Effect of formulations over a Periodic Capacitated Vehicle Routing Problem with multiple depots, heterogeneous fleet, and hard time-windows.

This article presents a novel contribution to the Periodic Vehicle Routing Problem (PVRP) by introducing two new problem formulations which differ in the usage of the crucial flow variable. The formulations are tailored to meet the specific demands of the vending machine industry in Medellín, Colombia, and require considering a PVRP with time windows, a heterogeneous fleet, and multiple depots. This scenario, tailored to address real-world complexity and computational challenges, brings to light an exponential surge in integer variables as customer numbers increase. The research presents an analysis of PVRPs that include the four mentioned attributes, compares their similarities, and delves into their nuances. From the analysis it is derived that the variant of the PVRP presented has not been considered previously, taking into account not only these attributes, but also the restrictions involved. Empirical experiments are conducted to examine the intricate interplay between the two proposed formulations, highlighting their impact on the performance of the GUROBI solver. The study provides valuable insights into problem-specific adaptations and algorithmic approaches, emphasizing the significance of the proposed formulations in addressing multifaceted PVRPs. In essence, this research positions the introduction of these two formulations as a pioneering step, offering a new paradigm for approaching the PVRP.

Cost-effectiveness of SARS-CoV-2 self-testing at routine gatherings to minimize community-level infections in lower-middle income countries: A mathematical modeling study.

Places of worship serve as a venue for both mass and routine gathering around the world, and therefore are associated with risk of large-scale SARS-CoV-2 transmission. However, such routine gatherings also offer an opportunity to distribute self-tests to members of the community to potentially help mitigate transmission and reduce broader community spread of SARS-CoV-2. Over the past four years, self-testing strategies have been an impactful tool for countries' response to the COVID-19 pandemic, especially early on to mitigate the spread when vaccination and treatment options were limited. We used an agent-based mathematical model to estimate the impact of various strategies of symptomatic and asymptomatic self-testing for a fixed percentage of weekly routine gatherings at places of worship on community transmission of SARS-CoV-2 in Brazil, Georgia, and Zambia. Testing strategies assessed included weekly and bi-weekly self-testing across varying levels of vaccine effectiveness, vaccine coverage, and reproductive numbers to simulate developing stages of the COVID-19 pandemic. Self-testing symptomatic people attending routine gatherings can cost-effectively reduce the spread of SARS-CoV-2 within places of worship and the community, resulting in incremental cost-effectiveness ratios of $69-$303 USD. This trend is especially true in contexts where population level attendance at such gatherings is high, demonstrating that a distribution approach is more impactful when a greater proportion of the population is reached. Asymptomatic self-testing of attendees at 100% of places of worship in a country results in the greatest percent of infections averted and is consistently cost-effective but remains costly. Budgetary needs for asymptomatic testing are expensive and likely unaffordable for lower-middle income countries (520-1550x greater than that of symptomatic testing alone), promoting that strategies to strengthen symptomatic testing should remain a higher priority.

Limited open information sharing and mobility promotes sustainability of jaguar tourism in Pantanal wetland, Brazil.

Wildlife tourism plays a crucial role in biodiversity conservation. However, long-term sustainability is difficult to achieve. In this paper, we use property theory to produce a mathematical model that aims to better support stakeholders from the wildlife tourism industry to better guarantee a balance between sightings probability, tourists' overall experience and operators' sharing behaviour. We illustrate our model with the case study of Porto Jofre in the Pantanal wetland, Brazil. We show that while dealing with low sighting probability, tourist operators must share information about species' locations, leading to a system of open access regarding mobility and information. However, when sightings become common, sharing must be restricted to a bounded group avoiding overcrowding, a system of limited open access. Finally, when the sighting probability is high, no sharing is needed to achieve maximum overall experience. Our case study in Porto Jofre, Pantanal, Brazil, clearly shows these shifts in terms of governance strategies. We show that by looking at sighting probability it is possible to predict the best optimal social strategy that will guarantee long-term sustainability of the wildlife tourism initiatives. We also show the need for external support on adaptation in cases where current strategies do not match the predicted ones.

Hierarchical Control for the Oldroyd Equation in Memoriam to Professor Luiz Adauto Medeiros.

