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Engineering surface chemistry to precisely control interfacial interactions is crucial for fabricating superior antifouling coatings and separation membranes. Here, we present a hydrophobic chain engineering strategy to regulate membrane surface at a molecular scale. Hydrophilic phytic acid and hydrophobic perfluorocarboxylic acids are sequentially assembled on a graphene oxide membrane to form an amphiphilic surface. The surface energy is reduced by the introduction of the perfluoroalkyl chains while the surface hydration can be tuned by changing the hydrophobic chain length, thus synergistically optimizing both fouling-resistance and fouling-release properties. It is found that the surface hydration capacity changes nonlinearly as the perfluoroalkyl chain length increases from C 4 to C 10 , reaching the highest at C 6 as a result of the more uniform water orientation as demonstrated by molecular dynamics simulations. The as-prepared membrane exhibits superior antifouling efficacy (flux decline ratio <10%, flux recovery ratio ~100%) even at high permeance (~620 L m −2 h −1 bar −1 ) for oil-water separation. Fouling is a continuous challenge for the effective application of membranes in oily wastewater treatment. Here, the authors present a hydrophobic chain engineering strategy to regulate the surface of graphene oxide-based membranes at a molecular scale for increased antifouling even at high permeance.

As the continuous changes in environmental regulations have a non-negligible impact on the innovation activities of micro subjects, and economic policy uncertainty has become one of the important influencing factors to be considered in the development of enterprises. Therefore, based on the panel data of Chinese high-tech enterprises from 2012–2017, this paper explores the impact of heterogeneous environmental regulations on firms’ green innovation from the perspective of economic policy uncertainty as a moderating variable. The empirical results show that, first, market-incentivized environmental regulation instruments have an inverted U-shaped relationship with innovation output, while voluntary environmental regulation produces a significant positive impact. Second, the U-shaped relationship between market-based environmental regulation and innovation output becomes more pronounced when economic policy uncertainty is high. However, it plays a negative moderating role in regulating the relationship between voluntary-based environmental regulation and innovation output. This paper not only illustrates the process of technological innovation by revealing the intrinsic mechanism of environmental regulation on firm innovation, but also provides insights for government in environmental governance from the perspective of economic policy uncertainty as well.

With the rapid advancement of computer vision technology, traditional manual methods of reading meters are increasingly being replaced by automated water meter reading technologies based on image recognition. This technology can precisely locate and recognize the readings on captured images of water meter dials, laying a solid technical foundation for the implementation of remote automatic meter reading systems. However, in practical applications, the recognition of water meter readings still faces challenges due to interference from factors such as shooting angles and changes in environmental lighting. To address these challenges, this paper proposes an innovative method based on deep learning. Firstly, the ResNet-based Feature Pyramid Network (FPN) is used to detect the reading area of the water meter to ensure the accuracy of the detection. For the problem of digit character detection, the character detection attention mechanism is introduced to improve the performance of digit detection and reduce the interference of background noise while ensuring high accuracy. For numerical character recognition, the improved LeNet-5 network can better identify water meter readings in natural scenes. Additionally, the integration of a global average pooling layer within the network effectively alleviates the issue of overfitting. To verify the effectiveness of our method, we conducted experiments on the CCF real-world water meter reading automatic identification dataset. The experimental results show that by scaling the water meter reading area and introducing the character attention mechanism to assist in numerical character detection, the recognition accuracy of individual digits improved by 8.8% and 5.5%, respectively, and the overall recognition accuracy of the final water meter reading also increased by 7.0% and 2.2%. These significant improvements demonstrate the superiority and effectiveness of our method in practical applications.

As the coffee market continues to expand and new consumption patterns emerge, a brand’s cultural philosophy has become central to market differentiation. Consequently, innovative coffee packaging designs that integrate regional culture have drawn attention among designers and consumers. This study designed and evaluated wooden packaging prototypes. First, a repository was compiled of representative Yunnan cultural elements and four design schemes were developed. Next, 10 experts in the field of packaging design were invited. A multi-criteria evaluation framework was constructed, and the FAHP and CRITIC computed composite weights were determined for each index. Subsequently, TOPSIS was used to rank the scheme. An eye-tracking experiment was conducted with 44 test subjects to assess differences in visual attraction across schemes. Results showed that the FAHP–CRITIC–TOPSIS pipeline reliably identified the top-ranked scheme, and eye-tracking metrics corroborated its superior visual salience. The integrated approach combines subjective and objective evidence, rendering the weighting process more defensible and linking quantitative rankings with observed viewing behavior.

