Argon ion laser and halogen lamp activation of a dark and light resin composite: microhardness after long-term storage

The objective of this study was to evaluate in vitro light activation of the nano-filled resin composite Vita shade A1 and A3 with a halogen lamp (QTH) and argon ion laser by Knoop microhardness profile. Materials and methods: Specimens of nanofilled composite resin (Z350-3 M-ESPE) Vita shade A1 and A3 were prepared with a single increment inserted in 2.0-mm-thick and 3-mm diameter disc-shaped Teflon mold. The light activation was performed with QTH for 20 s (with an intensity of approximately 1,000 mW/cm(2) and 700 mW/cm(2)) and argon ion laser for 10 s (with a power of 150 mW and 200 mW). Knoop microhardness test was performed after 24 h and 6 months. The specimens were divided into the 16 experimental groups (n = 10), according to the factors under study: photoactivation form, resin shade, and storage time. Knoop microhardness data was analyzed by a factorial ANOVA and TukeyA ` s tests at the 0.05 level of significance. Results: Argon ion laser was not able to photo-activate the darker shade of the nanofilled resin composite evaluated but when used with 200 mW it can be as effective as QTH to photo-activate the lighter shade with only 50% of the time exposure. After 6 months storage, an increase in the means of Knoop microhardness values were observed. Conclusions: Light-activation significantly influenced the Knoop microhardness values for the darker nanofilled resin composite.

A comparison of low-storage strategies for spectral analysis in dissipative systems: filter diagonalisation in the Lanczos representation and harmonic inversion of the Chebychev-order-domain autocorrelation function

We compare the performance of two different low-storage filter diagonalisation (LSFD) strategies in the calculation of complex resonance energies of the HO2, radical. The first is carried out within a complex-symmetric Lanczos subspace representation [H. Zhang, S.C. Smith, Phys. Chem. Chem. Phys. 3 (2001) 2281]. The second involves harmonic inversion of a real autocorrelation function obtained via a damped Chebychev recursion [V.A. Mandelshtam, H.S. Taylor, J. Chem. Phys. 107 (1997) 6756]. We find that while the Chebychev approach has the advantage of utilizing real algebra in the time-consuming process of generating the vector recursion, the Lanczos, method (using complex vectors) requires fewer iterations, especially for low-energy part of the spectrum. The overall efficiency in calculating resonances for these two methods is comparable for this challenging system. (C) 2001 Elsevier Science B.V. All rights reserved.

Lanczos subspace filter diagonalization: Homogeneous recursive filtering and a low-storage method for the calculation of matrix elements

We develop a new iterative filter diagonalization (FD) scheme based on Lanczos subspaces and demonstrate its application to the calculation of bound-state and resonance eigenvalues. The new scheme combines the Lanczos three-term vector recursion for the generation of a tridiagonal representation of the Hamiltonian with a three-term scalar recursion to generate filtered states within the Lanczos representation. Eigenstates in the energy windows of interest can then be obtained by solving a small generalized eigenvalue problem in the subspace spanned by the filtered states. The scalar filtering recursion is based on the homogeneous eigenvalue equation of the tridiagonal representation of the Hamiltonian, and is simpler and more efficient than our previous quasi-minimum-residual filter diagonalization (QMRFD) scheme (H. G. Yu and S. C. Smith, Chem. Phys. Lett., 1998, 283, 69), which was based on solving for the action of the Green operator via an inhomogeneous equation. A low-storage method for the construction of Hamiltonian and overlap matrix elements in the filtered-basis representation is devised, in which contributions to the matrix elements are computed simultaneously as the recursion proceeds, allowing coefficients of the filtered states to be discarded once their contribution has been evaluated. Application to the HO2 system shows that the new scheme is highly efficient and can generate eigenvalues with the same numerical accuracy as the basic Lanczos algorithm.

