Results 500 resources
Sousa, J. L. D. de. (2021). O Autoconceito no Pré-Escolar em Contexto Educativo Multicultural e Plurilinguίstico=Preschool Children’s Self-concept in a Multicultural and Multilingual Educational Context [University of Saint Joseph]. https://library-opac.usj.edu.mo/cgi-bin/koha/opac-detail.pl?biblionumber=177010&query_desc=an%3A27020
Chau, M., & Lei, W. C. (2021). Nowcasting Gross Domestic Product and Gross Gaming Revenue of Macau: a Theoretical and Conceptual Framework. 113–136. https://www.researchgate.net/publication/362831464_Nowcasting_Gross_Domestic_Product_and_Gross_Gaming_Revenue_of_Macau_a_Theoretical_and_Conceptual_Framework
This paper aimed to build up the theorical and conceptual understanding of future forecasting study of Macau’s GDP and Gross Gaming Revenue (GGR) by co-movement of economic indicators. Macau GDP and GGR showed co-movements with a number of time series economic indicators, including China’s exports and imports, China’s manufacturing PMI, non-manufacturing PMI, China's electricity production growth, share price of some Macau’s gaming operators, etc. These time series data can be found in statistics departments of China, Macau and Hong Kong, stock exchanges, and international organizations such as the International Monetary Fund (IMF), the World Bank, the World Trade Organization (WTO). Burns and Mitchell’s study in 1946 identified co-movements between economic indicators and being further carried out and developed leading, coincident and lagging indicators, which is essential for future econometric models and nowcasting techniques developments to study these co-movements. In particular, with the proper application of nowcasting techniques, future studies can exploit the data of leading and coincident economic indicators to forecast Macau’s GDP and GGR within an acceptable level of error. Since Macau is a “monotown,” where the gaming revenue makes a significant contribution to the economy. The forecasting of gaming revenue is crucial as it aids the gambling and tourism industries in preparing supply and provides information to policymakers to plan for the near future. This research also contributes to understand Macau’s economy by investigating its internal and external economic variables.
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2020). Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering. In Predictive Models for Decision Support in the COVID-19 Crisis (pp. 55–68). Springer International Publishing. https://doi.org/10.1007%2F978-3-030-61913-8_4
Marques, J. A. L., Cortez, P. C., Madeiro, J. P. V., Albuquerque, V. H. C., Fong, S. J., & Schlindwein, F. S. (2020). Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. Journal of Supercomputing, 76(2), 1305–1320. https://doi.org/10.1007/s11227-018-2570-8
Breitzler, L., Lau, I. H., Fonseca, P. J., & Vasconcelos, R. O. (2020). Noise-induced hearing loss in zebrafish: investigating structural and functional inner ear damage and recovery. Hearing Research, 391, 107952. https://doi.org/10.1016/j.heares.2020.107952
Exposure to continuous moderate noise levels is known to impair the auditory system leading to Noise-Induced Hearing Loss (NIHL) in animals including humans. The mechanism underlying noise-dependent auditory Temporary Threshold Shifts (TTS) is not fully understood. In fact, only limited information is available on vertebrates such as fishes, which share homologous inner ear structures to mammals and have the ability to regenerate hair cells. The zebrafish Danio rerio is a well-established model in hearing research providing an unmatched opportunity to investigate the molecular and physiological mechanisms of NIHL at the sensory receptor level. Here we investigated for the first time the effects of noise exposure on TTS and functional recovery in zebrafish, as well as the associated morphological damage and regeneration of the inner ear saccular hair cells. Adult specimens were exposed for 24h to white noise at various amplitudes (130, 140 and 150 dB re. 1 μPa) and their auditory sensitivity was subsequently measured with the Auditory Evoked Potential (AEP) recording technique. Sensory recovery was tested at different times post-treatment (after 3, 7 and 14 days) and compared to individuals kept under quiet lab conditions. Results revealed noise level-dependent TTS up to 33 dB and increase in response latency. Recovery of hearing function occurred within 7 days for fish exposed to 130 and 140 dB noise levels, while fish subject to 150 dB only returned to baseline thresholds after 14 days. Hearing impairment was accompanied by significant loss of hair cells only at the highest noise treatment. Full regeneration of the sensory tissue (number of hair cell receptors) occurred within 7 days, which was prior to functional recovery. We provide first baseline data of NIHL in zebrafish and validate this species as an effective vertebrate model to investigate the impact of noise exposure on the structure and function of the adult inner ear and its recovery process.
