Upon virus entry into rice cells, facilitated by insect vectors, the viral CP is recognized and bound by the RING-type ubiquitin ligase RBRL, which subsequently activates its E3 ubiquitin ligase activity. This activation results in the ubiquitination and subsequent degradation of NINJA3, a known inhibitor of the jasmonic acid (JA) signaling pathway. Degradation of NINJA3 alleviates the inhibitor’s repression of the JA signaling pathway, facilitating pathway activation, which leads to the upregulation of genes responsible for JA biosynthesis and the expression of downstream antiviral factors, including AGO18. This mechanism also inhibits viral replication through enhanced RNA interference, ultimately enabling rice plants to mount a robust antiviral defense.
China’s electric vehicle (EV) boom will generate an unprecedented volume of battery waste, yet over 70% of end-of-life EV batteries currently flow to informal recyclers operating outside regulated channels. Low market entry barriers, high profit margins, and weak regulatory enforcement enable these informal operators to outcompete licensed enterprises, posing a major obstacle to China’s sustainable energy transition. By coupling the Global Change Analysis Model with dynamic material flow analysis, we project that retired EV battery volumes will reach 22.39 million tons by 2040 under the carbon neutrality scenario, representing 112-fold increases from the 2020 level. To address this escalating crisis, we urge coordinated actions among all stakeholders, including legislating extended producer responsibility, classifying end-of-life batteries as hazardous waste, raising market entry thresholds, supporting research and development for low-cost recycling technologies, and implementing ownership-based classification of retired batteries.
Geothermal energy is a sustainable power source with considerable potential to facilitate net-zero emissions targets. China possesses extensive deep geothermal reserves, which could play a pivotal role in advancing its national energy transition. This paper reviews the principal approaches for exploiting deep geothermal resources, with emphasis on direct-use applications, power generation, and energy storage. Key technical and economic challenges are identified, including underground thermal imbalance, wellbore corrosion and scaling, mineralization reactions with CO2, co-products extraction, brine reinjection blockage, and the advancement of drilling and stimulation technologies. Policy and regulatory constraints on geothermal development in China are critically examined and compared with established frameworks in leading geothermal-producing countries. Based on the insights, recommendations are proposed to advance deep geothermal development in China, including ① the establishment of a specific regulatory framework for geothermal exploration and development; ② enhanced investment in foundational research and the development of core technologies; ③ introduction of geothermal-specific fiscal policies; and ④ prioritization of the strategic implementation of geothermal projects across different regions.
Environmental input-output analysis suggests that we use one-third of all metals to produce machinery and equipment (ME) and that their production causes 5% of greenhouse gas emissions globally. Yet, our empirical understanding of material use and emissions associated with ME remains limited, making it the least researched major aspect of material consumption. Machines are not represented explicitly in climate change mitigation models and there is little research considering mitigation opportunities related to ME. Meanwhile the practice and potential for circular material flows, which have dynamic interactions with machinery, have yet to be explored. ME is a very diverse category and so economic statistics and input-output models are essential for a holistic understanding. Mitigation, however, can only be understood through bottom-up engineering research. We identify data sources for future empirical research and suggest how to combine these. Future demand for ME can in part be foreseen by assuming that lower-income countries will use machines to increase their productivity to levels seen in high-income countries. Additional demand will arise from the introduction of autonomous machines, service robots, and artificial intelligence in workplaces and homes. We describe knowledge gaps and outline research questions important for anticipating the future requirements for machines and their potential contributions as both causes of and solutions to climate change and resource overconsumption.
Effective identification and assessment of various energy transition risks are essential for ensuring energy security. This study conducts a systematic review of the literature on energy transition risk assessment, with three principal objectives: ① establishing a standardized risk taxonomy, ② analyzing the characteristics of current assessment methodologies, and ③ identifying the priority research directions. First, energy transition risks are structured into two categories: implementation risks and consequential risks. Subsequently, assessment methodologies are categorized into five methodological groups: the indicator approach, probabilistic risk assessment approach, econometric approach, simulation approach, and hybrid approach. This analysis reveals a growing scholarly focus on consequential risks, particularly those affecting the energy system and economic system. The evolution of research shows a progression from macro-level analyses to micro-level investigations, accompanied by methodological advancement from simple indicator approach to the more sophisticated computational models. Future research may prioritize investigating multi-risk interactions and developing comprehensive assessment frameworks to address the complex risk landscape faced by diverse stakeholders in the energy transition.
