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.

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%.

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.

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.

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.

首次复治菌阳肺结核合并糖尿病患者血清IAP、25(OH)D水平及对疗效的预测价值

目的探讨首次复治菌阳肺结核(TB)合并糖尿病(DM)患者血清免疫抑制酸性蛋白(IAP)、25-羟基维生素D[25(OH)D]表达变化及2者对治疗疗效的预测价值。方法选取本院2019年1月至2022年4月首次复治菌阳TB合并DM患者48例为实验组,另选同期单纯TB患者48例为对照组。测定并比较两组患者治疗前后血清IAP、25(OH)D水平;多因素Logistic回归分析影响菌阳TB合并DM患者痰菌阴转的可能因素;受试者工作特征(ROC)曲线评估IAP、25(OH)D对患者痰菌未转阴的预测价值。结果实验组痰菌阴转患者31例(64.58%)显著低于对照组痰菌阴转患者40例(83.33%)。治疗2个月后,实验组、对照组血清IAP均显著降低,25(OH)D水平均显著升高(P<0.05);且治疗2个月后实验组血清IAP水平显著低于对照组,25(OH)D水平显著高于对照组(P<0.05)。多因素Logistic回归分析显示,IAP高水平是首次复治菌阳TB合并DM患者痰菌未阴转的危险因素(P<0.05),25(OH)D高水平是首次复治菌阳TB合并DM患者痰菌未阴转的保护因素(P<0.05)。ROC曲线显示,IAP预测痰菌未阴转的AUC为0.719,25(OH)D预测痰菌未阴转的AUC为0.791,2者联合预测痰菌未阴转的AUC为0.871,明显高于2者单独诊断(Z_(联合vs IAP)=2.605、P=0.009;Z_(联合vs 25(OH)D)=2.408、P=0.016),且灵敏度、特异性分别为78.46%、82.86%。结论首次复治菌阳TB合并DM患者经治疗后血清IAP水平降低、25(OH)D水平升高,2者联合对菌阳TB合并DM患者痰菌未阴转具有一定预测价值。

1990—2020年波曲流域冰川冰湖时空变化及其对气候变化的响应

基于Landsat TM/ETM+/OLI遥感影像和数字高程模型(DEM)数据,结合比值阈值法与目视解译提取了1990—2020年波曲流域冰川边界,同时利用归一化水体指数与目视解译提取波曲流域冰湖数据,分析近30 a波曲流域冰川冰湖空间分布与变化特征以及母冰川-冰湖相对位置变化关系,探究波曲流域冰川冰湖协同演化及其与气候变化的响应关系。结果表明:(1)1990—2020年波曲流域冰川加速退缩,尤其是近10 a来冰川加速退缩趋势更为显著;波曲流域冰川主要分布在海拔5500~6100 m范围内,规模上看,大规模(≥10 km^(2))冰川数量保持稳定,小规模(≤0.5 km^(2))冰川数量呈增加趋势。(2)近30 a波曲流域冰湖数量、面积均不断增加,冰湖面积扩张率为74.24%;冰湖主要分布在海拔4900~5300 m之间,大冰湖(≥0.07 km^(2))面积不断扩张;小冰湖(≤0.03 km^(2))数量增加显著。(3)与母冰川相连冰湖是最重要的冰湖扩张类型,该类型冰湖面积增长量占总冰湖增量的72.08%。(4)近30 a波曲流域气温缓慢上升,降水量总体略微下降。气温上升和降水量下降是造成冰川退缩重要因素,同时冰川融水促进冰湖扩张。通过波曲流域冰川-冰湖分布面积、变化特征及其协同演化关系的探究为流域冰川面积变化趋势和冰湖溃决灾害的预测及防治提供数据支撑。

