University of Aveiro

This Comment discusses a lack of proof for the relationship of the nanogenerator performance reported in J. Mater. Chem. A , 2018, 6 , 16101 to the enhanced piezoelectricity and to the Al-doping of BaTiO 3 particles used as the nanogenerator core.

This paper introduces a new class of space-time integer-valued ARMA models referred to as STINARMA. This class arises as the natural space-time extension of the INARMA models and, simultaneously, as the integer-valued counterpart of the conventional STARMA models. In this work, the moving average subclass STINMA(qm1,…,mq) is studied in detail. Particular attention is given to the derivation of first- and second-order moments, including space-time autocorrelations. Due to its large potential use in real-data applications, the Poisson STINMA(11) process is analyzed in further detail. Estimation methods are also addressed and their performance is demonstrated through a simulation study and by analysing the daily number of hospital admissions observed over time in three Portuguese locations.

This work focuses on the problem of mortality prediction in patients with pneumonia after admission into an intensive care unit, by addressing it via logistic regression. This approach can model the relationship between clinical correlates and the probability of the binary outcome, with obvious advantages such as simplicity and interpretability of the predictive models. This work further inspects the potential of localized models, an approach based on different (parallel) predictive models each one constructed in clusters automatically identified in the training set. The predicted outcome is then obtained via membership separation (M, which corresponds to the outcome of the closest localized model) or weights (W, outcome as the weight average of localized outcomes via inverse distance). The results point out a similar balanced accuracy of 0.73 for the global model M24-48PS (without oversampling) and the W M24-48PSC model (weighted average of localized models without oversampling), which is partially explained by the small separability between the identified clusters. Therefore, a proof of concept was performed to support the usefulness of localized models in more separable data. This study considered a small amount of data for training and testing (chosen as that closest to the centroids of the identified clusters) and the results suggest that the localized approach can outperform the global one in more separable data.

The progress in computer vision has allowed the development of a diversity of precision agriculture systems, improving the efficiency and yield of several processes of farming. Among the different processes, crop monitoring has been extensively studied to decrease the resources consumed and increase the yield, where a myriad of computer vision strategies has been proposed for fruit analysis (e.g., fruit counting) or plant health estimation. Nevertheless, the problem of fruit ripeness estimation has received little attention, particularly when the fruits are still on the tree. As such, this paper introduces a strategy to estimate the maturation stage of fruits based on images acquired from handheld devices while the fruit is still on the tree. Our approach relies on an image segmentation strategy to crop and align fruit images, which a CNN subsequently processes to extract a compact visual descriptor of the fruit. A non-linear regression model is then used for learning a mapping between descriptors to a set of physicochemical parameters, acting as a proxy of the fruit maturation stage. The proposed method is robust to the variations in position, lighting, and complex backgrounds, being ideal for working in the wild with minimal image acquisition constraints. Source code is available at https://github.com/Diogo365/WildFruiP.

Pain is a highly subjective and complex phenomenon. Current methods used to measure pain mostly rely on the patient’s description, which may not always be possible. This way, pain recognition systems based on body language and physiological signals have emerged. As the emotional state of a person can also influence the way pain is perceived, in this work, a protocol for pain induction with previous emotional elicitation was conducted. Eletrocardiogram (ECG), Electrodermal Activity (EDA) and Eletromyogram (EMG) signals were collected during the protocol. Besides the physiological responses, perception was also assessed through reported-scores (using a numeric scale) and times for pain tolerance. In this protocol, 3 different emotional elicitation sessions, negative, positive and neutral, were performed through videos of excerpts of terror, comedy and documentary movies, respectively, and pain was induced using the Cold Pressor Task (CPT). A total of 56 participants performed the study (with 54 completing all three sessions). The results showed that during the negative emotional state, pain reported-scores were higher and pain threshold and tolerance times were smaller when compared with positive. As expected, the physiological response to pain remain similar despite the emotional elicitation.

