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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.

This paper aims to exploit shape-memory polymers as self-healable materials. The underlying mechanism involved the thermal transitions as well as the enrichment of the healing reagents and the closure of the crack surfaces due to shape recovery. The multi-stimuli-triggered shape memory composite was capable of self-healing under not only direct thermal but also electrical stimulations.

The shape memory epoxy polymer composites comprising the AgNWs and poly (ε-caprolactone) were fabricated by dry transfer process. The morphologies of the composites were investigated by the optical microscope and scanning electron microscopy (SEM). The electrical conduction and the Joule heating effect were measured. Furthermore, the healing efficiency under the different stimuli was calculated, whose dependence on the compositions was also discussed.

The AgNWs network maintained most of the pathways for the electrons transportation after the dry transfer process, leading to a superior conduction and flexibility. Consequently, the composites could trigger the healing within several minutes, as applied with relatively low voltages. It was found that the composites having more the AgNWs content had better electrically triggered performance, while 50 per cent poly (ε-caprolactone) content endowed the materials with max healing efficiency under thermal or electrical stimuli.

The findings may greatly benefit the application of the intelligent polymers in the fields of the multifunctional flexible electronics.

Most studies have by far emphasized on the direct thermal triggered cases. Herein, a novel, flexible and conductive shape memory-based composite, which was capable of self-healing under the thermal or electrical stimulations, has been proposed.