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Osteomyelitis is a complex infectious disease caused by bacterial infection, which has always been a difficult problem for clinical orthopedic surgeons. Although the traditional methods are effective in treating osteomyelitis, the heavy use of antibiotics and surgical treatment cause serious side effects and lead to bone loss and healing difficulties after surgery. Chitosan (CS), a natural polymer material, is a deacetylated derivative of chitin, which has good biodegradability, antibacterial, and osteogenic properties, and has been widely used in the treatment of osteomyelitis. In this review, the recent research progress in the application of CS based composites such as micro-nano drug delivery carriers, hydrogels, coating materials, and bone tissue engineering scaffolds for the treatment of osteomyelitis is summarized, in order to provide certain experimental and theoretical support for the development of CS based composites.

The cancer-associated alternative splicing (AS) events generate cancer-related transcripts which are involved in uncontrolled cell proliferation and drug resistance. However, the key AS variants implicated in tamoxifen (TAM) resistance in breast cancer remain elusive. In the current study, we investigated the landscape of AS events in nine pairs of primary and relapse breast tumors from patients receiving TAM-based therapy. We unrevealed a notable association between the inclusion of exon 7.2 in the 5’untranslated region (5’UTR) of ALDOA mRNA and TAM resistance. Mechanistically, the inclusion of ALDOA exon 7.2 enhances the translation efficiency of the transcript, resulting in increased ALDOA protein expression, mTOR pathway activity, and the promotion of TAM resistance in breast cancer cells. Moreover, the inclusion of exon 7.2 in ALDOA mRNA is mediated by MSI1 via direct interaction. In addition, elevated inclusion of ALDOA exon 7.2 or expression of MSI1 is associated with an unfavorable prognosis in patients undergoing endocrine therapy. Notably, treatment with Aldometanib, an ALDOA inhibitor, effectively restrains the growth of TAM-resistant breast cancer cells in vitro and in vivo. The present study unveils the pivotal role of an AS event in ALDOA, under the regulation of MSI1, in driving TAM resistance in breast cancer. Therefore, this study provides a promising therapeutic avenue targeting ALDOA to combat TAM resistance.

AbstractAllosteric effectors play an important role in regulating the oxygen supply efficiency of hemoglobin for blood storage and disease treatment. However, allosteric effectors that are approved by the US FDA are limited. In this study, cefmetazole sodium (CS) was found to bind adult hemoglobin (HbA) from FDA library (1338 compounds) using surface plasmon resonance imaging high-throughput screening. Using surface plasmon resonance (SPR), the interaction between CS and HbA was verified. The oxygen dissociation curve of HbA after CS interaction showed a significant increase in P50and theoretical oxygen-release capacity. Acid-base sensitivity (SI) exhibited a decreasing trend, although not significantly different. An oxygen dissociation assay indicated that CS accelerated HbA deoxygenation. Microfluidic modulated spectroscopy showed that CS changed the ratio of the alpha-helix to the beta-sheet of HbA. Molecular docking suggested CS bound to HbA's β-chains viahydrogen bonds, with key amino acids being N282, K225, H545, K625, K675, and V544.The results of molecular dynamics simulations (MD) revealed a stable orientation of the HbA-CS complex. CS did not significantly affect the P50of bovine hemoglobin, possibly due to the lack of Valβ1 and Hisβ2, indicating that these were the crucial amino acids involved in HbA's oxygen affinity. Competition between the 2,3-Diphosphoglycerate (2,3-DPG) and CS in the HbA interaction was also determined by SPR, molecular docking and MD. In summary, CS could interact with HbA and regulate the oxygen supply efficiency viaforming stable hydrogen bonds with the β-chains of HbA, and showed competition with 2,3-DPG.Communicated by Ramaswamy H. Sarma

Using a “core-first” approach with atom transfer radical polymerization, fluorescent center-functional star polymers of equivalent molecular weight but with varying numbers of arms (di-, tri-, and tetra-arm) were prepared. The sensitivity of fluorescence, combined with a dye-labeling technique introducing a fluorescent donor (carbazole) and an acceptor (anthracene) at the center of poly(methyl methacrylate) (PMMA) chains, enabled the application of time-resolved fluorescence resonance energy transfer to obtain quantitative insights into the conformation of the star polymer chains in the film state. When the results of star-branched polymers were compared with those of linear polymers of identical type and molecular weight, the impact of branching on polymer behavior was isolated for examination. Although the star topology does not alter the average intercoil distance, it affects the distance dispersity. Star polymers with higher arm numbers display decreased dispersity from distance due to reduced intermolecular aggregation at their geometric centers. This study presents the first spectroscopic evidence regarding the distribution of geometric centers in star polymers, offering a physical understanding of chain interpenetration and entanglement within star polymers.

