Energy efficiency modeling considered chemical process anomalies using contrastive learning-guided generative adversarial imputation network for operation-aligned data reconstruction

People living with amyotrophic lateral sclerosis (ALS) are at high risk of malnutrition, making it essential to monitor their nutritional status through measurements of body composition and energy expenditure. However, validity of equations, as alternatives to reference standards for assessing these parameters in ALS, is unclear. This systematic review evaluates criterion validity of equations to estimate body composition and energy expenditure in ALS. Four electronic databases (EMBASE, MEDLINE, CINAHL and Cochrane) were systematically searched from inception until July 7th, 2025. Studies were included if criterion validity of an instrument or method for estimating body composition or energy expenditure was examined in people diagnosed with ALS. Methodological quality was assessed using the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) risk of bias checklist. Criterion validity was rated as sufficient (+), indeterminate (?) or insufficient (-) based on COSMIN criteria for good measurement properties. Results were qualitatively summarised. Twelve studies were included: five evaluated the criterion validity of equations to estimate body composition using Bioelectrical Impedance Analysis (BIA) or anthropometry, and seven to estimate resting or total daily energy expenditure. No equation was rated as sufficient for criterion validity across studies. Equations to estimate body composition and energy expenditure should be applied with caution, as no equation exhibited high criterion validity in ALS. ALS-specific equations require further validation, and ideally, new equations tailored to the unique physiological characteristics of ALS should be developed. CRD42024573509.

This paper presents a portion of recent research outcomes from the LoLiPoP-IoT Chips JU project, which focuses on developing sustainable, long-life IoT platforms by integrating advanced energy harvesting, intelligent energy management strategies, and low-power HW/SW co-design techniques to optimize battery longevity with the intention of reducing the economic and ecological impacts of frequent battery replacements. The main objective of this research is to investigate how integrated energy harvesting, adaptive power management, and efficient data-processing techniques can significantly extend battery lifetime while maintaining performance and usability in real IoT deployments. Unlike many existing studies that address isolated aspects of low-power IoT design, this work provides a comprehensive and practical approach that combines energy harvesting dimensioning, including simulation of the deployment environment, real HW power profiling, adaptive energy planning algorithms, predictive maintenance modeling, and their deployment on resource-constrained devices. The holistic integration of available technologies with newly designed approaches, such as dynamic energy scheduling, enables improvements in the overall IoT experience and a more sustainable usage. Experimental results demonstrate several outcomes. The proposed dynamic energy planning framework, particularly the “Slope” algorithm, can extend battery lifetime by up to five times compared to baseline operation. If full energy autonomy is required, the photovoltaic panel area can be reduced by approximately 77 %. Our developed simulation toolkit enables accurate estimation of energy consumption and optimal sizing of photovoltaic harvesters, while predictive maintenance models based on statistical model checking enable forecasting fault probabilities of factory equipment based on collected data. Furthermore, we conducted experiments to confirm that optimized machine-learning models can achieve high accuracy with reduced memory footprint and inference time on embedded IoT platforms.

Severe traumatic injury triggers a profound hypermetabolic and hypercatabolic response, marked by increased resting energy expenditure, accelerated protein breakdown, muscle wasting, and heightened risk of malnutrition, infection, and mortality. Timely and targeted nutritional intervention is therefore essential to support immune function, promote wound healing, and facilitate recovery. Herein, we examine the metabolic demands of patients with severe trauma and synthesize current clinical guidelines and evidence-based strategies for nutritional management. This review outlines the neuroendocrine, inflammatory, and metabolic mechanisms that drive hypercatabolism following major trauma. It evaluates methods for estimating energy expenditure and their limitations, and discusses recommended caloric and protein targets, as well as timing of initiation and preferred routes of nutritional support. Additionally, our review examines the evidence for adjunctive immunonutrition, including supplementation of glutamine, omega-3 fatty acids, arginine, and ghrelin. In summary, early and individualized nutritional therapy is critical to mitigating hypercatabolism and improving clinical outcomes in trauma patients. Although foundational guidelines have been established, high-quality randomized controlled trials remain necessary to better define the role of specific immunonutrients across diverse trauma populations.

