Mean Energy Consumption Research Articles

Impact of Recent Job Loss on Sleep, Energy Consumption and Diet.

Adequate sleep and self reported dietary recall data from the Assessing Daily Activity Patterns through Occupational Transitions (ADAPT) study was analysed. Primary sleep indices used were total sleep time, time spent in bed after final awakening, and sleep quality as measured by the Daily Sleep Diary (DSD). Mean Energy consumption (MEC) was the primary nutritional index. Secondary indices included diet quality using the Health Eating Index 2015 (HEI), and self-reported intake of protein, carbohydrates and fats. The study participants were comprised mainly of women (61%) and non-Hispanic white. The participants had at least 2 years of college education and mean body mass index of 30.2±8.08 (kg/m 2). The average time in bed was 541.8 (9.0 hrs) ±77.55 minutes and total sleep time was 461.1 (7.6 hrs) ±56.49 minutes. Mean sleep efficiency was 91±6%, self-reported sleep quality was 2.40±0.57 (0-4 scale, 4 = very good), and minutes earlier than planned morning awakening were 14.36±24.15. Mean HEI score was 47.41±10.92. Although the MEC was below national average for both men and women, male sex was associated with higher MEC. In a fully adjusted model sleep quality was positively associated with MEC. Daily overall assessments of sleep quality among recently unemployed persons were positively associated with mean energy consumption. Additionally, the diet quality of unemployed persons was found to be unhealthier than the average American and consistent with the relationship between poor socioeconomic status and lower diet quality.

Learning-Based Cooperative Adaptive Cruise Control

Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were trained using the proximal policy optimization algorithm. A validation by comparison with a model-based controller was performed. The performance of the trained controllers was verified with respect to the mean energy consumption and the root mean squared distance error. In our evaluation scenarios, the learning-based controllers reduced energy consumption in comparison to the model-based controller by 17.9% on average.

Comparison of energy consumptions measured by metabolic monitor with standard equations in intensive care patients

Purpose: The aim of this study was to determine parameters affecting the energy consumption in intensive care patients, and the most reliable formulas for calculation of energy consumption.
 Materials and Methods: This prospective study was carried out in the intensive care unit of the Internal Medicine Department of Çukurova University Hospital. Total 71 patients above 18 years of age, with hemodynamical and respirational stability, and being followed up on a mechanical ventilator for more than 24 hours, were included to this study. We analyzed the correlation of calculated energy consumption values obtained from formulas, with the energy consumption values measured by indirect calorimeter. 
 Results: The study was executed on 71 patients. The mean energy consumption of the patients was 2078±794 kcal and the mean energy need per kilogram was found as 31.64±13.82 kcal. Indirect calorimeter measurements showed the strongest correlation with Swinamer formula.
 Conclusion: Indirect calorimetry has become not only a "gold standard" but an "achievable gold standard" in determining energy consumption. We recommend that the indirect calorimeter method should be used in intensive care patients to maintain nutrition properly. Under conditions where indirect calorimeter cannot be used, or it is not desirable to wait for 24 hours; feeding can be started according to the results of a 2-hour measurement, or reliable predictive equations.

A Combined Traffic and Workload-aware Optimized Virtual Machine Migration in Cloud Computing

In cloud computing paradigm, Virtual Machine (VM) migration strategies play a vital role in reducing the Energy Consumption (EC) and balancing the workloads in the cloud servers. From this viewpoint, an Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction and Multipath Traffic Routing (OH-BAC-FUP-MTR) strategy was designed to accomplish effective load-balancing and minimize the chance of traffic congestion during VM migration in data centres. But, the tradeoffs among workload efficiency and power-gain in heterogeneous cloud servers were not analyzed. Hence in this article, a VM consolidation strategy is proposed with OH-BAC-FUP-MTR to switch the idle Physical Machines (PMs) into hibernation mode, resulting in very less EC. In this strategy, the VM migration is targeted at consolidating the VMs depending on the workload to the smaller number of PMs for reducing the power use and encouraging green computing. Initially, the PMs are split into various groups depending on their workload levels and then a new Merge-and-Split-based Coalitional Game-theoretic (MSCG) method is applied to select associates from these groups to create efficient coalitions. After that, OH-BAC-FUP-MTR is performed amongst the coalition associate for maximizing the reward of each coalition and PMs are maintained to operate in the maximum power-efficient condition. Finally, the investigational outcomes exhibit the OH-BAC-FUP-MTR-MSCG achieves a mean EC of 60.63KWh which is 28.87% less than all other classical VM migration strategies.

