Producción científica y docente
Producción científica reciente
Advanced Wafer Hotspot Detection through Image Segmentation and Stacked Model
The wafer map is a data visualization of a thin semiconductor fabric made of crystalline silicon, such as defects or test results. The wafer map is a base for creating electronic coordinate circuits and photovoltaic cells. During the wafer map production, any fault results in a product failure. The wafer map faults are undetectable to the naked eye, which is a big challenge. Hotspot detection in wafer maps is significantly important to evaluate the manufacturing process and. improve product yield. The hotspot detection in the wafer maps is the primary aim of this research. A novel wafer map hotspot detector (WHD) is proposed based on three stack fully connected conventional neural network layers and a dense layer. Data augmentation uses the segmented images of the wafers to build the proposed model. The proposed model is evaluated through several evalua-tion parameters and state-of-the-art studies comparative analysis. The proposed model achieved a 94% training and 90% testing performance accuracy for hotspot detection and shows better results than existing approaches. This study helps semiconductor engineers improve wafer manufacturing designs and efficiency in the semiconductor industry.
Valorization of Purple Prickly Pear Peel By‐Products: Antiproliferative and Pro‐Apoptotic Effects on Human Colorectal Cancer Cells HCT116
Opuntia ficus-indica peel is known to possess antioxidant, anti-inflammatory, and anticancer activities and currently is discarded or used for animal feeding. Within this context, the aim of this work was to evaluate the antiproliferative and pro-apoptotic effect of purple prickly pear peel extract (PPE) on the human colon adenocarcinoma cancer cell line (HTC116). The methanolic extract of PPE was characterized in terms of betalain and polyphenols as well as total antioxidant capacity. Cell viability, apoptosis induction, cell cycle arrest, and reactive oxygen species (ROS) production assays were performed. Important proteins and genes related to proliferation and apoptosis were determined. PPE represents a good source of bioactive compounds with a high antioxidant capacity. Cell viability was reduced gradually by PPE treatments, with lower effects in nontumorigenic cells. Compared to the control group, a significant induction of apoptosis as well as cell cycle arrest in the sub-G1 phase and ROS production was observed in PPE-treated cells. Furthermore, the treatment induced the overexpression of p53 at protein levels and upregulated the mRNA expression of pro-apoptotic BAX, CASP9, BID, and CYCS, along with the significant decrease of anti-apoptotic BCL2 gene expression. Simultaneously, cyclin D1 and CDK4 gene expression were significantly decreased, while p21 increased considerably. The treatment also induced the downregulation of Her2 and PI3K at protein levels and caused the suppression of PI3KCA and mTOR expression at gene levels. Overall, these findings suggested that PPE has potential anticancer effects against human colon adenocarcinoma progression.
Infrared thermography to assess fatigue, injury risk factors and recovery in soccer: a systematic review of original studies
Background: Recovery after a training session or match is a key factor in injury prevention and sports performance. The purpose of this systematic review was to analyze and consolidate the available scientific evidence from the main databases on the use of infrared thermography in the assessment of fatigue, injury risk factors, and recovery in soccer players.Methods: The literature search was conducted following the PRISMA guidelines and the PICOS model until June 30, 2025, in the main scientific databases (ScienceDirect, EMBASE, Web of Science (WOS), Cochrane Library, SciELO, MEDLINE/PubMed, SPORTDiscus, and Scopus). The risk of bias and methodological quality were assessed using the Cochrane Handbook guidelines and the PEDro scale.”Results: The initial literature search yielded a total of 510 records. After applying the inclusion and exclusion criteria, the final sample consisted of 20 studies, which were of high methodological quality. The results showed the effects of infrared thermography in assessing fatigue, identifying injury risk factors, and monitoring recovery processes in soccer players. The studies also systematically reported the characterization of the population, the assessment methods used, the variables analyzed, the methodological design, the main results, and the effects of the intervention.Conclusions: Infrared thermography shows promise as a valid, reliable, and non-invasive tool for assessing skin temperature, reflecting temperature changes in response to physiological processes. It allows for the analysis of structural or metabolic fatigue and thermal asymmetries. Therefore, thermography could be used to design individualized recovery protocols.
Liquorice alters adipocyte–breast cancer cell crosstalk by modulating oxidative stress and suppressing aromatase and renin–angiotensin signalling
Obesity is recognised to be a risk factor for breast cancer since adipose tissue influences the tumour microenvironment. This study aims to investigate the effect of the secretome of 3T3-L1 adipocytes untreated or treated with liquorice root extract (LRE), containing flavonoids, phenolic acids, and saponins on MCF-7 breast cancer cells. By treating adipocytes with LRE, the secretion of certain pro-tumorigenic factors like IGFBP-6, resistin, and VEGF was reduced. MCF-7 cells exposed to conditioned medium from LRE-treated adipocytes exhibited an increase in reactive oxygen species levels, downregulation of the Nrf2 antioxidant pathway, and increased autophagy. Those conditions reduced cell viability, migration, and colony formation. Additionally, there was downregulation of genes associated with oestrogen signalling and tumour-related processes, including CYP19A1 (aromatase), ERα, Her2, and components of the renin–angiotensin system (RAS). These findings suggest that LRE can modulate the adipocyte secretome to influence breast cancer cell behaviour under obesity-related in vitro conditions.
A Hybrid Temporal-spectral Load Forecasting Model with Static Context Fusion for Smart Cities
Accurate short-term electricity load forecasting is essential for reliable and efficient smart city energy management, particularly in environments characterized by high-dimensional, heterogeneous, and noisy multivariate signals. However, existing forecasting models often struggle to simultaneously capture nonlinear temporal dependencies, multi-scale periodicity, and static contextual influences within a unified framework. To address this challenge, this study proposes a hybrid deep learning architecture that integrates Bidirectional Long Short-Term Memory (BiLSTM) for temporal modeling, an additive attention mechanism for adaptive time-step weighting, Fast Fourier Transform (FFT)-based frequency residual learning for periodicity extraction, and embedding-based static feature fusion for contextual representation. The model is evaluated on the ISO-NE Smart City Energy Dataset for next-hour electricity load forecasting using a two-week input window (336 hours). Experimental results demonstrate that the proposed hybrid framework significantly improves predictive accuracy, achieving an RMSE of 25.51 kW and an R of 0.9905, outperforming recurrent, convolutional, and transformer-based baselines under identical evaluation settings. Ablation analysis confirms that temporal attention and frequency-domain residual modeling contribute substantially to performance gains. These findings indicate that joint temporal–spectral modeling combined with static contextual fusion provides a robust and effective solution for complex smart-city electricity forecasting tasks.