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• The high-density nanocrystalline soft magnetic composites with bioinspired nacre-like structures were successfully fabricated using flaky-Fe 73.8 Si 13.5 B 8.7 Cu 1 Nb 3 powders in the supercooled liquid region. • These soft magnetic composites exhibit soft magnetism enhancement and eddy current suppression due to laminated structure and elaborate coating layer. • The designed soft magnetic composites exhibit excellent magnetic performances, which achieve a high maximum permeability of 2900 at 1000 Hz and low magnetic loss of 41.3 W/kg (at 1000 Hz and 1.0 T). It provides great potential for practical application in high-speed electrical machines. Bioinspired nacre-like structured high-density soft magnetic composites (SMCs) have been successfully constructed using flaky-Fe 73.8 Si 13.5 B 8.7 Cu 1 Nb 3 powders in the supercooled liquid region (SCLR). These densely arranged particles with a consistent planar orientation significantly enhance the soft magnetic properties of SMCs, including high permeability and low magnetic losses. The internal structures of the composites and microstructure evolution of the flaky nanocrystalline particles during the hot-pressing process have been thoroughly studied. Moreover, systematic investigations into the effects of coatings and particle sizes on the maximum permeability and magnetic losses of the composites are conducted. The SMC prepared using the coated particles with a size of 0–100 µm exhibits a high maximum permeability of 2170 (at 1000 Hz) and low magnetic loss of 41.61 W kg–1 (at 1000 Hz and 1.0 T). The losses and permeability analysis reveal that the superior performance of these soft magnetic materials is attributed to their laminated structure, insulation coating, and the reduced planar demagnetizing factor. Compared to the traditional silicon steel, this novel SMCs exhibits high magnetic permeability and reduced magnetic losses at frequencies above 1000 Hz, which possess immense application potential within high-frequency electric machines. [Display omitted] [ABSTRACT FROM AUTHOR]

• Single-phase anti-perovskite medium-entropy alloy nitride foams with hierarchical multi-level pore size were prepared by N-atom induced lattice expansion engineering. • The formation mechanism of single-phase anti-perovskite medium-entropy alloy nitride foams was revealed through microstructure evolution and theoretical calculation. • The foams exhibits a promotion of electromagnetic wave dissipation due to meticulous composition design and microstructure optimization. • The designed foams exhibit excellent performance, which achieves an EAB max of 5.28 GHz with a matching thickness of 1.69 mm, along with a low density about 0.028 g/cm3. It provides a great potential for practical application in the future. A single-phase anti-perovskite medium-entropy alloy nitride foams (MEANFs), as innovative materials for electromagnetic wave (EMW) absorption, have been successfully synthesized through the lattice expansion induced by nitrogen doping. This achievement notably overcomes the inherent constraints of conventional metal-based absorbers, including low resonance frequency, high conductivity, and elevated density, for the synergistic advantages provided by multimetallic alloys and foams. Microstructural analysis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism, electronic structure, and magnetic moment of MEANFs. Furthermore, deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and absorption. The results show that the MEANFs exhibit a minimum reflection loss (RL min) value of −60.32 dB and a maximum effective absorption bandwidth (EAB max) of 5.28 GHz at 1.69 mm. This augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure, interfacial polarization, defect-induced polarization, and magnetic resonance. This study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys, emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties. [Display omitted] [ABSTRACT FROM AUTHOR]

Forests are the most extensive terrestrial ecosystems in the world. Their vertical structure information not only reflects the spatial structure characteristics of the forest but also the physiological and ecological processes. The existing studies on forest vertical structure through remote sensing often encounter challenges such as high costs, difficulties in acquiring effective data, or complexities in data processing. In this study, a new technology for detecting the vertical structure of vegetation is proposed and validated, which can obtain the vertical structure of vegetation by single flight. In order to adopt the new technology, a radar system was developed, incorporating a modular design scheme that emphasizes high integration and lightweight characteristics, and it was deployed on an unmanned aerial vehicle (UAV) flight platform. It consists of a main control unit, a signal processing unit, and a data recording unit, which weighs only 0.92 kg in total. Meanwhile, a novel algorithm is proposed to reconstruct the vertical structure of targets, effectively addressing critical challenges in UAV radar signal processing, such as strong system coupled signal, significant noise in radar signals, and high sidelobes in images. In order to validate the capability of the new technology, UAV flight experiments, as well as in situ observation, were carried out in typical vegetation areas. The proposed algorithm was used to process the acquired radar echoes, yielding a root-mean-square error (RMSE) of 1.33 m for the vegetation height compared to the ground synchronous measurement.

