PARAMETRIC AND NON-PARAMETRIC APPROACHES FOR THE PREDICTION OF THE DIFFUSION OF THE ELECTRIC VEHICLE

Driven by environmental awareness and new regulations for fuel efficiency, electric vehicles (EVs) have significantly evolved in the last decade, yet their market share is still much lower than expected. In addition to understanding the reasons for this slow market penetration, it is crucial to have appropriate tools to correctly predict the diffusion of this innovative product. Recent works in forecasting the EV market combine substitution and diffusion models, where discrete choice specifications are used to address the former, and Bass-type to account for the latter. However, these methodologies are not dynamic and do not consider the fact that innovation occurs through social channels among members of a social system. This research presents two advanced methodologies that make use of real data to evaluate the adoption of the EVs in the State of Maryland. The first consists of a disaggregated substitution model that considers social influence and social conformity, which is then embedded in a diffusion model to predict electric vehicle sales. The second, in contrast, relies on non-parametric machine learning techniques for the classification of potential EV purchasers. Both make use of data collected through a stated choice experiment specifically designed to capture the inclination of users towards EVs

An interpretable machine learning approach to understanding the impacts of attitudinal and ridesourcing factors on electric vehicle adoption

13 p.The global electric vehicle (EV) market has been experiencing an impressive growth in recent times. Understanding consumer preferences on this cleaner, more eco-friendly mobility option could help guide public policy toward accelerating EV adoption and sustainable transportation systems. Previous studies suggest the strong influence of individual and external factors on EV adoption decisions. In this study, we apply machine learning techniques on EV stated preference survey data to predict EV adoption using attitudinal factors, ridesourcing factors (e.g., frequency of Uber/Lyft rides), as well as underlying sociodemographic and vehicle factors. To overcome machine learning models’ low interpretability, we adopt the innovative Local Interpretable Model-Agnostic Explanations (LIME) method to elaborate each factor’s contribution to the predicting outcomes. Besides what was found in previous EV preference literature, we find that the frequent usage of ridesourcing, knowledge about EVs, and awareness of environmental protection are important factors in explaining high willingness of adopting EVs

Classification of potential electric vehicle purchasers: A machine learning approach

13 p.Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption

How the design of Complete Streets affects mode choice: Understanding the behavioral responses to the level of traffic stress

Following a federal policy statement in 2010 supporting bicycle and pedestrian accommodation in federal-aid transportation projects, many cities across the US have implemented Complete Streets principles and invested in developing better-planned infrastructure that can be safely accessed by a diversity of modes of transportation by all types of users, in a mix of land uses. However, most of the travel demand forecasting models and planning tools used in practice are not sensitive to changes in demand for non-motorized modes such as walking and cycling in response to road infrastructure improvements. Hence, there is a need for models and tools that are capable of evaluating impacts of infrastructure changes that include Complete Streets implementations on the travel behavior, and estimate shifts in mode choices from motorized to non-motorized modes. This paper proposes a specific data collection plan, a multi-modal choice model, and strategies to update traditional trip-based transportation models to forecast rates of non-motorized trips for evaluating Complete Streets plans at a higher level. Concretely, we estimate elasticities to Level of Traffic Stress, which defines the comfort or discomfort experienced by walkers and bikers, segmented by income levels and trip purposes. We then use them to compute the new non-motorized mode shares that would be achieved by improving CS attributes leading to lower levels of traffic stress. The proposed modeling framework has been successfully applied to the Maryland Statewide Transportation Model, producing reliable non-motorized trip rates, and can be extended to other methodological frameworks used by public agenciesThis research was sponsored by the Maryland Department of Transportation State Highway Administration (Project No: MD-21- SHA/UM/5-25, Erdogan et al., 2021), and the Urban Mobility & Equity Center (UMEC), based at Morgan State Universit

Effects of ankle position during the Nordic Hamstring exercise on range of motion, heel contact force and hamstring muscle activation

