Showing papers by "Zhejiang Gongshang University published in 2020"

Recent advances in MOF-based photocatalysis: environmental remediation under visible light

Abstract: Visible light-induced photocatalysis is a promising way for environmental remediation due to efficient utilization of solar energy. Recently, metal–organic frameworks (MOFs) have attracted increasing attention in the field of photocatalysis. In comparison with traditional metal oxide semiconductors, MOFs have many advantages, such as high specific surface area, rich topology and easily tunable porous structure. In this review, we aim to summarize and illustrate recent advances in MOF-based photocatalysis for environmental remediation under visible light, including wastewater treatment, air purification and disinfection. A series of strategies have been designed to modify and regulate pristine MOFs for enhanced photocatalytic performance, such as ligand functionalization, mixed-metal/linker strategy, metal ion/ligand immobilization, dye sensitization, metal nanoparticle loading, carbon material decoration, semiconductor coupling, MOF/COF coupling, carrier loading and magnetic recycling. The above modifications may result in extended visible light absorption, efficient generation, separation and transfer of photogenerated charges, as well as good recyclability. However, there are still many challenges and obstacles. In order to meet the requirements of using MOF photocatalysis as a friendly and stable technology for low-cost practical applications, its future development prospects are also discussed.

MTES: An Intelligent Trust Evaluation Scheme in Sensor-Cloud-Enabled Industrial Internet of Things

Abstract: As an enabler for smart industrial Internet of Things (IoT), sensor cloud facilitates data collection, processing, analysis, storage, and sharing on demand. However, compromised or malicious sensor nodes may cause the collected data to be invalid or even endanger the normal operation of an entire IoT system. Therefore, designing an effective mechanism to ensure the trustworthiness of sensor nodes is a critical issue. However, existing cloud computing models cannot provide direct and effective management for the sensor nodes. Meanwhile, the insufficient computation and storage ability of sensor nodes makes them incapable of performing complex intelligent algorithms. To this end, mobile edge nodes with relatively strong computation and storage ability are exploited to provide intelligent trust evaluation and management for sensor nodes. In this article, a mobile edge computing-based intelligent trust evaluation scheme is proposed to comprehensively evaluate the trustworthiness of sensor nodes using probabilistic graphical model. The proposed mechanism evaluates the trustworthiness of sensor nodes from data collection and communication behavior. Moreover, the moving path for the edge nodes is scheduled to improve the probability of direct trust evaluation and decrease the moving distance. An approximation algorithm with provable performance is designed. Extensive experiments validate that our method can effectively ensure the trustworthiness of sensor nodes and decrease the energy consumption.

The Impact of the COVID-19 Pandemic on User Experience with Online Education Platforms in China

Abstract: During the COVID-19 pandemic, social education has shifted from face to face to online in order to avoid large gatherings and crowds for blocking the transmission of the virus. To analyze the impact of virus on user experience and deeply retrieve users’ requirements, this paper constructs a reasonable evaluation index system through obtaining user reviews about seven major online education platforms before and after the outbreak of COVID-19, and by combining the emotional analysis, hot mining technology, as well as relevant literature. At the same time, the variation coefficient method is chosen to weigh each index based on the difference of index values. Furthermore, this paper adopts the comprehensive evaluation method to analyze user experience before and after the outbreak of COVID-19, and finally finds out the change of users’ concerns regarding the online education platform. In terms of access speed, reliability, timely transmission technology of video information, course management, communication and interaction, and learning and technical support, this paper explores the supporting abilities and response levels of online education platforms during COVID-19, and puts forward corresponding measures to improve how these platforms function.