Assessment of physical activity intensity is essential for guiding rehabilitation after stroke. Wearable activity monitors are used to estimate energy expenditure during walking; however, their validity in stroke populations under real-life walking conditions remains uncertain. This study aimed to evaluate the validity of an ActiGraph GT3X for estimating energy expenditure during walking tasks in stroke survivors using indirect calorimetry as the reference method. Thirty stroke survivors and 30 healthy controls performed indoor and outdoor walking tasks, including overground walking, stair ascent and descent, irregular terrain walking, and uphill and downhill walking. Energy expenditure expressed in metabolic equivalents was measured using the ActiGraph GT3X worn at the ankle and a portable gas exchange analyzer. Agreement between methods was assessed using Bland-Altman analyses and linear regression models. The ActiGraph GT3X overestimated energy expenditure across most walking tasks in both groups, with percentage differences ranging from 25.7 to 112% in stroke survivors and from 18.1 to 149.1% in healthy controls. In stroke survivors, walking speed was significantly associated with the difference in energy expenditure (ActiGraph GT3X - indirect calorimetry) across most tasks, with higher speeds leading to greater overestimation (e.g. β = 9.01, P < 0.001, R2 = 0.41 for stair descent). In healthy controls, this association was observed only for selected tasks, including overground walking and stair descent. These findings indicate that the ActiGraph GT3X has limited validity for energy expenditure estimation during ecological walking tasks. Although the device may provide relative indicators of effort, its outputs should be interpreted with caution.

As electric vehicles gain traction across the globe, one persistent worry among drivers is whether their battery will last long enough to reach the next charging point, a concern commonly referred to as range anxiety. In this paper, we describe a practical route planning tool that tackles this problem head-on. At its core sits a Gradient Boosting Regressor trained on 20,000 synthetically generated trip records whose parameters are rooted in real-world physics. The model takes in the vehicle type, how much cargo is on board, the trip distance, driving speed, terrain changes, and outside temperature, and outputs an energy consumption estimate. On the server side, a FastAPI application pulls together driving directions from OSRM, live weather readings from OpenWeatherMap, elevation data from Open-Elevation, and nearby charger locations from OpenChargeMap. A step-by-step greedy algorithm then figures out where the driver should stop to recharge, while also factoring in how much the battery may have degraded over time. The accompanying mobile app, built with Flutter, shows the planned route on an interactive map and even works offline thanks to local caching. In our tests, the prediction model achieved an R² above 0.95 on unseen data.

Background/Objective: Accurate resting energy expenditure (REE) estimation is paramount for the nutritional management of older Asian adults with multimorbidity. However, standard predictive equations (PEs) lack precision for this cohort. This study aimed to recalibrate PEs using BMI-stratified, slope-only regression to enhance bedside accuracy. Methods: REE was measured via indirect calorimetry in 400 hospitalized patients (age ≥ 65). Sensitivity analyses identified significant proportional bias in existing models. Models were recalibrated and validated using 1000-iteration bootstrap resampling. Results: Standard PEs exhibited significant bias, particularly overpredicting requirements for 68% of underweight patients. The new Singapore Older Adults Resting energy expenditure (SOAR) PE 1 (963.67 + 8.56 × weight − 5.6 × age) eliminated weight-dependent systematic errors. The recalibrated models utilizing actual body weight achieved accuracy rates of up to 64% in obese cohorts, comparable to complex adjusted-weight protocols. Conclusions: Population-specific recalibration is essential to mitigate the bidirectional risks of malnutrition and overfeeding in geriatric rehabilitation. The BMI-stratified multipliers provided offer a robust, clinically efficient framework for individualized nutritional care.

