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Asthma is a complex and heterogeneous inflammatory response characterized by various immune cells, including myeloid-derived suppressor cells (MDSCs) and CD4+ T-cell subsets. However, few studies on MDSC subsets and the association between MDSCs and CD4+ T-cell subsets in asthma are reported. In the present study, we detected CD4+ T cells and MDSC subsets and evaluated the relationship of these cells in mice with ovalbumin-induced asthma. We found that asthmatic mice showed severe airway inflammatory response and inflammatory cell infiltration in the lungs and bronchoalveolar lavage fluid. We also noted increased numbers of Th2, Th17, and MDSCs; decreased proportion of Th1 and Treg cells in the splenocytes and lungs; and increased expression of pro-inflammatory cytokines in splenocytes and lungs. Granulocytic MDSCs (G-MDSCs) and Th17 cells were closely related. Gemcitabine treatment reduced the G-MDSC level and the iNOS expression, alleviated the inflammatory response, and decreased the proportion and number of Th2 and Th17 cells in asthmatic mice. Besides the increase in Th2 and Th17 cells, the findings indicate that G-MDSC elevation plays a crucial role in asthmatic mice.

Great progress has been made in the field of pedestrian detection, but the following two problems have not yet been well addressed. One problem is the missed detection of small scale pedestrain as false negative failure, and the other one is confusion with anthropomorphic negative samples like vertical structures as false positive failure. In this paper, to tackle the above two problems, we use the light-field camera to capture pedestrian images for the following reasons: (i) the light-field camera can obtain multi-depth refocused images in a single exposure using one sensor, (ii) compared with 2D images, these refocused images can provide different key representations for different parts of the image. We further establish a light-field pedestrian dataset with 1766 images for pedestrian detection. A multi-focus detection network proposed in this work consists of multiple-branch detection models and takes multiple refocused images as inputs. In order to select the appropriate candidate proposal bounding box as final detection results, we design a cumulative probability selection (CPS) layer to combine each refocused image branch and accumulate the probability of each candidate neighboring proposal. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods on our light-field pedestrian dataset.

Background Cytidine nucleotide triphosphate synthase 1 (CTPS1) is a CTP synthase which play critical roles in DNA synthesis. However, its biological regulation and mechanism in triple-negative breast cancer (TNBC) has not been reported yet. Methods The expression of CTPS1 in TNBC tissues was determined by GEO, TCGA databases and immunohistochemistry (IHC). The effect of CTPS1 on TNBC cell proliferation, migration, invasion, apoptosis and tumorigenesis were explored in vivo and in vitro. In addition, the transcription factor Y-box binding protein 1 (YBX1) was identified by bioinformatics methods, dual luciferase reporter and chromatin immunoprecipitation (CHIP) assays. Pearson correlation analysis was utilized to assess the association between YBX1 and CTPS1 expression. Results CTPS1 expression was significantly upregulated in TNBC tissues and cell lines. Higher CTPS1 expression was correlated with a poorer disease-free survival (DFS) and overall survival (OS) in TNBC patients. Silencing of CTPS1 dramatically inhibited the proliferation, migration, invasion ability and induced apoptosis of MDA-MB-231 and HCC1937 cells. Xenograft tumor model also indicated that CTPS1 knockdown remarkably reduced tumor growth in mice. Mechanically, YBX1 could bind to the promoter of CTPS1 to promote its transcription. Furthermore, the expression of YBX1 was positively correlated with CTPS1 in TNBC tissues. Rescue experiments confirmed that the enhanced cell proliferation and invasion ability induced by YBX1 overexpression could be reversed by CTPS1 knockdown. Conclusion Our data demonstrate that YBX1/CTPS1 axis plays an important role in the progression of TNBC. CTPS1 might be a promising prognosis biomarker and potential therapeutic target for patients with triple-negative breast cancer.

