Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computational resources for end users, by which low latency can be guaranteed. In this paper, we put forward a feasible ...solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming to minimize the average response time for events reported by vehicles. First, we construct a distributed city-wide traffic management system, in which vehicles close to road side units can be utilized as fog nodes. Then, we model parked and moving vehicle-based fog nodes according to a queueing theory, and draw the conclusion that moving vehicle-based fog nodes can be modeled as an M/M/1 queue. An approximate approach is developed to solve the offloading optimization problem by decomposing it into two subproblems and scheduling traffic flows among different fog nodes. Performance analyses based on a real-world taxi-trajectory datasets are conducted to illustrate the superiority of our method.
Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ...ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted.
As the most abundant biopolymer in nature, cellulose has become a fascinating building block for the design of functional nanomaterials. Owing to the presence of numerous hydroxyl groups, cellulose ...provides a unique platform for the preparation of new materials via versatile chemical modifications. This critical review aims to present the advances about nanomaterials based on cellulose derivatives with the focus on cellulose esters within the last two decades, including the chemistry and application of these nanostructured materials. This review starts with the introduction on first fundamental aspects about diverse esterification techniques used up to now to modify cellulose. The in situ esterification for the isolation of nanocelluloses and diverse post esterification methods of nanocelluloses for the surface functionalization were highlighted in the following description. Various esterification strategies and further nanostructure constructions have been developed aiming to confer specific properties to cellulose esters, extending therefore their feasibility for highly sophisticated applications, which were summarized with respect to the categories of the introduced ester moieties. Thus, this review assembles and emphasizes the state-of-art knowledge of functional nanomaterials derived from diverse esterified cellulose compounds.
Chloroplasts are important for photosynthesis and for plant immunity against microbial pathogens. Here we identify a haustorium-specific protein (Pst_12806) from the wheat stripe rust fungus, ...Puccinia striiformis f. sp. tritici (Pst), that is translocated into chloroplasts and affects chloroplast function. Transient expression of Pst_12806 inhibits BAX-induced cell death in tobacco plants and reduces Pseudomonas-induced hypersensitive response in wheat. It suppresses plant basal immunity by reducing callose deposition and the expression of defense-related genes. Pst_12806 is upregulated during infection, and its knockdown (by host-induced gene silencing) reduces Pst growth and development, likely due to increased ROS accumulation. Pst_12806 interacts with the C-terminal Rieske domain of the wheat TaISP protein (a putative component of the cytochrome b6-f complex). Expression of Pst_12806 in plants reduces electron transport rate, photosynthesis, and production of chloroplast-derived ROS. Silencing TaISP by virus-induced gene silencing in a susceptible wheat cultivar reduces fungal growth and uredinium development, suggesting an increase in resistance against Pst infection.
Recently, Internet of Vehicles (IoV) has become one of the most active research fields in both academic and industry, which exploits resources of vehicles and Road Side Units (RSUs) to execute ...various vehicular applications. Due to the increasing number of vehicles and the asymmetrical distribution of traffic flows, it is essential for the network operator to design intelligent offloading strategies to improve network performance and provide high-quality services for users. However, the lack of global information and the time-variety of IoVs make it challenging to perform effective offloading and caching decisions under long-term energy constraints of RSUs. Since Artificial Intelligence (AI) and machine learning can greatly enhance the intelligence and the performance of IoVs, we push AI inspired computing, caching and communication resources to the proximity of smart vehicles, which jointly enable RSU peer offloading, vehicle-to-RSU offloading and content caching in the IoV framework. A Mix Integer Non-Linear Programming (MINLP) problem is formulated to minimize total network delay, consisting of communication delay, computation delay, network congestion delay and content downloading delay of all users. Then, we develop an online multi-decision making scheme (named OMEN) by leveraging Lyapunov optimization method to solve the formulated problem, and prove that OMEN achieves near-optimal performance. Leveraging strong cognition of AI, we put forward an imitation learning enabled branch-and-bound solution in edge intelligent IoVs to speed up the problem solving process with few training samples. Experimental results based on real-world traffic data demonstrate that our proposed method outperforms other methods from various aspects.
Fine particulate matter (Particulate matter with diameter ≤ 2.5 μm) is associated with multiple health outcomes, with varying effects across seasons and locations. It remains largely unknown that ...which components of PM2.5 are most harmful to human health.
We systematically searched all the relevent studies published before August 1, 2018, on the associations of fine particulate matter constituents with mortality and morbidity, using Web of Science, MEDLINE, PubMed and EMBASE. Studies were included if they explored the associations between short term or long term exposure of fine particulate matter constituents and natural, cardiovascular or respiratory health endpoints. The criteria for the risk of bias was adapted from OHAT and New Castle Ottawa. We applied a random-effects model to derive the risk estimates for each constituent. We performed main analyses restricted to studies which adjusted the PM2.5 mass in their models.
