Correction: Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation.

Correction for 'Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation' by Bingjie Guo et al., Mater. Horiz., 2024, https://doi.org/10.1039/D3MH01950K.

Research on Isomorphic Task Transfer Algorithm Based on Knowledge Distillation in Multi-Agent Collaborative Systems.

In response to the increasing number of agents and changing task scenarios in multi-agent collaborative systems, existing collaborative strategies struggle to effectively adapt to new task scenarios. To address this challenge, this paper proposes a knowledge distillation method combined with a domain separation network (DSN-KD). This method leverages the well-performing policy network from a source task as the teacher model, utilizes a domain-separated neural network structure to correct the teacher model's outputs as supervision, and guides the learning of agents in new tasks. The proposed method does not require the pre-design or training of complex state-action mappings, thereby reducing the cost of transfer. Experimental results in scenarios such as UAV surveillance and UAV cooperative target occupation, robot cooperative box pushing, UAV cooperative target strike, and multi-agent cooperative resource recovery in a particle simulation environment demonstrate that the DSN-KD transfer method effectively enhances the learning speed of new task policies and improves the proximity of the policy model to the theoretically optimal policy in practical tasks.

Affective computing for human-machine interaction via a bionic organic memristor exhibiting selective in situ activation.

Affective computing, representing the forefront of human-machine interaction, is confronted with the pressing challenges of the execution speed and power consumption brought by the transmission of massive data. Herein, we introduce a bionic organic memristor inspired by the ligand-gated ion channels (LGICs) to facilitate near-sensor affective computing based on electroencephalography (EEG). It is constructed from a coordination polymer comprising Co ions and benzothiadiazole (Co-BTA), featuring multiple switching sites for redox reactions. Through advanced characterizations and theoretical calculations, we demonstrate that when subjected to a bias voltage, only the site where Co ions bind with N atoms from four BTA molecules becomes activated, while others remain inert. This remarkable phenomenon resembles the selective in situ activation of LGICs on the postsynaptic membrane for neural signal regulation. Consequently, the bionic organic memristor network exhibits outstanding reliability (200 000 cycles), exceptional integration level (210 pixels), ultra-low energy consumption (4.05 pJ), and fast switching speed (94 ns). Moreover, the built near-sensor system based on it achieves emotion recognition with an accuracy exceeding 95%. This research substantively adds to the ambition of realizing empathetic interaction and presents an appealing bionic approach for the development of novel electronic devices.

Recent Advances in Whiskers: Properties and Clinical Applications in Dentistry.

Whiskers are nanoscale, high-strength fibrous crystals with a wide range of potential applications in dentistry owing to their unique mechanical, thermal, electrical, and biological properties. They possess high strength, a high modulus of elasticity and good biocompatibility. Hence, adding these crystals to dental composites as reinforcement can considerably improve the mechanical properties and durability of restorations. Additionally, whiskers are involved in inducing the value-added differentiation of osteoblasts, odontogenic osteocytes, and pulp stem cells, and promoting the regeneration of alveolar bone, periodontal tissue, and pulp tissue. They can also enhance the mucosal barrier function, inhibit the proliferation of tumor cells, control inflammation, and aid in cancer prevention. This review comprehensively summarizes the classification, properties, growth mechanisms and preparation methods of whiskers and focuses on their application in dentistry. Due to their unique physicochemical properties, excellent biological properties, and nanoscale characteristics, whiskers show great potential for application in bone, periodontal, and pulp tissue regeneration. Additionally, they can be used to prevent and treat oral cancer and improve medical devices, thus making them a promising new material in dentistry.

Emerindanols A and B: Two Bipolyhydroindenol Sesterterpenes with 5/6-6/5 Coupled Ring System Discovered by Genome Mining.

Genome mining of Emericella sp. XL-029 achieved a new type E sesterterpene synthase, EmES, which affored a novel bipolyhydroindenol sesterterpene, emerindanol A. Heterologous coexpression with the upstream P450 oxidase revealed C-4 hydroxylated product, emerindanol B. Notably, emerindanols A and B represented the first sesterterpenes featuring a unique 5/6-6/5 coupled ring system. EmES was postulated to initiate through C1-IV-V pathway and convert the fused ring intermediate into the final coupled ring product through a spiro skeleton.