• Oxide scales are mainly composed of α-Al 2 O 3 and no obvious internal oxidation occurs in both solid and semi-solid intervals. • During solid oxidation, different scale formation kinetics occurs on various phases, which is caused by outward Al3+ diffusion rate difference in various component systems. • Increased degree of chaos and number of vacancies in liquid phase contribute to the rise of oxygen diffusion rate and flux, causing the oxidation corrosion around liquid phase region. • Oxidation resistance in semi-solid state attributes to dense Al 2 O 3 scales formed by selective oxidation of Al. In this work, possible short-term thermal exposure oxidation in hot processing including semi-solid processing is focused. We aim to investigate the short-term oxidation behavior of a novel powder metallurgy Al 0.8 Co 0.5 Cr 1.5 CuFeNi high entropy alloy (HEA) in high-temperature solid (1000–1100 °C) and semi-solid (1125–1225 °C) intervals. From 1000 to 1225 °C, mass gain increases from 0.31 to 0.45 mg cm–2 after 60 min oxidation, presenting superior oxidation resistance in the semi-solid state compared with other semi-solid alloys. Equiaxed α-Al 2 O 3 grains are the predominant component of oxide scales and no obvious internal oxidation layer occurs in both solid and semi-solid intervals. Al 2 O 3 scale growth is predominantly determined by outward Al3+ diffusion. For solid oxidation, different scale formation kinetics occur on various phases and the BCC(B2) phase has faster scale formation kinetics. This is attributed to outward Al3+ diffusion rate differences in various component systems. During semi-solid oxidation, the increased degree of chaos and number of vacancies in the liquid phase provide a diffusion channel, leading to rapid Al 2 O 3 formation. Oxidation resistance in the semi-solid state is attributed to dense Al 2 O 3 scales formed through selective oxidation of Al. [Display omitted] [ABSTRACT FROM AUTHOR]

α-Amino-CF3compounds are widely employed in bio- and pharmaceutical chemistry for improved stability and bioactivities. Traditional methods often face challenges with functional group tolerance and lack a general approach for late-stage functionalization. Herein, we report a new type of redox-active α-amino-CF3reagents, easily prepared from trifluoro acetaldehyde hydrates. These α-amino-CF3reagents can serve as versatile building blocks for coupling with alkynyl bromides, aryl bromides, and enol triflates under nickel catalysis.

Electrochemical CO2reduction reaction (eCO2RR) is a meaningful way for CO2depletion and value addition. And electrocatalysts play great roles in the CO2reduction product selectivity. SnOx-based catalysts have been widely used in formate production. However, the activity and selectivity are still unsatisfying, especially at the industrial level current density (>0.2 A cm–2) for pure HCOOH production. In this work, we proposed a triple-metal composite electrocatalyst with high oxygen vacancy contents and an abundant grain-boundary strategy for the eCO2RR. In particular, oxygen vacancies in the designed catalyst reached 13.9%. When catalysts were employed as catalysts for eCO2RR, both high HCOOH FE and long-term electrolysis stability under acid electrolytes were obtained. The FE of HCOOH was around 87.9% at 0.3 A cm–2and was stable even after 15,000 s of electrolysis. This work provides a new path for electrocatalyst design and highly efficient HCOOH production.

ABSTRACTThis paper provides a critical realist reflection on methodological decisions that frame examination of the effectiveness of destination branding using food and cuisine. The authors use a multi-method strategy to evaluate the degree of congruence amongst (1) destination marketers’ promotional food images, (2) internal stakeholders’ perception of images, and (3) tourists’ views of food destinations. Results reveal that critical realism enhances the capacity to explore the less observable forces influencing food image across diverse stakeholder groups and builds a more coherent attribute-based framework to measure gaps in perception.

β-Myrcene is an important monoterpene compound widely used in the fragrance, agricultural, and food industries. The microbial production of β-myrcene conforms to the trend of green biological manufacturing, which has great potential for development. The poor catalytic activity of β-myrcene synthase (MS) and the insufficient supply of precursors are considered to be the bottlenecks of β-myrcene production. Here, source screening, subcellular localization, enzyme fusion, and precursor-enhancing strategies were integrated for β-myrcene biosynthesis with Saccharomyces cerevisiae. The β-myrcene titer gradually increased by 218-fold (up to 63.59 mg/L) compared to that of the initial titer of the shake flask. Moreover, the titer reached 66.82 mg/L after the addition of antioxidants (1 mM glutathione, GSH, and 1% butylated hydroxytoluene, BHT). Ultimately, 142.64 mg/L β-myrcene in S. cerevisiaewas achieved in 5.0 L of fed-batch fermentation under a carbon restriction strategy, which was the highest reported titer in yeast thus far. This study not only established a platform for β-myrcene production but also provided a reference for the efficient biosynthesis of other monoterpene compounds.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is envisioned as a key enabler for future six-generation (6G) networks. Nevertheless, the pronounced expansion in both antenna aperture and operational frequency mandates the consideration of near-field conditions. This shift complicates traditional far-field beam training designs, which struggle with near-field spherical-wave-based modeling, thereby compromising channel capacity. Specifically, in near-field systems, unfavorable computational complexity and pilot overheads become almost inevitable, given that beam training codebooks are coupled with both angles and distances of scatterers in significant propagation paths. Moreover, the power leakage effect in the near-field system can erode beamforming gains if not addressed. To counter these challenges, we introduce a novel two-stage learning-based beam training protocol that independently manages angles and distances. Building on this foundation, we present a hierarchical codebook design with modular narrow codeword units termed hierarchical codebook design via accumulation (HCD-A) to alleviate the power leakage problem. Our simulation results underscore the superior performance of our proposed solutions at different signal-to-noise ratio (SNR).

