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Direct hydrogen production from inexhaustible seawater using abundant offshore wind power offers a promising pathway for achieving a sustainable energy industry and fuel economy. Various direct seawater electrolysis methods have been demonstrated to be effective at the laboratory scale. However, larger-scale in situ demonstrations that are completely free of corrosion and side reactions in fluctuating oceans are lacking. Here, fluctuating conditions of the ocean were considered for the first time, and seawater electrolysis in wave motion environment was achieved. We present the successful scaling of a floating seawater electrolysis system that employed wind power in Xinghua Bay and the integration of a 1.2 Nm 3  h −1 -scale pilot system. Stable electrolysis operation was achieved for over 240 h with an electrolytic energy consumption of 5 kWh Nm −3 H 2 and a high purity (>99.9%) of hydrogen under fluctuating ocean conditions (0~0.9 m wave height, 0~15 m s −1 wind speed), which is comparable to that during onshore water electrolysis. The concentration of impurity ions in the electrolyte was low and stable over a long period of time under complex and changing scenarios. We identified the technological challenges and performances of the key system components and examined the future outlook for this emerging technology. Seawater electrolysis shows promising potential toward sustainable energy generation, but large-scale in-situ demonstrations are still lacking. Here, authors report a floating platform integrating a 1.2 Nm 3 h −1 seawater direct electrolysis system with wind power for energy input in the Xinghua Bay.

Abnormal activation of the oncogene YAP in the Hippo pathway is a major feature in liver cancer and inactivation of MST1/2 has been shown to be responsible for the overactivation of YAP that led to tumorigenesis. However, mechanisms underlying MST1/2 dysregulation remain poorly understood. RNA‐seq analysis and genome (KEGG) pathway enrichment analysis were used to identify genes and pathways that were regulated by SIRT7. qRT‐PCR, ChIP, and luciferase assay were used to investigate transcriptional regulation. Mass spectrometry, co‐immunoprecipitation and immunoprecipitation were used to exam protein–protein interaction and post‐transcriptional modification. A xenograft mouse model was used to confirm the effect of SIRT7 and SIRT7 inhibitors on hepatocellular carcinoma (HCC) proliferation in vivo. We found that SIRT7 suppresses MST1 by both transcriptional regulation and post‐transcriptional modification, which in turn promotes YAP nuclear localization and transcriptional activation in liver cancer. Mechanistically, we revealed that SIRT7 suppresses MST1 transcription by binding to the MST1 promoter and inducing H3K18 deacetylation in its promoter region. In addition, SIRT7 directly binds to and deacetylates MST1, which primes acetylation‐dependent MST1 ubiquitination and protein degradation. In clinical samples, we confirmed a negative correlation between SIRT7 and MST1 protein levels, and high SIRT7 expression correlated with elevated YAP expression and nuclear localization. In addition, SIRT7 specific inhibitor 2800Z sufficiently inhibited HCC growth by disrupting the SIRT7/MST1/YAP axis. Our data thus revealed the previously undescribed function of SIRT7 in regulating the Hippo pathway in HCC and further proved that targeting SIRT7 might provide novel therapeutic options for the treatment of liver cancer. SIRT7 promotes HCC proliferation by regulating YAP activation; SIRT7 downregulates the expression of MST1 through transcriptional and post‐translational regulation and promotes YAP nuclear localization and transcriptional activity; downregulation of SIRT7 can inhibit the occurrence of liver cancer by preserving Hippo signal.

Advancements in Industry 4.0 and smart manufacturing have increased the demand for precise and intricate part surface geometries, making the analysis of surface morphology essential for ensuring assembly precision and product quality. This study presents an innovative fitness landscape-based methodology for extreme point analysis of part surface morphology, effectively addressing the limitations of existing techniques in accurately identifying and analyzing extremum points. The proposed approach integrates adaptive Fitness-Distance Correlation (FDC) with a roughness index to dynamically determine the number and spatial distribution of initial points within the pattern search algorithm, based on variations in surface roughness. By partitioning the feasible domain into subregions according to FDC values, the algorithm significantly reduces optimization complexity. Regions with high ruggedness are further subdivided, facilitating the parallel implementation of the pattern search algorithm within each subregion. This adaptive strategy ensures that areas with intricate surface features are allocated a greater number of initial points, thereby enhancing the probability of locating both regional and global extremum points. To validate the effectiveness and robustness of the proposed method, extensive testing was conducted using five diverse test functions treated as black-box functions. The results demonstrate the method’s capability to accurately locate extremum points across varying surface profiles. Additionally, the proposed method was applied to flatness error evaluation. The results indicate that, compared to using only the raw measurement data, the flatness error increases by approximately 3% when extremum points are taken into account.

