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.

An updated inventory of marine opisthobranch(Mollusca,Gastropoda)from the territorial waters of the Republic of Mauritius

Island ecosystems support diverse aquatic invertebrate communities comprising endemic taxa.Documentation of existing species is important for conservation.In this study,a checklist of marine opisthobranch from the Republic of Mauritius is presented.A combination of benthic surveys(50 m×5 m in triplicates),rover diving techniques and photo documentation were used over two years(2018–2020)within 35 sheltered and unsheltered lagoons.Morphological and molecular analysis were used for identification.Species composition within sheltered and unsheltered areas in Mauritius was estimated using the Bray-Curtis similarity.The checklist featured 117 species belonging to 61 genera and 28families,of which 13 are new records.The findings increased the knowledge of opisthobranch diversity from the Mauritius by 15.4%.Among the listed species,the distribution range of Cyerce nigra,Actinocyclus papillatus,and Phyllidia picta extended from the Western Pacific to the South Western Indian Ocean.Molecular analysis of the undescribed Gymnodoris sp.showed it resembled Gymnodoris sp.from Hawaii and were different by a genetic distance value of 10.6%.The species richness and evenness were higher within the sheltered regions of Mauritius which harboured the food resource of opisthobranch.These areas as compared to unsheltered regions were heavily populated,suggesting the probable influence of wave actions on opisthobranch diversity and abundance.The order Nudibranchia was reported as most speciose,with 86 species.The Sacoglossa and Nudibranchia were observed only on macroalgae and sponges respectively.High abundance was also recorded on shipwrecks which are the most common form of artificial reefs.With the inclusion of observations from previous studies,201species belonging to 94 genera and 36 families are now known from the Mauritius.

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.

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.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

A Note on an Order Level Inventory Model with Varying Two-Phased Demand and Time-Proportional Deterioration

The main purpose of this paper is to generalize the effect of two-phased demand and variable deterioration within the EOQ (Economic Order Quantity) framework. The rate of deterioration is a linear function of time. The two-phased demand function states the constant function for a certain period and the quadratic function of time for the rest part of the cycle time. No shortages as well as partial backlogging are allowed to occur. The mathematical expressions are derived for determining the optimal cycle time, order quantity and total cost function. An easy-to-use working procedure is provided to calculate the above quantities. A couple of numerical examples are cited to explain the theoretical results and sensitivity analysis of some selected examples is carried out.

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.

An open-accessed inventory of landslides triggered by the M_(S)6.8 Luding earthquake,China on September 5,2022

This study constructs a preliminary inventory of landslides triggered by the M_(S) 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images.The results show that this earthquake triggered at least 5007 landslides,with a total landslide area of 17.36 km^(2),of which the smallest landslide area is 65 m^(2)and the largest landslide area reaches 120747 m^(2),with an average landslide area of about 3500 m^(2).The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault,and the area density and point density of landslides are 13.8%,and 35.73 km^(-2) peaks with 2 km as the search radius.It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images.Based on the available post-earthquake remote sensing images,the number of landslides triggered by this earthquake is roughly estimated to be up to 10000.This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region,and the prevention and control of landslide hazards in the seismic area.

Spatial distribution and inventory of natural gas hydrate in the Qiongdongnan Basin,northern South China Sea

