Search Results (1,197)

Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. Full article

The large-scale integration of volatile and intermittent renewables necessitates greater flexibility in the power system. Improving this flexibility is key to achieving a high proportion of renewable energy consumption. In this context, the scientific selection of energy storage technology is of great significance for the construction of new power systems. From the perspective of life cycle cost analysis, this paper conducts an economic evaluation of four mainstream energy storage technologies: lithium iron phosphate battery, pumped storage, compressed air energy storage, and hydrogen energy storage, and quantifies and compares the life cycle cost of multiple energy storage technologies. On this basis, a three-dimensional multi-energy storage comprehensive evaluation indicator system covering economy, technology, and environment is constructed. The improved grade one method and entropy weight method are used to determine the comprehensive performance, and the fuzzy comprehensive evaluation method is used to carry out multi-attribute decision-making on the multi-energy storage technology in the source, network, and load scenarios. The results show that pumped storage and compressed air energy storage have significant economic advantages in long-term and large-scale application scenarios. With its fast response ability and excellent economic and technical characteristics, the lithium iron phosphate battery has the smallest score change rate (15.2%) in various scenarios, showing high adaptability. However, hydrogen energy storage technology still lacks economic and technological maturity, and breakthrough progress is still needed for its wide application in various application scenarios in the future. Full article

The report examines food availability and demand in the Anthropocene era, exploring the connections between global population growth and carrying capacity through an extended version of Cohen’s Condorcet concept. It recalls the super-Malthus and Verhulst-type scalings, matched with the recently introduced analytic relative growth rate. It focuses particularly on the ongoing Fifth Industrial Revolution (IR) and its interaction with the concept of a sustainable civilization. In this context, the significance of innovative food preservation technologies that can yield high-quality foods with health-promoting features, while simultaneously increasing food quantities and reducing adverse environmental impacts, is discussed. To achieve this, high-pressure preservation and processing (HPP) can play a dominant role. High-pressure ‘cold pasteurization’, related to room-temperature processing, has already achieved a global scale. Its superior features are notable and are fairly correlated with social expectations of a sustainable society and the technological tasks of the Fifth Industrial Revolution. The discussion is based on the authors’ experiences in HPP-related research and applications. The next breakthrough could be HPP-related sterilization. The innovative HPP path, supported by the colossal barocaloric effect, is presented. The mass implementation of pressure-related sterilization could lead to milestone societal, pro-health, environmental, and economic benefits. Full article

Bamboo gene science has witnessed significant advancements over the past two decades, driven by breakthroughs in gene cloning, marker-assisted breeding, sequencing, gene transformation, and gene editing technologies. These developments have not only enhanced our understanding of bamboo’s genetic diversity and adaptability but also provided critical tools for its genetic improvement. Compared to other crops, bamboo faces unique challenges, including its long vegetative growth cycle, environmental dependency, and limited genetic transformation efficiency. Then, the launch of China’s “Bamboo as a Substitute for Plastic” initiative in 2022, supported by the International Bamboo and Rattan Organization, has opened new opportunities for bamboo gene science as well as for bamboo production systems. This policy framework has spurred research into bamboo genetic regulation, fiber-oriented recombination, and green separation technologies, aiming to develop sustainable alternatives to plastic. Future research directions include overcoming bamboo’s environmental limitations, improving genetic transformation efficiency, and deciphering the mechanisms behind its flowering. By addressing these challenges, bamboo genetic science can enhance its economic and ecological value, contributing to global sustainability goals and the “dual-carbon” strategy. Full article

Background/Objectives: Traditional hearing aid noise reduction algorithms offer no additional benefit in noisy situations for bimodal cochlear implant (CI) users with a CI in one ear and a hearing aid (HA) in the other. Recent breakthroughs in deep neural network (DNN)-based noise reduction have improved speech understanding for hearing aid users in noisy environments. These advancements could also boost speech perception in noise for bimodal CI users. This study investigated the effectiveness of DNN-based noise reduction in the HAs used by bimodal CI patients. Methods: Eleven bimodal CI patients, aged 71–89 years old, were fit with a Phonak Audéo Sphere Infinio 90 HA in their non-implanted ear and were provided with a Calm Situation program and Spheric Speech in Loud Noise program that uses DNN-based noise reduction. Sentence recognition scores were measured using AzBio sentences in quiet and in noise with the CI alone, hearing aid alone, and bimodally with both the Calm Situation and DNN HA programs. Results: The DNN program in the hearing aid significantly improved bimodal performance in noise, with sentence recognition scores reaching 79% compared to 60% with Calm Situation (a 19% average benefit, p < 0.001). When compared to the CI-alone condition in multi-talker babble, the DNN HA program offered a 40% bimodal benefit, significantly higher than the 21% score seen with the Calm Situation program. Conclusions: DNN-based noise reduction in HA significantly improves speech understanding in noise for bimodal CI users. Utilization of this technology is a promising option to address patients’ common complaint of speech understanding in noise. Full article

