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During adolescence, many young people gain greater food choice agency but also become increasingly exposed and susceptible to environmental pressures that influence their food choices. This coincides with increased nutritional needs, especially for girls. In urban Colombia, adolescent diets are often high in undesirable foods and low in nutritious foods, contributing to overweight and micronutrient deficiencies. This study aimed to explore the potential of improving diet quality using food-based recommendations (FBRs) within the parameters of local food systems and adolescents' existing dietary patterns to inform context-specific programmatic responses to malnutrition. We applied linear programming analysis to dietary data from 13- to 20-year-old girls in Medellin to identify problem nutrients, local micronutrient sources, and promising FBRs. Iron and, to a lesser extent, calcium targets were difficult to meet using optimized diets based on local foods, especially for 13- to 17-year-olds. High habitual consumption of foods with excessive salt, fat, or sugar provided >5% of micronutrients in optimized diets. Otherwise, significant micronutrient sources included legumes, meat, dairy, bread, potatoes, and fruit. FBRs met targets for 10 micronutrients but only 32%-39% recommended nutrient intake for iron. FBRs, including occasionally consumed foods and supplements, met all intake targets for less cost, indicating a need to increase access to nutrient-dense products.

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The transition to a neutral carbon and sustainable urban water cycle requires improving eco-efficiency in wastewater treatment processes. To support decision-making based on eco-efficiency evaluations, reliable estimations are fundamental. In this study, the eco-efficiency of a sample of 109 WWTPs was evaluated using efficiency analysis tree method. It combines machine learning and linear programming techniques and therefore, overcomes overfitting limitations of non-parametric methods used by past research on this topic. Results from the case study revealed that optimal costs and greenhouse gas emissions depend on the quantity of organic matter and suspended solids removed from wastewater. The estimated average eco-efficiency is 0.373 which involves that the assessed WWTPs could save 0.32 €/m3 and 0.11 kg of CO2 equivalent/m3. Moreover, only 4 out of 109 WWTPs are identified as eco-efficient which implies that the majority of the evaluated facilities can achieve substantial savings in operational costs and greenhouse gas emissions.

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Adequate calcium intake is essential for health, especially for infants, children, adolescents, and women, yet is difficult to achieve with local foods in many low- and middle-income countries. Previous analysis found it was not always possible to identify food-based recommendations (FBRs) that reached the calcium population recommended intake (PRI) for these groups in Bangladesh, Guatemala, and Uganda. We have modeled the potential contribution of calcium-fortified drinking water or wheat flour to FBR sets, to fill the remaining intake gaps. Optimized diets containing fortified products, with calcium-rich local foods, achieved the calcium PRI for all target groups. Combining fortified water or flour with FBRs met dietary intake targets for adolescent girls in all geographies and allowed a reduction from 3-4 to the more feasible 1-2 FBRs. Water with a calcium concentration of 100 mg/L with FBRs was sufficient to meet calcium targets in Uganda, but higher concentrations (400-500 mg/L) were mostly required in Guatemala and Bangladesh. Combining calcium-fortified wheat flour at 400 mg/100 g of flour and the FBR for small fish resulted in diets meeting the calcium PRI in Bangladesh. Calcium-fortified water or flour could improve calcium intake for vulnerable populations, especially when combined with FBRs based on locally available foods.

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Globally, dietary intake of calcium is often insufficient, and it is unclear if adequacy could be achieved by promoting calcium-rich local foods. This study used linear programming and household consumption data from Uganda, Bangladesh, and Guatemala to assess whether local foods could meet calcium population reference intakes (Ca PRIs). The most promising food-based approaches to promote dietary calcium adequacy were identified for 12- to 23-month-old breastfed children, 4- to 6-year-old children, 10- to 14-year-old girls, and nonpregnant and nonbreastfeeding (NPNB) women of reproductive age living in two regions of each country. Calcium-optimized diets achieved 75-253% of the Ca PRI, depending on the population, and were <100% for 4- to 6-year-olds in one region of each country and 10- to 14-year-old girls in Sylhet, Bangladesh. The best food sources of calcium were green leafy vegetables and milk, across geographic locations, and species of small fish, nixtamalized (lime-treated) maize products, sesame seeds, and bean varieties, where consumed. Food-based recommendations (FBRs) achieving the minimum calcium threshold were identified for 12- to 23-month-olds and NPNB women across geographic locations, and for 4- to 6-year-olds and 10-to 14-year-old girls in Uganda. However, for 4- to 6-year-olds and 10- to 14-year-old girls in Bangladesh and Guatemala, calcium-adequate FBRs could not be identified, indicating a need for alternative calcium sources or increased access to and consumption of local calcium-rich foods.