Evaluation of Impact Hammer Mill for Limestone Crushing for Acidic Soil

Soil acidity becomes a serious threat to crop production in most highlands of Ethiopia particularly in Western parts of Oromia. Frequent tillage, removal of crop residues and mono-cropping and heavy rainfall contributes to soil acidification by leaching of cations. Agricultural limestone raises soil pH and reduces solubility of potentially toxic elements such as hydrogen, aluminum (Al3+) and manganese (Mn) at optimum nutrient uptake by crops. To elucidate problems associated with soil acidity, a motorized agricultural limestone crusher was fabricated and evaluated. Performance of the prototype hammer mill machine, in terms of crushing capacity (kg/h), crushing efficiency (%), mean particle size (mm), fuel consumption (ml/kg) or energy consumption (wh/kg) was evaluated. Tests were carried out at engine speeds of 540, 720, 900 rpm, screen hole diameter of 2, 4, 6 mm and feed rates of 3.50, 7.00, 10.50 kg/min. The highest crushing capacity 630.32 kg/hr was recorded at 900 rpm engine speed, 6mm screen hole diameter and at 10.50 kg/min feed rate whereas the minimum 65.62 kg/h was observed at 540 rpm hammer mill speed, 2 mm screen hole diameter and at 3.50 kg/min feed rate. The mean consumed energy ranged from 15.47 to 149.16 Wh/kg with hammer rotor speed of 540 to 900 rpm, screen hole diameter of 2 to 6 mm and the feeding rate of 3.5 to 10.5 kg/min. The mean particle size ranged from 0.121 to 0.448 mm with hammer rotor speed of 540 to 900 rpm, screen holes diameter of 2 to 6 mm and the feeding rate of 3.5 to 10.5 kg/min. It could be noticed that the lowest values of mean particle size were obtained at engine speed of 900 rpm, screen hole diameter of 2 mm and feed rate of 10.5 kg/min.

An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system

Recently, growth in e-commerce has resulted in substantially increased investment in robotic technologies in warehouses, especially for storage and retrieval devices. A shuttle-based storage and retrieval system (SBS/RS) is one of the robotic automated storage and retrieval technologies mostly used for a miniload material handling system in e-commerce warehouses. This new technology has been developed as an alternative to the traditional miniload crane-based automated storage and retrieval system. Because it has an autonomous order picker robot in each tier of the racking system, the process rate for this new technology is very high. To evaluate several design options for an SBS/RS warehouse quickly, we have developed analytical models for estimating important performance outputs of these systems. Specifically, this study presents an open queuing network-based software tool that can quickly estimate some important performance metrics for a predefined SBS/RS warehouse design. Once the user enters the required input data values in the tool (e.g., the arrival rate of transactions in the system, warehouse metrics, number of tiers, aisles, and bays, and velocity profiles of devices), the performance metric estimates can be obtained in terms of the mean cycle time of a transaction, the mean energy consumption and energy regeneration per transaction, the mean waiting time of a transaction, the mean number of transactions waiting in a server (lift/shuttle) queue, and the mean utilization of servers. The software tool developed can be downloaded for free from the Web site http//:www.sbsrscalculator.com.

