This work outlines the current status of ozone (O3) pollution in China, which has become increasingly prominent in recent years, and control strategies that can be used to address this issue. O3 is a secondary product from the complex photochemical reactions of volatile organic compounds (VOCs) coupled with the nitrogen oxide (NOx) cycle. Considering the sources of precursors (i.e., VOCs and NOx) and the maturity of corresponding control technologies, substantially reducing NOx is a more feasible strategy for reducing O3 concentrations than focusing on VOCs, although it is undeniable that implementing coordinated control of NOx and VOCs in an optimal reduction ratio based on the specific conditions of different regions is the most effective strategy for controlling O3 pollution. Additionally, direct O3-decomposition technologies using catalytic materials coated on artificial surfaces offer a promising solution: These technologies can remove O3 without additional energy consumption, providing a practical complement to traditional emission-control strategies.
Low spatial resolution (LR) remote sensing data is widely adopted because of its lower cost, although its limited analytical precision constrains its full use in precision agriculture. By contrast, the acquisition of high spatial resolution (HR) data often requires substantial expense. To address this limitation, this study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images without requiring paired HR-LR training data. The main contributions are as follows: ① the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image (HIS) cubes, and a contrast-learning-based degradation-aware module is integrated to address unknown real-world degradation; ② comprehensive evaluation is conducted using image quality metrics, spectral consistency analysis, and performance in crop remote sensing tasks, such as chlorophyll content estimation; ③ the generalization capability of the model is assessed using data from three imaging devices, two spatial scales (near-ground and unmanned aerial vehicle (UAV)), and two geographic regions. The results show that the proposed method achieves the best overall performance in the comprehensive evaluation, with a mean peak signal-to-noise ratio ($\bar{PSNR}$) of 32.78, a mean root mean squared error ($\bar{RMSE}$) of 6.93, a mean structural similarity index ($\bar{SSIM}$) of 0.89, and a mean spectral angle mapper ($\bar{SAM}$) of 0.131. The method effectively reduces the degradation in chlorophyll detection accuracy caused by spatial resolution reduction. The evaluation of generalization capability further shows that the proposed method demonstrates strong generalization across different spatial scales, geographic regions, devices, and data types. These results indicate that UDAMSR provides a robust, efficient, and cost-effective software solution that can compensate for hardware limitations and support high-quality crop phenotyping detection in diverse application scenarios.
Spatial frequency domain imaging (SFDI) has been widely applied in fruit quality inspection because of its noncontact and wide-field advantages. However, conventional multispectral SFDI systems remain constrained by low transmission efficiency, limited spectral range, and reliance on mechanical scanning. To overcome these limitations, we developed a continuously tunable wavelength SFDI system (450-1040 nm) that enables both continuous-spectrum and selectable-band imaging through patterned monochromatic illumination. The system adopts a modular design that integrates a monochromatic light generation module, a projection module, an imaging module, and a motorized imaging platform. This configuration allows flexible coupling and replacement of light sources and projection modules, enabling automated measurement of optical properties across different wavelength ranges according to application needs. With its high tunability, the system supports customized measurements at specific wavelengths via dedicated acquisition software, and it also provides the potential for spectral extension into longer infrared bands by simply upgrading the light source and infrared-sensitive projection module. Leveraging its wavelength tunability, we further demonstrated the system’s capability for depth-resolved imaging by jointly regulating the spatial frequency and wavelength. The results showed that the system achieved an imaging depth of 3-4 mm. The optical property measurements of various fruits obtained using our system were in close agreement with the reference values provided by the integrating sphere (IS). The mean measurement error of the absorption coefficient was approximately 0.002 mm-1, while that of the reduced scattering coefficient was approximately 0.02 mm-1. In the application case of peach firmness prediction, the developed model achieved a coefficient of determination for prediction of 0.786. These results demonstrate that our system is more accurate than existing multiwavelength SFDI devices. This improvement indicates that the extended spectral range of the proposed SFDI system provides richer tissue information, thereby highlighting its potential for fruit quality evaluation. More importantly, this work establishes a new paradigm for SFDI instrumentation by transitioning from fixed multispectral sensing to customizable, spectrally continuous imaging, thereby broadening its applicability in the nondestructive evaluation of agricultural products and potentially other biological tissues.
