Abstract
Reducing the costs associated with health care services is on the agenda, if possible, improving their quality. The Lean management approach has proven to provide good results in creating value and reducing waste. This paper is based on an exploratory case study in the logistic operations of a Northern Portuguese hospital, focusing on the delivery plans of products needed between the central warehouse and the internal medicine ward. Using PDCA improvement cycles and other lean tools, this study analyzed the actual delivery system, identified inefficiencies, and proposed and evaluated some solutions. The aim was to address different types of waste, such as the time the ward head nurse spent to launch orders and perform the reception/arrangement of the products or the excess of products leaving the central warehouse. Although a daily delivery with a fixed stock level seems to be a good delivery system for a large group of products, the recorded or possible failures have led us to devise an optimization model to improve the deliveries. The preliminary results suggest that a weekly plan with a daily delivery of products (to be repeated every week) is even more optimal, not only because it relieves the head nurse of logistical tasks but also because it takes into account the units of products per package. Although this model can be generalized to other nursing wards, some limitations are addressed, namely its non (daily) standardization, leading to some complexity in its handling by the logistic central warehouse operators.

Abstract
Teaching project management to engineering students demands realworld experiences in which they can apply and develop work-ready skills, such as critical thinking, empathy, and teamwork. While a shortage of these skills in new graduates is frequently claimed by engineering companies and educational bodies, there is still a lack of higher education research studies on how to foster them through teaching practice. This paper intends to contribute to filling this gap by presenting an exploratory case study research of a Project-Based Learning (PjBL) experience aimed at designing and implementing a professional (re)integration plan for social and economic deprived people (e.g., long/short-term unemployed), who depend on external food supply provided by a non-profit organization called REFOOD. The experience was carried out in Portugal, from February to June 2021, with 7 MSc mechanical engineering students from the University of Trasos-Montes and Alto Douro (UTAD). We firstly describe the PjBL experience in terms of the key driving question, the learning goals, the educational activities, the collaboration among students and stakeholders, the scaffolding activities, and the tangible learning artefacts produced. We further discuss the preliminary results of the study from data collected through documental analysis, participant observation, and self-completion questionnaires on students' perceptions of the PjBL experience. Data analysis shows that this experience positively impacted the development of students' project management, empathy, critical thinking, and team-working skills, by mainly having challenged their personal belief systems and biases related to the real-world scenarios they dealt with. Finally, we outline implications for the teaching practice concerning the development of similar PjBL experiences, as well as future research directions.

Abstract
This study aims at solving a harvesting scheduling optimization problem with constraints on the clearcut area with additional constraints on clearcut proximity. The objective function is defined as the net present value generated by harvesting discounted by a penalty for each clearcut. This problem arises to reduce the negative environmental impact of excessive harvesting. We propose the connected-bucket model, the so-called bucket model with additional constraints on bucket connectivity and two definitions of stand adjacency, and a Dantzig-Wolfe decomposition. The decomposed model is solved by branch-and-price and the connected-bucket model by a general-purpose mixed integer programming solver (CPLEX). We compare the quality of the solutions obtained with both approaches for real instances. The branch-and-price approach found better solutions for the majority of the instances.

Abstract

Abstract
This study aims to examine if the pricing strategy "Pay What You Want" can be the best option for the industry of digital products' distribution, when compared with other fixed prices policies. To verify the adequacy of Pay What You Want Pricing strategy, we conducted an online survey using a sample of online consumers, to evaluate their buying intention and the willingness to pay regarding a set of digital products. Results show that, in some instances, the Pay What You Want Pricing strategy yields a greater sales revenue through the reduction of the individual amounts paid, which is counter-balanced by the increasing number of transactions. We conclude that this pricing strategy is as much suitable for companies, as they may potentially increase their sales revenue.

Abstract
Background and objective: Cell migration is essential for many biological phenomena with direct impact on human health and disease. One conventional approach to study cell migration involves the quantitative analysis of individual cell trajectories recorded by time-lapse video microscopy. Dedicated software tools exist to assist the automated or semi-automated tracking of cells and translate these into coordinate positions along time. However, cell biologists usually bump into the difficulty of plotting and computing these data sets into biologically meaningful figures and metrics. Methods: This report describes MigraR, an intuitive graphical user interface executed from the RStudio (TM) (via the R package Shiny), which greatly simplifies the task of translating coordinate positions of moving cells into measurable parameters of cell migration (velocity, straightness, and direction of movement), as well as of plotting cell trajectories and migration metrics. One innovative function of this interface is that it allows users to refine their data sets by setting limits based on time, velocity and straightness. Results: MigraR was tested on different data to assess its applicability. Intended users of MigraR are cell biologists with no prior knowledge of data analysis, seeking to accelerate the quantification and visualization of cell migration data sets delivered in the format of Excel files by available cell-tracking software. Conclusions: Through the graphics it provides, MigraR is an useful tool for the analysis of migration parameters and cellular trajectories. Since its source code is open, it can be subject of refinement by expert users to best suit the needs of other researchers. It is available at GitHub and can be easily reproduced.