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In this paper, a model generation algorithm for non-equal diameter particles with a specified probability density distribution is proposed. Based on considering the randomness of the size and distribution of the particles, the compact stacking of the particles is realized by the compactness algorithm, and then the spatial distribution of the tightly compacted particles is made to meet the random distribution of the specified probability density and the specified volume fraction by the filtering algorithm. The computational efficiency and effectiveness of the algorithm are verified, and the effects of the particle size and volume fraction on the distribution are analyzed. Finally, the proposed model has been used to study the permeability of a titanium porous filter cartridge. The results show that the size and location of the particle samples that are generated by the proposed algorithm follow specified probability distributions according to the requirements, and the volume fraction can be adjusted. Compared with the traditional algorithm, the computational effort and complexity are reduced. The resultant model can be used to study the permeability of porous materials and provide modeling support for structural optimization and further simulation of porous materials.

Most face recognition methods rely on deep convolutional neural networks (CNNs) that construct multiple layers of processing units in a cascaded form and employ convolution operations to fuse local features. However, these methods are not conducive to modeling the global semantic information of the face and lack attention to important facial feature regions and their spatial relationships. In this work, a Group Depth-Wise Transpose Attention (GDTA) block is designed to effectively capture both local and global representations, mitigate the issue of limited receptive fields in CNNs, and establish long-range dependencies among different feature regions. Based on GDTA and CNNs, a novel, efficient, and lightweight face recognition model called CFormerFaceNet, which combines a CNN and Transformer, is proposed. The model significantly reduces the parameters and computational cost without compromising performance, greatly improving the computational efficiency of deep neural networks in face recognition tasks. The model achieves competitive accuracy on multiple challenging benchmark face datasets, including LFW, CPLFW, CALFW, SLLFW, CFP_FF, CFP_FP, and AgeDB-30, while maintaining the minimum computational cost compared to all other advanced face recognition models. The experimental results using computers and embedded devices also demonstrate that it can meet real-time requirements in practical applications.

Traditional YOLO models face a dilemma when it comes to dim detection targets: the detection accuracy increases while the speed inevitably reduces, or vice versa. To resolve this issue, we propose a novel DME-YOLO model, which is characterized by the establishment of a backbone based on the YOLOv7 and Dense blocks. Moreover, through the application of feature multiplexing, both the parameters and floating-point computation were decreased; therefore, the defect detection process was accelerated. We also designed a multi-source attention mechanism module called MSAM, which is capable of integrating spatial information from multiple sources. Due to its outstanding quality, the addition of MSAM as the neck of the original YOLOv7 model compensated for the loss of spatial information in the process of forward propagation, thereby improving the detection accuracy of small target defects and simultaneously ensuring real-time detection. Finally, EIOU was adopted as a loss function to bolster the target frame regression process. The results of the experiment indicated detection accuracy and speed values of up to 97.6 mAP and 51.2 FPS, respectively, suggesting the superiority of the model. Compared with the YOLOv7 model, the experimental parameters for the novel DME-YOLO increased by 2.8% for mAP and 15.7 for FPS, respectively. In conclusion, the novel DME-YOLO model had excellent overall performance regarding detection speed and accuracy.