Magnetized bentonite modified rice straw biochar: Qualitative and quantitative analysis of Cd(II) adsorption mechanism.

Industrialization has caused a significant global issue with cadmium (Cd) pollution. In this study, Biochar (Bc), generated through initial pyrolysis of rice straw, underwent thorough mixing with magnetized bentonite clay, followed by activation with KOH and subsequent pyrolysis. Consequently, a magnetized bentonite modified rice straw biochar (Fe3O4@B-Bc) was successfully synthesized for effective treatment and remediation of this problem. Fe3O4@B-Bc not only overcomes the challenges associated with the difficult separation of individual bentonite or biochar from water, but also exhibited a maximum adsorption capacity of Cd(II) up to 241.52 mg g-1. The characterization of Fe3O4@B-Bc revealed that its surface was rich in C, O and Fe functional groups, which enable efficient adsorption. The quantitative calculation of the contribution to the adsorption mechanism indicates that cation exchange and physical adsorption accounted for 65.87% of the total adsorption capacity. In conclusion, Fe3O4@B-Bc can be considered a low-cost and recyclable green adsorbent, with broad potential for treating cadmium-polluted water.

Automatic three-dimensional facial symmetry reference plane construction based on facial planar reflective symmetry net.

OBJECTIVES: Three-dimensional (3D) facial symmetry analysis is based on the 3D symmetry reference plane (SRP). Artificial intelligence (AI) is widely used in the dental and oral sciences. This study developed a novel deep learning model called the facial planar reflective symmetry net (FPRS-Net) to automatically construct an SRP and established a method for defining a 3D point-cloud region of interest (ROI) and high-dimensional feature computations suitable for this network model. METHODS: Overall, 240 patients were enroled. The deep learning model was trained and predicted using 200 samples, and its clinical suitability was evaluated with 40 samples. Four FPRS-Net models were prepared, each using supervised and unsupervised learning approaches based on full facial and ROI data (FPRS-NetS, FPRS-NetSR, FPRS-NetU, and FPRS-NetUR). These models were trained on 160 3D facial datasets, validated on 20 cases, and tested on another 20 cases. The model predictions were evaluated using an additional 40 clinical 3D facial datasets by comparing the mean square error of the SRP between the parameters predicted by the four FPRS-Net models and the truth plane. The clinical suitability of FPRS-Net models was evaluated by measuring the angle error between the predicted and ground-truth planes; experts evaluated the predicted SRP of the four FPRS-Net models using the visual analogue scales (VAS) method. RESULTS: The FPRS-NetSR and FPRS-NetU models achieved an average angle error of 0.84° and 0.99° in predicting 3D facial SRP, respectively, with a VAS value of >8. Using the four FPRS-Net models to create an SRP in 40 cases of 3D facial data required <4 s. CONCLUSIONS: Our study demonstrated a new solution for automatically constructing oral clinical 3D facial SRPs. CLINICAL SIGNIFICANCE: This study proposes a novel deep learning algorithm (FPRS-Net) to construct a symmetry reference plane that can reduce workload, shorten the time required for digital design, reduce dependence on expert experience, and improve therapeutic efficiency and effectiveness in dental clinics.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception.

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis.

Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher-student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices.

A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Entering the era of AI 2.0, bio-inspired target recognition facilitates life. However, target recognition may suffer from some risks when the target is hijacked. Therefore, it is significantly important to provide an encryption process prior to neuromorphic computing. In this work, enlightened from time-varied synaptic rule, an in-memory asymmetric encryption as pre-authentication is utilized with subsequent convolutional neural network (ConvNet) for target recognition, achieving in-memory two-factor authentication (IM-2FA). The unipolar self-oscillated synaptic behavior is adopted to function as in-memory asymmetric encryption, which can greatly decrease the complexity of the peripheral circuit compared to bipolar stimulation. Results show that without passing the encryption process with suitable weights at the correct time, the ConvNet for target recognition will not work properly with an extremely low accuracy lower than 0.86%, thus effectively blocking out the potential risks of involuntary access. When a set of correct weights is evolved at a suitable time, a recognition rate as high as 99.82% can be implemented for target recognition, which verifies the effectiveness of the IM-2FA strategy.