The quality of fresh meat inevitably deteriorates during refrigerated storage, and ammonia is a critical volatile marker of spoilage. Nevertheless, temperature and humidity fluctuations within the cold chain environment can decrease the reliability of ammonia detection. To overcome this limitation and increase the sensing precision, in this work, a microwave ammonia sensor with temperature and humidity compensation is proposed on the basis of a backpropagation (BP) neural network. By analyzing the correlation between the radiation gain of the sensor and the ammonia concentration and integrating a wireless power transmission model, a new wireless microwave ammonia sensing model was established. The sensing system was experimentally validated through real-time monitoring of ammonia released during the spoilage of refrigerated meat. The results indicate that BP neural network-based temperature and humidity (THBP) compensation with Pearson correlation analysis reduced the radio frequency signal zero-point frequency fluctuation by 14 MHz, limited the absolute error to 0.06 parts per million (ppm), and increased the detection accuracy by 31.11%. This work provides a reliable theoretical framework and practical approach for high-sensitivity, non-destructive monitoring of the quality of fresh meat in dynamic cold-chain.
Deep learning (DL) methods, particularly those that combine camera and light detection and ranging (LiDAR) data, have demonstrated remarkable accuracy in three-dimensional (3D) obstacle detection. This is crucial for achieving rigorous and reliable autonomous navigation of agricultural machinery. However, recent approaches heavily rely on large-scale labeled datasets during training, which creates challenges for their application in agriculture because of presence of scarce and distinct agricultural samples. To overcome this limitation, this paper proposes a novel 3D detection method for agricultural obstacles with few or zero samples based on a multimodal feature representation mechanism. Image and point cloud attitude adjusters are integrated to increase the accuracy, reliability, and uniformity of multimodal data. Semantic and geometry-intensity feature encoders are integrated to capture essential relationships among categories. The Bird’s Eye View (BEV) fusion decoder is designed to discern intracategory similarities and intercategory distinctions. Multicategory experiments in various field scenarios reveal that the proposed method reduces the dependence on training samples by 30%-40%, and the precision rate, recall rate, F1 score, and detection speed are 95.03%, 97.01%, 96.01%, and 16.56 frames per second (FPS), respectively. Even in completely unknown scenarios (i.e., obstacle categories that lack any corresponding training samples), the proposed method still achieves an acceptable F1 score of 81.63%. As indicated by the results, the proposed method achieves a sophisticated trade-off among detection performance, operational efficiency, and data dependency, providing an effective safety guarantee for the autonomous navigation of agricultural machinery.
Production traits such as growth, disease resistance, and fatty acid content in engineered animals are anticipated to be enhanced via transgenesis (TG) or genome editing (GE). It is, however, unclear whether this expectation is upheld when making global comparisons across taxa. In this study, we performed a meta-analysis of 154 research papers covering 72 species and 55 genes, with the aim of quantifying and comparing the effects of TG and GE on animal production traits through overexpressing or disrupting key genes. Although TG is more commonly used for trait enhancement, GE has more pronounced and widespread effects, particularly on growth and disease resistance traits. This is reflected in larger effect sizes and broader impacts across trait responses. Yet, we observe differences in patterns of trait enhancement that are specific to taxon and parameter. For instance, TG reduces pathogen load in chickens and cattle, but not in pigs; conversely, GE lowers virus RNA levels in pigs, but is less successful in chickens and cattle. In contrast, both TG and GE significantly increase growth rates in ray-finned fish. It is notable that, although transgenes or edited genes remain highly expressed or repressed in Filial 1 (F1) offspring, the magnitude of trait improvement is diminished compared to the founder generations. This study provides evidence-based insights to assist researchers in refining their methods and directing future investigations into trait enhancement in genetically engineered animals, while also informing policymaking.
