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ABSTRACTBackgroundThis study aimed to evaluate the pharmacokinetic (PK), pharmacodynamic (PD) similarity, comparable safety, and immunogenicity between LY06006, European Union-sourced denosumab (EU-DEN), and United States-sourced denosumab (US-DEN).Research design and methodsIn this double-blind, parallel-group, and single-dose study, 300 healthy male subjects were randomized 1:1:1 to receive a 60 mg dose of either LY06006, EU-DEN, or US-DEN subcutaneously. This study lasted for 253 days. Primary PK endpoints included maximum serum concentration (Cmax), area under the concentration-time curve (AUC) from time zero to last quantifiable concentration (AUC0-t), and AUC from time zero to infinity (AUC0-inf). Pharmacokinetic equivalence was concluded if the two-sided 90% confidence interval (CI) for the geometric least squares mean ratio (GLSMR) of primary endpoints were within 80%-125%. Other PK parameters, PD parameters, safety, and immunogenicity assessments were also conducted during the study.ResultsThe 90% CIs for ratios of GLSMR were within the predefined equivalence margin for AUC0-inf(89.0%–111.1%), AUC0-t(89.7%–111.3%), and Cmax(92.3%–106.7%). The PD parameters, safety, and immunogenicity of LY06006 were also comparable to US-DEN and EU-DEN.ConclusionLY06006 was highly similar to US-DEN and EU-DEN in terms of PK, PD, safety and immunogenicity in healthy male subjects.Clinical trial registrationwww.clinicaltrials.govidentifier is NCT06095427

Asymmetric radical transformations (ARTs) are vital for constructing chiral drugs and materials, while the highly reactive nature of radicals often imposes a challenge in selectivity control of these processes. Inspired by the principles of enzyme-cofactor cooperation to enhance stereochemical induction in enantioselective radical transformations, we developed an enantioselective asymmetric radical azidation viacooperative organo- and transition metal catalysis. This approach enables the efficient synthesis of heavily functionalized tertiary azides from readily available aldehydes. The key to this enantioselective process is the use of both chiral organocatalysts to transiently convert aldehydes to the corresponding chiral radical cationic species upon oxidation along with a detailed screening of chiral metal-azide catalysts to cooperatively enhance stereoinduction in carbon-azide bond formation. DFT studies suggest a favorable stereocontrol model and validate the crucial roles of chirality pairing of both catalytic schemes. We envision that this copper/amine cooperative catalysis could offer a useful strategy of constructing tetrasubstituted stereogenic carbon in asymmetric radical transformation development.

Circularly polarized thermally activated delayed fluorescence materials not only possess high exciton utilization efficiency but also have the capability to emit circularly polarized light for potential information storage and sensing. In this work, chiral blue TADF enantiomers are prepared. The energy difference between singlet and triplet, ΔEST, increases with the strength of chirality. The chiral orbit-induced spin degeneracy elimination could enhance spin relaxation, where spin could flip easily to lead to an effective transition from triplet to singlet states. This induces a pronounced enhancement in fluorescence quantum yield. Furthermore, circularly polarized emission of chiral TADF materials under different external magnetic fields are studied. Magnetic field control of glumpresents a mirror symmetry effect for chiral TADF enantiomers, which provides evidence for the transition between the photon spin and electron spin.

The trade-off between strength and ductility remains a persistent obstacle in the development of advanced structural materials. In the present study, a novel dual-heterogeneous structure with a bimodal grain distribution in both ferrite and austenite phases was fabricated via cold rolling and partial recrystallization annealing on solution-treated 2205 duplex stainless steel (DSS). The processed steel exhibited superior mechanical properties, with the yield strength increasing from 586 MPa to 903 MPa, and the ultimate tensile strength from 796 MPa to 1082 MPa, while maintaining a high total elongation of 35.3 %. Based on in-situ electron backscatter diffraction (EBSD) and scanning electron microscope (SEM) analyses, the microstructural deformation behavior and strengthening mechanisms of the dual-heterostructured 2205 DSS were elucidated. The outstanding combination of strength and ductility was ascribed to the synergistic effects of grain refinement, dislocation strengthening, and hetero-deformation induced (HDI) strengthening. Moreover, the high ductility in DSS was attributed to the co-activation of cross-slip systems in ferrite {110} and {112} along with the single-slip systems in austenite {111}. These findings provide a new strategy for the design and development of high-strength and ultra-high-strength DSSs. [Display omitted] [ABSTRACT FROM AUTHOR]

A great number of studies have demonstrated that many complex systems could benefit a lot from complex networks, through either a direct modeling on which dynamics among agents could be investigated in a global view or an indirect representation by the aid of that the leading factors could be captured more clearly. Hence, in the context of networks, this article copes with the continuous network dismantling problem which aims to find the key node set whose removal would break down a given network more thoroughly and thus is more capable of suppressing virus or misinformation. To achieve this goal effectively and efficiently, we propose the external-degree and internal-size component suppression (EDIS) framework based on the network percolation, where we constrain the search space by a well-designed local goal function and candidate selection approach such that EDIS could obtain better results than the-state-of-the-art in networks of millions of nodes in seconds. We also contribute two strategies with time complexity ${\mathcal {O}}(m\log _{\vartheta } m)$ and space complexity ${\mathcal {O}}(m)$ , of networks of m edges, under such framework by well studying the evolving characteristics of the associated connected components as nodes are occupied, where $\vartheta \gt 1$ is a hyperparameter. Our results on 12 empirical networks from various domains demonstrate that the proposed method has far better performance than the-state-of-the-art over both effectiveness and computing time. Our study could play important roles in many real-world scenarios, such as the containment of misinformation or epidemics, the distribution of resources or vaccine, the decision of which group of individuals set to quarantine, or the detection of the resilience of a network-based system under intentional attacks.

