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The existing underwater Internet of Things (UIoT) is based on IP architecture, which is not conducive to the efficient storage and distribution of huge amounts of content generated in underwater. Actively pushing all content to users causes much unnecessary resource consumption in the UIoT. The information centric networking (ICN) architecture opens new horizons up for these challenges. However, the slowness of underwater propagation speed makes traditional ICN not suitable for UIoT, especially considering about delay time. In this paper, we propose an information-centric wireless sensor networking scheme with water-depth-aware content caching (ICWSN-WDA) to solve the above challenges. First, we design a naming scheme and a hybrid communication mode suitable for ICWSN-WDA. The communication mode in underwater we design is divided into push and pull traffic, which balances energy consumption and delay time. Secondly, we define a push level to decide which water depth the content actively pushes to, finding a suitable junction point of two modes. Thirdly, as the water depth is deeper, it becomes more difficult to replace sensor battery. To save energy consumption of deep-water sensors, water-depth-aware caching mechanism is proposed based on water depth, popularity and senor energy. Our extensive evaluation confirms the effectiveness of our proposed scheme, and it balances energy consumption constraints and latency.

Ambient fine particulate matter (PM2.5) is one of the main environmental air pollutants that is closely related to the development of lung cancer, but the mechanisms are unclear. In this study, A549 cells were exposed to ambient PM2.5 to investigate the alterations of biological behaviors, and the possible role of miR-582-3p in the effects was further explored. The findings showed that PM2.5 exposure could significantly enhance the biological behaviors of A549 cells, and promote their epithelial-mesenchymal transition (EMT) transformation, especially at relatively low doses. Over-activation of Wnt/β-catenin signaling pathway and increased expression of miR-582-3p were also found in A549 cells after PM2.5 exposure. After the knockdown of miR-582-3p in A549 cells, the effects of PM2.5 on malignant biological behavior changes, EMT, and the activation of Wnt/β-catenin signaling pathway were all significantly alleviated. Furthermore, the inhibition of Wnt/β-catenin signaling pathway also inhibited the EMT process of A549 cells, which was rescued by the overexpression of miR-582-3p. Therefore, this study showed that ambient PM2.5 can upregulate the expression of miR-582-3p, consequently activate the Wnt/β-catenin signaling pathway, and thereby enhance EMT transformation and promote the malignant biological behaviors of A549 cells. These findings provide evidence for further research into the mechanisms by which exposure to PM2.5 in the environment promotes lung cancer.

Effects of prenatal ambient air pollution exposure could increase the risk of adverse pregnancy outcomes, which have been well documented by various studies. However, only very few studies investigated the effects on macrosomia. This study investigated the effects of prenatal air pollution exposure on the risk of macrosomia in a coastal city of China. Data of birth outcomes and air pollution in a coastal city in China between November 1, 2013, and December 31, 2017, were collected. Finally, 58,713 eligible births, including 8159 (13.9%) macrosomia and 50554 (86.1%) normal birth weight (NBW) infants, were included in the analysis. Logistic regression analyses were used to evaluate the effects of prenatal air pollution exposure on macrosomia. In the single-pollutant models, each 10 μg/m3 increase of PM2.5, PM10, and SO2 exposures, during the entire pregnancy or three trimesters, were related to elevated risk of macrosomia (adjusted RR, 95% CI) ranging from 1.018 (1.001, 1.035) to 1.314 (1.188, 1.454). In addition, O3 exposure in the first trimester (adjusted RR =1.034, 95% CI 1.009, 1.059) also increased the macrosomia risk. Prenatal PM2.5, PM10, and SO2 exposure could significantly increase the risk of macrosomia. These findings need to be further verified in more studies with multiple coastal cities included.

