Construction of a Multi-Level Strategic System for Cultivating Cultural Industry Management Talents in Colleges and Universities

Through SWOT(strengths,weaknesses,opportunities,and threats)and PEST(political,economic,social,and technological)analysis,this study discusses the construction of a multi-level strategic system for the cultivation of cultural industry management talents in colleges and universities.First of all,based on SWOT analysis,it is found that colleges and universities have rich educational resources and policy support,but they face challenges such as insufficient practical teaching and intensified international competition.External opportunities come from the rapid development of the cultivation of cultural industry management talents and policy promotion,while threats come from global market competition and talent flow.Secondly,PEST analysis reveals the key factors in the macro-environment:at the political level,the state vigorously supports the cultivation of cultural industry management talents;at the economic level,the market demand for cultural industries is strong;at the social level,the public cultural consumption is upgraded;at the technological level,digital transformation promotes industry innovation.On this basis,this paper puts forward a multi-level strategic system covering theoretical education,practical skill improvement,interdisciplinary integration,and international vision training.The system aims to solve the problems existing in talent training in colleges and universities and cultivate high-quality cultural industry management talents with theoretical knowledge,practical skills,and global vision,so as to adapt to the increasingly complex and diversified cultural industry management talents market demand and promote the long-term development of the industry.

尺度适应性起沙机制在CMA-CUACE/Dust中的应用

沙尘暴是影响我国重要的灾害性天气之一,针对中国气象局亚洲沙尘暴数值预报系统CMA-CUACE/Dust(China Meteorological Administration Unified Atmospheric Environment for Dust)的沙尘质量浓度在中亚高估、蒙古北部低估、在我国消散过快以及极端沙尘暴预报峰值偏低等问题,应用与模式格距匹配的尺度适应性起沙机制并更新风蚀资料库对模式进行改进。对2021年3月13—17日东亚最强沙尘暴个例和2023年3—5月与业务运行环境一致的连续预报试验表明,改进后的模式(CMA-CUACE/Dust V1.5)有效改善了上述不足,极端沙尘暴过程传输至我国后的沙尘质量浓度峰值与观测接近。连续预报试验TS(threat score)评分显示:CMA-CUACE/Dust V1.5预报一致性和连续性较好,1~5 d不同时效预报TS评分明显高于改进前和韩国模式ADAM(the Asian Dust Aerosol Model),漏报率明显降低,对2023年5次沙尘过程的4次预报TS评分高于0.3,部分过程超过0.5。

Deflagration characteristics of freely propagating flames in magnesium hydride dust clouds

The flame propagation processes of MgH_(2)dust clouds with four different particle sizes were recorded by a high-speed camera.The dynamic flame temperature distributions of MgH_(2)dust clouds were reconstructed by the two-color pyrometer technique,and the chemical composition of solid combustion residues were analyzed.The experimental results showed that the average flame propagation velocities of 23μm,40μm,60μm and 103μm MgH_(2)dust clouds in the stable propagation stage were 3.7 m/s,2.8 m/s,2.1 m/s and 0.9 m/s,respectively.The dust clouds with smaller particle sizes had faster flame propagation velocity and stronger oscillation intensity,and their flame temperature distributions were more even and the temperature gradients were smaller.The flame structures of MgH_(2)dust clouds were significantly affected by the particle sinking velocity,and the combustion processes were accompanied by micro-explosion of particles.The falling velocities of 23μm and 40μm MgH_(2)particles were 2.24 cm/s and 6.71 cm/s,respectively.While the falling velocities of 60μm and 103μm MgH_(2)particles were as high as 15.07 cm/s and 44.42 cm/s,respectively,leading to a more rapid downward development and irregular shape of the flame.Furthermore,the dehydrogenation reaction had a significant effect on the combustion performance of MgH_(2)dust.The combustion of H_(2)enhanced the ignition and combustion characteristics of MgH_(2)dust,resulting in a much higher explosion power than the pure Mg dust.The micro-structure characteristics and combustion residues composition analysis of MgH_(2)dust indicated that the combustion control mechanism of MgH_(2)dust flame was mainly the heterogeneous reaction,which was affected by the dehydrogenation reaction.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Effects of dust controls on respirable coal mine dust composition and particle sizes:case studies on auxiliary scrubbers and canopy air curtain

