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Layered transition-metal oxides have attracted intensive interest for cathode materials of sodium-ion batteries. However, they are hindered by the limited capacity and inferior phase transition due to the gliding of transition-metal layers upon Na + extraction and insertion in the cathode materials. Here, we report that the large-sized K + is riveted in the prismatic Na + sites of P2-Na 0.612 K 0.056 MnO 2 to enable more thermodynamically favorable Na + vacancies. The Mn-O bonds are reinforced to reduce phase transition during charge and discharge. 0.901 Na + per formula are reversibly extracted and inserted, in which only the two-phase transition of P2 ↔ P’2 occurs at low voltages. It exhibits the highest specific capacity of 240.5 mAh g −1 and energy density of 654 Wh kg −1 based on the redox of Mn 3+ /Mn 4+ , and a capacity retention of 98.2% after 100 cycles. This investigation will shed lights on the tuneable chemical environments of transition-metal oxides for advanced cathode materials and promote the development of sodium-ion batteries. High-capacity and structural stable cathode materials are challenges for sodium-ion batteries. Here, the authors report a layered P2-Na 0.612 K 0.056 MnO 2 with large-sized K + riveted in the Na-layers to enable 0.9 Na + (de)insertion with a reversible phase transition of P2-P’2.

Chronic stress is a significant risk factor for depression as well as anxiety disorders. Yet, the stress-induced specific and common molecular dysregulations of these disorders have not been fully understood. Previously, we constructed a chronic mild stress (CMS) rat model to separate and obtain depression-susceptible, anxiety-susceptible, and insusceptible groups. In this study, the prefrontal cortical proteomes of the three stressed groups were comparatively profiled utilizing isobaric tags for relative and absolute quantitation (iTRAQ)-coupled tandem mass spectrometry approach. A total of 212 protein dysregulations were identified, potentially correlating to susceptibility or resilience to CMS-induced depression or anxiety, and thus might serve as potential protein targets for further investigation. In addition, independent analysis by parallel reaction monitoring identified changes in Gfap, Rhog, Gnai2, Ppp1r1b, and Uqcrh; Tubb6, Urod, Cul1, Spred1, and Gpcpd1; Acadl, Ppp1r1a, Grm2, Mtor, Lsm8, Cplx2, and Tsta3 that were distinctly correlated to depression-susceptible, anxiety-susceptible, or insusceptible groups, respectively. This suggested that identical CMS had different effects on the protein regulation system of the rat prefrontal cortex. Collectively, the present proteomics study of the prefrontal cortex established a significant molecular basis and offered new insights into the specificity and commonality of pathophysiologic mechanisms underlying susceptibility and resiliency to stress-induced depression or anxiety.

The integration of cyber and physical networks in modern power system introduces complex interdependencies that necessitate effective robustness assessment frameworks. In this paper, we propose a novel robustness assessment method for cyber-physical power systems (CPPS), which integrates structural and functional robustness. Firstly, an interdependent dynamic hierarchical network model that accounts for static topological structure, functional attributes and dynamic operational characteristics of cyber-physical power system is established. Based on the model, a probabilistic cascading failure model considering topological connectivity loss, power flow overload, cyber functional failures, and cyber-physical dependence is proposed. The proposed model quantifies the cross-layer impact of cyber-layer impairments (such as communication delay and data loss) on physical-layer operation. Finally, the impacts of cyber network performance and initial failure modes on the robustness of the coupled system are analyzed. The results show that an excellent processing performance and topological connectivity of cyber network can enhance the robustness of the coupled system, and the failure of high-degree nodes is more likely to trigger more severe cascading failure results than the failure of high-betweenness nodes.

Treatment for acute liver failure (ALF) is constrained by shortages of liver transplant donors and immune rejection. Porcine bone marrow mesenchymal stem cells (pBMSCs) demonstrate clinical potential in xenotransplantation due to their abundant availability, low immunogenicity, and strong proliferative activity. This study is the first to investigate the reparative effects and mechanisms of pBMSCs derived from Luopan Mountain pigs in a D-galactosamine (D-GalN)-induced ALF rat model. The results demonstrated that tail-vein transplantation of pBMSCs significantly improved survival rates in ALF rats; reduced serum ALT, AST, and TBIL levels; enhanced hepatic glycogen metabolism; and mitigated histopathological liver damage. Additionally, pBMSC transplantation upregulated serum HGF, IGF-1, and VEGF levels while inhibiting hepatocyte apoptosis. Mechanistic studies indicate that pBMSCs promote liver function recovery and regeneration by activating the PI3K/Akt/mTOR signaling pathway and suppressing its key negative regulator, PTEN, by regulating the expression of key genes involved in inflammation, fibrosis, proliferation, and apoptosis. This study provides crucial experimental evidence for the use of pBMSCs in treating acute liver failure (ALF) and lays the groundwork for its clinical translation in the field of xenotransplantation.

Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discusses feasible techniques for prediction across time scales, building levels, and energy consumption types in the context of the factors affecting data-driven building energy prediction. The review results revealed that the outdoor dry-bulb temperature is a vital factor affecting building energy consumption. In data-driven building energy consumption prediction, data preprocessing enables prediction across time scales, energy consumption feature extraction enables prediction across energy consumption types, and hyperparameter optimization enables prediction across time scales and building layers.

Background: While machine learning has advanced molecular conformation generation, existing models often suffer from limited generalization and inaccuracies, especially for complex molecular structures. These limitations hinder their reliability in downstream applications. Methods: We proposed a molecular conformation model combined with a molecular graph pre-training module and a diffusion model (PDCG). Feature embeddings are obtained from a pre-trained model and concatenated with the molecular graph information. Fusion features are used for generating conformations in the model. The model was trained and evaluated on the GEOM-QM9 and GEOM-Drugs datasets. Results: PDCG significantly outperforms existing baselines, which shows markedly superior results. Furthermore, in downstream molecular property prediction tasks, conformations generated by PDCG yield results comparable to those derived from DFT-optimized geometries. Conclusions: Our work provides a robust and generalizable model for accurate conformation generation. PDCG offers a reliable tool for downstream computational tasks, such as the virtual screening of functional materials and drug-like molecules.

The Stöber method is a widely-used sol-gel route for synthesizing amorphous SiO 2 colloids and conformal coatings. However, the material systems compatible with this method are still limited. Herein, we have extended the approach to metal-organic frameworks (MOFs) and coordination polymers (CPs) by mimicking the Stöber method. We introduce a general synthesis route to amorphous MOFs or CPs by making use of a base-vapor diffusion method, which allows to precisely control the growth kinetics. Twenty-four different amorphous CPs colloids were successfully synthesized by selecting 12 metal ions and 17 organic ligands. Moreover, by introducing functional nanoparticles (NPs), a conformal amorphous MOFs coating with controllable thickness can be grown on NPs to form core-shell colloids. The versatility of this amorphous coating technology was demonstrated by synthesizing over 100 core-shell composites from 20 amorphous CPs shells and over 30 different NPs. Besides, various multifunctional nanostructures, such as conformal yolk-amorphous MOF shell, core@metal oxides, and core@carbon, can be obtained through one-step transformation of the core@amorphous MOFs. This work significantly enriches the Stöber method and introduces a platform, enabling the systematic design of colloids exhibiting different level of functionality and complexity. What happens when the classical and famous Stöber method collides with popular Metal-Organic Frameworks and Coordination Polymers? The answer is Big Bang of the Amorphous Functional Colloids.

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot’s work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

Under the premise of ensuring the safe and stable operation of a wind–thermal–storage power system, this paper proposes an optimization model aimed at improving its overall economic efficiency and effectively reducing the peak-to-valley load difference. The model transforms the multi-objective optimization problem to solve a feasible interval of weight coefficients. We introduce a novel fusion model, where a Convolutional Neural Network (CNN) is melded with a Long Short-Term Memory Network (LSTM) to form the target network structure. Additionally, for datasets with limited samples, we incorporate a Self-Attention Mechanism (SAM) into the Model-Agnostic Meta-Learning (MAML). Ultimately, we build an MAML-SAM-CNN-LSTM network model to solve the interval of weight coefficients. An arithmetic validation of a modified IEEE 30-node system demonstrates that the MAML-SAM-CNN-LSTM network proposed in this paper can adeptly solve the feasible intervals of weight coefficients in the optimization model of the wind-thermal storage system. This is achieved under the constraints of the specified wind-thermal storage power system operation indexes. The evaluation indexes of the network model, including its accuracy, precision, recall, and F1 score, all exceed 98.72%, 98.57%, 98.30%, and 98.57%, respectively. This denotes a superior performance compared to the other three network models, offering an effective reference for optimizing decision-making and facilitating the enhanced realization of multi-objective, on-demand scheduling in the wind-thermal storage power system.

Despite the high energy density of lithium-rich (Li-rich) cathodes, their implementation is hampered by the unsatisfied rate capacity and poor cycling performance accompanied with substantial voltage decay. To address these issues, the hierarchical yolk-shell structured Li 1.2 Mn 0.54 Ni 0.13 Co 0.13 O 2 cathodes (YK-LMNCO) was proposed and synthesized through a facile glycerol assisted solvothermal approach and the following lithiation process. Benefitting from the shortened lithium diffusion lengths and the enhanced tolerance to the large volume variation upon lithium ions intercalation/de-intercalation, the unique structure reciprocates an initial coulombic efficiency of 85.8%, an outstanding capacity retention rate of 89.1% after cycling at 2.0 C for 200 cycles with a minor voltage drop, and a capacity retention rate of 93.8% after cycling at 10.0 C for 500 cycles, 85.2% for 1,000 cycles. When assembled with graphite as anode, the YK-LMNCO//graphite full cell shows a remarkable capacity retention rate of 87.2% after cycling at 5.0 C for 50 cycles. Our facile strategy for constructing the yolk-shell structured Li-rich cathodes with high capacity and voltage stability sheds light on synthesizing other lithium storage materials.