Artificial intelligence (AI) is reshaping the operation and management of energy pipelines that transport oil, natural gas, hydrogen, and carbon dioxide. This review provides a critical synthesis of current developments, highlighting how AI is being applied across pipeline life cycle stages—from route design and construction quality assurance to operation, maintenance, and eventual decommissioning. Recent advances in deep learning, transformer architectures, graph neural networks, and physics-informed models demonstrate the ability to detect anomalies, optimize multiphase flows, and support predictive maintenance with greater adaptability and robustness than conventional methods. Case studies illustrate how industrial operators are deploying AI platforms to improve efficiency, reduce environmental impact, and enhance system resilience. Alongside these opportunities, the review identifies systemic risks, including data bias, algorithmic opacity, cybersecurity vulnerabilities, and overreliance on automation, and discusses emerging mitigation strategies such as synthetic data generation, explainable AI, defense-in-depth frameworks, and mixed-initiative human-machine collaboration. Looking ahead, the field is moving toward multi-source heterogeneous data fusion, few-shot and transfer learning for data-scarce environments, and causal inference for decision support. AI applications in hydrogen and CO2 pipelines, though nascent, offer promising directions for building safer and more sustainable infrastructures. This review underscores the need for continued interdisciplinary efforts to balance innovation with reliability, ensuring that AI becomes a trusted enabler of next-generation pipeline systems in the global energy transition.
Rising geopolitical tensions present significant risks to national energy security, coinciding with a critical moment in the global energy transition. Securing stable energy availability has thus grown increasingly vital for pursuing nations’ climate targets. Deep decarbonization of the energy system remains a cornerstone of China’s long-term modernization agenda but is hindered by challenges like mitigating oil supply risks. External oil supply of China is exposed to high risk due to potential interruption in global chokepoints such as Strait of Hormuz and Strait of Malacca. This study therefore investigates how the ongoing energy transition will reshape China’s long-term oil security landscape and its short-term resilience to such supply disruptions. In this study, we employed our self-developed integrated assessment model (IAM), called IAMET, to quantify future energy development pathways and evaluate China’s oil security by integrating IAM with a forward-looking assessment framework. System reliance is used to simulate oil security under oil supply disruptions and the enhancement space of resilience are estimated. Results indicate that: ① By 2060, China’s oil demand will be around 200-300 million tons under carbon neutrality constraint, reducing the country’s oil import dependency ratio from the current 72% to 41%-60%, while oil security will face acute risks in the next 10-15 years. ② Energy transition will help to enhance oil supply resilience. Under a simulated disruption of 39%-66% of overseas oil imports, oil supply resilience will be “moderate” under carbon neutrality scenario. ③ Strategic petroleum reserves and demand rationing policies have different temporal effects on oil supply resilience enhancement. By adjusting energy demand structure, resilience will rise sharply in the early phase with greater possibility to belong to “good” zone, while current oil strategic stock has larger effects in the later phase as the oil demand shrink. This study underscores the growing role of IAM in evaluating oil security and emphasizes that reducing oil consumption through demand-side strategies can effectively complement supply-side actions to safeguard national energy security.