Direct air capture (DAC), as a complementary strategy to carbon capture and storage (CCS), offers a scalable and sustainable pathway to remove CO₂ directly from the ambient air. This study presents a detailed evaluation of the amine-functionalised metal-organic framework (MOF) sorbent, mmen-Mg₂(dobpdc), for DAC using a temperature–vacuum swing adsorption (TVSA) process. While this sorbent has demonstrated promising performance in point-source CO₂ capture, this is the first dynamic simulation-based study to rigorously assess its effectiveness for low-concentration atmospheric CO₂ removal. A transient one-dimensional TVSA model was developed in Aspen Adsorption and validated against experimental breakthrough data to ensure accuracy in capturing both the sharp and gradual adsorption kinetics. To enhance process efficiency and sustainability, this work provides a comprehensive parametric analysis of key operational factors, including air flow rate, temperature, adsorption/desorption durations, vacuum pressure, and heat exchanger temperature, on process performance, including CO₂ purity, recovery, productivity, and specific energy consumption. Under optimal conditions for this sorbent (vacuum pressure lower than 0.15 bar and feed temperature below 15 °C), the TVSA process achieved ~98% CO₂ purity, recovery over 70%, and specific energy consumption of about 3.5 MJ/KgCO₂. These findings demonstrate that mmen-Mg₂(dobpdc) can achieve performance comparable to benchmark DAC sorbents in terms of CO₂ purity and recovery, underscoring its potential for scalable DAC applications. This work advances the development of energy-efficient carbon removal technologies and highlights the value of step-shape isotherm adsorbents in supporting global carbon-neutrality goals. Full article

High-speed trains have revolutionized modern transportation with their exceptional speeds, yet the essence of this technological breakthrough resides in the train’s wheels. These components are engineered to endure extreme mechanical stresses while ensuring high safety and reliability. In this paper, we selected the rim and web as representative components of the wheel and conducted a comprehensive and systematic study on their microstructure and mechanical properties. The wheels are typically produced through integral forging. To improve the mechanical performance of the wheel/rail contact surface (i.e., the tread), the rim is subjected to surface quenching or other heat treatments. This endows the rim with strength and hardness second only to the tread and lowers its ductility. This results in a more isotropic structure with improved fatigue resistance in low-cycle and high-cycle regimes under rotating bending. The web connects the wheel axle to the rim and retains the microstructure formed during the forging process. Its strength is lower than that of the rim, while its ductility is slightly better. The web satisfies current property standards, although the microstructure suggests further optimization may be achievable through heat treatment refinement. Full article

The widespread and persistent occurrence of micropollutants—such as pesticides, pharmaceuticals, heavy metals, personal care products, microplastics, and per- and polyfluoroalkyl substances (PFAS)—has emerged as a critical environmental and public health concern, necessitating the development of highly sensitive, selective, and field-deployable detection technologies. Microfluidic sensors, including biosensors, have gained prominence as versatile and transformative tools for real-time environmental monitoring, enabling precise and rapid detection of trace-level contaminants in complex environmental matrices. Their miniaturized design, low reagent consumption, and compatibility with portable and smartphone-assisted platforms make them particularly suited for on-site applications. Recent breakthroughs in nanomaterials, synthetic recognition elements (e.g., aptamers and molecularly imprinted polymers), and enzyme-free detection strategies have significantly enhanced the performance of these biosensors in terms of sensitivity, specificity, and multiplexing capabilities. Moreover, the integration of artificial intelligence (AI) and machine learning algorithms into microfluidic platforms has opened new frontiers in data analysis, enabling automated signal processing, anomaly detection, and adaptive calibration for improved diagnostic accuracy and reliability. This review presents a comprehensive overview of cutting-edge microfluidic sensor technologies for micropollutant detection, emphasizing fabrication strategies, sensing mechanisms, and their application across diverse pollutant categories. We also address current challenges, such as device robustness, scalability, and potential signal interference, while highlighting emerging solutions including biodegradable substrates, modular integration, and AI-driven interpretive frameworks. Collectively, these innovations underscore the potential of microfluidic sensors to redefine environmental diagnostics and advance sustainable pollution monitoring and management strategies. Full article

