Free Lunch Research Articles

Free Lunch for Health

Free Lunch for Health

Universal free school meals and children’s bodyweight. Impacts by age and duration of exposure

We study the impact on children’s bodyweight of switching from means-tested to universal provision of nutritious free school meals in England. We exploit the roll-out of universal provision across Local Authorities to estimate effects at different ages and durations of exposure, based on nurse-collected, population-wide health data for state school children. Exposure to universal free lunches reduces obesity prevalence and BMI among children aged 4–5 and 10–11, but older children’s bodyweights are less responsive than younger children’s. We find tentative evidence that effects may be cumulative, as impacts are largest for those exposed to universal free school meals throughout primary school.

Effects of a classwide reading intervention on reading fluency and comprehension of content area text with students in middle school

Partner reading with paragraph shrinking has been shown to be an effective classwide reading intervention in elementary school, but has yet to be studied with eighth-grade students or with content area reading such as science and social studies. The current study examined the effects of implementing the classwide intervention for 3 wk with 86 students in eighth grade that were randomly assigned to the treatment or a control group. The intervention led to significantly higher oral reading fluency (η2 = .49), and higher scores on a researcher-created maze test for science (η2 = .10) and social studies (η2 = .13). Student variables (i.e., receiving English as a second language services, special education, or a free or reduced-price lunch) did not predict student outcomes and added negligible to small amounts of variance. Therefore, the effects seemed to be positive regardless of student-level factors. Potential implications for practice and research are included.

GP4ESP: a hybrid genetic algorithm and particle swarm optimization algorithm for edge server placement

Edge computing has attracted wide attention due to its ultra-low latency services, as well as the prevalence of smart devices and intelligent applications. Edge server placement (ESP) is one of the key issues needed to be addressed for effective and efficient request processing, by deciding which edge stations to equip with limited edge resources. Due to NP-hardness of ESP, some works have designed meta-heuristic algorithms for solving it. While these algorithms either exploited only one kind of meta-heuristic search strategies or separately perform two different meta-heuristic algorithms. This can result in limit performance of ESP solutions due to the “No Free Lunch” theorem. In addition, existing algorithms ignored the computing delay of edge servers (ESs) on request process, resulting in overestimation of the service quality. To address these issues, in this article, we first formulate ESP problem with the objective of minimizing the overall response time, considering heterogeneous edge servers with various service capacity. Then, to search effective or even the best ESP solutions, we propose a hybrid meta-heuristic algorithm (named GP4ESP) by taking advantage of both the powerful global search ability of genetic algorithm (GA) and the fast convergence of particle swarm optimization (PSO). GP4ESP effectively fuses the merits of GA and PS by integrating the swarm cognition of PSO into the evolutionary strategy of GA. At last, we conducted extensive simulation experiments to evaluate the performance of GP4ESP, and results show that GP4ESP achieves 18.2%–20.7% shorter overall response time, compared with eleven up-to-date ESP solving algorithms, and the performance improvement is stable as the scale of ESP is varied.

Identifying parentally perceived barriers for children with celiac disease to participate in elementary school meal programs

AbstractSeveral states have recently enacted laws permanently granting all public school students access to free breakfast and lunch. However, children with dietary restrictions, such as celiac disease, may encounter barriers to participation in these meal programs. We surveyed caregivers of school‐aged children with celiac disease to study barriers to universal school meals. More than half of the children with celiac disease did not participate in school meal programs due to concerns about the cafeteria's ability to prepare gluten‐free meals safely. Moreover, among those who were food insecure and gluten‐free food insecure, 50% had never consumed free school lunch and breakfast. Parental perception of nutritional quality, communication regarding gluten‐free options, and safety of school kitchens emerged as common obstacles to participation in these programs. Addressing these concerns is paramount to ensuring equitable access to nutritious meals for all students.

