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Societal Impact Statement Nitrogen, phosphorus, and potassium are essential elements for plant growth and are the primary nutrients in commercial fertilizers. However, the extent to which these nutrients individually limit plant growth is still unclear, as is the influence of their interactions. Using a meta‐analysis approach, plant growth was found to be co‐limited by nitrogen, phosphorus, and potassium. Furthermore, these findings demonstrate that multiple nutrient additions are primarily additive, with the exception of synergistic effects observed with NP additions on plant growth. These findings are important as they provide a timely and valuable reference for assessing the ecosystem‐scale consequences of increasing nutrient deposition caused by human activities. Summary Numerous field studies and a few meta‐analyses have confirmed that terrestrial primary production is individually or jointly limited by nitrogen (N) and phosphorus (P). Meanwhile, a recent synthesis emphasized that potassium (K) limitation in terrestrial ecosystems is more widespread than previously thought. However, it remains unclear whether N, P, and K co‐limit terrestrial production and how their interactions influence plant growth. Therefore, we conducted a globally comprehensive synthesis of a total 2877 observations to explore the individual and interactive effects of N, P, and K additions on plant growth. We found that N, P, K, NP, NK, PK, and NPK additions significantly increased aboveground biomass production (AGP) by 30.8%, 15.3%, 12.3%, 68.4%, 36.5%, 21.1%, and 78.7%, respectively. The combined addition of multiple nutrients resulted in a larger increase in AGP than a single addition of nutrients. These findings suggested that terrestrial production is widely co‐limited by N, P, and K. However, no significant responses of belowground biomass production (BGP) to nutrient additions were observed. Except for synergistic interactions between N and P additions, the responses of AGP to other pairs of nutrient additions were predominantly additive. This meta‐analysis highlights the importance of multiple‐nutrient limitation in regulating terrestrial primary production while pointing out that non‐additive effects (including synergistic and antagonistic) of multiple‐nutrient additions on terrestrial production are relatively rare.

Background: Teacher well-being, encompassing emotional, psychological, and social dimensions, significantly influences teaching effectiveness. Understanding the factors that enhance teacher well-being and effectiveness is crucial, particularly amidst global teacher shortages and high turnover rates. Method: The present study gathered data from 252 university-level foreign language teachers from eight provinces in China. Participants completed measures assessing well-being (using the PERMA model), teaching effectiveness, and life satisfaction scales through online surveys. Data were analyzed using JASP and SPSS for regression and mediation analyses. Results: The findings revealed that Positive emotions and relationships significantly predicted life satisfaction, while accomplishment significantly predicted teaching effectiveness. Both teaching effectiveness and overall well-being were significant predictors of life satisfaction. However, teaching effectiveness did not significantly mediate the relationship between overall well-being and life satisfaction, indicating that these factors independently contribute to life satisfaction. Conclusion: Our findings share the inclusion of PERMA principles in enhancing teaching effectiveness and life satisfaction among foreign language teachers. Constructive future directions are suggested for professional development for improving teacher well-being and effectiveness.

In order to develop functional rice flour products with anti-digestive and antioxidant activities, the effects of adding Wuyi rock tea (WRT) on the quality, in vitro digestive characteristics and antioxidant activity of rice noodles were studied. The texture and sensory quality of rice noodles were determined by texture analyzer and sensory evaluation method, respectively. The flavor components were characterized by GC-MS, and the antioxidant activity was assessed by FRAP, DPPH and ABTS+ assays. The digestive characteristics of rice noodles were detected by the in vitro simulated digestion method. The results showed that compared to the control rice noodles, the breaking rate of rice noodles added with 2.9%~7.0%. WRT was not significantly changed (P>0.05), while the breaking rate of rice noodles added with 8.3% WRT was significantly increased (P

ObjectivesThe emergency response to the COVID-19 pandemic may disrupt hospital management activities of antimicrobial resistance (AMR). This study aimed to determine the changing AMR trend over the period in China when stringent COVID-19 response measures were implemented.MethodsThis retrospective study was conducted in a designated hospital for COVID-19 patients in Guangzhou, China from April 2018 to September 2021. The prevalence of 13 antimicrobial-resistant bacteria was compared before and after the COVID-19 responses through Chi-square tests. Interrupted time series (ITS) models on the weekly prevalence of AMR were established to determine the changing trend. Controlled ITS models were performed to compare the differences between subgroups.ResultsA total of 10,134 isolates over 1,265 days were collected. And antimicrobial-resistant strains presented in 38.6% of the testing isolates. The weekly AMR prevalence decreased by 0.29 percentage point (95% CI [0.05–0.80]) after antimicrobial stewardship (AMS) policy, despite an increase in the prevalence of penicillin-resistant Streptococcus pneumoniae (from 0/43 to 15/43, p

Objectives: To evaluate the impact of pay-for-performance on antimicrobial consumption and antimicrobial expenditure in a large teaching hospital in Guangzhou, China. Methods: We collected data from hospital information system from January 2018 through September 2022 in the inpatient wards. Antimicrobial consumption was evaluated using antibiotic use density (AUD) and antibiotic use rate (AUR). The economic impact of intervention was assessed by antimicrobial expenditure percentage. The data was analyzed using interrupted time series (ITS) analysis. Results: Following the implementation of the intervention, immediate decreases in the level of AUD were observed in Department of Hematology Unit 3 (β = −66.93 DDDs/100PD, P = 0.002), Urology (β = −32.80 DDDs/100PD, P

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4.

Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced ECL signal boosted by nanochannel-confined Au nanomaterial, which enables sensitive detection of the tumor biomarker—carcinoembryonic antigen (CEA). Vertically-ordered mesoporous silica film (VMSF) with a nanochannel array and amine groups was rapidly grown on a simple and low-cost indium tin oxide (ITO) electrode using the electrochemically assisted self-assembly (EASA) method. Au nanomaterials were confined in situ on the VMSF through electrodeposition, which catalyzed both the conversion of dissolved oxygen (O2) to reactive oxygen species (ROS) and the oxidation of a luminol emitter and improved the electrode active surface. The ECL signal was enhanced fivefold after Au nanomaterial deposition. The recognitive interface was fabricated by covalent immobilization of the CEA antibody on the outer surface of the VMSF, followed with the blocking of non-specific binding sites. In the presence of CEA, the formed immunocomplex reduced the diffusion of the luminol emitter, resulting in the reduction of the ECL signal. Based on this mechanism, the constructed immunosensor was able to provide sensitive detection of CEA ranging from 1 pg·mL−1 to 100 ng·mL−1 with a low limit of detection (LOD, 0.37 pg·mL−1, S/N = 3). The developed immunosensor exhibited high selectivity and good stability. ECL determination of CEA in fetal bovine serum was achieved.

