Search Results (499)

Background/Objectives: Traditional predictive equations derived from regression analyses exhibit varying degrees of accuracy in estimating resting energy expenditure (REE). AI models can increase the predictability of such equations, even for population-specific ones. This work aimed to improve the prediction of REE in a dataset of Saudi population-specific equations using suitable AI tools. Methods: The dataset from the previously published Saudi population-specific equation by Almajwal and Abulmeaty (AA) in 2019 was used to develop an artificial neural network (ANN)-based version to better predict REE in the adult population. Anthropometric and body composition parameters were used as proposed features. The proposed hybrid prediction model underwent an extensive two-stage, iterative training process. First, the Extreme Gradient Boosting (XGBoost) model is used to compute feature importance scores. Then, the most prominent features were identified and incorporated into the ANN model. These significant features were used to train the ANN model to capture nonlinear correlations among them and make accurate predictions. Subsequently, XGBoost and Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) are used for their ability to provide a multi-layer abstraction of complex input data. Results: A total of 423 participants (208 male, 215 female) were divided into three non-overlapping sets: training (295, 70%), validation (64, 15%), and testing (64, 15%). The ANN model, combined with XGBoost, helped us to develop two equations: AA_ANN1= 2.47 × BMI + 11.9 × AdjBW + 962.5 and AA_ANN2 = 4.29 × age + 9.4 × fat mass + 15.71 × FFMI + 1289.3, where BMI is Body Mass Index (kg/m²), AdjBW is Adjusted Body Weight (kg), and FFMI is Fat Free Mass Index (kg/m²). The AA_ANN1 presented a Root Mean Square Error (RMSE) of 215 and an accuracy of 66.2%, whereas AA_ANN2 presented a lower RMSE of 193 and a higher accuracy of 71.4%. The ANN model was trained on the top 10 features ranked by XGBoost, achieving an average accuracy of 90.2%. Conclusions: The two new predictive equations, developed using an ANN combined with XGBoost, significantly improved REE prediction accuracy to 90.2%, achieved only with the full ANN model. Future external validation in an independent cohort is essential before clinical application of these equations. Full article

This study presents a computational investigation of an ingenious Twain co-flow jet (CFJ) airfoil system featuring independently controlled micro-compressors for active flow control. Unlike conventional single-point or synchronously controlled CFJ configurations, the proposed system enables independent tuning of jet momentum coefficients at multiple locations along the airfoil surface. Reynolds-averaged Navier–Stokes (RANS) simulations are employed to analyze the impact of this independent control strategy on boundary layer behavior, lift enhancement, stall delay, and aerodynamic efficiency. The objective of this work is to establish a quantitative relationship between jet momentum distribution and aerodynamic performance, while also evaluating the associated energy consumption characteristics of the system. This technology works incredibly well at low speeds, significantly increasing stall angles and lift coefficients; at higher speeds, it uses less energy and improves the lift-to-drag ratio. Twain configuration offers more accurate control over pressure gradients, enabling adaptive performance during all flight phases. In this work, a Twain-compressor-integrated CFJ system is presented, in which jet momentum coefficients (Cμ = 0.05 and 0.1) are dynamically controlled by two independently controlled micro-compressors across various flight conditions (11.34 m/s, 138 m/s, 208 m/s). By optimizing injection at the leading edge and mid-chord—paired with synchronized suction at strategic withdrawal points—the system achieves precise boundary layer control with near-zero net mass flux. Modulating Cμ improves aerodynamic efficiency while limiting the total propulsion energy expenditure, allowing a smooth transition from high-lift takeoff to low-drag cruise, according to computational fluid dynamics (CFD) analysis. Due to these developments, Twain-compressor CFJ systems are now a scalable option for aircraft that need to be extremely aerodynamically versatile without sacrificing efficiency. Full article