On Feasibility and Limitations of Detecting False Data Injection Attacks on Power Grid State Estimation Using D-FACTS Devices

Abstract: Recent studies have investigated the possibilities of proactively detecting the high-profile false data injection (FDI) attacks on power grid state estimation by using the distributed flexible ac transmission system (D-FACTS) devices, termed as proactive false data detection (PFDD) approach. However, the feasibility and limitations of such an approach have not been systematically studied in the existing literature. In this paper, we explore the feasibility and limitations of adopting the PFDD approach to thwart FDI attacks on power grid state estimation. Specifically, we thoroughly study the feasibility of using PFDD to detect FDI attacks by considering single-bus, uncoordinated multiple-bus, and coordinated multiple-bus FDI attacks, respectively. We prove that PFDD can detect all these three types of FDI attacks targeted on buses or super-buses with degrees larger than 1, if and only if the deployment of D-FACTS devices covers branches at least containing a spanning tree of the grid graph. The minimum efforts required for activating D-FACTS devices to detect each type of FDI attacks are, respectively, evaluated. In addition, we also discuss the limitations of this approach; it is strictly proved that PFDD is not able to detect FDI attacks targeted on buses or super-buses with degrees equalling 1.

Detecting breaking news rumors of emerging topics in social media

Abstract: Users of social media websites tend to rapidly spread breaking news and trending stories without considering their truthfulness. This facilitates the spread of rumors through social networks. A rumor is a story or statement for which truthfulness has not been verified. Efficiently detecting and acting upon rumors throughout social networks is of high importance to minimizing their harmful effect. However, detecting them is not a trivial task. They belong to unseen topics or events that are not covered in the training dataset. In this paper, we study the problem of detecting breaking news rumors, instead of long-lasting rumors, that spread in social media. We propose a new approach that jointly learns word embeddings and trains a recurrent neural network with two different objectives to automatically identify rumors. The proposed strategy is simple but effective to mitigate the topic shift issues. Emerging rumors do not have to be false at the time of the detection. They can be deemed later to be true or false. However, most previous studies on rumor detection focus on long-standing rumors and assume that rumors are always false. In contrast, our experiment simulates a cross-topic emerging rumor detection scenario with a real-life rumor dataset. Experimental results suggest that our proposed model outperforms state-of-the-art methods in terms of precision, recall, and F1.

Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms

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Smart Contract Vulnerability Detection using Graph Neural Network.

Abstract: The security problems of smart contracts have drawn extensive attention due to the enormous financial losses caused by vulnerabilities. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low detection accuracy. In this paper, we explore using graph neural networks (GNNs) for smart contract vulnerability detection. Particularly, we construct a contract graph to represent both syntactic and semantic structures of a smart contract function. To highlight the major nodes, we design an elimination phase to normalize the graph. Then, we propose a degree-free graph convolutional neural network (DR-GCN) and a novel temporal message propagation network (TMP) to learn from the normalized graphs for vulnerability detection. Extensive experiments show that our proposed approach significantly outperforms state-of-the-art methods in detecting three different types of vulnerabilities.

Sensory attributes of edible insects and insect-based foods – Future outlooks for enhancing consumer appeal

Abstract: Background Edible insects are considered a new alternative sustainable source of proteins that exhibits higher feed-conversion efficiency and has a less negative environmental impact, compared to conventional animal-derived protein sources. Highly nutritional edible insects may help solve issues of global malnutrition and food insecurity, while byproducts have the potential for practical application in the food industry, agriculture, and medicine. Despite these numerous benefits, negative attitudes toward insects as food exist in societies where it is not part of their culture to consume insects, raising barriers to the wider introduction of edible insects in the diet on a regular basis. Scope and approach In this review, we discuss the sensory characteristics, such as flavor and texture, of edible insect and insect-based foods, their contribution to consumers’ attitudes toward edible insects, and approaches to alter the quality of sensory attributes—from insect farming and processing to novel product formulation and taste education. Key findings and conclusions Sensory and visual characteristics of edible insects, and availability of information on their origin and safety, are important factors in consumer appeal and their willingness to try eating insects in the future. Flavor and texture of edible insects depend largely on the insect species, its development stage, and the way it is processed. Recent findings on insect production, processing, and formulation of insect-based foods, as well as ways to increase familiarity with edible insects, demonstrate the potential to enhance the acceptability of insects as a novel food.