Impact of climate change in cultural heritage: from energy consumption to artefacts’ conservation and building rehabilitation

Historic artefacts must be properly preserved if they are to be transmitted to future generations. Indeed, several methodologies and guidelines that aim to safeguard artefacts by limiting the ranges in which the indoor temperature and relative humidity vary exist, which means energy consumption. This paper aims to quantify the energy consumption associated to three different setpoints and respective financial cost, as well as their future trend to demonstrate the positive outcome of passive retrofit measures, since they will be responsible for decreasing the building’s energy consumption and mitigating the effects of climate change in artefacts’ preservation. A validated whole-building hygrothermal model of a historic building was used coupled to climate change weather files to obtain the expected future indoor conditions for three types of climates, which were also assessed using a risk-based analysis. The positive potential of passive retrofit measures on the building’s energy consumption was shown, but the risk-based analysis showed that the measures performance are not universal since, for example, whilst the selected measures decrease the risk of chemical decay for Seville, they have the contrary behaviour for Oslo. To achieve these goals more than 1400 simulations were run in WUFI®Plus, which took more than 1600 h.

Energy and Exergy Performances of Floor, Ceiling, Wall Radiator and Stratum Ventilation Heating Systems for Residential Buildings

To provide a highly efficient heating for the residential buildings, different heating systems, including the floor and ceiling heating, wall radiator heating and stratum ventilation heating, are compared based on a typical sleeping environment in this study. Performance indices, including occupied zone predicted mean vote (OPMV), energy consumption and exergy consumption, are analyzed under different conditions. The results show that stratum ventilation heating performs better than the conventional floor, ceiling and wall radiator heating in terms of energy and exergy consumptions. Firstly, under stratum ventilation heating, the OPMV equals zero at a low supply air temperature (20.0°C for stratum ventilation heating at the supply air angle of 45° and 19.7°C for stratum ventilation heating mode at the supply air angle of 75°). Secondly, stratum ventilation heating consumes less energy than the floor, ceiling or wall radiator heating of identical OPMV increments. In addition, compared with the floor, ceiling, wall radiator heating and stratum ventilation heating at the supply air angle of 45°, the energy-saving ratio of stratum ventilation heating at the supply air angle of 75° reaches 29.7%, 21.2%, 22.4% and 4.2% respectively when OPMV equals zero. Lastly, for stratum ventilation heating, the supply and return water temperatures in the air handling unit are significantly lower. Therefore, a lower exergy consumption is realized under stratum ventilation heating in comparison with the conventional floor, ceiling and wall radiator heating.

Minimizing virtual machine migration probability in cloud computing environments

Virtualization provides the flexibility to distribute the workload among physical servers to reduce overall electrical power consumption, through the consolidation of Virtual Machines (VMs). Many research projects have been done on VM migration to reduce energy consumption in data centers while ensuring a high level of adherence to the Service Level Agreements (SLA). Service levels of running applications are likely to be negatively affected during a live VM migration. For this reason, in this paper, we propose a new intelligent VM migration approach, called CLANFIC, which utilizes modified Cellular Learning Automata based Evolutionary Computing (CLA-EC) and neuro-fuzzy to minimize the number of VM migrations and improve energy consumption. This goal is achieved by utilizing an optimized placement method and delaying migration time based on future resource demand prediction. This algorithm reduces the number of migrations in two steps (i) finding the optimal virtual machine placement and replacement on physical servers by using modified CLA-EC (ii) predicting future resource usage in each host by a neuro-fuzzy algorithm to prevent unnecessary migrations. The experimental results on the real workload traces from PlanetLab show that the proposed method reduces the mean migration number, energy consumption, and SLA violation of the data center by 59.05%, 8.5%, and 70.76%, respectively.

An Efficient Multi-Objective Model for Data Replication in Cloud Computing Environment

The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.

Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption

Self-Organized Proactive Routing Protocol for Non-Uniformly Deployed Underwater Networks.