Empirical parameter settings in steam-based soil disinfestation for Panax notoginseng (a valuable medicinal plant) often hinder the simultaneous optimization of pathogen control and energy efficiency. To address this limitation, this study aims to develop a parameter regulation framework that integrates multi-output regression with scenario-oriented intelligent decision-making. Initially, a comprehensive dataset comprising critical parameters—steam pressure (P_steam), soil compaction (C_soil), and heating time (t_heat)—was established. A random search (RS) hyperparameter optimization scheme was employed to comparatively evaluate the multi-output predictive performance of Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) for the joint estimation of soil temperature (T_soil) and root-rot pathogen kill rate (Kill_rate). Subsequently, by integrating total energy consumption (E_total) and operating electricity cost models, a constrained search algorithm was implemented to develop three objective-oriented decision modes: “maximize Kill_rate”, “minimize C_electricity”, and “maximize Efficiency”. Results demonstrate that the RS-optimized SVR yielded superior multi-output performance, achieving R² of 0.968 for T_soil (MAE = 2.44 °C) and 0.808 for Kill_rate (MAE = 7.85%). Compared to conventional empirical configurations, the proposed decision modes exhibited significant advantages across diverse scenarios. In the “maximize Kill_rate” mode, dynamic extensions of t_heat facilitated theoretical complete inactivation even under challenging heating conditions, effectively eliminating disinfection “blind spots” inherent in fixed-duration strategies. Under the “minimize C_electricity” mode, precise regulation of P_steam reduced operational electricity costs by 18.2% while satisfying the constraint of Kill_rate ≥ 95%. Furthermore, the “maximize Efficiency” mode identified an optimal operating point at C_soil = 64 kPa (P_steam = 0.4 MPa, t_heat = 13 min), thereby mitigating performance degradation associated with excessive tillage or high media rigidity and achieving an optimized cost–benefit ratio. By synthesizing high-fidelity multi-output regression with a flexible multi-mode decision-making framework, this study provides an intelligent solution for soil disinfestation in protected agriculture, facilitating the coordinated optimization of phytosanitary efficacy, energy expenditure, and economic viability. Full article

Introduction: Accurately meeting the energy requirements of patients in intensive care units (ICUs) is crucial to prevent catabolism, muscle loss, and complications. We assessed their energy needs in this study using indirect calorimetry (IC) and predictive formulas, comparing the results with delivered energy intake and evaluating agreement. Materials and Methods: A total of 38 mechanically ventilated patients in seven ICUs at Kayseri City Hospital were included; eligible patients were ≥18 years old and mechanically ventilated for at least 24 h. Disease severity and nutritional risk were evaluated using validated indices (prognostic nutritional index (PNI) and Modified Nutrition Risk in the Critically Ill (mNUTRIC)), and basal energy expenditure (BEE) was measured by IC and calculated using the Harris–Benedict (HB) and ESPEN formulas. IC measurements lasted 15 min under resting conditions in conscious patients and, according to acute phase criteria, in unconscious patients in a quiet, temperature-controlled environment. Nutrition was provided enterally or parenterally based on patient condition and disease severity. Agreement between IC and predictive formulas was assessed using Bland–Altman analysis, a statistical method that evaluates agreement between two measurement techniques. Results: Estimated energy requirements differed significantly from delivered energy intake (p < 0.001). IC-derived values were significantly lower than those estimated by the HB equation and ESPEN recommendations (p < 0.001), suggesting that predictive equations may overestimate energy requirements in this population. By contrast, delivered energy intake was lower than IC-measured values, with a mean difference of approximately 503 kcal, indicating a potential risk of underfeeding in clinical practice. Weak correlations were observed between methods (IC vs. HB: r = 0.35, p = 0.003; IC vs. ESPEN: r = −0.21, p = 0.02), indicating limited agreement between predictive equations and IC measurements, and Passing–Bablok regression analysis further supported this lack of agreement between methods. Conclusions: The energy intake delivered to patients was lower than the calculated values. Indirect calorimetry is important for accurately monitoring and determining energy requirements based on delivered energy intake, and further research in this area is needed. These findings highlight the importance of individualized monitoring of energy expenditure in critically ill patients and suggest that reliance solely on predictive equations may lead to clinically relevant discrepancies in energy delivery. Full article