Noisy labels, inevitably existing in pseudo-segmentation labels generated from weak object-level annotations, severely hamper model optimization for semantic segmentation. Previous works often rely on massive handcrafted losses and carefully tuned hyperparameters to resist noise, suffering poor generalization capability and high model complexity. Inspired by recent advances in meta-learning, we argue that rather than struggling to tolerate noise hidden behind clean labels passively, a more feasible solution would be to find out the noisy regions actively, so as to simply ignore them during model optimization. With this in mind, this work presents a novel meta-learning-based semantic segmentation method, MetaSeg, that comprises a primary content-aware meta-net (CAM-Net) to serve as a noise indicator for an arbitrary segmentation model counterpart. Specifically, CAM-Net learns to generate pixel-wise weights to suppress noisy regions with incorrect pseudo-labels while highlighting clean ones by exploiting hybrid strengthened features from image content, providing straightforward and reliable guidance for optimizing the segmentation model. Moreover, to break the barrier of time-consuming training when applying meta-learning to common large segmentation models, we further present a new decoupled training strategy that optimizes different model layers in a divide-and-conquer manner. Extensive experiments on object, medical, remote sensing, and human segmentation show that our method achieves superior performance, approaching that of fully supervised settings, which paves a new promising way for omni-supervised semantic segmentation.

This study Present a novel group three-way decision (TWD) method that effectively addresses decision-making problems characterized by interval-valued uncertain information by integrating regret theory and the consensus reaching process (CRP) model. The proposed method explores several key principles derived from regret theory to transform the loss functions into regret theory-based interval-valued loss functions. In addition, improvements are made to the TOPSIS method for calculating conditional probabilities of alternatives under an interval-valued uncertain environment. Subsequently, several novel interval-valued TWD rules are established to classify alternatives into three predefined decision regions without the requirements for information transformation. Moreover, a personalized CRP model is developed based on the classification results, assisting experts with low consensus in revising their opinions to achieve the desired consensus. To assess the effectiveness of the proposed method, an illustrative example is presented, accompanied by comparative and sensitivity analyses that highlight its advantages and robustness. The results emphasize the innovative perspective and methodology provided by the proposed method for effectively addressing practical decision problems in uncertain and risk-laden environments.

Industrial defect detection plays a critical role in controlling product quality. Obtaining industrial defects with diverse and balanced classes in natural environments is often challenging. Most methods tend to uniformly augment all classes in small-sample datasets, which wastes computing resources and the classification performance is not always good. To achieve the purposive data augmentation, we propose a minority class imbalance rate (MiCIR) and an MiCIR-based data augmentation strategy that can determine the class and the number of samples to be augmented. In addition, to address the misclassification problem of classes with relatively large sample sizes, we introduce a lightweight classification model, ShcNet. We construct convolution-batchnorm-hard-swish (CBH) and convolution-batchnorm-hard-swish-convolutional block attention mechanism (CBHC) modules in ShcNet to improve classification performance. Experimental results demonstrate that our data augmentation strategy can significantly improve the classification results with generalizability across different datasets. The ShcNet outperforms the baseline models on classification accuracy while maintaining fewer parameters and model complexity.

General graph neural networks (GNNs) implement convolution operations on graphs based on polynomial spectral filters. Existing filters with high-order polynomial approximations can detect more structural information when reaching high-order neighborhoods but produce indistinguishable representations of nodes, which indicates their inefficiency of processing information in high-order neighborhoods, resulting in performance degradation. In this article, we theoretically identify the feasibility of avoiding this problem and attribute it to overfitting polynomial coefficients. To cope with it, the coefficients are restricted in two steps, dimensionality reduction of the coefficients’ domain and sequential assignment of the forgetting factor. We transform the optimization of coefficients to the tuning of a hyperparameter and propose a flexible spectral-domain graph filter, which significantly reduces the memory demand and the adverse impacts on message transmission under large receptive fields. Utilizing our filter, the performance of GNNs is improved significantly in large receptive fields and the receptive fields of GNNs are multiplied as well. Meanwhile, the superiority of applying a high-order approximation is verified across various datasets, notably in strongly hyperbolic datasets. Codes are publicly available at: https://github.com/cengzeyuan/TNNLS-FFKSF.

Longer overnight fasting (ONF) is a potential strategy for weight control. Although promising, the evidence from large population-based studies is limited.

Entanglement, the non-local correlations present in multipartite quantum systems, is a key resource for quantum technologies. It is therefore a major priority to develop simple and energy-efficient methods for generating high-fidelity entangled states. In the case of light, entanglement can be realized by interactions with matter but the required nonlinear interaction is often impractically weak. Here we show how a single two-level emitter deterministically coupled to light in a nanophotonic waveguide can be used to realize photonic quantum entanglement by excitation at the single-photon level. Efficient optical coupling enables mediation of two-photon interactions by the emitter, creating a strong nonlinearity that leads to entanglement. We experimentally verify energy–time entanglement by violating a Bell inequality in an interferometric measurement of the two-photon scattering response. The on-chip two-level emitter acts as a passive scatterer, so that no advanced spin control is required. As such, our method may provide a more efficient approach to synthesizing photonic entangled states for quantum simulators or metrology.

