Zhejiang Ocean University

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great diversity of several applications such as military, construction, image and video mapping, medical, search and rescue, parcel delivery, hidden area exploration, oil rigs and power line monitoring, precision farming, wireless communication and aerial surveillance. The drone industry has been getting significant attention as a model of manufacturing, service and delivery convergence, introducing synergy with the coexistence of different emerging domains. UAVs offer implicit peculiarities such as increased airborne time and payload capabilities, swift mobility, and access to remote and disaster areas. Despite these potential features, including extensive variety of usage, high maneuverability, and cost-efficiency, drones are still limited in terms of battery endurance, flight autonomy and constrained flight time to perform persistent missions. Other critical concerns are battery endurance and the weight of drones, which must be kept low. Intuitively it is not suggested to load them with heavy batteries. This study highlights the importance of drones, goals and functionality problems. In this review, a comprehensive study on UAVs, swarms, types, classification, charging, and standardization is presented. In particular, UAV applications, challenges, and security issues are explored in the light of recent research studies and development. Finally, this review identifies the research gap and presents future research directions regarding UAVs.

With the development of computing and information processing techniques, artificial intelligence (AI) has been extensively applied in education. Artificial intelligence in education (AIEd) opens new opportunities, potentials, and challenges in educational practices. In its short history, AIEd has been undergoing several paradigmatic shifts, which are characterized into three paradigms in this position paper: AI-directed, learner-as-recipient, AI-supported, learner-as-collaborator, and AI-empowered, learner-as-leader. In three paradigms, AI techniques are used to address educational and learning issues in varied ways. AI is used to represent knowledge models and direct cognitive learning while learners are recipients of AI service in Paradigm One; AI is used to support learning while learners work as collaborators with AI in Paradigm Two; AI is used to empower learning while learners take agency to learn in Paradigm Three. Overall, the development trend of AIEd has been developing to empower learner agency and personalization, enable learners to reflect on learning and inform AI systems to adapt accordingly, and lead to an iterative development of the learner-centered, data-driven, personalized learning.

This work aims to evaluate the antimicrobial potential of ethanolic and water extracts of roselle (Hibiscus sabdariffa), rosemary (Rosmarinus officin), clove (Syzygium aromaticum), and thyme (Thymus vulgaris) on some food pathogens and spoilage microorganisms. Agar well diffusion method has been used to determine the antimicrobial activities and Minimum Inhibitory Concentrations (MIC) of different plant extracts against Gram-positive bacteria (Bacillus cereus, Staphylococcus aureus), Gram-negative bacteria (Escherichia coli, Salmonella enteritidis, Vibrio parahaemolyticus, Pseudomonas aeruginosa), and one fungus (Candida albicans). The extracts exhibited both antibacterial and antifungal activities against tested microorganisms. Ethanolic roselle extract showed significant antibacterial activity (P<0.05) against all tested bacterial strains, while no inhibitory effect on Candida albicans (CA) was observed. Only the ethanolic extracts of clove and thyme showed antifungal effects against CA with inhibition zones ranging from 25.2±1.4 and 15.8±1.2 mm, respectively. Bacillus cereus (BC) appears to be the most sensitive strain to the aqueous extract of clove with a MIC of 0.315%. To enhance our understanding of antimicrobial activity mechanism of plant extracts, the changes in internal pH (pHin), and membrane potential were measured in Staphylococcus aureus (SA), and Escherichia coli (EC) cells after exposure to the plant extracts. The results indicated that the plant extracts significantly affected the cell membrane of Gram-positive and Gram-negative bacteria, as demonstrated by the decline in pHin as well as cell membrane hyperpolarization. In conclusion, plant extracts are of great value as natural antimicrobials and can use safely as food preservatives.

Seaweed aquaculture, the fastest-growing component of global food production, offers a slate of opportunities to mitigate and adapt to climate change. Seaweed farms release carbon that maybe buried in sediments or exported to the deep sea, therefore acting as a CO2 sink. The crop can also be used, in total or in part, for biofuel production, with a potential CO2 mitigation capacity, in terms of avoided emissions from fossil fuels, of about 1500 tons CO2 km-2 year-1. Seaweed aquaculture can also help reduce the emissions from agriculture, by improving soil quality substituting synthetic fertilizer and, when included in cattle fed, lowering methane emissions from cattle. Seaweed aquaculture contributes to climate change adaptation by damping wave energy and protecting shorelines, and by elevating pH and supplying oxygen to the waters, thereby locally reducing the effects of ocean acidification and de-oxygenation. The scope to expand seaweed aquaculture is, however, limited by the availability of suitable areas and competition for suitable areas with other uses, engineering systems capable of coping with rough conditions offshore and an increasing market demand for seaweed products, among other factors. Despite these limitations, seaweed farming practices can be optimized to maximize climate benefits, which may, if economically compensated, improve the income of seaweed farmers.

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on mechanical metamaterials has focused on passive mechanical metamaterials and the tunability of their mechanical properties. Deep integration of multifunctionality, sensing, electrical actuation, information processing, and advancing data-driven designs are grand challenges in the mechanical metamaterials community that could lead to truly intelligent mechanical metamaterials. In this perspective, we provide an overview of mechanical metamaterials within and beyond their classical mechanical functionalities. We discuss various aspects of data-driven approaches for inverse design and optimization of multifunctional mechanical metamaterials. Our aim is to provide new roadmaps for design and discovery of next-generation active and responsive mechanical metamaterials that can interact with the surrounding environment and adapt to various conditions while inheriting all outstanding mechanical features of classical mechanical metamaterials. Next, we deliberate the emerging mechanical metamaterials with specific functionalities to design informative and scientific intelligent devices. We highlight open challenges ahead of mechanical metamaterial systems at the component and integration levels and their transition into the domain of application beyond their mechanical capabilities.

Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.

Millions of metric tons of plastics are produced annually and transported from land to the oceans. Finding the fate of the plastic debris will help define the impacts of plastic pollution in the ocean. Here, we report the abundances of microplastic in the deepest part of the world's ocean. We found that microplastic abundances in hadal bottom waters range from 2.06 to 13.51 pieces per litre, several times higher than those in open ocean subsurface water. Moreover, microplastic abundances in hadal sediments of the Mariana Trench vary from 200 to 2200 pieces per litre, distinctly higher than those in most deep sea sediments. These results suggest that manmade plastics have contaminated the most remote and deepest places on the planet. The hadal zone is likely one of the largest sinks for microplastic debris on Earth, with unknown but potentially damaging impacts on this fragile ecosystem.

Attributed to the recent COVID-19 pandemic, panic buying is now a frequent occurrence in many countries, leading to stockouts and supply chain disruptions. Consequently, it has received much attention from academics and the retail industry. The aim of this study is to review, identify, and synthesise the psychological causes of panic buying, which is a relatively new and unexplored area in consumer behaviour research. A systematic review of the related literature is conducted. The review suggests that panic buying is influenced by (1) individuals' perception of the threat of the health crisis and scarcity of products; (2) fear of the unknown, which is caused by negative emotions and uncertainty; (3) coping behaviour, which views panic buying as a venue to relieve anxiety and regain control over the crisis; and (4) social psychological factors, which account for the influence of the social network of an individual. This study contributes to the literature by consolidating the scarce and scattered research on the causes of panic buying, drawing greater theoretical insights into each cause and also offers some implications for health professionals, policy makers, and retailers on implementing appropriate policies and strategies to manage panic buying. Recommendations for future research are also provided.

The development of distinguished photocatalysts with high photo-carrier disassociation and photo-redox power for efficient elimination of pollutants in water is of great significance but still a grand challenge. Herein, a novel Cd0.5Zn0.5S/Bi2WO6 S-scheme heterojunction was built up by integrating Cd0.5Zn0.5S nanoparticles on Bi2WO6 microspheres via a simple route. The S-scheme charge transfer mode substantially boosts the high-energetic electrons/holes spatial detachment and conservation on the Cd0.5Zn0.5S (reduction) and Bi2WO6 (oxidation), respectively, as well as effectively suppresses the photo-corrosion of Cd0.5Zn0.5S, rendering Cd0.5Zn0.5S/Bi2WO6 photocatalysts with superior redox ability. The optimal Cd0.5Zn0.5S/Bi2WO6 heterojunction achieves exceptional visible-light-driven photocatalytic tetracycline degradation and Cr(VI) reduction efficiency, 3.2 (1.9)-time and 33.6 (1.6)-time stronger than that of neat Bi2WO6 (Cd0.5Zn0.5S), while retaining the superior stability and reusability. Quenching test, mass spectrometry analysis, and toxicity assessment based on Quantitative Structure Activity Relationships. calculation unravel the prime active substances, intermediates, photo-degradation pathway, and intermediate eco-toxicity in photocatalytic process. This research not only offers a potential photocatalyst for aquatic environment protection but also promotes the exploration of novel and powerful chalcogenides-based S-scheme photocatalysts for environment protection.