Stable, high-performance sodium-based plasmonic devices in the near infrared.

Abstract: Plasmonics enables the manipulation of light beyond the optical diffraction limit1–4 and may therefore confer advantages in applications such as photonic devices5–7, optical cloaking8,9, biochemical sensing10,11 and super-resolution imaging12,13 However, the essential field-confinement capability of plasmonic devices is always accompanied by a parasitic Ohmic loss, which severely reduces their performance Therefore, plasmonic materials (those with collective oscillations of electrons) with a lower loss than noble metals have long been sought14–16 Here we present stable sodium-based plasmonic devices with state-of-the-art performance at near-infrared wavelengths We fabricated high-quality sodium films with electron relaxation times as long as 042 picoseconds using a thermo-assisted spin-coating process A direct-waveguide experiment shows that the propagation length of surface plasmon polaritons supported at the sodium–quartz interface can reach 200 micrometres at near-infrared wavelengths We further demonstrate a room-temperature sodium-based plasmonic nanolaser with a lasing threshold of 140 kilowatts per square centimetre, lower than values previously reported for plasmonic nanolasers at near-infrared wavelengths These sodium-based plasmonic devices show stable performance under ambient conditions over a period of several months after packaging with epoxy These results indicate that the performance of plasmonic devices can be greatly improved beyond that of devices using noble metals, with implications for applications in plasmonics, nanophotonics and metamaterials A thermo-assisted spin-coating process followed by packaging is used to fabricate sodium films that are stable for several months, enabling the realization of plasmonic devices with state-of-the-art performance at near-infrared wavelengths

Formulation of More Efficacious Curcumin Delivery Systems Using Colloid Science: Enhanced Solubility, Stability, and Bioavailability

Abstract: Curcumin is a bioactive constituent isolated from turmeric that has historically been used as a seasoning, pigment, and herbal medicine in food. Recently, it has become one of the most commonly studied nutraceuticals in the pharmaceutical, supplement, and food areas because of its myriad of potential health benefits. For instance, it is claimed to exhibit antioxidant, anti-inflammatory, antimicrobial, antiparasite, and anticancer activities when ingested as a drug, supplement, or food. Toxicity studies suggest that it is safe to consume, even at relatively high levels. Its broad-spectrum biological activities and low toxicity have meant that it has been widely explored as a nutraceutical ingredient for application in functional foods. However, there are several hurdles that formulators must overcome when incorporating curcumin into commercial products, such as its low water solubility (especially under acidic and neutral conditions), chemical instability (especially under neutral and alkaline conditions), rapid metabolism by enzymes in the human body, and limited bioavailability. As a result, only a small fraction of ingested curcumin is actually absorbed into the bloodstream. These hurdles can be at least partially overcome by using encapsulation technologies, which involve trapping the curcumin within small particles. Some of the most commonly used edible microparticles or nanoparticles utilized for this purpose are micelles, liposomes, emulsions, solid lipid particles, and biopolymer particles. Each of these encapsulation technologies has its own benefits and limitations for particular product applications and it is important to select the most appropriate one.

Genomic landscape and genetic manipulation of the black soldier fly Hermetia illucens, a natural waste recycler.

Abstract: The black soldier fly (BSF), Hermetia illucens (Diptera: Stratiomyidae), is renowned for its bioconversion of organic waste into a sustainable source of animal feed. We report a high-quality genome of 1.1 Gb and a consensus set of 16,770 gene models for this beneficial species. Compared to those of other dipteran species, the BSF genome has undergone a substantial expansion in functional modules related to septic adaptation, including immune system factors, olfactory receptors, and cytochrome P450s. We further profiled midgut transcriptomes and associated microbiomes of BSF larvae fed with representative types of organic waste. We find that the pathways related to digestive system and fighting infection are commonly enriched and that Firmicutes bacteria dominate the microbial community in BSF across all diets. To extend its potential practical applications, we further developed an efficient CRISPR/Cas9-based gene editing approach and implemented this to yield flightless and enhanced feeding capacity phenotypes, both of which could expand BSF production capabilities. Our study provides valuable genomic and technical resources for optimizing BSF lines for industrialization.

Privacy-Enhanced Data Collection Based on Deep Learning for Internet of Vehicles

Abstract: The development of smart cities and deep learning technology is changing our physical world to a cyber world. As one of the main applications, the Internet of Vehicles has been developing rapidly. However, privacy leakage and delay problem for data collection remain as the key concerns behind the fast development of the cyber intelligence technologies. If the original data collected are directly uploaded to the cloud for processing, it will bring huge load pressure and delay to the network communication. Moreover, during this process, it will lead to the leakage of data privacy. To this end, in this article we design a data collection and preprocessing scheme based on deep learning, which adopts the semisupervised learning algorithm of data augmentation and label guessing. Data filtering is performed at the edge layer, and a large amount of similar data and irrelevant data are cleared. If the edge device cannot process some complex data independently, it will send the processed and reliable data to the cloud for further processing, which maximizes the protection of user privacy. Our method significantly reduces the amount of data uploaded to the cloud, and meanwhile protects the user's data privacy effectively.

A Dynamic Membership Data Aggregation (DMDA) Protocol for Smart Grid

Abstract: In order to protect the privacy of individual data, meantime guaranteeing the utility of big data, the privacy preserving data aggregation is widely researched, which is a feasible solution since it not only preserves the statistical feature of the original data, but also masks single user's data. With smart meter owning the capability of connecting to Internet, the aggregation area extends to the virtual area rather than a traditional physical area. However, in a virtual aggregation area, the users’ membership maybe frequently changes, if while executing the aggregation protocol for the traditional area, the overhead is not ignorable. In this paper, the homomorphic encryption and ID-based signature are employed to design a dynamic membership data aggregation (DMDA) scheme, which reduces the complexity on a new user's joining and an old user's quitting. In addition, the operation center obtains the sum of the data in the virtual aggregation area, meantime knows nothing about single user's data. Comparing with traditional privacy-preserving data aggregation scheme, DMDA is more suitable for next-generation smart grid and other Internet of Things environments.

Dietary advanced glycation end‐products: Perspectives linking food processing with health implications

Abstract: Dietary advanced glycation end products (dAGEs) are complex and heterogeneous compounds derived from nonenzymatic glycation reactions during industrial processing and home cooking. There is mounting evidence showing that dAGEs are closely associated with various chronic diseases, where the absorbed dAGEs fuel the biological AGEs pool to exhibit noxious effects on human health. Currently, due to the uncertain bioavailability and rapid renal clearance of dAGEs, the relationship between dAGEs and biological AGEs remains debatable. In this review, we provide the most updated information on dAGEs including their generation in processed foods, analytical and characterization techniques, metabolic fates, interaction with AGE receptors, implications on human health and reducing strategies. Available evidence demonstrating a relevance between dAGEs and food allergy is also included. AGEs are ubiquitous in foods and their contents largely depend on the reactivity of carbonyl and amino groups, along with surrounding condition mainly pH and heating procedures. Once being digested and absorbed into the circulation, two separate pathways can be involved in the deleterious effects of dAGEs: an AGE receptor-dependent way to stimulate cell signals, and an AGE receptor-independent way to dysregulate proteins via forming complexes. Inhibition of AGEs formation during food processing and reduction in the diet are two potent approaches to restrict health-hazardous dAGEs. To elucidate the biological role of dAGEs toward human health, the following significant perspectives are raised: molecular size and complexity of dAGEs; interactions between unabsorbed dAGEs and gut microbiota; and roles played by concomitant compounds in the heat-processed foods.

