Ekotietoisuutta vai ekounholaa? : pohdintoja sulauttavasta musiikkikokemuksesta

Johdatus ekomusikologiaan, musiikkitiede, Turun yliopisto.

Study of lymphocyte subpopulations in bone marrow in a model of protein-energy malnutrition

Objective: Protein-energy malnutrition (PEM) is an important public health problem affecting millions of people worldwide. Hematopoietic tissue requires a high nutrient supply, and a reduction in leukocytes, especially lymphocytes, suggests that some nutritional deficiencies might be altering bone marrow function and decreasing its ability to produce lymphocytes. In this study, we evaluated the effect that PEM has on lymphocyte subtypes and the cell cycle of CD5(+) cells. Methods: Swiss mice were subjected to PEM using a low-protein diet containing 4% protein. When the experimental group had lost about 20% of their original body weight, we collected blood and bone marrow cells and evaluated the hemogram, the myelogram, bone marrow lymphoid markers using flow cytometry, and the cell cycle in CD5(+) bone marrow. Results: Malnourished animals presented anemia, reticulocytopenia, and leukopenia with lymphopenia. The bone marrow was hypocellular, and flow cytometric analyses of bone marrow cells showed cells that were CD45(+) (91.2%), CD2(+) (84.9%), CD5(+) (37.3%), CD3(+) (23.5%), CD19(+) (43.3%), CD22(+) (34.7%), CD19(+)/CD2(+) (51.2%), CD19(+)/CD3(+)(24.0%), CD19(+)/CD5(+) (13.2%), CD22(+)/CD2(+) (40.1%), CD22(+)/CD3(+) (30.3%), and CD22(+)/CD5(+) (1.1%) in malnourished animals and CD45(+) (97.5%), CD2(+) (42.9%), CD5(+) (91.5%), CD3(+) (92.0%), CD19(+) (52.0%), CD22(+) (75.6%), CD19(+)/CD2(+) (62.0%), CD19(+)/CD3(+) (55.4%), CD19(+)/CO5(+) (6.7%), CD22(+)/CD2(+) (70.3%), CD22(+)/CD3(+) (55.9%), and CD22(+)/ CD5(+) (8.4%) in control animals. Malnourished animals also presented more CD5(+) cells in the G0 phase of cell cycle development. Conclusion: Malnourished animals presented bone marrow hypoplasia, maturation interruption, prominent lymphopenia with depletion in the lymphoid lineage, and changes in cellular development. We suggest that these changes are some of the primary causes of lymphopenia in cases of PEM and partly explain the increase in susceptibility to infections found in malnourished individuals. Published by Elsevier Inc.

Resting energy expenditure of children attending a rehabilitation programme following head injury

Statement of purpose: Increased resting energy expenditure following head injury is well documented, but whether this increase extends into rehabilitation and whether this is affected by changes in body composition have not been studied. The aim of this study was to determine whether children attending a rehabilitation program following head injury had altered energy expenditure and body composition. Methods: Measurements of resting energy expenditure by indirect calorimetry were performed in 21 head injured children (mean age 10.2±3.8 years). Measurement of body composition was performed using total body potassium. Results: Measured resting energy expenditure values were widely distributed, ranging from 52.3-156.4% of predicted values, yet the mean percentage predicted using Schofield weight, Schofield weight and height and World Health Organization predictive equations were 97.5%, 97.4% and 98.6%, respectively. Mean percentage of expected total body potassium for weight, height and age for head injured children were 85.1 ± 15.5%, 89.1 ± 14.1% and 86.9 ± 15.9%, thus all showed significant depletion. Conclusions: During rehabilitation, using predictive equations to estimate resting energy expenditure in this group revealed a small bias on average but very large bias at the individual level. Head injured children had altered resting energy expenditure and body composition.

Centile reference for total energy expenditure in infants from 1 to 12 months

Background: A knowledge of energy expenditure in infancy is required for the estimation of recommended daily amounts of food energy, for designing artificial infant feeds, and as a reference standard for studies of energy metabolism in disease states. Objectives: The objectives of this study were to construct centile reference charts for total energy expenditure (TEE) in infants across the first year of life. Methods: Repeated measures of TEE using the doubly labeled water technique were made in 162 infants at 1.5, 3, 6, 9 and 12 months. In total, 322 TEE measurements were obtained. The LMS method with maximum penalized likelihood was used to construct the centile reference charts. Centiles were constructed for TEE expressed as MJ/day and also expressed relative to body weight (BW) and fat-free mass (FFM). Results: TEE increased with age and was 1.40,1.86, 2.64, 3.07 and 3.65 MJ/day at 1.5, 3, 6, 9 and 12 months, respectively. The standard deviations were 0.43, 0.47, 0.52, 0.66 and 0.88, respectively. TEE in MJ/kg increased from 0.29 to 0.36 and in MJ/day/kg FFM from 0.36 to 0.48. Conclusions: We have presented centile reference charts for TEE expressed as MJ/day and expressed relative to BW and FFM in infants across the first year of life. There was a wide variation or biological scatter in TEE values seen at all ages. We suggest that these centile charts may be used to assess and possibly quantify abnormal energy metabolism in disease states in infants.

