Size Of Items Research Articles

Determination of Single Sampling Attribute Plans Based Upon Dodge Roaming Model with Application

Bayesian sampling plans for production inspection involve using a sampling method to assess the features of the plan, under the assumption that defect rates fluctuate randomly among different production batches. This results in a likelihood distribution that can be established through experience and the quality information at hand. In this study, the parameters of a single Bayesian sampling plan were determined using the Beta-Binomial distribution, and were subsequently contrasted with parameters from other single sampling plans. Based on research findings, (Ala corporation for soft drinks) oversees the control of product quality. Since the variable fluctuates randomly between manufacturing batches, 120 batches were selected to calculate the defect rate by analyzing batch size and number of defective items. Applying Bayesian and decision-making models can lead to the development of a single sampling inspection procedure that closely approximates the actual quality level. When the decision-making model was used, the researchers discovered that the sample size was smaller and led to lower inspection costs compared to other inspection plans.

Improved YOLOv8 for Dangerous Goods Detection in X-ray Security Images

X-ray security images face significant challenges due to complex backgrounds, item overlap, and multi-scale target detection. Traditional methods often struggle to accurately identify objects, especially under cluttered conditions. This paper presents an advanced detection model, called YOLOv8n-GEMA, which incorporates several enhancements to address these issues. Firstly, the generalized efficient layer aggregation network (GELAN) module is employed to augment the feature fusion capabilities. Secondly, to tackle the problems of overlap and occlusion in X-ray images, the efficient multi-scale attention (EMA) module is utilized, effectively managing the feature capture and interdependencies among overlapping items, thereby boosting the model’s detection capability in such scenarios. Lastly, addressing the diverse sizes of items in X-ray images, the Inner-CIoU loss function uses auxiliary bounding boxes at varying scale ratios for loss calculation, ensuring faster and more effective bounding box predictions. The enhanced YOLOv8 model was tested on the public datasets SIXRay, HiXray, CLCXray, and PIDray, where the improved model’s mean average precision (mAP) reached 94.4%, 82.0%, 88.9%, and 85.9%, respectively, showing improvements of 3.6%, 1.6%, 0.9%, and 3.4% over the original YOLOv8. These results demonstrate the effectiveness and universality of the proposed method. Compared to current mainstream X-ray images of dangerous goods detection models, this model significantly reduces the false detection rate of dangerous goods in X-ray security images and achieves substantial improvements in the detection of overlapping and multi-scale targets, realizing higher accuracy in dangerous goods detection.

HocoPG: A Database System with Homomorphic Compression for Text Processing

Databases employ out-of-line storage and compression strategies to manage extensive text data. However, the growth in both the size of individual data items and overall data volume has significantly increased the burden of decompression, adversely affecting query performance. To address this challenge, we develop HocoPG, an innovative system that incorporates homomorphic compression theory within RDBMS, enabling direct computation on compressed data to enhance query efficiency and system usability. HocoPG performs homomorphic evaluations across a suite of basic text operations, enabling the execution of intricate text queries by combining these operations flexibly. In this demonstration, we showcase the deployment and usage of HocoPG through a database terminal. Additionally, we introduce the HocoPG Admin, a tool that provides insights to DBAs and general users for choosing the optimal compression scheme based on their individual needs.

Clothing suggestions based on comfort extracted from physiological and emotional parameters

PurposeThe comfort of apparel is not only a feeling of perception but also a tangible measure. The fit and fabric of clothing can exert a perception of comfort for the wearer, whereas actual comfort largely depends on physiological and emotional soothing. However, there is still no solid work on connecting the bridge between physiological and emotional feelings to the comfort of clothing. In this study, we have conceptualized, formulated and proven the relation between physiological and emotional parameters with clothing fit and fabric to find the true comfort of the wearer.Design/methodology/approachA mixed-method research design using physiological and emotional parameters for different fabric and fit combinations were used for this study. The physiological comfort parameters (i.e. heart rate and respiration rate) are extracted from the subjects using gold-standard clinical devices for various fit and fabric combinations. For the emotional response, a survey was conducted for the same subjects wearing all the fit and fabric combinations. Statistical analysis and modeling were performed to obtain the results.FindingsPhysiological indicators such as heart rate are closely linked with user comfort. Due to the limitations in environmental control, the physiological changes obtained did not significantly vary for different fabric and fit combinations of the clothing. However, a significant change in emotional response indicated a definite relationship between different fabric and fit types. Based on the participants’ responses, weather conditions, size of the clothing item, types of fabrics and style also influence the participants’ choice of clothing.Originality/valueThe research was conducted to discover the relation between true comfort (physiological and emotional parameters) and clothing (fit and fabric), which is unique to the field. This study closes the gap and builds up the relationship, which can help introduce clothing comfort to users in the future. The findings of this study help us understand how fabric types (natural or synthetic) and clothing fit types (loose or fitted) can affect physiological and emotional responses, which can provide the consumer with satisfactory clothing with the suitable properties needed.

