Weight History Correlates of Resting Energy Expenditure in Women With Bulimia Nervosa.

OBJECTIVE: Among those with bulimia nervosa, weight suppression has been associated with illness severity and treatment prognosis. Although significant weight loss is known to reduce metabolic rate, the relation between weight suppression and resting energy expenditure (REE) in bulimia nervosa has not been examined. This study tested the hypothesis of an inverse relation between weight suppression and REE in a sample of women with bulimia nervosa (N = 84). METHODS: In primary analyses, linear regressions were conducted between weight suppression and REE, corrected for fat-free mass. In follow-up, exploratory analyses, stepwise linear regressions were conducted to explore the main and interaction effects of weight history and weight suppression on REE. RESULTS: Neither traditional (TWS) nor developmental weight suppression (DWS) correlated with REE. Results from exploratory analyses, however, revealed a medium-to-large inverse relation between several weight history variables and REE (highest past weight, sr2 = 0.05; lowest postmorbid weight, sr2 = 0.07; current weight, sr2 = 0.05). Additionally, DWS interacted with current (sr2 = 0.08) and highest premorbid (sr2 = 0.05) z-BMI to influence REE with a medium-to-large effect. For individuals low in current and premorbid z-BMIs, higher DWS associated with lower REE levels. However, for individuals at higher premorbid z-BMIs, higher DWS unexpectedly associated with greater REE levels. DISCUSSION: In this sample of women with bulimia nervosa, reduced REE associated with higher weights across all timepoints. If the interaction effect between DWS and z-BMI history persists in future studies, this may indicate unique challenges faced by individuals low in z-BMI and high in DWS related to weight gain and normalization of eating.

Body Composition and Energy Expenditure in Youth With Spina Bifida: Protocol for a Multisite, Cross-Sectional Study.

BACKGROUND: Obesity prevalence in youth with spina bifida is higher than in their typically developing peers. Obesity is associated with lifelong medical, psychological, and economic burdens. Successful prevention or treatment of obesity in individuals with spina bifida is compromised by (1) the lack of valid and reliable methods to identify body fat in a clinical setting and (2) limited data on energy expenditure that are necessary to provide daily caloric recommendations. OBJECTIVE: The objectives of this study will be to develop 2 algorithms for use in youth with spina bifida in a clinical setting, one to model body fat and one to predict total daily energy expenditure. In addition, physical activity and dietary intake will be described for the sample. METHODS: This multisite, prospective, national clinical study will enroll 232 youth with myelomeningocele aged 5 to 18 years (stratified by age and mobility). Participants will be enrolled for 1 week. Data obtained include 4 measures of body composition, up to 5 height measures, a ramped activity protocol, and a nutrition and physical activity screener. Participants will wear an accelerometer for the week. On the final study day, 2 samples of urine or saliva, which complete the doubly labeled water protocol, will be obtained. The analysis will include descriptive statistics, Bland-Altman plots, concordance correlation, and regression analysis. RESULTS: The study received extramural federal funding in July 2019. Data collection was initiated in March 2020. As of April 2024, a total of 143 (female participants: n=76, 53.1%; male participants: n=67, 46.9%) out of 232 participants have been enrolled. Data collection is expected to continue throughout 2024. A no-cost extension until November 2025 will be requested for data analysis and dissemination of findings. CONCLUSIONS: This study furthers previous pilot work that confirmed the acceptability and feasibility of obtaining alternate height, body composition, and energy expenditure measures. The findings from this study will enhance screening, prevention, and treatment of abnormal weight status by facilitating the accurate identification of youths' weight status category and recommendations of daily caloric needs for this population that is at higher risk of obesity. Furthermore, the findings have the potential to impact outcomes for youth diagnosed with disabilities other than spina bifida who experience similar challenges related to alterations in body composition or fat distribution or measurement challenges secondary to mobility issues or musculoskeletal problems. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52779.

Resting Energy Expenditure, comparison of predictive formulas with bioimpedance: Peruvian population / Gasto Energético en Reposo, comparación de fórmulas predictivas con la bioimpedancia: población peruana

Introduction: In areas with limited access to healthcare systems, Resting Energy Expenditure (REE) estimation is performed using predictive equations to calculate an individual's caloric requirement. One problem is that these equations were validated in populations with different characteristics from those in Latin America, such as race, height, or body mass, leading to potential errors in the prediction of this parameter. Objective: To determine the REE using predictive formulas compared with bioimpedance in Peruvians. Materials and methods: A comparative analytical cross-sectional study with secondary database analysis of the CRONICAS cohort. Results: we worked with a total of 666 subjects. The Mjeor equation was the one with the highest rating of 0.95, a lower mean absolute percentage error (MAPE) of 4.69%, and equivalence was found with the REE values. In the multiple regression, it was observed that the Mjeor equation was the one that least overestimated the REE, increasing 0.77 Kcal/day (95% CI: 0.769-0.814; p<0.001) for each point that increased the REE determined by bioimpedance. The strength of association between Mjeor and bioimpedance was 0.9037. Furthermore, in the regression of the data (weight, height, age) in the Mjeor equation it was observed that the coefficients obtained were the same as those used in the original equation. Conclusions: The Mjeor equation seems to be the most adequate to estimate the REE in the Peruvian population. Future prospective studies should confirm the usefulness of this formula with potential utility in primary health care(AU)

