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- Assessing the differences of two vineyards soils’ by NIR spectroscopy and chemometricsPublication . Machado, Sandia; Barreiros, Luísa; Graça, António R.; Madeira, Manuel; Páscoa, Ricardo N. M. J.; Segundo, Marcela A.; Lopes, João A.Soil properties influence greatly the status of vine plants which consequently influences the quality of wine. Therefore, in the context of viticulture management, it is extremely important to assess the physical and chemical parameters of vineyards soils. In this study, the soils of two vineyards were analysed by near-infrared (NIR) spectroscopy and established analytical reference procedures. The main objective of this study was to verify if NIR spectroscopy is a potential tool to discriminate the soils of both vineyards as well as to quantify differences of soil’s parameters. For that, a total of eight sampling spots were selected at each vineyard taking into consideration the soil type and sampled at different depths. The data analysis was performed using analysis of variance (ANOVA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression. The ANOVA results revealed that 12 out of the 18 parameters analysed through the reference procedures can be considered statistically different (p < 0.05). Regarding PCA, the obtained results revealed a clear separation between the scores of both vineyards either considering NIR spectra or the chemical parameters. The PLS-DA model was able to obtain 100 % of correct predictions for the discrimination of both vineyards. PLS regression analysis using NIR spectra revealed R2 P and RER values higher than 0.85 and 10, respectively, for 8 (pH (H2O), N, Ca2+, Mg2+, SB, CEC, ECEC and GSB) of the 18 chemical parameters evaluated. Concluding, these results demonstrate that it is possible to discriminate the soils of the different vineyards through NIR spectroscopy as well as to quantify several chemical parameters through soils NIR spectra in a rapid, accurate, cost-effective, simple and environmentally friendly way when compared to the reference procedures.
- An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidentsPublication . Gholamizadeh, Kamran; Zarei, Esmaeil; Yazdi, Mohammad; Rodrigues, Matilde A.; Shirmohammadi-Khorram, Nasrin; Mohammadfam, IrajOccupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-being
- ATILGP - Associação de Tradutores e Intérpretes de LGP - Uma História de Sucesso para a ComunicaçãoPublication . Barbosa, Susana; Macedo, Vera; Pereira, Guadalupe; Silva, Joana Filipa; Ferreira, Jorge; Loureiro, Rita; Borges, Francisca; Pedreira, Cláudia; Santos, Mónica; Fernandes, Paula Sofia; Barbosa, Sara; Branco, Susana; Oliveira, Ana; Almeida, Liliana; Sousa, Sara; Brito, Margarida de; Coelho, DinaUm dos propósitos deste livro é proporcionar uma viagem à origem da ATILGP - Associação de Tradutores e Intérpretes de Língua Gestual Portuguesa, contando e registando a sua história e a dos seus associados e, deste modo, contribuir para que as novas gerações de profissionais conheçam o passado e os feitos conquistados em prol da valorização e dignificação da profissão. A virtuosa contribuição dos trabalhos compilados neste livro representa, na sua essência, a forte comunidade de intérpretes que a ATILGP foi capaz de congregar ao longo destes 16 anos e cuja força associativa tem respaldo direto em todos os capítulos. Dá-se voz às questões profissionais, passando pelo processo de criação efetiva de uma associação, da construção de uma identidade coletiva e de uma imagem representativa dos seus valores e ambições, à luta incessante pela defesa por melhores condições de trabalho, por legislação atualizada e por uma sociedade cada vez mais inclusiva, destacando-se a importância de uma liderança sólida ao longo do tempo, que tem sido capaz de motivar os seus membros em torno de princípios e objetivos que unem a classe profissional, as boas práticas como fator determinante para o empoderamento e afirmação profissional, sem descurar o olhar sobre os valores éticos e deontológicos pelos quais a bússola associativa sempre se norteou.
- The effects of the feedback through the "like" feature on brain activity: a systematic reviewPublication . Dores, Artemisa RochaThe excessive use of social media platforms (SM) is a growing concern, especially in more vulnerable populations. Tied to its usage is the need for social rewards, and the “like” feature is shown to activate various brain circuits related to reward processing and continued SM usage.
- The effect of immersive reminiscence therapy on anxiety and depression in people with dementia: a pilot randomized controlled trial using virtual reality headsetsPublication . Soares, Maria; Coelho, Tiago; Portugal, Paula; Coelho, TiagoThis study aims to analyze the effect of reminiscence therapy using immersive virtual reality technology, in comparison with conventional reminiscence therapy, on anxiety and depressive symptoms of people with dementia. This pilot study followed a randomized controlled trial design. A convenience sample of 16 people with dementia was randomly divided in experimental and control groups. Due to health-related factors, 2 participants abandoned the study before completing the intervention. Intervention consisted of 8 biweekly individual reminiscence sessions conducted by trained researchers, in which participants in the experimental group viewed 360o videos of locations with personal relevance considering their life narratives, using virtual reality headsets to promote an immersive experience. Intervention in the control group was similar, except the videos were displayed in a computer monitor. The assessment was carried out before and after the intervention, using the Geriatric Depression Scale (15 items) and the Generalized Anxiety Disorder scale (7 items). Intervention and control groups were compared regarding sociodemographic variables and level of dementia progression at baseline. No statistically significant differences were found. Regarding the comparison of anxiety and depressive symptoms pre- and post-intervention, a slight decrease was observed in both groups, although statistical significance was not reached (p>0.05). The results of the present study do not illustrate an added value regarding the use of immersive stimuli with virtual reality technology, in reminiscence therapy programs with people with dementia. Further research is warranted to better ascertain the cost effectiveness of using these technologies in the nonpharmacological treatment of people with dementia.
