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The chiral binaphthol-catalyzed enantioselective conjugate addition of alkenylboronic acids and heteroarylboronic acids to cyclic N-sulfonyl ketimines is reported, providing the 1,4-addition products in high yields and moderate to excellent enantioselectivities (up to >99% ee). This mild, scalable catalytic system exhibits high efficiency and broad substrate scopes. Additionally, arylboronic acids were viable nucleophiles under more forcing conditions.

ABSTRACTTuned mass dampers (TMDs) are attractive vibration control devices, and their seismic attenuation depends on their frequency and damping coefficient. Structural characteristics are changed by the soil-structure interaction (SSI); therefore, SSI should be considered in the optimization of TMD. A high-rise building with 40 stories, including four different soil conditions, is proposed as a case study. For the objective function, considering the randomness of earthquakes, a stationary white noise stochastic process based on the Kanai-Tajimi spectrum was used as the input. To improve the performance of a TMD in controlling multimodal and overall dynamic responses, three objectives were considered in the objective function: the maximum and root mean square (RMS) displacements of the top story and the maximum displacement of all stories, respectively. The weight coefficient of each objective function was obtained according to the modal mass participation coefficient. To clearly observe the damping effect of the TMD with different parameter combinations, an ergodic parameter calculation is used. The TMD optimization parameters for each benchmark model with different soil conditions were proposed. Far-field ground motions were considered, and four optimized TMDs from reference were compared. The results show that the proposed TMDs can significantly mitigate earthquake oscillations in a great degree. They performed better than contrastively optimized TMDs under several earthquakes.

With the advent of mobile networks, we are witnessing an unprecedented shift in the landscape of mobile network services, evolving from traditional voice calls to advanced artificial intelligence (AI) services. This paper delves into the intricacies of this evolution, particularly emphasizing the deep integration of AI agents into 6G networks. Despite recent researches in using large language model (LLM) and AI agent for network automation, the fundamental mobile AI agent use cases, their network requirements, potential network architecture and enabling technologies for supporting the pervasive AI agents in 6G era are largely unexplored. In this article, we present an in-depth analysis of typical mobile AI agent use cases in 6G, consisting of AI agent-based 6G network automation, handheld personalized agents, connected robotics and autonomous systems, and wearable AI agent. Then, we elucidate a novel system architecture that supports identified use cases. The article also addresses core aspects of enabling technologies, including 6G agent and application agent collaboration, efficient model and memory management, coordinated agent-to-agent communication and support of multi-modal data transmission. A proof of concept prototype is also presented to demonstrate 6G agent and application AI agent collaboration. Finally, three challenges and research directions: energy saving, security protection and AI agent tailored communication are discussed. This article lays a foundation for understanding the role of 6G in realizing the full potential of AI agents in various applications.

Data-free knowledge distillation (DFKD) improves the student model (S) by mimicking the class probability from a pre-trained teacher model (T) without training data. Under such setting, an ideal scenario is that T can help generate ”good” samples from a generator (G) to maximally benefit S. However, existing arts suffer from the non-ideal generated samples under the disturbance of the gap (i.e., either too large or small) between the class probabilities of T and S; for example, the generated samples with too large gap may exhibit excessive information for S, while too small gap leads to the limited knowledge in the samples, resulting into the poor generalization. Meanwhile, they fail to judge the “goodness” of the generated samples for S since the fixed T is not necessarily ideal. In this paper, we aim to answer what is inside the gap box; together with how to yield ”good” generated samples for DFKD? To this end, we propose a Gap-Sensitive Sample Generation (GapSSG) approach, by revisiting the empirical distilled risk from a data-free perspective, which confirms the existence of an ideal teacher (T$^*$*), while theoretically implying: (1) the gap disturbance originates from the mismatch between T and T$^*$*, hence the class probabilities of T enable the approximation to those of T$^*$*; and (2) ”good” samples should maximally benefit S via T's class probabilities, owing to unknown T$^*$*. To this end, we unpack the gap box between T and S as two findings: inherent gap to perceive T and T$^*$*; derived gap to monitor S and T$^*$*. Benefiting from the derived gap that focuses on the adaptability of generated sample to S, we attempt to track student's training route (a series of training epochs) to capture the category distribution of S; upon which, a regulatory factor is further devised to approximate T$^*$* over inherent gap, so as to generate ”good” samples to S. Furthermore, during the distillation process, a sample-balanced strategy comes up to tackle the overfitting and missing knowledge issues between the generated partial and critical samples by training G. The theoretical and empirical studies verify the advantages of GapSSG over the state-of-the-arts.