Smartwatches are widely used to monitor physiological responses during exercise; however, their accuracy in measuring heart rate (HR) and energy expenditure (EE) across different exercise modalities remains insufficiently characterized. This study evaluated the accuracy of HR and EE measurements obtained from four commercially available smartwatches in comparison with gold-standard reference methods. Sixty-two healthy adult men performed standardized endurance and resistance exercise protocols while simultaneously wearing four smartwatches (Apple, Galaxy, Fitbit, and Garmin). HR was measured using electrocardiography (ECG), and EE was determined using indirect calorimetry. Measurement accuracy was assessed using repeated-measures analysis of variance, Pearson's correlation analysis, intraclass correlation coefficients (ICCs), and Bland-Altman analyses. All smartwatches demonstrated high accuracy in HR measurements during both endurance and resistance exercises. During endurance exercise, HR measurements from all smartwatch brands were comparable to those obtained via ECG, whereas during resistance exercise, only the Apple Watch showed no significant difference from the ECG. HRs showed strong correlations with ECG readings (r = 0.64-0.97), excellent reliability (ICC > 0.94), and narrow limits of agreement (approximately ±10 bpm). In contrast, the EE measurements exhibited limited accuracy across all devices. During endurance exercise, EE was consistently underestimated with wide limits of agreement. EE accuracy further deteriorated during resistance exercise, showing weak correlations with indirect calorimetry (r = 0.10-0.34) and poor reliability (ICC < 0.45). Overall, smartwatches provide accurate HR measurements across endurance and resistance exercise modalities, supporting their use in exercise intensity monitoring and HR-based training. However, smartwatch-derived EE estimates do not accurately reflect the metabolic demands, particularly during resistance exercises. Future research should focus on improving EE estimation algorithms through multimodal biosignal integration and machine-learning approaches, and validating these methods across diverse populations and exercise modalities.

Qiao, Z, Guo, B, Gao, Y, Wang, Y, Jiang, M, Yu, J, Yi, L, Yan, B, Qiu, J, and Girard, O. Energy expenditure analysis and prediction in Smith machine squats integrating mechanical loads and sex. J Strength Cond Res 40(5): 519-527, 2026-Calculating energy expenditure (EE) and metabolic equivalents (METs) from oxygen uptake (V̇o2) alone often underestimates resistance training intensity because of its intermittent, brief, and high-intensity nature. This study aims to refine EE and METs estimation during Smith machine squats by integrating mechanical work, glycolytic contribution, and postexercise EE, with particular emphasis on weight-dependent and sex-specific metabolic responses. Fifty-one subjects (31 men) performed weight-bearing squats (4 kg-80% 1 repetition maximum) for 5 repetitions across 3 sets with 4-minute rest intervals. Aerobic, glycolytic, and postexercise EE were estimated from V̇o2, blood lactate, and excess postexercise oxygen consumption, respectively. Mechanical work was measured using a linear-position transducer system. Training load (p < 0.05) and sex (p < 0.01) significantly influenced total EE. Mechanical efficiency was significantly influenced by training load (p < 0.001). Energy expenditure-based METs increased with training weight (p < 0.05) and were higher than V̇o2-based METs (p < 0.001). Training weight and mechanical work strongly correlated with total EE (r ≥ 0.80, p < 0.001). The optimal model, including body weight, training load, peak heart rate, and movement distance, demonstrated high accuracy (R2 = 0.885, cross-validated R2 = 0.803, MAE = 10.067). A simplified model using only body weight and training weight achieved comparable accuracy (R2 = 0.847, cross-validated R2 = 0.750, MAE = 10.883). Total EE increased with training weights, but low-to-moderate-weight squats showed superior mechanical efficiency. Women tended to expend less energy than men during squats, but no difference in mechanical efficiency was observed. V̇o2-based METs underestimated squat intensity. Both the simplified and optimal models accurately estimated squat EE, with the optimal model being more accurate.

This study evaluates residential carbon neutrality by examining carbon emissions, vegetation carbon sequestration, and occupant energy behavior within urban housing. Global warming, driven by carbon dioxide emissions, underscores the need for integrated solutions addressing household carbon footprints. The objective of this research is to develop a comprehensive model that incorporates building carbon emissions, vegetation carbon sequestration, and occupant behavior to assess residential carbon neutrality. This study focuses on 49 households in Vila Dago Housing, Pamulang, South Tangerang, utilizing surveys and field data to estimate energy consumption, carbon emissions, and vegetation absorption. The findings reveal that operational carbon emissions are primarily driven by electricity usage, with average emissions ranging from 10,000 to 27,000 kg CO₂e annually per household. Embodied carbon emissions from construction materials also contribute significantly, with variations depending on house type. Vegetation in the study area provides limited carbon sequestration, unable to offset the carbon emissions generated by household activities. Additionally, occupant behavior, including lighting, air conditioning, LPG use, and waste management, directly influences overall emissions. In conclusion, achieving carbon-neutral housing requires improved energy behavior and enhanced green spaces, with an integrated approach to passive building design and carbon sequestration strategies being essential for mitigation efforts.

