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Hierarchically porous structured materials have potential applications in catalysis, biology, biomedical and other fields, because of their variety advantages, such as high surface and pore volume, excellent permeability storage performance etc. Therefore, it has always been one of the hot spots in scientific research. In the past decades, a variety of hierarchically porous materials have been prepared, however, there are a lot of problems that have not be solved, such as hardly control of the well-defined morphology and well-ordered mesostructured as the particle size becomes smaller, complicated synthesis method and so on. So, it is meaningful to develop a simple and effective method for synthesis of hierarchically porous materials. Mesoporous silica nanoparticles have unique advantages in the areas of catalysis, adsorption, separation, biosensing, and biological sciences due to their dual function of porous materials and nanoparticle materials. However, the preparation of uniform, monodisperse, size-controllable mesoporous silica nanoparticles is still a difficult problem. Surfactants or polymers have been widely used as templates for the formation of porous materials. According to the types of polyelectrolyte, surfactant and the condition of assembly, the complex of polyelectrolyte and surfactant can have abundant ordered mesostructures. So it has a wide range of applications in the synthesis of hierarchically porous structured materials. In this thesis, a series of hierarchical mesoporous silica nanoparticles were prepared using a complex system of polyelectrolyte and surfactant. We also studied the effects of different length of surfactant complexes and different silicon precursor on the morphology and structure of the hierarchically porous materials, and highly ordered HMS nanoparticles with 2D hexagonal structure (p6mm) were firstly obtained in polymer-surfactant synthetic system. Finally apply it to the adsorption experiment to explore the rate of adsorption of mesoporous materials with different structures.介孔材料是指孔径介于 2-50 nm 的一类多孔材料。由于其较的高比表面积、规则有序的介孔孔道、孔径分布连续可调等特点,在吸附、分离、催化、电极材料、光电器件、化学传感器、非线性光学材料等领域有重要的应用价值。 多级孔结构材料由于其具有高比表面积和较大的孔体积、优异的通透性能和吸附性能等诸多优点,在催化、生物医药等领域具有重要的应用价值,因而一直是科学研究热点之一。近年来,多级孔结构材料的制备和研究已经得得到了突破性进展,各种样各的多级孔结构材料被不断地合成出来,但是在多级孔结构材料的合成与研究中仍然存在很多未能解决的问题,如其形貌和相态结构难以控制,有序结构随着粒子的变小转变为无序结构,复杂的合成方法等。因此,寻求一类简单、易操作表面活性剂或聚合物作为模板来制备多级孔结构材料,并研究其合成机制仍然具有重要的意义。介孔二氧化硅纳米粒子由于具有介孔材料和纳米粒子材料的双重功能,所以在催化,吸附,分离,生物传感和生物科学领域具有独特的优势。但是制备粒径均一的,单分散的,尺寸可控的介孔二氧化硅纳米粒子仍然是一难点。 聚电解质-表面活性剂作为模板已广泛应用于多孔材料的合成。聚电解质和表面活性剂的复合物是具有有序液晶相的介晶化合物。根据聚电解质的类型,表面活性剂和组装条件,聚电解质与表面活性剂的复合物可以具有丰富的有序介观结构,所以它在合成多级孔材料有着广泛的应用前景。在本论文中,使用聚电解质与表面活性剂的复合物体系制备了一系列多级孔介孔二氧化硅纳米粒子,并且研究了不同长度烷基链的表面活性剂和不同硅前驱体对多级孔材料的形貌和结构的影响,并首次在聚合物-表面活性剂合成体系中首次得到了具有二维六方结构(P6mm)的高度有序介孔二氧化硅纳米粒子。最后将其应用于吸附实验,探究不同结构介孔材料对丁基罗丹明吸附速率的快慢。
As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency.
Recently, Parallel Factor-Direct (PARAFAC-Direct) method has been proposed for parameter estimation including velocity disambiguation for Doppler Division Multiple Access (DDMA) Multiple-Input Multiple-Output (MIMO) radar. However, DDMA MIMO radar spreads the overall transmit energy into the entire spatial region, and therefore, suffers from beam-shape loss that can limit the performance of PARAFAC-Direct method. To solve this problem, a Transmit Beamspace (TB) Slow-Time MIMO (ST-MIMO) approach is proposed that focuses the transmit energy within a desired spatial region. Unlike traditional DDMA MIMO radars, the Doppler spectrum is divided into more subbands than the number of transmit elements to reduce the mainlobe intervals between adjacent beams formed by DDMA modulation vectors. Then, the TB ST-MIMO beam set can be directed to the spatial region of interest via a proper selection of DDMA modulation vectors. Parameter estimation performance of TB ST-MIMO is improved as compared to conventional DDMA MIMO techniques. Simulations are conducted to validate the proposed method.