T he Effectiveness of Likelihood Ratio Test & Multiple Indicators Multiple Causes method in detecting differential item functioning according to sample size and percentage of item with DIF and ability distribution

T he Effectiveness of Likelihood Ratio Test & Multiple Indicators Multiple Causes method in detecting differential item functioning according to sample size and percentage of item with DIF and ability distribution

Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis.

Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, potentially inaccurate, and time-intensive. Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, prior AI methodologies tackle dietary assessment in a fragmented landscape (e.g., merely recognizing food types or estimating portion size), and encounter challenges in their ability to generalize across a diverse range of food categories, dietary behaviors, and cultural contexts. Recently, the emergence of multimodal foundation models, such as GPT-4V, has exhibited transformative potential across a wide range of tasks (e.g., scene understanding and image captioning) in various research domains. These models have demonstrated remarkable generalist intelligence and accuracy, owing to their large-scale pre-training on broad datasets and substantially scaled model size. In this study, we explore the application of GPT-4V powering multimodal ChatGPT for dietary assessment, along with prompt engineering and passive monitoring techniques. We evaluated the proposed pipeline using a self-collected, semi free-living dietary intake dataset comprising 16 real-life eating episodes, captured through wearable cameras. Our findings reveal that GPT-4V excels in food detection under challenging conditions without any fine-tuning or adaptation using food-specific datasets. By guiding the model with specific language prompts (e.g., African cuisine), it shifts from recognizing common staples like rice and bread to accurately identifying regional dishes like banku and ugali. Another GPT-4V's standout feature is its contextual awareness. GPT-4V can leverage surrounding objects as scale references to deduce the portion sizes of food items, further facilitating the process of dietary assessment.

Online Unit Profit Knapsack with Predictions

A variant of the online knapsack problem is considered in the setting of predictions. In Unit Profit Knapsack, the items have unit profit, i.e., the goal is to pack as many items as possible. For Online Unit Profit Knapsack, the competitive ratio is unbounded. In contrast, it is easy to find an optimal solution offline: Pack as many of the smallest items as possible into the knapsack. The prediction available to the online algorithm is the average size of those smallest items that fit in the knapsack. 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To obtain a positive competitive ratio for all r, we adjust the algorithm, resulting in a competitive ratio of 12r\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{1}{2r}$$\\end{document} for r≥1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r\\ge 1$$\\end{document} and r2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{r}{2}$$\\end{document} for r≤1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r\\le 1$$\\end{document}. We show that improving the result for any r<1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r< 1$$\\end{document} leads to a worse result for some r>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$r>1$$\\end{document}.

Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes

In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement varies over time as a Markov chain while in service. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in the steady state. Under our problem formulation, we develop a job dispatch policy, named Join-Requesting-Server (JRS). We show that in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r, JRS achieves an additive optimality gap of O(√r) in the objective value, where the optimal objective value is Θ(r). Our approach highlights a novel policy conversion framework that utilizes the solution of a single-server problem.

Semi-Online Algorithms for the Hierarchical Extensible Bin-Packing Problem and Early Work Problem

In this paper, we consider two types of semi-online problems with hierarchies. In the extensible bin-packing problem with two hierarchical bins, one bin can pack all items, while the other bin can only pack some items. The initial size of the bin can be expanded, and the goal is to minimize the total size of the two bins. When the largest item size is given in advance, we provide some lower bounds and propose online algorithms. When the total item size is given in advance, we provide some lower bounds and propose online algorithms. In addition, we also consider the relevant early-work-maximization problem on two hierarchical machines; one machine can process any job, while the other machine can only process some jobs. Each job shares a common due date, and the goal is to maximize the total early work. When the largest job size is known, we provide some lower bounds and propose two online algorithms whose competitive ratios are close to the lower bounds.