Laying the under-sleeper pad (USP) is one of the effective measures commonly used to delay ballast degradation and reduce maintenance workload. To explore the impact of application of the USP on the dynamic and static mechanical behavior of the ballast track in the heavy-haul railway system, numerical simulation models of the ballast bed with USP and without USP are presented in this paper by using the discrete element method (DEM)—multi-flexible body dynamic (MFBD) coupling analysis method. The ballast bed support stiffness test and dynamic displacement tests were carried out on the actual operation of a heavy-haul railway line to verify the validity of the models. The results show that using the USP results in a 43.01% reduction in the ballast bed support stiffness and achieves a more uniform distribution of track loads on the sleepers. It effectively reduces the load borne by the sleeper directly under the wheel load, with a 7.89% reduction in the pressure on the sleeper. Furthermore, the laying of the USP changes the lateral resistance sharing ratio of the ballast bed, significantly reducing the stress level of the ballast bed under train loads, with an average stress reduction of 42.19 kPa. It also reduces the plastic displacement of ballast particles and lowers the peak value of rotational angular velocity by about 50% to 70%, which is conducive to slowing down ballast bed settlement deformation and reducing maintenance costs. In summary, laying the USP has a potential value in enhancing the stability and extending the lifespan of the ballast bed in heavy-haul railway systems.

Soybean protein has high nutritional value, but its functional properties are easily affected by external factors, which limits its application in food industry. In the study, soybean protein isolate (SPI) was modified by dry heat glycation of galactooligosaccharides (GOS). The gel properties, antioxidant properties and structural changes of SPI-GOS conjugates were investigated. The application of SPI-GOS conjugates in noodles was also explored. The results observed that the glycation degree of SPI increased with the increasing reaction time. SDS-PAGE and spectral analysis showed the changes of spatial conformation of SPI after glycation. The antioxidant activity of SPI increased after glycation and DPPH radical scavenging activity of SPI-GOS peaked at 48 h of reaction. The hardness, elasticity and resilience of soybean protein gel reached their relative maximum at 48 h, 48 h and 12 h of glycation reaction, respectively. Moreover, the appropriate addition of glycated SPI improved the quality of noodles. The noodles with 4% addition of SPI-GOS had higher hardness, elasticity and tensile properties. This study will provide an effective method to modify soybean protein and expand the use of soybean protein in food industry.

ABSTRACTAkkermansia muciniphila(A. muciniphila) is a ‘star strain’ that has attracted much attention in recent years. A. muciniphilacan effectively regulate host metabolism, significantly affect host immune function, and play an important role in balancing host health and disease. As one of the organs most closely related to the gut (the two can communicate through the hepatic portal vein and bile duct system), liver is widely affected by intestinal microorganisms. A growing body of evidence suggests that A. muciniphilamay alleviate liver-related diseases by improving the intestinal barrier, energy metabolism and regulating inflammation through its protein components and metabolites. This paper systematically reviews the key roles of A. muciniphilaand its derivatives in maintaining liver health and improving liver disease.

Graph Contrastive Learning (GCL) has achieved great success in self-supervised representation learning throughout positive and negative pairs based on graph neural networks (GNNs), where one critical issue lies in how to handle the false hard negatives that share the large similarity to the same referenced class as the anchor, which is critical to message passing of GNNs to exploit the graph structure. However, the existing arts either mistakenly identify or miss the false hard negatives, hence resulting into poor node representation. Building on this, there are several crucial bottlenecks – Where do false hard negatives exist upon the anchor? How to well seek false hard negatives? Whether are more false hard negatives better? To answer these questions, in this paper, we propose a novel Locally Weighted Graph Contrastive Learning method, named LocWGCL, while revealing that false hard negatives are primarily distributed in the first-order and second-order neighborhoods of the anchor. Benefiting from the tightness between the first-order nodes and the anchor, representation similarity is calculated to select false hard negatives. For the second-order case, false hard negatives are identified, such that they share the similar passed message with the anchor over the common first-order nodes, along with the large similarity. Upon the seeking process, we devise a weighted strategy to false hard negatives for better node representation. Empirical studies verify the advantages of LocWGCL over the state-of-the-arts on six benchmarks.