This article presents a two-stage, biologically inspired neuromorphic framework for autonomous robot navigation in cluttered environments. The proposed architecture combines unsupervised spiking neural network (SNN)-based sensory abstraction with reward-modulated spike-timing-dependent plasticity (R-STDP) for decision-making. In the first stage, raw 360° LiDAR measurements are transformed into a compact, interpretable obstacle state through a lateral-inhibition-driven STDP network, yielding a low-dimensional, behaviorally relevant perception of the environment. In the second stage, navigation actions are learned via reward-modulated STDP operating on this abstracted state, supporting long-horizon goal-directed behavior without backpropagation or deep reinforcement learning. Two navigation paradigms are investigated: conventional goal-oriented navigation using relative bearing information, and a probabilistic field–based formulation that enables source-seeking behavior under goal uncertainty. The proposed approach is evaluated extensively in Gazebo and NVIDIA Isaac Sim using a TurtleBot3 platform across static, dynamic, and near-realistic environments. Experimental results demonstrate reliable navigation performance, competitive success and collision rates compared to state-of-the-art SNN and hybrid SNN–RL methods, and substantially lower estimated energy consumption. These findings highlight the effectiveness of modular, biologically plausible neuromorphic architectures for energy-efficient autonomous navigation in complex environments.

This review examines the current state of development and application of artificial intelligence (AI) tools for monitoring nutrition and physical activity in individuals with obesity, with a focus on the physiological complexity of energy balance and the role of chrono-nutrition. Energy intake and expenditure are dynamically coupled and circadian-regulated: meal timing and movement patterns influence insulin sensitivity, thermogenesis, and Non-Exercise Activity Thermogenesis within the same day. Traditional monitoring methods suffer from recall bias and low granularity, while isolated sensors operate in data silos, limiting accuracy. Effective solutions require multimodal, continuous, and temporally aligned data streams. Current AI models exhibit critical limitations in obesity-specific contexts: inaccurate gait and energy expenditure estimates due to biomechanical differences, dietary models underestimating glycemic variability, poor performance on mixed dishes, sauces, and culturally diverse foods, and a lack of validation against gold standards such as doubly labelled water (DLW) and weighed food records. This review proposes a paradigm shift toward obesity-specific AI design, including enriched datasets and multimodal integration. Physical activity monitoring faces similar challenges: systematic measurement bias in wearables, sensor placement issues, and algorithms trained on normal-weight cohorts. In the GLP-1/GIP era, if transparency, ethical safeguards, and equitable access are ensured, AI will act as a catalyst for personalized care, remote monitoring, trial optimization, and next-generation drug discovery. In conclusion, the integration of AI with rigorous validation procedures and inclusive sampling strategies is essential to achieve reliable, fair, and clinically relevant monitoring approaches for obesity management.

The Industrial Internet of Things (IIoT) increasingly integrates heterogeneous sensing platforms, including mobile agents such as robots and drones, to enable real-time monitoring and control in dynamic environments. Although Time-Slotted Channel Hopping (TSCH) protocols offer deterministic scheduling and energy efficiency, their limited adaptability to mobility and dynamic topologies restricts their effectiveness in mobile IIoT scenarios. To overcome these limitations, this paper introduces MoC-TSCH, a mobility-aware TSCH framework enhanced with multi-objective Mixed-Integer Linear Programming. MoC-TSCH jointly optimizing initial static node placement at the boundary and mobile node trajectory planning, while dynamically adjusting timeslot and channel allocations in response to changing network conditions. Simulation results indicate that MoC-TSCH improves performance across spatial, temporal, and reliability metrics under evaluated scenarios. Coverage increases from 40% to over 88%, average joining time drops from 3.7 s to 2.4 s, and reliability reaches 92%. End-to-end latency is reduced from 128 ms to 89 ms. Estimated energy consumption, calculated using a supply voltage of 3.3 V, decreases from approximately 0.43 J (130 mAs) to 0.25 J (76 mAs). Handover analysis indicate adaptive behavior, with MoC-TSCH achieving higher handover rates than standard TSCH under the tested scenarios. Compared to MTSH, MoC-TSCH coordinates static and mobile nodes under a multi-objective optimization, yielding clearer gains in coverage, connectivity, join time, reliability consistency, and delay in dynamic IIoT scenarios. To validate these findings, a custom indoor testbed was deployed using mobile and static OpenMote B nodes, Nano33BLE sensors, and a TurtleBot 4 Lite platform. The results suggest that MoC-TSCH exhibits improve reliability and reduced delay relative to baseline TSCH in the evaluated IIoT scenarios.