ABSTRACTMacroautophagy/autophagy activation in renal tubular epithelial cells protects against acute kidney injury (AKI). However, the role of immune cell autophagy, such as that involving macrophages, in AKI remains unclear. In this study, we discovered that macrophage autophagy was an adaptive response during AKI as mice with macrophage-specific autophagy deficiency (atg5−/−) exhibited higher serum creatinine, more severe renal tubule injury, increased infiltration of ADGRE1/F4/80+macrophages, and elevated expression of inflammatory factors compared to WT mice during AKI induced by either LPS or unilateral ischemia-reperfusion. This was further supported by adoptive transfer of atg5−/−macrophages, but not WT macrophages, to cause more severe AKI in clodronate liposomes-induced macrophage depletion mice. Similar results were also obtained in vitro that bone marrow-derived macrophages (BMDMs) lacking Atg5largely increased pro-inflammatory cytokine expression in response to LPS and IFNG. Mechanistically, we uncovered that atg5deletion significantly upregulated the protein expression of TARM1 (T cell-interacting, activating receptor on myeloid cells 1), whereas inhibition of TARM1 suppressed LPS- and IFNG-induced inflammatory responses in atg5−/−RAW 264.7 macrophages. The E3 ubiquitin ligases MARCHF1 and MARCHF8 ubiquitinated TARM1 and promoted its degradation in an autophagy-dependent manner, whereas silencing or mutation of the functional domains of MARCHF1 and MARCHF8 abolished TARM1 degradation. Furthermore, we found that ubiquitinated TARM1 was internalized from plasma membrane into endosomes, and then recruited by the ubiquitin-binding autophagy receptors TAX1BP1 and SQSTM1 into the autophagy-lysosome pathway for degradation. In conclusion, macrophage autophagy protects against AKI by inhibiting renal inflammation through the MARCHF1- and MARCHF8-mediated degradation of TARM1.Abbreviations:AKI, acute kidney injury; ATG, autophagy related; Baf, bafilomycin A1; BMDMs, bone marrow-derived macrophages; CCL2/MCP-1, C-C motif chemokine ligand 2; CHX, cycloheximide; CQ, chloroquine; IFNG, interferon gamma; IL, interleukin; IR, ischemia-reperfusion; MAP1LC3/LC3, microtubule-associated protein 1 light chain 3; LPS, lipopolysaccharide; MARCHF, membrane associated ring-CH-type finger; NC, negative control; NFKB, nuclear factor of kappa light polypeptide gene enhancer in B cells; NLRP3, NLR family, pyrin domain containing 3; NOS2, nitric oxide synthase 2, inducible; Rap, rapamycin; Wort, wortmannin; RT-qPCR, real-time quantitative polymerase chain reaction; Scr, serum creatinine; SEM, standard error of mean; siRNA, small interfering RNA; SYK, spleen tyrosine kinase; TARM1, T cell-interacting, activating receptor on myeloid cells 1; TAX1BP1, Tax1 (human T cell leukemia virus type I) binding protein 1; TECs, tubule epithelial cells; TNF, tumor necrosis factor; WT, wild type.