Sufficient vascular system and adequate blood perfusion is crucial for ensuring nutrient and oxygen supply within biomaterials. Actively exploring the optimal physical properties of biomaterials in various application scenarios has provided clues for enhancing vascularization within materials, leading to improved outcomes in tissue engineering and clinical translation. Here we focus on reviewing the physical properties of biomaterials, including pore structure, surface topography, and stiffness, and their effects on promoting vascularization. This angiogenic capability has the potential to provide better standardized research models and personalized treatment strategies for bone regeneration, wound healing, islet transplantation and cardiac repair.

Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions. We introduce SubA, an innovative substrate-aware descriptor that is inspired by the fact that specific atoms or motifs in reactants drive the reaction outcomes. By employing graph matching algorithms for molecular backbone identification and incorporating atomic and molecular properties derived from density functional theory calculations, SubA extracts essential information at both the atomic level and the molecular level. Compared to four mainstream descriptors, SubA achieves reduced dimensionality and enhanced prediction accuracy with over 2% mean absolute error reduction in both random and scaffold splitting evaluations. It also demonstrates better generalization when confronted with previously unreported substrate combinations in extended experiments. Furthermore, an interpretable analysis of SubA shows that the predictor focuses on key molecular and atomic features, offering insights into reaction mechanisms.

In response to the 2023 “Action Plan for Methane Emission Control” in China, which mandates precise methane (CH4) emission accounting, we developed a dynamic model to estimate CH4emissions from fossil-fuel and food systems in China for the period 1990–2020. We also analyzed their socioeconomic drivers through the Logarithmic Mean Divisia Index (LMDI) model. Our analysis revealed an accelerated emission increase (850.4 Gg/year) during 2005–2015, compared to 570.4 Gg/year in the preceding period (1990–2005), with a downward trend (−1216.6 Gg/year) detected after 2015. The fossil-fuel system was the primary contributor to these changes, with emissions positively correlated with per capita GDP and negatively influenced by energy intensity at the production stage and wastewater discharge intensity at the disposal stage. In the food system, CH4emission intensity and waste treatment practices were the most significant negative drivers at production and disposal stages, respectively. Urbanization also played a notable role, contributing to 19.3% and 18.1% in livestock and rice cultivation emission reductions, respectively. Despite the observed changes, coal mining, livestock, and rice remain the dominant sources of CH4emissions. Our findings suggest that effective CH4emission mitigation can be achieved through strategies such as reducing energy intensity, improving agricultural production efficiency, and advancing urbanization efforts.

A refined synthesis of 1,4,2-diazaphospholidine-3,5-dione derivatives was achieved through a cyclization reaction involving Na(OCP) and isocyanates. Na(OCP) was demonstrated to be a relatively stable and safe source of phosphorus, enabling the production of diverse 1,4,2-diazaphospholidine-3,5-dione derivatives with high yields. The reaction proceeds efficiently under catalyst-free and mild conditions. Both experimental findings and density functional theory calculations have elucidated that the process involves a crucial step of carbon monoxide elimination, which provides deeper insight into the reaction mechanism.

The surface passivation with the heterostructure of the 2D/3D stack has been widely used for boosting the efficiency of n-i-p perovskite solar cells (PSCs). However, the disordered quantum well width distribution of 2D perovskites leads to energy landscape inhomogeneity and crystalline instability, which limits the further development of n-i-p PSCs. Here, a versatile approach, ligand-mediated surface passivation, was developed to produce a phase-pure 2D perovskite passivation layer with a homogeneous energy landscape by dual-ligand codeposition. The preferential adsorption of 3,6-dimethyl-carbazole-9-ethylammonium iodide with a large molecular size and lower adsorption energy could regulate the surface reaction between the m-fluorophenylethylammonium iodide and perovskite surface, resulting in a 2D perovskite with a narrow quantum well distribution and a uniform surface potential distribution. Beyond this, the preservation of the surface-confined 2D passivation layer retained a higher electric field at the interface of perovskite and the hole transport layer. As a result, the champion device reached an efficiency of 25.86% for the 0.05 cm2device and 25.08% for the 1 cm2device, with enhanced operational stability (T90> 1000 h) and much better thermal stability. Our work provides deeper insights into efficient and stable 2D passivation for PSCs.

To validate the feasibility of using near-infrared (NIR) spectroscopy for real-time monitoring of multiple active pharmaceutical ingredients dissolution, this study focused on Guizhi Fuling capsules and tablets. The NIR spectroscopy fiber probe was inserted into the dissolution apparatus and connected to a Fourier transform near-infrared spectrometer (FT-NIR) to capture spectral data. During the dissolution tests, dissolution behavior curves for seven components, gallic acid (GA), alibiflorin (ALI), paeoniflorin (PF), paeonol (PAE), amygdalin (AMY), cinnamaldehyde (CL), and cinnamic acid (CA) in the capsules, were obtained by sampling from the dissolution cups at specific time intervals. Linear regression was applied to models corrected using various pre-process techniques with the partial least squares (PLS) algorithm. Additionally, an artificial neural network (ANN), a nonlinear regression algorithm, was utilized to explore the complex relationship between spectra and multicomponent dissolution. Ultimately, the ANN model achieved a lower prediction mean square error (RMSEP) and relative error compared to the PLS model, with significantly higher correlation coefficient (Rp) for the validation set. The highest Rpvalue reached 0.8825. The paired t-test results also indicated no significant difference between predicted and measured values. Furthermore, the ANN model demonstrated the best predictive performance in the tablet experiments, achieving an Rpof 0.8134. The findings indicate that real-time monitoring of multicomponent drug dissolution using NIR spectroscopy combined with chemometric methods is feasible, offering a promising new direction to replace traditional dissolution testing.