The Near Exact Bin Covering Problem

AbstractWe present a new generalization of the bin covering problem that is known to be a strongly NP-hard problem. In our generalization there is a positive constant $$\varDelta $$ Δ , and we are given a set of items each of which has a positive size. We would like to find a partition of the items into bins. We say that a bin is near exact covered if the total size of items packed into the bin is between 1 and $$1+\varDelta $$ 1 + Δ . Our goal is to maximize the number of near exact covered bins. If $$\varDelta =0$$ Δ = 0 or $$\varDelta &gt;0$$ Δ &gt; 0 is given as part of the input, our problem is shown here to have no approximation algorithm with a bounded asymptotic approximation ratio (assuming that $$P\ne NP$$ P ≠ N P ). However, for the case where $$\varDelta &gt;0$$ Δ &gt; 0 is seen as a constant, we present an asymptotic fully polynomial time approximation scheme (AFPTAS) that is our main contribution.

Seasonal organization of tintinnid community by temperature preference and LOD size-class in a subtropical brackish embayment

Abstract Most annual studies of tintinnid and phytoplankton in coastal habitats are influenced by alien species introduced by water masses exchange, and therefore cannot be used as direct evidence for exploring the mechanism of annual succession in indigenous community. For brackish communities, very limited annual studies exist because of the difficulty of conducting stable sampling in the transition between freshwater and seawater. The special topography of Xiangshan Bay isolates the external water mass and provides a suitable habitat for exploring the annual succession mechanism of the brackish tintinnid community. In this study, samples were collected from seven stations in Tie Harbor, Xiangshan Bay (East China Sea) during 10 months from 2022 to 2023. A total of 23 tintinnid species of five genera were identified throughout the year. These tintinnid species can be clustered into four seasonal groups, winter, summer, autumn and spring-autumn, with three temperature preferences. The Lorica Oral Diameter (LOD) size-class of each tintinnid species, which determines the food item size of tintinnid, separated their different ecological niches within each seasonal group. The seasonal succession of tintinnid community in Xiangshan Bay was organized by both temperature preference and LOD size of the tintinnid species.

A pattern-based algorithm with fuzzy logic bin selector for online bin packing problem

The online bin packing problem is a well-known optimization challenge that finds application in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called FuzzyPatternPack(FPP), which leverages fuzzy inference and pattern-based predictions of the distribution of item sizes in online bin packing. In comparison to traditional heuristics like BestFit(BF) and FirstFit(FF), as well as the more recent PatternPack(PaP) and ProfilePacking(PrP) algorithm based on online predictions, FPP demonstrates competitive and superior performance in solving various benchmark problems. Particularly, it excels in addressing problems with evolving distributions, making it a promising solution for real-world applications where the item sizes may change over time. This research unveils the promising potential of employing fuzzy logic to effectively address uncertainty in scheduling and planning problems.

Increasing mercury bioaccumulation and biomagnification rates of Nile perch (Lates niloticus L.) in Winam Gulf, Lake Victoria, Kenya

The Nile perch (Lates niloticus L.) commercial fishery for Lake Victoria in East Africa is an important source of revenue and employment. We focused on shifts in food web structure and total mercury (THg) bioaccumulation and biomagnification in Nile perch, and lower food web items collected from Winam Gulf (Kenya) sampled 24 years apart (1998 and 2022). Stable isotope carbon (δ13C) values were higher in all species from 2022 compared to 1998. Stable nitrogen isotope (δ15N) values in baseline organisms were lower in 2022 compared to 1998. In Nile perch, δ15N values were correlated with total length, but the δ15N-length regressions were steeper in 1998 compared to 2022 except for one large (158 cm) Nile perch from 1998 with an uncharacteristically low δ15N value. Total Hg concentrations were lower in lower trophic species from 2022 compared to 1998. However, the THg bioaccumulation rate (as a function of fish length) in Nile perch was greater in 2022 compared to 1998 resulting in 24.2 % to 42.4 % higher wet weight dorsal THg concentrations in 2022 Nile perch for market slot size (50 to 85 cm) fish. The contrasting observations of increased THg bioaccumulation with size in 2022 against decreases in the rate of trophic increase with size and lower THg concentrations of lower food web items imply reduced fish growth and potential bioenergetic stressors on Winam Gulf Nile perch. All samples except 1 large Nile perch (139 cm total length collected in 2022) had THg concentrations below the European Union trade limit (500 ng/g wet weight). However, for more vulnerable individuals (women, children and frequent fish eaters), we recommend a decrease in maximum monthly meal consumption for 55–75 cm Nile perch from 16 meals per month calculated for 1998 to a limit of 8 meals per month calculated for 2022.

