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Abstract Black soybean peels (BSPs) are rich in anthocyanins (ANCs), a naturally active substance added to manufactured foods. However, the instability of the ANC structure limits its application in the food industry. In the present study, an efficient and simple microencapsulation method to improve the stability of ANC was successfully established. BSP ANCs were encapsulated in a microencapsulated complex with fructo‐oligosaccharide (FOS) and whey protein (WP) associated with a high encapsulation rate (65.41%) and drug‐loading capacity (6.13%). The particle size and polydispersity index of anthocyanin‐loaded microencapsulation (ALM) were 4.648 ± 0.350 µm and 0.405 ± 0.107, respectively. The ANC stability test showed that ALM performed enhanced thermostability, pH stability, storage stability, and light stability. Meanwhile, in vitro release experiments indicated that ALM dramatically delayed the contact time of ANC with the environment and reduced the degradation. Moreover, animal experiments demonstrated that ALM better improved blood glucose homeostasis and lipid homeostasis in high‐fat‐ and high‐fructose‐treated mice and reduced liver lipid accumulation and damage. Additionally, the better in vivo bioactivity could be attributed to the extended bioavailability that was proved by a pharmacokinetics test. These results suggested that the microencapsulation employed WP‐FOS complexes as the wall materials are of great potential to improve the stability and health‐promoting properties of ANC, providing a promising solution as dietary supplementation.

This paper aims to assess the effects of various laser cutting parameters on the surface roughness of 2 mm thick titanium–zirconium amorphous alloy sheets. With multiple linear regression, a mathematical model was proposed to predict the maximum height of surface Sz as a function of two laser cutting parameters: cutting speed and assist gas pressure. The adequacy of the proposed model was evaluated using the analysis of variance method. Scanning electron microscopy was then used to observe the surface topography of the material following laser cutting under different conditions. It was found that for a fixed laser power, cutting speed dominates the parameter that determines the maximum height of surface roughness.

Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries.

Abstract This paper uses a novel numerical approach to approximate the coupled Cahn–Hilliard equations, which are a highly nonlinear system depicting the phase separation of the homopolymer and copolymer mixtures. The new method is named 3S-IEQ, and its construction and calculation are more straightforward than the invariant energy quadratization and scalar auxiliary variable methods. Notably, we only need to solve two linear decoupled constant-coefficient equations at each time step. Numerical simulations are shown

Over the past years, the emergence of intelligent networks empowered by machine learning techniques has brought great facilitates to different aspects of human life. However, using machine learning in intelligent networks also presents potential security and privacy threats. A common practice is the so-called poisoning attacks where malicious users inject fake training data with the aim of corrupting the learned model. In this survey, we comprehensively review existing poisoning attacks as well as the countermeasures in intelligent networks for the first time. We emphasize and compare the principles of the formal poisoning attacks employed in different categories of learning algorithms, and analyze the strengths and limitations of corresponding defense methods in a compact form. We also highlight some remaining challenges and future directions in the attack-defense confrontation to promote further research in this emerging yet promising area.

The present paper considers the blowup properties in trapped dipolar quantum gases modelled by the Gross-Pitaevskii equation. More precisely, through analyzing the temporal evolution of J′(t) in the form of uncertain principle [1], we provide some invariant evolution flows. Based on that, we establish the global existence versus blowup dichotomy of solutions above the mass-energy threshold. Meanwhile, we can estimate the behaviour of solutions with arbitrary large energy.

Abstract Mercury injection capillary pressure (MICP) tests, nuclear magnetic resonance (NMR), (fluorescence) thin section, X‐ray diffraction (XRD), and scanning electron microscope (SEM) analyses were used to describe the size and distribution of entire pore‐throat structures in the sandstones of the E1f3 (the third member of Paleogene Funing Formation) in the Subei Basin. The lithologies of E1f3 in the Subei Basin are mainly dark‐gray, very fine‐grained sandstones and siltstones, interbedded with dark mudstones. The pore systems predominantly feature secondary intergranular and intragranular dissolution pores, micropores coexisting with minor amounts of intergranular pores, and microfractures. The high threshold pressure and bulk volume of irreducible fluids values and the significant variation in the NMR and MICP parameters indicate that the E1f3 reservoirs are characterized by complex and heterogeneous microscopic pore structures. Microscopic pore‐throat parameters are linked with macroscopic properties through the reservoir quality index (RQI). The NMR T2 (transverse time relaxation) spectrum is unimodal or bimodal but with weak right peaks, indicating the rarity of large intergranular pores. However, large‐scale pore throats, though only account for a minor part of the total pore volume, significantly contribute to the total permeability. The abundance of small‐scale pore‐throat systems (short T2 components) results in high irreducible water content. Therefore, the oil saturation in E1f3 sandstones is low, and the pore structure, especially the number of micropores, determines the oil‐bearing property. Oil primarily occurs in the intragranular dissolution pores with minor amounts occurring in the large intergranular pores. Most of the micropores are bound by capillary water. The sandstones with chlorite clay minerals tend to be oil‐wet and have high oil‐bearing potential, while the abundance of detrital clay or illite contributes to a low oil‐bearing grade. The combination of core and microscopic observations and the MICP and NMR analyses have allowed the determination of the pore structure characteristics and their coupling effects on oil‐bearing property.

