Background: In boys with DMD, muscle weakness progresses in a proximal to distal pattern, leading to compensatory gait strategies, including hyperlordosis and equinus, that increase energy cost and accelerate the loss of walking capacity.
Research Question: The purpose of this study was to determine the changes in the energy cost of walking that occur with disease progression and to determine the optimal normalization scheme for the longitudinal assessment of the energy cost of walking in boys with DMD.
Methods: Energy cost was assessed with the COSMED K4b. Three normalization schemes were examined: gross energy cost (EC), net non-dimensional oxygen cost (NNcost) and speed-matched control energy cost (SMC-EC). Nonlinear mixed modeling procedures determined the rate of change with age. Linear regression was used to asses the relationship between each normalization scheme and age and body height.
Results: 74 boys with DMD were assessed for the energy cost of walking. Velocity decreased at a significant rate (-.00245/month, p = .03) across time; (Fig. 2), while gross EC (.003248/month, p = 0.0026), NNcost (.006155/month, p < 0.0001) and SMC-EC (.001690/month, p = 0.03) all increased significantly. Age and height were significantly associated with NNcost and SMC-EC. The sensitivity of NNcost and SMC-EC to age over time were similar, while SMC-EC was less sensitive to changes in height over time than NNcost.
Significance: In contrast to able-bodied peers, boys with DMD decrease their velocity while all walking energy cost measures increased over time. Both SMC-EC and NNcost proved appropriate normalization schemes for boys with DMD. Compared to gross EC, both NNcost and SMC-EC were less sensitive to changes in age over time, while SMC-EC was less sensitive to changes in height than NNcost. Therefore, both NNCost and SMC-EC are suggested normalization schemes for the longitudinal assessment of energy cost in boys with DMD.
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http://dx.doi.org/10.1016/j.gaitpost.2021.09.173 | DOI Listing |
Waste Manag Res
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Bohai Rim Energy Research Institute, Northeast Petroleum University, Daqing, Heilongjiang, China.
In this systematic review, advancements in plastic recycling technologies, including mechanical, thermolysis, chemical and biological methods, are examined. Comparisons among recycling technologies have identified current research trends, including a focus on pretreatment technologies for waste materials and the development of new organic chemistry or biological techniques that enable recycling with minimal energy consumption. Existing environmental and economic studies are also compared.
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Universidad Complutense de Madrid Facultad de Ciencias Quimicas, Inorganic Chemistry Department, 28034, Madrid, SPAIN.
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Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, California 94305, United States.
Significant hourly variation in the carbon intensity of electricity supplied to wastewater facilities introduces an opportunity to lower emissions by shifting the timing of their energy demand. This shift could be accomplished by storing wastewater, biogas from sludge digestion, or electricity from on-site biogas generation. However, the life cycle emissions and cost implications of these options are not clear.
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Electrical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, India.
Graphene quantum dots (GQDs) are highly valued for their chemical stability, tunable size, and biocompatibility. Utilizing green chemistry, a microwave-assisted synthesis method was employed to produce water-soluble GQDs from Mangifera Indica leaf extract. This approach is efficient, cost-effective, and environmentally friendly, offering reduced reaction times, energy consumption, and uniform particle sizes, and has proven advantageous over other methods.
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Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand.
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