Consumption Metrics Research Articles

Multi-objective cutting parameter optimization model of multi-pass turning in CNC machines for sustainable manufacturing

Sustainable manufacturing has grown widely owing to recent environmental issues. This study aims to develop a multi-objective multi-pass turning optimization model to determine the optimal cutting parameters, including spindle rotation speed, feed rate, depth of cut, and number of roughing passes. The optimization model considers several criteria in the key metrics of sustainable manufacturing, i.e., energy consumption, carbon emissions, production time, and production cost. A numerical example is provided to show the application of the model, including sensitivity analysis, to study the effects of several cutting parameters on the objective functions. The model can be used by manufacturing industries to improve their manufacturing process efficiency and simultaneously produce products that support sustainable manufacturing.

The Measure of the Middle Class

The term, ‘middle class’, is constantly used to describe some average group of individuals, households or families. Various definitions and measures are used within the public policy, media and academic arenas. And, yet, there is no definitive consensus as to what middle class means or how to measure it. It seems that there are a number of nuances to the concept of the middle class that may not lend itself to direct measurement, outside of specialized surveys. The most common approach to defining and measuring the middle class is through the use of some form of income. Generally the concept of the money income seems to the broadest and most useful, through it presents certain problems upon closer examination. Other metrics used or proposed include wealth and consumption. Wealth as a metric is often of interest especially where there is concern over inequality. The consumption metric is more directly related to income, but the manner in which it is defined and collected is where it may present certain advantages over income. There are more subjective aspects of this topic which is often used to better identify those belonging to the middle class. Its qualitative nature makes it difficult to measure on some consistent basis. However, it seems that this area needs further exploration. The purpose of this paper is not to delve into the areas of poverty or inequities of the income or wealth distribution. It sole purpose is to attempt to examine the definition of middle class that may lead to a useful metric to measure it. As to the various metrics available, an examination of the standard ones will be made, as well as some non-standard ones. The paper's objective is to highlight which metrics may be most appropriate and what changes should be made to provide a more robust method of measuring the middle class. Assuming one can define, albeit imperfectly, a middle class, then it naturally follows that a lower class and upper class will also be defined.

Investigation Misbehavior Configuration Nodes with Secure Neighborhood on Energy Consumption for DYMO Routing Protocol in MANETS

We calculate misbehavior of energy consumption during configuration nodes between neighborhood nodes with specific investigation on secure environment with DYMP routing Protocol. An experimental analysis of DYMO, M-DYMO (misbehavior DYMO), S-DYMO (Secure-DYMO) has been carried out using Qual Net 5.1 simulator. The simulation results have been derived using self-created network scenarios by incorporating secure neighborhood in de-facto DYMO by varying the network size as small, medium and large, Node Traversal Time, ART, Buffer Size. From the experiment results, it has been concluded that energy consumption increases as security is incorporated in the existing routing protocol. From the results, the variance of total energy consumed in all modes of energy (transmit, receive and idle) for nodes in DYMO,M-DYMO and S-DYMO under Random waypoint Mobility Model is maximum for larger network size which is 3.380037 mj , 3.363414 mj and 3.612123 mj. For random waypoint mobility model the variance of total energy consumed in all modes of energy is maximum at 0.2320866668 at 115 nodes. In this research paper, an effort has been made to investigate the impact of secure neighborhood on energy consumption and QoS metrics of Dynamic MANETs On-Demand (routing protocol) (DYMO) in MANETS.

Multistage fuzzy logic congestion-aware routing using dual-stage notification and the relative barring distance in wireless sensor networks

Congestion management in a wireless sensor network (WSN) is a key determinant of the quality of service. Congestion in a network causes data loss, a reduced transmission rate, increased delays, and excess energy consumption. The latter has a direct impact on tiny sensor devices with limited resources and processing, buffering, and transmitting capabilities. In addition, a WSN relies on multiple packet relays between nodes, which inevitably results in network congestion near the base station, whose neighboring nodes incur crowded traffic from multisource deliveries. Thus, this paper proposes a novel routing method that minimizes congestion. The adaptive routing strategy consists of 3 main modules. First, an optimal notification level for queue control is specified by using multistage fuzzy logic (MFL). The resulting weights evaluated from congestion-related parameters are then passed onto the subsequent modules. The second module adjusts the congestion notification, which makes the module more flexible to improve its routing discovery efficiency and to reduce the chance of loss during the rerouting stage. Finally, we propose a routing adjustment and control mechanism by using a novel navigation technique based on linear and angular distances and MFL to create weights for path assessment. Simulation results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of the packet loss ratio, average hop count, network lifetime, and energy consumption metrics.

Reduced Alcohol Use Is Sustained in Patients Provided Alcohol-Related Counseling During Direct-Acting Antiviral Therapy for Hepatitis C.

Patients with chronic hepatitis C and risky/harmful alcohol use experience poor outcomes. Granular data evaluating whether alcohol counseling during hepatitis C treatment impacts longitudinal alcohol consumption are lacking. To evaluate whether provider-delivered counseling in the context of direct-acting antiviral hepatitis C treatment associates with decreased longitudinal alcohol consumption. We performed secondary data analysis from the Hep ART study including adults with hepatitis C who underwent provider-delivered counseling during direct-acting antiviral treatment between October 2014 and September 2017. Demographics and disease characteristics were summarized. Alcohol consumption, abstinence, and heavy drinking were evaluated in periods before, during, and after direct-acting antiviral treatment. Multivariate regression analyses were performed to evaluate the association of alcohol consumption with each 12-week time period for all patients and a subsample with cirrhosis. One hundred twenty-three patients were included; 41 had cirrhosis. Most patients were male (74.0%) and Black (58.5%). Alcohol consumption improved during direct-acting antiviral treatment and was notably sustained (< 12weeks before treatment 32.5g/day; during treatment 20.0g/day; and 12-24weeks after treatment 23.7g/day). Multivariable analyses showed significantly improved alcohol consumption metrics during and after antiviral treatment compared to < 12weeks before treatment (during treatment 13.04g/day less, p = 0.0001; > 24weeks after treatment 15.29g/day less, p = 0.0001). The subsample with cirrhosis showed similar results (during treatment 13.21g/day less, p = 0.0001; > 24weeks after treatment 7.69g/day less, p = 0.0001). Patients with chronic HCV and risky/harmful alcohol use given provider-delivered alcohol-related counseling during HCV treatment sustain decreased alcohol consumption patterns during and after treatment.

Energy-Aware Scheduling of Workflow Using a Heuristic Method on Green Cloud

Energy consumption has been one of the main concerns to support the rapid growth of cloud data centers, as it not only increases the cost of electricity to service providers but also plays an important role in increasing greenhouse gas emissions and thus environmental pollution, and has a negative impact on system reliability and availability. As a result, energy consumption and efficiency metrics have become a vital issue for parallel scheduling applications based on tasks performed at cloud data centers. In this paper, we present a time and energy-aware two-phase scheduling algorithm called best heuristic scheduling (BHS) for directed acyclic graph (DAG) scheduling on cloud data center processors. In the first phase, the algorithm allocates resources to tasks by sorting, based on four heuristic methods and a grasshopper algorithm. It then selects the most appropriate method to perform each task, based on the importance factor determined by the end-user or service provider to achieve a solution designed at the right time. In the second phase, BHS minimizes the makespan and energy consumption according to the importance factor determined by the end-user or service provider and taking into account the start time, setup time, end time, and energy profile of virtual machines. Finally, a test dataset is developed to evaluate the proposed BHS algorithm compared to the multiheuristic resource allocation algorithm (MHRA). The results show that the proposed algorithm facilitates 19.71% more energy storage than the MHRA algorithm. Furthermore, the makespan is reduced by 56.12% in heterogeneous environments.

Multi-criteria HPC task scheduling on IaaS cloud infrastructures using meta-heuristics

