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تحقيق الحماية الكاملة لحقوق الإنسان كان ولا يزال الغاية الأساس التي عمل الشعب الصيني والحكومة الصينية من أجلها منذ فترات طويلة، فضلا عن كونه من التطلعات العامة التي يشترك فيها الشعب الصيني مع جميع شعوب العالم. وقد قام (تشانغ بينغ تشون)، بصفته أول ممثل للصين لدى الأمم المتحدة ونائب رئيس لجنة الأمم المتحدة لحقوق الإنسان، بصياغة الإعلان العالمي لحقوق الإنسان مع السيدة رزفلت وممثلين عن باقي دول العالم، وقدموا إسهاما أشاد به العالم أجمع. وتقر الحكومة الصينية بالمبادئ والقيم الخاصة بحقوق الإنسان واعتمدتها، كما حرصت أشد الحرص على استيفاء الواجبات المنصوص عليها في هذه الاتفاقيات، باذلة في الوقت نفسه، ومن واقع ظروفها الوطنية الخاصة، قصارى جهودها لاستكشاف مسار تنمية حقوق الإنسان الذي يتلاءم مع طبيعتها، وذلك لضمان تحقيق إصلاحات مستمرة لحماية حقوق الإنسان جنبا إلى جنب مع التنمية الاقتصادية والاجتماعية، وتحقيق التنسيق والتوازن في حقوق الإنسان، كما أنشأت الصين مجموعة كاملة من أنظمة الحماية الفعالة لحقوق الإنسان، فأصبح مبدأ «احترام الدولة لحقوق الإنسان وصونها» مبدأ دستوريا أساسيا للصين سطره دستور الحزب الشيوعي الصيني الحاكم ليكون المحدد الأساسي لخطط الصين نحو التنمية الاقتصادية والاجتماعية الوطنية قبل أن يصبح أحد المبادئ الرئيسة التي يلتزم بها الحزب والحكومة الصينية في حكم البلاد، ويشكل تطوير خطتي العمل الوطنيتين لحقوق الإنسان في الصين وتنفيذهما بداية مرحلة جديدة لقضية حقوق الإنسان في الصين التي تعرض التنمية المخطط لها والمستمرة والمطردة والتقدم الشامل.

Light-regime variability is an important limiting factor constraining tree growth in tropical forests. However, there is considerable debate about whether radiation-induced green-up during the dry season is real, or an apparent artifact of the remote-sensing techniques used to infer seasonal changes in canopy leaf area. Direct and widespread observations of vertical canopy structures that drive radiation regimes have been largely absent. Here we analyze seasonal dynamic patterns between the canopy and understory layers in Amazon evergreen forests using observations of vertical canopy structure from a spaceborne lidar. We discovered that net leaf flushing of the canopy layer mainly occurs in early dry season, and is followed by net abscission in late dry season that coincides with increasing leaf area of the understory layer. Our observations of understory development from lidar either weakly respond to or are not correlated to seasonal variations in precipitation or insolation, but are strongly related to the seasonal structural dynamics of the canopy layer. We hypothesize that understory growth is driven by increased light gaps caused by seasonal variations of the canopy. This lightregime variability that exists in both spatial and temporal domains can better reveal the drought-induced green-up phenomenon, which appears less obvious when treating the Amazon forests as a whole.

Radiocesium remediation is desirable for ecological protection, human health and sustainable development of nuclear energy. Effective capture of Cs + from acidic solutions is still challenging, mainly due to the low stability of the adsorbing materials and the competitive adsorption of protons. Herein, the rapid and highly selective capture of Cs + from strongly acidic solutions is achieved by a robust K + -directed layered metal sulfide KInSnS 4 (InSnS-1) that exhibits excellent acid and radiation resistance. InSnS-1 possesses high adsorption capacity for Cs + and can serve as the stationary phase in ion exchange columns to effectively remove Cs + from neutral and acidic solutions. The adsorption of Cs + and H 3 O + is monitored by single-crystal structure analysis, and thus the underlying mechanism of selective Cs + capture from acidic solutions is elucidated at the molecular level. The removal of radiocesium from acidic solutions is challenging. Here, the authors report the rapid and highly selective capture of cesium(I) from strongly acidic solutions by a robust layered metal sulfide.

Two-dimensional materials such as graphene are attractive materials for making smaller transistors because they are inherently nanoscale and can carry high currents. However, graphene has no band gap and the transistors are \"leaky\"; that is, they are hard to turn off. Related transition metal dichalcogenides (TMDCs) such as molybdenum sulfide have band gaps. Transistors based on these materials can have high ratios of \"on\" to \"off\" currents. However, it is often difficult to make a good voltage-biased (p-n) junction between different TMDC materials. Li et al. succeeded in making p-n heterojunctions between two of these materials, molybdenum sulfide and tungsten selenide. They did this not by stacking the layers, which make a weak junction, but by growing molybdenum sulfide on the edge of a triangle of tungsten selenide with an atomically sharp boundary Science, this issue p. 524 Two-dimensional transition metal dichalcogenides (TMDCs) such as molybdenum sulfide MoS2 and tungsten sulfide WSe2 have potential applications in electronics because they exhibit high on-off current ratios and distinctive electro-optical properties. Spatially connected TMDC lateral heterojunctions are key components for constructing monolayer p-n rectifying diodes, light-emitting diodes, photovoltaic devices, and bipolar junction transistors. However, such structures are not readily prepared via the layer-stacking techniques, and direct growth favors the thermodynamically preferred TMDC alloys. We report the two-step epitaxial growth of lateral WSe2-MoS2 heterojunction, where the edge of WSe2 induces the epitaxial MoS2 growth despite a large lattice mismatch. The epitaxial growth process offers a controllable method to obtain lateral heterojunction with an atomically sharp interface.