Control of dust in underground coal mines is critical for mitigating both safety and health hazards.For decades,the National Institute of Occupational Safety and Health(NIOSH)has led research to evaluate the effectiveness of various dust control technologies in coal mines.Recent studies have included the evaluation of auxiliary scrubbers to reduce respirable dust downstream of active mining and the use of canopy air curtains(CACs)to reduce respirable dust in key operator positions.While detailed dust characterization was not a focus of such studies,this is a growing area of interest.Using preserved filter samples from three previous NIOSH studies,the current work aims to explore the effect of two different scrubbers(one wet and one dry)and a roof bolter CAC on respirable dust composition and particle size distribution.For this,the preserved filter samples were analyzed by thermogravimetric analysis and/or scanning electron microscopy with energy dispersive X-ray.Results indicate that dust composition was not appreciably affected by either scrubber or the CAC.However,the wet scrubber and CAC appeared to decrease the overall particle size distribution.Such an effect of the dry scrubber was not consistently observed,but this is probably related to the particular sampling location downstream of the scrubber which allowed for significant mixing of the scrubber exhaust and other return air.Aside from the insights gained with respect to the three specific dust control case studies revisited here,this work demonstrates the value of preserved dust samples for follow-up investigation more broadly.

EGSNet:An Efficient Glass Segmentation Network Based on Multi-Level Heterogeneous Architecture and Boundary Awareness

Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.

Filtration performance and modeling of granular bed for dust removal from coal pyrolytic vapors

Dust removal from pyrolytic vapors at high temperatures is an obstacle to the industrialization of the coal pyrolysis process.In this work,a granular bed with expanded perlites as filtration media was designed and integrated into a 10 t·d^(–1)coal pyrolysis facility.The testing results showed that around 97.56%dust collection efficiency was achieved.As a result,dust content in tar was significantly lowered.The pressure drop of the granular bed maintained in the range of 356 Pa to 489 Pa.The dust size in the effluent after filtration exhibited a bimodal distribution,which was attributed to the heterogeneity of the dust components.The effects of filtration bed on pyrolytic product yields were also discussed.A modified filtration model based on the macroscopic phenomenological theory was proposed to describe the performance of the granular bed.The computation results were well agreed with the experimental data.

Experimental and numerical simulation study on forced ventilation and dust removal of coal mine heading surface

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface,an experimental platform for forced ventilation and dust removal was built based on the similar principles.Through the similar experiment and numerical simulation,the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined.The airflow field in the roadway can be divided into three zones:jet zone,vortex zone and reflux zone.The dust concentration gradually decreases from the head to the rear of the roadway.Under the forced ventilation conditions,there is a unilateral accumulation of dust,with higher dust concentrations away from the ducts.The position of the equipment has an interception effect on the dust.The maximum error between the test value and the simulation result is 12.9%,which verifies the accuracy of the experimental results.The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

Ion acoustic solitary waves in an adiabatic dusty plasma:Roles of superthermal electrons,ion loss and ionization

We investigate propagation of dust ion acoustic solitary wave(DIASW)in a multicomponent dusty plasma with adiabatic ions,superthermal electrons,and stationary dust.The reductive perturbation method is employed to derive the damped Korteweg-de Vries(DKdV)equation which describes DIASW.The result reveals that the adiabaticity of ions significantly modifies the basic features of the DIASW.The ionization effect makes the solitary wave grow,while collisions reduce the growth rate and even lead to the damping.With the increases in ionization cross sectionΔσ/σ_(0),ion-to-electron density ratioδ_(ie)and superthermal electrons parameterκ,the effect of ionization on DIASW enhances.

Oxygen-assisted zinc recovery from electric arc furnace dust using magnesium chloride

Electric arc furnace(EAF)dust is an important secondary resource containing metals,such as zinc(Zn)and iron(Fe).Recover-ing Zn from EAF dust can contribute to resource recycling and reduce environmental impacts.However,the high chemical stability of ZnFe_(2)O_(4)in EAF dust poses challenges to Zn recovery.To address this issue,a facile approach that involves oxygen-assisted chlorination using molten MgCl_(2)is proposed.This work focused on elucidating the role of O2 in the reaction between ZnFe_(2)O_(4)and molten MgCl_(2).The results demonstrate that MgCl_(2)effectively broke down the ZnFe_(2)O_(4)structure,and the high O2 atmosphere considerably promoted the sep-aration of Zn from other components in the form of ZnCl_(2).The presence of O2 facilitated the formation of MgFe_(2)O_(4),which stabilized Fe and prevented its chlorination.Furthermore,the excessive use of MgCl_(2)resulted in increased evaporation loss,and high temperatures pro-moted the rapid separation of Zn.Building on these findings,we successfully extracted ZnCl_(2)-enriched volatiles from practical EAF dust through oxygen-assisted chlorination.Under optimized conditions,this method achieved exceptional Zn chlorination percentage of over 97%within a short period,while Fe chlorination remained below 1%.The resulting volatiles contained 85wt%of ZnCl_(2),which can be further processed to produce metallic Zn.The findings offer guidance for the selective recovery of valuable metals,particularly from solid wastes such as EAF dust.

