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Mobile ad-hoc cloud can exploit the computing resources (e.g., smartphones, vehicles, and unmanned systems) scattered in the mobile environment to form a self-organized ad-hoc local resource pool for providing opportunistic computing services. However, the highly dynamic and distributed characteristics of the mobile ad-hoc network environment bring great challenges in privacy and security in such opportunistic resource sharing. In this article, we first discuss the attractive features of blockchain for providing such resource sharing services in the mobile ad-hoc network environment in a secure and trustful way and then discuss the problems caused when using existing consensus protocols in such an environment. We accordingly devise a blockchain based trustful mobile ad-hoc cloud architecture, AdChain cloud. We describe the functions at different layers in this architecture, including the network layer, blockchain layer, and smart contract layer. To adapt to the high dynamics of the mobile ad-hoc network environment, we design a stability-aware consensus protocol at the blockchain layer. Simulation results show that our solution can achieve improved performance as compared with existing work.

This study focused on the engineering properties of alkali-activated slag (AAS) pastes prepared by mixing an activator consisting of sodium silicate and sodium hydroxide at room temperature. The water-to-slag ratio of AAS paste was kept constant at 0.35 by mass. AAS pastes were prepared using the activator with five different silicate moduli of 1, 1.2, 1.4, 1.6, and 1.8 and three different Na2O contents of 6%, 8%, and 10%. The results showed that both the silicate moduli and Na2O contents had significant effects on the engineering properties of AAS pastes. All the AAS pastes exhibited properties such as fast setting, good workability, and high early compressive strength. The final setting time varied from 9 to 36 min, and the fluidity was in the range of 147–226 mm. The 1 d compressive strength of all the AAS pastes, which could be easily achieved, had values above 55 MPa, whereas the highest strength obtained was 102 MPa with the silicate modulus of 1 and Na2O content of 8% at room temperature. The drying shrinkage increased as the silicate modulus increased. Furthermore, the hydration products and microstructures of AAS pastes were explained according to the microanalysis methods.

Cache-enabled device-to-device (D2D) communication has emerged as a promising paradigm for caching popular contents at user devices and using D2D communication among adjacent users to relieve cellular traffic overload. To maximize traffic offloading, optimal content-selection for caching and helper-receiver matching are two key concerns. In this paper, we deal with the two key issues by leveraging user characteristics, which are crucial to D2D content sharing but often omitted by existing work in this field. We first propose a comprehensive model that profiles user characteristics including user preference, user activity degree, and user relationship. To provide an accurate estimation of user relationship, a new metric named expected correlation coefficient (ECC) is further proposed, which can reveal the potential offloading opportunities behind the contacts between users. We then design a novel user-centric caching policy that integrates the above-mentioned models into content caching policy to optimize the caching efficiency. Furthermore, we propose an online learning algorithm by leveraging the Combinatorial Multi-Armed Bandits (CMAB) method to achieve efficient helper-receiver matching among users. Experiments based on real-world traces and content requesting records were conducted. The results demonstrate that when compared with existing algorithms, our proposed algorithm improves the offloading ratio by nearly 10% and the system utility approximately by 20%.

Small RNAs (sRNAs) are short noncoding RNAs that play roles in many biological processes, including drought responses in plants. However, how the expression of sRNAs dynamically changes with the gradual imposition of drought stress in plants is largely unknown. We generated time-series sRNA sequence data from maize ( L.) seedlings under drought stress (DS) and under well-watered (WW) conditions at the same time points. Analyses of length, functional annotation, and abundance of 736,372 nonredundant sRNAs from both DS and WW data, as well as genome copy numbers at the corresponding genomic regions, revealed distinct patterns of abundance and genome organization for different sRNA classes. The analysis identified 6646 sRNAs whose regulation was altered in response to drought stress. Among drought-responsive sRNAs, 1325 showed transient downregulation by the seventh day, coinciding with visible symptoms of drought stress. The profiles revealed drought-responsive microRNAs, as well as other sRNAs that originated from ribosomal RNAs (rRNAs), splicing small nuclear RNAs, and small nucleolar RNAs (snoRNA). Expression profiles of their sRNA derivers indicated that snoRNAs might play a regulatory role through regulating the stability of rRNAs and splicing small nuclear RNAs under drought condition.

Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location-based topology control algorithms have been proposed. In this paper, we propose two location-assisted grid-based topology control GBP algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid-based topology control, a network is divided into equally spaced squares called grids. We accordingly design cross-sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near-minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross-sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

In this paper, a parametric experimental study developing the alkali-activated slag concrete hollow block (AASCHB) is discussed. Fourteen trial mixes of alkali-activated slag concrete containing pottery sand and ceramsite with different water-to-slag ratios, sand ratios, silicate moduli, and Na2O contents were evaluated to determine the optimal mix for high compressive strength and low drying shrinkage. All four factors evaluated were found to be significant for the desired properties. A series of 390 &times, 190 &times, 190 mm3 AASCHBs were prepared using the optimal mix with a water-to-slag ratio of 0.35, sand ratio of 0.64, silicate modulus of 1.2, and Na2O content of 8%. The compressive strength, flexural strength, water absorption, and moisture content tests of these blocks indicate that the resulting AASCHB can be classified under the strength grade of MU15 as a load-bearing hollow concrete block. The proposed AASCHBs appear to provide a viable solution to the environmental problems of industrial waste and cement production emissions, leading to more sustainable buildings without compromising structural performance.

UAV networks often partition into separated clusters due to the high node and link dynamic. As a result, network connectivity recovery is an important issue in this area. Existing solutions always need excessive movement of nodes and thus lead to low recovery efficiency in terms of the time and energy consumption. In this paper, we for the first time study the issue of how to utilize cooperative communication technology to improve the connectivity recovery efficiency in UAV networks. We propose a Cooperative Communication based Connectivity Recovery algorithm for UAV Networks, named C3RUN. The key novelty is C3RUN not only uses cooperative communication to enlarge node's communication range and thus achieve quick repair of network connectivity, but also enables nodes to proactively move to better places for ensuring the establishment of cooperative communication links. We conduct extensive simulations to evaluate the performance of C3RUN. The simulation results reveal that C3RUN can not only achieve connectivity recovery with less nodes and shorter distance to move, but also always finish recovery with less time, when comparing with existing work. Furthermore, C3RUN can achieve 100% success ratio for connectivity recovery.

Small RNAs (sRNAs) are short noncoding RNAs that play roles in many biological processes, including drought responses in plants. However, how the expression of sRNAs dynamically changes with the gradual imposition of drought stress in plants is largely unknown. We generated time-series sRNA sequence data from maize seedlings under drought stress and under well-watered conditions at the same time points. Analyses of length, functional annotation, and abundance of 736,372 non-redundant sRNAs from both drought and well-watered data, as well as genome copy number and chromatin modifications at the corresponding genomic regions, revealed distinct patterns of abundance, genome organization, and chromatin modifications for different sRNA classes of sRNAs. The analysis identified 6,646 sRNAs whose regulation was altered in response to drought stress. Among drought-responsive sRNAs, 1,325 showed transient down-regulation by the seventh day, coinciding with visible symptoms of drought stress. The profiles revealed drought-responsive microRNAs, as well as other sRNAs that originated from ribosomal RNAs (rRNAs), splicing small nuclear RNAs, and small nucleolar RNAs (snoRNA). Expression profiles of their sRNA derivers indicated that snoRNAs might play a regulatory role through regulating stability of rRNAs and splicing small nuclear RNAs under drought condition.

