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Energy efficiency has been a hot research topic for many years and many routing algorithms have been proposed to improve energy efficiency and to prolong lifetime for wireless sensor networks (WSNs). Since nodes close to the sink usually need to consume more energy to forward data of its neighbours to sink, they will exhaust energy more quickly. These nodes are called hot spot nodes and we call this phenomenon hot spot problem. In this paper, an Enhanced Power Efficient Gathering in Sensor Information Systems (EPEGASIS) algorithm is proposed to alleviate the hot spots problem from four aspects. Firstly, optimal communication distance is determined to reduce the energy consumption during transmission. Then threshold value is set to protect the dying nodes and mobile sink technology is used to balance the energy consumption among nodes. Next, the node can adjust its communication range according to its distance to the sink node. Finally, extensive experiments have been performed to show that our proposed EPEGASIS performs better in terms of lifetime, energy consumption, and network latency.

Many studies have examined the negative impact on smartphone addiction in adolescents. Recent concerns have focused on predictors of smartphone addiction. This study aimed to investigate the association of adolescents' smartphone addiction with family environment (specifically, domestic violence and parental addiction). We further investigated whether self-control and friendship quality, as predictors of smartphone addiction, may reduce the observed risk. We used the 2013 national survey on internet usage and utilization data from the National Information Agency of Korea. Information on exposure and covariates included self-reported experience of domestic violence and parental addiction, sociodemographic variables, and other variables potentially related to smartphone addiction. Smartphone addiction was estimated using a smartphone addiction proneness scale, a standardized measure developed by national institutions in Korea. Adolescents who had experienced domestic violence (OR = 1.74; 95% CI: 1.23-2.45) and parental addiction (OR = 2.01; 95% CI: 1.24-3.27) were found to be at an increased risk for smartphone addiction after controlling for all potential variables. Furthermore, on classifying adolescents according to their level of self-control and friendship quality the association between domestic violence and parental addiction, and smartphone addiction was found to be significant in the group with adolescents with lower levels of self-control (OR = 2.87; 95% CI: 1.68-4.90 and OR = 1.95; 95% CI: 1.34-2.83) and friendship quality (OR = 2.33; 95% CI: 1.41-3.85 and OR = 1.83; 95% CI: 1.26-2.64). Our findings suggest that family dysfunction was significantly associated with smartphone addiction. We also observed that self-control and friendship quality act as protective factors against adolescents' smartphone addiction.

Recently, wireless sensor network (WSN) has drawn wide attention. It can be viewed as a network with lots of sensors that are autonomously organized and cooperate with each other to collect, process, and transmit data around targets to some remote administrative center. As such, sensors may be deployed in harsh environments where it is impossible for battery replacement. Therefore, energy efficient routing is crucial for applications that introduce WSNs. In this paper, we present an energy efficient routing schema combined with clustering and sink mobility technology. We first divide the whole sensor field into sectors and each sector elects a Cluster Head (CH) by calculating its members’ weight. Member nodes calculate energy consumption of different routing paths to choose the optimal scenario. Then CHs are connected into a chain using the greedy algorithm for intercluster communication. Simulation results prove the presented schema outperforms some similar work such as Cluster-Chain Mobile Agent Routing (CCMAR) and Energy-efficient Cluster-based Dynamic Routing Algorithm (ECDRA). Additionally, we explore the influence of different network parameters on the performance of the network and further enhance its performance.

Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of the collected data. The privacy and security of data storage in WBAN devices cannot meet the needs of WBAN users. Therefore, this paper adopts blockchain technology to store data, which improves the security of the collected data. Moreover, a storage model based on blockchain in WBAN is proposed in our solution. However, blockchain storage brings new problems, for example, that the storage space of blockchain is small, and the stored content is open to unauthorized attackers. To solve the problems above, this paper proposed a sequential aggregate signature scheme with a designated verifier (DVSSA) to ensure that the user’s data can only be viewed by the designated person and to protect the privacy of the users of WBAN. In addition, the new signature scheme can also compress the size of the blockchain storage space.

In wireless sensor networks (WSNs), sensor devices must be equipped with the capabilities of sensing, computation and communication. These devices work continuously through non-rechargeable batteries under harsh conditions, the batter span of nodes determines the whole network lifetime. Network clustering adopts an energy neutral approach to extend the network life. The clustering methods can be divided into even and uneven clustering. If even clustering is adopted, it will cause the cluster head nodes (CHs) in vicinity of the base station to relay more data and cause energy hole phenomenon. Therefore, we adopt a non-uniform clustering method to alleviate the problem of energy hole. Furthermore, to further balance and remit resource overhead of the entire network, we combined the PEGASIS algorithm and the Hamilton loop algorithm, through a mixture of single-hop and multiple hops mechanisms, inserting a mobile agent node (MA) and designing an optimal empower Hamilton loop is obtained by the local optimization algorithm. MA is responsible for receiving and fusing packet from the CHs on the path. Network performance results show that the proposed routing algorithm can effectively prolong network lifetime, equalize resource expenditure and decrease the propagation delay.

