Global burden of type 1 diabetes in adults aged 40-64: trends from 1990 to 2021 and projections to 2036.

Advances in multimodal artificial intelligence models for epilepsy research.

A review of multi-motor coordinated control technologies: advanced strategies, intelligent algorithms, and future trends

As Simulation Engineers and Subject Matter Experts (SMEs), we often focus on solving complex technical challenges within a specific technical or physical domain by leveraging specific simulation approaches. However, to maximize the impact and integration of our work across all the engineering domains that are typically engaged in a project, it's essential to understand Systems Engineering (SE) and Model-Based Systems Engineering (MBSE) practices that are becoming increasingly more important in performing 21st century engineering projects. Elaborating a NAFEMS World Congress (NWC) 2025 presentation and complementing a NAFEMS ASSESS Insights, webinar, this paper aims to minimize the gap between Simulation Engineers/SMEs and the realm of SE/MBSE by offering insights into how SE/MBSE/MBE ideas and practices can enhance collaboration between engineers and improve (modeling & simulation) project outcomes. It shows how the adoption of SE and MBSE concepts and approaches can help to manage complexity, ensure traceability, and integrate simulation models more effectively within larger system architectures and system models. By understanding core SE and MBSE principles, workflows, and tools, Simulation Engineers can improve communication with SE and within cross-functional teams, contribute to system-level requirements and technical concepts, and elevate the impact and value of their work in modern engineering projects. Specifically, insights are provided on the following topics: SE and MBSE, key systems modeling language concepts relevant for simulation engineers such as Analysis, Verification, and TradeStudy, and integration of Engineering Simulation within SE and MBSE Frameworks. Furthermore, an outlook is provided on future trends and the evolving role of simulation engineers in the context of SE & MBSE.

Machine learning applications in nuclear power plant piping inspection: A review of methods, data, and future trends

Current and future trends in lithium-ion battery electrode production machinery: A comprehensive review

Global evolution of research on life cycle assessment: A data-driven visualization of collaboration, frontier identification, and future trend

Microgrids space applications: A comprehensive review and future trends

Molecularly imprinted optical sensors for disease biomarker analysis: current progress and future trends

Smart cold chain logistics for fresh agricultural products: key technologies, challenges, and future trends

Climate change exacerbates the challenge of soil salinity, directly threatening the agricultural yields of staple crops such as wheat. Salinity currently affects at least 20% of total arable land and this figure is expected to worsen depending on future climate change trends. Aiming at improving the salt tolerance of wheat and ensuring future food security, researchers use, for example, multi-omics tools and genome-wide association studies (GWAS) to understand plant stress tolerance, with transcription factors playing a crucial role in regulating stress responses. Here, we describe the use of a next-generation PCR method, Hyperplex PCR (hpPCR), to analyze a 20-plex transcription factor panel focusing on three underexplored wheat transcription factor families-bHLH, C3H, and RAV-to identify their involvement in salt stress. All measured transcription factors, except one of the RAV (related to ABI3 and VP1) targets in shoot tissue, were observed to be downregulated in the presence of 100mM NaCl. Compared to 2-step RT-qPCR, hpPCR measured the same upregulation and downregulation trends for all targets with up to 18 fewer PCR cycles.

This review provides insights into the existing and future research trends in experimental and hypothetical COFs, exploring the critical role of computational chemistry in their design and ubiquitous applications across diverse domains.

Cooling photovoltaic surfaces with vertical or rooftop greenery: a review of mechanisms, key factors, methods and future research trends

Future development trends of sodium-ion batteries: From the perspective of pairing and performance correlation

Applications of starch-based mucoadhesive drug delivery systems in cancer management.

<p>Since its clinical introduction in the 1970s, X-ray computed tomography (CT) has evolved from a simple tomographic imaging tool into a comprehensive imaging platform integrating system architecture, detector technology, and advanced reconstruction algorithms. Early CT systems were limited by slow acquisition speed and restricted clinical use, yet they provided the foundation for cross-sectional medical imaging. In recent years, breakthroughs in multi-source and static scanning configurations, energy-resolving photon-counting detectors (PCDs), and sophisticated reconstruction methods have significantly advanced CT performance in spatial resolution, spectral imaging, and dose efficiency. These developments have not only improved diagnostic accuracy but also expanded CT applications to cardiovascular imaging, oncology, pediatrics, and functional imaging studies. This paper reviews the evolution of CT technology from three perspectives: system architecture, detector technology, and image reconstruction, and further discusses future trends toward intelligent, personalized, and clinically translatable imaging solutions.</p>

Environmental drivers and ecological responses of seaweed litter production and decomposition: A comprehensive review.

Over the past few decades, significant progress has been made in the research of polysaccharides extracted from natural resources, which are often used in functional foods, medicines, cosmetics, and biomedical materials. However, traditional research heavily relies on trial-and-error screening, which is limited by challenges in elucidating structure-function relationships, low preparation efficiency, and poor application adaptability. The integration of artificial intelligence (AI) has provided a critical pathway to overcome these constraints. This review outlines recent AI applications in polysaccharide research, discusses current challenges, and identifies future trends. For polysaccharide extraction, AI employs models such as artificial neural networks and genetic algorithm-backpropagation to optimize processing conditions. Its prediction accuracy often reaches above 0.95, significantly higher than the 0.7-0.8 of traditional response surface methodology models. In practical applications, AI integrates multi-omics data to support personalized polysaccharide scheme design. For instance, graph convolutional networks can correlate structural features with biological activities (e.g., tumor cell inhibition rates and immune cell activation), thereby promoting the development of personalized functional products. However, the field still faces challenges such as inconsistent data quality, limited model interpretability, and difficulties in cross-disciplinary collaboration. Solving these problems is key to advancing AI from a supportive tool to a central driver of innovation in polysaccharide research, with potential impacts on precision medicine, functional foods, and advanced biomaterials.

Advances in sulphur-iron autotrophic denitrification research and exploration of its future application in wastewater treatment: A critical review.

The industrial sector is presently experiencing a significant transformation characterized by the integration of intelligent automation fueled by advancements in Artificial Intelligence (AI). This combination significantly boosts operational efficiency and facilitates data-driven decision-making. Such improvements allow for optimal resource distribution and enhance the accuracy of production planning. This paper intends to present the latest trends and ongoing innovations within the AI domain as it pertains to the manufacturing sector. Additionally, the review examines critical applications of AI in manufacturing, including predictive maintenance, quality assurance, process optimization, supply chain management, robotics and automation, as well as intelligent decision support systems. It also addresses the challenges faced by the manufacturing industry while exploring how AI can help alleviate these issues. Moreover, this study provides an in-depth analysis of recent developments in AI technologies such as explainable AI, collaboration between humans and robots, edge computing, and integration with the Internet of Things (IoT). The review concludes with recommendations that underscore best practices and identify potential collaborative opportunities.