利福平联合复方新诺明对复治涂片阳性肺结核炎性因子的影响

目的分析利福平联合复方新诺明辅助治疗对复治涂片阳性肺结核患者疗效和血清炎性因子水平的影响。方法将2017年5月—2022年8月我院收治80例复治涂片阳性肺结核患者使用随机信封法分为观察组及对照组各40例。对照组采用利福平治疗,观察组采用利福平联合复方新诺明辅助治疗;治疗后对患者临床疗效、复发率及痰菌阴转率进行评估,并检测治疗前后血清炎性因子水平。结果治疗后观察组病灶吸收率90.00%高于对照组77.50%,空洞缩小率95.00%高于对照组82.50%,差异均具有统计学意义(均P<0.05);观察组复发率2.50%低于对照组15.00%,痰菌阴转率97.50%高于对照组80.00%,差异均具有统计学意义(均P<0.05);观察组治愈率95.00%高于对照组85.00%,差异具有统计学意义(P<0.05)。治疗后观察组血中白细胞介素-37(IL-37)、触珠蛋白(HP)、骨桥蛋白(OPN)水平均下降,观察组3种因子水平分别为(4.63±0.83)pg·mL^(-1)、(810.38±31.64)μg·mL^(-1)、(189.19±20.74)pg·mL^(-1)低于对照组的(6.33±0.91)pg·mL^(-1)、(1283.73±101.67)μg·mL^(-1)、(456.28±45.92)pg·mL^(-1),差异均具有统计学意义(均P<0.05)。结论利福平联合复方新诺明对复治涂片阳性肺结核患者后有助于提高临床疗效,改善血液中IL-37、HP、OPN水平。

不同剂量异烟肼抗结核化疗方案对药物敏感复治肺结核的疗效

目的探讨药物敏感复治肺结核患者应用不同剂量异烟肼抗结核化疗方案治疗的效果及安全性。方法选取收治的对药物敏感的复治肺结核患者97例,按照随机抽样的方式将其分为标准组(n=48)和高剂量组(n=49),标准组依据国家统一的常规剂量(异烟肼0.3 g·d^(−1))给药,高剂量组根据患者体质量决定用药量,≥70 kg者异烟肼剂量为0.5 g·d^(−1),<70 kg者为0.4 g·d^(−1),疗程为8个月。比较2组患者治疗结束后X线摄影显示肺部病灶吸收情况的变化;痰菌阴转情况;血清细胞因子水平变化[白细胞介素-18(interleukin-18,IL-18)、γ干扰素(interferon-γ,IFN-γ)、肿瘤坏死因子-α(tumor necrosis factor-α,TNF-α)、白细胞介素-10(interleukin-10,IL-10)]、生活质量[肺癌生活质量评估量表(functional assessment of cancer therapylung,FACT-L)]评分以及不良反应情况。结果高剂量组病灶吸收率为87.76%,明显高于标准组的70.83%(P<0.05);治疗第1个月和第2个月,2组患者痰液转阴率比较差异无统计学意义,治疗第4个月和第8个月高剂量组患者痰菌转阴率显著高于标准组(P<0.05);治疗8个月后,2组患者血清IL-18、IFN-γ、TNF-α及IL-10水平均显著低于治疗前,且高剂量组显著低于标准组(P<0.05);2组患者FACT-L评分均显著高于治疗前,且高剂量组高于标准组(P<0.05);高剂量组不良发应发生率(18.37%)与标准组(16.67%)比较差异无统计学意义。结论对药物敏感复治肺结核采用高剂量异烟肼治疗疗效较好,能有效增强患者肺部病灶吸收,加快空洞闭合,痰液尽早恢复阴性,血清中的炎症因子水平降低,从而提高生活质量,且无明显不良反应。

