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

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

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

Feasibility of an innovative amorphous silicon photovoltaic/thermal system for medium temperature applications

Medium temperature photovoltaic/thermal (PV/T) systems have immense potential in the applications of absorption cooling, thermoelectric generation, and organic Rankine cycle power generation, etc. Amorphous silicon (a-Si) cells are promising in such applications regarding the low temperature coefficient, thermal annealing effect, thin film and avoidance of large thermal stress and breakdown at fluctuating temperatures. However, experimental study on the a-Si PV/T system is rarely reported. So far the feasibility of medium temperature PV/T systems using a-Si cells has not been demonstrated. In this study, the design and construction of an innovative a-Si PV/T system of stainless steel substrate are presented. Long-term outdoor performance of the system operating at medium temperature has been monitored in the past 15 months. The average electrical efficiency was 5.65%, 5.41% and 5.30% at the initial, intermediate and final phases of the long-test test, accompanied with a daily average thermal efficiency from about 21% to 31% in the non-heating season. The thermal and electrical performance of the system at 60 °C, 70 °C and 80 °C are also analyzed and compared. Moreover, a distributed parameter model with experimental validation is developed for an inside view of the heat transfer and power generation and to predict the system performance in various conditions. Technically, medium temperature operation has not resulted in interruption or observable deformation of the a-Si PV/T system during the period. The technical and thermodynamic feasibility of the a-Si PV/T system at medium operating temperature is demonstrated by the experimental and simulation results

Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations

The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly andWeddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data

Unified Medical Image Pre-training in Language-Guided Common Semantic Space

Vision-Language Pre-training (VLP) has shown the merits of analysing medical images, by leveraging the semantic congruence between medical images and their corresponding reports. It efficiently learns visual representations, which in turn facilitates enhanced analysis and interpretation of intricate imaging data. However, such observation is predominantly justified on single-modality data (mostly 2D images like X-rays), adapting VLP to learning unified representations for medical images in real scenario remains an open challenge. This arises from medical images often encompass a variety of modalities, especially modalities with different various number of dimensions (e.g., 3D images like Computed Tomography). To overcome the aforementioned challenges, we propose an Unified Medical Image Pre-training framework, namely UniMedI, which utilizes diagnostic reports as common semantic space to create unified representations for diverse modalities of medical images (especially for 2D and 3D images). Under the text's guidance, we effectively uncover visual modality information, identifying the affected areas in 2D X-rays and slices containing lesion in sophisticated 3D CT scans, ultimately enhancing the consistency across various medical imaging modalities. To demonstrate the effectiveness and versatility of UniMedI, we evaluate its performance on both 2D and 3D images across 10 different datasets, covering a wide range of medical image tasks such as classification, segmentation, and retrieval. UniMedI has demonstrated superior performance in downstream tasks, showcasing its effectiveness in establishing a universal medical visual representation