Prediction of the C-13 NMR chemical shifts of organic species adsorbed on H-ZSM-5 zeolite by the ONIOM-GIAO method

The ONIOM-GIAO method has been used to accurately predict C-13 NMR chemical shifts for a series of organic species adsorbed on H-ZSM-5 zeolite. This is useful for the spectroscopic identification of complicated catalytic systems

Synergetic treatment of dye contaminated wastewater using microparticles functionalized with carbon nanotubes/titanium dioxide nanocomposites

This journal is © The Royal Society of Chemistry. The highly efficient treatment of azo dye contaminated wastewater from the textile industry is an important but challenging problem. Herein, polydimethylsiloxane (PDMS) microparticles, incorporating multiple-walled carbon nanotubes/titanium dioxide (MWCNTs/TiO2) nanocomposites, were successfully synthesized to treat wastewater containing Rhodamine B (RhB) dyes in a synergetic approach, by combining sorption and photocatalytic degradation. The surfactant wrapping sol-gel method was applied to synthesize MWCNTs/TiO2 nanocomposites with TiO2 nanoparticles evenly distributed on the surface of the MWCNTs. The PDMS microparticles were fabricated with an oil-in-water (O/W) single emulsion template, using needle-based microfluidic devices. MWCNTs/TiO2 nanocomposites (at a weight ratio of 1%, and 2%, respectively) were mixed with the PDMS precursor as the dispersed phase, and an aqueous solution of polyvinyl alcohol (PVA) was used as the continuous phase. Highly monodispersed microparticles, with average diameters of 692.7 μm (Coefficient of Variation, CV = 0.74%) and 678.3 μm (CV = 1.04%), were formed at an applied flow rate of the dispersed and continuous phase of 30 and 200 μL min-1, respectively. The fabricated hybrid microparticles were employed for the treatment of RhB, involving a dark equilibrium for 5 hours and UV irradiation for 3 hours. The experimental conditions of applied PDMS type, mass loading amount, treatment duration, photodegradation kinetics, initial concentration of pollutants and environmental pH values were investigated in this work. The PDMS microparticles with 2 wt% MWCNTs/TiO2 nanocomposites can exhibit a removal efficiency of 85%. Remarkably, an efficiency of 70% can be retained after the microparticles have been recycled and reused for 3 cycles. The PDMS-MWCNTs/TiO2 microparticles possess a superior performance over conventional treatment approaches for dye contaminated wastewater, especially in recyclability and the prevention of secondary pollution. This work provides a feasible and eco-friendly route for developing an efficient and low-cost microfluidic method for treating complicated water environmental systems

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Empirical Study on the Reform of Water Resources Management in Xinjiang Rural Communities

With the case study of two rural communities of Hetian County and Shawan County in Xinjiang, the foundation, operation and development of the water management organizations in the two communities and their reform achievements were studied and compared. It was concluded that the reform of water resources management should be in accordance with the practical conditions of rural communities. Only with the same objectives of community people and by benefiting the farmers could the reform of water resources management be effectively implemented and achieve good results

A buyer-traceable DNN model IP protection method against piracy and misappropriation

Recently proposed model functionality and attribute extraction techniques have exacerbated unauthorized low-cost reproduction of deep neural network (DNN) models for similar applications. In particular, intellectual property (IP) theft and unauthorized distribution of DNN models by dishonest buyers are very difficult to trace by existing framework of digital rights management (DRM). This paper presents a new buyer-traceable DRM scheme against model piracy and misappropriation. Unlike existing methods that require white-box access to extract the latent information for verification, the proposed method utilizes data poisoning for distributorship embedding and black-box verification. Composite backdoors are installed into the target model during the training process. Each backdoor is created by applying a data augmentation method to some clean images of a selected class. The data-augmented images with a wrong label associated with a buyer are injected into the training dataset. The ownership and distributorship of a backdoor-trained user model can be validated by querying the suspect model with a set of composite triggers. A positive suspect will output the dirty labels that pinpoint the dishonest buyer while an innocent model will output the correct labels with high confidence. The tracking accuracy and robustness of the proposed IP protection method are evaluated on CIFAR-10, CIFAR-100 and GTSRB datasets for different applications. The results show an average of 100% piracy detection rate, 0% false positive rate and 96.81% traitor tracking success rate with negligible model accuracy degradation.National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: CHFA-GC1-AW01)