Electromagnetic (EM) waves cannot propagate more than few meters in sea water due to the high absorption rate. Acoustic waves are more suitable for underwater communication, but they travel very slowly compared to EM waves. The typical speed of acoustic waves in water is 1500 m/s, whereas speed of EM waves in air is approximately 3 × 108 m/s. Therefore, the terrestrial wireless sensor network (WSN) protocols assume that the propagation delay is negligible. Hence, reactive protocols are deemed acceptable for WSNs. Other important issues related to underwater wireless sensor networks (UWSNs) are determining the position of the underwater nodes and keeping a time synchronization among the nodes. Underwater nodes can neither determine their position nor synchronize using Global Navigation Satellite Systems (GNSS) because of the short penetration of EM waves in sea water. The limited mobility of UWSN nodes and variation in the propagation speed of acoustic waves make time synchronization a challenging task for underwater acoustic networks (UASNs). For all these reasons, WSN protocols cannot be readily used in UASNs. In this work, a protocol named SPRINT is designed to achieve high data throughput and low energy operation in the nodes. There is a tradeoff between the throughput and the energy consumption in the wireless networks. Longer links mean higher energy consumption. On the other hand, the number of relay nodes or hops between the source node and the final destination node is a key factor which affects the throughput. Each hop increases the delay in the packet forwarding and, as a result, decreases the throughput. Hence, energy consumption requires the nearest nodes to be chosen as forwarding nodes, whereas the throughput requires the farthest node to be selected to minimize the number of hops. SPRINT is a cross-layer, self-organized, proactive protocol which does not require positioning equipment to determine the location of the node. The routing path from the node to the gateway is formed based on the distance. The data sending node prefers to choose the neighbor node which is closest to it. The distance is measured by the signal strength between the two nodes.

Application to rigid memory mechanisms of a variable internal dynamic damping model

The paper presents a dynamic model that works with variable internal damping, applicable directly to rigid memory mechanisms. If the problem of elasticity is generally solved, the problem of system damping is not clear and well-established. It is usually considered a constant value for the internal damping of the system and sometimes the same value c and for the damping of the elastic spring supporting the valve. However, the approximation is much forced, as the elastic spring damping is variable, and for the conventional cylindrical spring with constant elasticity parameter (k) with linear displacement with force, the damping is small and can be considered zero. It should be specified that damping does not necessarily mean stopping (or opposition) movement, but damping means energy consumption to brake the motion (rubber elastic elements have considerable damping, as are hydraulic dampers). Metal helical springs generally have a low (negligible) damping. The braking effect of these springs increases with the elastic constant (the k-stiffness of the spring) and the force of the spring (P0 or F0) of the spring (in other words with the arc static arrow, x0=P0/k). Energy is constantly changing but does not dissipate (for this reason, the yield of these springs is generally higher). The paper presents a dynamic model with a degree of freedom, considering internal damping of the system (c), damping for which it is considered a special function. More precisely, the cushioning coefficient of the system (c) is defined as a variable parameter depending on the reduced mass of the mechanism (m* or J reduced) and the time, ie, c depends on the derivative of m reduced in time. The equation of the differential movement of the mechanism is written as the movement of the valve as a dynamic response.

Improved Artificial Bee Colony Optimization-Based Clustering Technique for WSNs

The core objective of controlling topology, conserving energy and extending the life cycle in sensor networks is highly indispensable for maximizing its lifetime. Meta-heuristic optimization algorithms are found to be optimal in facilitating the process of cluster head election and clustering. An Improved Artificial Bee Colony Optimization-based Clustering Technique (IABCOCT) is propounded by deriving the merits of Grenade Explosion and Cauchy operator for ensuring optimal clustering process and election of cluster heads. The potential of Grenade Explosion and Cauchy operator are embedded in the Onlooker Bee and scout bee phase for phenomenal improvement in the degree of exploitation and exploration of searching that aids in the optimal election of cluster heads. This incorporation of Grenade Explosion and Cauchy operator in the clustering process maximizes the rate of cluster head election for reducing energy consumption in sensor nodes that enhances lifetime of the network. The potential of IABCOCT is studied through simulation experiments and its performance is investigated against Enhanced Particle Swarm Optimization Technique (EPSOCT), Hierarchical Clustering-based Cluster Head Election (HCCHE) and Competitive Clustering Technique (CCT). The simulation results of IABCOCT prove that the mortality rate of sensor nodes is minimized for enhancing network lifetime. The results deduce that the relative energy, dropping ratio, Mean energy consumption and Network Routing Overhead is better than EPSOCT, HCCHE and CCT under the impact of varying number of rounds.