Lara, R. A., Breitzler, L., Lau, I. H., Gordillo-Martinez, F., Chen, F., Fonseca, P. J., Bass, A. H., & Vasconcelos, R. O. (2022). Noise-induced hearing loss correlates with inner ear hair cell decrease in larval zebrafish. Journal of Experimental Biology, 225(7), jeb243743. https://doi.org/10.1242/jeb.243743
Anthropogenic noise can be hazardous for the auditory system and wellbeing of animals, including humans. However, very limited information is known on how this global environmental pollutant affects auditory function and inner ear sensory receptors in early ontogeny. The zebrafish (Danio rerio) is a valuable model in hearing research, including investigations of developmental processes of the vertebrate inner ear. We tested the effects of chronic exposure to white noise in larval zebrafish on inner ear saccular sensitivity and morphology at 3 and 5 days post-fertilization (dpf), as well as on auditory-evoked swimming responses using the prepulse inhibition (PPI) paradigm at 5 dpf. Noise-exposed larvae showed a significant increase in microphonic potential thresholds at low frequencies, 100 and 200 Hz, while the PPI revealed a hypersensitization effect and a similar threshold shift at 200 Hz. Auditory sensitivity changes were accompanied by a decrease in saccular hair cell number and epithelium area. In aggregate, the results reveal noise-induced effects on inner ear structure–function in a larval fish paralleled by a decrease in auditory-evoked sensorimotor responses. More broadly, this study highlights the importance of investigating the impact of environmental noise on early development of sensory and behavioural responsiveness to acoustic stimuli.
Caldas, W. L., do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8
Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.
Hun, K. U. (2022). New Great Learning Oneness Epistemic Model for the Greater Peace: A Philosophical Analysis on the Concept of Oneness and its Application with Veganism [University of Saint Joseph]. https://library-opac.usj.edu.mo/cgi-bin/koha/opac-detail.pl?biblionumber=201694&query_desc=an%3A83493
Goncalves, M. V., Marques, J. A. L., Silva, B. R. S., Luther, V., & Hayes, S. (2022). Neuromarketing and Global Branding Reaction Analysis Based on Real-Time Monitoring of Multiple Consumer’s Biosignals and Emotions [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4071297
Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. Multicultural influences and societal conditions are also crucial aspects to consider from a global perspective. Applying neuroscience tools and techniques in international marketing and consumer behavior is an emergent and multidisciplinary field that aims to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. The primary purpose of this research was to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different types of branding advertisements. The non-advertisement exposition was also monitored during the gathering of survey responses at the end of each phase. A feature extraction module was implemented with a data analytics module to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.
Morgan, S. (2021). ‘Navigation for an ocean of interminable scepticism’ revisited: John Henry Newman and the place of theology in the university. International Journal for the Study of the Christian Church, 21(2), 1–15. https://doi.org/10.1080/1474225X.2021.1997030
The place of theology is under threat in the modern university. It is denied a place, except insofar as it is useful in the training of religious professionals or as a phenomenon in its own right, on the grounds that relate to an unscientific scientism that both makes metaphysical assumptions it itself does not recognise as scientific or denies its own epistemological commitments. This article argues that the notion of education in ‘liberal knowledge’ or ‘universal knowledge’, the idea at the heart of John Henry Newman’s The Idea of a University provides a sufficiently robust counter to these assaults on the place of theology proper in the modern university and that refusing such a place to it undermines the claim of universities to use the name at all. It is precisely the uselessness of theology that guarantees its place in the university committed to universal knowledge and universal enquiry.
Lan, K., Liu, L., Li, T., Chen, Y., Fong, S., Marques, J. A. L., Wong, R. K., & Tang, R. (2020). Multi-view convolutional neural network with leader and long-tail particle swarm optimizer for enhancing heart disease and breast cancer detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04769-y
Yan, J., Zhang, B., Zhou, M., Kwok, H. F., & Siu, S. W. I. (2022). Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network. Computers in Biology and Medicine, 147, 105717. https://doi.org/10.1016/j.compbiomed.2022.105717
Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cancers. In this work, we developed Multi-Branch-CNN, a CNN method with multiple input branches for identifying three types of ion channel peptide binders (sodium, potassium, and calcium) from intra- and inter-feature types. As for its real-world applications, prediction models that are able to recognize novel sequences having high or low similarities to training sequences are required. To this end, we tested our models on two test sets: a general test set including sequences spanning different similarity levels to those of the training set, and a novel-test set consisting of only sequences that bear little resemblance to sequences from the training set. Our experiments showed that the Multi-Branch-CNN method performs better than thirteen traditional ML algorithms (TML13), yielding an improvement in accuracy of 3.2%, 1.2%, and 2.3% on the test sets as well as 8.8%, 14.3%, and 14.6% on the novel-test sets for sodium, potassium, and calcium ion channels, respectively. We confirmed the effectiveness of Multi-Branch-CNN by comparing it to the standard CNN method with one input branch (Single-Branch-CNN) and an ensemble method (TML13-Stack). The data sets, script files to reproduce the experiments, and the final predictive models are freely available at https://github.com/jieluyan/Multi-Branch-CNN.
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