Background The current successful clinical use of agents promoting robust anti-tumor immunity in cancer patients warrants noting that radiation therapy (RT) induces immunogenic cell death (ICD) of tumor cells, which can generate anti-tumor immune responses. However, breast cancer stem cells (BCSCs) are resistant to RT and RT alone usually failed to mount an anti-tumor immune response. Methods High aldehyde dehydrogenase activity (ALDH) bright and CD44 + /CD24 − /ESA + cancer cells, previously shown to have BCSC properties, were isolated from human MDA-MB-231 and UACC-812 breast cancer cell lines by flow cytometer. Flow sorted BCSCs and non-BCSCs were further tested for their characteristic of stemness by mammosphere formation assay. Induction of ICD in BCSCs vs. non-BCSCs in response to different in vitro treatments was determined by assessing cell apoptosis and a panel of damage-associated molecular pattern molecules (DAMPs) by flow and enzyme-linked immunosorbent assay (ELISA). Results We found that ionizing radiation (IR) triggered a lower level of ICD in BCSCs than non-BCSCs. We then investigated the ability of disulfiram/cooper (DSF/Cu) which is known to preferentially induce cancer stem cells (CSCs) apoptosis to enhance IR-induced ICD of BCSCs. The results indicate that DSF/Cu induced a similar extent of IDC in both BCSCs and non-BCSCs and rendered IR-resistant BCSCs as sensitive as non-BCSCs to IR-induced ICD. IR and DSF/Cu induced ICD of BCSCs could be partly reversed by pre-treatment of BCSCs with a reactive oxygen species (ROS) scavenger and XBP1s inhibitors. Conclusion DSF/Cu rendered IR-resistant BCSCs as sensitive as non-BCSCs to IR-induced ICD. Our data demonstrate the potential of IR and DSF/Cu to induce ICD in BCSCs and non-BCSCs leading to robust immune responses against not only differentiated/differentiating breast cancer cells but also BCSCs, the root cause of cancer formation, progression and metastasis. Graphical abstract

In addition to chemical factors, physical conditions also play a key role in the growth of microalgae. In this study, solid sediment in rivers was simulated by pure quartz sand with different particle sizes and the physical effects of disturbance rate, solid–liquid ratio and particle size on the growth of Chlorella vulgaris (C. vulgaris) were investigated through orthogonal analysis and response surface methodology (RSM) during co-cultivation of C. vulgaris and sediment. The result of ANOVA in orthogonal analysis showed that the effect ability of a single factor on biomass can be ranked as disturbance rate > particle size > solid–liquid ratio, 100 r/min disturbance rate and 30–40 M particle size are the most significant at the 0.05 level. Furthermore, the specific growth rate can reach 0.25/d and 0.27/d, respectively. With the growth of C. vulgaris, the pH of the solution reached a maximum of 10.7 in a week. The results from the RSM showed that strong interactions are reflected in the combinations of disturbance rate and solid–liquid ratio, and disturbance rate and particle size. Ramp desirability of the biomass indicates that the optimum levels of the three variables are 105 r/min disturbance rate, 0.117 g/mL solid–liquid ratio and 30–40 M particle size. In this case, the biomass can grow seven times in a week with 0.27/d specific growth rate and a pH value of 7–10.4. This study shows that the growth of C. vulgaris can be regulated by changing physical conditions simultaneously, and the optimization of physical conditions can be applied to biomass production, algae prediction and acid water treatment in rivers, lakes and reservoirs.

The double-bearing vortex pump is a new type of high-efficiency vortex pump. Compared with the traditional vortex pump, its volume and mass are significantly reduced, and its reliability is greatly improved. As the core component of the pump, the impeller has a decisive impact on the overall performance of the pump. Therefore, in order to deeply understand the internal flow mechanism of the vortex pump and improve its hydraulic performance. The mesh model of the double-bearing vortex pump is established by using UG 12.0 and ANSYS Fluent 2022 R1 software, and the influence of different rotational speeds on the flow field characteristics, such as fluid velocity and pressure in the internal vortex pump, is analyzed. The accuracy of the numerical simulation results is verified through experiments. On this basis, the impeller structure of the vortex pump is optimized by introducing the orthogonal design method and taking the impeller diameter, blade groove radius, blade number, and impeller width as optimization parameters. The results indicate that the number of blades is the most critical factor affecting the performance of vortex pumps, and the optimized impeller design increases the head and efficiency of the vortex pump by 18.9% and 11.6%, respectively. This provides important reference for improving the structural design of vortex pump impellers and enhancing their hydraulic performance.

Aiming at solving the control problem of a constrained and perturbed underwater robot, a control method was proposed by combining the self-triggered mechanism and the nonlinear model predictive control (NMPC). The theoretical properties of the kinematic model of the underwater robot, as well as the corresponding MPC controller, are first studied. Then, a novel technique for determining the next update moment of both the optimal control problem and the system state is developed. It is further rigorously proved that the proposed algorithm can (1) stabilize the closed-loop underwater robot system, (2) reduce the time of solving the optimal control problem and (3) save the information transfer resources. Finally, a case study is provided to show the effectiveness of the developed researched scheme.