Global dust density in two-dimensional complex plasma

The driven-dissipative Langevin dynamics simulation is used to produce a two-dimensional(2D) dense cloud, which is composed of charged dust particles trapped in a quadratic potential. A 2D mesh grid is built to analyze the center-to-wall dust density. It is found that the local dust density in the outer region relative to that of the inner region is more nonuniform,being consistent with the feature of quadratic potential. The dependences of the global dust density on equilibrium temperature, particle size, confinement strength, and confinement shape are investigated. It is found that the particle size, the confinement strength, and the confinement shape strongly affect the global dust density, while the equilibrium temperature plays a minor effect on it. In the direction where there is a stronger confinement, the dust density gradient is bigger.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

Weather Classification for Autonomous Vehicles under Adverse Conditions Using Multi-Level Knowledge Distillation

Achieving reliable and efficient weather classification for autonomous vehicles is crucial for ensuring safety and operational effectiveness.However,accurately classifying diverse and complex weather conditions remains a significant challenge.While advanced techniques such as Vision Transformers have been developed,they face key limitations,including high computational costs and limited generalization across varying weather conditions.These challenges present a critical research gap,particularly in applications where scalable and efficient solutions are needed to handle weather phenomena’intricate and dynamic nature in real-time.To address this gap,we propose a Multi-level Knowledge Distillation(MLKD)framework,which leverages the complementary strengths of state-of-the-art pre-trained models to enhance classification performance while minimizing computational overhead.Specifically,we employ ResNet50V2 and EfficientNetV2B3 as teacher models,known for their ability to capture complex image features and distil their knowledge into a custom lightweight Convolutional Neural Network(CNN)student model.This framework balances the trade-off between high classification accuracy and efficient resource consumption,ensuring real-time applicability in autonomous systems.Our Response-based Multi-level Knowledge Distillation(R-MLKD)approach effectively transfers rich,high-level feature representations from the teacher models to the student model,allowing the student to perform robustly with significantly fewer parameters and lower computational demands.The proposed method was evaluated on three public datasets(DAWN,BDD100K,and CITS traffic alerts),each containing seven weather classes with 2000 samples per class.The results demonstrate the effectiveness of MLKD,achieving a 97.3%accuracy,which surpasses conventional deep learning models.This work improves classification accuracy and tackles the practical challenges of model complexity,resource consumption,and real-time deployment,offering a scalable solution for weather classification in autonomous driving systems.

Climatic and Environmental Impacts of Dust over the Tibetan Plateau: An Overview

The Tibetan Plateau (TP), located at a height of nearly 4000 m above sea level, has a unique setting that effects the environment of the whole of northern hemisphere. It acts as the “water reservoir” of Asia as several important rivers originate from this region. Therefore, even slight alternations in the TP’s hydrological cycle may have profound ecological and social impacts. However, it is experiencing a significant increase in accumulation of dust from local and global sources. The impact of dust on the region’s climate has become an active area of research. Further, the study of sources of dust arriving at the TP is also critical. Accumulation of dust is impacting temperature, snow cover, glaciers, water resources, biodiversity and soil desertification. This manuscript tries to provide a comprehensive summary of the impact of dust on weather, climate, and environmental components of the TP. The impact of dust on clouds, radiative energy, precipitation, atmospheric circulation, snow and ice cover, soil, air quality, and river water quality of the TP are discussed. It further discusses the steps immediately needed to mitigate the devastating impact of dust on the fragile ecosystem of the TP.

Advancing respirable coal mine dust source apportionment:a preliminary laboratory exploration of optical microscopy as a novel monitoring tool

Exposure to respirable coal mine dust(RCMD)can cause chronic and debilitating lung diseases.Real-time monitoring capabilities are sought which can enable a better understanding of dust components and sources.In many underground mines,RCMD includes three primary components which can be loosely associated with three major dust sources:coal dust from the coal seam itself,silicates from the surrounding rock strata,and carbonates from the inert‘rock dust’products that are applied to mitigate explosion hazards.A monitor which can reliably partition RCMD between these three components could thus allow source apportionment.And tracking silicates,specifically,could be valuable since the most serious health risks are typically associated with this component-particularly if abundant in crystalline silica.Envisioning a monitoring concept based on field microscopy,and following up on prior research using polarized light,the aim of the current study was to build and test a model to classify respirable-sized particles as either coal,silicates,or carbonates.For model development,composite dust samples were generated in the laboratory by successively depositing dust from high-purity materials onto a sticky transparent substrate,and imaging after each deposition event such that the identity of each particle was known a priori.Model testing followed a similar approach,except that real geologic materials were used as the source for each dust component.Results showed that the model had an overall accuracy of 86.5%,indicating that a field-microscopy based moni-tor could support RCMD source apportionment and silicates tracking in some coal mines.