To investigate the compressive dynamic properties of hydraulic asphalt concrete under various temperatures, four temperatures and four strain rates have been set to perform the uniaxial compression experiments using hydraulic servo machine in this paper. The influence of temperature and strain rate on the failure modes, stress-strain curves and mechanical characteristic parameters of hydraulic asphalt concrete is analyzed and the results reveal that the failure modes and stress-strain curves have significant temperature effect. When the temperature is between −20 °C and 0 °C, the failure mode is dominated by brittle failure of asphalt binder, and hydraulic asphalt concrete shows obvious strain softening. With the addition of temperature, the failure modes of specimens are transferred from brittle failure to ductile failure since the asphalt changes from elastic-brittleness to viscoelasticity. Influenced by temperature effect, the compressive stress-strain curves of hydraulic asphalt concrete show strain hardening while the peak stress of hydraulic asphalt concrete is obviously decreased, and the variation coefficient of peak stress has a power relation with temperature. With successive increases in strain rate, the stress-strain curves of hydraulic asphalt concrete gradually are transferred from strain hardening to strain softening. The peak stress and stiffness modulus of specimens under compression gradually increase, and the dynamic increase factor of peak stress is linearly related with the logarithm value of strain rate after dimensionless treatment. In terms of the quantitative analysis of the experimental data, two relationship models of the coupling effect between temperature and strain rate are proposed. The proposed models have good applicability to the quantitative analysis of the experimental results in the manuscript. This paper offers important insights into the application and development of hydraulic asphalt concrete in hydraulic engineering.

RSCD faces persistent challenges in high-resolution imagery due to complex spatial structures, temporal heterogeneity, and semantic ambiguity. While deep learning methods have significantly advanced the field, most existing models still rely on static and homogeneous processing, treating all channels and modalities equally, which limits their capacity to capture fine-grained semantic shifts or adapt to region-dependent variations. To address these issues, we propose SynerCD, a unified Siamese encoder–decoder framework that introduces dynamic, content-adaptive perception through channel decoupling, frequency-domain enhancement, and vision-language collaboration. The encoder employs a Tri-branch Synergistic Coupling (TSC) module that dynamically rebalances channel responses and captures multi-scale spatial-frequency dependencies via Mamba-based long-sequence modeling and wavelet decomposition. The decoder integrates a vision-aware language-guided attention (VAL-Att) module, which adaptively modulates visual-textual fusion using CLIP-based semantic prompts to guide attention toward meaningful change regions. Extensive experiments on four benchmark datasets verify that SynerCD achieves superior localization accuracy and semantic robustness, establishing a dynamic and adaptive paradigm for multimodal change detection.

Background Chronic kidney disease (CKD) leads to end-stage renal failure and cardiovascular events. An attribute to these progressions is abnormalities in inflammation, which can be evaluated using the neutrophil-to-lymphocyte ratio (NLR). We aimed to investigate the association of NLR with the progression of end stage of renal disease (ESRD), cardiovascular disease (CVD) and all-cause mortality in Chinese patients with stages 1–4 CKD. Methods Patients with stages 1–4 CKD (18–74 years of age) were recruited at 39 centers in 28 cities across 22 provinces in China since 2011. A total of 938 patients with complete NLR and other relevant clinical variables were included in the current analysis. Cox regression analysis was used to estimate the association between NLR and the outcomes including ESRD, CVD events or all-cause mortality. Results Baseline NLR was related to age, hypertension, serum triglycerides, total serum cholesterol, CVD history, urine albumin to creatinine ratio (ACR), chronic kidney disease-mineral and bone disorder (CKD-MBD), hyperlipidemia rate, diabetes, and estimated glomerular filtration rate (eGFR). The study duration was 4.55 years (IQR 3.52–5.28). Cox regression analysis revealed an association of NLR and the risk of ESRD only in patients with stage 4 CKD. We did not observe any significant associations between abnormal NLR and the risk of either CVD or all-cause mortality in CKD patients in general and CKD patients grouped according to the disease stages in particular. Conclusion Our results suggest that NLR is associated with the risk of ESRD in Chinese patients with stage 4 CKD. NLR can be used in risk assessment for ESRD among patients with advanced CKD; this application is appealing considering NLR being a routine test. Trial registration ClinicalTrials.gov Identifier NCT03041987. Registered January 1, 2012. (retrospectively registered) ( https://www.clinicaltrials.gov/ct2/show/NCT03041987?term=Chinese+Cohort+Study+of+Chronic+Kidney+Disease+%28C-STRIDE%29&rank=1 )

