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The study set out to understand how the regional competitiveness index (RCI) in the department of Ancash related to the human development index (HDI) from 2008 to 2021. For a more complete understanding of the findings, each component or dimension of the RCI was analyzed. Ancash's HDI and its competitiveness index over a 14-year period were used as the population for this applied, longitudinal, descriptive-correlational study, which was based on secondary data extracted from the “Instituto Nacional de Estadística e Informática” (INEI) and business school of the Pontificia Universidad Católica del Perú (CENTRUM) statistical databases. Multiple linear regression was used to find the relationship. The research found a strong and positive correlation between regional competitiveness and human development between 2008 and 2021. No correlations were found between the HDI and the health, education, employment, or institutional dimensions of regional competitiveness, but direct and significant correlations were established between the economic environment and the HDI and between the infrastructure dimension and the HDI.

Neurostrategy is an abductive decision-making process developed from the knowledge generated within the network of decision-makers. It links cognitive style with the team's decisional adaptability in relation to the organization's purpose. Neurostrategy differs from traditional methods, in which it addresses negative utility or decisional trauma, highlighting the variety of interests that are the main cause of team misalignment and allowing for the development of strategies to address them. Neurostrategy enables the classification of strategy deficiencies based on cohesion, coordination, communication and conduction (Co4).

This paper introduces a novel approach called relational neurostrategy (RNS) to elucidate the knowledge processes influencing decision-making. RNS aims to “capture” the intricate processes guiding decisions, enabling the network's decisional plasticity in both forms and contents. This adaptability is crucial for effectively addressing posed challenges, while simultaneously mitigating the impact of diverse interests. The methodology also ensures transparency in the decision-making process and generates an effective solution strategy.

The RNS addresses two critical aspects of the decision-making process. Firstly, it reduces unnecessary variety stemming from multiple interpretations and secondly, it minimizes the adverse impact of diverse interests within the decision-making network. This approach results in strong and credible decisions that reflect the collective intelligence, cooperation and collaborative efforts of the network, rather than being imposed as absolute truths.

The RNS stands out as a distinctive decision-making method, setting itself apart from existing approaches. Its uniqueness becomes evident in its ability to address the question “what prevents the authors from … ?” from this inquiry, RNS successfully integrates unrequired variety and negative utility. By doing so, it strategically narrows down the search field to the universe of distinctions that truly constitute the problem. This innovative process not only enhances efficiency but also fosters a high level of participation in the strategic design of potential solutions. In essence, RNS brings unprecedented value by effectively navigating the intricacies of decision-making and maximizing the relevance of the identified problem space.

Realistic composite vehicles with 2, 3, 5 and 9 axles, consisting of a truck with one or two trailers, are addressed in this paper by computational models for vehicle–bridge interaction analysis.

The vehicle–bridge interaction (VBI) models are formed by sets of 2-D rigid blocks interconnected by mass, damping and stiffness elements to simulate their suspension system. The passage of the vehicles is performed at different speeds. Several rolling surface profiles are admitted, considering the maintenance grade of the pavement. The spectral density functions are generated from an experimental database to form the longitudinal surface irregularity profiles. A computational code written in Phyton based on the finite element method was developed considering the Euler–Bernoulli beam model.

Several models of composite heavy vehicles are presented as manufactured and currently travel on major roads. Dynamic amplification factors are presented for each type of composite vehicle.

The VBI models for compound heavy vehicles are 2-D.

This work contributes to improving the safety and lifetime of the bridges, as well as the stability and comfort of the vehicles when passing over a bridge.

The structural response of the bridge is affected by the type and size of the compound vehicles, their speed and the conservative grade of the pavement. Moreover, one axle produces vibrations that can be superposed by the vibrations of the other axles. This effect can generate not usual dynamic responses.

The estimation of the burden of a disease is one of the tasks with the longest tradition in health economics, which allows us to know the volume of resources that a country allocates to a specific health problem, and to compare countries and diseases. Although the fundamental objective of health systems is not to reduce the cost of the disease, but to improve the health of the population, the studies of burden of disease establish the economic seriousness of the problem, orienting the priorities of action.

Government-funded medical expenditure in Uruguay for the last ten years has tripled in US dollars. The increase in the prevalence of overweight and obesity has contributed to this growth. According to the World Health Organization, Uruguay has the highest growing trend in the prevalence of both overweight and obesity in South America. We have previously estimated that economic burden linked to obesity will be more than US$500 million by 2020, a figure close to 1% of the country’s GDP.

In this study, we tried to generate a measure of value to ascertain the cost of inaction in the fight against obesity and its consequences linked to several non-communicable diseases. The cost of inaction is not defined as the cost of not doing, but as the cost of not implementing the right policies (in this case health prevention policies) at the right time.