In this paper, we propose a real-time gesture recognition model based on a feedforward artificial neural network for surface electromyography (sEMG) signals, which contains four processes: pre-processing, feature extraction, classification, and post-processing. In the feature extraction and selection stage, sliding windows are used to segment the data, and time-domain features and convolution results are used as input features. To avoid over-fitting of training, a dropout layer is added to the neural network model. In the post-processing part, two statistical methods are used: majority voting and continuous voting result verification. Finally, a test trial was performed using sEMG signals collected from the human body. The results show that the algorithm can achieve 92.7% recognition accuracy and has good real-time performance.

Helminths and their products can shape immune responses by modulating immune cells, which are dysfunctional in inflammatory diseases such as asthma. We previously identified SJMHE1, a small molecule peptide from the HSP60 protein of Schistosoma japonicum. SJMHE1 can inhibit delayed‐type hypersensitivity and collagen‐induced arthritis in mice. In the present study, we evaluated this peptide's potential intervention effect and mechanism on ovalbumin‐induced asthma in mice. SJMHE1 treatment suppressed airway inflammation in allergic mice, decreased the infiltrating inflammatory cells in the lungs and bronchoalveolar lavage fluid, modulated the production of pro‐inflammatory and anti‐inflammatory cytokines in the splenocytes and lungs of allergic mice, reduced the percentage of Th2 cells and increased the proportion of Th1 and regulatory T cells (Tregs). At the same time, Foxp3 and T‐bet expression increased, and GATA3 and RORγt decreased in the lungs of allergic mice. We proved that SJMHE1 can interrupt the development of asthma by diminishing airway inflammation in mice. The down‐regulation of Th2 response and the up‐regulation of Th1 and Tregs response may contribute to the protection induced by SJMHE1 in allergic mice. SJMHE1 can serve as a novel therapy for asthma and other allergic or inflammatory diseases.

Objective To identify the plasma protein biomarkers related to the chemoresistance of postoperative recurrence of epithelial ovarian cancers. Methods Forty plasma samples from patients in chemotherapy-sensitive and chemotherapy-resistant groups (20 for each group) were collected at Gynecology Department in the Fourth Hospital of Hebei Medical University from September 2013 to September 2014. The differentially expressed proteins between two groups were screened with two-dimensional gel electrophoresis (2-DE) and further analyzed by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF–MS). Results Thirty-four differentially expressed spots were identified between the two groups. Compared with the chemo-sensitive group, 21 protein spots were up-regulated and 13 were down-regulated in the chemoresistant group, in which 14 differentially expressed proteins were identified by the Mass spectrometry and Mascot search. Among the 14 proteins, complement C4-A, IgJ chain, clusterin, α-1-antitrypsin and carbonic anhydrase 1 were up-regulated, and transthyretin, haptoglobin, β-2-glycoprotein, Ig γ-2 chain C region, Ig γ-1 chain C region, complement factor I light chain, Igκ chain C region, complement C3 and apolipoprotein E were down-regulated in the chemoresistant group when compared with the chemosensitive group. Conclusion The up-regulated proteins including transthyretin, apolipoprotein E and haptoglobin proteins and the down-regulated proteins such as clusterin, carbonic anhydrase 1, alpha-1-antitrypsin were differentially expressed in the plasma between the chemo-sensitive group and the chemoresistant group, which may be potential biomarkers for predicting the chemotresistance of epithelial ovarian cancer patients.

Peripheral nerve injury results in limited nerve regeneration and severe functional impairment. Mesenchymal stem cells (MSCs) are a remarkable tool for peripheral nerve regeneration. The involvement of human umbilical cord MSC‐derived extracellular vesicles (hUCMSC‐EVs) in peripheral nerve regeneration, however, remains unknown. In this study, we evaluated functional recovery and nerve regeneration in rats that received hUCMSC‐EV treatment after nerve transection. We observed that hUCMSC‐EV treatment promoted the recovery of motor function and the regeneration of axons; increased the sciatic functional index; resulted in the generation of numerous axons and of several Schwann cells that surrounded individual axons; and attenuated the atrophy of the gastrocnemius muscle. hUCMSC‐EVs aggregated to rat nerve defects, down‐regulated interleukin (IL)‐6 and IL‐1β, up‐regulated IL‐10 and modulated inflammation in the injured nerve. These effects likely contributed to the promotion of nerve regeneration. Our findings indicate that hUCMSC‐EVs can improve functional recovery and nerve regeneration by providing a favourable microenvironment for nerve regeneration. Thus, hUCMSC‐EVs have considerable potential for application in the treatment of peripheral nerve injury.