This manuscript deals with a hierarchical control problem for Oldroyd equation under the Stackelberg-Nash strategy. The Oldroyd equation model is defined by non-regular coefficients, that is, they are bounded measurable functions. We assume that we can act in the dynamic of the system by a hierarchy of controls, where one main control (the leader) and several additional secondary control (the followers) act in order to accomplish their given tasks: controllability for the leader and optimization for followers. We obtain the existence and uniqueness of Nash equilibrium and its characterization, the approximate controllability with respect to the leader control, and the optimality system for leader control.

Numerical-experimental procedure for predicting fatigue life in SAE AMS 7475-T7351 aluminum alloy considering the effect of stress ratio.

This study addresses the prediction of fatigue life in SAE AMS 7475-T7351 aluminum alloys under variable loads, commonly used in the construction of aircraft fuselages. The main objective of the research was to develop a numerical-experimental procedure to analyze crack growth, using the Walker's approach which considers the effects of the stress ratio R on the fatigue crack growth rate d a / d N , combined with the Finite Element Method and Linear Regression of the Stress Intensity Factor. Observations showed that Walker's model effectively consolidated fatigue crack propagation data for various stress ratios when applied longitudinally to L-T rolling orientation, due to low dependence of exponent m on R -value in d a / d N equation. Simple averaging of m values effectively calculated Walker's exponent. The methodology employed experimental tests following ASTM standards for tension, fracture toughness, and fatigue, complemented by Finite Element Method (FEM) simulations. The Walker's model proved more effective, while the Paris-Erdogan model, which ignores the R effect, resulted in overly conservative service life estimates. The principle of similitude suggests that this methodology could be effective in predicting fatigue life in cases with complex geometries, where calculating the Stress Intensity Factor Fracture parameter is challenging and the Finite Element Method shows efficiency.

Modeling learning-oriented motivation in health students: a system dynamics approach.

BACKGROUND: Evidence shows that motivational practices focused on utility, importance, and autonomy shape university students' motivational orientation toward learning. On the other hand, the relationship between these variables and motivational orientation toward learning is not linear and requires models that describe their behavior over time. METHOD: In this study, mathematical modeling based on system dynamics methodology is used to simulate in health students the temporal dynamics of the motivational orientation toward learning based on the behavior of these variables in different scenarios. RESULTS: The results indicate that a) Mastery is sensitive to changes in frequency when importance and autonomy practices are performed; b) the development of Mastery is critical in the first three semesters of academic life, but its loss is hardly recoverable even when practices are incorporated in subsequent semesters; c) Utility-focused motivational practices have no significant effect on the development of learning-oriented motivation. CONCLUSION: These findings have significant practical implications for higher education. Understanding the critical role of Mastery in the early stages of academic life and the limited potential for recovery if lost can help raise awareness of the importance of early implementation of motivational practices focused on relevance and autonomy.

How much rainwater contributes to a spring discharge in the Guarani Aquifer System: insights from stable isotopes and a mass balance model.

Outcrops play an important role in groundwater recharge. Understanding groundwater origins, dynamics and its correlation with different water sources is essential for effective water resources management and planning in terms of quantity and quality. In the case of the Guarani Aquifer System (GAS) outcrop areas are particularly vulnerable to groundwater pollution due to direct recharge processes. This study focuses on the Alto Jacaré-Pepira sub-basin, a watershed near Brotas, a city in the central region of the state of São Paulo, Brazil, where groundwater is vital for supporting tourism, agriculture, urban water supply, creeks, river and wetlands. The area has a humid tropical climate with periods of both intense rainfall and drought, and the rivers remain perennial throughout the year. Therefore, the aim of this study is to investigate the interconnections between a spring and its potential sources of contribution, namely rain and groundwater, in order to elucidate the relationships between the different water sources. To achieve this, on-site monitoring of groundwater depth, rainfall amount, and stable isotope ratios (deuterium (2H) and oxygen-18 (18O)) from rain, spring discharge, and a monitoring well was carried out from 2013 to 2021. The results indicate that the mean and standard deviations for δ18O in rainwater exhibit higher variability, resulting in -4.49 ± 3.18 ‰ VSMOW, while δ18O values from the well show minor variations, similar to those of the spring, recording -7.25 ± 0.32 ‰ and -6.94 ± 0.28 ‰ VSMOW, respectively. The mixing model's outcomes reveal seasonal variations in water sources contribution and indicate that groundwater accounts for approximately 80 % of spring discharge throughout the year. Incorporating stable isotopes into hydrological monitoring provides valuable data for complementing watershed analysis. The values obtained support the significance of the aquifer as a primary source, thereby offering critical insights into stream dynamics of the region.

