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The operation of traditional aqueous-electrolyte zinc-ion batteries is adversely affected by the uncontrollable growth of zinc dendrites and the occurrence of side reactions. These problems can be avoided by the development of functional hydrogel electrolytes as replacements for aqueous electrolytes. However, the mechanism by which most hydrogel electrolytes inhibit the growth of zinc dendrites on a zinc anode has not been investigated in detail, and there is a lack of a large-scale recovery method for mainstream hydrogel electrolytes. In this paper, we describe the development of a recyclable and biodegradable hydrogel electrolyte based on natural biomaterials, namely chitosan and polyaspartic acid. The distinctive adsorptivity and inducibility of chitosan and polyaspartic acid in the hydrogel electrolyte triggers a double coupling network and an associated synergistic inhibition mechanism, thereby effectively inhibiting the side reactions on the zinc anode. In addition, this hydrogel electrolyte played a crucial role in an aqueous acid-based Zinc/MnO 2 battery, by maintaining its interior two-electron redox reaction and inhibiting the formation of zinc dendrites. Furthermore, the sustainable biomass-based hydrogel electrolyte is biodegradable, and could be recovered from the Zinc/MnO 2 battery for subsequent recycling. Functional hydrogel electrolytes show promising potential for enhancing the sustainability of aqueous zinc-ion batteries. Here, the authors introduce a biomass-based hydrogel electrolyte that not only prevents side reactions on the zinc anode but also enables easy retrieval from the zinc batteries.

How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes’ movement speed, rhythm and duration on the scoring, thus reflecting the athletes’ skill level more realistically. Second, to further improve the objectivity and accuracy, an adaptive weighted multi-model decision-making strategy is proposed. In addition, this study is the first to use interpretable artificial intelligence to provide feedback for teaching and learning Wushu. Experimental results indicate that the integrated model using the weighted average strategy not only outperforms other algorithms after feature alignment (on the XSQ dataset MAE is 0.237, RMSE is 0.442, sMAPE is 8.569, R is 0.633, Pearson’s correlation is 0.807, ICC is 0.856. on the UMONS-Taichi dataset, MAE is 0.29, RMSE is 0.438, sMAPE is 10.01, R is 0.844, Pearson correlation is 0.921, and ICC is 0.954. on the PBB dataset, MAE is 0.261, RMSE is 0.351, sMAPE is 6.765, R is 0.557, Pearson correlation is 0.753, and ICC is 0.82), but is also close to the performance of human experts. In conclusion, this study not only achieves a performance of movement evaluation comparable to human experts, but also provides a technical framework for the rapid realizing of automatic scoring in other martial arts styles, which will promote the popularization and development of martial arts education.

Liquid ammonia is an ideal zero-carbon fuel for internal combustion engines. High-pressure injection is a key technology in organizing ammonia combustion. Characteristics of high-pressure liquid ammonia injection is lack of research. Spray behaviors are likely to change when a high-pressure diesel injector uses liquid ammonia as its fuel. This study uses high-speed imaging with a DBI method to investigate the liquid penetration, width, and spray tip velocity of high-pressure liquid ammonia injection up to 100 MPa. Non-flash and flash boiling conditions were included in the experimental conditions. Simulation was also used to evaluate the results. In non-flash boiling conditions, the Hiroyasu model provided better accuracy than the Siebers model. In flash boiling conditions, a phenomenon was found that liquid penetration and spray tip velocity were strongly suppressed in the initial stage of the injection process, this being the “spray resistance phenomenon”. The “spray resistance phenomenon” was observed when ambient pressure was below 0.7 MPa during 0–0.05 ms ASOI and was highly related to the superheated degree. The shape of near-nozzle sprays abruptly changed at 0.05 ms ASOI, indicating that strong cavitation inside the nozzle caused by needle lift effects is the key reason for the “spray resistance phenomenon”.

Accurate daily surface soil moisture (SSM) mapping at high spatial resolution (e.g., 30 m) remains challenging due to individual satellite sensor limitations. Although passive microwave sensors provide frequent coarse-resolution observations and synthetic aperture radar (SAR) offers high-resolution data intermittently, achieving both simultaneously requires sensor synergy. This paper introduces the microwave–optical multi-stage synergistic downscaling framework (MMSDF) to generate daily 30 m SSM products. The framework integrates SMAP L4 (9 km), MODIS data (500 m–1 km), harmonized Landsat Sentinel-2 (HLS, 30 m), radiometric terrain corrected Sentinel-1 (RTC-S1, 30 m), and auxiliary geographic data. It comprises three stages: (1) downscaling SMAP L4 to 1 km via random forest; (2) calibrating Sentinel-1 water cloud model (WCM) using intermediate 1 km SSM to retrieve 30 m SSM without in situ calibration; and (3) fusing daily 1 km SSM and intermittent 30 m WCM-derived retrievals using the spatial–temporal fusion model (ESTARFM) to generate seamless daily 30 m SSM maps. Validation against in situ measurements from 16 sites in Hunan Province, China (summer 2024) yielded R of 0.54 and RMSE of 0.045 cm3/cm3. Results demonstrate the framework’s capability to synergize multi-source data for high-resolution daily SSM estimates valuable for hydrological and agricultural applications.

Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels.