Decarbonization and energy transition in modern energy systems require integrated tools that can analyze complex supply pathways, optimize infrastructure choices, and evaluate policy impacts. This study presents the algorithm for transportation pathways and patterns through pipeline network (ATP3), an open-source framework combining high‐resolution supply‐chain traceability, cost- and emissions‐aware network optimization, and scenario simulation. ATP3 reconstructs real-world flows by matching supply and demand nodes across an entire pipeline network, integrating the entropy weight method (EWM)-based allocation and a minimum-cost flow formulation with scenario‐driven computational simplification. In a California power generation case study, ATP3 accurately identified 154 processing-to-plant supply routes and 134 upstream field linkages. In addition, when using the EWM-based 40% baseline allocation, the model determined that 90.53% of natural gas used for electricity was imported—primarily from Arizona (48.66%), Oregon (27.43%), and Nevada (14.44%)—with Arizona supplying the largest single external volume (1034.51 MMscf·d−1, here MMscf is million standard cubic feet, and 1 MMscf ≈ 28 317 m3). In‐state production accounted for only 201.37 MMscf·d−1 (9.47%). Meanwhile, EWM allocation reduced transportation costs by 2.76% compared to uniform allocation by favoring geographically proximate sources. These results demonstrate the ability of ATP3 to bridge granular infrastructure mapping with system‐level planning, offering a robust and versatile platform for life‐cycle assessment, infrastructure planning, and policy evaluation across power, transportation, and industrial sectors. The continuously updated resource is available via the GitHub repository.
Sector-specific climate actions are widely recognized for their air pollution co-benefits. However, uncertainties in cross-sector climate action may weaken or reverse these co-benefits. This study quantified the potential negative impacts of uncertain climate actions in the passenger car (PC) and power sectors on air pollutant (AP) emissions by integrating a fleet model with a power plant model under various infrastructure-level transition pathway combinations. Our results show that uncertainties in cross-sector climate actions, particularly when fleet electrification lags behind the car sector’s ideal transition pathway across all power sector transition scenarios, are projected to lead to a maximum increase of ∼120% in nitrogen oxide emissions and ∼635% in particulate matter emissions from the power and PC sectors in 2050, compared to maintaining the 2020 action levels. Additionally, the indirect AP emissions from the electricity use of electric cars are reallocated to provinces that rely heavily on coal-fired power, such as Inner Mongolia, Shanxi, Shandong, and Guizhou, along the power supply chain, thereby leading to significant losses in air pollution reduction co-benefits. We highlight the necessity of shifting from the current single-sector-oriented climate-mitigation policy framework to a cross-sector-integrated perspective. Specifically, it is crucial to reduce car use demand and enhance AP emission control for coal plants in the near term, while accelerating renewable energy transition in the power sector as a long-term strategy to effectively enhance air pollution control co-benefits in response to uncertainties during policy implementation.
Tight gas reservoirs are promising candidates for CO2 geological sequestration. However, the fracturing treatments used for production may compromise the integrity of the caprock and increase the risk of gas leakage. This study examines the impact mechanisms of different fracturing schemes on caprock safety during CO2 sequestration in tight reservoirs to optimize sequestration strategies and provide theoretical support. Using a developed CO2 sequestration model, we systematically evaluate the leakage characteristics of caprock under various fracturing schemes. For the first time, this study introduces the use of the Analytically Modified Embedded Discrete Fracture Model (AEDFM) to describe the flow interactions between fractures and the matrix during long-term CO2 sequestration. Through an in-depth analysis of critical factors such as the different mechanisms of fracture parameters, sand concentration, flow rate, and fracturing fluid viscosity, we comprehensively evaluate how these fracturing schemes influence caprock safety for a case study. Validation results confirm that the AEDFM method successfully overcomes the low accuracy exhibited by the Embedded Discrete Fracture Model (EDFM) in simulating early-time transient flow. The proposed CO2 sequestration model demonstrates strong agreement with production history matching performed using commercial software, affirming its reliability and practical applicability. The results from a case study show that increasing the number, half-length, and height of hydraulic fractures in fracturing designs will increase the risk of CO2 leakage, thereby compromising caprock safety. If hydraulic fractures penetrate the caprock, the risk of CO2 leakage through the top caprock markedly increases. In scenarios where fractures do not penetrate the caprock, the volume of leaked CO2 over a 100-year sequestration period remains below 5% of the injected gas. However, leakage can rise to as much as 12% if caprock penetration occurs. Furthermore, increasing the flow rate and sand concentration and decreasing the viscosity lead to larger fracture dimensions, further increasing the risk of CO2 leakage and diminishing caprock safety. An investigation of various mechanisms affecting caprock safety reveals that heterogeneity-controlled preferential flow and hydrochemical reactions tend to compromise caprock safety, while capillary forces and stress-sensitive fracture behavior increase sealing capacity. This study offers theoretical insights and practical guidance for CO2 sequestration in tight reservoirs.
