In this work, the structural transformations of a suboxide vacuum-deposited film of SiO_1.3 composition annealed in an inert atmosphere in a wide temperature range of 100 °C–1100 °C were characterized by the reflection-transmission spectroscopy technique. The experimental spectroscopic data were used to obtain the spectra of the absorption coefficient α(hν) in the absorption edge region of the film. Based on their processing, the dependences of Urbach energy E_U and optical (Tauc) bandgap E_o on the annealing temperature were obtained. An assessment of the electronic band gap (mobility gap) E_g was also carried out. Analysis of these dependences allowed us to trace dynamics of thermally stimulated disproportionation of the suboxide film and the features of the formation of nanocomposites consisting of amorphous and/or crystalline silicon nanoparticles in an oxide matrix.

Molybdenum (Mo) is considered and described as an essential element for living organisms’ development. Until now, no studies have been performed on genes involved in the Mo transporter in ancestral Ipomoea species. This study aimed to identify potential Mo genes in Ipomoea trifida and I. triloba genomes using bioinformatics tools. We identified four Mo transporter genes, two in I. trifida and two in I. triloba. Based on the RNA-seq datasets, we observed that Mo genes are expressed (in silico) and present different mechanisms between the tissues analyzed. The information generated in this study fills missing gaps in the literature on the Mo gene in an important agronomic crop.

This study examined the impact of aluminium doping on the structural, electrical, and magnetic properties of Li_(0.5)Co_(0.75)Al_xFe_(2−x)O₄ spinel ferrites (x =0.15 to 0.60). The samples were synthesised using the sol-gel auto-combustion technique, and they were examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), dielectric measurements, and vibrating sample magnetometry (VSM). All samples possessed a single-phase cubic spinel structure with Fd-3m space group, according to XRD analyses. SEM images showed the creation of homogeneous particles with an average size of about 21 nm. All samples had spinel ferrite phases, confirmed from FTIR spectra. DC electrical conductivity studies showed that the conductivity increased with increasing aluminium content up to x = 0.45 before dropping at x = 0.60. The maximum saturation magnetization value was found at x = 0.45, according to VSM measurements, which demonstrated that the magnetic characteristics were strongly correlated with the amount of aluminium.

Artificial intelligence chatbots can be used to conduct research effectively and efficiently in the fifth industrial revolution. Artificial intelligence chatbots are software applications that utilize artificial intelligence technologies to assist researchers in various aspects of the research process. These chatbots are specifically designed to understand researchers’ inquiries, provide relevant information, and perform tasks related to data collection, analysis, literature review, collaboration, and more. The purpose of this study is to investigate the use of artificial intelligence chatbots for conducting research in the fifth industrial revolution. This qualitative study adopts content analysis as its research methodology, which is grounded in literature review incorporating insights from the researchers’ experiences with utilizing artificial intelligence. The findings reveal that researchers can use artificial intelligence chatbots to produce quality research. Researchers are exposed to various types of artificial intelligence chatbots that can be used to conduct research. Examples are information chatbots, question and answer chatbots, survey chatbots, conversational agents, peer review chatbots, personalised learning chatbots and language translation chatbots. Artificial intelligence chatbots can be used to perform functions such as literature review, data collection, writing assistance and peer review assistance. However, artificial intelligence chatbots can be biased, lack data privacy and security, limited in creativity and critical thinking. Researchers must be transparent and take in consideration issues of informed content and data privacy and security when using artificial intelligence chatbots. The study recommends a framework on artificial intelligence chatbots researchers can use to conduct research in the fifth industrial revolution.

The COVID-19 pandemic has instigated global lockdowns, profoundly altering daily life and resulting in widespread closures, except for essential services like healthcare and grocery stores. This scenario has notably intensified mental health challenges, particularly among children and adolescents. Influenced by a myriad of factors including developmental stages, educational backgrounds, existing psychiatric disorders, and socioeconomic status, the pandemic’s impact extends beyond the immediate health crisis. This paper critically examines the multifaceted effects of the pandemic on mental and physical health across various age groups. It highlights the increased incidence of stress, anxiety, and depression, underscoring the pandemic’s deep psychological footprint. Additionally, the paper explores the societal implications, from altered family dynamics and educational disruptions due to the shift to online learning, to workplace transformations. These changes have led to a mix of adaptive responses and adverse effects, including heightened domestic tensions and mental health issues. The paper also delves into the ethical challenges faced by medical professionals during this crisis, balancing urgent patient care with ongoing medical research and mental health considerations. This analysis aims to provide a comprehensive understanding of the COVID-19 pandemic’s extensive impact on health and society, emphasizing the importance of addressing mental health as a crucial component of the response strategy.