It has been well established that there is a connection between polycystic ovary syndrome pathology and gut microbiome dysbiosis. A marine-derived oligosaccharide, GV-971, has been reported to alter gut microbiota and alleviate Aβ amyloidosis. In this study, the effects of GV-971 on polycystic ovary syndrome–like mice were explored. Mice were randomly assigned into four groups: control, letrozole, letrozole + GV-971, and control + GV-971. Glucose metabolism in polycystic ovary syndrome–like mice was ameliorated by GV-971, while the reproductive endocrine disorder of polycystic ovary syndrome–like mice was partially reversed. The messenger ribonucleic acid levels of steroidogenic enzymes in ovaries of polycystic ovary syndrome–like mice were improved. GV-971 restored the fertility of polycystic ovary syndrome–like mice and significantly increase the number of litters. Furthermore, GV-971 treatment effectively mitigated abnormal bile acid metabolism. Notably, after GV-971 intervention, gut microbiota alpha-diversity was considerably raised and the relative abundance of Firmicuteswas reduced. In conclusion, the hyperinsulinemia and hyperandrogenemia of polycystic ovary syndrome–like mice were alleviated by GV-971 intervention, which was associated with mitigating bile acid metabolism and modulating gut microbiota.Sodium oligomannate (GV-971) treatment ameliorated metabolic disorders and reproductive dysfunction through remodeling gut microbiota in a letrozole-induced PCOS-like mice model.Graphical Abstract

In this article, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames. Traditional numerical simulation systems depend largely on the initial settings and correctness of the constructed partial differential equations (PDEs). Despite recent efforts yielding significant success in discovering data-driven PDEs with neural networks, the limitations posed by singular scenarios and the absence of local insights prevent them from performing effectively in a broader real-world context. To this end, this article propose the universal expert module – that is, optical flow estimation component, to capture the evolution laws of general physical processes in a data-driven fashion. To enhance local insight, we painstakingly design a finer-grained physical pipeline, since local characteristics may be influenced by various internal contextual information, which may contradict the macroscopic properties of the whole system. Further, we harness currently popular neural discrete learning to unveil the underlying important features in its latent space, this process better injects interpretability, which can help us obtain a powerful prior over these discrete random variables. We conduct extensive experiments and ablations to demonstrate that the proposed framework achieves large performance margins, compared with the existing SOTA baselines.

The utilization of modular chiplets in interposer-based 2.5-D heterogeneous systems simplifies fabrication and design, however, it also introduces significant noise challenges. This article presents a collaborative jitter-aware optimization in 2.5-D integrated circuits (ICs), incorporating power supply induced jitter (PSIJ), system impedance, target impedance, and decoupling capacitors, based on the hybrid precomputation and prestorage/duplicate removal-nondominated sorting differential evolution (PCPS/DR-NSDE) algorithm. An automatic channel model algorithm and a uniform decoupling capacitor placement strategy are proposed to improve the design efficiency. Then, the system transfer impedance, simultaneous switch current, sensitivity function, and amplification factor are individually modeled, leading to the assembly and verification of the final PSIJ in the 2.5-D system. A precomputation and prestorage (PCPS) strategy is proposed to handle high-time-consuming modules in the objective function and a duplicate removal (DR) operation is added to improve algorithm performance. The proposed PCPS/DR-NSDE is faster than traditional algorithms and has optimal hypervolume and coverage-metric (C-metric) indicators. The procedures for further obtaining desired solutions in the Pareto front are discussed. The impact of practical constraints and target impedance is also analyzed. This work provides a collaborative optimization and analysis of jitter, noise, and impedance in 2.5-D systems.

Polycystic ovary syndrome (PCOS) is the leading cause of anovulatory infertility. Inadequate understanding of the ovulation drivers hinders PCOS intervention. Herein, we report that follicle stimulating hormone (FSH) controls follicular fluid (FF) glutamine levels to determine ovulation. Murine ovulation starts from FF-exposing granulosa cell (GC) apoptosis. FF glutamine, which decreases in pre-ovulation porcine FF, elevates in PCOS patients FF. High-glutamine chow to elevate FF glutamine inhibits mouse GC apoptosis and induces hormonal, metabolic, and morphologic PCOS traits. Mechanistically, follicle-development-driving FSH promotes GC glutamine synthesis to elevate FF glutamine, which maintain follicle wall integrity by inhibiting GC apoptosis through inactivating ASK1-JNK apoptotic pathway. FSH and glutamine inhibit the rapture of cultured murine follicles. Glutamine removal or ASK1-JNK pathway activation with metformin or AT-101 reversed PCOS traits in PCOS models that are induced with either glutamine or EsR1-KO. These suggest that glutamine, FSH, and ASK1-JNK pathway are targetable to alleviate PCOS.