As an alternative model for studying the dynamic process of early mammalian embryonic development, much progress has been made in using mouse embryonic stem cells (mESCs) to generate embryo-like structures, especially by modifying the starting cells. A previous study has demonstrated that totipotent blastomere-like cells (TBLCs) can be obtained by continuous treatment of mESCs with a low-dose splicing inhibitor, Pladienolide B (PlaB). However, these totipotent mESCs have limited proliferative capacity. Here, we report that short-term high-dose PlaB treatment can also induce mESCs to acquire totipotency. This treatment equips this novel type of stem cells with the ability to self-organize into blastoids and recapitulate key preimplantation developmental processes. Therefore, the stem cells are termed transient totipotent blastomere-like stem cells (tTBLCs). Transcriptome analysis showed that tTBLC blastoids bore similarities to mouse E3.5 blastocysts, E4.5 blastocysts, and TBLC blastoids. Additionally, we found that tTBLC blastoids could develop beyond the implantation stage, forming egg-cylinder-like structures both in vitroand in vivo. In summary, our research provides an alternative rapid and convenient method to generate the starting cells capable of developing into blastoids, which have immense application in various fields, not only in the basic study of early mouse embryogenesis but also in high-throughput drug screening.

A 4H-SiC light-triggered thyristor is fabricated and triggered by a 355nm UV laser for fast switching performance by photoconductive assistance. The switching characteristics of the thyristor are tested in a resistive load circuit with a 220nF capacitor as an energy storage element. By combining the high-power UV light with a multi-gate structure, the thyristor is switched on fast through the photoconduction mechanism, overcoming the limitation of the internal positive feedback formation process. The peak current of the thyristor reaches to 328A, and the corresponding current density is about 8.2kA/cm2. The highest current rising rate (dI/dt) obtained in this work is 27.3kA/ $\mu $ s, and the corresponding current density rising rate is 682.5 kA/(cm $^{{2}}\cdot \mu $ s).

Industrial robots (IRs) have considerable energy-saving potential due to their vast application scale and wide range of applications. Although substantial work on the energy consumption (EC) optimization of IRs has emerged, most optimization approaches require prior knowledge of the IRs' dynamic characteristics and the electro-mechanical parameters of their drive systems, which are typically not provided by IR manufacturers. Therefore, this article proposes an EC modeling and optimization method based on the time-scaling technique and custom identification experimental data without joint torque information. Specifically, this article develops an energy characteristic parameter submodel (ECPSM) to formulate the EC resulting from configuration transitions. In addition, theoretical proof demonstrates that all coefficients in the proposed ECPSM can be identified based on the data of a finite number of identification experiments. Building upon the proposed EC model, a bidirectional dynamic programming (BDP) algorithm optimizes the IR's trajectory for energy-saving, while utilizing parallel processing significantly reduces the time required for the optimization process. Experimental results on the KUKA KR60-3 demonstrate that the proposed method achieves an average relative error of 1.59% for predicting the EC of linear scaling trajectories and 6.19% for nonlinear scaled trajectories. Moreover, the BDP-based optimization method dramatically reduces the computational time required to obtain the optimal scaling trajectory and its EC.

This article studies the diffusion-source-inference (DSI) problem, whose solution plays an important role in real-world scenarios such as combating misinformation and controlling diffusions of information or disease. The main task of the DSI problem is to optimize an estimator, such that the real source can be more precisely targeted. In this article, we assume that the state of a number of nodes, called observer set, in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the DSI problem. In particular, we find that the conventional error distance metric cannot precisely evaluate the effectiveness of varied DSI approaches in heterogeneous networks, and thus propose a novel and more general measurement, the candidate set, that is formulated to contain the diffusion source for sure. We propose the percolation-based evolutionary framework (PrEF) to optimize the observer set such that the candidate set can be minimized. Hence, one could further conduct more intensive investigation or search on only a few nodes to target the source. To achieve that, we first theoretically show that the size of the candidate set is bounded by the size of the largest component cover, and demonstrate that there are some similarities between the DSI problem and the network immunization problem. We find that, given the associated direction information of the diffusion is known on observers, the minimization of the candidate set is equivalent to the minimization of the order parameter if we view the observer set as the removal node set. Hence, PrEF is developed based on the network percolation and evolutionary algorithm. The effectiveness of the proposed method is validated on both synthetic and empirical networks in regard to varied circumstances. Our results show that the developed approach could achieve much smaller candidate sets compared to the state of the art in almost all cases, e.g., it is better in 26 out of 27 empirical networks and 155 out of 162 cases regarding the critical threshold. Meanwhile, our approach is also more stable, i.e., it works well irrespective of varied infection probabilities, diffusion models, and underlying networks. More importantly, we provide a framework for the analysis of the DSI problem in large-scale networks.

Concept-cognitive learning is an emerging area of cognitive computing, which refers to continuously learning new knowledge by imitating the human cognition process. However, the existing research on concept-cognitive learning is still at the level of complete cognition as well as cognitive operators, which is far from the real cognition process. Meanwhile, the current classification algorithms based on concept-cognitive learning models (CCLMs) are not mature enough yet since their cognitive results highly depend on the cognition order of attributes. To address the above problems, this article presents a novel concept-cognitive learning method, namely, stochastic incremental incomplete concept-cognitive learning method (SI2CCLM), whose cognition process adopts a stochastic strategy that is independent of the order of attributes. Moreover, a new classification algorithm based on SI2CCLM is developed, and the analysis of the parameters and convergence of the algorithm is made. Finally, we show the cognitive effectiveness of SI2CCLM by comparing it with other concept-cognitive learning methods. In addition, the average accuracy of our model on 24 datasets is 82.02%, which is higher than the compared 20 classification algorithms, and the elapsed time of our model also has advantages.