Significant associations were observed between several PM2.5 constituents and different health endpoints. Among them, black carbon and organic carbon were most robustly and consistently associated with all natural, cardiovascular mortality and morbidity. Other potential toxic constituents including nitrate, sulfate, Zinc, silicon, iron, nickel, vanadium, and potassium were associated with adverse cardiovascular health, while nitrate, sulfate and vanadium were relevant for adverse respiratory health outcomes.
Our analysis suggests that black carbon and organic carbon are important detrimental components of PM2.5, while other constituents are probably hazardous to human health. However, more studies are needed to further confirm our results.
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•The first systematic review of both short term and long term exposure to PM2.5 constituents and related health effects.•Both Mortality and Morbidity have been considered.•BC and OC are constituents that are most likely to cause adverse health effects.
The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources ...and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.
Although modern transportation systems facilitate the daily life of citizens, the ever-increasing energy consumption and air pollution challenge the establishment of green cities. Current studies on ...green IoV generally concentrate on energy management of either battery-enabled RSUs or electric vehicles. However, computing tasks and load balancing among RSUs have not been fully investigated. In order to satisfy heterogeneous requirements of communication, computation and storage in IoVs, this article constructs an energy-efficient scheduling framework for MEC-enabled IoVs to minimize the energy consumption of RSUs under task latency constraints. Specifically, a heuristic algorithm is put forward by jointly considering task scheduling among MEC servers and downlink energy consumption of RSUs. To the best of our knowledge, this is a prior work to focus on the energy consumption control issues of MEC-enabled RSUs. Performance evaluations demonstrate the effectiveness of our framework in terms of energy consumption, latency and task blocking possibility. Finally, this article elaborates some major challenges and open issues toward energy-efficient scheduling in IoVs.
The task of text-to-image synthesis is to generate photographic images conditioned on given textual descriptions. This challenging task has recently attracted considerable attention from the ...multimedia community due to its potential applications. Most of the up-to-date approaches are built based on generative adversarial network (GAN) models, and they synthesize images conditioned on the global linguistic representation. However, the sparsity of the global representation results in training difficulties on GANs and a shortage of fine-grained information in the generated images. To address this problem, we propose cross-modal global and local linguistic representations-based generative adversarial networks (CGL-GAN) by incorporating the local linguistic representation into the GAN. In our CGL-GAN, we construct a generator to synthesize the target images and a discriminator to judge whether the generated images conform with the text description. In the discriminator, we construct the cross-modal correlation by projecting the image representations at high and low levels onto the global and local linguistic representations, respectively. We design the hinge loss function to train our CGL-GAN model. We evaluate the proposed CGL-GAN on two publicly available datasets, the CUB and the MS-COCO. The extensive experiments demonstrate that incorporating fine-grained local linguistic information with cross-modal correlation can greatly improve the performance of text-to-image synthesis, even when generating high-resolution images.
While the composition and diversity of soil microbial communities play a central and essential role in biogeochemical cycling of nutrients, they are known to be shaped by the physical and chemical ...properties of soils and various environmental factors. This study investigated the composition and diversity of microbial communities in 48 samples of seasonally frozen soils collected from 16 sites in an alpine wetland region (Lhasa River basin) and an alpine forest region (Nyang River basin) on the Tibetan Plateau using high-throughput sequencing that targeted the V3-V4 region of 16S rRNA gene. The dominant soil microbial phyla included Proteobacteria, Acidobacteria, and Actinobacteria in the alpine wetland and alpine forest ecosystems, and no significant difference was observed for their microbial composition. Linear discriminant analysis Effect Size (LEfSe) analysis showed that significant enrichment of Hymenobacteraceae and Cytophagales (belonging to Bacteroidetes) existed in the alpine wetland soils, while the alpine forest soils were enriched with Alphaproteobacteria (belonging to Proteobacteria), suggesting that these species could be potential biomarkers for alpine wetland and alpine forest ecosystems. Results of redundancy analysis (RDA) suggest that the microbial community diversity and abundance in the seasonally frozen soils on the Tibetan Plateau were mainly related to the total potassium in the alpine wetland ecosystem, and available potassium and soil moisture in the alpine forest ecosystem, respectively. In addition, function prediction analysis by Tax4Fun revealed the existence of potential functional pathways involved in human diseases in all soil samples. These results provide insights on the structure and function of soil microbial communities in the alpine wetland and alpine forest ecosystems on the Tibetan Plateau, while the potential risk to human health from the pathogenic microbes in the seasonally frozen soils deserves attention.
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•Bacterial α-diversity decreased with altitude in the Tibetan alpine wetland soils.•Bacterial β-diversity was significantly different in alpine wetland and forest soils.•Wetland and forest ecosystems showed little difference in soil microbial composition.•Total/available K and moisture influenced the structure of soil microbial communities.•Potential functional pathways involved in human disease were identified in the soils.
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