Preparation of Amidoxime Polyacrylonitrile Chelating Nanofibers and Their Application for Adsorption of Metal Ions.

Polyacrylonitrile (PAN) nanofibers were prepared by electrospinning and they were modified with hydroxylamine to synthesize amidoxime polyacrylonitrile (AOPAN) chelating nanofibers, which were applied to adsorb copper and iron ions. The conversion of the nitrile group in PAN was calculated by the gravimetric method. The structure and surface morphology of the AOPAN nanofiber were characterized by a Fourier transform infrared spectrometer (FT-IR) and a scanning electron microscope (SEM), respectively. The adsorption abilities of Cu2+ and Fe3+ ions onto the AOPAN nanofiber mats were evaluated. FT-IR spectra showed nitrile groups in the PAN were partly converted into amidoxime groups. SEM examination demonstrated that there were no serious cracks or sign of degradation on the surface of the PAN nanofibers after chemical modification. The adsorption capacities of both copper and iron ions onto the AOPAN nanofiber mats were higher than those into the raw PAN nanofiber mats. The adsorption data of Cu2+ and Fe3+ ions fitted particularly well with the Langmuir isotherm. The maximal adsorption capacities of Cu2+ and Fe3+ ions were 215.18 and 221.37 mg/g, respectively

Substitutive First-party Content as a Strategic Decision for Platform Growth – Evidence from a B2B Platform

This paper examines the effect of substitutive first-party content (SFPC) as a strategic variable by a business-to-business (B2B) e-commerce platform. Constructing a unique time-series dataset, we find that SFPC’s impact differs in the early stage of the platform and in the later stage when it has a larger user base and has transformed itself into a service provider. In the early stage, increasing SFPC can attract more buyers to trade but may crowd out sellers, leading to an insignificant impact on total trading volume. In the second stage, however, SFPC no longer hurts seller participation and increases total trading volume. We also find that SFPC could attract new users consistently across the two stages. Our findings suggest a strategic role of SFPC to mitigate the “chicken‐and‐egg problem ” in the early stage of a two-sided B2B platform and to continuously grow platform size when it becomes more established

A new and efficient method for purification of poly-γ- glutamic acid from high-viscosity fermentation broth

Purpose: To devise an efficient strategy for the separation and recovery of high-quality γ-PGA by investigation of the physical properties, pigment properties and microfiltration mode of high-viscosity fermentation broth.Methods: The bacterial strain, Bacillus subtilis 115, was used in this study. The viscosity of the fermentation broth was determined by digital viscometer with spindle SP-2 at 25 oC. The concentrations of glucose and L-glutamate were analyzed with a biosensor equipped with both glucose oxidase and Lglutamate oxidase electrodes. The pigment in the fermentation liquid was scanned with a UV spectrophotometer at wavelength range of 200 - 500 nm and was removed using activated carbon. Measurement of IR spectrum was performed using an IR spectrophotometer with KBr pellet. Results: The results showed that the γ-PGA yield was 35 g/L. The viscosity of the fermentation broth was 1600 mPa.s at the end of the batch fermentation. After 3-fold dilution, the viscosity was reduced to one-fortieth of the original value at 65 °C for 30 min., which allowed effective removal of Bacillus subtilis 115 from the broth. Maximum UV absorption of the pigment was occurred at 260 nm. The pigment was removed by shaking with 0.6 % activated carbon powder at 50 rpm for 20 min, resulting in 88 % decolorization. Concentration with hollow-fiber membrane (MWCO 500,000) resulted in complete removal of residual glucose and glutamic acid from the aqueous solution of γ-PGA. The molecular weight of the γ-PGA was 1095 kDa, and its UV scanning spectrum showed an absorption peak at 216 nm. The decomposition temperature (Td) of the γ-PGA was 312.92 oC. Its IR spectrum was consistent with the presence of carboxyl, hydroxyl, carbonyl and amide groups.Conclusion: An efficient method for the extraction and purification of high-quality γ-PGA from highviscosity fermentation broth.Keywords: Bacillus subtilis 115, γ-Polyglutamic acid, De-pigmentation, Activated carbon, Ultra-filtration, High-viscosity fermentation brot