Introducción: En zonas con acceso limitado a sistemas de salud, la estimación del Gasto Energético en Reposo (GER) se realiza utilizando ecuaciones predictivas para calcular el requerimiento calórico de un individuo. Uno de los problemas es que estas ecuaciones fueron validadas en poblaciones con características diferentes a las latinoamericanas, como raza, talla o masa corporal, lo que conlleva a potenciales errores en la predicción de este parámetro. Objetivo: Determinar el GER mediante fórmulas predictivas comparadas con la bioimpedancia en peruanos. Materiales y métodos: Estudio transversal analítico comparativo con análisis secundario de base de datos de la cohorte CRONICAS. Resultados: Se trabajó con un total de 666 sujetos. La ecuación de Mjeor fue la que obtuvo la puntuación más alta de 0,95, un error medio porcentual absoluto (MAPE) inferior de 4,69%, y se encontró equivalencia con los valores del GER. En la regresión múltiple, se observó que la ecuación de Mjeor fue la que menos sobreestimó el GER, aumentando 0,77 Kcal/día (IC 95%: 0,769-0,814; p<0,001) por cada punto que aumentaba el GER determinado por bioimpedancia. La fuerza de asociación entre Mjeor y bioimpedancia fue de 0,9037. Además, en la regresión de los datos (peso, talla, edad) de la ecuación de Mjeor se observó que los coeficientes obtenidos eran los mismos que los utilizados en la ecuación original. Conclusiones: La ecuación de Mjeor parece ser la más adecuada para estimar el GER en la población peruana. Futuros estudios prospectivos deberán confirmar la utilidad de esta fórmula para su potencial utilidad en la atención primaria de salud(AU)

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

Market-based solution in China to finance the clean from the dirty.

Financial incentives play a key role in promoting renewable energy investments that can help China achieve the 'dual carbon' goal. The national emissions trading scheme (ETS) and the renewable energy portfolio standard (RPS) are two existing market-based policy instruments that can generate stable expected returns for low-carbon projects. This paper studies the interactive distribution effects of these two market-based instruments. We use the micro-level thermal power plant data to investigate the abatement effects of the national ETS, in which the details show that the existing rate-based ETS will result in higher negative impacts on power units, whose installed capacities are smaller than 400 MW. The interactive distribution effects between the two markets will occur when the permit allocation standards of the national ETS become stricter than the existing ones. Provinces in Eastern China and Northern China will face high pressure on costs in both ETS and RPS markets. When the levels of the permit allocation standards are set as 70% of the existing ones and the carbon price is assumed to be 200 yuan/ton in 2030, the annual market size of the national ETS will be nearly 100 billion yuan, and the annual market size is predicted to be 250 billion yuan. In the existing rate-based national ETS, the China Certified Emission Reduction (CCER) mechanism will have an offsetting effect, which should be taken into serious consideration during the policy-making processes in the future.

Comparative life cycle assessment of integrated renewable energy-based power systems.

Integrated renewable-based power cycles should be employed to produce more sustainable electricity. This is a comparative life cycle assessment (LCA) of three combined power plants, encompassing: case 1 involving combined geothermal and wind, case 2 featuring combined geothermal and solar, and case 3 integrating wind and solar systems. The base case perovskite solar cell (PSC) modelling assumes a 3-year lifespan and a power conversion efficiency of 17 %. However, diverse scenarios are evaluated through a sensitivity assessment involving enhancements in lifetime and efficiency. The base case evaluation emphasizes that the phases with the most significant negative environmental effects which includes the drilling of geothermal wells, construction of wind plants, and manufacturing and installation of PSCs. The midpoint findings indicate that boosting the power conversion efficiency of PSC from 17 % to 35 % yields a notable decrease in environmental impact. Moreover, extending the lifetime from 3 to 15 years led to reduction in CO2 emissions from 0.0373 and 0.0185 kg CO2 eq/kWh to 0.026 and 0.0079 kg CO2 eq/kWh in cases 2 and 3, respectively. Assessing worst and best-case scenarios highlights significant declines in certain impact categories. In case 3, terrestrial ecotoxicity (TE), photochemical oxidant formation (POF), human toxicity (HT), marine ecotoxicity (ME), and marine eutrophication (MU) saw reductions exceeding 88 % compared to worst-case results. The environmental effects observed in cases 2 and 3 stem from toxicity and metal depletion, mainly linked to the PSC. Endpoint results revealed that when considering a PSC lifespan of 10 years or more, the detrimental ecosystem impacts of cases 2 and 3 become less severe than those of case 1. Uncertainty assessment has been done for different cases and impact categories. The study's results are also novel in which it evaluated the innovative PSC technology when integrated with other renewable resources, contrasting it with other integrated plants.