Chronic kidney disease affects approximately 14.3% of people worldwide. Tubulointerstitial fibrosis is the final stage of almost all progressive CKD. To date, the pathogenesis of renal fibrosis remains unclear, and there is a lack of effective treatments, leading to renal replacement therapy. Mitophagy is a type of selective autophagy that has been recognized as an important way to remove dysfunctional mitochondria and abrogate the excessive accumulation of mitochondrial-derived reactive oxygen species (ROS) to balance the function of cells. However, the role of mitophagy and its regulation in renal fibrosis need further examination. In this study, we showed that mitophagy was induced in renal tubular epithelial cells in renal fibrosis. After silencing BNIP3, mitophagy was abolished in vivo and in vitro, indicating the important effect of the BNIP3-dependent pathway on mitophagy. Furthermore, in unilateral ureteral obstruction (UUO) models and hypoxic conditions, the production of mitochondrial ROS, mitochondrial damage, activation of the NLRP3 inflammasome, and the levels of αSMA and TGFβ1 increased significantly following BNIP3 gene deletion or silencing. Following silencing BNIP3 and pretreatment with mitoTEMPO or MCC950, the protein levels of αSMA and TGFβ1 decreased significantly in HK-2 cells under hypoxic conditions. These findings demonstrated that HIF1α-BNIP3-mediated mitophagy played a protective role against hypoxia-induced renal epithelial cell injury and renal fibrosis by reducing mitochondrial ROS and inhibiting activation of the NLRP3 inflammasome.

Most of the available divisible-load scheduling models assume that all servers in networked systems are idle before workloads arrive and that they can remain available online during workload computation. In fact, this assumption is not always valid. Different servers on networked systems may have heterogenous available times. If we ignore the availability constraints when dividing and distributing workloads among servers, some servers may not be able to start processing their assigned load fractions or deliver them on time. In view of this, we propose a new multi-installment scheduling model based on server availability time constraints. To solve this problem, we design an efficient heuristic algorithm consisting of a repair strategy and a local search strategy, by which an optimal load partitioning scheme is derived. The repair strategy guarantees time constraints, while the local search strategy achieves optimality. We evaluate the performance via rigorous simulation experiments and our results show that the proposed algorithm is suitable for solving large-scale scheduling problems employing heterogeneous servers with arbitrary available times. The proposed algorithm is shown to be superior to the existing algorithm in terms of achieving a shorter makespan of workloads.

We report a rare case of anterior cruciate ligament (ACL) tear combined with acute avulsion fracture of the medial and lateral meniscal posterior roots in a 23-year-old male patient. The management strategy was focused on how to do arthroscopic trans-tibial pull-out fixation of the avulsed fracture fragment and ACL reconstruction simultaneously, avoiding the possibility of the tibia tunnel interference during drilling. At the 8-year postoperative follow-up, the patient demonstrated excellent knee function and returned to non-competitive football activities, and manual knee laxity tests were negative. Plain radiographs of the knee showed complete bone healing of the avulsed fracture fragment without osteophyte formation and joint space narrowing. Magnetic resonance imaging showed good ACL graft integrity, and the tibial fragment was connected with the native insertion site.

Solid-state electrolytes (SSEs), particularly garnet-type Li6.4La3Zr1.4Ta0.6O12 (LLZTO), offer high stability and a wide electrochemical window. However, their grain boundaries limit ionic conductivity, necessitating high-temperature sintering for improved performance. Yet, this process results in brittle electrolytes prone to fracture during manufacturing. To address these difficulties, solvent-free solid-state electrolytes with a polyacrylonitrile (PAN) coating on LLZTO particles are reported in this work. Most notably, the PAN-coated LLZTO (PAN@LLZTO) electrolyte demonstrates self-supporting characteristics, eliminating the need for high-temperature sintering. Importantly, the homogeneous polymeric PAN coating, synthesized via the described method, facilitates efficient Li+ transport between LLZTO particles. This electrolyte not only achieves an ionic conductivity of up to 2.11 × 10−3 S cm−1 but also exhibits excellent interfacial compatibility with lithium. Furthermore, a lithium metal battery incorporating 3% PAN@LLZTO-3%PTFE as the solid-state electrolyte and LiFePO4 as the cathode demonstrates a remarkable specific discharge capacity of 169 mAh g−1 at 0.1 °C. The strategy of organic polymer-coated LLZTO provides the possibility of a green manufacturing process for preparing room-temperature sinter-free solid-state electrolytes, which shows significant cost-effectiveness.

Claudin18.2 (CLDN18.2) is overexpressed in cancers of the digestive system, rendering it an ideal drug target for antibody-drug conjugates (ADCs). Despite many CLDN18.2-directed ADCs undergoing clinical trials, the inconclusive underlying mechanisms pose a hurdle to extending the utility of these agents. In our study, αCLDN18.2-MMAE, an ADC composed of an anti-CLDN18.2 monoclonal antibody and the tubulin inhibitor MMAE, induced a dose-dependent apoptosis via the cleavage of caspase-9/PARP proteins in CLDN18.2-positive gastric cancer cells. It was worth noting that autophagy was remarkably activated during the αCLDN18.2-MMAE treatment, which was characterized by the accumulation of autophagosomes, the conversion of autophagy marker LC3 from its form I to II, and the complete autophagic flux. Inhibiting autophagy by autophagy inhibitor LY294002 remarkably enhanced αCLDN18.2-MMAE-induced cytotoxicity and caspase-mediated apoptosis, indicating the cytoprotective role of autophagy in CLDN18.2-directed ADC-treated gastric cancer cells. Combination with an autophagy inhibitor significantly potentiated the in vivo antitumoral efficacy of αCLDN18.2-MMAE. Besides, the Akt/mTOR pathway inactivation was demonstrated to be implicated in the autophagy initiation in αCLDN18.2-MMAE-treated gastric cancer cells. In conclusion, our study highlighted a groundbreaking investigation into the mechanism of the CLDN18.2-directed ADC, focusing on the crucial role of autophagy, providing a novel insight to treat gastric cancer by the combination of CLDN18.2-directed ADC and autophagy inhibitor.