Explore the vast range of titles available.

في السنوات القليلة الماضية ، سطع وهج النفوذ الصيني على الصعيد الدولي وبات كثير من الناس يرغب في التعرف إلى الصين وإلى حياة شعبها اليومية، ومع انتشار معاهد كونفوشيوس في شتى أرجاء المعمورة ، ازداد عدد الطلاب ولا سيما الصغار الذين يرغبون بتعلم اللغة الصينية ولهذا الغرض تمت ترجمة سلسلة كتب لمحة عن الصين المعاصرة لعدد من المؤلفين الصينين بإشراف لي لوشينغ وهي صادرة ومؤلفة من عشرة أجزاء يركز كل جزء منها على التعريف على أحد مجالات الحياة الصينية مثل الغذاء والنقل والأسرة والرياضية والفن والسياحة وما إلى ذلك بحيث يتعلم الطالب مابين 2500 إلى 3000 كلمة من كل كتاب وهذا ما يتطلبه امتحان كفاءة اللغة الصينية من الدرجة الخامسة وهكذا فإن القارئ على مدى صفحات الكتاب سيجد الكلمات الصينية متبوعة بشرح لها باللغة الإنجليزية وهذا ما يسهل على المتعلم القراءة والفهم.

Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed.

Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non‐invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT‐BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject‐specific dataset, which may have potential practical applications.

Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called ‘Russian Dolls’ ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition.

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.

C-type lectins (CTLs) are characterized by the presence of a C-type carbohydrate recognition domain (CTLD) that by recognizing microbial glycans, is responsible for their roles as pattern recognition receptors in the immune response to bacterial infection. In addition to the CTLD, however, some CTLs display additional domains that can carry out effector functions, such as the collagenous domain of the mannose-binding lectin. While in vertebrates, the mechanisms involved in these effector functions have been characterized in considerable detail, in invertebrates they remain poorly understood. In this study, we identified in the kuruma shrimp (Marsupenaeus japonicus) a structurally novel CTL (MjCC-CL) that in addition to the canonical CTLD, contains a coiled-coil domain (CCD) responsible for the effector functions that are key to the shrimp's antibacterial response mediated by antimicrobial peptides (AMPs). By the use of in vitro and in vivo experimental approaches we elucidated the mechanism by which the recognition of bacterial glycans by the CTLD of MjCC-CL leads to activation of the JAK/STAT pathway via interaction of the CCD with the surface receptor Domeless, and upregulation of AMP expression. Thus, our study of the shrimp MjCC-CL revealed a striking functional difference with vertebrates, in which the JAK/STAT pathway is indirectly activated by cell death and stress signals through cytokines or growth factors. Instead, by cross-linking microbial pathogens with the cell surface receptor Domeless, a lectin directly activates the JAK/STAT pathway, which plays a central role in the shrimp antibacterial immune responses by upregulating expression of selected AMPs.

Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn spatiotemporal features and hidden patterns in complex time series. Shown to have the potential of achieving higher-precision prediction in chaotic systems, those pioneering works led to a great amount of interest and follow-ups in the community of nonlinear dynamics and complex systems. To unlock the full capabilities of reservoir computing towards a fast, lightweight, and significantly more interpretable learning framework for temporal dynamical systems, substantially more research is needed. This Perspective intends to elucidate the parallel progress of mathematical theory, algorithm design and experimental realizations of reservoir computing, and identify emerging opportunities as well as existing challenges for large-scale industrial adoption of reservoir computing, together with a few ideas and viewpoints on how some of those challenges might be resolved with joint efforts by academic and industrial researchers across multiple disciplines. Reservoir Computing has shown advantageous performance in signal processing and learning tasks due to compact design and ability for fast training. Here, the authors discuss the parallel progress of mathematical theory, algorithm design and experimental realizations of Reservoir Computers, and identify emerging opportunities as well as existing challenges for their large-scale industrial adoption.

Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red–green–blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved.

In this paper, we discuss the properties for commutators and iterated commutators generated by the multilinear ω-CZO and weighted Lipschitz functions on Lebesgue space.

A large-scale silicon nanophotonic phased array with more than 4,000 antennas is demonstrated using a state-of-the-art complementary metal-oxide–semiconductor (CMOS) process, enabling arbitrary holograms with tunability, which brings phased arrays to many new technological territories. New dimension to photonic nanoarrays Nanophotonic approaches allow the construction of chip-scale arrays of optical nanoantennas capable of producing radiation patterns in the far field. This could be useful for a range of applications in communications, LADAR (laser detection and ranging) and three-dimensional holography. Until now this technology has been restricted to one-dimensional or small two-dimensional arrays. This paper reports the construction of a large-scale silicon nanophotonic phased array containing 4,096 optical nanoantennas balanced in power and aligned in phase. The array was used to generate a complex radiation pattern—the MIT logo—in the far field. The authors show that this type of nanophotonic phased array can be actively tuned, and in some cases the beam is steerable. Electromagnetic phased arrays at radio frequencies are well known and have enabled applications ranging from communications to radar, broadcasting and astronomy 1 . The ability to generate arbitrary radiation patterns with large-scale phased arrays has long been pursued. Although it is extremely expensive and cumbersome to deploy large-scale radiofrequency phased arrays 2 , optical phased arrays have a unique advantage in that the much shorter optical wavelength holds promise for large-scale integration 3 . However, the short optical wavelength also imposes stringent requirements on fabrication. As a consequence, although optical phased arrays have been studied with various platforms 4 , 5 , 6 , 7 , 8 and recently with chip-scale nanophotonics 9 , 10 , 11 , 12 , all of the demonstrations so far are restricted to one-dimensional or small-scale two-dimensional arrays. Here we report the demonstration of a large-scale two-dimensional nanophotonic phased array (NPA), in which 64 × 64 (4,096) optical nanoantennas are densely integrated on a silicon chip within a footprint of 576 μm × 576 μm with all of the nanoantennas precisely balanced in power and aligned in phase to generate a designed, sophisticated radiation pattern in the far field. We also show that active phase tunability can be realized in the proposed NPA by demonstrating dynamic beam steering and shaping with an 8 × 8 array. This work demonstrates that a robust design, together with state-of-the-art complementary metal-oxide–semiconductor technology, allows large-scale NPAs to be implemented on compact and inexpensive nanophotonic chips. In turn, this enables arbitrary radiation pattern generation using NPAs and therefore extends the functionalities of phased arrays beyond conventional beam focusing and steering, opening up possibilities for large-scale deployment in applications such as communication, laser detection and ranging, three-dimensional holography and biomedical sciences, to name just a few.