As semiconductor manufacturing advances into the angstrom-scale era characterized by three-dimensional integration, conventional metrology technologies face fundamental limitations regarding accuracy, speed, and non-destructiveness. Although optical spectroscopy has emerged as a prominent research focus, its application in complex manufacturing scenarios continues to confront significant technical barriers. This review establishes three concrete objectives: To categorize AI–optical spectroscopy integration paradigms spanning forward surrogate modeling, inverse prediction, physics-informed neural networks (PINNs), and multi-level architectures; to benchmark their efficacy against critical industrial metrology challenges including tool-to-tool (T2T) matching and high-aspect-ratio (HAR) structure characterization; and to identify unresolved bottlenecks for guiding next-generation intelligent semiconductor metrology. By categorically elaborating on the innovative applications of AI algorithms—such as forward surrogate models, inverse modeling techniques, physics-informed neural networks (PINNs), and multi-level network architectures—in optical spectroscopy, this work methodically assesses the implementation efficacy and limitations of each technical pathway. Through actual application case studies involving J-profiler software 5.0 and associated algorithms, this review validates the significant efficacy of AI technologies in addressing critical industrial challenges, including tool-to-tool (T2T) matching. The research demonstrates that the fusion of AI and optical spectroscopy delivers technological breakthroughs for semiconductor metrology; however, persistent challenges remain concerning data veracity, insufficient datasets, and cross-scale compatibility. Future research should prioritize enhancing model generalization capability, optimizing data acquisition and utilization strategies, and balancing algorithm real-time performance with accuracy, thereby catalyzing the transformation of semiconductor manufacturing towards an intelligence-driven advanced metrology paradigm. Full article

Trichomes—microscopic appendages on the plant epidermis—play vital roles as both protective barriers and specialized biosynthetic factories. Acting as the first line of defense against environmental stressors, they also produce a wide range of pharmaceutically valuable secondary metabolites. This mini-review highlights recent advances in understanding the development, structure, and function of trichomes, with a focus on glandular secretory trichomes (GSTs) in key species such as Artemisia annua and Solanum lycopersicum. We explore how insights from these systems are driving innovation in plant synthetic biology, including modular genetic engineering and metabolic channeling strategies. These breakthroughs are paving the way for scalable, plant-based platforms to produce high-value compounds. By integrating molecular mechanisms with emerging technologies, this review outlines a forward-looking framework for leveraging trichomes in sustainable agriculture, natural product discovery, and next-generation biomanufacturing. Full article

Physics-informed neural networks (PINNs) have emerged as a transformative methodology integrating deep learning with scientific computing. This review establishes a three-dimensional analytical framework to systematically decode PINNs’ development through methodological innovation, theoretical breakthroughs, and cross-disciplinary convergence. The contributions include threefold: First, identifying the co-evolutionary path of algorithmic architectures from adaptive optimization (neural tangent kernel-guided weighting achieving 230% convergence acceleration in Navier-Stokes solutions) to hybrid numerical-deep learning integration (5× speedup via domain decomposition) and second, constructing bidirectional theory-application mappings where convergence analysis (operator approximation theory) and generalization guarantees (Bayesian-physical hybrid frameworks) directly inform engineering implementations, as validated by $72\%$ cost reduction compared to FEM in high-dimensional spaces ($p<0.01,n=15$ benchmarks). Third, pioneering cross-domain knowledge transfer through application-specific architectures: TFE-PINN for turbulent flows ($5.12\pm 0.87\%$ error in NASA hypersonic tests), ReconPINN for medical imaging ($▵\mathrm{SSIM}=+0.18\pm 0.04$ on multi-institutional MRI), and SeisPINN for seismic systems ($0.52\pm 0.18$ km localization accuracy). We further present a technological roadmap highlighting three critical directions for PINN 2.0: neuro-symbolic, federated physics learning, and quantum-accelerated optimization. This work provides methodological guidelines and theoretical foundations for next-generation scientific machine learning systems. Full article