While 4G is speeding up its steps towards global markets, 5G has initiated its full development to satisfy an increasing demand on mobile data traffic and big data bandwidth. Centralized data processing, collaborative radio, real-time cloud infrastructure and Cloud Radio Access Network (C-RAN), along with their excellent advantages are being sought by more and more operators to meet end-user requirements. As a promising mobile wireless network architecture, compared with traditional RAN, C-RAN has incomparable advantages in terms of low power consumption, reduced Base Station (BS) numbers, and economic capital and operating expenditure. It can also improve network capacity and BS utilization rate. Recently, C-RAN security has aroused special attention and concern. However, the literature still lacks an overall review on it in order to guide current and future research. In this paper, we first overview the architecture, deployment scenarios and special characteristics of C-RAN. We then provide a thorough review on the existing security studies in the field of C-RAN based on its three logic layers and corresponding security threats and attacks. Particularly, we discuss whether the current literature can satisfy the expected security requirements in C-RAN. Based on this, we indicate open research issues and propose future research trends.
Soft robots that can move like living organisms and adapt to their surroundings are currently in the limelight from fundamental studies to technological applications, due to their advances in material flexibility, human-friendly interaction, and biological adaptation that surpass conventional rigid machines. Light-fueled smart actuators based on responsive soft materials are considered to be one of the most promising candidates to promote the field of untethered soft robotics, thereby attracting considerable attention amongst materials scientists and microroboticists to investigate photomechanics, photoswitch, bioinspired design, and actuation realization. In this review, we discuss the recent state-of-the-art advances in light-driven bimorph soft actuators, with the focus on bilayer strategy, i.e., integration between photoactive and passive layers within a single material system. Bilayer structures can endow soft actuators with unprecedented features such as ultrasensitivity, programmability, superior compatibility, robustness, and sophistication in controllability. We begin with an explanation about the working principle of bimorph soft actuators and introduction of a synthesis pathway toward light-responsive materials for soft robotics. Then, photothermal and photochemical bimorph soft actuators are sequentially introduced, with an emphasis on the design strategy, actuation performance, underlying mechanism, and emerging applications. Finally, this review is concluded with a perspective on the existing challenges and future opportunities in this nascent research Frontier.
Mobile crowd sensing (MCS) has been widely used as a cost-efcient way to collect data for smart cities, which typically starts with participant recruitment and task allocation. Previous work mainly focused on selecting a proper subset of humans for contributing sensing data. However, there often exist situations where humans are not able to reach the target areas, such as traffic jams or accidents. One solution is to complement manual data collection with autonomous data collection using unmanned aerial vehicles (UAVs) equipped with various sensors. In this paper, we focus on the scenarios of UAV-assisted MCS and propose a highly efficient task allocation method, called UMA (UAV-assisted Multi-task Allocation method) to jointly optimize the sensing coverage and data quality. The method incentivizes and guides human participants to contribute high-quality sensing data. Meanwhile, the UAVs are employed to sense data from rarely sensed points of interest, and calibrate data contributed by human participants. The method leverages emerging deep reinforcement learning techniques for directing UAVs sensing and movement actions based on the human participants locations and tasks achievement. The results well justify the efficiency of UMA in terms of coverage completed ratio, calibrating ratio, task fairness and energy efficiency, compared with the state-of-the-art.
Abstract The potential of the gut microbiome as a driver of individual cognitive differences in natural populations of animals remains unexplored. Here, using metagenomic sequencing of individual bumblebee hindguts, we find a positive correlation between the abundance of Lactobacillus Firm-5 cluster and memory retention on a visual discrimination task. Supplementation with the Firm-5 species Lactobacillus apis, but not other non-Firm-5 bacterial species, enhances bees’ memory. Untargeted metabolomics after L. apis supplementation show increased LPA (14:0) glycerophospholipid in the haemolymph. Oral administration of the LPA increases long-term memory significantly. Based on our findings and metagenomic/metabolomic analyses, we propose a molecular pathway for this gut-brain interaction. Our results provide insights into proximate and ultimate causes of cognitive differences in natural bumblebee populations.