PalmSpace: Leveraging the palm for touchless interaction on public touch screen devices

Touchscreen is the primary solution to interact with public devices such as Automated Teller Machines (ATMs). However, the touch modality raises health concerns since users have to touch the screens, and therefore risking the spread of contagious diseases. We design PalmSpace, an alternate input technique leveraging users’ hand palms to interact with public devices. With Palmspace, UI elements are mapped onto the users’ palms and can be accessed by touching various locations directly on the palm. We conduct a series of user studies to evaluate several design options, such as interface layout, item size, preferred item location, and suitable feedback for items. Based on the results, we design PalmSpace and compare its performance with mid-air input. We show that PalmSpace is a potential solution to interact with public devices without using their touchscreen. We conclude with design guidelines for using the palm as an alternative input space for touchscreen devices.

New roles of Lagrange multiplier method in generalizability theory: Inference of estimating the optimal sample size for teaching ability evaluation of college teachers.

Generalizability theory is widely used in psychological and educational measurement. Budget and cost are the problems that cannot be neglected in the measurement. When there is a budget constraint, the generalizability theory needs to consider how to design a measurement program with relatively high reliability and feasibility, which requires the optimal sample size to be estimated by some means. Lagrange multiplier method is a commonly used method for estimating the optimal sample size under budget constraints in generalizability theory. Unfortunately, to date, many formulas of estimating the optimal sample size for some more complex generalizability designs such as those with four facets or more facets have not been derived using the Lagrange multiplier method. The purpose of this article is to provide a detailed step-by-step derivation of the formula of estimating the optimal sample size for three typical complex generalizability designs using the Lagrange multiplier method under budget constraints in generalizability theory, which can demonstrate the new roles of the Lagrange multiplier method. This article derived the optimal sample size for teaching ability evaluation of college teachers with budget constraints in three generalizability designs such as the (s:t)×i, (s:t)× (i:v) and (s:t) × (i:v) ×o and explored their practical applications. By estimating the optimal sample size, the optimal generalizability design, which is more applicable in practice, can be compared to obtain. (1) Using the Lagrange multiplier method, the optimal sample size for students and items under budget constraints in different generalizability design can be derived. (2) For an example, based on teaching ability evaluation of college teachers in China, these designs of (s:t) ×i, (s:t) × (i:v) and (s:t) × (i:v) ×o were used to obtain the optimal sample size, which indicates the Lagrange multiplier method can been used in practice. (3) Under budget constraints, the (s:t) × (i:v) is the optimal generalizability design. The optimal sample size of students is 17 for each teacher and the optimal sample size of items is 4 for each dimension. The optimal sample size can be derived carefully using the Lagrange multiplier method under budget constraints in generalizability theory. The Lagrange multiplier method with new roles is worth recommending.

Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes

In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement may vary over time while in service, following a Markovian assumption. Once the job's service is complete, it departs from the system. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in steady state. Under our problem formulation, we develop a job dispatch policy, named Join-Reqesting-Server (JRS). Broadly, JRS lets each server independently evaluate its current job configuration and decide whether to accept additional jobs, balancing the competing objectives of maximizing throughput and minimizing the risk of resource capacity overruns. The JRS dispatcher then utilizes these individual evaluations to decide which server to dispatch each arriving job to. The theoretical performance guarantee of JRS is in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r. We show that JRS achieves an additive optimality gap of O(√r) in the objective value, where the optimal objective value is Θ(r). When specialized to constant job resource requirements, our result improves upon the state-of-the-art o(r) optimality gap. Our technical approach highlights a novel policy conversion framework that reduces the policy design problem into a single-server problem.

Ingestion of microplastics and microfibers by the invasive blue crab Callinectes sapidus (Rathbun 1896) in the Balearic Islands, Spain

The blue crab Callinectes sapidus Rathbun, 1896 is native to the western coasts of the Atlantic Ocean and is currently considered an invasive species in the Mediterranean Sea. In this study, we examined the stomach contents of C. sapidus to determine the frequency of occurrence of microplastics (MPs) and microfibers (MFs) in the Balearic Islands archipelago in the western Mediterranean Sea. A total of 120 individuals were collected from six locations between 2017 and 2020. Overall, 65.8% of the individuals had MPs and/or MFs particles with an average of 1.4 ± 1.6 particles ind.−1 of which an average of 1.0 ± 1.3 items ind.−1 were MFs and an average of 0.4 ± 0.8 items ind.−1 were MPs. In terms of type, fragments were the dominant type of MPs and the most common size of items ranged from between 0.5 and 1 mm (40%) followed by 1–5 mm (31%). The most prevalent polymers were low-density polyethylene (39%) and high-density polyethylene (26%). In terms of links to human activities, MP ingestion was positively correlated with an increase in drain pipes, whereas MF ingestion was positively correlated with an increase in sewage pipelines, providing evidence of potential sources and the bioavailability of these particles in various environments. This study confirms the widespread presence of MP and MF particles, even in areas that are currently managed under different protection statuses, in the stomach contents of invasive blue crab species throughout coastal communities.

