Determination of ultra-low level ¹²⁹I in vegetation using pyrolysis for iodine separation and accelerator mass spectrometry measurements

Radioactive isotopes of iodine are the most common radiological toxins from nuclear accidents due to their high release and high enrichment in human thyroid. Among the radioactive isotopes, long-lived 1291 can not only be used for the estimation of the radioactive risk of short-lived radioactive isotopes of iodine to humans and ecosystems, but also for the investigation of the biogeochemical cycle and environmental behavior of iodine. Accurate determination of I-129 in various environmental and biological samples is the key issue for these purposes. Due to its beta decay, low specific activity and ultra-low concentration in normal environmental and biological samples, it is important to efficiently separate iodine from the sample matrix and sensitively measure I-129. However, the complicated chemical properties of iodine and high organic content in biological samples make efficient separation of iodine very difficult using conventional acid digestion and alkaline ashing methods. By optimizing the key parameters related to the separation of iodine by pyrolysis using a tube furnace, including carbonization temperature, heating protocol, combustion assisting gas, iodine volatilization process and iodine trapping, a safe, robust and reliable pyrolysis method was established for the separation of trace amounts of iodine from biological samples with a recovery of more than 80%. By further separation of iodine, preparation of sample targets, and measurement of I-129 using AMS, a highly efficient and sensitive method for the determination of ultra low level I-129 in biological samples was developed. With this method, a detection limit of 6 x 10(-17) g g(-1) (or 0.4 nBq g(-1)) for I-129 was obtained. Compared with conventional methods, this method is easy to operate, provides highly efficient recovery of iodine, and has simple processing and less cross contamination. 5 different species of vegetation were analyzed using both the developed method and the conventional alkaline ashing method for sample decomposition, and the results agree very well with each other. The method has been successfully used for the determination of I-129 in a large number of vegetation samples

Universal scaling of strange particle pTp_{\rm T} spectra in pp collisions

As a complementary study to that performed on the transverse momentum ($p_{\rm T}$) spectra of charged pions, kaons and protons in proton-proton (pp) collisions at LHC energies 0.9, 2.76 and 7 TeV, we present a scaling behaviour in the $p_{\rm T}$ spectra of strange particles ($K_{S}^{0}$, $\rm \Lambda$, $\rm \Xi$ and $\phi$) at these three energies. This scaling behaviour is exhibited when the spectra are expressed in a suitable scaling variable $z=p_{\rm T}/K$, where the scaling parameter $K$ is determined by the quality factor method and increases with the center of mass energy ($\sqrt{s}$). The rates at which $K$ increases with $\mathrm{ln}\sqrt{s}$ for these strange particles are found to be identical within errors. In the framework of the colour string percolation model, we argue that these strange particles are produced through the decay of clusters that are formed by the colour strings overlapping. We observe that the strange mesons and baryons are produced from clusters with different size distributions, while the strange mesons (baryons) $K_{S}^{0}$ and $\phi$ ($\rm \Lambda$ and $\rm \Xi$) originate from clusters with the same size distributions. The cluster's size distributions for strange mesons are more dispersed than those for strange baryons. The scaling behaviour of the $p_{\rm T}$ spectra for these strange particles can be explained by the colour string percolation model in a quantitative way.Comment: 8 pages, 10 figures, accepted by EPJ

Are technical indicators helpful to investors in china’s stock market? A study based on some distribution forecast models and their combinations

Can investors use technical analysis to generate positive riskadjusted returns by observing historical transaction data? The study investigates whether technical indicators (TIs) are beneficial to the returns and risk management of China’s stock market investors. It is conducted from the perspective of a distribution forecast rather than a traditional point forecast. The study investigates the TIs’ predictability on the distribution of returns. It also examines the TIs’ impact on risk management. A high-dimensional-same-frequency information distribution forecasting model, the LASSO-EGARCH model, is built. The LASSO regression’s results show that the TIs have limited ‘explanatory power’ for the return prediction. However, the adaptive moving average and turnover rate show significant and robust effects. The statistical evaluation and economic evaluation show that the TIs information’s integration cannot improve the direction forecast’s accuracy, nor does it have excess profitability. However, it enables the return distribution to have a better calibration. The above conclusion reveals that the usefulness of the analysis for China’s stock market lies in its risk management when the stock price plunges, rather than in excess profits. This may provide a reference for investors who prefer the TIs’ analysis

Meta Pattern Concern Score: A Novel Evaluation Measure with Human Values for Multi-classifiers