Van der Waals heterostructures of transition metal dichalcogenides with interlayer coupling offer an exotic platform to realize fascinating phenomena. Due to the type II band alignment of these heterostructures, electrons and holes are separated into different layers. The localized electrons induced doping in one layer, in principle, would lift the Fermi level to cross the spin-polarized upper conduction band and lead to strong manipulation of valley magnetic response. Here, we report the significantly enhanced valley Zeeman splitting and magnetic tuning of polarization for the direct optical transition of MoS2 in MoS2/WS2 heterostructures. Such strong enhancement of valley magnetic response in MoS2 stems from the change of the spin-valley degeneracy from 2 to 4 and strong many-body Coulomb interactions induced by ultrafast charge transfer. Moreover, the magnetic splitting can be tuned monotonically by laser power, providing an effective all-optical route towards engineering and manipulating of valleytronic devices and quantum-computation.
With the popularity of sensor-rich mobile devices (e.g., smart phones and wearable devices), Mobile Crowdsourcing (MCS) has emerged as an effective method for data collection and processing. Compared with traditional Wireless Sensor Networking (WSN), MCS holds many advantages such as mobility, scalability, cost-efficiency, and human intelligence. However, MCS still faces many challenges with regard to security, privacy and trust. This paper provides a survey of these challenges and discusses potential solutions. We analyze the characteristics of MCS, identify its security threats, and outline essential requirements on a secure, privacy-preserving and trustworthy MCS system. Further, we review existing solutions based on these requirements and compare their pros and cons. Finally, we point out open issues and propose some future research directions
Abstract Identifying the top-K flows that require much more bandwidth resources in a large-scale Software-Defined Network (SDN) is essential for many network management tasks, such as load balancing, anomaly detection, and traffic engineering. However, identifying such top-K flows is not trivial, not only because of the fluctuations in flow bandwidth requirements but also because of the combinatorial explosion of problem instance sizes. In this paper, we weaken the tradeoff between exploration and exploitation and innovatively define the online top-K flows identification problem as identifying the top-K arms in a Combinatorial Multi-Armed Bandit (CMAB) model. Then, we propose a general greedy selection mechanism with some identification strategies that focus on temporal variations in the rewards. Extensive simulation experiments based on real traffic data are conducted to evaluate the performance of different strategies. In addition, the results of numerical simulations demonstrate that our proposed greedy selection mechanism significantly outperforms existing counterparts on top-K arms identification.
Recently, the development of Internet of Vehicles (IoV) and the increasing popularity of video applications have led to the fast-growing in-car video demand causing numerous challenges in wireless networks. Pre-caching and non-orthogonal multiple access (NOMA) have been regarded as two effective techniques to alleviate the mentioned challenge. In this paper, we propose a cache-aided cooperative transmission to maximize the quality of service (QoS) in the NOMA-based vehicular network. A QoS-oriented joint optimization problem is formulated, which incorporates power allocation, content caching, and delivery strategy. Considering, on the one hand, the slow update rate of cache content and, on the other hand, frequent handovers of vehicles between different transmitters, a mixed-timescale optimization is proposed where the serving cache is updated in a long-term phase, while content delivery and power allocation are optimized in a short-term phase. In the proposed approach, content caching is determined based on future user requests, vehicle tracking, and other delivery information. To make this possible, we leverage a substantial number of stochastic samples to approximate content caching in the long-term caching phase. Due to the NOMA-based transmission and integral variables, the setting leads to a Mixed Integer Non-Linear Programming (MINLP) problem, which is NP-hard. To solve this problem, an iterative method based on sample average approximation (SAA) and Successive Convex Approximation (SCA) is applied. Simulations demonstrate that the proposed algorithm can achieve better QoS than other recently proposed transmission schemes.
Abstract Herein, we report on the successful synthesis of photocatalytic Pb3(BTC)2·H2O polymers via different methods including the surfactant-assisted hydrothermal method, ultrasonic method and reflux method. As the crystal growth is subjected to preparation atmosphere, changes in reaction conditions do not alter the crystal structures of products, but vary their morphology. High ultraviolet-light-driven photocatalytic abilities are attributed to the stable Pb3(BTC)2·H2O, and the effective productions of h+ and ̇OH on the catalysts.