The linear and angular vibration sensors are essential to the motion control and measurement, which have been widely used in various industrial applications. However, the current vibration calibration methods always failed to determine their sensitivities in the low-frequency range with a high efficiency. In this letter, a new calibration method that is based on the multifrequency sinusoidal signal generation with prime ratio and excitation measurement with sine approximation fitting solution is proposed, which can significantly improve the efficiency by integrating the calibration of multiple frequencies. This method calibrates the sensitivities at different frequencies in the range from the base frequency to its ten times frequency, which only requires a single operation. Comparison experiments with the two conventional monocular vision method and laser interferometry show that the investigated method can improve the calibration efficiency by at least an order of magnitude with a similar accuracy in the 0.01–10 Hz.

Drawing on value co-creation theory, this study proposes a novel model that incorporates the hotel brand type and star rating into the “customer participation—customer brand engagement—brand preference” model to explore their impacts on the formation of customer brand preferences. Data collected from 330 hotel patrons were analyzed to test the proposed model. The results suggest that customer participation positively affected three dimensions of brand engagement, viz., cognitive engagement, affective engagement, and behavioral engagement, which in turn positively influenced brand preference. Contrary to expectation, hotel brand type and star rating weakened the relationships between affective engagement and brand preference. This study enriches the discussion on the determinants of brand preference and describes its formation path. Implications, as well as future research opportunities, are offered.

ReRAM-based computing-in-memory (CiM) architecture has been considered a promising solution to high-efficiency neural network accelerator, by conducting in-situ matrix multiplications and eliminating the movement of neural parameters from off-chip memory to computing units. However, we observed specific features of graph convolutional network (GCN) tasks pose design challenges to implement a high-efficiency ReRAM GCN accelerator. The ultralarge input feature data in some GCN tasks incur massive data movements, the extremely sparse adjacency matrix and input feature data involve the valid computation, and the super-large adjacency matrix that exceeds available ReRAM capacity causes frequent expensive write operations. To address the above challenges, we propose TARe, a task-adaptive CiM architecture, which consists of a hybrid in-situ computing mode to support the input feature in crossbar computing, a compact mapping scheme for efficient sparse matrix computing, and a write-free mapping to eliminate write activities in the computations with the super-large adjacency matrix. Additionally, TARe is facilitated with a task adaptive selection algorithm to generate optimized design schemes for graph neural network (GNN) tasks that have various operand sizes and data sparsity. We evaluate TARe on 11 diverse GNN tasks and compare it with different design counterparts, and the results show that achieves 168.06 $\times $ speedup and 10.95 $\times $ energy consumption reduction on average over the baseline in common GCN workloads.

Natural gap junctions are a type of channel protein responsible for intercellular signalling and mass communication. However, the scope of applications for these proteins is limited as they cannot be prepared at a large scale and are unable to spontaneously insert into cell membranes in vitro. The construction of artificial gap junctions may provide an alternative strategy for preparing analogues of the natural proteins and bottom–up building blocks necessary for the synthesis of artificial cells. Here we show the construction of artificial gap junction channels from unimolecular tubular molecules consisting of alternately arranged positively and negatively charged pillar[5]arene motifs. These molecules feature a hydrophobic–hydrophilic–hydrophobic triblock structure that allows them to efficiently insert into two adjacent plasma membranes and stretch across the gap between the two membranes to form gap junctions. Similar to natural gap junction channels, the synthetic channels could mediate intercellular signal coupling and reactive oxygen species transmission, leading to cellular activity.

In today's world, environmental safety has become a top priority, with a special focus on terminal disinfection of indoor environments, which has proven to be a crucial and formidable issue in infection control. The use of hydrogen peroxide disinfection through vaporization or atomization presents a viable solution to the challenges posed by traditional wiping disinfection and ultraviolet disinfection methods that are plagued by incomplete disinfection and other factors that can significantly reduce the virus content within a room. To improve the application of hydrogen peroxide disinfection spray, the CFD-DPM (Computational Fluid Dynamics-Discrete Particle Model) method is employed to simulate the particle distribution of traditional sprays in a 30 m3room. The study places emphasis on analyzing the impact of the placement position and bottle number of disinfection spray on the distribution of spray particles. The results demonstrate an impressive consistency between the numerical simulation method and experimental findings regarding the particle size distribution of disinfectant spray across various Schemes, validating the method's accuracy and reliability. However, the placement of a single disinfection spray source is insufficient to achieve uniformly distributed particles throughout the space when a bathroom is present within a given room. Nevertheless, by placing internal and external sources within the bathroom, the distribution of disinfectant spray particles becomes more even, thanks to a well conceived placement technique and an increased number of spray sources. Among all the schemes tested, Scheme 4 stands out for its ability to produce the greatest number of spray particles, with almost 60.71% of these particles remaining airborne for over 40 s. Therefore, it is also the most effective scheme for conducting disinfection.

Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension.

Spin-mechanical coupling is vital in diverse fields including spintronics, sensing, and quantum transduction. Two-dimensional (2D) magnetic materials provide a unique platform for investigating spin-mechanical coupling, attributed to their mechanical flexibility and novel spin orderings. However, studying their spin-mechanical coupling presents challenges in probing mechanical deformation and thermodynamic property changes at the nanoscale. Here we use nano-optoelectromechanical interferometry to mechanically detect the phase transition and magnetostriction effect in multilayer CrSBr, an air-stable antiferromagnet with large magnon-exciton coupling. The transitions among antiferromagnetism, spin-canted ferromagnetism, and paramagnetism are visualized. Nontrivial magnetostriction coefficient 2.3 × 10–5and magnetoelastic coupling strength on the order of 106J/m3have been found. Moreover, we demonstrate the substantial tunability of the magnetoelastic constant by nearly 50% via gate-induced strain. Our findings demonstrate the strong spin-mechanical coupling in CrSBr and pave the way for developing sensitive magnetic sensing and efficient quantum transduction at the atomically thin limit.

