Wuxi Institute of Technology

Developing advanced biomaterials with controllable nanostructures and biological multifunctionality is highly promising in biomedical fields. In this study, novel Sr²⁺/Zn²⁺ co‐doped mesoporous silica nanoparticles (MSNs) are fabricated using a designated substitution and etching method. The obtained SrZn‐MSNs possess unique hollow mesoporous structures, round spherical morphology, high specific surface areas, and suitable pore sizes. On this basis, gelatin methacrylate is combined with SrZn‐MSNs to construct injectable photo‐crosslinked hydrogels characterized by desired 3D interconnected porous structure, rough micro‐surface topography, sustained Sr/Zn ions releasing, improved biomineralization and mechanical properties. These satisfactory properties allow SrZn‐MSNs to fully exert their bioactivity in composite hydrogels, efficiently ameliorating osteoporotic osseointegration around Ti alloys without drug loading. Direct cell culture on composite hydrogels further confirms the superior biological multifunctionality of SrZn‐MSNs to positively manipulate the coupling of immunoregulation‐osteogenesis‐angiogenesis, depending on the synergetic actions of bioactive Sr²⁺/Zn²⁺. Furthermore, the underlying molecular mechanism responsible for enhanced osteogenesis of SrZn‐MSNs is clarified to be the upregulated PI3K‐AKT pathway, mainly mediated by activated integrin (Itgβ8, Itgα4) and toll‐like receptor (Tlr2) signaling. These findings throw new insights into the fabrication of novel SrZn‐MSNs and highlight its superior biological multifunctionality and osteogenic mechanism, thus may providing a new practical strategy for bone healing.

This research investigates the application of the Machine Learning (ML) model for effective and equitable essay scoring in education. Unlike their human counterpart, ML models have the capacity to rapidly analyze scores of essays, providing timely and equitable scores that take into account varying student demographics and styles of writing. This function helps in the identification of classroom problems and supports the design of focused teaching methodologies. For the study, a Light Gradient Boosting Classification (LGBC) model was optimized by three optimizers: Black Widow Optimization (BWO), Zebra Optimization Algorithm (ZOA), and Leader Harris Hawks Optimization (LHHO), for the development of the hybrid models with a focus on improved prediction quality. Comparison of these hybrid models with the base LGBC model was performed through different phases, such as Training, Validation, and Testing. The findings show that the LGLH model exhibited improved performance with an accuracy rate of 0.981, followed by the LGZO model with 0.971 and the LGBW model with 0.963. The lowest rate of accuracy was observed in the base LGBC model, which was 0.946. The results demonstrate the efficacy of hybrid models, which harness the optimality of several optimization techniques and provide more robust results for complicated tasks. The study emphasizes the importance of selecting the appropriate model architecture to achieve optimal performance, providing valuable insights into model efficacy at various stages of evaluation.

Inter-turn short circuit faults (ITSCFs) in induction motors (IMs) can lead to significant motor damage and system failure, particularly in electric drive systems. This paper proposes a novel digital twin (DT)-assisted method for ITSCF diagnosis, incorporating an analysis of the temperature rise characteristics induced by the faults. First, a DT model of the IM is developed, with state parameters updated in real-time using measured current data to accurately replicate motor behavior under various operating conditions. The DT model then simulates motor currents and temperature rise characteristics under healthy and faulty states, including inter-turn short circuit conditions. Combined with limited measured data, these synthetic data are used to create a physically-virtually fused dataset for training a deep learning-based fault diagnosis model. Additionally, the temperature rise characteristics are analyzed to reveal the distinctive thermal behavior of the stator windings under ITSCF conditions. Experimental results demonstrate that the proposed method not only achieves high diagnostic accuracy but also provides critical insights into the thermal effects of ITSCF in IMs. This approach offers a promising solution for real-time fault monitoring and thermal analysis in electric drive systems.

