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In this work, the Co–Al spinel catalyst and M–Co–Al (M = Fe and Ce) catalysts were synthesized to explore their catalytic performance for the NH3-SCR reaction. The physicochemical properties of these catalysts were further characterized utilizing N2-physisorption, XRD, NH3-TPD, H2-TPR, XPS, SEM, and EDS mapping. The results show that Fe/Ce addition has a significant influence on the catalytic performance and physicochemical properties of catalysts. The Co-Al spinel catalyst yields the lowest catalytic performance, while the Fe–Co–Al catalyst exhibits the best catalytic performance and more than 93% NOxconversion can be obtained in the range of 240–270 °C. Meanwhile, Fe or Ce addition can promote the oxidation of NO to NO2and inhibit the NH3oxidation reaction. The characterization results indicate that the addition of Fe or Ce remarkably reduces the crystallinity of spinel oxides and facilitates the dispersion of active species. Furthermore, the synergistic interaction between Fe/Ce and Co increases the ratio of Oβ/(Oα+ Oβ) and Co3+/(Co3++ Co2+), which can accelerate the catalytic performance of catalysts. Finally, better redox properties and moderate surface acidity of catalysts can be obtained with the addition of Fe or Ce. All of the mentioned above contribute to the promoted catalytic performance of catalysts and such an effect is more pronounced with Fe doping.

Inhibition of calcitonin gene-related peptide (CGRP) or its cognate CGRP receptor (CGRPR) has arisen as a major breakthrough in the treatment of migraine. However, a second CGRP-responsive receptor exists, the amylin (Amy) 1 receptor (AMY1R), yet its involvement in the pathology of migraine is poorly understood. AMY1R and CGRPR are heterodimers consisting of receptor activity-modifying protein 1 (RAMP1) with the calcitonin receptor (CTR) and the calcitonin receptor-like receptor (CLR), respectively. Here, we present the structure of AMY1R in complex with CGRP and Gs protein and compare it with the reported structures of the AMY1R complex with rat amylin (rAmy) and the CGRPR in complex with CGRP. Despite similar protein backbones observed within the receptors and the N- and C-termini of the two peptides bound to the AMY1R complexes, they have distinct organization in the peptide midregions (the bypass motif) that is correlated with differences in the dynamics of the respective receptor extracellular domains. Moreover, divergent conformations of extracellular loop (ECL) 3, intracellular loop (ICL) 2, and ICL3 within the CTR and CLR protomers are evident when comparing the CGRP bound to the CGRPR and AMY1R, which influences the binding mode of CGRP. However, the conserved interactions made by the C-terminus of CGRP to the CGRPR and AMY1R are likely to account for cross-reactivity of nonpeptide CGRPR antagonists observed at AMY1R, which also extends to other clinically used CGRPR blockers, including antibodies.

In the Loess Plateau of China, land-use pattern is a major factor in controlling underlying biological processes. Additionally, the process of land-use pattern was accompanied by abandoned lands, potentially impacting soil microbe. However, limited researches were conducted to study the impacts of land-use patterns on the diversity and community of soil microorganisms in this area. The study aimed to investigate soil microbial community diversity and composition using high-throughput deoxyribonucleic acid (DNA) sequencing under different land-use patterns (apricot tree land, apple tree land, peach tree land, corn land, and abandoned land). The results showed a substantial difference (P<0.050) in bacterial alpha-diversity and beta-diversity between abandoned land and other land-use patterns, with the exception of Shannon index. While fungal beta-diversity was not considerably impacted by land-use patterns, fungal alpha-diversity indices varied significantly The relative abundance of Actinobacteriota (34.90%), Proteobacteria (20.65%), and Ascomycota (77.42%) varied in soils with different land-use patterns. Soil pH exerted a dominant impact on the soil bacterial communities’ composition, whereas soil available phosphorus was the main factor shaping the soil fungal communities’ composition. These findings suggest that variations in land-use pattern had resulted in changes to soil properties, subsequently impacting diversity and structure of microbial community in the Loess Plateau. Given the strong interdependence between soil and its microbiota, it is imperative to reclaim abandoned lands to maintain soil fertility and sustain its function, which will have significant ecological service implications, particularly with regards to soil conservation in ecologically vulnerable areas.

When partial discharges occur in air-insulated equipment, the air decomposes to produce a variety of contamination products, resulting in a reduction in the insulation performance of the insulated equipment. By monitoring the concentration of typical decomposition products (CO, NO, and NO2) within the insulated equipment, potential insulation faults can be diagnosed. MoS2has shown promising applications as a gas-sensitive semiconductor material, and doping metal oxides can improve the gas-sensitive properties of the material. Therefore, in this work, MoS2has been doped using the popular metal oxides (ZnO, TiO2) of the day, and its gas-sensitive properties to the typical decomposition products of air have been analyzed and compared using density functional theory (DFT) calculations. The stability of the doped system was investigated using molecular dynamics methods. The related adsorption mechanism was analyzed by adsorption configuration, energy band structure, density of states (DOS) analysis, total electron density (TED) analysis, and differential charge density (DCD) analysis. Finally, the practical application of related sensing performance is evaluated. The results show that the doping of metal oxide nanoparticles greatly improves the conductivity, gas sensitivity, and adsorption selectivity of MoS2monolayer to air decomposition products. The sensing response of ZnO–MoS2for CO at room temperature (25 °C) reaches 161.86 with a good recovery time (0.046 s). TiO2–MoS2sensing response to NO2reaches 3.5 × 106at 25 °C with a good recovery time (0.108 s). This study theoretically solves the industrial challenge of recycling sensing materials and provides theoretical value for the application of resistive chemical sensors in air-insulated equipment.

G protein–coupled receptors (GPCRs) are the largest family of cell surface receptors. Class B1 GPCRs constitute a subfamily of 15 receptors that characteristically contain large extracellular domains (ECDs) and respond to long polypeptide hormones. Class B1 GPCRs are critical regulators of homeostasis, and, as such, many are important drug targets. While most transmembrane proteins, including GPCRs, are recalcitrant to crystallization, recent advances in cryo-electron microscopy (cryo-EM) have facilitated a rapid expansion of the structural understanding of membrane proteins. As a testament to this success, structures for all the class B1 receptors bound to G proteins have been determined by cryo-EM in the past 5 years. Further advances in cryo-EM have uncovered dynamics of these receptors, ligands, and signaling partners. Here, we examine the recent structural underpinnings of the class B1 GPCRs with an emphasis on structure–function relationships.

Amylin receptors (AMYRs), heterodimers of the calcitonin receptor (CTR) and one of three receptor activity-modifying proteins, are promising obesity targets. A hallmark of AMYR activation by Amy is the formation of a ‘bypass’ secondary structural motif (residues S19–P25). This study explored potential tuning of peptide selectivity through modification to residues 19–22, resulting in a selective AMYR agonist, San385, as well as nonselective dual amylin and calcitonin receptor agonists (DACRAs), with San45 being an exemplar. We determined the structure and dynamics of San385-bound AMY3R, and San45 bound to AMY3R or CTR. San45, via its conjugated lipid at position 21, was anchored at the edge of the receptor bundle, enabling a stable, alternative binding mode when bound to the CTR, in addition to the bypass mode of binding to AMY3R. Targeted lipid modification may provide a single intervention strategy for design of long-acting, nonselective, Amy-based DACRAs with potential anti-obesity effects.

Altitude affects leaf stoichiometry by regulating temperature and precipitation, and influencing soil properties in mountain ecosystems. Leaf carbon concentration (C), leaf nitrogen concentration (N), leaf phosphorous concentration (P), and their stoichiometric ratios of Leontopodium lentopodioides(Willd.) Beauv., a widespread species in degraded grasslands, were investigated to explore its response and adaptation strategy to environmental changes along four altitude gradients (2500, 3000, 3500, and 3800 m a.s.l.) on the northeastern Qinghai-Tibetan Plateau (QTP), China. The leaf C significantly varied but without any clear trend with increasing altitude. Leaf N showed an increasing trend, and leaf P showed a little change with increasing altitude, with a lower value of leaf P at 3500 m than those at other altitudes. Similarity, leaf C:P and N:P exhibited a little change with increasing altitude, which both had greater values at 3500 m than those at other altitudes. However, leaf C:N exhibited a decreasing trend with increasing altitude. Soil NH4+-N, soil pH, soil total phosphorus (STP), mean annual temperature (MAT), and mean annual precipitation (MAP) were identified as the main factors driving the variations in leaf stoichiometry of L. lentopodioidesacross all altitudes, with NH4+-N alone accounting for 50.8% of its total variation. Specifically, leaf C and N were mainly controlled by MAT, soil pH, and NH4+-N, while leaf P by MAP and STP. In the study area, it seems that the growth of L. lentopodioidesmay be mainly limited by STP. The results could help to strengthen our understanding of the plasticity of plant growth to environmental changes and provide new information on global grassland management and restoration.

The control system is an important part of the industrial infrastructure. Once the abnormal is likely to cause equipment failure, production interruption and other serious consequences, a timely and effective anomaly detection method is of great significance to the industrial control system. This paper presents a predictive maintenance strategy–early warning model that includes two modules, prediction and alarm, which can also realize online status monitoring. In the prediction module, a two-channel input model using a graphical neural network and a temporal convolutional network, and the graph structure learning module in the graph neural network is improved to optimize the graph structure. For the alarm module, combining expert experience and mathematical statistics knowledge, a hierarchical alarm mechanism based on abnormal scores and abnormal interval accumulation is proposed. We use the real data to verify the performance of the prediction algorithm and the effectiveness of the early warning index. Experimental results with the real data and the open data show that the prediction performance of the proposed early-warning model is better than the state-of-the-art methods. On the real TRT dataset, the evaluation indicator MAPE is 6.5693%, and on the open dataset, MAPE is 1.3368%. At the same time, its early-warning performance is better than the existing automatic interpretation and manual interpretation methods on the industrial internet.

Metalens is a planar optical component that uses nanostructures with a thickness on the order of the wavelength to manipulate the wavefront of the incident light. A key problem, especially in color imaging and display applications, is the correction of chromatic aberration, which is an inherent effect caused by the dispersion of periodic lattices and resonance modes. However, the current achromatic metalenses either use the PB phase method that is only valid for circularly polarized light or nanostructures with complex cross sections that are difficult to manufacture. Here, we designed a broadband achromatic metalens for linearly polarized light from 450 to 800 nm. Rectangular titanium dioxide nanofins of various lengths and widths were applied to modulate the phase and dispersion of the incident light. The metalens can fulfill three target phases simultaneously by using an optimization method. The designed metalens has a stable focus from 450 to 800 nm with an average focusing efficiency of 64%. It can be potentially applied in microscopes, lithography machines, sensors, and displays.

Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the $k$ -means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.

Emerging pollutants have become a significant concern in China’s efforts toward ecological and environmental protection. Trichloromethane, one of these emerging pollutants, is primarily released into soil and groundwater through various industrial activities. Over the past four decades, researchers have consistently focused on the remediation of trichloromethane-contaminated soil and groundwater using microorganisms and iron-based materials, which hold significant potential for practical application. Understanding the remediation process and the factors influencing trichloromethane degradation through these two methods is crucial for advancing both theoretical research and practical implementation. This paper focuses on the degradation mechanisms of trichloromethane in soil and groundwater by microbial and iron-based materials. It summarizes the active microorganisms and modified iron-based materials with high trichloromethane degradation capabilities, discusses enhancement measures for both methods in the remediation process, and finally, outlines the challenges faced by these methods. The goal is to provide theoretical references for the efficient remediation of trichloromethane-contaminated soil and groundwater.

Plasmonic color printing presents a sustainable solution for vibrant and durable color reproduction by leveraging the light-manipulating properties of nanostructures. However, the fabrication of plasmonic nanostructures has posed challenges, hindering widespread adoption. In this paper, we introduce plasmonic reflector arrays (PRAs) composed of three layers─Ag nanoparticles (NPs), an Al2O3spacer, and an Ag reflector─deposited via physical vapor deposition (PVD). By employing nanosecond and femtosecond laser writing techniques, we manipulate the surface morphology of silver nanoparticles on PRAs, resulting in a diverse range of structural colors that are both polarization-insensitive and polarization-sensitive. Furthermore, we demonstrate the versatility of nanosecond laser writing in creating intricate patterns on PRAs. Additionally, we propose a novel two-step method combining nanosecond and femtosecond laser processing to embed QR code patterns into PRAs, showcasing their potential for secure data encryption and transmission. This research underscores the promising applications of PRAs in advanced color printing and secure optical data encoding.

In light of Harris (2010) finding insufficient evidence to assert a causal linkage between any of the seven previously proposed causative factors and grassland degradation on the Qinghai-Tibetan Plateau (QTP), more recent empirical studies on QTP grassland degradation were explored to ascertain whether, in fact, these factors are casually linked to grassland degradation. The mischaracterization of the underlying causes of grassland degradation among policymakers has and continues to be an obstacle to sustainable regional grassland management practices. Accumulating evidence suggests that privatization and sedentarization, small mammals, climate change, harsh environments, fragile soils, and overgrazing contribute to grassland degradation. However, neither obsolete livestock husbandry methods nor the recent conversion of rangelands to agriculture had a meaningful influence. Estimates of the total area of degraded grasslands and the establishment of grassland degradation criteria have not been properly addressed in the literature. Both omissions constitute the basis for investigating the causes of grassland degradation across the QTP and the adoption of measures to manage these grasslands sustainably.

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model (DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Two-wave mixing is fundamental to applications in optical signal processing, holographic information, and imaging science. Conventionally, it takes place in a nonlinear medium where self-induced optical grating can effectively couple two optical waves. However, it demands high input power to trigger nonlinearities and is complicated by the phase shift of the optical grating for the given medium. Here, we separate these physical processes by studying near-degenerated two-wave mixing in a moving grating induced independently by an external acoustic optical modulator. Two-wave mixing is observed under Bragg’s phase matching condition, which is greatly affected by phase shifting. Moreover, we show that two-wave mixing here is unidirectional due to the propagation of acoustic waves, which may offer new avenues for applications in optical information processing.

In this paper, we discuss the concept of the smart city, summarize its development, analyze the motivation and goals of building smart cities in China, and illustrate the supporting technologies of the smart city. Next, we propose a smart city infrastructure that is based on the concept of the digital city and integrates the Internet of Things and cloud computing technologies, thereby achieving automatic control and intelligence services for people and logistics in physical cities. We analyze the big data of smart cities according to ubiquitous sensor networks. Facing the problems and challenges caused by big data of smart cities, we propose a strategy to handle big data that focuses on cloud computing and data mining. In addition, we present a basic framework for cloud computing platforms and propose the establishment of an operating center for smart cities. In closing, we discuss the bright prospects of smart cities. 本文探讨了智慧城市的概念, 总结了其发展历程, 剖析了中国建设智慧城市的动力和目标, 阐述了智慧城市的支撑技术, 并提出了智慧城市的基础架构, 即在数字城市的基础上有机地融合物联网和云计算技术, 以实现对现实城市中人和物的自动控制和智能服务。作者针对无所不在的传感器网对智慧城市的大数据进行了分析, 提出了应对大数据的策略和思路, 重点论述了云计算与数据挖掘, 并给出了云平台的基础框架, 提出了建立智慧城市运营中心的建议, 最后展望了智慧城市未来美好的前景。