With the rapid increase in the use of cloud computing systems, an efficient task scheduling policy, which deals with the assignment of tasks to resources, is required to obtain maximum performance. Cloud task scheduling (CTS) is an established NP-Hard optimization problem that can be effectively tackled with meta-heuristic algorithms. The cuckoo search (CS) algorithm is a powerful swarm-intelligence meta-heuristic that has been successfully applied over a wide-range of real-life optimization problems, including task scheduling problems. Besides its strong exploration ability, the CS algorithm suffers from insufficient local search, lack of solution diversity towards the end, and slow convergence problem. These drawbacks produce inefficient cloud task schedules resulting in sub-optimal performance. In this manuscript, an improved CS-based scheduling algorithm called CSDEO is introduced, which combines the features of the Opposition-based learning (OBL) method, Cuckoo search, and Differential evolution (DE) algorithms to optimize workload makespan and energy consumption of the cloud resources. Our CSDEO algorithm firstly uses the OBL method to produce an optimal initial population by providing solutions across the entire solution space. Then, the CSDEO uses an effective way of switching between the CS exploration phase and the DE exploitation phase, depending on each solution's fitness. Experiments are conducted on the CloudSim simulator by using the CEA-Curie and HPC2N supercomputing workloads. The observations show that in the case of CEA-Curie workloads, the proposed CSDEO algorithm achieves makespan improvement in the range of 6.29–29.76% and energy consumption improvement in the range of 3.76–201.98% over well-known scheduling algorithms. In the case of HPC2N workloads, the improvement ranges of the CSDEO approach for the makespan and energy consumption metrics are 9.86–281.69% and 6.12–233.3%, respectively compared to the tested scheduling algorithms.

Energy Consumption Analysis of Beamforming and Cooperative Schemes for Aircraft Wireless Sensor Networks

In this paper, the eight schemes for aircraft wireless sensor networks are investigated, which are single-hop array beamforming schemes (including analog beamforming (ABF), and digital beamforming (DBF)), non-cooperative schemes (including single-hop and multi-hop schemes), cooperative schemes (including amplify and forward (AF), decode and forward (DF)), and incremental cooperative schemes (incremental decode and forward (IDF), and incremental amplify and forward (IAF)). To set up the aircraft wireless communication environment, we design the aircraft channel model by referring to the experimental parameters of the ITU (International Telecommunication Union)-R M.2283, which is composed of path loss, shadowing fading, and multi-path fading channel responses. To evaluate the performance, the conditions energy consumption and throughput analysis are performed. Through simulation results, the incremental cooperative scheme outperformed by 66.8% better at spectral efficiency 2 than the DBF scheme in terms of the energy consumption metric. Whereas, in terms of throughput metric, overall SNR (signal-to-noise ratio) ranged from −20 to 30 dB the beamforming scheme had the best performance in which the beamforming scheme at SNR 0 dB achieved 85.4% better than the multi-hop scheme. Finally, in terms of normalized throughput metric in low SNR range between −20 and 1 dB the ABF scheme had the best performance over the others in which the ABF at SNR 0 dB achieved 75.4% better than the multi-hop scheme. Whereas, in high SNR range between 2 and 30 dB the IDF scheme had the best performance in which the IDF at SNR 10 dB achieved 62.2% better than the multi-hop scheme.

AndroDFA: Android Malware Classification Based on Resource Consumption

The vast majority of today’s mobile malware targets Android devices. An important task of malware analysis is the classification of malicious samples into known families. In this paper, we propose AndroDFA (DFA, detrended fluctuation analysis): an approach to Android malware family classification based on dynamic analysis of resource consumption metrics available from the proc file system. These metrics can be easily measured during sample execution. From each malware, we extract features through detrended fluctuation analysis (DFA) and Pearson’s correlation, then a support vector machine is employed to classify malware into families. We provide an experimental evaluation based on malware samples from two datasets, namely Drebin and AMD. With the Drebin dataset, we obtained a classification accuracy of 82%, comparable with works from the state-of-the-art like DroidScribe. However, compared to DroidScribe, our approach is easier to reproduce because it is based on publicly available tools only, does not require any modification to the emulated environment or Android OS, and by design, can also be used on physical devices rather than exclusively on emulators. The latter is a key factor because modern mobile malware can detect the emulated environment and hide its malicious behavior. The experiments on the AMD dataset gave similar results, with an overall mean accuracy of 78%. Furthermore, we made the software we developed publicly available, to ease the reproducibility of our results.

MW‐LEACH: Low energy adaptive clustering hierarchy approach for WSN

The authors propose a novel multiple weight low energy adaptive clustering hierarchy (MW-LEACH) protocol for wireless sensor networks. In MW-LEACH, they select the cluster heads (CHs) based on the residual energy, the distances between the CHs, and an optimal number of member nodes. The nodes are selected from the initial set based on the high residual energy closer to the centre of the density, thus forming an initial set of CH candidates. The candidates then move in different directions to collect data from their members sending it to the base station. The proposed approach has lower complexity in terms of time and message. It is also fast and offers a longer lifetime for the network. It also provides a proper level of fault tolerance. In the experimental simulation evaluation, their approach outperforms state-of-the-art protocols based on performance metrics of throughput, energy consumption, packet delivery, network lifetime, and latency.