Material hardship and use of social safety net programs among LGBT adults and their families

AbstractLesbian, gay, bisexual, and transgender (LGBT) people experience higher levels of economic insecurity and poverty compared to non‐LGBT people. Social safety net programs may reduce this disparity, but the uptake of these programs among LGBT adults has rarely been assessed. We conducted a secondary data analysis of the 2018–2020 Well‐Being and Basic Needs Survey to compare differences in material hardship and social safety net program utilization between LGBT and non‐LGBT adults in the United States. LGBT participants were more likely to report lack of an emergency fund, food insecurity, utilities hardship, medical hardship, and multiple material hardships. LGBT adults had higher odds compared to non‐LGBT adults of using SNAP, income‐ or disability‐based insurance, SSI or SSDI, and housing assistance. There were no differences in use of WIC, reduced‐price/free school lunch benefits, or charitable food. Future studies are needed to evaluate the efficacy of the social safety net programs for reducing material hardship.

Analisis Framing Portal Berita Kompas.Com dan Mediaindonesia.Com atas Pemberitaan Program Makan Siang Gratis oleh Paslon 02 (Prabowo-Gibran) Pada Periode 28 November 2023 - 10 Februari 2024

2024 is a crucial year for Indonesia as it marks the presidential and vice-presidential transition. The General Elections Commission (KPU) has nominated three candidate pairs, one of which is Prabowo Subianto-Gibran Rakabuming Raka (candidate pair 02), who proposed a free lunch program. This program has sparked considerable debate in various media, prompting this study to analyze the framing of news coverage about the free lunch program by candidate pair 02 (Prabowo-Gibran) on the online news portals Kompas.com and Mediaindonesia.com during the period of November 28, 2024, to February 10, 2024. The research employs a descriptive qualitative method to describe or explain the underlying meanings of reality. Data analysis is conducted using Zhongdang Pan and Gerald M. Kosicki's framing analysis model, which includes syntactic, script, thematic, and rhetorical structures. The findings indicate that Kompas.com’s coverage is supportive of the free lunch program. On the other hand, the coverage on Mediaindonesia.com is firmly against the free lunch program

VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication

In the current artificial intelligence (AI) era, the scale and quality of the dataset play a crucial role in training a high-quality AI model. However, good data is not a free lunch and is always hard to access due to privacy regulations like the General Data Protection Regulation (GDPR). A potential solution is to release a synthetic dataset with a similar distribution to that of the private dataset. Nevertheless, in some scenarios, it has been found that the attributes needed to train an AI model belong to different parties, and they cannot share the raw data for synthetic data publication due to privacy regulations. In PETS 2023, Xue et al. [29] proposed the first generative adversary network-based model, VertiGAN, for vertically partitioned data publication. However, after thoroughly investigating, we found that VertiGAN is less effective in preserving the correlation among the attributes of different parties. This article proposes a Vertical Federated Learning-based Generative Adversarial Network, VFLGAN, for vertically partitioned data publication to address the above issues. Our experimental results show that compared with VertiGAN, VFLGAN significantly improves the quality of synthetic data. Taking the MNIST dataset as an example, the quality of the synthetic dataset generated by VFLGAN is 3.2 times better than that generated by VertiGAN w.r.t. the Frechet Distance. We also designed a more efficient and effective Gaussian mechanism for the proposed VFLGAN to provide the synthetic dataset with a differential privacy guarantee. On the other hand, differential privacy only gives the upper bound of the worst-case privacy guarantee. This article also proposes a practical auditing scheme that applies membership inference attacks to estimate privacy leakage through the synthetic dataset.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM)

The free lunch program is an initiative that provides free lunches and milk in schools and Islamic boarding schools, as well as providing nutritional support for children under five and pregnant women. Even though efforts to overcome stunting with this program are considered too late, providing free milk to pregnant women is considered more relevant for dealing with the stunting problem. Based on experience from various countries, the free lunch program shows various positive impacts, including increasing children's nutritional intake, increasing school participation, developing student character, and supporting the family and national economy. Discussions about this program received various responses from netizens, including platform X users. Online platforms have an important role in reaching and disseminating information about various social programs. In this context, the support vector machine method can be used to classify netizen responses to the free lunch program into positive and negative categories. It is hoped that this analysis will provide a deeper understanding of how this program is received by the general public, as well as provide suggestions for policy makers in designing social programs in the future.

Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis

The work program promised by the 2024 Presidential Candidate and Vice Presidential Candidate pair, namely, Prabowo Subianto and Gibran Rakabuming Raka, one of which is a free lunch program, this program is an effort to improve community welfare, but this has attracted public attention on social media, one the other is platform X. The public response to the free lunch program is the main focus. In this research, researchers analyzed sentiment related to the free lunch program using the Naive Bayes method using the Python programming language on Google Colab to analyze sentiment towards social media users. X. the results obtained from 920 tweets data, there are 167positive value tweets, 744tweets are negative. The evaluation results with the confusion matrix showed an accuracy of 86.95%, with precision 93%, recall 61%, and F1-Score 65%.From the results of this research it can be concluded that the majority of the public who commented on social media X gave a negative response to the free lunch program. These results can be used as material for government evaluation in determining effective and efficient policies for society.

No Free Lunch? Welfare Analysis of Firms Selling through Expert Intermediaries

Abstract We study how firms target and influence expert intermediaries. In our context, pharmaceutical manufacturers provide payments to physicians during promotional interactions. We develop an identification strategy based on plausibly exogenous variation in payments driven by differential exposure to spillovers from academic medical centers’ conflict-of-interest policies. Using a case study of an important class of cardiovascular drugs, we estimate heterogeneous effects of payments on prescribing, with firms targeting highly responsive physicians. We also develop a model of supply and demand, which allows us to quantify how oligopoly prices reduce drug prescribing, and how payments move prescribing closer to the optimal level, but at great financial cost. In our estimated model, whether consumers are harmed by payments depends on whether there is substantial under-prescribing due to behavioral or other frictions. In a final exercise, we calibrate such frictions using clinical data and estimate that payments benefit consumers in this case study.

Ferociously Cute

Indonesia’s 2024 presidential election campaign was shaped by an alliance struck between outgoing incumbent Joko (Jokowi) Widodo and a former rival, Prabowo Subianto, who chose Jokowi’s son as his running mate. The seeming affirmation of dynastic power drew criticism, as did Prabowo’s former associations with the authoritarian New Order regime and its human rights abuses. But Prabowo remade his image with his promises of free school lunches and a new “cute” persona while pledging continuity with his popular predecessor’s administration, demonstrating the ambiguities of Indonesian democracy a quarter-century after the end of the New Order era.

Bobcat Optimization Algorithm: an effective bio-inspired metaheuristic algorithm for solving supply chain optimization problems

Supply chain efficiency is a major challenge in today’s business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat’s position change while moving towards the prey and (ii) exploitation based on simulating the bobcat’s position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

AbstractAccurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

Performance Analysis of Metaheuristic Algorithms on Benchmark Functions

The discipline of optimization can be used to maximize or minimize several problems. The use of metaheuristic algorithms is a strategy that often works well for global optimization. They are a type of stochastic algorithm that, via trial and error, finds workable solutions to difficult optimization problems in a reasonable amount of time, but they do not provide assurance that the answers are optimal. This paper aims to offer a comparative analysis of several metaheuristics in searching for the optimal solution. The selected metaheuristics are Artificial Bee Colony, Ant Lion Optimizer, Bat, Black Hole, Cuckoo Search, Cat Swarm Optimization, Dragonfly, Differential Evolution, Firefly, Genetic, Gravitational-Based Search, Grasshopper Optimization, Grey Wolf Optimizer, Harmony Search, Krill-Herd, Moth-Flame Optimizer, Particle Swarm Optimization, Sine Cosine, Shuffled Frog-Leaping, and Whale Optimization algorithms. For this evaluation, 18 benchmark test functions, categorized as unimodal, multimodal, and fixed-dimension multimodal are used to examine various properties, such as accuracy, escape from the local optimum, and convergence. As an indicator of how effectively these metaheuristics work, metrics like minimum, maximum, average, and standard deviation of fitness are provided. There are no optimization algorithms that are adequate for all problems, as the No Free Lunch theorem suggests, but the metaheuristics that are more effective than the others will be demonstrated. This study could be helpful for young researchers to identify the most prominent metaheuristics for achieving a better global optimum. Received: 8 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Optimization, Metaheuristic algorithm, Benchmark function, Performance metric

