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An experimental validation of a steady-state model for water-to-water heat pumps is conducted on a 10 kW test bench. The objective of the model is to predict the capacity and the required compressor power, based on the inlet conditions of the secondary fluids in the evaporator and condenser. Detailed manufacturer performance maps based on the AHRI 540-2020 standard are utilized to model the fixed-speed scroll compressor. A new semi-empirical model for the thermostatic expansion valve incorporates condensing temperature effects on superheating prediction. Sub-models for individual components, including detailed representations of the evaporator and condenser, are integrated into a global model, resulting in a set nonlinear equation solved using an equation solver with appropriate guess values. The validation of the model is conducted in an experimental test facility equipped with two precisely controlled secondary fluid loops. The heat pump is instrumented to measure condensation and evaporation pressures, the compressor discharge temperature, compressor power, superheating, and sub-cooling. The results are divided into three sub-sections: the first validates the complete heat pump model by comparing its power consumption and COPs in heating and cooling; the second compares the predicted and measured operational conditions; finally, it is shown how the model can be used to predict the non-operational conditions of the heat pump for specific scenarios.

This work is supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health. CIBER de Fisiopatología de la Obesidad y Nutrición is an initiative of ISCIII. The funding agency had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Even though the inevitable process of aging by itself cannot be considered a disease, it is directly linked to life span and is the driving force behind all age-related diseases. It is an undisputable fact that age-associated diseases are among the leading causes of death in the world, primarily in industrialized countries. During the last several years, an intensive search of antiaging treatments has led to the discovery of a variety of drugs that promote health span and/or life extension. The biguanide compound metformin is widely used for treating people with type 2 diabetes and appears to show protection against cancer, inflammation, and age-related pathologies. Here, we summarize the recent developments about metformin use in translational aging research and discuss its role as a potential geroprotector., Depto. de Genética, Fisiología y Microbiología, Fac. de Ciencias Biológicas, TRUE, pub

Skeletal muscle adapts to different exercise training modalities with age; however, the impact of both variables at the systemic and tissue levels is not fully understood. Here, adult and old C57BL/6 male mice were assigned to one of three groups: sedentary, daily high-intensity intermittent training (HIIT), or moderate intensity continuous training (MICT) for 4 weeks, compatible with the older group’s exercise capacity. Improvements in body composition, fasting blood glucose, and muscle strength were mostly observed in the MICT old group, while effects of HIIT training in adult and old animals was less clear. Skeletal muscle exhibited structural and functional adaptations to exercise training, as revealed by electron microscopy, OXPHOS assays, respirometry, and muscle protein biomarkers. Transcriptomics analysis of gastrocnemius muscle combined with liver and serum metabolomics unveiled an age-dependent metabolic remodeling in response to exercise training. These results support a tailored exercise prescription approach aimed at improving health and ameliorating age-associated loss of muscle strength and function in the elderly.

A new bottom-up white-box building stock energy modelling approach is presented in this paper. The model is a physics simulation-based stock model that can be used to compare the base case building stock with technological variations for comparative assessments. The model accuracy is compared to known stock data for a variety of categories, including end-use, energy source and building type. The model predicts the energy use of a building stock in a Canadian region with good agreement across all categories, with the total energy consumption of the model within 1.5% of the real stock energy use. Our analysis shows that modelling a sufficiently large sample of stock is required to reduce the expected deviation for lesser-represented portions of the stock. A case study illustrates how the model can be applied to compare different heating system distributions, in order to assess their impact on greenhouse gas emissions and peak electricity demand.

Reduced expression of the plasma membrane citrate transporter INDY (acronym I'm Not Dead, Yet) extends life span in lower organisms. Deletion of the mammalian Indy (mIndy) gene in rodents improves metabolism via mechanisms akin to caloric restriction, known to lower blood pressure {BP) by sympathoadrenal inhibition. We hypothesized that mIady deletion attenuates sympathoadrenal support of BP. Continuous arterial BP and heart rate (HR) were reduced in mINDY-KO mice. Concomitantly, urinary catecholamine content was lower, and the decreases in BP and HR by mIndy deletion were attenuated after autonomic ganglionic blockade. Catecholamine biosynthesis pathways were reduced in mINDY-KO adrenals using unbiased microarray analysis. Citrate, the main mINDY substrate, increased catecholamine content in pheochromocytoma cells, while pharmacological inhibition of citrate uptake blunted the effect. Our data suggest that deletion of mIndy reduces sympathoadrenal support of BP and HR by attenuating catecholamine biosynthesis. Deletion of mIndy recapitulates beneficial cardiovascular and metabolic responses to caloric restriction, making it an attractive therapeutic target.