Palmprint recognition, as a biometric recognition technology, has unique individual recognition and high accuracy, and is broadly utilized in fields such as identity verification and security monitoring. Therefore, a palm print recognition model that integrates regions of interest and Gabor filters has been proposed to solve the problem of difficulty in feature extraction caused by factors such as noise, lighting changes, and acquisition angles that often affect palm print images during the acquisition process. This model extracts standardized feature regions of palmprint images through the region of interest method, enhances texture features through multi-scale Gabor filters, and finally uses support vector machines for classification. The experiment findings denote that the region of interest model performs better than other methods in terms of signal-to-noise ratio and root mean square error, with a signal-to-noise ratio of 0.89 on the GPDS dataset and 0.97 on the CASIA dataset. The proposed model performs the best in recognition accuracy and error convergence speed, with a final accuracy of 95%. The proposed model has the shortest running time, less than 0.4 seconds in all groups, especially less than 0.3 seconds in Group 4, demonstrating high recognition efficiency. The research conclusion shows that the palmprint recognition method combining regions of interest and Gabor filters has high efficiency and performance, and can effectively improve recognition accuracy.

In the field of non-destructive testing of foreign objects in food, the high cost and low efficiency of manual labeling greatly limit the application of X-ray foreign object detection systems. To overcome this problem, this paper proposes a technique for fusing foreign object images with food images, and the fused images enable the automatic labeling of foreign objects. Firstly, X-ray images of foreign objects and food images were collected, and data augmentation was performed on the foreign object images to increase their diversity. Then the food images were fused with the enhanced foreign object images, and the foreign objects were automatically labeled in the fused food images. Finally, the foreign object detection models Model_Y2 and Model_Y1 were established using the dataset automatically annotated by the image fusion method and the dataset manually collected and annotated by traditional methods. The results demonstrate that the proposed method substantially decreases annotation time by 90% while concurrently improving annotation efficiency and accuracy. Comparatively, Model_Y2 outperforms Model_Y1 with a 4.5% higher mAP@0.5:0.95. This indicates that the method not only enhances data annotation efficiency and quality but also improves the accuracy of X-ray foreign object detection, providing a highly efficient and practical technical solution for the intelligent development of food safety inspection.

Heat waves (HWs), intensifying under climate change, critically modulate planetary boundary‐layer (PBL) turbulence through poorly constrained mechanisms. Leveraging unique radar wind profiler network measurements across three Beijing during the record‐breaking 2023 summer (16 HW days), we quantify the turbulence dissipation rate (ε) variations under anticyclone driven HWs (hereafter called Type AC, 40% dominance). The mean ε in the PBL during HWs is elevated by ∼55%, demonstrating heat‐amplified turbulence. Divergent forcing regimes emerges–surface‐air temperature difference (Ts−Ta) governs PBL turbulence generation while vertical wind shear (VWS) dominates mechanical mixing aloft. Intriguingly, clouds play a dual role: they enhance VWS‐induced turbulence under normal conditions but reduce heat driven turbulence. These findings establish the first observational evidence of synoptic‐scale thermal‐dynamic decoupling in urban PBLs during extreme heat, providing mechanistic insights for improving megacity air quality forecasting and heat‐stress resilience strategies.

Tissue engineering scaffolds with tailored mechanical properties are crucial for regenerative medicine. However, establishing the intricate relationships between scaffold design parameters and resulting mechanical behaviour remains a challenge. Here, we present a transferable, machine-learning-driven framework for the rapid customisation of triply periodic minimal surface (TPMS) scaffolds. We employ a genetic algorithm to optimise a back-propagation neural network (GA-BPNN) for forward prediction of elastic modulus from structural parameters (porosity, pore size). Transfer learning enables efficient adaptation of the model to different TPMS topologies (Primitive, Gyroid, Diamond, I-Wrapped Package) with minimal retraining. Furthermore, we develop a BPNN-SCLFPSO (synchronous changing learning factor particle swarm optimisation) reverse search model, allowing for the inverse design of TPMS structures with targeted mechanical properties. Simulations demonstrate high predictive accuracy (R² > 0.97) across diverse material compositions. Physical validation via additive manufacturing and compression testing confirms the model's reliability, with average relative errors of 7.6% and 4.8% for PCU and Ti alloy, respectively. This framework offers a powerful tool for on-demand design of TPMS scaffolds, accelerating the development of personalised tissue engineering solutions.