BackgroundAs one of the main functional forms of mesenchymal stem cells (MSCs), MSC-derived extracellular vesicles (MSC-EVs) have shown an alternative therapeutic option in experimental models of allergic asthma. Oxygen concentration plays an important role in the self-renewal, proliferation, and EV release of MSCs and a recent study found that the anti-asthma effect of MSCs was enhanced by culture in hypoxic conditions. However, the potential of hypoxic MSC-derived EVs (Hypo-EVs) in asthma is still unknown.MethodsBALB/c female mice were sensitized and challenged with ovalbumin (OVA), and each group received PBS, normoxic human umbilical cord MSC-EVs (Nor-EVs), or Hypo-EVs weekly. After treatment, the animals were euthanized, and their lungs and bronchoalveolar lavage fluid (BALF) were collected. With the use of hematoxylin and eosin (HE), periodic acid-Schiff (PAS) and Masson’s trichrome staining, enzyme-linked immune sorbent assay (ELISA), Western blot analysis, and real-time PCR, the inflammation and collagen fiber content of airways and lung parenchyma were investigated.ResultsHypoxic environment can promote human umbilical cord MSCs (hUCMSCs) to release more EVs. In OVA animals, the administration of Nor-EVs or Hypo-EVs significantly ameliorated the BALF total cells, eosinophils, and pro-inflammatory mediators (IL-4 and IL-13) in asthmatic mice. Moreover, Hypo-EVs were generally more potent than Nor-EVs in suppressing airway inflammation in asthmatic mice. Compared with Nor-EVs, Hypo-EVs further prevented mouse chronic allergic airway remodeling, concomitant with the decreased expression of pro-fibrogenic markers α-smooth muscle actin (α-SMA), collagen-1, and TGF-β1-p-smad2/3 signaling pathway. In vitro, Hypo-EVs decreased the expression of p-smad2/3, α-SMA, and collagen-1 in HLF-1 cells (human lung fibroblasts) stimulated by TGF-β1. In addition, we showed that miR-146a-5p was enriched in Hypo-EVs compared with that in Nor-EVs, and Hypo-EV administration unregulated the miR-146a-5p expression both in asthma mice lung tissues and in TGF-β1-treated HLF-1. More importantly, decreased miR-146a-5p expression in Hypo-EVs impaired Hypo-EV-mediated lung protection in OVA mice.ConclusionOur findings provided the first evidence that hypoxic hUCMSC-derived EVs attenuated allergic airway inflammation and airway remodeling in chronic asthma mice, potentially creating new avenues for the treatment of asthma.

Electric Vehicle (EV) sharing systems have recently experienced unprecedented growth across the world. One of the key challenges in their operation is vehicle rebalancing, i.e., repositioning the EVs across stations to better satisfy future user demand. This is particularly challenging in the shared EV context, because i) the range of EVs is limited while charging time is substantial, which constrains the rebalancing options; and ii) as a new mobility trend, most of the current EV sharing systems are still continuously expanding their station networks, i.e., the targets for rebalancing can change over time. To tackle these challenges, in this paper we model the rebalancing task as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We propose a novel approach of policy optimization with action cascading, which isolates the non-stationarity locally, and use two connected networks to solve the formulated MARL. We evaluate the proposed approach using a simulator calibrated with 1-year operation data from a real EV sharing system. Results show that our approach significantly outperforms the state-of-the-art, offering up to 14% gain in order satisfied rate and 12% increase in net revenue.

