Search for: All records

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors. While AI-enhanced NMR prediction models hold promise, challenges still persist in tasks such as molecular retrieval, iso- mer recognition, and peak assignment. In response, this paper introduces a novel solution, Knowledge-Guided Multi-Level Multimodal Alignment with Instance-Wise Discrimination (K-M3 AID), which establishes correspondences between two heterogeneous modalities: molecular graphs and NMR spectra. K- M3AID employs a dual-coordinated contrastive learning architecture with three key modules: a graph-level alignment module, a node-level alignment module, and a communication channel. Notably, K-M3AID introduces knowledge- guided instance-wise discrimination into contrastive learning within the node-level alignment module. In addition, K-M3 AID demonstrates that skills acquired during node-level alignment have a positive impact on graph-level alignment, acknowledging meta-learning as an inherent property. Empirical validation underscores the effectiveness of K-M3AID in multiple zero- shot tasks. 

Enhancing accurate molecular property predic- tion relies on effective and proficient representa- tion learning. It is crucial to incorporate diverse molecular relationships characterized by multi- similarity (self-similarity and relative similarities) (Wang et al., 2019) between molecules. However, current molecular representation learning meth- ods fall short in exploring multi-similarity and of- ten underestimate the complexity of relationships between molecules. Additionally, previous multi- similarity approaches require the specification of positive and negative pairs to attribute distinct pre- defined weights to different relative similarities, which can introduce potential bias. In this work, we introduce Graph Multi-Similarity Learning for Molecular Property Prediction (GraphMSL) framework, along with a novel approach to for- mulate a generalized multi-similarity metric with- out the need to define positive and negative pairs. In each of the chemical modality spaces (e.g., molecular depiction image, fingerprint, NMR, and SMILES) under consideration, we first de- fine a self-similarity metric (i.e., similarity be- tween an anchor molecule and another molecule), and then transform it into a generalized multi- similarity metric for the anchor through a pair weighting function. GraphMSL validates the effi- cacy of the multi-similarity metric across Molecu- leNet datasets. Furthermore, these metrics of all modalities are integrated into a multimodal multi-similarity metric, which showcases the po- tential to improve the performance. Moreover, the focus of the model can be redirected or cus- tomized by altering the fusion function. Last but not least, GraphMSL proves effective in drug dis- covery evaluations through post-hoc analyses of the learnt representations. 

Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless networks. One primary challenge in this integration is limited wireless network resources, particularly when serving a large number of users within distributed EV charging networks in the SIoT. Factors such as congestion during EV travel, varying EV user preferences, and uncertainties in decision-making regarding charging station resources significantly impact system operation and network resource allocation. To address these challenges, this paper develops a novel framework harnessing the potential of emerging technologies, specifically reconfigurable intelligent surfaces (RISs) and causal-structure-enhanced asynchronous advantage actor–critic (A3C) reinforcement learning techniques. This framework aims to optimize resource allocation, thereby enhancing communication support within EV charging networks. Through the integration of RIS technology, which enables control over electromagnetic waves, and the application of causal reinforcement learning algorithms, the framework dynamically adjusts resource allocation strategies to accommodate evolving conditions in EV charging networks. An essential aspect of this framework is its ability to simultaneously meet real-world social requirements, such as ensuring efficient utilization of network resources. Numerical simulation results validate the effectiveness and adaptability of this approach in improving wireless network efficiency and enhancing user experience within the SIoT context. Through these simulations, it becomes evident that the developed framework offers promising solutions to the challenges posed by integrating the SIoT with EV charging networks.

 

Recycling underutilized resources from food waste (FW) to agriculture through hydrothermal carbonization (HTC) has been proposed to promote a circular economy (CE) in food-energy-water (FEW) nexus. However, most HTC studies on FW were conducted at laboratory scale, and little is known on the efficacy and feasibility of field application of HTC products from FW, i.e. the aqueous phrase (AP) and solid hydrochar (HC), to support agriculture production. An integrated pilot-scale HTC system was established to investigate practical HTC reaction conditions treating FW. A peak temperature of 180 ◦C at a residence time of 60 min with 3 times AP recirculation were recommended as optimal HTC conditions to achieve efficient recovery of nutrients, and desirable AP and HC properties for agriculture application. Dilution of the raw AP and composting of the fresh HC are necessary as post-treatments to eliminate potential phytotoxicity. Applying properly diluted AP and the composted HC significantly improved plant growth and nutrient availability in both greenhouse and field trials, which were comparable to commercial chemical fertilizer and soil amendment. The HTC of FW followed with agricultural application of the products yielded net negative carbon emission of 􀀀 0.28 t CO2e t􀀀 1, which was much lower than the other alternatives of FW treatments. Economic profit could be potentially achieved by valorization of the AP as liquid fertilizer and HC as soil amendment. Our study provides solid evidences demonstrating the technical and economic feasibility of recycling FW to agriculture through HTC as a promising CE strategy to sustain the FEW nexus. 

The Hubbard model is an iconic model in quantum many-body physics and has been intensely studied, especially since the discovery of high-temperature cuprate superconductors. Combining the complementary capabilities of two computational methods, we found superconductivity in both the electron- and hole-doped regimes of the two-dimensional Hubbard model with next-nearest-neighbor hopping. In the electron-doped regime, superconductivity was weaker and was accompanied by antiferromagnetic Néel correlations at low doping. The strong superconductivity on the hole-doped side coexisted with stripe order, which persisted into the overdoped region with weaker hole-density modulation. These stripe orders varied in fillings between 0.6 and 0.8. Our results suggest the applicability of the Hubbard model with next-nearest hopping for describing cuprate high–transition temperature (T_c) superconductivity.