As the increasing popularity and development of the Internet of Vehicles (IoV), it is important to ensure the reliability and safety for IoV. However, communicating in an open-access environment makes road safety, communication security and privacy issues facing great challenges. Also, the high requirement of real-time detection and reponse to the security issues makes edge detection a more and more important research subject. Among all the challenges, Sybil Denial of Service (DoS) Attack is one of the most severe threats to the IoV safety, which can lead to traffic jams and other safety issues. In this paper, we introduce a Real-Time Edge Detection Scheme for Sybil DDoS in IoV. We use the entropy theory to quantify the traffic distribution, and further design an algorithm named Fast Quartile Deviation Check (FQDC) to recognize and locate the attack. Furthermore, because of the calculation limit in IoV scenarios, we also optimize the calculation with some useful techniques, such as the optimized sliding window, the incremental calculation of entropy values and make it suitable for the IoV environment. Finally, we proposed a temporal index. Temporal False Omission Rate (TFOR), to measure the performance of response speed and omission rate. In our evaluation, we successfully detect all the Sybil DDoS attacks provided in the F2MD datasets, and have the average alarm delay of 4.9193 seconds, and the average TFOR of 1.6024%.

According to the "fetal origin of disease" hypothesis, air pollution exposure in pregnancy may play an important role in stimulating the early programming of asthma and allergies. However, previous studies reported inconsistent findings. The aim of this meta-analysis was to provide higher grade evidence and quantitatively analyze the link between prenatal exposure to outdoor air pollutants and childhood asthma and wheezing. Databases (Web of Science and PubMed) were extensively searched for articles published from the start of the database to September 15, 2021. Either random-effect model or fixed-effect model was used to estimate the disease-specific relative risks (RR) with the corresponding 95% confidence intervals (CIs) to estimate the association. Newcastle-Ottawa Quality Score (NOS) was used to assess the quality of studies. This study finally included 13 cohort studies, and the findings showed that NO

Ambient air particulate exposure not only capable of elevating the risks of adverse pregnancy outcomes, but also has profound implications for human health, but the results are discrepant. This meta-analysis aimed to provide higher grade evidence on the impacts of air particulate on specific pregnancy outcomes. A total of 81 eligible cohort studies were included in this meta-analysis, of which the outcomes included preterm birth (PTB), moderate PTB, very PTB, extreme PTB, term low birth weight (TLBW), term birth weight (TBW), stillbirth (SB) and small for gestational age (SGA). The results showed that every 10 μg/m

Cooking oil fume (COF) is an important source of indoor air pollution which severely affects human health, and sufficient vitamin DTwenty-four pregnant rats were divided into 4 groups and treated with various treatments: normal feeding, COFs-PMWe successfully established an intrauterine growth restriction model in rats induced by COFs-PMThe findings of this study suggested that COF-PM

Ambient fine particulate matter (PM

Background Ambient fine particulate matter (PM2.5) is one of the main environmental air pollutants that closely related to the development of lung cancer, but the mechanisms are unclear. Results In this study, A549 cells were exposed to ambient PM2.5 to investigate the alterations of biological behaviors, and the possible role of miR-582-3p in the effects was further explored. The findings showed that that PM2.5 exposure could significantly enhance the biological behaviors of A549 cells, and promote their EMT transformation, especially at relatively low doses. Over-activation of Wnt/β-catenin signaling pathway and increased expression of miR-582-3p were also found in A549 cells after PM2.5 exposure. After the knockdown of miR-582-3p in A549 cells, the effects of PM2.5 on malignant biological behavior changes, EMT and the activation of Wnt/β-catenin signaling pathway were all significantly alleviated. Furthermore, the inhibition of Wnt/β-catenin signaling pathway also inhibited the EMT process of A549 cells, which was rescued by the overexpression of miR-582-3p. Conclusion This study showed that ambient PM2.5 can up-regulate the expression of miR-582-3p, consequently activate the Wnt/β-catenin signaling pathway, thereby enhance EMT transformation and promote the malignant biological behaviors of A549 cells. These findings provide evidence for further research into the mechanisms by which exposure to PM2.5 in the environment promotes lung cancer.