Weizmannia coagulans is increasingly employed in food processing owing to its health benefits. Our previous research developed Oolong tea-fortified rice noodles with unique flavor and potent antioxidant activity; however, their texture still requires improvement. In this study, Oolong tea-fortified rice noodles were fermented using W. coagulans PR06 at inoculation amounts of 1%, 3%, and 5% (v/v), and assessed for cooking quality, texture, and starch digestibility. The results indicated that fermentation with 3% and 5% W. coagulans PR06 altered the amylopectin length distribution in the rice noodles and increased the degree of starch short-range order. Furthermore, the fermentation process increased the storage modulus (G′) and loss modulus (G″) values, decreased the tan δ value, and strengthened the interactions among tea polyphenols, proteins, and starch in the rice flour gel. Consequently, this process increased the hardness and chewiness of the rice noodles, decreased their broken strip rate and cooking loss, and significantly reduced their in vitro starch digestibility. Overall, fermentation with W. coagulans PR06 markedly improved the texture and cooking quality of Oolong tea-fortified rice noodles while effectively delaying starch digestion. This study highlights the potential application of W. coagulans PR06 in developing diverse and functional rice noodle products.

Background: The challenge of emerging antimicrobial resistance and variation in antibiotic use across provinces in China call for knowledge on antibiotic utilization at the regional level. This study aims to evaluate the long-term trends and patterns of antibiotic usage in Xinjiang Province, the largest provincial-level division located in the northwest of China, aiming to provide evidence in enhancing provincial antimicrobial stewardship (AMS) and developing policy measures to optimize regional antimicrobial use. Methods: This was an ecological study with temporal trend analysis on inpatient antibiotic utilization, with antibiotic use data from 92 public hospitals covered by Xinjiang’s Center for Antibacterial Surveillance from 2012 to 2022. Antibiotic use was measured by the number of daily defined doses per 100 patient days (DDDs/100 pds). Patterns of antibiotic use were described by Anatomical Therapeutic Chemical (ATC) subgroups and the Access, Watch, Reserve (AWaRe) classification. The Average Annual Percent Change (AAPC) of antibiotic use and the corresponding 95% confidence intervals (CIs) were calculated to describe the trend of antibiotic use over time. Joinpoint regression was performed using the Weighted Bayesian Information Criteria (WBIC) model with a parametric method. A pairwise comparison between secondary and tertiary hospitals was conducted to explore disparities in antibiotic use across hospital levels. The most commonly used antibiotics were also analyzed. Results: The total inpatient antibiotic use in Xinjiang was 27.6 DDDs/100 patient days in 2022, with a significant decreasing trend during 2012–2022 (AAPC, −2.0%; 95% CI, −3.6% to −0.4%). The Watch group antibiotics were the most used AWaRe category, with the Access-to-Watch ratio decreasing significantly from 46.4% to 24.4% (AAPC, −6.8%; 95% CI, −8.4% to −5.1%). No significant difference was found in the trend of total antibiotic use between secondary and tertiary hospitals, but there were disparities across hospital levels in subgroups. Third-generation cephalosporins, second-generation cephalosporins, and fluoroquinolones remained the top three antibiotic class throughout the study period. The number of antibiotics accounting for 90% of the total antibiotic use decreased from 34 antibiotics in 2012 to 18 antibiotics in 2022. Conclusions: The decreasing trend of inpatient antibiotic use in Xinjiang’s public hospitals reflects the effects of continuous AMS implementation. Patterns of antibiotic use underscore the need for further efforts on evidence-based antibiotic selection and for analyses on the appropriateness of antibiotic use.

The significant differences in data domains between SAR images and the expensive and time-consuming process of data labeling pose significant challenges to terrain classification. Current terrain classification methodologies face challenges in addressing domain disparities and detecting uncommon terrain effectively. Based on Style Transformation and Domain Metrics (STDMs), we propose an unsupervised domain adaptive framework named STDM-UDA for terrain classification in this paper, which consists of two steps: image style transfer and domain adaptive segmentation. As a first step, image style transfer is performed within the image space to mitigate the differences in low-level features between SAR image domains. Subsequently, leveraging this process, the segmentation network extracts image features, employing domain metrics and adversarial training to enhance alignment between domain gaps in the semantic feature space. Finally, experiments conducted on several pairs of SAR images, each exhibiting varying degrees of differences in key imaging parameters such as source, resolution, band, and polarization, demonstrate the robustness of the proposed method. It achieves remarkably competitive classification accuracy, particularly for unlabeled, high-resolution broad scenes, effectively overcoming the domain gaps introduced by the diverse imaging parameters under studies.

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data.

The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over 30 FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than 10 datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various FMs and confirm that pretrained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize 12 open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area.

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This article summarizes and analyzes the essential properties of brain cognize learning and the recent advance of remote sensing interpretation. First, this article introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this article summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation, and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

IntroductionAntimicrobial resistance (AMR) of Klebsiella pneumoniae (K. pneumoniae) poses a significant global public health threat and is responsible for a high prevalence of infections and mortality. However, knowledge about how ambient temperature influences the AMR of K. pneumoniae is limited in the context of global warming.MethodsAMR data of 31 Chinese provinces was collected from the China Antimicrobial Resistance Surveillance System (CARSS) between 2014 and 2020. Socioeconomic and meteorological data were collected from the China Statistical Yearbook during the same period. A modified difference-in-differences (DID) approach was applied to estimate the association between ambient temperature and third-generation cephalosporin-resistant K. pneumoniae (3GCRKP) and carbapenem-resistant K. pneumoniae (CRKP). Furthermore, moderating effects of socioeconomic factors were also evaluated.ResultsEvery 1°C increase in annual average temperature was associated with a 4.7% (relative risk (RR):1.047, 95% confidence intervals (CI): 1.031–1.082) increase in the detection rate of 3GCRKP, and a 10.7% (RR:1.107, 95% CI: 1.011–1.211) increase in the detection rate of CRKP. The relationships between ambient temperature and 3GCRKP and CRKP were found to be moderated by socioeconomic status (GDP per capita, income per capita, and consumption per capita; the interaction p-values

The frequent occurrence of global flood disasters leads to millions of people falling into poverty each year, which poses immense pressure on governments and hinders social development. Therefore, providing more data support for flood disaster detection is of paramount importance. To facilitate the development of water body detection algorithms, we create the DaliWS dataset for water segmentation, which contains abundant pixel-level annotations, and consists of high spatial resolution SAR images collected from the GaoFen-3 (GF-3) satellite. For comprehensive analysis, extensive experiments are conducted on the DaliWS dataset to explore the performance of the state-of-the-art segmentation models, including FCN, SegNeXt, U-Net, and DeeplabV3+, and investigate the impact of different polarization modes on water segmentation. Additionally, to probe the generalization of our dataset, we further evaluate the models trained with the DaliWS dataset, on publicly available water segmentation datasets. Through detailed analysis of the experimental results, we establish a valuable benchmark and provide usage guidelines for future researchers working with the DaliWS dataset. The experimental results demonstrate the F1 scores of FCN, SegNeXt, U-Net, and DeeplabV3+ on the dual-polarization data of DaliWS dataset reach to 90.361%, 90.192%, 92.110%, and 91.199%, respectively, and these four models trained using the DaliWS dataset exhibit excellent generalization performance on the public dataset, which further confirms the research value of our dataset.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.