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Wastewater treatment plants (WWTPs) are energy intensive facilities. Controlling energy use in WWTPs could bring substantial benefits to people and environment. Understanding how energy efficient the wastewater treatment process is and what drives efficiency would allow treating wastewater in a more sustainable way. In this study, we employed the efficiency analysis trees approach, that combines machine learning and linear programming techniques, to estimate energy efficiency of wastewater treatment process. The findings indicated that considerable energy inefficiency among WWTPs in Chile existed. The mean energy efficiency was 0.287 suggesting that energy use should cut reduce by 71.3 % to treat the same volume of wastewater. This was equivalent to a reduction in energy use by 0.40 kWh/m3 on average. Moreover, only 4 out of 203 assessed WWTPs (1.97 %) were identified as energy efficient. It was also found that the age of treatment plant and type of secondary technology played an important role in explaining energy efficiency variations among WWTPs.

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In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the R Sequential package.

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In many countries, there is an energy pricing policy that varies according to the time-of-use. In this context, it is financially advantageous for the industries to plan their production considering this policy. This article introduces a new bi-objective unrelated parallel machine scheduling problem with sequence-dependent setup times, in which the objectives are to minimize the makespan and the total energy cost. We propose a mixed-integer linear programming formulation based on the weighted sum method to obtain the Pareto front. We also developed an NSGA-II method to address large instances of the problem since the formulation cannot solve it in an acceptable computational time for decision-making. The results showed that the proposed NSGA-II is able to find a good approximation for the Pareto front when compared with the weighted sum method in small instances. Besides, in large instances, NSGA-II outperforms, with 95% confidence level, the MOGA and NSGA-I multi-objective techniques concerning the hypervolume and hierarchical cluster counting metrics. Thus, the proposed algorithm finds non-dominated solutions with good convergence, diversity, uniformity, and amplitude.

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[This corrects the article DOI: 10.3389/fnut.2018.00048.].

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In this paper we evaluate different models and constraints to define strategic planning approaches. In addition, we analyze the best models to meet the expectations generated by the organization. A forest company situated in the province of Misiones, Argentina, provided the data. Hence, forest growth was simulated and, ultimately, optimized planning was used to evaluate different scenarios with 50-year horizon. The best results to stabilize log production were obtained when the harvest is relaxed in ±2 years. Relaxing the clear-cut age leads to a better balance in planting, thinning (1, 2, 3 and 4) and clear felling operations. We found that when maximizing the economic benefit, the NPV is slightly higher, however, this is not significant. In this sense, the planner chooses an economic or volumetric objective function. Furthermore, we demonstrated that model 1 presented better results than model 2 because it manages to stabilize production in the planning horizon. The results allow forest companies to see the implication of choosing the model for strategic planning.

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Achieving nutritional adequacy requires an increase in fresh foods consumption, which may increase pesticide intakes. This study aimed to identify required dietary modifications to achieve nutritional adequacy without exceeding the acceptable daily intake (ADI) for pesticides. Data from the National Dietary Survey 2017-2018 were linked to the pesticide database from the Program on Pesticide Residue Analysis in Food. We performed linear programming models to design nutritionally adequate diets constrained by food preferences for different constraints on pesticide intake at the least cost increment. Nutritional adequacy led to an increase in pesticide intakes without exceeding their ADI. Modifications in diets varied according to the model, but, in general, consisted in an increase in fruits and vegetables, dairy, and seafood, and a reduction in rice, red meat, and sugar-sweetened beverages quantities. In conclusion, meeting nutritional adequacy increases pesticide intake compared to the observed diets, without representing a health concern to consumers.