BackgroundSmartwatches have gained popularity for their potential to provide accurate measurements of various physiological parameters. However, the validity of energy expenditure (EE) across different smartwatch models remains a topic of ongoing investigation. Discrepancies between results obtained from different models and gold standard methods are particularly critical across varying exercise intensities and types, as validation studies have demonstrated overestimation when wearable activity monitors are compared with indirect calorimetry.ObjectiveThis study investigated the accuracy of 2 versions of the Samsung smartwatch (Galaxy Watch [GW] 6 and 7) in measuring EE during intermittent moderate-intensity running exercises, using indirect calorimetry as the gold standard method.MethodsThis study included 148 healthy adults, comprising 80 men and 68 women. Participants performed intermittent treadmill running, consisting of walking at 5 km·h⁻¹ for 1 minute and running between 8 and 16 km·h⁻¹ for 2 minutes, based on participant preference, for a total duration of 27 minutes. The GW6 and GW7 models were used and EE was measured by indirect calorimetry using a wearable portable metabolic gas analysis system (K5; Cosmed), which is considered a gold standard method.ResultsNo statistically significant differences were found between the GW models and the K5. The K5 showed a mean EE of 213.60 (SD 43.04) kilocalories, compared with 219.53 (SD 35.70) kilocalories for the GW6 and 202.67 (SD 47.42) kilocalories for the GW7 (all P>.05). Good Spearman correlations (0.63‐0.70) and moderate intraclass correlation coefficients (0.65‐0.74) were found. Mean absolute percentage error values ranged from 10.10% to 12.55%. Bland-Altman analysis revealed limits of agreement for all comparisons (K5 vs GW6 and GW7, −61.93 to 65.80 kcal).ConclusionsThe GW6 and GW7 devices showed moderate validity for estimating EE during intermittent running exercises, demonstrating the suitability of the GW as a low-cost and practical wearable option for daily physical activities.

The rise of artificial intelligence in urban computing, particularly in response to power crises caused by extreme events, demands fast simulations that balance physical accuracy with computational efficiency. This work presents a scalable software tool for predicting geothermal energy use in residential buildings. The tool uses a reduced-order model to estimate energy consumption and benchmarks the results against EnergyPlus simulations. Three distinct methods, Latin Hypercube Sampling, Saltelli, and eFast, were used to generate parameter datasets to explore model sensitivity to input parameters. The simulated outputs were then analyzed using the Extreme Gradient Boosting (XGBoost) algorithm, a machine learning approach based on gradient-boosted decision trees. The trained model achieved near-perfect accuracy in predicting energy usage. This software represents a significant step toward scalable, computationally efficient urban energy modeling and analysis.

Abstract This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with hyperdimensional computing (HDC). This decoding method is designed to achieve high accuracy, high noise robustness, low inference latency and low energy consumption. Compared to analogous architectures decoded with existing approaches, the SNN-HDC model attains generally better classification accuracy, lower inference latency, lower spike count and lower estimated energy consumption on multiple test cases from the literature. The SNN-HDC achieved spike count reductions of 1.74 × to 3.36 × on the DvsGesture dataset and 1.36 × to 2.70 × on the SL-Animals-DVS dataset. The SNN-HDC achieved estimated energy consumption reductions of 1.24 × to 3.67 × on the DvsGesture dataset and 1.38 × to 2.27 × on the SL-Animals-DVS dataset. The proposed decoding method enables detection of classes unseen during training. On the DvsGesture dataset, the SNN-HDC model can detect 100% of samples from an unseen/untrained class. The findings suggest the proposed decoding method is a compelling alternative to both rate and latency decoding.