In order to ameliorate microstructure and optimize properties of Inconel 718 alloy, a novel heat treatment method combining high-temperature ultra-short annealing at 980 °C for 1 min with subsequent double aging was proposed. The results showed that ultra-short annealing generated partially recrystallized microstructure and recrystallized nuclei were formed even in deformed grains, which was confirmed by KAM maps. Therefore, high-temperature ultra-short annealing overcame recrystallization barrier, and the subsequent recrystallization process could be completed during aging treatment with relatively low temperature, resulting in the uniformly refined microstructure (∼3.59 µm). The 980-1min-aged sample exhibited outstanding tensile properties. The room temperature ultimate strength and total elongation were 1600 MPa and 19%, respectively. The corresponding values of high- temperature (650 °C) tensile tests were ∼1350 MPa and ∼26%. Consequently, ultra-short annealing can be a promising route to enhance the properties of Inconel 718 alloy.

Due to the demand for high-performance electronic products, there has been a rapid development in three-dimensional integrated circuit (3D IC) packaging technology. The 3D stacking relies on microbumps and through silicon vias (TSVs) to connect multiple Si dies vertically. These high-performance electronic products usually need to consume high power, combined with limited heat dissipation due to dense packing, resulting in the issue of Joule heating. The problem of Joule heating impacts the reliability of products. This study investigated the factors contributing to Joule heating of microbumps and its subsequent reliability issues through experimental methods and finite element analysis (FEA) simulations. Joule heating was quantified by measuring the temperature increase at stable states using the temperature coefficient of resistance (TCR) equation. We first observed that environmental temperature influences Joule heating. Higher oven temperature can exacerbate the Joule heating effect and lead to higher temperature increase under the same current density. We also examined the effect of intermetallic compound (IMC) formation on Joule heating, with a 12.5% increase in heat generation after IMC formation. Furthermore, the adverse effect of voids induced by sidewall wetting during high-temperature storage (HTS) on Joule heating using FEA was demonstrated. Additionally, we discovered severe Joule heating to cause thermomigration (TM) in the microbumps at a system level. Finally, we proposed structural optimization of the microbump to mitigate Joule heating and enhance reliability.

Magnetic levitation actuators (MLAs) are employed in flexible manufacturing, precision positioning and machining, and haptic devices due to untethered motions. However, the MLAs with disc-magnet movers and static coils rely on multidimensional and memory-expensive lookup tables (LUTs) for operation, lacking online force and torque (wrench) models. We propose a deep neural network model to predict the wrench between individual disc magnets and each coil using a programmable logic controller. The approach utilizes residual blocks to deepen the architecture without enlarging network scales, and we train and validate the wrench model using datasets generated with random mover poses inside the operating range. We show that after training, the residual-based model outperforms a two-hidden-layer baseline model in implementation performance and test accuracy. The test/prediction accuracy is verified through load cell measurements and LUTs on two datasets with $\text{0.96}$ and 25 million samples. After obtaining the wrench matrix of the disc-magnet magnetic levitation actuator, the weighted pseudoinverse commutation law is adopted to decouple the system. Experimental validation shows multidisc-magnet mover control resolutions of $\pm$10 $\mu$m and $\pm$10 mdegrees in the translational and rotational axes, respectively, with a processing time of 4.1 $\mu$s. Full operating range sinusoidal responses demonstrate the capability of dynamic motion controls and motion ranges in all axes.

We study a sensor-weapon–target assignment (S-WTA) problem that considers the desired probability of target destruction and aims to minimize the total cost of combat resources. Lower and upper bounds for the S-WTA problem are obtained by constructing linear approximation models. We also propose an adaptive large neighborhood search (ALNS) algorithm characterized by a model-driven repair phase to solve this problem. The destruction phase adaptively selects a destruction operator to remove partial resource assignments and produces an incomplete reference solution. For the destroyed solution, the repair phase generates a reduced subproblem that optimizes only the destroyed parts while keeping the other parts fixed. Each subproblem is formulated as a mixed integer programming model and solved by a general-purpose solver to repair the destroyed solution. Computational experiments show that the approximation formulations can obtain tight lower and upper bounds for most problem instances. Moreover, our proposed ALNS algorithm is competitive with the solver for small instances and effectively solves large instances. In addition, we experimentally demonstrate that our ALNS outperforms state-of-the-art algorithms in the literature, and the proposed model-driven solution repair phase outperforms the traditional heuristic repair operators.