The body structures and motion stability of worm-like and snake-like robots have garnered significant research interest. Recently, innovative serial–parallel hybrid segmented robots have emerged as a fundamental platform for a wide range of motion modes. To address the hyper-redundancy characteristics of these hybrid structures, we propose a novel caterpillar-inspired Stable Segment Update (SSU) gait generation approach, establishing a unified framework for multi-segment robot gait generation. Drawing inspiration from the locomotion of natural caterpillars, the segments are modeled as rigid bodies with six degrees of freedom (DOF). The SSU gait generation method is specifically designed to parameterize caterpillar-like gaits. An inverse kinematics solution is derived by analyzing the forward kinematics and identifying the minimum lifting segment, framing the problem as a single-segment end-effector tracking task. Three distinct parameter sets are introduced within the SSU method to account for the stability of robot motion. These parameters, represented as discrete hump waves, are intended to improve motion efficiency during locomotion. Furthermore, the trajectories for each swinging segment are determined through kinematic analysis. Experimental results validate the effectiveness of the proposed SSU multi-gait generation method, demonstrating the successful traversal of gaps and rough terrain.

Chondrosarcoma (CS) is a high-morbidity, relatively common bone malignancy without well-established biomarkers. The proteins EAAT1, DHFR, and fetuin-A have been investigated in many cancers, but their specific relationship to CS has not been reported. The present study examined EAAT1, DHFR, and fetuin-A expression in CS and the clinicopathological significance of these proteins in CS pathogenesis, progression, and prognosis. EAAT1, DHFR, and fetuin-A protein levels in 80 CS and 25 chondroma specimens were measured by immunohistochemistry and related to histological and clinical factors with chi-squared tests. Following univariate survival analysis, ROC curves calculation, and multivariate analysis. EAAT1, DHFR, and fetuin-A expression levels were significantly higher in the CS group than in the chondroma group ( < 0.05). Their immunopositivity rates were significantly greater in tissues with moderate or poor tumor differentiation, AJCC stage III or IV, Enneking stage II or III, and metastasis ( <0.05 or <0.01 or <0.001). Kaplan-Meier survival analysis showed significantly shorter survival in patients with moderately or poorly differentiated tumors, AJCC stage III or IV CS, Enneking stage II or III CS, metastasis, invasion, or EAAT1, DHFR, and fetuin-A immunopositivity ( < 0.05 or < 0.001). Cox regression analysis showed that moderate or poor tumor differentiation, AJCC stage III or IV, Enneking stage II or III, metastasis, invasion, and EAAT1, DHFR, or fetuin-A immunopositivity correlated negatively with postoperative survival and positively with mortality ( < 0.05). The AUCs for EAAT1, DHFR, and fetuin-A were 0.654 (95% CI: 0.532-0.776, = 0.025), 0.638 (95% CI: 0.519-0.756, = 0.039), and 0.670 (95% CI: 0.556-0.784, = 0.011), respectively. EAAT1, DHFR, and fetuin-A may be important biomarkers of the pathogenesis and progression of CS and predictors of its prognosis.

Tissue engineered vascular grafts (TEVGs) generated from human primary cells represent a promising vascular interventional therapy. However, generation and application of these TEVGs may be significantly hindered by the limited accessibility, finite expandability, donor-donor functional variation and immune-incompatibility of primary seed cells from donors. Alternatively, human induced pluripotent stem cells (hiPSCs) offer an infinite source to obtain functional vascular cells in large quantity and comparable quality for TEVG construction. To date, TEVGs (hiPSC-TEVGs) with significant mechanical strength and implantability have been generated using hiPSC-derived seed cells. Despite being in its incipient stage, this emerging field of hiPSC-TEVG research has achieved significant progress and presented promising future potential. Meanwhile, a series of challenges pertaining hiPSC differentiation, vascular tissue engineering technologies and future production and application await to be addressed. Herein, we have composed this review to introduce progress in TEVG generation using hiPSCs, summarize the current major challenges, and encapsulate the future directions of research on hiPSC-based TEVGs.