Patterned micro/nanomaterials display efficient light management capabilities owing to their control of light propagation within multiscale periodic structures. Here a hierarchical photonic structure composed of polystyrene microspheres and cholesteric assembly of cellulose nanocrystals is described, acting as a polarization-sensitive retroreflective coating and microlens array. Micropatterned photonic films are prepared by casting an aqueous cellulose nanocrystal suspension onto a monolayer of polystyrene microspheres substrate through evaporation-assisted transfer imprinting lithography, integrating a bulk cholesteric matrix and patterned surface. By directing light at the as-assembled polystyrene surface, an enhanced structural color develops from the circularly polarized light retroreflection. Whereas when light travelling across the photonic film, the transparent layer of polystyrene microspheres forms into plano-convex microlens to converge the transmitted light into the focus plane and reduce centimeter-scale illuminated image into a high-fidelity miniaturized replica. This simple method, combining self-assembly with imprinting lithography, is expected to pave the way for designing custom-tailored optics with novel functions.
Moiré superlattices, the artificial quantum materials, have provided a wide range of possibilities for the exploration of completely new physics and device architectures. In this Review, we focus on the recent progress on emerging moiré photonics and optoelectronics, including but not limited to moiré excitons, trions, and polaritons; resonantly hybridized excitons; reconstructed collective excitations; strong mid- and far-infrared photoresponses; terahertz single-photon detection; and symmetry-breaking optoelectronics. We also discuss the future opportunities and research directions in this field, such as developing advanced techniques to probe the emergent photonics and optoelectronics in an individual moiré supercell; exploring new ferroelectric, magnetic, and multiferroic moiré systems; and using external degrees of freedom to engineer moiré properties for exciting physics and potential technological innovations.
Abstract Based on a great number of experimental data on various mechanical properties of rock in the literature, six empirical equations between the characteristic impedance (product of density and P-wave velocity) and mechanical properties of rock are proposed. These properties include uniaxial compressive strength, tensile strength, shear strength, mode I fracture toughness, Young’s modulus, and Poisson’s ratio. These empirical equations show that the values of the aforementioned properties increase with increase in characteristic impedance. It also implies that the characteristic impedance of rock may be considered as an index to represent the main properties of rock. In this sense, it is possible to consider using characteristic impedance to classify rock masses for studies in the future.
The growth of high-quality semiconducting single-wall carbon nanotubes with a narrow band-gap distribution is crucial for the fabrication of high-performance electronic devices. However, the single-wall carbon nanotubes grown from traditional metal catalysts usually have diversified structures and properties. Here we design and prepare an acorn-like, partially carbon-coated cobalt nanoparticle catalyst with a uniform size and structure by the thermal reduction of a [Co(CN)6 ]3- precursor adsorbed on a self-assembled block copolymer nanodomain. The inner cobalt nanoparticle functions as active catalytic phase for carbon nanotube growth, whereas the outer carbon layer prevents the aggregation of cobalt nanoparticles and ensures a perpendicular growth mode. The grown single-wall carbon nanotubes have a very narrow diameter distribution centred at 1.7 nm and a high semiconducting content of > 95%. These semiconducting single-wall carbon nanotubes have a very small band-gap difference of ~ .08 eV and show excellent thin-film transistor performance.