Conceptual assessment of energy input-output analysis and data envelopment analysis of greenhouse crops in Crete Island, Greece.

The current study observes the input/output energy quantities and its associated financial value, in vegetable greenhouses on Crete, Greece, for the 2015-2016 cropping seasons. The utilized data was taken in regular direct basis for the implementation of energy input-output analysis and data envelopment analysis. For input data analysis, four crop practices were used tomato, pepper, cucumber, and eggplant. The outcomes of the expended majority of energy input-output analysis showed that fertilizers count for 53%, fossil fuel counts for 16%, and electricity counts for 12%. The mean crop and energy consumption for each crop practice were estimated as 94,036MJ/ha and 171,950kg/ha, 115,473MJ/ha and 173,000kg/ha, 81,196MJ/ha and 128,893kg/ha, and 146,067MJ/ha and 209,501kg/ha, respectively. Inputs with the biggest shares in total inputs were manpower counts for 30%, fertilizers count for 22%, and crop protection counts for 16%. DEA method showed that the mean values of technical efficiency, pure technical efficiency, and scale efficiency were 0.90193, 0.97272, and 0.80322, respectively. The mean scale efficiencies were set as 0.80. The findings of this research would be valuable to the inefficient producers undertaking into consideration the recommendations made by this method, where the overall input in Euro could be meaningfully condensed without any reduction on the current overall output.

Theoretical Analysis on Edge Computation Offloading Policies for IoT Devices

The Internet of Things (IoT) has gained great attention in recent years, due to its significant role in industry innovations and promotions. However, it is still facing many technical challenges before fully gaining ground, mainly resulting from limited computational and energy resources of IoT devices and best-effort underlying network paradigms. Thanks to the emerging edge computing that optimizes the cloud computing by processing data at edge networks, IoT devices can offload computation-intensive tasks to their assigned edge computing servers with response time guaranteed and energy consumption saved. As a result, how to perform task offloading by IoT devices has become a key challenge widely discussed. Nevertheless, most of the existing works focus on the tradeoff between executing a task locally and remotely through techniques, such as optimization and game theory, rather than related theoretical model to analyze communication procedures of offloading policies. Thus, in this paper, we propose a multiqueue model to explore the impact of offloading policies on performance of the IoT devices with their assigned edge computing server. Particularly, we consider two simple policies, namely Locality-First policy and Probability-based policy, and obtain their analytic solution of the task mean response time and energy consumption of the IoT devices and edge computing server. Extensive simulations are performed and related results have proved accuracy of the proposed model.

Energy-Aware Motion and Disparity Estimation System for 3D-HEVC With Run-Time Adaptive Memory Hierarchy

The popularization of multimedia services has pushed forward the development of 2D/3D video-capable embedded mobile devices. Such devices require efficient energy/memory-management strategies to deal with severe memory/processing requirements and limited energy supply. Therefore, we propose a motion and disparity estimation (ME and DE) system—the most memory/processing demanding encoding steps—for the 3D High Efficiency Video Coding (3D-HEVC) standard. It was designed for low energy consumption, featuring a run-time adaptive memory hierarchy. The processing unit employs flexible coding order and optimizations to reduce the computational effort by exploring the inter-channel and inter-view redundancies. The memory hierarchy features window-based prefetching, data reuse, subsampling, and dynamic voltage scaling controlled by our depth-based dynamic search window resizing algorithm. Memory results demonstrate an average on-chip energy reduction of 79% in comparison to the widely used Level-C solution for a 45-nm technology. The proposed energy-aware ME and DE system dissipates 7.55 W while processing three HD 1080p views (video + depth) at 30 frames per second and presents a mean energy consumption of 0.107 J per access unit. To the best of our knowledge, this is the first work that proposes a real-time ME/DE system for the 3D-HEVC standard with an adaptive memory hierarchy.