Development of Next-Generation Nutritionally Fortified Plant-Based Milk Substitutes: Structural Design Principles

Abstract: Consumers are increasingly interested in decreasing their dietary intake of animal-based food products, due to health, sustainability, and ethical concerns. For this reason, the food industry is creating new products from plant-based ingredients that simulate many of the physicochemical and sensory attributes associated with animal-derived foods, including milk, eggs, and meat. An understanding of how the ingredient type, amount, and organization influence the desirable physicochemical, sensory, and nutritional attributes of these plant-based foods is required to achieve this goal. A potential problem with plant-based diets is that they lack key micronutrients, such as vitamin B12, vitamin D, calcium, and ω-3 fatty acids. The aim of this review is to present the science behind the creation of next-generation nutritionally fortified plant-based milk substitutes. These milk-like products may be formed by mechanically breaking down certain plant materials (including nuts, seeds, and legumes) to produce a dispersion of oil bodies and other colloidal matter in water, or by forming oil-in-water emulsions by homogenizing plant-based oils and emulsifiers with water. A brief overview of the formulation and fabrication of plant-based milks is given. The relationship between the optical properties, rheology, and stability of plant-based milks and their composition and structure is then covered. Approaches to fortify these products with micronutrients that may be missing from a plant-based diet are also highlighted. In conclusion, this article highlights how the knowledge of structural design principles can be used to facilitate the creation of higher quality and more sustainable plant-based food products.

Supplier encroachment strategy in the presence of retail strategic inventory: Centralization or decentralization?

Abstract: There has been extensive research on supplier encroachment. That is, a supplier sells to consumers through a direct retail subsidiary in addition to an independent retailer. However, most research assumes centralized encroachment, where the supplier makes decisions for the subsidiary. In this research, we make a major contribution by explicitly analyzing the option of decentralized encroachment, where the subsidiary makes its own pricing and/or quantity decisions. Furthermore, we make another major contribution by studying the retailer's possible use of strategic inventory as a countermeasure to achieve wholesale price concessions from the supplier. In a dual channel consisting of a retailer and a supplier and its subsidiary in a two-period model, we find that when the retailer can employ strategic inventory, decentralized encroachment outperforms centralized encroachment for the supplier and the retailer. Moreover, if the supplier adopts the strategy of decentralized encroachment, the retailer's use of strategic inventory always benefits the supplier, but it benefits the retailer only when the unit inventory holding cost is below a threshold. We also conduct numerical examples to further illustrate our analytical findings and gain more managerial insights.

Research progress on liposomes: Application in food, digestion behavior and absorption mechanism

Abstract: Background The application of liposomal system in food area allowing carried and controlled bioactive-molecules release has been considered one of the main strategies used to improve nutrients stability, to increase individualized absorptivity, and to enhance quality-of-food. Scope and approach The preparation methods including traditional and novel technologies and formation mechanism of liposomes are reviewed. Then we discuss how liposomes are designed and applied to alter properties for food quality improvement and nutrient-modification in dairy, beverages, meat and other products. Besides, the changes in liposomal structure and molecules bioavailability within the gastrointestinal tract, along with the cell uptake pathways of liposomes, generally employed adhesion, fusion, endocytosis, lipid exchange, and its relevant factors are emphasized. Key findings and conclusions So far, a rapidly increased achievement of liposomes in food science and nutraceuticals has already gained, and the digestion behavior and the followed absorption mechanism have been explored. For further successful development and wide use of liposomes, promising preparation approaches and safe formula in future study on food must be exploited, albeit with designing the efficacious and targeted deliver in human body.

Dietary protein-phenolic interactions: characterization, biochemical-physiological consequences, and potential food applications.

Abstract: Dietary proteins and phenolic compounds are commonly co-existing components that readily interact with each other to yield complexes in a wide range of food systems The formed complexes play a cri