Self-sustained catalytic combustion is a promising strategy to remove CO from the off-gas produced during steelmaking, where the potential catalysts are bulk copper-cerium-zirconium mixed oxides or those supported on TiO2 or ZSM-5 substrates. In this study, the effects of the catalyst support on the CO catalytic ignition performance and reaction pathways were investigated by FTIR coupled with a novel in-situ cell, together with the state-of-the-art characterization techniques. The Infrared (IR) transmission cell equipped with a magnetically driven system, could effectively prevent overlaps between active intermediate peaks (Cu+-CO and Cu+(CO)2) and gaseous CO peaks. The Cu+ cations located at the phase interface are the main active sites. The Cu and Ce interactions lead to the formation of solid solutions of CuCe0.75Zr0.25Oδ (CuCeZr). The monocarbonyls [Cu+-CO] are the dominant species during CO oxidation, and the vacancies in the solid solutions are occupied by oxygen, accelerating the oxygen cycle. The TiO2 or ZSM-5 supports promote copper dispersion over CuCe0.75Zr0.25Oδ/TiO2 (CuCeZr/T) and CuCe0.75Zr0.25Oδ/ZSM-5 (CuCeZr/Z) catalysts, which can be attributed to their high surface areas (168.2 and 346.3 m2/g, respectively), while the Cu-Ce interactions are less relevant. Hence, CO oxidation mainly occurs at the phase interface between copper oxide and TiO2/ZSM-5. Dicarbonyls [Cu+(CO)2] are the main intermediates for the CuCeZr/T and CuCeZr/Z catalysts, and the Cu2+ species are reduced to form dicarbonyls that also take part in the oxidation process. Although a well copper dispersion enhances the activity of individual copper sites on the CuCeZr/T and CuCeZr/Z catalysts, considering the redshift of the carbonyl bands and the increase in CO adsorption, the close interactions and high contents of Cu and Ce favor the local accumulation of heat and mass transfer over bulk CuCeZr, leading to the ignition of CO at low temperatures.
Abstract Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.
The cognitive processing mechanism of humor refers to how the system of neural circuitry and pathways in the brain deals with the incongruity in a humorous manner. The past research has revealed different stages and corresponding functional brain activities involved in humor-processing in terms of time and space dimensions, highlighting the effects of the time windows of about 400 ms, 600 ms, and 900 ms. However, much less is known about humor processing in light of the frequency dimension. A total of 36 Chinese participants were recruited in this experiment, with Chinese jokes, nonjokes, and nonsensical sentences used as the stimuli. The experimental results showed that there were significant differences among conditions in the P200 effect, which signified that the incongruity detection had already been integrated and perceived at about 200 ms, prior to the semantic integration at about 400 ms. This pre-processing is specific to Chinese verbal jokes due to the simultaneous involvement of both orthographic and phonologic parts in processing Chinese characters. The analysis on the frequency dimension indicated that beta’s power particularly reflected the characteristics of different stages in Chinese verbal humor processing. Jokes’ and nonsensical sentences’ relative power changes on the beta band ranked significantly higher than that of nonjokes at about 200 ms, which suggested the existence of more difficulties in meaning construction in pre-processing the incongruities. This indicated a continuity between the analysis of event related potential (ERP) components and neural oscillations and revealed the key role of the beta frequency band in Chinese verbal joke processing.
Carbon removals associated with incremental gains in soil organic carbon (SOC) at scale have enormous potential to mitigate global warming, yet confusion over contexts that elicit SOC accrual abound. Here, we examine how bespoke interventions (through irrigation, fertiliser, crop type and rotations), antecedent SOC levels and soil type impact on long-term SOC accrual and greenhouse gas (GHG) emissions. Using a whole farm systems modelling approach informed using participatory research, we discovered an inverse relationship between antecedent SOC stocks and SOC gains realised following intervention, with greater initial SOC levels resulting in lower ex poste change in SOC. We found that SOC accrual was greatest for clays and least for sands, although changes in SOC in sandy loam soils were also low. Diversified whole farm adaptations – implemented through inclusion of grain legumes within wheat/canola crop rotations – were more conducive to improvement in SOC stocks, followed by Intensified systems (implemented through greater rates of irrigation, farm areas under irrigation, nitrogen fertiliser and inclusion of rice and maize in crop rotations). Adaptations that Simplified farm systems by reducing irrigation and fertiliser use resulted in the lowest SOC accrual. In most cases, long-term SOC stocks fell when SOC at the outset was greater than 4–5%, regardless of intervention made, soil or crop type, crop rotation, production system or climate. We contend that (1) management interventions primarily impacted SOC in the soil surface (0–30 cm) and had de minimus impact on deep SOC stocks (30–100 cm), (2) crop rotations including wheat, canola and faba beans were more conducive to improvement in SOC stocks, (3) scenarios with high status quo SOC had little impact on crop productivity, and not necessarily the lowest GHG emissions intensity, (4) productivity and GHG emissions intensity were largely a function of the quantum of nitrogenous fertiliser added, rather than SOC stocks, and (5) aspirations for improving SOC are likely to be futile if antecedent SOC stocks are already high (4–5 %). We conclude that potential for improving SOC stocks exists in contexts where antecedent stocks are low (<1%), which may include regions with land degradation, chronic erosion and/ or other constraints to vegetative ground cover that could be sustainably and consistently alleviated.