Background Exposure to air pollutants is associated with adverse pregnancy outcomes. But evidence on the effects of preconceptional air pollution exposure on the risk of termination of pregnancy (TOP) caused by pregnancy losses and congenital malformations is lacking. Methods The distributed lag nonlinear model (DLNM) was used to evaluate the impact of short-term air pollutants exposure on the risk of TOP. Stratified analyses by age ( Results PM2.5, PM10, and O3 exposure were significantly associated with elevated risk of TOP. The risk of TOP was associated with PM2.5 exposure from lag11 to lag15 in the single-pollutant model, and the strongest association was observed at lag13 (RR = 1.021, 95%CI:1.002–1.040). PM10 exposure from lag10 to lag15 was associated with increased TOP risk, with the corresponding peak association being at lag13 (RR = 1.020, 95%CI: 1.004–1.037). For O3, the single-day lag association appeared to be statistically significant from lag26 to lag27, with the highest RR of TOP cases being at lag27 (RR = 1.044, 95%CI: 1.005–1.084). Similar results were observed for pregnancy losses (PL). However, no significantly association between air pollution exposure and the risk of congenital malformations (CM) was found in this study. Stratified analyses showed that pregnant women with more advanced ages were more susceptible to PM2.5, PM10, and O3 exposure. The effect of PM2.5 exposure was statistically significant in cold season subgroups. Conclusion The findings suggest that exposure to PM2.5, PM10, and O3 before pregnancy are associated with the risk of TOP in Lu'an, China, reflecting the significance of preconceptional environmental exposure in the development of adverse pregnancy outcomes.

Background The topic of congenital heart diseases (CHDs) has attracted more and more attentions. Accumulating evidence suggests that exposure to air pollutants during pregnancy is associated with CHDs, yet the results are inconsistent and study about weekly exposure is few. Our study evaluated the association between weekly air pollution and CHDs in Hefei, China. Materials and methods Daily CHDs admission data were obtained from eight hospitals in Hefei from October 2015 to September 2017. Meteorological data and air quality were collected from China Meteorological Data Network. Distributed lag nonlinear model (DLNM) considering both the lag effect of exposure factors and the nonlinear relationship of exposure-reaction was used to assess the effect of weekly air pollutants exposure on CHDs admission. Results During the study period, totally 47,046 cases of perinatal infants were recruited, and the incidence of CHDs was 9.71 per thousand. The findings showed PM2.5, PM10, SO2 and NO2 significantly increased the risk of CHDs. Each 10 μg/m3 increase in PM2.5 during gestational weeks 20–26 increased the risk of CHDs. The susceptibility windows of PM10 (weeks 0–2 and weeks 25–29 of pregnancy), SO2 (weeks 8–16 and weeks 29–38) and NO2 (week 40), while the strongest effects of these 4 pollutants on CHDs were observed in week 22 (RR = 1.034, 95% CI: 1.007–1.062), week 0 (RR = 1.081, 95% CI: 1.02–1.146), week 37 (RR = 1.528, 95% CI: 1.085–2.153) and week 40 (RR = 1.171, 95% CI: 1.006–1.364), respectively. Conclusions Air pollutants (SO2, NO2, PM10, and PM2.5) exposure could increase the risk of CHDs, while the most crucial susceptibility windows for the exposure were mainly in the second and third trimesters. Boys seemed to be more sensitive to air pollution. Our study contributes to the knowledge of the association between maternal exposure to air pollution and CHDs, but the associations need to be verified by further studies.

Previous studies have evaluated the relationship between prenatal air pollution exposure and low birth weight, but the results are inconsistent. The purpose of this meta-analysis is to quantitatively analyze the relationship between maternal air pollutant exposure and low birth weight (LBW). PubMed and Web of Science databases were searched to obtain the studies on the relationship between the prenatal exposure of air pollutants and LBW that published as of June 2020. The pooled effects of air pollutant exposure and LBW were calculated using random-effect model (for studies with significant heterogeneity) or fixed-effect model (for studies without significant heterogeneity). Totally, 54 studies were included in this meta-analysis. The pooled effect of PM2.5, PM10, NO2, CO, SO2, and O3 exposure on LBW were 1.081 (95% CI: 1.043, 1.120), 1.053 (95% CI: 1.030, 1.076), 1.030 (95% CI: 1.008, 1.053), 1.007 (95% CI: 1.001, 1.014), 1.125 (95% CI: 1.017, 1.244), and 1.045 (95% CI: 1.005, 1.086), respectively. NO2 (per 10 ppb increase) and CO (per 100 ppb increase) exposure in the first trimester were positively correlated with LBW, of which the pooled effect was 1.022 (95% CI: 1.009, 1. 035) and 1.008 (95% CI: 1.004, 1.012), respectively. PM2.5 (per 10 μg/m3 increase) exposure in the third trimester significantly affected the LBW, of which the pooled effect was 1.053 (95% CI: 1.010, 1.097). In addition, PM10 (per 10 μg/m3 increase) exposure in the second trimester also significantly affected the LBW, with the pooled effect of 1.011 (95% CI: 1.005, 1.017). Prenatal exposure of the major air pollutants during the entire pregnancy could increase the risk of LBW, while the susceptible window of the pollutants varied.