Agricultural water scarcity is increasingly conflicting with demands for both crop yield and crop nutri- tional quality, yet current irrigation strategies are failing to achieve synergistic improvements. This study explores how reducing soil moisture fluctuations (SMFs) affects crop yield and quality, using tomato plants under three irrigation treatments: fast wetting (FW), medium wetting (MW), and slow wetting (SW). We analyzed soil moisture dynamics, yield, fruit quality, soil bacteria, and plant molecular responses. Slowing the wetting process significantly improved tomato yield by 10%-20% and increased vitamin C and lycopene content by 10%-17% and 7%-29%, respectively, while reducing the irrigation quota by 30%-35%. The results showed a significant increase in the relative abundance of Myxococcota and Chloroflexi, while the relative abundance of Actinobacteria significantly decreased. Functional pre- diction showed that the abundance of aerobic chemotrophic heterotrophy was suppressed, whereas nitrate reduction was promoted. Based on a joint analysis of transcriptomics and metabolomics, several genes encoding key enzymes (GME, DHAR, IDH1, crtB, and crtH ) in the pathways of ascorbic acid, lycopene, and organic acid cycles were significantly affected. Structural equation modeling (SEM) revealed that the stabilized soil moisture directly increased microbial community diversity and soil fertility, which subse- quently activated transcriptional pathways associated with nutrient assimilation and antioxidant biosyn- thesis. This cascade of biological responses ultimately mediated improvements in crop productivity and quality. These findings challenge the conventional understanding of wet-dry cycles in irrigation. Reducing SMFs offers a practical approach to simultaneously improving water-use efficiency, crop yield, and fruit quality, with potential applications in sustainable agriculture.
Apramycin, an aminoglycoside antibiotic used exclusively in veterinary medicine, has attracted growing interest for its potential clinical application owing to its low toxicity and potent activity against multidrug-resistant (MDR) bacteria. Despite the completion of two Phase I clinical trials, apramycin resistance dynamics across One Health interfaces remain poorly understood. This study, conducted from 2020 to 2023 in Chengdu, Qingdao, and Shanghai, China, collected 5160 non-duplicate samples from hospitals, broiler and pig farms and slaughterhouses, and markets. We identified 1394 isolates of apramycin-resistant Escherichia coli (E. coli ) (AREC), with the highest detection rates in animal feces (58%, 700/1214), followed by animal carcasses (47%, 183/393), fresh meat (35%, 229/659), environments (21%, 127/593), human feces (7%, 103/1425), and clinical samples (5%, 42/876). Detection rates were higher in broiler-producing chains (57%, 742/1292) than in pig-producing chains (32%, 512/1609). Most AREC isolates (99.7%, 1390/1394) carried the aac(3)-IV gene, conferring resistance to apramycin, gentamicin, and tobramycin. Genomic analysis of 742 AREC isolates revealed sporadic clonal transmission events between animals and humans in Qingdao and Shanghai. Long-read sequencing of 66 representative isolates showed that aac(3)-IV genes were primarily located on IncHI2/IncHI2A plasmids, with high structural conservation across different sources. Temporal surveillance indicated a sharp increase in aac(3)-IV prevalence in livestock-associated E. coli following the adoption of apramycin in China. These findings demonstrate the rapid, plasmid-driven dissemination of apramycin resistance at the One Health interface, underscore the need for prudent veterinary stewardship and careful consideration of apramycin’s clinical repurposing for human use.
