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Topology and chirality of fermionic quasiparticles have enabled exciting discoveries, including quantum anomalous Hall liquids and topological superconductivity. Recently, topological and chiral phonons emerge as new and fast-evolving research directions. While these concepts are separately developed, they are intimately connected in the context of Weyl phonons. The couplings between chiral and topological phonons with various electronic and magnetic quasiparticles are predicted to give rise to new quantum states and giant magnetism with fundamental and applicational interests, ranging from quantum information science to dark matter detectors.

Background Robot-Assisted Gait Training (RAGT) is a novel technology widely employed in the field of neurological rehabilitation for patients with subacute stroke. However, the effectiveness of RAGT compared to conventional gait training (CGT) in improving lower extremity function remains a topic of debate. This study aimed to investigate and compare the effects of RAGT and CGT on lower extremity movement in patients with subacute stroke. Methods Comprehensive search was conducted across multiple databases, including PubMed, Web of Science, Cochrane Library, EBSCO, Embase, Scopus, China National Knowledge Infrastructure, Wan Fang, SinoMed and Vip Journal Integration Platform. The database retrieval was performed up until July 9, 2024. Meta-analysis was conducted using RevMan 5.4 software. Results A total of 24 RCTs were included in the analysis. The results indicate that, compared with CGT, RAGT led to significant improvements in the Fugl-Meyer Assessment for Lower Extremity [MD = 2.10, 95%CI (0.62, 3.59), P = 0.005], Functional Ambulation Category[MD = 0.44, 95%CI (0.23, 0.65), P < 0.001], Berg Balance Scale [MD = 4.55, 95%CI (3.00, 6.11), P < 0.001], Timed Up and Go test [MD = −4.05, 95%CI (−5.12, −2.98), P < 0.001], and 6-Minute Walk Test [MD = 30.66, 95%CI (22.36, 38.97), P < 0.001] for patients with subacute stroke. However, it did not show a significant effect on the 10-Meter Walk Test [MD = 0.06, 95%CI (−0.01, 0.14), P = 0.08]. Conclusions This study provides evidence that RAGT can enhance lower extremity function, balance function, walking ability, and endurance levels compared to CGT. However, the quality of evidence for improvements in gait speed remains low.

Potassium-ion batteries (KIBs) are promising electrochemical energy storage systems because of their low cost and high energy density. However, practical exploitation of KIBs is hampered by the lack of high-performance cathode materials. Here we report a potassium manganese hexacyanoferrate (K 2 Mn[Fe(CN) 6 ]) material, with a negligible content of defects and water, for efficient high-voltage K-ion storage. When tested in combination with a K metal anode, the K 2 Mn[Fe(CN) 6 ]-based electrode enables a cell specific energy of 609.7 Wh kg −1 and 80% capacity retention after 7800 cycles. Moreover, a K-ion full-cell consisting of graphite and K 2 Mn[Fe(CN) 6 ] as anode and cathode active materials, respectively, demonstrates a specific energy of 331.5 Wh kg −1 , remarkable rate capability, and negligible capacity decay for 300 cycles. The remarkable electrochemical energy storage performances of the K 2 Mn[Fe(CN) 6 ] material are attributed to its stable frameworks that benefit from the defect-free structure. Potassium-ion battery is a promising candidate for post-Li-ion energy storage but the lack of cathode materials hinders practical exploitation. Here the authors investigate defect-free potassium manganese hexacyanoferrate as cathode active material for high energy and long lifespan K-based cells.

Neural networks based on memristive devices 1 – 3 have the ability to improve throughput and energy efficiency for machine learning 4 , 5 and artificial intelligence 6 , especially in edge applications 7 – 21 . Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks 22 – 28 . This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even ‘mortal computing’ 25 , 29 , 30 . Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Chips with 256 × 256 memristor arrays that were monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry achieved 2,048 conductance levels in individual memristors.

The Wi‐Fi‐based human activity recognition shows immense potential, as it is device‐free, non‐intrusive to privacy, and low‐cost. However, current learning‐based recognition methods mostly adopt the hybrid representation without distinguished contributions of features to different activities, which will be seriously affected by environment variations and interference of other persons, and costly to extend to new activities. Therefore, this paper proposes HR‐HAR, a hierarchical relation representation for human activity recognition, to improve the performance, extensibility, and robustness by exploiting the hierarchical relation of features of activities. The hierarchical relation reflects the different contributions of features to recognize different activities and effectively distinguishes similar activities. It naturally leads to a layered structure that can be extended to new activities without re‐training the entire model. With the layered structure, HR‐HAR first detects the existence of other persons and then processes un‐interfered scene and interfered scene signals with different methods, so it is robust to the interference. The experimental results on the public dataset with 95.6% accuracy and on the self‐collected dataset with 95.4% accuracy for un‐interfered scene and 95.0% for interfered scene indicate that HR‐HAR is of reliable performance on human activity recognition and is robust to environmental changes and interference of other persons.