BackgroundTo delineate the clinical and mutational signatures of patients with CRB1-associated retinopathies.MethodsThis multicentre retrospective cohort study involved 40 patients with CRB1mutations and 40 age-matched and gender-matched inherited retinal diseases (IRDs). The detailed phenotyping and genotyping characteristics and genotype‐phenotype correlations of the patients were analysed.ResultsThe mean age of CRB1cohort was 27.33±14.63 years. Results showed that yellowish geographic macular degeneration (66.67%), small white or yellow dots (65.6%), hyperopia (62.5%), abnormally laminated retina (61.61%), epiretinal membrane (60.6%) and nummular pigment deposits (50%) were the most common signatures in patients with CRB1mutations. These clinical signatures were notably more prevalent among CRB1patients than among individuals in other IRD groups (p<0.001). Early-onset severe retinal dystrophy/Leber congenital amaurosis (EOSRD/LCA) patients are more likely to present these signatures than retinitis pigmentosa (RP) and macular dystrophy (MD) patients. Furthermore, a significant reduction in central foveal thickness coupled with pronounced thickening of the peripheral retina was observed more distinctly in patients with EOSRD/LCA (p<0.001). The choroidal thickness was not significantly altered compared to the normal controls, but was markedly reduced in the other IRD groups (p<0.001). 55 pathogenic variants were identified, 20 of which were novel. Null mutations were associated with EOSRD/LCA patients, and missense mutations were more prevalent in MD and RP patients.ConclusionsKey clinical and mutational signatures were demonstrated in this study, providing a comprehensive update on CRB1-associated retinopathies that will aid in diagnosis and lay the foundation for future therapeutic studies.

Battery production line is crucial for determining the performance of batteries, further significantly affecting the industrial applications of relevant energy systems. As a complex and multidisciplinary system involving electrical, mechanical, and chemical processes, efficient prediction of manufactured battery properties and explainable analysis of strongly coupled battery production variables becomes an important but challenging issue for the wider application of batteries. In this article, an interpretable AI solution based on generalized additive model with interactive features and interpretability (GAM-IFI) is proposed to effectively predict battery capacities in the early phase of battery manufacturing and explain the effects of involved coating features. The designed solution is evaluated by using reliable production data from a real battery manufacturing line. Illustrative results show that the proposed solution is able to accurately predict three different types of battery capacities with an $R^{2}$ over 0.98. Moreover, information regarding the importance ratio of both main effect and pairwise interaction terms derived from three coating features is identified, while global and local interpretations of the effects of these terms can be well explained. The developed interpretable solution opens a promising avenue to identify the importance of battery production features and explain how the variation of these features influences the properties of battery products. This can help engineers to better understand the underlying complex behaviors in battery production, which in turn will benefit the digitalization of battery manufacturing.

This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by using an offline iterative method under the time-triggered control framework. Then, starting from the stability of the constrained system, a nonperiodic ETC method and a periodic ETC method are developed by adopting an online learning algorithm. In addition, four kinds of neural networks are constructed for the implementation of the event-based online $H_{\infty }$ optimal control strategy. Finally, two experimental examples with physical backgrounds are provided to illustrate the effectiveness and superiority of the developed schemes.

This article focuses on addressing the distributed three-dimensional (3-D) formation maneuver control problem of multiple autonomous underwater vehicles (AUVs) in undersea exploration. The 3-D affine formation maneuver strategy by fusion of the stress matrix is employed to maneuver the multi-AUV formation for the sake of achieving the formation pattern maneuver transformations (e.g., rotation, scaling, shear, coplanarity, collineation, etc.). The desired yaw angle of each AUV is calculated in real time on the basis of its individual desired trajectory, which can guarantee the attitude configuration for the multi-AUV formation. Meanwhile, the two-layered leader-follower framework is employed to guarantee the flexible maneuvers, in which the AUVs are divided into three layers: 1) first leader AUV; 2) second leader AUV group; and 3) follower AUV group. The desired formation maneuver information only needs to be known by the first leader AUV. For the second AUV group, the formation maneuver information is estimated by the designed distributed estimators to acquire their desired reference trajectories. The follower AUV group only needs to track their desired trajectories affinely localized by the leader AUVs. Furthermore, the adaptive fast nonsingular integral terminal sliding-mode controllers are designed for the leader layers and follower layer to guarantee the fast and accurate tracking of their desired trajectories, such that the multi-AUV formation system is able to realize the precise maneuvers. Finally, the effectiveness of the proposed control strategy is verified by the high-fidelity simulations with real water current disturbances.

In wastewater treatment processes (WWTPs), data and knowledge are employed to build an effective model for monitoring its operation. Unfortunately, they are difficult to be fused due to their heterogeneity, which struggles to provide a united and reliable solution. To solve this issue, a data-knowledge-driven inductive learning (DKIL) method is introduced to WWTPs. First, a fuzzy-based expression strategy is introduced to describe the operational status of WWTPs. This strategy captures the available data, constraint knowledge and semantic knowledge for the modeling process. Second, a heterogeneous assimilation mechanism is designed to integrate data and knowledge. This mechanism supports their interaction to form a unified scheme through fusion operations. Third, a collaborative optimization algorithm is developed to extract the operational features of WWTPs. This algorithm updates the parameters using both error information and semantic knowledge, which enhances the modeling performance. In the experiment, the results have verified that DKIL can efficiently model WWTPs.