Methicillin-resistant Staphylococcus aureus (MRSA) represents a significant global public health threat. Combination therapy, particularly the use of antibiotics in conjunction with non-antibiotic agents, has emerged as a promising strategy to address the growing crisis of antibiotic resistance. Fosfomycin (FOS), increasingly utilized in clinical practice for treating drug-resistant bacterial infections, exhibits limited efficacy as a monotherapy. Here, we find that 5-fluorouracil (5-FU), a Food and Drug Administration (FDA)-approved anticancer drug, effectively enhances the antibacterial activity of FOS against MRSA, including biofilm-embedded MRSA cells. Mechanistically, 5-FU targets cytidine triphosphate (CTP) synthase, a rate-limiting enzyme responsible for the adenosine triphosphate (ATP)-dependent conversion of uridine triphosphate (UTP) to CTP. Moreover, we demonstrate that the synergistic effect of 5-FU and FOS arises from the perturbation of pyrimidine metabolism, which induces membrane damage, dissipation of the proton motive force (PMF), enhanced ATP synthesis, and accumulation of reactive oxygen species, culminating in bacterial death. In both Galleria mellonella (G. mellonella) and murine infection models, the combination of 5-FU and FOS markedly improves survival and reduces bacterial burdens. Collectively, our work demonstrates the therapeutic potential of 5-FU combined with FOS for tackling MRSA infections and highlights the pivotal role of perturbing pyrimidine metabolism in restoring antibiotic susceptibility.
Modern industrial systems have grown increasingly extensive, complex, and hierarchical, with operations relying on numerous knowledge-based queries. These queries necessitate considerable human resources while also requiring high levels of accuracy, subjectivity, and consistency, all of which critically influence operational efficiency. To overcome these challenges, this study proposes an industrial retrieval-augmented generation (RAG) method designed to enhance large language models (LLMs) using domain-specific knowledge, thereby improving the precision of question answering. A comprehensive industrial knowledge base was constructed from diverse sources, including journal articles, theses, books, and patents. A Text classification model based on bidirectional encoder representations from transformers (BERTs) was trained to accurately classify incoming queries. Furthermore, the general text embedding-dense passage retrieval (GTE-DPR) model was employed to perform word embedding and vector similarity retrieval, facilitating the alignment of query vectors with relevant entries in the knowledge base to obtain initial responses. These initial results were subsequently refined by LLMs to produce accurate final answers. Experimental evaluations confirm the effectiveness of the proposed approach. In particular, when applied to ChatGLM2-6B, the RAG method increased the ROUGE-L score from 32.52% to 55.04% and improved accuracy from 50.52% to 73.92%. Comparable improvements were also observed with LLaMA2-7B, underscoring the RAG framework’s capability to significantly enhance the accuracy and relevance of industrial question-answering (QA) systems.
This article presents a novel, lightweight, full-polarized Luneburg lens (LL) antenna featuring a diamond lattice structure for the first time. While retaining the essential attributes of a full-scale LL, such as pencil-beam generation capability, high gain, wide bandwidth, and omnidirectional scanning, this LL leverages the isotropic nature of its diamond crystal structure to seamlessly adapt to various polarizations and a wide range of incident wave angles. Utilizing advanced three-dimensional-printing (3DP) technology, an intricately designed structure was fabricated. An elaborate wideband feeding system was also developed to serve as the primary feed for the lens. The proposed system supports typical polarization states and consists of a dual-polarized horn antenna integrated with a sextuple-polarized feeding network. The results indicate that the operation bandwidth of the proposed LL antenna covers the entire X-band (8-12 GHz) under any polarization scenario. Furthermore, the antenna exhibits consistent and stable gains, thereby simplifying the design complexity of signal processing systems. Compared with conventional LLs constructed with a cubic lattice structure, this innovative LL antenna boasts a reduced weight and diminished sidelobe levels. These advancements position the proposed design as a promising candidate for satellite communications, integrated sensing and communication, microwave imaging, and diverse future endeavors.