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We aim to identify the dietary changes to improve nutrition and reduce diet-related greenhouse gas emission (GHGE) simultaneously in Brazil, taking into account the heterogeneity in food habits and prices across the country. Food consumption and prices were obtained from two nationwide surveys (n = 55,970 households and 34,003 individuals). Linear programming models were performed to design optimised diets most resembling the observed diets, and meeting different sets of constraints: (i) nutritional, for preventing chronic diseases and meeting nutrient adequacy; (ii) socio-cultural: by respecting food preferences; and (iii) environmental: by reducing GHGE by steps of 10%. Moving toward a diet that meets nutritional recommendations led to a 14% to 24% cost increase and 10% to 27% GHGE reduction, depending on the stringency of the acceptability constraints. Stronger GHGE reductions were achievable (up to about 70%), with greater departure from the current diet, but not achieving calcium and potassium goals. Diet cost increment tended to be mitigated with GHGE reduction in most models, along with reductions in red meat, chicken, eggs, rice, and high-fat sugar sodium foods.

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The cell expresses various genes in specific contexts with respect to internal and external perturbations to invoke appropriate responses. Transcription factors (TFs) orchestrate and define the expression level of genes by binding to their regulatory regions. Dysregulated expression of TFs often leads to aberrant expression changes of their target genes and is responsible for several diseases including cancers. In the last two decades, several studies experimentally identified target genes of several TFs. However, these studies are limited to a small fraction of the total TFs encoded by an organism, and only for those amenable to experimental settings. Experimental limitations lead to many computational techniques having been proposed to predict target genes of TFs. Linear modeling of gene expression is one of the most promising computational approaches, readily applicable to the thousands of expression datasets available in the public domain across diverse phenotypes. Linear models assume that the expression of a gene is the sum of expression of TFs regulating it. In this chapter, I introduce mathematical programming for the linear modeling of gene expression, which has certain advantages over the conventional statistical modeling approaches. It is fast, scalable to genome level and most importantly, allows mixed integer programming to tune the model outcome with prior knowledge on gene regulation.

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Genes are transcribed into various RNA molecules, and a portion of them called messenger RNA (mRNA) is then translated into proteins in the process known as gene expression. Gene expression is a high-energy demanding process, and aberrant expression changes often manifest into pathophysiology. Therefore, gene expression is tightly regulated by several factors at different levels. MicroRNAs (miRNAs) are one of the powerful post-transcriptional regulators involved in key biological processes and diseases. They inhibit the translation of their mRNA targets or degrade them in a sequence-specific manner, and hence control the rate of protein synthesis. In recent years, in response to experimental limitations, several computational methods have been proposed to predict miRNA target genes based on sequence complementarity and structural features. However, these predictions yield a large number of false positives. Integration of gene and miRNA expression data drastically alleviates this problem. Here, I describe a mathematical linear modeling approach to identify miRNA targets at the genome scale using gene and miRNA expression data. Mathematical modeling is faster and more scalable to genome-level compared to conventional statistical modeling approaches.

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Resumo O estudo avaliou a viabilidade da adequação de cardápios em relação às exigências nutricionais estabelecidas pelo Programa Nacional de Alimentação Escolar (PNAE) e a relação entre o custo do cardápio. Cada cardápio semanal contou de uma combinação de alimentos dentro de cada grupo. Para cada combinação de alimentos foi elaborado um modelo de otimização de dados para obter as quantidades de cada alimento de forma a atender às exigências do PNAE ao menor custo possível (cardápios com 20% e 30% da ingestão dietética de referência para energia, macronutrientes, cálcio, ferro, magnésio, zinco, vitaminas A e C, além de restrições para sódio, gorduras saturada e trans, e açúcar de adição). Não foi obtida nenhuma solução que acomodasse todas as exigências do PNAE. Os componentes limitantes foram cálcio, sódio e carboidratos; para os demais os cardápios foram adequados. O custo foi diretamente correlacionado com a frequência de carne e frutas, e inversamente com os conteúdos de sódio e carboidratos, e com as frequências de arroz e feijão. A probabilidade de adequação de carboidratos foi próxima de zero quando a frequência de carne foi acima de 1 vez por semana. Concluindo, é pouco provável a obtenção de cardápios que atendam à todas as exigências do PNAE.