To summarize the clinical characteristics and treatments of preeclampsia complicated with hyponatremia. We reported a new case of preeclampsia complicated with severe hyponatremia; searched for relevant articles from the PubMed, Scopus and Cochrane databases; and reviewed all reported cases. Twenty-one reported cases were found. Our case is 22nd, and the serum sodium level in this case was the lowest reported. After treatment comprising fluid restriction, hypertonic saline and caesarean section, a relatively good outcome was achieved. In all reported cases, SIADH, preeclampsia or the combined effect of preeclampsia and induced nephrotic syndrome were the speculated pathogeny. Termination was performed due to adverse manifestations; six cases underwent transvaginal deliveries, and sixteen cases underwent caesarean section. Fifteen patients recovered from hyponatremia within 72 h after delivery. The pathogenesis of hyponatremia occurring in patients with preeclampsia is still unclear. Termination of the pregnancy led to a stabilization of the sodium level, ICU monitoring was necessary, and fluid restriction and hypertonic saline intake were applied; however, there was no evidence of the effectiveness of the treatments.

Non-volatile random-access memory (NVRAM) technology is maturing rapidly and its byte-persistence feature allows the design of new and efficient fault tolerance mechanisms. In this paper we propose the versionized process (VerP), a new process model based on NVRAM that is natively non-volatile and fault tolerant. We introduce an intermediate software layer that allows us to run a process directly on NVRAM and to put all the process states into NVRAM, and then propose a mechanism to versionize all the process data. Each piece of the process data is given a special version number, which increases with the modification of that piece of data. The version number can effectively help us trace the modification of any data and recover it to a consistent state after a system crash. Compared with traditional checkpoint methods, our work can achieve fine-grained fault tolerance at very little cost.

BRCA1/2 mutations represent a high risk of breast cancer and are related to early-onset breast cancer. However, few studies have reported the relationship between BRCA1/2 mutations and their clinical characteristics in early-onset breast cancers. This study is the first article that characterizes the risk factor profiles in Chinese patients selected by the age of onset (≤ 40 years old). We found some differences in the prevalence of germline BRCA1/2 mutations between Asian and Western countries.A total of 1371 consecutive unselected Chinese early-onset breast cancer patients were enrolled from the Fujian Medical University Union Hospital, China, and screened for germline BRCA1/2 mutations. Full-exome sequencing in next-generation sequencing technology was performed in all patients to examine BRCA1/2 mutations.In our study, 25 (1.8%) and 61 (4.4%) patients were identified with BRCA1 and BRCA2 mutations, respectively, among the unselected early-onset breast cancer patients. BRCA1 mutations were associated with pregnancies (p = 0.026), and BRCA1 carriers had a higher likelihood of being HR positive (p 0.001), HER2 negative (p 0.001), or high grade (p = 0.002) than noncarriers. Among BRCA2 mutations, the age of onset was younger in carriers than in noncarriers (p = 0.017), and BRCA2 carriers were more likely to have lymph node metastasis (p = 0.004). HR-positive or HER2-negative patients were likely to be positive for BRCA2 mutations (p 0.001). Overall, 14 BRCA1 mutations and 8 BRCA2 mutations were first reported in our study CONCLUSION: This study provided some information about the spectrum of BRCA1/2 mutations and characterized the risk factors for early-onset breast cancer in China.

Real-time collaborative programming is an emerging approach that supports a team of programmers to view and edit shared source code at the same time. Each programmer can edit any part of the source code, and changes become instantly visible at other collaborating sites. With a broad range of application scenarios and benefits, real-time collaborative programming has attracted increasing interest from academia and industry. Lightweight integrated development environments (lightweight IDEs) have rapidly grown in popularity in the recent years, but there are serious problems and limitations with existing real-time collaboration support for lightweight IDEs. In this study, we contribute a novel real-time collaborative programming environment named CoVSCode that supports unconstrained and flexible real-time collaboration based on Visual Studio Code, one of the most popular and widely used lightweight IDEs. We present design objectives and rationales, the workflow and functional design from collaborating programmers&rsquo, perspectives, major technical issues and solutions, and prototype implementation, as well as a set of experimental evaluations that have demonstrated the technical feasibility and good performance of the prototype system. All approaches, techniques and solutions derived in this work are generic, which can also be applied in building real-time collaborative programming environments for other lightweight IDEs.