Objectives: The aim of this study was to explore the combined effects of resistance exercise and whey protein supplementation on plasma irisin levels in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) under a 30% calorie-restricted weight loss diet. Methods: Thirty adult patients with MASLD were randomized into the following three groups for a 4-week intervention: calorie restriction group (CR) (n = 8), CR with resistance exercise group (EX) (n = 11), and CR with resistance exercise and whey protein group (EX + P) (n = 11; 0.7 g/kg per day). All participants received boxed meals providing 70% of their total energy expenditure. The participants in the resistance exercise groups performed full-body resistance exercises 5 days/week (50–75% one-repetition maximum). Plasma irisin level, controlled attenuation parameter (CAP), and body composition were assessed before and after the intervention. Results: Plasma irisin levels significantly increased in the EX (+2.24 ng/mL, p = 0.016) and EX + P (+4.86 ng/mL, p = 0.004) groups but not in the CR group. Muscle mass increased significantly only in the EX + P group. The CAP decreased in all groups. The change in irisin level was negatively correlated with the change in CAP (r = −0.459, p = 0.032). Conclusions: Resistance exercise under calorie-restricted conditions effectively increased plasma irisin levels in patients with MASLD, whereas caloric restriction alone did not. Furthermore, a stronger increasing trend in the plasma irisin levels was observed with whey protein supplementation. An increase in irisin levels was significantly associated with hepatic fat reduction, suggesting that irisin may serve as a biomarker reflecting improvements in hepatic steatosis following lifestyle intervention. Full article

Background: Accurate estimation of water and energy requirements is fundamental for establishing dietary reference values in young adults. However, evidence integrating objectively measured energy expenditure with detailed water turnover components remains limited in Chinese populations. Objectives: To quantify water intake, water loss, and energy expenditure in healthy young college students, and to examine how energy metabolism is associated with specific components of water turnover under free-living conditions. Methods: Twenty-one healthy adults aged 18–25 years participated in a 14-day observational study conducted in Beijing, China. Total energy expenditure (TEE) was measured over 14 days using the doubly labeled water (DLW) method. Physical activity was monitored over 7 consecutive days using a triaxial accelerometer. Water intake was assessed using multiple methods: water from beverages (including plain drinking water and other beverages) was recorded over 7 days using 24 h fluid intake records, while water from food was measured during days 5–7 using weighed food records combined with duplicate portion and direct drying methods. Urinary and fecal water loss were quantified using 24 h collections conducted during days 5–7. Metabolic water production and insensible water losses were estimated using established physiological equations. Multivariable linear regression analyses were conducted to examine associations between energy-related variables and components of water turnover. Results: Mean total daily water intake was 3023 mL, with water from beverages accounting for 54.1%, water from food for 36.7%, and metabolic water for 9.1%. Mean total daily water loss was 1931 mL, predominantly from urinary excretion (81.0%). DLW-measured TEE averaged 2018.6 kcal/day and was higher in males than in females. Most regression models examining total water intake and beverage-derived water were not statistically significant, and no consistent associations were observed between these variables and total energy intake, TEE, or PAEE. In contrast, TEE was positively associated with metabolic water production and respiratory water loss (both p < 0.001). Significant associations with total energy intake were observed for water from food and fecal water loss (both p < 0.01), whereas other water intake components showed no significant associations. Conclusions: In young adults, energy metabolism appears to be more closely associated with physiologically regulated components of water turnover than with voluntary water intake. These findings suggest a divergence between endogenous and behaviorally regulated pathways of water turnover and highlight the importance of considering component-specific water dynamics when examining hydration and energy balance, although confirmation in larger studies is warranted. Full article

This paper presents a comprehensive life-cycle analysis of distribution transformers, based on realized measurements of the increased power losses as a result of their long-term service under real-world conditions. The study is based on aggregated measured data from extensive fleets of oil-immersed distribution transformers characterized by diverse designs, manufacturing vintages, and service lives. The evolution of no-load losses and short-circuit losses is analyzed as a function of operational duration, structural characteristics, and the specific technologies employed for windings and magnetic core construction. Statistical models describing the variation in these losses are presented, highlighting the limitations of the static assumptions commonly utilized in power distribution network planning. On this basis, an approximation of the time evolution of the transformer’s total power and energy losses is proposed as appropriate for implementation in a life-cycle analysis model. Furthermore, the impacts of thermal loading and abnormal operating conditions—such as unbalanced loads, frequent short circuits, and repeated overheating of the transformer oil—are analyzed as drivers of accelerated transformer aging. These effects are integrated into a unified life-cycle framework, enabling the quantitative assessment of loss variations and their associated operational expenditures (OPEX). A numerical example is provided to evaluate the cost-effectiveness of “repair vs. replacement” scenarios, utilizing a discounted cash flow analysis that incorporates a carbon component. The findings establish a methodological foundation for a broader assessment of technical condition and energy performance, identifying the optimal intervention point for repair or replacement to support decision-making for Distribution System Operators (DSOs) amidst increasing requirements for efficiency and decarbonization. Full article

