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Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications
Publication . Coletti, Roberta; Leiria de Mendonça, Mónica; Vinga, Susana; Lopes, Marta B.; CMA - Centro de Matemática e Aplicações; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; SAGE Publications
Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.
A blockchain architecture with smart contracts for an additive symbiotic network
Publication . Ferreira, Inês de Abreu; Palazzo, Guido; Pinto, António; Pinto, Pedro; Sousa, Pedro; Godina, Radu; Carvalho, Helena; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial; Springer New York
Adopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology’s immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.
Topology optimization of thermoelastic structures with single and functionally graded materials exploring energy and stress-based formulations
Publication . Silva, Rui F.; Coelho, Pedro G.; Conde, Fábio M.; Almeida, Cláudia J.; Custódio, Ana L.; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial; DM - Departamento de Matemática; Springer Science Business Media
Topology optimization problem formulations have lately included stresses, besides compliance, to ensure mechanical strength feasibility, which is of utmost importance in structural engineering practice. A mechanically induced stress field has often been considered in optimal structural design. However, one realizes that thermal stresses can also greatly influence efficient designs, especially when addressing highly constrained structures. Moreover, stress mitigation has been achieved by enlarging the design domain to multi-material solutions. This motivates to pursue stress-based topology optimization of thermoelastic structures and the extension of the multi-material setting to Functionally Graded Materials (FGMs), with greater potential in stress mitigation. Two optimization problems are investigated: (1) elastic strain energy minimization and (2) maximum von Mises stress minimization. In the former, the single-material problem is revisited, but in the frame of a multi-objective formulation, weighting mechanical and thermal strain energy terms, as they can be decoupled. Insights into thermal stresses allow to propose a well-posed stress-based formulation for the topology optimization thermoelastic problem. In the latter, stress mitigation is sought on account of optimizing the spatial mixture (composition) of two solids amidst prescribed or predicted voids. It is assumed that the RAMP interpolation scheme has the physical meaning of rendering the thermoelastic properties for the continuous variation of composition. Linear thermoelasticity and plane stress benchmarks are used. In the multi-objective energy-based problem, the trade-offs between the conflicting design objectives, in the Pareto sense, are highlighted. Regarding the stress-based problem, lower stress peaks are obtained in FGM solutions, as stresses are more evenly distributed.
Multi-scale topology optimization of structures with multi-material microstructures using stiffness and mass design criteria
Publication . Conde, Fábio M.; Coelho, Pedro G.; Guedes, José M.; DEMI - Departamento de Engenharia Mecânica e Industrial; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; Elsevier Science Publisher B.V.
Nowadays, there is a great interest on the part of the automotive and aerospace industry to design environmentally-friendly structures. To that purpose, stiffness-oriented designs are proposed here by extending previous work on multi-scale topology optimization to the multi-material setting reformulating the problem to include appropriately mass constraints and discussing different design domain parametrizations and algorithmic strategies. On the macroscale, the problem of minimizing the compliance subject to a global mass constraint is addressed. On the microstructure scale, the multi-material design is carried out by solving the problem of minimizing the local complementary strain energy density with mass density constraint. As a result, very efficient structures composed of spatially varying porous and multi-material microstructures are obtained. The optimal design of the multi-material microstructure can be done either in a pointwise manner or in larger subdomains, to promote design uniformity. These parametrizations are here compared and discussed. Moreover, two different algorithmic strategies to solve the multi-scale problem are proposed, and their pros and cons discussed. They differ in the way the macro and micro design variables are related and updated. The macro design variables consider the mass density distribution along the structure, while the micro variables define the microstructure's topology using a multi-material SIMP interpolation scheme. The results show very efficient structures with locally optimized multi-material microstructures, which can outperform their single-material counterparts with regards to stiffness while maintaining the same mass. Additionally, the maximum stress verified on multi-material structures tends to be lower than the one obtained in the single-material counterparts.
All Lives Matter
Publication . K. Eslamzadeh, Milad; Grilo, António; Espadinha-Cruz, Pedro; DEMI - Departamento de Engenharia Mecânica e Industrial; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; MDPI - Multidisciplinary Digital Publishing Institute
Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
Número da atribuição
UIDB/00667/2020