Evaluation of false-twist textured yarns by image processing

A new method has been introduced to determine the crimp features of false twist textured yarns by applying computer vision and image processing method. Hence, the test results, with accuracy, are achieved more quickly than by the other exciting method. The mean angle of filament orientation in false twist textured yarns with different texturizing variables (heater temperature, texturizing speed and twist) is determined. Similarly, the direct tracking algorithms to achieve a good correlation with crimp contraction are also used. The results show that by this new method a correlation coefficient of more than 95% is achieved between mean orientation angle and crimp contraction.

Réduction du modèle ASM 1 pour la commande optimale des petites stations d'épuration à boues activées

L'adoption par l'Union Européenne de normes de rejets plus contraignantes implique une meilleure gestion des stations d'épuration. L'utilisation de modèles de simulation dynamique dans des schémas de commande en boucle fermée constitue une alternative intéressante pour répondre à ce problème.Sur la base du modèle ASM 1, un modèle réduit est ici élaboré pour le procédé à boues activées en aération séquentielle, en vue de la commande optimale du système d'aération. Les simplifications considérées sont de deux types : (i) les dynamiques lentes du système sont identifiées au moyen d'une méthode d'homotopie, puis éliminées du modèle ; (ii) des simplifications plus heuristiques consistant à prendre en compte un composé organique unique et à éliminer la concentration des composés organiques azotés sont ensuite appliquées. Elles conduisent à un modèle simplifié de 5 variables. L'application d'une procédure d'identification paramétrique permet alors de démontrer que le comportement dynamique du modèle simplifié est en bonne adéquation avec celui du modèle ASM 1 sur un horizon de prédiction de plusieurs heures, même lorsque les concentrations de l'influent ne sont pas connues. Il est également vérifié que le modèle proposé est observable et structurellement identifiable, sous des conditions d'aérobiose et d'anoxie, à partir des mesures en ligne des concentrations en oxygène dissous, ammoniaque et nitrate.Le modèle simplifié développé présente ainsi toutes les propriétés requises pour une future utilisation au sein de schémas de commande en boucle fermée, en vue de la commande optimale des petites stations d'épuration à boues activées.In order to meet the stricter wastewater effluent guidelines adopted by the European Union, wastewater treatment plants require better management strategies. Wastewater treatment process models have become a major tool to design closed-loop control schemes. However, the dynamic models that are currently used in the simulation of activated sludge treatment plants (ASM 1, ASM 2 and, more recently, ASM 3 models) are highly dimensional and are not appropriate for on-line implementation (e.g., for model predictive control or optimal control). It is therefore important to develop reduced models that could be used for this purpose.A reduced model was developed to describe the behaviour of alternating activated sludge treatment plants, with the aim of applying it to the optimal control of an aeration system. The reduction scheme was based on appropriate simplifications to the ASM 1 model (which is more appropriate for open-loop control). The objective was to verify if accurate predictions could be made time periods of several hours (about 8 h).The present results are related to an existing small-size wastewater treatment plant. This plant was designed for 15,000 population-equivalents (p.e.) and consists of a primary treatment stage (screening, grit removal, primary sedimentation), followed by a secondary treatment stage (biological treatment). The latter consists of a single aeration tank of about 2,050 m3 equipped with 3 turbines which are operated cyclically to create alternating aerobic and anoxic conditions. Ammonia is converted into nitrate during air-on periods (nitrification step) and nitrate is subsequently removed during air-off periods (denitrification step). It is important to note that a dynamic model, based on the ASM 1 model and calibrated from a set of input/output measurements over a one-day period (Chachuat, 2001), was used here as a reference to perform model reduction. The following two-level simplification procedure was applied :· A homotopy method was first used to establish relationships between the states and the dynamics of the system, via an eigenvalue decomposition. The components that are associated with the slowest dynamics are then assumed constant to reduce the state space dimension. Heterotrophic (XB,H) and autotrophic (XB,A) biomass and inert particulate organic compounds (XI) were detected as the slow state variables. It was found that the short-term predictions of the dynamic model were not affected by assuming that XI, XB,H and XB,A concentrations were constant. Eliminating these 3 state variables, along with the concentrations of soluble inert organic compounds (SI), resulted in a 7-dimensional dynamic model.· However, further simplifications were required to enable the on-line optimisation of the bioreactor aeration profiles with reasonable computational times. These simplifications consisted of taking into account the process specifications in order to reduce the state space dimension to 4 or 5, and were therefore based on more heuristic considerations. Both organic and nitrogenous compounds are under consideration: (i) a single organic compound (denoted as XDCO) is formed by adding soluble and particulate organic compound concentrations, and (ii) the mathematical expression that describes the organic nitrogen hydrolysis process is simplified so that the dynamics with respect to soluble and particulate organic nitrogen are independent.The two previous simplification steps produced a reduced 5-dimensional dynamic model with state variables XDCO, SNO, SNH, SND and SO. It should also be noted that the resulting model involved the parameters YH, iNBM, KS, KNO, KO,H, KNH,A, ηNO,g and ηNO,h that are identical to those defined in the original ASM 1 model by Henze et al. (1987). In addition, 7 specific parameters were defined defined (θ1, θ2, θ3, θ4, θ5, KDCO, KND). These new parameters exhibited rather slow temporal variation, thus agreeing with the general ASM 1 model for short time periods.Afterwards, a two-step procedure was applied to calibrate the model. This procedure first consisted of determining a reduced set of identifiable parameters by the use of both sensitivity and principal component analyses. Note that the inlet concentrations of organic compounds, ammonia nitrogen and soluble organic nitrogen may be considered as additional parameters since they are generally not measured on-line. The selected parameters (θ1, θ2, θ3) and inlet concentrations (XinDCO, SinNH) were then estimated by the application of a local gradient search method (successive quadratic programming, SQP). Comparisons between the dynamic behaviour of both reduced and ASM 1 models show that accurate predictions can be obtained over time periods of several hours (8 h). It was also shown that the reduced model was observable and structurally identifiable under aerobic and anoxic conditions from dissolved oxygen, ammonia and nitrate concentration measurements. These results therefore demonstrate the ability of the reduced model to be embedded into closed-loop control schemes.The conclusions from this work are twofold: (i) The reduced model can be used as a basis to construct an on-line observer to estimate the unmeasured state variables, the unknown (most sensitive) parameters and inlet concentrations; (ii) Non-linear model predictive control (NMPC) schemes can then be implemented to operate the aeration system so that the nitrogen discharge or the energy consumption are minimised (optimal control).The initial results demonstrate that the application of NMPC strategies is likely to give large reductions of nitrogen discharge with respect to usual operating strategies (e.g., oxygen or redox control). Such closed-loop control schemes are particularly efficient in dealing with large influent variations (inlet flow rate, concentration and composition) resulting from both human activities and climatic conditions, and inherent modelling uncertainties. However, an experimental validation of this control strategy on a pilot scale or an industrial scale is required to confirm these results