Open-set domain adaptation (OSDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain containing novel classes. Traditional OSDA methods rarely account for the uncertainty in predictions and typically require additional training overhead. Evidential deep learning (EDL) transforms the model’s predictions from point estimates to distributions over the probability simplex by replacing the standard softmax output of classification neural networks with Dirichlet distributions. Considering the presence of out-of-distribution novel classes in OSDA and the additional overhead of existing methods, we propose EDL for open-set active domain adaptation (EOSADA). Leveraging EDL, we construct an open-set classifier and employ a two-round selection strategy guided by the data uncertainty of target domain samples and semantic similarity scores with known classes. This strategy balances the selection of samples from known and novel classes while identifying informative samples, thereby maximizing the performance of the model in OSDA scenarios without modifying the model structure and utilizing a limited annotation budget. Extensive experiments demonstrate the superiority of our approach.

This work focuses on the controllability problems of Hilfer fractional backward evolution equations. Firstly, existence and uniqueness results are displayed by employing the iteration sequence approach. Then, the approximate controllability problem is explored by iteration and approximation sequence techniques involving resolvent and integral contractor. Additionally, with the aid of suitable conditions, by Banach’s fixed point theorem, the exact null controllability result is exhibited without involving the inverse of the control operator. The idea can also be well adapted to the exact controllability problem. Moreover, an important example is supplied to manifest the feasibility of our findings. Eventually, the conclusion of this work is offered.

Background The coordinated development of the digital economy and public health services is essential for integrating the “Digital China” and “Healthy China” strategies and accelerating the modernization of the public health system. However, substantial regional disparities persist, necessitating a systematic evaluation of the coupling and coordination between these two domains, along with the identification of key influencing factors to support evidence-based policymaking. Methods This study utilizes panel data from 30 Chinese provinces spanning 2012–2021. The entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is employed to quantify the levels of digital economy development and public health services. A coupling coordination model is employed to evaluate the degree of coordinated development between these sectors, whereas a panel Tobit model serves to identify the key influencing factors. Results The overall trajectory of China’s digital economy and public health services exhibits an upward yet fluctuating trend. The degree of coupling coordination has progressed from a state of near imbalance to a marginally coordinated phase, although it remains relatively low. Spatially, the eastern regions exhibit a higher degree of coordination, whereas the central and western regions primarily experience imbalances characterized by a lagging digital economy. Furthermore, the coupling coordination degree demonstrates a significant spatial positive correlation. Economic development is identified as the primary driver of improved coordination, whereas factors such as population density and health status exert inhibitory effects to some extent. Conclusion To enhance overall coordination and achieve regional balance, policymakers should tailor development strategies to local resource endowments, optimize the synergy between the digital economy and public health services, and refine collaborative mechanisms.

Three organic solvents, cyclohexane, n-hexane and n-heptane were selected to dissolve the Ethylene-Propylene-Diene Monomer (EPDM) to keep the mass fractions of EPDM solution at 5 wt% and 10 wt%, respectively. The viscosities of three EPDM solutions at different temperatures were measured by a rotary viscometer. The experimental results show that the concentration and temperature exert significant influences on the viscosities of the EPDM solutions, compared with the rotor type and rotational speed having no obvious effect on the viscosities. An EPDM solution with higher concentration shows remarkable higher viscosity. The viscosities show almost linear decline with increasing temperature within the experimental temperature range, which is also called a viscosity–temperature curve. However, the temperature dependences of viscosity are varied for the three different EPDM solutions. The compatibility between EPDM and solvents could be characterized by the energy difference (Ra) and Flory–Huggins interaction parameter (χ), which has also been attempted to be correlated with the viscosity–temperature curve and solvent molar volume. It is found that the smaller Ra value relates to better compatibility of the EPDM solution and greater slope of the viscosity–temperature curve. Furthermore, the viscosity of EPDM solution and the slope of the viscosity–temperature curve are affected more significantly by the molar volume of solvent when the Ra value is similar. A formula for predicting the viscosity of EPDM solution has been established by using a new Flory–Huggins interaction parameter (χHSP), which can also be used to calculate the viscosity at the extreme temperature that is difficult to be measured. Finally, for the three EPDM solutions, the different dissolution temperatures corresponding to the same viscosity can be obtained by formula calculations with the achieved prediction formulas.