This paper evaluates the causal effects of China's anti-corruption campaign on corporate environmental costs. Using primary data and the investigations of the anti-corruption campaign as a quasi-natural experiment, our difference-in-differences estimation shows that: (1) the inspections encourages heavy pollution enterprises to increase investment in environmental protection; (2) our findings are particularly pronounced in subsamples of SOEs, non-SOEs with political connections, firms with more related party transaction, with less government’ intervention, with more financing constrains and information asymmetry. Overall, this paper links the corruption and environmental protection, which is helpful for the governments to improve the environment circumstances through political channel.

To evaluate the correlation between expression of p53, Livin, Excision repair cross-complementation group 1 (ERCC1), BRCA1 and Poly (ADP-ribose) polymerase 1 (PARP 1) in epithelial ovarian cancer (EOC) tissues with platinum-based chemotherapy and prognosis in patients who received either comprehensive surgical staging or cytoreductive surgery.The protein expressions level of five potential regulators involved in chemo-resistance, including p53, Livin, ERCC1, BRCA1 and PARP1 in EOC tissues from 66 patients were evaluated using immunohistochemistry method. We also measured preoperative CA125 level measured by an electrochemiluminescence immunoassay (ECLIA) in all patients. Cox proportional hazard regression model was established to identify whether these proteins are associated with overall survival.Chemo-resistance and poor overall survival were shown to be significantly related with positive expressions of p53, Livin, ERCC1, BRCA1 and PARP1. The evaluation of risk factors on the chemo-resistance showed that ERCC1 and BRCA1 are strong risk factors (OR: 21.12 and 21.61, all P0.01), while the positive expression of ERCC1, BRCA1 and PARP1 was significantly highly associated with the overall survival (HR: 3.9, 3.7 and 2.6, all P0.05, respectively). CA125 levels were significantly higher in patients with positive expression of P53, BRCA1, ERCC1 or Livin compared with those with negative expression (471:146, 667:260, 494:261 and 4589:89 U/ml, respectively, all P0.05).The elevated expression levels of ERCC1 and BRCA1 were identified as significant risk factors for chemo-resistance in EOC. Reduced expression levels of ERCC1, BRCA1 and PARP1 were significantly associated with better overall survival. The CA125 levels were significantly higher in patients with EOC specimens that were positive of p53, BRCA1, ERCC1 and Livin.

As a scenario experience project running on PC, this work takes roaming the Innovation and Entrepreneurship Education Center of the Department of Psychology of Beijing Normal University as the background, and takes the real scene inside the education center as the prototype, so that visitors can experience the roaming realized by virtual reality technology.

Page protection is often used to achieve memory access monitoring in many applications, dealing with program-analysis, checkpoint-based failure recovery, and garbage collection in managed runtime systems. Typically, low overhead access monitoring is limited by the relatively large page-level granularity of memory management unit hardware support for virtual memory protection. In this paper, we improve upon traditional page-level mechanisms by additionally using hardware support for virtualization in order to achieve fine and flexible granularities that can be smaller than a page. We first introduce a memory allocator based on page protection that can achieve fine-grained monitoring. Second, we explain how virtualization hardware support can be used to achieve dynamic adjustment of the monitoring granularity. In all, we propose a process-level virtual machine to achieve dynamic and fine-grained monitoring. Any application can run on our process-level virtual machine without modification. Experimental results for an incremental checkpoint tool provide a use-case to demonstrate our work. Comparing with traditional page-based checkpoint, our work can effectively reduce the amount of checkpoint data and improve performance.