Many different cultures and countries have fish as a central piece in their diet, particularly in coastal countries such as Portugal, with the fishery and aquaculture sectors playing an increasingly important role in the provision of food and nutrition. As a consequence, fish-freshness evaluation is very important, although so far it has relied on human judgement, which may not be the most reliable at times. This paper proposes an automated non-invasive system for fish-freshness classification, which takes fish images as input, as well as a seabream fish image dataset. The dataset will be made publicly available for academic and scientific purposes with the publication of this paper. The dataset includes metadata, such as manually generated segmentation masks corresponding to the fish eye and body regions, as well as the time since capture. For fish-freshness classification four freshness levels are considered: very-fresh, fresh, not-fresh and spoiled. The proposed system starts with an image segmentation stage, with the goal of automatically segmenting the fish eye region, followed by freshness classification based on the eye characteristics. The system employs transformers, for the first time in fish-freshness classification, both in the segmentation process with the Segformer and in feature extraction and freshness classification, using the Vision Transformer (ViT). Encouraging results have been obtained, with the automatic fish eye region segmentation reaching a detection rate of 98.77%, an accuracy of 96.28% and a value of the Intersection over Union (IoU) metric of 85.7%. The adopted ViT classification model, using a 5-fold cross-validation strategy, achieved a final classification accuracy of 80.8% and an F1 score of 81.0%, despite the relatively small dataset available for training purposes.

Appropriate pain treatment relies on an accurate assessment of pain. Limitations regarding subjective reporting of pain or observational bias, when pain is assessed by a healthcare professional, can lead to inadequate pain treatment. Therefore, pain assessment using physiological signals has been studied in past years due to the importance of objective measurement. The aim of this work is to use features extracted from Electrocardiogram (ECG) signals to classify pain induced by a Cold Pressor Task (CPT). Specifically, the goal is to determine the optimal hyperparameters of the classification algorithms and the optimal features for accurately distinguishing between higher and lower levels of pain. A model combining 15 ECG-features related to the P, R, S, and T waves and the Random Forest algorithm provided the best performance for predicting induced pain levels. This model achieved an accuracy of 95.3%, an F1-score of 94.0%, a precision of 97.9%, and a recall of 90.4%. These results show the feasibility of identifying pain through the physiological characteristics of the ECG.

Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering. Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity. Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.

Studies using retrospective memory tasks have revealed that animates/living beings are better remembered than are inanimates/nonliving things (the animacy effect). However, considering that memory is foremost future oriented, we hypothesized that the animacy effect would also occur in prospective memory (i.e., memory for future intentions). Using standard prospective memory (PM) procedures, we explored this hypothesis by manipulating the animacy status of the PM targets. Study 1a reports data collected from an American sample; these results were then replicated with a Portuguese sample (Study 1b). Study 2 employed a new procedure, and data were collected from a broader English-speaking sample. In these three studies, animate (vs. inanimate) targets consistently led to a better PM performance, revealing, for the first time, that the animacy advantage extends to PM. These results strengthen the adaptive approach to memory and stress the need to consider animacy as an important variable in memory studies.

Objectives to validate the internal structure of the Hospital Resources Assessment Scale for the Preservation of Urinary Continence in the Elderly. Methods validation study of the internal structure of a scale constructed based on the Donabedian conceptual model and an integrative review, with prior content validation. The scale was applied to the target population, and 124 nurses responded to the questionnaire. Exploratory Factor Analysis was performed using the FACTOR software, employing multiple techniques. Results a factorial model with 11 items organized into two dimensions (support for human resources and material resources) was obtained. The “physical structure” dimension was removed from the initial model and adopted as a complementary checklist to the instrument, as it was not possible to obtain a factorable model with this dimension. Conclusions we provide a valid scale that can measure indicators, identifying weaknesses and/or strengths related to hospital resources for the preservation of urinary continence in the elderly. Descriptors: Urinary Incontinence; Aged; Hospitalization; Nursing; Validation Study

Palladium nanohybrids were synthesized and applied to the one‐pot synthesis of bis(3‐indolyl)methanes by selective C‐C bond reaction from benzyl alcohol and indole. A T. lanuginosus lipase‐palladium nanoparticles hybrid (Pd@TLL) was synthesized, yielding PdNPs with an average diameter size of 5 nm. This heterogeneous catalyst was first tested in the selective oxidation of benzyl alcohol to benzaldehyde in different solvents. Then, the direct formation of bis(3‐indolyl)methane, by in situ oxidation and C‐C coupling, was successfully evaluated under different conditions, obtaining >99% conversion at 80 ºC in toluene, with a TOF value of 9 min‐1 and 89% in pure water, demonstrating the versatility of these biohybrids.

