ABSTRACT
Direct reduction of unactivated alkyl halides for C(sp3)-N couplings under mild conditions presents a significant challenge in organic synthesis due to their low reduction potential. Herein, we introduce an in situ formed pyridyl-carbene-ligated copper (I) catalyst that is capable of abstracting halide atom and generating alkyl radicals for general C(sp3)-N couplings under visible light. Control experiments confirmed that the mono-pyridyl-carbene-ligated copper complex is the active species responsible for catalysis. Mechanistic investigations using transient absorption spectroscopy across multiple decades of timescales revealed ultrafast intersystem crossing (260 ps) of the photoexcited copper (I) complexes into their long-lived triplet excited states (>2 µs). The non-Stern-Volmer quenching dynamics of the triplets by unactivated alkyl halides suggests an association between copper (I) complexes and alkyl halides, thereby facilitating the abstraction of halide atoms via inner-sphere single electron transfer (SET), rather than outer-sphere SET, for the formation of alkyl radicals for subsequent cross couplings.
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Relaxor ferroelectric (RFE) films are promising energy-storage candidates for miniaturizing high-power electronic systems, which is credited to their high energy density (Ue) and efficiency. However, advancing their Ue beyond 200 joules per cubic centimeter is challenging, limiting their potential for next-generation energy-storage devices. We implemented a partitioning polar-slush strategy in RFEs to push the boundary of Ue. Guided by phase-field simulations, we designed and fabricated high-performance Bi(Mg0.5Ti0.5)O3-SrTiO3-based RFE films with isolated slush-like polar clusters, which were realized through suppression of the nonpolar cubic matrix and introduction of highly insulating networks. The simultaneous enhancement of the reversible polarization and breakdown strength leads to a Ue of 202 joules per cubic centimeter with a high efficiency of ~79%. The proposed strategy provides a design freedom for next-generation high-performance dielectrics.
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Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students' perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students' perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students' engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students' emotions and achievement.
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Performing correction first is the most common methods to address feature matching issues for fisheye images, but corrections often result in significant loss of scene details or stretching of images, leaving peripheral regions without matches. In this paper, we propose a novel approach, named flattened-affine-SIFT, to find widely distributed feature matches between stereo fisheye images. Firstly, we establish a new imaging model that integrates a scalable model and a hemisphere model. Utilizing the extensibility of the imaging model, we design a flattened array model to reduce the distortion of fisheye images. Additionally, the affine transformation is performed on the flattened simulation images, which are computed using the differential expansion and the optimal rigidity transformation. Then feature matches are extracted and matched from the simulated images. Experiments on indoor and outdoor fisheye images show that the proposed algorithm can find a large number of reliable feature matches. Moreover, these matches tend to be dispersed over the entire effective image, including peripheral regions with dramatic distortion.
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This paper proposed a single-layer checkerboard metasurface with simultaneous wideband radar cross-section (RCS) reduction characteristics and low infrared (IR) emissivity. The metasurface consists of an indium tin oxide (ITO)-patterned film, a polyethylene terephthalate (PET) substrate and an ITO backplane from the top downwards, with a total ultra-thin thickness of 1.6 mm. This design also allows the metasurface to have good optical transparency and flexibility. Based on phase cancellation and absorption, the metasurface can achieve a wideband RCS reduction of 10 dB from 10.6 to 19.4 GHz under normal incidence. When the metasurface is slightly cylindrically curved, an RCS reduction of approximately 10 dB can still be achieved from 11 to 19 GHz. The polarization and angular stability of the metasurface have also been verified. The filling rate of the top ITO-patterned film is 0.81, which makes the metasurface have a low theoretical IR emissivity of 0.24. Both simulation and experimental results have verified the excellent characteristics of the proposed checkerboard metasurface, demonstrating its great potential application in radar-IR bi-stealth.
