A model for the development of programming courses to promote the participation of young women in STEM

There is a gender gap in science, technology, engineering, and mathematics (STEM), and this is a global problem that affects society. However, it is worth pointing out that the gap is not uniform in all STEM fields. Women’s underrepresentation is more marked in physics, engineering, and computer science fields. Nowadays, the labor market is becoming more competitive, technology-based and demands a diverse workforce. Therefore, it is important to continue promoting the participation of women in STEM, and the universities play a leading role in it. Previous research has shown that early learning experiences in STEM can show female students that they can succeed in this fields. This paper describes a model for developing programming courses for pre-university students to promote the participation of young women in STEM programs. The course was developed in one week, 25 students (16 girls and 9 boys) participated. The instructors of the course were four female professors. The programming language was Python, and the methodology used case-based learning. Both instructors and students gave positive comments on their experience in the course. The proposed model, including instruments, learning resources, and methodology, can be replicated and adapted to be used even in other learning field

Modified Spatio-Temporal Matched Filtering for Brain Responses Classification

In this article, we apply the method of spatio-temporal filtering (STF) to electroencephalographic (EEG) data processing for brain responses classification. The method operates similarly to linear discriminant analysis (LDA) but contrary to most applied classifiers, it uses the whole recorded EEG signal as a source of information instead of the precisely selected brain responses, only. This way it avoids the limitations of LDA and improves the classification accuracy. We emphasize the significance of the STF learning phase. To preclude the negative influence of super–Gaussian artifacts on accomplishment of this phase, we apply the discrete cosine transform (DCT) based method for their rejection. Later, we estimate the noise covariance matrix using all data available, and we improve the STF template construction. The further modifications are related with the constructed filters operation and consist in the changes of the STF interpretation rules. Consequently, a new tool for evoked potentials (EPs) classification has been developed. Applied to the analysis of signals stored in a publicly available database, prepared for the assessment of modern algorithms aimed in EPs detection (in the frames of the 2019 IFMBE Scientific Challenge), it allowed to achieve the second best result, very close to the best one, and significantly better than the ones achieved by other contestants of the challeng

Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of suffering stroke. Some people with AF do not have symptoms, so, its diagnosis can be difficult, especially in early stages of the disease. In this paper, we propose the use of the spatio-Temporal filter (STF) to characterize atrial activity in ECG recordings and distinguish between normal sinus rhythm (NSR) and atrial arrhythmias. This method allows the effective detection of P waves when they are synchronized with QRS complexes. The distances from the QRS complexes to the detected P waves are characterized by seven dispersion metrics that are used as inputs to three clustering algorithms. The results show classification accuracy of up to 98.88% of NSR and atrial arrhythmias

Classification of Cognitive Evoked Potentials for ADHD Detection in Children using Recurrence Plots and CNNs

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood-onset condition characterized by difficulty paying attention and hyperactivity. The diagnosis of ADHD is made from psychological tests and electroencephalography (EEG). However, patient cooperation is necessary, which is a challenge with ADHD children. This work proposes a method for classification of ADHD and control cases from cognitive event-related potentials using recurrence plots and deep learning. A total of 44 children were included in this study (22 children with ADHD and 22 case controls). The signals were processed by a high-pass filter to eliminate DC components, wavelets transform with six decomposition levels, and synchronized averaging for each of the six channels (F3, AF3, F4, AF4, F7 and F8). Subsequently, the recurrence plot of each of the processed signals was obtained and used as inputs for two convolutional neural networks (CNN). The proposed models showed accuracies of 69.44% and 77,78%. © 2021 IEE