Objectives to validate the internal structure of the Hospital Resources Assessment Scale for the Preservation of Urinary Continence in the Elderly. Methods validation study of the internal structure of a scale constructed based on the Donabedian conceptual model and an integrative review, with prior content validation. The scale was applied to the target population, and 124 nurses responded to the questionnaire. Exploratory Factor Analysis was performed using the FACTOR software, employing multiple techniques. Results a factorial model with 11 items organized into two dimensions (support for human resources and material resources) was obtained. The “physical structure” dimension was removed from the initial model and adopted as a complementary checklist to the instrument, as it was not possible to obtain a factorable model with this dimension. Conclusions we provide a valid scale that can measure indicators, identifying weaknesses and/or strengths related to hospital resources for the preservation of urinary continence in the elderly. Descriptors: Urinary Incontinence; Aged; Hospitalization; Nursing; Validation Study

Although wine tourism is predominantly described as an activity practised by middle-aged adults who travel without children to experience, learn about and buy wines, it has been considered to attract unexpected markets, such as families with children, mainly in rural destinations where wine and grape production occur in a particular natural, cultural and social context. However, research on families with children undertaking wine tourism is scarce and little is known about the features and heterogeneity of this market. This study analyses, through a survey research, visitors travelling as a family with children on three Portuguese wine routes (N=370), regarding general profile, travel motivation, attractions visited, satisfaction and loyalty. Results suggest two profiles of family visitors to Central Portugal wine routes: those focused on wine-related attractions and activities and those more interested in having fun and socialising in a wine region, enjoying its landscapes, culture and rural life. In our sample, the first group is almost totally composed of domestic visitors, accompanied mainly by children under 10 years. The second group includes some international visitors, and a higher presence of ‘older’ children and adolescents. Family visitors focused on wine attractions tend to indicate more suggestions for route improvement, primarily related to the availability of varied wine tourism experiences, including child-friendly activities.

This study examines the synchronisation of the business cycles between the Baltic States and the countries of Western Europe. The study covers the following countries: Latvia, Lithuania, France, the United Kingdom, Germany, and Estonia; and the quarterly GDP growth data during the period 1995-2017. The GDP growth data have been modified using the Hodrick-Prescott and Baxter filters to distinguish business cycles. To measure the synchronisation between the business cycles of the selected countries, the correlation between the business cycles of the countries was used. The results show that the business cycles of the Baltic and Western European countries were more synchronised in 2009-2014 than in 1998-2014. It shows that the Baltic economies are becoming more related to the European Union countries and less related to the post-Soviet countries.

This study was designed to fill two main research gaps: the need for a multidimensional perspective on the future of tourism and hospitality (T&H) labour and the lack of studies from the perspectives of key stakeholders who are representatives of tourism organisations and involved in policymaking. Therefore, it unveils the perceptions of policymakers responsible for tourism organisations in Portugal towards the future of T&H labour. Following an exploratory qualitative case study approach based on semi-structured interviews with eleven policymakers, the results illustrate that the most prominent challenges that the COVID-19 pandemic created are the damage to practical ability, finding a skilled and experienced workforce, and attracting tourism labour back to the sector. However, there are some requirements to overcome such challenges in the future. Moreover, future trends and skills for T&H employment were revealed as new working models, digitisation and robotisation, and the expected skills such as management, analytical, digital marketing, and customer behaviour analysis. Finally, technology was found to have positive and negative impacts on T&H employment. The results and implications will benefit Portugal and different destinations in understanding the dimensions of T&H labour for the future and developing future actions, strategies and policies.

A set of graphitic carbon nitride samples was prepared using a straightforward experimental procedure without templates and any subsequent treatments. The materials were studied in‐depth using a range of physical and chemical methods such as X‐ray diffraction, FTIR spectroscopy, elemental analysis (CHN), nitrogen physisorption, SEM, XPS, TPD CO2. The resulting g‐C3N4 was shown to be highly efficient in carboxymethylation of cinnamyl alcohol with dimethyl carbonate yielding up to ca. 82% of the desired cinnamyl methyl carbonate. In the studied conditions, an increase in the surface N atomic content leads to an increase in selectivity towards the desired carbonate, while a higher surface O content was beneficial for side products. Metal‐free graphitic carbon nitride was shown to be one of the most productive (ca. 2 mol /h kgcat) in the investigated reaction among studied heterogeneous catalysts.