As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very well for the type of tasks that requires continuous monitoring, e.g., WIFI signal sensing, where the WiFi signal may vary over time, and users are required to contribute continuous efforts. The incentive mechanism for continuous crowdsensing has yet to be investigated, where the corresponding tasks need continuous efforts of users, and the freshness of the sensed data is very important. In this paper, we design TIMCC, a privacy-preserving incentive mechanism for continuous crowdsensing. In contrast to most existing studies that treat tasks as one-shot tasks, we consider the tasks that require users to contribute continuous efforts, where the freshness of data is a key factor impacting the value of data, which further determines the rewards. We introduce a metric named age of data that is defined as the amount of time elapsed since the generation of the data to capture the freshness of data. We adopt the reverse auction framework to model the connection between the platform and the users. We prove that the proposed mechanism satisfies individual rationality, computational efficiency, and truthfulness. Simulation results further validate our theoretical analysis and the effectiveness of the proposed mechanism.

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Construction of plasmids from multiple fragments often uses customised parts and leaves scars where fragments are joined. Here the authors develop a method for barcoding fragments and constructing plasmids in a scarless manner from a collection of standard parts.

Finite battery capacity limits the network lifetime of wireless sensor networks, and thus severely impedes the deployment of large scale sensor networks. To prolong the lifetime, researchers utilize mobile chargers to recharge sensors with external power sources. In this paper, we study both periodic charging time scheduling and charging path planning with multiple chargers. First, we present an efficient slot-based periodic charging time scheduling algorithm with both a fine-grained node classification scheme to prevent unnecessary visits of energy-sufficient nodes, and a balanced charging task assignment scheme to avoid charging starvation. To further enhance charging efficiency, we also propose a charging path planning algorithm, which enables parallel power replenishment with multiple chargers. The simulation results show that our algorithms are effective and competitive when compared with existing algorithms.

The last decade has witnessed unmanned aerial vehicles (UAVs) emerging as a powerful platform for various industrial and civilian applications. However, refrained by limited battery capacities, the hovering time of UAVs is still limited, impeding them from achieving remote tasks, such as wide area inspection. To deal with such long-range applications, a common sense solution is to employ vehicles to transport, launch, and recycle UAVs. Efficient routing and scheduling for UAVs and vehicles can greatly reduce time consumption and financial expenses incurred in long-range inspection. Nevertheless, prior works in vehicle-assisted UAV inspection considered only one UAV, and was incapable of concurrently serving multiple targets distributed in an area. Leveraging multiple UAVs to serve multiple targets in parallel can significantly enhance efficiency and expand service areas. Therefore, in this paper, we propose a novel hybrid genetic algorithm (HGA), which supports the cooperation of one vehicle and multiple drones for wide area inspection applications. HGA allows multiple UAVs to launch and recycle in different locations, minimizing time wastage for both the vehicle and UAVs. Performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

The Internet of Things (IoT) is widely regarded as a key component of the Internet of the future and thereby has drawn significant interests in recent years. IoT consists of billions of intelligent and communicating “things”, which further extend borders of the world with physical and virtual entities. Such ubiquitous smart things produce massive data every day, posing urgent demands on quick data analysis on various smart mobile devices. Fortunately, the recent breakthroughs in deep learning have enabled us to address the problem in an elegant way. Deep models can be exported to process massive sensor data and learn underlying features quickly and efficiently for various IoT applications on smart devices. In this article, we survey the literature on leveraging deep learning to various IoT applications. We aim to give insights on how deep learning tools can be applied from diverse perspectives to empower IoT applications in four representative domains, including smart healthcare, smart home, smart transportation, and smart industry. A main thrust is to seamlessly merge the two disciplines of deep learning and IoT, resulting in a wide-range of new designs in IoT applications, such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection. We also discuss a set of issues, challenges, and future research directions that leverage deep learning to empower IoT applications, which may motivate and inspire further developments in this promising field.