Assessing the carbon footprint of soybean transportation in Brazil: a network equilibrium model approach.

Brazil has historically invested few resources in its transport infrastructure, leaving gaps and reducing its efficiency. The country presents a high dependence on road transport, which results in increased operational costs and higher greenhouse gas (GHG) emissions. Consequently, the performance of cargo transportation in Brazil has been deteriorating, accompanied by a rise in the consumption of fossil fuels and noteworthy levels of GHG emissions. This article assesses the carbon intensity of soybean transport operations within Brazil. Utilizing a network equilibrium model, this study estimated the soybean transportation flows that minimize the total cost of transporting this product across the origins and destinations within the grain handling system. The modeling also calculated the GHG emissions in transportation. The results show that the transportation of soybeans produced 2.74 million tonnes of CO2 equivalent annually, with road transport accounting for 81.7% of these emissions. The state of Mato Grosso, responsible for 44.08 kg CO2 equivalent per tonne of soybeans transported, contributed almost 49% of the total emissions due to the extensive distances involved. In contrast, states like Paraná and Rio Grande do Sul, located closer to southern ports, exhibited the lowest emissions, with rates of 11.55 kg CO2 eq/t and 12.52 kg CO2 eq/t, respectively. The analysis highlights the significant potential for reducing GHG emissions by increasing the use of rail and barge transport, particularly in high-emission regions such as Mato Grosso.

Feed Forward Modeling: an efficient approach for mathematical modeling of the force frequency relationship in the rabbit isolated ventricular myocyte.

Background and Objective. This study addresses the Force-Frequency relationship, a fundamental characteristic of cardiac muscle influenced byß1-adrenergic stimulation. This relationship reveals that heart rate (HR) changes at the sinoatrial node lead to alterations in ventricular cell contractility, increasing the force and decreasing relaxation time for higher beat rates. Traditional models lacking this relationship offer an incomplete physiological depiction, impacting the interpretation of in silico experiment results. To improve this, we propose a new mathematical model for ventricular myocytes, named 'Feed Forward Modeling' (FFM).Methods. FFM adjusts model parameters like channel conductance and Ca2+pump affinity according to stimulation frequency, in contrast to fixed parameter values. An empirical sigmoid curve guided the adaptation of each parameter, integrated into a rabbit ventricular cell electromechanical model. Model validation was achieved by comparing simulated data with experimental current-voltage (I-V) curves for L-type Calcium and slow Potassium currents.Results. FFM-enhanced simulations align more closely with physiological behaviors, accurately reflecting inotropic and lusitropic responses. For instance, action potential duration at 90% repolarization (APD90) decreased from 206 ms at 1 Hz to 173 ms at 4 Hz using FFM, contrary to the conventional model, where APD90 increased, limiting high-frequency heartbeats. Peak force also showed an increase with FFM, from 8.5 mN mm-2at 1 Hz to 11.9 mN mm-2at 4 Hz, while it barely changed without FFM. Relaxation time at 50% of maximum force (t50) similarly improved, dropping from 114 ms at 1 Hz to 75.9 ms at 4 Hz with FFM, a change not observed without the model.Conclusion. The FFM approach offers computational efficiency, bypassing the need to model all beta-adrenergic pathways, thus facilitating large-scale simulations. The study recommends that frequency change experiments include fractional dosing of isoproterenol to better replicate heart conditionsin vivo.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties.

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

A model of long-term population growth with an application to Central West Argentina.

We propose an Ideal Specialization Model to help explain the diversity of population growth trajectories exhibited across archaeological regions over thousands of years. The model provides a general set of expectations useful for guiding empirical research, and we provide a concrete example by conducting a preliminary evaluation of three expectations in Central West Argentina. We use kernel density estimates of archaeological radiocarbon, estimates of paleoclimate, and human bone stable isotopes from archaeological remains to evaluate three expectations drawn from the model's dynamics. Based on our results, we suggest that innovations in the production of food and social organization drove demographic transitions and population expansion in the region. The consistency of population expansion in the region positively associates with changes in diet and, potentially, innovations in settlement and social integration.

Leveraging the trend analysis for modeling of the greenhouse gas emissions associated with coal combustion.