BackgroundClimate change has resulted in long-term impacts on human health. Implementing efficient adaptation strategies among primary healthcare facilities is well determined by whether staff of different genders recognize the health risks related to climate change and are willing to take active measures.ObjectiveTo investigate gender differences on perceptions of health risks and attitude towards adaptation strategies among primary healthcare workers in China, and provide relevant suggestions.MethodsBy adopting a multi-stage cluster sampling method, we selected 21 urban and 10 rural healthcare facilities in Guangdong Province to collect information with a questionnaire. Then chi-square test, Wilcoxon rank sum test, and logistic regression analysis were used to explore the gender differences in climate change-related health risk cognition, knowledge acquisition channels, attitudes towards adaptation strategies, main obstacles, and resource requirements among the healthcare workers.ResultsOf 733 participants, 38.47% (282 participants) were male and 61.53% (451 participants) were female. The percentages of male healthcare workers who agreed that climate change is happening and recognized the causes of climate change (70.21%, 60.99%) were higher than the percentages of female counterparts (59.87%, 49.00%) (both Ps＜0.05), but no obvious gender differences existed in recognizing health risks of heatwaves and infectious diseases as well as sensitive population identification (all Ps＞0.05). Most of the participants (92.50%) received climate change and health-related information from mass media like TV, radio, and newspapers. Meanwhile less men chose new media channels than women (OR=0.62, 95%CI∶ 0.41-0.94). Only 30.56% of the participants (33.69% of men and 28.60% of women) reported involvement of relevant training and lectures. Most of them (90.96%) agreed to take active measures to deal with the health threats associated with climate change without gender differences for various measures (all Ps＞0.05). The leading obstacles were hard to integrate health adaptation to climate change into main work of institutions (most female agreed, 72.28%) and the lack of funds (most male agreed, 77.66%). Increasing funding for primary health care was regarded as the most needed resource by male (86.88%) and female (89.14%). After controlling the influence of other social factors, more men agreed with the lack of funds than women (OR=1.57, 95%CI∶ 1.10-2.24).ConclusionThere are some gender differences in the perceptions of health risks and adaptation strategies of climate change among primary healthcare workers: Male staff are more likely to agree with climate change and regard the lack of funds as the main obstacle, while women prefer to choose new media channels to obtain information. It’s suggested that the government and relevant institutions focus on the learning and training of climate change and health-related knowledge and expand diversified information access, and promote capacity building to cope with health threats at grass-root level, in the light of recognized gender differences among primary healthcare workers.

BackgroundClimate change leads to frequent heavy rainfall events, and higher incidences of bacillary dysentery after heavy rainfall have been observed. The impacts of heavy rainfall and its antecedent rainfall conditions on the disease are worth paying attention to.ObjectiveTo quantitatively analyze how the relationship between heavy rainfall events and bacillary dysentery occurrence is modified by antecedent rainfall conditions in Anhui Province and explore the different moderation effects in urban and rural contexts.MethodsCN05.1 meteorological data of Anhui Province and cases of bacillary dysentery of the same area were collected from January 1, 2006 to August 31, 2017. An exposure-response Poisson regression model of heavy rainfall events and the number of daily cases was constructed to explore the moderation effect of antecedent rainfall conditions on the incidence of bacillary dysentery, and further stratified by urban and rural areas.ResultsThis study included 129 459 cases of bacillary dysentery, with a daily average of 30.39. Compared with dry antecedent conditions without heavy rainfall, dry antecedent conditions with heavy rainfall had no obvious different effect on the incidence of bacillary dysentery for the whole province (P>0.05). But wet antecedent conditions significantly increased the risk of bacillary dysentery for the whole province after heavy rainfall (wet antecedent conditions without heavy rainfall: RR=1.281, 95%CI: 1.264-1.298; wet antecedent conditions with heavy rainfall: RR=1.267, 95%CI: 1.167-1.376). After urban and rural stratification, antecedent rainfall conditions also showed a significant moderation effect on the incidence of bacillary dysentery following heavy rainfall events. Compared with dry antecedent conditions without heavy rainfall, dry antecedent conditions with heavy rainfall had no obvious effect on the incidence of bacillary dysentery for the urban and the rural populations (P > 0.05). However, wet antecedent conditions without heavy rainfall (urban: RR=1.391, 95%CI: 1.362-1.421; rural: RR=1.222, 95%CI: 1.201-1.243) and wet antecedent conditions with heavy rainfall (urban: RR=1.364, 95%CI: 1.193-1.559; rural: RR=1.218, 95%CI: 1.098-1.352) significantly increased the risk of bacillary dysentery in both rural and urban areas.ConclusionIn the influence of heavy rainfall on the incidence of bacillary dysentery in Anhui Province, antecedent rainfall conditions have a certain moderation effect in the whole province and in both urban and rural areas, and the risk of bacillary dysentery is increased under wet antecedent conditions.

Infectious diarrhea is an important public health problem, which has a significant impact on global disease burden. Under the background of climate change, rainstorms increase and floods occur frequently. Most studies show that the incidences of infectious diarrhea disease increase significantly after rainstorm and flood events. However, there is a lack of systematic summary on the path of rainstorm and flood events affecting the incidence of infectious diarrhea, including the key links and mechanisms underlying environmental-social interaction. This study comprehensively combed the literature from environmental factors, socio-economic and cultural factors, and population and individual susceptibility factors. The potential mechanisms of infectious diarrhea caused by rainstorm and flood events were discussed from the aspects of spreading of pathogens, affecting sanitation facilities and (or) drinking water treatment infrastructure, the regulatory role of individual and behavioral factors, and long-term effects. Based on the "pressure-state-response" model, a social driving process model of rainstorm and flood leading to incidence of infectious diarrhea was constructed. This model could provide reference for future quantitative modeling and other research directions. It is helpful to guide the public health departments to accurately identify factors affecting the incidence of infectious diarrhea after rainstorm and flood, so as to take targeted intervention measures.