In this article, we have designed an optimized ferroelectric tunnel junction (FTJ) device structure that inserts 3-nm Al2O3 between Hf0.5Zr0.5O2 (HZO) films. The Al2O3 interlayer can block the longitudinal growth of HZO grains and increase the number of ferroelectric domains. Therefore, the FTJ devices with Al2O3 interlayer demonstrate amazing multilevel states (256) and ultralow computational power consumption (76.1 pW/bit). In addition, the proposed FTJ device shows high linearity ( $\alpha _{\text {p}} = -1.262$ ), wide modulation capability, and good reproducibility. The results indicate that the device has high potential in energy-efficient brain-like computing application.

ABSTRACTDuring tumor expansion, breast cancer (BC) cells often experience reactive oxygen species accumulation and mitochondrial damage because of glucose shortage. However, the mechanism by which BC cells deal with the glucose-shortage-induced oxidative stress remains unclear. Here, we showed that MANF (mesencephalic astrocyte derived neurotrophic factor)-mediated mitophagy facilitates BC cell survival under glucose-starvation conditions. MANF-mediated mitophagy also promotes fatty acid oxidation in glucose-starved BC cells. Moreover, during glucose starvation, SENP1-mediated de-SUMOylation of MANF increases cytoplasmic MANF expression through the inhibition of MANF’s nuclear translocation and hence renders mitochondrial distribution of MANF. MANF mediates mitophagy by binding to PRKN (parkin RBR E3 ubiquitin protein ligase), a key mitophagy regulator, in the mitochondria. Under conditions of glucose starvation, protein oxidation inhibits PRKN activity; nevertheless, the CXXC motif of MANF alleviates protein oxidation in RING II-domain of PRKN and restores its E3 ligase activity. Furthermore, MANF-PRKN interactions are essential for BC tumor growth and metastasis. High MANF expression predicts poor outcomes in patients with BC. Our results highlight the prosurvival role of MANF-mediated mitophagy in BC cells during glucose starvation, suggesting MANF as a potential therapeutic target.Abbreviation:2DG, 2-deoxy-D-glucose; 5TG, 5-thio-D-glucose; ACSL4/FACL4, acyl-CoA synthetase long chain family member 4; Baf A1, bafilomycin A1; BRCA, breast cancer; CHX, cycloheximide; DMF, distant metastasis-free; DMFS, distant metastasis-free survival; ECM, extracellular matrix; ER, endoplasmic reticulum; ERS, endoplasmic reticulum stress; F-1,6-BP, fructose-1,6-bisphosphate; FAO, fatty acid oxidation; GSH, reduced glutathione; GSVA, gene set variation analysis; HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; IF, immunofluorescence; MANF, mesencephalic astrocyte derived neurotrophic factor; Mdivi-1, mitochondrial division inhibitor 1; MFI, mean fluorescence intensity; NAC, N-acetyl-L-cysteine; OCR, oxygen-consumption rate; OS, overall survival; PMI, SQSTM1/p62-mediated mitophagy inducer; PPP, pentose phosphate pathway; PRKN, parkin RBR E3 ubiquitin protein ligase; RBR, RING in between RING; RFS, relapse-free survival; ROS, reactive oxygen species; SAPLIPs, saposin-like proteins; TCGA, The Cancer Genome Atlas; TNBC, triple-negative breast cancer; WT, wild type.

The geometric error compensation is one cost-effective approach to enhance the accuracy of the machine tool. Position-independent geometric errors (PIGEs) of rotary axes introduce extra great influence on the accuracy of the five-axis machine tool. In this work, the ballbar-based PIGEs identification of rotary axes is presented by analyzing the influences of PIGEs on the local frames. First, the influences of squareness errors for the rotary axis are explained in detail based on the physical definition-based mechanism analysis, which are angular errors in two directions of the local frame changing with the rotation of the rotary axis. The local error vectors of PIGEs for the rotary axis are established. Second, three measurement strategies and the identification formula for PIGEs of the rotary axis are proposed based on the ballbar reading model-based identification Jacobian matrix. Two reference points of the ballbar on the axial line of the measured rotary axis are designed according to the requirement of full rank of the identification Jacobian matrix. For each measurement strategy, the single motion of the measured rotary axis is needed. The accuracy of three measurement strategies and the universality of different rotary axes are also discussed. Third, simulations are proposed to testify three measurement strategies by considering the influences of position-dependent geometric errors (PDGEs). The comparisons with two existing methods are carried out to validate the effectiveness of the proposed methods. At last, measurement, compensation experiments, and the workpiece cutting experiments are conducted with the SmartCNC-DRTD five-axis machine tool for the validation.

Purpose: The aim of this study was to observe the influence of differential nutritional status on bone age (BA) change according to body mass index (BMI) and analyze the risk of advanced bone age in children with overweight and obesity. Methods: In total 23,305 children from Beijing were included in this cross-sectional study. Childhood overweight and obesity were defined according to the China and World Health Organization growth criteria. The data were analyzed by the R coding platform version 4.3.0. Results: Under the Chinese criteria, 29%, 15%, and 4% of boys with overweight; 33%, 33%, and 3% of boys with obesity; 39%, 25%, and 2% of girls with overweight; and 37%, 42% and 1% of girls with obesity had advanced, significantly advanced and delayed BA, respectively. After adjustment, overweight (odds ratio, 95% confidence interval, P under the Chinese criteria: 2.52, 2.30–2.75, <0.001 and 4.54, 4.06–5.09, <0.001) and obesity (4.31, 3.85–4.82, <0.001 and 14.01, 12.39–15.85, <0.001) were risk factors for both advanced BA and significantly advanced BA. Conclusions:: Different nutritional statuses lead to differences in children’s BA development. Children with overweight and obesity have higher rates of advanced BA under two growth criteria, and girls have more advances in BA than boys do. Overweight and obesity are risk factors for advanced BA.