Phagocytosis is the process by which myeloid phagocytes bind to and internalize potentially dangerous microorganisms1. During phagocytosis, innate immune receptors and associated signalling proteins are localized to the maturing phagosome compartment, forming an immune information processing hub brimming with microorganism-sensing features2–8. Here we developed proximity labelling of phagosomal contents (PhagoPL) to identify proteins localizing to phagosomes containing model yeast and bacteria. By comparing the protein composition of phagosomes containing evolutionarily and biochemically distinct microorganisms, we unexpectedly identified programmed death-ligand 1 (PD-L1) as a protein that specifically enriches in phagosomes containing yeast. We found that PD-L1 directly binds to yeast upon processing in phagosomes. By surface display library screening, we identified the ribosomal protein Rpl20b as a fungal protein ligand for PD-L1. Using an auxin-inducible depletion system, we found that detection of Rpl20b by macrophages cross-regulates production of distinct cytokines including interleukin-10 (IL-10) induced by the activation of other innate immune receptors. Thus, this study establishes PhagoPL as a useful approach to quantifying the collection of proteins enriched in phagosomes during host–microorganism interactions, exemplified by identifying PD-L1 as a receptor that binds to fungi.

Introduction: Persistent nasal carriage has been associated with Staphylococcus aureusinfection. Previous S. aureusstudies in Asia have primarily focused on clinical patients, providing limited information on persistent nasal carriage among the general adult population. Methods: This study examined 143 healthy adults in a community in Jiangsu, China. Nasal swab samples were collected 10 times. The colonization status was identified using SPA typing. We also determined antimicrobial susceptibility, genotype, and genomic characteristics of S. aureus. Results: The prevalence of S. aureusnasal carriage among the community individuals was on average 16.78%. The carriage rates of methicillin-resistant S. aureusand multidrug-resistant S. aureuswere 6.29% and 7.69%, respectively. We identified 8.39% persistent carriers, 39.16% intermittent carriers, and 52.45% noncarriers. Furthermore, family members displayed concordance in terms of genotype and genomic characteristics. Conclusion: Persistent nasal sampling captured intermittent carriers that were missed during short-term sampling, thus highlighting the necessity for regular community testing. SPA typing can serve as a rapid method for determining S. aureuscolonization. The potential for intrafamilial transmission of S. aureusis evident, with persistent carriers being the most probable source of infection.

Quasi-static and high-rate tension and compression properties of an extruded AZ80 Mg alloy rod were investigated at loading rates of 0.001 and 500 s−1. Acquired stress – strain results show that tension and compression plastic deformations of AZ80 rod are asymmetric, and such strength differential effect is dependent of strain rate and loading orientation. Strength differential effect is weakened at 500 s−1when the sample was loaded along the extrusion direction. Crystal plasticity simulations on the basis of the visco-plastic self-consistent method were performed. Numerical results indicate that the rate and orientation dependency of strength differential effect in AZ80 alloy is related to {101‾2}extension twinning. High-rate tests for pretension, unloading and reverse compression were realized to investigate the dynamic Bauschinger effect. Experimental results show that Bauschinger effect of AZ80 alloy is anisotropic and sensitive to the strain rate. A Bauschinger effect parameter considering strength differential effect was proposed, and it was found that the Bauchinger effect in the extrusion direction is reduced significantly at 500 s−1.

