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In recent years, various technologies have been developed for hybrid vehicles, ranging from energy management to the type of engines used, where Diesel engines have been used with the aim to improve fuel efficiency. This work presents a novel scheme for energy recovery from the exhaust gases of a turbocharged diesel engine integrated into a hybrid powertrain. The operation is explained through simulations conducted in MATLAB, using a fifth-order dynamic model where the dynamics of the compressor and turbine are not coupled and for which a feedback linearized controller is designed. Simulation results confirm the presence of excess energy in the exhaust gases. To demonstrate this, an engine velocity profile is simulated and the available o required power in both input and output manifolds is determined. The integral of the difference between these two powers clearly identifies an energy excess that can be converted to electric energy to be used in the propulsion system. This decoupling of the compressor and turbine is crucial as in conventional turbochargers they are mechanically coupled. The implementation of this schemes requires to couple to the shaft of the turbocompresor a high speed electrical engine that absorbs the available energy excess. Finally, the achieved recovery ranges from 1-3%, which demonstrates the effectiveness of the proposal.

An observer is proposed for a trapezoidal brushless DC motor composed of a cascade connection of a reduced-order Luenberger observer and a high-order sliding mode (HOSM) differentiator. This configuration can estimate the angular velocity and reconstruct the load torque, key elements for the control of this type of motor, under the mild assumption that the variable load torque and its k-th time derivatives are bounded. The proposed observer was tested on an experimental test bench based on Texas Instruments (TI) High Voltage Digital Motor Control (HVMTR Kit) using a Delfino F28379D micro controller. The results show that the velocity and load torque can be properly estimated, despite the presence of noise in the current measurements.

Understanding, promoting and managing sustainable urban mobility better is very critical in the midst of an unprecedented climate crisis. Identifying, evaluating, benchmarking and prioritising its key indicators is a way to ensure that policy-makers will develop those transport strategies and measures necessary to facilitate a more effective transition to liveable futures. After identifying from the literature and the European Commission (EC) directives the indicators that are underpinning the powerful scheme of Sustainable Urban Mobility Plans (SUMPs) that each municipality in Europe may implement to elevate the wellbeing of its population, we adopt a Cross Impact Matrix Multiplication Applied to Classification (MICMAC) approach to assess, contextualise and rank them. Through conducting a qualitative study that involved a narrative literature review and more importantly in-depth discussions with 28 elite participants, each of them with expertise in sustainable development, we are able to designate the Sustainable Urban Mobility Indicators (SUMIs) that are the most (and least) impactful. According to our analysis the most powerful indicator is traffic congestion, followed by affordability of public transport for the poorest, energy efficiency, access to mobility service and multimodal integration. This analysis allows us to then match them with the most applicable strategies that may ensure a holistic approach towards supporting in practical terms sustainable mobility in the city level. These are in ranking order: Transit Oriented Development (TOD); public and active transport enhancement; parking policies, vehicle circulation and ownership measures; telecommuting and car-pooling.

The development of sensitive and affordable testing devices for infectious diseases is essential to preserve public health, especially in pandemic scenarios. In this work, we have developed an attractive analytical method to monitor products of genetic amplification, particularly the loop-mediated isothermal amplification reaction (RT-LAMP). The method is based on electrochemical impedance measurements and the distribution of relaxation times model, to provide the so-called time-constant-domain spectroscopy (TCDS). The proposed method is tested for the SARS-CoV-2 genome, since it has been of worldwide interest due to the COVID-19 pandemic. Particularly, once the method is calibrated, its performance is demonstrated using real wastewater samples. Moreover, we propose a simple classification algorithm based on TCDS data to discriminate among positive and negative samples. Results show how a TCDS-based method provides an alternative mechanism for label-free and automated assays, exhibiting robustness and specificity for genetic detection.