In this paper, it is aimed, for the first time, at deriving simple models, leveraging the trend analysis in order to estimate the future greenhouse gas emissions associated with coal combustion. Due to the expectations of becoming the center of global economic development in the future, BRICS-T (Brazil, the Russian Federation, India, China, South Africa, and Turkiye) countries are adopted as cases in the study. Following the models' derivation, their statistical validations and estimating accuracies are also tested through various metrics. In addition, the future greenhouse gas emissions associated with coal combustion are estimated by the derived models. The results demonstrate that the derived models can be successfully used as a tool for estimating the greenhouse gas emissions associated with coal combustions with accuracy ranges from at least 90% to almost 98%. Moreover, the estimating results show that the total amount of greenhouse gas emissions associated with coal combustions in the relevant countries and in the world will increase to 14 BtCO2eq and 19 BtCO2eq by 2035, with an annual growth of 2.39% and 1.71%, respectively. In summary, the current study's findings affirm the usefulness of trend analysis in deriving models to estimate greenhouse gas emissions associated with coal combustion.

Parameter Estimation Using the Inverse Problem Method for Simulating Lateral Inflow and Runoff Depth in a small catchment of Amazon.

The inverse problem method can be applied to determine the properties of hydrological phenomena and estimate the parameters, which cannot be measured directly. This type of inverse focus can facilitate the implementation of the kinematic wave model (direct model-DM), to fill gaps for lateral inflow rate and runoff depth in watersheds. Thus, the goal of the study was the application of the inverse problem method (IP). The lateral inflow rate was generally obtained as a Fourier transform to represent any watersheds. The study was developed using a small catchment in the Amazon where intense rainfall events occur, producing runoff and sediments, which affect rural populations. Lateral inflow rate and runoff depth were derived using precipitation data and parameters estimated through the KINEROS2 (K2)/direct model (DM) model and the ensuing solution methods with MCMC (Markov chains Monte Carlo)/Fourier transform. The developed method was applied to four rainfall-runoff events, leading to a good fit between the observed and predicted data (Nash-Sutcliffe coefficients between 0.76 and 0.85 and RMSE values between 1.80 mm and 6.72 mm).

Methodology for the assessment of poor-data water resources.

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Dispersion analysis of the 2017 Persian Gulf oil spill based on remote sensing data and numerical modelling.

Oil spills, detected by SAR sensors as dark areas, are highly effective marine pollutants that affect the ocean surface. These spills change the water surface tension, attenuating capillary gravitational waves and causing specular reflections. We conducted a case study in the Persian Gulf (Arabian Sea to the Strait of Hormuz), where approximately 163,900 gal of crude oil spilled in March 2017. Our study examined the relationship between oil weathering processes and extracted backscatter values using zonal slices projected over SAR-detected oil spills. Internal backscatter values ranged from -22.5 to -23.5, indicating an oil chemical binding and minimal interaction with seawater. MEDSLIK-II simulations indicated increased oil solubilization and radar attenuation rates with wind, facilitating coastal dispersion. Higher backscatter at the spill edges compared to the core reflected different stages of oil weathering. These results highlight the complex dynamics of oil spills and their environmental impact on marine ecosystems.

Proposal of a domain model for 3D representation of buildings in Ecuador.

The accelerated urban sprawl of cities around the world presents major challenges for urban planning and land resource management. In this context, it is crucial to have a detailed 3D representation of buildings enriched with accurate alphanumeric information. A distinctive aspect of this proposal is its specific focus on the spatial unit corresponding to buildings. In order to propose a domain model for the 3D representation of buildings, the national standard of Ecuador and the international standard (ISO 19152:2012 LADM) were considered. The proposal includes a detailed specification of attributes, both for the general subclass of buildings and for their infrastructure. The application of the domain model proposal was crucial in a study area located in the Riobamba canton, due to the characteristics of the buildings in that area. For this purpose, a geodatabase was created in pgAdmin4 with official information, taking into account the structure of the proposed model and linking it with geospatial data for an adequate management and 3D representation of the buildings in an open-source Geographic Information System. This application improves cadastral management in the study region and has wider implications. This model is intended to serve as a benchmark for other countries facing similar challenges in cadastral management and 3D representation of buildings, promote efficient urban development and contribute to global sustainable development.

Complex artificial intelligence models for energy sustainability in educational buildings.

