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Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy R2 of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with RMSE of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt.

With the wide application of intense lasers, the protection of human eyes and detectors from laser damage is becoming more and more strict. In this paper, we study the nonlinear optical limiting (OL) properties of porous carbon with a super large specific surface area (2.9 × 103 m2/g) using the nanosecond Z-scan technique. Compared to the traditional OL material C60, the porous carbon material shows an excellent broadband limiting effect, and the limiting thresholds correspond to 0.11 J/cm2 for 532 nm and 0.25 J/cm2 for 1064 nm pulses, respectively. The nonlinear scattering experiments showed that the OL behavior was mainly attributed to the nonlinear scattering effect, which is caused by the rapid growth and expansion of bubbles in the dispersion induced by laser irradiation, and the scattered light distribution is consistent with the results of Mie’s scattering. These results suggest that porous carbon materials are expected to be applied to the field of laser protection in the future to further protect the human eye and precision optical instruments.

The measurement process of ground shock wave overpressure is influenced by complex field conditions, leading to notable errors in peak measurements. This study introduces a novel pressure measurement model that utilizes the Rankine−Hugoniot relation and an equilateral ternary array. The research delves into examining the influence of three key parameters (array size, shock wave incidence angle, and velocity) on the precision of pressure measurement through detailed simulations. The accuracy is compared with that of a dual-sensor array under the same conditions. Static explosion tests were conducted using bare charges of 0.3 kg and 3 kg TNT to verify the numerical simulation results. The findings indicate that the equilateral ternary array shock wave pressure measurement method demonstrates a strong anti-interference capability. It effectively reduces the peak overpressure error measured directly by the shock wave pressure sensor from 17.73% to 1.25% in the test environment. Furthermore, this method allows for velocity-based measurement of shock wave overpressure peaks in all propagation direction, with a maximum measurement error of 3.59% for shock wave overpressure peaks ≤ 9.08 MPa.

Urban ecological space (UES) is crucial to the sustainable development of cities and the improvement of human well-being. Rapid urbanization tends to lead to the fragmentation of urban ecological space and functional decline. China's coastal cities are developed regions with high urbanization levels and the unique geographical location makes them have two kinds of ecological spaces, namely marine ecological spaces (MES) and terrestrial ecological spaces (TES). During rapid urbanization, the spatiotemporal changes of the urban ecological space availability (UESA) and its driving factors in China's coastal cities are still unclear. For this, this work selected 53 coastal cities in China as the research samples and applied remote sensing monitoring data of land use to analyze the spatiotemporal patterns of the UESA under different travel modes (walking, public transport, and driving) from 1990 to 2015. Then, the spatial econometric model was applied to reveal the impact of urbanization on urban ecological space availability (UESA). The main research results show: (1) generally, the marine ecological spaces availability (MESA), terrestrial ecological spaces availability (TESA), and marine and terrestrial ecological spaces availability (MTESA) of the coastal cities in China showed an increasing trend from 1990 to 2015. With the increase in travel distance, the MESA, TESA, and MTESA in the coastal cities increased accordingly. (2) however, the spatial pattern of MESA in the coastal cities showed a significant heterogeneity, The hot spots of the MESA mostly appeared in coastal cities from Zhejiang to Fujian, and the cold spots were concentrated in the Liaoning, Hebei, and Shandong. (3) The TESA and MTESA in southern coastal cities were generally higher than that in northern coastal cities. (4) The expansion of the urban built-up area (UBUA) led to the improvement of the UESA in China's coastal cities. GDP and population density were positive factors affecting the TESA and MTESA. Our research can provide scientific planning basis for coordinating marine and terrestrial ecological space and optimizing territorial space planning, which is conducive to improving the life quality of residents and promoting sustainable urban development.

