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This study presents a novel phase-field model for ductile fracture by the introduction of both the plastic driving force and the degrading fracture toughness into crack phase-field computations based on the phenomenological justification for ductile fracture in elastoplastic materials. Assuming that the constitutive work density consists of elastic, pseudo-plastic and crack components, we derive the governing equations from local and global optimization problems within the continuum thermodynamics framework. In addition to the elastic strain energy, the plastic strain energy also works as a driving force to sustain damage evolution. Additionally, we introduce a degrading fracture toughness to reflect the evolution of micro-defects and their coalescences into each other that are caused by accumulated plastic deformation. Equipped with these ingredients, the proposed model realizes the reduction of both stiffness and fracture toughness to simulate the failure phenomena of elastoplastic materials. Several numerical examples are presented to demonstrate the capability of the proposed model in reproducing some typical ductile fracture behaviors. The findings and perspectives are subsequently summarized.

This study presents a gradient damage model for ductile fracture, in which the damage hardening modulus is degraded by the accumulation of plastic deformation and the volume expansion caused by negative hydrostatic pressure. The proposed model fulfills the thermodynamic requirements, and the governing equations are derived from energy minimization principles. Two parameter studies are carried out to confirm the basic performance of the proposed model, in which some typical ductile fracture responses are demonstrated by changing parameters for degrading the damage hardening modulus. Also, a series of numerical experiments are presented to reveal the ability of the proposed model to successfully simulate the fracture tests of advanced high strength steel sheets with different tensile strengths. It is indeed confirmed by the close agreement with experimental results that the proposed model is capable of realizing the breaking elongation, the transitional behavior from unstable to stable crack propagations, and the corresponding load–displacement curves. Also, the model successfully reproduces and predicts the crack initiation positions in notched specimens with different notch radii.

To meet the measurement needs of multidimensional high-g acceleration in fields such as weapon penetration, aerospace, and explosive shock, a biaxial piezoresistive accelerometer incorporating tension–compression is meticulously designed. This study begins by thoroughly examining the tension–compression measurement mechanism and designing the sensor’s sensitive structure. A signal test circuit is developed to effectively mitigate cross-interference, taking into account the stress variation characteristics of the cantilever beam. Subsequently, the signal test circuit of anti-cross-interference is designed according to the stress variation characteristics of the cantilever beam. Next, the finite element method is applied to analyze the structure and obtain the performance indices of the range, vibration modes, and sensitivity of the sensor. Finally, the process flow and packaging scheme of the chip are analyzed. The results show that the sensor has a full range of 200,000 g, a sensitivity of 1.39 µV/g in the X direction and 1.42 µV/g in the Y direction, and natural frequencies of 509.8 kHz and 510.2 kHz in the X and Y directions, respectively.

Focusing on the mTOR-HIF-1α signaling pathway, this study investigated the mechanism through which the traditional Chinese medicine compound Mingshi Formula delays the progression of form-deprivation myopia (FDM) in guinea pigs. The guinea pigs were divided into the normal control group (NC), FDM group, Mingshi Formula low-dose group (FDM + Low), medium-dose group (FDM + Medium), high-dose group (FDM + High), and MTOR inhibitor group (FDM + RapaLink-1). The guinea pig model of FDM was established by applying 3D-printed hoods modified by a latex balloon with 60% light transmission for 4 weeks. Refractive error changes were monitored using a refractometer. Axial length was quantitatively analyzed using A-scan ultrasound, choroidal thickness was measured with SD-OCT, and structural changes of the choroid and sclera were observed after hematoxylin-eosin (HE) staining. At the molecular level, the expression levels of the mammalian target of rapamycin (mTOR), phosphorylated mTOR (p-mTOR), HIF-1α, LDHA, PKM2, MMP2, Collagen I, and α-SMA in the sclera were measured by RT-qPCR and Western blotting. The spatial distribution characteristics of mTOR, p-mTOR, HIF-1α, and Collagen I were verified via immunofluorescence techniques. The results demonstrated that the Mingshi Formula significantly decreased myopic refractive error and axial length ( p  < 0.01), increased choroidal thickness ( p  < 0.05), downregulated the gene and protein expression of mTOR, p-mTOR, HIF-1α, LDHA, PKM2, MMP2, and α-SMA in response to hypoxia, and upregulated the expression of Collagen I compared to the FDM group. We demonstrated that MingShi formula modulates the mTOR/HIF-1α signaling axis to ameliorate scleral hypoxic metabolic homeostasis, regulate Collagen synthesis, inhibit aberrant extracellular matrix remodeling, and ultimately delay myopia progression.

Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution. Wang et al. propose EasIFA, an efficient enzyme active site annotation algorithm, to advance various fields including drug discovery, disease research, enzyme engineering, and synthetic biology.