Toxicity Evaluation of Different Exposure Scenarios of Road Dust Using Daphnia magna and Artemia salina as Aquatic Organisms, and Prosopis cineraria and Vachellia tortilis as Native Plant Species

Particulate matter (PM10) deposited as road dust is considered an important source of contamination from atmosphere. However, there are limited studies on the toxicity of road dust as such on different organisms. This study evaluates the toxicity of road dust using different extraction scenarios on Daphnia magna and Artemia salina as aquatic organisms and also on Prosopis cineraria and Vachellia tortilis as local plant species. Chemical analysis of different extracts shows considerable amount of trace metals, however the trace metals in the dust extract associated with suspended sediment were not absorbed by the receptors. On the other hand, the concentration of trace metals in the artificial mixture was found bioavailable and absorbed causing a high percentage of mortality. In the plant assay, significant difference was obtained in the germination percentage between the control and three different extraction exposures in both plant species. The mean root length of P. cineraria and V. tortilis were higher in 20% and 50% extracts than the control probably due to the availability of nutrients from the dust extract. Interestingly however, the seedling vigor index was the opposite with higher index in the control and lower in dust extracts that contain heavy metals.

Assessment of Heavy Metals Contamination of Topsoil and Street Dust around Cement Factory in Southern Jordan

Evaluation of assessment of the metal processes governing the metals distribution in soil and dust samples is very significant and protects the health of human and ecological system. Recently, special attention has given to the assessment of metals pollution impact on soil and dust within industrial areas. This study aims to assess the metal contamination levels in the topsoil and street dust around the cement factory in Qadissiya area, southern Jordan. The levels of seven metals (namely Fe, Zn, Cu, Pb, Cr, Cd, and Mn) were analyzed using Flame Atomic Absorption Spec-trophotometer (FAAS) to monitor, evaluate, and to compare topsoil and road dust pollution values of metals of the different types of urban area. The physicochemical parameters which believed to affect the mobility of metals in the soil of the study area were determined such as pH, EC, TOM, CaCO3 and CEC. The levels of metal in soil samples are greater on the surface but decrease in the lower part as a result of the basic nature of soil. The mean values of the metals in soil can be arranged in the following order: Zn > Pb > Mn > Fe > Cu > Cr > Cd. The relatively high concentration of metals in the soil sample was attributed to anthropogenic activities such as traffic emissions, cement factory and agricultural activities. Correlation coefficient analysis and the spatial distribution of indices and the results of statistical analysis indicate three groups of metals: Fe and Mn result by natural origin, Zn, Pb, Cu and Zn result by anthropogenic origin (mainly motor vehicle traffic and abrasion of tires) while Cd is mixed origin. The higher content level values of metals of anthropogenic source in soil samples indicate that it is a source of contamination of air in the studied area. .

Discussion on Dust Removal(Suppression)System of Crushing Station in Open-Pit Coal Mine

This paper introduces the method of coal dust treatment in crushing station and the present situation of dust removal system in typical open-pit coal mine crushing station in China,and expounds the research idea of determining comprehensive dust removal(suppression)system in crushing station inspired by the working principle of"range hood".Based on the design example and link optimization of the crush-ing station of open-pit coal mine I of Thar coalfield,this paper finally draws some conclusions on the key technologies of dust removal(suppression)system of open-pit coal mine crushing station.This study has certain reference value for the technical innovation of dust removal(suppression)system in crushing station,the realization of green mining in"crushing link",and the reduction and avoidance of ecological environment pollution in mining area.

Recovery of zinc and lead by simultaneously leaching from lead slag fuming dust with ammonium chloride solution

The effects of leaching temperature(60−105°C),NH4Cl concentration(3−7 mol/L),liquid/solid ratio(4:1−12:1 mL/g),stirring speed(150−750 r/min),and leaching time(5−90 min)on the leaching rates of Zn and Pb were investigated.The leaching kinetics of Zn-and Pb-rich fuming dust with a NH4Cl solution was also studied.The leaching rates of Zn and Pb respectively reached 98.2%and 75.6%at leaching temperature of 100°C,an NH4Cl concentration of 7.0 mol/L,a liquid/solid ratio of 10:1 mL/g,a stirring speed of 450 r/min and leaching time of 60 min.The kinetics results indicate that the leaching of Zn and Pb conforms to the shrinking unreacted core model and is controlled by the internal diffusion of NH4Cl through the reacted fuming dust layer and external diffusion of NH4Cl through the leaching solution boundary layer,respectively.The apparent activation energies of Zn and Pb are 23.922 and 19.139 kJ/mol,respectively.This study demonstrates that the use of NH4Cl solution,without ammonia,is an environmentally friendly method for simultaneous extracting Zn and Pb from the fuming dust of lead blast furnace slag.