Abstract We evaluated the feasibility of the menu adequacy regarding the nutritional constraints established by the National School Feeding Program (PNAE) and its relation to the cost. Each menu accounted for a given food combination within each food group. A diet optimization model comprising each set of foods was designed to obtain food quantities in order to meet the exigences of the PNAE at the lowest cost (menus with 20% and 30% of dietary reference intake for energy, macronutrients, calcium, iron, magnesium, zinc, vitamins A and C, also restrictions for sodium, saturated and trans fats, and added sugar). There was no feasible solution that accommodated all nutrient targets. Limiting components were calcium, sodium, and carbohydrates; but the menus were adequate for the other nutrients. There was a positive correlation between the menu cost and the frequency of meat and fruits, and a negative correlation with the contents of sodium and carbohydrates, and with the frequencies of rice and beans. The probability of obtaining carbohydrate adequacy was close to zero when the meat frequency was higher than one serving per week. In conclusion, it is unlikely to obtain menus that meet all the requirements of the PNAE.

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The aim was to design culturally acceptable and healthy diets with reduced energetic share of ultra-processed foods (UPF%) at no cost increment and to evaluate the impact of the change in the UPF% on diet quality. Food consumption and price data were obtained from the Household Budget Survey (n 55 970 households) and National Dietary Survey (n 32 749 individuals). Linear programming models were performed to design diets in which the mean population UPF% was reduced up to 5 % with no cost increment relative to the observed costs. The models were isoenergetic or allowed the energy content to vary according to the UPF%, and they were not constrained to nutritional goals (nutrient-free models) or maximised the compliance with dietary recommendations (nutrient-constrained models). Constraints regarding food preference were introduced in the models to obtain culturally acceptable diets. The mean population UPF% was 23·8 %. The lowest UPF% attained was approximately 10 %. The optimised diet cost was up to 20 % cheaper than the observed cost, depending on the model and the income level. In the optimised diets, the reduction in the UPF% was followed by an increase in fruits, vegetables, beans, tubers, dairy products, nuts, fibre, K, Mg, vitamin A and vitamin C in the nutrient-constrained models, compared with the observed consumption in the population. There was little variation in most nutrients across the UPF% reduction. The UPF% reduction in the nutrient-free models impacted only trans-fat and added sugar content. UPF% reduction and increase in diet quality are possible at no cost increment.

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The study aimed to analyze the economic impact of the adoption of optimized and nutritionally balanced diets to Brazilian families, considering the Brazilian dietary guidelines and the economic disparities of the population. Data from the Brazilian Household Budget Survey from 2008-2009 (550 strata; 55,970 households) were used. About 1,700 foods and beverages purchased by the Brazilians were classified into 4 groups according to NOVA system. Linear programming models estimated isoenergetic diets preserving the current diet as baseline and optimizing healthier diets gradually based on the "golden rule" of the Brazilian dietary guidelines, respecting nutritional restrictions for macronutrients and micronutrients (based on international recommendations) and food acceptance limits (10th and 90th percentiles of the per capita calorie distribution from the population). The diet cost was defined based on the sum of the average cost of each food group, both in the current and optimized diets (BRL per 2,000Kcal/person/day). The economic impact of the Brazilian dietary guidelines to Brazilian household budget was analyzed by comparison the cost of the optimized diets to the cost of the current diet, calculated for the total population and by income level. Three healthier diets were optimized. Current diet cost was BRL 3.37, differed among low- and high-income strata (BRL 2.62 and BRL 4.17, respectively). Regardless of income, diet cost decreased when approaching the guidelines. However, low-income strata compromised their household budget more than two times the high-income strata (20.2% and 7.96%, respectively). Thus, the adoption of healthier eating practices can be performed with the same or lower budget.