To address the challenges of high thermal loads and limited energy efficiency in an electric tractor operating under complex agricultural conditions, this paper proposes a hierarchical battery thermal management strategy based on liquid cooling. The method integrates an upper-level piecewise linear model predictive control to regulate battery temperature and a lower-level convex optimization scheme for dynamic actuator power allocation among the compressor, cooling fan, and expansion valve. By decomposing the nonlinear thermal dynamics into multiple local subregions, the predictive accuracy is enhanced while maintaining real-time computational feasibility. Comparative simulations reveal that under severe 45 °C ambient conditions, the proposed strategy limits the maximum temperature difference among battery cells to 1.34 °C and average temperature fluctuations to 0.231 °C, significantly outperforming conventional linear baseline methods which resulted in 1.66 °C and 0.349 °C, respectively. Furthermore, the optimized actuator coordination reduces total cooling energy expenditure by 11.4%, effectively minimizing transient peak loads on the high-voltage bus and preserving energy for primary traction tasks. These quantitative results confirm that the proposed control framework substantially improves battery thermal stability and powertrain energy efficiency, demonstrating robust potential for practical implementation in heavy-duty agricultural machinery. Full article

Background: Stable isotope-based analytical methods have brought about a significant transformation in the study of energy nutrient metabolism, enabling precise in vivo measurement of metabolic fluxes at systemic, tissue, and organ-specific levels in both healthy and diseased states. The regulation of these metabolic fluxes is governed by dynamic interactions between proteins, lipids, carbohydrates, and their precursors—such as glucose, fatty acids, and amino acids—as well as final metabolic products. Discussion: Advanced analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), which can offer enhanced precision, have been developed for investigating nutrient metabolism and fluxes in humans, providing precise information on metabolic pathways. These techniques have primarily utilized stable isotopes, such as ²H, ¹³C, ¹⁵N, and ¹⁸O, which have largely replaced radioactive isotopes and are now central to metabolic research. These isotopes have been used to label glucose, fatty acids, or amino acids—the main biomolecular precursors—enabling detailed investigation at systemic, tissue, and organ-specific levels of carbohydrate, lipid, and protein metabolism, and revealing pathway alterations associated with diseases conditions, such as diabetes, non-alcoholic fatty liver disease, cardiovascular disorders, and cancer. The use of deuterium oxide (D₂O) has allowed for long-term metabolic studies, providing a cost-effective and less invasive means to monitor metabolic changes over days to months. Total daily energy expenditure can be measured in free living conditions by the doubly stable isotopes ²H- and ¹⁸O-labeled water method. Stable isotope tracing, combined with advanced imaging and modeling, has also been instrumental in assessing body composition, energy expenditure, and nutrient bioavailability. Collectively, these methods have expanded our understanding of human physiology and disease, supporting the development of novel diagnostic tools, the identification of new biomarkers, and the tailoring of nutritional and therapeutic interventions. Conclusions: This review aimed to provide an overview of the applications of stable isotopes for the study of energy nutrient metabolic pathways. The ongoing integration of stable isotope approaches with artificial intelligence, omics technologies, and miniaturized detection techniques could promise to further refine our understanding of human metabolism and drive advances in personalized medicine. Full article