Energy cost of physical activity in cystic fibrosis

Objective: The purpose of this study was to compare the energy cost of standardized physical activity (ECA) between patients with cystic fibrosis (CF) and healthy control subjects. Design: Cross-sectional study using patients with CF and volunteers from the community. Setting: University laboratory. Subjects: Fifteen patients (age 24.6 +/- 4.6 y) recruited with consent from their treating physician and 16 healthy control subjects (age 25.3 +/- 3.2) recruited via local advertisement. Interventions. Patients and controls walked on a computerised treadmill at 1.5 km/h for 60 min followed by a 60 min recovery period and, on a second occasion, cycled at 0.5 kp (kilopond), 30 rpm followed by a 60 min recovery. The ECA was measured via indirect calorimetry. Resting energy expenditure (REE), nutritional status, pulmonary function and genotype were determined. Results: The REE in patients was significantly greater than the REE measured in controls (P = 0.03) and was not related to the severity of lung disease or genotype. There was a significant difference between groups when comparing the ECA for walking kg root FFM (P = 0.001) and cycling kg root FFM (P = 0.04). The ECA for each activity was adjusted (ECA(adj)) for the contribution of REE (ECA kJ kg root FFM 120 min(-1) - REE kJ kg root FFM 120 min(-1)). ECA(adj) revealed a significant difference between groups for the walking protocol (P = 0.001) but no difference for the cycling protocol (P = 0.45). This finding may be related to the fact that the work rate during walking was more highly regulated than during cycling. Conclusions ECA in CF is increased and is likely to be explained by an additional energy-requiring component related to the exercise itself and not an increased REE. Sponsorship. The Prince Charles Hospital Foundation; MLR was in receipt of a QUTPRA Scholarship.

Relationship of high energy expenditure and variation in dietary intake with reporting accuracy on 7 day food records and diet histories in a group of healthy adult volunteers

Objectives: To assess the accuracy of reporting from both a diet history and food record and identify some of the characteristics of more accurate reporters in a group of healthy adult volunteers for an energy balance study. Design: Prospective measurements in free-living people. Setting: Wollongong, Australia. Subjects: Fifteen healthy volunteers (seven male, eight female; aged 22 -59 y; body mass index (BMI) 19 - 33 kg/m(2)) from the local community in the city of Wollongong, Australia. Interventions: Measurement of energy intake via diet history interview and 7 day food records, total energy expenditure by the doubly labelled water technique over 14 days, physical activity by questionnaire, and body fat by dual-energy X-ray absorptiometry. Results: Increased misreporting of energy intake was associated with increased energy expenditure (r = 0.90, P < 0.0001, diet history; r(s)=0.79, P=0.0005, food records) but was not associated with age, sex, BMI or body fat. Range in number of recorded dinner foods correlated positively with energy expenditure (r(s)=0.63, P=0.01) and degree of misreporting (r(s)=0.71, P=0.003, diet history; r(s)=0.63, P=0.01, food records). Variation in energy intake at dinner and over the whole day identified by the food records correlated positively with energy expenditure (r=0.58, P = 0.02) and misreporting on the diet history (r=0.62, P=0.01). Conclusions: Subjects who are highly active or who have variable dietary and exercise behaviour may be less accurate in reporting dietary intake. Our findings indicate that it may be necessary to screen for these characteristics in studies where accuracy of reporting at an individual level is critical. Sponsorship: The study was supported in part by Australian Research Council funds made available through the University of Wollongong.

Patterns of lipid storage and mobilisation in the female green sea turtle (Chelonia mydas)

Reproductive data from southern Queensland indicate that vitellogenesis in female Chelonia mydas takes approximately 8 months and is followed by a migration to a breeding area. At Heron Island, females lay multiple clutches over approximately 3 months. To investigate how females mobilise and store lipid during the breeding season we collected plasma, yolk, and fat tissue samples from females at a variety of stages during the nesting season. In breeding females, concentrations of plasma triglyceride increased seasonally. They reached peak concentrations during vitellogenesis and courtship, remained high throughout the nesting season, and then declined to a nadir after the last clutch. Plasma protein concentration increased throughout the breeding season, peaking following the last clutch for the season. Yolk lipids were highest during courtship and were similar throughout the nesting season, suggesting that uptake of lipid by ovarian follicles is completed prior to the beginning of the nesting season. Plasma triglyceride decreases in females with prolonged periods of unsuccessful nesting, and total lipid levels in adipose tissue and follicle yolks were significantly lower in atretic females. It appears that: (1) endogenous energy reserves can be reduced by stochastic environmental events (such as those reducing nesting success), and (2) a metabolic shift signalling the end of the nesting season is characterised by a drop in plasma triglycerides and slight increase in total plasma protein.

