Terahertz imaging with sub-wavelength resolution by femtosecond laser filament in air

Terahertz (THz) imaging provides cutting edge technique in biology, medical sciences and non-destructive evaluation. However, due to the long wavelength of the THz wave, the obtained resolution of THz imaging is normally a few hundred microns and is much lower than that of the traditional optical imaging. We introduce a sub-wavelength resolution THz imaging technique which uses the THz radiation generated by a femtosecond laser filament in air as the probe. This method is based on the fact that the femtosecond laser filament forms a waveguide for the THz wave in air. The diameter of the THz beam, which propagates inside the filament, varies from 20 {\mu}m to 50 {\mu}m, which is significantly smaller than the wavelength of the THz wave. Using this highly spatially confined THz beam as the probe, THz imaging with resolution as high as 20 {\mu}m (~{\lambda}/38) can be realized.Comment: 10 pages, 7 figure

Brain imaging and forecasting::Insights from judgmental model selection

In this article, we shed light on the differences between two judgmental forecasting approaches for model selection — forecast selection and pattern identification — with regard to their forecasting performance and underlying cognitive processes. We designed a laboratory experiment using real-life time series as stimuli to record subjects' selections as well as their brain activity by means of electroencephalography (EEG). We found that their cognitive load, measured by the amplitude of parietal P300, can be effectively used as a neurological indicator of identification and forecast accuracy. As a result, judgmental forecasting based on pattern identification outperforms forecast selection. Time series with low trendiness and high noisiness have low forecasting accuracy because of the high cognitive load induced

Anti-Corrosive Properties of Alkaloids on Metals

Numerous organic inhibitors have been reported to be used for the corrosion inhibition of various metals, especially, the heterogeneous ring compounds bearing larger electronegativity atoms (i.e., N, O, S, and P), polar functional groups, and conjugated double bonds are the most effective inhibitors. Based on the concept of green chemistry, in recent years, the research of corrosion inhibitor has gradually extracted new environment-friendly corrosion inhibitor from natural animals and plants, because of its advantages in wide source, low cost, low toxicity and subsequent treatment. Alkaloids such as papaverine, strychnine, quinine, nicotine, etc., have been studied as inhibitors for metals corrosion in corrosive media. This chapter aims to review the application of alkaloids for the corrosion inhibition of metals in corrosive media, and the development trend in this field is prospected

Impacts of FDI Renewable Energy Technology Spillover on China's Energy Industry Performance

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China

Optimizacija prehospitalnih strategija upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou pomoću algortima za duboko učenje

The aim of the study was to optimize the pre-hospital first aid management strategy for patients with infectious diseases in Huizhou city, which is expected to provide a basis for the epidemic prevention and control, to save lives, and increase the pre-hospital first aid efficiency. At the Department of Emergency, Huizhou Third People’s Hospital as the research subject, the common pre-hospital first aid procedure for infectious diseases was identified. The Petri net was used to model and determine the execution time of each link of the pre-hospital first aid process. The isomorphic Markov chain was used to optimize the pre-hospital first aid procedure for infectious diseases. In terms of the emergency path, deep learning was combined with the reinforcement learning model to construct the reinforcement learning model for ambulance path planning. Isomorphic Markov chain analysis revealed that the patient status when returning to the hospital, the time needed for the ambulance to come to designated location, and the on-site treatment were the main problems in the first aid process, and the time needed for the pre-hospital first aid process was reduced by 25.17% after optimization. In conclusion, Petri net and isomorphic Markov chain can optimize the pre-hospital first aid management strategies for patients with infectious diseases, and the use of deep learning algorithm can effectively plan the emergency path, achieving intelligent and informationalized pre-hospital transfer, which provides a basis for reducing the suffering, mortality, and disability rate of patients with infectious diseases.Cilj istraživanja bio je optimizirati strategiju prehospitalnog upravljanja prvom pomoći za bolesnike sa zaraznim bolestima u gradu Huizhou, Kina, za koju se očekuje da pruži osnovu za prevenciju i kontrolu epidemije, da spasi živote te da poveća učinkovitost prehospitalne prve pomoći. Istraživanje je provedeno na Hitnom odjelu Treće narodne bolnice u gradu Huizhou, gdje je utvrđen opći prehospitalni postupak prve pomoći za zarazne bolesti. Petrijeva mreža je primijenjena kako bi se modeliralo i odredilo vrijeme izvršenja svake karike u procesu prehospitalne prve pomoći. Izomorfni Markovljev lanac primijenjen je za optimizaciju prehospitalnog postupka prve pomoći za zarazne bolesti. Za putanju hitnosti, duboko učenje je kombinirano s modelom pojačanog učenja kako bi se konstruirao model osnaživanja učenja za planiranje putanje vozila hitne pomoći. Analiza Markovljeva lanca pokazala je da su status bolesnika na povratku u bolnicu, vrijeme potrebno da vozilo hitne pomoći dođe na određenu lokaciju i skrb na mjestu događaja glavni problemi u procesu prve pomoći te da je vrijeme potrebno za prehospitalni proces prve pomoći smanjeno za 25,17% nakon optimizacije. Zaključeno je da Petrijeva mreža i izomorfni Markovljev lanac mogu optimizirati strategije upravljanja prehospitalnom prvom pomoći za bolesnike sa zaraznim bolestima te da primjena algoritma dubokog učenja može učinkovito planirati putanju tima hitne pomoći, čime se postiže pametan i informatizirani prehospitalni prijevoz, što čini osnovu za smanjenje patnje, smrtnosti i stope invalidnosti za bolesnike sa zaraznim bolestima

Sequence Variation and Expression Analysis of Seed Dormancy- and Germination-Associated ABA- and GA-Related Genes in Rice Cultivars

Abscisic acid (ABA) and Gibberellic acid (GA) play key roles in regulating seed dormancy and germination. First, when examining germination of different rice cultivars, we found that their germination timing and dormancy status are rather distinct, coupled with different GA/ABA ratio. Second, we studied genomic sequences of ABA and GA dormancy- and germination-associated genes in rice and discovered single nucleotide polymorphisms and insertions/deletions (Indels) in both coding and regulatory sequences. We aligned all these variations to the genome assemblies of 9311 and PA64s and demonstrated their relevance to seed dormancy both quantitatively and qualitatively based on gene expression data. Third, we surveyed and compared differentially expressed genes in dry seeds between 9311 and PA64s to show that these differentially expressed genes may play roles in seed dormancy and germination

Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data

IntroductionBrain degeneration is commonly caused by some chronic diseases, such as Alzheimer’s disease (AD) and diabetes mellitus (DM). The risk prediction of brain degeneration aims to forecast the situation of disease progression of patients in the near future based on their historical health records. It is beneficial for patients to make an accurate clinical diagnosis and early prevention of disease. Current risk predictions of brain degeneration mainly rely on single-modality medical data, such as Electronic Health Records (EHR) or magnetic resonance imaging (MRI). However, only leveraging EHR or MRI data for the pertinent and accurate prediction is insufficient because of single-modality information (e.g., pixel or volume information of image data or clinical context information of non-image data).MethodsSeveral deep learning-based methods have used multimodal data to predict the risks of specified diseases. However, most of them simply integrate different modalities in an early, intermediate, or late fusion structure and do not care about the intra-modal and intermodal dependencies. A lack of these dependencies would lead to sub-optimal prediction performance. Thus, we propose an encoder-decoder framework for better risk prediction of brain degeneration by using MRI and EHR. An encoder module is one of the key components and mainly focuses on feature extraction of input data. Specifically, we introduce an encoder module, which integrates intra-modal and inter-modal dependencies with the spatial-temporal attention and cross-attention mechanism. The corresponding decoder module is another key component and mainly parses the features from the encoder. In the decoder module, a disease-oriented module is used to extract the most relevant disease representation features. We take advantage of a multi-head attention module followed by a fully connected layer to produce the predicted results.ResultsAs different types of AD and DM influence the nature and severity of brain degeneration, we evaluate the proposed method for three-class prediction of AD and three-class prediction of DM. Our results show that the proposed method with integrated MRI and EHR data achieves an accuracy of 0.859 and 0.899 for the risk prediction of AD and DM, respectively.DiscussionThe prediction performance is significantly better than the benchmarks, including MRI-only, EHR-only, and state-of-the-art multimodal fusion methods

Long-term Microscopic Traffic Simulation with History-Masked Multi-agent Imitation Learning

A realistic long-term microscopic traffic simulator is necessary for understanding how microscopic changes affect traffic patterns at a larger scale. Traditional simulators that model human driving behavior with heuristic rules often fail to achieve accurate simulations due to real-world traffic complexity. To overcome this challenge, researchers have turned to neural networks, which are trained through imitation learning from human driver demonstrations. However, existing learning-based microscopic simulators often fail to generate stable long-term simulations due to the \textit{covariate shift} issue. To address this, we propose a history-masked multi-agent imitation learning method that removes all vehicles' historical trajectory information and applies perturbation to their current positions during learning. We apply our approach specifically to the urban traffic simulation problem and evaluate it on the real-world large-scale pNEUMA dataset, achieving better short-term microscopic and long-term macroscopic similarity to real-world data than state-of-the-art baselines.Comment: updat