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There has been an unprecedented increase in online learning worldwide, including in teacher education. However, student lurking can be a common issue, leading to a non-interactive learning environment.

The authors employed a qualitative case study with thematic analysis to examine a novel “self-directed” pre-service teacher online degree module that engaged students in regular peer-feedback, which intended to promote student engagement and interactivity. The research questions were as follows: To what extent did the seminar series represent the principles of self-directed learning and were learning outcomes effective from the process? And, how effective was the use of peer feedback?

The thematic analysis revealed that student progression and course completion was successful, and it represented some principles of self-directed learning; but (a) it cannot be presumed that pre-service teachers are competent in giving (peer) feedback and (b) pre-service teachers may need specific guidance and training for providing competent feedback.

This paper is highly original in respect of its combination of the self-directed learning framework with use of peer feedback, to engage students in an interactive learning environment. The present paper identifies that peer feedback is a powerful tool in online learning; peer feedback can supplement self- and teacher-assessment; but it should not be assumed that pre-service teachers are competent in providing (peer) feedback – pre-service teachers may need specific training in providing feedback.

This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.

This study aims to evaluate the caffeinated drinks, levels of vitamin D and calcium in the blood, and their relationship to bone mineral density (BMD) in osteoporotic women in Al-Ahsa Saudi Arabia.

This study included 85 women over approximately 18 months with ages ranging from 40 to 70 years who were evaluated in the laboratory via blood analysis. Moreover, sociodemographic data and information on health and nutritional awareness were collected via a questionnaire. The BMD was measured by dual-energy X-ray absorptiometry (DXA) and considered osteopenia when the T value total of the lumbar spine or hip was between −1 and −2.5 and osteoporosis < −2.5. Blood levels of vitamin D and calcium were measured via blood analysis.

There were 54.1% and 45.9% of women who suffered from osteoporosis and low BMD, respectively. There was a significant difference in the number of osteoporotic, and low bone mineral mass groups in marital status (p = 0.04), but no difference was found in age and educational level. A significant difference (p = 0.01) between low bone mass and osteoporosis groups in heights. BMD was significantly and negatively correlated with vitamin D in the blood in individuals with osteoporosis (r = −0.358, P = 0.014). In addition, there was a significant negative correlation between calcium in the blood and total samples (r = −0.355, P = 0.0578). There was a negative significant relationship between calcium supplements and BMD in individuals with low BMD (rt = −0.279, P = 0.041). There was a significant association between cola intake with the occurrence of low BMD (p = 0.027), whereas tea drinking was not associated with risk in both groups.

The study indicated that there is a direct relationship between osteoporosis and low bone mass with different variables. This study was conducted to address the lack of research related to the levels of vitamin D and calcium in the blood and their relationship with BMD in women with low BMD and osteoporosis in Al-Ahsa province-KSA.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.