The intensity and quality of the artificial light used in ‘‘green” plant cultivation greatly affect plant morphogenesis and physiological responses. Light-conversion films based on phosphors that can enable the precise conversion of ultraviolet (UV) radiation into photosynthetically active radiation (i.e., red light) hold great promise for next-generation ecological agriculture. However, conventional red phosphors often suffer from low stability or emit a short-wavelength red light more suitable for display than plant growth, limiting their agricultural applications. Herein, a weather-resistant Mn4+-doped yttrium aluminum garnet (YAG:Mn4+) deep red phosphor with a strong emission peak at 672 nm was synthesized. The phosphor’s luminescent properties were optimized by introducing Mg2+ as a charge compensator, thereby significantly increasing the phosphor’s emission intensity. Detailed photoluminescence and thermal quenching behaviors were investigated through comprehensive spectroscopic analyses. Light-conversion film made of biodegradable polyvinyl alcohol and the prepared phosphors was utilized to intensify the process of cultivating pea seedlings. As a proof of concept, a preliminary study under sunlight with an additional UV lamp demonstrated that treatment with the prepared light-conversion film enhanced the growth of pea seedlings. These improvements can be attributed to the effective conversion of UV radiation, which is useless for plant growth, into beneficial red light. The results demonstrate the potential of YAG:Mn4+–Mg2+-based phosphor films to improve agricultural productivity and promote eco-friendly cultivation practices.
Electrochemical carbon dioxide (CO2) reduction (ECO2R) is an increasingly valuable technology that converts CO2 into useful chemicals using various catalyst materials. This review underlines the advantages of ECO2R electrolyzers that use low-temperature electrolysis (25–80 °C) over other high-temperature electrolysis methods, particularly for applications in outer space. The potential of this technology for Mars missions is particularly intriguing, opening up new possibilities for in-situ resource utilization (ISRU) processes in upcoming space missions. This review also explores technology’s commercial potential and the importance of utilizing gas diffusion electrodes (GDEs) to enhance the ECO2R process. ECO2R has the potential to transform outer-space activities and space exploration by significantly reducing the use of Earth’s resources. It offers a sustainable alternative for continuous fuel and chemical production by utilizing gaseous CO2, thereby reducing its carbon footprint on Earth, and presents a promising process for outer-space applications.
The chemical looping steam reforming of methane (CL-SRM) holds immense potential for energy-efficient conversion of CH4 into syngas and high-purity hydrogen. However, its large-scale implementation remains limited by high operating temperatures and substantial energy requirements. This paper describes a non-thermal plasma-mediated CL-SRM process based on CH4/H2O redox cycles over lanthanum-based perovskites under mild conditions. The developed process achieves efficient CH4 activation at 600 °C, attaining 53.5% CH4 conversion and 0.57 mmol∙g−1 H2 with 92% purity over La0.5Ce0.5FeO3, while negligible conversion is observed under plasma-free conditions at the same furnace temperature. These performances surpass those observed under purely thermal conditions at 800 °C. Mechanistic insights reveal that plasma plays a crucial role in generating vibrationally excited CH4v species, thereby markedly lowering the reaction barrier for CH4 activation. The plasma-mediated CL-SRM process delivers energy through voltage-induced electron transfer, offering the potential for adiabatic reactor designs that minimize energy consumption compared with conventional combustion-based systems suffering from heat transfer limitations.
Uranium extraction from seawater is a promising strategy to alleviate global uranium scarcity, yet its implementation is hindered by extremely low concentrations and complex ionic environments. Concentrated seawater brine, a byproduct of salt production and desalination, contains 2-10 times more uranium than natural seawater, yet its high salinity presents additional challenges for extraction. Conventional polyamidoxime (PAO) hydrogels exhibit salt-induced shrinkage, compromising functional group accessibility and adsorption efficiency. Herein, we develop an anti-polyelectrolyte effect hydrogel by composing polyvinylphosphonic acid (PVPA) and the PAO. Under high-salinity conditions, cations and anions accumulate via diffusion around the positively charged amidoxime and negatively charged phosphonic acid groups, weakening interchain electrostatic attractions. This anti-polyelectrolyte effect promotes hydrogel swelling, significantly improving the exposure of binding sites and uranyl ion uptake. The PVPA-PAO hydrogel achieves a uranium adsorption capacity of 43.89 mg g−1 after 24 days in concentrated natural seawater derived from solar saltworks, significantly surpassing that of previously reported PAO hydrogels (∼10 mg g−1). In addition, it exhibits excellent antibacterial performance, mechanical robustness, and ion selectivity. This work presents an effective strategy for improving uranium recovery from marine resources and advances the comprehensive development and utilization of seawater resources.
