Abstract
Background: Diet and exercise interventions have been associated with improved body composition and physical fitness. However, evidence regarding their combined effects in older adults is scarce. This study aimed to investigate the impact of a combined 12-week Mediterranean diet-based sustainable healthy diet (SHD) and multicomponent training (MT) intervention on body composition, anthropometry, and physical fitness in older adults. Methods: Diet intervention groups received a weekly SHD food supply and four sessions, including a SHD culinary practical workshop. The exercise program included MT 50 min group session, three times a week, on non-consecutive days. Body composition and physical fitness variables were assessed through dual X-ray absorptiometry, anthropometric measurements, and senior fitness tests. Repeated measures ANOVA, with terms for group, time, and interaction, was performed. Results: Our results showed that a combined intervention significantly lowered BMI and total fat. Also, significant differences between assessments in all physical fitness tests, except for aerobic endurance, were observed. Adjusted models show significant differences in BMI (p = 0.049) and WHR (p = 0.037) between groups and in total fat (p = 0.030) for the interaction term. Body strength (p < 0.001), balance tests (p < 0.001), and aerobic endurance (p = 0.005) had significant differences amongst groups. Considering the interaction term, differences were observed for upper body strength (p = 0.046) and flexibility tests (p = 0.004 sit and reach, p = 0.048 back scratch). Conclusions: Our intervention study demonstrates the potential of implementing healthy lifestyle and sustainable models to promote healthy and active aging.

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Traditionally, scheduling literature has focused mainly on solving problems related to processing jobs with non- assembly operations. Despite the growing interest in the assembly literature in recent years, knowledge of the problem is still in its early stages in many aspects. In this regard, we are not aware of any previous contributions that address the assembly scheduling problem with just-in-time objectives. To fill this gap, this paper studies the 2-stage assembly scheduling problem minimising the sum of total earliness and total tardiness. We first analyse the relationship between the decision problem and the generation of the due dates of the jobs, and identify the equivalences with different related decision problems depending on the instances. The properties and conclusions obtained in the analysis are applied to design two constructive heuristics and a composite heuristic. To evaluate our proposals, different heuristics from the state-of-the-art of related scheduling problems are adapted, and a computational evaluation is carried out. The excellent behaviour of the proposed algorithms is demonstrated by an extensive computational evaluation.

Abstract
The Box-Cox (BC) transformation is widely used in data analysis for achieving approximate normality in the transformed scale. The transformation is only possible for non-negative data. This positiveness requirement implies a truncation to the distribution on the transformed scale and the distribution in the transformed scale is truncated normal. This fact has consequences for the estimation of the parameters specially if the truncated probability is high. In the seminal paper Box and Cox proposed to estimate parameters using the normal distribution which in practice means to ignore any consequences of the truncation on the estimation process. In this work we present the framework for exact likelihood estimation on the PN distribution to which we call method m(1) and how to calculate the parameters estimates using consistent estimators. We also present a pseudo-Likelihood function for the same model not taking into account truncation and allowing to replace parameters mu and sigma for their estimates. We call m(2) to this estimation method. We conclude that for cases where the truncated probability is low both methods give good estimation results. However for larger values of the truncated probability the m(2) method does not present the same efficiency.

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