This work introduces a new strategy of a single-atom nest catalyst, whereby several single atoms are positioned closely, aiming to achieve the dual benefits of high atom-utilization efficiency while avoiding the steric hindrance in the coupling reaction. As a proof of concept, Pt single-atom nests, where the adjacent Pt single atoms are approximately 4 Å apart, are precisely engineered on the TiO2photocatalyst for photocatalytic non-oxidative coupling of methane. The Pt single-atom nest photocatalyst demonstrates remarkable activity, achieving a C2H6yield and turnover frequency of 251.6 μmol gcat–1h–1and 20 h–1, respectively, representing a 3.2-fold improvement compared to the Pt single-atom photocatalyst. Density functional theory calculations reveal that the Pt single-atom nest can significantly decrease the energy barrier for the activation of both CH4molecules in the coupling process.

Intervertebral disc degeneration (IVDD) is a prevalent chronic condition causing spinal pain and functional impairment. This study investigates the role of extracellular vesicles (EVs) derived from human umbilical cord mesenchymal stem cells (hUCMSCs) in regulating IVDD. Using RNA-seq, we analyzed differential expressions of lncRNA and miRNA in nucleus pulposus tissues from various mouse groups. We identified key regulatory molecules, MALAT1 and miRNA-138-5p, which contribute to IVDD. Further experiments demonstrated that MALAT1 can up-regulate SLC7A11 expression by competitively binding to miR-138-5p, forming a MALAT1/miR-138-5p/SLC7A11 coexpression regulatory network. This study elucidates the molecular mechanism by which hUCMSC-derived EVs regulate IVDD and could help develop novel therapeutic strategies for treating this condition. Our findings demonstrate that hUCMSCs-EVs inhibit ferroptosis in nucleus pulposus cells, thereby improving IVDD. These results highlight the therapeutic potential of hUCMSCs-EVs in ameliorating the development of IVDD, offering significant scientific and clinical implications for new treatments.

AbstractThis study was conducted to investigate the effects of pantothenic acid (PA) on the lipid metabolism, and antioxidant function of Apis melliferaworkers. The results showed that ether extract (EE) content of bees’ body was significantly decreased when workers were supplemented with 147 mg/kg PA (P < 0.05). Further, supplemental PA reduced triglycerides (TG) and total cholesterol (TCHO) content in the hemolymph (P < 0.05), while 39.2 mg/kg PA supplementation improved hemolymph high-density lipoprotein cholesterol (HDL-c) concentration, while 261.6 mg/kg PA reduced low-density lipoprotein cholesterol (LDL-c) concentration (P < 0.05). C12:0, C14:1, and C18:0 content values were notably increased in PA-supplemented groups, while those of C14:0, C18:2n-6, C20:4n-6, polyunsaturated fatty acids (PUFA), and PUFA/saturated fatty acids were decreased (P < 0.05), while levels correlated with those of dietary PA. Malondialdehyde (MDA) content showed a downward trend in response to PA supplementation; however, total superoxide dismutase (T-SOD) was increased (P < 0.05). Furthermore, fatty acid synthase (FAS) mRNA levels were significantly downregulated in bees supplemented 9.8, 19.6, and 39.2 mg/kg (P < 0.05) and lipase activity significantly improved following supplementation with 39.2, 78.4, and 147 mg/kg PA (P < 0.05). Thus, dietary PA significantly influenced worker bee lipid metabolism, and improved antioxidant function.

Fiber-integrated micro/nanostructures play a crucial role in modern industry, mainly owing to their compact size, high sensitivity, and resistance to electromagnetic interference. However, the three-dimensional manufacturing of fiber-tip functional structures beyond organic polymers remains challenging. It is essential to construct fiber-integrated inorganic silica with designed functional nanostructures for microsystem applications. Here, we develop a strategy for the 3D nanolithography of fiber-integrated silica from hybrid organic–inorganic materials by ultrafast laser-induced multiphoton absorption. Without silica nanoparticles and polymer additives, the acrylate-functionalized precursors can be locally cross-linked through a nonlinear effect. Followed by annealing at low temperature, the as-printed micro/nanostructures are transformed to high-quality silica with sub-100 nm resolution. Silica microcantilever probes and microtoroid resonators are directly integrated onto the optical fiber, showing strong thermal stability and quality factors. This work provides a promising strategy for fabricating desired fiber-tip silica micro/nanostructures, which is helpful for the development of integrated functional device applications.

The computational complexity of the three-level space vector pulsewidth modulation (SVPWM) limits its application in high-switching-frequency converters. To address this issue, this article proposes a simplified SVPWM algorithm based on virtual chopping. First, the reference voltage vector (VV) is adjusted to avoid excessive line-to-line voltage jumps. Second, boundary VVs in the space vector diagram, with simple switching sequences, are chosen as standards. Third, the switching sequence of the reference VV is determined by virtual chopping the corresponding standard VV’s. Furthermore, to generate it in the microcontroller, the corresponding modulation waves are obtained. Finally, a method to balance dc-side capacitor voltages is proposed. Consequently, the proposed algorithm simplifies the process, requiring only four equations. The sector determination, dwell time calculation, and allocation are bypassed. Compared with the existing algorithms, the execution time and design complexity of the proposed algorithm in the microcontroller is reduced. The experimental results are presented to verify its effectiveness.