One of the main benefits of the Nordic Hamstring Exercise (NHE) is that it can be performed without the need of any extra material. However, numerous technical execution variables such as the ankle and pelvis position can influence the performance. The primary aims of this study were to investigate the effects of ankle position (i.e., plantar or dorsal flexion) on Nordic Hamstring Break Point (NHBP), repetition time and heel contact force. A secondary aim was to investigate differences in biceps femoris long head and semitendinosus muscle activation. Male professional field hockey players (n = 12) volunteered for the study. Paired t-tests were used to analyse the effect of ankle position on muscle NHBP, eccentric peak torque and repetition time. Ankle dorsal flexion resulted in a higher NHBP (p = 0.002, effect size [ES] = 1.48 [0.57 to 2.38]), repetition time (p = 0.004, ES = 0.98 [0.24 to 1.72]) and both absolute and relative heel contact force (p = 0.028, ES = 0.67 [0.01 to 1.34], p = 0.017, ES = 0.76 [0.07 to 1.44], respectively) compared to plantar flexion. Muscle activation was not significant different. This study showed a higher NHBP, absolute and relative heel contact force and repetition time with a dorsal flexed ankle vs. a plantar flexed ankle in the NHE, without changes in hamstrings muscle activation.Medicin

GoldenBraid 2.0: a comprehensive DNA assembly framework for plant synthetic biology

[EN] Plant synthetic biology aims to apply engineering principles to plant genetic design. One strategic requirement of plant synthetic biology is the adoption of common standardized technologies that facilitate the construction of increasingly complex multigene structures at the DNA level while enabling the exchange of genetic building blocks among plant bioengineers. Here, we describe GoldenBraid 2.0 (GB2.0), a comprehensive technological framework that aims to foster the exchange of standard DNA parts for plant synthetic biology. GB2.0 relies on the use of type IIS restriction enzymes for DNA assembly and proposes a modular cloning schema with positional notation that resembles the grammar of natural languages. Apart from providing an optimized cloning strategy that generates fully exchangeable genetic elements for multigene engineering, the GB2.0 toolkit offers an ever-growing open collection of DNA parts, including a group of functionally tested, premade genetic modules to build frequently used modules like constitutive and inducible expression cassettes, endogenous gene silencing and protein-protein interaction tools, etc. Use of the GB2.0 framework is facilitated by a number of Web resources that include a publicly available database, tutorials, and a software package that provides in silico simulations and laboratory protocols for GB2.0 part domestication and multigene engineering. In short, GB2.0 provides a framework to exchange both information and physical DNA elements among bioengineers to help implement plant synthetic biology projects.This work was supported by the Spanish Ministry of Economy and Competitiveness (grant no. BIO2010-15384), by a Research Personnel in Training fellowship to A.S.-P., and by a Junta de Ampliacion de Estudios fellowship to M.V.-V.Sarrion-Perdigones, A.; Vázquez Vilar, M.; Palací Bataller, J.; Castelijns, B.; Forment Millet, JJ.; Ziarsolo Areitioaurtena, P.; Blanca Postigo, JM.... (2013). GoldenBraid 2.0: a comprehensive DNA assembly framework for plant synthetic biology. Plant Physiology. 162(3):1618-1631. https://doi.org/10.1104/pp.113.217661S16181631162

Enfoques paramétricos y no paramétricos para la predicción de la difusión del vehículo eléctrico.

Tesis Doctoral inédita cotutelada por The University of Maryland y la Universidad Autónoma de Madrid, Facultad de Ciencias Económicas y Empresariales, Departamento de Análisis Económico, Teoría Económica e Historia EconómicaEsta Tesis tiene embargado el acceso al texto completo hasta el 05-12-202

Olimpiadas estadísticas

SIN FINANCIACIÓNNo data 201

Acercamiento a la profesión de investigador: una experiencia en el grado de creación y dirección de empresas

SIN FINANCIACIÓNNo data 201

T2 Hamstring Muscle Activation during the Single-Leg Roman Chair: Impact of Prior Injury

Previous studies have shown inhibition of previously injured hamstrings during eccentric exercises, but it is unknown whether this effect is also present during an isometric position-control exercise such as the single-leg Roman chair hold (SLRCH). Methods: This cross-sectional study investigated muscle activation during the SLRCH in individuals with prior hamstring injuries.Medicin