Apprenticeship Standard : Non-Destructive Testing Engineer

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems

Nonlinear terahertz metamaterials via field-enhanced carrier dynamics in GaAs

We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at terahertz frequencies. For SRRs on doped GaAs films, incident terahertz radiation with peak fields of ~20 - 160 kV/cm drives intervalley scattering. This reduces the carrier mobility and enhances the SRR LC response due to a conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field enhancement within the SRR gaps leads to efficient impact ionization, increasing the carrier density and the conductivity which, in turn, suppresses the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude in the carrier density in the SRR gaps on semi-insulating GaAs substrate. Furthermore, we show that the effective permittivity can be swept from negative to positive values with increasing terahertz field strength in the impact ionization regime, enabling new possibilities for nonlinear metamaterials.Comment: 5 pages, 4 figure

Variations of China's emission estimates:Response to uncertainties in energy statistics

The accuracy of China's energy statistics is of great concern because it contributes greatly to the uncertainties in estimates of global emissions. This study attempts to improve the understanding of uncertainties in China's energy statistics and evaluate their impacts on China's emissions during the period of 1990-2013. We employed the Multi-resolution Emission Inventory for China (MEIC) model to calculate China's emissions based on different official data sets of energy statistics using the same emission factors. We found that the apparent uncertainties (maximum discrepancy) in China's energy consumption increased from 2004 to 2012, reaching a maximum of 646Mtce (million tons of coal equivalent) in 2011 and that coal dominated these uncertainties. The discrepancies between the national and provincial energy statistics were reduced after the three economic censuses conducted during this period, and converging uncertainties were found in 2013. The emissions calculated from the provincial energy statistics are generally higher than those calculated from the national energy statistics, and the apparent uncertainty ratio (the ratio of the maximum discrepancy to the mean value) owing to energy uncertainties in 2012 took values of 30.0, 16.4, 7.7, 9.2 and 15.6%, for SO2, NOx, VOC, PM2.5 and CO2 emissions, respectively. SO2 emissions are most sensitive to energy uncertainties because of the high contributions from industrial coal combustion. The calculated emission trends are also greatly affected by energy uncertainties - from 1996 to 2012, CO2 and NOx emissions, respectively, increased by 191 and 197% according to the provincial energy statistics but by only 145 and 139% as determined from the original national energy statistics. The energy-induced emission uncertainties for some species such as SO2 and NOx are comparable to total uncertainties of emissions as estimated by previous studies, indicating variations in energy consumption could be an important source of China's emission uncertainties

NOx Emission Trends over Chinese Cities Estimated from OMI Observations During 2005 to 2015

Satellite NO2 observations have been widely used to evaluate emission changes. To determine trends in NOx emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations during 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52 from 2005 to 2011 and decreased by 21 from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r = 0.8 on average), but not for some cities (r = 0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies

Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding

A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding

Impacts of climate change on future air quality and human health in China.

In recent years, air pollution has caused more than 1 million deaths per year in China, making it a major focus of public health efforts. However, future climate change may exacerbate such human health impacts by increasing the frequency and duration of weather conditions that enhance air pollution exposure. Here, we use a combination of climate, air quality, and epidemiological models to assess future air pollution deaths in a changing climate under Representative Concentration Pathway 4.5 (RCP4.5). We find that, assuming pollution emissions and population are held constant at current levels, climate change would adversely affect future air quality for >85% of China's population (∼55% of land area) by the middle of the century, and would increase by 3% and 4% the population-weighted average concentrations of fine particulate matter (PM2.5) and ozone, respectively. As a result, we estimate an additional 12,100 and 8,900 Chinese (95% confidence interval: 10,300 to 13,800 and 2,300 to 14,700, respectively) will die per year from PM2.5 and ozone exposure, respectively. The important underlying climate mechanisms are changes in extreme conditions such as atmospheric stagnation and heat waves (contributing 39% and 6%, respectively, to the increase in mortality). Additionally, greater vulnerability of China's aging population will further increase the estimated deaths from PM2.5 and ozone in 2050 by factors of 1 and 3, respectively. Our results indicate that climate change and more intense extremes are likely to increase the risk of severe pollution events in China. Managing air quality in China in a changing climate will thus become more challenging

Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology

Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction

COVID-19 causes record decline in global CO2 emissions

The considerable cessation of human activities during the COVID-19 pandemic has affected global energy use and CO2 emissions. Here we show the unprecedented decrease in global fossil CO2 emissions from January to April 2020 was of 7.8% (938 Mt CO2 with a +6.8% of 2-{\sigma} uncertainty) when compared with the period last year. In addition other emerging estimates of COVID impacts based on monthly energy supply or estimated parameters, this study contributes to another step that constructed the near-real-time daily CO2 emission inventories based on activity from power generation (for 29 countries), industry (for 73 countries), road transportation (for 406 cities), aviation and maritime transportation and commercial and residential sectors emissions (for 206 countries). The estimates distinguished the decline of CO2 due to COVID-19 from the daily, weekly and seasonal variations as well as the holiday events. The COVID-related decreases in CO2 emissions in road transportation (340.4 Mt CO2, -15.5%), power (292.5 Mt CO2, -6.4% compared to 2019), industry (136.2 Mt CO2, -4.4%), aviation (92.8 Mt CO2, -28.9%), residential (43.4 Mt CO2, -2.7%), and international shipping (35.9Mt CO2, -15%). Regionally, decreases in China were the largest and earliest (234.5 Mt CO2,-6.9%), followed by Europe (EU-27 & UK) (138.3 Mt CO2, -12.0%) and the U.S. (162.4 Mt CO2, -9.5%). The declines of CO2 are consistent with regional nitrogen oxides concentrations observed by satellites and ground-based networks, but the calculated signal of emissions decreases (about 1Gt CO2) will have little impacts (less than 0.13ppm by April 30, 2020) on the overserved global CO2 concertation. However, with observed fast CO2 recovery in China and partial re-opening globally, our findings suggest the longer-term effects on CO2 emissions are unknown and should be carefully monitored using multiple measures

Learning Robust Correlation with Foundation Model for Weakly-Supervised Few-Shot Segmentation

Existing few-shot segmentation (FSS) only considers learning support-query correlation and segmenting unseen categories under the precise pixel masks. However, the cost of a large number of pixel masks during training is expensive. This paper considers a more challenging scenario, weakly-supervised few-shot segmentation (WS-FSS), which only provides category ($i.e.$ image-level) labels. It requires the model to learn robust support-query information when the generated mask is inaccurate. In this work, we design a Correlation Enhancement Network (CORENet) with foundation model, which utilizes multi-information guidance to learn robust correlation. Specifically, correlation-guided transformer (CGT) utilizes self-supervised ViT tokens to learn robust correlation from both local and global perspectives. From the perspective of semantic categories, the class-guided module (CGM) guides the model to locate valuable correlations through the pre-trained CLIP. Finally, the embedding-guided module (EGM) implicitly guides the model to supplement the inevitable information loss during the correlation learning by the original appearance embedding and finally generates the query mask. Extensive experiments on PASCAL-5$^i$ and COCO-20$^i$ have shown that CORENet exhibits excellent performance compared to existing methods