In the global transition to low-carbon energy, the multidimensional evaluation of solar energy utilization on building surfaces is crucial for sustainable urban development. Existing studies lack an understanding of the complex interactions between multiple periods, spatial orientations, radiation types, and dynamic thresholds, which hinders a revelation of the spatial-temporal variability of solar energy utilization. This study presents a novel urban-scale multidimensional utilization indicators prediction model with a multi-source heterogeneous data fusion method, employed in Hong Kong, China, with a coefficient of determination (R2) of 0.948 and a root mean square error (RMSE) of 0.228. Results demonstrate that the total solar energy (TSE) reserves of urban building surfaces reach 170 TW·h, with over 60% deemed usable. The mean shading ratio (MSR) is 39.97%, with the roof being the lowest at 10.76%, the south facade at approximately 40%, and the other facades at around 50%. Multiple coupled regional, seasonal, and orientation variability in the mean ratio of energy (MRE) over threshold is captured by combining a reasonable baseline utilization threshold (BUT) and a dynamic threshold field (DTF). Model interpretability and parameter sensitivity analyses reveal key variables that influence MRE across various orientations. The practical utilization potential analysis further uncovered spatial and temporal heterogeneity, offering new insights into optimizing installation deployment.
The residential clean heating transition has proven effective in reducing air pollution and improving public health. However, most research to date has focused on its economic and environmental implications, with limited attention being paid to household behavioral responses and heating experiences. To address this gap, we conducted household surveys in Linfen, a city in northern China, and analyzed residents’ self-reported indoor temperatures, heating costs, and heated areas before and after the transition. The results reveal substantial disparities across clean heating pathways. Compared with households that are continuing to use coal or biomass, those switching to gas experienced a 1.7 °C drop in indoor temperature and an annual cost increase of 1318.2 CNY, suggesting energy-limiting behaviors in response to higher expenses. In contrast, households adopting electric heating reported a 1.2 °C temperature increase without a significant rise in costs. Further analysis using the Gini coefficient and Oshima index shows that indoor temperature inequality increased among coal-to-gas households, while no such trend was observed among coal-to-electricity households. These findings highlight the importance of incorporating behavioral responses and thermal equity into the design of clean heating strategies.
As a major emitter of carbon dioxide (CO2), China is committed to achieving carbon neutrality (CN). However, unilateral climate policies risk carbon leakage through the relocation of emission-intensive industries, potentially undermining their intended benefits. Herein, we couple an integrated assessment model with a simple climate model to quantify the carbon leakage associated with China’s CN by 2100 and assess its impact on global warming. The results show that China achieving CN will lead to carbon leakage of 6.9% to 14.9%. While the emissions-level changes appear modest, the leakage leads to an increase in CO2 concentrations, rising from 2.1 ppm in the target year to 7.7 ppm by 2100. The global cooling effect of China’s CN is 0.15-0.19 °C, with leakage weakening this effect by 0.02 °C. Increased carbon sink capacity mitigates 8.9%-11.3% of the warming caused by leakage, and synergistic reductions in methane and nitrous oxide emissions offset over 70.5% of the leakage-induced warming. Therefore, solutions to carbon leakage should go beyond merely preventing increased emissions and focus on offsetting the broader climate impacts caused by leakage. These findings underscore the critical importance of addressing carbon leakage in climate policy design to mitigate the risks of global warming and maximize the benefits of CN efforts.
