Automotive observers based on multibody models and the extended Kalman filter

This work is part of a project aimed to develop automotive real-time observers based on detailed nonlinear multibody models and the extended Kalman filter (EKF). In previous works, a four-bar mechanism was studied to get insight into the problem. Regarding the formulation of the equations of motion, it was concluded that the state-space reduction method known as matrix-R is the most suitable one for this application. Regarding the sensors, it was shown that better stability, accuracy and efficiency are obtained as the sensored magnitude is a lower derivative and when it is a generalized coordinate of the problem. In the present work, the automotive problem has been addressed, through the selection of a Volkswagen Passat as a case-study. A model of the car containing fifteen degrees of freedom has been developed. The observer algorithm that combines the equations of motion and the integrator has been reformulated so that duplication of the problem size is avoided, in order to improve efficiency. A maneuver of acceleration from rest and double lane change has been defined, and tests have been run for the “prototype,” the “model” and the “observer,” all the three computational, with the model having 100 kg more than the prototype. Results have shown that good convergence is obtained for position level sensors, but the computational cost is high, still far from real-time performance.     

Evaluating the influence of multiplexing schemes and buffer implementation on perceived VoIP conversation quality

This work presents a study of RTP multiplexing schemes, which are compared with the normal use of RTP, in terms of experienced quality. Bandwidth saving, latency and packet loss for different options are studied, and some tests of Voice over IP (VoIP) traffic are carried out in order to compare the quality obtained using different implementations of the router buffer. Voice quality is calculated using ITU R-factor, which is a widely accepted quality estimator. The tests show the bandwidth savings of multiplexing, and also the importance of packet size for certain buffers, as latency and packet loss may be affected. The customer’s experience improvement is measured, showing that the use of multiplexing can be interesting in some scenarios, like an enterprise with different offices connected via the Internet. The system is also tested using different numbers of samples per packet, and the distribution of the flows into different tunnels is found to be an important factor in order to achieve an optimal perceived quality for each kind of buffer. Grouping all the flows into a single tunnel will not always be the best solution, as the increase of the number of flows does not improve bandwidth efficiency indefinitely. If the buffer penalizes big packets, it will be better to group the flows into a number of tunnels. The router processing capacity has to be taken into account too, as the limit of packets per second it can manage must not be exceeded. The obtained results show that multiplexing is a good way to improve customer’s experience of VoIP in scenarios where many RTP flows share the same path.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Solving differential equations with Fourier series and Evolution Strategies

A novel mesh-free approach for solving differential equations based on Evolution Strategies (ESs) is presented. Any structure is assumed in the equations making the process general and suitable for linear and nonlinear ordinary and partial differential equations (ODEs and PDEs), as well as systems of ordinary differential equations (SODEs). Candidate solutions are expressed as partial sums of Fourier series. Taking advantage of the decreasing absolute value of the harmonic coefficients with the harmonic order, several ES steps are performed. Harmonic coefficients are taken into account one by one starting with the lower order ones. Experimental results are reported on several problems extracted from the literature to illustrate the potential of the proposed approach. Two cases (an initial value problem and a boundary condition problem) have been solved using numerical methods and a quantitative comparative is performed. In terms of accuracy and storing requirements the proposed approach outperforms the numerical algorithm.     

Searching in peer-to-peer networks

As peer-to-peer networks are proving capable of handling huge volumes of data, the need for effective search tools is lasting and imperative. During the last years, a number of research studies have been published, which attempt to address the problem of search in large, decentralized networks. In this article, we mainly focus on content and concept-based retrieval. After providing a useful discussion on terminology, we introduce a representative sample of such studies and categorize them according to basic functional and non-functional characteristics. Following our analysis and discussion we conclude that future work should focus on information filtering, re-ranking and merging of results, relevance feedback and content replication as well as on related user-centric aspects of the problem.     

Changes in free amino acids content in albacore (Thunnus alalunga) muscle during thermal processing

Summary The effects of cooking and sterilization at several temperatures on the free amino acids (FAA) content in albacore (Thunnus alalunga) muscle were studied during the processing of canned tuna. FAAs were derivatized witho-phtalaldehyde, separated on a C18 column by HPLC and detected by both fluorescence and ultra-violet detectors. After cooking the loss of FAAs was not significant. However, in the final product sterilized at 115 °C and 110 °C (throughout the whole process) there were significant losses with regard to the start material, but not at 118 °C (all temperatures leading to the same lethal F-value). The influence of the thermal process time at 115 °C was evaluated for 60 and 100 min. Significant losses were found between both canned products (25%) and between the raw fish and the final product (12% and 34%, process time 60 and 100 min, respectively). The determination of the content of FAA present in canned albacore may be a useful indication of the severity of the thermal processing.