Pre-alloyed Al0.5CoCrFeNi high-entropy alloy (HEA) powders were prepared via gas atomization and followed by hot-pressing sintering (HPS) at 1200 °C for 1 h to fabricate bulk materials. The phase stability, microstructure evolution and mechanical properties of Al0.5CoCrFeNi HEA were mainly investigated. Results indicated that the atomized powders exhibited a fine-grained FCC + dendritic-like ordered BCC (B2) dual-phase structure. Rapid solidification was favorable to the metastable B2 phase formation during gas-atomization. Partial of the dendritic-like B2 phase dissolved after annealing (>600 °C), resulted in a homogeneous dual-phase structure with irregular B2 phase dispersed in the FCC matrix. The homogeneous structure was “inherited” in the sintered bulks and coarsened after HPS. Tensile test results suggested that the sintered bulks possessed excellent comprehensive mechanical properties with yield strength, ultimate tensile strength and elongation of 587.3 ± 18.7 MPa, 960.2 ± 23.6 MPa and 32.6 ± 5.1%, respectively. The excellent mechanical response during deformation was mainly attributed to the synergistic deformation of the FCC/B2 phase and considerable back stress hardening mechanism.

The impact of shear deformation on texture evolution in strip-cast Fe-1.5%Si non-oriented electrical steel was studied combined with in-suit experiments and crystal plasticity finite element method (CPFEM). Throughout the deformation process, the texture evolution predominantly follows a traditional route: transitioning from {211}<036> and {411}<148> to {111}<112> orientation. In certain regions with specific stress conditions, orientation evolution shifts from {310}<236> to {411}<148>, {100}<021>, and finally to {100}<001> orientation. Crystal plasticity finite element (CPFE) simulation results show that the rotation of {310}<236> through {411}<148> to {100}<021> orientation, {211}<236> and {112}<110> orientations transition to {111} and {110} orientations, respectively, which is consistent with the experimental observations. Recrystallization texture mainly includes favorable λ and near-λ (<100>∥ND, normal direction) textures, with detrimental γ (<111>∥ND) texture essentially diminishing. In-situ heating experiments revealed {411}<148> as the primary recrystallization orientation, the mechanism involves both oriented nucleation and growth. In the edge region lacking stress constraints, the Schmid factor (SF) reaches up to 0.48, the (21-3)[111] slip system is preferentially activated, with shear bands appearing earlier in the same region. Indicating that when slip mechanism alone are inadequate for sustaining plastic deformation, the shear deformation becomes the favored mode of activation. {411}<148> orientation can serve as a transitional orientation for transitioning to {100} orientation, and further as an advantageous transitional orientation during the nucleation and growth of recrystallization. Due to the texture improvement, superior magnetic properties were achieved, with the average magnetic induction (B50) and iron loss (P1.5/50) being 1.77 T and 4.25 W/kg, respectively.

The pursuit of materials with enhanced functionality has led to the emergence of metamaterials—artificially engineered materials whose properties are determined by their structure rather than composition. Traditionally, the building blocks of metamaterials are arranged in fixed positions within a lattice structure1–19. However, recent research has revealed the potential of mixing disconnected building blocks in a fluidic medium20–27. Inspired by these recent advances, here we show that by mixing highly deformable spherical capsules into an incompressible fluid, we can realize a ‘metafluid’ with programmable compressibility, optical behaviour and viscosity. First, we experimentally and numerically demonstrate that the buckling of the shells endows the fluid with a highly nonlinear behaviour. Subsequently, we harness this behaviour to develop smart robotic systems, highly tunable logic gates and optical elements with switchable characteristics. Finally, we demonstrate that the collapse of the shells upon buckling leads to a large increase in the suspension viscosity in the laminar regime. As such, the proposed metafluid provides a promising platform for enhancing the functionality of existing fluidic devices by expanding the capabilities of the fluid itself.

A chiral NHC-catalyzed [3 + 3] cycloaddition reaction of 3-aminobenzofurans with isatin-derived enals has been documented, furnishing 3,4′-piperidinoyl spirooxindoles bearing a quaternary stereocenter with good yields and excellent enantioselectivities. Further gram-scale preparation and synthetic transformation of the cycloadducts to δ-amino acid derivative demonstrated good practicality and applicability of this reaction.