Coal-based electricity generation stands as the largest single source of global carbon dioxide emissions. The United Nations Climate Change Conference 28 (COP28) reached a historic consensus by all parties to transition away from fossil energy. On a global scale, a tripling of renewable energy capacity and a doubling of energy efficiency improvements by 2030, while gradually phasing out unabated coal power. As we move toward a decarbonized power system in a just, orderly, and equitable manner, coal power can enhance the integration of renewable energy into the electricity system by providing flexible services such as rapid ramping, cycling, and reliable capacity backup, ensuring the secure and stable operation of the system. Herein, we contribute to clarifying the role of coal power flexibility in the different phases of renewable energy development. By analyzing the metrics and costs of coal flexibility, we find that flexibility retrofitting can improve the regulatory capacity of the power system, reduce carbon emissions, and provide economic benefits for operators. The research findings emphasize that coal flexibility can positively contribute to decarbonization through policy support and technological innovation.
The world has been facing climate-related problems in recent decades, resulting from environmental degradation, making it increasingly critical to reduce environmental degradation in order to combat climate change. In this context, transforming economies into eco-friendly ones, ensuring the global energy transition from fossil-fuel-based power to renewable energy, and applying stringent environmental policies have been recent focus points of policymakers. Accordingly, this study analyzes the marginal effect of the energy transition, market- and non-market-based environmental policy stringency, and income on ecological footprint (EFP) in Brazil, Russia, India, China, and South Africa (BRICS) countries using data between 2000 and 2020 and applying a kernel-based regularized least-squares (KRLS) approach. The results show that ① economic growth has a stimulating marginal effect on EFP in BRICS countries except for Brazil, where it has an insignificant effect; ② energy transition efforts are ineffective in all countries, having the undesired effect of increasing EFP; ③ market-based environmental policies have a curbing effect on EFP in Brazil, Russia, and India but an increasing effect in China and an insignificant effect in South Africa; ④ non-market-based environmental policies are highly ineffective in all countries; ⑤ the KRLS approach exhibits excellent performance in the estimation of EFP, reaching approximately 99.61%; and ⑥ the marginal effect of the studied factors on EFP varies across factors, countries, and percentiles. Thus, this study highlights that BRICS policymakers should consider the marginal effects of the studied factors on EFP in combating environmental degradation and climate-related problems.
Minimally invasive tumor ablation techniques, such as radiofrequency ablation, microwave ablation, and cryoablation, have emerged as important modalities for treating solid tumors, owing to their advantages such as rapid recovery after treatment and reduced surgical trauma. However, their clinical efficacy remains limited by various factors, including incomplete ablation, high rates of recurrence, and an immunosuppressive tumor microenvironment. In recent years, the advent of nanoengineering strategies has provided promising avenues to overcome these limitations. Owing to their unique properties, such as tumor-targeting, controlled drug release, and immunomodulatory functions, nanocarriers not only enhance the local efficacy of ablation therapies but also modulate cell death pathways, such as pyroptosis and ferroptosis, and reprogram the tumor immune microenvironment. These effects collectively activate systemic antitumor immune responses. This review summarizes the underlying mechanisms by which nanoengineered systems augment tumor ablation, with a specific focus on key pathways through which nanocarriers synergize with ablation therapies to improve therapeutic outcomes. This review also critically assesses current limitations, including biosafety concerns and translational challenges, and discusses future perspectives, such as the development of intelligent nanocarriers and multimodal combination strategies. Ultimately, this work aims to provide theoretical insights and practical guidance for advancing the efficacy and personalization of tumor ablation therapies.
