Abstract
In recent years, machine learning techniques, especially genetic programming (GP), have been a powerful approach for automated design of the priority rule-heuristics for the resource-constrained project scheduling problem (RCPSP). However, it requires intensive computing effort, carefully selected training data and appropriate assessment criteria. This research proposes a GP hyper-heuristic method with a duplicate removal technique to create new priority rules that outperform the traditional rules. The experiments have verified the efficiency of the proposed algorithm as compared to the standard GP approach. Furthermore, the impact of the training data selection and fitness evaluation have also been investigated. The results show that a compact training set can provide good output and existing evaluation methods are all usable for evolving efficient priority rules. The priority rules designed by the proposed approach are tested on extensive existing datasets and newly generated large projects with more than 1,000 activities. In order to achieve better performance on small-sized projects, we also develop a method to combine rules as efficient ensembles. Computational comparisons between GP-designed rules and traditional priority rules indicate the superiority and generalization capability of the proposed GP algorithm in solving the RCPSP.

Abstract
Robotics competitions encourage the development of solutions to new challenges that emerge in sync with the rise of Industry 4.0. In this context, robotic simulators are employed to facilitate the development of these solutions by disseminating knowledge in robotics, Education 4.0, and STEM. The RobotAtFactory 4.0 competition arises to promote improvements in industrial challenges related to autonomous robots. The official organization provides the simulation scene of the competition through the open-source SimTwo simulator. This paper aims to integrate the SiwTwo simulator with the Robot Operating System (ROS) middleware by developing a framework. This integration facilitates the design of robotic systems since ROS has a vast repository of packages that address common problems in robotics. Thus, competitors can use this framework to develop their solutions through ROS, allowing the simulated and real systems to be integrated.

Abstract
The fibre optic current sensor demonstrated here uses the intrinsic temperature and wavelength dependence of the Verdet constant of a terbium gallium garnet (TGG) magneto-optic material and the two micro-optic linear polarizers attached, to simultaneously extract the values of temperature and the optical Faraday rotation (induced by the presence of the magnetic field due an electric current on a conductor) without any extra optical component attached to the optical sensor head. The simultaneous measurement is achieved by illuminating the sensor head with a broadband optical source and by careful signal processing of the originated channelled-spectrum, compensate the sensor's temperature dependence.

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
In this work, we targeted the search engine of a sports-related website that presented an opportunity for search result quality improvement. We reframed the engine as a Federated Search instance, where each collection represented a searchable entity type within the system, using Apache Solr for querying each resource and a Python Flask server to merge results. We extend previous work on individual search term weighing, making use of past search terms as a relevance indicator for user selected documents. To incorporate term weights we define four strategies combining two binary variables: integration with default relevance (linear scaling or linear combination) and search term frequency (raw value or log-smoothed). To evaluate our solution, we extracted two query sets from search logs: one with frequently submitted queries, and another with ambiguous result access patterns. We used click-through information as a relevance proxy and tried to mitigate its limitations by evaluating under distinct IR metrics, including MRR, MAP and NDCG. Moreover, we also measured Spearman rank correlation coefficients to test similarities between produced rankings and reference orderings according to user access patterns. Results show consistency across all metrics in both sets. Previous search terms were key to obtaining a higher effectiveness, with runs that used pure search term frequency performing best. Compared to the baseline, our best strategies were able to maintain quality on frequent queries and improve retrieval effectiveness on ambiguous queries, with up to six percentage points better performance on most metrics.

Abstract