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When the polarization-maintaining fiber (PMF) delay coil of a fiber optic current transformer (FOCT) is impacted, external forces on the optical fibers and change of their birefringence may lead to extra phase errors during the propagation of optical signals in the fibers. These errors increase the error in current measurement. In the paper, the environmental impact mechanisms influencing the PMF delay coil of FOCT were investigated with a mathematical error model. The method of replacing PMF with a polarization-maintaining photonic crystal fiber (PCF) as the delay coil was proposed. Also, the relationship between structural parameters of air holes and linear birefringence of the polarization maintaining PCF subjected to stress was investigated. The structural parameters of optical fibers were also optimized subsequently. Simulated impact experiments of FOCT with polarization-maintaining PCF and PMF as the delay coil, respectively, demonstrated the effective impact resistance of FOCT with polarization-maintaining PCF delay coil after the optimization of its structural parameters. The current measurement errors of the FOCTs with polarization maintaining PCF delay coil reduced by approximately 35% with respect to the FOCT with PMF delay coil.

A comprehensive assessment of the environmental and employment impacts of international trade is essential for coordinating sustainable development between the economy, environment, and society. International trade is an important driving force for global economic growth. However, the environmental and social impacts of trade under global value chains (GVCs) have not yet been comprehensively characterized. This study aims to estimate the impacts of international trade from 2000–2014 on carbon emissions and employment using the gross export decomposition method, incorporating a counterfactual method based on the multi-region input–output model. We found that (1) at the global level, international trade has been conducive to carbon emission reductions since 2003 and played a role in increasing employment throughout the study period; however, the impacts of GVC-related trade have been mixed, generating global emissions savings, but leading to job losses. This implies that there is a trade-off between carbon emission reduction through GVC embedding and employment creation. (2) At the national level, a country’s participation in international trade benefits the environment, employment, or both. In this sense, international trade is no longer a zero-sum game; rather, it is likely to be a positive-sum game for all participants regarding environmental benefits and job creation. (3) In bilateral trade, developed and developing economies have positive and negative impacts, meaning that they should take joint actions to balance environment–employment–trade growth. These findings are useful for determining the responsibilities of different countries toward climate change and achieving sustainable development of the economy, society, and environment.

Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. Traditional approaches like Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples from such distributions but suffer from computational inefficiency, particularly when dealing with high-dimensional targets, as they require numerous iterations to generate a batch of samples. In this paper, we propose an efficient and scalable neural implicit sampler that overcomes these limitations. Our sampler can generate large batches of samples with low computational costs by leveraging a neural transformation that directly maps easily sampled latent vectors to target samples without the need for iterative procedures. To train the neural implicit sampler, we introduce two novel methods: the KL training method and the Fisher training method. The former minimizes the Kullback-Leibler divergence, while the latter minimizes the Fisher divergence. By employing these training methods, we effectively optimize the neural implicit sampler to capture the desired target distribution. To demonstrate the effectiveness, efficiency, and scalability of our proposed samplers, we evaluate them on three sampling benchmarks with different scales. These benchmarks include sampling from 2D targets, Bayesian inference, and sampling from high-dimensional energy-based models (EBMs). Notably, in the experiment involving high-dimensional EBMs, our sampler produces samples that are comparable to those generated by MCMC-based methods while being more than 100 times more efficient, showcasing the efficiency of our neural sampler. We believe that the theoretical and empirical contributions presented in this work will stimulate further research on developing efficient samplers for various applications beyond the ones explored in this study.

ConCave Ph.D. Symposium 2022 Proceedings, April 7-8, 2022. Georgia Institute of Technology, Atlanta, Georgia., This paper examines the reception and impacts of Western ideas of the “agrarian” in China. In particular, it traces how the agrarian philosophy of the Physiocrats traveled across space and time and how this line of thinking influenced the Chinese urban-rural transformation at the turn of the twentieth century. The paper examines Adam Smith’s interpretation of the Physiocracy, and how the agrarian idea was embedded in the liberal school of political economy. By tracing the significant role of Fukuzawa Yukichi and Liang Qichao in cross-cultural borrowing, the paper reveals the Western “agrarian” roots within the concept of “local self-government.” As the paper suggests, it was this line of thought that influenced the state regeneration in early modern China. As a representative case, Zhang Jian’s village-ism and his agrarian practice in Nantong are presented as the epitome of the local self-government movement in the early twentieth century, which marked one of the first rural modernization efforts in China. By tracing the intellectual transmission of the idea of the “agrarian,” the paper aims to unpack the connotation of the “agrarian modern” as an alternative to the mainstream model of high-dense cities and depopulated countryside. This paper offers a perspective to situate the urban-rural transformation in early modern China in a global context without the conventional West-East divide.

Adversarial attacks have the potential to mislead deep neural network classifiers by introducing slight perturbations. Developing algorithms that can mitigate the effects of these attacks is crucial for ensuring the safe use of artificial intelligence. Recent studies have suggested that score-based diffusion models are effective in adversarial defenses. However, existing diffusion-based defenses rely on the sequential simulation of the reversed stochastic differential equations of diffusion models, which are computationally inefficient and yield suboptimal results. In this paper, we introduce a novel adversarial defense scheme named ScoreOpt, which optimizes adversarial samples at test-time, towards original clean data in the direction guided by score-based priors. We conduct comprehensive experiments on multiple datasets, including CIFAR10, CIFAR100 and ImageNet. Our experimental results demonstrate that our approach outperforms existing adversarial defenses in terms of both robustness performance and inference speed.

Under the leadership of the Chinese Communist Party, generation after generation of women have made pioneering efforts and contributed to China's construction, reform and development. In the great leap of the Chinese nation from standing up, becoming rich to becoming strong, great changes have taken place in the status of Chinese women. On the one hand, the old feudal ideas are gradually broken down and new education and ideas are deeply rooted in the hearts of the people. On the other hand, economic development also needs and relies on the wisdom and strength of women to make continuous progress. Starting from the demographic indicators, this paper makes a gradual and progressive analysis of health and education. Finally, combining with the relevant contents of the female employment index report, this paper makes a prospect and puts forward some suggestions to promote the further development of the equal rights movement and women's cause.

This paper describes the work of the Data Science for Digital Health (DS4DH) group at the TREC Health Misinformation Track 2021. The TREC Health Misinformation track focused on the development of retrieval methods that provide relevant, correct and credible information for health related searches on the Web. In our methodology, we used a two-step ranking approach that includes i) a standard retrieval phase, based on BM25 model, and ii) a re-ranking phase, with a pipeline of models focused on the usefulness, supportiveness and credibility dimensions of the retrieved documents. To estimate the usefulness, we classified the initial rank list using pre-trained language models based on the transformers architecture fine-tuned on the MS MARCO corpus. To assess the supportiveness, we utilized BERT-based models fine-tuned on scientific and Wikipedia corpora. Finally, to evaluate the credibility of the documents, we employed a random forest model trained on the Microsoft Credibility dataset combined with a list of credible sites. The resulting ranked lists were then combined using the Reciprocal Rank Fusion algorithm to obtain the final list of useful, supporting and credible documents. Our approach achieved competitive results, being top-2 in the compatibility measurement for the automatic runs. Our findings suggest that integrating automatic ranking models created for each information quality dimension with transfer learning can increase the effectiveness of health-related information retrieval.

Additional file 1: Figure S1. a) TEM and HRTEM images of the water-dispersed BCDs-2. b) TEM and HRTEM images of the water-dispersed BCDs-3. Figure S2. a) AFM images of the water-dispersed BCDs-2. b) AFM images of the water-dispersed BCDs-3. Figure S3. XRD spectrum of BCDs-2 and BCDs-3. Figure S4. FT-IR spectrum of BCDs-2 and BCDs-3. Figure S5. a) XPS spectrum and b) high revolution spectra of C 1s. Figure S6. High revolution spectra of N 1s of MBCDs, BCDs-1, BCDs-2 and BCDs-3. Figure S7. High revolution spectra of O 1s of MBCDs, BCDs-1, BCDs-2 and BCDs-3. Figure S8. a, b) Fluorescence spectrums of BCDs-2 and BCDs-3 excited at different wavelengths, and pictures of solution under daylight and ultraviolet conditions. Figure S9. a) Food intake. b) Water intake. Figure S10. GO terms related to glucose metabolism. Figure S11. a) Fold change of CYP450 genes. b) mRNA expressions of CYP450. Figure S12. MBCDs affected 53 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in mice liver. Figure S13. Body weight of mice treated with MBCDs and MBCDs+antioxidants. Figure S14. Organ index and organ weight of mice treated with MBCDs and MBCDs+antioxidants. Table S1. Primer Sequence.

OBJECTIVE To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on the 1-year postoperative survival rate. METHODS A retrospective study was conducted on 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorders-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, one-way tests were performed using the Log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, modified Katagiri score were examined by plotting the subject operating characteristic curve (ROC) and calculating the area under curve (AUC) The AUC was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG web version for 1-year postoperative survival in patients with spinal metastases from breast cancer. RESULTS The average age was 51.3±8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 ml. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken of fixation. The mean survival time was 32.2 months, including a 6-month survival rate of 90.7%, a 1-year survival rate of 77.8%, and a 2-year survival rate of 60.3%. Univariate analysis showed that pre-operation with neurological deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer. Conclusion Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival time after surgery. Patients with hormone-insensitive type, with brain metastasis have poor prognosis, and SORG web version can predict patients' 1-year survival more accurately.

With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. A surrogate model or emulator, is often employed as a fast substitute for the simulator. Meanwhile, a common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, i.e., the experimental design problem. This dissertation focuses on the design problem under Gaussian process surrogates. The first work demonstrates empirically that space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. A purely random design is shown to be superior to higher-powered alternatives in many cases. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (one-shot design) and sequential settings. The second contribution is motivated by an agent-based model(ABM) of delta smelt conservation. The ABM is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. A batch sequential design scheme is proposed, generalizing one-at-a-time variance-based active learning, as a means of keeping multi-core cluster nodes fully engaged with expensive runs. The acquisition strategy is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. Design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream high-fidelity input sensitivity analysis. Doctor of Philosophy With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. Thus, a statistical model built upon input-output observations, i.e., a so-called surrogate model or emulator, is needed as a fast substitute for the simulator. Design of experiments, i.e., how to select samples from the input space under budget constraints, is also worth studying. This dissertation focuses on the design problem under Gaussian process (GP) surrogates. The first work demonstrates empirically that commonly-used space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (design points are allocated at one shot) and sequential settings (data are sampled sequentially). The second contribution is motivated by a stochastic computer simulator of delta smelt conservation. This simulator is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. An innovative batch sequential design method is proposed, generalizing one-at-a-time sequential design to one-batch-at-a-time scheme with the goal of parallel computing. The criterion for subsequent data acquisition is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. The design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream input sensitivity analysis.

A novel method is introduced for preparing iron nanoparticles from iron pentacarbonyl using an atmospheric microwave plasma. The prepared iron nanoparticles were characterized by transmission electron microscopy and X-ray diffraction. The results show that the size of the particles can be controlled by adjusting the microwave power and the flow rate of the carrier gas. The magnetic properties of the synthesized iron particles were studied and a saturation magnetization of ~95 emu/g was obtained. The convenient preparation process and considerable production rate were also found to be satisfactory for industrial applications.

In coastal waters, the phenomenon of migration and diffusion of radionuclides plays an important role in emergency decision-making and mitigation of accidents in the nuclear power plant. Based on Lagrangian and Euler methods, radionuclide diffusion models have been established for use in coastal waters of the nuclear power plant, respectively. The model resolution is 1′ × 1′ and the model reliability is verified. The principle of radionuclide migration has been analyzed. Studies are made on the influence of tidal currents and decay effect, on radionuclide concentration. Lagrangian results show that radionuclides advance by way of rotation due to the influence of semidiurnal tidal waves. Euler results show that radionuclides concentrate toward the shore during the high tide, and the radionuclide concentration is high. Radionuclides migrate toward the sea during the ebb tide, and the radionuclide concentration is low. The concentration of Cs-137 near the source can be reduced by an order of magnitude with one week. The radioactivity of I-131 is about 70% lower than that of Cs-137 after two weeks. It can provide theoretical basis for emergency measures.

With the advancement of high school information technology curriculum reform, the university information technology curriculum reform is imperative. This study focus on how to use new technology to promote university information technology curriculum reform effectively. After analyzing the status quo of university information technology curriculum and the possibility of promoting university information technology curriculum reform based on MOOC and SPOC, the study proposes that university information technology curriculum should adopt hierarchical teaching to teach students in accordance with their aptitude, as well as use MOOC and SPOC to promote reform, and expounds implementation plans of university information technology curriculum reform based on MOOC and SPOC.

The location service provided by the Global Navigation Satellite System (GNSS) has gained great impact on our life. However, the signal blockage, jamming and spoofing attack lead to a serious impediment to its further application. The receivers are able to be aware of the signal blockage and jamming through signal processing technology. The goal of the signal blockage and jamming is to prevent the receiver from receiving the broadcast signals and make the receivers fail to provide navigation information. Unlike the signal blockage and jamming, the spoofing attack feeds the receivers false signals and make the receivers to output erroneous navigation information. Thus the spoofing attack detection is more difficult. Under the circumstance that no spoofing attack existing, each antenna receives unique signals. Consequently, the baseline between each pair of receivers can be calculated. Assuming the baseline length is subject to Gaussian distribution, then mean value is fixed with some noise between the pairs of receivers. When the spoofing attack occurs, the antennas receive the identical spoofing signals, which result the position solutions are almost the same. Under this condition, baseline length mean value becomes to be zero. The separation is employed as an indicator to detect the spoofing attack occurrence in the hypothesis test. In this paper, we analyzed the detectors based the baseline data. Three cases are investigated with different setups: (1) single fixed baseline; (2) two fixed and independent baselines; (3) Max/Min model for two independent baselines. In addition, the influence of the baseline length on the detection performance is analyzed, and the performance of three studied cases are evaluated by comparing the receiver operating characteristic (ROC) curves. The results demonstrate effectiveness of the model.

The Global Navigation Satellite System (GNSS) broadcast navigation signals and provides global, accurate and real-time position, navigation and timing (PNT) information for various users including vehicles, UAVs, mobile devices et. However, the safety of the system is faced with new challenges. Signal spoofing attack of satellite navigation system seriously endangers the safety of satellite navigation system. Spoofing detection mitigation and detection methods are necessary and critical for ensuring safety of this system. This paper analyses effectiveness of a spoofing detection algorithm based on the covariance eigenvalues of a detection matrix formed by position vectors from different receivers. The eigenvalues of the covariance matrix under spoofing and no spoofing are computed and compared. The analysis shows that there is obvious change of the eigenvalues, which can be employed for spoofing detection. The experiment is carried out for modeling and analyzing effectiveness of this method. The results show that the eigenvalues of the covariance matrix obviously changed under the two different conditions, and three of the eigenvalues in the covariance matrix under spoofing are obviously larger, and three of them under no spoofing decrease obviously. A spoofing detection factor is established based on the changing eigenvalues. Relevant equations and proofings are also given in this paper. The experiment results are in agreement with the theoretical analysis.

Microelectromechanical Systems (MEMS) Inertial Measurement Unit (IMU) containing a three-orthogonal gyroscope and three-orthogonal accelerometer has been widely utilized in position and navigation, due to gradually improved accuracy and its small size and low cost. However, the errors of a MEMS IMU based standalone Inertial Navigation System (INS) will diverge over time dramatically, since there are various and nonlinear errors contained in the MEMS IMU measurements. Therefore, MEMS INS is usually integrated with a Global Positioning System (GPS) for providing reliable navigation solutions. The GPS receiver is able to generate stable and precise position and time information in open sky environment. However, under signal challenging conditions, for instance dense forests, city canyons, or mountain valleys, if the GPS signal is weak and even is blocked, the GPS receiver will fail to output reliable positioning information, and the integration system will fade to an INS standalone system. A number of effects have been devoted to improving the accuracy of INS, and de-nosing or modelling the random errors contained in the MEMS IMU have been demonstrated to be an effective way of improving MEMS INS performance. In this paper, an Artificial Intelligence (AI) method was proposed to de-noise the MEMS IMU output signals, specifically, a popular variant of Recurrent Neural Network (RNN) Long Short Term Memory (LSTM) RNN was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. A MEMS IMU (MSI3200, manufactured by MT Microsystems Company, Hebei, China) was employed to test the proposed method, a 2 min raw gyroscope data with 400 Hz sampling rate was collected and employed in this testing. The results show that the standard deviation (STD) of the gyroscope data decreased by 60.3%, 37%, and 44.6% respectively compared with raw signals, and on the other way, the three-axis attitude errors decreased by 15.8%, 18.3% and 51.3% individually. Further, compared with an Auto Regressive and Moving Average (ARMA) model with fixed parameters, the STD of the three-axis gyroscope outputs decreased by 42.4%, 21.4% and 21.4%, and the attitude errors decreased by 47.6%, 42.3% and 52.0%. The results indicated that the de-noising scheme was effective for improving MEMS INS accuracy, and the proposed LSTM-RNN method was more preferable in this application.

In view of the neurological disease rehabilitation opponent eye coordination training needs, we designed game software based on intelligent mobile devices. It trains patients' hand movements by shaking pulling or some other behaviors in Einstein game process. It makes the patients exercise actively and accelerate rehabilitation in the game entertainment. By probability calculation skills training such as people's understanding and computing skills. Collecting the patient training effect at the same time, doctors can make an admirable rehabilitation program by the decision-making reference.

Overhead cranes are important equipments that are used in many industries. They belong to underactuated mechanical systems. Since it is hard to obtain an accurate model for control design, this paper presents a design scheme for the transport control problem of overhead cranes with uncertainties. In this scheme, a variable structure control law based on sliding mode is designed for the nominal model and neural networks are utilized to learn the upper bound of system uncertainties. In the sense of Lyapunov theorem, the update formulas of the network weights are deduced to approximate the system uncertainties. From the design process and comparisons, it can be seen that: 1) the neural approximator is able to compensate the system uncertainties, 2) the control system possesses asymptotic stability, 3) better performance can be achieved.

To study the preventive effects of cordyceps sinensis (CS) against contrast induced nephropathy (CIN) in type 2 diabetics with renal insufficiency undergoing coronary angiography. A total of 210 patients with type 2 diabetes and estimated glomerular filtration rate (eGFR) of 60ml/min/1.73m2 or less