Pingping Wu

Accurate medical image segmentation with low model complexity remains difficult because lesions are often small in scale and boundary cues are easily corrupted by noise. Although recent segmentation methods have achieved strong performance, many of them rely on increasingly complex architectures with high computational costs, limiting their applicability in resource‐constrained settings. To alleviate this issue, we propose PLNet, a lightweight hybrid segmentation framework for medical image analysis. The network improves representation learning by integrating fine‐grained local‐structure perception with long‐range contextual modelling, thereby enhancing the representation of complex medical images. In addition, a feature selection mechanism is employed to emphasise informative feature responses and suppress redundant activations while preserving discriminative representations. Extensive experiments on multiple datasets demonstrate that PLNet achieves competitive IoU and Dice scores with relatively low model complexity. Overall, these results highlight the potential of PLNet as an effective lightweight framework that balances segmentation performance and computational complexity.

In this paper, we present an innovative solution tailored for the intricate challenges of the virtual try-on task–our novel Hierarchical Cross-Attention Network, HCANet. HCANet is meticulously crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic and visually convincing virtual try-on outcomes. A distinctive feature of HCANet is the incorporation of a novel Hierarchical Cross-Attention (HCA) block into both stages, enabling the effective capture of long-range correlations between individual and clothing modalities. The HCA block functions as a cornerstone, enhancing the depth and robustness of the network. By adopting a hierarchical approach, it facilitates a nuanced representation of the interaction between the person and clothing, capturing intricate details essential for an authentic virtual try-on experience. Our extensive set of experiments establishes the prowess of HCANet. The results showcase its cutting-edge performance across both objective quantitative metrics and subjective evaluations of visual realism. HCANet stands out as a state-of-the-art solution, demonstrating its capability to generate virtual try-on results that not only excel in accuracy but also satisfy subjective criteria of realism. This marks a significant step forward in advancing the field of virtual try-on technologies.

The reliability and sustainability of innovation in megaprojects are of critical importance with complex stakeholders and substantial resource investments. In that respect, responsible innovation management is the key to the sustainable development of megaprojects. This paper proposes the concept of megaproject responsible innovation (MRI) and develops a holistic framework for MRI with four dimensions: (1) anticipation across the megaproject life cycle, (2) inclusion of various parties in a relationship of symbiosis, competition, and cooperation, (3) reflexivity of social responsibility, and (4) responsiveness to social expectations. Based on an ecosystem approach to megaproject innovation, we propose an ecological governance strategy for MIR. Moreover, different approaches are needed in innovation organizations which are part of specific ecological niches for effective and efficient governance.

In this paper, we present an innovative solution for the challenges of the virtual try-on task: our novel Hierarchical Cross-Attention Network (HCANet). HCANet is crafted with two primary stages: geometric matching and try-on, each playing a crucial role in delivering realistic virtual try-on outcomes. A key feature of HCANet is the incorporation of a novel Hierarchical Cross-Attention (HCA) block into both stages, enabling the effective capture of long-range correlations between individual and clothing modalities. The HCA block enhances the depth and robustness of the network. By adopting a hierarchical approach, it facilitates a nuanced representation of the interaction between the person and clothing, capturing intricate details essential for an authentic virtual try-on experience. Our experiments establish the prowess of HCANet. The results showcase its performance across both quantitative metrics and subjective evaluations of visual realism. HCANet stands out as a state-of-the-art solution, demonstrating its capability to generate virtual try-on results that excel in accuracy and realism. This marks a significant step in advancing virtual try-on technologies.

The interest of advertising designers and operators in crafting appealing images is steadily increasing. With a primary focus on image attractiveness, this study endeavors to uncover the correlation between image features and attractiveness. The ultimate objective is to enhance the accuracy of predicting image attractiveness to achieve visually captivating effects. The experimental subjects encompass images sourced from the Shutterstock website, and the correlation between image features and attractiveness is analyzed through image attractiveness scores. In our experiments, we extracted traditional features such as color, shape, and texture from the images. Through a detailed analysis and comparison of the accuracy in predicting image attractiveness before and after feature selection using Lasso and LassoNet,, we confirmed that feature selection is an effective method for improving prediction accuracy. Subsequently, the Lasso and LassoNet feature selection methods were applied to a dataset containing image content features. The results verified an enhancement in the model’s accuracy for predicting image attractiveness with the inclusion of image content features. Finally, through an analysis of the four-dimensional features of color, texture, shape, and content, we identified specific features influencing image attractiveness, providing a robust reference for image design.

As human‐machine interaction (HMI) in healthcare continues to evolve, the issue of trust in HMI in healthcare has been raised and explored. It is critical for the development and safety of healthcare that humans have proper trust in medical machines. Intelligent machines that have applied machine learning (ML) technologies continue to penetrate deeper into the medical environment, which also places higher demands on intelligent healthcare. In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious, the authors need to build good human‐machine trust (HMT) in healthcare. This article provides a systematic overview of the prominent research on ML and HMT in healthcare. In addition, this study explores and analyses ML and three important factors that influence HMT in healthcare, and then proposes a HMT model in healthcare. Finally, general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

In this paper, we propose a novel feature compensation algorithm based on independent noise estimation, which employs a Gaussian mixture model (GMM) with fewer Gaussian components to rapidly estimate the noise parameters from the noisy speech and monitor the noise variation. The estimated noise model is combined with a GMM with sufficient Gaussian mixtures to produce the noisy GMM for the clean speech estimation so that parameters are updated if and only if the noise variation occurs. Experimental results show that the proposed algorithm can achieve the recognition accuracy similar to that of the traditional GMM-based feature compensation, but significantly reduces the computational cost, and thereby is more useful for resource-limited mobile devices.

This paper designs an infusion monitoring system based on STM32 microcontroller. The purpose is to solve some safety problems that exist in the hospital infusion hall. This design realizes the functions of information collection, liquid exhaust warning, screen display, and doctor-patient interaction. With this system, nurses at the nurse station can observe the liquid quality of each patient in the infusion hall in real time through the screen. When the liquid is about to run out, the system automatically warns nurses at the nurse station and the patient at the same time, reminding the patient and nurse to deal with it as soon as possible. The paper elaborated on the hardware design of the system in more detail, and explained the scheme design of the system.