BaCe0.8Y0.2O3–αceramics exhibit superior conductivity among related materials. However, the high-temperature sintering makes it difficult to prepare electrochemical devices with a BaCe0.8Y0.2O3–αmultilayer film, and very few studies have examined the conductivity and transport properties of unsintered BaCe0.8Y0.2O3–α. In nominally dry conditions, the instability of this material in a water-containing atmosphere can be minimized, allowing the unsintered BaCe0.8Y0.2O3–αto be applied in some particular test environment as a component of electrochemical devices. Hence, the conductivity of unsintered BaCe0.8Y0.2O3–αin dry conditions was measured via AC impedance spectroscopy in the temperature range of 500–800 °C. The unsintered BaCe0.8Y0.2O3–αexhibits high conductivity and hydration ability, as well as low proton activation energy. In addition, it shows high oxygen vacancy and low proton transport numbers at high temperature, limited by its grain boundaries. This work provides insights into the conductivity and proton transport of unsintered BaCe0.8Y0.2O3–αand demonstrates its potential as a proton-conducting electrolyte.

The applications of flexible sensors that can mimic biological skin have enormous potential in areas, such as artificial intelligence and healthcare detection. The existing rigid/flexible pressure sensors mainly rely on resistive or capacitive sensors, necessitating a continuous external power supply. The current self-powered sensors, such as triboelectric and piezoelectric, struggle to continuously measure static forces. Some piezoelectric sensors can detect static forces but require additional power or external resistors for static measurements. Developing a flexible sensor that can monitor various external stimuli like skin and does not require power can address these shortcomings. This article proposes a single-mode flexible pressure sensor based on the principle of air battery. By introducing different structures to the cathode and solid electrolyte of the battery, the sensor’s linearity and pressure detection range can be significantly improved. The sensor’s linearity exceeds 99% under pressures ranging from 0 to 45 kPa, with a response time of less than 50 ms. Besides, self-powered sensor can work continuously for over eight days. Testing shows that the sensor can mimic the functions of rapid adaptive (RA), slow adaptive (SA) cutaneous mechanoreceptors, and Krause corpuscles to detect mechanical stimuli and temperature. This work introduces novel strategy to enhance the performance of battery powered self-powered sensors, with anticipated applications in areas, such as medical health detection and artificial intelligence.

The highlight reflectance on machined metal surfaces dramatically influences the performance of optical measurement sensors, such as fringe projector profilometers. Previous research addressed such an issue through fusion of images under different camera exposure times or modulation of the lighting intensity of the projector. Differently, this article provides a new research perspective that resorts to the modeling of complicated anisotropic reflectance on machined metal surfaces, which essentially causes the decrease in the quality of fringe images. In this article, an anisotropic neural reflectance model is proposed for the effective description of machined metal reflectance. Then, a facility with one camera, one projector, and several lighting emittance didoes is designed for the application of the proposed model in the measurement of curved machined metal surfaces. A complete and accurate point cloud is finally obtained through fitting of the real reflectance using the proposed model in a self-supervision way. Synthetic experiments illustrate that the capability of the proposed model to describe the machined metal reflectance is considerably enhanced compared with the traditional parameterized reflectance model and isotropic neural reflectance model. Real experiments prove that the proposed method can achieve measurement accuracy of 23 μm on machined metal surfaces with repeated accuracy of 0.15 μm, where the ground truth is given by the coordinate measuring machine.

Grid security is threatened by the uncontrolled access of large-scale electric vehicles (EVs) to the grid. The scheduling problem of EVs charging and discharging is described as a Markov decision process (MDP) to develop an efficient charging and discharging scheduling strategy. Furthermore, a deep reinforcement learning (DRL)-based model-free method is suggested to address such issue. The proposed method aims to enhance EVs charging and discharging profits while reducing drivers' electricity anxiety and ensuring grid security. Drivers' electricity anxiety is described by fuzzy mathematical theory, and analyze the effect of EV current power and remaining charging time on it. Variable electricity pricing is calculated by real-time residential load. A dynamic charging environment is constructed considering the stochasticity of electricity prices, driver’s behavior, and residential load. A soft actor-critic (SAC) framework is used to train the agent, which learns the optimal charging and discharging scheduling strategies by interacting with the dynamic charging environment. Finally, simulation with actual data is used to verify that the suggested approach can reduce drivers' charging costs and electricity anxiety while avoiding transformer overload.

The dynamic load distribution within in-service axlebox bearings of high-speed trains is crucial for the fatigue reliability assessment and forward design of axlebox bearings. This paper presents an in situ measurement of the dynamic load distribution in the four rows of two axlebox bearings on a bogie wheelset of a high-speed train under polygonal wheel–rail excitation. The measurement employed an improved strain-based method to measure the dynamic radial load distribution of roller bearings. The four rows of two axlebox bearings on a wheelset exhibited different ranges of loaded zones and different means of distributed loads. Besides, the mean value and standard deviation of measured roller–raceway contact loads showed non-monotonic variations with the frequency of wheel–rail excitation. The fatigue life of the four bearing rows under polygonal wheel–rail excitation was quantitatively predicted by compiling the measured roller–raceway contact load spectra of the most loaded position and considering the load spectra as input.

Some patients with pulmonary tuberculosis (PTB) do not display typical clinical features, leading to delays in diagnosis and treatment.

The optical quantum control has been successfully applied in modulating biological processes such as energy transfer and bond isomerization. Among the reactions in realizing biological functions, the electron transfer (ET) process is fundamental; hence, the quantum control over such an ET reaction is of far-reaching significance. Here, we realized optical quantum control over ultrafast ET processes in a protein, flavodoxin, by applying various chirped excitation pulses. We observed the wavepacket dynamics within a dephasing time of less than 1 ps. Within this time window, we found that the ultrafast photoinduced ET reaction can be controlled by different chirped excitations with a rate change by a factor of about 2. Furthermore, the control effect is propagated into the subsequent ultrafast back ET reaction, showing a variation of the BET dynamics with different excitation chirps. The underlying mechanism is the initial wavepacket dynamics; the differently prepared wavepackets with chirped excitation evolve along various pathways, resulting in the changes of ET rates. The successful demonstration of optical quantum control of ultrafast biological ET is significant and opens a new avenue to explore the quantum control of real biological ET reactions.