With shrinking design rules, the overall patterning requirements are getting aggressively tighter and tighter. For the 5-nm node and beyond, on-product overlay below 2.5nm is required. Achieving such performance levels will not only need optimization of scanner performance but a holistic tuning of all process steps. In previous work, it has been shown that process-induced pattern asymmetry has significant impact on overlay performance at wafer edge and can be partially compensated by applying high-order scanner corrections or optimizing metrology targets. Today, we present the reduction of process-induced pattern asymmetry in a tunable etch system and demonstrate the related on-product overlay improvement combined with scanner corrections. In our work we utilize etch tools (Lam Kiyo® conductor etch systems) with proprietary edge tuning technology that can be used to reduce the etch-related asymmetry at the wafer edge. In combination to this unique method, we evaluate the impact of high order corrections per exposure field to compensate for process asymmetry at the wafer edge with a state-of-the-art 1.35 NA immersion scanner (NXT:1970Ci). The study is done on dedicated test wafers with 10-nm logic node design. We use angle-resolved scatterometry (YieldStar® S-250), atomic force microscopy, and SEM cross-sections to characterize process asymmetry. We present experimental investigation of the effect of etch tuning and scanner corrections on the pattern shift and the resulting overlay. In particular, we present results showing a reduction of etch-induced pattern shift by 12nm at wafer radius 147mm. Results show that asymmetry can be addressed by both, litho compensation and etch tuning, and bring on-product overlay down to the required level. We discuss the benefit of the correction techniques especially for thick hard mask layers (the pattern shift scales linear with hard mask thickness) and evaluate a combined correction scenario, where preventive etch tuning and feed-back based scanner corrections are used. We conclude that a holistic tuning of all process steps will be required to fulfill overlay requirements of future nodes.

Web page segmentation aims to break a page into sections that can reveal the information presentation structure and appear coherent to readers. In this paper, we propose a new web page segmentation framework based on the process of analyzing and understanding web page structure. After extracting the segmentation graph structure, we formulate the label assignment task which determines whether each boundary should segment current block or not on a graph as a structured learning problem. Computation of highest scoring label assignment relies on Viterbi algorithm and joint feature function captures the dependency among boundaries. To solve the learning of parameters, we adopt a learning model based on perceptron algorithm. Furthermore, utilizing the previous framework, we propose a web information crawling application framework which integrates web page segmentation and semantic block classification process.

Currently, various Medium Access Control (MAC) protocols have been proposed for underwater Wireless sensor networks. Unlike terrestrial networks, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves incurs long propagation delays that typically lead to low throughput especially in protocols that require receiver feedback such as multimedia stream delivery and the energy cost of transmission is much higher than receptions. Thus, collision and retransmission should be reduced in practice in order to reduce energy cost and improve throughput. Based on these motivations, we propose a novel MAC protocol called NCDC-MAC. NCDC-MAC leverages network coding and duty cycle, the combination of which is seldom explored, to solve these challenges. Heterogeneous wireless networks and node roles are considered while designing our algorithms. Meanwhile, fairness including schedule and service time assignment is supported in our approach. Extensive simulations show that our approach can achieve significantly better performance.

Hybrid sensor networks (HSNs) comprise of mobile and static sensor nodes setup for purpose of collaboratively performing tasks like sensing a phenomenon or monitoring a region. In this paper, we present target interception as a novel application using mobile sensor nodes as executor. Static sensor nodes sense, compute and communicate with each other for navigation. Mobile nodes are guided to intercept target by the static nodes nearby. Our approach does not require any prior maps of the environment thus, cutting down the cost of the overall energy consumption. As to multi-targets multi-mobile nodes case, we present a PMB algorithm for task assignment. Simulation results have verified the feasibility and effectiveness of our approach proposed.

