the formation of the aggregate orientation and structure of an asphalt mixture and therefore has a profound influence on its final volumetric and mechanical performance. This article describes the influence of various forms of laboratory (gyratory, vibratory and slab-roller) and field compaction on the internal structure of asphalt specimens and subsequently on their mechanical properties, particularly stiffness and permanent deformation. A 2D image capturing and image analysis system has been used together with alternative specimen sizes and orientations to quantify the internal aggregate structure (orientation and segregation) for a range of typically used continuously graded asphalt mixtures. The results show that in terms of aggregate orientation, slab-compacted specimens tend to mimic field compaction better than gyratory and vibratory compaction. The mechanical properties of slab-compacted specimens also tend to be closer to that of field cores. However, the results also show that through careful selection of specimen size, specimen orientation and compaction variables, even mould-based compaction methods can be utilised with particular asphalt mixtures to represent field-compacted asphalt mixtures

Using neuroevolution for predicting mobile marketing conversion

This paper addresses user Conversion Rate (CVR) prediction within the context of Mobile Performance Marketing. Specifically, we adapt two main neuroevolution methods: Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT). First, we discuss two mechanisms for increasing execution speed (parallelism and data sampling); a strategy for preventing excessive network complexity with NEAT; and a rolling window scheme for performing an online learning. Then, we present experimental results, using distinct datasets and testing both offline and online learning environments.ThisarticleisaresultoftheprojectNORTE-01-0247-FEDER-017497,supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples

This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units.This work has been supported by FCT – Funda ̧c ̃ao para a Ciˆencia e Tecnologiawithin the R&D Units Project Scope: UIDB/00319/2020. The authors also wishto thank the chemical company staff involved with this project for providing thedata and also the valuable domain feedback

Innovation for an inclusive future

This workshop will focus on setting the agenda for research, practice and policy in support of inclusive design for third generation computer-based products. The next generation of technology represents an unprecedented opportunity to improve the quality of life for groups of users who have previously faced exclusion, such as those with impairments and older citizens. At the same time it risks creating a greater digital divide and further exclusion. How we approach design for this new generation will determine whether or not the third wave will provide positive advances towards an inclusive digital world. We therefore need to put forward both a rationale for inclusive design and provide pointers towards technical development and design practice in support of inclusion. It is our belief that there is not only a strong moral case for design for inclusion but also significant commercial incentive, which may be key to persuading influential players to focus on inclusion. Therefore one of our key objectives is to describe and promote the advantages of designing ‘in from the edges’ of the user population rather than designing for a notional ‘average’ user

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Extrapolation for Time-Series and Cross-Sectional Data

Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts’ commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased

ACL injuries identifiable for pre-participation imagiological analysis: Risk factors

Identification of pre-participation risk factors for noncontact anterior cruciate ligament (ACL) injuries has been attracting a great deal of interest in the sports medicine and traumatology communities. Appropriate methods that enable predicting which patients could benefit from pre- ventive strategies are most welcome. This would enable athlete-specific training and conditioning or tailored equipment in order to develop appropriate strategies to reduce incidence of injury. In order to accomplish these goals, the ideal system should be able to assess both anatomic and functional features. Complementarily, the screening method must be cost-effective and suited for widespread application. Anatomic study protocol requiring only standard X rays could answer some of such demands. Dynamic MRI/CT evaluation and electronically assisted pivot-shift evaluation can be powerful tools providing complementary information. These upcoming insights, when validated and properly combined, envision changing pre-participation knee examination in the near future. Herein different methods (validated or under research) aiming to improve the capacity to identify persons/athletes with higher risk for ACL injury are overviewed.

Rotational knee laxity: Reliability of a simple measurement device in vivo

<p>Abstract</p> <p>Background</p> <p>Double bundle ACL reconstruction has been demonstrated to decrease rotational knee laxity. However, there is no simple, commercially-available device to measure knee rotation. The investigators developed a simple, non-invasive device to measure knee rotation. In conjunction with a rigid boot to rotate the tibia and a force/moment sensor to allow precise determination of torque about the knee, a magnetic tracking system measures the axial rotation of the tibia with respect to the femur. This device has been shown to have acceptable levels of test re-test reliability to measure knee rotation in cadaveric knees.</p> <p>Methods</p> <p>The objective of this study was to determine reliability of the device in measuring knee rotation of human subjects. Specifically, the intra-tester reliability within a single testing session, test-retest reliability between two testing sessions, and inter-tester reliability were assessed for 11 male subjects with normal knees.</p> <p>Results</p> <p>The 95% confidence interval for rotation was less than 5° for intra-tester, test-retest, and inter-tester reliability, and the standard error of measurement for the differences between left and right knees was found to be less than 3°.</p> <p>Conclusion</p> <p>It was found that the knee rotation measurements obtained with this device have acceptable limits of reliability for clinical use and interpretation.</p