Background and Objectives: Obesity affects over 1.9 billion adults globally, with a disproportionately higher prevalence in Saudi Arabia among women. While excessive adiposity is known to impair respiratory mechanics and lung function, its relationship with respiratory muscle strength and exercise energy expenditure remains inadequately elucidated. This study examined differences in respiratory muscle strength, metabolic equivalents (METs) of physical activity, and energy expenditure during exercise between adults with normal and high body fat percentage (BF%) and explored the statistical role of body fat as a potential mediator in the cross-sectional association between respiratory muscle strength and energy expenditure. Methods: In this cross-sectional study, 126 Saudi women aged 18–45 years (mean age: 21.7 ± 4.2 years) were stratified into normal (n = 63) and high (n = 63) BF% groups. Body composition was assessed via bioelectrical impedance analysis, and respiratory muscle strength (MIP and MEP) was measured using a MicroRPM device. Peak oxygen consumption (VO_2peak) and energy expenditure were obtained through the Bruce Submaximal Treadmill Protocol, and physical activity was self-reported via the IPAQ. Hierarchical regression and structural equation modeling were used to examine variable associations and explore statistical mediation patterns. Results: Participants with high body fat demonstrated significantly low MIP (−26%) and MEP (−31%), low VO_2peak (−13%), and approximately 26% high energy expenditure during exercise compared to the normal-BF group (all p < 0.001), despite comparable self-reported physical activity levels. Body fat percentage was the most strongly associated with energy expenditure (β = 0.078, R² = 0.329), with maximal inspiratory pressure contributing an additional 7.3% of explained variance in hierarchical regression (total R² = 0.414). Mediation analyses revealed that body fat percentage was statistically consistent with a partial mediation model in the relationship between MIP and energy expenditure (indirect association = −0.016, p = 0.033), accounting for 27% of the total association, and between MEP and energy expenditure (indirect association = −0.013, p = 0.035), accounting for 38% of the total association. Conclusions: High BF% is independently associated with low respiratory muscle strength and high exercise metabolic cost. Body fat is statistically associated with (and consistent with a mediating role in) an inverse relationship between respiratory muscle strength and energy expenditure. Alternative directional relationships and shared underlying factors may explain these observations. Full article

The rapid deployment of Large Language Models (LLMs) in multilingual, production-scale systems has made inference-time energy consumption a critical yet systematically under-evaluated dimension of model quality. While accuracy-centric benchmarks dominate current evaluation practice, they fail to capture the energy cost of reasoning, particularly across languages and task complexities where consumption profiles diverge substantially. In this work, we present a comprehensive energy–performance evaluation of five instruction-tuned LLMs, spanning Transformer, Grouped-Query Attention, and State Space Model architectures, across thirteen typologically diverse languages and multiple task difficulty levels under controlled GPU-level energy measurement on NVIDIA H200 hardware. Our analysis encompasses 65 model–language configurations totaling over 5100 individual inference runs, supported by rigorous non-parametric statistical testing (Friedman tests, pairwise Wilcoxon signed-rank with Holm correction, and paired Cohen’s d effect sizes). We report four principal findings. First, energy consumption varies up to threefold across models under identical workloads (${\chi }^2=49.42$, $p=4.78\times {10}^{-10}$, Friedman test), stratifying into three distinct energy regimes driven by architecture and generation dynamics rather than parameter count. Second, energy expenditure and reasoning performance are only weakly coupled, as confirmed by Spearman rank correlation analysis ($r_s=0.109$, $p=0.386$). Third, task category and difficulty level introduce substantial and model-dependent variation in both energy demand and performance, with cross-lingual performance variance amplifying at higher difficulty levels. Fourth, language choice acts as a measurable deployment parameter as follows: Romance languages on average achieve lower energy consumption than English across multiple models, while model efficiency rankings shift across languages, yielding language-dependent Pareto-optimal frontiers. We formalize these trade-offs through multi-objective Pareto analysis and introduce a composite AI Energy Score metric that captures reasoning quality per unit of energy. Of the 65 evaluated configurations, only four are Pareto-optimal, three Mistral-7B configurations at the low-energy extreme and one Phi-4-mini-instruct configuration at the high-performance end, while three of the five models are entirely dominated across all language configurations. These findings provide actionable guidelines for energy-aware model selection in multilingual deployments and support the integration of AI Energy Scores as a standard complementary criterion in LLM evaluation frameworks. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are effective treatments for obesity, but substantial weight regain is common after therapy is discontinued. This study investigated whether probiotic strains with anti-obesity effects could enhance GLP-1RA-induced weight loss and attenuate post-treatment weight rebound. Methods: Candidate lactic acid bacteria were screened for anti-obesity efficacy in a high-fat-diet (HFD)-induced obese mouse model, and the selected strain was further characterized using metabolomic profiling of culture supernatants. To examine its interaction with GLP-1RA therapy, obese mice received dulaglutide for 4 weeks and were monitored for 2 weeks after treatment withdrawal, while the probiotic was orally administered for a total of 6 weeks. Body weight, glycemic parameters, and muscle strength were assessed throughout the study. Results: Limosilactobacillus fermentum GB102 reduced body weight and improved glycemic control in HFD-fed mice. These metabolic benefits were associated with alterations in circulating metabolic hormones, including adipokines, along with attenuated inflammatory responses in adipose tissue. Metabolomic profiling revealed that GB102 produced high levels of succinic acid, a metabolite previously linked to thermogenic activation. This strain increased whole-body energy expenditure in HFD-fed mice, produced glutamine, and showed enhanced conversion of arginine into ornithine and citrulline. When combined with dulaglutide, GB102 enhanced weight loss, preserved muscle strength, and attenuated both weight regain and glycemic rebound following dulaglutide withdrawal. Conclusions: These findings suggest that energy-metabolism-enhancing probiotics such as GB102 may enhance the metabolic effects of GLP-1RA therapy and help attenuate weight regain after treatment discontinuation. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