Inconspicuous data augmentation based backdoor attack on deep neural networks

With new applications made possible by the fusion of edge computing and artificial intelligence (AI) technologies, the global market capitalization of edge AI has risen tremendously in recent years. Deployment of pre-trained deep neural network (DNN) models on edge computing platforms, however, does not alleviate the fundamental trust assurance issue arising from the lack of interpretability of end-to-end DNN solutions. The most notorious threat of DNNs is the backdoor attack. Most backdoor attacks require a relatively large injection rate (≈ 10%) to achieve a high attack success rate. The trigger patterns are not always stealthy and can be easily detected or removed by backdoor detectors. Moreover, these attacks are only tested on DNN models implemented on general-purpose computing platforms. This paper proposes to use data augmentation for backdoor attacks to increase the stealth, attack success rate, and robustness. Different data augmentation techniques are applied independently on three color channels to embed a composite trigger. The data augmentation strength is tuned based on the Gradient Magnitude Similarity Deviation, which is used to objectively assess the visual imperceptibility of the poisoned samples. A rich set of composite triggers can be created for different dirty labels. The proposed attacks are evaluated on pre-activation ResNet18 trained with CIFAR-10 and GTSRB datasets, and EfficientNet-B0 trained with adapted 10-class ImageNet dataset. A high attack success rate of above 97% with only 1% injection rate is achieved on these DNN models implemented on both general-purpose computing platforms and Intel Neural Compute Stick 2 edge AI device. The accuracy loss of the poisoned DNNs on benign inputs is kept below 0.6%. The proposed attack is also tested to be resilient to state-of-the-art backdoor defense methods.National Research Foundation (NRF)Submitted/Accepted versionThis research is supported by the National Research Foundation, Singapore, under its National Cybersecurity Research & Development Programme/Cyber-Hardware Forensic & Assurance Evaluation R&D Programme (Award: CHFA-GC1- AW01)

An imperceptible data augmentation based blackbox clean-label backdoor attack on deep neural networks

Deep neural networks (DNNs) have permeated into many diverse application domains, making them attractive targets of malicious attacks. DNNs are particularly susceptible to data poisoning attacks. Such attacks can be made more venomous and harder to detect by poisoning the training samples without changing their ground-truth labels. Despite its pragmatism, the clean-label requirement imposes a stiff restriction and strong conflict in simultaneous optimization of attack stealth, success rate, and utility of the poisoned model. Attempts to circumvent the pitfalls often lead to a high injection rate, ineffective embedded backdoors, unnatural triggers, low transferability, and/or poor robustness. In this paper, we overcome these constraints by amalgamating different data augmentation techniques for the backdoor trigger. The spatial intensities of the augmentation methods are iteratively adjusted by interpolating the clean sample and its augmented version according to their tolerance to perceptual loss and augmented feature saliency to target class activation. Our proposed attack is comprehensively evaluated on different network models and datasets. Compared with state-of-the-art clean-label backdoor attacks, it has lower injection rate, stealthier poisoned samples, higher attack success rate, and greater backdoor mitigation resistance while preserving high benign accuracy. Similar attack success rates are also demonstrated on the Intel Neural Compute Stick 2 edge AI device implementation of the poisoned model after weight-pruning and quantization.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThis work was supported in part by the National Research Foundation, Singapore; in part by the Cyber Security Agency of Singapore under its National Cybersecurity Research and Development Program/Cyber-Hardware Forensic and Assurance Evaluation Research and Development Program under Grant NRF2018NCR-NCR009-0001 and Grant CHFA-GC1-AW01; and in part by the Ministry of Education, Singapore, through the Academic Research Fund Tier 2 under Grant MOE-T2EP50220-0003