Impact of emotional states on the effective range of electric vehicles

Over the last decade, a large interest in reducing transportation dependence on fossil fuels as well as the cost reduction in battery technologies, have driven the electric cars market uptake. However, information is scarce about factors that affect the driving range. Besides the battery’s capacity, other factors may affect the overall vehicle’s range, for instance: driving behavior, fluctuations in temperature, number of battery cycles, etc. Accordingly, this paper proposes an approach to evaluate the impact of emotions and driving behavior on the range of electric cars using physiological signals and vehicle performance features. This work was developed in three stages. During the first stage, the heart rate and galvanic skin response of 20 volunteers were recorded from biosensors. The vehicle’s data was obtained from a driving simulator. Afterward, the driving profile was used as an input source to simulate an object-oriented electric vehicle model to estimate the driving range. Finally, during the third stage, feature selection techniques and subject-dependent classifiers were evaluated using metrics such as the accuracy and the area under the curve. Support-vector machines with radial kernel and tree-bagged models provided the best global performance with the bio-signals and driving performance subsets to discriminate between calm and aggressive driving. Results showed that driving behavior could be evaluated from physiological and vehicle features. Furthermore, the subjects’ statements showed that users’ beliefs, thoughts, and prior social contexts influence the way they perceive driving behavior. Reductions in the range of up to 68% when driving aggressively compared to a calm manner were found. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature

A small vocabulary database of ultrasound image sequences of vocal tract dynamics

This paper presents a new database consisting of concurrent articulatory and acoustic speech data. The articulatory data correspond to ultrasound videos of the vocal tract dynamics, which allow the visualization of the tongue upper contour during the speech production process. Acoustic data is composed of 30 short sentences that were acquired by a directional cardioid microphone. This database includes data from 17 young subjects (8 male and 9 female) from the Santander region in Colombia, who reported not having any speech pathology

Automated Atrial Fibrillation Detection by ECG Signal Processing: A Review

Cardiovascular diseases are the main cause of death in the world, according to the World Health Organization. Among them, ischemic heart disease is at the top, followed by a stroke. Several studies have revealed that atrial fibrillation (AF), which is the most common cardiac arrhythmia, increases up to five fold the overall risk of stroke. As AF can be asymptomatic, approximately 20% of the AF cases remain undiagnosed. AF can be detected by analyzing electrocardiography records. Many studies have been conducted to develop automatic methods for AF detection. This paper reviews some of the most relevant methods, classified into three groups: analysis of heart rate variability, analysis of the atrial activity, and hybrid methods. Their benefits and limitations are analyzed and compared, and our beliefs about where AF automatic detection research could be addressed are presented to improve its effectiveness and performance. © 2021 by Begell House, Inc

Guidance and support of women in engineering programs at Universidad Tecnologica de Bolivar

Gender equality is one of the sustainable development goals. In STEM areas, it is a requirement to expand and improve women's careers. To increase the participation of women in STEM it is not enough to attract more young women to these programs. It is necessary to provide orientation during the academic programs and ensure safe and rewarding environments so that women can succeed in their studies. The Faculty of Engineering of Universidad Tecnológica de Bolívar has gender gaps in most of its programs and several strategies are being implemented to overcome this situation. This paper analyzes five factors to guide and support women in engineering programs: academic success, protection of women, scholarships and financial assistance, international mobility, and leadership. We present several activities and strategies developed in the last three years in these five factors. The impacts of these strategies cannot be evaluated yet, but we expect that they can improve the well-being of women in engineering and contribute to the reduction of the gender gaps. © 2023 IEEE

Detection and Analysis of ERPs for Social Cognition Evaluation

This paper describes an approach for elicitation, acquisition, and analysis of event-related potentials (ERPs) for social cognition evaluation. We used images of emotional content that were classified into three groups according to their valence: pleasant, unpleasant, and neutral. An application for stimuli generation based on the emotional oddball paradigm (EOP) was developed, and a commercial wireless EEG headset was used for signal acquisition. The ERPs of 13 volunteers for the three types of stimuli were obtained and analyzed to extract the N100 and P300 components. The results show increased amplitudes in ERP components due to unpleasant stimuli and longer latencies observed in neutral stimuli. © 2022 IEEE

Towards gender equality in engineering programs : A case study

The low participation of women in STEM fields is considered a critical issue in our society. We analyzed the student population by gender of the school of engineering at Universidad TecnolÓgica de Bolívar. We found that the share of female first-time students shows a decreasing trend since 2015 and was only 24% in 2020. Gender gaps are wider in electrical, electronic, systems, mechanic, and mechatronic engineering. We also observed that females have lower access to engineering programs, especially in the last three years. The effects of the COVID-19 pandemic have been observed as a reduction of the share of enrolled students vs applicants. In order to increase the participation of women in the programs with higher gender gaps, we developed several activities in 2020 specially designed for secondary students with the participation of female instructors as role models