The Effect of Universal Free School Meals on Child BMI

Abstract We estimate the effect of universal free school meal access through the Community Eligibility Program (CEP) on child body mass index (BMI). Through the CEP, schools with high percentages of students qualified for free or reduced-priced meals can offer free breakfast and lunch to all students. With administrative data from a large school district in Georgia, we use student-level BMI measures from the FitnessGram to compare within-student outcomes before and after CEP implementation across eligible and non-eligible schools. We find one year of CEP exposure increased expected BMI percentile by about 0.085 standard deviation, equivalent to a nearly 1.88-pound weight increase for a student of average height. We also find that the program led to a small increase in the likelihood of overweight and limited evidence of a small decrease in the likelihood of underweight. We do not find that the program increased student obesity risk. Examining the effects of CEP on child BMI by grade suggests that the overall effect is largely driven by students in middle schools, highlighting potential heterogeneity in the program's impact across grades. The findings of this paper are relevant for researchers and policy makers concerned with the effects of universal free school meals on student health.

Impact of free school lunch program on nutritional status and academic outcomes among school children in India: A systematic review

IntroductionMid-day meal programmes (MDM) were introduced in India to improve school attendance, nutritional status and educational outcomes of school children. Numerous primary studies have examined the impact of the MDM...

Feed the children, free the women? Evidence from the China rural nutrition improvement program

This study establishes a causal impact of China Rural Nutrition Improvement Program (CRNIP) on the labour force participation of Chinese rural mothers, utilizing quasi-exogenous variation in the timing of program implementation across the country. Drawing on 1% samples of the 2010 and 2015 Chinese Population Censuses, we find that the CRNIP, which provided free lunch meals to students during their compulsory education, significantly enhanced maternal labour force participation by an estimated 4.25 percentage points, while showing no effects for fathers. The provision of school lunches reduced the time mothers spent on childcare, primarily benefiting less-educated mothers, who opted for informal, off-farm work in their local area. While only small in magnitude, this finding has significant policy implications for a country facing a declining population in general, and declining female labour force participation in particular.

Multi-objective design optimization of hermetically sealed core-type distribution transformer considering current harmonics of power grid using NSGA III

In this paper, the design optimization of hermetically sealed oil-immersed distribution transformers is studied. For the first time in the design of this type of transformer, harmonics of the power grid affecting the transformer design, along with all design variables, are included, and their boundaries suitable for the production line are determined. The design probability (search space) for a total of 21 design variables is 1.32×1025. The Brute Force method is not a time-efficient approach to finding the optimal result in this search space. Due to the mixed integer nonlinear programming (MINLP) nature of the transformer design problem, heuristic optimization algorithms are selected because of their shorter solution time and near-optimal results. Considering the principle of the “No Free Lunch” theorem, extensive evaluations were conducted on various heuristic algorithms to identify the most effective ones for addressing the transformer design problem. The most successful algorithms for solving the problem were identified after thorough evaluation, considering three distinct scenarios within a multi-objective optimization framework. These algorithms are namely MOEA/D, MOGWO, MOWOA, and NSGA III. In this study, an optimization tool is developed for the design optimization of hermetically sealed distribution transformers, covering a range from 50 kVA to 3150 kVA and encompassing all design variables. The developed optimization program is fully compatible with the production line and can be easily adjusted to meet all national and international standards, such as IEC and IEEE, as well as customer specifications.