Threats from the air: Damselfly predation on diverse prey taxa.

To understand the diversity and strength of predation in natural communities, researchers must quantify the total amount of prey species in the diet of predators. Metabarcoding approaches have allowed widespread characterization of predator diets with high taxonomic resolution. To determine the wider impacts of predators, researchers should combine DNA techniques with estimates of population size of predators using mark-release-recapture (MRR) methods, and with accurate metrics of food consumption by individuals. Herein, we estimate the scale of predation exerted by four damselfly species on diverse prey taxa within a well-defined 12-ha study area, resolving the prey species of individual damselflies, to what extent the diets of predatory species overlap, and which fraction of the main prey populations are consumed. We identify the taxonomic composition of diets using DNA metabarcoding and quantify damselfly population sizes by MRR. We also use predator-specific estimates of consumption rates, and independent data on prey emergence rates to estimate the collective predation pressure summed over all prey taxa and specific to their main prey (non-biting midges or chironomids) of the four damselfly species. The four damselfly species collectively consumed a prey mass equivalent to roughly 870 (95% CL 410-1,800) g, over 2months. Each individual consumed 29%-66% (95% CL 9.4-123) of its body weight during its relatively short life span (2.1-4.7days; 95% CL 0.74-7.9) in the focal population. This predation pressure was widely distributed across the local invertebrate prey community, including 4 classes, 19 orders and c. 140 genera. Different predator species showed extensive overlap in diets, with an average of 30% of prey shared by at least two predator species. Of the available prey individuals in the widely consumed family Chironomidae, only a relatively small proportion (0.76%; 95% CL 0.35%-1.61%) were consumed. Our synthesis of population sizes, per-capita consumption rates and taxonomic distribution of diets identifies damselflies as a comparatively minor predator group of aerial insects. As the next step, we should add estimates of predation by larger odonate species, and experimental removal of odonates, thereby establishing the full impact of odonate predation on prey communities.

The early life stages of the orange-spotted grouper,Epinephelus coioides, exhibit robustness to hypercapnia

AbstractOcean acidification (OA) and other climate change-induced environmental alterations are resulting in unprecedented rates of environmental degradation. This environmental change is generally thought to be too fast for adaptation using evolutionary process dependent on natural selection, and thus, resilience may be related to the presence of existing tolerant genotypes and species. Estuaries undergo natural partial pressure carbon dioxide (pCO2) fluctuations, with levels regularly exceeding predicted end of the century values. In this study, we use the estuarine orange-spotted grouper (Epinephelus coioides) to explore the intrinsic resilience to elevated pCO2. Our sensitivity endpoints included: survival, heart rate, growth, and yolk consumption. Furthermore, we attempted to determine whether their acid–base regulatory machinery was plastic in response to elevated pCO2 by analysing the gene expression of key transporters and ionocyte density. Survival was not significantly altered by exposure to elevated pCO2. Interestingly, the heart rate was significantly elevated at both 1500 and 3100 μatm exposure. However, other metrics of energetic consumption, such as yolk consumption and growth, were not significantly altered. Furthermore, we found no changes in gene expression in vha, nhe3, and nbc, as well as ionocyte density at elevated pCO2. Overall, these results support the hypothesis that estuarine species are resilient to the impacts of OA.

Making energy metrics relevant to service firms: from energy conservation to energy productivity

Although energy conservation and reduction in environmental impact are on the international and most national agendas, service firms rarely include energy consumption metrics in their strategic decision-making. One reason for the omission is that for service industries, firm level energy utilization is most commonly measured in kilowatt hours per square meter of office space where changes often related to the space rather than the firm performance. The measure also presents several problems for firms in service industries. First, energy conservation and reduction may be counterproductive for service firms that are growing and require energy to sustain that growth. Second, it may not relate to national and international goals which are often focused on the amount of carbon dioxide produced generating energy than the total amount of energy consumed. Third, it treats energy as a utility rather than a resource in firms’ value creation. Results from a field study focused on service firms in Sweden suggests that focusing on energy productivity overcomes the limitations of existing measures and produces positive results. By conceptualizing energy productivity as output per unit of energy, we create a conservation metric that enables service firms to measure their contributions to energy consumption relative to national economic growth. As a result, energy productivity aligns the interests of service organizations with those of policy makers and conservationists.