Abstract In this study, we report a facile solution route to prepare superhydrophobic and superoleophilic ZnC4O4 concave microspheres. The surface morphologies and chemical compositions were determined through scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray spectroscopy (EDX), X-ray powder diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The wettability of the as-synthesized ZnC4O4 coordination compound surface was studied by measuring the contact angle (CA). A static CA for water over 160° was observed, which was closely related to both the structure and chemical modification of ZnC4O4, and a 0° static CA for octane was observed, which showed that the as-prepared ZnC4O4 surface had superoleophilic properties. Furthermore, the as-prepared ZnC4O4 surface showed superhydrophobicity for some corrosive liquids, such as acidic and basic aqueous solutions.
Abstract Oils and solvable organic pollutants in wastewater demand separations of the components along with efficient photocatalysis in water treatment. Herein, we report on a practical purification strategy by using the multifunctional nickel-dimethylglyoxime [Ni(DMG)2] microtubes to separate the liquid mixture and degrade organic pollutants. The self-assembled [Ni(DMG)2] tubes was synthesized by a facile co-precipitation method. The static contact angle of the film prepared by mixing [Ni(DMG)2] powder (1 : 2 wt%) into polydimethylsilicone (PDMS) to water can reach 161.3°, which can still remain superhydrophobic but oil-friendly under corrosion conditions. PDMS imparts good mechanical properties and serves as both the adhesive and hydrophobic material. PFOTS methanol solution contains a large number of low surface energy groups, which can reduce the surface free energy of [Ni(DMG)2] rough structure. The superhydrophobic rough surface prepared by hollow micron tubular [Ni(DMG)2] samples must have both low surface energy substance and hollow micron tubular morphology. Due to the unique wettability, oil and water were efficiently separated from the oil–water mixture through the films. The coated film itself is photocatalytic in degrading quinoline blue, rhodamine B, methyl orange and methylene blue. By using the film’s multifunctionality, a practical wastewater treatment was realized via water–oil separation, followed by fast photocatalytic degradation of solvable dyes.
Societal Impact Statement Despite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates. Summary Research aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land-use changes in space and time. Here, we demonstrate how a new framework—WaterCan Profit—can be used to elicit such complexity using an irrigated case study farm with four whole-farm adaptation scenarios (Baseline, Diversified, Intensified and Simplified) with four types of irrigated infrastructure (Gravity, Pipe & Riser, Pivot and Drip). Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole-farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long-term economic returns. We conclude with four key insights: (1) When assessing whole-farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity-based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole-farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole-farm intensification was able to raise profitability under future climates.
Increasing agricultural water scarcity is threatening food security and ecosystem sustainability in China. Previous studies showed a deceleration in the growth of irrigation water use in China due to reducing water use intensities of irrigation. However, a finer-scale analysis at the prefecture level is urgently needed to account for the impacts of land management policies and the impact of international food trade in water stress mitigation. Here, we address these gaps and demonstrate that the scarce irrigation water use trend reversed to rising after 2011 through shifting to irrigated cropland, even if grain import reduced water stress at the national scale, and we highlight the specificity of relationships between scarce water use and irrigated cropland change at both the river-basin and prefecture scales. These results call for an urgent re-evaluation of the implementation guidelines of China's Land Requisition-Compensation Balance policy on scarce irrigation water use.
Abstract Facial kinship verification refers to automatically determining whether two people have a kin relation from their faces. It has become a popular research topic due to potential practical applications. Over the past decade, many efforts have been devoted to improving the verification performance from human faces only while lacking other biometric information, for example, speaking voice. In this article, to interpret and benefit from multiple modalities, we propose for the first time to combine human faces and voices to verify kinship, which we refer it as the audio-visual kinship verification study. We first establish a comprehensive audio-visual kinship dataset that consists of familial talking facial videos under various scenarios, called TALKIN-Family. Based on the dataset, we present the extensive evaluation of kinship verification from faces and voices. In particular, we propose a deep-learning-based fusion method, called unified adaptive adversarial multimodal learning (UAAML). It consists of the adversarial network and the attention module on the basis of unified multimodal features. Experiments show that audio (voice) information is complementary to facial features and useful for the kinship verification problem. Furthermore, the proposed fusion method outperforms baseline methods. In addition, we also evaluate the human verification ability on a subset of TALKIN-Family. It indicates that humans have higher accuracy when they have access to both faces and voices. The machine-learning methods could effectively and efficiently outperform the human ability. Finally, we include the future work and research opportunities with the TALKIN-Family dataset.
