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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 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.

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.

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.

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.

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.

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.

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.

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.

It is known that measurement and extraction of the tiny amplitude of second harmonic Lamb waves are the main difficulties for practical applications of the nonlinear Lamb wave technique. In this study, phase-reversal approaches and phase matching technique are combined to build up the second-harmonic generation (SHG) of Lamb waves. A specific Lamb wave mode pair, which satisfied phase matching conditions, is selected to ensure the generation of cumulative second harmonic waves. Lamb wave signals with the same frequency but in reverse phase, propagating in the given specimen, are added together to counteract the fundamental waves, and simultaneously to enhance the signals of the second harmonic generated. The obtained results show that the phase-reversal approach can enhance the signals of second harmonic Lamb waves, and effectively counteract that of the fundamental waves. The approach is applied to assess the thermal-induced material degradation in the stainless steel plates. Distinctions of the acoustic nonlinearity parameters under different degraded levels are clearly shown in an improved repeatable and reliable manner, while those of linear wave velocity in the specimens are neglectable. The experimental investigations performed indicate that the proposed approach can be taken as a promising alternative for assessment of material degradation in its early stages.

Laminate composites which are widely used in the aeronautical industry, are usually subjected to frequency variation of environmental temperature and excessive humidity in the in-service environment. The thermal fatigue and moisture absorption in composites may induce material degradation. There is a demand to investigate the coupling damages mechanism and characterize the degradation evolution of composite laminates for the particular application. In this paper, the degradation evolution in unidirectional carbon/epoxy composite laminates subjected to thermal fatigue and moisture absorption is characterized by Lamb waves. The decrease rate of Lamb wave velocity is used to track the degradation evolution in the specimens. The results show that there are two stages for the progressive degradation of composites under the coupling effect of thermal cyclic loading and moisture diffusion. The present work provides an alternative to monitoring the degradation evolution of in-service aircraft composite Laminates.

Pyrroline-5-carboxylate reductase (PYCR) is pivotal in converting pyrroline-5-carboxylate (P5C) to proline, the final step in proline synthesis. Three isoforms, PYCR1, PYCR2, and PYCR3, existed and played significant regulatory roles in tumor initiation and progression. In this study, we first assessed the molecular and immune characteristics of PYCRs by a pan-cancer analysis, especially focusing on their prognostic relevance. Then, a kidney renal clear cell carcinoma (KIRC)-specific prognostic model was established, incorporating pathomics features to enhance predictive capabilities. The biological functions and regulatory mechanisms of PYCR1 and PYCR2 were investigated by in vitro experiments in renal cancer cells. The PYCRs’ expressions were elevated in diverse tumors, correlating with unfavorable clinical outcomes. PYCRs were enriched in cancer signaling pathways, significantly correlating with immune cell infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI). In KIRC, a prognostic model based on PYCR1 and PYCR2 was independently validated statistically. Leveraging features from H&E-stained images, a pathomics feature model reliably predicted patient prognosis. In vitro experiments demonstrated that PYCR1 and PYCR2 enhanced the proliferation and migration of renal carcinoma cells by activating the mTOR pathway, at least in part. This study underscores PYCRs’ pivotal role in various tumors, positioning them as potential prognostic biomarkers and therapeutic targets, particularly in malignancies like KIRC. The findings emphasize the need for a broader exploration of PYCRs’ implications in pan-cancer contexts.

Floods are common and inevitable natural disasters. Achieve Sustainable Development Goal (SDG) 11.5 is a critical challenge for coastal cities, especially those in deltaic lowlands such as in the case of Guangzhou, China. Regarding the spatial planning and design of such urban regions, it is crucial to study the impacts of flooding in compact or decentralized spatial development pathways. This reinforces the understanding of the relationship between strategic decisions for spatial planning and flood mitigation. However, the lack of a computer model to assess spatial evolution paths is a significant limitation. The non-dominated Sorting Genetic Algorithm II (NSGA-II) explores the possibility of a compact built-up land layout in 2030. The results showed that, concerning the 2030 decentralized scenario, the 2030 compact scenario presents a large increase in the integrated fitness function value from 0.618 to 0.771 (the increase is equivalent to 0.153 or about 24.75%). In addition, different development scenarios were constructed by setting different target weights. Compared to the decentralized scenario results, the fitness function values of the optimization results of each scenario showed better results at different levels. They could also serve as a reference for other similar coastal areas to achieve SDG 11.5 by 2030.