This letter investigates the sparse channel estimation for holographic multiple-input multiple-output (HMIMO) systems. Given that the wavenumber-domain representation is based on a series of Fourier harmonics that are in essence a series of orthogonal basis functions, a novel wavenumber-domain sparsifying basis is designed to expose the sparsity inherent in HMIMO channels. Furthermore, by harnessing the beneficial sparsity in the wavenumber domain, the sparse estimation of HMIMO channels is structured as a compressed sensing problem, which can be efficiently solved by our proposed wavenumber-domain orthogonal matching pursuit (WD-OMP) algorithm. Finally, numerical results demonstrate that the proposed wavenumber-domain sparsifying basis maintains its detection accuracy regardless of the number of antenna elements and antenna spacing. Additionally, in the case of antenna spacing being much less than half a wavelength, the wavenumber-domain approach remains highly accurate in identifying the significant angular power of HMIMO channels.

In this letter, we investigate a multi-receiver communication system enabled by movable antennas (MAs). Specifically, the transmit beamforming and the double-side (DS) antenna movement at the transceiver are jointly designed to maximize the sum-rate of all receivers under imperfect channel state information (CSI). Since the formulated problem is non-convex with highly coupled variables, conventional optimization methods cannot solve it efficiently. To address these challenges, an effective learning-based algorithm is proposed, namely heterogeneous multi-agent deep deterministic policy gradient (MADDPG), which incorporates two agents to learn policies for beamforming and movement of MAs, respectively. Based on the offline learning under numerous imperfect CSI, the proposed heterogeneous MADDPG can output the solutions for transmit beamforming and antenna movement in real time. Simulation results validate the effectiveness of the proposed algorithm, and the MA can significantly improve the sum-rate performance of multiple receivers compared to other benchmark schemes.

The joint study of agriculture and rural areas is of great significance for safeguarding agricultural development, revitalizing rural areas, and enhancing farmers’ well-being. This paper aims to assess the spatiotemporal evolution characteristics of the coupling and coordination degree of agricultural resilience and rural land use efficiency and their dynamic transfer law and driving mechanisms, based on panel data of 31 provinces (municipalities and autonomous regions) in China from 2010 to 2020. The results showed: (1) Good coupling and coordination of agricultural resilience and rural land use efficiency, with reduced temporal differentiation degrees between regions; (2) Significant spatial autocorrelation between the overall coupling and coordination degrees of agricultural resilience and rural land use efficiency, forming cold spot and hot spot spatial patterns in the western and eastern parts, respectively, with a central transition area; (3) A spillover effect of the dynamic transfer process, with a manifested specific law as “club convergence”, “Matthew effect”, and progressive development characteristics; (4) The key roles of the natural, social, economic, and policy indicators in the coupling and coordination development process of agricultural resilience and rural land use efficiency. However, the selected indicators showed substantial spatial differences in their influences on the coupling and coordination process between provinces.

Ni, Co-induced highly distributed NiCoP nanoparticles embedded nitrogen-doped carbon nanotubes (NCNTs) (NiCo/NiCoP-NCNTs) were directly synthesized by a one-step phosphorization and carbonization process. As a bifunctional electrocatalyst for water splitting, NiCo/NiCoP NCNTs show impressive catalytic performance with an overpotential of only 206 mV for the hydrogen evolution reaction and 360 mV for the oxygen evolution reaction in 0.5 mol/L H2SO4and 1 mol/L KOH solutions, respectively. In addition, NiCo/NiCoP NCNTs maintain a stable cell voltage of 1.68 V at 10 mA/cm2with only a 10% decrease in current density over 48 h, showing remarkable stability. The improved catalytic activity can be attributed to the integration of NiCoP nanoparticles and the synergies between NCNTs and NiCo alloy. Additionally, the improved electrocatalytic performance can be attributed to the increased electrochemically active surface area and the reduced electron transfer resistance of the NiCo/NiCoP-NCNTs. Overall, the NiCo/NiCoP-NCNTs demonstrated significant performance for advanced water electrolysis applications.

A 1200-V rated CoolSiC Trench MOSFET structure has excellent overall electrical characteristics due to its unique asymmetric structure. However, it is sensitive to space heavy ion radiation. In this study, heavy ion irradiation experiments were conducted on 1.2 kV CoolSiC Trench MOSFET devices to calibrate the simulation structure model and critical failure conditions. The 2-D numerical simulator SILVACO TCAD was used to investigate the single event burnout (SEB) hardening method for the 1.2 kV-rated CoolSiC Trench MOSFET under high linear energy transfer (LET) values. The simulation results show that the improved CoolSiC Trench MOSFET structure can effectively enhance the SEB capability of the device without sacrificing its excellent overall electrical characteristics compared to the traditional CoolSiC Trench MOSFET. At 360 V voltage and 0.5 pC/ $\mu $ m LET value, the highest temperature is reduced from 3097 to 1502 K, and the lowest burnout threshold is increased by 200 V in the improved structure while maintaining almost unchanged characteristics at other locations.

