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Intelligent computing provides efficient, real-time, and secure data analysis services for the Internet of Things (IoT). As the number of IoT devices increases, IoT generates massive, diverse, and multisourcing datasets that can be used to improve IoT services further. Models trained by intelligent computing from a single system or sensor are often not global, and sending all data directly to the computing platform wastes network bandwidth and may cause network congestion and even privacy leakage. To ensure IoT applications’ quality of service and privacy, we propose a framework that integrates edge computing and blockchain to provide lightweight data fusion and secure data analysis for IoT. We propose a lightweight data fusion method that can reduce the amount of data at the node level and prevent network congestion and bandwidth waste. Furthermore, we propose a hierarchical fuzzy hashing method to check and locate anomalies of IoT machine learning models to ensure the validity of IoT intelligent computing and the security of sensitive data. Finally, we demonstrate the effectiveness of the method proposed in this paper through experiments.

The underlying mechanisms connecting obesity and periodontal diseases remain unclear. This study investigates the potential causal association of obesity with periodontal diseases using Mendelian randomization (MR).Single-nucleotide polymorphisms of obesity traits including body mass index (BMI), waist circumference (WC), and WC adjusted for BMI (WCadjBMI) from large-scale genome-wide association studies were screened for instrumental variables. The single trait periodontitis and the combined trait comprising periodontitis and loose teeth were adopted as surrogates for periodontal diseases. Inverse-variance weighted (IVW), series of sensitivity analyses and multivariable MR were employed to determine the association of obesity with periodontal diseases.IVW results showed that per 1-SD increment in BMI (odds ratio, OR = 1.115; 95% confidence interval [CI] = 1.064-1.169; p .001) and WC (OR = 1.117; 95% CI = 1.052-1.185; p .001), but not WCadjBMI, were significantly associated with an increased risk of periodontitis/loose teeth. Moreover, the MR estimates were consistent across other MR sensitivity analyses and multivariable MR. However, a causal association of obesity with the single trait periodontitis was not identified.The presented evidence supports previous epidemiological findings by showing a potential causal association of genetic liability to obesity with periodontal diseases. The biological mechanisms underlying this association warrant further investigation.

Background Despite the widespread use of functional appliances, broad variations are reported on the treatment response. This retrospective study aims to investigate the pre-treatment cephalometric predictors on the treatment outcome of Twin block in growing patients with class II malocclusion Methods After screening, a total of 90 patients treated by Twin block were included in the study. The treatment outcome was the advancement of the chin position, assessed by the distance of skeletal pogonion to the vertical reference plane perpendicular to Frankfurt plane (△Pog-VRP). Moreover, △Pog-VRP is adjusted by facial growth evaluated by △N-Ba to minimize the growth discrepancy among individuals (adj△Pog-VRP). The pre-treatment cephalometric measurements were compared with independent t-test. Stepwise multivariate regression analysis was performed to determine the pre-treatment cephalometric predictors for the treatment outcome. Results Patients were equally divided into good response group (GRG/adjGRG, N = 45) and poor response group (PRG/adjPRG, N = 45) respectively based on the median value of △Pog-VRP/adj△Pog-VRP. Patients with greater chin advancement had a significantly reduced cephalometric measurements in the vertical dimensions including ∠Na-Go-Me, ∠MP-OP and sum of angels (P P = 0.01) and after adjustments (β=-0.29,95%CI -0.06, -0.01, P

The accidental spilling of cryogenic liquid leads to formation of a spreading pool, which may result in pool fires, BLEVE(boiled liquid evaporate vapor explosion) or vapor cloud fire, such as liquefied natural gas, is flammable. The key aspect of evaluating the consequence of such a disaster is to predict vaporization rate of the spreading cryogenic liquid pool. In this study, an empirical function was established to predict the temperature gradient of concrete. Afterwards an improved 1-D heat conduction equation was established to predict heat conduction of the spreading cryogenic liquid, and then vaporization rate was measured. In addition, to validate accuracy of the improved 1-D heat conduction equation, small-scale experiments were conducted to calculate vaporization rate for a spreading cryogenic liquid pool. The resulting vaporization rate decreased with discharge time, and increased with spill rate. The established empirical function was used to predict the temperature gradient displayed satisfactory accuracy with absolute average relative errors (AAREs) less than 10%; the improved 1-D heat transfer model AAREs were less than 13% compared with the experimental value. In summary, the improved 1-D heat transfer model can be applied to predict vaporization rate if the spill rate and discharge time are confirmed.

Standard orifice flowmeter has been widely used in the field of natural gas metering. However, the traditional stainless steel orifice flowmeter (TSSOF) is not resistant to wear and corrosion, especially the entrance sharpness will gradually increase. In order to improve the measurement accuracy and working life of the orifice flowmeter, the cemented carbide is embedded in the opening place of the TSSOF, thus, an improved carbide orifice flowmeter (ICOF) is manufactured. For conducting the field comparison experiment, four ICOF and four TSSOF were installed in four natural gas pipelines, respectively. The experiment results show that the variation ranges of the entrance sharpness of them are 9.863–26.438 µm and 9.192–57.329 µm, respectively; the entrance sharpness is increased with the increasing of the use time, but the change rate of the ICOF is smaller. The CFD simulation was also carried out. The simulated discharge coefficient values were compared with the calculated discharge coefficient values by the ISO empirical correlation. The results show that the accuracy of the two types of orifice flowmeter display a same decreasing trend with the increasing of the use time, and the absolute value of the relative error ranges are 0.51–2.28%, 0.13–4.25%, respectively, but the descent rate of the ICOF is smaller; the measurement accuracy of the ICOF is improved up to 2.39 times compared with the TSSOF, and it is more stable when the gas flow rate changes in the pipeline. Additionally, the effect of downstream inclination angle was studied. The result demonstrate that the measurement accuracy can be improved by 0.34%, when the downstream inclination angle is 60° at the gas flow rate of 5–15 m/s, which is superior to the commonly used inclination angle of 45°. In summary, the ICOF can be better applied to engineering practice.

Heterostructured TiO2 nanorod@nanobowl (NR@NB) arrays consisting of rutile TiO2 nanorods grown on the inner surface of arrayed anatase TiO2 nanobowls are designed and fabricated as a new type of photoanodes for photoelectrochemical (PEC) water splitting. The unique heterostructures with a hierarchical architecture are readily fabricated by interfacial nanosphere lithography followed by hydrothermal growth. Owing to the two-dimensionally arrayed structure of anatase nanobowls and the nearly radial alignment of rutile nanorods, the TiO2 NR@NB arrays provide multiple scattering centers and hence exhibit an enhanced light harvesting ability. Meanwhile, the large surface area of the NR@NB arrays enhances the contact with the electrolyte while the nanorods offer direct pathways for fast electron transfer. Moreover, the rutile/anatase phase junction in the NR@NB heterostructure improves charge separation because of the facilitated electron transfer. Accordingly, the PEC measurements of the TiO2 NR@NB arrays on the fluoride-doped tin oxide (FTO) substrate show significantly enhanced photocatalytic properties for water splitting. Under AM1.5G solar light irradiation, the unmodified TiO2 NR@NB array photoelectrode yields a photocurrent density of 1.24 mA cm(-2) at 1.23 V with respect to the reversible hydrogen electrode, which is almost two times higher than that of the TiO2 nanorods grown directly on the FTO substrate.

Gas condensate is mainly composed of highly valued hydrocarbons. Its high molecular weight is derived from the quantity of heavy ends. Predicting the dew point pressure of gas condensate is important in reservoir and gas transportation engineering. In this study, a thermodynamic model based on a modified Peng-Robinson equation of state is established to predict the dew point pressure. The presented model, using the MPR2 equation of state combined with a four-coefficient molar distribution function and the Kesler-Lee characterization method, is superior to four empirical correlations. This study also presents a comparison between the adopted MPR2 equation and ten equations of state, including eight PR families of equations, i.e., PR, PRSV, PRT, PRSF, PRHF, PRGF, PRTBD and PRJF, and two other modified versions of the SRK equation, i.e., SRKMCC and SRKNB. The statistical error analysis indicates that the model outperforms the other methods, with an average absolute relative error of 11.05% and a coefficient of determination of 0.829. The relative deviation range of the calculated values by the model is −0.20%–0.37%, which is in better agreement with the experimental values than the other methods employed in this study. Therefore, the presented model is effective and practical for predicting the dew point pressure for gas condensate systems.

The gas compressibility factor as an essential thermodynamic parameter is often used to analyze PVT behavior in natural gas engineering. To accurately predicate it for various gas condensates, a data base containing 916 data sets covering a wide range of experimental conditions is employed to establish and test the prediction model based on a three-parameter cubic equation of state (EoS). The presented model which based on EPT EoS combines with Elliott–Daubert binary interaction coefficients correlation, Ahmed et al. splitting and Hosseinifar–Jamshidi characterization methods is superior to conventional empirical correlations with three mixing rules and two-parameter EoSs as SRK, PR, PRSV and MPR2. Statistical error analysis shows that it outperforms empirical correlations with average absolute relative errors of 1.45% and coefficient of determination of 0.989. At pressure and temperature up to 95.04 MPa and 429.3 K, respectively, the model also outperforms two-parameter EoSs with average absolute relative errors of 1.65% and coefficient of determination of 0.992. The presented model is efficient and practical for predicting the compressibility factor of gas condensate.

Large-area ZnS nanobowl arrays with a monolayer inverse opal structure were synthesized facilely by nanosphere lithography at solution surface (NSLSS) through direct solution deposition. It was shown that the well-defined ZnS nanobowl arrays resulted from preferential deposition of ZnS at the polystyrene sphere–solution interface. Owing to the 2D periodic structure of the hexagonally ordered nanobowl arrays and the high refractive index of ZnS, the as-prepared ZnS nanobowl arrays showed intensive structural colors and tunable photonic band-gap properties. These ZnS nanobowl arrays are able to act as effective, fast-responsive 2D photonic crystal sensors based on the refractive index changes because of their easy accessibility for both solvents and analyte molecules. The as-prepared ZnS nanobowl arrays can be used as sensitive 2D photonic crystal sensors for fast detection of organic solvents and for visual oil sensing through color changes. After surface functionalization, the ZnS nanobowl arrays can be used as effective 2D photonic crystal biosensors for sensitive detection of avidin molecules with low detection limits (less than 100 pM) and broad working range.