This study evaluates the quality and dynamics of the renewable energy transition in Poland’s electricity sector during the years 2010–2023 through an integrated Fuzzy Time Series (FTS) and Fuzzy Multi-Criteria Evaluation (FMCE) methodology. The evaluation is based on five production-related criteria: the production of renewable electricity, the capacity of installed renewable energy sources, investment costs, innovation costs, and total electricity production. Contrary to trend projection and elasticity ratio methods, the new approach determines qualitative transition states (Low, Medium, High) and their transitions over time in the presence of non-linearities and partial progress. The outcome shows a protracted pre-transformational period from 2010 to 2014, with features of perpetual Low → Low transitions and high system inertia. The first qualitatively detectable transition takes place in 2015, where the renewable electricity output regime shifts from Low to Medium, symbolizing the beginning of the moderate transition phase. The Medium regime continues until 2021, with little innovation expenditure, signifying a consolidation rather than acceleration phase. The most significant transition regime shift takes place in 2022, where the system advances from Medium to High, fueled by the cumulative growth of renewable electricity output, capacity, and total electricity production. The High regime is maintained in 2023, indicating a systemic rather than a temporary transition. The results show that the transition of Poland towards renewable energy sources has been following a non-linear and regime-dependent path, with turning points marking observable qualitative state transitions rather than the beginning of trends. The FTS-FMCE approach is a powerful method for separating growth from transformation, and it has been shown to be useful for coal-dependent economies that experience a delayed but accelerating energy transition. Full article

Background: Resting energy expenditure (REE) represents 60–75% of total daily energy expenditure and is mainly determined by fat-free mass (FFM). Indeed, the predictive equations vary according to FFM techniques and population characteristics. Therefore, this study aimed to explore the influence of dual-energy X-ray absorptiometry (DXA)-derived FFM on REE prediction by different predictive equations in a large and diverse cohort. Methods: A total of 1987 active and sedentary participants of both sexes (43.8 ± 19.4 years) underwent body composition assessment by DXA. REE was predicted using the Harris–Benedict, Schofield, Mifflin–St Jeor (weight- and height-based), and Mifflin (FFM-based) equations. Statistical analyses included Kruskal–Wallis, Spearman correlations, and linear regression. Results: Men presented higher absolute FFM, whereas women exhibited higher relative fat mass (FM) (p < 0.01). Across age groups, FFM declined progressively, while FM increased (p < 0.01). The REE differed significantly (p < 0.001) between equations, with the lowest values predicted from the FFM-based model, while the Harris–Benedict and Schofield equations showed the highest REE, especially in women. Strong correlations were observed between FFM and REE (r = 0.77–0.98; p < 0.01) for all age groups and equations, whereas FM showed strong correlations (r = 0.77–0.85; p < 0.01) only for the ≥60 years group. REE tended to be higher in active than sedentary participants, with the correlations to FFM and FM exhibiting a similar profile to that observed for the whole group. Conclusions: FFM showed a strong association with the estimate of REE in active and sedentary participants from both sexes and different age groups, but FM showed a similar trend in older participants only. Therefore, the increase or the maintenance of FFM with an active lifestyle is important to keep REE at high and efficient levels regardless of sex and age. Full article