This study utilizes the unique merits of an 8-L laboratory upflow anaerobic sludge blanket (UASB) reactor for treating synthetic wastewater containing trichloroethylene (TCE). The reactor was operated at different hydraulic retention times (HRT) of 25, 20, 15, 10, and 5 h. TCE removal efficiency decreased from 99 to 85 % when the HRT was lowered down from 25 to 5 h, as well as chemical oxygen demand (COD) removal efficiency (from 95 to 84.15 %). Using Illumina 16S rRNA gene MiSeq sequencing, we investigated the evolution of bacterial communities in the anaerobic sludge under five different conditions of HRT. In total, 106,387 effective sequences of the 16S rRNA gene were generated from 5 samples that widely represented the diversity of microbial community. Sequence analysis consisting of several novel taxonomic levels ranging from phyla to genera revealed the percentages of these bacterial groups in each sample under different HRTs. The differences found among the five samples indicated that HRT had effects on the structures of bacterial communities and the changes of bacterial communities associated with the effect of HRT on the performance of the reactor. Sequence analyses showed that Bacteroidetes and Firmicutes were the dominant phyla. It is notable that the class Dehalococcoidia was found in the samples at HRT of 5, 10, 20, and 25 h, respectively, in which there were some dechlorination strains. Moreover, a tremendous rise of TCE removal efficiency from HRT of 5 h to HRT of 10 h was found.

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency. Memristor-based neural networks hold promise for neuromorphic computing, yet large-scale experimental execution remains difficult. Here, Xia et al. create a multi-layer memristor neural network with in-situ machine learning and achieve competitive image classification accuracy on a standard dataset.

The light instability of CH3NH3PbIxBr3–x is one of the biggest challenges for its application in tandem solar cells. Here we show that an improved crystallinity and grain size of CH3NH3PbIxBr3–x films could stabilize these materials under one sun illumination, improving both the efficiency and stability of the wide‐bandgap perovskite solar cells.

This study investigates the impact of servant leadership on employees’ taking charge behavior, focusing on the mediating roles of intrinsic motivation and supervisor-subordinate relationships, and the moderating effect of employees’ hierarchical levels. Grounded in self-determination and social exchange theories, the research employs a moderated mediation model and utilizes ordinary least squares (OLS) regression to analyze data from 356 employees in China. The findings reveal that servant leadership significantly enhances employees’ taking charge behavior. Intrinsic motivation and supervisor-subordinate relationships serve as essential mediators in this process. Furthermore, employees’ hierarchical level moderates the indirect effects of servant leadership on taking charge behavior, with a stronger influence observed among those in higher hierarchical positions. This study advances the understanding of taking charge behavior by elucidating the mechanisms through which servant leadership influences proactive employee actions. Additionally, it contributes to the literature on organizational hierarchy within the context of leadership and employee behavior.

Direct photocatalytic conversion of methane to value-added C 1 oxygenate with O 2 is of great interest but presents a significant challenge in achieving highly selective product formation. Herein, a general strategy for the construction of copper single-atom catalysts with a well-defined coordination microenvironment is developed on the basis of metal-organic framework for selective photo-oxidation of CH 4 to HCHO. We propose the directional activation of O 2 on the mono-copper site breaks the original equilibrium and tilts the balance of radical formation almost completely toward •OOH. The synchronously generated •OOH and •CH 3 radicals rapidly combine to form HCHO while inhibiting competing reactions, thus resulting in ultra-highly selective HCHO production (nearly 100%) with a time yield of 2.75 mmol g cat −1  h −1 . This work highlights the potential of rationally designing reaction sites to manipulate reaction pathways and achieve selective CH 4 photo-oxidation, and could guide the further design of high-performance single-atom catalysts to meet future demand. Selective photo-oxidation of CH 4 to value-added C 1 oxygenates remains challenging. Here, the authors propose construct mono-copper sites within a framework platform to optimize O 2 activation, enabling highly selective conversion of CH 4 to HCHO.