Advanced beyond-silicon electronic technology requires both channel materials and also ultralow-resistance contacts to be discovered 1 , 2 . Atomically thin two-dimensional semiconductors have great potential for realizing high-performance electronic devices 1 , 3 . However, owing to metal-induced gap states (MIGS) 4 – 7 , energy barriers at the metal–semiconductor interface—which fundamentally lead to high contact resistance and poor current-delivery capability—have constrained the improvement of two-dimensional semiconductor transistors so far 2 , 8 , 9 . Here we report ohmic contact between semimetallic bismuth and semiconducting monolayer transition metal dichalcogenides (TMDs) where the MIGS are sufficiently suppressed and degenerate states in the TMD are spontaneously formed in contact with bismuth. Through this approach, we achieve zero Schottky barrier height, a contact resistance of 123 ohm micrometres and an on-state current density of 1,135 microamps per micrometre on monolayer MoS 2 ; these two values are, to the best of our knowledge, the lowest and highest yet recorded, respectively. We also demonstrate that excellent ohmic contacts can be formed on various monolayer semiconductors, including MoS 2 , WS 2 and WSe 2 . Our reported contact resistances are a substantial improvement for two-dimensional semiconductors, and approach the quantum limit. This technology unveils the potential of high-performance monolayer transistors that are on par with state-of-the-art three-dimensional semiconductors, enabling further device downscaling and extending Moore’s law. Electric contacts of semimetallic bismuth on monolayer semiconductors are shown to suppress metal-induced gap states and thus have very low contact resistance and a zero Schottky barrier height.

Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

Vascular smooth muscle cells (VSMCs), the major cell type in the arterial vessel wall, have a contractile phenotype that maintains the normal vessel structure and function under physiological conditions. In response to stress or vascular injury, contractile VSMCs can switch to a less differentiated state (synthetic phenotype) to acquire the proliferative, migratory, and synthetic capabilities for tissue reparation. Imbalances in VSMCs phenotypic switching can result in a variety of cardiovascular diseases, including atherosclerosis, in-stent restenosis, aortic aneurysms, and vascular calcification. It is very important to identify the molecular mechanisms regulating VSMCs phenotypic switching to prevent and treat cardiovascular diseases with high morbidity and mortality. However, the key molecular mechanisms and signaling pathways participating in VSMCs phenotypic switching have still not been fully elucidated despite long-term efforts by cardiovascular researchers. In this review, we provide an updated summary of the recent studies and systematic knowledge of VSMCs phenotypic switching in atherosclerosis, in-stent restenosis, aortic aneurysms, and vascular calcification, which may help guide future research and provide novel insights into the prevention and treatment of related diseases.

Fusarium graminearum is a destructive wheat pathogen. No fully resistant cultivars are available. Knowledge concerning the molecular weapons of F. graminearum to achieve infection remains limited. Here, we report that deletion of the putative secondary metabolite biosynthesis gene cluster fg3_54 compromises the pathogen’s ability to infect wheat through cell-to-cell penetration. Ectopic expression of fgm4 , a pathway-specific bANK-like regulatory gene, activates the transcription of the fg3_54 cluster in vitro. We identify a linear, C- terminally reduced and d -amino acid residue-rich octapeptide, fusaoctaxin A, as the product of the two nonribosomal peptide synthetases encoded by fg3_54 . Chemically-synthesized fusaoctaxin A restores cell-to-cell invasiveness in fg3_54 -deleted F. graminearum , and enables colonization of wheat coleoptiles by two Fusarium strains that lack the fg3_54 homolog and are nonpathogenic to wheat. In conclusion, our results identify fusaoctaxin A as a virulence factor required for cell-to-cell invasion of wheat by F. graminearum . Fusarium graminearum is a fungal pathogen of wheat and other cereals. Here the authors identify a gene cluster in F. graminearum encoding the production of a non-ribosomal peptide that is required for infection of wheat through cell-to-cell penetration.

Neural networks based on memristive devices 1 – 3 have the ability to improve throughput and energy efficiency for machine learning 4 , 5 and artificial intelligence 6 , especially in edge applications 7 – 21 . Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks 22 – 28 . This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even ‘mortal computing’ 25 , 29 , 30 . Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Chips with 256 × 256 memristor arrays that were monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry achieved 2,048 conductance levels in individual memristors.

As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.