Aging is associated with distinct phenotypical, physiological, and functional changes, leading to disease and death. The progression of aging-related traits varies widely among individuals, influenced by their environment, lifestyle, and genetics. In this study, we conducted physiologic and functional tests cross-sectionally throughout the entire lifespan of male C57BL/6N mice. In parallel, metabolomics analyses in serum, brain, liver, heart, and skeletal muscle were also performed to identify signatures associated with frailty and age-dependent functional decline. Our findings indicate that declines in gait speed as a function of age and frailty are associated with a dramatic increase in the energetic cost of physical activity and decreases in working capacity. Aging and functional decline prompt organs to rewire their metabolism and substrate selection and toward redox-related pathways, mainly in liver and heart. Collectively, the data provide a framework to further understand and characterize processes of aging at the individual organism and organ levels.

Chronic nutrient excess leads to metabolic disorders and insulin resistance. Activation of stress-responsive pathways via Nrf2 activation contributes to energy metabolism regulation. Here, inducible activation of Nrf2 in mice and transgenesis of the Nrf2 target, NQO1, conferred protection from diet-induced metabolic defects through preservation of glucose homeostasis, insulin sensitivity, and lipid handling with improved physiological outcomes. NQO1-RNA interaction mediated the association with and inhibition of the translational machinery in skeletal muscle of NQO1 transgenic mice. NQO1-Tg mice on high-fat diet had lower adipose tissue macrophages and enhanced expression of lipogenic enzymes coincident with reduction in circulating and hepatic lipids. Metabolomics data revealed a systemic metabolic signature of improved glucose handling, cellular redox, and NAD+ metabolism while label-free quantitative mass spectrometry in skeletal muscle uncovered a distinct diet- and genotype-dependent acetylation pattern of SIRT3 targets across the core of intermediary metabolism. Thus, under nutritional excess, NQO1 transgenesis preserves healthful benefits.

Existing electricity smart meter data sets lack sufficient details on building parameters to evaluate the impact that home characteristics can have on electricity consumption. An extensive, open-source virtual smart meter (VSM) data set with corresponding building characteristics is provided. The methodology used to develop the VSM data is presented in detail. The data set consists of a variety of homes representative of a subset of the Canadian single-family home building stock. The building characteristics cover a wide range of values that are based on probability distributions developed using a segmentation and characterization process. The resulting framework and VSM data set can be used by researchers to develop classification models, verify load disaggregation algorithms, and for a variety of other purposes.

Thermal models of buildings are helpful to forecast their energy use and to enhance the control of their mechanical systems. However, these models are building-specific and require a tedious, error-prone and time-consuming development effort relying on skilled building energy modelers. Compared to white-box and gray-box models, data-driven (black-box) models require less development time and a minimal amount of information about the building characteristics. In this paper, autoregressive neural network models are compared to gray-box and black-box linear models to simulate indoor temperatures. These models are trained, validated and compared to actual experimental data obtained for an existing commercial building in Montreal (QC, Canada) equipped with roof top units for air conditioning. Results show that neural networks mimic more accurately the thermal behavior of the building when limited information is available, compared to gray-box and black-box linear models. The gray-box model does not perform adequately due to its under-parameterized nature, while the linear models cannot capture non-linear phenomena such as radiative heat transfer and occupancy. Therefore, the neural network models outperform the alternative models in the presented application, reaching a coefficient of determination R2 up to 0.824 and a root mean square error down to 1.11 °C, including the error propagation over time for a 1-week period with a 5-minute time-step. When considering a 50-hour time horizon, the best neural networks reach a much lower root mean square error of around 0.6 °C, which is suitable for applications such as model predictive control.