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In the current global context, digital finance (DF) and sustainable economic development (SED) are important topics. The synergies between DF and SED have already been proven. However, the measurement and quantitative analysis of the coupling coordination degree (CCD) of DF and SED have not received sufficient attention to date. Based on data from 55 cities in the Yellow River Basin (YRB) from 2011 to 2021, this study constructs an evaluation index system of DF and SED and measures their level, respectively. The proposed CCD model is then used to measure the CCD between the two systems. In addition, kernel density estimation, Markov chain, σ-convergence, ß-convergence, and the quadratic assignment procedure (QAP) method are used to study the spatial pattern, distribution dynamic evolution trend, convergence, and influencing factors of the regional differences in the CCD. The results show that: (1) From 2011 to 2021, the CCD level showed a stable upward trend and regional heterogeneity, and the time stage characteristics were more obvious. (2) The center position and change interval of the overall distribution curve of the kernel density estimation gradually shifted to the right. The Markov transfer probability matrix shows that the CCD is more stable among different levels, indicating a phenomenon of "club convergence". (3) A convergence analysis shows that there are significant σ-convergence, absolute ß-convergence, and conditional ß-convergence. (4) The QAP regression shows that factors such as the regional differences in GDP per capita have a significant impact on the regional differences in the CCD. This study offers a comprehensive structure that can be used to examine the synergistic effects between DF and SED; the research findings can also provide perspectives for other areas.
Subject(s)
Economic Development , Rivers , China , Cities , HeadABSTRACT
How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem.
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A transparent metamaterial absorber (MMA) with both tunable absorption bandwidth and low infrared (IR) emissivity is proposed in this paper. The MMA is hierarchical, which consists of an infrared shielding layer (IRSL), two radar-absorption layers (RALs), an air/water layer, and an indium-tin-oxide (ITO) backplane from the top downwards. The IRSL and the RALs are made of ITO patterns etched on polyethylene terephthalate (PET) substrates. By changing the thickness of the water, the 90% absorption bandwidth can be tuned from 6.4-11.3 GHz to 12.7-20.6 GHz, while retaining good polarization and angular stability. An equivalent circuit model (ECM) is present, to reveal the physical mechanism of absorption. The proposed MMA has a low theoretical IR emissivity of about 0.24. A sample was fabricated and measured, and the experimental results are consistent with the simulation results, showing its potential applications in stealth glass and multifunctional radome.
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Elucidating the effect of fertigation on soil hydraulic parameters and water-solute transportation is fundamental to the design of farmland irrigation systems and their sustainable utilization. Few studies have focused on soil hydraulic parameters or water infiltration characteristics or how they are influenced by urea solution concentration. In this study, the clay loam and sandy loam in Yangling District of Shaanxi Province, China, were used as test soil, and experiments involving seven urea solution concentrations (0.2, 0.4, 0.6, 0.8, 1, 3, and 5 g/L) and a control treatment (0 g/L) were conducted to explore the influence of the various urea solution concentrations on soil hydraulic parameters and water infiltration characteristics. The results indicated that the cumulative infiltration and wetting front migration depth increased with urea solution concentration, as accurately estimated using the Kostiakov model and a power function, respectively. In addition, the coefficients of the Kostiakov model and the power function increased with urea solution concentration. Treatment with multiple concentrations of urea solution resulted in an increase in the volume of macro pores in the soil but a reduction in the volume of mesopores and micro pores in the soil, leading to increases in the saturated water content, saturated hydraulic conductivity, soil water diffusivity, and infiltration capacity and a reduction in the water-holding capacity of the soil. The effect of urea solute potential on the inhibition of soil water movement is small, and this inhibitory effect is far weaker than the improvement effect of the urea solution on soil structure, and hence enhance the soil water infiltration capacity. Our results increase the understanding of soil hydrological mechanisms and may be usefully applied for improving the management of fertigation.
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Photoinduced homolysis of NiII-carbon and -heteroatom bonds has been well studied for carbon-heteroatom couplings, but homolysis of the NiII-P bond is still undisclosed. Herein, we describe the homolysis of NiII-P bonds via ligand to metal charge transfer to access active nickel(I) complexes and phosphorus-centered radicals under visible-light irradiation for C-P couplings of diaryl phosphine oxides with aryl bromides. Experimental studies demonstrated that visible light enabled homolysis of the NiII-P bond and the NiI/NiIII self-sustained cycle was involved in C-P bond formation. Furthermore, homolysis of the NiII-P bond can be applied to the hydrophosphination of [1.1.1]propellane in single-nickel photocatalysis.
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The measurement model of binocular vision is inaccurate when the measurement distance is much different from the calibration distance, which affects its practicality. To tackle this challenge, we proposed what we believe to be a novel LiDAR-assisted accuracy improvement strategy for binocular visual measurement. First, the 3D points cloud and 2D images were aligned by the Perspective-n-Point (PNP) algorithm to realize calibration between LiDAR and binocular camera. Then, we established a nonlinear optimization function and proposed a depth-optimization strategy to lessen the error of binocular depth. Finally, the size measurement model of binocular vision based on the optimized depth is built to verify the effectiveness of our strategy. The experimental results show that our strategy can improve the depth accuracy compared to three stereo matching methods. The mean error of binocular visual measurement decreased from 33.46% to 1.70% at different distances. This paper provides an effective strategy for improving the measurement accuracy of binocular vision at different distances.
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Marine Predators Algorithm (MPA) is a newly nature-inspired meta-heuristic algorithm, which is proposed based on the Lévy flight and Brownian motion of ocean predators. Since the MPA was proposed, it has been successfully applied in many fields. However, it includes several shortcomings, such as falling into local optimum easily and precocious convergence. To balance the exploitation and exploration ability of MPA, a modified marine predators algorithm hybridized with teaching-learning mechanism is proposed in this paper, namely MTLMPA. Compared with MPA, the proposed MTLMPA has two highlights. Firstly, a kind of teaching mechanism is introduced in the first phase of MPA to improve the global searching ability. Secondly, a novel learning mechanism is introduced in the third phase of MPA to enhance the chance encounter rate between predator and prey and to avoid premature convergence. MTLMPA is verified by 23 benchmark numerical testing functions and 29 CEC-2017 testing functions. Experimental results reveal that the MTLMPA is more competitive compared with several state-of-the-art heuristic optimization algorithms.
Subject(s)
Algorithms , Learning , Problem Solving , Heuristics , MotionABSTRACT
Defects in crystals play a fundamental role in modulating mechanical, electrical, luminescent, and magnetic behaviors of materials. However, accurate measurement of defect structures is hindered by symmetry breaking and the corresponding complex modifications in atomic configuration and/or crystal tilt at the defects. Here, we report the deep-sub-angstrom resolution imaging of dislocation cores via multislice electron ptychography with adaptive propagator, which allows sub-nanometer scale mapping of crystal tilt in the vicinity of dislocation cores and simultaneous recovery of depth-dependent atomic structure of dislocations. The realization of deep-sub-angstrom resolution and depth-dependent imaging of defects shows great potential in revealing microstructures and properties of real materials and devices.
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The oriental fruit fly, Bactrocera dorsalis (Hendel), has very strong ecological adaptability and phenotypic plasticity. Here, the genome of B. dorsalis was assembled into 549.45 Mb sequences with a contig N50 length of 12.81 Mb. Among, 95.67 % assembled genome sequences were anchored on six chromosomes with an N50 length of 94.63 Mb. According to the basic characteristics of the sex chromosomes of Tephritidae, the X chromosome of B. dorsalis was identified. Significant gene expansions were detected in several important gene families related to adaptability. In particular, we annotated 50 histone modification enzymes (HMEs) in this genome. A comparative transcriptome analysis indicated that 12 HME genes were differentially expressed in two thermo-tolerant strains (heat and cold). Interestingly, four and seven of the 12 HME genes responded to heat shock or cold hardening, respectively. These evidences suggested that the histone modification as an epigenetic modification may be involved in the thermal tolerance of B. dorsalis, but with different regulation mechanisms in thermal acclimation and hardening. The high quality genome of B. dorsalis provides an invaluable resource for further functional genomic study. Moreover, comparative genomic analysis will shed insights on revealing the mechanisms of adaptive evolution in this fly.
Subject(s)
Tephritidae , Animals , Tephritidae/genetics , Gene Expression Profiling , Transcriptome , Drosophila/genetics , Chromosomes , Epigenesis, Genetic/geneticsABSTRACT
Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms.
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Herein, we report visible light-promoted single nickel catalysis for diverse carbon-heteroatom couplings under mild conditions. This mild, general, and robust method to couple diverse nitrogen, oxygen, and sulfur nucleophiles with aryl(heteroaryl)/alkenyl iodides/bromides exhibits a wide functional group tolerance and is applicable to late-stage modification of pharmaceuticals and natural products. On the base of preliminary mechanistic studies, a NiI /NiIII cycle via the generation of active NiI complexes that appear from homolysis of NiII -I rather than NiII -aryl bond was tentatively proposed.
