Engineering最新文献

The in-band full-duplex (IBFD) wireless system is a promising candidate for 6G and beyond, as it can double data throughput and enormously lower transmission latency by supporting simultaneous in-band transmission and reception of signals. Enabling IBFD systems requires a substantial mitigation of a transmitter (Tx)’s strong self-interference (SI) signal into the receiver (Rx) channel. However, current state-of-the-art approaches to tackle this challenge are inefficient in terms of performance, cost, and complexity, hindering the commercialization of IBFD techniques. In this work, we devise and demonstrate an innovative approach to realize IBFD systems that exhibit superior performance with a low-cost and less-complex architecture in an all-passive module. Our scheme is based on meticulously combining polarization-division multiplexing (PDM) with ferromagnetic nonreciprocity to achieve ultra-high isolation between Tx and Rx channels. Such an unprecedented conception has become feasible thanks to a concurrent dual-mode circulator—a new component introduced for the first time—as a key feature of our module, and a dual-mode waveguide that transforms two orthogonally polarized waves into two orthogonal waveguide modes. In addition, we propose a unique passive tunable secondary SI cancellation (SIC) mechanism, which is embedded within the proposed module and boosts the isolation over a relatively broad bandwidth. We report, solely in the analog domain, experimental isolation levels of 50, 70, and 80 dB over 340, 101, and 33 MHz bandwidth at the center frequency of interest, respectively, with excellent tuning capability. Furthermore, the module is tested in two real IBFD scenarios to assess its performance in connection with Tx-to-Rx leakage and modulation error in the presence of a Tx’s strong interference signal.

The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

This paper introduces a systems theory-driven framework to integration artificial intelligence (AI) into traditional Chinese medicine (TCM) research, enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles. Utilizing the System Function Decoding Model (SFDM), the research progresses through define, quantify, infer, and validate phases to systematically explore TCM’s material basis. It employs a dual analytical approach that combines top-down, systems theory-guided perspectives with bottom-up, elements–structure–function methodologies, provides comprehensive insights into TCM’s holistic material basis. Moreover, the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components, proposing two specific AI-driven technical applications. This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.

The incorporation of commercial flame retardants into fiber-reinforced polymer (FRP) composites has been proposed as a potential solution to improve the latter’s poor flame resistance. However, this approach often poses a challenge, as it can adversely affect the mechanical properties of the FRP. Thus, balancing the need for improved flame resistance with the preservation of mechanical integrity remains a complex issue in FRP research. Addressing this critical concern, this study introduces a novel additive system featuring a combination of one-dimensional (1D) hollow tubular structured halloysite nanotubes (HNTs) and two-dimensional (2D) polygonal flake-shaped nano kaolinite (NKN). By employing a 1D/2D hybrid kaolinite nanoclay system, this research aims to simultaneously improve the flame retardancy and mechanical properties. This innovative approach offers several advantages. During combustion and pyrolysis processes, the 1D/2D hybrid kaolinite nanoclay system proves effective in reducing heat release and volatile leaching. Furthermore, the system facilitates the formation of reinforcing skeletons through a crosslinking mechanism during pyrolysis, resulting in the development of a compact char layer. This char layer acts as a protective barrier, enhancing the material’s resistance to heat and flames. In terms of mechanical properties, the multilayered polygonal flake-shaped 2D NKN plays a crucial role by impeding the formation of cracks that typically arise from vulnerable areas, such as adhesive phase particles. Simultaneously, the 1D HNT bridges these cracks within the matrix, ensuring the structural integrity of the composite material. In an optimal scenario, the homogeneously distributed 1D/2D hybrid kaolinite nanoclays exhibit remarkable results, with a 51.0% improvement in mode II fracture toughness (G_IIC), indicating increased resistance to crack propagation. In addition, there is a 34.5% reduction in total heat release, signifying improved flame retardancy. This study represents a significant step forward in the field of composite materials. The innovative use of hybrid low-dimensional nanomaterials offers a promising avenue for the development of multifunctional composites. By carefully designing and incorporating these nanoclays, researchers can potentially create a new generation of FRP composites that excel in both flame resistance and mechanical strength.

Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several alternatives beyond the famous ChatGPT. However, training, fine-tuning, and updating such intelligent chatbots consume substantial amounts of electricity, resulting in significant carbon emissions. The research and development of all intelligent LLMs and software, hardware manufacturing (e.g., graphics processing units and supercomputers), related data/operations management, and material recycling supporting chatbot services are associated with carbon emissions to varying extents. Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact. In this work, we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots. Based on a life-cycle and interaction analysis of these phases, we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints. While anticipating the enormous potential of this advanced technology and its products, we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.

The prevalence of metabolic-dysfunction-associated steatotic liver disease (MASLD) is alarmingly high; it is estimated to affect up to a quarter of the global population, making it the most common liver disorder worldwide. MASLD is characterized by excessive hepatic fat accumulation and is commonly associated with comorbidities such as obesity, dyslipidemia, and insulin resistance; however, it can also manifest in lean individuals. Therefore, it is crucial to develop effective therapies for this complex condition. Currently, there are no approved medications for MASLD treatment, so there is a pressing need to investigate alternative approaches. Extensive research has characterized MASLD as a multifaceted disease, frequently linked to metabolic disorders that stem from dietary habits. Evidence suggests that changes in the gut microbiome play a fundamental role in the development and progression of MASLD from simple steatosis to steatohepatitis and even hepatocellular carcinoma (HCC). In this review, we critically examine the literature on the emerging field of gut-microbiota-based therapies for MASLD and metabolic-dysfunction-associated steatohepatitis (MASH), including interventions such as fecal microbiota transplantation (FMT), probiotics, prebiotics, short-chain fatty acids, antibiotics, metabolic pathway targeting, and immune checkpoint kinase blockade.