Abstract
The storage policy has a tremendous impact on the efficiency of order-picking operations, which can account for up to 50% of operating costs. The coronavirus pandemic has reinforced the importance of managers making the right operational decisions, namely regarding the definition of the storage policy. It is therefore important to consider handling constraints. This article is inspired by a Portuguese retail company and it considers two handling constraints: weight and shape. We define the location of products by using a zero-one quadratic assignment model. In this model, in addition to the demand and similarity, we considered the weight and shape of the products. We used both weight and shape parameters to set products with similar shapes together, placing aside products with odd shapes. Our analysis shows that the inclusion of the shape and weight into the problem improved the current operations. We found that our method allowed for a reduction of up to 24% in the picking distance, a percentage higher than the one that only considers weight constraints. The inclusion of the shape parameter into the study enabled the company to increase the flow and efficiency of the order-picking operations. Thus, it can be an asset for other warehouses.

Abstract
This paper proposes a multi-objective mixed-integer linear programming to model a cold chain with complementary operations on a hierarchical hub network. Central hubs are linked to each other in the first level of the network and to the star network of the lower-level hubs. As for a case study, different hub levels provide various refreshing or freezing operations to keep the perishable goods fresh along the network. Disruption is formulated by the consideration of stochastic demand and multi-level freshness time windows. Regarding the solution, a genetic algorithm is also developed and compared for competing the large-sized networks.

Abstract
Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.

Abstract
Risk management is one of the ten knowledge areas discussed in the Project Management Body of Knowledge (PMBOK), which serves as a guide that should be followed to increase the chances of project success. The popularity of research regarding the application of risk management in software projects has been consistently growing in recent years, especially with the application of machine learning techniques to help identify risk levels of risk factors of a project before its development begins, with the goal of improving the likelihood of success of these projects. This paper presents the results of the application of machine learning techniques for risk assessment in software projects. A Python application was developed and, using Scikit-learn, two machine learning models, trained using software project risk data shared by a partner company of this project, were created to predict risk impact and likelihood levels on a scale of 1 to 3. Different algorithms were tested to compare the results obtained by high performance but non-interpretable algorithms (e.g., Support Vector Machine) and the ones obtained by interpretable algorithms (e.g., Random Forest), whose performance tends to be lower than their non-interpretable counterparts. The results showed that Support Vector Machine and Naive Bayes were the best performing algorithms. Support Vector Machine had an accuracy of 69% in predicting impact levels, and Naive Bayes had an accuracy of 63% in predicting likelihood levels, but the results presented in other evaluation metrics (e.g., AUC, Precision) show the potential of the approach presented in this use case.

Abstract
Forecasting COVID-19 disease severity is key to supporting clinical decision making and assisting resource allocation, particularly in intensive care units (ICUs). Here, we investigated the utility of time- and frequency-related features of the backscattered signal of serum patient samples to predict COVID-19 disease severity immediately after diagnosis. ICU admission was the primary outcome used to define disease severity. We developed a stacking ensemble machine learning model including the backscattered signal features (optical fingerprint), patient comorbidities, and age (AUROC = 0.80), which significantly outperformed the predictive value of clinical and laboratory variables available at hospital admission (AUROC = 0.71). The information derived from patient optical fingerprints was not strongly correlated with any clinical/laboratory variable, suggesting that optical fingerprinting brings unique information for COVID-19 severity risk assessment. Optical fingerprinting is a label-free, real-time, and low-cost technology that can be easily integrated as a front-line tool to facilitate the triage and clinical management of COVID-19 patients.

Abstract
The study of phenotypic evolution in island birds following colonization is a classic topic in island biogeography. However, few studies explicitly test for the role of selection in shaping trait evolution in these taxa. Here, we studied the Azores woodpigeon (Columba palumbus azorica) to investigate differences between island and mainland populations, between females and males, and interactions between geographical origin and sex, by using spectrophotometry to quantify plumage colour and linear measurements to examine external and skeletal morphology. We further tested if selection explains the observed patterns by comparing phenotypic differentiation to genome-wide neutral differentiation. Our findings are consistent with several predictions of morphological evolution in island birds, namely differences in bill, flight and leg morphology and coloration differences between island and mainland birds. Interestingly, some plumage and morphological traits that differ between females and males respond differently according to geographical origin. Sexual dimorphism in colour saturation is more pronounced in the mainland, but this is driven by selection on female plumage coloration. Differences in flight morphology between females and males are also more pronounced in the mainland, possibly to accommodate contrasting pressures between migration and flight displays. Overall, our results suggest that phenotypic differentiation between mainland and island populations leading to divergent sexual dimorphism patterns can arise from selection acting on both females and males on traits that are likely under the influence of natural and sexual selection.