In the context of the worldwide quest for green and sustainable development, there is a growing importance in enhancing the environmental, social, and governance (ESG) performance of manufacturing companies. With the rise of digital transformation and pressing environmental challenges, artificial intelligence (AI) has emerged as a crucial tool for manufacturing organizations to gain a competitive edge in sustainability. While the role of digital technologies in driving ESG improvements has been widely discussed, there is limited scholarly exploration of the specific impact of AI on the ESG performance of manufacturing firms, as well as the underlying mechanisms at play from an AI perspective. Addressing this research gap, this study constructs a theoretical model of AI affecting manufacturing firms’ ESG performance using a sample of Chinese-listed manufacturing firms from 2012–2022. Additionally, this study examines the role of mediating mechanisms of ambidextrous green innovation as well as differences in the intrinsic mechanisms triggered by the equilibrium of ambidextrous green innovation and firm size. The findings indicate that the application of AI technology effectively promotes improvements in the ESG performance of manufacturing firms, with ambidextrous green innovation playing a positive mediating role. Furthermore, manufacturing companies with a strong balance of ambidextrous green innovation and larger scale exhibit enhanced effects of AI on ESG performance. This study serves to supplement existing literature on ESG performance enhancement, elucidate the theoretical rationale behind the non-economic performance of AI-enabled firms, and extend the application of organizational dualism theory to new contexts.

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate “biomarker - disease” causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention. The metabolomic changes over the course of aging are complex. Here, the authors present a comprehensive metabolomic profile of aging and construct a metabolomic aging score, which has potential for personalized aging monitoring and early disease-risk identification.

The microstructural characteristics of feathers are useful for species identification. In this study, scanning electron microscopy was employed to examine the microstructures of contour feathers, rectrices, and down feathers from both the Scaly-sided Merganser ( Mergus squamatus ) and Common Merganser ( Mergus merganser ). The primary objective was to assess inter-species differences and evaluate the potential of these microstructural characteristics as reliable indicators for distinguishing species. Several microstructural characteristics of feathers exhibited significant variations between the two species. In rectrices, significant variations were observed in the prong length, base length, hooklet number, and prong number of distal barbules. Similarly, down feathers exhibited marked differences in the node number, distance between nodes, internode width, and barbule length of downy barbules. Stepwise discriminant analysis, combined with the leave-one-out cross-validation test, further validated the discriminatory power of all microstructural characteristics. For contour feathers, incorporating the base length into the model achieved a 56.9% correct classification rate. In rectrices, the hooklet number and prong length emerged as key discriminators, with a correct classification rate of 91.3%. Most notably, the barbule length, node number, and distance between nodes of down feathers demonstrated exceptional discriminative capabilities, attaining a perfect 100% correct classification rate. Consequently, the barbule length, node number, and distance between nodes of down feathers, may serve as potentially useful morphological markers for differentiating the Scaly-sided Merganser from the Common Merganser.