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.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Distribution system operation supported by contextual energy resource management based on intelligent SCADA

Future distribution systems will have to deal with an intensive penetration of distributed energy resources ensuring reliable and secure operation according to the smart grid paradigm. SCADA (Supervisory Control and Data Acquisition) is an essential infrastructure for this evolution. This paper proposes a new conceptual design of an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). This SCADA model is used to support the energy resource management undertaken by a distribution network operator (DNO). Resource management considers all the involved costs, power flows, and electricity prices, allowing the use of network reconfiguration and load curtailment. Locational Marginal Prices (LMP) are evaluated and used in specific situations to apply Demand Response (DR) programs on a global or a local basis. The paper includes a case study using a 114 bus distribution network and load demand based on real data.

Wind energy technology

Electricity is regarded as one of the indispensable means to growth of any country’s economy. This source of power is the heartbeat of everything from the huge metropolitans, industries, worldwide computer networks and our global communication systems down to our homes. Electrical energy is the lifeline for any economic and societal development of a region or country. It is central to develop countries for maintaining acquired life styles and essential to developing countries for industrialisation and escaping poverty.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Energy-aware task mapping onto heterogeneous platforms using DVFS and sleep states

Heterogeneous multicore platforms are becoming an interesting alternative for embedded computing systems with limited power supply as they can execute specific tasks in an efficient manner. Nonetheless, one of the main challenges of such platforms consists of optimising the energy consumption in the presence of temporal constraints. This paper addresses the problem of task-to-core allocation onto heterogeneous multicore platforms such that the overall energy consumption of the system is minimised. To this end, we propose a two-phase approach that considers both dynamic and leakage energy consumption: (i) the first phase allocates tasks to the cores such that the dynamic energy consumption is reduced; (ii) the second phase refines the allocation performed in the first phase in order to achieve better sleep states by trading off the dynamic energy consumption with the reduction in leakage energy consumption. This hybrid approach considers core frequency set-points, tasks energy consumption and sleep states of the cores to reduce the energy consumption of the system. Major value has been placed on a realistic power model which increases the practical relevance of the proposed approach. Finally, extensive simulations have been carried out to demonstrate the effectiveness of the proposed algorithm. In the best-case, savings up to 18% of energy are reached over the first fit algorithm, which has shown, in previous works, to perform better than other bin-packing heuristics for the target heterogeneous multicore platform.

TEA: Timing and Energy Aware compression architecture for Efficient Configuration in CGRAs

Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern applications (e.g. 4G, CDMA, etc.). Recently proposed CGRAs offer time-multiplexing and dynamic applications parallelism to enhance device utilization and reduce energy consumption at the cost of additional memory (up to 50% area of the overall platform). To reduce the memory overheads, novel CGRAs employ either statistical compression, intermediate compact representation, or multicasting. Each compaction technique has different properties (i.e. compression ratio, decompression time and decompression energy) and is best suited for a particular class of applications. However, existing research only deals with these methods separately. Moreover, they only analyze the compaction ratio and do not evaluate the associated energy overheads. To tackle these issues, we propose a polymorphic compression architecture that interleaves these techniques in a unique platform. The proposed architecture allows each application to take advantage of a separate compression/decompression hierarchy (consisting of various types and implementations of hardware/software decoders) tailored to its needs. Simulation results, using different applications (FFT, Matrix multiplication, and WLAN), reveal that the choice of compression hierarchy has a significant impact on compression ratio (up to 52%), decompression energy (up to 4 orders of magnitude), and configuration time (from 33 n to 1.5 s) for the tested applications. Synthesis results reveal that introducing adaptivity incurs negligible additional overheads (1%) compared to the overall platform area.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Changes in body fluid and energy compartments during prolonged hunger strike

Prolonged total food deprivation in non-obese adults is rare, and few studies have documented body composition changes in this setting. In a group of eight hunger strikers who refused alimentation for 43 days, water and energy compartments were estimated, aiming to assess the impact of progressive starvation. Measurements included body mass index (BMI), triceps skinfold (TSF), arm muscle circumference (AMC), and bioimpedance (BIA) determinations of water, fat, lean body mass (LBM), and total resistance. Indirect calorimetry was also performed in one occasion. The age of the group was 43.3±6.2 years (seven males, one female). Only water, intermittent vitamins and electrolytes were ingested, and average weight loss reached 17.9%. On the last two days of the fast (43rd-44th day) rapid intravenous fluid, electrolyte, and vitamin replenishment were provided before proceeding with realimentation. Body fat decreased approximately 60% (BIA and TSF), whereas BMI reduced only 18%. Initial fat was estimated by BIA as 52.2±5.4% of body weight, and even on the 43rd day it was still measured as 19.7±3.8% of weight. TSF findings were much lower and commensurate with other anthropometric results. Water was comparatively low with high total resistance, and these findings rapidly reversed upon the intravenous rapid hydration. At the end of the starvation period, BMI (21.5±2.6 kg/m²) and most anthropometric determinations were still acceptable, suggesting efficient energy and muscle conservation. Conclusions: 1) All compartments diminished during fasting, but body fat was by far the most affected; 2) Total water was low and total body resistance comparatively elevated, but these findings rapidly reversed upon rehydration; 3) Exaggerated fat percentage estimates from BIA tests and simultaneous increase in lean body mass estimates suggested that this method was inappropriate for assessing energy compartments in the studied population; 4) Patients were not morphologically malnourished after 43 days of fasting; however, the prognostic impact of other impairments was not considered in this analysis.

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