Currently, oil-coated PVA fibers are the most commonly used material in ECC research. However, the high price limits the application of PVA-ECC in practical engineering. In order to reduce the cost, one of the methods is to partially replace the PVA fibers in ECC. In order to demonstrate the feasibility of PVA/BF-ECC and PVA/PP-ECC, polyvinyl alcohol fibers (PVA), basalt fibers (BFs) and polypropylene fibers (PP) were added at 0.5%, 1.0% and 1.5% by volume of PVA in addition to 1% by volume of PVA. Subsequently, tensile, compression and drop-weight impact tests were conducted on single or hybrid fiber concrete. The results showed that the post-peak compression toughness, tensile strength, and initial cracking impact strength of PVA/BF-ECC and PVA/PP-ECC increased significantly with the increase in the volume ratio of BF and PP fibers, while the performance of PVA-ECC materials with the same fiber volume ratio decreased slightly. Therefore, the cost can be reduced by designing hybrid PVA/BF-ECC materials that meet the performance requirements. The experimental evidence presented in this study demonstrates the feasibility and reasonable prospect of the new hybrid PVA/BF-ECC.

Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and spatial characteristics between landslides and environmental factors may not be captured, and nonlandslide points may be randomly selected in the susceptibility mapping process. To overcome these shortcomings, in this paper, an information-gate recurrent unit residual network (Information-GRUResNet) model is proposed to produce a landslide susceptibility map by combining existing landslide records and environmental factor data. The model uses the information theory method to produce the initial landslide susceptibility map. Then, representative grid units and landslide points are selected as input variables of the GRUResNet model, from which nonlinear temporal and spatial characteristics are extracted to produce a landslide susceptibility map. Changzhou town in Wuzhou, China, is selected as a case study, and it is verified that the Information-GRUResNet model can accurately produce a landslide susceptibility map for the selected area. Finally, the Information-GRUResNet model is compared with GRU, RF, and LR models. The experimental results show that the Information-GRUResNet model is more accurate than the other three models.

The traditional method of related commodity discovery mainly focuses on direct co-occurrence association of commodities and ignores their indirect connection. Link prediction can estimate the likelihood of links between nodes and predict the existent yet unknown future links. This paper proposes a potentially related commodities discovery method based on link prediction (PRCD) to predict the undiscovered association. The method first builds a network with the discovered binary association rules among items and uses link prediction approaches to assess possible future links in the network. The experimental results show that the accuracy of the proposed method is better than traditional methods. In addition, it outperforms the link prediction based on graph neural network in some datasets.

Traditional topic research divides similar topics into the same cluster according to clustering or classification from the perspective of users, which ignores the deep relationship within and between topics. In this paper, topic analysis is achieved from the perspective of the topic network. Based on the initial core topics obtained by the keyword importance and affinity propagation clustering, co-occurrence time series between topics are constructed according to time sequence and topic frequency. Subsequence segments of each topic co-occurrence time series are divided by sliding windows, and the similarity between subsequence segments is calculated. Based on the topic similarity matrix, the topic network is constructed. The topic network is divided according to the community detection algorithm, which realizes the topic re-clustering and reveals the deep relationship between topics in fine-grained. The results show there is no relationship between topic center representation and keyword popularity, and topics with a wide range of concepts are more likely to become topic network centers. The proposed approach takes into account the influence of time factors on topic analysis, which not only expands the analysis in the field of topic research but also improves the quality of topic research.

Based on the time series of articles obtained from the literature, we propose three analysis methods to deeply examine the characteristics of these articles. This method can be used to analyze the construction and development of various disciplines in institutions, and to explore the features of the publications in important periodicals in the disciplines. By defining the concepts and methods relevant to research and discipline innovation, we propose three methods for analyzing the characteristics of agency publications: numerical distribution, trend, and correlation network analyses. The time series of the issuance of articles in 30 important journals in the field of management sciences were taken, and the new analysis methods were used to discover some valuable results. The results showed that by using the proposed methods to analyze the characteristics of institution publications, not only did we find similar levels of discipline development or similar trends in institutions, achieving a more reasonable division of the academic levels, but we also determined the preferences of the journals selected by the institutions, which provides a reference for subject construction and development.

In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

Parkinson’s disease (PD) is a common neurodegenerative disease that has a significant impact on people’s lives. Early diagnosis is imperative since proper treatment stops the disease’s progression. With the rapid development of CAD techniques, there have been numerous applications of computer-aided diagnostic (CAD) techniques in the diagnosis of PD. In recent years, image fusion has been applied in various fields and is valuable in medical diagnosis. This paper mainly adopts a multi-focus image fusion method primarily based on deep convolutional neural networks to fuse magnetic resonance images (MRI) and positron emission tomography (PET) neural photographs into multi-modal images. Additionally, the study selected Alexnet, Densenet, ResNeSt, and Efficientnet neural networks to classify the single-modal MRI dataset and the multi-modal dataset. The test accuracy rates of the single-modal MRI dataset are 83.31%, 87.76%, 86.37%, and 86.44% on the Alexnet, Densenet, ResNeSt, and Efficientnet, respectively. Moreover, the test accuracy rates of the multi-modal fusion dataset on the Alexnet, Densenet, ResNeSt, and Efficientnet are 90.52%, 97.19%, 94.15%, and 93.39%. As per all four networks discussed above, it can be concluded that the test results for the multi-modal dataset are better than those for the single-modal MRI dataset. The experimental results showed that the multi-focus image fusion method according to deep learning can enhance the accuracy of PD image classification.