The use of smartphones has become an inherent part of daily human life. It allows users to keep personal information, emails, pictures, social media accounts, and financial data in one place. Consequently, smartphones are an attractive target for malware developers to spread malicious content, aiming at extracting information without the user’s knowledge. Therefore, understanding malware propagation characteristics could provide a means to evaluate how they behave in order to plan security solutions accordingly. Bluetooth antennas are a channel for spreading malware through smartphones, where the probability of infection, similar to biological viruses, depends mainly on the attacker’s physical proximity. This work presents a model based on cellular automata and epidemiological compartmental models for studying the spatial and temporal propagation of Bluetooth worms in smartphones. The proposed model incorporates the individual characteristics of each device, such as security settings, latency time, operating system, different classes of Bluetooth antennas (range and transfer rate), and different user mobility patterns. Several simulation scenarios are analyzed in order to study the spreading dynamics of Bluetooth-based worms, considering the location where the outbreak begins, and the different types of antennas integrated into the smart devices. Simulation results indicated that the proposed model is appropriate for studying how the users’ demographics affect the worm’s propagation dynamics in time and space. Moreover, the model permits an analysis of the impact of users’ awareness about the risks inherent in using smart devices in Bluetooth networks, based on the acceptance of incoming communication and the effects of recovery and immunity to threats. Finally, the proposed model preserves simplicity and computational efficiency, with the possibility of extending beyond Bluetooth in order to include other transmission media.

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue’s stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.

Transportation system and its Urban Transportation Infrastructure (UTI) influence an area´s economic health and quality of life because they provide the background in order the people to perform their everyday activities. Nevertheless, transportation activity generates certain negative externalities like congestion, road accidents, poor air quality that affect the people physically, emotionally and socially. These externalities become severe via the urbanization-metropolisation phenomenon that increase the distances for territorial displacement, generating major mobility demand and car usage. Consequently, an approach to urban planning is required, which would prioritize sustainable mobility. This paper’s objective is to help the implementation of Integral Mobility Plans by focusing on the case of Mexico City. This can be achieved through the application of structural analysis method in two steps: first, we facilitate the identification of the interlinks among the essential urban structure’s components for sustainable mobility. Second, we evaluate the effectiveness of the public policies-strategies that form part of Mexico City’s Integral Mobility Plan and organize them in order of importance. The results show which UTI elements should be considered primarily for sustainable mobility and which public policies-strategies should be prioritized in order to implement the aforementioned plan effectively.

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.

The use of recursive subspace-based identification methods is analyzed in the estimation of the most significant vibration frequencies along an elevated railroad segment of México City Metro Line 12 using ambient vibration measurements recorded from 2012, when the line was opened, to 2018. Due to the railroad characteristics, the use of high-order models and the systematic tuning of the methods are required to achieve low uncertainty in frequency estimation. A frequency history is generated using these high-order models in order to check for variations along the seven years where important events took place: two low- and one high-intensity earthquakes, paving, and construction of sidewalks and planters around the sensor station. Results are consistent for all methods under analysis in the identified frequencies, suggesting that the system has preserved its structural health. To produce independent results, spectral analysis was performed showing that the associated frequency history is again consistent with that generated with recursive subspace-based identification methods. Overall, results indicate that these subspace methods are suitable for frequency monitoring in the studied system offering, in the case of recursive N4SID, important advantages in terms of low computational cost, real-time implementation, and smaller uncertainty.

Transportation systems help in shaping an area’s economic health and quality of life, providing the infrastructure for the mobility of people and goods. Nevertheless, the negative externalities of car-oriented urban-metropolitan planning have heightened awareness for the need of urban planning approaches that incorporate sustainable mobility. Consequently, cities worldwide have increasingly produced sustainable mobility plans. This points to the need of creating mechanisms to implement these sustainable plans, particularly in large, complex, and fast-growing cities. This paper provides guidelines to facilitate the implementation of Sustainable Mobility Plans by focusing on the case of Mexico City. This is achieved by applying the complex large-scale integrated open systems (CLIOS) systemic analysis, in two steps: first, we facilitate the identification of the complexities and relationships among the essential systems of Mexico City’s urban structure, along with the recognition of their most important components and the institutions involved within the urban planning process. Second, we assess the effectiveness of the public policies–strategies that form part of Mexico City’s Sustainable Mobility Plan and organize them in order of importance. The results show which principal subsystems should be considered for sustainable mobility and which public policies–strategies should be prioritized in order to implement the aforementioned plan effectively.

