氢闸流管驱动三同轴电缆Blumlein线的kHz重频脉冲功率源

闪光X射线摄影技术在军事和民用等多个领域具有重要场景。针对重频直线感应加速器高重频的要求,提出了基于氢闸流管驱动三同轴电缆Blumlein线的脉冲功率源方案,设计和研制了一种三同轴电缆,研究了氢闸流管的导通特性,搭建了氢闸流管驱动三同轴电缆Blumlein线脉冲功率源的验证平台,开展了kHz重频脉冲功率源实验研究,以及kHz重频脉冲功率源驱动重频感应腔的实验研究,结果表明:基于氢闸流管驱动三同轴电缆Blumlein线脉冲功率源实现了波形品质优异的kHz重频方波脉冲输出。

基于PFN-Marx技术的紧凑型重频脉冲功率源

针对低磁场相对论磁控管高功率微波器件实验驱动需求,对基于脉冲形成网络(PFN)储能的高功率脉冲产生技术进行了研究。为了使其结构紧凑且具有较好的输出脉冲波形,设计了半环形PFN脉冲形成单元,两个半环形带状PFN与一体化开关、绝缘盘组成圆环形高压脉冲产生模块。PFN脉冲形成单元由13个陶瓷电容与半环形金属电极板构成,多个高压脉冲产生模块同轴层叠,所有开关导通后各模块PFN串联放电,产生快前沿高功率方波脉冲,再通过对触发开关和充电电源的同步控制实现重频工作。采用电磁仿真软件对PFN物理结构进行优化设计,研制的高压脉冲产生模块充电51 kV在负载8.5Ω上输出电压峰值49.6 kV、脉冲半高宽108 ns、脉冲前沿14 ns、平顶(90%~90%) 74 ns,具有较好的方波特性;11个高压脉冲产生模块层叠集成为1个22级紧凑PFN-Marx装置,在充电51 kV的条件下,84Ω负载上获得峰值516 kV的高电压脉冲输出,半高宽104 ns、平顶63 ns、脉冲前沿11 ns,实现了20 Hz连续15 s重频稳定工作,输出波形完全一致。

不同酸碱体系中Pt/SiO_(2)催化肼分解产生自由基

采用电子自旋共振(electron spin resonance spectroscopy, ESR)法,探讨高氯酸体系和氢氧化钠体系中H^(+)浓度与OH-浓度对催化剂催化肼断键及断键产生的自由基的状态及行为的影响,明确了酸碱体系下Pt/SiO_(2)催化肼分解产生自由基的差异。结果表明:碱性体系中,随着OH-浓度升高,N-H断键速率显著升高,肼分解以氢解反应为主;酸性体系中,在pH=6.9到pH=1.1范围内,随着H^(+)浓度升高,N-N断键速率急速升高并远大于N-H断键速率,在pH<1.1范围内,随着H^(+)浓度升高,N-H断键速率和N-N断键速率均快速下降。在酸碱变化过程中,N-H断键起着主导作用,决定了肼分解速率,随着酸度升高,N-H断键速率下降,肼分解速率下降。

乏燃料后处理碱性流程的研究进展

乏燃料后处理碱性流程是用碳酸盐、氢氧化物等碱性物质的溶液作为介质进行乏燃料的溶解及铀、钚等元素的分离与纯化的方法。碱性条件下,乏燃料中的大部分裂变产物和次锕系元素并不溶解或者在溶解过程中转变为碳酸盐、氢氧化物沉淀。与已经实现工业化的PUREX(plutonium uranium redox extraction)酸性流程相比,碱性流程具有腐蚀性更小、流程更简单等潜在的优点。鉴于碱性流程的优点及其在乏燃料后处理中的潜在应用,日本、美国、俄罗斯、韩国等国家的科研人员已经围绕该流程开展了一些研究工作。本文首先介绍了各国建议的碱性流程的技术路线;然后逐一介绍了与主要工艺环节相关的基础研究的进展,包括乏燃料的氧化溶解、核素分离、试剂的回收等;最后对该领域面临的挑战和前景进行了讨论。