El objetivo del estudio fue analizar el impacto económico de la adopción de dietas optimizadas y nutricionalmente equilibradas en familias brasileñas, considerando las guías alimentarias y las disparidades económicas de la población. Los datos se recopilaron de la Encuesta de Presupuestos por Hogares desde 2008-2009 (550 estratos; 55 970 hogares). Se clasificaron alrededor de 1.700 comidas y bebidas, adquiridas por brasileños en 4 grupos, según el sistema NOVA. Los modelos de programación lineal estimaron dietas isoenergéticas, preservando la dieta actual como base de referencia, y optimizando las dietas más sanas gradualmente, basadas en la "regla de oro" de las guías alimentarias, respecto a restricciones nutricionales en macronutrientes y micronutrientes (basadas en recomendaciones internacionales), y los límites de aceptación alimentarios (10o y 90o percentiles de la distribución calórica per cápita de la población). El coste de la dieta se definió basado en el total del coste medio de cada grupo de comida, ambos en las dietas actual y optimizada (BRL por 2.000Kcal/persona/día). El impacto económico de las guías alimentarias para el presupuesto por hogar se analizó mediante comparación del coste de dietas optimizadas con el coste de la dieta actual, calculado para el total de población y por nivel de ingresos. Se optimizaron las tres dietas más saludables. El coste de la dieta actual fue BRL 3,37, diferenciando los estratos entre bajo- y altos-ingresos (BRL 2,62 y BRL 4,17, respectivamente). A pesar del ingreso, el coste de la dieta decreció cuando se aproximaba a las recomendaciones de las guías. No obstante, los estratos de bajos ingresos comprometieron su presupuesto doméstico más de dos veces, respecto a los estratos de ingresos más altos (20,2% y 7,96%, respectivamente). Por ello, la adopción de prácticas alimentarias más sanas se puede conseguir con el mismo presupuesto o incluso más bajo.

O objetivo desse estudo foi analisar o impacto econômico da adoção de dietas otimizadas e nutricionalmente balanceadas para famílias brasileiras, considerando as diretrizes alimentares para a população brasileira e as disparidades econômicas da população. Foram usados dados da Pesquisa Nacional de Orçamentos Familiares de 2008-2009 (550 estratos; 55.970 domicílios). Cerca de 1,7 mil alimentos e bebidas adquiridos pelos brasileiros foram classificados em quatro grupos de acordo com o sistema NOVA. Modelos de programação linear estimaram dietas isoenergéticas preservando a dieta atual como linha de base e otimizando dietas mais saudáveis gradativamente com base na "regra de ouro" das diretrizes alimentares, respeitando restrições nutricionais para macronutrientes e micronutrientes (com base em recomendações internacionais) e limites de aceitação alimentar (10º e 90º percentis da distribuição de calorias per capita da população). O custo da dieta foi definido a partir da soma do custo médio de cada grupo de alimentos, tanto na dieta atual quanto na otimizada (R$ por 2.000Kcal/pessoa/dia). O impacto econômico das diretrizes alimentares para o orçamento familiar brasileiro foi analisado comparando-se o custo das dietas otimizadas com o custo da dieta atual, calculado para a população total e por nível de renda. Três dietas mais saudáveis foram otimizadas. O custo da dieta atual era de R$ 3,37, diferindo entre os estratos de baixa e alta renda (R$ 2,62 vs. R$ 4,17). Independentemente da renda, o custo da dieta diminuiu com a abordagem das diretrizes. No entanto, os estratos de baixa renda comprometeram seu orçamento familiar mais de duas vezes que os estratos de alta renda (20,20% vs. 7,96%). Assim, a adoção de práticas alimentares mais saudáveis pode ser realizada com orçamento igual ou inferior.

