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Abstract Background Blastocyst morphology has been demonstrated to be associated with ploidy status. Existing artificial intelligence models use manual grading or 2D images as the input for euploidy prediction, which suffer from subjectivity from observers and information loss due to incomplete features from 2D images. Here we aim to predict euploidy in human blastocysts using quantitative morphological parameters obtained by 3D morphology measurement. Methods Multi-view images of 226 blastocysts on Day 6 were captured by manually rotating blastocysts during the preparation stage of trophectoderm biopsy. Quantitative morphological parameters were obtained by 3D morphology measurement. Six machine learning models were trained using 3D morphological parameters as the input and PGT-A results as the ground truth outcome. Model performance, including sensitivity, specificity, precision, accuracy and AUC, was evaluated on an additional test dataset. Model interpretation was conducted on the best-performing model. Results All the 3D morphological parameters were significantly different between euploid and non-euploid blastocysts. Multivariate analysis revealed that three of the five parameters including trophectoderm cell number, trophectoderm cell size variance and inner cell mass area maintained statistical significance (P

Abstract In order to enhance the dynamic performance of aircraft high‐voltage direct current wound rotor synchronous generator (WRSG) system under load variations, a dual internal model control (DIMC) method utilising improved load current feedback (ILCF) is proposed. It employs internal model controllers in both voltage and current loops, thereby significantly augmenting the system's disturbance rejection capabilities. Improved load current feedback is incorporated between the DC voltage loop and the field current loop. Additionally, a method for designing a feedback fitting function is established, enhancing the system's adaptability to changes in operational speed and load. The authors outline parameter design methods for voltage and current loops in DIMC, based on the internal model control principle. Adjustments to these parameters can further enhance the system's disturbance rejection capabilities. Finally, comparative experiments between the PI method with ILCF and the DIMC method with ILCF are conducted on a 15 kW WRSG experimental platform. These experiments demonstrate that the proposed DIMC method with ILCF optimises dynamic performance across the entire speed range, enhancing the competitiveness of WRSG systems in aircraft applications.

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

Abstract Crystallization speed of phase change material is one of the main obstacles for the application of phase change memory (PCM) as storage class memory in computing systems, which requires the combination of nonvolatility with ultra‐fast operation speed in nanoseconds. Here, we propose a novel approach to speed up crystallization process of the only commercial phase change chalcogenide Ge2Sb2Te5 (GST). By employing TiO2 as the dielectric layer in phase change device, operation speed of 650 ps has been achieved, which is the fastest among existing representative PCM, and is comparable to the programing speed of commercial dynamic random access memory (DRAM). Because of its octahedral atomic configuration, TiO2 can provide nucleation interfaces for GST, thus facilitating the crystal growth at the determinate interface area. Ti–O–Ti–O four‐fold rings on the (110) plane of tetragonal TiO2 is critical for the fast‐atomic rearrangement in the amorphous matrix of GST that enables ultra‐fast operation speed. The significant improvement of operation speed in PCM through incorporating standard dielectric material TiO2 in DRAM paves the way for the application of phase change memory in high performance cache‐type data storage.

Abstract Background Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example. Methods Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. Results Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. Conclusions The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics.

Variable flux reluctance machines (VFRMs) are increasingly attracting research interest due to their magnetless and robust brushless structure. Under the modulation effect of the airgap permeance, the VFRM operates with a series of field harmonics, distinguishing it from conventional AC synchronous machines. This paper deals with the analysis and preliminary design of the VFRM from the perspective of multiple working airgap field harmonics. Firstly, the spatial and temporal order of the working field harmonics are defined. The systematic winding theory, including the unified star of slots and winding factor calculation method, is established to consider all these working harmonics. Then, an average torque model is built and simplified. The key role of 1st-order rotor permeance, 1st- and 3rd-order polarized stator permeance is deduced. The relationship between key parameters and average torque is computed, providing a guideline for the preliminary design of the VFRM.

Permanent magnet synchronous machines (PMSMs) have been widely used in various applications such as robotics, electric vehicles, and aerospace due to their fast dynamic response, high-power/torque density, and high efficiency. These features make them attractive candidates for aeronautical applications, where the weight and volume of onboard systems are critically important. This paper aims to provide an overview of recent developments in PMSMs. Key design considerations for aeronautical PMSMs across different applications are highlighted based on the analysis of industrial cases and research literature. Additionally, emerging techniques that are vital in enhancing the performance of aeronautical PMSMs are discussed.