Hydrogen production from renewable energy is a promising solution for clean and efficient hydrogen generation. The hybrid electrolyzers system (HES) consists of alkaline (ALK) and proton exchange mem-brane (PEM) electrolyzers. It balances PEM’s economic benefits and ALK’s hydrogen production capabil-ities. To enhance hydrogen production efficiency and ensure the operational stability of HES, this study proposes a novel multi-timescale rolling optimization strategy considering flexible hydrogen demand. A joint wind-photovoltaic power prediction model is used to provide accurate forecast data for schedul-ing optimization. The operating characteristics of the electrolyzers, including various operating states, start-stop behaviors, load variations, and hydrogen production features of ALK and PEM, are modeled in detail. Multi-timescale modeling is employed for rolling optimization to obtain the optimal scheduling solution. Finally, the validity of the proposed method is verified under varying weather types in Macheng, Hubei, China. The results show that HES significantly improves hydrogen production capacity and eco-nomics compared to ALK-only production, with a 25% increase in net revenue under extreme weather. Flexible hydrogen load demand response synchronizes fluctuations on both the supply and demand sides, multiplying grid trading benefits. The multi-timescale scheduling strategy enabled each electrolyzer to achieve over 96% execution of the day-ahead schedule across various weather conditions. The system’s economy achieves 98% of the ideal maximum benefit and 80% under extreme weather. This demonstrates that the proposed scheme holds promise for providing effective solutions for the optimal design and scheduling of renewable energy hydrogen production systems.
Arch bridges are well-suited to mountainous regions because their force characteristics align with local site conditions. However, construction in such areas faces challenges including large temperature differentials, complex canyon wind fields, and rugged terrain. Arch-forming also entails extensive work at height, high construction risk, and difficulties in achieving precise alignment after forming. To overcome these issues, this study presents an intelligent arch-forming method for large-span arch bridges. First, an optimization model for the entire arch-forming process is established to compute cable forces that meet objectives during construction. Second, a digital preassembly-based manufacturing control scheme is developed, allowing high-precision virtual assembly of arch rib segments in a digital environment. Finally, an automatic installation attitude adjustment strategy is proposed, based on restoring the structure to its designed shape, enabling high-precision, automated adjustment of the three-dimensional installation attitude of arch rib segments. The proposed method has been successfully applied to the Deyu Expressway Wujiang Bridge (with a main span of 504 m) located in Guizhou Province, China, demonstrating its reliability and practicality. This approach offers guidance for low-labor, resource-efficient, rapid, and automated construction of large-span arch bridges.
Indoor air purification, as a typical gas–solid interface process, involves the transfer of airborne pollutants to purification material surfaces through mass transfer, enabling their removal. While research on indoor air purification materials has expanded remarkably, studies on enhancing mass transfer have been relatively limited. In this work, we proposed a new concept of “integration of mass transfer and material regulation,” aiming to provide a design methodology for indoor air purification. Taking solid-phase particulate matter (PM) filtration and gas-phase pollutant adsorption as examples, we summarized the novel approach that shifts from passive material usage to active control of mass transfer through multiscale (milli–micro–nano) and multifield (mass–flow–force). For PM removal, the external electric force can enhance the migration velocity of PM towards the fiber approximately fivefold, resulting in 1−3 orders of magnitude higher comprehensive quality factor than commercial filters, considering filtration efficiency, pressure drop, and energy consumption. For gas removal, the hierarchical structure can increase the gas–solid contact area by 58%, resulting in a 37% improvement in single-pass removal efficiency and a 152% enhancement in dynamic adsorption capacity. We bridge the gap between high-performance materials and technologies by providing a design methodology for controlling surface forces and structures to improve mass transfer.