地下水体系核素化学形态模拟系统软件开发

为评估处置库的安全性,核素化学形态信息是准确预测核素的运移行为研究的前提。鉴于我国核能工业的迅速发展以及建立环境中核素的确认需求,在我国开展处置库周围核素的化学形态研究具有十分重要的意义。针对地下水中元素种态分布的研究需求,以JAVA为开发工具、MySQL作为数据库、Tomcat为容器,开发了一套B/S架构的元素种态分布模拟软件。针对模拟计算中遇到的化学反应平衡非线性方程组求解收敛困难的问题,引入了根据化学反应势能求解的方法和反应因子控制迭代步长,实现了对化学反应非线性方程快速地求解,并可拓展多相平衡计算。以塔木素地下水中镎为考察对象,利用所开发化学形态模拟软件(simulation software on chemical species,SSCS)计算环境中镎的形态和量,并对比PHREEQC的计算结果,相对偏差在10%以内,针对环境中痕量元素的分布,提供了理论计算的解决方案。

含磷类萃取剂萃取性能及辐射稳定性的研究进展

目前乏燃料后处理技术的研究特别是溶剂萃取方面,即利用含萃取剂的有机稀释剂萃取分离水溶液中的放射性核素,受到广泛关注。本文对近十多年来乏燃料后处理(溶剂萃取)中含磷类萃取剂特别是中性膦类萃取剂的萃取性能及辐射稳定性等进行了综述与讨论。对于中性膦类萃取剂而言,萃取性能及辐射稳定性受自身结构、稀释剂类型等其他因素的影响,萃取剂中P—O键数目的减少会提升萃取性能,烷基链长度的增加或支链(如甲基、乙基及苯基)的引入均能提高辐射稳定性。此外,采用离子液体作为稀释剂可以减小有机相的辐解。因此,研究含磷类萃取剂结构与萃取性能及辐射稳定性的关系,不仅有利于筛选出适用于乏燃料后处理过程的萃取剂,对于选择兼具优异萃取性能和辐射稳定性的结构从而指导新型含磷类萃取剂的合成同样具有重要意义。

细长氚光管内壁荧光涂层制备工艺与性能

针对小尺寸气态氚光源研制需求,自主开发了一种细长玻璃管内壁制备微米级ZnS(Cu)荧光粉的湿法涂覆及烧结工艺,设计并建立了一套可控压力、流量、温度的涂覆装置,利用此涂覆装置进行了规格为φ1.1 mm×550 mm的细长玻璃管内壁荧光涂层的制备工艺研究,分析了料浆配方对混合液体配方黏度以及涂层厚度的影响;优化了干燥工艺参数,最终在细长玻璃管内获得了满足功能要求的荧光涂层。截面扫描电镜(SEM)分析表明:涂层具有较好的致密性,其厚度为(16.9±0.6)μm(n=32),涂层厚度均匀性良好,且长时间放置稳定不脱落。使用该工艺充氚得到的氚光管样品发光亮度为0.642 cd/m^(2),发光均匀性、一致性良好,可为后续气态氚光源产品的开发提供装置条件与工艺优化依据。

Assessment of compressive strength of jet grouting by machine learning

Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.

消费者用药依从行为探析:基于两阶段理论模型

个体健康行为改变是否存在阶段性是当前争议的焦点。用药依从行为作为关键的影响医疗效果指标,对个体的身心健康产生重要影响。前人对于用药依从水平的研究综述大多是从医学角度,着眼于特定疾病的用药依从行为。然而,在医疗保健行业市场化的背景下,鲜有研究从消费者的视角出发,探究信息加工方式和心理过程对消费者用药依从行为的影响。同时,现有研究也缺乏对依从行为的理论分类和论述。基于两阶段理论模型回顾了营销领域影响消费者用药依从行为的影响因素,梳理了干预策略,并提出未来研究趋势与展望。在理论上,这有助于从健康行为改变阶段上理解个体的用药依从行为,丰富健康领域的阶段理论。在实践上有助于更好地理解消费者的心理健康和行为规律,并为慢性病管理提供了营销方面的启示。

The effects of data aggregation on long-term projections of forest stands development

Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.