Patients supported by extracorporeal membrane oxygenation (ECMO) are often in a state of severe hypermetabolism and hypercatabolism, making nutritional support difficult, and there are significant deviations in estimating energy consumption by traditional formulas. On May 1, 2025, a 42-year-old male patient with acute extensive anterior myocardial infarction was admitted to the First Dongguan Affiliated Hospital of Guangdong Medical University. After admission, the patient underwent coronary stent implantation, and subsequently developed severe complications such as cardiac failure, cardiogenic shock and electrical storm, with an Acute Physiology and Chronic Health Evaluation II of 34 points. A series of invasive supportive treatments were successively administered to the patient, including tracheal intubation with mechanical ventilation, intra-aortic balloon pump (IABP), veno-arterial ECMO (VA-ECMO), continuous renal replacement therapy (CRRT) and cardiac pacemaker implantation. During the treatment, resting energy expenditure (REE) of the patient was monitored in real time by metabolic cart (indirect calorimetry), and the monitoring results were compared with those calculated by the traditional Harris-Benedict formula. Individualized nutritional support regimens were formulated based on the metabolic cart monitoring data: the ratio of enteral to parenteral nutrition was 1 : 2 during VA-ECMO treatment, and adjusted to 2 : 1 in the late post-weaning period with the addition of ω-3 polyunsaturated fatty acids. Meanwhile, an integrated traditional Chinese and Western medicine approach was adopted to regulate gastrointestinal function and repair digestive tract damage. Monitoring results showed that the REE value measured by metabolic cart decreased to one-third of the pre-VA-ECMO level during VA-ECMO treatment, which was correlated with ECMO machine parameters. Various invasive treatments exerted complex effects on the body's nutritional and metabolic status and also interfered with the monitoring indicators of the metabolic cart. Guided by metabolic cart monitoring, precise nutritional therapy was implemented in combination with multidisciplinary comprehensive treatment, which maintained the patient's metabolic stability, promoted the gradual improvement of organ functions, and laid a solid foundation for successful weaning from supportive devices and favorable prognosis. This case suggests that individualized clinical nutritional therapy under the guidance of metabolic cart is conducive to optimizing energy supply for ECMO patients and improving clinical outcomes, thus possessing important clinical application value.

Estimating energy consumption in the processing of cotton raw materials in a linter, determining the patterns of changes in electrical loads and relative energy consumption and the most favorable modes. Also, it is important to correctly determine the installed power of the electric drives of linter machines and determine the required power on the shaft of their motors, and analyze the energy performance of the enterprise and workshops.

The article has considered renewable energy sources with high energy potential, which in the near future will become the fastest growing source of electricity. Generation sources include solar, wind, and biomass resources, which contribute to economic growth and reduce pollution. Optimizing the renewable and sustainable energy project is a key factor as a reliable alternative to conventional hydrocarbons, as well as as an energy source. It can play a significant role in the future of renewable and sustainable energy in Iraq. In the work, Helioscope and HOMER Pro software are used to create a small model connected to a network and to estimate energy consumption for optimization purposes. The results have showed an internal rate of return of 12 %, as well as about 8.5 % return on investment, and the share of the renewable energy component is almost 99.7 %. The proposed method proved to be effective in terms of using renewable energy. The research can be applied in any country, especially in the neighboring countries of Iraq.

This study developed a model for estimating energy consumption and CO2 emissions up to 2050 in the household sector in Japan. The total national energy consumption of the BAU case would be 1,136 PJ, a 53.4 % reduction compared to 2013. The total national CO2 emissions would be 10.18 Mt-CO2, a 94.1 % reduction compared to 2013. In addition, energy consumption and CO2 emissions will not reach zero even if Japan achieves current policy targets, suggesting that further efforts will be needed to reduce CO2 emissions to zero.