We investigate speeding up of relaxation of Markovian open quantum systems with the Liouvillian exceptional point (LEP), where the slowest decay mode degenerates with a faster decay mode. The degeneracy significantly increases the gap of the Liouvillian operator, which determines the time scale of such systems in converging to stationarity and thus accelerates the relaxation process. We explore an experimentally relevant three-level atomic system whose eigenmatrices and eigenspectra are obtained completely analytically. This allows us to gain insights into the LEP and examine the respective dynamics with details. We illustrate that the gap can be further widened by Floquet engineering, which further accelerates the relaxation process. Finally, we extend this approach to analyze laser cooling of trapped ions, where vibrations (phonons) couple to the electronic states. An optimal cooling condition is obtained analytically, which agrees with both existing experiments and numerical simulations. In this paper, we provide analytical insights into understanding LEP as well as controlling and optimizing the dissipative dynamics of atoms and trapped ions.

In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is proposed to enhance the extraction of WB edge information, while an Atrous Spatial Pyramid Pooling (ASPP) module is employed to enhance the extraction of small WBs. Furthermore, we analyzed the impact of four kinds of band combinations on WB extraction by the MF-SegFormer model, including true color composite images, false color images, true color images, and false color images enhanced by Gaussian stretch. We also compared our proposed method with several different approaches. The results suggested that false color composite images enhanced by Gaussian stretching are beneficial for extracting WBs, and the MF-SegFormer model achieves the highest accuracy across the study area with a precision of 77.6%, recall of 84.4%, F1-score of 80.9%, and mean intersection over union (mIoU) of 83.9%. In addition, we used the determination coefficient (R2) and root-mean-square error (RMSE) to evaluate the performance of river width extraction. Our extraction results in an overall R2 of 0.946 and an RMSE of 28.21 m for the mainstream width in the “Xi’an-Xianyang” section of the Weihe River. The proposed MF-SegFormer method used in this study outperformed other methods and was found to be more robust for WB extraction.

Summary: Background: Antibiotic resistance leads to longer hospital stays, higher medical costs, and increased mortality. However, research into the relationship between climate change and antibiotic resistance remains inconclusive. This study aims to address the gap in the literature by exploring the association of antibiotic resistance with regional ambient temperature and its changes over time. Methods: Data were obtained from the China Antimicrobial Surveillance Network (CHINET), monitoring the prevalence of carbapenem-resistant Acinetobacter baumannii (CRAB), Klebsiella pneumoniae (CRKP) and Pseudomonas aeruginosa (CRPA) in 28 provinces/regions over the period from 2005 to 2019. Log-linear regression models were established to determine the association between ambient temperature and antibiotic resistance after adjustment for variations in socioeconomic, health service, and environmental factors. Findings: A 1 °C increase in average ambient temperature was associated with 1.14-fold increase (95%-CI [1.07–1.23]) in CRKP prevalence and 1.06-fold increase (95%-CI [1.03–1.08]) in CRPA prevalence. There was an accumulative effect of year-by-year changes in ambient temperature, with the four-year sum showing the greatest effect on antibiotic resistance. Higher prevalence of antibiotic resistance was also associated with higher antibiotic consumption, lower density of health facilities, higher density of hospital beds and higher level of corruption. Interpretation: Higher prevalence of antibiotic resistance is associated with increased regional ambient temperature. The development of antibiotic resistance under rising ambient temperature differs across various strains of bacteria. Funding: The National Key R&D Program of China (grant number: 2018YFA0606200), National Natural Science Foundation of China (grant number: 72074234), Fundamental Scientific Research Funds for Central Universities, P.R. China (grant number: 22qntd4201), China Medical Board (grant number: CMB-OC-19-337).

Abstract Objectives The chemokine CXCL1, known as growth-related oncogene α (GRO-α), is a potent chemoattractant and regulator of neutrophils. The purpose of our study was to evaluate the regulatory response of CXCL1 in the serum of patients with systemic lupus erythematosus (SLE) in the active stage of disease and to assess whether it was implicated in the pathogenesis/inflammatory process in lupus. Methods CXCL1 serum concentrations were examined in 90 SLE patients, 56 other autoimmune diseases (OADs) patients and 100 healthy controls using enzyme-linked immunosorbent methodology. Results SLE patients exhibited significant increases in serum CXCL1 concentrations [1492.86 (735.47–2887.34) pg/ml] compared with OADs patients [155.88 (10.77–366.78) pg/ml] and healthy controls [13.58 (8.46–37.22) pg/ml] (p

Crack detection plays an important role in disease assessment of concrete buildings. However, factors such as complex background, irregular edge, and the real-time and accuracy requirement also make crack detection a challenging task. Aiming at the above challenges, an improved U-Net model for concrete crack detection is proposed, which has strong capability to extract the linear object, improving the performance in crack detection. The model is named Residual Linear Attention U-Net (RLAU-Net). There are three key measures in this paper. First, mirror padding the source image before convolution. Second, the multi-level features are obtained by aggregating the multi-scale features level by level. Third, strip pooling kernels are used to extract global contextual information, reducing information interference from the background. We tested the performance of RLAU-Net on our crack dataset, and the experimental results exhibited that it can improve the quantitative results of mean Intersection Over Union to 81.69%. In addition, F1 score has increased to, 78.21%, the Intersection Over Union of crack increased to 64.47%. We also compared the detect time-consuming of RLAU-Net and that of the original U-Net. Results demonstrate that the proposed model has a short processing time while maintaining a high detection accuracy for crack detection.

Abstract Background Inputs of above- and belowground litter into forest soils are changing at an unprecedented rate due to continuing human disturbances and climate change. Microorganisms drive the soil carbon (C) cycle, but the roles of above- and belowground litter in regulating the soil microbial community have not been evaluated at a global scale. Methods Here, we conducted a meta-analysis based on 68 aboveground litter removal and root exclusion studies across forest ecosystems to quantify the roles of above- and belowground litter on soil microbial community and compare their relative importance. Results Aboveground litter removal significantly declined soil microbial biomass by 4.9% but root exclusion inhibited it stronger, up to 11.7%. Moreover, the aboveground litter removal significantly raised fungi by 10.1% without altering bacteria, leading to a 46.7% increase in the fungi-to-bacteria (F/B) ratio. Differently, root exclusion significantly decreased the fungi by 26.2% but increased the bacteria by 5.7%, causing a 13.3% decrease in the F/B ratio. Specifically, root exclusion significantly inhibited arbuscular mycorrhizal fungi, ectomycorrhizal fungi, and actinomycetes by 22.9%, 43.8%, and 7.9%, respectively. The negative effects of aboveground litter removal on microbial biomass increased with mean annual temperature and precipitation, whereas that of root exclusion on microbial biomass did not change with climatic factors but amplified with treatment duration. More importantly, greater effects of root exclusion on microbial biomass than aboveground litter removal were consistent across diverse forest biomes (expect boreal forests) and durations. Conclusions These data provide a global evidence that root litter inputs exert a larger control on microbial biomass than aboveground litter inputs in forest ecosystems. Our study also highlights that changes in above- and belowground litter inputs could alter soil C stability differently by shifting the microbial community structure in the opposite direction. These findings are useful for predicting microbe-mediated C processes in response to changes in forest management or climate.