Association between maternal zinc intake and preterm birth in Gansu Province in 2010-2012

To analyze the association between maternal dietary zinc levels at different stages and preterm birth. The subjects were from the 2010-2012 birth cohort of Maternal and Child Health Hospital of Gansu Province, and a total of 10 179 pregnant women were included in the final study. Dietary information before and during pregnancy was collected through semi-quantitative food frequency questionnaire, and daily zinc intake in each period was estimated according to the frequency of consumption and portion size of food items using the Chinese Standard Tables of Food Consumption. Logistic regression was used to analyze the OR value of preterm birth under different maternal dietary zinc levels. The detection rate of preterm birth was 10.0%, of which 81.8% were moderate preterm, 18.3% were very preterm, 33.2% were medically indicated preterm and 66.8% were spontaneous preterm birth. After adjusting for general conditions such as maternal age, education level, and history of preterm birth, compared with the group with the highest zinc intake before and during pregnancy, those with low zinc intake before or during pregnancy had a significantly higher risk of moderate, very, and spontaneous preterm birth. After further controlled of folic acid and fiber intake during pregnancy, compared with the group with the highest zinc intake before and during pregnancy, the risk of very preterm birth(OR=2.04, 95%CI 1.07-3.90) and spontaneous preterm birth(OR=1.38, 95%CI 1.01-1.88) was significantly increased in those with the lowest zinc intake during the third trimester. For pregnant women aged≥30 years(OR=1.55, 95%CI 1.12-2.13) and normal or less normal weight gain during pregnancy(OR=1.51, 95%CI 1.15-1.97), insufficient zinc intake was significantly associated with preterm birth. Maternal low dietary zinc intake during the third trimester was associated with very premature and spontaneous preterm birth.

The incomplete analytic hierarchy process and Bradley–Terry model: (In)consistency and information retrieval

Several methods of preference modeling, ranking, voting and multi-criteria decision-making include pairwise comparisons. It is usually simpler to compare two objects at a time, furthermore, some relations (e.g., the outcome of sports matches) are naturally known for pairs. This paper investigates and compares pairwise comparison models and the stochastic Bradley–Terry model. It is proved that they provide the same priority vectors for consistent (complete or incomplete) comparisons. For incomplete comparisons, all filling in levels are considered. Recent results identified the optimal subsets and sequences of multiplicative/additive/reciprocal pairwise comparisons for small sizes of items (up to n=6). Simulations of this paper show that the same subsets and sequences are optimal in the case of the Bradley–Terry and the Thurstone models as well. This somehow surprising coincidence suggests the existence of a more general result. Further models of information and preference theory are subject to future investigation to identify optimal subsets of input data.

Food provision in Australian aged care homes does not meet protein needs of residents: A call for reform.

Malnutrition is common in older adults in aged care homes, partly due to inadequate protein intake. Menu planning guidelines are available however, adherence to guidelines is unknown. This study aimed to determine; (i) what are the average serving sizes of menu items provided and do they meet recommended portion sizes? (ii) does consumption from a 'typical' menu provide sufficient protein? and (iii) can substituting a 'typical' menu with high-protein options enable residents to achieve protein adequacy? This study involved 572 residents (73% female; aged 86.4 ± 7.3 years) from 60 aged-care homes in Australia involved in a 2-year cluster-randomised trial. During the trial, food intake was recorded quarterly using visual estimation of plate-waste and 42 061 foods analysed. As part of a secondary analysis of these data, portion sizes of foods were compared to guidelines by calculating the mean (95% confidence interval). Items were deemed inadequate if the upper 95% confidence interval remained below recommended portion sizes. On average 47% of breakfast and 80% of lunch/dinner items were below recommended portion sizes. Relative protein intakes, from a typical menu (most consumed foods), was 0.9 g and 0.8 g/kg body weight/day for females and males; both below recommendations. Substituting regular items with higher protein equivalents increased protein intake to 1.3 g and 1.2 g/kg body weight/day, for females and males, respectively. Aged care homes in Australia are not meeting menu planning guidelines resulting in insufficient protein being provided. Reform to menu guidelines including provision of high-protein foods, will ensure protein adequacy in older adults in aged-care homes.