Generating character-consistent and personalized dialogue for Non-Player Characters (NPCs) in Role-Playing Games (RPGs) poses significant challenges, especially due to limited memory retention and inconsistent character representation. This paper proposes a framework for generating personalized dialogues based on character-specific knowledge. By combining static knowledge fine-tuning and dynamic knowledge graph technology, the framework generates dialogue content that is more aligned with character settings and is highly personalized. Specifically, the paper introduces a protective static knowledge fine-tuning approach to ensure that the language model does not generate content beyond the character’s cognitive scope during conversations. Additionally, dynamic knowledge graphs are employed to store and update the interaction history between NPCs and players, forming unique “experience-response” patterns. During dialogue generation, the paper first parses player input into an Abstract Meaning Representation (AMR) graph, retrieves relevant memory nodes from the knowledge graph, and constructs a fused graph structure. This integrated graph is encoded via a graph neural network to generate high-dimensional semantic vectors, which are then used to retrieve and supplement knowledge from the vector database. Ultimately, the model generates personalized responses consistent with the NPC’s identity. Experimental results demonstrate that the framework significantly enhances the authenticity of NPC dialogues and player immersion and performs well on multiple large-scale language models.

A VAQ-based DAC switching scheme is proposed to improve the power efficiency of SAR ADCs. The input signals are sampled onto bottom-plates of the most significant bit (MSB) capacitors, thereby eliminating the reset energy. The reference voltage VCM rather than VREF is switched during the third-bit cycle, thus significantly reducing the power consumption. Additionally, an energy-efficient one-sided switching technique is employed from the fourth-bit cycle. This proposed switching scheme achieves a 99.51% reduction in switching energy over the classic scheme. The ADC with the proposed switching scheme is designed in 0.18-μm CMOS technology. It consumes 37.7 nW at a sampling rate of 20 KS/s and 0.6 V supply, and achieves the ENOB of 9.59 bits, resulting in a figure of merit (FOM) of 2.45 fJ/conversion-step.

Purpose Based on the ego depletion theory, this study reveals the mechanism of the leader’s nighttime sleep deprivation inducing daytime abusive supervision and investigates the mediation role of the leader’s resource depletion. At the same time, the moderation effects of the leader’s time pressure and the leader’s mindfulness on the mediation path are discussed. Design/methodology/approach In this study, 227 leaders and their employees from 5 Chinese enterprises were selected as research objects by the experience sampling method. A paired survey was conducted for 10 consecutive working days. We used Mplus 7.4 and adopted a bootstrapping technique for data analysis. Findings The leader’s nighttime sleep deprivation can lead to resource depletion, which can lead to daytime abusive supervision. At the same time, this study also finds that the leader’s time pressure and the leader’s mindfulness are the “gate valve” for the leader’s nighttime sleep deprivation to promote daytime abusive supervision. That is, when the leader’s time pressure is higher, the resource depletion caused by the leader’s nighttime sleep deprivation is higher, which induces more daytime abusive supervision; when the leader’s mindfulness is high, the leader’s nighttime sleep deprivation results in less abusive supervision through the leader’s resource depletion. Originality/value From the perspective of ego depletion, this study explores why a leader’s nighttime sleep deprivation leads to daytime abusive supervision. The above results expand the research on the mechanism and boundary conditions of leaders’ nighttime sleep deprivation in China’s organizational context, contribute to deepening the understanding of the relationship between leaders’ sleep and work, and provide useful references for leadership development and organizational management.

As Internet of Vehicles (IoV) technology continues to advance, edge computing has become an important tool for assisting vehicles in handling complex tasks. However, the process of offloading tasks to edge servers may expose vehicles to malicious external attacks, resulting in information loss or even tampering, thereby creating serious security vulnerabilities. Blockchain technology can maintain a shared ledger among servers. In the Raft consensus mechanism, as long as more than half of the nodes remain operational, the system will not collapse, effectively maintaining the system’s robustness and security. To protect vehicle information, we propose a security framework that integrates the Raft consensus mechanism from blockchain technology with edge computing. To address the additional latency introduced by blockchain, we derived a theoretical formula for system delay and proposed a convex optimization solution to minimize the system latency, ensuring that the system meets the requirements for low latency and high reliability. Simulation results demonstrate that the optimized data extraction rate significantly reduces system delay, with relatively stable variations in latency. Moreover, the proposed optimization solution based on this model can provide valuable insights for enhancing security and efficiency in future network environments, such as 5G and next-generation smart city systems.