The principles of microbial community assembly in engineered ecosystems (particularly biotreatment processes) exposed to high-intensity environmental disturbances remain poorly understood. In this study, we conducted a meta-analysis of microbial communities across nine datasets from bioreactors operated under varied conditions. Null model-based analysis indicated that stochastic processes predominantly govern community assembly during contaminant-free selection phases or stable operation. In contrast, deterministic processes consistently govern initial acclimation of activated sludge. These processes then shift toward stochastic dominance as the operation stabilizes. The presence of environmental disturbances—such as shock, refeeding, or stress—during operation increases the relative contribution of deterministic processes to community structuring. Notably, low bioreactor performance is associated with deterministic assembly, whereas sustained stable operation corresponds to stochastic dynamics, irrespective of reactor type or condition. These findings were integrated across four categories of biotreatment operations to illustrate how specific disturbances influence microbial community succession and performance stability. This work advances the theoretical understanding of microbial dynamics in engineered systems and offers practical insights into linking community assembly mechanisms with bioreactor function.
Multimetal oxide with asymmetric atomic sites offers potential solutions for Fenton-like reactions, while their pilot-scale synthesis remains challenging. Herein, we develop a continuous flash Joule heating method using a programmable logic controller with robotic arms to accomplish proof-of-concept of scalable production. The pilot-scale product (178.3 kg·h−1·m−2 electrode) of fusion ternary metal oxides was achieved for flow-through water treatment. Integrating multiple reaction electrodes with respective independent power further outlined an increased production path. Experiments and density functional theory calculations proved that fusion CuVFeO structure achieved the dual functionality of organics adsorption on Cu sites and peroxydisulfate activation on Fe sites. The synergistic reaction can be strengthened by V doping endowed with a d band center, leading to an increased Fe Bader charge. Therefore, triple site effects shorten the reaction distance between free radicals (SO4•− and •OH) and organics, enhancing free radicals’ utilization and production efficiency. CuVFe secures superior performance during long-term operations (1455 min) in a continuous flow-through device. flash Joule heating characterization determined multi transition metals (CuVFe, CoVFe, MgVFe) can be generally synthesized with a superior catalytic performance. Undoubtedly, continuous flash Joule heating sheds light on developing advanced oxidation materials for pilot-scale wastewater treatment.
Atherosclerosis (AS) is a chronic inflammatory disease in which macrophages play an indispensable role. Exploration of the effects of aortic cell subpopulations in AS remains challenging due to cellular heterogeneity. Phytosterol oxidation products (POPs) are key dietary factors influencing AS due to their potential pro-inflammatory effects in atherosclerotic mice. However, the contribution of alterations in cellular heterogeneity to this outcome and the exact mechanisms remain elusive. Here, we constructed a novel single-cell transcriptomic landscape of arteries in ApoE−/− mice fed an atherosclerotic diet without or with POPs. Combining single-cell RNA sequencing (scRNA-seq) with in vitro functional validation, we demonstrated that 7-ketositosterol, a major component of POPs, induced macrophages to skew the pro-inflammatory (M1) phenotype through the TLR4-IRF5 axis, thereby amplifying the inflammatory response. Notably, we verified the presence of this pro-inflammatory immune niche with the same molecular features using publicly available human arterial scRNA-seq data. This demonstrates that this is a reproducible characteristic in human AS. Our study shifts the current paradigm of exploring the biological effects of food components, and provides unprecedented perspectives for the application of single-cell technology to food nutrition research.