Sample scheduling is a crucial issue in wireless sensor networks (WSNs). The design objectives of efficient sample scheduling are in general two-folds: to achieve a low sample rate and also high sensing quality. Recently, compressive sensing (CS) has been regarded as an effective paradigm for achieving high sensing quality at a low sample rate. However, most existing work in the area of CS for WSNs use fixed sample rates, which may make sensor nodes in a WSN unable to capture significant changes of target phenomenon, unless the sample rate is sufficiently high, and thus degrades the sensing quality. In this paper, to pursue high sensing quality at low sample rate, we propose an adaptive CS based sample scheduling mechanism (ACS) for WSNs. ACS estimates the minimum required sample rate subject to given sensing quality on a per-sampling-window basis and accordingly adjusts sensors' sample rates. ACS can be useful in many applications such as environment monitoring, and spectrum sensing in cognitive sensor networks. Extensive trace-driven experiments are conducted and the numerical results show that ACS can obtain high sensing quality at low sample rate.

This paper investigated the curing effects on the mechanical properties of calcium-containing geopolymer mortar. Three precursors are used: Class C fly ash, Class F fly ash plus calcium hydroxide and Class F fly ash plus slag. Curing conditions included: (1) standard curing at 20 ± 3 °C and RH 95% (C); (2) steam curing at 60 °C for 24 h (S); (3) steam curing at 60 °C for 6 h (S6); and (4) oven curing at 60 °C for 24 h (O), then the latter three followed by the standard curing. Under the standard conditions, the flexural strength and compressive strength of Class C fly ash geopolymer mortars developed quickly until the age of 7 days, followed by a gradual increase. Specimens with Class F fly ash plus Ca(OH)2 showed slow increase till the age of 28 days. Under these non-standard conditions (2–4), all specimens showed higher 3-day strength, while later strengths were either higher or lower than those in standard conditions, depending on the type of the precursor.

Achieving high data quality and efficient network resource utilization is two major design objectives of wireless sensor networks WSNs. However, these two objectives are often conflictive. By allowing sensors to report sampled data at high rates, fine-grained data quality can be obtained. However, the limited resources of a WSN make it difficult to support very high traffic rate. Therefore, the capability of adaptively adjusting sensor nodes' traffic-generating rates on the basis of the availability of network resources and application requirements is critical. This issue has attracted much attention recently, and some work has been carried out.

Backpressure based scheduling has been considered as a promising technique for improving the throughput of a wide range of communication networks. However, this scheduling technique has not been well studied for heterogeneous wireless networks. In this paper, we propose a virtual-queue based backpressure scheduling (VQB) algorithm for heterogeneous multi-hop wireless networks. The VQB algorithm introduces a simple virtual queue for each flow at a node for backpressure scheduling, whose length depends on the cache size of the node. When calculating flow weights and making scheduling decisions, the length of a virtual queue is used instead of the length of a real queue. We theoretically prove that VQB is throughput-optimal. Simulation results show that the VQB algorithm significantly outperforms a classical backpressure scheduling algorithm in heterogeneous multi-hop wireless networks in terms of the packet delivery ratio, packet delivery time, and average sum of the queue lengths of all nodes per timeslot.

In this paper, we design a new virtual queue-based back-pressure scheduling algorithm (VBR) for achieving significant delay reduction in wireless sensor networks (WSN). Our algorithm design comes from an observation that classical back-pressure scheduling algorithm usually needs a long period of time to form a queue backlog-based gradient in a network, which decreases towards the sink in the network, before achieving stable packet delivery performance. To address this issue, VBR is designed to pre-build proper virtual queue-based gradient at nodes in a WSN, which is chosen to be a function of traffic arrival rate, link rate, and distance to sink, in order to be adaptive to different network and application environments while achieving high network performance. Moreover, the queue backlog differential between each pair of neighbor nodes is decided by their actual queue lengths and also their virtual queue lengths (gradient values). We prove that VBR can maintain back-pressure scheduling’s throughput optimality. Simulation result shows that VBR can obtain significant performance improvement in terms of packet delivery ratio, average end-to-end delay, and average queue length as compared with existing work.