Disentangling the effects of management and climate change on habitat suitability for saproxylic species in boreal forests

Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood,the limiting factor for habitats for saproxylic species in European boreal forests.The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood.We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland.We simulated seven forest management regimes and three climate scenarios(reference,RCP4.5 and RCP8.5)over 100 years.Management regimes included set aside,continuous cover forestry,business-as-usual(BAU)and four modifications of BAU.Habitat suitability was assessed using a speciesspecific habitat suitability index,including 21 fungal and invertebrate species groups.“Winner”and“loser”species were identified based on the modelled impacts of forest management and climate change on their habitat suitability.We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change.Habitat suitability index varied by over 250%among management regimes,while overall change in habitat suitability index caused by climate change was on average only 2%.More species groups were identified as winners than losers from impacts of climate change(52%–95%were winners,depending on the climate change scenario and management regime).The largest increase in habitat suitability index was achieved under set aside(254%)and the climate scenario RCP8.5(>2%),while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species(up to+11%)across all climate change scenarios.Our results show that close-to-nature management regimes(e.g.,continuous cover forestry and set aside)can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime.This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices,even as climate change progresses.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Community perspectives on the effectiveness of watershed management institutions in the Himalayas

The sustainability of environmental management initiatives,such as watershed management programs,relies on the presence of effective institutions at the watershed level.However,there needs to be more empirical evidence from evaluating the effectiveness of watershed-level institutions.Therefore,this study presents a pioneering effort to evaluate the effectiveness of Nepal’s first watershed conservation committee at the watershed scale,focusing on the case of the Khageri Khola watershed in Central Nepal.The study involved conducting a household survey,key informant interviews,focus group discussions,and field observations to collect and analyze the data.Descriptive analysis,index value calculation,and chi-square statistics were then employed to summarize the results regarding local respondents’perceptions of twelve institutional characteristics,their rationalities,and their association with socio-demographic variables.The results reveal that the watershed conservation committee was perceived as performing well in managing the watershed.Specifically,good interaction,appropriate scale,technical,environmental,social,organizational,and government rationality were perceived as highly effective,with an average index value of less than 0.36.In contrast,clarity of objectives and economic rationality showed moderate effectiveness,with an average index value ranging from 0.36 to 0.65.However,the results suggested that adaptiveness,compliance capacity,and financial rationality merit increased attention,intending to improve their performance.Further,the results showed the association of socio-demographics with respondents’perceptions of various indicators of institutional characteristics and their rationalities.Therefore,the study provides valuable insights for policymakers,researchers,and development practitioners charged with designing sustainable and effective programs and institutions.To enhance the effectiveness and sustainability of watershed management programs,we recommend establishing a policy-guided institutional mechanism at the watershed scale.This mechanism should be based on various institutional characteristics and rationalities and should consider the extant variability in the socio-demographic and topographic characteristics of the watershed.

Refined Anam-Net:Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.

Formation of Natural Melanin/TiO_(2) Nanostructure Hybrids with Enhanced Optical,Thermal and Magnetic Properties as a Soft Material

The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated by using Fourier transform infrared spectroscopy,thermogravimetric analysis and UV-Vis spectroscopy.It was observed that incorporating natural melanin on TiO_(2) nanoparticles(TiO_(2)-Mel)occurred at 2.01 eV with a low value of Urbach energy around 100 meV indicating improvement in the crystalline structure.Magnetic measurement at room temperature showed diamagnetic behavior.Furthermore,thermal results showed that TiO_(2)-Mel is stable even at temperatures up to 400℃.According to the results obtained by the thermal stability of melanin with titanium dioxide,it can be a good candidate in many applications such as solar cells and optoelectronics.

Post processing of additive manufactured Mg alloys:Current status,challenges,and opportunities

Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.Optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.

Revealing Al-O/Al-F reaction dynamic effects on the combustion of aluminum nanoparticles in oxygen/fluorine containing environments:A reactive molecular dynamics study meshing together experimental validation

Improving the energy conversion efficiency in metallic fuel(e.g.,Al)combustion is always desirable but challenging,which often involves redox reactions of aluminum(Al)with various mixed oxidizing environments.For instance,Al-O reaction is the most common pathway to release limited energy while Al-F reaction has received much attentions to enhance Al combustion efficiency.However,microscopic understanding of the Al-O/Al-F reaction dynamics remains unsolved,which is fundamentally necessary to further improve Al combustion efficiency.In this work,for the first time,Al-O/Al-F reaction dynamic effects on the combustion of aluminum nanoparticles(n-Al)in oxygen/fluorine containing environments have been revealed via reactive molecular dynamics(RMD)simulations meshing together combustion experiments.Three RMD simulation systems of Al core/O_(2)/HF,n-Al/O_(2)/HF,and n-Al/O_(2)/CF4 with oxygen percentage ranging from 0%to 100%have been performed.The n-Al combustion in mixed O_(2)/CF_4 environments have been conducted by constant volume combustion experiments.RMD results show that Al-O reaction exhibits kinetic benefits while Al-F reaction owns thermodynamic benefits for n-Al combustion.In n-Al/O_(2)/HF,Al-O reaction gives faster energy release rate than Al-F reaction(1.1 times).The optimal energy release efficiency can be achieved with suitable oxygen percentage of 10%and 50%for n-Al/O_(2)/HF and n-Al/O_(2)/CF_4,respectively.In combustion experiments,90%of oxygen percentage can optimally enhance the peak pressure,pressurization rate and combustion heat.Importantly,Al-O reaction prefers to occur on the surface regions while Al-F reaction prefers to proceed in the interior regions of n-Al,confirming the kinetic/thermodynamic benefits of Al-O/Al-F reactions.The synergistic effect of Al-O/Al-F reaction for greatly enhancing n-Al combustion efficiency is demonstrated at atomicscale,which is beneficial for optimizing the combustion performance of metallic fuel.

Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

Experimental and computational study of annealed nickel sulfide quantum dots for catalytic and antibacterial activity

This research investigates the hydrothermal synthesis and annealing duration effects on nickel sulfide(NiS_(2)quantum dots(QDs)for catalytic decolorization of methylene blue(MB)dye and antimicrobial efficacy.QD size increased with longer annealing,reducing catalytic activity.UV–vis,XRD,TEM,and FTIR analyses probed optical structural,morphological,and vibrational features.XRD confirmed NiS2's anorthic structure,with crystallite size growing from 6.53 to 7.81 nm during extended annealing.UV–Vis exhibited a bathochromic shift,reflecting reduced band gap energy(Eg)in NiS_(2).TEM revealed NiS_(2)QD formation,with agglomerated QD average size increasing from 7.13 to 9.65 nm with prolonged annealing.Pure NiS_(2)showed significant MB decolorization(89.85%)in acidic conditions.Annealed NiS_(2) QDs demonstrated notable antibacterial activity,yielding a 6.15mm inhibition zone against Escherichia coli(E.coli)compared to Ciprofloxacin.First-principles computation supported a robust interaction between MB and NiS_(2),evidenced by obtained adsorption energies.This study highlights the nuanced relationship between annealing duration,structural changes,and functional properties in NiS_(2)QDs,emphasizing their potential applications in catalysis and antibacterial interventions.

Improving Channel Estimation in a NOMA Modulation Environment Based on Ensemble Learning

This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.