Nanofibers-based piezoelectric energy harvester for self-powered wearable technologies

The demands for wearable technologies continue to grow and novel approaches for powering these devices are being enabled by the advent of new energy materials and novel manufacturing strategies. In addition, decreasing the energy consumption of portable electronic devices has created a huge demand for the development of cost-effective and environment friendly alternate energy sources. Energy harvesting materials including piezoelectric polymer with its special properties make this demand possible. Herein, we develop a flexible and lightweight nanogenerator package based on polyvinyledene fluoride (PVDF)/LiCl electrospun nanofibers. The piezoelectric performance of the developed nanogenator is investigated to evaluate effect of the thickness of the as-spun mat on the output voltage using a vibration and impact test. It is found that the output voltage increases from 1.3 V to 5 V by adding LiCl as additive into the spinning solution compared with pure PVDF. The prepared PVDF/LiCl nanogenerator is able to generate voltage and current output of 3 V and 0.5 µA with a power density output of 0.3 µW cm−2 at the frequency of 200 Hz. It is found also that the developed nanogenerator can be utilized as a sensor to measure temperature changes from 30◦C to 90◦C under static pressure. The developed electrospun temperature sensor showed sensitivity of 0.16%/◦C under 100 Pa pressure and 0.06%/◦C under 220 Pa pressure. The obtained results suggested the developed energy harvesting textiles have promising applications for various wearable self-powered electrical devices and systems

The Prevalent Cases of Endometrial Carcinoma in Different Life Periods of Women – Clinical and Histopathological Diagnosis