Postmenopausal women with obesity are at a significantly elevated risk of cognitive impairment. There is therefore an urgent need to uncover the underlying mechanisms of this impairment and develop effective therapeutic strategies. In this study, we demonstrated that treatment with the traditional Chinese herbal formulation Bazi Bushen capsule (BZBS; 1.4 and 2.8 g∙kg-1∙d-1) significantly alleviated cognitive deficits and restored hippocampal theta rhythm power and frequency in ovariectomized (OVX) high-fat diet-fed (OXHF) mice. Furthermore, we showed that BZBS improved mitochondrial function in the hippocampus, as evidenced by mitochondrial crista structure restoration, decreased reactive oxygen species (ROS) accumulation, and reduced adenosine triphosphate (ATP) depletion. By integrating bioinformatic, molecular, and neurophysiological methods, we found that BZBS inhibited the aryl hydrocarbon receptor (AhR)-miR-23a axis, which reactivated both the nicotinamide phosphoribosyltransferase (NAMPT)-nicotinamide adenine dinucleotide (NAD+)-sirtuin 1 (SIRT1) and proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α)-SIRT3 pathways, thus restoring mitochondrial function and increasing ATP levels. This study mechanistically validated the Qi-replenishing bioactivity of BZBS, which aligned with its therapeutic mechanism as informed by traditional Chinese medicine theory. Our findings identified BZBS as a promising therapeutic candidate for alleviating cognitive decline induced by menopausal obesity. Moreover, by elucidating the mechanisms of BZBS at the molecular level, we established a crucial foundation for the clinical use of this Chinese herbal medicine.
MicroRNAs (miRNAs) are promising biomarkers for cancer, offering a minimally invasive approach for liquid biopsy. However, their low abundance in clinical samples poses a major challenge for detection sensitivity and analytical reliability. Herein, this study reports a fluorescence-electrochemical dual-mode sensor that integrates multiple signal amplification strategies for the ultrasensitive detection of miRNA-21. MXene-gold nanoparticle (AuNP) composite were first modified on the electrode surface to enable efficient probe loading and enhanced electron transfer to amplify signal. The urchin-shaped covalent organic frameworks (COFs) facilitated more efficient methylene blue loading, with the unique structure further enhancing signal response. Combined with the miRNA-21 triggered entropy-driven catalysis (EDC)-DNA catalysis (DNAzyme) cascade amplification, the sensor realized a multi-level synergistic signal enhancement, demonstrating remarkable sensitivity. Notably, the urchin-shaped COFs served dual roles as both fluorescent reporters and nanocarriers, facilitating dual-mode signal readout. Under optimized conditions, the sensor exhibited a linear response to miRNA-21 in the range of 1-105 fmol∙L-1, with detection limits of 0.285 and 0.342 fmol∙L-1 for the electrochemical and fluorescence modes, respectively. The platform enabled accurate detection of miRNA-21 in human serum and cancer cells. It provides a viable pathway for early cancer diagnosis with sensitivity and reliability.
Biodegradable polyesters offer eco-friendly alternatives to traditional fossil-based plastics, but their natural biodegradation requires long periods and produce CO2. Biosynthesis provides a sustainable approach to utilizing waste polyesters for low-carbon development, yet the bioconversion capability of plastics remains extremely low. Herein, we demonstrate the piezo-driven biosynthesis of CH4 fuel from polyester waste hydrolysates and CO2 by harvesting hydraulic mechanical energy from flowing water to promote microbial metabolism and energy cycling. Polarized BaTiO3 (BTO) generates piezoholes to oxidize polylactic acid (PLA) plastic hydrolysates into small organic molecules, while the piezoelectrons are accepted by Methanosarcina barkeri as bioavailable reducing equivalents for CO2 reduction and intermediates oxidation into CH4. This results in 100% utilization of PLA waste hydrolysates for CH4 production with an ultrahigh selectivity of > 98% and an impressive reaction turnover frequency (TOF) of 1.24 × 105 s−1·cell−1. Moreover, the biotic-abiotic platform successfully utilizes CO2 to convert real-world postconsumer plastic products (straws, cups, knives, and forks) and various polyester-based plastics, such as polyglycolic acid (PGA) and polycaprolactone (PCL) into CH4 fuels. Critically, this piezo-driven strategy can be readily extended to other microbial cell factories, significantly enhancing the efficiency of the production of valuable chemicals such as acetate and polyhydroxybutyrate (PHB). This work establishes a mechanical energy-driven, carbon-negative platform for sustainable waste upgrading, paving a low-carbon and resource-circular avenue for plastic waste management and renewable energy production.