A surface double-coupling engineering is developed via constructing a spinel-like interlayer to mitigate bulk strain, curb surface-side reactions, and assist stable and durable Li-rich Mn-based cathode materials.

Solar-driven carbon dioxide (CO2) reduction into C2+products such as ethylene represents an enticing route toward achieving carbon neutrality. However, due to sluggish electron transfer and intricate C–C coupling, it remains challenging to achieve highly efficient and selective ethylene production from CO2and H2O beyond capitalizing on Cu-based catalysts. Herein, we report a judicious design to attain asymmetric C–C coupling through interfacial defect-rendered tandem catalytic centers within a sulfur-vacancy-rich MoSx/Fe2O3photocatalyst sheet, enabling a robust CO2photoreduction to ethylene without the need for copper, noble metals, and sacrificial agents. Specifically, interfacial S vacancies induce adjacent under-coordinated S atoms to form Fe–S bonds as a rapid electron-transfer pathway for yielding a Z-scheme band alignment. Moreover, these S vacancies further modulate the strong coupling interaction to generate a nitrogenase-analogous Mo–Fe heteronuclear unit and induce the upward shift of the d-band center. This bioinspired interface structure effectively suppresses electrostatic repulsion between neighboring *CO and *COH intermediates via d-p hybridization, ultimately facilitating an asymmetric C–C coupling to achieve a remarkable solar-to-chemical efficiency of 0.565% with a superior selectivity of 84.9% for ethylene production. Further strengthened by MoSx/WO3, our design unveils a promising platform for optimizing interfacial electron transfer and offers a new option for C2+synthesis from CO2and H2O using copper-free and noble metal-free catalysts.

A chlorotrimethylsilane (TMSCl)-mediated cascade phosphorylation and cycloisomerization of enynones with diphenylphosphine oxides is presented. This methodology enables the highly selective synthesis of monophosphorylated 2H-pyrans and bisphosphorylated dihydropyrans through precise solvent-reagent stoichiometry control. The strategy demonstrated excellent functional group compatibility and high yields (up to 96%), providing facile access to structurally diverse phosphorylated heterocycles with potential applications in medicinal chemistry and materials science.

van der Waals (vdW) structures host a broad range of physical phenomena. New opportunities arise if different functional layers are remotely modulated or coupled in a device structure. Here we demonstrate the in situ coherent modulation of moiré excitons and correlated Mott insulators in transition metal dichalcogenide (TMD) moirés with on-chip terahertz (THz) waves. Using common dual-gated device structures of a TMD moiré bilayer sandwiched between two few-layer graphene (fl-Gr) gates with hexagonal boron nitride (h-BN) spacers, we launch coherent phonon wavepackets at ∼0.4–1 THz from the fl-Gr gates by femtosecond laser excitation. The waves travel through the h-BN spacer, arrive at the TMD bilayer with precise timing, and coherently modulate the moiré excitons or Mott states. These results demonstrate that the fl-Gr gates, often used for electrical control, can serve as on-chip opto-elastic transducers to generate THz waves for coherent control and vibrational entanglement of functional layers in moiré devices.

This paper presents the design of a novel honeycomb structure with a double curved beam. The purpose of this design is to achieve vibration isolation for the main engine of an offshore platform and reduce impact loads. An analytical formula for the force-displacement relationship of the honeycomb single-cell structure is presented based on the modal superposition method. This formula provides a theoretical basis for predicting the compression performance of honeycomb structures. The effects of structural geometric parameters, series and parallel connection methods on the mechanical and energy absorption properties are investigated through mathematical modeling and experimental methods. Furthermore, the study focuses on the vibration isolation and impact resistance performance of honeycomb panels. The results show that the designed honeycomb structure has good mechanical and energy absorption performance, and its energy absorption effect is related to the geometric parameters and series and parallel connection methods of the structure. The isolation efficiency of the honeycomb with 4 rows and 3 columns reaches 38%. The initial isolation frequency of the isolator is 11.7 Hz.

Sarcopenia is an important indicator of ill health and is linked to increased mortality and a reduced quality of life. Age‐associated muscle mass indices provide a critical tool to help understand the development of sarcopenia. This study aimed to develop sex‐ and age‐specific percentiles for muscle mass indices in a Chinese population and to compare those indices with those from other ethnicities using the National Health and Nutrition Examination Survey (NHANES) data. Whole‐body and regional muscle mass was measured by dual‐energy X‐ray absorptiometry (DXA) in participants of the China Body Composition Life‐course (BCL) study (17 203 healthy Chinese aged 3–60 years, male 48.9%) and NHANES (12 663 healthy Americans aged 8–59 years, male 50.4%). Age‐ and sex‐specific percentile curves were generated for whole‐body muscle mass and appendicular skeletal muscle mass using the Generalized Additive Model for Location Scale and Shape statistical method. Values of upper and lower muscle mass across ages had three periods: an increase from age 3 to a peak at age 25 in males (with the 5th and 95th values of 41.5 and 66.4 kg, respectively) and age 23 in females (with the 5th and 95th values of 28.4 and 45.1 kg, respectively), a plateau through midlife (30s–50s) and then a decline after their early 50s. The age at which muscle mass began to decline was 52 years in men with the 5th and 95th percentile values of 43.5 and 64.6 kg, and 51 years in women with the 5th and 95th percentile values of 31.6 and 46.9 kg. Appendicular skeletal muscle mass decreased earlier than whole body muscle mass, especially leg skeletal muscle mass, which decreased slightly after age 49 years in both sexes. In comparison with their US counterparts in the NHANES, the Chinese participants had lower muscle mass indices (all P< 0.001) and reached a muscle mass peak earlier with a lower muscle mass, with the exception of similar values compared with adult Mexican and White participants. The muscle mass growth rate of Chinese children decreased faster than that of other races after the age of 13. We present the sex‐ and age‐specific percentiles for muscle mass and appendicular skeletal muscle mass by DXA in participants aged 3–60 from China and compare them with those of different ethnic groups in NHANES. The rich data characterize the trajectories of key muscle mass indices that may facilitate the clinical appraisal of muscle mass and improve the early diagnosis of sarcopenia in the Chinese population.