With the rapid growth in number of connected devices, edge and fog computing paradigms become promising solutions for handling computation with low latency and reduced bandwidth usage. They allow to execute a computation across IoT devices, e.g., cameras, an edge aggregator/processor, and on the cloud. In this paper, we consider a pervasive computing scenario where end devices such as laptops, smart phones or roadside units are willing to offer compute services along with the traditional infrastructure-based edge computing resources. Our work uses an Information Centric Network (ICN), which is a promising technique for resource-constrained end devices to offload time-sensitive computing tasks to nearby distributed network resources, e.g., to an edge server. We expand the Named Data Networking (NDN), which is an ICN implementation, to operate in a dynamic resource-constrained multi-hop environment and propose i) a fast and efficient capability discovery scheme to assist each node in discovering other nodes' capabilities in a distributed manner; and ii) efficient schemes for server/path selection, to optimize the end-to-end latency including discovery, data transmission and computation. Our results show that the proposed solutions can improve the total latency by around 50% for most of the tasks.
The CuMn/ZSM-5 (CM/Z), CuMn/Beta (CM/B), and CuMn/SSZ-13 (CM/S) catalyst samples were prepared via an excess impregnation method to investigate the metal-support effect. The results show that the activity at 550 °C for NO direct decomposition follows the order of CM/Z (53.4%) > CM/B (49.2%) > CM/S (7.9%). The good activity and stability of CM/Z are attributed to a strong support effect including forming more active copper sites of the (Cu2+-O2--Cu2+)2+ and (Cu+-□-Cu+)2+ dimers, which have higher redox activity, capacity of oxygen mobility, and NO sorption capability. The competitive adsorption between NO and O2 on the oxygen vacancy of dimers over CM/Z results in the activity decrease from 53.4% to 40.3% at 550 °C after adding 1 vol% O2. The reaction mechanism over CM/Z was discussed based on the in situ DRIFT and isotopic (18O2) experiments. Two NO adsorbs firstly on (Cu+-□-Cu+)2+ to form N2O, and N2O is activated to produce N2, followed by the NO adsorption on (Cu2+-O2--Cu2+)2+ to form nitrite and nitrate species and decomposition to NO and O2.
The growth kinetics play key roles in determining the chirality distribution of the grown single-walled carbon nanotubes (SWCNTs). However, the lack of comprehensive understandings on the SWCNT’s growth mechanism at the atomic scale greatly hinders SWCNT chirality-selective synthesis. Here, we establish a general model, where the dislocation theory is a specific case, to describe the etching agent–dependent growth kinetics of SWCNTs on solid catalyst particles. In particular, the growth kinetics of SWCNTs in the absence of etching agent is validated by both in situ environmental transmission electron microscopy and ex situ chemical vapor deposition growth of SWCNTs. On the basis of the new theory of SWCNT’s growth kinetics, we successfully explained the selective growth of (2n, n) SWCNTs. This study provides another degree of freedom for SWCNT controlled synthesis and opens a new strategy to achieve chirality-selective synthesis of (2n, n) SWCNTs using solid catalysts.
Cylindrical-vector beams (CVBs) with axial symmetry in polarization and field intensity are gathering increasing attention from fundamental research to practical applications. However, a majority of the CVBs are generated by modulating light beams in free space, and the temporal durations are far away from the ultrafast regime. Here, an ultrafast all-fiber based CVB laser is demonstrated via intermodal coupling in two mode fibers. In the temporal domain, chirp-free pulses are formed with combined actions of the ultrafast saturable absorption, self-phase modulation, and anomalous dispersion. In the spatial domain, the lateral offset splicing technique and a two mode fiber Bragg grating are adopted to excite and extract CVBs, respectively. The ultrafast CVB has an annular profile with a duration of 6.87 ps and a fundamental repetition rate of 13.16 MHz, and the output polarization status is switchable between radially and azimuthally polarized states. This all-fiber-based ultrafast CVB laser is a simple, low-cost source for diversified applications of nanoparticle manipulation, high-resolution imaging, material processing, spatiotemporal nonlinear optics, etc.