Natural gas hydrate is a potential clean energy source and is related to submarine geohazard,climate change,and global carbon cycle.Multidisciplinary investigations have revealed the occurrence of hydrate in the Qiongdongnan Basin,northern South China Sea.However,the spatial distribution,controlling factors,and favorable areas are not well defined.Here we use the available high-resolution seismic lines,well logging,and heat flow data to explore the issues by calculating the thickness of gas hydrate stability zone(GHSZ)and estimating the inventory.Results show that the GHSZ thickness ranges between mostly~200 and 400 m at water depths>500 m.The gas hydrate inventory is~6.5×109-t carbon over an area of~6×104 km2.Three areas including the lower uplift to the south of the Lingshui sub-basin,the Songnan and Baodao sub-basins,and the Changchang sub-basin have a thick GHSZ of~250-310 m,250-330 m,and 350-400 m,respectively,where water depths are~1000-1600 m,1000-2000 m,and2400-3000 m,respectively.In these deep waters,bottom water temperatures vary slightly from~4 to 2℃.However,heat flow increases significantly with water depth and reaches the highest value of~80-100 mW/m2 in the deepest water area of Changchang sub-basin.High heat flow tends to reduce GHSZ thickness,but the thickest GHSZ still occurs in the Changchang sub-basin,highlighting the role of water depth in controlling GHSZ.The lower uplift to the south of the Lingshui sub-basin has high deposition rate(~270-830 m/Ma in 1.8-0 Ma);the thick Cenozoic sediment,rich biogenic and thermogenic gas supplies,and excellent transport systems(faults,diapirs,and gas chimneys)enables it a promising area of hydrate accumulation,from which hydrate-related bottom simulating reflectors,gas chimneys,and active cold seeps were widely revealed.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

FPGA based hardware platform for trapped-ion-based multi-level quantum systems

We report a design and implementation of a field-programmable-gate-arrays(FPGA)based hardware platform,which is used to realize control and signal readout of trapped-ion-based multi-level quantum systems.This platform integrates a four-channel 2.8 Gsps@14 bits arbitrary waveform generator,a 16-channel 1 Gsps@14 bits direct-digital-synthesisbased radio-frequency generator,a 16-channel 8 ns resolution pulse generator,a 10-channel 16 bits digital-to-analogconverter module,and a 2-channel proportion integration differentiation controller.The hardware platform can be applied in the trapped-ion-based multi-level quantum systems,enabling quantum control of multi-level quantum system and highdimensional quantum simulation.The platform is scalable and more channels for control and signal readout can be implemented by utilizing more parallel duplications of the hardware.The hardware platform also has a bright future to be applied in scaled trapped-ion-based quantum systems.

Inventory of Host Plants and Parasitoids of the Fall Armyworm (FAW), Spodoptera frugiperda (JE Smith), in the Southern Agricultural Zone of Niger

The fall armyworm, Spodoptera frugiperda (JE Smith), is a polyphagous pest reported in sub-Saharan Africa since 2016 and has expanded rapidly in almost Africa. In Niger, Spodoptera frugiperda (JE Smith) is considered like a major pest of maize, to which it causes significant damage, in a context where proven control methods against this moth remain almost non-existent. The objective of the present study was to determine the economic importance of FAW through the damage caused to the different host plants and to identify the parasitoids of this caterpillar. The study was conducted in the southern agricultural zone of Niger, specifically in the regions of Dosso, Maradi, Tahoua and Zinder. FAW eggs and caterpillars were collected from six villages in each region and then incubated and reared in the entomology laboratory of INRAN in Maradi. The rate of infestation of the different crops by FAW was determined as well as the observation of the beneficiaries. The results obtained indicate the presence of FAW on millet with an attack rate varying from 45.7% to 68%, sorghum with 47.2% to 62.25% and sesame with 9.7%. This work also revealed an oophagous parasitoid, Telenomus remus with 138 ± 23 and larval parasitoids, Cotesia sp with 16 ± 1 maximum number of individuals emerged from the collected material. Also, it was identified the parasitoid Cotesia icipe with a rate of parasitism from 4.6% to 5.75%;the Charops ater whose rate of parasitism varies from 4.5% and 12.25% but for Chelonus insularis with 17.25% and Tachnidae with 53%. These very interesting results will constitute a basis for the development of biological control and a component of an agroecological management strategy of caterpillar.

