Highly Manufacturable Packaging of an Implantable Episcleral Surface Stimulator with Reliability and Safety Validations

This study presents an innovative packaging scheme for an implantable medical device which is an episcleral surface stimulator applying trans-scleral electrical stimulation. The stimulator employs an application-specific integrated circuit to facilitate wireless connection and electrical stimulation. The proposed packaging scheme considers the safety, functionality, reliability, and particularly manufacturability of the stimulator. The packaging process is structured into chip-level, component-level, and device-level. At the initial chip-level packaging, a silicon interposer is introduced to enhance the manufacturability and increase the bonding strength. At the component-level packaging, a flexible printed circuit (FPC) integrates all the components to enable the electrical functions. The FPC is curved by the bending method. The specialized design of the stack and layout addresses the biocompatibility concern. Finally, the device-level packaging incorporates Parylene-C coating and silicone encapsulation to form the final stimulator. At this packaging stage, a novel strategy is introduced eliminating the challenging Parylene-C etching process while streamlining the bending and the silicone encapsulation processes. The outstanding manufacturability achieved from the proposed packaging scheme allows consistent product quality, leading to trustable reliability validation. The reliability of the stimulator is approved by a high-temperature saline soaking test which indicates a minimum 6.1-year lifespan. Additionally, trial surgeries on a rabbit validate the shape and softness, and the biocompatibility of the stimulator is also confirmed by implantation tests on mice. © 2011-2012 IEEE

Using global sensitivity analysis to quantify the uncertainty of root reinforcement in vegetated slope stability

Aims: Variabilities of vegetation and soil cause uncertainty to the factor of safety (FoS) of unsaturated vegetated slopes, yet the significance of these variabilities on the uncertainty of FoS is unclear. This study aims to quantify the effect of the uncertainties of root reinforcement and soil hydromechanical properties to the uncertainty of the FoS. Methods: The variance‐based global sensitivity analysis was adopted to evaluate how the variance of FoS of vegetated slopes can be apportioned by the variabilities of soil and root parameters. A copula theory was applied to model the correlation amongst the parameters. Results: For slip depths shallower than 0.30 m, the major source of the variance of the FoS included the parameters that define root reinforcement, followed by the parameters of soil shear strength. The variation of transpiration‐induced soil suction had limited effect on the FoS variance under heavy rainfall. Taking into account the correlations amongst the parameters had minor influence on their contribution to the variance of the FoS. Conclusions: We observed threshold slip depths, where the relative contribution of uncertainties in root and soil parameters on the FoS uncertainty underwent a transition. Root reinforcement for slips as deep as 0.60 m can provide reliable slope stabilisation effects. © The Author(s) 2025

Advances in 3D Neural Stylization: A Survey

Modern artificial intelligence offers a novel and transformative approach to creating digital art across diverse styles and modalities like images, videos and 3D data, unleashing the power of creativity and revolutionizing the way that we perceive and interact with visual content. This paper reports on recent advances in stylized 3D asset creation and manipulation with the expressive power of neural networks. We establish a taxonomy for neural stylization, considering crucial design choices such as scene representation, guidance data, optimization strategies, and output styles. Building on such taxonomy, our survey first revisits the background of neural stylization on 2D images, and then presents in-depth discussions on recent neural stylization methods for 3D data, accompanied by a benchmark evaluating selected mesh and neural field stylization methods. Based on the insights gained from the survey, we highlight the practical significance, open challenges, future research, and potential impacts of neural stylization, which facilitates researchers and practitioners to navigate the rapidly evolving landscape of 3D content creation using modern artificial intelligence. © The Author(s) 2025

Democratizing value alignment: from authoritarian to democratic AI ethics

Value alignment is essential for ensuring that AI systems act in ways that are consistent with human values. Existing approaches, such as reinforcement learning with human feedback and constitutional AI, however, exhibit power asymmetries and lack transparency. These “authoritarian” approaches fail to adequately accommodate a broad array of human opinions, raising concerns about whose values are being prioritized. In response, we introduce the Dynamic Value Alignment approach, theoretically grounded in the principles of parallel constraint satisfaction, which models moral reasoning as a dynamic process that balances multiple value principles. Our approach also enhances users’ moral and epistemic agency by granting users greater control over the values that influence AI behavior. As a more user-centric, transparent, and participatory framework for AI ethics, our approach not only addresses the democratic deficits inherent in current practices but also ensures that AI systems are flexibly aligned with a diverse array of human values