Optimal scheduling of microgrids (MGs) is a crucial component of smart grid optimization, playing a vital role in minimizing energy consumption and environmental degradation. However, existing methods tend to consider only a single optimization and do not consider the multiobjective optimization problem of MGs in a comprehensive and integrated way. This study proposes a comprehensive multiobjective optimal scheduling methodology for renewable energy MGs, incorporating demand-side management (DSM) considerations. Initially, a DSM multiobjective optimization model is formulated, focusing on the load shifting of controllable devices within the MG to refine the electricity consumption structure. This model contemplates the renewable energy consumption of the MG, customer electricity purchase costs, and load smoothness. Subsequently, a multiobjective optimization model for grid-connected MGs, encompassing wind and photovoltaic power generation, is constructed with the dual objectives of economic and environmental optimization for the MG. Ultimately, a multimodal multiobjective optimization algorithm, amalgamating a local convergence index and an environment selection strategy, is proposed to solve the model. The experimental results show that compared with other methods, the proposed method in this paper can reduce the integrated cost by 32.6% and 38.9% in summer and 19.4% and 40.2% in winter. This stands out as a unique contribution in the field of MG optimization, as it integrates DSM considerations into a multiobjective optimization model. This methodology achieves a balance between minimizing energy consumption and environmental degradation while also enhancing economic efficiency.

The uplift-restricted and slip-permitted (URSP) connector is a new type of connector used in steel–concrete composite structures that has been proven to improve the structural performance of negative moment regions. Since this connector changes the interface restraint between the slab and steel beam, there is an imperative to study the seismic performance of steel–concrete composite frame systems with this new type of connector. In this study, the dynamic behavior of composite frame structures with URSP connectors under seismic loads was numerically investigated. First, a beam–shell mixed model was used and complex interfaces of different connectors were considered while establishing a numerical model to conduct elasto–plastic time history analysis under various seismic loads. This numerical model was validated with the frame sub-assemblage experimental results of quasi-static cyclic tests. Second, the model analysis results of structures with URSP connectors were obtained and compared with those of traditional structures. Third, dynamic response results including roof displacement, inter-story displacement, and the distribution and failure modes of plastic hinges were analyzed and compared. The comparisons indicated that the arrangement of full-span URSP connectors had a non-negligible influence on the dynamic behavior of the systems. The arrangement increased the maximum inter-story displacement by 31.5% and induced adverse effects in certain cases, which is not suggested in the application of URSP connectors. The partial arrangement of URSP connectors had little influence on the dynamic behavior of the systems, and the frame systems still showed a good seismic performance, which was the same as the traditional composite structural system. These findings may promote the application of URSP connectors in composite structures.

Background Chemotherapy-induced nausea and/or vomiting (CINV) is an intractable adverse effect of anticancer drugs. Although prophylactic use of fosaprepitant may be effective in reducing CINV, there is a lack of studies evaluating the application of fosaprepitant in real world. Aims and methods This study prospectively observed the effectiveness and safety for the prophylaxis of CINV in a real-world clinical setting. A single dose fosaprepitant 150 mg was intravenously administered to enrolled patients 30 min prior to the chemotherapy drug. Initial data were recorded and patients were followed for 120 h (5 days). The primary endpoint is the complete response (CR) rate and the incidence of serious adverse events (SAEs). The second endpoint is the use of rescue therapy. We also performed stratified analyses to investigate the impact of different factors on fosaprepitant for the prevention of CINV in the acute phase. Results Between March 2021 to August 2021, 1001 patients were enrolled in this study. CR was 77.32%, 93.61%, and 76.72% for vomiting control in 0–24 h, 24–120 h, and 0–120 h respectively, and 97.4%, 99.1%, and 96.9% for nausea control. No SAEs were recorded. 23.48% or 3.1% of patients needed rescue therapy for vomiting or nausea control respectively, most of which occurred in the acute phase. CR rate decreased with increasing emetogenicity of chemotherapeutic agents. Conclusions Single-dose fosaprepitant has shown good performance in real-world clinical practice. This study is the first to prospectively evaluate the efficacy and safety of fosaprepitant for the prevention of CINV in a real-world clinical setting and may be a good complement to the clinical data.

Improving the efficacy of gastric cancer (GC) treatment remains an ongoing challenge. Considering the increasing importance of PANoptosis, a novel form of programmed cell death, the current study integrates photodynamic therapy (PDT) and chemodynamic therapy (CDT) into nanoliposomes. This approach utilizes the ability of photosensitizer Chlorin e6 (Ce6) to generate reactive oxygen species (ROS) and the function of the natural targeting agent Jolkinolide B to activate the PANoptosis molecular switch, inducing the ROS‐caspase8/PANoptosis pathway to promote GC cell death. The designed CJP–TiN liposome targets GC via internalizing RGD peptide (iRGD), and demonstrates ROS/pH dual responsiveness in the tumor microenvironment. In vitro and in vivo experiments show effective ROS generation ability under light exposure, killing tumor cells and triggers thioether bond cleavage for dual‐controlled drug release. The combined therapy enhances antitumor effect, converting “cold tumors” into “hot tumors,” thereby enhancing the success of immunotherapy. The role of CJP–TiN as a PANoptosis inducer in the tumor microenvironment is confirmed, thereby expanding its application potential as a molecularly targeted therapy for GC treatment, and providing a novel perspective for therapeutic strategies. Schematic model of CJP–TiN liposomes for gastric cancer (GC) treatment. Internalizing RGD peptide (iRGD) targets tumor cells, while Chlorin e6 (Ce6) generates reactive oxygen species (ROS). Jolkinolide B (JB) activates the PANoptosis pathway via caspase‐8, enhancing GC cell death and immune response, transforming “cold tumors” into “hot tumors” by promoting M1 macrophage and T‐cell infiltration.