Lignin, the most abundant renewable aromatic biopolymer on Earth, is rapidly emerging as a powerful enabler of next-generation sustainable technologies. This review shifts the focus to the latest industrial breakthroughs that exploit lignin’s multifunctional properties across energy, agriculture, healthcare, and environmental sectors. Lignin-derived carbon materials are offering scalable, low-cost alternatives to critical raw materials in batteries and supercapacitors. In agriculture, lignin-based biostimulants and controlled-release fertilizers support resilient, low-impact food systems. Cosmetic and pharmaceutical industries are leveraging lignin’s antioxidant, UV-protective, and antimicrobial properties to create bio-based, clean-label products. In water purification, lignin-based adsorbents are enabling efficient and biodegradable solutions for persistent pollutants. These technological leaps are not merely incremental, they represent a paradigm shift toward a materials economy powered by renewable carbon. Backed by global sustainability roadmaps like the European Green Deal and China’s 14th Five-Year Plan, lignin is moving from industrial residue to strategic asset, driven by unprecedented investment and cross-sector collaboration. Breakthroughs in lignin upgrading, smart formulation, and application-driven design are dismantling long-standing barriers to scale, performance, and standardization. As showcased in this review, lignin is no longer just a promising biopolymer, it is a catalytic force accelerating the global transition toward circularity, climate resilience, and green industrial transformation. The future of sustainable innovation is lignin-enabled. Full article

Additive manufacturing (AM) has brought significant breakthroughs to the construction sector, such as the ability to fabricate complex geometries, enhance efficiency, and reduce both material usage and construction waste. However, several challenges must still be addressed to fully transition from conventional construction practices to innovative and sustainable green alternatives. This study investigates the use of non-cementitious traditional mixtures for green construction applications through 3D printing using Liquid Deposition Modeling (LDM) technology. To explore the development of mixtures with enhanced physical and mechanical properties, natural pine and cypress wood shavings were added in varying proportions (1%, 3%, and 5%) as sustainable additives. The aim of this study is twofold: first, to demonstrate the printability of these eco-friendly mortars that can be used for conservation purposes and overcome the challenges of incorporating bio-products in 3D printing; and second, to develop sustainable composites that align with the objectives of the European Green Deal, offering low-emission construction solutions. The proposed mortars use hydrated lime and natural pozzolan as binders, river sand as an aggregate, and a polycarboxylate superplasticizer. While most studies with bio-products focus on traditional methods, this research provides proof of concept for their use in 3D printing. The study results indicate that, at low percentages, both additives had minimal effect on the physical and mechanical properties of the tested mortars, whereas higher percentages led to progressively more significant deterioration. Additionally, compared to molded specimens, the 3D-printed mortars exhibited slightly reduced mechanical strength and increased porosity, attributable to insufficient compaction during the printing process. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

The relentless emergence of RNA viruses poses a perpetual threat to global public health, necessitating continuous efforts in surveillance, discovery, and understanding of these pathogens. This review provides a comprehensive update on recent advancements in RNA virus discovery, highlighting breakthroughs in technology and methodologies that have significantly enhanced our ability to identify novel viruses across diverse host organisms. We explore the expanding landscape of viral diversity, emphasizing the discovery of previously unknown viral families and the role of zoonotic transmissions in shaping the viral ecosystem. Additionally, we discuss the potential implications of RNA virus discovery on disease emergence and pandemic preparedness. Despite remarkable progress, current challenges in sample collection, data interpretation, and the characterization of newly identified viruses persist. Our ability to anticipate and respond to emerging respiratory threats relies on virus discovery as a cornerstone for understanding RNA virus evolution. We address these challenges and propose future directions for research, emphasizing the integration of multi-omic approaches, advanced computational tools, and international collaboration to overcome barriers in the field. This comprehensive overview aims to guide researchers, policymakers, and public health professionals in navigating the intricate landscape of RNA virus discovery, fostering a proactive and collaborative approach to anticipate and mitigate emerging viral threats. Full article