This paper presents an extensive dataset of real-world oscillograms that capture voltage and current signals from electrical substations. The dataset aims to advance research on power system analysis, fault detection, and machine learning-driven relay protection. It includes approximately 50,000 oscillograms recorded with sampling rates up to 8 kHz. A manually annotated subset of 480 oscillograms categorizes events into four groups: noise-dominated signals without deviations; routine equipment operations such as load switching, circuit breaker actions, motor startups, or transformer energization; non-critical deviations compliant with operational standards, including single-phase ground faults or minor voltage dips; critical faults such as short-circuits or voltage collapses requiring immediate relay protection activation. The unannotated part of the data supports self-supervised and unsupervised machine learning methods, enabling tasks like feature extraction, anomaly detection, and latent pattern identification in power networks. The dataset facilitates various applications, including the validation of synthetically trained models, the refinement of adaptive relay protection algorithms, and the development of fault detection and diagnosis systems.
This study presents a novel, to the best of our knowledge, ultra-wideband nanobiosensor based on a double-negative (DNG) metamaterial perfect absorber for early cancer detection through exosomal biomarker analysis. Our biosensor operates across a broad frequency range from 70 THz to 3 PHz, exhibiting near-unity absorption, i.e., exceeding 99%, and angular and polarization insensitivity, i.e., providing polarization-independent absorption across the full spectrum of polarization angles (0° to 90°), ensuring stable performance under both transverse electric (TE) and transverse magnetic (TM) polarized waves. Of particular interest is its performance in the near-infrared (NIR) region (70–400 THz), where the sensor’s DNG characteristics manifest through simultaneously negative permittivity and permeability, enhancing field confinement and sensitivity. This spectral window is especially conducive to label-free, non-invasive detection of circulating exosomes, critical indicators of early stage oncogenesis. The sensor is constructed using a tri-layer metal–insulator–metal (MIM) architecture comprising nickel (Ni) layers and a silicon dioxide (SiO 2 ) dielectric spacer. The design leverages the plasmonic and thermal stability properties of Ni and the low optical attenuation of SiO 2 to achieve optimal absorption and structural robustness. Electromagnetic simulations demonstrate strong electric and magnetic resonances, producing significant near-field enhancements. These improve the detection of subtle dielectric changes associated with exosomal binding events. The sensor maintains high absorption efficiency across oblique incidence angles and various polarization states, making it suitable for real-world biomedical diagnostic applications. By focusing on the NIR regime where tissue transparency and molecular vibrational modes intersect, the proposed biosensor enables the discrimination between cancer-derived exosomes and their normal counterparts, as confirmed through spectral and field distribution analyses. The demonstrated performance highlights the sensor’s promise for next-generation photonic platforms targeting early cancer diagnostics, with potential extension to environmental monitoring and energy harvesting technologies.
Relay Protection and Automation (RPA) devices maintain power system stability by isolating faults using predefined or adaptive thresholds. However, even flexible configurations require manual pre-tuning, limiting their adaptability in dynamic grid environments, such as those with renewable energy integration. Artificial Intelligence (AI) augments RPA functionality by generalizing to new, unseen data, improving fault detection accuracy in transient or noisy scenarios. Our early studies demonstrate that embedded AI systems outperform static thresholds while operating in real time. These systems, implemented via neural networks on System-on-Chip (SoC) platforms with dedicated Neural Processing Units (NPUs), avoid cloud-dependent latency.
Micro, small, and medium-sized enterprises (MSMEs) are a fundamental pillar for Angola’s economic development, particularly within the context of Lubango. However, despite their relevance, many face continuity challenges and difficulties related to the adoption of consistent ethical conduct. In this regard, the present study aims to analyze the relationship between ethical practices and organizational performance among MSMEs in the municipality of Lubango. The research followed a quantitative and correlational approach, focusing on the analysis of the association between ethical and performance variables. The sample consisted of 132 MSME managers selected through convenience sampling, who were surveyed using a structured Likert-scale questionnaire measuring perceptions of ethics and business performance. Data analysis included descriptive statistics, Mann-Whitney and Kruskal-Wallis tests, and multiple regression, with internal reliability confirmed by Cronbach’s alpha coefficient (α > 0.70). The results show that MSMEs in Lubango adopt ethical practices at a moderate level, with internal policies aimed at preventing unethical behaviors. Furthermore, the educational level of managers significantly influences the adoption of ethical practices, whereas gender showed no statistically significant differences. Regression analyses confirmed a positive and significant relationship between ethical practices and MSME performance, indicating that ethics is a key determinant of sustainability and competitiveness among MSMEs in the local context.
Este documento apresenta um guia introdutório sobre a configuração inicial do Auto Multiple Choice (AMC), uma ferramenta open-source voltada para a criação e correção automatizada de exames de múltipla escolha. O tutorial abrange desde a instalação e atualização do software até a configuração dos pacotes necessários para seu funcionamento adequado, com ênfase no uso do WSL para ambientes Windows.