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Veränderung im Gehalt an freien Aminosäuren in Thunfischmuskel (Tunnus alalunga) während der Wärmebehandlung

Zusammenfassung Die Wirkung des Kochens und Sterilisierens bei verschiedenen Temperaturen auf den Gehalt an freien Aminosäuren (FAA) im Thunfischmuskel (Thunnus alalunga) wurde während der Herstellung von Thunfischkonserven untersucht. Die freien Aminosäuren wurden mito-Phtalaldehyd derivatisiert, auf einer C-18-Kolonne mit HPLC abgetrennt und durch Fluorescenz und UV-Detektoren nachgewiesen. Nach dem Kochen war der Verlust an FAA nicht signifikant, jedoch in dem bei 110° und 115 °C sterilisierten Endprodukt ergaben sich signifikante Verluste bezogen auf das Ausgangsmaterial, aber nicht auf das bei 118 °C erhitzte; alle Erhitzungstemperaturen führen zu demselben letalen F-Wert. Der Zeiteinfluß der Erhitzung bei 115 °C wurde bei 60 und 100 min bewertet. Signifikante Verluste sind bei beiden Dosenkonserven aufgetreten (25%) und zwischen diesen und dem Rohfisch ( 12% und 34%) bei einer Erhitzungszeit von 60 und 100 min). Die Bestimmung des FAA-Gehalts in Thunfischkonserven kann für den Nachweis der Einwandfreiheit des thermischen Prozesses sehr nützlich sein.

     

Functionalized Reduced Graphene Oxide as a Versatile Tool for Cancer Therapy

Cancer is one of the deadliest diseases in human history with extremely poor prognosis. Although many traditional therapeutic modalities—such as surgery, chemotherapy, and radiation therapy—have proved to be successful in inhibiting the growth of tumor cells, their side effects may vastly limited the actual benefits and patient acceptance. In this context, a nanomedicine approach for cancer therapy using functionalized nanomaterial has been gaining ground recently. Considering the ability to carry various anticancer drugs and to act as a photothermal agent, the use of carbon-based nanomaterials for cancer therapy has advanced rapidly. Within those nanomaterials, reduced graphene oxide (rGO), a graphene family 2D carbon nanomaterial, emerged as a good candidate for cancer photothermal therapy due to its excellent photothermal conversion in the near infrared range, large specific surface area for drug loading, as well as functional groups for functionalization with molecules such as photosensitizers, siRNA, ligands, etc. By unique design, multifunctional nanosystems could be designed based on rGO, which are endowed with promising temperature/pH-dependent drug/gene delivery abilities for multimodal cancer therapy. This could be further augmented by additional advantages offered by functionalized rGO, such as high biocompatibility, targeted delivery, and enhanced photothermal effects. Herewith, we first provide an overview of the most effective reducing agents for rGO synthesis via chemical reduction. This was followed by in-depth review of application of functionalized rGO in different cancer treatment modalities such as chemotherapy, photothermal therapy and/or photodynamic therapy, gene therapy, chemotherapy/phototherapy, and photothermal/immunotherapy.     

Ivabradine Induces Cardiac Protection against Myocardial Infarction by Preventing Cyclophilin-A Secretion in Pigs under Coronary Ischemia/Reperfusion

In response to cardiac ischemia/reperfusion, proteolysis mediated by extracellular matrix metalloproteinase inducer (EMMPRIN) and its secreted ligand cyclophilin-A (CyPA) significantly contributes to cardiac injury and necrosis. Here, we aimed to investigate if, in addition to the effect on the funny current (I(f)), Ivabradine may also play a role against cardiac necrosis by reducing EMMPRIN/CyPA-mediated cardiac inflammation. In a porcine model of cardiac ischemia/reperfusion (IR), we found that administration of 0.3 mg/kg Ivabradine significantly improved cardiac function and reduced cardiac necrosis by day 7 after IR, detecting a significant increase in cardiac CyPA in the necrotic compared to the risk areas, which was inversely correlated with the levels of circulating CyPA detected in plasma samples from the same subjects. In testing whether Ivabradine may regulate the levels of CyPA, no changes in tissue CyPA were found in healthy pigs treated with 0.3 mg/kg Ivabradine, but interestingly, when analyzing the complex EMMPRIN/CyPA, rather high glycosylated EMMPRIN, which is required for EMMPRIN-mediated matrix metalloproteinase (MMP) activation and increased CyPA bonding to low-glycosylated forms of EMMPRIN were detected by day 7 after IR in pigs treated with Ivabradine. To study the mechanism by which Ivabradine may prevent secretion of CyPA, we first found that Ivabradine was time-dependent in inhibiting co-localization of CyPA with the granule exocytosis marker vesicle-associated membrane protein 1 (VAMP1). However, Ivabradine had no effect on mRNA expression nor in the proteasome and lysosome degradation of CyPA. In conclusion, our results point toward CyPA, its ligand EMMPRIN, and the complex CyPA/EMMPRIN as important targets of Ivabradine in cardiac protection against IR.     

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.     

Biomarkers for Cancer Cachexia: A Mini Review

Cancer cachexia is a common condition in many cancer patients, particularly those with advanced disease. Cancer cachexia patients are generally less tolerant to chemotherapies and radiotherapies, largely limiting their treatment options. While the search for treatments of this condition are ongoing, standards for the efficacy of treatments have yet to be developed. Current diagnostic criteria for cancer cachexia are primarily based on loss of body mass and muscle function. However, these criteria are rather limiting, and in time, when weight loss is noticeable, it may be too late for treatment. Consequently, biomarkers for cancer cachexia would be valuable adjuncts to current diagnostic criteria, and for assessing potential treatments. Using high throughput methods such as “omics approaches”, a plethora of potential biomarkers have been identified. This article reviews and summarizes current studies of biomarkers for cancer cachexia.