Free-space optical (FSO) technologies face significant challenges from atmospheric turbulence, which can degrade transmission performance. Adaptive optics (AO) techniques offer a promising solution by compensating for turbulence-induced optical aberrations in real-time. In this paper, we propose a deep learning-based AO compensation scheme utilizing a mixed convolutional channel attention prediction network (MCCA-PNet). The network establishes a mapping relationship between distorted beam intensity information and atmospheric turbulence, enabling real-time compensation on a spatial light modulator. In order to address the challenge of limited datasets across diverse atmospheric conditions, we introduce a personalized federated learning approach named federated layer similarity aggregation algorithm (FedLaySim). The proposed method enables the exchange of feature information while preserving data privacy through the use of a layer similarity algorithm. Compared to conventional local training methods, personalized federated training enhances phase screen prediction accuracy by 1.98 dB. The average mode purity of the compensated vortex beams (VB) reaches 0.96. Furthermore, the applicability of our AO compensation scheme in actual, real-world environments is verified through the use of atmospheric simulation in an environmental chamber.

Model predictive control (MPC) is a practical method for addressing control issues in constrained systems. System identification and constrained optimization are two key problems that affect MPC performance. In this work, a self-organizing MPC (SOMPC) strategy is proposed for constrained nonlinear systems with unknown dynamics to achieve constraint satisfaction and improve control performance. First, the generalized multiplier method is introduced into the MPC framework to redesign the objective function. In this way, the constrained optimal control problem is reconstructed into an easily solvable unconstrained optimal problem. Second, a self-organizing fuzzy neural network (SOFNN) is adopted to identify unknown nonlinear system. Then, the performance of SOFNN is optimized by parameter updating and structure self-organization to provide accurate prediction output. Third, the gradient descent algorithm is utilized to solve nonlinear optimization problem of MPC to obtain control input. To ensure practical application, the convergence of SOFNN, the feasibility and stability of SOMPC strategy are proved. Finally, the proposed SOMPC strategy is demonstrated by a numerical experiment and an industrial process control simulation experiment, and the results show that it exhibits outstanding control performance and constraint satisfaction ability.

In order to improve the voltage distribution and operation cost for ADN, A scheduling strategy is designed to integrate flexible resources, particularly mobile energy storage systems, within the coupling of ADN and TN, under an uncertain environment. A day-ahead Volt-VAR coordinated scheduling framework for ADN and TN can be proposed through incorporating a data-driven day-ahead scenario generation method based on denoising diffusion probabilistic model. First, the historical data may be employed to learn the error relationship between real power curves and predicted power curves for generating RES scenarios. The probability distribution for prediction error is constructed which can describe the day-ahead output power curve of RES. Subsequently, a SCCO approach is employed to measure voltage operating risk in uncertain environment, which can effectively utilize the controllability of different resource at timescale and spatial scale in ADN to fulfill the anticipated operational requirement. Finally, The ADN coupled with TN model can be linearized and converted into the mixed-integer linear programming method. Numerical simulations based on the IEEE 33-bus distribution system coupled with 15-node transportation network may verify the effectiveness of the proposed method.

With the advancement of deep Earth exploration and urban underground space detection, addressing the challenges posed by environmental complexity and increasing data acquisition accuracy are increasingly critical. Conventional geophysical exploration techniques and their associated networking approaches cannot fulfill contemporary exploration requirements. Herein, the fundamental principles and inherent connections between seismic exploration and electromagnetic exploration are analyzed by comparing the circuit design for seismic and electromagnetic data-acquisition systems. Consequently, a technical solution for a seismo-electromagnetic joint acquisition system with high accuracies in both seismic and electromagnetic explorations is developed. A real-time host monitoring system is developed for both personal computers and mobile terminals. Moreover, a distributed hybrid networking scheme for this distributed seismo-electromagnetic high-accuracy acquisition system that incorporates wired, wireless, and self-storage intelligent arbitrary clipping is innovatively implemented, achieving a synchronization accuracy of <100 ns among various acquisition systems. The developed system is used for networking deployment in Zhangjiakou (Hebei, China), verification of key indicators in a coal mine with a depth of 848 m in Huainan (Anhui, China), and practical onsite explorations in Changzhi (Shanxi, China). The experimental and application results verify the reliability, stability, and practicality of the system. The proposed joint exploration technique fully leverages the advantages of distributed data-acquisition systems and can potentially shed light on the development of other joint research methodologies. The findings can increase the efficiency and accuracy of joint geophysical explorations, providing a solid foundation with regard to both machinery and technical aspects.