The continuous increase in microplastic (MP) pollution poses significant risks to human health and environmental sustainability, especially in agroecosystems. This study focused on identifying and managing MP risk to crops in agricultural soils in China, which is among the world’s largest consumers of plastic. Via the use of 3243 site-year field observations, we developed intelligent agriculture models to predict MP-related crop risks and identify key drivers, such as climate, livestock density, and fertilizer application, other than the use of agricultural plastic film. Rice was most sensitive to MPs, with an average risk quotient (RQ; unitless) of (3.76 ± 1.95), which is 2.19 and 1.93 times greater than those of maize and wheat, respectively. Climate factors are closely related to livestock density and agricultural management practices, potentially exacerbating MP risk under future conditions. Optimizing livestock density and fertilizer use levels reduced MP risk by 20.9%, 22.9%, and 20.3% and increased crop yields by 9.0%, 6.0%, and 5.6% for maize, rice, and wheat, respectively. Despite limitations related to model uncertainty and policy implementation, the proposed intelligent agriculture model provides a comprehensive basis and potential solutions for assessing and managing MP risk to crops.
As a core sensing and decision-making technology in industrial and automation domains, machine vision is required to maintain high-precision image-processing performance while simultaneously meeting multiple engineering constraints, such as real-time operation, miniaturization, and energy efficiency. These demands have driven the deep integration of machine vision and edge intelligence, giving rise to a new research frontier—real-time intelligent machine vision systems. This article systematically reviews the progress of this interdisciplinary field from three perspectives: algorithm evolution, hardware-software co-design, and engineering applications. First, it summarizes the development of intelligent vision algorithms represented by convolutional neural networks and vision transformers, emphasizing the role of model compression techniques—including pruning, quantization, and lightweight architecture design—in enabling edge deployment. Second, it explores edge computing platforms suitable for intelligent machine vision tasks and discusses typical strategies for hardware-software co-optimization in deploying artificial intelligence algorithms. Furthermore, through analyses of representative applications such as intelligent connected vehicles, urban surveillance systems, and robotics, the paper highlights the advantages and potential of edge vision systems in latency-sensitive scenarios. Finally, it identifies current challenges, including system integration, computational efficiency, energy efficiency, and intelligence level, and envisions emerging directions such as in-sensor computing, in-memory computing, neuromorphic computing, and on-device deployment of large models at the edge. Overall, this review emphasizes that the deep synergy among algorithms, hardware, and applications will be the key pathway toward realizing autonomous, adaptive, and perceptually intelligent machine vision empowered by edge intelligence.
Potential resource constraints for batteries hinder the electric vehicle (EV) deployment which is necessary for meeting Europe’s climate goals. Most previous studies on the material implications of car electrification do not explicitly consider lifetime differences between EVs and batteries, particularly the dynamics of battery technology development and recycling pathways. Herein, we developed a product-component dynamic material flow analysis model for 32 European countries, including the European Union of 28 Member States (EU28), Norway, Iceland, Switzerland, and Türkiye, and explored their supply-demand gaps for lithium (Li), cobalt (Co), manganese (Mn), and nickel (Ni) under various scenarios. We found that achieving 100% electrification in Europe under the “combined mitigation strategies” scenario (i.e., advanced lithium iron phosphate (LFP) battery technology, extended battery lifetimes, and shifts toward a smaller car segment) requires a high cumulative demand (2021-2050) for primary Li (2815 kt), Co (398 kt), Mn (397 kt), and Ni (2667 kt). Among various mitigation strategies, deploying advanced LFP battery technology appears to be the most critical for relieving resource pressure. We also reveal a trade-off effect for the second-life use of EV batteries: It may worsen critical material circularity but mitigate the carbon impact for prospective energy storage systems. Our product-component model can be used for other product systems and suggests the need for a systematic framework to maximize the synergies among material, energy, and climate strategies in the e-mobility transition.