Efficient Adaptation of Large Vision Transformer via Adapter Re-Composing

The advent of high-capacity pre-trained models has revolutionized problem-solving in computer vision, shifting the focus from training task-specific models to adapting pre-trained models. Consequently, effectively adapting large pre-trained models to downstream tasks in an efficient manner has become a prominent research area. Existing solutions primarily concentrate on designing lightweight adapters and their interaction with pre-trained models, with the goal of minimizing the number of parameters requiring updates. In this study, we propose a novel Adapter Re-Composing (ARC) strategy that addresses efficient pre-trained model adaptation from a fresh perspective. Our approach considers the reusability of adaptation parameters and introduces a parameter-sharing scheme. Specifically, we leverage symmetric down-/up-projections to construct bottleneck operations, which are shared across layers. By learning low-dimensional re-scaling coefficients, we can effectively re-compose layer-adaptive adapters. This parameter-sharing strategy in adapter design allows us to significantly reduce the number of new parameters while maintaining satisfactory performance, thereby offering a promising approach to compress the adaptation cost. We conduct experiments on 24 downstream image classification tasks using various Vision Transformer variants to evaluate our method. The results demonstrate that our approach achieves compelling transfer learning performance with a reduced parameter count. Our code is available at \href{https://github.com/DavidYanAnDe/ARC}{https://github.com/DavidYanAnDe/ARC}.Comment: Paper is accepted to NeurIPS 202

Early and automatic processing of written Chinese: visual mismatch negativity studies

Fluent reading entails multiple levels of analysis including orthography, syntax and semantics but is also characterised by fast speed and apparent ease in understanding the various linguistic input. This thesis therefore focuses on the earliness and automaticity of single word recognition, which is a fundamental component of reading process. Exactly when a visual stimulus is recognised as a word and comprehended, and to what extent this is an automatic and not a controlled process, are two of the most debated issues in psycholinguistic research. A series of six Event-Related Potential (ERP) studies were carried out in this study, with the first five of these investigating Chinese single character words and pseudowords and the sixth investigating Spanish words and word-like strings. The critical ERP component of interest is visual Mismatch Negativity (vMMN), a visual counterpart of the well-documented auditory MMN (Näätänen, Gaillard, & Mäntysalo, 1978). VMMN has recently been demonstrated to be a neural index of automatic processing of not only generic visual features but also written words. To overcome the compounding of physical differences between stimulus conditions, a “same-stimulus” identity oddball paradigm was adopted throughout the studies. The vMMN was computed by comparing the ERP responses to deviant and standard stimuli of the same lexical/semantic category. It was found that lexical and semantic vMMN effects could be obtained within the first 250 ms after the stimulus onset, even when the critical words were presented briefly and outside of the focus of attention (perifoveally) and participants were instructed to carry out a non-linguistic distraction task, indicating automaticity of processing. The similarity in the timing of these early vMMN responses lends support to parallel processing models of linguistic information processing. In addition, vMMN to changes in lexicality was subject to configurations in the cognitive system, with attention and the magnitude of deviance revealed as two important variables. Language vMMN effects in normal adults as revealed in this thesis may serve as a benchmark for assessing the reading abilities of first or second language readers, as well as of people with linguistic impairments, such as dyslexia

Adaptive neural network control of a robotic manipulator with unknown backlash-like hysteresis

This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlashlike hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers

Representation Learning with Large Language Models for Recommendation

Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. However, these graph-based recommenders heavily depend on ID-based data, potentially disregarding valuable textual information associated with users and items, resulting in less informative learned representations. Moreover, the utilization of implicit feedback data introduces potential noise and bias, posing challenges for the effectiveness of user preference learning. While the integration of large language models (LLMs) into traditional ID-based recommenders has gained attention, challenges such as scalability issues, limitations in text-only reliance, and prompt input constraints need to be addressed for effective implementation in practical recommender systems. To address these challenges, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. It proposes a recommendation paradigm that integrates representation learning with LLMs to capture intricate semantic aspects of user behaviors and preferences. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework. This work further establish a theoretical foundation demonstrating that incorporating textual signals through mutual information maximization enhances the quality of representations. In our evaluation, we integrate RLMRec with state-of-the-art recommender models, while also analyzing its efficiency and robustness to noise data. Our implementation codes are available at https://github.com/HKUDS/RLMRec.Comment: Published as a WWW'24 full pape