RCIPL
Repositório Institucional do Politécnico de Lisboa
Entradas recentes
Educação estética na Formação de professores
Publication . Manera, Lorenzo; Neitzel, Adair; Steil, Isleide; Carvalho, Carla; Ramos, Flávia; Reis, Gesiele; Ostetto, Luciana; Góes, Margarete; Pillotto, Silvia; Moraes, Taíza; Matos Pereira, Teresa; Antunes, Sandra Sofia Pereira
Application of the hirshfeld atom refinement to determine the spin state of the novel Fe(III) schiff base complex
Publication . Buvaylo, Elena A.; Goreshnik , Evgeny; Nesterova, Oksana V.; Nesterov, Dmytro
The novel complex [FeIII(HL)(L)]·2dmf (1) was successfully synthesized by reacting iron chloride with dimethylformamide solution of the Schiff base ligand (H2L). The ligand was prepared in situ through the condensation of 5-bromo-salicylaldehyde and benzhydrazide. The compound 1 was investigated by single crystal X-ray diffraction, IR spectroscopy, CASSCF/NEVPT2 and DFT theoretical calculations, revealing the high-spin (S = 5/2) state of Fe(III). The Hirshfeld Atom Refinement (HAR) of the crystal structure confirmed the sextet ground spin state of 1 through the comparison of R-factors and Hirshfeld deformation maps of the structures refined assuming high, indeterminate (S = 3/2) and low (S = 1/2) ground spin states. A series of basis sets and DFT functionals was screened, indicating that in most cases the combinations allow the correct determination of the ground state of 1. The def2-SVP double-zeta basis set combined with the GGA (Generalized Gradient Approximation) functional such as PBE or BP86 was found to be a sufficiently precise low-cost computational level. While further investigation is necessary, the results of the work tentatively suggest that the HAR could be successfully used for determination of the ground spin state of Fe(III) complexes even in the case of regular quality X-ray structures.
Prediction of chronic kidney disease based on simulated serum analysis by vibrational spectroscopy
Publication . Serrano, Diogo; Zoio, Paulo; Fonseca, Luís P.; Calado, Cecília
The development of new technologies enabling rapid, frequent, and reagent-free moni toring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R2 > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions en abled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from dis eased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems.
Risk phenotyping before graft implantation: FTIR spectroscopy and machine learning for complementary risk stratification in kidney transplantation
Publication . Ramalhete, Luís; Araújo, Rúben Alexandre Dinis; Vigia, Emanuel; Bigotte Vieira, Miguel; Antunes Ferreira, Manuel Aníbal; Calado, Cecília
Background: Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection. Methods: In this retrospective single-center study, 80 pre-transplant serum samples collected on the day of transplantation were initially evaluated; after spectral quality control, 79 samples were retained for analysis. FTIR spectra were acquired in transmission mode and analyzed in the 600–1900 cm−1 and 2800–3400 cm−1 regions. Multiple preprocessing strategies were assessed, including Rubber Band baseline correction, vector normalization, and first- and second-derivative transformation, with and without normalization. Naïve Bayes classifiers with Leave-One-Out Cross-Validation and Fast Correlation-Based Filter feature selection were applied. Results: Exploratory analysis showed broad overlap between groups, indicating a subtle multivariate spectral signal. In the initial exploratory workflow, classifier performance depended strongly on preprocessing and feature selection. Because non-nested feature selection may produce optimistic estimates, the main supervised analysis was repeated using FCBF nested within each LOOCV training fold. The best-performing nested model was obtained using second derivative transformation followed by normalization in the combined 600–1900 and 2800–3400 cm−1 regions, achieving an AUC of 0.837, accuracy of 0.747, sensitivity of 0.675, specificity of 0.821, balanced accuracy of 0.748, and F1-score of 0.730. Permutation testing with 1000 label-randomized repetitions supported performance above chance expectation, with no permuted model reaching the observed AUC (empirical p = 0.000999). Conclusions: Pre-transplant serum FTIR spectroscopy combined with leakage-aware nested machine learning analysis identified an internally validated spectral signal associated with subsequent biopsy-proven rejection. These findings support FTIR as a promising complementary and hypothesis-generating approach for pre-transplant biochemical risk phenotyping, requiring external multicenter validation before clinical application.
Embedding computational thinking in engineering and energy fundamentals: A PBL–experiential design supporting conceptual engagement and skill development
Publication . Soares Domingues, Nuno Alexandre; Costa, Maria Cristina; Domingues, António
Introductory electrical engineering courses frequently emphasise procedural calculation rather than conceptual reasoning and systems‐level problem solving. Although computational thinking (CT) has been recognised as relevant for engineering education, its explicit integration within undergraduate engineering practice remains insufficiently explored. This study investigates how CT can be embedded and analysed within an ICT‐supported problem‐based learning (PBL) environment aligned with Kolb's experiential learning cycle in undergraduate electrical engineering education. A design‐based research approach was implemented during a 14‐week intervention in electrotechnics and electrical machines courses. Custom‐developed simulation tools were integrated into lecture and laboratory activities to support collaborative problem solving, parameter exploration and student‐generated engineering problems. Data sources included student‐generated artefacts, reflection reports, simulationbased activities and structured classroom observations. Analysis employed predefined coding rubrics aligned with five CT dimensions: abstraction, decomposition, algorithmic thinking, automation and generalisation. Inter‐rater agreement exceeded Cohen's κ > 0.80. Student‐generated artefacts increasingly demonstrated explicit circuit decomposition, structured analytical procedures and transfer of reasoning strategies across DC and AC contexts. Simulation tools evolved from being used primarily for answer verification to supporting exploratory modelling and comparative analysis of alternative circuit conditions. Classroom observations indicated increased collaborative reasoning, engagement and autonomous problem formulation during later intervention phases. Descriptive coding frequencies across artefacts and observations provided convergent evidence of increased enactment of CT‐related behaviours over time. The study demonstrates how CT can be operationalised within foundational electrical engineering education through the integration of ICT‐supported simulation environments, PBL activities and experiential learning structures. The work contributes a replicable instructional and analytical framework linking CT dimensions with observable engineering problem‐solving practices and technology‐supported learning activities.