Universidade Dili

Voltammetry is a promising technique for estimating heavy metal pollution such as Cadmium (Cd2+) and Lead (Pb2+) in water. Its advantages include rapid analysis and cost-effectiveness over established methods like Atomic Absorption Spectroscopy (AAS) and Inductively Coupled Plasma - Mass Spectrometry (ICP-MS). However, current analysis often depends only on peak data, ignoring the rest of the voltammetric signal which may contain useful information that could potentially improve measurement accuracy. To address this limitation, the Cross-Attention Feature Fusion (CAFF) network is proposed to analyze Cyclic Voltammetry (CV) signals acquired using a 3-electrode setup with a Glassy Carbon Electrode (GCE) as the working electrode, Platinum as the counter, and Ag/AgCl as the reference. Unlike standard self-attention mechanisms or simple concatenation fusion methods, CAFF introduces a novel dual-stream architecture that dynamically captures the inter-dependencies between raw CV signals and extracted peak data—an approach previously unexplored in electrochemical sensing. The model integrates an Improved Beluga Whale Optimization (IBWO) algorithm that automatically determines the optimal hyperparameters, resulting in a more robust model. Robustness was assessed using Chemically-Informed Degradation Simulation (CIDS). As a result, the proposed CAFF-IBWO model demonstrated superior performance, achieving R2 values of 0.97 for Cd2+ and 1.00 for Pb2+. It also significantly reduced the Mean Absolute Percentage Error (MAPE) by 65.79% for Cd2+ and 72.50% for Pb2+ compared to single-input attention networks. Furthermore, CAFF-IBWO exhibited remarkable resilience against signal degradation, maintaining stable prediction performance across varying noise conditions. While the study focuses specifically on Cd2+ and Pb2+ and requires further validation for broader generalization, the demonstrated performance is highly promising. These findings underscore the model’s potential for real-world environmental sensing applications.

Introduction: Most of the data on metastatic breast cancer (MBC) originate from hospital-based studies or controlled trials involving specific populations and controlled treatments. In this respect, few population-based studies have analyzed the profile of MBC in low- and middle-income countries. Objective: To describe the epidemiological profile of women with de novo MBC using data from a population-based cancer registry (PBCR). Methods: An ecological study conducted in a PBCR in Goiânia, Brazil, for the 1995–2011 period. Women with MBC at diagnosis were included and the standardized incidence rate and annual percent change (APC) over the period were calculated. The women’s clinical and demographic characteristics and data on diagnosis and treatment were analyzed. Results: Overall, 5,289 cases of breast cancer were registered in the Goiânia PBCR, 277 (5.2%) at metastatic stage. The adjusted incidence was 8.9/100,000 in 1995 and 6.04/100,000 in 2011 (APC: 1.1; p=0.6). Most of the patients (70.3%) were receiving care within the public healthcare system and the mean age at diagnosis was 54.7±14.5 years. Additional data for a subpopulation of 156 patients were identified at the city’s two main treatment centers. According to immunohistochemistry, 53 women (67.1%) had hormone receptor-positive cancer. Of these, 14.0% (6/43) received endocrine therapy as first-line systemic treatment and 48.5% (17/35) as second-line treatment. A comparison of clinical data between the 1995–2003 and 2004–2011 periods revealed no significant differences in age, histological grade, locoregional staging, the presence of symptoms at diagnosis, or in treatment. Conclusion: This study population of women with MBC consisted predominantly of locally advanced tumors and the luminal-like subtype. The incidence rate of MBC in Goiânia did not change over the 17-year period. Most cases received chemotherapy as first-line systemic treatment irrespective of the tumor phenotype.

Este relato técnico é relevante por documentar a experiência prática de desenvolvimento de um software de gestão de plataformas de Internet das Coisas (IoT) por uma equipe de trainees em formação. O objetivo foi apresentar o impacto desse projeto, tanto para os trainees quanto para a empresa, considerando o contexto da IoT. A metodologia utilizada foi qualitativa e descritiva, baseada na experiência do gestor de projetos e em entrevistas semiestruturadas com o diretor e os trainees. Os resultados demonstram que os trainees adquiriram habilidades técnicas e comportamentais significativas, enquanto o projeto atendeu às expectativas da empresa em termos de valor, especificações e usabilidade. A pesquisa contribui com a literatura sobre IoT e gestão de equipes, oferecendo um estudo de caso prático para organizações e gestores de projetos. Como limitação, o estudo se baseou em entrevistas e na perspectiva do gestor do projeto, sem análises qualitativas ou quantitativas mais aprofundadas.

Collaborative business processes increasingly span multiple autonomous entities, relying on asynchronous message exchanges. While Business Process Model and Notation (BPMN) 2.0 is the standard for modeling these interactions, it lacks formal semantics to verify structural correctness, leading to potential deadlocks during execution. Existing centralized verification methods often face state explosion issues or fail to model the asynchronous nature of message buffering. This paper proposes a formal verification framework by transforming inter-organizational BPMN models into Petri Nets, introducing explicit buffer places to capture decoupled interactions. The approach utilizes Linear Temporal Logic (LTL) over a Reachability Graph to verify liveness properties. The method was validated using a synthetic dataset of 30 test cases with varying complexity (20-150 nodes). Experimental results show a Precision of 0.96 and Recall of 0.98, with an average detection time of 0.15 seconds $(\mathbf{S D}=\mathbf{0. 0 2})$. Limitations include the reliance on centralized model availability.

Bothrops jararacussu (BjsuV) snake bites cause severe local and systemic effects that are counteracted with antivenom that is ineffective in preventing damage to muscle tissue and amputations. Photobiomodulation (PBM) has emerged as a promising adjunct anti-inflammatory therapy. This study investigated whether PBM can modulate macrophage functions via TGF-β1 following exposure to BjsuV. Cell survival, migration, and phagocytosis of macrophages were evaluated after treatments with BjsuV, TGF-β1, a TGF-β inhibitor-SB431542, and PBM (660 nm red or 810 nm near-infrared laser at 10 mW/cm2, 400 and 500 s, 4 J/cm2, and 5 J/cm2, 7.5 pJ/cm2, 1.7ɇ). BjsuV-reduced macrophage survival and migration but increased phagocytosis that was significantly counteracted by PBM treatments. These observations suggest the anti-inflammatory effects of PBM in macrophages contribute to its therapeutic potential in mitigating venom-induced multiple signaling pathways. These mechanistic insights could improve the rigor and reproducibility of future clinical PBM anti-inflammatory treatments.