Improving catalytic ability of enzymes is critical to the success of many metabolic engineering projects, but the search space of possible protein mutants is too large to explore exhaustively through experiments. To some extent, highly soluble enzymes tend to exhibit high activity due to their better folding quality. Here, we demonstrate that an optimization algorithm based on a regression model can effectively design short peptide tags to improve solubility of a few model enzymes. Based on the protein sequence information, a support vector regression model we recently developed was used to evaluate protein solubility after small peptide tags were introduced to a target protein. The optimization algorithm guided the sequences of the tags to evolve towards variants that had higher solubility. The optimization results were validated successfully by measuring solubility and activity of the model enzyme with and without the identified tags. The solubility of one protein (tyrosine ammonia lyase) was more than doubled and its activity was improved by 250%. This strategy successfully increased solubility of another two enzymes (aldehyde dehydrogenase and 1-deoxy-D-xylulose-5-phosphate synthase) we tested. The presented optimization methodology thus provides a valuable tool for improving enzyme performance for metabolic engineering and other biotechnology projects.

Conventional sensing methodologies for smart home are known to be labor-intensive and complicated for practical deployment. Thus, researchers are resorting to alternative sensing mechanisms. Wi-Fi is one of the key technologies that enable connectivity for smart home services. Apart from its primary use for communication, Wi-Fi signal has now been widely leveraged for various sensing tasks, such as gesture recognition and fall detection, due to its sensitivity to environmental dynamics. Building smart home based on Wi-Fi sensing is cost-effective, non-invasive, and enjoys convenient deployment. In this paper, we survey the recent advances in the smart home systems based on the Wi-Fi sensing, mainly in such areas as health monitoring, gesture recognition, contextual information acquisition, and authentication.

Finite network lifetime severely limits the usage of sensor networks. To prolong the lifetime, researchers adopt mobile chargers to recharge sensors with external power sources. Prior studies customarily assumed that a mobile charger sequentially visits and charges sensors. However, this method is inefficient, since it normally incurs considerable charging waiting time. To address the limitation, in this paper, we propose to replenish sensors with a mobile worker carrying multiple portable chargers. The worker simultaneously charges multiple sensors in a small region by deploying the chargers onto the sensors. We claim that this parallel charging mode significantly reduces waiting time incurred per sensor and thus improves charging efficiency. Based on the novel design, this paper contributes two novel approaches called periodic multi-charger (PMC) and on-demand multi-charger (OMC). The PMC offers guaranteed and sustainable power supplies via periodic charging schedules, while the OMC derives charging schedules in real time to adapt to the dynamism in energy consumption patterns. Both the algorithms achieve high charging efficiency by simultaneously charging sensors with multiple chargers in each charging round. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches.

Recent years have witnessed significant advances in indoor positioning. Existing approaches either require cumbersome site surveys or customized hardware, thus hindering their popularity. In this paper, we propose Stationary peers-Assisted indoor Positioning (SAP), a practical indoor positioning solution that is highly scalable and easy-to-deploy. SAP greatly alleviates the pain of fingerprint collection by smartly leveraging the relatively stationary people, namely stationary peers that are largely available in common indoor environments to assist positioning. Once SPs' locations and the relative distance between SPs and a target are obtained, SAP can apply trilateration to locate the target. SAP incorporates three key modules: a novel accelerometer-based filter that can accurately identify SP, an enhanced fingerprint-based positioning method that can accurately pinpoint SPs' locations, and a robust acoustic ranging method. We implement a prototype of SAP on the Android platform and evaluate its performance in representative real-world environments. SAP achieves an 80% positioning error of 2.2 m, which is comparable to the most existing smartphone-assisted approaches.

Combining powerful sensors and near ubiquitous distribution, the smartphone has become an irreplaceable part of modern day life. Using its pervasive sensing capabilities, the smart-phone guides us to our destination with precise step-by-step directions, advises us on what to have for lunch, and improves our photography skills through stabilizing our camera. Using the popular augmented reality (AR) smartphone app Pokemon Go as a case study, we explore the world of pervasive sensing. In this paper, we show both the current state of the art that enable applications such as Pokemon Go to thrive, as well as the limitations and opportunities inherent in current pervasive sensing applications.