Application of the reallocated smoothed pseudo Wigner-Ville distribution to knock detection

Knock energy estimation has always been essential for car manufacturers . Because of recent anti-pollution norms reinforcement , knock must be detected as soon as possible in order to reach optimal operating points for the engine . Knock effects are directl y visible on the pressure signal extracted from the combustion chamber through the oscillations caraterizing the cavity resonanc e frequencies . That is the reason why this signal is usually used as a reference for knock intensity estimation . The use of time - frequency distributions along with image processing has led to a great improvement in knock detection . However, knock images are sometimes poorly readable and difficulties arise in the extraction of relevant information . In this paper, a method using th e reallocated smoothed pseudo Wigner-Ville distribution associated to a labeling technique is proposed . The resulting process i s easy to implement . It is applied to actual measurements recorded on two different spark ignition engines . Compared to those o f existing techniques, the present results show a better knock intensity estimation . An improvement in the separation of knock from noisy combustions is also observed .Estimer l'énergie du cliquetis a toujours été un enjeu important pour les constructeurs automobile. Le renforcement des normes antipollution contraint à détecter l'apparition du cliquetis de plus en plus tôt afin de se placer dans des conditions optimales de fonctionnement du moteur. Les effets du cliquetis sont directement visibles sur le signal de pression issu de la chambre de combustion à travers les oscillations caractéristiques des fréquences propres de la cavité. C'est pourquoi ce signal sert généralement de référence à l'estimation de l'énergie du cliquetis. L'utilisation de techniques temps-fréquence et de traitements d'image a permis d'améliorer grandement l'estimation et la détection du cliquetis. Cependant les images sont parfois peu lisibles et les traitements nécessaires à l'extraction de l'information pertinente sont souvent complexes. Nous proposons ici d'utiliser une méthode de réallocation de la distribution de Wigner-Ville associée à une technique d'étiquetage. La mise en oeuvre d'un tel procédé est simple et il a été appliqué à des données issues d'essais réalisés sur différents moteurs. Les résultats obtenus sont comparés à ceux des méthodes existantes. Ils montrent une amélioration de l'estimation de l'intensité du cliquetis et une meilleure réjection des phénomènes de combustions bruyantes

An Inverse Method for Cracks Characterization from Ultrasonic Bscan Images

Concern has been expressed about the capabilities of performing non destructive evaluation (NDE) of flaws located near to the outer surface in nuclear pressurized water reactor (PWR) vessels. The ultrasonic examination of PWR is accomplished from the inside with ultrasonic focused transducers working in the pulse echo mode. By recording the echoes as a function of time, the Ascan representation may be obtained. Many ultrasonic flaw detectors used for NDE are based on the simple Ascan concept involving measuring a time interval called “time of flight”. By combining the Ascan concept with synchronized transducer scanning, one can produce Bscan images that are two dimensional descriptions of the flaw interaction with the ultrasonic field

Dog‐assisted interventions and outcomes for older adults in residential long‐term care facilities: a systematic review and meta‐analysis

Objective To comprehensively review studies on dog‐assisted interventions (DAIs) among older people in residential long‐term care facilities (RLTCFs) and to provide an overview of their interventions, outcomes and methodological quality. Method We searched 18 electronic databases to identify English articles (published January 2000–December 2019) reporting on well‐defined DAIs targeting older adults (≥65 years) in RLTCF. Data were extracted by two independent reviewers. Descriptive statistics were produced for quantitative studies, with key themes identified among qualitative studies. Where possible, estimates were pooled from randomised controlled trials using random effects meta‐analyses. Results Forty‐three relevant studies (39 quantitative; 4 qualitative) were identified. The majority of quantitative studies were assessed as low‐quality according to the MMAT criteria (n = 26, 67%). Almost half of the quantitative studies (n = 18, 46%) found no significant changes over time or between groups across outcomes measured. The most salient intervention effects included improved social functioning (n = 10), reduced depressive symptoms (n = 6) and loneliness (n = 5). A random‐effects meta‐analysis revealed a medium effect in favour of DAT on reducing depressive or loneliness symptoms (pooled SMD: 0.66, 95%CI 0.21–1.11; I2 = 50.5; five trials), relative to treatment as usual. However, compared to treatment as usual, no overall effect of DAI on activities of daily living was detected (p = .737). Key themes from qualitative studies included (a) animals as effective transitional objects, (b) the therapeutic value of pets and (c) the significance of the care environment and stakeholders in facilitating DAI. Implications for practice The findings of this review indicate that while DAI has value for older people in RLTCF, challenges remain in accurately measuring its impact to provide a stronger evidence‐base. Standardisation of DAI service design, delivery and evaluation is required for future research and practice in providing holistic care for older adults

An Ultra-Fast Metabolite Prediction Algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Why Pleiotropic Interventions are Needed for Alzheimer's Disease

Alzheimer's disease (AD) involves a complex pathological cascade thought to be initially triggered by the accumulation of β-amyloid (Aβ) peptide aggregates or aberrant amyloid precursor protein (APP) processing. Much is known of the factors initiating the disease process decades prior to the onset of cognitive deficits, but an unclear understanding of events immediately preceding and precipitating cognitive decline is a major factor limiting the rapid development of adequate prevention and treatment strategies. Multiple pathways are known to contribute to cognitive deficits by disruption of neuronal signal transduction pathways involved in memory. These pathways are altered by aberrant signaling, inflammation, oxidative damage, tau pathology, neuron loss, and synapse loss. We need to develop stage-specific interventions that not only block causal events in pathogenesis (aberrant tau phosphorylation, Aβ production and accumulation, and oxidative damage), but also address damage from these pathways that will not be reversed by targeting prodromal pathways. This approach would not only focus on blocking early events in pathogenesis, but also adequately correct for loss of synapses, substrates for neuroprotective pathways (e.g., docosahexaenoic acid), defects in energy metabolism, and adverse consequences of inappropriate compensatory responses (aberrant sprouting). Monotherapy targeting early single steps in this complicated cascade may explain disappointments in trials with agents inhibiting production, clearance, or aggregation of the initiating Aβ peptide or its aggregates. Both plaque and tangle pathogenesis have already reached AD levels in the more vulnerable brain regions during the “prodromal” period prior to conversion to “mild cognitive impairment (MCI).” Furthermore, many of the pathological events are no longer proceeding in series, but are going on in parallel. By the MCI stage, we stand a greater chance of success by considering pleiotropic drugs or cocktails that can independently limit the parallel steps of the AD cascade at all stages, but that do not completely inhibit the constitutive normal functions of these pathways. Based on this hypothesis, efforts in our laboratories have focused on the pleiotropic activities of omega-3 fatty acids and the anti-inflammatory, antioxidant, and anti-amyloid activity of curcumin in multiple models that cover many steps of the AD pathogenic cascade (Cole and Frautschy, Alzheimers Dement 2:284–286, 2006)

Parkinson’s disease mouse models in translational research

Animal models with high predictive power are a prerequisite for translational research. The closer the similarity of a model to Parkinson’s disease (PD), the higher is the predictive value for clinical trials. An ideal PD model should present behavioral signs and pathology that resemble the human disease. The increasing understanding of PD stratification and etiology, however, complicates the choice of adequate animal models for preclinical studies. An ultimate mouse model, relevant to address all PD-related questions, is yet to be developed. However, many of the existing models are useful in answering specific questions. An appropriate model should be chosen after considering both the context of the research and the model properties. This review addresses the validity, strengths, and limitations of current PD mouse models for translational research

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)