海洋型冰川表碛与退缩区土壤微生物群落特征——以阿扎冰川和米堆冰川为例

以藏东南阿扎冰川和米堆冰川为研究区,采用细菌16S rRNA基因扩增子测序和真菌ITS测序技术,研究了冰川表碛与退缩区不同植被演替阶段土壤中微生物群落特征及土壤化学性质影响.结果表明,两条冰川表碛与退缩区土壤中共发现细菌门38个,其中Proteobacteria(40%)、Actinobacteriota(23%)、Bacteroidota(14%)为优势群,共发现真菌门7个,Basidiomycota(47%)和Ascomycota(45%)为优势群.阿扎和米堆冰川表碛中Patescibacteria、RCP2-54、Bacteroidota、Gemmatimonadota和Acidobacteriota的菌群丰度存在显著差异,米堆冰川表碛和退缩区土壤中微生物群落结构和丰度存在显著差异(P<0.05),而退缩区三个演替阶段之间的群落结构差异不显著.适应极端环境的微生物群落在表碛中丰度最高,而随着演替进程参与植物定殖或与植物共生的微生物群落丰度与多样性显著增加,微生物群落的α多样性上升,NMDS模型也逐渐接近重叠,群落结构在植被演替后期逐渐趋于稳定,.除全钾外,养分含量在两条冰川表碛中均较低,随着植被演替土壤pH值逐渐降低而养分逐渐增加,演替后期显著高于前两阶段.表碛中的微生物群落与pH值和全钾显著正相关(P<0.05),而退缩区土壤中则与速效钾、硝酸盐氮、全磷、可溶性有机碳显著正相关(P<0.05),并以磷限制为主.

农田退水生态削减净化技术研究进展

针对农田退水产生的来源和农田退水污染的特点,综述可选用治理农田退水的国内外源头管控措施及生态净化技术的应用场景。经过多年发展应用,源头管控措施可以在源头削减退水量及退水污染负荷,是控制退水的关键策略。生态处理技术因其生态效益、建设成本等方面的优势成为农村地区可选的最佳退水处理方法。对现有面源污染治理模式进行总结,提供一种农田退水生态逐级截留净化的治理思路,对农田退水处理技术的未来发展方向进行展望并为农田退水未来治理提供参考。

Pixelated non-volatile programmable photonic integrated circuits with 20-level intermediate states

Multi-level programmable photonic integrated circuits(PICs)and optical metasurfaces have gained widespread attention in many fields,such as neuromorphic photonics,opticalcommunications,and quantum information.In this paper,we propose pixelated programmable Si_(3)N_(4)PICs with record-high 20-level intermediate states at 785 nm wavelength.Such flexibility in phase or amplitude modulation is achieved by a programmable Sb_(2)S_(3)matrix,the footprint of whose elements can be as small as 1.2μm,limited only by the optical diffraction limit of anin-house developed pulsed laser writing system.We believe our work lays the foundation for laser-writing ultra-high-level(20 levels and even more)programmable photonic systems and metasurfaces based on phase change materials,which could catalyze diverse applications such as programmable neuromorphic photonics,biosensing,optical computing,photonic quantum computing,and reconfigurable metasurfaces.

下分层停采线外延后回撤通道顶板预裂卸压研究

分层开采时,当上分层已经回采完毕,为了对下分层尽量多回收煤炭资源,需要下分层安全通过上分层停采线后,继续向前外延回采。为了避免下分层外延的末采期间发生矿压灾害,需保证下分层的采空区顶板能够及时冒落。因而提出了工作面外延末采时伪斜调整渐进式通过上分层停采线,此外在上覆顶板采用水力压裂超前预裂的方式,对坚硬厚层砂岩弱化卸压。结果表明:下分层在安全通过上分层停产线后又安全外延回采了70 m,多回收煤炭资源近3万t;采用水力压裂对下分层外延后的顶板致裂卸压后,顶板厚层砂岩能够及时垮落,垮落的周期来压步距小于15 m,未发生矿压灾害,保证了安全生产。