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal interstitial lung disease characterized by myofibroblast accumulation and extracellular matrix deposition, which lead to irreversible damage of the lung’s architecture and the formation of fibrotic lesions. IPF is also a sequela in serious patients with the coronavirus disease 2019 (COVID-19). The molecular mechanisms under pulmonary fibrosis remain unclear, and there is no satisfactory treatment currently available. Piceatannol (PIC) is a naturally occurring resveratrol analog found in a variety of dietary sources such as grapes, passion fruit, and white tea. It has been reported to inhibit liver fibroblast growth and exhibited various antitumor activities, although its role in pulmonary fibrosis has not been established yet. In the present study, we evaluated the anti-fibrotic role of PIC in bleomycin (BLM)-induced pulmonary fibrosis in mice.Methods: Mice with BLM-induced pulmonary fibrosis were treated with PIC, and fibrotic changes were measured by hematoxylin-eosin (H&E) staining and hydroxyproline assay. Luciferase assay, Western blot assay, histological analysis, and immunofluorescence staining were used to evaluate the effect of PIC on fibroblast activation and autophagy in mouse embryonic fibroblast cells (NIH-3T3) and human lung fibroblast cells (HFL1). The anti-fibrotic mechanisms of PIC were either confirmed in vivo.Results: Our results showed that PIC significantly alleviated the bleomycin-induced collagen deposition and myofibroblast accumulation. In vitro and in vivo studies indicated that PIC plays a role in activating autophagy in the process of anti-fibroblast activation. Further mechanism studies demonstrated that PIC can promote autophagy via inhibiting the TGF-β1-Smad3/ERK/P38 signaling pathway, which leads to a decreased number of activated myofibroblasts.Conclusion: Our study demonstrated for the first time that PIC possesses the protective effects against bleomycin-induced pulmonary fibrosis due to the direct pulmonary protective effects which enhance the effect of autophagy in vitro and in vivo and finally leads to the decreased number of activated myofibroblasts. PIC may serve as a candidate compound for pulmonary fibrosis therapy and attenuates the sequelae of SARS-COV-2 pulmonary fibrosis.

We studied signatures of quantum chaos in dynamics of Rydberg-dressed bosonic atoms held in a one-dimensional triple-well potential. Long-range nearest-neighbor and next-nearest-neighbor interactions, induced by laser dressing atoms to strongly interacting Rydberg states, drastically affect mean-field and quantum many-body dynamics. By analyzing the mean-field dynamics, classical chaos regions with positive and large Lyapunov exponents were identified as a function of the potential well tilting and dressed interactions. In the quantum regime, it was found that level statistics of the eigen-energies gain a Wigner–Dyson distribution when the Lyapunov exponents are large, giving rise to signatures of strong quantum chaos. We found that both the time-averaged entanglement entropy and survival probability of the initial state have distinctively large values in the quantum chaos regime. We further showed that population variances could be used as an indicator of the emergence of quantum chaos. This might provide a way to directly probe quantum chaotic dynamics through analyzing population dynamics in individual potential wells.

Bone homeostasis only exists when the physical function of osteoblast and osteoclast stays in the balance between bone formation and resorption. Bone resorption occurs when the two processes are uncoupled, shifting the balance in favour of bone resorption. Excessive activation of osteoclasts leads to a range of osteolytic bone diseases including osteoporosis, aseptic prosthesis loosening, rheumatoid arthritis, and osteoarthritis. Receptor activator of nuclear factor kappa-B ligand (RANKL) and its downstream signaling pathways are recognized as key mediators that drive the formation and activation of osteoclastic function. Hence, osteoclast formation and/or its function remain as dominant targets for research and development of agents reaching the treatment towards osteolytic diseases. Chrysin (CHR) is a flavonoid with a wide range of anti-inflammatory and anti-tumor effects. However, its effect on osteoclasts remains unknown. In this study, we found the effects of CHR on inhibiting osteoclast differentiation which were assessed in terms of the number and size of TRAcP positive multinucleated osteoclasts (OCs). Further, the inhibitory effects of CHR on bone resorption and osteoclast fusion of pre-OC were assessed by hydroxyapatite resorption pit assay and F-actin belts staining; respectively. Western blotting analysis of RANKL-induced signaling pathways and immunofluorescence analysis for p65 nuclear translocation in response to RANKL-induced osteoclasts were used to analyze the mechanism of action of CHR affecting osteoclasts. Lastly, the murine calvarial osteolysis model revealed that CHR could protect against particle-induced bone destruction in vivo. Collectively, our data strongly suggested that CHR with its promising anti-tumor effects would also be a potential therapeutic agent for osteolytic diseases.

Combination of probabilistic damage imaging (PDI) algorithm and the fusion ultrasonic guided wave damage index can be used to detect the damage in a complicated composite structure with acceptable accuracy. However, the damage signatures associated with PDI algorithm cannot accurately identify multi-defects features such as the number, position and dimension information, which greatly restrict the inspection effect in real practice. In this paper, the damage shape factor βM of each damage-impaired path is optimized to enhance the resolution of PDI algorithm. Then, a modified probabilistic damage imaging (MPDI) algorithm with the βM is proposed to identify the multi-defects existed in the curved carbon/epoxy composite structures. The validity of the algorithm is firstly checked in the curved composite structures with the single defect. The MPDI with the βM is applied to identify the depth of defect. The obtained results show that the precision of different defects identification by the MPDI algorithm is much higher than that used by the PDI algorithm. The number, position and dimension of multi-defects existed in the curved composite structure are clearly imaged by the method. This study also verifies that the majorization of damage shape factor in the MPDI algorithm is practicable and necessary for better identification of multi-defects in composite structure.