Introduction The novel systemic inflammation markers are a class of indicators combining clinically common laboratory indices, reflecting the underlying immune and inflammatory status. Minimal change disease (MCD) is an important cause of idiopathic nephrotic syndrome (INS) in adults. Novel systemic inflammation markers were evaluated for their ability to predict treatment response to steroids and subsequent relapse in adult-onset MCD. Methods This unicentric, retrospective study included the clinical data of adult-onset INS patients who were pathologically diagnosed with MCD at Xiangya Hospital of Central South University from January 2010 to December 2021 and followed up with the patients’ remission after adequate steroid treatment, starting from the time of initial complete remission to May 31, 2022, with recurrence as the end-point event. Eight common and associated novel systemic inflammation markers were collected, and univariate analysis of these markers was performed using dummy variable assignment or maximally selected rank statistics. Multivariate logistic regression and Cox regression statistics identified the independent risk factors for steroid resistance and relapse after initial remission in adult-onset MCD, respectively. Results A total of 121 patients were included; the median age was 22 (19,40) years, and there were 92 (76%) men. Adequate corticosteroids were the initial treatment: 98 (81%) patients achieved remission, and 23 (19%) developed steroid resistance. Of the 98 patients with remission, the median age was 25 (19, 44) years, 76 (77.6%) were male, the median follow-up time was 11.4 (6.3, 33.4) months, and 46 (46.9%) experienced relapse. The multivariate analysis showed that elevated C-reactive protein to albumin ratio (CAR) (≥ 0.196) and derived neutrophil/ (leukocyte minus neutrophil) ratio (dNLR) (≥ 1.32) were independent predictors of steroid resistance and relapse in adult-onset MCD, respectively. Conclusions The novel systemic inflammation markers CAR and dNLR may play significant roles in steroid treatment and the prognosis of MCD in adults, and deeper clinical studies in the future are warranted.

Despite the advantages of hydrogels, such as softness and biological affinity, their applications are often severely limited by the inadequate mechanical properties that result from their loose and homogeneous structure. Here, we propose an enhanced hydrogel structure prepared via a universal freeze-assisted bidirectional ion migration strategy, yielding energy dissipating structures from the millimeter scale to the nanometer scale. A dense‒porous‒dense sandwich structure surrounds a mineralized physical crosslinking center, which allows stress to pass between the multilayers of the interlayers and the mineralized center under force, resulting in hydrogels with a strength of 33.51 MPa, an fracture energy of 286.39 kJ·m -2 , and similar mechanical properties in the perpendicular and parallel directions in the plane. This strategy for constructing tough, robust hydrogels is broadly applicable to different combinations of mineralized ions and facilitates recyclability. This simple approach provides a general strategy for overcoming the long-standing application problems of hydrogels in harsh mechanical loading applications. Hydrogels have some desirable properties, but applications can be limited if they are too fragile. Here, the authors report the development of a freeze-assisted bidirectional ion migration strategy for the fabrication of tough hydrogels.

Precision improvement in mechanical manufacturing faces challenges due to nonlinear effects impacting assembly accuracy analysis models. An assembly accuracy analysis method based on multi‐stage linearized contact is proposed to address this issue. A model of part surface asperities considering morphological errors is established using the linear superposition of discrete cosine transform (DCT) kernel functions and the assembly interface is simplified. By employing homogeneous coordinate transformation (HCT), the prediction of the part's pose during the assembly process with the rigid body assumption is achieved. The elastic contact process is divided into multiple stages according to the order of the asperity participating in the contact and further subdivided into several linear processes by adding nodes at each stage. The relationship between the assembly load and the deformation amount is established based on the related theories of contact mechanics and the geometric relationship between the assembly interfaces, thus enabling the prediction of the part's pose during the assembly process. Taking a multi‐way hydraulic valve as an object, by comparing the accuracy of pose prediction of the algorithm before and after adding nodes, it is proved that the proposed method can significantly improve the precision of assembly accuracy analysis. The elastic contact process during assembly is described. The elastic contact process is divided into multiple stages according to the order of the asperity participating in the contact and further subdivided into several linear processes by adding nodes at each stage.

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward.