Interdependence between the United States (U.S.), European Union (EU) and Asia in the semiconductor industry, driven by specialization, can serve as a preventive measure against disruptions in the global semiconductor supply chain. Moreover, with rising geopolitical tensions, the cost-intensive nature of the semiconductor industry and a slowdown in demand, interdependence and partnership provide countries with opportunities and benefits. Specifically, by analyzing global trade patterns, developing the Interdependence Index within the semiconductor market, and applying the Grubel-Lloyd Index to the U.S., the EU, and Asian countries from 2011 to 2022, our findings reveal that interdependence enhances regional semiconductor supply chains, such as the establishment of semiconductor foundries in the U.S., Japan, and the EU; reduces dependence on a single supplier, such as the U.S. distancing from China; and increases market share in different semiconductor segments, as demonstrated by Taiwan in automobile chips. The evidence indicates that China heavily depends on foreign sources to meet its semiconductor demand, while Taiwan and South Korea specialize as foundry service providers with lower Interdependence Index values. The U.S., with a robust presence in semiconductor manufacturing and design, has a moderate dependence on semiconductor imports, whereas the EU demonstrates a higher level of interdependence because it lacks semiconductor foundries. The stage-specific analyses indicate that the U.S. and the EU rely on Asia for semiconductor devices, while China and Taiwan have a higher dependence on American intermediate inputs and European lithography machines.

Water pollution has become a serious threat to our ecosystem. Water contamination due to human, commercial, and industrial activities has negatively affected the whole world. Owing to the global demanding challenges of water pollution treatments and achieving sustainability, membrane technology has gained increasing research attention. Although numerous membrane materials have focused, the sustainable water purification membranes are most effective for environmental needs. In this regard sustainable, green, and recyclable polymeric and nanocomposite membranes have been developed. Materials fulfilling sustainable environmental demands usually include wide-ranging polyesters, polyamides, polysulfones, and recyclable/biodegradable petroleum polymers plus non-toxic solvents. Consequently, water purification membranes for nanofiltration, microfiltration, reverse osmosis, ultrafiltration, and related filtration processes have been designed. Sustainable polymer membranes for water purification have been manufactured using facile techniques. The resulting membranes have been tested for desalination, dye removal, ion separation, and antibacterial processes for wastewater. Environmental sustainability studies have also pointed towards desired life cycle assessment results for these water purification membranes. Recycling of water treatment membranes have been performed by three major processes mechanical recycling, chemical recycling, or thermal recycling. Moreover, use of sustainable membranes has caused positive environmental impacts for safe waste water treatment. Importantly, worth of sustainable water purification membranes has been analyzed for the environmentally friendly water purification applications. There is vast scope of developing and investigating water purification membranes using countless sustainable polymers, materials, and nanomaterials. Hence, value of sustainable membranes has been analyzed to meet the global demands and challenges to attain future clean water and ecosystem.

Renewable energy is gaining momentum in developing countries as an alternative to non-renewable sources, with rooftop solar power systems emerging as a noteworthy option. These systems have been implemented across various provinces and cities in Vietnam, accompanied by government policies aimed at fostering their adoption. This study, conducted in Ho Chi Minh City, Vietnam investigates the factors influencing the utilization of rooftop solar power systems by 309 individuals. The research findings, analyzed through the Partial least squares structural equation modeling (PLS-SEM) model, reveal that policies encouragement and support, strategic investment costs, product knowledge and experience, perceived benefits assessment, and environmental attitudes collectively serve as predictors for the decision to use rooftop solar power systems. Furthermore, the study delves into mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also furnishes policymakers with evidence to chart new directions for encouraging the widespread adoption of solar power systems.

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.

Photocatalysis, an innovative technology, holds promise for addressing industrial pollution issues across aqueous solutions, surfaces, and gaseous effluents. The efficiency of photodegradation is notably influenced by light intensity and duration, underscoring the importance of optimizing these parameters. Furthermore, temperature and pH have a significant impact on pollutant speciation, surface chemistry, and reaction kinetics; therefore, process optimization must consider these factors. Photocatalytic degradation is an effective method for treating water in environmental remediation, providing a flexible and eco-friendly way to eliminate organic contaminants from wastewater. Selectivity in photocatalytic degradation is achieved by a multidisciplinary approach that includes reaction optimization, catalyst design, and profound awareness of chemical processes. To create efficient and environmentally responsible methods for pollution removal and environmental remediation, researchers are working to improve these components.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.