Energy consumption of constructed educational facilities significantly impacts economic, social and environment sustainable development. It contributes to approximately 37% of the carbon dioxide emissions associated with energy use and procedures. This paper aims to introduce a study that investigates several artificial intelligence-based models to predict the energy consumption of the most important educational buildings; schools. These models include decision trees, K-nearest neighbors, gradient boosting, and long-term memory networks. The research also investigates the relationship between the input parameters and the yearly energy usage of educational buildings. It has been discovered that the school sizes and AC capacities are the most impact variable associated with higher energy consumption. While 'Type of School' is less direct or weaker correlation with 'Annual Consumption'. The four developed models were evaluated and compared in training and testing stages. The Decision Tree model demonstrates strong performance on the training data with an average prediction error of about 3.58%. The K-Nearest Neighbors model has significantly higher errors, with RMSE on training data as high as 38,429.4, which may be indicative of overfitting. In contrast, Gradient Boosting can almost perfectly predict the variations within the training dataset. The performance metrics suggest that some models manage this variability better than others, with Gradient Boosting and LSTM standing out in terms of their ability to handle diverse data ranges, from the minimum consumption of approximately 99,274.95 to the maximum of 683,191.8. This research underscores the importance of sustainable educational buildings not only as physical learning spaces but also as dynamic environments that contribute to informal educational processes. Sustainable buildings serve as real-world examples of environmental stewardship, teaching students about energy efficiency and sustainability through their design and operation. By incorporating advanced AI-driven tools to optimize energy consumption, educational facilities can become interactive learning hubs that encourage students to engage with concepts of sustainability in their everyday surroundings.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model.

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

On the uncertainty in numerical modeling of wireless communication devices operating at frequencies of 900 MHz, 1800 MHz, and 28 GHz.

Some of the difficulties in numerical modeling of wireless communication devices for dosimetric evaluations arise from, e.g. incomplete documentation available for the numerical model, such as missing information on dielectric materials or the antenna matching circuitry. This study investigates the impact of these difficulties on the dosimetric results, such as the peak spatial average specific absorption rate at 900 and 1800 MHz and the peak spatial average power density at 28 GHz. The impact of dielectric losses, detuning, and mesh resolution is quantified using different generic and Computer Aided Design (CAD) based models of wireless transmitters. The findings show that the uncertainties of the numerical results due to detuning and mesh resolution can be reduced by normalization to the antenna feedpoint power instead of the feedpoint current. Uncertainties due to variations in dielectric losses can largely be compensated by normalization to the radiated power.

Chemical and isotopic tracers combined with mixing models for tracking nitrate contamination in the Pampa de Pocho aquifer, Argentina.

In recent years, it has become evident that human activities have significantly disrupted the nitrogen cycle surpassing acceptable environmental thresholds. In this study, chemical and isotopic tracers were combined with a mathematical mass balance model (EMMA), PHREEQC inverse mixing model, and statistical analyses to evaluate groundwater quality, across an area experiencing substantial human activities, with a specific focus on tracing the origin of nitrate (NO3-) with potential water mixing processes. This multi-technique approach was applied to an unconfined aquifer underlying an agricultural area setting in an inter-mountain depression (i.e., the "Pampa de Pocho Plain" in Argentina). Here, the primary identified geochemical processes occurring in the investigated groundwater system include the dissolution of carbonate salts, cation exchange, and hydrolysis of alumino-silicates along with incorporating ions from precipitation. It was observed that the chemistry of groundwater, predominantly of sodium bicarbonate with sulfate water types, is controlled by the area's geology, recharge from precipitation, and stream water infiltration originating from the surrounding hills. Chemical results reveal that 60% of groundwater samples have NO3- concentrations exceeding the regional natural background level, confirming the impact of human activities on groundwater quality. The dual plot of δ15NNO3 versus δ18ONO3 values indicates that groundwater is affected by NO3- sources overlapping manure/sewage with organic-rich soil. The mathematical EMMA model and PHREEQC inverse modeling, suggest organic-rich soil as an important source of nitrogen in the aquifer. Here, 64 % of samples exhibit a main mixture of organic-rich soil with manure, whereas 36 % of samples are affected mainly by a mixture of manure and fertilizer. This study demonstrates the utility of combining isotope tracers with mathematical modeling and statistical analyses for a better understanding of groundwater quality deterioration in situations where isotopic signatures of contamination sources overlap.