Developing a dynamic life cycle assessment framework for buildings through integrating building information modeling and building energy modeling program.

The construction and building sector is one of the largest contributors to the global carbon emissions. Therefore, it is imperative to accurately assess the carbon emissions of buildings throughout the life cycle. Many studies conducted life cycle assessment (LCA) of buildings to evaluate carbon emissions. However, due to the lack of dynamic data, most studies adopted the static LCA methodology, which neglected the dynamic variations during life cycle stages of a building. Unlike previous studies that collected static data from questionnaires and documents, the present study aims to establish a novel dynamic life cycle assessment (D-LCA) framework for buildings by incorporating the building information modeling (BIM) and the building energy modeling program (BEMP) into the static LCA. First, a static LCA is established as the baseline scenario that covers the "cradle-to-grave" life cycle stages. A BIM model is established using Revit to obtain the inventory of building materials. The Designer Simulation Toolkit (DeST) is used as a BEMP to simulate the operating energy consumption of the studied building, taking into account changes in energy mix, climate change, and occupant behavior. At the same time, the DeST results are further used as a data input for dynamic scenarios. The D-LCA framework is applied to a high-rise commercial building in China. This study found that the difference between static and dynamic scenarios was up to 66.7 %, mainly reflected in the dynamic energy consumption during the operation phase, indicating the inaccuracy of traditional static LCA. Therefore, a D-LCA by integrating BIM and BEMP can facilitate dynamic modeling and improve the accuracy and reliability of LCA for buildings.

Can green finance promote the low-carbon transformation of the energy system? New evidence from city-level data in China.

This study explores the comprehensive effects of green finance (GF) on the low-carbon transition of the energy system (LTES) by analyzing panel data from 281 cities in China from 2006 to 2021. It is found that GF significantly reduces overall energy consumption and exhibits a U-shaped association with energy efficiency, while its relationship with the energy consumption structure is inverted U-shaped. After accounting for endogeneity in the robustness tests, these findings remain consistent and are therefore deemed reliable. A mechanistic analysis reveals that GF promotes industrial upgrading, technological progress, and economic agglomeration, collectively facilitating the LTES. The impact of GF on LTES shows considerable variation among regions, influenced by their levels of economic growth, extents of marketization, and governmental environmental preferences. Our findings provide new evidence for the relationship between GF and LTES, offering a scientific basis for formulating GF policies to accelerate this transformation.

Forecasting synergistic pathways between rare earth elements, renewable energy, and product and economic complexities in achieving a low-carbon future.

Recent decades have witnessed an unprecedented transformation in the global energy landscape, driven by environmental concerns and the quest for sustainable economic growth. As the world grapples with the urgent need for decarbonization, the utilization of renewable energy technologies with the instrumental role of rare earth elements (REEs) has come to the forefront. However, empirical investigations into their synergistic pathways for product and economic complexities concerning achieving a low-carbon future remain scarce. Therefore, we forecast synergistic pathways between the REE supply, renewable energy, economic and product complexities, and GDP growth using a panel dataset of 11 REE-producing countries from 1990 to 2023. We used Common Correlated Effects and Temporal Causal Models as primary methods to estimate panel long-run elasticities and subsequently forecast mutual causal synergies between the variables. The results indicated that REE supply led to renewable energy and economic growth that further elevated the countries' product and economic complexities rankings. GDP growth increased REE production, economic complexity, and renewable energy directly, and consequently, product complexity and REE production through them. This underscores the positive role of REE production coupled with renewable energy technologies in achieving a low-carbon future based on economic diversification, enhanced industrial capabilities, and technological sophistication.

Actual cost of electricity: An economic index to overcome levelized cost of electricity limits.

The selection of renewable energy technologies is widely based on the economic index levelized cost of electricity (LCOE). However, the LCOE ignores the potential temporal mismatch between electricity generation and actual grid demand: this aspect is accounted for in the new index named actual cost of electricity (ACOE), here proposed. This index provides a more accurate economic assessment of renewable energy, minimizing the number of assumptions to be made and outlining the benefits of including a storage. The proposed index is tested across ten cases encompassing three renewable technologies: wind, photovoltaic, and concentrated solar power. The outcomes show that the actual renewable electricity generation of a plant can be reduced by 40%-50% when accounting for the actual electricity demand, resulting in an ACOE exceeding the LCOE by up to 100/150 $/MWh. In addition, the ACOE enables the identification of breakthrough conditions that make storage adoption economically feasible.