Vehicular ad hoc networks (VANETs) have stimulated interest in both academic and industry settings because, once deployed, they would bring a new driving experience to drivers. However, communicating in an open-access environment makes security and privacy issues a real challenge, which may affect the large-scale deployment of VANETs. Researchers have proposed many solutions to these issues. We start this paper by providing background information of VANETs and classifying security threats that challenge VANETs. After clarifying the requirements that the proposed solutions to security and privacy problems in VANETs should meet, on the one hand, we present the general secure process and point out authentication methods involved in these processes. Detailed survey of these authentication algorithms followed by discussions comes afterward. On the other hand, privacy preserving methods are reviewed, and the tradeoff between security and privacy is discussed. Finally, we provide an outlook on how to detect and revoke malicious nodes more efficiently and challenges that have yet been solved.

, electrochromic, electrochemical actuated, stretchable, self-healing and stimuli-sensitive ones, are then presented. The self-sustainable SCs which integrate SC units with energy-harvesting units in one compact configuration are also introduced. The last section highlights some current challenges and future perspectives of this thriving field.

Plants respond to environmental pollutants such as heavy metal(s) by triggering the expression of genes that encode proteins involved in stress response. Toxic metal ions profoundly affect the cellular protein homeostasis by interfering with the folding process and aggregation of nascent or non-native proteins leading to decreased cell viability. However, plants possess a range of ubiquitous cellular surveillance systems that enable them to efficiently detoxify heavy metals toward enhanced tolerance to metal stress. As proteins constitute the major workhorses of living cells, the chelation of metal ions in cytosol with phytochelatins and metallothioneins followed by compartmentalization of metals in the vacuoles as well as the repair of stress-damaged proteins or removal and degradation of proteins that fail to achieve their native conformations are critical for plant tolerance to heavy metal stress. In this review, we provide a broad overview of recent advances in cellular protein research with regards to heavy metal tolerance in plants. We also discuss how plants maintain functional and healthy proteomes for survival under such capricious surroundings.

Present photocatalysts for the synchronous cleanup of pharmaceuticals and heavy metals have several drawbacks, including inadequate reactive sites, inefficient electron–hole disassociation, and insufficient oxidation and reduction power. In this research, we sought to address these issues by using a facile solvothermal-photoreduction route to develop an innovative plasmonic S-scheme heterojunction, Au/MIL-101(Fe)/BiOBr. The screened-out Au/MIL-101(Fe)/BiOBr (AMB-2) works in a durable and high-performance manner for both Cr(VI) and norfloxacin (NOR) eradication under visible light, manifesting up to 53.3 and 2 times greater Cr(VI) and NOR abatement rates, respectively, than BiOBr. Remarkably, AMB-2’s ability to remove Cr(VI) in a Cr(VI)-NOR co-existence system is appreciably better than in a sole-Cr(VI) environment; the synergy among Cr(VI), NOR, and AMB-2 results in the better utilization of photo-induced carriers, yielding a desirable capacity for decontaminating Cr(VI) and NOR synchronously. The integration of MOF-based S-scheme heterojunctions and a plasmonic effect contributes to markedly reinforced photocatalytic ability by increasing the number of active sites, augmenting the visible-light absorbance, boosting the efficient disassociation and redistribution of powerful photo-carriers, and elevating the generation of reactive substances. We provide details of the photocatalytic mechanism, NOR decomposition process, and bio-toxicity of the intermediates. This synergistic strategy of modifying S-scheme heterojunctions with a noble metal opens new horizons for devising excellent MOF-based photosystems with a plasmonic effect for environment purification.

This article describes the basic optical principles in the lithographic tool, with the resulting positioning accuracy requirements. For three generations of lithographic tools, the mechatronic architecture and control implications are discussed. Then, six degrees of freedom (DOF) stage control is described with the main focus on actuator force decoupling, allowing the use of classical single-input, single-output (SISO) controllers.

The development of easily recyclable and advanced photosystems is a promising strategy for achieving sustainable water decontamination in industrial applications. In this study, a flexible, floatable, and easily recyclable S-scheme photosystem of oxygen vacancy (OV)-rich Bi2WO6/C3N4/carbon fiber cloth (BWOV/CN/CF) was fabricated via sequential in situ growth of C3N4 and Bi2WO6 with oxygen vacancies on CF cloth. The integrated BWOV/CN/CF photosystem exhibited outstanding photocatalytic decontamination rates for TC (0.0353 min−1) and Cr(VI) (0.0187 min−1), significantly exceeding CN/CF by 0.5 and 30.2 folds, respectively. This appreciable improvement is derived from the unique hierarchical S-scheme heterostructure with OV, which enables the enhanced capability of BWOV/CN/CF in light use and powerful photocarrier detachment, as well as offering abundant active centers. Significantly, the BWOV/CN/CF cloth shows intriguing industrial application prospects owing to its high anti-interference properties, broad pH applicability, good durability, easy recycling and operation, and extensive adaptability for diverse contaminant purification. Furthermore, the photocatalytic TC decomposition process, by-product biotoxicity, and photocatalysis mechanism were systematically evaluated. The ingenious design of floatable cloth-shaped photosystems offers an effective strategy for environmental purification.

Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.

Abstract The excessive enrichment of nitrate in the environment can be converted into ammonia (NH 3 ) through electrochemical processes, offering significant implications for modern agriculture and the potential to reduce the burden of the Haber–Bosch (HB) process while achieving environmentally friendly NH 3 production. Emerging research on electrocatalytic nitrate reduction (eNitRR) to NH 3 has gained considerable momentum in recent years for efficient NH 3 synthesis. However, existing reviews on nitrate reduction have primarily focused on limited aspects, often lacking a comprehensive summary of catalysts, reaction systems, reaction mechanisms, and detection methods employed in nitrate reduction. This review aims to provide a timely and comprehensive analysis of the eNitRR field by integrating existing research progress and identifying current challenges. This review offers a comprehensive overview of the research progress achieved using various materials in electrochemical nitrate reduction, elucidates the underlying theoretical mechanism behind eNitRR, and discusses effective strategies based on numerous case studies to enhance the electrochemical reduction from NO 3 ˗ to NH 3 . Finally, this review discusses challenges and development prospects in the eNitRR field with an aim to guide design and development of large‐scale sustainable nitrate reduction electrocatalysts.

Marine biofouling is a well-known massive problem: within the shortest time, ship hulls and other man-made submerged surfaces are inevitably populated by various marine organisms. Marine biofouling causes severe economic and environmental problems. Thus, effective biofouling control on submerged surfaces is of utmost importance. Since the middle of the 20th century, scientists and engineers have developed antifouling coatings mainly based on the continuous release of toxic metal ions and accompanying booster biocides to repel or kill organisms approaching the surface. However, these coatings caused serious harm to non-target organisms and the ocean. Therefore, the development of environmentally friendly alternative coatings is an urgent need, and research in this field is growing rapidly. This review includes concise basic theory from biology, chemistry, and physics. It provides an introduction into the biofouling formation, as well as physicochemical surface properties that can be manipulated to achieve an effective biofouling control. Furthermore, a complete overview of the currently developed biofouling control coatings is presented and summarized. This overview includes coatings based on surface wettability, self-renewable coatings, coatings containing antifouling agents, switchable coatings, and biomimetic coatings.

As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students' learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students' collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

// Islam Khairul 1 , Qian Qian Wang 1,2 , Yu Han Jiang 1,3 , Chao Wang 1 and Hua Naranmandura 1,2,3 1 Department of Toxicology, School of Medicine and Public Health, Zhejiang University, Hangzhou, China 2 College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China 3 Ocean College, Zhejiang University, Hangzhou, China Correspondence to: Hua Naranmandura, email: // Keywords : arsenite, acute promyelocytic leukemia Received : November 23, 2016 Accepted : January 11, 2017 Published : January 18, 2017 Abstract A variety of studies indicated that inorganic arsenic and its methylated metabolites have paradoxical effects, namely, carcinogenic and anticancer effects. Epidemiological studies have shown that long term exposure to arsenic can increase the risk of cancers of lung, skin or bladder in man, which is probably associated with the arsenic metabolism. In fact, the enzymatic conversion of inorganic arsenic by Arsenic (+3 oxidation state) methyltransferase (AS3MT) to mono- and dimethylated arsenic species has long been considered as a major route for detoxification. However, several studies have also indicated that biomethylation of inorganic arsenic, particularly the production of trivalent methylated metabolites, is a process that activates arsenic as a toxin and a carcinogen. On the other hand, arsenic trioxide (As 2 O 3 ) has recently been recognized as one of the most effective drugs for the treatment of APL. However, elaboration of the cytotoxic mechanisms of arsenic and its methylated metabolites in eradicating cancer is sorely lacking. To provide a deeper understanding of the toxicity and carcinogenicity along with them use of arsenic in chemotherapy, caution is required considering the poor understanding of its various mechanisms of exerting toxicity. Thereby, in this review, we have focused on arsenic metabolic pathway, the roles of the methylated arsenic metabolites in toxicity and in the therapeutic efficacy for the treatments of solid tumors, APL and/or non-APL malignancies.