The Impact of Atrial Fibrillation on In-Hospital Outcomes in Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock Undergoing Coronary Revascularization with Percutaneous Ventricular Assist Device Support.

Atrial fibrillation (AF) is common in acute myocardial infarction complicated by cardiogenic shock (AMI-CS) requiring percutaneous ventricular assist device (pVAD-Impella®) support during percutaneous coronary interventions (PCI). We evaluated the effects of a coexistent diagnosis of AF on clinical outcomes in patients with AMI-CS undergoing PCI with pVAD support. The National Inpatient Sample (2008-2014) was queried to identify patients with AMICS requiring PCI with pVAD support and had a concomitant diagnosis of AF. Propensity-matched cohorts (AF+ vs AF-) were compared for in-hospital outcomes. A total of 840 patients with AMICS requiring PCI with pVAD support (420 AF+ vs 420 AF-) were identified in the matched cohort. Patients with AF were older (mean 69.7±12.0 vs 67.9±11.3 yrs, p=0.030). All-cause in-hospital mortality rates between the two groups were similar (40.5% vs 36.7%, p=0.245); however, higher postprocedural respiratory complications (9.5% vs 4.8%, p=0.007) were seen in AF+ group. In-hospital cardiac arrests were more frequent in the AF- group (32.0% vs 19.2%, p<0.001). We examined the length of stay (LOS), transfer to other facilities, and hospital charges as metrics of health care resource consumption and found that the AF+ cohort experienced fewer routine discharges (13.1% vs 30.2%), more frequent transfers to other facilities including skilled nursing facilities or intermediate care facilities (27.3% vs 17.8%; p<0.001), more frequently required the use of home health care (14.3% vs 7.1%; p<0.001). The mean LOS (11.9±10.1 vs 9.11±6.8, p<0.001) and hospital charges ($308,478 vs $277,982, p=0.008) were higher in the AF+ group. In patients suffering AMICS requiring PCI and pVAD support, a coexistent diagnosis of AF was not associated with an increase in all-cause in-hospital mortality as compared to patients without AF. However, healthcare resource consumption as assessed by various metrics was consistently greater in the AF+ group.

Metrics of energy consumption in software systems: a systematic literature review

The current situation when using tight time frames and frequently changing requirements when creating software dictates the need to create a system for monitoring energy consumption at any stage of production of a software product. At the first stage, we need to evaluate the state-of-the-art on this topic. To this goal, we conducted a systematic literature review. During the review more than 500 studies were observed and 124 of them were selected for detailed analysis. Among these papers, 169 metrics were derived and assessed from the point of their applicability within invasive software development process analysis. The study demonstrates the relevance of the questions posed and shows the immaturity of the area. There is no evolutionary study and the possibility of assessment at any stage of the development of a software product. The data show the importance and relevance of technical work and the importance of its further development.

Fast Radix-32 Approximate DFTs for 1024-Beam Digital RF Beamforming

The discrete Fourier transform (DFT) is widely employed for multi-beam digital beamforming. The DFT can be efficiently implemented through the use of fast Fourier transform (FFT) algorithms, thus reducing chip area, power consumption, processing time, and consumption of other hardware resources. This paper proposes three new hybrid DFT 1024-point DFT approximations and their respective fast algorithms. These approximate DFT (ADFT) algorithms have significantly reduced circuit complexity and power consumption compared to traditional FFT approaches while trading off a subtle loss in computational precision which is acceptable for digital beamforming applications in RF antenna implementations. ADFT algorithms have not been introduced for beamforming beyond $N = 32$, but this paper anticipates the need for massively large adaptive arrays for future 5G and 6G systems. Digital CMOS circuit designs for the ADFTs show the resulting improvements in both circuit complexity and power consumption metrics. Simulation results show similar or lower critical path delay with up to 48.5% lower chip area compared to a standard Cooley-Tukey FFT. The time-area and dynamic power metrics are reduced up to 66.0%. The 1024-point ADFT beamformers produce signal-to-noise ratio (SNR) gains between 29.2--30.1 dB, which is a loss of $\le$ 0.9 dB SNR gain compared to exact 1024-point DFT beamformers (worst case) realizable at using an FFT.

Spectral Flux-Based Convolutional Neural Network Architecture for Speech Source Localization and Its Real-Time Implementation.