Direct Synthesis of Topology-Controlled BODIPY and CO₂-Based Zirconium Metal-Organic Frameworks for Efficient Photocatalytic CO₂ Reduction

Boron dipyrromethene (BODIPY)-based zirconium metal–organic frameworks (Zr-MOFs) possess strong light-harvesting capabilities and great potential for artificial photosynthesis without the use of sacrificial reagents. However, their direct preparation has not yet been achieved due to challenges in synthesizing suitable ligands. Herein, we reported the first successful direct synthesis of BODIPY-based Zr-MOFs, utilizing CO2 as a feedstock. By controlling synthetic conditions, we successfully obtained two distinct Zr-MOFs. The first, CO2-Zr6-DEPB, exhibits a face-centered cubic (fcu) topology based on a Zr6(μ3-O)4(μ3-OH)4 node, while the second, CO2-Zr12-DEPB, features a hexagonal closed packed (hcp) topology, structured around a Zr12(μ3-O)8(μ3-OH)8(μ2-OH)6 node. Both MOFs demonstrated excellent crystallinity, as verified through powder X-ray diffraction and high-resolution transmission electron microscopy analyses. These MOF catalysts displayed suitable photocatalytic redox potentials for the reduction of CO2 to CO using H2O as the electron donor in the absence of co-catalyst or toxic sacrificial reagent. Under light irradiation, CO2-Zr12-DEPB and CO2-Zr6-DEPB offered high CO yields of 16.72 and 13.91 μmol g−1 h−1, respectively, with nearly 100 % selectivity. CO2 uptake and photoelectrochemical experiments revealed key insights into the mechanisms driving the different catalytic activities of the two MOFs. These BODIPY and CO2-based, light-responsive Zr-MOFs represent a promising platform for the development of efficient photocatalysts. © 2025 Wiley-VCH GmbH

A deep learning-based system for automatic detection of emesis with high accuracy in Suncus murinus

Quantifying emesis in Suncus murinus (S. murinus) has traditionally relied on direct observation or reviewing recorded behaviour, which are laborious, time-consuming processes that are susceptible to operator error. With rapid advancements in deep learning, automated animal behaviour quantification tools with high accuracy have emerged. In this study, we pioneere the use of both three-dimensional convolutional neural networks and self-attention mechanisms to develop the Automatic Emesis Detection (AED) tool for the quantification of emesis in S. murinus, achieving an overall accuracy of 98.92%. Specifically, we use motion-induced emesis videos as training datasets, with validation results demonstrating an accuracy of 99.42% for motion-induced emesis. In our model generalisation and application studies, we assess the AED tool using various emetics, including resiniferatoxin, nicotine, copper sulphate, naloxone, U46619, cyclophosphamide, exendin-4, and cisplatin. The prediction accuracies for these emetics are 97.10%, 100%, 100%, 97.10%, 98.97%, 96.93%, 98.91%, and 98.41%, respectively. In conclusion, employing deep learning-based automatic analysis improves efficiency and accuracy and mitigates human bias and errors. Our study provides valuable insights into the development of deep learning neural network models aimed at automating the analysis of various behaviours in S. murinus, with potential applications in preclinical research and drug development. © The Author(s) 2025

Online Stochastic Optimization with Wasserstein-Based Nonstationarity

We consider a general online stochastic optimization problem with multiple resource constraints over a horizon of finite time periods. In each time period, a reward function and multiple cost functions are revealed, and the decision maker needs to specify an action from a convex and compact action set to collect the reward and consume the resources. Each cost function corresponds to the consumption of one resource. The reward function and the cost functions of each time period are drawn from an unknown distribution, which is nonstationary across time. The objective of the decision maker is to maximize the cumulative reward subject to the resource constraints. This formulation captures a wide range of applications including online linear programming and network revenue management, among others. In this paper, we consider two settings: (i) a data-driven setting where the true distribution is unknown but a prior estimate (possibly inaccurate) is available and (ii) an uninformative setting where the true distribution is completely unknown. We propose a unified Wasserstein distance–based measure to quantify the inaccuracy of the prior estimate in setting (i) and the nonstationarity of the environment in setting (ii). We show that the proposed measure leads to a necessary and sufficient condition for the attainability of a sublinear regret in both settings. For setting (i), we propose an informative gradient descent algorithm. The algorithm takes a primal-dual perspective, and it integrates the prior information of the underlying distributions into an online gradient descent procedure in the dual space. The algorithm also naturally extends to the uninformative setting (ii). Under both settings, we show the corresponding algorithm achieves a regret of optimal order. We illustrate the algorithm’s performance through numerical experiments