In view of the application prospects in biomedicine of (E)-1-(4-(diethyla-mino)-2-hydroxybenzylidene)-4,4-dimethylthiosemicarbazide (DAHTS), the behavior of excited-state dynamics and photophysical properties were studied using the density functional theory/time-dependent density functional theory method. A series of studies indicated that the intramolecular hydrogen-bond (IHB) intensity of DAHTS was enhanced after photoexcitation. This was conducive to promoting the excited-state intramolecular proton-transfer (ESIPT) process. Combining the analysis of the IHB and hole–electron, it revealed that the molecule underwent both the ESIPT process and the twisted charge-transfer (TICT) process. Relying on exploration of the potential energy surface, it was proposed that the different competitive mechanisms between the ESIPT and TICT processes were regulated by solvent polarity. In acetonitrile (ACN) solvent, the ESIPT process occurred first, and the TICT process occurred later. In contrast, in the CYH solvent, the molecule first underwent the TICT process and then the ESIPT process. Furthermore, we raised the possibility that the TICT behavior was the cause of weak fluorescence emission for the DAHTS in CYH and ACN solvents. By the dimer correlation analysis, the corresponding components of triple fluorescence emission were clearly assigned, corresponding to the monomer, dimer, and ESIPT isomer in turn. Our work precisely elucidated the photophysical mechanism of DAHTS and the attribution of the triple fluorescence emission components, which provided valuable guidance for the development and regulation of bioactive fluorescence probes with multiband and multicolor emission characteristics.

•Prof. Tsubaki's professional career spanning over 35 years is covered.•Innovative achievements of Prof. Noritatsu Tsubaki in C1 chemistry are listed.•State-of-the-art technologies on C1 chemistry and catalyst design are reviewed.•The role of Tsubaki's team in the development of C1 catalysis is pointed out.•Other contributions of Prof. Tsubaki to the catalysis community are highlighted.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

A catalyst-free and atom-economical [4 ​+ ​3] cycloaddition of azadienes with C,N-cyclic azomethine imines has been developed, providing an efficient and environmentally benign access to 1,2,4-triazepines with high diastereoselectivities and yields. This reaction can be performed in 2-methyltetrahydrofuran under mild conditions smoothly.

A single-layer, electrically small, quasi-isotropic antenna with filtering response is developed and experimentally investigated in this article. This electrically small antenna (ESA) incorporates a pair of orthogonally arranged crossed electric dipoles and the C-shaped elements serving as the near-field resonant parasitic (NFRP) structures and the driven elements. The driven elements are utilized to motivate a 90° phase difference, and then, with almost the same amplitudes and 90° phase difference between the crossed NFRP electric dipoles stimulated by the driven elements, a quasi-isotropic coverage can be realized. Owing to the C-shaped NFRP elements and the Egyptian axe structures of the crossed dipoles, the electrical size of the antenna can be efficiently decreased. Additionally, these C-shaped NFRP elements can introduce two radiation nulls in the lower and higher stopbands to enhance the frequency selectivity, respectively. The prototype of this quasi-isotropic ESA with filtering response was fabricated and measured, and the tested results exhibit good consistency with the simulated ones. This developed quasi-isotropic ESA demonstrates an electrically small size [ $\pi \times $ ( $0.145\,\,\lambda)^{2} \times 0.004\,\,\lambda $ and ka = 0.91]. The measured −10-dB impedance bandwidth is 14.2%. Furthermore, the bandwidth of 6-dB realized gain variation is 7%, which demonstrates that it possesses good isotropic coverage within the entire bandwidth. Specifically, the minimum realized gain difference of the quasi-isotropic ESA is 4.6 dB (4.2 dB in simulation). Moreover, due to the introduction of the two radiation nulls, this quasi-isotropic ESA exhibits a good frequency selectivity.

To achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging technology. The RIS-assisted mmWave massive MIMO vehicle-to-everything (V2X) communications can support enhanced V2X applications for connected and automated vehicles. The design of RIS-assisted mmWave V2X communications is though not exempt of challenges as a result of fast time-varying propagation and highly dynamic vehicular networks and topologies. To address some of these challenges, we propose an attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted mmWave massive MIMO system to improving the safety and traffic efficiency of cooperative automated driving. The received signal of the vehicle is decomposed into four-dimensional tensors and study the channel, frequency and time attention (CFTA) algorithm using the low order tensor characteristics of mmWave channels. This algorithm can extract the frequency and time characteristics of the channel, and obtain the millimeter wave characteristic channel between the corresponding base station (BS) and the vehicles. The problem of jointly optimization of beamforming matrix at the BS and the phase shift matrix on the RIS is constructed and solved using deep reinforcement learning (DRL). Unlike traditional alternating optimization methods, the integration of DRL techniques into the optimal design of the system enables the observation of immediate rewards, learning from the environment and improving it to obtain optimal solutions to high-dimensional jointly beamforming optimization problems ultimately. The simulation results show that the proposed algorithm has meaningful performance compared with other methods.