Background: To investigate the epidemiological characteristics and resistance changes of carbapenem-resistant organisms (CROs) under the COVID-19 outbreak to provide evidence for precise prevention and control measures against hospital-acquired infections during the pandemic. Methods: The distribution characteristics of CROs (i.e., carbapenem-resistant Klebsiella pneumoniae and Acinetobacter baumannii) were analyzed by collecting the results of the antibiotic susceptibility tests of diagnostic isolates from all patients. Using interrupted time series analysis, we applied Poisson and linear segmented regression models to evaluate the effects of COVID-19 on the numbers and drug resistance of CROs. We also conducted a stratified analysis using the Cochran–Mantel–Haenszel test. Results: The resistance rate of carbapenem-resistant Acinetobacter baumannii (CRAB) was 38.73% higher after the COVID-19 outbreak compared with before (p < 0.05). In addition, the long-term effect indicated that the prevalence of CRAB had a decreasing trend (p < 0.05). However, the overall resistance rate of Klebsiella pneumoniae did not significantly change after the COVID-19 outbreak. Stratified analysis revealed that the carbapenem-resistant Klebsiella pneumoniae (CRKP) rate increased in females (OR = 1.98, p < 0.05), those over 65 years old (OR = 1.49, p < 0.05), those with sputum samples (OR = 1.40, p < 0.05), and those in the neurology group (OR = 2.14, p < 0.05). Conclusion: The COVID-19 pandemic has affected the change in nosocomial infections and resistance rates in CROs, highlighting the need for hospitals to closely monitor CROs, especially in high-risk populations and clinical departments. It is possible that lower adherence to infection control in crowded wards and staffing shortages may have contributed to this trend during the COVID-19 pandemic, which warrants further research.

Abstract Gaining control over the grafting geometry is critically important for any application of surface‐supported single‐molecule magnets (SMMs) in data storage, spintronics, and quantum information science. Here, tetrairon(III) SMMs with a propeller‐like structure are functionalized with thioacetyl‐terminated alkyl chains to promote chemisorption on gold surfaces from solution and to evaluate differences in adsorption geometry and magnetic properties as a function of chain length. The prepared monolayers are investigated using X‐ray absorption techniques with linearly and circularly polarized light to extract geometrical and magnetic information, respectively. All derivatives remain intact and form partially oriented monolayers on the gold surface. A ligand‐field analysis of the observed X‐ray natural linear dichroism shows that the threefold molecular axis is invariably biased toward the surface normal, in agreement with ab initio calculations. This preferential orientation is most pronounced in monolayers of the shortest‐chain derivative, which are further studied with an ultralow temperature X‐ray magnetic circular dichroism setup operating down to 350 mK. The isothermal field sweeps with the magnetic field at normal incidence show an open hysteresis loop below 1 K, while measurements at different incidence angles prove the magnetic anisotropy of the monolayers.

We investigate Floquet dynamics of a particle held in a three-well system driven by a two-frequency field and identify integer and fractional photon resonances due to the dual-frequency driving. It is found that pairs of photon-assisted tunneling near the resonance originate from avoided level crossings in the Floquet spectra which, in essence, are quantum features of the hybridization between different quantum states. In particular, we establish a close connection between fractional-order resonances and Floquet mode properties under two-frequency driving conditions and illustrate their dependence on driving parameters. These results provide us a possibility to realize coherent control of quantum states with the assistance of classical external driving fields.

In this paper, the mechanical properties of hollow precast utility tunnels are studied by experimental and numerical methods. Through full-scale experiments, the failure modes of ordinary and hollow utility tunnels are studied, and the failure stages of the structures are classified based on the bearing capacity and damage to the structures. The nonlinear finite element model is used to simulate the behavior of the structure, and the optimal design of the structure under load type and the hollow ratio are discussed based on the finite element method. The theoretical calculation method of the bearing capacity for hollow structures in each stage is proposed, and its application scope is discussed. The finite element analysis can effectively predict the mechanical properties of the structure, and the failure of the utility tunnel structure is dependent on the shear bearing capacity. Although hollow design advances the structural damage under point load, the hollow structure has significant advantages under uniform loads or reasonable hollow ratios. It is reasonable to calculate the cracking load considering moment distribution at section centroid and the failure load considering the combined action of flexural and shear stress, but the hollow ratio should be less than 16%. Under reasonable hollow ratio or load conditions, the hollow design has little effect on the bearing capacity of the structure and can reduce the weight, which has practical value for architecture and construction.

Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.

In the urban environment, there are strict control requirements for deep foundation pit deformation, resulting in earth pressure on the flexible supports often being in a nonlimiting state. Therefore, it is important to consider displacement when calculating earth pressure. In this study, lateral unloading stress path triaxial compression tests were performed to investigate the radial stress–strain relationship of soft clay in an active region. Herein, a displacement-dependent earth pressure model is proposed with the assumption of the soil strain distribution in the disturbed area. From the surface of the ground to the deepest part of the support structure, the sufficient active displacement inversed by the proposed model decreased, which confirmed that the earth pressure along the entire wall could not achieve its active conditions simultaneously. The efficacy of the proposed model is demonstrated through a comparison of the predicted earth pressure with the experimental results reported in the published literature.

The generation of second- and third-order combined harmonics induced by the interaction of two primary Lamb waves with different frequencies has been analyzed theoretically and observed experimentally in our previous research [W. Li et al., J. Appl. Phys. 124, 044901 (2018)]. However, only third-order combined harmonics were observed experimentally because in the experimental investigations, the conditions for both synchronism and the symmetric feature were not satisfied simultaneously for the generation of the second-order combined harmonics. In this paper, first, the numerical perspective of the second-order combined harmonic generation by the collinear interaction of two primary Lamb waves with different frequencies in an isotropic and homogeneous plate is investigated. The results indicate that the synchronism and symmetric feature of two primary Lamb waves selected significantly affect the generation of second-order combined harmonics at the mixing frequencies. Next, an experimental scheme is proposed to measure the second-order combined harmonics predicted theoretically and numerically, through which it is found that the experimental results agree well with that of the theoretical and numerical predictions. The numerical and experimental results in this paper provide a clear physical insight, not previously available, into the second-order combined harmonic generation induced by the collinear interaction of two primary ultrasonic Lamb waves.

The association process of Feshbach molecules is well described by a Landau–Zener (LZ) transition above the Fermi temperature, such that two-body physics dominates the dynamics. However, using ^6 Li atoms and the associated Feshbach resonance at B _r = 834.1 G, we observe an enhancement of the atom–molecule coupling as the fermionic atoms reach degeneracy, demonstrating the importance of many-body coherence not captured by the conventional LZ model. In the experiment, we apply a linear association ramp ranging from adiabatic to non-equilibrium molecule association for various temperatures. We develop a theoretical model that explains the temperature dependence of the atom–molecule coupling. Furthermore, we characterize this dependence experimentally and extract the atom–molecule coupling coefficient as a function of temperature, finding qualitative agreement between our model and experimental results. In addition, we simulate the dynamics of molecular association during a nonlinear field ramp. We find that, in the non-equilibrium regime, molecular association efficiency can be enhanced by sweeping the magnetic field cubically with time. Accurate measurement of the atom–molecule coupling coefficient is important for both theoretical and experimental studies of molecular association and many-body collective dynamics.

