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This study empirically investigates the long-run and interactive effect of Chinese foreign direct investment (CFDI) on Africa's industrialization process.

The authors employed industry and manufacturing value-added (% GDP) as the dependent variables and applied the two-step GMM and panel-corrected standard errors' (PCSE) techniques involving a panel of 49 African countries from 2003 to 2020.

The industry value-added (% GDP) results show that the presence of CFDI propels industrial productivity by contributing to value-addition in the short and long run. Moreover, the study shows that the magnitude of the CFDI effect on industrialization is pronounced in the short-run when it is associated with labor and natural resources. This result reveals efficiency-seeking behavior of CFDI and the CFDI-Africa industrialization nexus is not primarily resource-driven. More importantly, the authors found human capital, electricity and political stability, as primary factors that magnify CFDI's effect on industrialization in the short and long run.

This study is the first to use macro-level data to empirically investigate and find the significant effect of CFDI on Africa's industrialization in the long run. More importantly, the authors investigated channels through which CFDI magnifies industrialization in Africa in the short and long run.

The objective of this study is to examine the various factors that exert an influence on the green economy. This study also investigates the impact of foreign direct investment (FDI) on the Malaysian economy, specifically focusing on its position as a mediator. This research also examines the correlation between FDI and its influence on the contemporary green economy.

The authors employed quantitative methodologies and a self-administered survey to evaluate data and derive a definitive conclusion. The result was constructed using SPSS and SEM-PLS as the analytical software.

The study reveals that technological advancement, investment country and government policy significantly and positively affect the green economy, catalyse SDG goals and restructure the economy in better shape.

The current empirical research bridges the research gap in the context of technology advancement in government policy from emerging economies by exploring important factors, proposing their impact on the performance of the green economy, and empirically testing those hypothesized relationships. This study deciphers that FDI influences the green economy, where the investment country plays a significant role. Also, for a graphical presentation of this abstract, see the online appendix.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.