In this article, we present a real-time convolutional neural network (CNN)-based Speech source localization (SSL) algorithm that is robust to realistic background acoustic conditions (noise and reverberation). We have implemented and tested the proposed method on a prototype (Raspberry Pi) for real-time operation. We have used the combination of the imaginary-real coefficients of the short-time Fourier transform (STFT) and Spectral Flux (SF) with delay-and-sum (DAS) beamforming as the input feature. We have trained the CNN model using noisy speech recordings collected from different rooms and inference on an unseen room. We provide quantitative comparison with five other previously published SSL algorithms under several realistic noisy conditions, and show significant improvements by incorporating the Spectral Flux (SF) with beamforming as an additional feature to learn temporal variation in speech spectra. We perform real-time inferencing of our CNN model on the prototyped platform with low latency (21 milliseconds (ms) per frame with a frame length of 30 ms) and high accuracy (i.e. 89.68% under Babble noise condition at 5dB SNR). Lastly, we provide a detailed explanation of real-time implementation and on-device performance (including peak power consumption metrics) that sets this work apart from previously published works. This work has several notable implications for improving the audio-processing algorithms for portable battery-operated Smart loudspeakers and hearing improvement (HI) devices.

Function-Level Dynamic Monitoring and Analysis System for Smart Contract

The close integration of blockchain and smart contract technology has become an important foundation for current trusted applications. High-quality, high-efficiency and high-security codes have become basic requirements for smart contract applications because they are not easy to be modified after being deployed on blockchain. This paper proposes a function-level dynamic monitoring and analysis method for smart contract, and implements a prototype system. The method adds a “shadow stack” and related data structures to virtual machine of testing blockchain platform by analyzing the principle of function management with original stack, then monitors the bytecode after code instrumentation, records the function calling relationships as well as the relevant metrics of time, instruction number and gas consumption. The prototype system identifies contract inefficient behaviors using visualization and intelligent analysis methods, then forms a smart contract optimization closed loop through iterative improvement. Finally, the paper verified the high feasibility and applicability of the monitoring and analyzing method as well as prototype system’s performance through experiments.

Service-aware objective function with QoS for RPL routing

The routing protocol for low power and lossy network (RPL), constructed according to the objective function (OF), is the standard for routing selection in low power and lossy networks (LLNs). Most schemes utilise the expected transmission count (ETX) as the routing metric for the next hop. Designing effective and efficient routing protocols in LLNs require the mechanism to meet the criteria of different applications or services. In this paper, we propose a service-aware OF with quality of service (SOF-QoS) mechanism which integrates received signal strength indicator (RSSI), energy consumption, and hop count metrics into RPL OF for path selection according to different services, such as normal data, video packets and emergency messages transmission in LLNs. The proposed SOF-QoS mechanism defines various weights with fuzzy logic for the next routing selection in response to each service. We conduct the simulation under different network topologies with static and mobile scenarios to analyse the performance of the proposed SOF-QoS mechanism and compare them with the traditional RPL schemes. The simulation result shows that our proposed SOF-QoS mechanism provides relatively better or superior performance with reliability than traditional ones for different services.

Exploratory Analysis of Nutritional Quality and Metrics of Snack Consumption among Nepali Children during the Complementary Feeding Period.

The World Health Organization recommends feeding snacks between meals to young children. This study explored nutritional quality of snacks consumed between meals and consumption metrics (% total energy intakes (%TEI) and amount of kcal from snacks) to understand correlations with dietary outcomes (total energy intakes and dietary adequacy) and body-mass-index-for-age z-scores (BMIZ). Data used were 24-h dietary recalls and anthropometric measurements among a representative sample (n = 679) of one-year-olds in Nepal. Nepali meal patterns for young children were identified through formative research and all foods/beverages consumed outside of meals were categorized as snacks. A nutrient profiling model was used to categorize snacks as healthy or unhealthy, based on positive and negative nutrient content. Snacks consumed between meals provided half of all energy consumed, and were associated with increased energy and nutrient intakes. The positive effect of snacks between meals on dietary adequacy was greater when these snacks were healthy, while increasing %TEI from unhealthy snacks consumed between meals was negatively associated with dietary adequacy. Consumption of snacks between meals was not associated with mean BMIZ among the children. These findings indicate that the provision of and nutritional quality of snacks are important considerations to communicate to caregivers. Discouragement of unhealthy, nutrient-poor snacks is critical for complementary feeding dietary guidelines in contexts experiencing nutrition transition.