A parameter identification method and a high gain observer are proposed in order to identify the model and to recover the state of a seismically excited shear building using acceleration responses of the ground and instrumented floors levels, as well as the responses at noninstrumented floors, which are reconstructed by means of cubic spline shape functions. The identification method can be implemented online or offline and uses Linear Integral Filters, whose bandwidth must enclose the spectrum of a seismically excited building. On the other hand, the proposed state observer estimates the displacements and velocities of all the structure floors using the model estimated by the identification method. The observer allows obtaining a fast response and reducing the state estimation error, while depending on a single gain. The performance of the parameter and state estimators is verified through experiments carried out on a five-story small scale building.

The urban transportation infrastructure (UTI) is an essential asset for any city and it affects citizens, governments, economic growth and urban functioning. The UTI is more valuable than ever due to the urbanization process that concentrates more people in the urban centers, generating a greater mobility demand and urban sprawl that encourages the automobile usage with its associated consequences like congestion, roads accidents, noise etc. Hence, a new UTI management approach is required in order to enhance the sustainable mobility paradigm. The objective of this paper is to construct a set of indicators related to the sustainable transportation modes that could help the decision-making of urban planning via a Geographic Information System. As a case study, the indicators are determined for a Census Tract within Iztapalapa County, the results revealed opportunities for improvement through lines of action that belong to an integral mobility plan rather than partial implemented countermeasures.

Urban Transportation Infrastructure (UTI) is a key asset for any city as it affects economic growth and urban functioning; UTI is more valuable than ever due to the urbanization process that concentrates many people to the urban centers, generating a major mobility demand and car usage. Hence, an approach to UTI management is required, which would provide a greater significance to sustainable mobility. This paper’s objective is to structure in hierarchical manner UTI and urban surroundings indicators related to sustainable mobility in order to create a decision-making instrument related to UTI management in the form of a weighted index and to evaluate public policies-strategies associated to these indicators. This was accomplished through the structural analysis-MICMAC method; the results showed which indicators are the most important and the necessity to prioritize them in order certain public policies to be more effective conforming to their impact on the weighted index.

A methodology to estimate the shear model of seismically excited, torsionally coupled buildings using acceleration measurements of the ground and floors is presented. A vector parameterization that considers Rayleigh damping for the building is introduced that allows identifying the stiffness/mass and damping/mass ratios of the structure, as well as their eccentricities and radii of gyration. This parameterization has the advantage that its number of parameters is smaller than that obtained with matrix parameterizations or when Rayleigh damping is not used. Thus, the number of spectral components of the excitation signal required to identity the structural parameters is reduced. To deal with constant disturbances and measurement noise that corrupt acceleration measurements, Linear Integral Filters are used that guarantee elimination of constant disturbances and attenuation of noise.

Resumen: Se presenta un método de identificación por mínimos cuadrados de los parámetros de un modelo por cortante de un edificio que considera tres grados de libertad por cada piso. La excitación para la identificación se supone proveniente de dos componentes ortogonales horizontales de movimiento del terreno inducido por un sismo. El conocimiento de estos parámetros resulta útil en la aplicación de técnicas de control semiactivas que atenúen la vibración de los edificios durante el sismo y prevengan daños permanentes. El método usado introduce una variación en la formulación convencional de mínimos cuadrados que permite una reducción sustancial en el número de cálculos involucrados, lo que hace factible la aplicación de la metodología en tiempo real. Para probar el método se utiliza un conjunto de datos registrados en dos edificios instrumentados durante la ocurrencia de un sismo. Los resultados obtenidos permiten comprobar la eficacia del algoritmo para reconstruir las aceleraciones de los entrepisos y recuperar las frecuencias fundamentales de vibración de la estructura : identificación paramétrica en tiempo real, control semiactivo de estructuras, mínimos cuadrados, cálculo de estructuras, integridad de estructuras

Resumen: Se presenta un observador adaptable para un edificio equipado con un amortiguador magneto-reológico. Este observador es capaz de reconstruir los parámetros estructurales, las posiciones y las velocidades absolutas de los pisos a partir de mediciones de aceleración. Estas señales son reconstruidas en forma explícita gracias a que el esquema propuesto no requiere de formas canónicas ni transformaciones de estados. El desempeño de este observador se ilustra con resultados experimentales obtenidos en una estructura de escala reducida. Palabras clave: Observador adaptable, amortiguador magneto-reológico, identificación de parámetros, control de estructuras