In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based approaches have gained traction due to their optimal balance between efficiency and precision. However, UAV scenarios often present challenges such as insufficient sampling resolution, fast motion and small objects with limited feature information. As a result, temporal context in UAV tracking tasks plays a pivotal role in target location, overshadowing the target’s precise features. In this paper, we introduce MT-Track, a streamlined and efficient multi-step temporal modeling framework designed to harness the temporal context from historical frames for enhanced UAV tracking. This temporal integration occurs in two steps: correlation map generation and correlation map refinement. Specifically, we unveil a unique temporal correlation module that dynamically assesses the interplay between the template and search region features. This module leverages temporal information to refresh the template feature, yielding a more precise correlation map. Subsequently, we propose a mutual transformer module to refine the correlation maps of historical and current frames by modeling the temporal knowledge in the tracking sequence. This method significantly trims computational demands compared to the raw transformer. The compact yet potent nature of our tracking framework ensures commendable tracking outcomes, particularly in extended tracking scenarios. Comprehensive tests across four renowned UAV benchmarks substantiate the superior efficacy of our approach, delivering real-time performance at 84.7 FPS on a single GPU. Real-world test on the NVIDIA AGX hardware platform achieves a speed exceeding 30 FPS, validating the practicality of our method.

The insulin signaling (IIS) pathway plays a key role in the regulation of various physiological functions in animals. However, the involvement of IIS pathway in the reproduction of natural enemy insects remains enigmatic. Here, 3 key genes (named ClInR, ClPI3K, and ClAKT) related to IIS pathway were cloned from Cyrtorhinus lividipennis(Reuter) (Hemiptera: Miridae), an important natural enemy in the rice ecosystem. These 3 proteins had the typical features of corresponding protein families and shared high similarity with their respective homologs from the Hemipteran species. The ClInR, ClPI3K, and ClAKTwere highly expressed in the adult stage. Tissue distribution analysis revealed that ClInR, ClPI3K, and ClAKTwere highly expressed in the midgut and ovary of adults. Silencing of ClInR, ClPI3K, and ClAKTcaused 92.1%, 72.1%, and 57.8% reduction in the expression of ClVg, respectively. Depletion of these 3 genes impaired vitellogenin synthesis and ovary development. Moreover, the fecundity in the dsInR, dsPI3K, and dsAKT injected females were 53.9%, 50.8%, and 48.5% lower than the control treatment, respectively. These results indicated that ClInR, ClPI3K, and ClAKTare of great importance for the reproduction of C. lividipennis. Our results advance the knowledge about the molecular mechanism of reproduction regulation in natural enemy insects.

Estimating the severity of lightning-caused voltage sag events lays the foundation of voltage sag mitigation by differentiated lightning protection. Traditional methods require numerous parameters for modeling power grid and lightning-caused voltage sags. In real-world scenarios, obtaining these parameters is challenging due to security issues or management problems. Therefore, a generic severity estimation method for lightning-caused voltage sags by using multiplatform monitoring data mining is proposed in this paper. The generic estimation method can be divided into three main modules: data discretization, association rule mining, and rule matching. Association rules are mined to discover hidden patterns between available lightning-related parameters and the corresponding sag severity based on historical records in multiplatform. For unmonitored sites, the severity of voltage sag caused by newly recorded lightning can be estimated by matching lightning-parameters with mined rules. Field records from multiplatform are applied to verify the performance and effectiveness of the proposed method. The proposed method yields acceptable performance as long as a few essential monitoring parameters are available. All the parameters are monitored in two widely used monitoring platforms, i.e., Lightning Location System (LLS) and Power Quality Monitoring System (PQMS), indicating the proposed method is generic. Moreover, the supplementation of more parameters from other platforms can further improve the estimated accuracy.

Corticosteroids have beneficial effects in improving outcomes in hospitalized patients with severe COVID-19 by suppressing excessive immune responses. However, the effect of corticosteroids on the humoral and T-cell responses of survivors of COVID-19 1 year after infection remains uncertain, as it relates to the extent of immediate, antigen-specific defense provided by protective memory.

With the advancement of modern science and technology, large scientific facilities are increasingly oriented toward demand and application, and can be used for basic research as well as serving multiple disciplines. Developing large scientific facilities and related analytical technologies enhances understanding of large scientific facilities and popularizes their application in research across multiple disciplines. The combination of light or neutron sources from large scientific facilities and advanced analytical technologies can be achieved for materials structure information, dynamics study of chemical reactions, high dissociation of biomolecules, 3D visualization of energy materials or biological samples, etc. We first introduce the progress of domestic large scientific facilities of synchrotron radiation (SR) and free electron lasers (FELs) with different wavelengths and neutron sources. We further discuss the comparison between Chinese and typical foreign facilities in X-ray radiation from X-ray tubes, synchrotrons, X-ray FELs, and neutron sources based on physical parameters of light and neutron sources. In addition, we focus on the technological progress and perspectives combined with advanced X-ray radiation and neutron sources of large scientific facilities in China, especially in the nanoscience fields of energy catalysis and biological science. We hope that this roadmap will provide references on technology and methods to experimental users, as well as prospects for future development of technologies based on large research infrastructure facilities. Comprehensive studies and guidelines for basic research to practical application in various disciplines can be made with the assistance of large scientific facilities.