Free-space optical (FSO) links offer gigabit per second data rates and low system complexity, but suffer from atmospheric loss due to fog and scintillation. Radio-frequency (RF) links have lower data rates, but are relatively insensitive to weather. Hybrid FSO/RF links combine the advantages of both links. Currently, selection or "hard-switching" is performed between FSO or RF links depending on feedback from the receiver. This technique is inefficient since only one medium is used at a time. In this paper, we develop a "soft-switching" scheme for hybrid FSO/RF links using short-length Raptor codes. Raptor encoded packets are sent simultaneously on both links and the code adapts to the conditions on either link with very limited feedback. A set of short-length Raptor codes (k = 16 to 1024) are presented which are amenable to highspeed implementation. A practical Raptor encoder and decoder are implemented in an FPGA and shown to support a 714 Mbps data rate with a 97 mW power consumption and 26360 gate circuit scale. The performance of the switching algorithms is simulated in a realistic channel model based on climate data. For a 1 Gbps FSO link combined with a 96 Mbps WiMAX RF link, an average rate of over 472 Mbps is achieved using the implemented Raptor code while hard-switching techniques achieved 112 Mbps on average.

Wireless sensor networks may be destroyed by targets in field surveillance. Self-protection technology focuses on using sensor nodes to protect themselves so as to resist attacks. Selecting protecting nodes has been proved to be a NP-complete problem. In this paper, we propose a new distributed layered negotiation based approximation algorithm to find a protecting set using local topology information. Experimental result manifests the feasibility and efficiency of our algorithms proposed.

Indoor localization based on WiFi signal strength fingerprinting techniques have been attracting many research efforts in past decades. Many localization algorithms have been proposed in order to achieve higher localization accuracy. In this paper, we investigate Bayes learning algorithms and some common-used machine learning algorithms. We identify a general problem of Zero Probability (ZP) which may cause significant decrease of accuracy. In order to solve this problem, we propose an Improved Naive Bayes Simple learning algorithm, namely INBS, based on our data set characteristic. INBS is applicable even though Zero Probability problem occurs. We design experiments based on off-the-shelf WiFi devices, mobile phones and well-known machine learning tool Weka. Our experiments are conducted on a floor covering 560m 2 in a campus building and a laboratory covering 78m 2 . Experiment results show that INBS outperforms traditional Naive Bayes and k-Nearest Neighbors (k-NN) algorithms and two common-used machine learning algorithms in terms of accuracy.

The network lifetime is a fundamental criterion for evaluating WSNs. Receiving and sending data packets consume energy so judicious routing strategies can effectively extend operational time. The ant colony optimization algorithm, known as ACO, is a probabilistic technique for solving computational problems. In this paper, an ACO-based routing algorithm called PACO is proposed. We take three factors into consideration. First of all, the hops are to be minimized. Additionally, energy distance is defined and needs minimizing. We also define the popularity of the sensors given the necessity to avoid those nodes that are used too often while routing. The definition of the lifetime and energy model is discussed as well. By conducting the simulation, it is proved that PACO outperforms other ACO routing algorithms, indicating that PACO is a promising routing algorithm for prolonging the lifetime of WSNs.

Field surveillance is one of the most important applications for wireless sensor networks. Many sensors are deployed in a region of concern to detect any potential targets. On the contrary, intelligent target finds out the deployed sensors to destroy for fear of being detected and leads to defunct surveillance. Due to the importance of sensor nodes, it is necessary to hide them from the scope of target. In this paper, we introduce Proactive Stealthiness as the natural immune system covered on sensor networks. We model the stealthiness problem, and propose a distributed marking algorithm based on Breadth-First Spanning Tree. Different from previous works which only introduce self protection to wireless sensor networks, we not only maintain the connectivity of all dominating sensors but achieve the minimum hops of urgent message to gateway. This makes the method proposed more practical and sensible. Extensive simulation confirms the performance of algorithms proposed and Experimental result manifests the feasibility and efficiency.

