Energy Recovering Using Regenerative Braking in Diesel–Electric Passenger Trains: Economical and Technical Analysis of Fuel Savings and GHG Emission Reductions

Rail transport, specifically diesel–electric trains, faces fundamental challenges in reducing fuel consumption to improve financial performance and reduce GHG emissions. One solution to improve energy efficiency is the electric brake regenerative technique. This technique was first applied on electric trains several years ago, but it is still considered to improve diesel–electric trains efficiency. Numerous parameters influence the detailed estimation of brake regenerative technique performance, which makes this process particularly difficult. This paper proposes a simplified energetic approach for a diesel–electric train with different storage systems to assess these performances. The feasibility and profitability of using a brake regenerative system depend on the quantity of energy that can be recuperated and stored during the train’s full and partial stop. Based on a simplified energetic calculation and cost estimation, we present a comprehensive and realistic calculation to evaluate ROI, net annual revenues, and GHG emission reduction. The feasibility of the solution is studied for different train journeys, and the most significant parameters affecting the impact of using this technique are identified. In addition, we study the influence of electric storage devices and low temperatures. The proposed method is validated using experimental results available in the literature showing that this technique resulted in annual energy savings of 3400 MWh for 34 trains, worth USD 425,000 in fuel savings.

An Energetic Approach to Modeling Cytoskeletal Architecture in Maturing Cardiomyocytes

Through a variety of mechanisms, a healthy heart is able to regulate its structure and dynamics across multiple length scales. Disruption of these mechanisms can have a cascad- ing effect, resulting in severe structural and/or functional changes that permeate across different length scales. Due to this hierarchical structure, there is interest in understand- ing how the components at the various scales coordinate and influence each other. However, much is unknown regarding how myofibril bundles are organized within a densely packed cell and the influence of the subcellular components on the architecture that is formed. To elucidate potential factors influencing cytoskeletal development, we proposed a compu- tational model that integrated interactions at both the cel- lular and subcelluar scale to predict the location of indi- vidual myofibril bundles that contributed to the formation of an energetically favorable cytoskeletal network. Our model was tested and validated using experimental metrics derived from analyzing single cell cardiomyocytes. We demonstrated that our model-generated networks were capable of repro- ducing the variation observed in experimental cells at different length scales as a result of the stochasticity inher- ent in the different interaction between the various cellu- lar components. Additionally, we showed that incorporat- ing length-scale parameters resulted in physical constraints that directed cytoskeletal architecture towards a structurally consistent motif. Understanding the mechanisms guiding the formation and organization of the cytoskeleton in individual cardiomyocytes can aid tissue engineers towards developing functional cardiac tissue.

The effective Equations for the Ultrasonic Response of Wet Cortical Bone

We outline the mathematical model of the ultrasonic response of wet cortical bone and its time harmonic formulation. We employ an energetic approach based on the Reuss-bound of the free energy of a porous material consisting of a piezo-electric solid and a conducting fluid part. Magnetic effects are taken into consideration. Corresponding boundary value problems are stated and associated theorems established. A conclusion is included concerning future developments of this formulation.} \keywords{wet bone, ultrasonic response, Maxwell equations}

A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis

The various hurdles in machine learning are beaten by deep learning techniques and then the deep learning has gradually become preeminent in artificial intelligence. Deep learning uses neural networks to kindle decisions like humans. Deep learning flourished as an energetic approach and clarity marked its success in various domains. The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect and diagnose of cancer at an early stage. As early as the detection of cancer than to treat the disease is uncomplicated. Early diagnosis was particularly relevant for some cancers such as breast, skin, colon, and rectum, which prohibit the chance to grow and spread. Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. The paper presents the study of a few deep learning software frameworks such as tensor flow, theano, caffe, torch, and keras. Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. The survey winds up by presenting several future avenues and open challenges that should be addressed by the researcher in the future.

Mathematical myocardium elasticity properties modeling in echocardiography study of diastolic and systolic function of the left ventricle (Preprint)

BACKGROUND A systematic and quantitative comparative analysis for this subject has not been done so far. Thus defined, the coefficient of elasticity is a whole new dimension. OBJECTIVE This study proposes a new mathematical myocardium elasticity property modeling in characterizing of the ventricular diastole and systole. METHODS The study group consisted of 2283 consecutive patients evaluated by echocardiography. The mathematical approach is made starting from energetic consideration, by applying the energy conservation low for the blood entering from left atrium into left ventricle during diastole period. RESULTS Analyzing all the data obtained we developed two brand new coefficients to describe the cardiac cycle and we had verified if the coefficients are correlated with classically used parameters. We consider that the energetic approach take into consideration the whole mechanical movement that is happening inside the heart and can offer a very synthetic and scientific solid view about the cardiac cycle. CONCLUSIONS The new coefficients are simply to be calculated and as you will see from our research the correlation with other classically used parameters is obvious. The direct physical approach of blood flow within the heart can generate new, beneficial perspectives in diagnosing various heart conditions, or even in understanding how works the filling of the ventricles and atria during a heartbeat.