A Simple Graphene NH₃ Gas Sensor via Laser Direct Writing.

Ammonia gas sensors are very essential in many industries and everyday life. However, their complicated fabrication process, severe environmental fabrication requirements and desorption of residual ammonia molecules result in high cost and hinder their market acceptance. Here, laser direct writing is used to fabricate three parallel porous 3D graphene lines on a polyimide (PI) tape to simply construct an ammonia gas sensor. The middle one works as an ammonia sensing element and the other two on both sides work as heaters to improve the desorption performance of the sensing element to ammonia gas molecules. The graphene lines were characterized by scanning electron microscopy and Raman spectroscopy. The response and recovery time of the sensor without heating are 214 s and 222 s with a sensitivity of 0.087% ppm-1 for sensing 75 ppm ammonia gas, respectively. The experimental results prove that under the optimized heating temperature of about 70 °C the heaters successfully help implement complete desorption of residual NH₃ showing a good sensitivity and cyclic stability

Improved Differential Analysis of Block Cipher PRIDE

In CRYPTO 2014 Albrecht \emph{et al.} brought in a 20-round iterative lightweight block cipher PRIDE which is based on a good linear layer for achieving a tradeoff between security and efficiency. A recent analysis is presented by Zhao \emph{et al.}. Inspired by their work, we use an automatic search method to find out 56 iterative differential characteristics of PRIDE, containing 24 1-round iterative characteristics, based on three of them we construct a 15-round differential and perform a differential attack on the 19-round PRIDE, with data, time and memory complexity of $2^{62}$, $2^{63}$ and $2^{71}$ respectively

Retrieval of atmospheric CFC-11 and CFC-12 from high-resolution FTIR observations at Hefei and comparisons with other independent datasets

peer reviewedAbstract. Synthetic halogenated organic chlorofluorocarbons (CFCs) play an important role in stratospheric ozone depletion and contribute significantly to the greenhouse effect. In this work, the mid-infrared solar spectra measured by ground-based high-resolution Fourier transform infrared spectroscopy (FTIR) were used to retrieve atmospheric CFC-11 (CCl3F) and CFC-12 (CCl2F2) at Hefei, China. The CFC-11 columns observed from January 2017 to December 2020 and CFC-12 columns from September 2015 to December 2020 show a similar annual decreasing trend and seasonal cycle, with an annual rate of -0.47±0.06 % yr−1 and -0.68±0.03 % yr−1, respectively. So the decline rate of CFC-11 is significantly lower than that of CFC-12. CFC-11 total columns were higher in summer, and CFC-12 total columns were higher in summer and autumn. Both CFC-11 and CFC-12 total columns reached the lowest in spring. Further, FTIR data of NDACC (Network for the Detection of Atmospheric Composition Change) candidate station Hefei were compared with the ACE-FTS (Atmospheric Chemistry Experiment Fourier transform spectrometer) satellite data, WACCM (Whole Atmosphere Community Climate Model) data, and the data from other NDACC-IRWG (InfraRed Working Group) stations (St. Petersburg, Jungfraujoch, and Réunion). The mean relative difference between the vertical profiles observed by FTIR and ACE-FTS is -5.6±3.3 % and 4.8±0.9 % for CFC-11 and CFC-12 for an altitude of 5.5 to 17.5 km, respectively. The results demonstrate that our FTIR data agree relatively well with the ACE-FTS satellite data. The annual decreasing rate of CFC-11 measured from ACE-FTS and calculated by WACCM is -1.15±0.22 % yr−1 and -1.68±0.18 % yr−1, respectively. The interannual decreasing rates of atmospheric CFC-11 obtained from ACE-FTS and WACCM data are higher than that from FTIR observations. Also, the annual decreasing rate of CFC-12 from ACE-FTS and WACCM is -0.85±0.15 % yr−1 and -0.81±0.05 % yr−1, respectively, close to the corresponding values from the FTIR measurements. The total columns of CFC-11 and CFC-12 at the Hefei and St. Petersburg stations are significantly higher than those at the Jungfraujoch and Réunion (Maïdo) stations, and the two values reached the maximum in local summer or autumn and the minimum in local spring or winter at the four stations. The seasonal variability at the three stations in the Northern Hemisphere is higher than that at the station in the Southern Hemisphere

A Strategy for the Proliferation of Ulva prolifera, Main Causative Species of Green Tides, with Formation of Sporangia by Fragmentation

Ulva prolifera, a common green seaweed, is one of the causative species of green tides that occurred frequently along the shores of Qingdao in 2008 and had detrimental effects on the preparations for the 2008 Beijing Olympic Games sailing competition, since more than 30 percent of the area of the games was invaded. In view of the rapid accumulation of the vast biomass of floating U. prolifera in green tides, we investigated the formation of sporangia in disks of different diameters excised from U. prolifera, changes of the photosynthetic properties of cells during sporangia formation, and development of spores. The results suggested that disks less than 1.00 mm in diameter were optimal for the formation of sporangia, but there was a small amount of spore release in these. The highest percentage of area of spore release occurred in disks that were 2.50 mm in diameter. In contrast, sporangia were formed only at the cut edges of larger disks (3.00 mm, 3.50 mm, and 4.00 mm in diameter). Additionally, the majority of spores liberated from the disks appeared vigorous and developed successfully into new individuals. These results implied that fragments of the appropriate size from the U. prolifera thalli broken by a variety of factors via producing spores gave rise to the rapid proliferation of the seaweed under field conditions, which may be one of the most important factors to the rapid accumulation of the vast biomass of U. prolifera in the green tide that occurred in Qingdao, 2008