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Sizing and allocating health-care professionals are a critical problem in the management of emergency departments (EDs) managed by a public company in Rio de Janeiro (Brazil). An efficient ED configuration that is cost and time effective must be developed by this company for hospital managers. In this paper, the problem of health-care professional configurations in EDs is modelled to minimise the total labour cost while satisfying patient queues and waiting times as defined by the actual ED capacity and current clinical protocols. To solve this issue, mixed integer linear programming (MILP) that allocates health-care professionals and specifies the amount of professionals who must be hired is proposed. To consider the uncertainties in this environment and evaluate their impacts, a discrete-event simulation model is developed to reflect patient flow. An optimisation and simulation approach is used to search for efficiency leads for different ED configurations. These configurations change depending on the shift and the day of the week.

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The mining industry is one of the world's massive scrap off-the-road (OTR) tire generators. Reverse logistics (RL) allows for a cost-effective treatment of residual items with environmentally friendly practices. This paper aims to design a RL network for large OTR tires discarded from scattered mining sites, which requires a substantial initial investment in tire downsizing equipment. Therefore, the on-site use of a single shared shredding resource set is proposed with a schedule of visits between mines. Our contribution is a mixed-integer linear programming model set to determine the OTR tires optimal network, including decisions regarding a tire-fuelled power plant location, tire shredding and transport amounts, and a shredding resource set schedule to maximize the profits of the RL network while considering whole waste tire stockpile limits per year. Results showed that the proposed RL network starting from mine shredding tires and supplying a power generation plant might be a profitable solution that could help mines to comply with the legal regulations and turn this waste into a positive economic value good. Environmental and social implications are the mitigation of scrap tires, an increased job demand triggered by power plants, and public health improvement in the vicinity of the mining sites.

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BACKGROUND: Meeting nutrient intake recommendations may demand substantial modifications in dietary patterns, and may increase diet cost. Incentives for modifying one's dietary intake that disregard prices are unlikely to be effective in the general population, especially among low-income strata, due to the high percentage of income committed to food purchases. The aim of this study is to evaluate how much the nutrient content can be increased through a modeled diet, without any cost increase, for low-income Brazilian households. METHODS: Low-income households were selected from the Household Budget Survey (24,688 households) and National Dietary Survey (6,032 households, 16,962 individuals), from where we obtained food prices and consumption data. Food quantities were modeled using linear programming to find diets that meet nutritional recommendations in two sets of models: cost-constrained (the cost should not be higher than the observed diet cost) and cost-free. Minimum and maximum amounts of each food in the modelled diets were allowed at three levels of food acceptability: rigorous (least deviance from the current observed diets), moderate, and flexible (higher deviance from the current observed diets). RESULTS: We found no feasible solution that would accommodate all the nutritional targets. The most frequent limiting nutrients were calcium; vitamins D, E, and A; zinc; fiber; sodium; and saturated and trans-fats. However, increases in nutrient contents were observed, especially for fiber, calcium, copper, magnesium, vitamin A, vitamin C, and vitamin E. In general, the best achievement was obtained with cost-free models. Fruits and beans increased in all models; large increase in whole cereals was observed only in the flexible models; large increase in vegetables was observed only in the cost-free models; and fish increased only in the cost-free models. Reductions were observed for rice, red and processed meats, sugar-sweetened beverages, and sweets. The mean observed cost was US$2.16 per person/day. The mean cost in the cost-free models was US$2.90 (moderate), US$2.70 (rigorous), and US$2.60 (flexible). CONCLUSION: The complete nutritional adequacy is unattainable, although feasible changes would substantially improve diet quality by improving nutrient content without additional costs.

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In the fog computing paradigm, fog nodes are placed on the network edge to meet end-user demands with low latency, providing the possibility of new applications. Although the role of the cloud remains unchanged, a new network infrastructure for fog nodes must be created. The design of such an infrastructure must consider user mobility, which causes variations in workload demand over time in different regions. Properly deciding on the location of fog nodes is important to reduce the costs associated with their deployment and maintenance. To meet these demands, this paper discusses the problem of locating fog nodes and proposes a solution which considers time-varying demands, with two classes of workload in terms of latency. The solution was modeled as a mixed-integer linear programming formulation with multiple criteria. An evaluation with real data showed that an improvement in end-user service can be obtained in conjunction with the minimization of the costs by deploying fewer servers in the infrastructure. Furthermore, results show that costs can be further reduced if a limited blocking of requests is tolerated.