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Anthropogenic noise of variable temporal patterns is increasing in aquatic environments, causing physiological stress and sensory impairment. However, scarce information exists on exposure effects to continuous versus intermittent disturbances, which is critical for noise sustainable management. We tested the effects of different noise regimes on the auditory system and behaviour in the zebrafish (Danio rerio). Adult zebrafish were exposed for 24 h to either white noise (150 ± 10 dB re 1 μPa) or silent control. Acoustic playbacks varied in temporal patterns—continuous, fast and slow regular intermittent, and irregular intermittent. Auditory sensitivity was assessed with Auditory Evoked Potential recordings, revealing hearing loss and increased response latency in all noise-treated groups. The highest mean threshold shifts (c. 13 dB) were registered in continuous and fast intermittent treatments, and no differences were found between regular and irregular regimes. Inner ear saccule did not reveal significant hair cell loss but showed a decrease in presynaptic Ribeye b protein especially after continuous exposure. Behavioural assessment using the standardized Novel Tank Diving assay showed that all noise-treated fish spent > 98% time in the bottom within the first minute compared to 82% in control, indicating noise-induced anxiety/stress. We provide first data on how different noise time regimes impact a reference fish model, suggesting that overall acoustic energy is more important than regularity when predicting noise effects.
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This study examined responses from 508 full-time teachers working in inclusive schools in Macao (SAR). The intention was to understand the teachers’ perceptions about their roles and how they responded to inclusive practices in their school. Teachers’ perceived levels of emotional exhaustion and cognitive work engagement were assessed in relation to several professional competencies (self-efficacy with using inclusive instruction, collaborating with parents and paraprofessionals, and managing disruptive behaviours), as well as the organisational variable of role understanding. Regression analysis showed that teachers’ self-efficacy with using inclusive instruction was found to be the most powerful negative predictor of emotional exhaustion; while self-efficacy for managing disruptive behaviours was a positive predictor of teachers’ cognitive work engagement. Teachers’ level of understanding of their role and that of their schools was a negative predictor of emotional exhaustion and a positive predictor of cognitive work engagement. Moreover, it further confirmed that the concept of co-existence between work engagement and burnout can be applied to inclusive teachers. Results were interpreted in relation to management in inclusive schools in Macao and were followed by a discussion on the implications of enhancing inclusive education.
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The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.
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Macau, Macau Business, MAG, MB, MB Featured, Opinion | As Macau rebuilds its post-pandemic economy, one could be forgiven for believing that the good life for all has arrived, as evidenced in the manifest opulence on display in its up-market shopping malls. However, this is not the case; social justice needs attention in Macau.
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Online shopping in Macau has developed rapidly in recent years. And the success of Taobao is significantly hard to not notice. Its’ sales are breaking the record every year. However, there are a lot of negative comments towards Taobao. Various researches and data have shown that live-streaming is one of the biggest contributions towards Taobao’s sales and record breaking. This research aims to investigate deeply to understand how Taobao counters those issues and the role of live-streaming in relation to it. Based on a review of the literature in the relevant areas , qualitative methodology is adopted after thorough considerations. A small sample size of 10 were selected to conduct semi-structured in-depth interviews and the participants agreed to respond to answer the original interview questions and the follow-up questions. Analysis of the responses demonstrated e-customer service is the most influential variable towards repurchase intention. Live-streaming strategy can effectively and directly reduce constomer’s uncertainty of products and increase the efficiency of responsiveness. And product uncertainty and responsiveness speed are variables that impact purchase intention. The result demonstrated live-streaming's effectiveness in combating multiple negative aspects of Taobao and strengthening the positive aspects. On this basis, live-streaming is an impactful method to combat Taobao. In addition, e-service in terms of sufficiency of the staff’s communication skill have been found important towards customer’s satisfaction. A gap related to such an issue has been recommended in the further research recommendations along with other factors or sample groups, which are needed to explore deeply in the future
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The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.
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