This paper aims to effectively preserve the spatial and spectral information of hyperspectral (HS) images. For this purpose, the author proposed a new image fusion method based on coupled tensor decomposition (CTD). First, the wanted high spatial resolution HS (HRHS) images were decomposed into the core tensor and basis matrices. Assuming that the basis matrices can be calculated from low spatial resolution HS (LRHS) images, the core tensor was estimated from the high spatial resolution multispectral (HRMS) images based on the relationship between HRHS and HRMS images. Finally, the HRHS images were obtained by reconstructing the core tensor with basis matrices. Owing to the good properties of tensor, the proposed method achieved better fusion results on different data sets than those of the contrastive methods. The research findings shed new light on hyperspectral pansharpening.

The perception layer of Internet of Things (IOT) consists of various sensors. It is the source of the IOT to identify objects and collect information. Information fusion collected from multi-sensor has been widely used in various fields, such as intelligent industry, intelligent agriculture, intelligent transportation, and intelligent environmental protection. In this paper, multi-sensor image fusion, multispectral (MS) and panchromatic (PAN) images, is studied, and the fused images are used in target detection, recognition, and classification. However, traditional methods based on an injection model generally consider the MS images as a whole to compute the spectral weights. They ignore the local information of MS images and produce some spectral distortions, because for different objects, the spectral response will be different. Therefore, we propose a novel multi-sensor image fusion based on application layer of IOT (IFIOT) to preserve the spectral information of MS images. In this method, local homogeneous areas are found first by superpixel segmentation. Due to good properties of superpixel, the homogeneous areas are uniform and contain only one kind of object. Then, we estimate the spectral weights for different bands on the homogeneous area. The injection gain has an important influence on fusion results. Therefore, we adaptively compute the gain coefficients by minimizing the error between the spectral degraded MS and PAN images. Finally, after the injection of spatial details obtaining from the PAN image, fused images are produced. Experimental results reveal that the IFIOT method can give good fusion results and the spectral information is preserved well.

We study the chaos and hyperchaos of Rydberg-dressed Bose–Einstein condensates (BECs) in a one-dimensional optical lattice. Due to the long-range, soft-core interaction between the dressed atoms, the dynamics of the BECs are described by the extended Bose-Hubbard model. In the mean-field regime, we analyze the dynamical stability of the BEC by focusing on the ground state and localized state configurations. Lyapunov exponents of the two configurations are calculated by varying the soft-core interaction strength, potential bias, and length of the lattice. Both configurations can have multiple positive Lyapunov exponents, exhibiting hyperchaotic dynamics. We show the dependence of the number of the positive Lyapunov exponents and the largest Lyapunov exponent on the length of the optical lattice. The largest Lyapunov exponent is directly proportional to areas of phase space encompassed by the associated Poincaré sections. We demonstrate that linear and hysteresis quenches of the lattice potential and the dressed interaction lead to distinct dynamics due to the chaos and hyperchaos. Our work is relevant to current research on chaos as well as collective and emergent nonlinear dynamics of BECs with long-range interactions.

Metals which are widely used in many types of industries are usually subjected to fatigue and surface corrosion. There is a demand to detect the surface damage caused by fatigue and corrosion at an early stage to ensure the structural integrity. In this paper, a novel nonlinear ultrasonic technique based on the measure of third-order combined harmonic generation, is proposed to detect and locate the surface damage in 6061 aluminum alloy. Third-order combined harmonic generation caused by non-collinear mixing of one longitudinal wave and one transverse wave at different frequencies, is firstly analyzed and experimentally observed. An experimental procedure of nonlinear scanning is proposed for the damage detection and location by checking the variation of frequency nonlinear response. The correlations of nonlinear frequency mixing responses and surface damage in the specimens are obtained. Results show that the nonlinear response caused by fatigue damage and surface corrosion can be identified and located by this method. In addition, this approach can exclude the nonlinearity induced by the instruments and simplify the signal processing.

This study aimed to screen DNA aptamers against the signal molecule C4-HSL of the rhl system for the inhibition of biofilm formation of Pseudomonas aeruginosa using an improved systematic evolution of ligand by exponential enrichment (SELEX) method based on a structure-switching fluorescent activating bead. The aptamers against the C4-HSL with a high affinity and specifity were successfully obtained and evaluated in real-time by this method. Results of biofilm inhibition experiments in vitro showed that the biofilm formation of P. aeruginosa was efficiently reduced to about 1/3 by the aptamers compared with that of the groups without the aptamers. Independent secondary structure simulation and computer-aided tertiary structure prediction (3dRNA) showed that the aptamers contained a highly conserved Y-shaped structural unit. Therefore, this study benefits the search for new methods for the detection and treatment of P. aeruginosa biofilm formation.

We propose an adiabatic method for optimal phonon temperature estimation using trapped ions which can be operated beyond the Lamb-Dicke regime. The quantum sensing technique relies on a time-dependent red-sideband transition of phonon modes, described by the nonlinear Jaynes-Cummings model in general. A unique feature of our sensing technique is that the relevant information of the phonon thermal distributions can be transferred to the collective spin-degree of freedom. We show that each of the thermal state probabilities is adiabatically mapped onto the respective collective spin-excitation configuration and thus the temperature estimation is carried out simply by performing a spin-dependent laser fluorescence measurement at the end of the adiabatic transition. We characterize the temperature uncertainty in terms of the Fisher information and show that the state projection measurement saturates the fundamental quantum Cramér-Rao bound for a quantum oscillator at thermal equilibrium.

An ensemble of excited atoms can synchronize emission of light collectively in a process known as superradiance when its characteristic size is smaller than the wavelength of emitted photons. The underlying superradiance depends strongly on electromagnetic (photon) fields surrounding the atomic ensemble. High mode densities of microwave photons from 300 K blackbody radiation (BBR) significantly enhance decay rates of Rydberg states to neighbouring states, enabling superradiance that is not possible with bare vacuum induced spontaneous decay. Here we report observations of the superradiance of ultracold Rydberg atoms embedded in a bath of room-temperature photons. The temporal evolution of the Rydberg | nD ⟩ to |( n + 1) P ⟩ superradiant decay of Cs atoms ( n the principal quantum number) is measured directly in free space. Theoretical simulations confirm the BBR enhanced superradiance in large Rydberg ensembles. We demonstrate that the van der Waals interactions between Rydberg atoms change the superradiant dynamics and modify the scaling of the superradiance. In the presence of static electric fields, we find that the superradiance becomes slow, potentially due to many-body interaction induced dephasing. Our study provides insights into many-body dynamics of interacting atoms coupled to thermal BBR, and might open a route to the design of blackbody thermometry at microwave frequencies via collective, dissipative photon-atom interactions.

