Language is fundamental to human communication, allowing individuals to express and exchange ideas, thoughts, and emotions. In early childhood, some children experience communication disorders that impede their ability to articulate words correctly, posing significant challenges to their learning and development. This issue is exacerbated in developing countries, where limited resources and a lack of technological tools hinder access to effective speech therapy. Traditional speech therapy remains vital, but the latest technological advancements have introduced robotic assistants to enhance therapy for communication disorders. Despite their potential, these technologies are often inaccessible in developing regions due to high production costs and a lack of sustainable manufacturing models. For these reasons, this paper presents “FONA,” a robotic assistant that employs rule-based expert systems to provide tactile, auditory, and visual stimuli. FONA supports children aged 3 to 6 in speech therapy by delivering exercises such as syllable production, word formation, and pictographic storytelling of various phonemes. Notably, FONA was successfully tested on children with cochlear implants, reducing the number of sessions required to produce isolated phonemes. The paper also introduces an innovative analysis of the Make To Order (MTO) manufacturing system for producing FONA in developing countries. This analysis explores two key perspectives: collaborative networks and entrepreneurship, offering a sustainable production model. In a pilot experiment, FONA significantly improved children’s attention spans, increasing the period by 17 min. Furthermore, the economic analysis demonstrates that producing FONA through collaborative networks can significantly reduce costs, making it more accessible to institutions in developing countries. The findings suggest that the project is viable for a five-year period, providing a sustainable and effective solution for addressing communication disorders in children.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

Nanoparticle V₂O₅ is prepared by the measurement of X-ray diffraction (XRD) and atomic force microscopy (AFM) analyses. The crystallite size = 19.59 nm, optical energy gap = 2.6 eV, an average particle size of 29.58 nm and, RMS roughness of ~6.8 nm. Also, Fourier transformer infrared spectrophotometer (FTIR) showed a porous free morphology with homogeneity and uniformity on the sample surface. The film surface exhibited no apparent cracking and, the grains exhibited large nicely separated conical columnar growth combined grains throughout the surface with coalescence of some columnar grains at a few places. The fabrication of a thin film of V₂O₅ NPs/PSi heterojunction photodetector was characterized and investigated.

This article aims to analyze the form of promotion and its policies in increasing tourists in Indonesia. Ecotourism is one of the nation’s vital sectors that can improve the economy, preserve nature and introduce local culture. Sadly, today, ecotourism has yet to be discovered by the public, which cumulatively causes much damage. Therefore, The Ministry of Tourism and Creative Economy is tasked with educating the public in order to create a collaborative synergy. This article uses qualitative research with a phenomenological approach. The primary data sources in this study are Twitter netizens’ tweets and The Ministry of Tourism and Creative Economy ‘s social media accounts. At the same time, the secondary data used in this study are articles, books, and reportage. Then the data will be analyzed through several procedures, namely, 1) data matrix, 2) data reduction, 3) coding, and 4) conclusion drawing. The results showed that the messages conveyed by The Ministry of Tourism and Creative Economy regarding ecotourism were good, and the intensity was relatively high. Public conversations about ecotourism have also been substantive in accordance with ideal ecotourism. Unfortunately, the intensity of The Ministry of Tourism and Creative Economy ‘s message is not accompanied by the intensity of ecotourism conversations in the community. However, the Ministry of Tourism and Creative Economy has issued a communication policy in promoting ecotourism in Indonesia. This aims to benefit the wider community, such as community productivity, economic improvement, and the introduction of local culture to the international community.

With the rapid development of modern AI painting, Chinese university fine arts education is facing numerous challenges and opportunities. This paper analyzes the impact of modern AI painting on traditional art creation and its implications for student skill development. Additionally, it explores the key areas where Chinese university fine arts education needs to transform, including curriculum, teaching methods, and teacher training, while proposing corresponding strategies.