Background Though the association between air pollution and incident type 2 diabetes (T2D) has been well documented, evidence on the association with development of subsequent diabetes complications and post-diabetes mortality is scarce. We investigate whether air pollution is associated with different progressions and outcomes of T2D. Methods Based on the UK Biobank, 398,993 participants free of diabetes and diabetes-related events at recruitment were included in this analysis. Exposures to particulate matter with a diameter ≤ 10 μm (PM 10 ), PM 2.5 , nitrogen oxides (NO x ), and NO 2 for each transition stage were estimated at each participant’s residential addresses using data from the UK’s Department for Environment, Food and Rural Affairs. The outcomes were incident T2D, diabetes complications (diabetic kidney disease, diabetic eye disease, diabetic neuropathy disease, peripheral vascular disease, cardiovascular events, and metabolic events), all-cause mortality, and cause-specific mortality. Multi-state model was used to analyze the impact of air pollution on different progressions of T2D. Cumulative transition probabilities of different stages of T2D under different air pollution levels were estimated. Results During the 12-year follow-up, 13,393 incident T2D patients were identified, of whom, 3791 developed diabetes complications and 1335 died. We observed that air pollution was associated with different progression stages of T2D with different magnitudes. In a multivariate model, the hazard ratios [95% confidence interval (CI)] per interquartile range elevation in PM 2.5 were 1.63 (1.59, 1.67) and 1.08 (1.03, 1.13) for transitions from healthy to T2D and from T2D to complications, and 1.50 (1.47, 1.53), 1.49 (1.36, 1.64), and 1.54 (1.35, 1.76) for mortality risk from baseline, T2D, and diabetes complications, respectively. Generally, we observed stronger estimates of four air pollutants on transition from baseline to incident T2D than those on other transitions. Moreover, we found significant associations between four air pollutants and mortality risk due to cancer and cardiovascular diseases from T2D or diabetes complications. The cumulative transition probability was generally higher among those with higher levels of air pollution exposure. Conclusions This study indicates that ambient air pollution exposure may contribute to increased risk of incidence and progressions of T2D, but to diverse extents for different progressions.

In the United States, increasing access to the internet, the increasing costs of large-scale face-to-face data collections, and the general reluctance of the public to participate in intrusive in-person data collections all mean that new approaches to nationally representative surveys are urgently needed. The COVID-19 pandemic accelerated the need for faster, higher-quality alternatives to face-to-face data collection. These trends place a high priority on the evaluation of innovative web-based data collection methods that are convenient for the U.S. public and yield scientific information of high quality. The web mode is particularly appealing because it is relatively inexpensive, it is logistically flexible to implement, and it affords a high level of privacy and confidentiality when correctly implemented. With this study, we aimed to conduct a methodological evaluation of a sequential mixed-mode web/mail data collection protocol, including modular survey design concepts, which was implemented on a national probability sample in the U.S. in 2020–2021. We implemented randomized experiments to test theoretically-informed hypotheses that 1) the use of mail and increased incentives to follow up with households that did not respond to an invitation to complete a household screening questionnaire online would help to recruit different types of households; and 2) the use of modular survey design, which involves splitting a lengthy self-administered survey up into multiple parts that can be completed at a respondent’s convenience, would improve survey completion rates. We find support for the use of mail and increased incentives to follow up with households that have not responded to a web-based screening questionnaire. We did not find support for the use of modular design in this context. Simple descriptive analyses also suggest that attempted telephone reminders may be helpful for the main survey.

Current treatment strategy for kidney renal clear cell carcinoma (KIRC) is limited. Tumor-associated antigens, especially neoantigen-based personalized mRNA vaccines represent new strategies and manifest clinical benefits in solid tumors, but only a small proportion of patients could benefit from them, which prompts us to identify effective antigens and suitable populations to facilitate mRNA vaccines application in cancer therapy. Through performing expression, mutation, survival and correlation analyses in TCGA-KIRC dataset, we identified four genes including DNA topoisomerase II alpha (TOP2A), neutrophil cytosol factor 4 (NCF4), formin-like protein 1 (FMNL1) and docking protein 3 (DOK3) as potential KIRC-specific neoantigen candidates. These four genes were upregulated, mutated and positively associated with survival and antigen-presenting cells in TCGA-KIRC. Furthermore, we identified two immune subtypes, named renal cell carcinoma immune subtype 1 (RIS1) and RIS2, of KIRC. Distinct clinical, molecular and immune-related signatures were observed between RIS1 and RIS2. Patients of RIS2 had better survival outcomes than those of RIS1. Further comprehensive immune-related analyses indicated that RIS1 is immunologically “hot” and represent an immunosuppressive phenotype, whereas RIS2 represents an immunologically “cold” phenotype. RIS1 and RIS2 also showed differential features with regard to tumor infiltrating immune cells and immune checkpoint-related genes. Moreover, the immune landscape construction identified the immune cell components of each KIRC patient, predicted their survival outcomes, and assisted the development of personalized mRNA vaccines. In summary, our study identified TOP2A, NCF4, FMNL1 and DOK3 as potential effective neoantigens for KIRC mRNA vaccine development, and patients with RIS2 tumor might benefit more from mRNA vaccination.