Achieving selective C−H activation at a single and strategic site in the presence of multiple C−H bonds can provide a powerful and generally useful retrosynthetic disconnection. In this context, a ...directing group serves as a compass to guide the transition metal to C−H bonds by using distance and geometry as powerful recognition parameters to distinguish between proximal and distal C−H bonds. However, the installation and removal of directing groups is a practical drawback. To improve the utility of this approach, one can seek solutions in three directions: 1) Simplifying the directing group, 2) using common functional groups or protecting groups as directing groups, and 3) attaching the directing group to substrates via a transient covalent bond to render the directing group catalytic. This Review describes the rational development of an extremely simple and yet broadly applicable directing group for PdII, RhIII, and RuII catalysts, namely the N‐methoxy amide (CONHOMe) moiety. Through collective efforts in the community, a wide range of C−H activation transformations using this type of simple directing group have been developed.
Clear directions: The simple N‐methoxy amide moiety is a versatile directing group for a wide range of C−H functionalization reactions using Pd, Rh, and Ru catalysis. Whereas it was initially used for Pd‐catalyzed C−H activation, it has also been employed in RhIII‐catalyzed variants and in combination with cheaper ruthenium catalysts.
Recent experimental observation of the first bilayer clusters B48−/0 reveals a new structural domain in boron nanostructures. Inspired by the previously reported bilayer B48, B54, B60, and B62 and ...based on extensive global‐minimum searches and density‐functional theory calculations, we predict herein a new series of medium‐sized bilayer boron nanoclusters C2 B64 (I), D2 B66 (II), D2 B68 (III), C1 B70 (IV), and Ci B72 (V) which all contain an elongated B46 bilayer hexagonal prism at the center with four effective interlayer 2c–2e B−B σ bonds formed between the top and bottom layers and the bilayer to core‐shell structural transition at B74 where core‐shell species start to dominate in thermodynamics, defining the up‐limit of the bilayer boron nanoclusters at B72. The newly obtained bilayer C2 B64 (I), D2 B68 (III), and C1 B70 (IV) appear to be systematically more stable than the previously reported cage‐like D4d B64, core‐shell C1 B68, and quasi‐planar C3v B70, respectively. Detailed bonding analyses indicate that these bilayer species follow the universal bonding pattern of σ+π double delocalization, rendering three‐dimensional aromaticity to the systems. The IR, Raman, and UV‐vis spectra of the concerned bilayer species are computationally simulated to facilitate their future characterizations.
Extensive first‐principles theory calculations predict the most stable bilayer structures of C2 B64, D2 B66, D2 B68, C1 B70, and Ci B72 in the size range between B64∼B72 and structural transition at core‐shell C1 B74.
Distributed Denial of Service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking ...(SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.
Planar, tubular, cage‐like, and bilayer boron clusters Bn+/0/− (n=3∼48) have been observed in joint experimental and theoretical investigations in the past two decades. Based on extensive global ...searches augmented with first‐principles theory calculations, we predict herein the smallest perfect core‐shell octahedral borospherene Oh B@B38+ (1) and its endohedral metallo‐borospherene analogs Oh Be@B38 (2), and Oh Zn@B38 (3) which, with an octa‐coordinate B, Be or Zn atom located exactly at the center, turn out to be the well‐defined global minima of the systems highly stable both thermodynamically and dynamically. B@B38+ (1) represents the first boron‐containing molecule reported to date which contains an octa‐coordinate B center covalently coordinated by eight face‐capping boron atoms at the corners of a perfect cube in the first coordination sphere. Detailed natural bonding orbital (NBO) and adaptive natural density partitioning (AdNDP) bonding analyses indicate that these high‐symmetry core‐shell complexes X@B38+/0/− (X=B, Be, Zn) as super‐noble gas atoms follow the octet rule in coordination bonding patterns (1S21P6), with one delocalized 9c‐2e S‐type coordination bond and three delocalized 39c‐2e P‐type coordination bonds formed between the octa‐coordinate X center and its octahedral Oh B38 ligand to effectively stabilize the systems. Their IR, Raman, and UV‐Vis spectra are computationally simulated to facilitate their spectroscopic characterizations.
Extensive global minimum searches augmented with first‐principles theory calculations predict the smallest perfect core‐shell octahedral borospherene Oh B@B38+ and its endohedral metallo‐borospherene analogs Oh Be@B38 and Oh Zn@B38 which, as super‐noble gas atoms, follow the octet rule and are spherically aromatic in nature.
In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained ...from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.
•Mid-to-long term wind and solar joint generation prediction model established.•Copula for extracting nonlinear and non-Gaussian distributed key weather factors.•Long short term memory network to ...construct the prediction model with fewer data.•The proposed method is able to improve prediction accuracy validated by real cases.
The accurate estimation of mid-to-long term wind and photovoltaic power generation is important to the power grid's plan improvement, dispatching optimization, management development, and consumption enhancement. These constitute key factors for the realization of power mutual assistance and complementary dispatch of power generation in the broad area of renewable energy. However, owing to the large time scale of mid-to-long term prediction, the low accuracy of weather prediction, the limited data samples of historical power generation, and the significant difference between power generation prediction and short-term power prediction, short-term power prediction technology cannot be directly copied. Thus, the industry has not established yet an effective approach for mid-to-long term wind and photovoltaic power generation predictions. To solve these problems, this study proposed a method for the mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network to achieve an effective extraction of the key meteorological factors that affect power generation owing to nonlinear effects and tendencies, and to deeply exploit the long-term dependencies and tendencies from the limited available data samples. Therefore, the proposed approach is suitable for mid-to-long term wind and photovoltaic power generation prediction using limited data samples. Firstly, the non-linear effects and tendency correlation measurements of the copula function were used to extract the key meteorological factors that influence wind and photovoltaic power generation. Secondly, independent wind/photovoltaic prediction models were established based on long short term memory network using the best input condition obtained by comparing these models to the persistence model. Additionally, the independent wind/photovoltaic models were further compared to support vector machine model with the optimal input condition. Thirdly, the joint prediction models of wind and photovoltaic power generation based on long short term memory network were established using different inputs. The persistence model and the support vector machine model were used as benchmarks to compare the elicited performances. Finally, the validity and applicability of the proposed approach were extensively evaluated using actual data from wind farms and photovoltaic power stations in China and the United States. The independent and joint power generation prediction results demonstrated that the proposed approach outperforms both the persistence model and the support vector machine model, and can have widespread applicability in limited data sample cases.
Flexible covalent organic frameworks (COFs) are intriguing for their dynamic properties distinctive from rigid counterparts but still suffer from limited accessibility. Especially, controlling ...flexibility of COFs is challenging and the impact of different flexibility on properties of COFs has rarely been unveiled. This article reports stepwise adjustment on flexibility of two-dimensional COFs, which is realized by the designed synthesis of rigid COF (R-COF), semi-flexible COF (SF-COF), and flexible COF (F-COF) through polymerization, linker exchange, and linkage conversion with a newly developed method for reduction of hydrazone, respectively. Significant difference in breathing behavior and self-adaptive capability of the three COFs are uncovered through vapor response and iodine capture experiments. Gas sorption experiments indicate that the porosity of F-COF could switch from "close" state in nitrogen to "open" state in carbon dioxide, which are not observed for R-COF and SF-COF. This study not only develops a strategy to adjust the flexibility of COFs by tuning their linkers and linkages, but also provides a deep insight into the impact of different flexibility on properties of COFs, which lays a foundation for the development of this new class of dynamic porous materials.
This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by ...recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.
Physical hydrogels from existing polymers consisting of noncovalent interacting networks are highly desired due to their well‐controlled compositions and environmental friendliness; and therefore, ...applied as adhesives, artificial tissues, and soft machines. Nevertheless, these gels have suffered from weak mechanical strength and low water resistance. Current methodologies used to fabricate these hydrogels mainly involve the freezing–thawing process (cryogels), which are complicated in preparation and short in adjustment of polymer conformation. Here, taking the merits of noncovalent bonds in adjustability and reversibility, a solvent‐exchange strategy is developed to construct a class of exogels. Based on the exchange from a good solvent subsequently to a poor one, the intra‐ and interpolymer interactions are initially suppressed and then recovered, resulting in dissolving and cross‐linking to polymers, respectively. Key to this approach is the good solvent, which favors of a stretched polymer conformation to homogenize the network, forming cross‐linked hydrogel networks with remarkable stiffness, toughness, antiswelling properties, and thus underwater adhesive performance. The exogels highlight a facile but highly effective strategy of turning the solvent and consequently the noncovalent interactions to achieve the rational design of enhanced hydrogels and hydrogel‐based soft materials.
A solvent‐exchange strategy is developed to construct a class of stiff and antiswelling hydrogels (named as exogels). Key to this approach is the rational regulation of noncovalent interactions and thus the conformation of polymers and structure of the cross‐linked networks. Moreover, the unique sol–gel progression via solvent exchange makes exogels applicable for robust underwater adhesion.
Ridge regression (RR) is an important machine learning technique which introduces a regularization hyperparameter <inline-formula><tex-math notation="LaTeX">\alpha</tex-math> ...<mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq1-2937491.gif"/> </inline-formula> to ordinary multiple linear regression for analyzing data suffering from multicollinearity. In this paper, we present a quantum algorithm for RR, where the technique of parallel Hamiltonian simulation to simulate a number of Hermitian matrices in parallel is proposed and used to develop a quantum version of <inline-formula><tex-math notation="LaTeX">K</tex-math> <mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq2-2937491.gif"/> </inline-formula>-fold cross-validation approach, which can efficiently estimate the predictive performance of RR. Our algorithm consists of two phases: (1) using quantum <inline-formula><tex-math notation="LaTeX">K</tex-math> <mml:math><mml:mi>K</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq3-2937491.gif"/> </inline-formula>-fold cross-validation to efficiently determine a good <inline-formula><tex-math notation="LaTeX">\alpha</tex-math> <mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq4-2937491.gif"/> </inline-formula> with which RR can achieve good predictive performance, and then (2) generating a quantum state encoding the optimal fitting parameters of RR with such <inline-formula><tex-math notation="LaTeX">\alpha</tex-math> <mml:math><mml:mi>α</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq5-2937491.gif"/> </inline-formula>, which can be further utilized to predict new data. Since indefinite dense Hamiltonian simulation has been adopted as a key subroutine, our algorithm can efficiently handle non-sparse data matrices. It is shown that our algorithm can achieve exponential speedup over the classical counterpart for (low-rank) data matrices with low condition numbers. But when the condition numbers of data matrices are large to be amenable to full or approximately full ranks of data matrices, only polynomial speedup can be achieved.
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