Objective This study was to evaluate effects of mixed plant oils (identified as mixed oil 1 [MO1] and mixed oil 2 [MO2]) on performance, serum composition, viscera percentages, meat quality, and fatty acid deposition of broilers. Methods A total of 126 one-day-old Arbor Acres male broiler chicks (weighing 44.91± 0.92 g) were randomly allocated to 1 of 3 treatments with 7 replicate pens per treatment (6 broilers per pen). Dietary treatments included a corn-soybean basal diet supplemented with 3% soybean oil (CTR), basal diet with 3% MO1 (a mixture of 15% corn oil, 10% coconut oil, 15% linseed oil, 20% palm oil, 15% peanut oil and 25% soybean oil; MO1), or basal diet with 3% MO2 (a combination of 50% MO1 and 50% extruded corn; MO2). The trial consisted of phase 1 (d 1 to 21) and phase 2 (d 22 to 42). Results Compared to CTR, broilers fed MO (MO1 or MO2) had greater (p

Preterm birth (PTB), a major public health impact, has been shown to be associated with prenatal air pollution exposure, but the results are still inconsistent. This meta-analysis was performed to quantitatively evaluate the correlation between maternal air pollutant exposure and PTB, and provide evidence of higher grade to help improving the pregnancy outcomes. Databases including Web of Science and PubMed were searched to retrieve eligible studies published up to October 2020. The quality of the articles was assessed by the Newcastle-Ottawa Quality Score (NOS), after which the pooled estimate of the effect was calculated. The robustness of the joint estimates was confirmed by sensitivity analysis of excluded studies one by one, and the sources of heterogeneity were discussed by stratification analysis. Egger's and Begg's tests were performed to examine publication bias. Sixty studies that met the eligible criteria were finally included in this study. The findings showed combined relative risks of 1.032-1.070 for PTB, 0.859-1.081 for moderate PTB, 1.119-1.194 for very PTB and 1.128-1.259 for extremely PTB when mothers were exposed to PM2.5, PM10, NO2, O3, SO2, CO and NOx during pregnancy, while the sensitive windows varied for different air pollutants. Notably, PM2.5 exposure in only the 2nd trimester, NO2 exposure in only the 3rd trimester, and O3 exposure in all three trimesters were positively associated with PTB, while NO2 exposure in the 1st trimester was negatively associated with PTB. In addition, exposure of PM2.5 and PM10 in the 2nd trimester was positively associated with moderate PTB, and in the 1st and 2nd trimesters were positively associated with very PTB. These findings demonstrated that PM2.5, PM10, O3, NO2 were associated with PTB (including moderate PTB, very PTB, and/or extremely PTB), while NOx was not, and the relationship between CO and SO2 and PTB was not stable.

This paper presents a novel approach to achieve optimization for the audio features in compressed domain, which is the PSO (particle swarm optimization) algorithm basing on the attribute importance criterion of rough set theory. Our method firstly extracts the attributes of audio to form the feature vectors and pre-processes these vectors, then realizes the optimization using the proposed PSO algorithm, and finally determines the optimal feature subset. The experimental results show that feature optimization not only greatly reduces the training time of classifier, but also improves the classification accuracy. The performance of the classification model developed on the optimal feature subset. It achieves effective dimensionality reduction.