Screening Tree Species for Carbon Storage Potential through Urban Tree Inventory in Planted Vegetation

Urban tree inventory is a great tool for gathering data that can be used by different end users. This study attempted to chart the species diversity in planted areas and measure their tree diameter at breast height to screen them for the carbon storage potential. A total of 2860 trees belonging to 36 species were recorded in the planted vegetation in parks and avenue plantation. The dominant species were Azadirachta indicia (25.5%), Conocarpus erectus (19.2%), Ficus spp. (15.5%), Tabebuia rosea (9.2%), Peitophorum pterocarpum (9.0%) and the remaining represents (21.6%) of the tree identified in this study. It was found that the highest contribution of carbon sequestration (CO2 equivalent) is dominated by the Ficus spp. (30.3%) with a total of 3399.3 tCO2eq, followed by Azadirachta indicia (25.4%) with a total of 2845.2 tCO2eq and Conocarpus erectus (20.4%) with a total of 2286 tCO2eq. The entire area has the capability to sequester around 11,213.3 tCO2eq and on average of 3.9 ± 0.1 tCO2eq. In accordance with the findings, it is imperative for the preservation of a sustainable environment to have vegetation that has the capacity to store carbon. The study suggests, there is potential to increase carbon sequestration in urban cities through plantation programs on existing and new land uses and along roads.

Inventory of the Thermo-Physiological Behavior of Fabrics—A Review

A comprehensive literature review was performed to create an inventory of thermal-physiological quantities for fabrics from different fiber materials, material blends, and fabric structures. The goal was to derive over-arching concepts that cannot be seen by the individual studies alone. Equations of best fits suggest non-linear changes for fabric thickness, thermal and water-vapor resistance with changes in material blend ratio. Air permeability decreases with increasing fabric density and fabric weight wherein the degree of decrease differs among fabric materials, blend ratio, and fabric structure. Water-vapor transmission rates strongly depend on fabric thickness, material, and blend, but marginally depend on fabric structure as long as the fabric and material thickness remain the same.

Neighborhood Effects and Political Trust: A Multi-level Analysis of Chinese Rural-to-Urban Migrants’ Trust in County Government

Massive rural-to-urban migration in China is consequential for political trust: rural-to-urban migrants have been found to hold lower levels of trust in local government than their rural peers who choose to stay in the countryside (mean 4.92 and 6.34 out of 10, respectively, p < 0.001). This article explores why migrants have a certain level of political trust in their county-level government. Using data of rural-to-urban migrants from the China Family Panel Survey, this study performs a hierarchical linear modeling (HLM) to unpack the multi-level explanatory factors of rural-to-urban migrants’ political trust. Findings show that the individual-level socio-economic characteristics and perceptions of government performance (Level-1), the neighborhood-level characteristics-the physical and social status and environment of neighborhoods (Level-2), and the objective macroeconomic performance of county-level government (Level-3), work together to explain migrants’ trust levels. These results suggest that considering the effects of neighborhood-level factors on rural-to-urban migrants’ political trust merits policy and public management attention in rapidly urbanizing countries.

基于零库存目标的体外诊断试剂供应链质量评价与改进研究

目的:制定体外诊断试剂供应链的零库存管理目标,评价供应链质量,改进试剂供应中存在的问题。方法:从体外诊断试剂的库存量、供应效率和产品质量方面分析体外诊断试剂管理中存在的问题,制定医院运营、管理质量和患者利益最优化的管理体系,构建零库存管理路径和质量评价模型。选取2020年1月至2023年3月江苏省苏北人民医院采购的21种类型85个型号的体外诊断试剂,根据供应链质量管理方式不同,分别采用按需库存管理模式(模式1)和零库存管理模式(模式2),对比两种管理模式的需求采购、库存管理和临床使用效果。结果:采用模式2管理方式的体外诊断试剂的采购需求达标率、供应能力优质率和临床使用匹配度分别为(93.35±3.62)%、(94.87±2.63)%和(96.08±2.31)%,均高于模式1,其差异有统计学意义(Z=2.489、2.836、2.838,P<0.05),存产品长期积压、环境不达标和信息不及时的例数分别为(2.92±2.54)例、(2.83±1.59)例和(5.58±3.12)例,均低于模式1,其差异有统计学意义(Z=2.959、3.037、3.703,P<0.05);临床科室、医技科室及采购中心对模式2的体外诊断试剂的供应配送和信息沟通的满意度分别为97.8%和93.3%、97.0%和87.9%、100%和84.6%,均高于模式1,其差异有统计学意义(χ^(2)_(临床科室)=5.428、6.133,χ^(2)_(医技科室)=3.958、3.937,χ^(2)_(采购中心)=5.159、4.996,P<0.05)。结论:零库存管理模式能够提高体外诊断试剂需求采购的规范性,降低库存管理中积压失效问题的发生率,提升临床供应服务质量。

