Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods

The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.

Responses of frozen fine-grained soils due to multiple packets of cyclic stress with variable-amplitude and an empirical prediction model

A suite of stress-controlled cyclic triaxial tests were performed on frozen fine-grained soils to assess the characteristics of the accumulated and cyclic behaviours under packets of cyclic stress with variable-amplitude. The influence of cyclic stress sequences is highlighted, and the results of the variable-amplitude cyclic tests indicate that the applied cyclic stress amplitude sequence is of significant importance regarding the final accumulated deformations. A step change is observed in the accumulated strain when the applied cyclic stress amplitude is beyond that of the previous stress package, while a decreasing level of cyclic stress below the previous loading package results in slight additional strain accumulation. The results of these tests also indicate that the cyclic stiffness is dependent on both the past accumulated strain and the current cyclic stress amplitude. For frozen soil, a higher past accumulated strain and lower cyclic stress levels yield a higher cyclic stiffness. An empirical approach for representing the response of frozen fine-grained soils under multiple packets of cyclic loading are proposed and then verified by the test data. The results show that the proposed empirical model is able to extract and predict the accumulated and cyclic behaviours under multiple packets of cyclic loading.

Empirical prediction of flight effect on subsonic coaxial-jet noise by introducing an adjusted flight velocity term

The effect of forward flight on jet noise is difficult to quantify through flyover tests since only the total noise is measured in a full-scale flyover test, and the contribution of the jet noise is difficult and sometimes nearly impossible to identify. Thus, most studies on the flight effect have been carried out through model-scale experiments with a single-stream jet simulator in a free jet facility. In this paper, the effect of forward flight was captured by using an adjusted flight velocity term (αV) to describe jet velocity in a new prediction of coaxial-jet noise. The new jet noise prediction method assumes that there are three components: primary, secondary, and mixed components with no filter functions. The coefficient α is determined by a thorough investigation of the model-scale data gained from an experiment in the anechoic wind tunnel of ONERA. The value of α is 1 for the primary component, 0.5 for the secondary component, and a linear function of the angle for the mixed component. The simple adjustment of the flight velocity successfully embodied the effect of forward flight at all angles, with no separate velocity exponent or an additional term.

Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

Predicting which cryptic-donors may be activated by a genetic variant is notoriously difficult. Through analysis of 5,145 cryptic-donors activated by 4,811 variants (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the authentic-donor appear important determinants of cryptic-donor activation. However, other factors such as auxiliary splicing elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequent mis-splicing events at each exon-intron junction, mined from 40,233 RNA-sequencing samples, predict with remarkable accuracy which cryptic-donor will be activated in rare disease. Aggregate RNA-Sequencing splice-junction data provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

Empirical prediction of variant-associated cryptic-donors with 87% sensitivity and 95% specificity

Predicting which cryptic-donors may be activated by a genetic variant is notoriously difficult. Through analysis of 5,145 cryptic-donors activated by 4,811 variants (versus 86,963 decoy-donors not used; any GT or GC), we define an empirical method predicting cryptic-donor activation with 87% sensitivity and 95% specificity. Strength (according to four algorithms) and proximity to the authentic-donor appear important determinants of cryptic-donor activation. However, other factors such as auxiliary splicing elements, which are difficult to identify, play an important role and are likely responsible for current prediction inaccuracies. We find that the most frequent mis-splicing events at each exon-intron junction, mined from 40,233 RNA-sequencing samples, predict with remarkable accuracy which cryptic-donor will be activated in rare disease. Aggregate RNA-Sequencing splice-junction data provides an accurate, evidence-based method to predict variant-activated cryptic-donors in genetic disorders, assisting pathology consideration of possible consequences of a variant for the encoded protein and RNA diagnostic testing strategies.

Oceanic Drivers and Empirical Prediction of Interannual Rainfall Variability in Late Summer Over Northeast China

Northeast China (NEC) is located between the subtropical monsoon and temperate-frigid monsoon regions and exhibits two successive rainy seasons with different natures: the northeast cold vortex rainy season in early summer (May–June) and the monsoon rainy season in late summer (July–August). Summer rainfall over NEC (NECR) has a fundamental influence on society, yet its successful seasonal prediction remains a long-term scientific challenge to current dynamical models. The poor NECR prediction skill is partly attributed to the large NECR variability at both the interannual and interdecadal time scales. Here, we focus on the oceanic drivers of the late summer NECR variability and associated physical processes at interannual time scale. Then, we establish an empirical prediction model to predict the interannual variability of summer NECR at one-month lead time (in June). The analysis of observations spanning 40 years (1963–2002) reveals three physically and synergistically influencing predictors of the late summer NECR interannual variability. Above-normal NECR is preceded in the previous spring by (a) warm sea surface temperature (SST) anomalies in the tropical northern Indian Ocean, (b) a positive thermal contrast tendency in the tropical West–East Pacific SST, and (c) a positive tendency of the North Atlantic tripolar SST. These precursors enhance the anomalous low-level anticyclone over the Northwest Pacific and southerly anomalies over NEC in late summer, which are beneficial to enhancing NECR. An empirical prediction model built on these three predictors achieves a forecast temporal correlation coefficient (TCC) skill of 0.72 for 1961–2019, and a 17-year (2003–2019) independent forecast shows a significant TCC skill of 0.70. The skill is substantially higher than that of five state-of-the-art dynamical models and their ensemble mean for 1979–2019 (TCC=0.24). These results suggest that the proposed empirical model is a very meaningful approach for the prediction of NECR, although the dynamical prediction of NECR has considerable room for improvement.

Prophecy of Stock Price of Bharat Immunological & Biological Corporation Ltd using Hybrid ANN and PSO Model

The prediction of the time series has always attracted much interest from investors and researchers to evaluate financial risk. Stock market movements are extremely complex and are influenced by different factors. Hence it is very important to find the most important factors for the stock market. But the high level of noise and complexity of the financial data makes this job very difficult. Many authors have already used artificial neural network for this kind of forecasting tasks, but hybridization model of artificial neural network is considered to be widely used and better performing forecasting model among others. The dormant high noises data mess up the performance, so to enhance the prediction accuracy. We considered a set of seven technical attribute of stock market to perform the hybrid model of Artificial Neural Network (ANN) and Particle Swarm Optimization algorithms. The efficiency of the proposed method is measured by the stock price of Bharat Immunological & Biological Corporation Ltd with 3945 number of daily transactional data. Empirical prediction analysis shows that the proposed model enhances the performance in comparison to simple ANN model.

Prediction of Cadmium Transfer From Soil to Potato in Karst Soils, China

Contamination of food with the heavy metal Cd is a significant global concern. In this study, a field survey was performed to investigate the characteristics of Cd transfer from soil to potato tubers (n = 105). The results showed that the bioaccumulation factor of the potato tuber ranged from about 0.1 to 1. The soil threshold of Cd derived from the cumulative probability distribution was 0.15 mg kg−1 in order to protect 95% of potatoes. Additionally, prediction models for Cd transfer were constructed based on soil properties and the concentration of CaCl2-extractable soil Cd. The results of the analysis showed that pH was the critical factor affecting Cd uptake by potatoes. Additionally, the R2 of different empirical models increased from 0.354 to 0.715 as the number of soil parameters was increased, and the predicted soil Cd concentration approached the measured values at values of about 0–15 mg kg−1. The results of this study suggest that the probability distribution method was stricter than the empirical prediction models for estimating the ecological risk of Cd contamination of potatoes in karst soils.