In this paper, we construct a MDCT-based auditory spectrum model and investigate the calculation of auditory spectrum in audio classification applications. It is focus on the compressed-audio. The MDCT coefficient maintains some of the acoustic features itself, and we build the auditory spectrum model based on the coefficient to simulate the human ear characteristics better. The proposed model is composed by three steps: outer and middle ear modelling; critical band decomposition and the spectrum spreading. Filter is the key point in the processes, and its band architecture would realize cochlea implants or auditory processors of biorealism. To assess the performance of the proposed MDCT-based auditory spectrum model, a range of various speech/music classification tasks is implemented wherein a support vector machine (SVM) algorithm is used as the classifiers. Features used for classification include MFSCs derived from the improvement of conventional MFCCs, as well as other features of the energy category. Compared to the conventional features, the features derived from the proposed MDCT-based auditory spectrum model show higher robust performance in noisy test. Experiments also indicate that, using the new features, the performance of the proposed MDCT-based auditory spectrum is slightly better than that of the original auditory spectrum.

Music style classification is important in numerous research fields. In this paper, a novel method is presented to classify six style music samples. The method makes use of the multi-class support vector machines (SVMs) model based on Mel-frequency cepstrum coefficients to classify different music style files. The "voting strategy" is chosen and "AND" gate is used to combine all of the k(k -1)/ 2 binary classifiers to test samples. After training the data, a model file is created. Then, new input data can be predicted according to the pre-trained model and get the result. The experimental results show that the multi-class support vector machine learning method has fine performance in music classification. Furthermore, the length of the training time and the parameters search using cross-validation are also discussed in this paper.

This paper proposes an approach to output-based objective music articulation assessment using M-SVMs (Multi-category support Vector Machines). This method extract MFSC ( Mel Frequency Spectral Coefficient ) as features, then mapping from MFSC vectors to subjective articulation evaluation levels by M-SVMs. The output results are objective music articulation assessment levels. Besides, we compare it with the situation that extracts MFCC (Mel Frequency Cesptral Coefficient) as characteristic parameters. Experimental results show that the MFSC is better and the correlation degree reaches over 0.96 when using the train samples and attains more than 0.82 when using the test samples.

With the amount of MP3 compressed data increasing, automatic scene change detection is becoming more and more important. Several studies have proposed some interesting approaches. However, none of these techniques analyze the audio signals in a low SNR noisy environment in compressed domain. In this paper, a new scene-change detection algorithm is proposed to detect the composed speech and music scene change automatically in a low SNR noisy environment in compressed domain. From the Modified Discrete Cosine Transform (MDCT) matrix, three robust features are extracted which include the Compressed MDCT Average Envelope (CMAE) parameters, the Compressed Possibilities Density Ratio (CPDR), and the Compressed Possibilities density Ratio local Average (CPRA). A statistical activity detection model in compressed domain employs the CMA to segment the audio signal into pure noise and noisy audio segments for compressed data sets. The automatic scene detection for composed speech and music sound with noise under low SNR in compressed domain is implemented. The experimental results show that the proposed methods can detect the pure noise, noisy speech and noisy music effectively even when SNR is as low as 0dB.

As one of the important multimedia information carrier, audio signal effectively enriches and satisfies people's apperception and acquirement of the information; in order to improve the accuracy of audio classification, we adopt the modified SVM that is based on hierarchical clustering analysis and binary decision tree to classify the seven types of audio signal in this paper, a number of the samples are used for training of each audio signal so as to obtain the excellent training templates, and then to test the audio signal. Experimental results show that the method has a good classification performance, compared with the traditional one-to-one and other algorithms, our algorithm not only reduces the training and testing time, but also further improves the accuracy rate, up to over 90%.

This paper proposed a quick audio search algorithm for specific audio retrieval. In the previous work, the time-series active search method with the upper bound proof reduces the matching calculations compare to the exhaustive search, while a lot of time still wasted in lower similarity matching calculations. Therefore, we redefine the similarity equation, predict equation and skipping equation to overcome this problem. Then a general time-series active search algorithm is proposed based on all those equations. Experimental results show that the proposed method reduces the number of matching calculations by 5 to 6 times compare to the time-series active search method and improves the search speed significantly.