Metabolic diseases, such as diabetes, obesity, and steatotic liver disease, represent a global epidemic. The pathogenesis of these disorders involves systemic disturbances in glucose homeostasis, lipid metabolism, energy balance, and inflammation, yet effective therapeutic strategies to correct these core disturbances remain limited. Silent information regulator 3 (sirtuin 3 (SIRT3)), a major mitochondrial deacetylase that we defined as the “head goose molecule,” acts as a central regulator and can initiate a coordinated rescue of metabolic homeostasis. We integrate evidence that SIRT3 activation triggers a “negentropic mechanism,” a suite of processes that collectively counteract systemic metabolic disorders by enhancing insulin sensitivity, promoting lipid oxidation, fine-tuning redox equilibrium, optimizing energy expenditure, and suppressing inflammation. The therapeutic potential of SIRT3 activators derived from natural products, synthetic compounds, and nicotinamide adenine dinucleotide (NAD+) precursors is evaluated, highlighting their promise as safe and sustainable treatment options. This review establishes the role of SIRT3 as a master regulator and suggests that it should be targeted to reconstitute systemic metabolic homeostasis.
Spinal cord injury (SCI) is a devastating traumatic disorder of the central nervous system that severely impairs sensory, motor, and autonomic functions, placing heavy burdens on patients, families, and society. This review summarizes engineering advances in SCI repair, emphasizing neuromodulation therapies, surgical approaches, cell therapy, pharmacological and gene therapies, and biomaterial-based tissue engineering. It also discusses challenges in clinical translation, such as ethical considerations, multimodal technology integration, and interindividual variability. The review underscores the importance of strengthening interdisciplinary collaboration to integrate multiple-model treatments and accelerate their clinical application.
With the rapid expansion of cloud computing and large-scale artificial intelligence models, building accurate and transparent energy-use statistics for data centers has become a critical challenge for global energy systems and climate governance. Existing studies report strikingly divergent estimates of global data center electricity consumption, ranging from 196 to 1200 TW h in 2020, a more than sixfold difference. Such discrepancies reveal profound uncertainties and structural deficiencies in current energy accounting frameworks. Conventional estimation approaches rely heavily on indirect assumptions, proxy indicators, or highly aggregated regional and national statistics, obscuring the true electricity demand of data centers. This lack of statistical transparency distorts energy and carbon accounting, weakens power system planning, and constrains the effective integration of renewable energy with rapidly growing computing demand. This paper highlights that data centers should be treated as a distinct and strategically important end-use energy sector. It emphasizes the need for grid-informed energy registration, enhanced artificial intelligence identification techniques to improve the accuracy and verifiability of energy statistics. Furthermore, the paper emphasizes that policymakers should establish coordinated policy frameworks, enforce standardized energy reporting, and design appropriate incentive mechanisms to encourage data centers to participate in demand response programs and electricity markets, thereby unlocking load flexibility and supporting a secure, low-carbon energy transition.
Wind farm operators always need a better maintenance strategy to increase resource utilization efficiency while controlling operation and maintenance costs. However, conventional maintenance decision-making approaches are time-consuming and have poor flexibility and adaptability to various scenarios. This study addressed these challenges by using a large language model (LLM) to understand, generate, and plan maintenance strategies for wind farms characterized by various failure modes and maintenance costs. A labelled-data-supervised fine-tuning LLM for maintenance, named LLM4M, is proposed. The proposed LLM4M model is trained on an extensive dataset of mathematical programs for maintenance to generate optimal strategies for wind farms. Compared with other large parameter LLMs, the fine-tuned LLM4M model demonstrates remarkable accuracy, with an error of approximately 2% from the optimal strategy. In addition, the generalization of the proposed LLM4M model has achieved remarkable results. If the LLM4M model correctly generates the maintenance strategy, the maintenance cost deviates from the optimal solution by only approximately 5%. Furthermore, phase transition behavior is observed, which provides considerable guidance for the development of domain-specific LLMs for the maintenance domain.