国民党军队撤离新疆始末研究

1949年夏,人民解放军第一野战军向西北五省国统区发起“大进军”,随着陕、甘、宁、青四省解放,孤悬塞外的新疆局势亦风起云涌:一方面,对国民党当局失去信心的美国政府希冀经营新疆为抵御共产主义阵营南下印度之最后堡垒,故对新疆采取种种渗透和颠覆活动;另一方面,国民党军方高层或认为与中共仍未决胜负,遂打起“内调”新疆驻军参战的算盘。新疆的十万驻军不仅是可以支援战局的机动力量,也是在险恶的外部环境下保全领土的关键因素。在“保全国土”与“戡平内乱”的纠结中,国民党决策部门与地方军政当局就新疆驻军及国民党上层派驻新疆人员何去何从的问题展开了一系列争论,最后作出“起义”和“撤退”两种相对和平的选择,这也为新疆的和平解放创造了条件。

青藏高原海螺沟冰川退缩区原生演替序列优势乔木氮磷化学计量及重吸收特征研究

以海螺沟冰川退缩区原生演替序列为对象,研究各演替阶段优势乔木氮(N)、磷(P)化学计量及重吸收效率特征。结果表明:优势乔木鲜叶及凋落叶N、P浓度随演替下降,且鲜叶的N∶P在整个演替序列中总体小于14,表明该演替序列优势乔木的生长主要受N元素限制;优势乔木N、P的重吸收效率在演替中期最高,表明植物较高的生长速率会增加其对养分的吸收利用效率;优势乔木N、P的重吸收效率与其生长速率呈正相关关系,而与土壤的N、P储量呈负相关关系,说明树木生长速率(需求)和土壤养分状况(供给)共同调节植物对养分的吸收模式;优势乔木N重吸收效率与其鲜叶N∶P呈正相关关系,表明植物对N的重吸收过程对维持其体内N∶P的平衡有着重要意义。研究结果有助于深入认识原生演替过程中植被对养分的利用机制,可为植被恢复提供理论依据。

植物多样性与物种组成随原生演替进程的变化规律——以贡嘎山冰川退缩区为例

研究原生演替过程中植物多样性与物种组成的演变规律有助于解析植物群落的演替动态与构建机制。以冰川退缩区原生演替序列上7个演替阶段(S1~S7)的植物群落为研究对象,于2022年开展群落调查工作,结合α、β多样性两方面,分析其乔木层、灌木层、草本层植物多样性和物种组成的变化规律。结果显示:(1)本次调查在海螺沟冰川退缩区原生演替序列中记录到植物共41科66属100种,其中单(寡)种属居多,初步反映了本研究区植物物种组成的复杂性和多样性;(2)原生演替序列上,各层植物的优势种组成在不同演替阶段差异明显,出现明显的更替;(3)随着演替的进行,乔木层和灌木层的多项α多样性指数均呈“单峰型”响应格局,演替初期上升,达到峰值后缓慢下降;(4)不同生活型植物在演替序列上的β多样性差异明显,物种周转在物种组成变化中起主要作用,其中草本层的周转程度最高,为92.16%,而灌木层的周转程度最低,为58.01%;(5)冰川退缩区植物群落的主林层(乔木层)主要经历了先锋群落、落叶阔叶林、针阔混交林、顶级针叶林4个演替阶段。研究结果可为原生演替过程中植物群落的构建机制提供参考。

航海模拟器中登陆艇抢滩退滩运动建模与仿真

本文提出一种应用于模拟器的登陆艇抢滩退滩运动数学模型。该模型基于动量及动量矩定理建立海床作用力模型,基于悬链线理论建立锚链力模型,并结合分离型建模理论,建立了登陆艇抢滩退滩的六自由度运动数学模型。该模型考虑了海床底质、初始速度和海床坡面角的影响,能够更准确地模拟登陆艇在抢滩和退滩过程中的运动轨迹。此外,该模型还扩展了航海模拟器在登陆艇操纵以及船舶搁浅脱浅应急操纵方面的功能,为相关领域的研究和应用提供了有力支持。