Factors influencing teachers’ level of digital citizenship in underdeveloped regions of China

In digital times, new demands for higher levels of digital citizenship (DC) have aroused concern. Based on the study reported on here, we propose that 4 predictive factors, i.e., internet self-efficacy, internet attitudes, internet use behaviour and demographic characteristics affect teachers’ level of DC in underdeveloped regions of China. From 21 different provinces, 240 primary teachers in underdeveloped regions in China participated in this quantitative research. The description, significance, correlation, and structural equation modelling (SEM) were statistically performed and analysed. We concluded the following: 1) The average score for DC is low and its 5 dimensions score differently with the highest being the ethical element and the lowest being networking agency and critical perspective; no statistically-significant differences exist for gender, school types, teaching subject and professional rank in predicting DC, but do exist for birth-era, suggesting that young teachers have a higher level of DC. 2) Internet self-efficacy, internet attitudes and internet use behaviour are positively correlated with DC. 3) In the SEM test, internet use behaviour acts as a mediator in the research model; internet self-efficacy is the major determinant of DC, followed by internet use behaviour and internet attitudes. The results were analysed and recommendations to promote teachers’ high-level DC in underdeveloped regions are proposed

Synthesis and optical property of one-dimensional spinel ZnMn2O4 nanorods

Spinel zinc manganese oxide (ZnMn2O4) nanorods were successfully prepared using the previously synthesized α-MnO2 nanorods by a hydrothermal method as template. The nanorods were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, UV-Vis absorption, X-ray photoelectron spectroscopy, surface photovoltage spectroscopy, and Fourier transform infrared spectroscopy. The ZnMn2O4 nanorods in well-formed crystallinity and phase purity appeared with the width in 50-100 nm and the length in 1.5-2 μm. They exhibited strong absorption below 500 nm with the threshold edges around 700 nm. A significant photovoltage response in the region below 400 nm could be observed for the nanorods calcined at 650 and 800°C

Facile preparation of antifouling nanofiltration membrane by grafting zwitterions for reuse of shale gas wastewater

Complex organic matter causes severe fouling when membranes are applied for shale gas wastewater (SGW) treatment. This study reports the grafting of a zwitterionic polymer brush consisting of poly (sulfobetaine methacrylate) (PSBMA) onto the surface of a commercial nanofiltration (NF) membrane via electron transferatom transfer radical polymerization (ARGET-ATRP) to achieve anti-fouling property, especially against organic foulants. Compared to the pristine NF membranes, the PSBMA-grafted NF membrane showed high performance when challenged by SGW as a feed stream: (1) The flux stability was significantly improved during long-term operation, with a 64.28% increase in flux normalization at 50% recovery rate of SGW, while maintaining a suitable initial flux and near constant ion removal rate; (2) Based on excitation-emission-matrix spectra integrated in the fluorescence region, the removal of protein-like organic matters and humus-like organic matters increased by 34% and 16.5%, respectively; (3) The XDLVO theory supports the hypothesis that the hydrophobic interactions between the membrane surface and organic foulants were reduced by enhancing the Lewis acid-base interaction energy. The proposed anti-fouling zwitterionic membranes has potential in industrial application for the on-site reuse of SGW

3,3,4,4,5,5-Hexafluoro-1,2-bis­(5-formyl-2-methylsulfanyl-3-thienyl)cyclo­pent-1-ene

In the crystal structure of the title diaryl­ethyl­ene compound, C17H10F6O2S4, the two 3-thienyl substituents are aligned at 44.9 (1) and 40.2 (1)° with respect to the –C—C=C—C– fragment of the central cyclo­pentenyl ring. The five-membered cyclo­pentenyl ring adopts an envelope conformation. The flap atom of this ring and the two F atoms bonded to it are disordered over two positions with occupancies 0.810 (5)/0.190 (5)

MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications

The recent surge in the field of Large Language Models (LLMs) has gained significant attention in numerous domains. In order to tailor an LLM to a specific domain such as a web-based healthcare system, fine-tuning with domain knowledge is necessary. However, two issues arise during fine-tuning LLMs for medical applications. The first is the problem of task variety, where there are numerous distinct tasks in real-world medical scenarios. This diversity often results in suboptimal fine-tuning due to data imbalance and seesawing problems. Additionally, the high cost of fine-tuning can be prohibitive, impeding the application of LLMs. The large number of parameters in LLMs results in enormous time and computational consumption during fine-tuning, which is difficult to justify. To address these two issues simultaneously, we propose a novel parameter-efficient fine-tuning framework for multi-task medical applications called MOELoRA. The framework aims to capitalize on the benefits of both MOE for multi-task learning and LoRA for parameter-efficient fine-tuning. To unify MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to maintain a small number of trainable parameters. Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks. To validate the effectiveness and practicality of the proposed method, we conducted comprehensive experiments on a public multi-task Chinese medical dataset. The experimental results demonstrate that MOELoRA outperforms existing parameter-efficient fine-tuning methods. The implementation is available online for convenient reproduction of our experiments

Diffusion Augmentation for Sequential Recommendation

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation

Superwettable PVDF/PVDF-g-PEGMA Ultrafiltration Membranes

Poly(vinylidene fluoride) (PVDF) is a common and inexpensive polymeric material used for membrane fabrication, but the inherent hydrophobicity of this polymer induces severe membranes fouling, which limits its applications and further developments. Herein, we prepared superwettable PVDF membranes by selecting suitable polymer concentration and blending with PVDF-graft-poly(ethylene glycol) methyl ether methacrylate (PVDF-g-PEGMA). This fascinating interfacial phenomenon causes the contact angle of water droplets to drop from the initial value of over 70° to virtually 0° in 0.5 s for the best fabricated membrane. The wetting properties of the membranes were studied by calculating the surface free energy by surface thermodynamic analysis, by evaluating the peak height ratio from Raman spectra, and other surface characterization methods. The superwettability phenomenon is the result of the synergetic effects of high surface free energy, the Wenzel model of wetting, and the crystalline phase of PVDF. Besides superwettability, the PVDF/PVDF-g-PEGMA membranes show great improvements in flux performance, sodium alginate (SA) rejection, and flux recovery upon fouling

1,2-Bis[5-(4-cyano­phen­yl)-2-methyl-3-thien­yl]-3,3,4,4,5,5-hexa­fluoro­cyclo­pent-1-ene: a photochromic diaryl­ethene compound

The mol­ecules of the title compound, C29H16F6N2S2, a photochromic dithienylethene with 4-cyano­phenyl substituents, adopt an anti­parallel arrangement that is reponsible for photoactivity. The mol­ecule lies on a twofold rotation axis. The dihedral angle between the nearly planar cyclo­pentenyl and heteroaryl rings is 142.5 (3)°, and that between the heteroaryl and benzene rings is 22.4 (3)°. The distance between the heteroaryl rings of adjacent mol­ecules is 3.601 (2) Å, indicating a π–π interaction

RePAST: A ReRAM-based PIM Accelerator for Second-order Training of DNN

The second-order training methods can converge much faster than first-order optimizers in DNN training. This is because the second-order training utilizes the inversion of the second-order information (SOI) matrix to find a more accurate descent direction and step size. However, the huge SOI matrices bring significant computational and memory overheads in the traditional architectures like GPU and CPU. On the other side, the ReRAM-based process-in-memory (PIM) technology is suitable for the second-order training because of the following three reasons: First, PIM's computation happens in memory, which reduces data movement overheads; Second, ReRAM crossbars can compute SOI's inversion in $O\left(1\right)$ time; Third, if architected properly, ReRAM crossbars can perform matrix inversion and vector-matrix multiplications which are important to the second-order training algorithms. Nevertheless, current ReRAM-based PIM techniques still face a key challenge for accelerating the second-order training. The existing ReRAM-based matrix inversion circuitry can only support 8-bit accuracy matrix inversion and the computational precision is not sufficient for the second-order training that needs at least 16-bit accurate matrix inversion. In this work, we propose a method to achieve high-precision matrix inversion based on a proven 8-bit matrix inversion (INV) circuitry and vector-matrix multiplication (VMM) circuitry. We design \archname{}, a ReRAM-based PIM accelerator architecture for the second-order training. Moreover, we propose a software mapping scheme for \archname{} to further optimize the performance by fusing VMM and INV crossbar. Experiment shows that \archname{} can achieve an average of 115.8$\times$/11.4$\times$ speedup and 41.9$\times$/12.8$\times$energy saving compared to a GPU counterpart and PipeLayer on large-scale DNNs.Comment: 13pages, 13 figure