Abstract This article presents a dual‐winding permanent magnet generator (PMG) system with integrated dual‐channel controller to meet the requirements of high power density, high reliability, and high output performance of aircraft electric power system. The dual‐winding of the PMG are designed to have the same phase spatially, with which the same rotor position can be applied in independent vector control. In addition, a dual‐channel controller is applied to control the two sets of windings, which achieves high‐performance control under normal state and fault‐tolerant control under fault state. The integrated dual‐channel controller integrates two sets of main power circuits into one controller and uses a main control unit, which simplifies the PMG system leading to reduction in the overall volume and weight. Reliability analysis of dual‐winding PMG system is proposed to be compared with that of the single‐winding PMG system. A dual‐winding cooperative control strategy and a fault‐tolerant control strategy are proposed to achieve high‐performance control of DC‐link voltage under normal condition and redundant functions under fault condition. As a result, the reliability of the PMG system is improved. The experimental results validate the effectiveness of the proposed PMG system and the control strategy.

In recent years, microrobots have drawn extensive attention due to their promising potential in biomedical applications [...]

Decoy oligodeoxynucleotides (ODNs) allow targeting undruggable transcription factors, such as STAT3, but their limited potency and lack of delivery methods hampered translation. To overcome these challenges, we conjugated a STAT3-specific decoy to thalidomide, a ligand to cereblon in E3 ubiquitin ligase complex, to generate a proteolysis-targeting chimera (STAT3DPROTAC). STAT3DPROTAC downregulated STAT3 in target cells, but not STAT1 or STAT5. Computational modeling of the STAT3DPROTAC ternary complex predicted two surface lysines, K601 and K626, in STAT3 as potential ubiquitination sites. Accordingly, K601/K626 point mutations in STAT3, as well as proteasome inhibition or cereblon deletion, alleviated STAT3DPROTAC effect. Next, we conjugated STAT3DPROTAC to a CpG oligonucleotide targeting Toll-like receptor 9 (TLR9) to generate myeloid/B cell-selective C-STAT3DPROTAC. Naked C-STAT3DPROTAC was spontaneously internalized by TLR9+ myeloid cells, B cells, and human and mouse lymphoma cells but not by T cells. C-STAT3DPROTAC effectively decreased STAT3 protein levels and also STAT3-regulated target genes critical for lymphoma cell proliferation and/or survival (BCL2L1, CCND2, and MYC). Finally, local C-STAT3DPROTAC administration to human Ly3 lymphoma-bearing mice triggered tumor regression, while control C-STAT3D and C-SCR treatments had limited effects. Our results underscore the feasibility of using a PROTAC strategy for cell-selective, decoy oligonucleotide-based STAT3 targeting of and potentially other tumorigenic transcription factors for cancer therapy.

The intumescent flame retardant (IFR) technique is an alternative to halogen-based flame retardants for reducing fire hazards in polymers. However, IFR has drawbacks like unsatisfactory flame-retardant efficiency and high loading requirements. In this study, MIL-125 (Ti-based metal–organic framework) is added to ABS/IFR composites to improve flame retardancy and reduce smoke emissions. Thermogravimetric analysis (TGA) results indicate that combining ammonium polyphosphate (APP) and expandable graphite (EG) increases charred residue and slows mass loss compared with the original ABS resin. The ABS/IFR/MIL-125 system stabilizes the char layer, serving as a protective shield against combustible gases during combustion. Additionally, MIL-125 enhances performance in microscale combustion calorimetry (MCC) flammability testing. In fire tests (UL-94, limiting oxygen index (LOI), and cone calorimeter), the ABS/IFR/MIL-125 system achieves a UL-94 V0 rating and the highest LOI value of 31.5% ± 0.1%. Peak heat lease rate (PHRR) values in the cone calorimeter are reduced by 72% with 20 wt.% of additives, and smoke production decreases by 53% compared with neat ABS. These results demonstrate the efficient synergistic effects of MIL-125 and IFR additives in improving the formation and stability of the intumescent char layer, thereby protecting ABS from intense burning.

Optical field echo scattering characteristic is one of the essential technologies for the future integration of underwater laser communication and detection. Vortex beam (e.g. Laguerre-Gaussian (LG) beam) with a helical wavefront structure is better suited to reduce the effects of ocean turbulence. A double-path transmission model of LG beam scattering through rough surface in ocean turbulence is innovatively established. We developed a closed analytical expression for the echo speckle intensity of LG beams reflected by a rough surface with a Gaussian distribution in ocean turbulence based on the generalized Huygens-Fresnel principle. The autocorrelation function of intensity and complex degree of coherence (DOC) of echo speckle field were numerically simulated and analyzed. From the results we found a rule that when coherence length of rough surfaces (ρh) is greater than the coherence length (ρ0) of spherical waves propagating across ocean turbulence, the impact of a rough surface on DOC is much less than that of ocean turbulence. This conclusion provides the theoretical basis that the echo wave field cannot be obtained in target detection under ocean turbulence.