While advanced classifiers have been increasingly used in real-world safety-critical applications, how to properly evaluate the black-box models given specific human values remains a concern in the community. Such human values include punishing error cases of different severity in varying degrees and making compromises in general performance to reduce specific dangerous cases. In this paper, we propose a novel evaluation measure named Meta Pattern Concern Score based on the abstract representation of probabilistic prediction and the adjustable threshold for the concession in prediction confidence, to introduce the human values into multi-classifiers. Technically, we learn from the advantages and disadvantages of two kinds of common metrics, namely the confusion matrix-based evaluation measures and the loss values, so that our measure is effective as them even under general tasks, and the cross entropy loss becomes a special case of our measure in the limit. Besides, our measure can also be used to refine the model training by dynamically adjusting the learning rate. The experiments on four kinds of models and six datasets confirm the effectiveness and efficiency of our measure. And a case study shows it can not only find the ideal model reducing 0.53% of dangerous cases by only sacrificing 0.04% of training accuracy, but also refine the learning rate to train a new model averagely outperforming the original one with a 1.62% lower value of itself and 0.36% fewer number of dangerous cases.Comment: Published at the 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 9 pages, 6 figure

TSFool: Crafting Highly-imperceptible Adversarial Time Series through Multi-objective Black-box Attack to Fool RNN Classifiers

Neural network (NN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks achieve state-of-the-art performance in feed-forward NNs and image recognition tasks, they do not perform as well on time series classification with recurrent neural network (RNN) models. This is because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity of time series data to perturbations challenges the traditional local optimization objective of the adversarial attack. In this paper, a black-box method called TSFool is proposed to efficiently craft highly-imperceptible adversarial time series for RNN classifiers. We propose a novel global optimization objective named Camouflage Coefficient to consider the imperceptibility of adversarial samples from the perspective of class distribution, and accordingly refine the adversarial attack as a multi-objective optimization problem to enhance the perturbation quality. To get rid of the dependence on gradient information, we also propose a new idea that introduces a representation model for RNN to capture deeply embedded vulnerable samples having otherness between their features and latent manifold, based on which the optimization solution can be heuristically approximated. Experiments on 10 UCR datasets are conducted to confirm that TSFool averagely outperforms existing methods with a 46.3% higher attack success rate, 87.4% smaller perturbation and 25.6% better Camouflage Coefficient at a similar time cost.Comment: 9 pages, 7 figure

Influence of Acetaldehyde Induction on Monomeric and Polymeric Polyphenols in Wine using the Polyphenol/Protein-binding Model

Polyphenols make a substantial contribution to the sensory properties of wine, and their evolution is affected by the acetaldehyde present during fermentation and ageing. In this work, five typical monomeric phenolic standards and three different polymeric flavanol fractions separated from wine were tested for polyphenol/protein binding by means of circular dichroism measurement and fluorescence spectrum assay in the presence or absence of acetaldehyde, and the formation of new oligomeric compounds linked by ethyl bridges was observed through HPLC-MS analyses. The results show that the protein-binding ability of these monomers was in the order of gallic acid > caffeic acid > quercetin > (+)-catechin > (-)-epicatechin, while acetaldehyde exerted a stronger effect on (+)-catechin and (-)-epicatechin monomers. Moreover, different wine fractions had different responses when reacted with proteins with the participation of acetaldehyde, while the polymeric proanthocyanidins produced the largest value (84.67%) of the salivary protein precipitation index and the strongest fluorescence-quenching effect

Spatio-Temporal Patterns of Water Table and Vegetation Status of a Deserted Area

Understanding groundwater-vegetation interactions is crucial for sustaining fragile environments of desert areas such as the Horqin Sandy Land (HSL) in northern China. This study examined spatio-temporal variations in the water table and the associated vegetation status of a 9.71 km2 area that contains meadowland, sandy dunes, and intermediate transitional zones. The depth of the water table and hydrometeorologic parameters were monitored and Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were utilized to assess the vegetation cover. Spatio-temporal variations over the six-year study period were examined and descriptive groundwater-vegetation associations developed by overlaying a water table depth map onto a vegetation index map derived from MODIS. The results indicate that the water table depends on the local topography, localized geological settings, and human activities such as reclamation, with fluctuations occurring at annual and monthly scales as a function of precipitation and potential evapotranspiration. Locations where the water table is closer to the surface tend to have more dense and productive vegetation. The water table depth is more closely associated with vegetative density in meadowlands than in transitional zones, and only poorly associated with vegetation in sandy dunes