- Creation of a fungal library and screening of antimicrobial and anticancer activityPublication . Ferreira, Diogo; Hermida, Lara Areal; Rocha, Ana Catarina; Baylina, Pilar; Sieiro, Carmen; Fernandes, Rúben; BAYLINA MACHADO, PILARAccording to the World Health Organization, cancer and infectious diseases are two of the most problematic diseases nowadays. Cancer kills 10 million people every year and the emergence of resistance to antitumoral drugs is an important medical challenge. At the same time, antimicrobial resistance (AMR) is also a serious threat to human and environmental health. Besides mortality, AMR burdens healthcare services and dampens medical procedures such as surgeries, cancer treatments and other invasive procedures. The development of new drug therapies to fight drug resistance is essential to contest the rising of resistant bacteria and reduction of the effectiveness of antitumoral drugs. Microorganisms have been a major source for natural compounds throughout the years. Fungi, renowned for their ability to produce an array of broad and diverse secondary metabolites, offer a rich resource for drug discovery. We built a collection of fungal species, isolated from chestnuts, sunflower seeds, and chestnut flour, and explored their extracts for potential antimicrobial and anticancer activity. Fungi cultures for secondary metabolite biosynthesis were done in submerged fermentation in Malt Extract broth for 15 days at 26 °C. Liquid-liquid extraction techniques, with ethyl acetate as a solvent, were applied to obtain crude secondary metabolite extracts. Clinical resistant bacteria, yeasts, and prostate cell lines (human prostate epithelial cells – HpepiC; human caucasian prostate adenocarcinoma cells - PC3) were exposed to fungal extracts at a single concentration of 100 µg/mL. Our results so far show several extracts with antimicrobial and/or anticancer activity without decreasing cell viability of non-tumoral cells, showing their potential as therapeutic drugs without possible secondary effects. Although, more studies should be done, and pending fungal identification will allow us to select which extracts will be further investigated to find if the displayed bioactivity could be happening due to unknown natural compounds
- Inking cell blocks improves scanner detection for diagnosis in pathologyPublication . Eloy, Catarina; Neves, Beatriz; Vale, João; Campelos, Sofia; Curado, Mónica; Polónia, AntónioCell blocks may be hard to be totally automatically detected by the scanner (ADS),generating incomplete whole slide images (WSIs), with areas that are not scanned,leading to possible false negative diagnosis. The aim of this study is to test if inkingthe cell blocks helps increasing ADS. Test 1: 15 cell blocks were sectioned, one halfinked black (1HB) and the other inked green (1HG). Each of the halves was individu-ally processed to generate a WSI stained by the H&E. 1HBs and 1HGs had similarscanning time (median 59 s vs. 65 s, p = .126) and file sizes (median 382 Mbvs. 381 Mb, p = .567). The black ink interfered less in the observation (2.2%vs. 44.4%; p < .001) than in the green one. Test 2: 15 cell blocks were sectioned, onehalf inked black (2HB) and the other left unstained/null (2HN). Each of the halveswas individually processed to generate three WSIs—one HE, one periodic-acid Schiff(PAS), and one immunostained by cytokeratin AE1&AE3 (CKAE1AE3). HE and PASWSIs from both 2HN and 2HB groups were all totally ADS and had similar scanningtimes and file sizes. Concerning immunostaining with CKAE1AE3: ADS (46.7%vs. 93.3%; p = .014), median time for scanning (57 s vs. 83 s; p < .001) and file size(178 Mb vs. 338 Mb; p < .001) were reduced significantly in the 2HN group in com-parison with the 2HB. Although increasing scanning time and file size, inking the cellblocks helps increasing ADS after immunostaining, improving the safety and effi-ciency of the digital pathology workflow.
- A Hybrid Deep Learning Model for UAVs Detection in Day and Night Dual VisionsPublication . Noor, Alam; Li, Kai; Ammar, Adel; Koubâa, Anis; Benjdira, Bilel; Tovar, EduardoUnmanned Aerial Vehicle (UAV) detection for public safety protection is becoming a critical issue in non-fly zones. There are plenty of attempts of the UAV detection using single stream (day or night vision). In this paper, we propose a new hybrid deep learning model to detect the UAVs in day and night visions with a high detection precision and accurate bounding box localization. The proposed hybrid deep learning model is developed with cosine annealing and rethinking transformation to improve the detection precision and accelerate the training convergence. To validate the hybrid deep learning model, real-world experiments are conducted outdoor in daytime and nighttime, where a surveillance video camera on the ground is set up for capturing the UAV. In addition, the UAV-Catch open database is adopted for offline training of the proposed hybrid model, which enriches training datasets and improves the detection precision. The experimental results show that the proposed hybrid deep learning model achieves 65% in terms of the mean average detection precision given the input videos in day and night visions.
- Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground ReflectionsPublication . Gaitán, Miguel Gutiérrez; Javanmardi, Gowhar; Robles, RamiroThis work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS.