Coagulation Behavior of Aluminum Salts in Eutrophic Water: Significance of Al13Species and pH Control

The coagulation behavior of aluminum salts in a eutrophic source water was investigated from the viewpoint of Al(III) hydrolysis species transformation. Particular emphasis was paid to the coagulation effect of Al-13 species on removing particles and organic matter. The coagulation behavior of Al coagulants with different basicities was examined through jar tests and hydrolyzed Al(III) speciation distribution characterization in the coagulation process. The results showed that the coagulation efficiency of Al coagulants positively correlated with the content of Al-13 in the coagulation process rather than in the initial coagulants. Aluminum chloride (AlCl3) was more effective than polyaluminum chloride (PACT) in removing turbidity and dissolved organic matter in eutrophic water because AlCl3 could not only generate Al-13 species but also function as a pH control agent in the coagulation process. The solid-state Al-27 NMR spectra revealed that the precipitates formed from AlCl3 and PACT were significantly different and proved that the preformed Al-13 polymer was more stable than the in situ formed one during the coagulation process. Through regulating Al speciation, pH control could improve the coagulation process especially in DOC removal, and AlCl3 benefited most from pH control

E-Syn: E-Graph Rewriting with Technology-Aware Cost Functions for Logic Synthesis

Logic synthesis plays a crucial role in the digital design flow. It has a decisive influence on the final Quality of Results (QoR) of the circuit implementations. However, existing multi-level logic optimization algorithms often employ greedy approaches with a series of local optimization steps. Each step breaks the circuit into small pieces (e.g., k-feasible cuts) and applies incremental changes to individual pieces separately. These local optimization steps could limit the exploration space and may miss opportunities for significant improvements. To address the limitation, this paper proposes using e-graph in logic synthesis. The new workflow, named Esyn, makes use of the well-established e-graph infrastructure to efficiently perform logic rewriting. It explores a diverse set of equivalent Boolean representations while allowing technology-aware cost functions to better support delay-oriented and area-oriented logic synthesis. Experiments over a wide range of benchmark designs show our proposed logic optimization approach reaches a wider design space compared to the commonly used AIG-based logic synthesis flow. It achieves on average 15.29% delay saving in delay-oriented synthesis and 6.42% area saving for area-oriented synthesis.Comment: Accepted by DAC 2024; Please note that this is not the final camera-ready versio

KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion

The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and dialog systems. Leveraging them for KGC re-ranking is beneficial for leveraging the extensive pre-trained knowledge and powerful generative capabilities. However, it may encounter new problems when accomplishing the task, namely mismatch, misordering and omission. To this end, we introduce KC-GenRe, a knowledge-constrained generative re-ranking method based on LLMs for KGC. To overcome the mismatch issue, we formulate the KGC re-ranking task as a candidate identifier sorting generation problem implemented by generative LLMs. To tackle the misordering issue, we develop a knowledge-guided interactive training method that enhances the identification and ranking of candidates. To address the omission issue, we design a knowledge-augmented constrained inference method that enables contextual prompting and controlled generation, so as to obtain valid rankings. Experimental results show that KG-GenRe achieves state-of-the-art performance on four datasets, with gains of up to 6.7% and 7.7% in the MRR and Hits@1 metric compared to previous methods, and 9.0% and 11.1% compared to that without re-ranking. Extensive analysis demonstrates the effectiveness of components in KG-GenRe.Comment: This paper has been accepted for publication in the proceedings of LREC-COLING 202

Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a challenging setting, popular reconstruction-based networks with continuous latent representation assumption always suffer from the "identical shortcut" issue, where both normal and abnormal samples can be well recovered and difficult to distinguish. To address this pivotal issue, we propose a hierarchical vector quantized prototype-oriented Transformer under a probabilistic framework. First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut. The vector quantized iconic prototype is integrated into the Transformer for reconstruction, such that the abnormal data point is flipped to a normal data point.Second, we investigate an exquisite hierarchical framework to relieve the codebook collapse issue and replenish frail normal patterns. Third, a prototype-oriented optimal transport method is proposed to better regulate the prototypes and hierarchically evaluate the abnormal score. By evaluating on MVTec-AD and VisA datasets, our model surpasses the state-of-the-art alternatives and possesses good interpretability. The code is available at https://github.com/RuiyingLu/HVQ-Trans

Cost and thermodynamic analysis of wind-hydrogen production via multi-energy systems

With rising temperatures, extreme weather events, and environmental challenges, there is a strong push towards decarbonization and an emphasis on renewable energy, with wind energy emerging as a key player. The concept of multi-energy systems offers an innovative approach to decarbonization, with the potential to produce hydrogen as one of the output streams, creating another avenue for clean energy production. Hydrogen has significant potential for decarbonizing multiple sectors across buildings, transport, and industries. This paper explores the integration of wind energy and hydrogen production, particularly in areas where clean energy solutions are crucial, such as impoverished villages in Africa. It models three systems: distinct configurations of micro-multi-energy systems that generate electricity, space cooling, hot water, and hydrogen using the thermodynamics and cost approach. System 1 combines a wind turbine, a hydrogen-producing electrolyzer, and a heat pump for cooling and hot water. System 2 integrates this with a biomass-fired reheat-regenerative power cycle to balance out the intermittency of wind power. System 3 incorporates hydrogen production, a solid oxide fuel cell for continuous electricity production, an absorption cooling system for refrigeration, and a heat exchanger for hot water production. These systems are modeled with Engineering Equation Solver, and analyzed based on energy and exergy efficiencies, and on economic metrics like levelized cost of electricity (LCOE), cooling (LCOC), refrigeration (LCOR), and hydrogen (LCOH) under steady-state conditions. A sensitivity analysis of various parameters is presented to assess the change in performance. Systems were optimized using a multi-objective method, with maximizing exergy efficiency and minimizing total product unit cost used as objective functions. The results show that System 1 achieves 79.78 % energy efficiency and 53.94 % exergy efficiency. System 2 achieves efficiencies of 55.26 % and 27.05 % respectively, while System 3 attains 78.73 % and 58.51 % respectively. The levelized costs for micro-multi-energy System 1 are LCOE = 0.04993 $/kWh, LCOC = 0.004722$/kWh, and LCOH = 0.03328 $/kWh. For System 2, these values are 0.03653$/kWh, 0.003743 $/kWh, and 0.03328$/kWh. In the case of System 3, they are 0.03736 $/kWh, 0.004726$/kWh, and 0.03335 $/kWh, and LCOR = 0.03309$/kWh. The results show that the systems modeled here have competitive performance with existing multi-energy systems, powered by other renewables. Integrating these systems will further the sustainable and net zero energy system transition, especially in rural communities.</p

Dzyaloshinskii-Moriya torque-driven resonance in antiferromagnetic {\alpha}-Fe2O3

We examine the high-frequency optical mode of {\alpha}-Fe2O3 and report that Dzyaloshinskii-Moriya (DM) interaction generates a new type of torque on the magnetic resonance. Using a continuous-wave terahertz interferometer, we measure the optical mode spectra, where the asymmetric absorption with a large amplitude and broad linewidth is observed near the magnetic transition point, Morin temperature (TM ~ 254.3 K). Based on the spin wave model, the spectral anomaly is attributed to the DM interaction-induced torque, enabling to extract the strength of DM interaction field of 4 T. Our work opens a new avenue to characterize the spin resonance behaviors at an antiferromagnetic singular point for next-generation and high-frequency spin-based information technologies.Comment: 4 figure