Since the sensor node is important and critical in Wireless Sensor Networks (WSNs), it is necessary to provide wireless sensor network certain level of protection. The self protection problem focuses on using the sensor nodes of the network to protect themselves rather than other objects so that the network can resist attacks. In this paper, we study both the minimum 1-self protection and minimum k-self protection problems, and propose our distributed local optimum algorithms. In addition, we give our method of finding multiple protection sets when the network is redundant, so that the protection sets can work by turns. Experimental result manifests the efficiency and feasibility of our algorithms proposed.

Hybrid sensor network (HSN) is composed of a set of static and mobile sensor nodes. Besides the capabilities of sensing, computing and communicating like static nodes, mobile nodes have locomotion capability particularly. Area coverage is one of the most fundamental metrics for hybrid sensor network because it is crucial to the quality of monitoring. In order to improve the network coverage, we employ the mobility of mobile nodes to compensate for the uneven deployment of static sensors. In this paper we propose a 3-phase algorithm based on Hamilton Circuit. Conducted simulation shows that the algorithm can improve coverage performance with less energy conservation. Experimental results verify the feasibility and validity of the algorithm proposed.

A robust solution for context-aware multimedia delivery in the ubiquitous computing environment remains a challenging problem. Its heterogeneous and dynamic nature demands a more flexible and intelligent framework than Internet does. We propose a layered middleware solution to address this issue. The system responds to the condition of the network to offer extensibility and efficiency. Furthermore, we propose an active controlling scheme, in which the multimedia delivery is able to adapt to the environment variation in a timely fashion. In addition, we describe an application scenario of our framework, which opens out a wide prospect.

Metadata are abstraction and knowledge of wireless sensor networks and are used to provide adequate information for query processing. The purpose of metadata management is to provide adequate information for query processing, while at the same time to make the cost of maintaining the metadata as low as possible. In this paper, we discuss new issues about metadata management in wireless sensor networks; and propose a metadata management solution which includes an architecture and a model-aided approach for the base station to collect meta-data from sensor nodes. Experimental results show the effectiveness of our solution.

A wireless sensor network may consist of a large number of small, battery-powered, wireless sensor nodes and works in an unattended way. In order to manage the sensor network and collect data from the network efficiently, we need to know the state of the WSN. In this paper, we propose a model-aided approach to support the monitoring of the state of WSNs. In this approach, models are created on base station to support the monitoring of the network, and mobile agents are injected into the network to collect state information. Experimental results show the effectiveness of our approach.

A robust solution for context-aware multimedia delivery in the pervasive computing environment remains a challenging problem. Its heterogeneous and dynamic nature demands a more flexible and intelligent framework than Internet does. We propose an adaptive middleware solution to address this issue. The system responds to the condition of the network to offer extensibility and efficiency. Furthermore, we propose an active controlling scheme, in which the multimedia delivery is able to adapt to the environment variation in a timely fashion. In addition, we describe an application scenario of our framework, which opens out a wide prospect.

Intelligent city traffic for travelling navigation, traffic prediction and decision support needs to collect large-scale real-time data from numerous vehicles. As a small, economical yet reasonably efficient device, wireless sensors can conveniently serve for this purpose. In this paper, we investigate how to deploy wireless sensor networks in buses to gather traffic data for intelligent city traffic. The paper presents a self-organization mechanism and a routing protocol for the proposed sensor networks. Our work has three advantages: (1)adaptive network topology, which satisfies highly mobile city traffic environment, (2)directed data transmission, saving energy consumption of sensor nodes with limited power resource, and (3)longer lifetime because of fewer redundant network communication and balanced power usage of sensor nodes in a network.

Most sensor networks are deployed to monitor a region of interest for any potential target. An intelligent target looks for the best path to traverse the sensing field. In this paper, we model this anti-detection problem, propose a number of path-finding algorithms, and evaluate these algorithms. Different from previous works which assume complete information of the sensing field, we assume that the target only can detect part of the deployment of sensor network. This makes the proposed method more practical. Experimental results manifest the effectiveness of the path-finding algorithms.