Topics in recurrent event prediction with generalized non-homogeneous Poisson process (NHPP) and electronic circuit troubleshooting with Bayesian inference

This dissertation consists of three projects focused on seasonal recurrent event prediction, electronic circuit troubleshooting and the development of an open source software, RSpice, respectively. Big challenges when making recurrent event prediction include (1) the recurrent event rate may show a seasonal pattern and the pattern may also be affected by locations; (2) individual level variabilities can also affect the recurrent event rate. We present a general methodology to solve these challenges by using hierarchical clustering for the seasonal patterns and introducing random effects into our models. These help us in improving both the model fitting and prediction performance. This work is illustrated with two motivating product warranty applications in Chapter 2. Electronic circuits are widely used in industry, and the troubleshooting process of an electronic circuit based on previous experience may suggest a replacement of the whole circuit or some components, however, the failure of the circuit may just due to a less number of specific electronic components. The unnecessary removal of components can increase the costs significantly. Motivated by finding a more precise and faster troubleshooting procedure based on limited data, we propose the use of data simulation and Bayesian inference in circuit troubleshooting in Chapter 3. In the third project (Chapter 4), we address challenges that arise when doing data simulation and Bayesian inference in the second project (Chapter 3). In order to do exploratory analysis of an electronic circuit and make inference on which specific electronic components cause the failure of a circuit, we need to use a circuit simulator, ngspice, to generate the response (e.g., voltage values at specified nodes of a circuit) given the circuit setup. We also need to ensure that the component values generated by Bayesian inference algorithm can be passed to the circuit simulator and the output from the circuit simulator can be passed back to the algorithm for evaluation interactively. We present our work of writing an R package called RSpice to accomplish the above tasks.</p

Topics in recurrent event prediction with generalized non-homogeneous Poisson process (NHPP) and electronic circuit troubleshooting with Bayesian inference

This dissertation consists of three projects focused on seasonal recurrent event prediction, electronic circuit troubleshooting and the development of an open source software, RSpice, respectively. Big challenges when making recurrent event prediction include (1) the recurrent event rate may show a seasonal pattern and the pattern may also be affected by locations; (2) individual level variabilities can also affect the recurrent event rate. We present a general methodology to solve these challenges by using hierarchical clustering for the seasonal patterns and introducing random effects into our models. These help us in improving both the model fitting and prediction performance. This work is illustrated with two motivating product warranty applications in Chapter 2. Electronic circuits are widely used in industry, and the troubleshooting process of an electronic circuit based on previous experience may suggest a replacement of the whole circuit or some components, however, the failure of the circuit may just due to a less number of specific electronic components. The unnecessary removal of components can increase the costs significantly. Motivated by finding a more precise and faster troubleshooting procedure based on limited data, we propose the use of data simulation and Bayesian inference in circuit troubleshooting in Chapter 3. In the third project (Chapter 4), we address challenges that arise when doing data simulation and Bayesian inference in the second project (Chapter 3). In order to do exploratory analysis of an electronic circuit and make inference on which specific electronic components cause the failure of a circuit, we need to use a circuit simulator, ngspice, to generate the response (e.g., voltage values at specified nodes of a circuit) given the circuit setup. We also need to ensure that the component values generated by Bayesian inference algorithm can be passed to the circuit simulator and the output from the circuit simulator can be passed back to the algorithm for evaluation interactively. We present our work of writing an R package called RSpice to accomplish the above tasks

Seasonal Warranty Prediction Based on Recurrent Event Data

Warranty return data from repairable systems, such as vehicles, usually result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data. Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the than the overall population. The stratification facilitates modeling the recurrent event data with both time-varying and time-constant covariates. We demonstrate and validate the models using warranty claims data for two different vehicles. The results show that our approach provides important improvements in the predictive power of monthly events compared with models that do not take the season-ality into account.This is a pre-print of a submitted article.</p

Integrated Evaluation of Rivers Based upon the River Happiness Index (RHI): Happy Rivers in China

How to better harmonize the relationship between humans and rivers is a global issue of widespread concern at home and abroad, and science-based and integrated evaluation of rivers themselves is crucial to river management. Based on Maslow&rsquo;s hierarchy of needs and according to the World Happiness Report and the 2030 Agenda for Sustainable Development, this paper argues that a happy river is a river that can maintain its own health, support high-quality economic and social development in the river basin and the region, reflect harmony between humans and water, and give people in the river basin a high sense of security and the ability to gain and satisfaction. This paper also analyzes happy rivers at five levels, including water security, water resources, water environment, water ecology, and water culture, and develops the River Happiness Index (RHI) and its indicator system, as well as assesses the overall river happiness in China&rsquo;s 10 first-grade water resource zones. The results show that China&rsquo;s RHI is at a medium level, with flood control capacity at a near-good level. On the grounds of the RHI evaluation results, the paper puts forward targeted measures for river basin governance, and provides a systematic solution to national river protection and governance