Subject(s)
Biological Products , Nickel , Nickel/chemistry , Carbon/chemistry , Catalysis , Oxygen/chemistryABSTRACT
With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.
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Introduction: In acute type B aortic dissection (TBAD) patients, thoracic endovascular aorta repair (TEVAR) and best medical treatment (BMT) have both been employed for the clinical management of this condition. The relative efficacy of TEVAR and BMT when used to manage cases of acute uncomplicated TBAD, however, remains to be clarified. Aim: To conduct a pooled meta-analysis comparing acute uncomplicated TBAD patient outcomes associated with primary TEVAR or BMT treatment. Material and methods: Relevant articles published up to July 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases. Pooled analyses of endpoints from these studies were then conducted. Results: Six relevant studies were included in this meta-analysis, involving 522 and 535 patients who underwent TEVAR and BMT treatment, respectively. No significant differences were observed between these two groups with respect to pooled hospitalization duration, re-intervention rates, early mortality, organ failure incidence, stroke incidence, or the incidence of retrograde type A dissection (p = 0.89, 0.12, 0.09, 0.36, 0.09, and 0.95, respectively). TEVAR, however, was associated with significantly better pooled thrombosed/obliterated false lumen, late mortality, aorta-related mortality, and rupture rates relative to BMT (p = 0.00001, 0.002, 0.0001, and 0.04, respectively). TEVAR was associated with a 7% pooled type I endoleak incidence rate. Endpoints exhibiting significant heterogeneity included hospitalization duration, thrombosed/obliterated false lumen rates, and rupture rates (I 2 = 96%, 73%, and 61%, respectively). Conclusions: While TEVAR and BMT yield similar short-term outcomes for acute uncomplicated TBAD patients, TEVAR may be associated with a better long-term patient prognosis.
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In order to investigate the expression levels of Lipoprotein A (LPa), B-type Natriuretic Peptide (BNP) and Monocyte chemoattractor Protein-1 (McP-1) in serum of patients with coronary heart disease (CHD) are used to detect significance and to analyze the correlation between these indicators and parameters of echocardiography. The clinical data of 132 CHD patients in our hospital from January 2021 to October 2021 are retrospectively analyzed and included in the CHD group. Another 100 healthy people who came to our hospital for general physical examination were selected as the control group. The expressions of Serum McP-1 and BNP are detected by the ELISA. The expression of Serum LPa is detected by immunoturbidimetry, and the expressions of SERUM McP-1, BNP, and LPa are compared between the two groups. The experiments show that the expressions of McP-1, BNP, and LPa in serum of control group are significantly lower than those of the CHD group (P < 0.05). Echocardiography results show that left ventricular ejection fraction (LVEF) in CHD group is significantly lower than that in control group, but left ventricular end-systolic volume (LVESV) and left ventricular end-diastolic volume (LVEDV) are significantly higher than those in control group (P < 0.05).
Subject(s)
Chemokine CCL2/metabolism , Coronary Disease , Natriuretic Peptide, Brain , Echocardiography , Humans , Lipoprotein(a) , Monocytes , Retrospective Studies , Stroke Volume , Ventricular Function, LeftABSTRACT
External obstacle detection is a significant task in transmission line inspection and is related to the safe operation of the power transmission grid. In recent years, unmanned aerial vehicles (UAVs) equipped with different devices have been widely used for transmission line inspection. However, because of the complex environment of transmission lines and weak power line textures in the obtained images, most existing methods and systems cannot meet the requirements for real-time and high-accuracy external obstacle detection of transmission lines. In this paper, a novel, to the best of our knowledge, UAV system integrated trinocular vision technology with remote sensing is developed to achieve better external obstacle detection of transmission lines in real time, which is composed of a DJ-Innovations (DJI) UAV equipped with a global positioning system (GPS), angle sensors, trinocular vision including three visible cameras with the same parameters, and a small processor with a pre-implanted software algorithm. In this paper, a new method for external obstacle detection of transmission lines is proposed to satisfy the requirements for real-time and high-accuracy practical inspection applications. First, the original trinocular images need to be rectified. Then, the rectified trinocular images are adopted to achieve three-dimensional reconstruction of power lines. Finally, based on trinocular vision, bag of feature, and GPS, the clearance distance measurement, obstacle classification, and obstacle location are realized. Experimental tests on 220 kV transmission lines reveal that our proposed system can be applied in practical inspection environments and has good performance.