Atmospheric nitrogen (N) deposition has been found to significantly affect plant growth and physiological performance in terrestrial ecosystems. Many individual studies have investigated how N addition influences plant functional traits, however these investigations have usually been limited to a single species, and thereby do not allow derivation of general patterns or underlying mechanisms. We synthesized data from 56 papers and conducted a meta-analysis to assess the general responses of 15 variables related to leaf economics, gas exchange, and hydraulic traits to N addition among 61 woody plant species, primarily from temperate and subtropical regions. Results showed that under N addition, leaf area index (+10.3%), foliar N content (+7.3%), intrinsic water-use efficiency (+3.1%) and net photosynthetic rate (+16.1%) significantly increased, while specific leaf area, stomatal conductance, and transpiration rate did not change. For plant hydraulics, N addition significantly increased vessel diameter (+7.0%), hydraulic conductance in stems/shoots (+6.7%), and water potential corresponding to 50% loss of hydraulic conductivity (P50, +21.5%; i.e., P50 became less negative), while water potential in leaves (−6.7%) decreased (became more negative). N addition had little effect on vessel density, hydraulic conductance in leaves and roots, or water potential in stems/shoots. N addition had greater effects on gymnosperms than angiosperms and ammonium nitrate fertilization had larger effects than fertilization with urea, and high levels of N addition affected more traits than low levels. Our results demonstrate that N addition has coupled effects on both carbon and water dynamics of woody plants. Increased leaf N, likely fixed in photosynthetic enzymes and pigments leads to higher photosynthesis and water use efficiency, which may increase leaf growth, as reflected in LAI results. These changes appear to have downstream effects on hydraulic function through increases in vessel diameter, which leads to higher hydraulic conductance, but lower water potential and increased vulnerability to embolism. Overall, our results suggest that N addition will shift plant function along a tradeoff between C and hydraulic economies by enhancing C uptake while simultaneously increasing the risk of hydraulic dysfunction.

The penetration paths of chloride ions in recycled aggregate concrete (RAC) are of significant interest and have not been well studied previously. This study used X-ray computed tomography (X-CT) as a novel approach to investigate chloride ion penetration paths in RAC. The results indicate that X-CT can be used for the constant monitoring of chloride ion penetration paths in RAC, and the influence of mix proportions on the chloride ion penetration can be understood through the X-CT visualization.

Under cyclic and repetitive loads, fatigue cracks can be further propagated to a crucial level by accumulation, causing detrimental effects to structural integrity and potentially resulting in catastrophic consequences. Therefore, there is a demand to develop a reliable technique to monitor fatigue cracks quantitatively at an early stage. The objective of this paper is to characterize the propagation of fatigue cracks using the damage index (DI) calculated by various acoustic features of ultrasonic guided waves. A hybrid DI scheme for monitoring fatigue crack propagation is proposed using the linear fusion of damage indices (DIs) and differential fusion of DIs. An experiment is conducted on an SMA490BW steel plate-like structure to verify the proposed hybrid DIs scheme. The experimental results show that the hybrid DIs from various acoustic features can be used to quantitatively characterize the propagation of fatigue cracks, respectively. It is found that the fused DIs calculated by the acoustic features in the frequency domain have an improved reliable manner over those of the time domain. It is also clear that the linear and differential amplitude fusion DIs in the frequency domain are more promising to indicate the propagation of fatigue cracks quantitatively than other fused ones.

The use of an acoustic nonlinear response has been accepted as a promising alternative for the assessment of micro-structural damage in metallic solids. However, the full mechanism of the acoustic nonlinear response caused by the material micro-damages is quite complex and not yet well understood. In this paper, the effect of material microstructural evolution on acoustic nonlinear response of ultrasonic waves is investigated in rolled copper and brass. Microstructural evolution in the specimens is artificially controlled by cold rolling and annealing treatments. The correlations of acoustic nonlinear responses of ultrasonic waves in the specimens corresponding to the microstructural changes are obtained experimentally. To eliminate the influence of attenuation, which was induced by microstructural changes in specimens, experimentally-measured nonlinear parameters are corrected by an attenuation correction term. An obvious decrease of nonlinearity with the increase of grain size is found in the study. In addition, the influences of material micro-damages introduced by cold rolling on the acoustic nonlinear response in specimens are compared with the ones of grain boundaries controlled by heat treatment in specimens. The experimental results show that the degradation of material mechanical properties is not always accompanied by the increase of acoustic nonlinearity generated. It suggested that the nonlinear ultrasonic technique can be used to effectively characterize the material degradations, under the condition that the variations of grain sizes in the specimens under different damage states are negligible.

Purpose Evidence shows that adenosine triphosphate (ATP) is involved in the transmission of multiple chronic pain via P2X7 receptor. This study was to investigate the P2X7 and microglial cells in the chronic prostatitis pain. Materials and Methods Rats were divided into control group and chronic prostatitis group (n = 24 per group). A chronic prostatitis animal model was established by injecting complete Freund's adjuvant (CFA) to the prostate of rats, and the thermal withdrawal latency (TWL) was detected on days 0, 4, 12 and 24 (n = 6 at each time point in each group). Animals were sacrificed and the pathological examination of the prostate, detection of mRNA expression of P2X7 and ionized calcium binding adaptor molecule 1 (IBA-1) and measurement of content of tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) in the dorsal horn of L5-S2 spinal cord were performed on days 0, 4, 12 and 24. In addition, the content of TNF-α and IL-1β in the dorsal horn of L5-S2 spinal cord was measured after intrathecal injection of inhibitors of microglial cells and/or P2X7 for 5 days. Results The chronic prostatitis was confirmed by pathological examination. The expression of P2X7 and IBA-1 and the content of TNF-α and IL-1β in rats with chronic prostatitis were significantly higher than those in the control group. On day 4, the expressions of pro-inflammatory cytokines became to increase, reaching a maximal level on day 12 and started to reduce on day 24, but remained higher than that in the control group. Following suppression of microglial cells and P2X7 receptor, the secretion of TNF-α and IL-1β was markedly reduced. Conclusion In chronic prostatitis pain, the microglial cells and P2X7 receptor are activated resulting in the increased expression of TNF-α and IL-1β in the L5-S2 spinal cord, which might attribute to the maintenance and intensification of pain in chronic prostatitis.