Arthrospira platensis is the widely available source of spirulina that contains distinctive natural pigments, including carotenoids and C-phycocyanin (C-PC). In this study, the major carotenoid and C-PC contents were determined in seven commercially available spirulina powder products and laboratory-prepared A. platensis trichomes (AP-1) by an LC-DAD method and UV-Visible spectrometry, respectively. The correlation of these two pigment content levels with Hunter color coordinates and antioxidant activity was also evaluated. The L* value failed to show a significant correlation with pigment content, but a positive correlation was observed between a* values and the contents of total carotenoid and C-PC. As b* values decreased, the chlorophyll a and C-PC contents increased. AP-1 exhibited the highest content of total carotenoids, chlorophyll a and C-PC, and antioxidant activities among the samples. This observation could be related to degradation of these pigments during the mass production process. The carotenoid profiles suggested that the commercial spirulina powders originated from two different sources, A. platensis and A. maxima. Total carotenoid and C-PC content exhibited positive significant correlations with antioxidant activities measured by 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) assays. These results provide a strong scientific foundation for the establishment of standards for the commercial distribution of quality spirulina products.

Although physiological changes are the most evident indicators of skin aging by alteration of the skin’s structure and function, we question whether skin aging is also affected by the structure and assembly process of the skin microbiome. We analysed the skin microbiomes of 73 healthy Chinese women in two age groups (25–35 years old and 56–63 years old) using 16S rRNA gene amplicon sequencing; the overall microbiome structure was significantly different between the two age groups. An analysis using ecological theory to evaluate the process of microbial community assembly processes revealed that the microbiomes of the older group were formed under a greater influence of the niche-based process, with the network of microbes being more collapsed than that of the younger group. Inferred metagenomic functional pathways associated with replication and repair were relatively more predominant in the younger group whereas, among the various metabolism-related pathways, those associated with biodegradation were more predominant in the older group. Interestingly, we found two segregated sub-typing patterns in the younger group which were also observed in the skin microbiomes of young Chinese women living in four other cities in China. The results of our study highlights candidate microbes and functional pathways that are important for future research into preventing skin aging and which could lead to a comprehensive understanding of age-related skin microbiome characteristics.

The convolutional neural networks (CNN) applied in remote sensing scene classification have two common problems. One is that these models have large number of parameters, which causes over-fitting easily. The other is that the network is not deep enough, thus more abstract semantic information cannot be extracted. To solve these two problems, we propose a simple and efficient full convolutional network based on DenseNet for remote sensing scene classification. We construct a small number of convolutional kernels to generate a large number of reusable feature maps by dense connections, which makes the network deeper, but does not increase the number of parameters significantly. Our network is so deep that it has more than 100 layers. However, it has only about 7 million parameters, which is far less than the number of VGG's parameters. Then we incorporate an adaptive average 3D pooling operation in our network. This operation fixes feature maps of size 7 × 7 from the last DenseBlock to 1 × 1 and decreases the number of channels from 1024 to 512, thus the whole network can accept input images with different sizes. Furthermore, we design the convolutional layer instead of the fully connected layer that is used as a classifier usually, so that the output features of the network can be classified without flattening operation, which simplifies the classification operation. Finally, a good model is trained by exploiting pre-trained weights and data augmentation technology. Compared with several state-of-the-art algorithms, our algorithm improves classification performance significantly on UCM dataset, AID dataset, OPTIMAL-31 dataset and NWPU-RESISC45 dataset.

Ischemia/reperfusion injury triggers acute kidney injury (AKI) by aggravating oxidative stress mediated mitochondria dysfunction. The peroxisome proliferator-activated receptor gamma coactivator 1α (PGC-1α) is a master player that regulates mitochondrial biogenesis and the antioxidant response. We postulated that PGC-1α functions as cytoprotective effector in renal cells and that its regulation mechanism is coordinated by nuclear factor erythroid 2-related factor 2 (Nrf-2). In this study, to understand the effect and molecular mechanisms of PGC-1α, we developed an empty vector or PGC-1α-overexpressing stable cell lines in HK-2 cells (Mock or PGC-1α stable cells). PGC-1α overexpression increased the viability of cells affected by H 2 O 2 mediated injury, protected against H 2 O 2 -mediated apoptotic events and inhibited reactive oxygen species accumulation in the cytosol and mitochondria as compared to that in Mock cells. The cytoprotective effect of PGC-1α was related to Nrf-2 upregulation, which was counteracted by Nrf-2-specific knockdown. Using inhibitor of p38, we found that regulation of the p38/glycogen synthase kinase 3β (GSK3β)/Nrf-2 axis was involved in the protective effects of PGC-1α. Taken together, we suggest that PGC-1α protects human renal tubule cells from H 2 O 2 -mediated apoptotic injury by upregulating Nrf-2 via GSK3β inactivation mediated by activated p38.