The rapid development of the field of aerospace engineering has driven the advancement of special materials with extreme temperature tolerance, mechanical durability, and the capability to monitor physiological signals under extreme environments. However, a significant challenge has emerged in striking a balance between thermal insulation and mechanical properties due to special materials’ unstable lamellar structures and uncontrollable molding processes. Herein, we pioneer the scalable production of carbon nanofiber aerogels (CNFAs) using a volatilization-hygroscopicity synergistically induced phase-separation three-dimensional (3D) electrospinning molding technology. Their advanced structures grant the CNFAs superb compression resistance (15 000 cycles), an extraordinarily low density (20.15 mg·cm−3), and ultra-low thermal conductivity (0.028 W·m−1·K−1). Thus, CNFAs can maintain their original morphology and mechanical behavior at temperatures ranging from −196 to 1500 °C. This breakthrough paves the way for designing and producing functional materials suitable for frontier fields such as space exploration.
The operation of a tidal lagoon (or barrage) involves impounding a substantial volume of water, creating a head difference across the impoundment wall, and converting the potential energy associated with the head difference into kinetic energy through turbines. Consequently, the functioning of a tidal lagoon facilitates the movement of substantial volumes of water between the water body and impounded basin, altering water exchange and tidal flushing patterns, which potentially affect water quality. In this study, three tidal lagoon schemes proposed in the United Kingdom, West Somerset Lagoon (WSL), Swansea Bay Lagoon (SBL), and North Wales Tidal Lagoon (NWTL), were investigated for their regional water renewal capacities under different operational schemes and tidal phase conditions within a comparative framework. The exchange processes were investigated by modeling tracer changes within a control domain, with and without the lagoon, under different operational procedures. The results revealed different tracer-exchange characteristics across all schemes. Regardless of the operation scheme, the exchange concentration distribution tended to converge to a near-steady-state distribution at the end of each tidal cycle over a longer timescale. This study indicated that although the tracer dispersion rate was not highly sensitive to the initial tidal conditions, the tracer concentration declined more rapidly when released during spring tides. The inclusion of the WSL significantly improved the water exchange capacity within the lagoon basin region and the Bristol Channel and Severn Estuary. By contrast, the SBL and NWTL schemes resulted in decreased water renewal capacity in the lagoon area.
Polyethylene (PE) biodegradation is critical, yet microbial degradation of its ultra-long-chain alkane (ULCA, > C22) components remains limited and poorly understood beyond C40. This study identified Acinetobacter calcoaceticus (A. calcoaceticus) SDH15, a novel strain capable of degrading solid n-alkanes up to C56, thereby significantly extending the known biological range of alkane catabolism. Integrated genomic and transcriptomic analyses revealed a four-module enzymatic system driving this process. The first module, chemotaxis and adsorption, employs an upregulated PilH-Chp chemosensory system and type IV fimbriae (T4P), with fimF/G expression increased up to 332-fold, for substrate sensing and attachment. The second module, transmembrane transport, involves upregulated genes encoding outer membrane proteins (encoded by fadL, ompW, and tonB) and inner membrane transporters (encoded by mfs/emrAB-tolC; up to 18.9-fold) that facilitate alkane uptake. The third module, oxidative degradation, is initiated by the AlkB-type alkane monooxygenase AlkMa, which is encoded by the dramatically upregulated alkMa gene (327.7-fold for C22) and is essential for ULCAs oxidation, followed by alcohol and aldehyde dehydrogenases (ALDHs). The fourth module, energy metabolism, couples β-oxidation with an adaptive glyoxylate cycle to conserve carbon. Furthermore, electrospray ionization mass spectrometry enabled direct detection of C56 fatty acid intermediates, providing molecular evidence of the pathway’s broad substrate capacity. Functional validation through alkMa gene deletion confirmed its indispensable role in the initial oxidation of C22-C30 alkanes. This study presents a comprehensive model of native multi-enzyme synergy underlying ULCAs biodegradation, offering a framework for engineering microbial systems for PE remediation.