The arid region in China with rich mineral resources are belongs to ecological fragile region with inadequate recovering capacity. Hundred years of continues mining of mineral resources for economic purposes, leads to the problem of land degradation caused by the spatial coupling of large-scale mining disturbance and arid fragile ecological environment. The environmental problems created by mining have not been addressed promptly, while at the same time, new problems have emerged. On the basis of our 20 years of practical experience on ecological restoration in mining areas, review and synthesis of selective literature, with interpretation and perspective, the mining situation and its ecological impacts, the factors impeding ecological restoration capability in the arid region in China were examined, the key challenges and promotion strategy were presented. The main findings of this study are as follows: Land degradation is the most prominent problem resulting in mining activities, and mainly reflected by immediate disturbance, long term effects and ecological degradation. The main challenges faced by mining area restoration in arid areas are unclear objectives of ecological restoration, lack of ecological water consumption, unreasonable selection of indigenous plants, serious land degradation and wide restoration area. Our restoration experiments in typical mining areas have proved that the micro-topography reconstruction measures can increase surface roughness, redistribute limited precipitation resources (especially snow), and make soil water and nutrients carried by surface runoff and collected in valleys to regulate soil infiltration and water supply, so as to achieve water-saving and non-irrigated vegetation restoration. The ideal restoration effects are the improvement of vegetation coverage, the increase of available land resources, and the improvement of ecosystem services in mining areas. This study would be helpful to carried out related implementation on ecological restoration and could act as a valuable reference for the researchers involved in the ecological restoration in arid region.

Vegetation phenology changes caused by urbanization could lead to shifts in ecosystem services in urban areas and impact on human health. The characteristics of urbanization affect vegetation phenology need to be emphasized, especially in China with a complex natural environment and rapid urbanization background. In this study, we used remote sensing-based phenological data (MODIS MCD12Q2) to analyze the spatial heterogeneity of vegetation phenology caused by urbanization between urban and non-urban areas in 320 cities across China. We found a significant difference between vegetation phenology in urban and its corresponding non-urban area at national and the regional scale. For national scale, the start of the growing season (SOS) was significantly advanced by 2.53 days (P

Honey bees use a complex form of spatial referential communication. Their waggle dance communicates to nestmates the direction, distance, and quality of a resource by encoding celestial cues, retinal optic flow, and relative food value into motion and sound within the nest. This protocol was developed to investigate the potential for social learning of this waggle dance. Using this protocol, we showed that correct waggle dancing requires social learning. Bees (Apis mellifera) that did not follow any dances before they first danced produced significantly more disordered dances, with larger waggle angle divergence errors, and encoded distance incorrectly. The former deficits improved with experience, but distance encoding was set for life. The first dances of bees that could follow other dancers had none of these impairments. Social learning, therefore, shapes honey bee signaling, as it does communication in human infants, birds, and multiple other vertebrate species. However, much remains to be learned about insects’ social learning, and this protocol will help to address knowledge gaps in the understanding of sophisticated social signal learning, particularly in understanding the molecular bases for such learning.Key features• It was unclear if honey bees (Apis mellifera) could improve their waggle dance by following experienced dancers before they first waggle dance.• Honey bees perform their first waggle dances with more errors if they cannot follow experienced waggle dancers first.• Directional and disorder errors improved over time, but distance error was maintained. Bees in experimental colonies continued to communicate longer distances than control bees.• Dancing correctly, with less directional error and disorder, requires social learning.• Distance encoding in the honey bee dance is largely genetic but may also include a component of cultural transmission.

ABSTRACT Theileria, a tick-borne intracellular protozoan, can cause infections of various livestock and wildlife around the world, posing a threat to veterinary health. Although more and more Theileria species have been identified, genomes have been available only from four Theileria species to date. Here, we assembled a whole genome of Theileria luwenshuni, an emerging Theileria, through next-generation sequencing of purified erythrocytes from the blood of a naturally infected goat. We designated it T. luwenshuni str. Cheeloo because its genome was assembled by the researchers at Cheeloo College of Medicine, Shandong University, China. The genome of T. lunwenshuni str. Cheeloo was the smallest in comparison with the other four Theileria species. T. luwenshuni str. Cheeloo possessed the fewest gene gains and gene family expansion. The protein count of each category was always comparable between T. luwenshuni str. Cheeloo and T. orientalis str. Shintoku in the Eukaryote Orthologs annotation, though there were remarkable differences in genome size. T. luwenshuni str. Cheeloo had lower counts than the other four Theileria species in most categories at level 3 of Gene Ontology annotation. Kyoto Encyclopedia of Genes and Genomes annotation revealed a loss of the c-Myb in T. luwenshuni str. Cheeloo. The infection rate of T. luwenshuni str. Cheeloo was up to 81.5% in a total of 54 goats from three flocks. The phylogenetic analyses based on both 18S rRNA and cox1 genes indicated that T. luwenshuni had relatively low diversity. The first characterization of the T. luwenshuni genome will promote better understanding of the emerging Theileria. IMPORTANCE Theileria has led to substantial economic losses in animal husbandry. Whole-genome sequencing data of the genus Theileria are currently limited, which has prohibited us from further understanding their molecular features. This work depicted whole-genome sequences of T. luwenshuni str. Cheeloo, an emerging Theileria species, and reported a high prevalence of T. luwenshuni str. Cheeloo infection in goats. The first assembly and characterization of T. luwenshuni genome will benefit exploring the infective and pathogenic mechanisms of the emerging Theileria to provide scientific basis for future control strategies of theileriosis.

Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer relations, rules, and knowledge hidden in the data. When the physics behind data becomes extremely complex, inexplicit, or even unclear/unknown, machine learning approaches have the advantage of being more flexible with wider applicability over conventional physics-based interpretation models. Moreover, machine learning can be utilized to assist many labor-intensive human interpretation tasks such as bad data identification, facies classification, and geo-features segmentation out of imagery data.However, the validity of the outcome from machine learning largely depends on the quantity, quality, representativeness, and relevance of the feeding data including accurate labels. To achieve the best performance, it requires significant effort in data preparation, feature engineering, algorithm selection, architecture design hyperparameter tuning, and regularization. In addition, it needs to overcome technical issues such as imbalanced population, overfitting, and underfitting.In this paper, advantages, limitations, and conditions of using machine learning to solve petrophysics challenges are discussed. The capability of machine learning algorithms in accomplishing different challenging tasks can only be achieved by overcoming its own limitations. Machine learning, if properly utilized, can become a powerful disruptive tool for assisting a series of critical petrophysics tasks.

As an emerging financial tool, green bonds can broaden the financing channels of enterprises and stimulate the green innovation of enterprises. Based on the A-share data of Chinese listed companies from 2012 to 2020, this paper analyzes the impact of green bonds on green technology innovation by using a method of Difference in Difference with Propensity Score Matching (PSM-DID). We found that green bonds can significantly improve enterprise green technology innovation. Its positive impact is attributed to increases in media attention and R&D capital investment and a reduction in financing constraints. Green bonds play a greater role in the green innovation of strong financial constraints enterprises, non-SOEs and large-scale enterprises. Our findings have important reference significance for the improvement of the resource allocation role of green bonds and achievement of sustainable growth.

Abstract Main goal of the JUNO experiment is to determine the neutrino mass ordering using a 20 kt liquid-scintillator detector. Its key feature is an excellent energy resolution of at least 3% at 1 MeV, for which its instruments need to meet a certain quality and thus have to be fully characterized. More than 20,000 20-inch PMTs have been received and assessed by JUNO after a detailed testing program which began in 2017 and elapsed for about four years. Based on this mass characterization and a set of specific requirements, a good quality of all accepted PMTs could be ascertained. This paper presents the performed testing procedure with the designed testing systems as well as the statistical characteristics of all 20-inch PMTs intended to be used in the JUNO experiment, covering more than fifteen performance parameters including the photocathode uniformity. This constitutes the largest sample of 20-inch PMTs ever produced and studied in detail to date, i.e. 15,000 of the newly developed 20-inch MCP-PMTs from Northern Night Vision Technology Co. (NNVT) and 5000 of dynode PMTs from Hamamatsu Photonics K. K.(HPK).

With the progress of society, the use of intelligent flowmeters has become more and more widespread. The powerful functions of intelligent flowmeters have made people’s production and living more convenient. Still, at the same time, public data, including industry and agriculture privacy and information security, have exposed huge security risks. This paper aims to study the information security of flowmeter communication networks based on multi-sensor data fusion and artificial intelligence-driven. The present study introduces network information security and its development direction, proposes a cloud computing-based network security situation prediction algorithm, uses the cloud model to predict the security situation of the cloud computing network, and gives a cloud computing network security situation assessment system model. At the same time, it analyzes and explores network information security, and explores network security based on multi-sensor data fusion and artificial intelligence driving. The experimental research results show that due to the unique advantages of the universal flowmeter intelligent processing system, such as high resource utilization, uniform standards, and wide application range, it has a lot of room for development. With the development of science and technology and application fields, and the continuous progress and expansion of general flowmeters, the future space of general category flowmeters is huge. The popularity of the Internet is getting faster and faster, and the requirements for information security are getting higher and higher, and the information security issues faced in the future will become more and more complicated. Therefore, it is necessary to speed up the construction of an information security system, and take measures to strictly manage from the aspects of law, management, technology and talents.