Aiming for the temperature drift phenomenon of SiC phototransistor during ultraviolet detection process, the p-n junction is integrated into the 4H-SiC n-p-n phototransistor chip to enable on-line temperature monitoring characteristic in this work. The SiC phototransistor is fabricated and investigated. Basic characteristics testing results demonstrate that the device’s peak ultraviolet wavelength is 342 nm, with a peak responsivity of 6.92 mA/W at zero bias. When the temperature increases from room temperature (RT) to 300 °C, the ultraviolet responsivity increases by approximately 64.7 times. However, temperature characteristics of the experimental results demonstrate that the device has temperature-sensing capability under reverse biased conditions. When the current is constant, the reverse voltage shows good linearity with temperature, with a sensitivity of approximately 2.76 mV/°C at $- 0.1~\mu $ A. The dynamic characteristics testing in a resistive load circuit demonstrates that the phototransistor has achieved on-line temperature monitoring ability. The equation between reverse current and temperature is also established for temperature extraction.

This paper characterizes the dark current development of p-on-n type HgCdTe multi-layer heterojunction long wavelength infrared (LWIR) and very long wavelength infrared (VLWIR) photodiodes. Four devices that operate at different wavelengths are fabricated by employing a multi-layer structure. The results demonstrate the favorable dark current which is close to the “Rule 07” limitation is obtained with a 50% cutoff wavelength of $16.6~\mu $ m. Besides, the influence mechanisms on the device are extracted by analyzing the temperature-dependent dark current from 40 K to 130 K. The results suggest that the proposed devices perform comparable to those of conventional double-layer heterojunction devices. Furthermore, it can be noted that by precisely controlling the composition distribution and depletion region positions as well as improving the process, we can further achieve superior LWIR and VLWIR devices.

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.

The Narasaka–Heck reaction is one of the most straightforward methods for constructing pyrroline derivatives. Herein, we report a novel nickel-catalyzed three-component carbonylation reaction, which cleverly realizes the continuous construction of C(sp3)–N bonds and C(sp3)–C(sp2) bonds and effectively promotes the synthesis of acyl-substituted pyrroline derivatives. Furthermore, this strategy not only expands the conversion pathway of γ,δ-unsaturated oxime esters but also provides a new method for the synthesis of nitrogen-containing heterocyclic compounds.

Hydrogel, as a promising material for a wide range of applications, has demonstrated considerable potential for use in flexible wearable devices and engineering technologies. However, simultaneously realizing the ultrastretchability, low hysteresis, and high toughness of hydrogels is still a great challenge. Here, we present a dual physically cross-linked polyacrylamide (PAM)/sodium hyaluronate (HA)/montmorillonite (MMT) hydrogel. The introduction of HA increases the degree of chain entanglement, and the addition of MMT acts as a stress dissipation center and cross-linking agent, resulting in a hydrogel with high toughness and low hysteretic properties. This hydrogel synthesized by a simple strategy exhibited ultrahigh stretchability (3165%), high breaking stress (228 kPa), high toughness (4.149 MJ/m3), and ultralow hysteresis (≈2% at 100% of strain). The fabricated hydrogel flexible strain sensors possessed fast response and recovery times (62.5:75 ms), a wide strain detection range (2000%), a strain detection limit of 1%, and excellent cycling stability over 500 cycles. Furthermore, the hydrogel flexible strain sensor can be used for human motion monitoring, gesture recognition, and pressure recognition assisted by deep learning algorithms, showing enormous promise for applications.

Recently, organic–inorganic hybrid perovskites with their unique physicochemical properties have become a vibrant frontier of research. Here, we report a novel organic–inorganic hybrid phase transition material, Me3HyPbBr3(Me3Hy+= 1,1,1-trimethylhydrazine), and explore its thermal, structural, and optical properties. X-ray diffraction shows that Me3HyPbBr3belongs to the R3̅m, R3̅, and R3̅mspace groups in the low-temperature (LTP), intermediate-temperature (ITP), and high-temperature (HTP) phases, respectively. In addition, the compound has a unique structure, a 9R-type hexagonal perovskite polytype structure, and the inorganic framework is alternately connected by tetrahedral and octahedral [PbBr3]−. Differential scanning calorimetry (DSC) tests showed that the compound underwent a reversible phase transition near 262 and 215 K. In addition, Me3HyPbBr3possesses semiconducting and fluorescent properties with a band-gap value of 2.91 eV, a photoluminescence (PL) peak located at 518 nm, and a quantum yield of 17.95%, suggesting its potential for optoelectronic applications.

