Abstract
Digital transformation creates a number of barriers that need to be surpassed by companies from the technological and organizational points of view. Concurrently, the complexity and nature of current market environments often demand new products, services, processes and business models, oftentimes supported by digital technologies. The objective of this paper is to contribute to a better understanding on the impact of a severe global crisis on the digital technologies' adoption process (and their associate drivers and barriers), with a special look on the strategies adopted by companies in terms of supply chain resilience. Specificities of the Portuguese industry are discussed through the analysis of five case studies.

Abstract
This paper presents several case studies that show that it is possible to use clean energy to produce electricity, we have environmental benefits and benefits for the management of the electrical transmission network. In this case wind energy are used.For this work, software was developed in Matlab for the model we developed and the results of this were compared with the results obtained by the simulator Power World.To make the decision to replace generators fossil generators with wind generators, Local Marginal Prices (LMP) were used. Some case studies were created using a model system, with the objective of evaluating the benefits of this allocation based on the LMP.The test network presented in this paper is a 9 Bus network. However, the developed software was also tested on an IEEE 30 Bus network. © 2023 IEEE.

Abstract
This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP-hard since it extends NP-hard problems. Good-quality solutions are efficiently found by an operation-based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state-of-the-art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper.

Abstract
This paper aims to thoroughly discuss the use of Digital Twin technology in complex operations environments, highlighting its potential applications and the research challenges that need to be addressed. This is necessitated by the fact that currently there is no comprehensive literature review and framework for implementing Digital Twin technology in complex operations environments. Furthermore, existing interpretations of DT implementation are inadequately detailed and not very informative in this area. This may be a consequence of the difficulties of collecting and extracting useful information from data in real-time. Another drawback worth mentioning is that Digital twins at the moment center on an individual or isolated part instead of integrating the whole system and no current work talks about this holistic approach. This paper will focus on Digital Twins in complex operations environments and their applications. A review of scientific literature on the use of Digital Twins in complex operations environments is performed and the articles are categorized by the problems and challenges that they address requiring DT as a solution. A selection of papers that focus on this topic and represent the current situation of research will be emphasized. In conclusion, this work will be utilized as a baseline study to propose a Digital Twin reference framework, which eventually leads to implementing and evaluating a comprehensive Digital Twin methodology in complex systems. © 2023 IEEE.

Abstract
This paper introduces a tomography-like method for assessing grape maturation. It analyses inner tissue spectra through point-of-measurement (POM) sensing. A multi-block hierarchical principal component analysis (MHPCA) algorithm was used for the spectral reconstruction of total grapes (skin, pulp, and seed). Two grape cultivars, Loureiro (white; n = 216) and Vinhao (red; n = 205) were measured at 12 dates after veraison (DAV). The reconstructed spectra showed no significant differences (p < 0.001) from the originals for both grapes. Loureiro had better statistical metrics (Person's correlation coefficient (r) values for: total grape: 0.99, skin: 1; pulp: 1, seed: 0.94) than Vinhao (r values for: total grape: 0.92, skin: 0.92; pulp: 0.95, seed: 0.95). Using self learning artificial intelligence (SL-AI), the following parameters were predicted for both grapes: soluble solids content (%; MAPE <13%), puncture force (N; MAPE <29%), chlorophyll content (a.u.; MAPE <29%), and anthocyanin content (a.u.; MAPE <17%, Vinhao only). When comparing observed values with predicted skin, pulp, and seed spectra, Vinhao showed no statistical differences for most parameters, except pulp chlorophyll on one DAV in the final maturation stage. The same was done with the Loureiro cultivar. Although Loureiro mostly showed no statistical differences in assessed parameters across tissues and dates, variations were found in pulp and skin chlorophyll content and puncture force. This tomography-like approach based on tissue maturation can help viticulturists to access instant data on grape maturation, supporting informed decision-making and promoting more sustainable agricultural practices.

Abstract
Cartilage repair after a trauma or a degenerative disease like osteoarthritis (OA) continues to be a big challenge in current medicine due to the limited self-regenerative capacity of the articular cartilage tissues. To overcome the current limitations, tissue engineering and regenerative medicine (TERM) and adjacent areas have focused their efforts on new therapeutical procedures and materials capable of restoring normal tissue functionalities through polymeric scaffolding and stem cell engineering approaches. For this, the sustainable exploration of marine origin materials has emerged in the last years as a natural alternative to mammal sources, benefiting from their biological properties (e.g., biocompatibility, biodegradability, no toxicity, among others) for the develop-ment of several types of scaffolds. In this study, marine collagen(jCOL)-chitosan(sCHT)-fucoidan(aFUC)/ chondroitin sulfate(aCS) were cryo-processed (-20 degrees C,-80 degrees C, and-196 degrees C) and a chemical-free cross -linking approach was explored to establish cohesive and stable cryogel materials. The cryogels were intensively characterized to assess their oscillatory behavior, thermal structural stability, thixotropic properties (around 45 % for the best formulations), injectability, and surface structural organization. Additionally, the cryogels demonstrate an interesting microenvironment in in vitro studies using human adipose-derived stem cells (hASCs), supporting their viability and proliferation. In both physic-chemical and in vitro studies, the systems that contain fucoidan in their formulations, i.e., C1 (jCOL, sCHT, aFUC) and C3 (jCOL, sCHT, aFUC, aCS), submitted at-80 degrees C, are those that demonstrated most promising results for future application in articular cartilage tissues.