Abstract Renal cell carcinoma (RCC) seriously threatens people's lives and health. The identification of some precise biomarkers during the process of RCC progression and the pathophysiologic procedure is critical for improving the diagnosis and management of RCC. Evidence suggests that ferroptosis may play a pivotal role in eradicating clear cell RCC (ccRCC, KIRC) tumor cells. We screened out the target prognostic ferroptosis‐associated genes and examined the functions of farnesyl‐diphosphate farnesyltransferase (FDFT1) in 786‐O cells by plasmid transfection. In our study, we identified FDFT1 as a potential marker correlating with ferroptosis in KIRC. Upregulated FDFT1 inhibited cell proliferation, migration, and invasion, and the underlying antitumor effects may occur via the AKT signaling pathway. Our study provides helpful evidence to study the complex physiopathology of KIRC.

AIM: To explore that double steep axial transparent corneal incision can effectively minimize corneal astigmatism and high-order aberration(HOA)in cataract patients with moderate to low degree corneal astigmatism, as well as improve postoperative visual quality.METHODS: A total of 60 cataract patients diagnosed with moderate to low corneal astigmatism in our hospital from October 2020 to July 2021 were randomly divided into group A(normal 135° incision, 30 cases)and group B(double steep axial transparent corneal incision, 30 cases). The uncorrected visual acuity(UCVA), astigmatism, astigmatism vector decomposition, polar keratometry(Polar K), second-order astigmatism, spherical, coma and trefoil aberration within 3mm of the central pupil were compared at 1 and 3mo before and after surgery.RESULTS: UCVA of group A and group B at 1 and 3mo after surgery was significantly higher than that before surgery(P0.05). The corneal astigmatism of group A at 1 and 3mo postoperative were not significantly different from that before surgery(P>0.05).The mean corneal astigmatism of group B was 0.66±0.13 and 0.61±0.12D at 1 and 3mo after surgery, respectively, which was significantly lower than the preoperative value of 1.38±0.24D(P0.05). In group A, CJ0/CJ45 were both significantly different from those at 1 and 3mo before surgery(P0.05). The coma aberration of group B at 3mo after surgery was lower than that before surgery and group A.CONCLUSION: For cataract patients with moderate to low corneal astigmatism, there was no significant difference in postoperative UCVA and HOA between double steep axial transparent corneal incision and normal incision. However, compared with normal incision, double steep axial transparent corneal incision can improve postoperative visual quality by effectively reducing corneal astigmatism and coma aberration.

Distributed optimization (DO) for solving optimal power flow (OPF) problems is a fundamental task for coordinated planning, scheduling, and operation of interconnected power grids. Among various distributed OPF (D-OPF) studies, the method based on the auxiliary problem principle (APP), has the advantage of fully parallel and decentralized implementation, which only involves neighbor-to-neighbor communication and no need for a central coordinator. However, due to it taking a fixed quadratic core as the estimator of boundary information, the convergent rate is slow when solving the ill-conditioned D-OPF problems, since the search direction simply relies on the first-order gradient information. Besides, for the non-convexity of AC-OPF, only empirical criteria for the APP’s parameter settings have been provided. Thus, convergence is not always guaranteed when the operation state changes without the criterion to select and readjust algorithm parameters. In this paper, to further exploit the potentialities of the APP during online coordinated scheduling and operation of interconnected power grids, the D-OPF method based on an adaptive core APP (AC-APP) is proposed, in which second-order cone programming (SOCP) is adopted to guarantee the finite convergence. The adaptive core is updated according to the Hessians of the boundary variables at each iterative step to correct the search path with second-order information. Compared to the existing fixed core APP (FC-APP) based D-OPF method, the proposed AC-APP based D-OPF method can obtain branch flow results at a higher accuracy with fewer iterations, which facilitates the online application. On the other hand, a boundary equivalence approach for the D-OPF with SOCP convexification is developed, guaranteeing the convergence prerequisite of the AC-APP. Case studies based on the IEEE-118 bus system verified the effectiveness and superiority of the proposed method.