This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.

Our environment has been significantly impacted by man-made pollutants, primarily due to industries making substantial use of synthetic chemicals, resulting in significant environmental consequences. In this research investigation, the co-precipitation approach was employed for the synthesis of cellulose-based ferric oxide (Fe₂O₃/cellulose) and copper oxide nanoparticles (CuO_x-NPs). Scanning electron microscopy (SEM) analyses were conducted to determine the properties of the newly synthesised nanoparticles. Furthermore, the synthesized nanoparticles were employed for eliminating chromium from aqueous media under various conditions, including temperature, contact time, adsorbent concentration, adsorbate concentration, and pH. Additionally, the synthesised materials were used to recover Cr(VI) ions from real samples, including tap water, seawater, and industrial water, and the adsorptive capacity of both materials was evaluated under optimal conditions. The synthesis of Fe₂O₃/cellulose and CuO_x-NPs proved to be effective, as indicated by the outcomes of the study. 

ZnO nanostructures were obtained by electrodeposition on Ni foam, where graphene was previously grown by chemical vapor deposition (CVD). The resulting heterostructures were characterized by X-ray diffraction and SEM microscopy, and their potential application as a catalyst for the photodegradation of methylene blue (MB) was evaluated. The incorporation of graphene to the Ni substrate increases the amount of deposited ZnO at low potentials in comparison to bare Ni. SEM images show homogeneous growth of ZnO on Ni/G but not on bare Ni foam. A percent removal of almost 60% of MB was achieved by the Ni/G/ZnO sample, which represents a double quantity than the other catalysts proved in this work. The synergistic effects of ZnO-graphene heterojunctions play a key role in achieving better adsorption and photocatalytic performance. The results demonstrate the ease of depositing ZnO on seedless graphene by electrodeposition. The use of the film as a photocatalyst delivers interesting and competitive removal percentages for a potentially scalable degradation process enhanced by a non-toxic compound such as graphene.

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.

Researchers from all over the world have been working tirelessly to combat the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) COVID-19 pandemic since the World Health Organization (WHO) proclaimed it to be a pandemic in 2019. Expanding testing capacities, creating efficient medications, and creating safe and efficient COVID-19 (SARS CoV-2) vaccinations that provide the human body with long-lasting protection are a few tactics that need to be investigated. In clinical studies, drug delivery techniques, including nanoparticles, have been used since the early 1990s. Since then, as technology has advanced and the need for improved medication delivery has increased, the field of nanomedicine has recently seen significant development. PNPs, or polymeric nanoparticles, are solid particles or particulate dispersions that range in size from 10 to 1000 nm, and their ability to efficiently deliver therapeutics to specific targets makes them ideal drug carriers. This review article discusses the many polymeric nanoparticle (PNP) platforms developed to counteract the recent COVID-19 pandemic-related severe acute respiratory syndrome coronavirus (SARS-CoV-2). The primary subjects of this article are the size, shape, cytotoxicity, and release mechanism of each nanoparticle. The two kinds of preparation methods in the synthesis of polymeric nanoparticles have been discussed: the first group uses premade polymers, while the other group depends on the direct polymerization of monomers. A few of the PNPs that have been utilized to combat previous viral outbreaks against SARS-CoV-2 are also covered.

Healthcare mobile applications satisfy different aims by frequently exploiting the built-in features found in smart devices. The accessibility of cloud computing upgrades the extra room, whereby substances can be stored on external servers and obtained directly from mobile devices. In this study, we use cloud computing in the mobile healthcare model to reduce the waste of time in crisis healthcare once an accident occurs and the patient operates the application. Then, the mobile application determines the patient’s location and allows him to book the closest medical center or expert in some crisis cases. Once the patient makes a reservation, he will request help from the medical center. This process includes pre-registering a patient online at a medical center to save time on patient registration. The E-Health model allows patients to review their data and the experiences of each specialist or medical center, book appointments, and seek medical advice.