Guided bone regeneration (GBR) technology has been demonstrated to be an effective method for reconstructing bone defects. A membrane is used to cover the bone defect to stop soft tissue from growing into it. The biosurface design of the barrier membrane is key to the technology. In this work, an asymmetric functional gradient Janus membrane was designed to address the bidirectional environment of the bone and soft tissue during bone reconstruction. The Janus membrane was simply and efficiently prepared by the multilayer self-assembly technique, and it was divided into the polycaprolactone isolation layer (PCL layer, GBR-A) and the nanohydroxyapatite/polycaprolactone/polyethylene glycol osteogenic layer (HAn/PCL/PEG layer, GBR-B). The morphology, composition, roughness, hydrophilicity, biocompatibility, cell attachment, and osteogenic mineralization ability of the double surfaces of the Janus membrane were systematically evaluated. The GBR-A layer was smooth, dense, and hydrophobic, which could inhibit cell adhesion and resist soft tissue invasion. The GBR-B layer was rough, porous, hydrophilic, and bioactive, promoting cell adhesion, proliferation, matrix mineralization, and expression of alkaline phosphatase and RUNX2. In vitro and in vivo results showed that the membrane could bind tightly to bone, maintain long-term space stability, and significantly promote new bone formation. Moreover, the membrane could fix the bone filling material in the defect for a better healing effect. This work presents a straightforward and viable methodology for the fabrication of GBR membranes with Janus-based bioactive surfaces. This work may provide insights for the design of biomaterial surfaces and treatment of bone defects.

A reinvestigation of “Phosphine-Mediated Reductive Condensation of γ-Acyloxy Butynoates: A Diversity Oriented Strategy for the Construction of Substituted Furans” (J. Am. Chem. Soc.2004, 126, 4118–4119) revealed different chemoselectivity of triphenylphosphine in the reactions with the γ-acyloxy butynoate substrates of varying substitution patterns/electronics. Furthermore, the electronics of the triaryl phosphine reagent could be tuned to trap a putative intermediate such as A, leading to the semihydrogenation of propiolamide substrates.

To achieve the brazed diamond tools with low thermal damage, the diamonds were brazed using Ni–Cr base filler metals containing up to 1.5 weight percent Nd. The microstructure of Ni–Cr base filler metals and the surface morphology and interfacial microstructure of brazed samples were characterized. The results show that by the addition of Nd, the microstructure of filler metals is obviously refined and becomes uniform. Meanwhile, Nd doping effectively reduces the graphitization degree and thermal residual stress of brazed diamonds. In addition, a variety of carbides (such as Cr3C2, Cr7C3, and SiC) which have low hot cracking tendencies are formed through occurring chemical/metallurgical reactions. The microstructural NdNi4B particles contribute to reducing the thermal damage of the brazed diamonds after brazing. At a doping level of 1.0 wt.% of rare-earth Nd, the maximum residual stress of brazed diamond was reduced by 20% and the material removal increased by 45.1%.

This study investigates the advantages of combining Spark Plasma Sintering (SPS) with Laser Cladding (LC) technology in the production of AlCoCrFeNi high-entropy alloy (HEA). By comprehensively applying SPS and LC composite processing techniques, we achieve rapid preparation of high-performance AlCoCrFeNi HEA. SPS technology enables rapid sintering under high-temperature conditions, while LC technology allows precise control of microstructure generation on the workpiece surface. Experimental results show that AlCoCrFeNi HEA prepared by single SPS technology exhibits a body-centered cubic (BCC) solid solution structure, while AlCoCrFeNi HEA processed with a combination of SPS and LC techniques demonstrates a complex microstructure consisting of equiaxed BCC phase, uniformly distributed granular face-centered cubic (FCC) solid solution, and α-Al2O3ceramic phase. Composite processing also eliminates voids and pores caused by single SPS processing, resulting in AlCoCrFeNi HEA with low porosity. Furthermore, the maximum hardness of AlCoCrFeNi HEA produced by composite processing technology is approximately 644.1HV, significantly higher than that of AlCoCrFeNi HEA processed solely by SPS (approximately 328.7HV). Wear test results show that AlCoCrFeNi HEA processed by composite machining exhibits significant advantages in wear resistance, with the width and depth of wear tracks significantly reduced compared to AlCoCrFeNi HEA processed solely by SPS technology, with a 33.17% higher wear rate. In conclusion, composite processing using SPS combined with LC technology enables rapid preparation of AlCoCrFeNi HEA with low porosity, high hardness, and high wear resistance.

One-shot talking head generation has no explicit head movement reference, thus it is difficult to generate talking heads with head motions. Some existing works only edit the mouth area and generate still talking heads, leading to unreal talking head performance. Other works construct one-to-one mapping between audio signal and head motion sequences, introducing ambiguity correspondences into the mapping since people can behave differently in head motions when speaking the same content. This unreasonable mapping form fails to model the diversity and produces either nearly static or even exaggerated head motions, which are unnatural and strange. Therefore, the one-shot talking head generation task is actually a one-to-many ill-posed problem and people present diverse head motions when speaking. Based on the above observation, we propose OSM-Net, a one-to-many one-shot talking head generation network with natural head motions. OSM-Net constructs a motion space that contains rich and various clip-level head motion features. Each basis of the space represents a feature of meaningful head motion in a clip rather than just a frame, thus providing more coherent and natural motion changes in talking heads. The driving audio is mapped into the motion space, around which various motion features can be sampled within a reasonable range to achieve the one-to-many mapping. Besides, the landmark constraint and time window feature input improve the accurate expression feature extraction and video generation. Extensive experiments show that OSM-Net generates more natural realistic head motions under reasonable one-to-many mapping paradigm compared with other methods.

Compositional zero-shot learning (CZSL) aims to recognize novel concepts from known subconcepts. However, it is still challenging since the intricate interaction between subconcepts is entangled with their corresponding visual features, which affects the recognition accuracy of concepts. Besides, the domain gap between training and testing data leads to the model poor generalization. In this article, we tackle these problems by exploring partial semantic consistency (PSC) to eliminate visual deviation to guarantee the discrimination and generalization of representations. Considering the complicated interaction between subconcepts and their visual features, we decompose seen images into visual elements according to their labels and obtain the instance-level subdeviations from compositions, which is utilized to excavate the category-level primitives of subconcepts. Furthermore, we present a multiscale concept composition (MSCC) approach to produce virtual samples from two aspects, which augments the sufficiency and diversity of samples so that the proposed model can generalize to novel compositions. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three benchmark datasets.