With the progress of information technology, especially the widespread use of artificial intelligence technology, it has shown an important role in promoting economic and social development. Art and design in universities is a new discipline that combines modern technology with humanities and art. Only by emphasizing the development of science and technology, adapting to the requirements of the times, and closely integrating humanities and art with science and technology, can we gradually expand the educational channels for cultivating composite and innovative talents. Effectively organizing different types of scientific research activities, building a sound and comprehensive education system, plays an important role in adjusting teaching relationships, innovating teaching models, enhancing students' professional and comprehensive qualities, and improving their academic performance and employment competitiveness.

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.

This study employs a transfer matrix, dynamic degree, stability index, and the PLUS model to analyze the spatiotemporal changes in forest land and their driving factors in Yibin City from 2000 to 2022. The results reveal the following: (1) The land use in Yibin City is predominantly characterized by cultivated land and forest land (accounting for over 95% of the total area). The area of cultivated land initially increased and then decreased, while forest land continued to decline and construction land expanded significantly. The rate of forest land loss has slowed (with the dynamic degree decreasing from −0.62% to −0.04%), and ecosystem stability has improved (the F-value increased from 2.27 to 2.9). The conversion of cultivated land to forest land is the primary driver of forest recovery, whereas the conversion of forest land to cultivated land is the main cause of reduction; (2) cultivated land is concentrated in the central and northeastern regions, while forest land is distributed in the western and southern mountainous areas. Construction land is predominantly located in urban areas and along transportation routes. Areas of forest land reduction are mainly found in the central and southern regions with rapid economic development, while areas of forest land increase are concentrated in high-altitude zones or key ecological protection areas. Stable forest land is distributed in the western and southern ecological conservation zones; (3) changes in forest land are primarily influenced by annual precipitation, elevation, and distance to rivers. Road accessibility and GDP have significant impacts, while slope, annual average temperature, and population density exert moderate influences. Distance to railways, aspect, and soil type have relatively minor effects. The findings of this study provide a scientific basis for the sustainable management of forest resources and ecological conservation in Yibin City.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

Water splitting, the process of converting water into hydrogen and oxygen gases, has garnered significant attention as a promising avenue for sustainable energy production. One area of focus has been the development of efficient and cost-effective catalysts for water splitting. Researchers have explored catalysts based on abundant and inexpensive materials such as nickel, iron, and cobalt, which have demonstrated improved performance and stability. These catalysts show promise for large-scale implementation and offer potential for reducing the reliance on expensive and scarce materials. Another avenue of research involves photoelectrochemical (PEC) cells, which utilize solar energy to drive the water-splitting reaction. Scientists have been working on designing novel materials, including metal oxides and semiconductors, to enhance light absorption and charge separation properties. These advancements in PEC technology aim to maximize the conversion of sunlight into chemical energy. Inspired by natural photosynthesis, artificial photosynthesis approaches have also gained traction. By integrating light-absorbing materials, catalysts, and membranes, these systems aim to mimic the complex processes of natural photosynthesis and produce hydrogen fuel from water. The development of efficient and stable artificial photosynthesis systems holds promise for sustainable and clean energy production. Tandem cells, which combine multiple light-absorbing materials with different bandgaps, have emerged as a strategy to enhance the efficiency of water-splitting systems. By capturing a broader range of the solar spectrum, tandem cells optimize light absorption and improve overall system performance. Lastly, advancements in electrocatalysis have played a critical role in water splitting. Researchers have focused on developing advanced electrocatalysts with high activity, selectivity, and stability for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). These electrocatalysts contribute to overall water-splitting efficiency and pave the way for practical implementation.