Backpressure based scheduling has revealed remarkable performance in wireless multihop networks as reported in a lot of previous work. However, its lack of consideration on energy use efficiency is still an obstacle for backpressure based algorithms to be deployed in resource-constrained wireless sensor networks (WSNs). In this paper, we focus on studying the design of energy efficient backpressure based algorithm. For this purpose, we propose a gradient-assisted energy-efficient backpressure scheduling algorithm (GRAPE) for WSNs. GRAPE introduces a new link-weight calculation method, based on which gradient information and nodal residual energy are taken into account when making decisions on backpressure based transmission scheduling. According to the decisions made by this new method, packets are encouraged to be forwarded to nodes with more residual energy. We theoretically prove the throughput-optimality of GRAPE. Simulation results demonstrate that GRAPE can achieve significant performance improvements in terms of energy use efficiency, network throughput, and packet delivery ratio as compared with existing work.

Positioning technique has become an indispensable part of our daily lives. Compared with the well-solved positioning issue in outdoor areas, indoor positioning is still far from being well explored, and existing techniques in this area are still far from being sufficiently mature to be widely used in practice. Recently, visible light communication based indoor positioning, also referred to as visible light based positioning, has attracted great attention and much work has been carried out, which reveals remarkable positioning accuracy. In this article, we provide a comprehensive survey of visible light based positioning. We first introduce some fundamental issues in visible light based positioning and further categorize existing systems in this field according to different design criteria. We then give a comprehensive survey of state-of-the-art systems, and introduce how each of the systems works and discuss their merits and deficiencies. Finally, we discuss challenging issues in this area and also point out future directions.

Epidemiologic studies have provided solid evidence for the neurotoxic effect of lead for decades of years. In view of the fact that children are more vulnerable to the neurotoxicity of lead, lead exposure has been an urgent public health concern for decades of years. The modes of action of lead neurotoxic effects include disturbance of neurotransmitter storage and release, damage of mitochondria, as well as induction of apoptosis in cerebrovascular endothelial cells, astroglia and oligodendroglia. Our studies here, from a novel point of view, demonstrates that lead specifically caused induction of COX-2, a well known inflammatory mediator in neurons and glia cells. Furthermore, we revealed that COX-2 was induced by lead in a transcription-dependent manner, which relayed on transcription factor NFAT, rather than AP-1 and NFκB, in glial cells. Considering the important functions of COX-2 in mediation of inflammation reaction and oxidative stress, our studies here provide a mechanistic insight into the understanding of lead-associated inflammatory neurotoxicity effect via activation of pro-inflammatory NFAT3/COX-2 axis.

TNFα is one of the initial and important mediators to activate downstream signaling pathways by binding to trimerized TNFα receptors (TNFR), and thus is an ideal drug target for cancer therapy. Taking advantage of intrinsic homotimerization of the globular domain of adiponectin (gAD), we have developed a novel TNFα antagonist, the trimerized fusion protein named sTNFRII-gAD. However, our previously-used CHO expression system yielded less than 10 mg/L of sTNFRII-gAD. To produce large quantities of sTNFRII-gAD efficiently, we used a modified CHO-S cell expression system, which is based on a pMH3 vector with non-coding GC-rich DNA fragments for high-level gene expression. We obtained stable clones that produced 75 mg/L of sTNFRII-gAD in the 96-well plate, adapted the clones to 40 ml suspension serum-free batch culture, then optimized the culturing conditions to scale up the fed-batch culture in a 3 L shake-flask and finally in a 5 L AP30 bioreactor. We achieved a final yield of 52 mg/L of sTNFRII-gAD. The trimerized sTNFRII-gAD exhibited the higher affinity to TNFα with a dissociation constant (Kd) of 5.63 nM than the dimerized sTNFRII-Fc with a Kd of 13.4 nM, and further displayed the higher TNFα-neutralizing activity than sTNFRII-Fc (p