The time-dependent viscoelastic response of cement-based materials to applied deformation is far from fully understood at the atomic level. Calcium silicate hydrate (C-S-H), the main hydration product of Portland cement, is responsible for the viscoelastic mechanism of cement-based materials. In this study, a molecular model of C-S-H was developed to explain the stress relaxation characteristics of C-S-H at different initial deformation states, Ca/Si ratios, temperatures, and water contents, which cannot be accessed experimentally. The stress relaxation of C-S-H occurs regardless of whether it is subjected to initial shear, tensile, or compressive deformation, and shows a heterogeneous characteristic. Water plays a crucial role in the stress relaxation process. A large Ca/Si ratio and high temperature reduce the cohesion between the calcium-silicate layer and the interlayer region, and the viscosity of the interlayer region, thereby accelerating the stress relaxation of C-S-H. The effect of the hydrogen bond network and the morphology of C-S-H on the evolution of the stress relaxation characteristics of C-S-H at different water contents was elucidated by nonaffine mean squared displacement. Our results shed light on the stress relaxation characteristics of C-S-H from a microscopic perspective, bridging the gap between the microscopic phenomena and the underlying atomic-level mechanisms.

In order to support the transmission of orbital angular momentum (OAM) modes effectively, the ring-core fiber has received a lot of attention due to the similar structure of the electric field distribution with the intensity profile of OAM mode. Both photonic crystal fibers and other micro-structured fibers almost contain a ring-core structure to support the transmission of more OAM modes. However, the middle of the ring-core fiber is usually a large air hole, and such a structure makes it difficult to manufacture the ring-core perfectly. Concave damage may occur in the ring-core. The effects of concave damage on the transmission of OAM modes are analyzed. The concave damage leads to a large effective refractive index difference between the odd and even modes of vector mode, and a method for proper compensation is proposed. The lining is added to the inner side of the ring-core, which is made of the same material as the cladding, and slightly increasing the refractive index of the lining material promotes the performance of the compensation. After numerical simulation, the walk-off distance can be increased by a factor of 2–20 after compensation.

To improve the bottomside ionospheric electron density profile (EDP) described by the International Reference Ionosphere (IRI), a global bottomside EDP updated model for geographic latitudes between ±40° is constructed. This model is based on the spherical harmonic analysis (SHA) method and utilizes radio occultation (RO) data from Formosa Satellite 7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (Formosat-7/COSMIC-2) to improve IRI. Using incoherent scatter radar (ISR) and ionosonde data as references, the corrected parameters and the bottomside EDP of the SHA model were compared with the IRI data. The results show that the time-series curve trends of hmF2 and NmF2 corrected by the SHA model are closer to those of the ISR/ionosonde curve. The standard deviation (STD) of hmF2 and NmF2 at different time segments improved, with the highest improvement ranges of 41.41% and 57.58%, respectively. The total time period statistical results for individual stations also showed an average reduction in STD of 18.80% and 10.11% for hmF2 and NmF2, respectively. Furthermore, the bottomside EDPs of the IRI and SHA models were compared with the ISR data, and we found that owing to the improvements in thickness parameter ${B}0$ and shape parameter ${B}1$ , the bottomside profile shape was also significantly improved. Finally, the bottomside EDPs generated by SHA model were used as vertical constraints to improve the existing computerized ionosphere tomography (CIT) algorithm. The 3-D evolution process of a moderate geomagnetic storm on February 2–3, 2022, was quantified. The 3-D ionospheric morphological changes and evolution characteristics in the region of 18°–36°N and 98°–123°E are clearly demonstrated.

This paper develops a novel approach for online Lithium-ion (Li-ion) battery model identification and state of charge (SOC) estimation. To account for the SOC-dependent battery dynamics and the static nonlinearity between the open-circuit voltage (OCV) and SOC, we formulate a grey box nonlinear state-space model, in which elements depend on SOC in a polynomial way. For model identification, we propose an online concurrent state and parameter estimation by alternating the recursive least squares algorithm and particle filter; the SOC is computed via Coulomb counting during the modeling. The identified grey box model is then applied for SOC estimation using the particle filter. Simulation with real-world battery measurements demonstrates the effectiveness of the model structure and the estimation approach, which is reflected in accurate terminal voltage estimation and nonlinear OCV-SOC relation, and superior performance regarding SOC estimation compared to state-of-the-art approaches.