Abnormal uterine bleeding is a term used to describe any irregular bleeding from the uterus that is not part of a woman’s normal menstrual cycle and can happen during different life periods. Abnormal uterine bleeding can have structural etiologies (polyp, adenomyosis, leiomyoma, malignancy) or nonstructural etiologies (coagulopathy, ovulatory dysfunction, and not-classified). The post- and pre-menopausal period requires a detailed investigation to establish the etiology of the bleeding because the chances that the woman has a malignancy are plentiful in this period. The treatment plan is determined based on the underlying cause of the bleeding. The main objective of this paper was to determine the prevalence of endometrial carcinoma in different periods, focusing on the postmenopausal period. This research was a cross-sectional study and included 79 cases of abnormal uterine bleeding in different periods of women’s lives. Our study found that abnormal uterine bleeding occurs more frequently during the premenopausal period (59%) and that the causes of irregular bleeding during this period were mainly benign. Our study focused on the postmenopausal period, where we found that 32.9% of irregular uterine bleeding occurs during this period, and the leading causes were malignant pathologies (83.3%), mainly endometrial adenocarcinomas. The average age was 66.7 years. During the study, it was observed that 50% of women with abnormal uterine bleeding due to malignancy were classified as overweight, 25% were obese, and 25% had normal weight. It was confirmed that obesity is a co-factor of malignancy in the postmenopausal period, and the pathophysiological mechanism is well understood now. Our study also found a high number of co-morbidities among women with abnormal uterine bleeding due to malignancy. We came to the conclusion that any abnormal uterine bleeding should be initially investigated in a multidisciplinary manner, an accurate diagnosis should be determined, and then a treatment plan should be established. The treatment strategy should be determined depending on the cause of abnormal bleeding

Cementitious composites reinforced with polypropylene, nylon and polyacrylonitile fibres

This paper compares the adhesion strength between three polymeric fibres (polypropylene (PP), nylon66 (N66) and polyacrylonitrile (PAN)) embedded in a cement paste. The specimens were prepared at a water to cement ratio (w/c) of 0.5, and tested after 7, 14 and 28 curing days. It was found that although the adhesion between the polymeric fibres to the cement matrix is an important factor, the energy absorption capacity or energy dissipation ability of the fibres plays a more important role in the improvement of the cementitious composites fracture toughness. Scanning electron micrographs was used to characterize the fibres surface before and after the Pullout tests

Evaluation of adhesion in polymeric fibre reinforced cementitious composites

In this study adhesion of some polymeric fibres to a cement matrix was evaluated both by a theoretical and by an experimental approach. In common methods adhesion of the fibres to the cementitious materials is determined by pull-out test. This test evaluates the energy failure during the fibre drawing out. This paper analyzes the adhesion theory for fibre reinforced cementitious composites to separate the share of the chemical and mechanical adhesion expressing new parameters, which are effective in the adhesion behaviour. Explanations about how the different fibres, matrix properties, test factors and environmental conditions can affect the adhesion results are given. It was found that for fibre/cement composites the fracture energy due to the interfacial interactions is for several orders of magnitude smaller that the polymeric fibre losses function. By employing adhesion theory, fibre/cement interac- tions are better described by a simple relationship of their surface free energy. Determination of the loss function (energy dissipation) leads us to predict the fibre behaviour in the cement matrix and the selection of the appropriate reinforcement

A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure

Total and acylated ghrelin levels in children and adolescents with idiopathic short stature and poor appetite

Context. Ghrelin is a hormone secreted primarily from stomach that can affect growth by its somatotropic and orexigenic activities. Objective. The aim of this study was to investigate the relationship of ghrelin and growth in children and adolescents with idiopathic short stature. Subjects and Methods. After thorough clinical examination, 56 subjects including 31 with normal weight and height and 25 with short stature were evaluated for fasting total (TG) and acylated (active) ghrelin (AG) levels. All the parameters of growth including growth hormone and IGF-1 levels, bone age and body mass index were also investigated. Appetite was also assessed and all the studied subjects were also divided into two groups, poor or good appetite. Results. TG and AG levels were not significantly different in the two groups. There was not any significant correlation between ghrelin and parameters of growth. On the other hand, TG concentration was significantly higher in subjects with poor appetite, but AG was not significantly different. Conclusions. The results of this study show that ghrelin is not significantly altered in idiopathic short stature. Although TG is increased in children with poor appetite its acylation is not increased concomitantly. © 2015, Editura Academiei Romane. All rights reserved

A robust scalable demand-side management based on diffusion-ADMM strategy for smart grid

Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework