Circular Convolution Filter Bank Multicarrier (FBMC) System with Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), which uses the subcarrier indices as a source of information, has attracted considerable interest recently. Motivated by the index modulation (IM) concept, we build a circular convolution filter bank multicarrier with index modulation (C-FBMC-IM) system in this paper. The advantages of the C-FBMC-IM system are investigated by comparing the interference power with the conventional C-FBMC system. As some subcarriers carry nothing but zeros, the minimum mean square error (MMSE) equalization bias power will be smaller comparing to the conventional C-FBMC system. As a result, our C-FBMC-IM system outperforms the conventional C-FBMC system. The simulation results demonstrate that both BER and spectral efficiency improvement can be achieved when we apply IM into the C-FBMC system

Suppression of methane uptake by precipitation pulses and long-term nitrogen addition in a semi-arid meadow steppe in northeast China

In the context of global change, the frequency of precipitation pulses is expected to decrease while nitrogen (N) addition is expected to increase, which will have a crucial effect on soil C cycling processes as well as methane (CH4) fluxes. The interactive effects of precipitation pulses and N addition on ecosystem CH4 fluxes, however, remain largely unknown in grassland. In this study, a series of precipitation pulses (0, 5, 10, 20, and 50 mm) and long-term N addition (0 and 10 g N m-2 yr-1, 10 years) was simulated to investigate their effects on CH4 fluxes in a semi-arid grassland. The results showed that large precipitation pulses (10 mm, 20 mm, and 50 mm) had a negative pulsing effect on CH4 fluxes and relatively decreased the peak CH4 fluxes by 203-362% compared with 0 mm precipitation pulse. The large precipitation pulses significantly inhibited CH4 absorption and decreased the cumulative CH4 fluxes by 68-88%, but small precipitation pulses (5 mm) did not significantly alter it. For the first time, we found that precipitation pulse size increased cumulative CH4 fluxes quadratically in both control and N addition treatments. The increased soil moisture caused by precipitation pulses inhibited CH4 absorption by suppressing CH4 uptake and promoting CH4 release. Nitrogen addition significantly decreased the absorption of CH4 by increasing NH4+-N content and NO3–-N content and increased the production of CH4 by increasing aboveground biomass, ultimately suppressing CH4 uptake. Surprisingly, precipitation pulses and N addition did not interact to affect CH4 uptake because precipitation pulses and N addition had an offset effect on pH and affected CH4 fluxes through different pathways. In summary, precipitation pulses and N addition were able to suppress the absorption of CH4 from the atmosphere by soil, reducing the CH4 sink capacity of grassland ecosystems

Symmetry Analysis and Conservation Laws for a Time-Fractional Generalized Porous Media Equation

The symmetry group method is applied to study a class of time-fractional generalized porous media equations with Riemann–Liouville fractional derivatives. All point symmetry groups and the corresponding optimal subgroups are determined. Then, the similarity reduction is performed to the given equation and some explicit solutions are derived. The asymptotic behaviours for the solutions are also discussed. Through the concept of nonlinear self-adjointness, the conservation laws arising from the admitted point symmetries are listed

Evaluation of the sustainable development level of countries along the Belt and Road and its impact factors: Empirical analysis based on the Super-efficiency slacks-based measure and Tobit measure models

Sustainable development is an important component of the Belt and Road Initiative (BRI) and is of great significance for evaluating the levels of sustainable development of countries along this route (henceforth, BRI countries). Therefore, this study aims to identify the factors that influence the levels of sustainable development of BRI countries in a reasonable and objective manner. Eventually, this study employs the super-efficiency slacks-based measure (Super-SBM) model, which considers unexpected outputs to measure the level of sustainable development of BRI countries. The dynamic change and composition of the sustainable development level of these countries are calculated using the global Malmquist–Luenberger index. Furthermore, the Tobit model is used to identify the factors influencing the level of sustainable development of BRI countries in general and in various categories. The empirical results suggest the following points. (a) The overall level of sustainable development of BRI countries is low, whereas those of high-income and middle- and high-income countries are relatively high. (b) The overall sustainable development levels of BRI countries declined to a certain extent in 2008 owing to the effect of the financial crisis,. However, the sustainable development level of other countries, barring low-income countries, has gradually increased since 2011. (c) Since 2008, technological progress has replaced technical efficiency as the main driving force behind the improvement of the sustainable development level of BRI countries. (d) A U-shaped relationship is observed between the economic and sustainable development levels of these countries. (e) The level of science and technology and the proportion of renewable energy consumption can promote the sustainable development of these countries. Moreover, a negative correlation exists between the level of opening to the outside world and that of sustainable development of countries that mainly export resource-based products and are dominated by labor-intensive export industries. Barring low-income countries, the energy structure plays an effective role in improving the level of sustainable development. Finally, the study presents suggestions for China in the process of coping with the sustainable development of relevant countries during its promotion of the BRI

Research on Strategies and Methods of Improving Teachers’ Digital Literacy in Classroom Teaching in Higher Vocational Colleges

With the continuous development and application of digital technology, higher vocational education is also facing the demand of digital transformation. The digital transformation of higher vocational education is the key to the high quality development of higher vocational education. In order to promote the digitization of education and enhance the adaptability of vocational education, it is necessary to continuously improve teachers’ ability and literacy. In order to adapt to and lead the digital new normal, it is an important starting point for the high-quality development of vocational education to build a comprehensive and multi-channel digital literacy of higher vocational teachers and the digital improvement path of classroom teaching. The research of this paper will provide guidance and reference for the cultivation of teachers’ digital literacy and the improvement of classroom teaching effect in higher vocational colleges

Dynamic paid peak shaving benchmarks and bidding strategies adapted to the high proportion of new energy system

This article mainly analyze the shortcomings of the inherent peak shaving auxiliary service mechanism in the high-proportion of new energy access scenarios in Northeast China, which restricts the enthusiasm of thermal power units to participate in peak shaving. For this reason, a dynamic peak shaving compensation benchmark is proposed which follows load changes. At the same time, in order to standardize market behavior and facilitate market supervision, a guiding formula is proposed for quotation of thermal power units. Based on the above, a dynamic auxiliary service market mechanism is established which used actual operating data of Liaoning province power grid as a calculation example to verify that the mechanism can effectively improve the enthusiasm of thermal power units to participate in peak shaving, which is conducive to market operation and supervision as well

Mowing enhances the positive effects of nitrogen addition on ecosystem carbon fluxes and water use efficiency in a semi-arid meadow steppe

Grasslands are now facing a continuously increasing supply of nitrogen (N) fertilizers, resulting in alterations in ecosystem functioning, including changes in carbon (C) and water cycling. Mowing, one of the most widely used grassland management techniques, has been shown to mitigate the negative impacts of increased N availability on species richness. However, knowledge of how N addition and mowing, alone and/or in combination, affect ecosystem-level C fluxes and water use efficiency (WN) is still limited. We experimentally manipulated N fertilization (0 and 10 g N m−2 yr−1) and mowing (once per year at the end of the growing season) following a randomized block design in a meadow steppe characterized by salinization and alkalinization in northeastern China. We found that, compared to the control plots, N addition, mowing, and their interaction increased net ecosystem CO2 exchange by 65.1%, 14.7%, and 133%, and WN by 40.7%, 18.5%, and 96.1%, respectively. Nitrogen enrichment also decreased soil pH, which resulted in greater aboveground biomass (AGB). Moreover, N addition indirectly increased AGB by inducing changes in species richness. Our results indicate that mowing enhances the positive effects of N addition on ecosystem C fluxes and WN. Therefore, appropriate grassland management practices are essential to improve ecosystem C sequestration, WN, and mitigate future species diversity declines due to ecosystem eutrophication

Accurate breast cancer diagnosis using a stable feature ranking algorithm

Abstract Background Breast cancer (BC) is one of the most common cancers among women. Since diverse features can be collected, how to stably select the powerful ones for accurate BC diagnosis remains challenging. Methods A hybrid framework is designed for successively investigating both feature ranking (FR) stability and cancer diagnosis effectiveness. Specifically, on 4 BC datasets (BCDR-F03, WDBC, GSE10810 and GSE15852), the stability of 23 FR algorithms is evaluated via an advanced estimator (S), and the predictive power of the stable feature ranks is further tested by using different machine learning classifiers. Results Experimental results identify 3 algorithms achieving good stability ( $$S \ge 0.55$$ S ≥ 0.55 ) on the four datasets and generalized Fisher score (GFS) leading to state-of-the-art performance. Moreover, GFS ranks suggest that shape features are crucial in BC image analysis (BCDR-F03 and WDBC) and that using a few genes can well differentiate benign and malignant tumor cases (GSE10810 and GSE15852). Conclusions The proposed framework recognizes a stable FR algorithm for accurate BC diagnosis. Stable and effective features could deepen the understanding of BC diagnosis and related decision-making applications

A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images

The deep neural network is a research hotspot for histopathological image analysis, which can improve the efficiency and accuracy of diagnosis for pathologists or be used for disease screening. The whole slide pathological image can reach one gigapixel and contains abundant tissue feature information, which needs to be divided into a lot of patches in the training and inference stages. This will lead to a long convergence time and large memory consumption. Furthermore, well-annotated data sets are also in short supply in the field of digital pathology. Inspired by the pathologist's clinical diagnosis process, we propose a weakly supervised deep reinforcement learning framework, which can greatly reduce the time required for network inference. We use neural network to construct the search model and decision model of reinforcement learning agent respectively. The search model predicts the next action through the image features of different magnifications in the current field of view, and the decision model is used to return the predicted probability of the current field of view image. In addition, an expert-guided model is constructed by multi-instance learning, which not only provides rewards for search model, but also guides decision model learning by the knowledge distillation method. Experimental results show that our proposed method can achieve fast inference and accurate prediction of whole slide images without any pixel-level annotations