GaoFen-3, the first polarimetric SAR satellite of China, carried out polarimetric calibration experiments using C-band polarimetric active radar calibrators (PARCs), trihedral corner reflectors (TCRs), and dihedral corner reflectors (DCRs). The calibration data were firstly processed referring to the classic 2 × 2 receive R and transmit T model for radar polarimeter systems, first proposed by Zebker, Zyl, and Held, and Freeman’s method based on PARCs, but the results were not good enough. After detailed analysis about the GaoFen-3 polarimetric system, we found that the system had some nonlinearity, then a new imbalance parameter was introduced to the classic model, which is equivalent to the γ proposed in Freeman’s paper about a general polarimetric system model. Then, we proposed the calibration data processing algorithm for GaoFen-3 based on the improved model and obtained better results. The algorithm proposed here is verified to be suitable for GaoFen-3 and can be applied to other spaceborne and airborne fully-polarimetric SAR systems.

New-generation retroviral vectors have potential applications in vaccination and gene therapy. Foamy viruses are particularly interesting as vectors, because they are not associated to any disease. Vector research is mainly based on primate foamy viruses (PFV), but cats are an alternative animal model, due to their smaller size and the existence of a cognate feline foamy virus (FFV). The potential of replication-competent (RC) FFV vectors for vaccination and replication-deficient (RD) FFV-based vectors for gene delivery purposes has been studied over the past years. In this review, the key achievements and functional evaluation of the existing vectors from in vitro cell culture systems to out-bred cats will be described. The data presented here demonstrate the broad application spectrum of FFV-based vectors, especially in pathogen-specific prophylactic and therapeutic vaccination using RD vectors in cats and in classical gene delivery. In the cat-based system, FFV-based vectors provide an advantageous platform to evaluate and optimize the applicability, efficacy and safety of foamy virus (FV) vectors, especially the understudied aspect of FV cell and organ tropism.

China is experiencing a high speed economic development which may exert great pressure on the environment and energy systems. To measure the environmental and energy performance during the economic development process, this paper selected 30 provinces, cities or autonomous regions as the decision making unit (DMU), and proposed a Green GDP index (GGI) in view of energy intensity and pollution intensity using the generalized Data Envelopment Analysis (DEA) method, and the developing trends of integrated energy and environment efficiency of DMUs from 2006 to 2010 are also demonstrated by the Malmquist index. Results show that the integrated energy and environment efficiency varies for each DMU. GGI were both 1 in Beijing and Shanghai. GGI values for the developed cities in Eastern China, such as Guangdong, Fujian, Zhejiang, Tianjin, Jiangsu, and Hainan, ranked high, while those in the Northeast and Middle China remained relatively low. Moreover, there is a positive relationship between the GGI and per capita GDP with a correlation coefficient of 0.75. Increases in GGI are also observed in the results, representing great achievements are acquired in energy conservation and emission reduction. However, the GGIs do not converge to the green frontier across the provinces.

Single-cell capture plays an important role in single-cell manipulation and analysis. This paper presents a microfluidic device for deterministic single-cell trapping based on the hydrodynamic trapping mechanism. The device is composed of an S-shaped loop channel and thousands of aligned trap units. This arrayed structure enables each row of the device to be treated equally and independently, as it has row periodicity. A theoretical model was established and a simulation was conducted to optimize the key geometric parameters, and the performance was evaluated by conducting experiments on MCF-7 and Jurkat cells. The results showed improvements in single-cell trapping ability, including loading efficiency, capture speed, and the density of the patterned cells. The optimized device can achieve a capture efficiency of up to 100% and single-cell capture efficiency of up to 95%. This device offers 200 trap units in an area of 1 mm2, which enables 100 single cells to be observed simultaneously using a microscope with a 20× objective lens. One thousand cells can be trapped sequentially within 2 min; this is faster than the values obtained with previously reported devices. Furthermore, the cells can also be recovered by reversely infusing solutions. The structure can be easily extended to a large scale, and a patterned array with 32,000 trap sites was accomplished on a single chip. This device can be a powerful tool for high-throughput single-cell analysis, cell heterogeneity investigation, and drug screening.

The paper proposes a new method for measuring the azimuth pointing of spaceborne synthetic aperture radar (SAR) antenna beams based on the ground receiver, which can receive and record complex sampling data of the pulse signals transmitted from the spaceborne SAR. The center of the antenna pattern is extracted from the complex sampling data amplitude envelope to obtain the time when the beam main lobe center irradiates the ground receiver, and the range migration information is extracted from the complex sampling data to obtain the time when the satellite is over the top of the ground receiver. The results of Chinese civilian remote sensing GaoFen-3 SAR satellite experiment data processing show that the measurement accuracy of this method is better than 0.002°, which can be applied to the accurate measurement of azimuth pointing of various low Earth orbit (LEO) SAR antenna beams.