Epidemiological studies from diverse global regions suggest a correlation between the accumulation of aluminum in the brain and the onset of various neurodegenerative diseases, including Alzheimer's disease, of which, neuronal cells death happen. Our previous research has found the potential of aluminum to induce neuronal cell death. A comprehensive exploration of the regulatory pathways influenced by aluminum in neuronal cell death could contribute to the development of strategies aimed at preventing the detrimental impact of aluminum on neuronal cells. This study is dedicated to exploring the impact of aluminum on mitochondrial homeostasis through the RIP3-PGAM5-Drp1 pathway, with a specific focus on its potential role in necroptosis. We observed that the inhibition of RIP3 function and the reduction in PGAM5 protein expression both mitigate aluminum-induced necroptosis in PC12 cells and enhance mitochondrial function. However, the inhibition of PGAM5 protein expression does not exert an impact on the expression of RIP3 and MLKL proteins. In summary, our study posits that aluminum can induce necroptosis in PC12 cells through the RIP3-PGAM5-Drp1 pathway.

Due to the unique advantages of untethered connections and a high level of safety, magnetic actuation is a commonly used technique in microrobotics for propelling microswimmers, manipulating fluidics, and navigating medical devices. However, the microrobots or actuated targets are exposed to identical and homogeneous driving magnetic fields, which makes it challenging to selectively control a single robot or a specific group among multiple targets. This paper reviews recent advances in selective and independent control for multi-microrobot or multi-joint microrobot systems driven by magnetic fields. These selective and independent control approaches decode the global magnetic field into specific configurations for the individualized actuation of multiple microrobots. The methods include applying distinct properties for each microrobot or creating heterogeneous magnetic fields at different locations. Independent control of the selected targets enables the effective cooperation of multiple microrobots to accomplish more complicated operations. In this review, we provide a unique perspective to explain how to manipulate individual microrobots to achieve a high level of group intelligence on a small scale, which could help accelerate the translational development of microrobotic technology for real-life applications.

In order to enhance the transient performance of aircraft high voltage DC (HVDC) generation system with wound rotor synchronous machine (WRSM) under a wide speed range, the nonlinear PI multi-loop control strategy is proposed in this paper. Traditional voltage control method is hard to achieve the dynamic performance requirements of the HVDC generation system under a wide speed range, so the nonlinear PI parameter adjustment, load current feedback and speed feedback are added to the voltage and excitation current double loop control. The transfer function of the HVDC generation system is derived, and the relationship between speed, load current and PI parameters is obtained. The PI parameters corresponding to the load at certain speed are used to shorten the adjusting time when the load suddenly changes. The dynamic responses in transient processes are analyzed by experiment. The results illustrate that the WRSM HVDC generator system with this method has better dynamic performance.

Parent’s nutrition knowledge, attitudes, and dietary practices (KAP) play imperative roles in preventing malnutrition for themselves and their children. Our study aimed to determine the status and contributing factors of nutrition KAP among parents of children and adolescents. A total of 1746 parents (mean age 39.67 ± 5.38 years, females accounting for 69.82%) of primary and junior high school students in Weifang, China, completed a self-reported KAP questionnaire in August 2021. An analysis of Pearson product-moment correlation was conducted to determine the relationship between knowledge, attitudes, and practices. Chi-square test, followed by a multivariable robust Poisson regression analysis, was performed to identify the contributing factors to parents’ KAP. A 65.94% awareness rate of nutritional knowledge was observed. The correlations between nutrition knowledge and attitudes (r = 0.03, P = 0.23), knowledge and practices (r = 0.02, P = 0.34), and attitudes and practices (r = 0.16, P

Nighttime semantic segmentation due to issues such as low contrast, fuzzy imaging, and low-quality annotation results in significant degradation of masks. In this paper, we introduce a domain adaptive approach for nighttime semantic segmentation that overcomes the reliance on low-light image annotations to transfer the source domain model to the target domain. On the front end, a low-light image enhancement sub-network combining lightweight deep learning with mapping curve iteration is adopted to enhance nighttime foreground contrast. In the segmentation network, the body generation and edge preservation branches are implemented to generate consistent representations within the same semantic region. Additionally, a pixel weighting strategy is embedded to increase the prediction accuracy for small targets. During the training, a discriminator is implemented to distinguish features between the source and target domains, thereby guiding the segmentation network for adversarial transfer learning. The proposed approach’s effectiveness is verified through testing on Dark Zurich, Nighttime Driving, and CityScapes, including evaluations of mIoU, PSNR, and SSIM. They confirm that our approach surpasses existing baselines in segmentation scenarios.

Objective To explore the application of team-based learning(TBL) combined with case-based learning (CBL) in training program of thyroid surgery on medical intern. Methods A prospective study was conducted in 94 interns with training program from November 2020 to June 2021 in thyroid surgery, Ruijin Hospital. The interns were grouped chronologically. Forty-six interns from November 2020 to February 2021 were included into control group and 48 interns from March 2021 to June 2021 into study group. Interns in control group were given traditional training and interns in study group with TBL with CBL training. Basic theory and knowledge, basic operative skill and ability of managing crisis were evaluated and compared between two groups. Results The basic theory and knowledge in study group were significantly better than that in control group (t=12.610, P0.05) and ability of managing crisis (t=1.313, P>0.05) between two groups. As for crisis management abi-lity, the team work ability (t=3.610, P