Addressing the goal-oriented design for mission-critical services like remote control, stands as a pivotal challenge in the space-air-ground integrated network (SAGIN). This paper investigates an energy-efficient wireless networked control system with local and remote controllers in SAGIN. Frequent state transmission from sensor to remote controller would improve the quality of control (QoC) at the expense of nontrivial energy consumption. Nevertheless, such a tradeoff for the considered system has received little attention. In this sense, we propose a goal-oriented transmission scheduling design to balance QoC and energy consumption. In particular, we formulate an optimization problem on minimizing the weighted sum of control cost and power consumption by making decisions about whether to transmit the state or not. By formulating a Markov Decision Process framework, the optimal transmission scheduling policy is proven to have a threshold structure tied to the age of information (AoI). Then, the optimal AoI threshold is obtained by means of a discrete-time Markov chain. Equipped with this, we propose an AoI-thresholding mechanism that can provide insights into when to transmit the state in a computationally-efficient manner. Numerical results shed light on the advantages of the proposed policy over the periodical and greedy transmission policies for the considered system.

The role of incommensurate (IC) modulation in the evolution of the pinched polarization–electric field (P–E) hysteresis loops has been investigated and discussed based on the structure and polarization evolution in Ba4(Sm1–xLax)2Ti4Nb6O30tetragonal tungsten bronzes. The relaxor behavior in the La-rich compound is accompanied by an IC modulation structure. Introduction of smaller Sm in the system increases the driving force for the transition from an IC modulation structure to a commensurate superstructure, which coupled with the ferroelectric transition in the middle composition with x= 0.5. In the Sm-rich compounds, the IC modulation structure reappears as a metastable state to balance the structural instability caused by the too small average ionic radius of the rare-earth ion; meanwhile, the field-induced transition from the IC modulation structure to the commensurate superstructure is confirmed by selected area electron diffraction using an in situ bias technique as the structural origin for the pinched P–Eloops. A phase diagram has been established by combining the ferroelectric phase transition and the modulation structure transition, and a new region with both very small A-site size (A1 + A2)/2 and A1-site tolerance factor (tA1) related to the ferroelectric compounds with pinched P–Eloops (pinched FE) was added into the previously reported crystal-chemical framework (Chem. Mater.2015,27,3250–3261). The present work expands the composition–structure–property relationships in tungsten bronze ferroelectrics by including the recently reported “pinched FE” and meanwhile extends the composition manipulation ranges from the crossover between relaxor and normal ferroelectrics to ferroelectrics with pinched P–Eloops.

The strategic design of catalysts for the oxygen evolution reaction (OER) is crucial in tackling the substantial energy demands associated with hydrogen production in electrolytic water splitting. Despite extensive research on birnessite (δ-MnO2) manganese oxides to enhance catalytic activity by modulating Mn3+species, the ongoing challenge is to simultaneously stabilize Mn3+while improving overall activity. Herein, oxygen (O) vacancies and nitrogen (N) doping have been simultaneously introduced into the MnO2through a simple nitrogen plasma approach, resulting in efficient OER performance. The optimized N-MnO2velectrocatalyst exhibits outstanding OER activity in alkaline electrolyte, reducing the overpotential by nearly 160 mV compared to pure pristine MnO2(from 476 to 312 mV) at 10 mA cm–2, and a small Tafel slope of 89 mV dec–1. Moreover, it demonstrates excellent durability over a 122 h stability test. The introduction of O vacancies and incorporation of N not only fine-tune the electronic structure of MnO2, increasing the Mn3+content to enhance overall activity, but also play a crucial role in stabilizing Mn3+, thereby leading to exceptional stability over time. Subsequently, density functional theory calculations validate the optimized electronic structure of MnO2achieved through the two engineering methods, effectively lowering the intermediate adsorption free energy barrier. Our synergistic approach, utilizing nitrogen plasma treatment, opens a pathway to concurrently enhance the activity and stability of OER electrocatalysts, applicable not only to Mn-based but also to other transition metal oxides.

Point-of-care testing (POCT) technologies facilitate onsite detection of pathogens in minutes to hours. Among various POCT approaches, pressure-based sensors that utilize gas-generating reactions, particularly those catalyzed by nanozymes (e.g., platinum nanoparticles, PtNPs, or platinum-coated gold nanoparticles, and Au@PtNPs) have been shown to provide rapid and sensitive detection capabilities. The current study introduces Au–Pt alloy-coated gold nanoparticles (Au@AuPtNPs), an innovative nanozyme with enhanced catalytic activity and relatively high stability. For pathogen detection, Au@AuPtNPs are modified with H1 or H2 hairpin DNAs that can be triggered to undergo a hybridization chain reaction (HCR) that leads to their aggregation upon recognition by an initiator strand (Ini) with H1-/H2-complementary aptamers tethered to magnetic beads (MBs). Pathogen binding to the aptamer exposes Ini, which then binds Au@AuPtNPs and initiates a HCR, resulting in Au@AuPtNP aggregation on MBs. These Au@AuPtNP aggregates exhibit strong catalysis of O2from the H2O2substrate, which is measured by a pressure meter, enabling detection of Escherichia coli (E. coli)O157:H7 at concentrations as low as 3 CFU/mL with high specificity. Additionally, E. coliO157:H7 could also be detected in simulated water and tea samples. This method eliminates the need for costly, labor- and training-intensive instruments, supporting its further testing and validation for deployment as a rapid-response POCT application in the detection of bacterial contaminants.