临床医学博士后Kolb学习风格调查分析

目的通过评价临床医学博士后的学习风格特征,提升学习者对自我学习风格的认知,提高学习灵活性,以指导教学实践。方法选取50名参与内科规范化培训的临床医学博士后,采用Kolb学习风格量表4.0版开展问卷调查,分析临床医学博士后学习风格特点,比较不同特征被调查者学习风格的分布情况、学习环节得分差异及学习弹性差异。结果临床医学博士后学习风格分布:思考型占20.0%,分析型占20.0%,平衡型占16.0%,经验型占14.0%,回顾型占10%,行动型占8.0%,决定型占8.0%,想象型占4.0%,无被调查者归属创始型。在女性博士后中,平衡型最多,其次是思考型、分析型和经验型,男性博士后则以思考型、分析型为主,平衡型显著少于女性博士后。8年制项目毕业博士后以思考型、分析型为主。在未来专业方向为内科的博士后中,思考型占比最多,其次为分析型和经验型,未来专业非内科的博士后以平衡型和分析型为主。毕业后第1年的被调查者具体经验(CE)得分显著低于其他毕业后年限组(P<0.05),其他学习环节得分及学习弹性在各类型分组中的差异均无统计学意义。结论临床医学博士后的学习风格以思考型和分析型为主,擅长抽象概念与反思观察。可以根据博士后学习风格来调整教学模式,以期提高教学质量。

城市存量土地更新的动力机制研究

随着城市化的加速发展,城市面临着空间资源有限、环境压力不断加大的问题。城市存量土地更新成为优化城市发展格局、提高资源利用效率的重要手段。论文阐述了城市存量土地的内涵,分析了城市存量土地类型与基本特征,构建了“权力—资本—民生”三大驱动力交互作用影响的更新动力分析框架,重点分析讨论了居住类存量土地的“政府+社会”协作更新机制、产业类存量土地的“政府+市场”协作更新机制、公共设施类存量土地的政府主导更新机制、公共空间类存量土地的政府主导更新机制以及历史文化类存量土地的多方协作更新机制等五种更新驱动作用机制,以期为城市存量土地更新制度设计与治理优化路径制定提供依据,更好地推动城市更新的协同、健康与可持续发展。

我国垃圾填埋碳排放核算标准的编制建议

填埋作为我国主要的垃圾处理方式之一,是碳排放的重要来源;编制我国垃圾填埋碳排放核算标准,将支持碳排放统计核算体系建设,推进碳达峰、碳中和目标进程。本文梳理了国内外垃圾填埋碳排放核算的相关标准和指南研制进展,分析了我国垃圾填埋碳排放核算标准编制面临的挑战,在深入讨论相关标准的基准方法、编制重点任务的基础上提出了有关发展建议。研究认为,国际上已有的标准和指南以及提供的缺省值不完全适用于我国垃圾填埋碳排放核算的实际情况,目前我国缺少规范统一的垃圾填埋碳排放核算方法,也缺乏可靠的核算清单数据。为此,针对我国填埋垃圾特征和技术现状,提出了我国垃圾填埋碳排放核算方法、核算范围和清单的建议内容;明晰了我国垃圾填埋碳排放核算标准编制的重点任务,提供了适合我国垃圾填埋场景的清单数据获取方式。后续,可充分考虑地区因素,采用我国特征性数据,以获得更贴合实际情况的垃圾填埋碳排放核算结果,据此开展我国垃圾填埋碳排放核算标准的编制工作。