Purpose:The goals of our study were to develop a noninvasive prenatal test for autosomal recessive monogenic conditions and to prove its overall feasibility and potential for clinical integration.Methods:We recruited a pregnant woman and her spouse, who had a proband child suffering from congenital deafness, and obtained the target-region sequencing data from a semicustom array that used genomic and maternal plasma DNA from three generations of this family. A haplotype-assisted strategy was developed to detect whether the fetus inherited the pathogenic mutations in the causative gene, GJB2. The parental haplotype was constructed using a trio strategy through two different processes, namely, the grandparent-assisted haplotype phasing process and the proband-assisted haplotype phasing process. The fetal haplotype was deduced afterward based on both the maternal plasma sequencing data and the parental haplotype.Results:The accuracy levels of paternal and maternal haplotypes obtained by grandparent-assisted haplotype phasing were 99.01 and 97.36%, respectively, and the proband-assisted haplotype phasing process yielded slightly lower accuracies of 98.73 and 96.79%, respectively. Fetal inheritance of the pathogenic gene was deduced correctly in both processes.Conclusion:Our study indicates that the strategy of haplotype-based noninvasive prenatal testing for monogenic conditions has potential applications in clinical practice.Genet Med 16 12, 972–976.

We propose an efficient and compact plasmonic surface-enhanced terahertz generation scheme based on nonlinear difference-frequency generation inside a metal–insulator–metal structure. Gold nanowire arrays are planted on top of the surface of a lithium niobate (LN) substrate with second-order nonlinearity to enhance both the nonlinear wavelength conversion and waveguide terahertz waves at the same time. Our numerical simulations show that our structures are capable of generating both tunable continuous and ultrafast-pulsed terahertz sources. We also discuss further improvements on the conversion efficiency by combining with Ti-diffusing LN waveguides.

Climate change and overgrazing are believed by policy makers to have been the drivers of grassland degradation in China over the past thirty years. However, recent work has suggested that policies that have led to privatization of use rights and household enclosures are more important drivers of degradation. An analysis of available evidence from scienti fic publications and national survey data relating to climate change, livestock numbers, and management policy was undertaken to evaluate the case for each of these possible drivers of degradation. The analysis suggests that, to date, policy measures imposed to avoid the 'tragedy of the commons' have been more important than either climate change or overgrazing as a driver of degradation. The resulting fragmentation of the pastoral landscape has caused localized grazing pressure and spatial and temporal mismatches between grassland production and livestock access to forage. Grazing rights negotiated and allocated at a multiple-household level would allow greater productivity, better rangeland condition, possible advantages of scale and pooling of herder resources, and better potential to adapt to future climate change.

Implementation of the Grain for Green program (GGP) resulted in great changes in land use and cover in northwest China, and presumably in the region's carbon. However, accurate assessment of the effects of the GGP on carbon storage remains a challenge. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model can map and evaluate natural goods and services that sustain life and quantify their impact from the changes in the ecosystem. Taking Huining County in China's Loess Plateau region as a case, the land use and land cover changes (LUCC) following the GGP were quantified, and their effects on carbon storage between 2000 and 2016 were evaluated using the InVEST model. The results showed that, a total area of about 3996.13 km2underwent changes following the GGP in Huining County during the study period, accounting roughly for 74% of the county. The total carbon storage increased from 6470.72 Gg in 2000 to 7335.07 Gg in 2016. Spatially, carbon density in the south of this county was greater than the northern part. Directly induced by the GGP (conversions of farmlands to forests and grasslands), the carbon storage increased by 786.84 Gg in total, with a rate of 46.28 Gg per year, the majority of which occurred during the first stage of the study period (from 2000 to 2008). The increase of carbon storage was mostly attributable to the increases in vegetation pool and the carbon density showed a strong spatial correlation with the growing season Normalized Difference Vegetation Index (NDVI). The similarities between carbon density and NDVI spatial patterns during the study period may have been shaped by the county's climate patterns, but not to any significant extent by climate change during the study period. This county-scale study allows for a deeper understanding of the links between LUCC induced by environmental restoration programs and carbon storage changes, and contributes to a greater accuracy in the estimation of carbon storage at the provincial (or larger) scale.