Dynamic Models for Rigid Memory Mechanisms

The paper presents a dynamic model that works with variable internal damping, applicable directly to rigid memory mechanisms. If the problem of elasticity is generally solved, the problem of system damping is not clear and well-established. It is usually considered a constant value for the internal damping of the system and sometimes the same value c and for the damping of the elastic spring supporting the valve. However, the approximation is much forced, as the elastic spring damping is variable and for the conventional cylindrical spring with constant elasticity parameter (k) with linear displacement with force, the damping is small and can be considered zero. It should be specified that damping does not necessarily mean stopping (or opposition) movement, but damping means energy consumption to brake the motion (rubber elastic elements have considerable damping, as are hydraulic dampers). Metal helical springs generally have a low (negligible) damping. The braking effect of these springs increases with the elastic constant (the k-stiffness of the spring) and the force of the spring (P0 or F0) of the spring (in other words with the arc static arrow, x0 = P0/k). Energy is constantly changing but does not dissipate (for this reason, the yield of these springs is generally higher). The paper presents a dynamic model with a degree of freedom, considering internal damping of the system (c), damping for which it is considered a special function. More precisely, the cushioning coefficient of the system (c) is defined as a variable parameter depending on the reduced mass of the mechanism (m* or J reduced) and the time, i.e., c depends on the derivative of m reduced in time. The equation of the differential movement of the mechanism is written as the movement of the valve as a dynamic response.

Dynamic Models for Rigid Memory Mechanisms

The paper presents a dynamic model that works with variable internal damping, applicable directly to rigid memory mechanisms. If the problem of elasticity is generally solved, the problem of system damping is not clear and well-established. It is usually considered a constant value for the internal damping of the system and sometimes the same value c and for the damping of the elastic spring supporting the valve. However, the approximation is much forced, as the elastic spring damping is variable and for the conventional cylindrical spring with constant elasticity parameter (k) with linear displacement with force, the damping is small and can be considered zero. It should be specified that damping does not necessarily mean stopping (or opposition) movement, but damping means energy consumption to brake the motion (rubber elastic elements have considerable damping, as are hydraulic dampers). Metal helical springs generally have a low (negligible) damping. The braking effect of these springs increases with the elastic constant (the k-stiffness of the spring) and the force of the spring (P0 or F0) of the spring (in other words with the arc static arrow, x0 = P0/k). Energy is constantly changing but does not dissipate (for this reason, the yield of these springs is generally higher). The paper presents a dynamic model with a degree of freedom, considering internal damping of the system (c), damping for which it is considered a special function. More precisely, the cushioning coefficient of the system (c) is defined as a variable parameter depending on the reduced mass of the mechanism (m* or J reduced) and the time, i.e., c depends on the derivative of m reduced in time. The equation of the differential movement of the mechanism is written as the movement of the valve as a dynamic response.

Association of exclusive breastfeeding duration with consumption of ultra-processed foods, fruit and vegetables in Brazilian children.

There has been little evidence of the influence of breastfeeding on childhood eating habits. To evaluate the association between duration of exclusive breastfeeding (EB) and consumption of ultra-processed foods, fruits and vegetables by Brazilian children. This is a cross-sectional study nested in a cohort of 403 children age 4-7years. Food consumption was evaluated by three food records and the foods were grouped by the amount of processing involved (minimal, including fruit and vegetables to ultra-processed foods). Adjusted linear and multinomial regression models were proposed to evaluate the association of EB with children's food intake. Median duration of EB was 4.0(2.0-6.0)months and the prevalence of EB of 4months or more was 60.9%. Mean energy consumption was 1536kcal/day, 38% of energy was from ultra-processed foods. Mean intake of fruit and vegetables was 130g/day. For each 1-month increase in duration of EB there was a 0.7% decrease in energy from ultra-processed foods (β: - 0.7; 95% CI: - 1.3 to 0.1; p = 0.038). EB of less than 4months was associated with the child being in the lowest tercile for consumption of fruit and vegetables (Adjusted OR: 1.8; 95% CI: 1.1-3.0; p = 0.030) and the highest for consumption of ultra-processed foods (adjusted OR: 1.7; 95% CI: 1.1-2.9; p = 0.040). In addition to other benefits of EB, we emphasize its importance for the formation of healthy eating habits, contributing to a higher consumption of fruit and vegetables and lower consumption of ultra-processed foods in childhood.