The peroxisome proliferator-activated receptor beta/delta agonist, GW501516, regulates the expression of genes involved in lipid catabolism and energy uncoupling in skeletal muscle cells

Lipid homeostasis is controlled by the peroxisome proliferator-activated receptors (PPARalpha, -beta/delta, and -gamma) that function as fatty acid-dependent DNA-binding proteins that regulate lipid metabolism. In vitro and in vivo genetic and pharmacological studies have demonstrated PPARalpha regulates lipid catabolism. In contrast, PPARgamma regulates the conflicting process of lipid storage. However, relatively little is known about PPARbeta/delta in the context of target tissues, target genes, lipid homeostasis, and functional overlap with PPARalpha and -gamma. PPARbeta/delta, a very low-density lipoprotein sensor, is abundantly expressed in skeletal muscle, a major mass peripheral tissue that accounts for approximately 40% of total body weight. Skeletal muscle is a metabolically active tissue, and a primary site of glucose metabolism, fatty acid oxidation, and cholesterol efflux. Consequently, it has a significant role in insulin sensitivity, the blood-lipid profile, and lipid homeostasis. Surprisingly, the role of PPARbeta/delta in skeletal muscle has not been investigated. We utilize selective PPARalpha, -beta/delta, -gamma, and liver X receptor agonists in skeletal muscle cells to understand the functional role of PPARbeta/delta, and the complementary and/or contrasting roles of PPARs in this major mass peripheral tissue. Activation of PPARbeta/delta by GW501516 in skeletal muscle cells induces the expression of genes involved in preferential lipid utilization, beta-oxidation, cholesterol efflux, and energy uncoupling. Furthermore, we show that treatment of muscle cells with GW501516 increases apolipoprotein-A1 specific efflux of intracellular cholesterol, thus identifying this tissue as an important target of PPARbeta/delta agonists. Interestingly, fenofibrate induces genes involved in fructose uptake, and glycogen formation. In contrast, rosiglitazone-mediated activation of PPARgamma induces gene expression associated with glucose uptake, fatty acid synthesis, and lipid storage. Furthermore, we show that the PPAR-dependent reporter in the muscle carnitine palmitoyltransferase-1 promoter is directly regulated by PPARbeta/delta, and not PPARalpha in skeletal muscle cells in a PPARgamma coactivator-1-dependent manner. This study demonstrates that PPARs have distinct roles in skeletal muscle cells with respect to the regulation of lipid, carbohydrate, and energy homeostasis. Moreover, we surmise that PPARgamma/delta agonists would increase fatty acid catabolism, cholesterol efflux, and energy expenditure in muscle, and speculate selective activators of PPARbeta/delta may have therapeutic utility in the treatment of hyperlipidemia, atherosclerosis, and obesity.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Intelligent energy resources management in the context of smart grids

Smart grids are envisaged as infrastructures able to accommodate all centralized and distributed energy resources (DER), including intensive use of renewable and distributed generation (DG), storage, demand response (DR), and also electric vehicles (EV), from which plug-in vehicles, i.e. gridable vehicles, are especially relevant. Moreover, smart grids must accommodate a large number of diverse types or players in the context of a competitive business environment. Smart grids should also provide the required means to efficiently manage all these resources what is especially important in order to make the better possible use of renewable based power generation, namely to minimize wind curtailment. An integrated approach, considering all the available energy resources, including demand response and storage, is crucial to attain these goals. This paper proposes a methodology for energy resource management that considers several Virtual Power Players (VPPs) managing a network with high penetration of distributed generation, demand response, storage units and network reconfiguration. The resources are controlled through a flexible SCADA (Supervisory Control And Data Acquisition) system that can be accessed by the evolved entities (VPPs) under contracted use conditions. A case study evidences the advantages of the proposed methodology to support a Virtual Power Player (VPP) managing the energy resources that it can access in an incident situation.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.