In action recognition, models often suffer from representation bias, focusing too much on background context rather than the action itself, which limits their ability to generalize. Existing methods suggest that incorporating differential inputs and utilizing dual-path structural designs could separate spatial and temporal representations. However, these approaches still rely on spatial hints and struggle to capture fine-grained temporal features. We propose a novel regularization technique, called Active Embedding Distance Correlation (AEDC), which is integrated into dual-path networks. AEDC minimizes the distance correlation between temporal and spatial embeddings, enabling spatially and temporally independent modeling. Our experiments show AEDC improves performance by 0.6% on SSV2 and 2.4% on TA50 compared to existing dual-path baselines. Ablation studies confirm that AEDC reduces scene bias and boosts robustness against video input variations.

Compared with TZ1 and TZ2, TZ3 seriously impacts colposcopic diagnosis and warrants serious consideration in clinical practice.

Copper slag is mostly considered a waste material and around 41 wt% of total iron content present in it is lost. Iron is mostly present in copper slag as fayalite (Fe2SiO4). In this study, Iron was recovered by the decomposition of Fe2SiO4using the synergistic effect of carbothermal reduction and sodium salts leaching for the first time. The results showed that 68.39 % of iron was recovered from copper slag by carbothermal reduction at 1000 °C for 1 hr. The iron recovery increased to 83.45, 93.39 and 97.8 %, respectively, after further 1 hr leaching by 2 M NaOH, 1 M Na2CO3and 2 M NaOH+1 M Na2CO3at 100 °C. The leaching results indicated that the increased NaOH concentration from 1 to 2 M improved the removal of silica, which in turn led to higher iron recovery. The increase of Na2CO3concentration from 1 to 2 M showed a negative effect on iron recovery. The combination of 2 M NaOH and 1 M Na2CO3, however, led to the highest iron recovery of 97.8 % because NaOH+Na2CO3gives Na2O that is easier to react with SiO2with only −194.3 kJ energy requirement. Mass balance calculations indicated that before the leaching process, 345 kg mass was lost, whereas, after the leaching process the amount of discarded waste was only 260.8 g. Therefore, the present study not only provides 97.8 % iron recovery but also reduces the discharge of secondary solid waste due to no addition of additives.

The concentrator system is widely used in photovoltaic power generation technology. This letter introduces the concentrator system into orientation detection. It proposes an innovative method combining a non-imaging concentrator system, a data collection electronic system, and a deep residual network(ResNet) algorithm optimized through hyperparameter search. The concentrator system comprises a primary optical element(POE) and a secondary optical element(SOE) array, while the electronic system is built around a PIN photodiode array. Experimental results validate the feasibility of this novel orientation detection method for the first time. This research offers a fresh perspective and technical foundation for orientation detection and identification.

Conventional microwave imaging algorithms excel in scenarios involving only single scattering, but often encounter imaging artifacts when processing higher order scattering. Previous studies have demonstrated the possibility for accurate imaging of higher order scattering fields in typical planar concave objects. However, these algorithms still encounter difficulties when applied to curved objects with unfixed reflection planes. To solve this problem, we propose an iterative tracing algorithm to establish the forward propagation model of the curved objects. Then an iterative reconstruction method is introduced to automatically separate the scattered fields of each order and accurately reconstruct the target by compensating for both amplitude and phase using the forward model. The illumination areas on the target for different high-order scattering components are further quantitatively analyzed using the proposed forward model. We compare our proposed method with the state-of-the-art imaging algorithms in terms of computational complexity and imaging accuracy. Numerical simulations and experiments are performed to validate the effectiveness of our algorithm.